Memoirs of a Defective Brain

The Even Earlier Discovery of Antibiotic Resistance

2013-05-21T12:00:00.000+01:00

So about a month ago, I wrote about how amazing it was that penicillin resistance was discovered as early as 1940, two years before it went on general sale. But whilst researching that article, I realised that Sulphonamide drugs entered the market long before penicillin, with their discoverer, Gerhard Domagk, being nominated for a Nobel prize in 1939. He had been tasked by Bayer pharmaceuticals to test out a gargantuan number of dye molecules to see whether they could kill off bacteria, and in the process , stumbled across the first antibiotic.
You may recall from the previous instalment that Heinrich Hoerlein was the man to recruit Gerhard Domagk into Bayer. Heinrich Hoerlein was a talented chemist, who had specialised in developing dyes for wool. How did this dye maker end up working to create one of the most important pharmaceuticals that the world had seen up until that point ? Why was it that when Bayer decided to devise new treatments against bacterial disease, they focussed on the compounds used to colour clothes ?

To understand how this state of affairs occurred, we need to go back further in time to the 1800s, and look at bacteria. If we were to do this in this era, we would need to get a good microscope. Bacteria tend not to be visible to the naked eye. So let us look down our microscope, what would we see ?
We would probably see tiny transparent blobs. This could mean that we either have lots of bacteria, or are looking at some air bubbles. There are various legends of scientists proclaiming that they have found an entirely new type of bacteria, only to later realise that this bacteria was nothing more than a bubble of air in the wrong place at the wrong time.
This is where dyes become important. The odds are that you are wearing clothes which have undergone the dye process. A dye works by chemically binding to the surface of whatever you want coloured.
A number of scientists of that era began to work on dyes that bind to cells, allowing them to be more easily seen under a microscope. This allowed scientists to see that bacteria came in all sorts of shapes and sizes, and that different species were associated with different diseases.
One of the pioneers of this research was a man named Paul Ehrlich. His PhD had been dedicated to studying how aniline dyes, which had previously only been used for fabrics, could actually be used to colour cells. He also noticed that dyes he used would colour some cells differently to others. Some cells would take up a lot of the dye, whilst others would not and remain transparent. At the age of 24, using the new "staining" techniques, he had managed to discover a new type of cell, known as a Mast cell.
A lot of his scientific achievements could be tracked down to the one simple question he asked himself when he saw this effect: Why did some cells take up more dye than others?
He theorised that cells took up specific nutrients, and that receptors on the surface of these cells played a crucial role in this process. Different cell types have different receptors on their surface. some of these receptors allow dye molecules to enter the cell, and some do not. The reason that different cells "stained" differently was due to the different receptors on their surfaces.
He suggested that toxins on the surface of the bacteria bound the receptors on the surface of the host during infection. In response, the host cell would secrete these receptors to flood the toxins on the surface of the bacteria, thus neutralising them. He called these secreted receptors antibodies.
Whilst this is far from our modern understanding of antibodies, it was a crucial step in the right direction, and he would be credited as one of the founders of immunology as a result of it.
He also suspected that bacteria also had receptors which they used to ingest dye molecules. He noted that some dyes were taken up by bacterial cells, but not human cells. He suggested that this was because the dye molecules resembled nutrients that the bacteria eat. If he could manufacture the chemical structures of these dye molecules to include poison, then he could have a chemical that kills of bacterial cells, and leave human cells alone. He termed these chemicals "Magic Bullets".
In 1904 came his first breakthrough, with a compound, known as Trypan Red, due to it's colour, and sucess for treating mice infected with trypanosomes. Whilst this was useful as a proof of concept, Trypan Red only worked against the types of trypanosomes that infect mice, but not those which attack humans
It was while he was working on this problem that researchers at the Liverpool School of Tropical Hygiene, Anton Breinl and Harold Wolferstan Thomas, discovered that a compound known as Atoxyl, though to be non-toxic for humans, could kill off trypanosomes. From 1906, a number of expeditions to Africa took it with them to protect themselves from the Sleeping Sickness caused by these organisms. Robert Koch, one of the founders of microbiology, used it to treat patients on the shore of Lake Victoria. It became incredibly popular at the time.
Intrigued, Paul Ehrlich investigated this wonder drug, in addition to the other dye based drugs he was developing. During the course of his research, he noticed a worrying trend. After prolonged therapy with these drugs, the resistance of the trypanosomes to these chemicals increased, until they were completely resistant to the therapy. He coined the term "fastness" to describe this trait in bacteria. The fact that he had observed this "fastness" occurring in response to such a broad range of chemicals suggested to him that this was an inevitable event.
Ehrlich was unexpectedly energised by this discovery of resistant organisms. This was because of the finding that once an organism became resistant compound, it was also resistant to chemicals with the same shape and structure. This provided evidence for his fledgling theory of surface receptors which bind to specific chemicals based on their shape and structure.
But his discovery of resistance put him on a crash course with Robert Koch, who had not observed this effect, and thus disputed that it had ever occurred outside of the lab. The main differences were that Robert Koch used much higher doses of Atoxyl than Ehrlich. Either way, Atoxyl was fast falling from popularity. Patients treated with it would go blind due to its severe side effects. A study in 1910 would show that it merely halted progression of trypanosome disease, and that patients were no better off using it.
Ehrlichs lab was still screening drugs to fight off pathogens, and it came across compound 606, a derivative of atoxyl that not only had less severe side effects, but had proven utility against syphilis.
This was marketed as Salversan, and became an important drug in the fight against syphilis, and was used up until the 1940's, when penicillin replaced it.
The discovery of these drugs, and the apt demonstration that dye molecules could make good antibiotics cemented his place in history. One of his assistants, Wilhelm Roehl, would go on to head a research department at Bayer. It was he who would recruit a Dye chemist, Heinrich Hoerlein, to find the next big drug.
So whilst Ehrlichs theory of "magic bullets" would live on, and laid the foundations for the discoveries of Domagk and Fleming, what happened to his theories on antibiotic resistance ?
There were a number of weaknesses in Ehrlichs theories that a number of researchers called into question. Ehrlichs theories of antibiotics and antibiotic resistances were too simplistic. He posited that for each compound, there was a single path to resistance through the mutation of a single receptor. But we know that bacteria and other pathogens can adapt to antibiotics in multiple ways. This made it difficult to replicate his results, and even more difficult for him to explain why they didn't replicate. The rules he had set out for explaining antibiotic resistance did not always hold true. The disparity observed between his experiments of Atoxyl, and of Kochs experiments did not help his case.
This apparent wooliness would mask the threat of antibiotic resistance as a merely theoretical phenomenon. It would for the next 30 years be regarded as a curiosity that would never pose a threat to people.
Over a hundred years after Ehrlich's initial observation of antibiotic resistance, we have a slightly different perspective on his discovery than his contemporaries.

References

Gradmann C. (2011). Magic bullets and moving targets: antibiotic resistance and experimental chemotherapy, 1900-1940, Dynamis, 31 (2) 305-321. DOI: 10.4321/S0211-95362011000200003

Titford M. (2010). Paul Ehrlich: Histological Staining, Immunology, Chemotherapy, Laboratory Medicine, 41 (8) 497-498. DOI: 10.1309/LMHJS86N5ICBIBWM

Casanova J.M. (1992). Bacteria and their dyes: Hans Christian Joachim Gram, Historia de La Immunologia, 11 (4) DOI:

Ehrlich P. Address in Pathology, ON CHEMIOTHERAPY: Delivered before the Seventeenth International Congress of Medicine., British medical journal, PMID: 20766753

Kaufmann S.H.E. (2008). Immunology's foundation: the 100-year anniversary of the Nobel Prize to Paul Ehrlich and Elie Metchnikoff, Nature Immunology, 9 (7) 705-712. DOI: 10.1038/ni0708-705

The Earlier discovery of Antibiotic Resistance

2013-05-20T12:00:00.000+01:00

A couple of weeks ago, I wrote about how quickly penicillin resistance was discovered not long before it was distributed to the public, and how even Alexander Fleming noted his worries over penicillin resistance in the closing of his Nobel prize acceptance speech.

But even in the process of researching this article, I realised that I was merely scratching the surface. You see penicillin was not the first antibiotic discovered. If I want to talk about the first discovery of antibiotic resistance, then I will need to tell this story as well.

In 1932 in Germany, a scientist patented an incredibly important discovery, one that would eventually win him the Nobel prize.

Domagk had been working at Bayer pharmaceuticals at the time of his discovery. In the early 1920's, Bayer had begun to experimenting with different methods for treating bacterial diseases. The experiences of World War 1 had left many researchers with the desire to find ways of preventing deaths from wound infections. Domagk had served in World War 1, and had worked in a cholera hospital near the eastern front. He noted the seeming futility of treating patients with infections.

He came to the attention of Bayer pharmaceuticals after Professor Heinrich Hoerlein* had come across his thesis and decided to hire him. Hoerlein believed that dye molecules could be the key to solving bacterial infection.

The chemists at Bayer would synthesise new chemicals, and then send them to Domagk, and he would then test them on whether they could kill bacteria in vitro, or whether they could prevent mouse deaths from Streptococcus infection. Domagk managed to speed up this process to the point where he could test 30 new chemicals every week.

The chemist on the other end of this process was a man named Josef Klarer. He was the one rushing to make the chemicals for testing. He had tried a number of quinine derivatives, but had no luck. However, in 1932, things would change when he decided to make products based off of Azo Dye molecules. His first success came with Kl-695**, which Domagk found to protect mice during an infection, even though it didn't seem to kill the bacteria in the petri dish. But based off of this finding, Klarer modified Kl-695 again and again. Until it came to a red dye compound that was at the time named Kl-730.

Of course, even though this chemical had been proven in mice, it was as of yet unknown whether it would work in humans. But then Domagks daughter fell ill with a streptococcal disease, and desperate, he gave her a dose of the drug, curing her of the disease.

By 1935, Prontosil Red was being trialled internationally, with Leonard Colebrook, himself a frequent experimenter with antibiotics, demonstrating the effectiveness of Prontosil Red in treating pregnant women, albeit with the side effect of turning his patients bright red. Prontosil Red was the first Sulphanilamide drugs.

Such was the success of this drug that he was nominated for a Nobel Prize in 1939. However, at this time the Nazi's were running Germany. They held a dim view of the Nobel prizes due to the previous German to win a prize. Carl von Ossietzky was a pacifist, who exposed the Nazi's breaking of the treaty of Versailles by training an air corp, and won the Nobel peace prize for his opposition to the Nazi's. As a result of this, the Nazi's forbade any German from accepting Nobel prizes

So when Domagk won a Nobel prize, he was immediately thrown in jail for a week by the Gestapo. This was enough to convince him not to accept the Nobel prize until 1947, two years after Fleming.

By this time, Doctors were already discovering the limits of antibiotics. A.J. Cokkinis wrote in 1938

Inadequate dosage and too short a period not only fail to do any good but seem to lead to the development of acquired resistance on the part of the organism to the drug

Amongst the first to analyse these limitations were a group of researchers based at St Mary's, one of whom was Alexander Fleming**. They had discovered that bacteria could adapt to antibiotic concentrations. The same year, Connor Macleod, a researcher based in New York, investigated this in more detail. He discovered that gradually increasing the amount of antibiotics in broth could increase the numbers of resistant bacteria.

Sulfa drugs like Prontosil Red changed the way medicine worked, and laid down the foundations upon which modern medicine would arise. Unlike penicillin, Prontosil and the related sulphonamide and sulphanilamide drugs could be created entirely synthetically from available chemicals.

Bayer's technique for finding drugs could best be compared to throwing spaghetti against a wall until it sticks, testing random chemicals until they produced the effects they wanted. and people say that Alexander Fleming relied on luck ! Bayer appeared to be basing its company policy on it.

But the question remains as to why they decided to use dye compounds as antibiotics, how did they even know it could work. It's not like there was someone before them who discovered antibiotics even earlier...was there ?

To be continued tomorrow........

References
Wollheim Memorial- Phillip Heinrich Hoerlein
Bayer- Gerhard Domagk
Nobel Prize- Gerhard Domagk

Bentley R. (2009). Different roads to discovery; Prontosil (hence sulfa drugs) and penicillin (hence β-lactams), Journal of Industrial Microbiology & Biotechnology, 36 (6) 775-786. DOI: 10.1007/s10295-009-0553-8

Macleod C. & Daddi G. (1939). A ''Sulfapyridine-Fast'' Strain of Pneumococcus Type 1, Proceedings of the Society for Experimental Biology and Medicine, 41 69-71. DOI: 10.3181/00379727-41-10575P

Cokkinis A.J. (1938). SULPHONAMIDE CHEMOTHERAPY IN SURGICAL INFECTIONS--I, BMJ, 2 (4059) 845-847. DOI: 10.1136/bmj.2.4059.845

Gerhardt Domagk: The First Man to Triumph Over Infectious Diseases By Ekkehard Grundmann

* Heinrich Hoerlein would eventually rise up to the managing board of IG farben, which was the conglomerate which ran a number of companies, including Bayer. Originally, it was primarily a dye making company. But it's activities during World War 2 were infamous. It was the company that developed Zyklon B, in the time that Hoerlein served on its board, which is why he found himself at the Nuremberg trials alongside many of the other company directors. It didn't help that at least one of these directors had been conducting experiments at Auschwitz under the direction of the SS. These experiments involved inducing artificial infections deliberately, and then giving the test subject antibiotics to cure the disease. Heinrich Hoerlein was amongst a number of IG Farbens executives who tried to stop the supply of these chemicals once he had found out what the Nazis were doing with them. When this came to light, the charges were dropped, but the reputation of IG Farben never really recovered, and the conglomerate didn't last long after the war, although some of it's constituent companies are still around today.

** Unfortunately the original paper is locked in the vaults of the Lancet, and so I am forced to diminish his role in the discovery of Antibiotic resistance, because there is no way for me to find out exactly what he did.

TMI Friday: Taking it to third base ..literally

2013-05-17T12:32:00.000+01:00

The variety of foreign bodies in the rectum tests a surgeon's ingenuity to solve a myriad of geometric puzzles

So begins Major PT Mcdonald's 1976 paper, in which he has to deal with a patient with a somewhat unique problem.
The patient, a 49 year old baseball fan, who had serious trouble with his bowels ever since the Oakland A's won the world series in 1974. The doctors examined him, and noticed

" a firm, fixed, round object barely palpable which was lodged high in the rectum"

It was a baseball. To celebrate the Oakland A's victory, he had his sexual partner force the hardball up his rectum, where it got lodged. Unable to get any purchase on the surface of the ball, retrieval seemed impossible.
Thus it was left to the surgeon to figure out how to get the ball out. They drained the man's bladder using a catheter to take some of the pressure off the baseball. They tried to hook the ball, and drag it out as you would a particularly large fish. But this anal fishing expedition was for naught, as they only managed to rip some of the skin from the baseball.
They then decided that perhaps a better way of extracting the ball was through using obstetrics forceps. For those of you who don't know, these are generally used to deliver babies. So they pumped a little bit of air around the baseball, and tried to use the forceps to grab the ball.
It didn't work. They realised what the problem was. The baseball had travelled up through the pelvic arches, and after it had done so, it had become swollen with fluid, and become lodged in the pelvis.
It was a dire situation. The surgeon decided to cut into the man's abdomen to get access to the baseball. It was still stuck fast, and he needed to get some grip on the surface. So he skewered the baseball with a corkscrew, and tried to use it to pull it out. It still wasn't enough. So he got an assistant to stick their fingers up the patients arse from the other end whilst also pulling on the corkscrew, and with " a force enough to lift the patient off the table", popped the baseball out.

McDonald M.P.T. & Rosenthal C.D. (1977). An unusual foreign body in the rectum—A baseball report of a case, Diseases of the Colon & Rectum, 20 (1) 56-57. DOI: 10.1007/BF02587455

TMI Friday: Using a Bottle for a Throttle

2013-05-10T14:32:00.000+01:00

Today we once again must again take a look at men who take incredible risks in order to find new and grotesque methods of masturbation. You have been warned.

This week, the object of their fascination is.. the plastic bottle.

In the grand scheme of things, at least the plastic bottles don't have spinning blades inside them, so in theory, these individuals are better off than those who turn on the vacuum cleaner for stimulation.

The first case we shall be examining comes from 2004, when a 27 year old man in India was admitted to hospital with a peculiar problem. His penis was stuck in a hard plastic bottle. There apparently were no attempts at an excuse, just the simple explanation that he had attempted to use it for masturbation. They called in the hospital carpenter to cut away the bottle *very* carefully using an Iron cutting saw. After 15 minutes of struggle, the bottle was removed.

In 2009, a 77 year old man in Singapore was admitted into hospital with complaints of blood in his urine, and difficulty urinating. Initially he wasn't forthcoming about his case history, for reasons that will soon become clear. You see, one week previously he had pushed a 1.5 litre bottle over his genitals, and got stuck. Over the next 3 days, he managed to cut away most of the bottle. But he still couldn't remove the neck of the bottle, despite attempts at lubricating it with soap. The surgeons managed to pry off the bottle neck with scissors, and they managed to repair some of the damage, but he died 3 days after admission.

The third case we will look at came from 2010 , and also occurred in India. A 47 year old man came in 14 hours after attempting to masturbate himself with a plastic bottle. That in itself is not the hair raising part of this case. We are told that the bottle neck was placed in such a position that it was impossible to access with a normal cutting device. So what did they use ? A soldering iron. Think about that. They mitigated the heat somewhat through adding cold saline in order to regulate the temperature. Still, it's not exactly a pleasant thought.

The final case I'll be talking about involves a 58 year old man. His flatmate called an ambulance for him after recognising that he was behaving oddly. But he sent them away, claiming that he didn't have anything wrong with him. Two days later his flatmate found him dead. The autopsy revealed that his genitals had been constricted with a plastic bottleneck. This bottleneck had cut off the circulation to this region, and allowing parts of his genitals to begin decaying. This lead to bacteria entering the bloodstream, and eventually caused multiple organ failure.

So how could these people stick their members into such small openings, and then get stuck. In order to answer this, we must examine how the penis works. Essentially it is a balloon filled with blood. To become erect, arteries dilate in order to increase blood flow. However, if the veins that take the blood out of the penis are constricted, by say, a plastic bottleneck, then blood takes longer to escape, and so it swells up and gets stuck. This can actually be very dangerous. when the circulation is cut off, it can become gangrenous and in severe cases of penile strangulation, the only option is amputation. this is not even the worst case scenario, as we have seen, if this is not dealt with as soon as possible, then there is a risk of death.

Penile strangulation appears to have a higher body count than men who stick their appendages into the whirling blades of a vacuum cleaner !

References

Jain S., Gupta A., Singh T., Aggarwal N., Sharma S. & Jain S. (2004). Penile strangulation by a hard plastic bottle : A case report, Indian Journal of Surgery, 66 (3)

Ooi C.K., Goh H.K., Chong K.T. & Lim G.H. Penile strangulation: report of two unusual cases., Singapore medical journal, PMID: 19296009

Shamrao Kumbhar U., Dasharathimurumu & Bhargavpak (2011). Acute penile incarceration injury caused by a plastic bottle neck., International Journal of Biological & Medical Research, 2 (4)

Morentin B., Biritxinaga B. & Crespo L. (2011). Penile strangulation: report of a fatal case., The American journal of forensic medicine and pathology, PMID: 22101437

TMI Friday: An Unusual Rectal Injury

2013-05-03T12:00:00.000+01:00

The year was 1953, it was the fifth of November and a 24 year old man stumbled into Beckett Hospital complaining of abdominal pains. He told the doctors that it was a regular occurrence, that he had been plagued by abdominal pain for the past ten years. He told them that the evening before, he had noticed blood issuing from his bowels, and that he had vomited that morning.

As the doctor noting his horribly swollen and tender belly, he fainted. This made finding the source of the problem more difficult. The doctor checked for tumours, and ended up trying to perform a proctoscopy. This involved the insertion of an instrument known as a proctoscope up the anus in order to get a look at the inside of the rectum. However, the copious amount of blood and faecal matter belching from the anus made it impossible to see anything.

This called for more drastic measures. The patient was anaesthetised, and the abdomen was opened up to get some idea what was going on. The abdominal cavity was full of blood, that was likely caused by some form of internal bruising. In the absence of an obvious injury, the abdomen was closed up. The doctor took another examination of the rectum, which was easier now that the patient was sedated, and found the source of the bleeding, a three cm rip in the rectum. With this established, surgery was performed to heal the wound, and to reverse the damage. But an important question still remained.

How did this injury occur ? Clearly, the patient had not told the whole story about what had happened to him. The three centimetre rip was clearly caused by a traumatic injury. The patient admitted this, and then gave them another explanation for what happened.

It was the fifth of November, Bonfire night, where the English stage fireworks displays to celebrate the foiling of the gunpowder plot. The man said that he had bent over at the wrong moment, and a carefully aimed firework had shot up his anus.

But this story didn't make sense. There would be damage to the anal sphincter and the butt cheeks had this been the case. When confronted with this evidence, the man told them a third story. I shall now quote directly from the article.

For domestic reasons he had become unhappy and morose, and on the evening of November 4 he decided to explode a firework up his seat. He accordingly fashioned a narrow tube, using cartridge paper, and with the aid of a pencil introduced one end of this tube, approximately 6 inches (15cm.) in length, into his rectum. He then placed a lighted firework into the end of the tube projecting out of his anus...

This story still left the question as to where the firework went, as no fragments were found. and there was a distinct lack of singeing. It is possible that the sheer volume of effluence issuing from the anus could have washed out the bits of firework. The patient maintained their story under psychiatric evaluation, so we must assume that this was the true story.There is no escaping the image of a man not only lighting a firework up his own anus, but then hitching up his pants and waiting until the next day to actually go to hospital.
The frustrating thing about this tale is that the patient went to some length to deceive the doctors with a fake medical history. Had he told the doctors the truth, he would have been put under anaesthesia, and have been treated more rapidly. That is one of the lessons we can draw from this study, aside from the obvious one.

Butters A.G. (1955). Unusual Rectal Injury, The British Medical Journal, 2 (4939) 602-603. DOI: 10.1136/bmj.2.4939.602

TMI Friday: Vacuum Cleaners Suck

2013-04-26T12:00:00.000+01:00

When you look at a vacuum cleaner, what do you see ? A tool perhaps, to help you deal with the crisps trodden into the carpet from a party the night before. A way to keep armies of dust from taking over your house and to frighten your pet dog.

But there are some people (Who am I kidding, It's men.) who look at vacuum cleaners in a different way. Who observe the coquettish expression on a Henry vacuum cleaner and contemplate a universe of intimacy. Not much is known about these people. Are they a real sub-culture ? Do they compare notes on Rule 34 ?

What we do know is that these individuals tend to end up in an emergency room, crossing their legs out of embarrassment and to stem the flow of blood.

Dr Ralph Benson, in his article "Vacuum Cleaner Injury: A Common Urologic Problem ?", he examines five cases where men had attempted intimacy with these household implements... and suffered the consequences. All of these cases occurred when men attempted to use vacuum cleaners as masturbatory aids. I don't want to get into the details of the injuries, but the words "laceration" and "penis" should give you an idea of what happened. If you don't know what the terms "avulsion" and "degloving" mean, then don't worry, the paper comes with helpful photographs. Why not download the paper and look at them ?

Dr Benson notes in the paper that the cases he describes are not isolated incidents. Reports of vacuum cleaner injury go right back to the 1960's. But the interesting thing that Dr Benson notes is that even though he lived in a relatively small town, he encountered a cluster of these vacuum cleaner cases. In the absence of a tangible connection between these people, he came to a somewhat incredible conclusion.

His conclusion was that this was not an isolated cluster of vacuum abusers. Instead , he suggests that vacuum cleaner abuse may be far more common than is generally recognised. He notes that often, people will lie about the causes for the injury, and will only reveal the truth if the doctor subjects them to more intense questioning. He proposes that in busier emergency rooms, medics don't have the time or the inclination to probe into these lies, and just get on with repairing the injury.

Nevertheless, there is an important lesson in here, that I never would have thought needed to be iterated, but nonetheless here it is- Do not stick any part of your body into a hole that contains whirling blades.

C. Benson R. (1985). Vacuum cleaner injury to penis: A common urologic problem?, Urology, 25 (1) 41-44. DOI: 10.1016/0090-4295(85)90561-8

TMI Friday: A Vexacious Consequence of a Vasectomy

2013-04-19T12:00:00.000+01:00

It was an emergency. The patient was 51 years of age, running a high fever, and pain and swelling in a particularly sensitive area, in which an operation had been performed a week previous. Gentlemen of delicate dispositions may wish to avoid reading further, for that operation was a vasectomy.

The purpose of a vasectomy is contraception, to make sure that a man cannot impregnate a woman with his sperm. A vasectomy works through preventing sperm from escaping from your testicles, where they are manufactured. It does this by cutting the vas deferens, the tube through which the sperm travel out of the testicles. This procedure has become relatively advanced in recent years.

The gentleman in question had a "No scalpel" incision vasectomy. This has a number of benefits , not least that it doesn't involve a scalpel being wielded near to a "gentleman's dangling region". It its quicker, leaves a tiny operation scar, which means less bleeding pain and infection, and more importantly, a quicker return to sexual activity.

The 51 year old gentleman however had clearly acquired some form of infection after the operation. Infection after a vasectomy is generally uncommon. They then found the identity of the bacterium causing this infection. It was Streptococcus pyogenes, the bacterium that commonly causes sore throats. Long time readers of this blog probably have some idea of where this is going...

His wife had been looking after their children who were suffering from sore throats. And unbeknownst to anyone, the Streptococcus pyogenes had been passed to her, and was settling on her tonsils. The night before the fateful emergency visit, she and her husband had an intimate moment. During this process, the bacteria on the wife's tonsils somehow ended up on the husbands genitals. The paper describing this clinical case describes the infectious process that followed:

It is reasonable to assume that the vasectomy incision was only superﬁcially healed, and therefore, violated and impregnated during the “trauma” of oral intercourse.

This is one of those cases where a series of events coincide, which results in a bizarre disease complication.

That was my TMI Friday, I hope you endured it as well as I did.

Ramaswamy K. &; Kaminetsky J. (2011). Unique Infective Complication after Routine Vasectomy: A Case Report, The Journal of Sexual Medicine, 8 (9) 2655-2658. DOI: 10.1111/j.1743-6109.2011.02345.x

TMI Friday: Taking a Bite out of Love

2013-04-12T12:00:00.000+01:00

Love isn't commonly encountered within the medical literature. The romantic lives of two people in love is a subject that rarely requires the attention of a doctor.

But occasionally in the violent throes of a passionate embrace, there is an emphasis on the violence. With this in mind, let us consider the Hickey.

Once when I was in school, I met up with my friend and the first thing I said to him was "What the f**k happened to your neck ? Did you get attacked ?" at which point I realised the girl next to him started to giggle.
I later learned that during the violent throes of passion, that occasional nibble may occur, leaving a bruise, or just a red mark. But on occasion, some couples go a little bit too far. This is where medical professionals get involved.
In a set of case reports published by the British Journal of Surgery in 1990, 7 cases of what are described as "Traumatic" love bites are reported. I shall summarise them below

Patient 1, a 35 year old man, came into hospital complaining of a hard lump in his shoulder that had been bothering him for long time. It was a worrying lump, and the doctors initially suggested that it was some sort of cyst. When they removed the cyst, they were dismayed to find... a plastic tooth. It turned out that he had engaged in intimate relations with a lady who was not only dressed as a vampire, but possessed an incredible commitment to the lifestyle.
Patient 2 arrived at the hospital with an abscess in his neck that was swollen with bacteria, caused by a particularly violent love bite that had happened 3 weeks previously
Patient 3 arrived at the emergency room, bleeding from the jugular vein that was caused by deep bite marks that had been inflicted 2 hours earlier under undisclosed circumstances.
Patient 4, a 26 year old woman had been suffering from cellulitis in the neck for about 3 days before she went to hospital, and later confessed it was due to "ferocious love biting" by her boyfriend, and in response, the surgeons gave her a course of antibiotics and also a tetanus shot.
Patient 5 appeared to accept some form of responsibility for the infected wound on his neck, as the wounds were inflicted after he had returned from a long holiday by his "frustrated" girlfriend.
I feel sorry for Patient 6, who had to cut off her honeymoon early after her drunken husband accidentally bit off her left nipple. The doctors don't mention the fate of this relationship, but I would be very surprised if "Divorce" was not a key feature of it.
Patient 7 suggested that the primary reason for the infected injury in the left breast was due to the short stature of her paramour.

Only in two of these cases is the injury in itself severe enough to merit an immediate visit to hospital. The majority of the problems caused by these human bites come from infection. The human mouth is generally full to the brim with bacteria, that could potentially become hazardous if introduced into a wound.

When one attempts a lovebite, always remember to take a sensible nibble, if you end up with a mouthful of blood then you are probably doing it wrong. Unless you are a vampire, in which case, check that you still have all of your teeth at the end of it.

Al Fallouji M. (1990). Traumatic love bites, British Journal of Surgery, 77 (1) 100-101. DOI: 10.1002/bjs.1800770134

The Early Emergence of Antibiotic Resistance

2013-04-07T16:15:00.001+01:00

The development of resistance to the antibiotics is a phenomenon of great theoretic interest to a bacteriologist, and it may some day become a matter of major concern to the clinician.

This is the opening line in C. Phillip Miller's paper on the development of resistance to antibiotics, which he published in 1947. Penicillin had only just been in production for five years. It was saving countless lives. It was emerging as a miracle drug. But even in this relatively optimistic era, a number of scientists were getting a taste of things to come.

Alexander Fleming, the discoverer of penicillin, was well aware that it does not kill all bacteria. Whilst it was effective against bacteria like Staphylococcus aureus, he found that E. coli (it was called B.coli at this stage in history) was not killed by penicillin. These bacteria appeared to be naturally resistant to penicillin. The question of what caused this resistance in some species of bacteria was taken up ten years later, by Edward Abraham and Ernest Chain. In 1940 they took cultures of E.coli and then broke them down into a mush. They filtered penicillin through this mush, and then tested whether it still killed Staphylococcus aureus. It turned out that the penicillin no longer worked, and they inferred that E.coli had some form of enzyme that broke down penicillin.

But this paper only talked about E.coli’s natural ability to break down penicillin. Surely this wouldn’t be a problem. This just means that penicillin won’t be useful against infections caused by E.coli. It’s not as if it would ever be a problem for fighting bacteria which they knew to be sensitive to penicillin, like Staphylococcus aureus ? Right ?

In 1942** it became clear that Staphylococcus aureus could become resistant to penicillin. Charles Rammelkamp and Thelma Maxon were trying out penicillin therapy for human infections. They noticed that penicillin therapy was not always effective for treating patients infected with Staphylococcus aureus. Some of the Staphylococcus isolated from these patients were now resistant to penicillin.

They then took some strains of Staphylococcus aureus that they knew could be killed by penicillin. The exposed these bacteria to increasing concentrations of penicillin, and over the course of 50 days they managed to dramatically increase the resistance of these bacteria to penicillin. The reason this had not been spotted before was because it had taken so long to develop. In the years after, similar methods were used to show that other bacteria could also develop resistance to penicillin.

By the time Alexander Fleming gave his Nobel Prize lecture in 1945, he was very much aware of the threat that antibiotic resistance posed. At the end of his lecture, he issued a warning about the perils of underdosing penicillin, and how it could eventually lead to the proliferation of penicillin resistance.

The time may come when penicillin can be bought by anyone in the shops. Then there is the danger that the ignorant man may easily underdose himself and by exposing his microbes to non-lethal quantities of the drug make them resistant.

In this speech, he also proposed his solution to this problem. He observed that since penicillin wasn’t toxic, there was no such thing as an overdose.

C. Phillip Miller’s paper further expands on this theory. He compared the development of resistance to Penicillin and Streptomycin. Other works had shown that antibiotic resistance had developed when low doses of penicillin were used to treat animal infection. Miller however disputed that this was relevant to the human situation, as penicillin was usually given in high doses to produce a “margin of safety”. He said that most bacteria develop resistance so slowly that infections are brought under control before a detectable degree of resistance has begun to build up. In his work, he noted how comparatively slowly organisms developed resistance to penicillin, and thus predicted the rise in Streptomycin resistant strains of bacteria, but not penicillin resistant strains. At this point in time, penicillin resistance could be dismissed as a rare event.

By 1957, the emergence of Staphylococcus aureus strain 52/42B/81, which possessed resistance to penicillin, streptomycin and tetracyclines, put paid to the idea that the threat from antibiotic resistance was only theoretical. When we talk about the emergence of antibiotic resistant strains, it must be remembered that they have been around for nearly as long as antibiotics themselves, and even the earliest pioneers could foresee the danger they presented.

References

Miller C.P. (1947). Development of Bacterial Resistance to Antibiotics, The Journal of the American Medical Association, 135 (12) 749. DOI: 10.1001/jama.1947.02890120003002

Abraham E.P. & Chain E. (1940). An Enzyme from Bacteria able to Destroy Penicillin, Nature, 146 (3713) 837-837. DOI: 10.1038/146837a0

Rammelkamp C. (1942). Resistance of Staphylococcus aureus to the Action of Penicillin, Proceedings of the Society for Experimental biology and Medicine, DOI: 10.3181/00379727-51-13986

Alexander Fleming's Nobel Prize Lecture (1945)
http://www.nobelprize.org/nobel_prizes/medicine/laureates/1945/fleming-lecture.html

Finland M. (1979). Emergence of antibiotic resistance in hospitals, 1935-1975, Reviews of infectious diseases, PMID: 45521

Footnotes

*They called resistant bacteria “antibiotic-fast”, for linguistic reasons that are unclear to me.

** The earliest paper that I could find which demonstrates penicillin resistance was from 1942. In the paper the authors say that penicillin resistance had been described before in 1941, and then cites those papers…. incorrectly. The first paper cited only looked at resistant to sulphonamide antibiotics. The second paper they cite was by howard florey, and in that paper there is no reference to either resistant (or “fast” as the term would be) Staphylococcus aureus. So somewhere out there there may be a paper in 1941 that shows the development of antibiotic resistance in Staphylococcus aureus, but it has been expunged from the record for completely unknown reasons.

#Microtwjc: The Evolution of Virulence

2013-03-31T18:37:00.000+01:00

A long time ago, a bacterium noticed the odd behaviour of its cousins. It had noticed that they had formed a group, and were spending a lot of time together. An unsettling amount of time together. The bacterium's friends told it to not worry. It is perfectly fine for related bacteria to stick together, to live in colonies of individuals.

The bacterium told its friends how its cousins behaviour was different. The cousins had forfeited the life of independence, and had reduced themselves to mere parts of a greater whole, a multicellular organism. it was disgusting, it was socialism. It had to be stopped.

Bit by bit, it rallied other bacteria to it's cause, and together they came up with a plan. They would become virulent. They would evolve the resources to fight their multicellular competitors, invade their cells and feast on their nutrients. So they went to Professor Oak, who happened to have just the right thing for them...

Okay, maybe my "Pokemon Virus Vendetta" theory of bacterial evolution is a bit off the mark. In my defence, a microbiologist looking at bacterial evolution doesn't have any fossils to help them. And even if they did, they won't really tell them anything other than the shape of the bacteria, which has no real bearing on whether a bacterium is virulent or not.

Evolutionary bacteriology is primarily based on using the bacteria's genetic code. We can look at bacteria that inhabit different environments, and look at the similarities and differences between them and formulate ideas about bacterial evolution.

So there are questions as to how bacteria evolved to live inside animals, and more importantly, how they learned how to survive the immune system. One of the great threats to bacteria are phagocytes, which roam the body gobbling up bacteria and digesting them. Yet some bacteria have not only evolved ways to prevent themselves being eaten, but to survive, even thrive, within phagocytes, such as Mycobacterium tuberculosis. One of the first steps of infection with this pathogen actually requires a phagocyte to consume it, and then it grows and proliferates within this phagocyte in order to eventually cause disease.

M. tuberculosis possesses many genes which help its survival within the host, and the set which is studied in this paper is known as "Mammalian Cell Entry" genes, so called because when E.coli were given these genes, it enabled them to invade mammalian cells*. In M. tuberculosis it is thought to in some way control its survival within phagocytes.

So when the "Mammalian Cell Entry" genes were found in a soil microbe, Streptomyces coelicolor , a question is raised. Why would S. coelicolor need these genes ? The answer may tell us something interesting about how bacteria evolved to attack us and cause disease.

Does Streptomyces actually use these genes ?

Just because it's in your genome doesn't mean you use it. If the genes have no function in S. coelicolor then we can only speculate on what function their ancestors had for these genes. So they measured the presence of mRNA within these cells at the different stages of Streptomyces growth, and checked its presence on different media.

This is shown on Figure 2 A, with the white bands indicating the presence of mRNA.

hrdB is a house keeping gene which is always active, and mceA is one of the "mammalian cell entry" genes. mtrA controls whether mceA is switched on or not.

So from figure 2A, we can see that mceA is switched on when grown in YEME medium, but not in MS medium, and mtrA is switched on in this medium. This suggested that the medium's effect on mceA works independently of mtrA.

In Figure 2 B, we are shown what happens if S.coelicolor grown in cholesterol compared to YEME. A mutant with no functional mtrA was also grown as well. This shows that mceA is not active when cholesterol is present within the medium, and it is not present when mtrA is removed from the medium as well. This indicates that mtrA is needed for the expression of mceA, except when the bacterium is grown in MS medium, in which case not even mtrA will not prevent mceA being repressed.

So at least we know that the mammalian cell entry genes are doing something in Streptomyces, but what are they doing ?

How well does Streptomyces do without the Mammalian Cell Entry (mce) genes?

One way of working out what a gene does is getting rid of it, and watching what the bacterium now cannot do without those genes.

Figure 3 looks at a number of things that change when Mammalian Cell Entry (mce) genes are removed.

They took a lawn of streptomyces, and then they added a drop of SDS into the middle of it. SDS is a surfactant, that attacks bacterial cell membranes kills off the streptomyces, which produces a dark pigment as if to protest***. As you can see from the image, the darker patches are larger when 20% SDS is applied compared to the 10%.

The mce mutation appears to give the two colonies we are shown an edge when growing with lysozyme, a molecule which acts to break down cell walls. Whilst the dark patches are still there, you can see some bits of white poking through, showing that some of the cells have survived, although they are still injured***

Electron microscopy revealed that mce mutants have more "wrinkled" cells, that were on average shorter than the normal cells from a sample of a hundred cells.

So what is the significance of these effects?

What happens if we feed our bacteria to an amoeba ?

An Amoeba is the natural predator of bacteria like S. coelicolor in its natural habitat within the soil. So what happens when we feed these bacteria to an amoeba, and what effect does the mutant have on this ?

In Figure 4, this is what they do.

It turns out that the mce mutant kills the amoeba. If you look under a microscope in 1 A , the bacteria germinate and grow within the amoeba after they are eaten, and kill the amoeba in 24 hours.

In the next experiment, they made a "lawn" of S. coelicolor grow on the surface of an agar plate, before adding amoeba to the centre of the plate. When the bacteria are eaten, clear patches form on the surface of the plate. So we can see that the deletion of mce, and the Mtr gene that promotes its growth prevents the bacteria getting eaten. If you try to correct the mutation by adding a plasmid (pLS006) with functioning mce, then you find that the bacteria once again get eaten by the amoebae.

So mce expression enables bacteria to be eaten by amoebae, and when its gone, the amobae stop being a threat.

So if all mce does is allow bacteria to get eaten, then what is it's point ?

What effect does mce mutation have on root colonisation ?

S. coelicolor lives on roots, and its possible that mce plays a part in root colonisation. And it turns out that when mce is deleted, bad things happen to the plants it colonises, which is shown in 5A. On the microscopic pictures we are shown, we can see that there are less bacteria on the roots when the mce is knocked out.

In 5B, we can see that there are less of the mce mutant on these plants compared to S. coelicolor with functioning mce.

So now we must consider what this data has told us, and then what we can deduce from it.

Summary

This paper characterises the function of a gene system in S. coelicolor which is somewhat related to an important gene in Mycobacterium tuberculosis. So what have we found out about this gene in this paper ?

When mce is active :

It decreases resistance to Sodium dodecyl sulphate (SDS)***
It does not prevent resistance to lysozyme.
It makes nice and healthy looking cells.
It does not prevent amoebae eating it
It allows growth of the bacteria on roots

When mce is inactive:

It increases resistance to SDS***
In increases resistance to lysozyme, which is lucky because lysozyme is an important digestive enzyme used by amoebae to eat their prey.
It also kills off amoebae when they attempt to eat S. coelicolor.
It prevents plants growing very well if they get into the roots
It also doesn't allow S. coelicolor to grow very well either on the surface of roots.

What does this tell us about the evolution of virulence ?

On its own, this data only really tells us about S. coelicolor, and what the mce gene does in this bacterium. But when we compare it to the action of the mce in M. tuberculosis, we can start thinking about the different ways these genes have evolved to match the life cycles of their respective bacteria.

There are multiple mce's in M.tuberculosis which have slightly different functions, but mostly they play roles in surviving within macrophages, primarily in reducing the expression of cytokines by macrophages after they've been infected with the bacterium, and its general survival. It is very difficult to compare the functions of the mce's in Mycobacterium to Streptomyces because of this.

But this isn't the point of the paper. They say in the paper that Streptomyces split off from the ancestor of Mycobacteria at around 440 million years ago**. Based on this, we assume that the common ancestor of these two bacteria probably expressed some variant of the mce gene. So based on this, we can try to deduce what this ancient bacteria from the Silurian era used these genes for. This was around the time that plants were colonising the land. The authors suggest that these genes evolved to allow soil bacteria to colonize the surface of plants, and allow it to control when it expresses the genes that allow it to survive being eaten by amoebae.

In many ways, phagocytes behave like amoebae. Like amoebae, phagocytes use lysozymes to digest their prey. So soil microbes which have methods of resisting amoebae come ready made with methods of resisting phagocytes.

So the idea is that a long long time ago, these soil microbes were minding their own business, when they end up in the body of a mammal. The mammals immune system immediately recognises them as foreign, and sends phagocytes to destroy them. The bacteria, assuming that they are being eaten by amoebae, shut down their mce system to resist being eaten, and end up causing a disease in the mammal.

Do you see the irony here ?

The presence of these macrophages actually make a host more likely to get a disease after ingesting these microbes.

Why does any of this matter ?

So let's imagine a world where we fully eradicate tuberculosis, and other diseases. The primary threat of disease comes from the fat tail of emerging pathogens which can now exploit the empty niches left by the other bacteria. What was originally just one bacterium causing a disease is now a hundred bacteria causing a disease.

Understanding the evolution of virulence allows us to get an idea of where possible threats can come from.

There are Mycobacteria, Legionella and Chlamydia like bacteria which currently reside in our environment and attack amoebae, which can cause pneumonia in humans if they end up in the lung. If we understand the evolutionary process which allows soil bacterium to cause disease in humans, perhaps we can devise strategies to prevent it from happening.

This paper was up for discussion by the microbiology twitter journal club this Tuesday (2nd April) at 8pm BST.

The transcript of that discussion can be found here

Reference

Clark L.C., Seipke R.F., Prieto P., Willemse J., van Wezel G.P., Hutchings M.I. & Hoskisson P.A. (2013). Mammalian cell entry genes in Streptomyces may provide clues to the evolution of bacterial virulence., Scientific reports, PMID: 23346366

*Now I should note here that it is unclear at this time how the "Mammalian Cell Entry" system actually works, and whether it actually serves any function in helping mammalian cell entry, or whether it simply allows bacteria to survive better after naturally being eaten by cells.

**I couldn't find the reference for this observation. Well, they do give a reference for fungal and plant evolution occurring around 400 million years ago, but streptomyces is a bacterium, and I'm not sure how this would apply.

*** In the original article, I assumed that the black patches were the colonies themselves, rather than zones of dead bacteria, when in actuality, they represent dead bacteria, and I thought that the SDS had the reverse effect to what it really does. Had I known that S. coelicolor painted itself black upon death, I would have drawn a picture of it dressed as a goth. Hat tip to @clonemanager and for explaining this to me.

The Ten things I can do now I have a PhD

2013-03-27T21:05:00.000Z

Ten Things I can now do with a PhD

I can introduce myself as the "lu-uurve" doctor to members of the opposite gender.
I can finally publish a book about the "Lint" diet , and have it fester on bookshelves around the country as its devotees staunchly defend it even as they succumb to rickets, scurvy and starvation.
I can start an advice column, in which I couch my advice in impenetrable jargon to conceal its vacuity.
I can drop my PhD into conversations, knowing that it makes me knowledgeable on all subjects, as opposed to the one I actually specialised in.
I can now end any argument by taking a tally of people in the room with PhDs, and those without, and then cement my victory by blowing raspberry lasting not less than 3 minutes.
I can buy a pair of glasses for the sole purpose of peering over them when talking to people. And occasionally ripping them off dramatically to let everyone know that shit just got real.
Whenever someone utters the words "Doctor Who?", I can shout my own name really loudly and pointing at myself. Raucous applause will no doubt follow.
I can now defuse "Doctor Doctor" jokes by simply saying "Yes, what is it?". This will always be funnier than the actual joke.
I can line up the photos from my BSc, MSc and PhD graduations, put attack statistics under them and pretend that I am a extremely rare and esoteric pokemon, and that these were my evolutions.
I can don a cape and mask to begin a career in super-villainy. It's the profession with the best hiring prospects in this economy.

The Final Hurdle

2013-03-25T21:08:00.002Z

I'd pretty much done everything at this point. Through struggle and sweat and 263 pages of thesis, I found myself edging closer to the finish line.
In theory, anyone can write a long thousand page tract on a topic without applying observation, logic, creativity or even basic grammar to it.
The manuscript I wrote needed to be assessed. But herein lies the problem.
A PhD, unlike many other qualifications, is granted based on the synthesis of new knowledge. The problem of this becomes apparent when it comes to examining a PhD. I'll compare it with learning in a school.
The learning that occurs in schools occurs in the classroom, by a teacher who at least know the standardised answers on their test sheets, if not the subject itself. As a result, examining students knowledge of these subjects is the simple task of comparing their answers to the standardised tests.
A PhD is very different. A student does not do most of their learning in lecture rooms regurgitating the knowledge that is fed to them. A student must use the tools, and the advice of experts (including , but not exclusively their supervisor(s)) in order to gain new knowledge or insight about a subject. In the case of the sciences, the teacher is nature. This becomes problematic when examining PhD, because Nature doesn't have a mark scheme. If it did, it would be behind an infinitely expensive paywall.
This is where the Viva Voce comes in. The exact details for the examination differ between countries, but the basic jist is that it requires the thesis to be assessed by a group of experts in that field, who are able to assess the work, and discuss the new findings of the thesis with the candidate.
A number of people in my lab told me that I would be fine, that it would amount to a nice conversation about my thesis by the only people* who would actually read it. I guess I should have found this reassuring. An experienced tight rope walker may tell you that a hundred metre walk on spider silk over hot lava was simple enough when they did it, but does that mean you'll have no trouble on your first go ?
As it turned out, this was pretty much the case. Aside from one moment where we got into a somewhat philosophical argument about the possible detriments of eradicating an infectious disease, a few moments where I talked myself into a corner, it went well.
In the end, I passed !

*not including those involved with its production

Elementary; The Science of the Perfect Murder

2013-03-11T14:11:00.001Z

It was television that inspired me to devise the perfect murder. Not for the usual reasons relating to scientific inaccuracy. This isn’t about watching a CSI unbalance a centrifuge, contaminate a sample or holding a pippette backwards. Small errors like this just don’t bother me anymore. I long ago accepted that most television shows are set in a science fiction fantasy world, where science only works when the gods of plot convenience allow it. It was in this frame of mind of that I watched a recent episode of Elementary (S1e17 “Possibility Two”).

For those of you who aren’t familiar with it, “Elementary” is a procedural cop drama with the twist that it is also a Sherlock Holmes pastiche. It follows the detective (played by Johnny Lee Miller) and Joan Watson (played by Lucy Liu) as they solve crime in modern day New York. Johnny Lee Millers interpretation of Holmes can be described by shouting “QUIRKY” and doing the jazz hands so hard that they violently detach and fly across the room. This is a show that proudly flies the flag and wears the matching underpants of silliness. The very definition of TV to turn your brain off and enjoy. Unfortunately, my brain’s off switch is always slightly faulty and against all odds, this episode did make me think. I am going to spend the rest of this post exploring these thoughts, so I should warn you that this article contains major spoilers.

This episode kicks off with millionaire Gerald Lydon attempting to get Holmes to reveal that he has hereditary Cerebral Amyloid Angiopathy (CAA). The twist is that he believes that someone gave him this hereditary genetic disease deliberately. To Sherlock, the answer to this particular puzzle is as simple as cluedo. The culprits were Lydon’s parents in the bedroom using the *ahem*. But they have to be ruled out as suspects, because they didn’t have CAA (and it is a dominant genetic trait).

And then Sherlock asks “Have you considered that you may not know your true parentage ?”, and they spend the next forty minutes of the episode tracing Lydon’s real father whilst discussing the degree to which parents are responsible for the genetic inheritance they bestow on their children and the episode finishes with Sherlock explaining the mystery to his dementia ridden client using sock puppets. Just kidding, this wouldn't be a murder mystery procedural drama without an actual murder mystery.

Someone did find a way to give Gerald Lydon CAA, and they did it by using science. In the process of this episode, they frequently invoke fictional geneticists that say “this is totally possible” in a naked attempt to justify the absurdity at the heart of the episode. Sherlock’s investigations lead him to a slick biotech company where their one jobbing scientist to explain the basis of CAA.
CAA is essentially caused by a an excess build up of beta amyloid protein in capillaries that carry blood within the brain. as these build up, blood is prevented from going where it’s needed, leading to brain cells dying off. As the scientist explains, the hereditary forms can occur through a mutation in the APP gene. This gene encodes the Amyloid precursor protein, which forms the harmful Beta Amyloid that is the cause of CAA. Later in the episode, that scientist sends the Sherlock a chemical structure, with a note that says something akin to “This is the murder weapon”.
.

Unfortunately, I’m not an expert on chemistry so I don’t know the identity of this chemical*. In this episode, it acts as a mutagen which specifically interacts with the APP gene to cause the build up of amyloid beta. How it does this is not explained, and I think I am safe in saying that this compound wouldn't actually work in real life**. But just because this compound is unlikely to give someone CAA, it doesn’t mean that it isn’t possible.
For the past 10 years, people have been developing gene therapies to cure hereditary diseases through manipulating the human genome. But what if we re-purposed the tools used for gene therapy into a murder weapon, in order to cause a disease rather than to cure it ? What if we put ourselves in the shoes of the murderer of this episode. How would we go about giving someone CAA for real ?
Let’s get back to the APP gene, which encodes the Amyloid precursor protein. We want to alter this gene so that it ends up forming as much Beta amyloid protein as possible. The APP gene, through a clever trick using short RNAs, actually codes for a number of different APP’s, only a few of which can be broken down to form Beta amyloid. The gene itself is structured into segments (which are called “exons”) which are transcribed depending on the presence or absence of microRNA’s.
It is known that the production of Beta amyloid is related to the presence of one particular section of the APP gene, known as the “Kunitz protein inhibitor” (KPI) is associated with the build up of beta amyloid [1,2]. However, this section of the gene often excluded from APP transcripts by a microRNA known as MiR-124 [3].

When the DNA code is being translated, this MiR-124 binds to the KPI section of the gene, and excludes it from the APP. If we stop MiR-124 from doing this, we increase the amount of APP with the KPI section in it, and in theory this could increase the production of beta amyloid, which would give an individual CAA. How do we do this ?
We can do it by using some nifty chemicals known as Morpholinos. They mimic RNA, except they don’t get broken down as easily, and they can be attached to other chemicals to make sure that they enter cells. We could design a morpholino to mimic the action of MiR-124 if our intention was to prevent someone from getting a build up of Beta amyloid in their brain. But that isn’t what we want to do. We want to stop MiR-124. So we can design a morpholino with a sequence that binds to MiR-124. This would effectively halt its function, and allow for more beta amyloid to build up.
But how do we give this lethal morpholino to a patient without them noticing ?
The murderer from Elementary fortunately solved this one for us. We are told that the murderer targeted three millionaires, and gave the drug intravenously whilst they were staying as patients in hospital. Delivering a drug intravenously allows it to go straight to the blood. Blood goes everywhere in the body, including the brain. While these patients are being infused with the anti-MiR-124 morpholino, Beta amyloid builds up, eventually to the levels needed for them to develop CAA ***.
Now we need to know how much Morpholino to give our murder victims. The main challenge here is the blood brain barrier, which prevents many drugs, including morpholinos, entering the brain. We are going to try to overcome this by simply putting in such a high dose that it overcomes the blood brain barrier.
In studies in rats, researchers have administered a dosage of approximately 300pmol of morpholino per rat brain [1]. Let’s imagine that to get the same effect in humans, all we need to do is increase the dose based on weight****. A rat brain weighs around 2 grams, and a human brain weighs around 1400g.

Anti-MiR-124 dose/Rat Brain= 300pmol/2 g
Human brain= x/1400g
Therefore the dose = 150 x 1400g

210,000pmol of morpholino needs to enter the human brain in order to have the desired effect. I could only find one study that makes quantitative measurements of morpholino concentrations in the brain after an intravenous dose, and it suggests that the dosage of morpholino can be reduced by around a thousand fold [5] *****. So we’re going to have to up that dose to 210,000,000 pmol, per person.
At this point, one may wonder why anybody would go to this much trouble to kill some rich philanthropists in such a convoluted way ? The answer is as demented as it is ludicrous. The killer is a scientist suffering from CAA. In order to cure himself, he needs more funding. So he decided that the best way to go about this was to deliberately give rich people CAA, which would compel them to donate money to him in order to fight the disease. I will demonstrate the ludicrousness of this plan through the use of simple arithmetic.
10,000,000 pmol of a morpholino costs at least $9,000 dollars (based on numbers from Gene-tools.com). So the total cost of giving one dose of the drug to reach their brain would be around $189,000 dollars. I am going to assume that they were given the drug throughout their stay in hospital. All of these individuals are over the age of fifty, and the average hospital stay for that age group in the United States is 5-6 days. So at most, it would cost $ 1,134,000 to poison one person.
During the course of this episode, not one, but three people have been poisoned successfully in this way, bringing the total budget of this endeavour to $3,402,000. This assumes that it all went according to plan (which science so often doesn’t) and that the murderer only produced enough chemical for three people. Let’s not forget that the technology for morpholinos already exists . If the murderer wanted to develop some other method to cause this disease, they would have to start from scratch, requiring even more money. This highlights the central contradiction within the plot.
As the scientists often say during the episode, with unlimited money and resources a scientist could develop the technology to give someone CAA. As I have demonstrated, the killer had plenty of money around to throw at this project. Yet the whole reason behind this convoluted murder scheme is to allow the perpetrator to acquire money. It’s almost like the murderer has some sort of degenerative brain disease.
In thinking through this murder plot, I am forced to consider the many barriers to actually pursuing it in real life, which I have boiled down to three points

To induce a genetic disease, we need to know a lot about it. I kept coming up against this problem with CAA. It’s not that I couldn’t find a cause, but that there were so many different genes that could play a role that I couldn’t point to any of them being the root cause.
Our technology isn’t there yet. There are a lot of promising developments in gene therapy that are just over the horizon. I’ve spent most of the this post talking about morpholinos, but there are other treatments in the works. Viruses that have been re-purposed to deliver genes are now commonplace in some labs, and advances in nanotechnology may allow for promising developments in the future. Very few of these have ended up in successful human trials, and they are still being tweaked to make them more effective.
Resources. The cost of using this technology immediately puts it out of the hands of a casual amateur. And if you are a professional scientist, you will eventually be called in to explain the half million dollar hole in your lab finances that is designated “convoluted murder plot”.

Our knowledge of genetic diseases is expanding as are the gene therapies that are being developed to combat them. The “Genetic disease” method I have described here may be plausible at some point in the future. But I have to note that the three points I have described are also barriers to curing genetic diseases.
When we do get to the point where our knowledge and technology is advanced enough to manipulate a person’s genome so subtly that we can give them a genetic disease, we will also be at the point where these advancements will also allow for the treatment, prevention and possibly even a cure for it.
It is somewhat amazing that a run-of-the-mill procedural drama catalysed this thought experiment. But sometimes inspiration can come from the unlikeliest places. Sherlock sums it up the best in the last line of the episode.

“A good detective knows that every task, every interaction no matter how seemingly banal, has the potential to contain multitudes.”

References

[1] Matsui T., Ingelsson M., Fukumoto H., Ramasamy K., Kowa H., Frosch M., Irizarry M. &; Hyman B. (2007). Expression of APP pathway mRNAs and proteins in Alzheimer’s disease, Brain Research, 1161 (3) 116-123. DOI: dx.doi.org/10.1016/j.brainres.2007.05.050
[2] Tharp W.G., Lee Y.H., Greene S.M., Vincellete E., Beach T.G. & Pratley R.E. (2012). Measurement of altered AβPP isoform expression in frontal cortex of patients with Alzheimer's disease by absolute quantification real-time PCR., Journal of Alzheimer's disease, 29 (1) 449-457. DOI: dx.doi.org/10.3233/JAD-2011-111337
[3] Smith P., Al Hashimi A., Girard J., Delay C. & Hébert S.S. (2011). In vivo regulation of amyloid precursor protein neuronal splicing by microRNAs, Journal of Neurochemistry, 116 (2) 240-247. DOI: 10.1111/j.1471-4159.2010.07097.x
[4] Fujikawa T., Tamura K., Kawase T., Mori Y., Sakai R., Sakawa K., Yamaguch A., Ogata M., Soya H. & Nakashima K.; (2005). Prolactin Receptor Knockdown in the Rat Paraventricular Nucleus by a Morpholino-Antisense Oligonucleotide Causes Hypocalcemia and Stress Gastric Erosion, Endocrinology, 146 (8) 3471-3480. DOI: 10.1210/en.2004-1528
[5] Stein D.A., Huang C.Y.H., Silengo S., Amantana A., Crumley S., Blouch R.E., Iversen P.L. & Kinney R.M. (2008). Treatment of AG129 mice with antisense morpholino oligomers increases survival time following challenge with dengue 2 virus, Journal of Antimicrobial Chemotherapy, 62 (3) 555-565. DOI: 10.1093/jac/dkn221

Footnotes

*It looks like a unsaturated aromatic ring linked via three keto groups to branched carbon chains that end in either a napthalene group or an adenine, and these speculations are purely based on the diligent use of google image search.
** If I am wrong about this compound being ineffective, then not only does this cement my inexperience as a chemist, but also worryingly suggests that not only is one of the scriptwriters for the show devising bioweapons, but they are broadcasting them on air for the whole world to see. That’s a scary thought. What will we see next, plans for an Antimatter bomb in the background of Sherlock’s study ?
*** You’ll need to expose the victims to the drug for a long time for it to be effective, as people who are born with the mutation we are attempting to replicate often take an entire lifetime for symptoms to become visible. And this is not to mention that the link between the presence of KPI and high levels of amyloid beta have only really been shown to have a correlational relationship, so this entire method of inducing it could be completely wrong. But let us assume that it works as a method of murder.
**** The actual art of comparing the biological reactions of all the different creatures within the animal kingdom (known as Allometry) is a massive subject which touches on evolution as well as medical science, and boiling it down to a mathematics exercise misses out a lot of its more subtler details.
***** I experienced severe math fail when reading reference [5]. They administered the drug at 10mg/kg to a mouse. In 200 micrograms of morpholino compound was given to a mouse, equating to approximately 0.26 micromols. This is assuming a mouse weighing 20 g, that a 24 nucleotide morpholino has a similar molecular weight to an RNA oligo of approximately ~ 7500 m.w. They later measured the presence of morpholino in the brain, and found that it hovered at around 0.02 micromols, which would make it look like it goes to the brain very well. However, in this same experiment they detected 9.34 micromols in the liver, which would suggest either that the morpholinos are replicating inside the mouse, or that my math went wrong somewhere. It is almost certainly the second option, because Figure 6 of that particular paper shows quite clearly that barely any of the morpholino reaches the brain, and my thousand fold guess comes from looking at the graph, and also comparing the amount of morpholino in the brain compared to other organs in table 2 of the paper.

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I've written a shorter version of this article on Kinja (http://faz-alam.kinja.com/). to make it clear, it's that article where I'm self plagiarising, as I wrote it after this one.

Taking DRACOnian measures against viruses

2013-02-09T16:45:00.001Z

When Alexander Fleming shamelessly took credit for a drug produced by the penicillium bread mould, our fight against bacteria completely changed. Even now, bacterial diseases are nowhere near the threat they were before that discovery. But today, we are seemingly having to put up with emerging viruses. Every year, we have to put up with entirely new strains of flu. There is nothing we can do about the viruses that cause the common cold. There are various antivirals out there, but they are specific for specific diseases, like flu, or HIV.
Generally, it’s been fairly difficult to make anti-viral drugs. Often, a lot of the problem comes from the way viruses work. When they are outside the cell, they don’t have any active metabolic processes that can be targeted by drugs. And when they are inside the host cells, the metabolic processes they’re mostly using are ours. So it’s very hard to target the viruses without attacking the host. Compared to bacteria, viruses present a very small target.
Could a paper published in PLOS One called “Broad Spectrum Anti-viral Therapeutics” represent a penicillin moment for viruses ? A trailblazer that can transform our relationship with viruses ? To work this out, let’s take a good long look at what these anti-viral therapeutics are, and what they do. To do that, we have to talk about DRACO.

Are we talking about the DRACO from Harry Potter, or the dragon voiced by Sean Connery in Dragonheart ?

Whilst I compliment you on your knowledge of pop culture, you are wrong on both counts. DRACO is a handy acronym that stands for Double-stranded Ribonucleic acid Activated Caspase Oligomizer. DRACO is the drug that is being purported to extinguish all of those nasty viruses. To understand how it works, we need to take a look at how some of our most pernicious viruses work.
You may have heard about DNA, and how it’s genetic code is a blueprint for life. Your genome is encoded in your DNA. The nucleus of your cell holds all of the DNA, and acts like a library for your genetic code. When your cell needs a specific genetic code to make a certain protein, the nucleus makes an RNA copy of the appropriate gene and sends it out into the rest of the cell, where it can be used to construct a protein.
The goal of a virus is to enter a cell, and to hijack this process to make more viruses. Usually at some point, the virus will attempt to substitute it’s own genetic code for that of the host cell, tricking the host cell to make more viruses ^[1].
However, some viruses can store their genetic code using RNA only. Whilst RNA is less stable than the DNA, it means that these viruses can go straight to the cell machinery that translate RNA into protein, and get ahead with making virus based proteins.

Influenza, Hanta Virus , Dengue Fever, Rhinovirus, Lassa fever virus, and reoviruses all use RNA as the basis of their genomes, and at some point during their replication, form double stranded RNA. This last bit is important, because double stranded RNA is not usually found in human cells, and when they are found, they don’t last for very long^[3]. So essentially the creation of double stranded RNA has been the achilles heel for many viruses.
So naturally, humans (and other animals) have evolved ways to detect the presence of double stranded RNA. I’ll give one example, a protein known as TLR-3^[4].

TLR-3 detects dsRNA, and then it signals to other proteins within the cell, so that it can do a number of things that the virus won’t like. For instance, it leads the cell to produce interferon, which acts as a distress call to summon the immune system. The most drastic reaction to the detection of viruses is the activation of caspases, which are the enzymatic equivalent of a self destruct button.

However viruses can evolve very quickly, and have matched pace with us in this evolutionary arms race. Viruses have evolved a number of tricks to get past our natural defences against them. While they can’t evolve ways out of being detected by RNA binding proteins, they have evolved ways to short circuit the signalling cascade that can occur after their initial detection. Let’s say that TLR-3 binds dsRNA, it tells another protein, and then a virus protein get’s in the way, and the message is lost.

So the idea underlying DRACO is taking the dsRNA detecting ability of enzymes like TLR-3, and shortcutting all of the signalling pathways and go straight to pushing the self destruct button Calm down, it’s not as bad as it sounds.

Your body naturally kills of virus infected cells. a cell infected with virus is not your friend any more it’s a factory for the enemy, churning out viruses to infect other cells. The normal immune response against viruses often involves killing off these cells, so don’t worry too much about them.

How does one make DRACOs ?

Now we’re getting into the actual paper, and away from the background. So how did this research group go about making DRACOs ?
They looked at a whole raft of different RNA binding proteins and looked at the parts of them which directly bind to the RNA. And then they looked at caspase proteins, and worked out which parts of those tend to cause human (or mouse cells) to self destruct. and they took those bits, and stuck them end to end.

Figure 1.A. of the paper shows the basic blueprint for these proteins. They also deliberately made versions of these DRACOs that were broken, to use as controls, just in case randomly giving cells proteins will protect them for no good reason. Figure 1.B. shows a western blot to prove that they created these proteins.

That’s great ! Do they work ?

So now that these DRACOs have been “designed” and created, the question is whether they work. So the first thing that we need to look at is whether these proteins actually get into cells. If they can’t do that, then there isn’t much hope that they can be effective.
The thing is, cells don’t just take up every protein that they get into contact with willy nilly. In order to gain passage into the cell, these proteins must have special tags on the end of them. In this case, they tried out DRACOs with PTD and TAT tags. They then added them to a culture with cells derived from humans (HeLa cells). They then extracted the cells from this,and tested whether the DRACOs had managed to get into the cells.

The western blot in Figure 2 A shows that DRACOs managed to get into the cells when they had the PTD or TAT tag added to them, but not without it. Furthermore, in Figure 2 B, they added the DRACOs to a culture of cells, and took out samples at specific time points to test whether the DRACOs had entered. Judging by the image, the DRACOs were taken up as early as 10^[5]-15 minutes after administration. Figure 2C shows that the DRACOs were retained by these cells for about 6-7^[5] days after they were first applied.
So now we know it actually gets into the cells, the question is whether they can detect the presence of double stranded RNA (dsRNA), and furthermore whether they can cause cells to “self destruct” when they are present. So they took some human cells, and genetically modified them to produce dsRNA. If the DRACO’s worked , then they would immediately kill off these cells.

How do you work out whether these cells have caspases becoming active ?

To work out whether the cells were pushing their self destruct button by activating caspases, the scientists here did some clever cell manipulation. They added the gene for luciferase to the cells. Luciferases are enzymes that produce light when they grab onto specific chemicals called luciferins.
To these cells, they introduced a protein which mimics the kind of proteins that caspases usually act upon when they are activated, with one difference. Tied up within the structure of these proteins is Luciferin. So when the caspases are activated, they bind this substrate, the process causes luciferin to be released into the cell. This is then found by the luciferase enzymes which then cause light to be produced. So the researchers could work out how active the caspases are in these cells just by looking at how much light they are making.
So they gave some of these cells caspase inhibitors. If DRACO’s were naturally lethal to these cells, the presence of these inhibitors would make no difference to whether the cells would self destruct (a process known as apoptosis).

In Figure 3, they added the DRACO’s to these cells, either with, or without the inhibitor. They also included a product which makes cells which have just self destructed glow in the dark. The first four sections on the graph are simply controls, to pick up the background levels of cell death. Since the main function of caspases is to cause cell death to occur, you can guess what would happen if we were to add caspase inhibitors to a normal set of cells. The blue and red bars are both lower than the green bar , because they have the caspase inhibitors added. The next three sections show what happens when DRACO’s are added to the mix, and they show that they kill off a lot of cells. And importantly, you can tell that it’s performed using caspases, because in the presence of inhibitor, the cells do not die as much. In fact, the levels of death seen is more or less the same as the other controls with inhibitors.
Ah yes, but this doesn’t necessarily prove anything ! The DRACO’s were created using e.coli cells, and it could be possible that when extracting these cells, some other nasty substances were pulled out that could account for this effect. So in the next set of data, they add some of the e.coli extract, and show that actually it doesn’t have any effect, when compared with controls.
But you forget, cells naturally try to off themselves when confronted with dsRNA ! how do we know that the supposed effect of the DRACO isn’t caused by that ? Because they then tested the cells with added dsRNA. And while the cells did indeed die off more than controls, it was still much lower than when the DRACO’s were added. They also added a compound known as camptothecin to deliberately trigger the self destruct in these cells.
So at the end of this last figure, we know that DRACO’s do what they say on the tin. When DsRNA is present, it activates Caspases and causes infected cells to Off themselves. But we haven’t yet tested them with real life actual viruses.
Whilst in the last experiment, we were looking at caspases only, in the next one we want to know whether it actually improves cell survival.
The theory behind DRACO’s is that the first cells to get infected should be the last cells to get infected, and die off before they can spread virus to the rest of the cells in the culture.

In Figure 4 A, they use rhinovirus, one of the viruses that cause the common cold, and add it to Normal Human Lung Fibroblasts, which are a cell type usually found in the lung. They are the normal target for rhinovirus. So they added the rhinovirus to the culture of lung cells, to see whether the presence of DRACO would save them. The first four sections show the controls, showing that no one part of the DRACO extract protects the cells on their own. Within 12 days post infection, all of the cells are dead.
The last two sections are the most interesting part of this graph. The last section shows that when the complete , fully functional DRACO is added to these cells, they are protected against rhinovirus infection.
Okay, that’s good. I should note that for this experiment, the DRACO’s were already in the cell culture when the viruses attacked. But what happens if you got rid of the DRACO? Could the virus bounce back, only being momentarily delayed ? How long do these cells need to be given DRACO to stop the infection?
Figure 4B goes some way to attempting to answer this question. A number of different types of DRACO were added to cell cultures, which were then exposed to the rhinovirus. After three days of marinating in DRACO filled medium^[6], the some cells were removed and left in plain old normal medium. Whether or not media were changed, the effects were still the same after seven days. This shows that whatever DRACO is doing, it’s happening within three days of infection.
In Figure 4C, we drive down further into this question. If you remember earlier, that DRACO’s can stick around in cells for around 6 days? So what happens if we treat cells with DRACO, and add in rhinovirus after 6 days ? it turns out that the cells survive. But the question is, can DRACO work if it is given after infection ? The last 3 sections of 4C show that it can work for up to 3 days after the initial infection.
So they next tested a whole raft of different types of DRACO’s (Figure 5 A) to see their efficacy against rhinovirus infection. In the previous studies, they showed that DRACOs can allow increased cell survival. But they haven’t yet shown a reduction in the levels of virus. Figure 5B solves this. Cultures of human lung cells were tested four days after infection to see detect the whether any rhinovirus was present. Turns out, the cultures with DRACO’s did not have any viruses, whereas the controls did.
The next experiment was a basic dose response, which asked the question ; What concentration of DRACO will save all of the cells in a culture ?
So they took a set of cell cultures, and added different concentrations of DRACO to them. They then infected each of these cultures with different species of virus. They used Rhinovirus, murine encephomyelitis and intriguingly murine adenovirus. At concentrations of 0.1 nM, no protective effects were found. The DRACOs seemed to be effective against all the viruses at around 10nM. As with all experiments that involve a line graph and phenomena that could possibly be described by an equation , I wonder where the regression’s at ? But I’ll talk more about that later.
But the interesting thing here is that for some reason the DRACO is effective against adenoviruses. This is interesting because adenoviruses do not have a genome of RNA- they have a genome of DNA, and are not noted for using dsRNA at any point during their life cycle.
In figure 6, they tested whether DRACOs were as effective against adenovirus as they were against rhinoviruses. DRACOs were effective against rhinovirus seemed to be even more effective against adenovirus, with it conferring 100% protection even if applied 3 days after infection. And a whole raft of DRACOs were effective against the adenoviruses. Similar tests were applied to the murine encephomyelitis, amapari, dengue and guama viruses, which are each viruses from different families, and all of them form dsRNA at some point in their replication cycle. I could go into more detail about all of the viruses they’ve tested, but suffice to say that DRACOs look like they do what they say on the tin.
But a drug needs to do more than just stop viruses in a cell culture. A cell culture is basically just a culture of one cell type floating in fluid. The interior of an organism has connective tissue, a circulatory system and a whole variety of cell types all interacting with eachother in a complex structured mass which can’t be replicated in a cell culture. If this drug is to be effective, then it needs to be able to be absorbed into the body, and survive long enough to reach the same cells that the virus intends to infect. Since we’re talking about this drug fighting against different types of virus, then it’ll have to go into different places in the body. If it needs to fight against hepatitis C, then it’ll need to get to the liver. For rhinovirus, the lungs, and for something really horrible like hantavirus, it pretty much needs to get to the entire circulation.
So they need to know how fast this drug can pass through the circulatory system. Drugs have two routes out of the circulation system after they enter. Route 1 is via the kidneys, which act to filter out waste and toxic products from the blood. Route 2 is via the liver, which generally drains blood coming from the gut, and is the main place in the body where drugs accumulate and are detoxified. So when drugs end up in these areas, a fair bet is that they’ve gone through the circulation.

In figure 9 A, they inject the drug into the circulation of a mouse, and then tested out specific organs at specific times to see how long it took for the drug to appear in these organs. within 2 hours, the DRACOs appear in the lung and the kidneys. It starts to appear in the liver not long after, and it stays there for over a day.
So the question arises- can this drug actually work when given to a real live whole organism ? To assess this, they performed a survival experiment (Figure 9 B). They gave two groups of mice different types of DRACO, and one group no treatment at all. The treatment was given the day before, and for three days after infection with H1N1 flu virus. 13 days after infection, most of the mice that didn’t receive the DRACO were dead, whilst most of the mice who received the drug survived. Some mice had their lungs extracted, and the numbers of viruses present in their lungs were assessed. The mice treated with the DRACOs had much fewer viruses within them than the untreated mice. In Figure 9C , a repeat of this experiment was performed , only using different DRACOs, and smaller animal numbers.
But wait ! Let’s take another look at this infection model. The mice received intraperitoneal injections of DRACO of 200 microlitres. Now this may not be the most practical methods of application for use in people, it’s sort of (but not really) the equivalent of an iv drip. But perhaps if the DRACO was administered through a different method, such as via the lungs, it may be either more or less effective.

True to this, Figure 10 A shows the distribution of different types of DRACO to the lungs, after they were administered to the lung. The DRACO that remained in the lung the longest was also the one which was the most protective for mice infected with influenza.
So those were the experiments, but the question is, what can we take away from this research.

Summary

Cons

Error Bars: I know always tend to bang on about this when I review papers, but it’s really important that when you put error bars on a graph, that you explain what the hell they are. Now, to be fair, this isn’t a problem for all the figures, as some do note that the error bars show standard error of the mean. But when I see a graph with error bars, but doesn’t tell you what they mean, I often assume the worst, that they’ve let excel do it’s automatic error bar thing, or they are too embarrassed to show you the actual standard deviation.
Statistics: This paper was relatively thin on the statistics. Often people tend to think that the only point of stats is to get a small p-value to “prove” that their pet theory is true. But it’s so much more than that ! It’s the best way to really get to grips with the shape of your data without your preconceptions getting in the way. The statistics they use are so basically to convert the numbers of dead/live cells into “percentage viabilities”. But they don’t seem to use any statistical analysis. I made a comment earlier about how they could have fitted their dose response data to an equation using regression, as it does look like a traditional hill slope. With that sort of information, you can actually get some interesting information about the similarities/ differences between the viruses. Without the stats, I have to make a guess that the variations in the cell viability are not large enough to account for these differences.
dsCARE. In 2009, a group based in boston/ china also created anti-viral based dsRNA that had a similar design to the DRACOs described here , except there they were called dsRNA dependant caspase recruiters. There are only a few differences with this paper- they use microscopy to judge whether viruses are there or not, as well as cell viability, and use direct counts for virus.They even discovered adenoviruses vulnerability to dsRNA based treatment. But they don’t look at the drug distribution, a much broader set of viruses and cell types, show no animal work, and didn’t get the patents. Read it here.

Pros

Breadth: They have shown quite effectively (and exhaustively) that DRACOs can prevent an infection propagating throughout a culture of cells. Now, I should note that without statistics, I am giving them the benefit of the doubt on this. But I think that the evidence they present is really compelling, and grounds for optimism. I mean the great thing about this paper is the sheer breadth of it. I may have name checked an earlier paper with a similar concept, but personally I like this one a fair bit more. This is the kind of paper that simply couldn’t have been published anywhere else, I mean there are 22 f***ing figures. That’s a huge amount of data. I mean, I’ve seen papers jump to far greater conclusions with less than a quarter of what this group is presenting.

So should we get excited ? Well a little bit. You can allow yourself a little chuckle perhaps. This is good news in the fight against viruses. They may not have been the first to come up and test the concept for this drug, but they have done a lot to build a case for it being broad spectrum, and for the possibility of it being useful for treating viral diseases. Even though the outlook is promising, this treatment is still in it’s early stages. The proteins may need to be altered, improved. They may even be ditched altogether and the genes encoding the drugs may be given. The treatment that will eventually result from these findings may look nothing like what was used here. It may look promising all the way to human tests, and then something could go wrong then.
This potentially could be a penicillin moment for viruses. But it’s too soon to tell. Such historic moments can only ever be judged with hindsight. I only hope that when I’m judging this moment in the future, it’ll be a future in which viruses have been knocked back as badly as bacteria once were.

Footnotes

[1] I should note that whilst this is the way that many viruses work, there are some exceptions. Mimiviruses, which generally are found in the sea infecting amoebas, may work in a completely different way that is mostly unknown at this moment.
[2] This footnote has no point to it. There is no reference in the text. It exists for its own sake, at the expense of the rest of the article.
[3] One exception is small interfering RNA. small interfering RNA’s do form double stranded RNA, which is recognised and rapidly broken down by the cell. The fact that double stranded RNA is broken down rapidly by the cell is why siRNA’s are so useful for a cell. For the first time, I can say that is the exception that proves the rule.
[4] TLR-3 stands for “Toll-Like receptor 3” because it was similar to a protein called Toll. and there were three more discovered before it that also looked like Toll. Yeah, it doesn’t really have anything to do with it’s function, but most proteins have names that occur by accident, and have nothing to do with their function.
[5] you may really need to squint your eyes to see the protein band here. This may be an instance of what my supervisor would refer to as “visible by photoshop”
[6] Feel free to use this phrase in your harry potter slash fic

Rider T.H., Zook C.E., Boettcher T.L., Wick S.T., Pancoast J.S., Zusman B.D. & Sambhara S. (2011). Broad-Spectrum Antiviral Therapeutics, PLoS ONE, 6 (7) e22572. DOI: 10.1371/journal.pone.0022572.t002

End of Hiatus

2013-02-07T18:15:00.003Z

A couple of months ago, for reasons too irritating to get into, my internet got cut off, which made blogging nearly impossible. But then again, I needed to focus on writing up my thesis. That didn't go exactly as well as I planned.....

It turns out, wasting time and procrastinating can occur even in the absence of the internet. As you can tell, internet access is no longer a problem, and so once again I can procrastinate by researching and writing posts for this blog ! Expect more posts soon.

#Microtwjc Why FRET about Biosensors ?

2012-08-11T18:32:00.000+01:00

The latest paper for #microtwjc focuses on finding a new way to sense metabolites inside of living bacteria. Bacteria need to regulate their nutrient intake, because like people, they don't want to get too fat or thin.

If there isn't enough nutrient in the environment, they need to conserve what they have, and if more appears, they need to consume it as efficiently as possible, but if they did that all the time, it could cause them problems.

But we don't have many great ways of observing how bacteria do this. There is a need for the creation of biosensors, which can sit in the bacteria without killing them and tell us what they are doing with their nutrients in whatever situation they find themselves. In this paper, they were looking at the localisation of a particular nutrient, citrate. So how do you do this? where do you find these sensors? Luckily, mother nature's got you covered.

Lots of bacteria have proteins which are used to grab citrate from the environment, like a periplasmic binding proteins which pretty much do exactly that. Bacteria also have "sensor kinases", which essentially work as sensors for the bacteria themselves, so that they know how much nutrient they themselves have. All of these proteins bind citrate. When they do bind, they change their shape.

But we can't just put them under a microscope, because these proteins are too small to see, and the only microscopes powerful enough to work would kill the bacteria. So what's a microbiologist to do ?

Don't fret ! FRET will come to the rescue ! What is FRET, and why am I shouting about it ?

FRET is an acronym, which stands for Fluorescence Resonance Energy Transfer. There, I've explained it, no more need to dwell on... okay fine. Let's quickly go through what Fluorescence Resonance Energy Transfer is.

Essentially, fluorescence is when you use photons to excite electrons that are bonded to atoms. Every electron has a specific orbit around an atom. This orbit relates to the level of energy it possesses.

If you shoot a photon of a specific wavelength, it will cause the electron to go into a higher energy state for a while. and then the electron will jump back down , releasing a photon.

Not just any photon will do either, it has to have a specific frequency. A molecule that fluoresces (which we're calling a fluorophore) under UV light will absorb ultraviolet radiation in order to get its electrons excited. But the light it emits afterwards is usually of a lower frequency, in this case it's blue light.

There are a lot of biological fluorophores around, most of them derived from green fluorescent protein. These can be encoded onto DNA and inserted into cells to make them glow when excited by specific light.

So that's Fluorescence sorted out. What about Resonance Energy Transfer?

Here, we'll need two fluorescent proteins. One of them absorbs say absorbs violet light, and emits blue light, and the second absorbs blue light and emits yellow light.

So if we shine just the violet light, we'd see blue light from the first protein, and none from the second one. But if we bring those two fluorophores close together, this changes.

In simple terms, they "get in eachothers bizniss".

In the excited fluorophore, we have electrons in a high energy state. But they interact with the electrons in the other fluorophore, and end up transferring their energy to them in order to get into a lower ground state. This is done via a "virtual photon" that barely has time to exist before exciting the other fluorophore.

So this way, you can shine violet light in a mixture of these two fluorphores. And you'll be able to tell how close they are to eachother by the light you get back off of them. If you get yellow light, they are close, and if you get blue light they are not. In fact the proportion of yellow light compared to blue light produced relates to how distant these fluorophores are from eachother.

But it also gets more subtle. So let's get back to the citrate binding proteins, and how we detect them changing shape when they bind to citrate.

If you attach a fluorophore to one end of the protein and another to the other end, then if the shape of the binding protein changes, so should the distance between the fluorophores. Essentially, you have created a biosensor. It will emit a different spectrum of light depending on whether it has bound to citrate or not.

So now we've gone through some of the basic concepts of the paper, let's start going through the paper.

Figure 1

The first thing they did was to try out a number of different citrate binding proteins. For their fluorophores, they decided to use Cyan Fluorescent Protein, and Venus, which is a derivative of Yellow Fluorescent protein.

They tried out a number of different citrate binding proteins. They had no idea which ones would work, or even why they'd work. The basically worked things out empirically, which is short hand here for "try out everything until something works"

In they end, they found that the CitA protein from Klebsiella was the one which worked. And by worked, I mean that it showed some difference in wavelength when citrate was added.

The bottom axis is the wavelength of light, with 480 being violet/blue and 540 being around the red/yellow part of the spectrum. The fluorescene intesnity is basically how bright the light is coming off of the sensors.

The dotted line is the protein without the citrate added, and the black one is when it has been added.

When citrate is added, more light is emitted from the blue spectrum, and less is emitted from the yellow spectrum, implying (if i've got my physics right) that the fluophores have slightly moved further from eachother.

Using this, you can calculate the fluorescence intensity ratio, by looking at the amount of yellow (530) light produced divided by the amount of blue (488) light produced. If there is no change, then the ratio stays the same. If the fluorophores move apart, then this the light at 488 decreases, and 530 increases, and fluorescence intensity ratio get's much higher.

But this change is still small, which would could produce detection problems.

Figure 2

The next thing the researchers did was to try and make these small changes in wavelength bigger. They looked at the protein structure of CitA, and removed flexible bits, as the "wobbling" of a protein will make the distance between the fluorophores vary, and make it more difficult to tell whether any shape change is due to binding of citrate, or due to the proteins natural wobbliness.

In they end, they eliminated two sections of the protein, and found that this improved their signal.

So what next ?

Next, they want to look at how sensitive their biosensor was at detecting citrate.

A : They added different concentrations of citrate, and then marked up the fluorescence intensity ratio. They then looked at how much the signal increased in response to adding more citrate. What you can see is a basic enzyme binding curve. Essentially, it shows that this biosensor cannot really detect concentrations of citrate lower that 1 micro mol, and that it is less sensitive at detecting concentrations in excess of 100 micromols.

B: But it's not enough to have a sensor that is very sensitive, it has to be specific. So they added a variety of different metabolites to this sensor,t o see if they made any difference. Only one did, an isomer of citrate, isocitrate. In this case, faint detection only occurred at around 10-1000 micro mols, far more than you ever expect to see in a cell. They also mixed together isocitrate with the citrate to check if there was any binding. while they don't show the actual data, they say that no competition is happening.

Table 1

I noted before that the biosensor was less sensitive when the amounts of citrate exceeded 100 micro molar. This is not great, as in a real cell, the sensor might have to deal with concentrations that high. So they did some more engineering on the CitA protein, to alter the way it binds so that it could detect higher concentrations of citrate.

The way they had to do this was to make the CitA bind less well to citrate. This would mean that the concentration of citrate would need to be higher in order for binding to occur.

They introduced a few mutations into CitA to do this, and then repeated the above experiment. They only display the KD as this basically shows the halfway point, where the area where the most sensitive detection can occur lies on the above graph.

The below table shows the binding properties of six mutated versions of the biosensor (CIT8u is the name of the normal biosensor)

They combined the mutations for CIT50u and CIT96u to create sensor with an affinity for 470 uM of citrate.

So now they had a panel of different biosensors which could cover a wide range of concentrations found in the living cell. So how can we tell which ones work ?

Figure 3

The best way for them to test these sensors was to try the out in living cells. They put the genes for these biosensors into a lab strain of E. coli. They used the CIT8u, CIT96u, CIT1.8m, to check which one would be the most appropriate. They also included a sensor which didn't bind anything, CIT0, as a control.

They were grown at room temperature for 60 hours, and then starved of nutrients on M9 media for four hours. After this, they then either added, glucose, acetate or citrate to the cells at a concentration of 2ug/ul.

So the first thing to address here is which sensor works ? Well, none of them really. When using biosensors properly, it's quite difficult to gauge how sensitive they are because you are relying on the bacteria to make them themselves. They don't really know how much biosensor a bacterium is going to make, and whether they all make the same numbers of biosensors per cell. They don't mention the plasmid they used for this study, so I cannot tell what kind of copy number to expect from it, or how active the promoter is for the biosensor.

While it will likely hover around the same average, this variation can throw out the kind of sensitive analysis performed in the in vitro experiments. So the only clue to the amount of citrate present comes from using sensors of different affinities, and seeing how well they compare to eachother.

Now we can get into what the data shows- firstly, when bacteria metabolise glucose, citrate is produced as the end point of glycolysis. Citrate build up for a time, and then falls as the TCA cycle starts up and begins to metabolise the excess citrate.

When Acetate is metabolised, citrate levels are increased for a bit longer, as citrate is one

Whereas when citrate is added, the E.coli do not absorb it, as it tends only to be absorbed by these bacteria under anaerobic conditions.

So now that they've seen how these biosensors can detect citrate when it is produced during the metabolism of glucose and acetate, they wanted to see whether they could find out anything new with their fancy new biosensors.

Figure 4

They took E.coli cells containing the CIT8u biosensor, and then starved them for 24 hours. At the end of these 24 hours, they added either glucose or acetate, and measured how quickly they were metabolised to citrate in the E.coli cells.

Turns out that both of these get metabolised very quickly, with the change due to glucose uptake (in black dots) being very rapid and similar results for acetate. This suggests that glucose uptake is very quick after 24 hours. But how do we know it's quicker than uptake after different levels of starvation?

In this graph, we have measurements taken every ten seconds being compared to measurements taken every minute. Will this make much difference to the results ? Probably not.

But it would have been nice to see how uptake of glucose/acetate changes at different degrees of starvation using directly comparable data sets. You could use statistics to compare the data, and perhaps reveal something interesting about how quickly bacteria adapt to starvation.

Summary

The Good

I couldn't find much wrong with this paper when it was dealing with the basic engineering issues for creating the citrate biosensor.

The focus was on getting a product that works. The work needed to find the crystal structures for all of these biosensors is huge, and not necessary. We don't need to know the exact mechanics, all we want is something that can bind citrate, and then give us the signal that it has.

They have pretty much shown that they have made a series of biosensors that are specific to citrate, and sensitive over a wide panel of concentrations. The methods they use when creating their biosensor are really good, and made an interesting read.

The Bad

Where the paper slightly falls down is when they try to prove that their sensors work in real living bacteria. Did they do enough to work out whether their biosensor is being expressed well enough ?

Did the biosensor inflict any fitness cost on the bacteria carrying it ? How stable is it on minimal and rich media? If I grow a batch of E.coli with it, will they still express it in a week ?

These are vitally important questions for the application of biosensors like this one. If you take one reading at say 4 hours, and another at 24 hours and half the cells may have already spat out your plasmid, and you'll be none the wiser if you only look at the fluorescence ratio, other than noticing that your data gets more variable the longer you leave your bacteria.

The data presentation left me annoyed. Some of the data was presented really well, other times not so much. Sometimes you get to hear how many replicates happened in each experiment, sometimes you won't. You may see error bars in one, and they are gone in the next experiment.

Sometimes they appear, and are not explained. If you don't mention what the error bars mean on a graph, then they can only be judged as pretty little lines.

Verdict

The thing about this paper is that it's got a lot of good stuff going for it, and only starts getting shaky when we leap into working in living bacteria. They haven't fully characterised their sensor in living bacteria. But even if I let that slide, we still have problems with data presentation that make it difficult to work out what they actually did. It's a small point, but an important one if a scientist like me needs to implement a biosensor like the one described here.

Despite this, there is still enough good stuff here for me to have some respect for this work. I am pretty much convinced that they've created an interesting set of biosensors that could potentially be useful. It's a great paper, but not perfect.

(2011). Engineering Genetically Encoded Nanosensors for Real-Time In Vivo Measurements of Citrate Concentrations, PLoS One, 6 (12) DOI: 10.1371/journal.pone.0028245.t002

Microbial Phylomon- Salmonella enterica

2012-07-31T16:31:00.000+01:00

To get everyone in the mood for #microtwjc tonight, I give you the microbial phylomon card for Salmonella enterica.

Previous Microbial phylomon posts can be found below:
http://defectivebrain.fieldofscience.com/2011/08/more-microbial-phylomon.html
http://defectivebrain.fieldofscience.com/2011/09/even-more-microbial-phylomon.html
http://defectivebrain.fieldofscience.com/2011/12/when-phylomon-fall-from-sky.html
http://defectivebrain.fieldofscience.com/2011/08/microbial-phylomon.html
http://defectivebrain.fieldofscience.com/2011/10/microbial-phylomon-it-came-from-beneath.html
http://defectivebrain.fieldofscience.com/2011/08/microbial-phylomon-and-friends.html

#Microtwjc A Salmonella of Doubt ?

2012-07-29T19:29:00.001+01:00

The microbiology twitter journal club beckons, and it is now time to wade through the latest paper, on Salmonella, and how to spot different subtypes of this bacteria. This allows us to get a look at the recent evolutionary history of the bacteria, and to track new subtypes as they arise. In the past, the ways these new subtypes were tracked and analysed through serotyping.

So how does serotyping work?

It essentially relies on antisera detecting proteins/sugars (antigens) on the surface of the bacteria. These antibodies are specific for specific antigens, like the H and O antigens. You can use different antisera to detect each of these antigens,
The expression of these antigens differs between different subtypes of salmonella, so you can identify different strains based on whether or not they have these antigens.
When the bacteria are mixed in with antisera, the antibodies within bind to their targets and then bind to eachother. So a positive reaction will cause the bacteria to stick to eachother, or agglutinate.
an example of the reaction is below.

This reaction is a fairly quick way of identifying these different types of salmonella.
So in the past different subspecies of salmonella have been classed into groups based on they way they are serotyped. These groups are known as serotypes or serovars.

Figure 1

The first figure essentially paints a picture of how salmonella types are classified from the species level down to the serovar level, and an example of how species are divided at different levels.
To define a species, DNA hybridisation and MLST are used, although biochemistry and serology are more commonly used.
They can be then differentiated by the different diseases they cause. Once the disease is identified, antisera for different O and H antigens can also be used to identify different species.
One can look at this figure, and perhaps draw the assumption that certain serovars are related to specific diseases. Indeed, this assumption is widespread. If you track one serovar as it spreads through a community, you will consequently track the outbreak of a specific type of disease such as gastroenteritis.

However, if we take a closer at this assumption, cracks appear. Serovars are classified by their surface antigens- the O and H antigens primarily. What are these antigens? O is lipopolysaccharide, a component of the bacterial cell wall, and H is for flagella, which act as propellers for bacterial movement. These components differ enough between serotypes to allow them to be discerned from eachother, but are the accurate markers of disease ? Is it possible for two salmonella subtypes with different evolutionary ancestry to appear as one serotype through the sheer luck of having the correct combination of antigens. Do each of these serotypes represent just one genetic line ?

Track virulence genes perhaps ? Whilst this is tempting , as virulence genes often have direct effects on disease pathogenesis, there is a problem with that- Many virulence genes are carried by phages- viruses that can infect bacteria and bring foreign DNA sequences. These phages can transfer DNA, and genes between different strains of bacteria through a process known as horizontal gene transfer. In fact, its possible that the H &O antigens themselves can be transferred between different bacteria.

The truth is that you would need to get Sequences at Multiple locations on the bacterial genome. This better allows scientists to more fully characterize the evolutionary history of the salmonella, rather than one specific gene within Salmonella. This was done by Multi-Locus-Sequence-Typing (MLST).
How does MLST work for Salmonella ?
Seven different genes, aroC, dnaN, hemD, hisD,purE, sucA, and thrA, had been previously used to analyse the heritage of salmonella typhi. Only small fragments of these genes were needed to differentiate different subtypes of this bacteria.
As a subtype of salmonella evolves, the sequences of these seven genes change. Through these gene alterations , it's possible to look at multiple subtypes and deduce how related they are to eachother.
Subtypes with the exact same sequences for all of these genes are designated Sequence Types (ST's).
The eBURST algorithm was then used to find the most closely related Sequence types and to assign them into a group. This algorithm will group Sequence Types together if they have 6/7 identical genes in common.
So these groups were defined as eBURST groups (eBG's), based on the algorithm used to define them.
(in the same way that serotypes are named after the process of serotyping)
When ten or more different salmonella strains were found to be part of the same Sequence type, they were upgraded to eBURST group. This added a layer of complication, as this means that an eBURST group, which itself contains a collection of related Sequence types could itself contain another eBURST group if one of those sequence types numbered more than ten.
If eBGs were found to share a common serovar with sequence types that differed only in 5 genes, they allowed those sequence types to be grouped in with the eBG.
Using these methods, they managed to distill 3,550 separate salmonella strains, isolated from around the world, into 138 eBURST groups.
It should be noted that whilst the eBURST algorithm is great at spotting close relationships between strains, it is somewhat less trustworthy at looking at less close relationships.

Figure 2

This diagram shows a Minimal Spanning tree, which broadly shows each sequence type (which are shown as dots) and the distance between each sequence type speaks to the distance of relatedness. The thick lines linking the dots indicate that the dots share 6/7 genes, and thin lines 5/7 genes.
The colour codes indicate which serovar to which each of these sequence types belong. (with white being unidentified)
But as I said before, eBG is not the be all and end all here, and it is always preferable to use multiple lines of evidence to double check the validity of the results presented here. So the authors used three different methods that have been used in previous work to re-check the results shown above. I should point out here that all of these techniques are completely new to me, and I have tried to simplify the descriptions as best as possible and emphasise the differences between these models as best I can.

Clonal Frame was used. This works slightly differently from eBURST, by taking into account the genes which do not undergo recombination, and treating those as a way of looking at how the bacteria are related.
gene by gene bootstrap analysis. How does gene by gene bootstrap analysis work? This is based on the molecular clock, that each set of genes are subject to mutation over a period of time, and accumulated mutations can be used to trace the genealogy of each set of genes. Moreover that the changes within each of these genes can make some homologous recombination events more likely than others, and these in themselves can be used to determine how each sequence type evolves. This can be used to generate a UPGMA tree.
Bayesian analysis of Population structure (BAPS) was the final check they used against their data. This model uses Bayesian inference to examine the question of whether a population of bacteria consists of subgroups which have genetically drifted apart.

So each of these methods were used to generate clusters of related subtypes, and these were mapped against the eBG's defined above to check whether they were in agreement.

This produced the next figure:

Figure 2

This Venn diagram shows the overlap and the differences between the the three models. The models are in agreement for 108 clusters, and BAPS and Cloneframe have a fairly large overlap of 21 clusters. Unfortunately, I am not yet familiar enough with any of the three other models and their construction to understand why there is more overlap between the BAPS and cloneframe than with Gene by Gene bootstrapping.

Nor can I say whether testing each of these three models will naturally reflect the state of nature, or that these models have the same base assumptions but approach them from different directions. Considering that they are all based off of the same data, that conclusion can easily be drawn.

Nevertheless, even with those thoughts taken into account, the agreement with these different groups is very convincing, and ensures that any weakness in one model is supplanted by the strength in another.

So with this done, we can look back on the Figure 2 and look at the relationships shown in it with more confidence. And what becomes more clear is that the relationship between serotype and eBG is not as clear cut as one would first assume. It looks like serovars like Newport, Paratyphi and Oranienberg encompass at least three eBG clusters each.

Figure 4

This figure focuses on salmonella typhimurium, which correlates the eBG1 in the Figure 2. Typhimurium as serotype variants, which don't express some of the antigens seen with other strains. These are the Mononphasic strain (in red) non motile/rough (in brown)
This is where things get interesting.
Each circle represents a Sequence Type, in which all of the 7 genes tested were identical. And we can see that with the biggest one includes a large chunk of monophasic variants. In fact monophasic variants never seem to represent their own cluster. Previous work suggests that the "monophasic" phenotype in these cases must occur through multiple unrealted genetic events, and so doesn't really form it's own independant subtype.
Also, this data suggests that typhimurium itself comprises more than just one eBURSTgroup, as the smaller eBG138 shares only have three similar genes with the larger eBG1 cluster.
The serotypes Hato, Kunduchi and Farsta are not generally associated with typhimurium, so seeing some of them group in the same cluster is unexpected. This suggests that serotyping is unreliable, although that blade can cut both ways.

Figure 5

So now we must look at eBG4, which tends to correlate serologically with enteridis mostly, although there is some relationship to gallinarum, and gallinarium pullorum.
eBG53 seems to enclose most of the Dublin.
But group eBG93 appears to serotype for dublin and enteridis, suggesting that it may be related to both. eBG32 is slightly more diverse, both of the previous ones plus paratyphi B var Java (monophasic).
This was relatively interesting, so the authors delved a little bit deeper, sequencing the genes for the antigens of ST74 (which is in eBG32). The gene which encodes this difference is FliC. If this has Ala220* and Thr315, then it will test positive as enteridis. If it has Ala318, then it tests positive for dublin. If it has all three, then it will test positive for both. Salmonella in ST74 test positive for both. The reasons that they were assigned to either dublin or enteridis is likely due to random chance depending on the lab in which they were first identified.

Also shown in this figure are various eBG's which serotype as paratyphi and if they can digest d-Tartrate, they are classified as Java. These two serotypes are believed to cause different diseases. However, these serotypes are characterised by multiple eBG's. D-tartrate digestion is controlled by a single nucleotide change, and this work suggests that the mutation can arise multiple times. Further analysis suggests that the virulence factors can vary between eBG's, as well as the presence of antibiotic resistance.
What this data shows is that even within one sequence type, there can be variables that change between different strains, and these need to be investigated with further sequence typing.

Figure 6

This figure shows the structure of 6,7:c:1,5, strains, which are not as well characterised as the ones previously described, mainly because they primarily occur in Asia, as opposed to Europe and the US.

The serotypes are mainly classified through the way in which they digest tartrate and dulcitol, as these seem to correlate with the difference in diseases caused by the bacteria.

A lot of these diseases affect swine (suis in latin means pig, hence cholerasuis= cholera in swine, typhisuis= typhoid in swine), whereas paratyphi C is associated with disease in humans.

Again, the authors note that they had to overcome some difficulties with serotyping, but most of these sequence types tend to map to closely related eBGs 6 and 20. The main exceptions are strains that serotype as Decatur, which seem to be unrelated to eachother as well as the main complex, calling into question it's definition as a type in itself.

But this raises an interesting question- how did these unrelated strains come to have the same serotype, despite being unrelated ?

The answer lies in the flagella, which are the targets of serotyping in this instance. They examined the full flaggellar genes for each of these strains and subjected them to analysis.

In addition, they searched the BLAST database of genomes to include genes that were similar, in order to work out what it was about Decatur types that separate them from all of the other ones.

What they found suggested that actually these flagella are related to eachother, despite the 7 genes for multilocus serotyping being different, suggesting that a horizontal gene transfer event has occurred, at least for the FliC flagella protein. The FljB protein however varies a lot, and could lead to serotyping confusion.

An interesting wrinkle in this data is the nature of the mutations shown in the decatur group.

with a lot of mutations, the ones that are favoured are generally the ones which don't affect the structure of the protein, i.e . that don't change the amino acids. But with Decatur, changes which seemed to favour different amino acids (relative to the standard sequence) seem to have occurred.

Wthese changes due to some form of evolutionary adaptation?

To test this they looked at ω. This measurement tells us about the accumulation of mutations which change protein structure relative to mutations which do not, and we can compare this to other genes which may not be subject to the same evolutionary pressures to check whether this specific gene is undergoing selection.

Whilst the ω seemed to be different, it was within the range of variation seen in the seven genes used for MLST, thus suggesting that any selective pressures that are affecting the flagella genes are also affecting these seven housekeeping genes as well.

Summary

This paper gave me a lot to chew on whilst reading it. It touches on multiple facets of salmonella epidemiology, as well as MLST and serotyping. It's big, it's ambitious and it's thorough. Moreover, towards the end of the paper, you could feel the joy of discovery here.

I mean, it wasn't enough for them to simply create their eBURST groups, look at the serotypes and simply say that their technique was better. They made a concerted effort with Dublin and Enteridis and Decatur to explain why these serotypes may be different, elevating this above a simple re-classification exercise and turning it into interesting science.

I also have to give props to them for not taking their eBG at face value, and subjecting them to alternative methods of classification to demonstrate their robustness. I really got the impression (as someone who isn't very familiar with MLST) that these data were scrutinized to the limits that current techniques allow.

The major problem of trying to look at the development of Salmonella is Horizontal gene transfer. The techniques they use minimise some of the problems caused by this by looking at multiple genes simultaneously.

Now, I have a confession to make. I don't often comment on the discussion of papers. Usually because for good papers, it doesn't usually add anything that the results themselves don't implicitly suggest. For bad papers it allows for authors to run wild with evidence free speculation (See dinosaurs in space,) and I can't face cutting through all of the bullshit.

But I'm going to comment on this discussion, because it provides something different. A warning for researchers who seek to follow in this research. They realise that the eBG map they have created is the starting point, and that there are gaps that may be filled through others following in their footsteps.

The warn against filling gaps for it's own sake, that potential errors can arise if nothing less than the best practice is performed when isolating new sequence types.

They also warn that Lineage 3, one of the few already established trees of Salmonella, may break down under further analysis, as it undergoes recombination frequently between sequence types of the same lineage.

So all in all, what have they shown in this paper ?

To sum it up in one sentence, they have shown that serotyping and traditional methods miss out a lot of important details that eBG's do capture. And the differences in serotyping across eBG groups tells us something new and interesting about Salmonella types and how they evolve.

Moreover, the eBG's defined here are robust and stand up to other analysis methods. Using MLST allows for a standard technique that also has the potential to track new groups as they develop.

Will this method be adopted in labs ? Well, I would have to hand that question over to microbiologists.

As seen above, the serotyping reaction is relatively quick. These sorts of classifications are often done in hospitals which may not have access to advanced molecular biology equipment, or the advanced refridgeration required to maintain the enzymes for PCR. in terms of cost, but this can be solved by having core reference labs dedicated to doing this sort of research.

But I'm wondering whether MLST is the minidisc player of the epidemiological world. At this moment it is probably the best method available. But just over the horizon is whole genome sequencing, which will blow MLST out of the water when it becomes reliable and cheap enough ? Shall we build our infrastructure for MLST, only to have to re-do it all for whole genome sequencing ?

I don't have the answers to these questions, but it speaks to the confidence of this paper that the authors themselves draw attention to these problems themselves.

(2012). Multilocus Sequence Typing as a Replacement for Serotyping in Salmonella enterica, PLoS Pathogens, 8 (6) DOI: 10.1371/journal.ppat.1002776.t004 *these are amino acid positions within proteins. ala220= alanine is present 220 amino acids along the protein.

#microtwjc Do Electric Guts Dream of Android Burgers ? part 2

2012-07-17T11:53:00.000+01:00

In part 1 of this post, I briefly described the key piece of equipment used in this study- the TIM2 machine, designed to model the large intestine.

So let's see what this machine can tell us about antibiotic associated diarrhoea. This disease happens after patients have been exposed to a long course of antibiotics, and the body's natural microbiota is wiped out. The usual treatments for this are the application of probiotics. But when do we apply them so that they have the most beneficial effect ? Do we give probiotics simultaneously with antibiotics and hope they flourish as well as they would without ? How will this affect the way the bacteria metabolise nutrients ?

Figure 2.

Quite helpfully, they put in a diagram of their experimental design. Essentially they took stool samples from 10 healthy adults of both genders *. From these samples they prepared a standardised set of microbiota **. They then put these microbes through the system for three treatments. The Control simply had no added clindamycin or VSL#3 and was monitored for seven days. The second group had both clindamycin and VSL#3 added, and was also monitored for seven days. The third group was essentially the same as the second group, except that it was followed for seven days longer with VSL#3 alone.***

For each day that these experiments were run, samples from the dialysis fluid (essentially representing the nutrients absorbed from the stomach) and samples from within the fluid of the machine. I chip analysis was performed every 7 days- this could be used to assess the bacteria present within this electric gut.

Figure 3

So the first question that was asked was what effect these different treatments have on the production of beneficial metabolites. So in this figure, they looked at short chain fatty acids, such as acetate, propionate and butyrate, measured daily using gas chromatography. The line graphs show cumulative frequency, i.e. the total accumulation of these metabolites throughout the experiment.

B and C shows what happens to these metabolites with and without the clindamycin + VSL#3 treatment, and found these there wasn't much of a difference, save for butyrate production.

A shows the clindamycin+VSL#3 followed by VSL#3 alone. Whilst it looks the same as the other groups for the first 7 days, after that there seems to be a sharp rise in the amounts of metabolites present. Considering that clindamycin is a bacteriostatic, it isn't such a great surprise that metabolites increase when it is removed.

At least until you realise the controls haven't received any antibiotics. So we could speculate that the presence of VSL#3 increases the presence of short chain fatty acids.

D- Ignore the title, as it's really misleading. What this shows is the total amount of short chain fatty acids that have accumulated over the first seven days for the first three groups, and then the accumulation of fatty acids that have accumulated over the last seven days for the long term group. This graph shows that the final seven days of the long term group (with VSL#3 alone) produced more propionate than the other groups.

Figure 4

So what about other compounds, such as lactate ? They look at that in their next figure.

These are another set of graphs showing the cumulative production of a compound, in this case- Lactate. This is structured the same way as the previous figure, and this time the differences are far more clear cut. L-Lactate and D-lactate (molecules of lactate that are structured as mirror images) are produced in roughly equal amounts for all groups. There is a drastic difference in cumulative production between the controls and the groups receiving the treatment. And as before, once clindamycin is removed, bacteria (most probably from the VSL#3 formulation) dramatically increase their production of lactate.

D demonstrates that when VSL#3 is acting alone, there is an increase in D-Lactate which accounts for much of the increase in lactate production.

Figure. 5

So we've looked at some of the beneficial compounds that can be made by probiotics, but can these probiotics prevent the production of potentially harmful products ?

This figure looks at the production of branched chain fatty acids (BCFA's), which are a signifier of protein fermentation which can lead to production of toxic metabolites.

This figure is set up in the same way as the previous ones, only this time showing the production of branched chain fatty acids. Keep in mind that for the first 7 days, the lines on A and B should be very similar. And in this figure, they are quite similar (just put a ruler against your screen to check, and try that out for all of the graphs)

Although the total levels in A are higher than in B, but I cannot tell whether it's outside the natural variation because I don't know whether the error bars they aren't showing are standard deviation or standard error.

Anyway, what this graph shows is that the clindamycin + VSL#3 treatment appears to reduce the presence of these branched chain fatty acids. But in group A , after clindamycin is no longer applied there is a dramatic increase in these products, and this is mirrored in D.

What they write about this in the results section is rather baffling though. What they set down in their study design was that for group A, VSL#3 was given throughout. What they imply in their results is that Clindamycin was the only treatment given for the first three days in this group, then follwed by VSL#3, and thus the reduction of BCFA's must be due to the probiotics, but only when given with clindamycin. But then if that was the case the whole time, then we could attribute the higher rate of lactate production in the previous experiment to clindamycin alone, which is insane.

The data seems to show that the probiotics do produce more branched chain fatty acids when left unchecked by clindamycin. There is a small decrease when both clindamycin and VSL#3 are administered with respect to the control.

Figure 6

So what about ammonia production. As you are probably aware, ammonia is a fairly hazardous compound, which is produced as part of protein digestion. Reducing the presence of detectable ammonia would be beneficial, so they analysed the production of ammonia over time.

This graph is simpler to understand than the others. A shows that the control group produces more ammonia than the other two groups over the last seven days. The change in the gradient of the longer term group indicates that ammonia production is increased when clindamycin is removed. Looking at the absolute amounts comparatively, it looks like the reduction of ammonia is due to the presence of clindamycin, as when it is removed ammonia production returns to the level seen in the controls.

Figure 7

So now we'll take a look at the bacteria that have been causing all of the chemical changes seen in the previous figures. So they used a microarray designed to detect the presence of genes expressed by specific species of bacteria. It can only detect a certain species of bacteria if there are more then ten million present per gram of fluid, so this analysis will miss out some of the less abundant bacteria.

The data shown is fold change compared to the controls, and this time they tested clindamycin alone compared with clindamycin + VSL#3, and VSL#3 after a weeks application of clindamycin. Red indicates that certain species are reduced with respect to the controls, and green indicates where some are increased, and blank spaces indicate no change.

The interesting thing is looking at the blank spaces, and seeing that clindamycin and VSL#3 together induce the greatest changes in bacterial flora. and when clindamycin is removed, and VSL#3 is added, a lot of bacterial species had a reduction in their presence.

Lactobacilli are increased when both clindamycin and VSL#3 are given together, but when clindamycin is given alone, there is no change, and when it is given and then VSL#3 is given, then the numbers of lactobacilli decrease.

Summary

The Good

Probiotics - They show that probiotics may still be active even in the presence of antibiotics, and their data suggest that it would be better for them to be administered with antibiotics rather than straight after.
TIM-2- They make the TIM-2 system seem like an interesting machine, although that they resorted to cumulative data indicates that there must be a great deal of variance on a day to day basis which could hide certain trends.
I-Chip- I think that what they did here was quite good, not just making a list of bacteria, but actually trying to understand how the various treatments are affecting these species.

The Bad

Graphs- For figures 3-5, the data was presented in a way to confound. If you want to show the effect of a treatment on a specific output (like BCFA, or lactate production) then it would be great to see all those different treatments on the same graph. Like how they presented figure 6. I really don't like pressing a ruler against my computer screen to check if there are massive differences between groups.

The Ugly

Poorly communicated experimental design :Figure 2 throws open some of the interesting problems of this paper. I generally quite like it when people sketch out their experimental design. But the problem with figure 2 is that the design sketched there isn't the design they keep to. In fact, for half the paper I was baffled as to what they were supposed to be doing. In figure 2 they suggest that for the long term treatment group they were giving VSL#3 throughout, but towards the end of the paper they straight up contradict this and say they only gave VSL#3 after 7 days to this group. Which is it ?
The Clindamycin +VSL#3 followed by just VSL#3 The long term group itself is problematic. In the original paper where the TIM-2 machine was devised, it was noted that for bacteria derived from fecal samples, the compositions can change over time. So why didn't they keep all the groups running for 14 days? They could have generated directly comparable data. As it stands , it is very difficult to say that the late stage changes in bacterial and metabolite composition are not due to them being kept in the machine longer than the other groups.
Error Bars- Where are the error bars? I see that some of the graphs have horizontal lines dangling off them, but what are those? are they standard deviation? standard error of the mean ?. The bar charts are handily labelled with numerical values which purport to show the averages. But without showing SEM, we are still in the dark as to how certain we can be of those averages.
Replicates- How many replicates were performed for each of these experiments ?
Statistical analysis-We got some statistics for looking at the microarrays, which is fine. However, where are the statistics for all of the other experiments ? How do we know whether any of these findings are not simply due to the natural variation of bacteria and metabolites within the TIM2 system? We don't know.

So did the VSL#3 decrease toxic metabolites? Don't know, the graphs say one thing and the authors say another.

Does it increase beneficial metabolites? Don't know, it may, but I'm not sure enough about what was done in each experiment to tell whether it was VSL#3 or Clindamycin doing the hard work.

What about when clindamycin is given followed by VSL#3 ? Still not sure, as I have no proper control to compare that data with. And to be honest,the confusion in the methods left me baffled as to what they actually did.

I am certain that if these issues were cleared up, then the rest of the paper would fall into place. There is enough here to convince me that there could be a great paper in here somewhere, but not enough for me to find it.

Rehman, A., Heinsen, F.A., Koenen, M.E., Venema, K., Knecht, H., Hellmig, S., Schreiber, S. & Ott, S.J. (2012). Effects of probiotics and antibiotics on the intestinal homeostasis in a computer controlled model of the large intestine, BMC Microbiology, 12 (1) DOI: 10.1186/1471-2180-12-47

*Yes, I saw the spelling error, and as someone who constantly makes spelling errors, I can say that it does not bother me.

**I have no idea how, as the paper with this technique was impossible for me to access- venema et al 2000 in Ernährung/Nutrition

Do Electric Guts Dream of Android Burgers? Part 1 #microtwjc

2012-07-16T20:02:00.001+01:00

The next paper for #microtwjc raises some interesting questions about what effects probiotics have on the gut. If you want to study the gut, and the bacteria within it, there are a number of little problems. For instance, many of the bacteria in the gut are anaerobes, and will shrivel up the moment they get exposed to air. It is thought that the distribution of different bacteria varies throughout the gut, so just taking faecal samples won't cut it.
So what can you do? There are animal models, where you can try and sample the bacteria in different areas of the intestine after dissection, or perhaps use techniques like bioluminescence to look at bacteria directly within the digestive tract. I remember hearing someone pitch a project to use magnetic resonance spectroscopy (using some fancy MRI technology) to analyse bacterial metabolites within the guts of human volunteers.
There is another way that researchers can try and work out what's happening in the gut. Through the creation of an artificial gut.
Consider for a second that in simple terms, your gut is basically a fancy bioreactor. It is a vessel in which enzymes, bile and bacteria all get mixed together to help you digest your food. So what if we tried to create this system artificially, and what would reveal about digestion
Minekus et al (published in 1999) developed the system, where they essentially created an artificial model of the large intestine, which over the last ten years has been developed to the one seen in the week 6 #microtwjc paper.
So how does this machine replicate the way that the Large Intestine works ?

Peristalsis- Your gut moves food around through squeezing it's walls and pushing the food along. The inside of this machine has a series of flexible walled tubes, which have water pumped along the outside of them. The pumping of the water passing through the system causes periodic compressions to be transferred through the system, mirroring the peristaltic movement seen in actual human guts.
Temperature: The water passing through the gut is heated to body temperature, which is 37 °C.
Bile: Bile performs a number of functions, such as introducing a number of salts which act to emulsify fats. Another function it performs is neutralising gut acid, and keeping the pH of the large intestine stable. To ensure that the pH of the electric version of this system is stable, pH sensors were present to control the infusion of sodium hydroxide, preventing it from getting too acidic.
Absorption: The main function of the gut is to absorb nutrients, such as short chain fatty acids. To replicate this, fibre membranes placed at strategic points along the machine allow for small molecules to pass through them. These are hooked up to pumps an pressure sensors to ensure that the pressure within the system remains constant.
Metabolites: In the human large intestine, partially digested food comes in from the small intestine. To replicate the present of this food, metabolites are added into this system, and because of the way the absorption works (via osmosis) the action of that system also acts to keep the metabolite levels within the gut fairly constant.
Anaerobic- It is generally thought that the human gut is mostly colonised by anaerobic bacteria*. So air was prevented from entering the system by flushing it with gaseous nitrogen.

This is the basis for the TIM2 system. Now, anyone can easily see that it isn't a perfect model for the gut. With any model, physical or computational, there are limitations. What you get is based off of your assumptions when creating it, and if you forget those, then you can be lead badly astray.
So the question is is it useful?

The only way to determine this is to validate the model.
In the initial paper, they found that the system absorbed short chain fatty acids, and maintained pH fairly well with "standard" fermenter flora and allowed them to survive in the system.
Also "Three successive experiments were carried out using fecal inocula from three different methane-excreting volunteers". They did not mention whether these people were chosen on the basis of their methane excretion. They looked at how well the faecal flora survived in the large intestine.
So from this first paper, they showed that this model of the gut could keep bacteria from the human gut alive, and that this system could absorb short chain fatty acids.

The point I would like to make about this model system is that yes, it does have limitations. It is not a perfect representation of the human gut. Even though we can't extrapolate findings with this equipment directly into humans, we can still put this machine to good use. It can be use to grow bacteria with different nutrients, and furthermore elucidate some of the more complex relationships between them within the human large intestine.
It is with that in mind that I shall look at the actual #microtwjc paper in part 2 of this post.

Minekus, M., Smeets-Peeters, M., Bernalier, A., Marol-Bonnin, S., Havenaar, R., Marteau, P., Alric, M., Fonty, G. & Huis in't Veld, J.H.J. (1999). A computer-controlled system to simulate conditions of the large intestine with peristaltic mixing, water absorption and absorption of fermentation products, Applied Microbiology and Biotechnology, 53 (1) 114. DOI: 10.1007/s002530051622

Part 2 is up here

* I should point out right now that I am not entirely certain whether the mammalian gut is entirely an anaerobic environment. For instance, I pointed out a study using bioluminescence to study bacteria as they invaded the gut. The bioluminescence reaction shown there requires oxygen to be present in order to function. So this indicates strongly that there is oxygen in the mammalian gut, but again, this conflicts with what is known about most of the microbiota present, which tend to shrivel and die at the mere mention of that element.

Cower Before Pretzlcoatlus !

2012-07-05T12:56:00.000+01:00

The long neck of the Quetzlcoatlus always looked really odd to me, until I realised that all of the fossil conformations looked completely off.And when I zoomed in to the pictures of quetzlcoatlus fossils, I noticed that all of the structures were strangely square, almost like pixels. I propose that these are salt crystals ! Taking this new evidence into account, I suggest an entirely new reconstruction for this beast. Ladies and Gentlemen, I present Pretzlcoatlus !

No, I'm not just overinterpreting every blemish on the paleontological images just to support my theory. I have verified it with tracing paper and crayon. And I need your support to get this new creature recognised by the paleontological community.
Ignore the unsavoury musings that can be found at tetrapod zoology http://blogs.scientificamerican.com/tetrapod-zoology/2012/07/03/world-must-ignore-reptileevolution-com/
And the distasteful take found in Laelaps http://www.wired.com/wiredscience/2012/07/pterosaurs-done-wrong/
And you should almost certainly disregard the "lacking in snacky-goodness" views of the paleo king
http://paleoking.blogspot.co.uk/2011/05/strange-journey-of-david-peters.html

Hail Pretzlcoatlus !

sciseekclaimtoken-4ffc4957aa832

#microtwjc- What's living in your bathroom.. Weeee

2012-07-02T20:10:00.000+01:00

And so dawns another week of the microbiology twitter journal club. This week we're discuss public toilets. WEEEEEEE!

Because the greatest concern when anyone goes to the restroom is the bacteria. Yes, you can't see them, but you know they're there.... watching...waiting...plotting.

But who are these mysterious bacteria? Can the 6 layers of two-ply really protect you as you sit straining atop your cushioned throne? This paper probably won't answer that.

This paper was an attempt to work out the identities of the bacteria in the restrooms, and where they come from.

But there is a problem. You can't just smudge a petri dish across the surface of everything and expect stuff to grow it. And for many years, most conventional microbiology techniques relied on getting living bacteria to the lab. In recent years we've realised that most bacteria don't survive the journey, so we don't know much about them. So we've turned to techniques that don't require living bacteria to work.

These techniques rely on detecting DNA. DNA is a relatively stable compound, and can stick around long after that bacteria that had it died out. It's like in CSI when they get DNA evidence from the crime scene to identify the criminal. Except the crime scene here is the toilet, and the criminals are bacteria.

So what did they do?

They looked in two buildings of the boulder campus in the University of Colorado, and sampled six girls restrooms, and six boys restrooms for bacteria. They took sterile swabs and wiped them over the main points of contact between the users and the wash room, and took samples of tap water.

They then extracted the genomic DNA from all of their samples.

So how do you identify a bacteria just by it's genome ? Well, there is this gene for 16s rRNA. What is this? this is the sequence for the ribosome, which all you biobuffs will probably know is essential to protein synthesis. So this sequence is found in all bacterial species, but the genetic code differs between species. So we can class each bacteria into a known species based on their specific 16s rRNA code.

"What about bacteria that don't have that sequence? " Shut up voice of cynical doubt in my head, if that were possible, 16s rRNA typing would surely have detected it.

So lets begin !

Figure 1

So what is this figure showing?

This figure shows the 19 most abundant bacterial species found in total, and how those species were distributed across different surfaces within the toilet.

The also sampled lots of human volunteers (all who presumably who worked in the building) as well, so that you can compare the bacterial species.

A) This picture shows all of the data from all of the volunteers and all of the toilets combined. They didn't note down the genders of their volunteers, so we can't tell if there is any bias in the bacterial communities samples based on genders. Luckily this won't be a problem if all the data is lumped together, and stick your fingers in your ears and scream doodoodalallala then none of this will pose any problems. I mean, its not as if in any point in the paper they suggest that gender has any effect on the microbiota.

Anyway, back to the actual figure. It shows the proportions of bacteria found from each sampled surface. You may notice that none of these graphs go to a hundred percent, but that's due to all the bacterial communities not shown, because they are rare and variable. So the floor has the highest number of these variable bacteria, because it's graph is the shortest.

With this graph, you can compare the species found on a bacterial surface, such as a toilet handle, and compare it to the bacteria found in faeces, and you can say for certain that we get out faecal bacteria from rubbing our smelly bits on flush handles.

B) Here, they show the same data as above, but this time broken down by boys toilets and girls toilets, and we can marvel at how guys have very few lactobacilli, which actually isn't surprising since lactobacilli are commonly found in the female genital tract, and have been posited as being an extra line of defence against infection.

So anyway, what this data certainly points to is that toilet seats and flush handles generally harbour bacteria that are commonly found in the gut suggesting that these surfaces are more likely to be contaminated with faeces. Yes Shit Sherlock !

Figure 2.

This is a PCoA plot. "What on earth is that and why did you just spit at me?" you may ask.

"Why, it's a principle coordinates analysis plot, an because I always try to say acronyms phonetically" I reply.

"Yes, but what is it?"

"Complicated"

Alright , I'll try to give a very very simplified explanation of this. Each dot represents a data point i.e. they sampled 12 toilets seats and flush handles, hence each data point represents the bacterial community for say each toilet seat sample, or every toilet floor sampled. But what do they represent from each bacterial community?

Well, they represent the the variation in the bacterial communities in two dimensions. Using magic (or Sufficiently Advanced Statistics,.) the variation can be classified into two components, PCO1 and PCO2. Essentially, the more each bacterial community has in common, the closer together they'll be on this graph.

So actually it suggests that in one particular toilet had a very similar community to the toilet floor. They say that people may be operating toilets with their feet.Either someone dropped a swab on the floor and told no-one. I know, that's crazy, lets go with the feet explanation !

Who cares about any of that when we can bask in the glow of...

Figure 3

This figure essentially breaks down the data from the previous sections and displayed it in a user friendly way. Each of these pictures shows an average toilet (for those two buildings that were tested). The light blue indicates low bacterial contamination, and dark blue means high. There are no intermediates.

A) This picture shows the distribution of skin associated bacteria, which were found to be common on all surfaces, but mostly on places like door handles and the soap dispenser. Wait, they don't get any hand dryers or toilet paper?

B) This picture shows the distribution of gut associated bacteria, which is mostly on the toilet seat and the flush handle. Which is a freaking miracle ! Considering that they must be wiping themselves out with their fingers, it's amazing that this entire stall isn't covered in desperate deep blue handprints.

C) This picture shows the distribution of the soil bacteria within the restroom, and it is mostly on the floor. Because shoes.

And now it's time for the final figure:

Figure 4

Here they ask themselves whether they can define the relationships of each surface to the source of the bacteria, using SourceTracker analysis. So in the previous few figures we looked at a couple of surfaces and then said to ourselves that these communities look similar. But Sourcetracker allows us to go one step further.

So you basically give it details about the numbers of species of bacteria in your urine. Then you set it to analyse the microbial communities in a stall, and it will tell you to proportion of those that are similar to the ones found in your urine from each surface.

So they took samples from faeces, to represent the gut. They took samples from the mouths of volunteers to represent bacteria in the mouth. They took urine samples to represent the bacteria in the urine.The samples of tap water were use to represent bacteria which may come from the tap.A variety of soil samples were sampled to represent the soil. Still with me?

So the surfaces of the face and hands, and hair were use to represent skin "skin". Oh, and the skin on the surface of genitalia were also lumped in with the rest. Seems legit.

So this source tracker analysis found that actually the skin is the greatest contributor of microbial flora in the toilets, and that faecal flora are ineed more common on toilet seats, and the flush handles.

So what does this all mean? Is it actually useful?

That's up to you. This was a basic look-see type of paper. They had a machine, and they had to go and use it for something, and if they got a publication out of it, then brilliant. Other studies tend to go into more detail , with actually trying to identify these species, like where any of the staphylococci are community MRSA's or whether there were any other pathogens hanging around. It really didn't go into much detail at all.

Honestly, the conclusions I can draw from this paper are no different from say this paper written in the mide 70s. Sure, they've got new technology,but the conclusions to be drawn are essentially the same.

In fact they can argue that this is yet more confirmation that their techniques work, because they prove what we already know.

I'm so glad I spent my time reading this. That was time well spent

Rule of 6ix has an alternate take on this paper that is worth reading

Science, It's a meritocratic thing?

2012-06-30T12:21:00.000+01:00

#sciencegirlthing meme confused me, because when I started at university, there was a 60:40 girl boy ratio on my science course. There seem to be plenty of girls going into science courses, but where do they go? This got me thinking about something that I wanted to blog about a while ago, but was too angry to do anything other than mash the keyboard with my own face.

In my early days working in the lab, I had the fortune to work with an amazing post doc. This post doc always knew what they were doing. Some days, they would be the first one in the lab, and last one out. Not because anyone told them to, not because they felt it gave them an air of arrogant superiority, but because the experiments demanded it. Through an extraordinary level of organisation, they managed to do a huge amount of work with very little time. It was an acknowledged fact in our lab that this post doc was some sort of superhero. Seeing that level of dedication, organisation and productivity affected me a lot, and I still am attempting to emulate their example.

So hearing that she wanted to get out of science sent my head spinning.

After which I was directed to look at the academics in higher positions. Most of them were men. It doesn't take a genius to see that something was going on.

There is a phrase that I've heard bandied about a number of places, "Too smart for science". Occasionally a lab will be blessed with an amazing student who, despite being excellent at science decides that they'll be better off doing another career, because the job opportunities are so small. And they do well, because they are smart enough to excel at anything they do. But it is still a loss for science.

So when you present such a person with a game so stacked against them, is it any surprise when they cash their chips and go elsewhere ? Why go through the sacrifice and hard work of a career in science when there is very likely no pay off at the end of it.

So seeing someone who had played such a key inspiration for me, decide that science wasn't the best use for her talents, was devastating. Hence the face/keyboard/laptop chewing implied earlier. Because I had my main preconceptions of science shattered.

I realised that no matter how good I am as a scientist, some arbitrary crap that I have no control over can prevent me from pursuing a career. Actually we don't always have the best people solving our scientific problems because the system is tipped against a lot of them. This is a career where the smartest people don't get to the top, they get out.

Can we really claim science is a meritocracy when we are haemorrhaging some of our best talent ?

#Microtwjc EHEC strain 86-24 gives it 100%

2012-06-16T16:49:00.001+01:00

It is time for me to again delve into the joyful quagmire that is the microbiology twitter journal club, to discuss "Hfq Virulence Regulation in Enterohemorrhagic Escherichia coli O157:H7 Strain 86-24", brought to you by the lab responsible for the best damn figure ever . So what are we waiting for ? Let's dive in!

So when studying bacteria as they cause disease, one is often faced with the question- why do they do things ? why do bacteria suddenly make the decision to become virulent ?
In the simplest terms, bacteria make decisions based on various sensory inputs, such as the presence of other bacteria and chemical signals from them, changes in the environment, food availability, and even the host immune response. How do bacteria take these signals, and direct them into meaningful outputs?
The paper I'm going to talk about contributes an answer to one of these questions- How does a bacterium decide when it can and cannot become virulent ?
In the link to a previous post, I touched on some of the bacterial factors related to expression of the LEE, and it's different roles in cows and in humans. In this paper, we'll be taking a much more in depth look at how the LEE is regulated. In most bacteria, there are a number of virulence regulators used to determine when the bacteria should express certain genes, and when they should not.
The main focus of this paper is a regulator called Hfq (pronounced hayfukyu*), which acts by grabbing sRNA and mRNA and putting them together. There is not much known about these proteins, save for them being important for the regulation of virulence in a number of species of bacteria, including Escherischia coli, Pseudomonas aeruginosa and Staphylococcus aureus (although the last one is debatable). In previous work, LEE expression was noted to increase when Hfq was removed.
Let's see what this paper finds in its results.

http://jb.asm.org/content/193/24/6843/T3.expansion.html
Table 3**.
One way of determining what Hfq does is removing it from the bacteria, and then looking at what happens. Does taking this one gene out of the network affect the expression of others, and if so, how?
So they performed microarray analysis, which measures the levels of mRNA expression for multiple different genes. and by multiple, I mean ALL of the recorded genes for EHEC. Since the expression of these genes can be affected by multiple factors, such as growth conditions, the authors were careful to ensure that the growth conditions were the same for both the control (which had Hfq) and the knockout (which did not), i.e. they were both grown to late log phase (when LEE genes are usually expressed).
They then collected the bacterial cultures, and extracted mRNA from them, and assessed the relative numbers of mRNA transcripts for each gene, and then compared the differences in expression between the two strains of bacteria. They found that a lot of genes were changed in their expression by this mutation, indicating that the absence of Hfq shows differences in gene expression when they test genes common in lab strains, in other EHEC isolates. The point here is that they looked at a wide variety of genomes, seemingly to ensure that they got coverage of every possible gene in the bacterium. And they found that the deletion of this gene caused the expression of mRNA to change a lot.
Some authors tend to display their mRNA data by listing out the genes which expressed the most change, and the statistics underlying this, so that they can then focus on the genes which either showed the most difference, or genes with hitherto unknown function that were also significantly increased in order to determine their role. Of course, those figures tend to take statistical probability as an output (which in itself can be a questionable practice) , so they've denied me a great opportunity for criticism.
They say that the main genes involved are to do with iron acquisition, Virulence regulation, LEE expression and the Quorum sensing E.coli regulators BC (qseBC). We don't actually get a sense from what they present as to the statistics relating to this expression. Microarray is used as a starting point and provides the impetus for the next sequence of studies where they will analyse these genes in greater detail.

Figure 1
In the previous set of results, it was implied that Hfq impacts the expression of LEE. It is known that the expression of LEE is controlled by a gene called ler, so does hfq change the expression of ler leading to a change in LEE ? Have you gone cross eyed yet?
So they again looked at mRNA transcripts, this time focussing on ler expression in the wild type bacteria, and the knockout. A housekeeping gene standard, rpoB was used to standardise the data for bacterial numbers.
They looked at the ratios of the transcripts at different stages of bacterial growth.
a) shows 86-24 (the wild type) compared to the knockout (86-24 hfq-) and a complemented strain where they added the gene back into the bacteria to show that the knockout didn't interfere with any other genes that could also be responsible for the effect. All this was done at late log (where f you remember LEE is expressed). And it shows that without hfq, there is no ler.
b) shows this is true for mid log, and c) shows it's true for stationary phase,
Since other groups grew their bacteria in LB broth (as opposed to DMEM as this group does) they repeated their results in LB, and this is what is shown in D) for bacteria at mid log.
The bottom line of this figure hints at the main point of interest in this study. This group took a different strain that caused EHEC called EDL933, and a mutant of that strain without the Hfq, and compared the expression of ler at E) mid log and F) late log. The deletion of hfq in this strain had the opposite effect to the mutation of ler in the 86-24 strain, and is more in line with the literature surrounding the subject. They even confirmed this result in LB to ensure that it wasn't some quirk of the media they used G).
So without hfq, there is less ler in the 86-24 strain, but there is more ler when Hfq is removed from EDL933.

http://jb.asm.org/content/193/24/6843/T1.expansion.html
Table 4
Since ler affects the expression of LEE, then you would expect changes in this expression to correspond with it. Table 4 looks in more detail at the fold differences in the expression of genes between the 86-24 mutant (which is now called MK08 to "simplify" things). And indeed, it confirms that the changes you'd expect if ler expression was changed, actually happen.

Figure 2
So we've seen that the change in hfq expression changes the primary regulator of the LEE, but does that constitute actual changes in the LEE genes? Considering how important the LEE is to infection, it is entirely conceivable that this particular strain may have made compensatory changes that would maintain normal LEE expression, which would lead to another interesting avenue of inquiry.
A)-D) Looks at the expression of a number of important LEE genes at different growth phases and in different media. The grey bars are the wild type, and the check bars which are almost completely absent are from the knockout. Suffice to say, this supports the idea that in this particular strain, hfq is necessary for the activation of LEE production.
E) We've been looking mostly at mRNA transcripts, but there is a question of whether these changes are manifested as protein expression. Specifically, they looked at EspA (one of the LEE genes) and using a western blot, found that there was less protein in the hfq knockout.

Figure 3
All right, well we've seen that bacteria without the gene are different, but what does this mean really? Does it have an effect on actual infection ?
To show this, the researchers looked how this strain of bacteria creates attaching effacing (AE) lesions when incubated with HeLa cells using fluorescent microscope, which are shown in the composite images above. Attaching effacing lesions are caused when bacteria stick to the surface of a cell, and through the action of various genes encoded by the LEE. The proteins encoded by the LEE recruit the cytoskeleton of the host cells, causing them to build around the bacteria.
The green in the images above shows the cytoskeleton (which is stained with a fluorescent dye) and how it interacts with bacteria (the smaller red dots). The nuclei of the cells were also stained (they are the bigger red bits) and give you an idea of the relation of the HeLa cells to the bacteria.
The first image is with the wild type bacteria, and you can barely see the bacteria as they have formed lesions of actin in close proximity to themselves. These are the attaching effacing lesions.
But in the second image, you can quite clearly see the bacteria, and the actin are in different parts of the cell. The difference between these two images? The last one does not have hfq, and therefore does not have LEE.
These images confirm that the way 86-24 EHEC regulates itself is quite different to what has been seen in other strains studied in the literature.

Figure 4

Enough about the LEE, what about the other virulence factors used by EHEC... what about Shiga toxin ?
What's Shiga toxin ? It is a deadly toxin produced by EHEC (and Shigella) which kills of cells and is pretty nasty. Will the deletion of hfq have an effect on its production ?
A) So again, they looked at the transcription of mRNA by 86-24, 86-24 hfq knockout, and 86-24 hfq knockout with the hfq returned on a plasmid for reasons that I noted above, and they found that the knockout produced more shiga toxin mRNA.
B) they then looked at the actual presence of shiga toxin by western blotting and showed results that mirrored that of the mRNA- that when hfq is removed, more shiga toxin is made.
C) & D) They compared this with EDL933 strain of EHEC to check that it worked the same way, and it did.
But the point here is that hfq does play a role in the production of the protein, and moreover, it is the same between both the 86-24 and the EDL933. This is in direct contrast with the regulation of the LEE, which is promoted by Hfq in 86-24 and repressed by hfq in EDL933.

Figure 5
In the previous microarray, they found that the qseBC regulatory system was also affected by the removal of hfq.
QseBC is a two component system, with one gene acting as a sensor and the other which acts as a transducer, transcribing genes when the sensor is activated. This particular sensor detects human epinephrine and a substance called auto-inducer-3. These two hormones tend to coincide with the onset of an immune response***, so the qseBC can allow bacteria to detect this response, and respond by regulating it's expression of virulence factors.
But to confirm this properly, they checked out the levels of transcription of this gene in 86-24 hfq, 86-24 hfq knockout, and in the complemented strain. And they confirmed that hfq activates this system in 86-24.

Figure 6

Remember in No Country For Old Men when they killed Josh Brolin off screen randomly, and the rest of the movie became Tommy Lee Jones moaning about how old he was ? This figure is kind of like that. Oh yeah, spoiler alert by the way. This comes out of nowhere, and we lose our main character, Hfq.
This figure focuses on qseBC.
In previous work, they created knockouts of qseC and qseB in the 86-24 strain. In the study where they knocked out qseC, it was replaced with beta-galactosidase, and the strain qseB- had a truncated version of the gene. They decided to look at how the absence of working QseB or QseC affect their own transcription, because this is what had been found in previous work. the implication is that Hfq plays a role in this, but lets see what the data shows,
A) Here, they looked at the mRNA transcripts of the whole qseBC operon (because qseb and qseC are usually transcribed together as one big chunk). This figure shows the effect of the addition of either autoinducer 3, or epinephrine (as previously noted, these are the activators of the qseBC system) when qse C is or is not present. It shows that when qseC is removed, there is an increase in transcription of qseBC, suggesting that qseC acts to block transcription, and that qseB is what promotes it, and is responsive to the addition of epinephrine or autoinducer 3.
B) This is a northern blot with the wild type, the qseC knockout and the qseB knockout to confirm the previous figure. well, I presume it does, but while there are three lanes for each strain, these could just be replicates for all the figure legends tell me.
While this is all very interesting, this last figure feels a bit out of place. The implication of figure 5 is that hfq regulates qseBC transcription, but this last figure doesn't really shed any new light on that. They don't even refer to it in the discussion. Actually, thinking about it, it's more like a big-lipped alligator moment. But for a scientific paper.

Summary
So here in this paper, we have a strain of EHEC which just won't behave the way you've expect. It regulates LEE in the opposite way to other strains, but regulates shiga toxin in the same way. They also found that this has an effect on the qseBC system.
The EDL933 and the 86-24 bacteria were both isolated in the 80's during two different disease outbreaks. The 86-24 was more severe, which makes sense considering how much it loves to make attaching effacing lesions. I've been struggling to pick holes in individual experiments but there are some lingering questions that I do have-
We've established that hfq acts differently in 86-24, but why is it acting differently ?
Is there a mutation in it perhaps? I haven't seen sequencing data between the strains, so I as a reader cannot rule it out. But I think there is a far more interesting question here.
The main thing that you need to remember with Hfq is that it doesn't act alone. The main way it functions is through using sRNA's. Focusing just on Hfq is only going to get us half the story, the other half lies in how the sRNA's are regulated, and which ones are working with Hfq to do its job.

The surprising finding here is that bacterial strains can show a lot of variation in the community, although I suspect that as labs work on greater numbers of clinical isolates, we'll see more variation like this, and hopefully we can use these differences to further understand how these bacteria are evolving.

* by me only. Sorry for making you scroll down so far for just three words.
**I'm starting with table 3, because the previous two tables simply listed out the various strains/ primers used in this study, so that other scientists can replicate and expand on the work performed here, and I don't feel a powerful need to comment on them.
*** and a helluva lot of other stuff that I don't want to get into here.

Whenever my experiments fail...

2012-06-14T16:02:00.002+01:00