Miguel,  Rojas-Cherto,; Michael,  van Vliet,; E,  Peironcely, Julio; Ronnie,  van Doorn,; Maarten,  Kooyman,; Te,  Beek, Tim; a,  van Driel, Marc; Thomas,  Hankemeier,; Theo,  Reijmers, MetiTree: a web application to organize and process high resolution multi-stage mass spectrometry metabolomics data (Article) Bioinformatics, Page(s): 2–4, 2012.

Abstract

Miquel, Rojas-Cherto,; E, Peironcely, Julio; T, Kasper, Piotr; J, van der Hooft, Justin J; H, de Vos, Ric C; Rob, Vreeken,; Thomas, Hankemeier,; Theo, Reijmers, Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral Trees (Article) Analytical chemistry, 2012.

Abstract

Multistage mass spectrometry (MS(n)) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS(n) data.

Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS(n) spectra acquired from 549 metabolites using Orbitrap FTMS(n).

For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS(n) spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS(n) spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree.

Ultimately, this tool may pave the way for constructing and populating large MS(n) spectral libraries that can be used for searching and matching experimental MS(n) spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies.

 In Simple Words

Similar metabolites have similar mass spectral trees. Imagine you have a collection or database of mass spectral trees of known metabolites, i.e. for which you know the chemical structure.

Now you have a mass spectral tree of an unknown metabolite. We propose a method to use the similarity between trees to identify the unknown either:

• By finding in the database a tree that is 100% similar. Here we could assign the identity to the unknown because we had it in the database.
• We might find several similar metabolites (not 100%) in the database. If these metabolites belong to a sub-class, let’s say amino acids, we assign the class of the unknown, it should also be a amino acid.
• These similar metabolites can have a common piece (a ring or a scaffold) which we speculate that is also present in the structure of the unknown metabolite. This piece, the Maximum Common Substructure, can help in metabolite identification and be used in Computer Assisted Structure Elucidation process to propose candidate structures for the unknown that contain such piece.

Smartphone games like Draw Something rose fast because its social approach and fell because … yes, it’s social approach.

In this Forbes article Zynga Nightmare Continues Tero Kuittinen tells us how the popular smartphone game company Zinga is struggling to keep people playing Draw Something.

Zynga recently acquired OMGPOP, a small company that created the game Draw Something. This game in a matter of days became a hit and was played by millions of people.

But here it seems that what easy comes easy goes. People are stopping to play Draw Something.

Tero argues that

Not only may it be impossible to predict the future success of games based on the first months of sales. But the quality of upgrades may ultimately turn out to have a decisive role in determining long-term success of a title.

From my point of view Tero gets part of this wrong.

He gets right that predicting the success of a game is difficult. It is not a precise science.

What he gets wrong is that upgrades played a role in the long-term success of games. In the case of Draw Something or WordFeud, their social approach is responsible for killing the game.

How smartphone games like Draw Something rise?

I read in Mashable that this game is getting hot. Next thing I do is to download the app and try to play with it. But I need a friend to play against. It’s not a single player game.

So I spam my friends with invitations, some of them will accept and spam forward to their contacts.

Draw Something grew socially because in order for a person to play, two have to download the app.

The same applies to stopping to play. If one player quits, the other cannot continue playing. In the extreme case, if one person stops playing, two will end up deleting the app.

And here lays the reason for the rapid decline on Draw Something. After a 2 hour spree of non-stop drawing, we get caught up by the busyness of our daily life and after a while we forget we were playing with a friend. We get bored and delete the app.

The same cascading effect of that makes people to massively download the smartphone games, also makes people massively uninstall them.

This is my little thank you card for them. Guys, you rock!

Enrique Dans

Enrique Dans is one of the first and best known bloggers in the Spanish bloggosphere. He mainly blogs about the Internet economy, new technologies and the impact copyrights and new policies have on Internet users.

What I like the most about Enrique Dans?

He’s open minded towards new technologies and very optimistic. Where others see new technologies as a threat, as a challenge to the status quo, he sees a positive disruption. He is always very excited about the new opportunities technology offers us.

I also like his scientific background. Thanks to this he writes very well documented posts, with abundant links to other information sources.

Hector Garcia

Hector Garcia, aka Kirai, is a Spanish blogger that lives in Japan. He writes about Japan, technology, being a programmer, and photography (boy he has nice pictures of Japan!).  I have been reading both Enrique’s and Hector’s blogs as early as 2004, when there was no Mashable yet.

What I like the most about Hector Garcia?

He has had a big influence in who I am now. At a time I was living in a narrow world, his blog I discovered it was possible to study and work abroad, and he showed how amazing Japan is. Fast-forward 8 years, I have studied a MSc and a PhD in The Netherlands, I have travelled throughout Europe and America, and I am planning a trip to Down Under and Japan. Thanks Kirai!!

Martin Varsavsky

Martin Varsavsky is a successful tech entrepreneur that has made a ton of money with his companies. This guy is very sharp and open, so when he shares his opinion on any kind of topic, you can expect a long debate. This is why I like to read his blog. He posts cover from what is it like to be a jewish father to his opinion on Internet business to how to use social media.

Follow also interesting updates in Martin’s Google+ account.

What I like the most about Martin Varsavsky?

He could be the most macho bragging blogger in the list. Yet he writes about many delicate topics with the upmost tact, respecting all points of view. Martin even writes about his mansions and his mega-yacht and when you read him, it feels like a normal conversation you could have with a friend at the bar.

Xavier Verdaguer

Xavier Verdaguer is a dreamer from Barcelona that lives in San Francisco. He writes about his entrepreneurial projects and shares pictures of his supertramps. He also has time to run marathons, walk barefoot a whole day, or train a team of young Spanish entrepreneurs in San Francisco.

What I like the most about Xavier Verdaguer?

His blog charges me with lots of energy. I don’t know if it is because of his dreams or his pictures of San Francisco, but Xavier’s blog makes me want to get more out of life. He is a great example of the motto “work hard play hard”.

Tim Ferriss

Tim Ferriss, Mister 4 Hours, is the new American dream. It is not about making a lot of money. Now it is about becoming anything you want and living the life you want to live, even if you don’t have money. He blogs mainly about the tricks and ideas presented in his book The 4 Hour Workweek.

What I like the most about Tim Ferriss?

When I read his blog or his books I end up having one of these two thoughts:

• How come nobody thought about this before?
• I have been thinking the same all my life and now this guy puts it into clear words, does it, and teaches me how to do it.

What do these great bloggers have in common?

They are humble achievers. You will not find in their blog posts a feeling of entitlement.

Some of them have tons of money, others have thousands of fans, or they create successful businesses.  In any case, they talk about what they have achieved in a normal way. They don’t show off nor they try to hide it. They are not ashamed of being what they are since they have worked hard to earn being up there.

Thank you again guys.

Photo by 401K

The difference between web and biotech startups is risk.

For a web startup it is easy to build a product that works. Hire some talented programmers and designers and you will have a working version of your idea. The risk lies in getting people to like and choose this product.

For a biotech startup the risky part involves going from an idea to a finished product. A possible treatment for a disease should work well and be safe. But hiring reputed scientists to lead the development phase is no guarantee to prevent the complexity of biology from threatening your startup. Once the product is finished, adoption by customers is easy.

Would you build a metabolite retention time prediction model with these data?

I got the a dataset from some HPLC experiments with the mission: can you build a Retention Time (RT) prediction model with these data?

I shared a Google Spreadsheet with the Retention Time of the metabolites.

We are doing metabolite identification and the plan would be to use such a model to reject candidate structures for unknown metabolites. When we would like to identify a metabolite, we will have LC-MSn data, this is retention time, mass and maybe known substructures for an unknown metabolite.

I would propose candidate structures either by mining databases like HMDB or PubChem, or via computer assisted structure generation.  Next I would use my model to predict the RT and reject those structures whose predicted RT is way off from the experimental.

My concerns about the data

What kind of data is this? 

If your answer is doing the HPLC experiment again

Image via Wikipedia

The future for drug discovery is dark grey. Big Pharma as we know it is going to to die. Only a few brave companies will survive, but only after a painful adaptation to the new reality.

Some are trying to adapt, hoping their pink hammer will fix the situation.

My concern has hit a maximum after reading this article by Simon Campbell (ex-Pfizer)  What is the Future for Drug Discovery in the UK? In it he describes the roadmap pharma industry should follow in order to survive. Some of the ideas are frightening.

A new and sustainable funding model with public sector participation is urgently required for world class UK scientists to invent and develop innovative medicines that meet the medical needs of the 21^st Century.

Public money is needed, but the profits will go for the companies, I guess. Does it sound like what happened with banks all over Europe?

We should build a consensus of expert stakeholders with the common objective of informing and influencing future development of world class healthcare innovation in the UK.

Lobbying and creating brain washed scientists is not the most ethical way to create a new future for drug discovery.

The government has stated a firm commitment to life sciences as a catalyst for growth, but emphasis appears to be on clinical trials, biologics and cell therapies rather than on cost effective and orally delivered small molecules that are the bedrock of any healthcare system.

More lobbying. What if “orally delivered small molecules” can’t solve every single disease? Why not to look a bit further from our belly button? What happened to personalized medicine? Prevention? Promoting a healthy life style?

Future R&D should focus on therapeutic areas of significant medical need where transformative new drugs will improve quality of life, and bring economic benefit.

No long tail business in the future for drug discovery, only focus on what makes big bucks.

We must address attrition [...] expand pre-competitive collaborations between industry/academia to focus on target selection/validation, predictive toxicology and to identify patient sub-groups that respond to agents with novel mechanisms of action…

This sounds a bit better. Collaborating with academia and targeting certain subgroups of patients.

…These initiatives would reduce risk, simplify clinical trails and lower costs

Ah my friend! We thought they want to target on sub-groups to improve the quality of their products, but it was just to reduce costs and maximize benefits. Stupid me, for a moment I forgot they are here for the money.

A network of Therapeutic Centres of Excellence should be established [...] expert medicinal chemists released by industry [...] Unused assets from Pharma could be included.

Experienced industrial scientists should be also embedded in world class biomedical centres by creating new chairs in medicinal chemistry

So these released experts working in Centres of Excellence created by Pharma are going to continue to use pharm.’s ideas to push pharm.’s agenda? Or on the other hand are going to push patients’ agenda?

If world class Pharma/Biotech talent is allowed to fade away, it will be extremely difficult to re-build quality in the future.

Many times something has to die so a better version can be born. It can be difficult, like many profound changes, but at the end of the process it will pay off.

Reallocation of current budgets…

Government, Funding Councils and Charities should now invest for future growth, as economic benefits will largely depend on a strong UK drug discovery capability that invents new medicines for world wide commercialisation

Some old school Fear, Uncertainty, Doubt (FUD) to end with. If you don’t give pharma big public bucks we are all screwed. It is again the same situation with banks, they take risks and when in trouble, public money has to be given. This only creates the incentives to be stupid again, since public institutions will take care of you no matter what.

If your only tool is a hammer, everything looks like a nail.

“Orally delivered small molecules” is the hammer that pharmaceutical companies have been using for decades. They have realized they only have a hammer. Unfortunately, they are asking public money to paint it pink and try to fix problems now with a pink hammer.

Sorry to disappoint you but in biology and health there is much more than meets the eye.

Your genetic heritage might indicate that your have more chances than usual of developing a disease, but there are many other factors that will contribute to this finally occuring.

Metabolomics studies the footprint of these factors in order to give a more accurate picture of your condition.

In this post you will find what is metabolomics explained in simple words and with examples of application areas.

Metabolomics in simple words

Metabolomics surveys the metabolites that are present in organisms.

What are metabolites?

Metabolites are small molecules, smaller than genes or proteins. They are involved in metabolism as intermediates or products of metabolic processes.

Human examples of metabolites are hormones, amino acids, lipids, vitamins, and antioxidants, among others. Not only humans have metabolites, in fact all live beings need metabolites for their functioning.

Metabolites are responsible for the taste of tomatoes, the smell of plants, the color of fishes, and the poison of spiders.

Why is metabolomics useful?

Metabolomics describes the phenotype of an organism, which quoting Wikipedia is:

the composite of an organism’s observable characteristics or traits: such as its morphology, development, biochemical or physiological properties, phenology, behavior, and products of behavior (such as a bird’s nest).

Phenotypes result from the expression of an organism’s genes as well as the influence of environmental factors and the interactions between the two.

The presence or absence of metabolites (and their concentrations) describes the state of an organism.

A practical application of this definition: Metabolomics can also describe the metabolic signatures of disease versus normality.

Imagine two identical twin brothers (who share almost identical genes). One has a stroke at 45 while the other lives healthy till he dies at 90. How can this happen if they are almost identical? Easy, lifestyle.

Can you guess who was having 2 meals a day at McRonald’s?

If you would have used metabolomics to check their lipid profile, you would have seen that the one with the stroke had a LDL cholesterol level abnormally high.

Examples Of Metabolomics Research

Metabolomics has proved useful to answer biological questions, for instance to determine the effect of a clinical intervention (aka, if I do this to these people, what do I see?) or to unravel the mechanisms of disease, to name a few.

If you are wondering what is metabolomics doing in the real world and what kind of biological questions metabolomics can answer, check these two examples.

How can I make my tomatoes tastier?

Imagine a company that sells tomato seeds want to improve their tomatoes in terms of taste, texture, aroma, and other properties you could not imagine a tomato possess.

Here the company can have two options, either modify genetically the tomatoes or to cross different breeds.

Next, a panel of tomato tasters (you guessed it, taster like with wine) give scores for these properties of each tomato sample.

Now, remember that metabolites are responsible for the taste and other properties of tomatoes.

By comparing the metabolic profiles of tomato types that scored high and low in each property, one can discover which metabolites contribute positively and negatively to the quality of tomatoes.

Is this diet good for reducing cholesterol?

Since you are interested in nutrition and health, you are curious about which diet improves your cholesterol, this is, bad cholesterol goes down, good cholesterol goes up.

The study is designed as follows: 4 different diets plus an extra control diet are chosen. You select 5 groups of people and each one gets a diet.

After some weeks, blood samples from the participants are collected and measured to determine the concentration of different cholesterol metabolites. And there you have your ideal diet to improve you cholesterol levels.

Sounds simple, huh? In fact, this kind of research, if you want solid results, requires a joint effort of many research centers and hundreds of participants.

Check this research about the best diet for  weight-loss maintenance.

Their goal was to decide which combination of high/low protein and high/low glycemic index was the, so you avoid the “rebound effect” (spoiler alert: the best diet is high protein and low GI).

Future Directions Of Metabolomics

Single Cell Metabolomics

Two individuals have different metabolic profiles if we measure their urine or blood. In the same fashion, different cell types in our body have different profiles.

In a not-so-far future, we will be able to extract a tiny sample from a single liver cell and compare its metabolic profile to the profile of a white blood cell, for instance.

By doing so, we will be able to focus on the specific organ or tissue involved in our research.

Metabolomics For Personalized Health

Have you heard of what 23andme.com is doing with gene analysis? you spit in a tube, send it by post and you get a detailed report on your genetic predisposition for a long list of diseases.

Now imagine the same, maybe instead of saliva samples with urine, but you will get a metabolic report, which describes your current health and if you are in any developmental stage of a disease.

If you want to receive future posts with new information on metabolomics, please subscribe to the RSS feed

This is me in Koh Chang (Thailand) during Xmas 2011 holidays

Dear readers, thanks for coming to my site. These are the 10 most viewed posts in my blog during 2011. I started to blog seriously from September 2011 and I am quite happy with the amount of readers I got in 3 months.

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Understanding and Classifying Metabolite Space and Metabolite-Likeness. 2011, PLoS ONE
Julio E. Peironcely, Theo Reijmers, Leon Coulier, Andreas Bender and Thomas Hankemeier.

Short Descritption Of The Paper

We have studied the chemical space of human metabolites.

We have built a computational model that assigns a metabolite-likeness score to input molecules.

We have studied which physicochemical properties and moieties are characteristic of human metabolites.

Long Description Of The Paper

This work describes the application of computational tools to predict the metabolite-likeness score of a molecule, i.e. how much it resembles a metabolite, and to acquire a global understanding of how the space of human metabolites is organized. In order to achieve this we made use of the state of the art in machine learning and the most comprehensive collection of human metabolites. To our understanding, we are the first to provide a metabolite-likeness score and to elaborate on its use for metabolomics, specifically in metabolite identification.

The use of computational models to classify metabolites and to compare them to other families of compounds has previously been published, but no prospectively validated papers have appeared.  The major contribution of the current work is threefold:

1. We employ and validate a variety of computational methods to not only predict if a molecule is a metabolite or not, but also to assign a metabolite-likeness score to molecules.
2. Three prospective validation sets are used to demonstrate the generalizability of our models and their applicability for drug research and metabolomics.
3. We investigate which physicochemical properties and chemical moieties are characteristic of the metabolite space.

We believe that our work would be of interest to medicinal chemists performing drug research, but specially to scientists in the field of metabolomics. Our aim is to show the metabolomics community how they can benefit from chemoinformatics.

Therefore, we devise further development and application of this work to accelerate discoveries concerning metabolite identification.

Calculate Metabolite-Likeness Of Your Molecules

If you would like to calculate the Metabolite-Likeness of some molecules, please contact me ( peyron #at# gmail.com)

Ditto if you want to know more about this work or to collaborate with me.