Bioinformatics 471

ABSTRACT

The Human Microbiome Project has highlighted the need to further examine the mutualistic relationships between humans and their intestinal bacteria. Turnbauch and his colleagues studied the bacterial diversity between twins and their mothers of varying weight classes in order to draw possible conclusions on the link between intestinal bacteria composition and obesity rates. They found that obese individuals tended to have lower bacterial diversity than their lean counterparts, and attempted to characterize the metabolic roles of these intestinal “guilds”, but their description fails to adequately describe the roles in a way that they could be applied in future studies. In thie project, I attempt to integrate newly published characterization of enterotypes to the Turnbaugh data in order to see possible diversifcation connections that can be made.

INTRODUCTION

It’s no secret to biologists that bacteria have formed close mutualistic relationships to their hosts as examples of such partnerships can be seen throughout the animal kingdom. For example, some species of moths have developed relationships with bacteria so they can live off of their relatively low-nutrient diets (Futuyma 2009). On a whole, moths tend to subside on the nectar of plants that they pollinate which comprises of carbohydrates mainly with little to no essential amino acids present. Moths have coevolved with certain species of bacteria that live in their guts who are able to produce all the essential amino acids that the moth is lacking by drinking nectar alone, and only for the price of sharing some of the sugars that it ingests with its microscopic partners. Humans, like moths and other organisms, have also developed microscopic partnerships with certain bacteria; however, the specific functions and varieties of these important bacteria weren’t very well understood until the Human Microbiome Project began (http://commonfund.nih.gov/hmp/). The project began as a survey of the possible functions and species of bacteria associated with the human body, which is literally covered on all surfaces. In truth, the human body is composed of about 10 trillion cells, but it also contains about 100 trillion bacteria (Yong, 2011). In addition, for every human gene, there are about 100 bacterial genes that can be taken from the human body (Yong, 2011). With such a vast population of bacteria to identify, the goal of the Human Microbiome project may have seemed insurmountable; however, by applying modern metagenomic technology, we are able to do wide scale analysis of genetic sequences instead of going sequence by sequence.

In addition, the project has been joined by other researchers who see the possible benefits of such research and applying to many common human health problems. Recently, there has been a big push to analyze the bacterial composition of the intestine and examine a possible correlation with obesity. Like the moths mentioned above, humans too have bacteria in our intestinal tract that allow us to more efficiently breakdown the food we try to pass through our systems. These partnerships in part contribute to our basic metabolic processes, but depending on the numbers and varieties present, they may be pushing some individuals into obesity. In a study done by Turnbaugh and their colleagues, they analyzed the bacterial composition of stool samples provided by twins (both fraternal and identical) and their mothers in order to see if the relative abundance and diversity differed between those of various weight classes (obese to lean) and between family members (Turnbaugh et al. 2009). They found that on a whole, individuals had more similar bacteria to those they were related to than those they had no relation to in the least (Turnbaugh et al. 2009). They also found that obese individuals had a lower bacterial diversity than lean individuals (Turnbaugh et al. 2009).  Though Turnbaugh and his colleagues characterized these bacterial groups some, overall very little was actually said about larger-scale bacterial groupings that could be used for future identification and characterization as the Human Microbiome Project continues to mature.

Recently, a new massively collaborative study was published by Manimozhiyan and their colleagues that aimed to elucidate many of the same goals that the Turnbaugh study did (Manimozhiyan et al., 2011). Instead of sampling twins, Manimozhiyan and his colleagues took stool samples from people of diverse European ancestry (not twins). Using their analytical techniques, they identified three main “enterotypes”, a more well-defined guild-like ecological identification system, for the microbiomes analyzed in the study’s subjects: Bacteriodetes, Prevotella, and Ruminoccus (Manimozhiyan et al, 2011). These enterotypes are diagnosed by the largest percentage of bacterial classes present in the subject’s samples and have been further studied in order to identify the metabolic “specialty” that such an enterotypic system would have. Using this new enterotypic information, it’s my intention to revise information in the Turnbaugh bacterial classification and draw some commonalities between the enterotypes found between the lean and obese subjects in the study.

METHODS

Original Data was collected by Turnbaugh and his colleagues and are publicly available on MG-RAST online (http://metagenomics.anl.gov/). Original data was taken from groups of monozygotic, dizygotic twins and their mother’s enrolled in the Missouri Adolescent Female Twin Study (MOAFTS). The twins ranged in age from 21-32 years old and were from either European or African ancestry (Turnbaugh et al. 2009). Although all of the twins were born in the state of Missouri, few actually remained in the state. However, fecal samples were still collected from them and frozen immediately for sequencing. Turnbaugh then took the samples and using multiplex pyrosequencing and 16S rRNA sequencing, analyzed the genetic

composition of the stool sample. Sequences were then matched up with known bacterial sequences and stored online on MG-RAST. During my project’s period, I was able to access the original data from the Turnbaugh study and randomly selected 14 nonrelated obese and lean subjects for comparison. A Table was built using their abundance totals, as well as phylogenetic trees and heat maps.

RESULTS

The total number of classes of bacteria can be seen above in table 1. The 14 randomly selected lean subjects from the Turnbaugh study had a total abundance of 66,214 and the obese sample only had 38,273 (only 58% of the diversity found in the lean subjects. The table also initially identifies two major phyla present in the two microbiome types: Firmicutes and Bacteriodetes. For the obese subjects, firmicutes bacteria comprised of 84% of the total bacteria identified in their systems, while it only comprised of 73% of the lean subject’s diversity. In addition, bacteriodetes sequences comprised only 16% of the total diversity in obese subjects and 24% in the lean subjects. Analyzing these proportions, both the lean and the obese subjects seem to have a Manimozhiyan enterotype consistent with rhuminococcus-type systems; however, obese candidates have an even higher of the characteristic rhuminococcus-type bacteria of the phyla firmicutes than the lean subjects, but enterotypic classification is solely based on the greatest proported phyla of bacteria found, which remains firmicutes for both categories.

These results are both echoed in figures 2 and 3. In figure 2, phylogenetic trees were built for the bacteria isolated from obese and lean samples on MG-RAST. The bacteria are sorted by species and color-coded by class to illustrate the diversity difference between the two groups. The obese tree has fewer colors encircling it compared to the lean tree so we then conclude that it has a lower overall diversity.

In addition, the largest section for both trees are the bacteria under the phyla firmicutes. In figure 3, two heat maps were drawn on MG-RAST in order to illustrate the diversity “hot spots” of the samples. The overarching pattern shows more green “active” spaces in the lean heat map, a greater “intensity” on its green spots, and also a longer map when compared to the obese heat map. In both maps, a well-defined horizontal green bar can be observed, marking the location of the firmicute species on the map

DISCUSSION

According to Manimozhiyan, the rhuminococcus enterotype represents an overrepresentation in the haem biosynthesis pathway and functions greatly in the iron transport system of the body (Manimozhiyan et al. 2011). In addition, the other major component to both systems, bacteriodetes, functions in Biotin bosynthesis, a vitamin necessary for proper digestion (Manimozhiyan et al. 2011). I find the function of the bacteriodetes bacteria to be most interesting of the two major components because of Biotin’s direct relation to digestion. Without proper amounts of biotin, it is difficult for the body to digest foods in the most efficient way possible, perhaps indicating a possible correlation for future study in obese patients.

It was unsure exactly what he proportion of bacteria found in the samples belonged to the last enterotype, prevotella, due to unclear bacterial criteria for being a member of that guild; however, its system mainly functions in thiamine (B1) biosynthesis, a vitamin that is thought to stimulate digestion by improving hydrochloric acid production. All of these pathways would be important in a healthy digestive system, and a functional balance between these and all enterotypes to be defined in the future are necessary for optimum health. More research should be done in this discipline in order to further identify enterotypes and function to extend the possible applications of this knowledge. Right now, it is difficult to draw definitive relationships due to limits on the amount of available public data to analyze, but this will easily be remedied with the continued development of the Human Microbiome Project.

REFERENCES

Arumugam, M, J. Raes, E Pelletier, D. Le Paslier, T. Yamada, D. R. Mende, G. R.           Fernandes, J. Tap, T. Bruls, J. M. Batto, M. Bertalan, N. Borruel, F. Casellas, L.           Fernandez, L. Gautier, T. Hansen, M. Hattori, T. Hayashi, M. Kleerebezem, K.            Kurokawa, M. Leclerc, F. Levenez, C. Manichanh, H. B. Nielson, T. Nielson.             2011. Enterotypes of the human gut microbiome. Nature.            Doi:10.1038/nature09944

Futuyma, D.J. 2009. Evolution. Sinauer Associates, Inc.

Human Microbiome Project. NIH. January, 27, 2011.            http://commonfund.nih.gov/hmp/

The Metagenomics RAST server – A public resource for the automatic phylogenetic           and functional analysis of metagenomes F. Meyer, D. Paarmann, M. D’Souza,           R. Olson , E. M. Glass, M. Kubal, T. Paczian , A. Rodriguez , R. Stevens, A. Wilke,           J. Wilkening, R. A. Edwards
BMC Bioinformatics 2008, 9:386.

Tschöp MH, Hugenholtz P, Karp CL. Getting to the core of the gut microbiome. Nat.           Biotechnol 2009 Apr;27(4):344-346. PMID: 19352371

Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML,  Jones WJ, Roe BA, Affourtit JP, Egholm M, Henrissat B, Heath AC, Knight R,     Gordon JI. 2009. A core gut microbiome in obese and lean twins. Nature            Jan;457(7228):480-484. PMID: 19043404

Yong, E. 2011. Divided by language, united by gut bacteria-people have three common  gut types. Discover Magazine.            http://blogs.discovermagazine.com/notrocketscience/2011/04/20/divided-by            -language-united-by-gut-bacteria-–-people-have-three-common-gut-types/

As described in a paper by Peter J Turnbaugh et al, the microbiome of an individual plays a crucial role in metabolic functions and weight control. In this project I have taken some information published by Turnbaugh ont he Site MGRAST and analyzed it in a KEGG map to see how the metabolism of a person (in this case a lean vs obese person) is affected by the metabolism of their gut bacteria.

My page is up. Just thought I would post here to let you know.

Have a great summer!

This was posted just a few hours ago: Divided by language, united by gut bacteria – people have three common gut types. That post lead me to this one:Enterotypes of the human gut microbiome

I thought I would post them in case someone doesn’t like where their paper is going and needs inspiration

So last week’s MeV project was a total bust for me. The software simply didn’t work on my laptop. I tried looking at it again this past weekend to make sense of what went wrong and found that my Java files were updated but there was a single piece of associated software that went missing or something when my Java update came up last week (since MeV slowed down my computer soooo much, I had decided to remove a lot of stuff I wasn’t using). I’ve since re-installed Java and it worked for a brief while before doing the same thing, so I was thinking that the UMW servers probably were blocking something on my computer, but when I went to use Starbucks’ wifi, I found that MeV still didn’t work, so that means there’s something wrong with my computer itself. I really hope it’s not a virus or spyware or something because I’ve noticed my computer has slowed down significantly in the past few weeks so I’m probably gonna get that checked out.

This week we are looking at Turnbaugh article and related figures/software to engineer our final project, which will be involved in making phylogenic trees and maps. The article talks about research involved with the diversity of microbes in the guts of obese versus lean twins. The findings are that a lot of the bacteria in the gut can be inherited; i.e. that the bacteria is shared within families. There are some major differences, however, that have to do with the environment of each individual; in other words, there’s a “base” group of microbes within families but variance occurs according to individual diets and environmental exposures. The major population of bacteria is different (Actinobacteria) in obese twins vs. lean twins (Bacteriodetes). Furthermore, obesity has been linked to a much less diverse bacterial population in feces. I think this is going to be an interesting project and I hope it works much better than our previous one.

This week, we were introduced to microarrays and began work on our next project, which is analyzing Dr. Zies’ microarray data for renal carcinoma cells. We spent most of the week looking at MeV, or multiple experiment viewer, which utilizes multiple different clustering functions and algorithms to sort through microarrays. It’s extremely confusing to use so far, but I made some headway by just playing around with the data. The self-organizing tree algorithm is particularly interesting and I like the way it clusters so I’m probably going to be using that for my project. Dr. Zies’ research student presented to us on Friday about the actual project we’re doing. I’m a little excited because we’ll be analyzing original data instead of looking at stuff that’s already been analyzed. I can see, though, the problems with that -I don’t think we’re informed enough to really know exactly what to look for or what we’re looking at and it’s going to take some work to get to that level. Especially since all we were given was a brief run-through, it’ll be interesting to see how everyone looks at the project through different angles. Just a quick look-through makes it look, to me, like there is a lot of albumin/interferon/immune activity going on; given what we’re studying in Immunology, I guess this makes sense. I hope things run smoothly for me as they always have but that’s always a gamble, especially using software that none of us really know how to use.

This was such an interesting article. I had never really thought about the importance of the bacteria in our gut the way it was explained by Tschop et al. It makes sense that family members have similar bacteria species in their intestines, however I found it a bit surprising that Obese people have less diverse systems. Once it was explained it was clear why that is true. This week (our final week) I am going to attempt to figure out our new program early so that Finals do not become even more stressful than they are already promising to be. I am really glad our MeV project is over mostly because the software was so confusing. I still need to look up a few things in the MeV manual simply because I am not entirely sure how the analysis methods used by my colleagues worked. I understand variant filtering, but the other types of filters confused me so I want to see how they work so I understand the criteria for removing genes.

Amid the glee of the “Death” of microarray in our lives, we were assigned to read this paper reviewing the study of intestional gut bacteria. It really didn’t hit me until I started reading the paper just what this study entailed (and then I saw the irony of the situation with all of us at the “butt” of our spring semesters (excuse the pun)). Anywho, I found their project surprisingly interesting. Who knew gut bacteria could potentially be so influential on obesity! I think that this project this week will be interesting for a few reasons:

1) How current the research is/it’s perspective: The review was published less than 2 years ago, which means that this study is still pretty fresh. The analytical technique applied here is also fresh, as I’ve never heard of a study analyzing the “ecology” of a medical condition.

2) The Obesity tie-in: Obesity is still a major problem in the united states, and any research like this that brings to light a new side to obesity-contributing conditions can help us to better think of ways to combat it.

3) THERE IS NO MICROARRAY: Can I get an amen?

All in all, I’m excited to do this project this week!

This article is looking into the nature of the microbiome of the gut as it relates to obesity. They are looking at studies in mice as well as in human twins. They made a number of different findings, formed a few new hypotheses, and recognized some areas for potential further research. They found that the gut microbiome of obese and lean individuals is varied. Obese individuals also had a reduction in microbiome diversity in comparison with lean counterparts. If a transfer was made from obese to lean or vise versa, the metabolism would be affected in a predictable manner. It was found that gut microbiota likely affect hormones, glucose production, and signaling to the brain. All of this research shows a potentially relevant connection between microbial function and pathways controlling energy. More research needs o be done in these areas to be able to have any solid and conclusive evidence.

Much to the happiness of everyone in the class, microarrays are done. I actually do feel like I have gotten some things out of the process besides the feeling of satisfaction over one success. I really did learn a lot about hierarchical clustering and many of the other analytical techniques. I may not ever use MeV again but surely clustering and relevance networks will come into account again and because of that I really do feel like the project worked out in the end. Next we are working on microbiomes. I read the paper and found it highly interesting. The new wave in weight loss will be fecal transplants from models. (GROSS) I don’t really know about all of that but the subject matter is very interesting and I am excited to look further into it.

I hope everyone’s last week of classes goes ok!