Metagenomics Project: Role of High Fat Diet on Obesity

Unravelling the Microbial Diversity through Metagenomics 

Metagenomics is the application of modern genomic techniques to the study of communities of microbial organisms. Metagenomics is based on direct sequencing of microbial communities from nature, such as seawater or the surface of human skin, which bypasses the need for laboratory cultivation of microbes. This approach has allowed the field of environmental biology to leap forward, since it enabled the sequencing of countless formerly unknown microorganisms. Before metagenomics, biologists were only able to conduct genetic analyses on organisms they knew something about. With few exceptions, biologists could only study microorganisms they could culture (or grow) within the laboratory. However, culturable microorganisms make up only a very small fraction of all of the microorganisms that exist. Using metagenomics, a biologist can simply sequence an environmental sample; for example, water from a certain lake. From the sequence output, the biologist gets a picture of all of the microorganisms living in that lake, including organisms that have never been sequenced or characterized in any other way. This is particularly valuable for studying organisms living in extreme environments, such as thermophiles in volcanic vents. Culturing such organisms can be quite challenging. Environmental sequencing allowed the literature on previously unknown organisms to increase exponentially, and we have quickly come to realize that the level of functional and taxonomic diversity in the earth’s microbial communities is far beyond the assumptions of biologists in the past.

This information is not only interesting in terms of basic biology, but it is also practical. Through environmental metagenomics, environmental scientists are able to study the effect of changes in the environment on microbial communities. Changes in microbial communities can also indicate changes in the environment. For example, environmental sequencing can reveal changes in the microbial population that could alert environmental scientists to a change in the environment that may not be obvious otherwise. In addition to looking at DNA to identify the organisms present in a population and their numbers, environmental researchers can also look at RNA– changes in transcription in the same population can reveal environmental changes. Differences in the expression of genes involved in key pathways, such as metabolism of toxins, can yield particularly valuable information.

Relevance of Metagenomic Data Analysis (Role in Microbial Abundance Analysis , Oncology etc.)

Metagenomics can be used to answer two broad questions. The first is “who is there?”; the second is “what are they doing?” Environmental sequencing can solve both of these important questions. There are three main metagenomics sequencing methods: 16S rRNA sequencing, whole metagenome shotgun sequencing (of historical importance), and high-throughput microbial whole-genome sequencing. Metagenomic sequencing data can be used to analyze microbiome composition and species diversity. It finds its application in identifying and studying microorganisms within their habitat. In cancer research, this approach has revolutionized the way of identifying, analyzing and targeting the microbial diversity present in the tissue specimens of cancer patients. In this course, you will learn about processing 16s rRNA data with DADA2, visualizing OTU abundance tables for diversity and using the Phyloseq R package that can help build phylogeny-aware species richness, as well as alpha diversity plots that measures the diversity within a single sample and beta diversity plots that represents species diversity between any two patches and their communities. In this course, you will learn about key concepts involved in metagenomic data analysis.

To learn more about metagenomics, its role in different fields of life sciences, you can visit the OmicsLogic Learn portal and get your hands dirty with Course 4 on Metagenomics.

Now let's have a look at what a metagenomics pipeline looks like on the T-Bioinfo server.

Metagenomic Data Analysis on T-Bioinfo Server

The pipeline is based on running a number of programs, including DADA2, Ape, and Phyloseq algorithms. DADA2 generates amplicon sequence variant (ASV) tables, which are similar to OTU tables but detailed in that they tabulate the number of identical amplicon sequence variants from different samples. Microbial studies utilizing DADA2 provide high resolution accurately reconstructed amplicon sequences that improve the detection of sample diversity and biological variants. 


After the pipeline has completed its processing, you will obtain a list of output files that could be downloaded to carry out statistical analysis and interpret biological insights. You will also obtain data visualizations in your output files that make sense to understand meaningful patterns or significant results. 


To understand what type of data is required to process the pipeline, what are the different algorithms involved and finally what does the output files include, please visit  16S RNA Amplicon Data Analysis (DADA2) on T-BioinfoServer (

On the OmicsLogic Learn Portal, experts have put together an example project which explains the application of metagenomic data analysis. The aim of this project is to understand the microbial diversity after feeding a high fat and normal diet and decipher its role in obesity and underlying diseases.

Example Project on OmicsLogic Learn Portal : Role of High Fat Diet on Obesity 

The composition of gut microbiota is unique to each individual, is variable between persons, and is reasonably stable following the first year of life. Despite this, emerging literature implicates diet as an important influence on the gut microbial profile. As such, lack of adequate nutrition has been linked to dysfunctional microbiota and dysbiosis.

Recent research has focused on the influence of HFD consumption on gut microbial composition. For example, it has been reported that HFD promotes a decrease in Bacteroidetes and an increase in both Firmicutes and Proteobacteria. Similar phylum-level shifts were reported following high-fat and high-sucrose feedings. Carmody et al., used over 200 strains of mice to determine whether variations in gut microbiota are primarily driven by host genetics or by dietary factors. 

“A high fat diet (HFD) can result in significant changes in gut microbial composition. A large number of studies to date are simply associations between HFD consumption, altered gut bacterial composition, and promotion of obesity. Significant mechanistic research is needed to link specific gut phylotypes to obesity traits and subsequent risk for chronic disease. Although the available literature on therapeutic targeting of the microbiota to counteract diet-induced obesity appears promising, whether this presents a realistic approach is unclear. Viable agents have yet to be fully recognized, and specifics on the dose, timing and frequency of administration are still unknown”.

Interestingly, HFD-induced changes in gut microbiota and resulting metabolic perturbations appear to be dependent on the fat content as milk fat-based, lard-based (saturated fatty acid sources), or safflower oil (polyunsaturated fatty acid)-based HFDs induced dramatic and specific 16S rRNA phylogenetic profiles that were associated with different inflammatory and lipogenic mediator profiles of mesenteric and gonadal fat depots. However, not all the data supports a positive association as a few studies have reported that the absence of intestinal microbiota does not protect mice from diet-induced obesity. Inconsistencies in the literature are likely due, at least in part, to microbial adaptation to diet and time.

To learn more about the “Role of high fat diet on Obesity”, visit OmicsLogic Learn Portal: Project 13: and get a good hands on practical experience with real time data, run the pipeline yourself, learn to analyze and interpret biological insights to understand the role of high fat diet on obesity.  

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