Looking into the Microbiome through multiple lenses

Article by: Dr. Darshan Dhakan

The microbiome may be referred to as the collection of microbial communities coexisting to perform specific functions within the host.

Superior Microbial Characterization through Microbiomics?

Next Generation Sequencing (NGS) has emerged as a primary tool to reveal the diversity and functions of these microbial communities [1]. A number of pioneering studies in the early 2000s helped lay the foundations for the ‘microbiome’ field and the development of major research programs. Earlier, Amplicon based sequencing using 16SrRNA (called Molecular clock) was used to generate an identification map of the Microbiome [2]. Based on the sequence identity of these 16SrRNA molecules they may be categorised into groups called Operational Taxonomic Units (OTUs) which further gives us an estimate of the diversity of these microbial communities [3]. By the mid 2000s, as the sequencing costs declined, the NIH’s initiative to study the microbial communities associated with multiple body sites called the “Human Microbiome Project'' was launched [4]. The study, engaging the ‘Whole Metagenome Shotgun’ approach not only revealed extensive diversity of the microbiome associated with multiple body sites but also shed light on the core microbiome functions important for their growth and survival.?

Owing to the success of these large scale projects, several satellite projects were initiated to understand the linkage of the microbiome with several disease conditions. Most of these studies were based on the association of the microbiome with a specific type of population. A number of bacterial species were discovered to have associations with disease phenotypes. For example, Short Chain Fatty Acid (SCFAs) producers such as Faecalibacterium prausnitzii and Roseburia intestinalis which are anti-inflammatory microbes leading to the suppression of the immune system was found to be associated with progression of Tuberculosis (TB) and obesity [5,6]. Flavonifractor plautii species which are found in the gut microbiome were reported as being? strongly associated with Colorectal cancer [7]. These association based studies establish potential bacterial markers for each of the disease phenotypes giving another dimension of investigation and also diagnosis.

Insightful Mechanistic Microbe-Disease Associations through Metabolomics?

To advance from microbiome-disease association to functions of the microbiome, additional data and experiments are required to explore potential causality between microbial components and the disease of interest. Metabolomics data is considered as a robust readout of these disease phenotypes [9].?

Metabolomics is the study of all the metabolites in a host under given conditions.?

These metabolites are analyzed using chromatography combined with Mass Spectrometry (LC-MS/GC-MS) [10]. Apart from that they can also be analyzed using Nuclear Magnetic Resonance (NMR) imaging technique. Metabolites serve as robust markers of the microbiome and the host combined, thus establishing a functional association with biochemical metabolic processes. Integrative analysis of both microbiome and metabolome datasets captures candidate biosynthetic gene clusters (BGCs) of bacteria and the encoded metabolites [11]. A large-scale study using serum metabolites of deeply phenotyped populations showed that the microbiome greatly explained the levels of xenobiotics and other unknown compounds in the serum, and the origins of these unidentified compounds [12]. Metabolomics, thus, may serve as an important dataset apart from genomics and transcriptomics in providing another dimension of looking into the mechanisms of specific phenotypes.?

Since microbial metabolism is diverse, metabolomics helps us? establish a link between microbial species and their metabolic readouts. As both sequencing and mass spectrometry have their pros and cons, integration of these datasets remains a challenge which needs to be addressed. Future studies employing mass spectrometry along with sequencing are likely to be heard of, as researchers now aim to look into the mechanisms and map the functions of these microbes in consortium.

References

1. Lynch, Susan V., and Oluf Pedersen. "The human intestinal microbiome in health and disease." New England Journal of Medicine 375.24 (2016): 2369-2379.

2. Shin, Jongoh, et al. "Analysis of the mouse gut microbiome using full-length 16S rRNA amplicon sequencing." Scientific reports 6.1 (2016): 1-10.

3. Johnson, Jethro S., et al. "Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis." Nature communications 10.1 (2019): 1-11.

4. Mitreva, Makedonka, and Human Microbiome Project Consortium. "Structure, function and diversity of the healthy human microbiome." Nature 486 (2012): 207-214.

5. Maji, Abhijit, et al. "Gut microbiome contributes to impairment of immunity in pulmonary tuberculosis patients by alteration of butyrate and propionate producers." Environmental microbiology 20.1 (2018): 402-419.

6. Tims, Sebastian, et al. "Microbiota conservation and BMI signatures in adult monozygotic twins." The ISME journal 7.4 (2013): 707-717.

7. Dhakan, D. B., et al. "The unique composition of Indian gut microbiome, gene catalogue, and associated fecal metabolome deciphered using multi-omics approaches." Gigascience 8.3 (2019): giz004.

8. Gupta, Ankit, et al. "Association of Flavonifractor plautii, a flavonoid-degrading bacterium, with the gut microbiome of colorectal cancer patients in India." MSystems 4.6 (2019): e00438-19.

9. Johnson, Caroline H., Julijana Ivanisevic, and Gary Siuzdak. "Metabolomics: beyond biomarkers and towards mechanisms." Nature reviews Molecular cell biology 17.7 (2016): 451-459.

10. De Hoffmann, Edmond. "Tandem mass spectrometry: a primer." Journal of mass spectrometry 31.2 (1996): 129-137.

11. Sugimoto, Yuki, et al. "A metagenomic strategy for harnessing the chemical repertoire of the human microbiome." Science 366.6471 (2019): eaax9176.

12. Bar, Noam, et al. "A reference map of potential determinants for the human serum metabolome." Nature 588.7836 (2020): 135-140.

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