Identification of the core human microbiome

[ad_1]

at recent days Nutrients Periodically studying, researchers provide a critical review of the concept of the “essential human microbiome.” Here, researchers discuss the technical, analytical, and conceptual issues that must be resolved in order to gain a comprehensive understanding of the underlying human microbiome.

Stady: The basic human microbiome: does it exist and how do we find it? A critical review of the concept. Image Credit: Juliasuena / Shutterstock.com

background

The core microbiome is of great scientific interest as a result of the critical participation of the microbiome in nutrient absorption, immune defense, and gut metabolism. Aside from diseases such as inflammatory bowel disease (IBD) that have been directly linked to altered gut microbiome composition, many other medical conditions such as depression and autism have been postulated to arise due to altered interactions between the gut microbiota and the brain.

Over the past several decades, the human microbiome has been the focus of significant research worldwide. Several large national and international initiatives have been undertaken, including the Human Microbiome Project (HMP) and MetaHIT, for example, to better understand the complexity of the microbiome.

What causes differences in the core of the microbiome?

Across different geographic regions and populations, the core human microbiome varies greatly. These differences can be attributed to different environmental and individual conditions such as diet, host genetics, and various other factors.

Additional differences in the core microbiome can be identified within a single individual, as the gut microbiome has a distinct composition compared to the vaginal and oral microbiome.

Furthermore, different parts of the gut may contain different microenvironments that support a distinct core microbiome. For example, mucosal-associated microorganisms have more profound effects on immune and health indicators than luminal and fecal microorganisms.

Compared with mice, gorillas, and chickens, the core human microbiome contains an abundance more common to certain species. Moreover, compared to rodents and birds, human microbes are most similar to those of gorillas.

Distinctive differences also exist between Westernized and non-Western populations. In fact, studies have shown that many microbial species that are abundant in one human group may not be ubiquitous in other human groups. but, fecal feces It was frequently identified in more than 90% of samples within six human groups.

Approaches to understanding the basic microbiome

The human body contains various types of microorganisms, including symbionts and pathogens. The term “basic human microbiome” describes components of the microbiome that remain relatively constant over time and between individuals.

There are primarily four different approaches to identifying the core human microbiome, the most common of which involve community composition and functional profile approaches. The community composition approach describes the core microbiome in terms of common taxa, while the description of the functional profile is based on a set of common functions.

The ecology method identifies the core microbiome based on taxa abundance, interactions, co-occurrence, and other patterns at the community level. The stability approach takes into account the characteristics that support the stability and resilience of the community.

Community profiling of nine groups: HMP stages 1 (n = 138), 2 (n = 91), and 3 (n = 42);  Healthy individuals from Denmark (n = 64);  Individuals with IBD from Spain (n = 16);  hunters, pickers, and traditional farmers (n = 19);  gorilla (n = 15);  mice (n = 141);  and chicken (n = 121).  (a) The fraction of samples containing each species, for species detected in at least 70% of samples in at least one group (a total of 107 species).  Refer to Supplementary Table S3 for a complete list of all species detected in all cohorts.  (b) The first two components of a UniFrac-based unweighted MDS analysis considering all samples.  (c) The number of species detected in at least 90% of the samples of each group.  Only one species (F. prausnitzii) was detected in 90% or more of the samples in all healthy Western human data sets (n = 4) and all human data sets (n = 6).  (d) The number of tracks detected in >90% of samples across all healthy Western human data sets (n = 4);  All human data sets (n = 6);  human and gorilla data sets (n = 7);  human, gorilla and mouse data sets (n = 8);  and all data sets, including chickens (n ​​= 9).” class=”rounded-img enlarge-image-child” src=”https://d2jx2rerrg6sh3.cloudfront.net/images/news/ImageForNews_719795_16582675788144472.jpg” srcset=”https://d2jx2rerrg6sh3.cloudfront.net/image-handler/ts/20220719055305/ri/1867/src/images/news/ImageForNews_719795_16582675788144472.jpg 1867w, https://d2jx2rerrg6sh3.cloudfront.net/image-handler/ts/20220719055305/ri/1850/src/images/news/ImageForNews_719795_16582675788144472.jpg 1850w, https://d2jx2rerrg6sh3.cloudfront.net/image-handler/ts/20220719055305/ri/1650/src/images/news/ImageForNews_719795_16582675788144472.jpg 1650w, https://d2jx2rerrg6sh3.cloudfront.net/image-handler/ts/20220719055305/ri/1450/src/images/news/ImageForNews_719795_16582675788144472.jpg 1450w, https://d2jx2rerrg6sh3.cloudfront.net/image-handler/ts/20220719055305/ri/1250/src/images/news/ImageForNews_719795_16582675788144472.jpg 1250w, https://d2jx2rerrg6sh3.cloudfront.net/image-handler/ts/20220719055305/ri/1050/src/images/news/ImageForNews_719795_16582675788144472.jpg 1050w, https://d2jx2rerrg6sh3.cloudfront.net/image-handler/ts/20220719055305/ri/850/src/images/news/ImageForNews_719795_16582675788144472.jpg 850w, https://d2jx2rerrg6sh3.cloudfront.net/image-handler/ts/20220719055305/ri/650/src/images/news/ImageForNews_719795_16582675788144472.jpg 650w, https://d2jx2rerrg6sh3.cloudfront.net/image-handler/ts/20220719055305/ri/450/src/images/news/ImageForNews_719795_16582675788144472.jpg 450w” sizes=”(min-width: 1200px) 673px, (min-width: 1090px) 667px, (min-width: 992px) calc(66.6vw – 60px), (min-width: 480px) calc(100vw – 40px), calc(100vw – 30px)” style=”width: 1867px; height: 2000px;” width=”1867″ height=”2000″/></a></p><p style=Community profiling of nine groups: HMP stages 1 (n = 138), 2 (n = 91), and 3 (n = 42); Healthy individuals from Denmark (n = 64); Individuals with IBD from Spain (n = 16); hunters, pickers, and traditional farmers (n = 19); gorilla (n = 15); mice (n = 141); and chicken (n = 121). (a) The fraction of samples containing each species for the species detected in at least 70% of the samples in at least one group (a total of 107 species). Refer to Supplementary Table S3 for a complete list of all species detected in all cohorts. (b) The first two components of a UniFrac-based unweighted MDS analysis considering all samples. (c) The number of species detected in at least 90% of the samples of each group. Only one species (F. prausnitzii) was detected in 90% or more of the samples in all healthy Western human data sets (n = 4) and all human data sets (n = 6). (d) The number of tracks detected in >90% of samples across all healthy Western human data sets (n = 4); All human data sets (n = 6); human and gorilla data sets (n = 7); human, gorilla and mouse data sets (n = 8); and all data sets, including chickens (n ​​= 9).

omics of the core of the microbiome

Currently, most research into the basic human microbiome is based on next-generation sequencing (NGS)-based technologies, which, when combined with appropriate bioinformatic methodologies, create massive amounts of data. Meanwhile, taxonomic and functional accuracy at the species level can only be achieved using metagenomicsmaking it the preferred method compared to amplicon surveys.

A large proportion of the available data have been collected by amplicon-based community surveys, which search for a conserved region of the genomes of the target population using polymerase chain reaction (PCR) assay and specific primers.

In recent years, microbiome research has shifted its focus to amplicon-based community surveys and metagenomics, the latter being a method of studying microbial communities without culturing. Metagenomics is based on the ‘gun’ sequencing of all microbial genomes present in the sample.

The European Nucleotide Archive (ENA) contains more than 610,000 samples of the human microbiome, 85% of which are amplicons of 16S ribosomal ribosomal acid (rRNA). Notably, in microbiome surveys, different 16S rRNA gene sites are commonly used to detect prokaryotes. In combination with additional samples accessible through other platforms, ENA samples provide a sufficient amount of data to assess the core human microbiome.

Technological challenges

Future approaches will justify the resolution of insufficient samples from non-Western populations, as well as the lack of complete reference databases and data on the abundance of eukaryotes, eukaryotic viruses, and bacteriophages. Other technological hurdles, such as sampling directly from different sections of the gut, should also be simplified in the future.

Therefore, the ability to accurately assess the core microbiome of distinct regions within the GI tract is likely to remain deferred for the foreseeable future due to the ongoing paucity of highly specific data.

In terms of metabolic processes, proton nuclear magnetic resonance (1H NMR), one of the primary analytical methods used in these investigations, has low sensitivity and may not detect low-abundance metabolites.

In addition to NMR, high-performance gas-liquid chromatography techniques combined with quadrupole (time-of-flight) mass spectrometry have been successfully used to characterize a large number of metabolites from microbial samples. In contrast, TOF-based detection requires sample pretreatment and chromatographic segmentation techniques before a suitable solution can be obtained in the discovery-based metabolic processes.

To overcome these limitations, new high-resolution micro-mass (HRAM) analytical tools have been used to study complex microbial community metabolites. Off-target detection of new metabolites is made possible by ultra-high-performance liquid chromatography (UHPLC) coupled with quadrupole mass spectrometry.

The advanced platform used in discovery- and metabolomics-based proteomics known as the Orbitrap mass analyzer provides essential tools in manipulating the gray areas of host microbiome metabolism, despite being an expensive tool that requires complex data analysis capabilities.

Machine learning and cloud computing

In the context of the core microbiome, the whole picture remains elusive due to the lack of a high-throughput method capable of communicating essential information. Computational and statistical methods are required to integrate layers of data and reveal complex patterns as more and more non-genetic data are collected.

To elucidate such patterns, machine learning and cloud computing are promising research areas. Cloud computing and other high-throughput computing technologies allow vast amounts of data to be examined, including possibly all data in public databases.

Conclusions

As 16S rRNA gene assessments continue to play an important role in this area, researchers will be able to make more accurate assessments of the core human microbiome using genetics with the help of improved bioinformatics and data.

Experts in nutrition, immunology, human genetics, microbiome, bioinformatics, and machine learning can collaborate to develop comprehensive studies to uncover patterns and processes that contribute to the core human microbiome.

Journal reference:

  • Sharon, I.; Quejada, N.; Bassoli, E. et al. (2022). The basic human microbiome: does it exist and how do we find it? A critical review of the concept. Nutrients. doi: 10.3390/no 14142872

[ad_2]

Source link

Related Posts

Precaliga