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Currently, there are numerous barriers that are hindering progress. Researchers in low- and middle-income countries (LMICs) frequently face challenges such as a lack of transport services, freezers, and reliable power supplies, which are essential for obtaining samples and rapidly freezing them at extremely low temperatures (−80 °C or lower). This freezing process is crucial for maintaining the integrity of DNA and RNA for sequencing, as well as ensuring bacterial viability for culturing. Additionally, LMICs often do not have the computational resources necessary to analyze and store large data sets. Access to sealed anaerobic cabinets, which are vital for culturing oxygen-intolerant gut bacteria, archiving the cultured organisms, sequencing their genomes, and characterizing their growth requirements and metabolic outputs, is also limited. Furthermore, these countries frequently lack the facilities required to conduct studies in gnotobiotic mice.
Similar to genome-wide association studies, a limited selection of participants raises questions about the applicability of established connections between the microbiome and human health.
Recent analytic approaches to studying the microbiome have revealed that all mammals are meta-organisms, relying not only on their own genome but also on the genomes of colonizing microbes
Understanding the differences in the microbiome between different world regions, cultures, and social groups is of great importance. Nevertheless, many populations are scarcely represented in the scientific literature.
Conventional analytical methods, including taxonomy-based and gene-centric approaches, frequently produce inconsistent results. For example, taxonomic groups such as Lactobacillus or Firmicutes have been associated with positive, negative, or no correlation with diseases like obesity or diabetes in various studies.
The Human Microbiome Project (HMP) and the MetaHIT project are significant initiatives studying the impact of the human microbiome on health and disease
Emerging Technologies in Microbiome Research
Microbial research has two main paths. One is through marker genes. An example is 16S rRNA next-generation sequencing . The other is through reference alignment. This involves shotgun sequencing . In both methods, genetic content is deduced. This is based on known genome information.
Microbial Culturing and Meta-omic Profiling Technologies reveal taxonomic variations. They show functional variations impacting host processes.
Microbial Cultivation-Independent Methods aid in bacterial quantification, enhancing our understanding of microbiome composition, function, stability, and resilience across various body sites
High Throughput Sequencing (HTS) / Next-generation sequencing (NGS) and Metagenomics have progressed. These technologies allow rapid studies focussing on microorganisms and their hosts
Metagenomic Shotgun Sequencing (MSS) reveals molecular activities of microbial communities
Studies show that Metagenomic Shotgun Sequencing (MSS) and metatranscriptomics improve taxonomy resolution. They provide a functional profile of the microbiome.
Metagenomics (DNA-based), metatranscriptomics (total transcribed RNA), metaproteomics (protein-based), and metabolomics (metabolic profiles based) are various approaches used to study different aspects of microbial communities
Several emerging technologies are shaping the study of the microbiome:
These technologies are crucial for understanding microbial communities and their interactions with hosts
Research Methodologies
Research on the human gut microbiome typically follows four main steps:
Efforts to refine these processes aim to produce high-quality, unbiased outcomes, helping to identify taxonomic profiles and gene functions of the gut microbiome. This is essential for distinguishing between healthy and disease-associated microbiomes, which is crucial for future disease treatment strategies
Sample Types and Their Implications
Research indicates that fecal samples may contain numerous microbial species unrelated to the disease site, while tissue (mucosal) samples better represent underlying dysbiosis. Fecal samples seem not to be appropriate to detect shifts in microbial composition
Limitations and Challenges
Advanced computer-based tools allow us to quickly and accurately identify different microbes down to the species level
But studying the human gut microbiome only at the species level miss important differences that exist within those species - differences that can have real impacts on health and disease.
However, within the same species, different strains can possess different genes, leading to different behaviors. In different scientific fields, the term "strain" can have different definitions.
In metagenomics, a strain usually refers to a genome or a group of genomes. Recently, it has become possible to study specific bacteria in the microbiome at the strain level, although typically limited to one or a few species.
If one currently focuses on the species level in the context of health and disease, one inevitably overlooks these differences between strains. Strains represent the most detailed classification and are often host-specific
Both species- and strain-level approaches have problems: species-level is too broad and misses meaningful differences, but strain-level is too fragmented and not consistent between studies
Recently, scientists have started to focus on an intermediate level between species and strains called "subspecies" - groups within a species that are more similar to each other and have shared consistent traits that can relate to specific health effects. In comparison to its sibling subspecies, which are delineated from the same species, this show functional, phenotype-specific differences. These differences are observed either in one or in a limited range of selected parental species, within which the sibling subspecies are delineated.
A catalog of human gut microbiota subspecies was created and operational subspecies units (OSUs) were defined. These are stable genome groups. Their coding sequence sketches were grouped. This resolution works worldwide. Subspecies have unique information that is missing at the species level. An example is the bacteria associated with colorectal cancer (CRC). Certain subspecies are linked to the disease, while their sibling subspecies or parent species are not, even though sibling subspecies share much of their genome. A rapid OSU quantification method identifies genes driving subspecies variations. Identifying disease-related microbial genes is crucial. It helps to understand microbiome-disease interactions.
A new methodological framework is urgently needed to effectively identify key microbiome members as biomarkers for maintaining health and managing diseases.
Current high-throughput sequencing technologies have limitations, such as restricted read lengths, which affect the detection of bacterial species and strains. Analyses of viruses and eukaryotes are still emerging
One factor, among others, to consider when defining diversity and a core microbiome is that methodological artifacts can significantly increase the apparent numbers of OTUs in a sample
Model Organisms in Microbiome Research
Classical invertebrates like Caenorhabditis elegans and Drosophila melanogaster serve as effective models for microbiome research due to their simple gut microbiota and short lifespans. For example, there is one bacterial species in Caenorhabditis elegans (C. elegans) and 5-20 species in Drosophila. all of these bacteria can be cultured in vitro.
see also:
Application & Fecal Microbiota Transplantation (FMT)
Assessment Microbes / Assessment Microbiome
Bacteria
Bifidobacterium longum infantis
Bifidobacterium longum longum
Development of Gut Microbiota
Geography & Gut microbiota
Gut microbiota & Organoids
Gut microbiota & Variability
High Phylogenetic Resolution Fluorescence In Situ Hybridization (HiPR-FISH)
Human microbiota / Human microbiome
Human microbiota / Human microbiome & Research
Human microbiota / Human microbiome & Variability
Intestinal Models & Personalized Medicine
Live bacterial therapeutics (LBTs)