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The Role of the Gut Microbiome in Cardiovascular Disease
Published in Stephen T. Sinatra, Mark C. Houston, Nutritional and Integrative Strategies in Cardiovascular Medicine, 2022
As the cost of metatranscriptomic or RNA sequencing continues to come down, research of the gut microbiome will continue to improve. Currently, metatranscriptomic technology is the best means we have of analyzing the gut microbiome because of its ability to see function and pathways of the gut microbiome. Defining what a healthy gut microbiome looks like and its variations among diseases is a meticulous and arduous process that requires a high input of data for accurate statistical analysis.
Computational Biology and Bioinformatics in Anti-SARS-CoV-2 Drug Development
Published in Debmalya Barh, Kenneth Lundstrom, COVID-19, 2022
Whole genome sequencing represents an important means for tracing the origin, spread, and transmission chains of SARS-CoV-2, and is crucial for monitoring the evolution of this virus and the emergence of reinfections. The first complete genomic sequences of SARS-CoV-2 were reported in late December 2019 [16–18]. The reference genome assembly was achieved through metatranscriptomic approaches augmented by PCR and Sanger sequencing [16–18]. This information was crucial as it provided vital means for the development of diagnostic tests based on real-time PCR [19]. This success in the identification of SARS-CoV-2 was a result of the use of next-generation sequencing (NGS) and readily available bioinformatics pipelines, which can be assembled into an NGS data analysis workflow consisting of several essential steps, such as quality control of the NGS data, removal of host/rRNA data, reads assembly, taxonomic classification, and virus genome verification [20]. A recent review describes numerous bioinformatics tools, which are currently available for every step of the NGS data analysis, and discusses the advantages and disadvantages of these bioinformatics resources [20]. Importantly, these same NGS technologies and bioinformatics resources can be used efficiently for the ongoing genomic surveillance of SARS-CoV-2 worldwide, tracking its spread, evolution, and patterns of variation on a global scale [20]. Another comprehensive review introduces currently available platforms and methodological approaches for the sequencing of SARS-CoV-2 genomes, and outlines some of the repositories and databases delivering access to SARS-CoV-2 genomic data and associated metadata [21].
Gut Microbiome
Published in Nathalie Bergeron, Patty W. Siri-Tarino, George A. Bray, Ronald M. Krauss, Nutrition and Cardiometabolic Health, 2017
Brian J. Bennett, Katie A. Meyer, Nathalie Bergeron, Patty W. Siri-Tarino, George A. Bray, Ronald M. Krauss
The classification schemes described earlier focus on 16S rRNA characterizations of the microbiota. An alternative approach is to focus on the overall functional characteristics of the microbiota. Although relatively fewer in number, studies that have conducted whole-genome metagenomics have shown that the large variation observed in compositional measures of the microbiota does not imply large variability in the functional potential or activity of the microbiota. In fact, these studies point to clear redundancy in gene presence and expression, suggesting a core set of activities that can be fulfilled by different microbiota (Turnbaugh et al. 2009, Qin et al. 2010, Human Microbiome Project, Consortium 2012). We know from shotgun metagenomics studies that the total microbial community DNA encompasses a rich set of genes involved with carbohydrate and amino acid metabolism, illustrating a core functional role of the gut microbiome in digestion and metabolism. The presence of specific microbial genes only reflects functional potential, and to truly characterize functional activity of the microbiome, it will be necessary to employ microbial measures of messenger RNA (metatranscriptomics), proteins (metaproteomics), and metabolites (metametabolomics) (Integrative 2014). These approaches may reveal variability related to health and disease not captured through 16S rRNA characterization of the microbiota and contribute to understanding a healthy core microbiome from a functional perspective. For example, a study utilizing metagenomics and metatranscriptomics analysis revealed that there is greater variability in gene expression than in gene presence (Franzosa et al. 2014). Similarly, proteins related to carbohydrate metabolism have been shown to be expressed at a level greater than expected from metagenomics profiles (Verberkmoes et al. 2009). Furthermore, there is an indication that copy-number variation may also impact the functional capacity of the microbiota (Greenblum, Carr, and Borenstein 2015). Understanding the factors that regulate differences in microbiota gene expression and their relationship to disease is still a critical gap in our knowledge.
Use of omic technologies in early life gastrointestinal health and disease: from bench to bedside
Published in Expert Review of Proteomics, 2021
Lauren C Beck, Claire L Granger, Andrea C Masi, Christopher J Stewart
To give insight into the active functional profile of the gut microbiome, metatranscriptomics studies are used [42]. Currently, metatranscriptomics analyses rely on the same methods as used in transcriptomic experiments, i.e. RNA-seq, but aim to study all the transcripts from microbial communities within a sample. Since metagenomics focuses only on the presence of organisms or genes within a community, it fails to capture whether these organisms are active members [43]. Metatranscriptomics offers a solution, allowing a deeper insight into gene expression within the microbiome, looking, in particular, at longitudinal changes in response to the environment. However, in clinical samples, such as stool, the transcription profiles will be derived from a range of cell types, including cells that have sloughed off at varying hours previously. The impacts on gene expression levels as cells transit through the gut GI tract are not well understood, which limits the ability to make accurate biological associations.
Microbial and metabolic features associated with outcome of infliximab therapy in pediatric Crohn’s disease
Published in Gut Microbes, 2021
Yizhong Wang, Xuefeng Gao, Xinyue Zhang, Fangfei Xiao, Hui Hu, Xiaolu Li, Fang Dong, Mingming Sun, Yongmei Xiao, Ting Ge, Dan Li, Guangjun Yu, Zhanju Liu, Ting Zhang
There are several limitations that exist in this study. First, although this is the largest longitudinal cohort study of pediatric CD using metabonomic and metataxonomic profiling from China to date, the subgroups within this cohort were small, which may lead to unintentional bias, a discovery-validation cohort study is needed to validate the findings in the future. Second, metabolome and microbiome analysis performed only on fecal samples collected from two time points after IFX treatment that may dilute the time-dependent effect of IFX, and the inadequate sequencing of samples from patients with severe diarrhea may cause a possible bias despite no significant difference was obtained by comparing the serum inflammatory factors in patients with diarrhea to those with normal/lose stools at baseline (data not shown). Third, metatranscriptomic analysis is needed to further investigate the proposed mechanistic relationships between gut microbial community dynamics and metabolic changes.
An integrated workflow for enhanced taxonomic and functional coverage of the mouse fecal metaproteome
Published in Gut Microbes, 2021
Nicolas Nalpas, Lesley Hoyles, Viktoria Anselm, Tariq Ganief, Laura Martinez-Gili, Cristina Grau, Irina Droste-Borel, Laetitia Davidovic, Xavier Altafaj, Marc-Emmanuel Dumas, Boris Macek
Here, we observed an overall positive correlation between gene and protein abundances derived from metaproteome and matching-metagenome analysis. This was previously reported in a longitudinal study of metaproteome/metagenome fluctuations from one individual with Crohn’s Disease.52 In our case the significantly correlated entries were associated with core bacterial metabolic functions, such as carbon and energy metabolism or electron transfer activity.72 Despite such correlations, we also reported extensive differences in quantified functions between metagenomics and metaproteomics. Notably, with regard to genetic information processing (KEGG level 2), the ribosome pathway was over-represented in entries with higher abundance in metaproteomes, whereas pathways associated with DNA repair, replication or recombination were over-represented in entries with increased abundance in metagenomes. However, several studies have shown positive correlation between metatranscriptomics and metaproteomics at the gene or function levels. For example, a microbial community study from wastewater treatment plant73 revealed overall positive correlation in functional categories abundance between transcripts and proteins. In another multi-omics study of the gut microbiome of human diabetic patients,55 while a positive correlation was observed between transcripts and proteins, this correlation did not translate to the derived functional profiles.