China
Africa
Explore chapters and articles related to this topic
The Microbiome in Multiple Sclerosis
Published in David Perlmutter, The Microbiome and the Brain, 2019
Helen Tremlett, Emmanuelle Waubant
There have been few studies using metagenomics to directly interrogate the functional capacity of the gut microbiota in people with multiple sclerosis. To date, most studies have employed animal models or looked at the effects of specific gut microbes on cells grown in the laboratory to explore specific aspects of microbial function. The German-based twin study reported using “bacterial metagenomics,” and found no differences between 32 monozygotic twin pairs discordant for multiple sclerosis. However, discordant twins were more similar than unrelated twins, potentially inferring what others have observed—that the host genetics influences the gut microbiome.29 Metagenomics is a costly endeavor, creating vast quantities of data requiring intense bioinformatics and complex analyses. However, a proxy-measure of the bacterial metagenome has been validated using the “PICRUSt” algorithm (“Phylogenetic Investigation of Communities by Reconstruction of Unobserved States”).61 Key findings from the limited number of studies in multiple sclerosis to explore this “predicted metagenome” include significant differences between multiple sclerosis cases and controls for pathways involving fatty acid metabolism, lipopolysaccharide biosynthesis, and glycolysis/glutathione metabolism, with multiple sclerosis disease-modifying drug exposure affecting some findings.2,62 A French team re-used publicly available raw 16S rRNA sequences from two US-based studies44,62 and focused on the inferred relative abundance of the enzyme EC 2.4.1.87 (N-acetylactosaminide 3-alpha-galactosyltransferase) via the PICRUSt algorithm.63 This enzyme corresponds to the GGTA1 gene, which is not found in humans, but has been associated with autoimmune diseases. A lower abundance was found in the multiple sclerosis cases relative to controls, which the authors proposed could suggest a role for the enzyme in multiple sclerosis, possibly mediated via IgG.63 These hypotheses-generating findings have yet to be validated.
Impact of type 1 diabetes on the composition and functional potential of gut microbiome in children and adolescents: possible mechanisms, current knowledge, and challenges
Published in Gut Microbes, 2021
Pari Mokhtari, Julie Metos, Pon Velayutham Anandh Babu
Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) is a bioinformatics software which predicts metagenome function from marker gene (e.g. 16s rRNA) surveys. PICRUSt analysis in previous studies indicated that numerous bacterial functions were over- or underrepresented in individuals with diabetes vs healthy individuals related to their different microbiome composition.47,60 An impairment in the pathways related to glucose metabolism and iron complex levels was predicted in Italian children and adolecents with T1D.60 Another study predicted that the abundance of genes related to energy and carbohydrate metabolism pathways deplete in T1D group compared to healthy controls.47 However, the genes linked with lipid metabolism and amino acid metabolism, LPS biosynthesis, arachidonic acid metabolism, ATP-binding-cassette transport, antigen processing and presentation, and chemokine signaling pathways related to inflammation and immune response were overrepresented in T1D.47 Further, this study demonstrated a significant increase in LPS, proinflammatory cytokines (IL-1β, IL-6, TNF-α) and a significant depletion of anti-inflammatory cytokines (IL-10 and IL-13) in subjects with T1D. This situation together with lower abundance of anti-inflammatory bacteria, promotes dysregulation of epithelial integrity and autoimmune responses in T1D.47,79
Acetaldehyde production by Rothia mucilaginosa isolates from patients with oral leukoplakia
Published in Journal of Oral Microbiology, 2020
Abdrazak Amer, Aine Whelan, Nezar N. Al-Hebshi, Claire M. Healy, Gary P. Moran
PICRUSt can be used to predict gene family abundance (e.g. the metagenome) in microbial communities for which only marker gene (e.g. 16S rRNA gene) data are available [33]. This analysis was carried out on our previously published microbiome dataset comparing the microbiomes of oral leukoplakia (OLK) and normal contralateral (NC) healthy tissues from 36 patients [29]. We have previously shown that the OLK microbiome exhibits increased levels of R. mucilaginosa and reduced levels of streptococci compared to NC healthy mucosa from the same patient [29]. Normal contralateral (NC) sites were deemed healthy following examination by an experienced consultant in oral medicine and acted as an age and oral hygiene matched control to the diseased sites. The reads were reclassified with Mothur using Wang’s method and Greengenes 97% clustered OTUs (version 13.5) as reference [34]. The reads were then assigned to OTUs based on their taxonomy (phylotype command) and the generated file was converted into a BIOM (Biological Observation Matrix) table. Output files showing the distribution of genes (catalogued by KEGG ontologies) and pathway abundances were generated. These files were analysed using the linear discriminant analysis (LDA) effect size (LEfSe) method to identify categories enriched in OLK or healthy NC tissues [35].
Characterization of gut microbiota composition and functions in patients with chronic alcohol overconsumption
Published in Gut Microbes, 2019
Steinar Traae Bjørkhaug, Håvard Aanes, Sudan Prasad Neupane, Jørgen G. Bramness, Stine Malvik, Christine Henriksen, Viggo Skar, Asle W. Medhus, Jørgen Valeur
Using PICRUSt, we inferred the gene content of the microbiota based on the 16S rRNA sequences (Figure 4(c)). The inferred gene counts were merged into larger categories (level 3 KEGG orthology), based on their molecular function, involvement in disease, metabolic pathways or cellular function. Notably, we found that the gut microbiota of patients with alcohol overconsumption had relatively more bacteria containing genes for “Bacterial invasion of epithelial cells”. A closer inspection revealed that a subset of these patients had very high counts (reaching up to >4500 reads), while none of the controls had a read count >350 (median 85). The observed difference was almost entirely determined by higher levels of genes in the adhesion/invasion gene category (K13735).