Gene expression profile based classification models of psoriasis

AbstractPsoriasis is an autoimmune disease, which symptoms can significantly impair the patient's life quality. It is mainly diagnosed through the visual inspection of the lesion skin by experienced dermatologists. Currently no cure for psoriasis is available due to limited knowledge about its pathogenesis and development mechanisms. Previous studies have profiled hundreds of differentially expressed genes related to psoriasis, however with no robust psoriasis prediction model available. This study integrated the knowledge of three feature selection algorithms that revealed 21 features belonging to 18 genes as candidate markers. The final psoriasis classification model was established using the novel Incremental Feature Selection algorithm that utilizes only 3 features from 2 unique genes, IGFL1 and C10orf99. This model has demonstrated highly stable prediction accuracy (averaged at 99.81%) over three independent validation strategies. The two marker genes, IGFL1 and C10orf99, were revealed as the upstream components of growth signal transduction pathway of psoriatic pathogenesis

Metagenomic Insights Into a Cellulose-Rich Niche Reveal Microbial Cooperation in Cellulose Degradation

BackgroundCellulose is the most abundant organic polymer mainly produced by plants in nature. It is insoluble and highly resistant to enzymatic hydrolysis. Cellulolytic microorganisms that are capable of producing a battery of related enzymes play an important role in recycling cellulose-rich plant biomass. Effective cellulose degradation by multiple synergic microorganisms has been observed within a defined microbial consortium in the lab culture. Metagenomic analysis may enable us to understand how microbes cooperate in cellulose degradation in a more complex microbial free-living ecosystem in nature.ResultsHere we investigated a typical cellulose-rich and alkaline niche where constituent microbes survive through inter-genera cooperation in cellulose utilization. The niche has been generated in an ancient paper-making plant, which has served as an isolated habitat for over 7 centuries. Combined amplicon-based sequencing of 16S rRNA genes and metagenomic sequencing, our analyses showed a microbial composition with 6 dominant genera including Cloacibacterium, Paludibacter, Exiguobacterium, Acetivibrio, Tolumonas, and Clostridium in this cellulose-rich niche; the composition is distinct from other cellulose-rich niches including a modern paper mill, bamboo soil, wild giant panda guts, and termite hindguts. In total, 11,676 genes of 96 glucoside hydrolase (GH) families, as well as 1,744 genes of carbohydrate transporters were identified, and modeling analysis of two representative genes suggested that these glucoside hydrolases likely evolved to adapt to alkaline environments. Further reconstruction of the microbial draft genomes by binning the assembled contigs predicted a mutualistic interaction between the dominant microbes regarding the cellulolytic process in the niche, with Paludibacter and Clostridium acting as helpers that produce endoglucanases, and Cloacibacterium, Exiguobacterium, Acetivibrio, and Tolumonas being beneficiaries that cross-feed on the cellodextrins by oligosaccharide uptake.ConclusionThe analysis of the key genes involved in cellulose degradation and reconstruction of the microbial draft genomes by binning the assembled contigs predicted a mutualistic interaction based on public goods regarding the cellulolytic process in the niche, suggesting that in the studied microbial consortium, free-living bacteria likely survive on each other by acquisition and exchange of metabolites. Knowledge gained from this study will facilitate the design of complex microbial communities with a better performance in industrial bioprocesses

A Comprehensive Curation Shows the Dynamic Evolutionary Patterns of Prokaryotic CRISPRs

Motivation. Clustered regularly interspaced short palindromic repeat (CRISPR) is a genetic element with active regulation roles for foreign invasive genes in the prokaryotic genomes and has been engineered to work with the CRISPR-associated sequence (Cas) gene Cas9 as one of the modern genome editing technologies. Due to inconsistent definitions, the existing CRISPR detection programs seem to have missed some weak CRISPR signals. Results. This study manually curates all the currently annotated CRISPR elements in the prokaryotic genomes and proposes 95 updates to the annotations. A new definition is proposed to cover all the CRISPRs. The comprehensive comparison of CRISPR numbers on the taxonomic levels of both domains and genus shows high variations for closely related species even in the same genus. The detailed investigation of how CRISPRs are evolutionarily manipulated in the 8 completely sequenced species in the genus Thermoanaerobacter demonstrates that transposons act as a frequent tool for splitting long CRISPRs into shorter ones along a long evolutionary history

Constraint Programming Based Biomarker Optimization

Efficient and intuitive characterization of biological big data is becoming a major challenge for modern bio-OMIC based scientists. Interactive visualization and exploration of big data is proven to be one of the successful solutions. Most of the existing feature selection algorithms do not allow the interactive inputs from users in the optimizing process of feature selection. This study investigates this question as fixing a few user-input features in the finally selected feature subset and formulates these user-input features as constraints for a programming model. The proposed algorithm, fsCoP (feature selection based on constrained programming), performs well similar to or much better than the existing feature selection algorithms, even with the constraints from both literature and the existing algorithms. An fsCoP biomarker may be intriguing for further wet lab validation, since it satisfies both the classification optimization function and the biomedical knowledge. fsCoP may also be used for the interactive exploration of bio-OMIC big data by interactively adding user-defined constraints for modeling

A human gut phage catalog correlates the gut phageome with type 2 diabetes

Abstract Background Substantial efforts have been made to link the gut bacterial community to many complex human diseases. Nevertheless, the gut phages are often neglected. Results In this study, we used multiple bioinformatic methods to catalog gut phages from whole-community metagenomic sequencing data of fecal samples collected from both type II diabetes (T2D) patients (n = 71) and normal Chinese adults (n = 74). The definition of phage operational taxonomic units (pOTUs) and identification of large phage scaffolds (n = 2567, ≥ 10 k) revealed a comprehensive human gut phageome with a substantial number of novel sequences encoding genes that were unrelated to those in known phages. Interestingly, we observed a significant increase in the number of gut phages in the T2D group and, in particular, identified 7 pOTUs specific to T2D. This finding was further validated in an independent dataset of 116 T2D and 109 control samples. Co-occurrence/exclusion analysis of the bacterial genera and pOTUs identified a complex core interaction between bacteria and phages in the human gut ecosystem, suggesting that the significant alterations of the gut phageome cannot be explained simply by co-variation with the altered bacterial hosts. Conclusions Alterations in the gut bacterial community have been linked to the chronic disease T2D, but the role of gut phages therein is not well understood. This is the first study to identify a T2D-specific gut phageome, indicating the existence of other mechanisms that might govern the gut phageome in T2D patients. These findings suggest the importance of the phageome in T2D risk, which warrants further investigation

MUSTv2: An Improved De Novo Detection Program for Recently Active Miniature Inverted Repeat Transposable Elements (MITEs)

Miniature inverted repeat transposable element (MITE) is a short transposable element, carrying no protein-coding regions. However, its high proliferation rate and sequence-specific insertion preference renders it as a good genetic tool for both natural evolution and experimental insertion mutagenesis. Recently active MITE copies are those with clear signals of Terminal Inverted Repeats (TIRs) and Direct Repeats (DRs), and are recently translocated into their current sites. Their proliferation ability renders them good candidates for the investigation of genomic evolution

Metagenomic Insights Into a Cellulose-Rich Niche Reveal Microbial Cooperation in Cellulose Degradation

Background: Cellulose is the most abundant organic polymer mainly produced by plants in nature. It is insoluble and highly resistant to enzymatic hydrolysis. Cellulolytic microorganisms that are capable of producing a battery of related enzymes play an important role in recycling cellulose-rich plant biomass. Effective cellulose degradation by multiple synergic microorganisms has been observed within a defined microbial consortium in the lab culture. Metagenomic analysis may enable us to understand how microbes cooperate in cellulose degradation in a more complex microbial free-living ecosystem in nature. Results: Here we investigated a typical cellulose-rich and alkaline niche where constituent microbes survive through inter-genera cooperation in cellulose utilization. The niche has been generated in an ancient paper-making plant, which has served as an isolated habitat for over 7 centuries. Combined amplicon-based sequencing of 16S rRNA genes and metagenomic sequencing, our analyses showed a microbial composition with 6 dominant genera including Cloacibacterium, Paludibacter, Exiguobacterium, Acetivibrio, Tolumonas, and Clostridium in this cellulose-rich niche; the composition is distinct from other cellulose-rich niches including a modern paper mill, bamboo soil, wild giant panda guts, and termite hindguts. In total, 11,676 genes of 96 glucoside hydrolase (GH) families, as well as 1,744 genes of carbohydrate transporters were identified, and modeling analysis of two representative genes suggested that these glucoside hydrolases likely evolved to adapt to alkaline environments. Further reconstruction of the microbial draft genomes by binning the assembled contigs predicted a mutualistic interaction between the dominant microbes regarding the cellulolytic process in the niche, with Paludibacter and Clostridium acting as helpers that produce endoglucanases, and Cloacibacterium, Exiguobacterium, Acetivibrio, and Tolumonas being beneficiaries that cross-feed on the cellodextrins by oligosaccharide uptake. Conclusion: The analysis of the key genes involved in cellulose degradation and reconstruction of the microbial draft genomes by binning the assembled contigs predicted a mutualistic interaction based on public goods regarding the cellulolytic process in the niche, suggesting that in the studied microbial consortium, free-living bacteria likely survive on each other by acquisition and exchange of metabolites. Knowledge gained from this study will facilitate the design of complex microbial communities with a better performance in industrial bioprocesses

Le Peuple : organe quotidien du syndicalisme

16 décembre 19331933/12/16 (A13,N4717)-1933/12/16