Genomic Insights into the Distribution and Phylogeny of Glycopeptide Resistance Determinants within the Actinobacteria Phylum

The spread of antimicrobial resistance (AMR) creates a challenge for global health security, rendering many previously successful classes of antibiotics useless. Unfortunately, this also includes glycopeptide antibiotics (GPAs), such as vancomycin and teicoplanin, which are currently being considered last-resort drugs. Emerging resistance towards GPAs risks limiting the clinical use of this class of antibiotics—our ultimate line of defense against multidrug-resistant (MDR) Gram-positive pathogens. But where does this resistance come from? It is widely recognized that the GPA resistance determinants—van genes—might have originated from GPA producers, such as soil-dwelling Gram-positive actinobacteria, that use them for self-protection. In the current work, we present a comprehensive bioinformatics study on the distribution and phylogeny of GPA resistance determinants within the Actinobacteria phylum. Interestingly, van-like genes (vlgs) were found distributed in different arrangements not only among GPA-producing actinobacteria but also in the non-producers: more than 10% of the screened actinobacterial genomes contained one or multiple vlgs, while less than 1% encoded for a biosynthetic gene cluster (BGC). By phylogenetic reconstructions, our results highlight the co-evolution of the different vlgs, indicating that the most diffused are the ones coding for putative VanY carboxypeptidases, which can be found alone in the genomes or associated with a vanS/R regulatory pair.

SARS-CoV-2 spike protein displays sequence similarities with paramyxovirus surface proteins; a bioinformatics study

Recent emergence of SARS-CoV-2 and associated COVID-19 pandemic have posed a great challenge for the scientific community. In this study, we performed bioinformatic analyses on SARS-CoV-2 protein sequences, trying to unravel potential molecular similarities between this newly emerged pathogen with non-coronavirus ssRNA viruses. Comparing the proteins of SARS-CoV-2 with non-coronavirus positive and negative strand ssRNA viruses revealed multiple sequence similarities between SARS-CoV-2 and non-coronaviruses, including similarities between RNA-dependent RNA-polymerases and helicases (two highly-conserved proteins). We also observed similarities between SARS-CoV-2 surface (i.e. spike) protein with paramyxovirus fusion proteins. This similarity was restricted to a segment of spike protein S2 subunit which is involved in cell fusion. We next analyzed spike proteins from SARS-CoV-2 “variants of concern” (VOCs) and “variants of interests” (VOIs) and found that some of these variants show considerably higher spike-fusion similarity with paramyxoviruses. The ‘spike-fusion’ similarity was also observed for some pathogenic coronaviruses other than SARS-CoV-2. Epitope analysis using experimentally verified data deposited in Immune Epitope Database (IEDB) revealed that several B cell epitopes as well as T cell and MHC binding epitopes map within the spike-fusion similarity region. These data indicate that there might be a degree of convergent evolution between SARS-CoV-2 and paramyxovirus surface proteins which could be of pathogenic and immunological importance.

SNPs, InDels, and Microsatellites within and near to Rice NBS-LRR Resistance Gene Candidates

Plant resistance genes (R-genes) drive the immune responses of crops against specific pathotypes of disease-causing organisms. Over time, genetic diversity in R-genes and R-pseudogenes has arisen among different rice varieties. This bioinformatics study was carried out to (i) predict the full sets of candidate nucleotide-binding site leucine-rich repeat (NLR) R-genes present in six rice genomes; (ii) detect variation within candidate R-genes; (iii) identify potential selectable markers within and near to LRR genes among 75 diverse indica rice genomes. Four high quality indica genomes, plus the standard japonica and indica reference genomes, were analysed with widely available bioinformatic tools to identify candidate R-genes and R-pseudogenes. They were detected in clusters, consistent with previous studies. BLAST analysis of cloned protein sequences of 31 R-gene loci gave confidence in this approach for detection of cloned NLR R-genes. Approximately 10% of candidate R-genes were located within 1 kb of a microsatellite (SSR) marker. Sequence comparisons among indica rice genomes detected SNPs or InDels in 334 candidate rice R-genes. There were significantly more SNPs and InDels within the identified NLR R-gene candidates than in other types of gene. The genome-wide locations of candidate R-genes and their associated markers are presented here for the potential future development of improved disease-resistant varieties. Limitations of in silico approaches used for R-gene discovery are discussed.

Similarity and Potential Relation Between Periimplantitis and Rheumatoid Arthritis on Transcriptomic Level: Results of a Bioinformatics Study

BackgroundThis bioinformatics study aimed to reveal potential cross-talk genes, related pathways, and transcription factors between periimplantitis and rheumatoid arthritis (RA).MethodsThe datasets GSE33774 (seven periimplantitis and eight control samples) and GSE106090 (six periimplantitis and six control samples) were included from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). A differential expression analysis (p < 0.05 and |logFC (fold change)| ≥ 1) and a functional enrichment analysis (p < 0.05) were performed. Based on this, a protein–protein interaction (PPI) network was constructed by Cytoscape. RA-related genes were extracted from DisGeNET database, and an overlap between periimplantitis-related genes and these RA-related genes was examined to identify potential cross-talk genes. Gene expression was merged between two datasets, and feature selection was performed by Recursive Feature Elimination (RFE) algorithm. For the feature selection cross-talk genes, support vector machine (SVM) models were constructed. The expression of these feature genes was determined from GSE93272 for RA. Finally, a network including cross-talk genes, related pathways, and transcription factors was constructed.ResultsPeriimplantitis datasets included 138 common differentially expressed genes (DEGs) including 101 up- and 37 downregulated DEGs. The PPI interwork of periimplantitis comprised 1,818 nodes and 2,517 edges. The RFE method selected six features, i.e., MERTK, CD14, MAPT, CCR1, C3AR1, and FCGR2B, which had the highest prediction. Out of these feature genes, CD14 and FCGR2B were most highly expressed in periimplantitis and RA. The final activated pathway–gene network contained 181 nodes and 360 edges. Nuclear factor (NF) kappa B signaling pathway and osteoclast differentiation were identified as potentially relevant pathways.ConclusionsThis current study revealed FCGR2B and CD14 as the most relevant potential cross-talk genes between RA and periimplantitis, which suggests a similarity between RA and periimplantitis and can serve as a theoretical basis for future research.

Molecular evolution of PCSK family: Analysis of natural selection rate and gene loss

Proprotein convertases subtilisin kexins are serine endoproteases, playing critical roles in the biological functions, including lipid, glucose, and bile acid metabolism, as well as cell proliferation, migration, and metastasis. Experimental studies have demonstrated the physiological functions of PCSKs and their association with diseases; however, studies on the evolutionary history and diversification of these proteins are missing. In the present research, a bioinformatics study was conducted on the molecular evolution of several PCSKs family members and gene loss events across placental mammalian. In order to detect evolutionary constraints and positive selection, the CodeML program of the PAML package was used. The results showed the positive selection to occur in PCSK1, PCSK3, PCSK5, and PCSK7. A decelerated rate of evolution was observed in PCSK7, PCSK3, and MBTPS1 in Carnivores compared to the rest of phylogeny, and an accelerated evolution of PCSK1, PCSK7, and MBTPS1 in Muridae family of rodents was found. Additionally, our results indicated pcsk9 gene loss in 12 species comprising Carnivores and bats (Chiroptera). Future studies are required to evaluate the functional relevance and selective evolutionary advantages associated with these modifications in PCSK proteins during evolution.