Background:Osteoporosis is a bone disease that increases the patient’s risk of fracture. We aimed to identify robust marker genes related to osteoporosis based on different bioinformatic methods and multiple datasets.Methods:Three datasets from Gene Expression Omnibus (GEO) were utilized for analysis separately. Significantly differentially expressed genes (DEGs) from comparing high hip and low hip low bone mineral density (BMD) groups in the first dataset were identified for Gene Ontology (GO), Gene set enrichment analysis (GSEA) and Kyoto encyclopedia of genes and genomes (KEGG) to investigate the discrepantly enriched biological processes between high hip and low hip group. Last absolute shrinkage and selection operator (LASSO), SVM model and protein-protein interaction (PPI) regulatory network were performed and generated robust marker genes for downstream TF-target and miRNA-target prediction.Results:Several DEGs between high hip BMD group and low hip BMD group were obtained. And the metabolism-related pathways such as metabolic pathways, carbon metabolism, glyoxylate and dicarboxylate metabolism shown enrichment in these DEGs. Integration with LASSO regression analysis, 8 differential expression genes (SH3BP1, NARF, ANKRD34B, RNF40, ZNF473, AKT1, SHMT1, and VASH1) in GSE62402 were identified as the optimal differential genes combination. Moreover, the SVM validation analysis in GSE56814 and GSE56815 datasets showed that the characteristic gene combinations presented high diagnostic effects, and the model AUC areas for GSE56814 was 0.899 and for GSE56815 was 0.921. Furthermore, the subcellular localization analysis of the 8 genes revealed that 4 proteins were located in the cytoplasm, 3 proteins were located in the nucleus, and 1 protein was located in the mitochondria. Additionally, the related TFs and miRNAs by performing TF-target and miRNA-target prediction for 5 genes (AKT1, SHMT1, ZNF473, RNF40 and VASH1) were investigated from PPI network.Conclusion:The optimal differential genes combination (SH3BP1, NARF, ANKRD34B, RNF40, ZNF473, AKT1, SHMT1, and VASH1) presented high diagnostic effect for osteoporosis risk.

The bacterium Oceanobacillus jordanicus strain GSFE11 is a halotolerant endophyte isolated from sterilized roots of Durum wheat (Triticum turgidum ssp. Durum) growing in hot and arid environments of Ghor Safi area in the Jordan Valley. The draft genome sequence and annotation of this plant growth-promoting endophytic bacterium are reported in this study. The draft genome sequence of Oceanobacillus jordanicus strain GSFE11 has 3 839 208 bp with a G + C content of 39.09%. A total of 3893 protein-coding genes and 68 RNA coding genes were predicted. Several putative genes that are involved in secretion and delivery systems, transport, adhesion, motility, membrane proteins, plant cell wall modification, and detoxification were identified, some are characteristics of endophytes lifestyle including genes that are involved in metabolism of carbohydrate, genes for xylose, fructose and chitin utilization, quinone cofactors biosynthesis, genes associated with nitrogen, sulfur, phosphate and iron acquisition, in addition to genes involved in the biosynthesis of plant hormone auxin. This study highlights the importance of using genome analysis and phylogenomic analysis to resolve the differences between closely related species, such analysis showed Oceanobacillus jordanicus strain GSFE11 to be a new species closely related to Oceanobacillus picturae (genome size 3.67 Mb), Oceanobacillus jordanicus has higher a number of predicted genes compared with Oceanobacillus picturae (3961 genes vs 3823 genes).

Background:Pharmacogenomics (PGx), forming the basis of precision medicine, has revolutionized traditional medical practice. Currently, drug responses such as drug efficacy, drug dosage, and drug adverse reactions can be anticipated based on the genetic makeup of the patients. The pharmacogenomic data of Pakistani populations are limited. This study investigates the frequencies of pharmacogenetic variants and their clinical relevance among ethnic groups in Pakistan.Methods:The Pharmacogenomics Knowledge Base (PharmGKB) database was used to extract pharmacogenetic variants that are involved in medical conditions with high (1A + 1B) to moderate (2A + 2B) clinical evidence. Subsequently, the allele frequencies of these variants were searched among multiethnic groups of Pakistan (Balochi, Brahui, Burusho, Hazara, Kalash, Pashtun, Punjabi, and Sindhi) using the 1000 Genomes Project (1KGP) and ALlele FREquency Database (ALFRED). Furthermore, the published Pharmacogenomics literature on the Pakistani population was reviewed in PubMed and Google Scholar.Results:Our search retrieved (n = 29) pharmacogenetic genes and their (n = 44) variants with high to moderate evidence of clinical association. These pharmacogenetic variants correspond to drug-metabolizing enzymes (n = 22), drug-metabolizing transporters (n = 8), and PGx gene regulators, etc. (n = 14). We found 5 pharmacogenetic variants present at >50% among 8 ethnic groups of Pakistan. These pharmacogenetic variants include CYP2B6 (rs2279345, C; 70%-86%), CYP3A5 (rs776746, C; 64%-88%), FLT3 (rs1933437, T; 54%-74%), CETP (rs1532624, A; 50%-70%), and DPP6 (rs6977820, C; 61%-86%) genes that are involved in drug response for acquired immune deficiency syndrome, transplantation, cancer, heart disease, and mental health therapy, respectively.Conclusions:This study highlights the frequency of important clinical pharmacogenetic variants (1A, 1B, 2A, and 2B) among multi-ethnic Pakistani populations. The high prevalence (>50%) of single nucleotide pharmacogenetic variants may contribute to the drug response/diseases outcome. These PGx data could be used as pharmacogenetic markers in the selection of appropriate therapeutic regimens for specific ethnic groups of Pakistan.

SARS-CoV-2 needs to efficiently make use of the resources from hosts in order to survive and propagate. Among the multiple layers of regulatory network, mRNA translation is the rate-limiting step in gene expression. Synonymous codon usage usually conforms with tRNA concentration to allow fast decoding during translation. It is acknowledged that SARS-CoV-2 has adapted to the codon usage of human lungs so that the virus could rapidly proliferate in the lung environment. While this notion seems to nicely explain the adaptation of SARS-CoV-2 to lungs, it is unable to tell why other viruses do not have this advantage. In this study, we retrieve the GTEx RNA-seq data for 30 tissues (belonging to over 17 000 individuals). We calculate the RSCU (relative synonymous codon usage) weighted by gene expression in each human sample, and investigate the correlation of RSCU between the human tissues and SARS-CoV-2 or RaTG13 (the closest coronavirus to SARS-CoV-2). Lung has the highest correlation of RSCU to SARS-CoV-2 among all tissues, suggesting that the lung environment is generally suitable for SARS-CoV-2. Interestingly, for most tissues, SARS-CoV-2 has higher correlations with the human samples compared with the RaTG13-human correlation. This difference is most significant for lungs. In conclusion, the codon usage of SARS-CoV-2 has adapted to human lungs to allow fast decoding and translation. This adaptation probably took place after SARS-CoV-2 split from RaTG13 because RaTG13 is less perfectly correlated with human. This finding depicts the trajectory of adaptive evolution from ancestral sequence to SARS-CoV-2, and also well explains why SARS-CoV-2 rather than other viruses could perfectly adapt to human lung environment.

Protein-protein interactions (PPIs) play a crucial role in the life cycles of living cells. Thus, it is important to understand the underlying mechanisms of PPIs. Although many high-throughput technologies have generated large amounts of PPI data in different organisms, the experiments for detecting PPIs are still costly and time-consuming. Therefore, novel computational methods are urgently needed for predicting PPIs. For this reason, developing a new computational method for predicting PPIs is drawing more and more attention. In this study, we proposed a novel computational method based on texture feature of protein sequence for predicting PPIs. Especially, the Gabor feature is used to extract texture feature and protein evolutionary information from Position-Specific Scoring Matrix, which is generated by Position-Specific Iterated Basic Local Alignment Search Tool. Then, random forest–based classifiers are used to infer the protein interactions. When performed on PPI data sets of yeast, human, and Helicobacter pylori, we obtained good results with average accuracies of 92.10%, 97.03%, and 86.45%, respectively. To better evaluate the proposed method, we compared Gabor feature, Discrete Cosine Transform, and Local Phase Quantization. Our results show that the proposed method is both feasible and stable and the Gabor feature descriptor is reliable in extracting protein sequence information. Furthermore, additional experiments have been conducted to predict PPIs of other 4 species data sets. The promising results indicate that our proposed method is both powerful and robust.

Communication systems within and between plant cells involve the transfer of ions and molecules between compartments, and are essential for development and responses to biotic and abiotic stresses. This in turn requires the regulated movement and fusion of membrane systems with their associated cargo. Recent advances in genomics has provided new resources with which to investigate the evolutionary relationships between membrane proteins across plant species. Members of the soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) are known to play important roles in vesicle trafficking across plant, animal and microbial species. Using recent public expression and transcriptomic data from 9 representative green plants, we investigated the evolution of the SNARE classes and linked protein changes to functional specialization (expression patterns). We identified an additional 3 putative SNARE genes in the model plant Arabidopsis. We found that all SNARE classes have expanded in number to a greater or lesser degree alongside the evolution of multicellularity, and that within-species expansions are also common. These gene expansions appear to be associated with the accumulation of amino acid changes and with sub-functionalization of SNARE family members to different tissues. These results provide an insight into SNARE protein evolution and functional specialization. The work provides a platform for hypothesis-building and future research into the precise functions of these proteins in plant development and responses to the environment.

Transposable elements (TEs) are known to play a role in genome evolution, gene regulation, and epigenetics, representing potential tools for genetics research in and breeding of conifers. Recently, thanks to the development of high-throughput sequencing, more conifer genomes have been reported. Using bioinformatics tools, the TEs of 3 important conifers (Picea abies, Picea glauce, and Pinus taeda) were identified in our previous study, which provided a foundation for accelerating the use of TEs in conifer breeding and genetic study. Here, we review recent studies on the functional biology of TEs and discuss the potential applications for TEs in conifers.

Monitoring the mutation and evolution of the virus is important for tracing its ongoing transmission and facilitating effective vaccine development. A total of 342 complete genomic sequences of SARS-CoV-2 were analyzed in this study. Compared to the reference genome reported in December 2019, 465 mutations were found, among which, 347 occurred in only 1 sequence, while 26 occurred in more than 5 sequences. For these 26 further identified as SNPs, 14 were closely linked and were grouped into 5 profiles. Phylogenetic analysis revealed the sequences formed 2 major groups. Most of the sequences in late period (March and April) constituted the Cluster II, while the sequences before March in this study and the reported S/L and A/B/C types in previous studies were all in Cluster I. The distributions of some mutations were specific geographically or temporally, the potential effect of which on the transmission and pathogenicity of SARS-CoV-2 deserves further evaluation and monitoring. Two mutations were found in the receptor-binding domain (RBD) but outside the receptor-binding motif (RBM), indicating that mutations may only have marginal biological effects but merit further attention. The observed novel sequence divergence is of great significance to the study of the transmission, pathogenicity, and development of an effective vaccine for SARS-CoV-2.

Time is one crucial dimension conveying information in animal communication. Evolution has shaped animals’ nervous systems to produce signals with temporal properties fitting their socio-ecological niches. Many quantitative models of mechanisms underlying rhythmic behaviour exist, spanning insects, crustaceans, birds, amphibians, and mammals. However, these computational and mathematical models are often presented in isolation. Here, we provide an overview of the main mathematical models employed in the study of animal rhythmic communication among conspecifics. After presenting basic definitions and mathematical formalisms, we discuss each individual model. These computational models are then compared using simulated data to uncover similarities and key differences in the underlying mechanisms found across species. Our review of the empirical literature is admittedly limited. We stress the need of using comparative computer simulations – both before and after animal experiments – to better understand animal timing in interaction. We hope this article will serve as a potential first step towards a common computational framework to describe temporal interactions in animals, including humans.

Current research has identified several potential biomarkers for lung cancer diagnosis or prognosis. However, most of these biomarkers are derived from a relatively small number of samples using algorithms at the gene level. Hence, gene expression signatures discovered in these studies have little overlaps. In this study, we proposed a new strategy to identify biomarkers from multiple datasets at the pathway level. We integrated the genome-wide expression data of lung cancer tissues from 13 published studies and applied our strategy to identify lung cancer diagnostic and prognostic biomarkers. We identified a 32-gene signature that differentiates lung adenocarcinomas from other lung cancer subtypes. We also discovered a 43-gene signature that can predict the outcome of human lung cancers. We tested their performance in several independent cohorts, which confirmed their robust prognostic and diagnostic power. Furthermore, we showed that the proposed gene expression signatures were independent of several traditional clinical indicators in lung cancer management. Our results suggest that the pathway-based strategy is useful to identify transcriptomic biomarkers from large-scale gene expression datasets that were collected from multiple sources.