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Background: The alkalistable and thermostable xylanases are in high demand for pulp bleaching in paper industry and generating xylooligosaccharides by hydrolyzing xylan component of agro-residues. The compost-soil samples, one of the hot environments, are expected to be a rich source of microbes with thermostable enzymes. Methodology/Principal Findings: Metagenomic DNA from hot environmental samples could be a rich source of novel biocatalysts. While screening metagenomic library constructed from DNA extracted from the compost-soil in the p18GFP vector, a clone (TSDV-MX1) was detected that exhibited clear zone of xylan hydrolysis on RBB xylan plate. The sequencing of 6.321 kb DNA insert and its BLAST analysis detected the presence of xylanase gene that comprised 1077 bp. The deduced protein sequence (358 amino acids) displayed homology with glycosyl hydrolase (GH) family 11 xylanases. The gene was subcloned into pET28a vector and expressed in E. coli BL21 (DE3). The recombinant xylanase (rMxyl) exhibited activity over a broad range of pH and temperature with optima at pH 9.0 and 80uC. The recombinant xylanase is highly thermostable having T1/2 of 2 h at 80°C and 15 min at 90°C. Conclusion/Significance: This is the first report on the retrieval of xylanase gene through metagenomic approach that encodes an enzyme with alkalistability and thermostability. The recombinant xylanase has a potential application in paper and pulp industry in pulp bleaching and generating xylooligosaccharides from the abundantly available agro-residues. [ABSTRACT FROM AUTHOR]

Increasing yield is an important goal of barley breeding. In this study, 54 papers published from 2001–2022 on QTL mapping for yield and yield-related traits in barley were collected, which contained 1080 QTLs mapped to the barley high-density consensus map for QTL meta-analysis. These initial QTLs were integrated into 85 meta-QTLs (MQTL) with a mean confidence interval (CI) of 2.76 cM, which was 7.86-fold narrower than the CI of the initial QTL. Among these 85 MQTLs, 68 MQTLs were validated in GWAS studies, and 25 breeder's MQTLs were screened from them. Seventeen barley orthologs of yield-related genes in rice and maize were identified within the hcMQTL region based on comparative genomics strategy and were presumed to be reliable candidates for controlling yield-related traits. The results of this study provide useful information for molecular marker-assisted breeding and candidate gene mining of yield-related traits in barley. [ABSTRACT FROM AUTHOR]

The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that lead to diminished physical or intellectual capabilities. The process of determining which gene drives disease, known as "gene prioritization," is not entirely understood. Genome-wide searches for gene-disease associations are still underdeveloped due to reliance on previous discoveries and evidence sources with false positive or negative relations. This paper introduces DeepGenePrior, a model based on deep neural networks that prioritizes candidate genes in genetic diseases. Using the well-studied Variational AutoEncoder (VAE), we developed a score to measure the impact of genes on target diseases. Unlike other methods that use prior data to select candidate genes, based on the "guilt by association" principle and auxiliary data sources like protein networks, our study exclusively employs copy number variants (CNVs) for gene prioritization. By analyzing CNVs from 74,811 individuals with autism, schizophrenia, and developmental delay, we identified genes that best distinguish cases from controls. Our findings indicate a 12% increase in fold enrichment in brain-expressed genes compared to previous studies and a 15% increase in genes associated with mouse nervous system phenotypes. Furthermore, we identified common deletions in ZDHHC8, DGCR5, and CATG00000022283 among the top genes related to all three disorders, suggesting a common etiology among these clinically distinct conditions. DeepGenePrior is publicly available online at http://git.dml.ir/z%5frahaie/DGP to address obstacles in existing gene prioritization studies identifying candidate genes. Author summary: DeepGenePrior is a deep learning-based method for prioritizing genes in genetic diseases. Conventional tools utilize the guilt by association principle, which relies on prior knowledge to identify novel genes. In contrast, our method does not use any prior information. Furthermore, other tools rely on auxiliary data, including false positive or negative relations, which may lead to erroneous associations. Another group of methods relies on hypothesis testing, and fundamental issues regarding this group have been widely discussed in different papers. We compared the results of DeepGenePrior with both statistical and machine learning studies against biological and classification benchmarks. Our method's results outperformed current works in three brain disorders: autism, schizophrenia, and developmental delay. [ABSTRACT FROM AUTHOR]

In genetic epidemiology, genome-wide association studies (GWAS) are used to rapidly scan a large set of genetic variants and thus to identify associations with a particular trait or disease. The GWAS philosophy is different to that of conventional candidate-gene-based approaches, which directly test the effects of genetic variants of potentially contributory genes in an association study. One controversial question is whether GWAS provide relevant scientific outcomes by comparison with candidate-gene studies. We thus performed a bibliometric study using two citation metrics to assess whether the GWAS have contributed a capital gain in knowledge discovery by comparison with candidate-gene approaches. We selected GWAS published between 2005 and 2009 and matched them with candidate-gene studies on the same topic and published in the same period of time. We observed that the GWAS papers have received, on average, 30±55 citations more than the candidate gene papers, 1 year after their publication date, and 39±58 citations more 2 years after their publication date. The GWAS papers were, on average, 2.8±2.4 and 2.9±2.4 times more cited than expected, 1 and 2 years after their publication date; whereas the candidate gene papers were 1.5±1.2 and 1.5±1.4 times more cited than expected. While the evaluation of the contribution to scientific research through citation metrics may be challenged, it cannot be denied that GWAS are great hypothesis generators, and are a powerful complement to candidate gene studies. [ABSTRACT FROM AUTHOR]

The identification of anammox bacteria is mostly relied on PCR with various marker genes. However, the community composition revealed by different marker genes and whether the marker genes influence the resulted community composition remain unclear. We compared the community structure of anammox bacteria in enriched and natural environments revealed by 16S rRNA and functional genes (hzo, hzsA and hzsB) from public database and published papers. The genus of Ca. Scalindua showed the lowest similarities with other genera, especially for the hzsA gene (66.9%-68.6%). The 16S rRNA gene is the most commonly used marker gene in natural habitats with 151 out 221 papers in total. The anammox bacterial community composition is distributed according to the source of habitat regardless the use of various marker genes. The role of marker gene is limited with explanatory of 5.4% for variance of community composition, versus 20.5% of habitat. The effect of marker gene is mainly acted on freshwater habitat, which shows significant different community composition revealed by 16S rRNA and hzo, with Ca. Brocadia and Ca. Jettenia as dominant genus, respectively. [ABSTRACT FROM AUTHOR]

We describe a novel single sample gene set testing method for cancer transcriptomics data named tissue-adjusted pathway analysis of cancer (TPAC). The TPAC method leverages information about the normal tissue-specificity of human genes to compute a robust multivariate distance score that quantifies gene set dysregulation in each profiled tumor. Because the null distribution of the TPAC scores has an accurate gamma approximation, both population and sample-level inference is supported. As we demonstrate through an analysis of gene expression data for 21 solid human cancers from The Cancer Genome Atlas (TCGA) and associated normal tissue expression data from the Human Protein Atlas (HPA), TPAC gene set scores are more strongly associated with patient prognosis than the scores generated by existing single sample gene set testing methods. Author summary: Cancer biology is highly tissue-specific: most cancer-driving somatic alterations occur in just a limited number of tissues and inherited mutations frequently have a tissue-specific functional impact. To leverage the associations between gene activity in normal and malignant tissue for gene set testing, we have developed a new single sample gene set testing method for tumor-derived transcriptomics data named TPAC (tissue-adjusted pathway analysis of cancer). The TPAC method uses normal tissue-specificity to quantifies gene set dysregulation in each profiled tumor. Importantly, we found that TPAC gene set scores are more strongly associated with patient prognosis than the scores generated by existing single sample gene set testing methods. [ABSTRACT FROM AUTHOR]

Gene expression sample data, which usually contains massive expression profiles of genes, is commonly used for disease related gene analysis. The selection of relevant genes from huge amount of genes is always a fundamental process in applications of gene expression data. As more and more genes have been detected, the size of gene expression data becomes larger and larger; this challenges the computing efficiency for extracting the relevant and important genes from gene expression data. In this paper, we provide a novel Bi-dimensional Principal Feature Selection (BPFS) method for efficiently extracting critical genes from big gene expression data. It applies the principal component analysis (PCA) method on sample and gene domains successively, aiming at extracting the relevant gene features and reducing redundancies while losing less information. The experimental results on four real-world cancer gene expression datasets show that the proposed BPFS method greatly reduces the data size and achieves a nearly double processing speed compared to the counterpart methods, while maintaining better accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

Accurate assessment and monitoring of the Plasmodium falciparum Kelch 13 (pfk13) gene associated with artemisinin resistance is critical to understand the emergence and spread of drug-resistant parasites in malaria-endemic regions. In this study, we evaluated the genomic profile of the pfk13 gene associated with artemisinin resistance in P. falciparum in Nigerian children by targeted sequencing of the pfk13 gene. Genomic DNA was extracted from 332 dried blood (DBS) spot filter paper samples from three Nigerian States. The pfk13 gene was amplified by nested polymerase chain reaction (PCR), and amplicons were sequenced to detect known and novel polymorphisms across the gene. Consensus sequences of samples were mapped to the reference gene sequence obtained from the National Center for Biotechnology Information (NCBI). Out of the 13 single nucleotide polymorphisms (SNPs) detected in the pfk13 gene, five (F451L, N664I, V487E, V692G and Q661H) have not been reported in other endemic countries to the best of our knowledge. Three of these SNPs (V692G, N664I and Q661H) and a non-novel SNP, C469C, were consistent with late parasitological failure (LPF) in two States (Enugu and Plateau States). There was no validated mutation associated with artemisinin resistance in this study. However, a correlation of our study with in vivo and in vitro phenotypes is needed to establish the functional role of detected mutations as markers of artemisinin resistance in Nigeria. This baseline information will be essential in tracking and monitoring P. falciparum resistance to artemisinin in Nigeria. [ABSTRACT FROM AUTHOR]

The Guilt-by-Association (GBA) principle, according to which genes with similar expression profiles are functionally associated, is widely applied for functional analyses using large heterogeneous collections of transcriptomics data. However, the use of such large collections could hamper GBA functional analysis for genes whose expression is condition specific. In these cases a smaller set of condition related experiments should instead be used, but identifying such functionally relevant experiments from large collections based on literature knowledge alone is an impractical task. We begin this paper by analyzing, both from a mathematical and a biological point of view, why only condition specific experiments should be used in GBA functional analysis. We are able to show that this phenomenon is independent of the functional categorization scheme and of the organisms being analyzed. We then present a semi-supervised algorithm that can select functionally relevant experiments from large collections of transcriptomics experiments. Our algorithm is able to select experiments relevant to a given GO term, MIPS FunCat term or even KEGG pathways. We extensively test our algorithm on large dataset collections for yeast and Arabidopsis. We demonstrate that: using the selected experiments there is a statistically significant improvement in correlation between genes in the functional category of interest; the selected experiments improve GBA-based gene function prediction; the effectiveness of the selected experiments increases with annotation specificity; our algorithm can be successfully applied to GBA- based pathway reconstruction. Importantly, the set of experiments selected by the algorithm reflects the existing literature knowledge about the experiments. [ABSTRACT FROM AUTHOR]

The aim of this systematic review and meta-analysis was to identify a list of common, candidate genes associated with the three components of fitness, specifically cardiovascular fitness, muscular strength, and anaerobic power, and how these genes are associated with exercise response phenotype variability, in previously untrained participants. A total of 3,969 potentially relevant papers were identified and processed for inclusion. After eligibility and study selection assessment, 24 studies were selected for meta-analysis, comprising a total of 3,012 participants (male n = 1,512; females n = 1,239; not stated n = 261; age 28 ± 9 years). Meta-Essentials spreadsheet 1.4 (Microsoft Excel) was used in creating the forest plots and meta-analysis. IBM SPSS statistics V24 was implemented for the statistical analyses and the alpha was set at p ≤ 0.05. 13 candidate genes and their associated alleles were identified, which were associated with the phenotypes of interest. Analysis of training group data showed significant differential phenotypic responses. Subgroup analysis showed; 44%, 72% and 10% of the response variance in aerobic, strength and power phenotypes, respectively, were explained by genetic influences. This analysis established that genetic variability explained a significant proportion of the adaptation differences across the three components of fitness in the participants post-training. The results also showed the importance of analysing and reporting specific gene alleles. Information obtained from these findings has the potential to inform and influence future exercise-related genes and training studies. [ABSTRACT FROM AUTHOR]

C. albicans is a commensal organism present in the human microbiome of more than 60% of the healthy population. Transition from commensalism to invasive candidiasis may occur after a local or a general failure of host's immune system. This transition to a more virulent phenotype may reside either on the capacity to form hyphae or on an acquired resistance to antifungal drugs. Indeed, overexpression of genes coding drug efflux pumps or adhesins, cell wall proteins facilitating the contact between the fungus and the host, usually marks the virulence profile of invasive Candida spp. In this paper, we compare virulence of two clinical isolates of C. albicans with that of laboratory-induced resistant strains by challenging G. mellonella larvae with these pathogens along with monitoring transcriptional profiles of drug efflux pumps genes CDR1, CDR2, MDR1 and the adhesin genes ALS1 and HWP1. Although both clinical isolates were found resistant to both fluconazole and micafungin they were found less virulent than laboratory-induced resistant strains. An unexpected behavior emerged for the former clinical isolate in which three genes, CDR1, CDR2 and HWP1, usually correlated with virulence, although hyperexpressed, conferred a less aggressive phenotype. On the contrary, in the other isolate, we observed a decreased expression of CDR1, CDR2 and HWP1as well as of MDR1 and ALS1 that may be consistent with the less aggressive performance observed in this strain. These altered gene expressions might directly influence Candida virulence or they might be an epiphenomenon of a vaster rearrangement occurred in these strains during the challenge with the host's environment. An in-deepth comprehension of this scenario could be crucial for developing interventions able to counteract C. albicans invasiveness and lethality. [ABSTRACT FROM AUTHOR]

High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney. Author summary: Biological tissues are composed of different types of structurally organized cell units playing distinct functional roles. The exciting new spatial gene expression profiling methods have enabled the analysis of spatially resolved transcriptomes to understand the spatial and functional characteristics of these cells in the context of eco-environment of tissue. Due to the technical limitations, spatial transcriptomics data suffers from only sparsely measured mRNAs by in-situ capture and possibly missing spots in tissue regions that entirely failed fixing and permeabilizing RNAs. Our method, FIST (Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion), focuses on the spatial and high-sparsity nature of spatial transcriptomics data by modeling the data as a 3-way gene-by-(x, y)-location tensor and a product graph of a spatial graph and a protein-protein interaction network. Our comprehensive evaluation of FIST on ten 10x Genomics Visium spatial genomics datasets and comparison with the methods for single-cell RNA sequencing data imputation demonstrate that FIST is a better method more suitable for spatial gene expression imputation. Overall, we found FIST a useful new method for analyzing spatially resolved gene expressions based on novel modeling of spatial and functional information. [ABSTRACT FROM AUTHOR]

Background: Delayed Plasmodium falciparum parasite clearance has been associated with Single Nucleotide Polymorphisms (SNPs) in the kelch protein propeller domain (coded by pfk13 gene). SNPs in the Plasmodium falciparum multidrug resistance gene 1 (pfmdr1) are associated with multi-drug resistance including the combination artemether-lumefantrine. To our knowledge, this is the first work providing information on the prevalence of k13-propeller and pfmdr1 mutations from Sédhiou, a region in the south of Senegal. Methods: 147 dried blood spots on filter papers were collected from symptomatic patients attending a hospital located in Bounkiling City, Sédhiou Region, Southern Senegal. All samples were collected between 2015–2017 during the malaria transmission season. Specific regions of the gene pfk13 and pfmdr1 were analyzed using PCR amplification and Sanger sequencing. Results: The majority of parasites (92.9%) harboured the pfk13 wild type sequence and 6 samples harboured synonymous changes. Regarding pfmdr1, wild-type alleles represented the majority except at codon 184. Overall, prevalence of 86Y was 11.9%, 184F was 56.3% and 1246Y was 1.5%. The mutant allele 184F decreased from 73.7% in 2015 to 40.7% in 2017. The prevalence of haplotype NFD decreased from 71.4% in 2015 to 20.8% in 2017. Conclusions: This study provides the first description of pfk13 and pfmdr1 genes variations in Bounkiling, a city in the Sédhiou Region of Senegal, contributing to closing the gap of information on anti-malaria drug resistance molecular markers in southern Senegal. [ABSTRACT FROM AUTHOR]

SREBP1 and 2, are cholesterol sensors able to modulate cholesterol-related gene expression responses. SREBPs binding sites are characterized by the presence of multiple target sequences as SRE, NFY and SP1, that can be arranged differently in different genes, so that it is not easy to identify the binding site on the basis of direct DNA sequence analysis. This paper presents a complete workflow based on a one-dimensional Convolutional Neural Network (CNN) model able to detect putative SREBPs binding sites irrespective of target elements arrangements. The strategy is based on the recognition of SRE linked (less than 250 bp) to NFY sequences according to chromosomal localization derived from TF Immunoprecipitation (TF ChIP) experiments. The CNN is trained with several 100 bp sequences containing both SRE and NF-Y. Once trained, the model is used to predict the presence of SRE-NFY in the first 500 bp of all the known gene promoters. Finally, genes are grouped according to biological process and the processes enriched in genes containing SRE-NFY in their promoters are analyzed in details. This workflow allowed to identify biological processes enriched in SRE containing genes not directly linked to cholesterol metabolism and possible novel DNA patterns able to fill in for missing classical SRE sequences. [ABSTRACT FROM AUTHOR]

Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature. Author summary: Understanding the individual role that genes play in life is a key issue in biomedical science. While information regarding gene functions is continuously growing, the number of genes with uncharacterized biological functions is still greater. Because of this, scientists have dedicated much of their time to build and design tools that automatically infer gene functions. One of the most promising approaches (sometimes called "phylogenomics") attempts to construct a model of inheritance and divergence of function along branches of the phylogenetic tree that relates different members of a gene family. If the functions of one or more of the family members has been characterized experimentally, the presence or absence of these functions for other family members can be predicted, in a probabilistic framework, based on the evolutionary relationships. Previously proposed Bayesian approaches to parameter estimation have proved to be computationally intractable, preventing development of such a probabilistic model. In this paper, we present a simple model of gene-function evolution that is highly-scalable, which means that it is possible to perform parameter estimation not only on one family, but simultaneously for hundreds of gene-families, comprising thousands of genes. The parameter estimates we obtain coherently agree with what theory dictates regarding how gene-functions evolved. Finally, notwithstanding its simplicity, the model's prediction quality is comparable to other more complex alternatives. Although we believe further improvements can be made to our model, even this simple model makes verifiable predictions, and suggests areas in which existing annotations show inconsistencies that may indicate errors or controversies. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is one of the most prevalent types of cancer in the world and ranks second in cancer deaths in the US. Despite the recent improvements in screening and treatment, the number of deaths associated with CRC is still very significant. The complexities involved in CRC therapy stem from multiple oncogenic mutations and crosstalk between abnormal pathways. This calls for using advanced molecular genetics to understand the underlying pathway interactions responsible for this cancer. In this paper, we construct the CRC pathway from the literature and using an existing public dataset on healthy vs tumor colon cells, we identify the genes and pathways that are mutated and are possibly responsible for the disease progression. We then introduce drugs in the CRC pathway, and using a boolean modeling technique, we deduce the drug combinations that produce maximum cell death. Our theoretical simulations demonstrate the effectiveness of Cryptotanshinone, a traditional Chinese herb derivative, achieved by targeting critical oncogenic mutations and enhancing cell death. Finally, we validate our theoretical results using wet lab experiments on HT29 and HCT116 human colorectal carcinoma cell lines. [ABSTRACT FROM AUTHOR]

Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance. [ABSTRACT FROM AUTHOR]

In a recent paper published in PLOS ONE, Wang et al. challenge our finding that the identity of optimal codons in different genomes follows a set of clear rules. Here we provide a rebuttal of their paper and demonstrate that the results of our original PLOS Genetics paper stand. This provides us with an opportunity to bring up an aspect of how codon usage has been studied that should be of general interest. The Wang et al. study, as well as many other studies, used ribosomal genes as a reference set for the study of patterns of codon usage. We discuss here the assumptions that are made in order to justify using ribosomal genes to study codon bias, suggest that this practice can at times be problematic, and discuss its limitations. [ABSTRACT FROM AUTHOR]

The article discusses how genes contribute to one's unique abilities, could it be cognitive or physical that differentiate them from apes. These genes that are fixed within one's lineage are those that make her up as a human. Due to recent advances in genomic sequencing and analysis, many papers had come out depending on many dimensions including detecting selection, single-gene studies and genome-wide analysis.

Background: As research into alternative splicing reveals the fundamental importance of this phenomenon in the genome expression of higher organisms, there is an increasing need for a standardized, consistent and unique identifier for alternatively spliced isoforms. Such an identifier would be useful to eliminate ambiguities in references to gene isoforms, and would allow for the reliable comparison of isoforms from different sources (e.g., known genes vs. computational predictions). Commonly used identifiers for gene transcripts prove to be unsuitable for this purpose. Methodology: We propose an algorithm to compute an isoform signature based on the arrangement of exons and introns in a primary transcript. The isoform signature uniquely identifies a transcript structure, and can therefore be used as a key in databases of alternatively spliced isoforms, or to compare alternative splicing predictions produced by different methods. In this paper we present the algorithm to generate isoform signatures, we provide some examples of its application, and we describe a web-based resource to generate isoform signatures and use them in database searches. Conclusions: Isoform signatures are simple, so that they can be easily generated and included in publications and databases, but flexible enough to unambiguously represent all possible isoform structures, including information about coding sequence position and variable transcription start and end sites. We believe that the adoption of isoform signatures can help establish a consistent, unambiguous nomenclature for alternative splicing isoforms. The system described in this paper is freely available at http://genome.ufl.edu/genesig/, and supplementary materials can be found at http://genome.ufl.edu/genesig-files/. [ABSTRACT FROM AUTHOR]

The fundamental difference between classic and modern biology is that technological innovations allow to generate highthroughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first. [ABSTRACT FROM AUTHOR]

The efflux pumps from the Resistance-Nodulation-Division family, RND, are main contributors to intrinsic antibiotic resistance in Gram-negative bacteria. Among this family, the MdtABC pump is unusual by having two inner membrane components. The two components, MdtB and MdtC are homologs, therefore it is evident that the two components arose by gene duplication. In this paper, we describe the results obtained from a phylogenetic analysis of the MdtBC pumps in the context of other RNDs. We show that the individual inner membrane components (MdtB and MdtC) are conserved throughout the Proteobacterial species and that their existence is a result of a single gene duplication. We argue that this gene duplication was an ancient event which occurred before the split of Proteobacteria into Alpha-, Beta- and Gamma- classes. Moreover, we find that the MdtABC pumps and the MexMN pump from Pseudomonas aeruginosa share a close common ancestor, suggesting the MexMN pump arose by another gene duplication event of the original Mdt ancestor. Taken together, these results shed light on the evolution of the RND efflux pumps and demonstrate the ancient origin of the Mdt pumps and suggest that the core bacterial efflux pump repertoires have been generally stable throughout the course of evolution. [ABSTRACT FROM AUTHOR]

Despite strong vetting for disease activity, only 10% of candidate new molecular entities in early stage clinical trials are eventually approved. Analyzing historical pipeline data, Nelson et al. 2015 (Nat. Genet.) concluded pipeline drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. Taking advantage of recent clinical development advances and rapid growth in GWAS datasets, we extend the original work using updated data, test whether genetic evidence predicts future successes and introduce statistical models adjusting for target and indication-level properties. Our work confirms drugs with genetically supported targets were more likely to be successful in Phases II and III. When causal genes are clear (Mendelian traits and GWAS associations linked to coding variants), we find the use of human genetic evidence increases approval by greater than two-fold, and, for Mendelian associations, the positive association holds prospectively. Our findings suggest investments into genomics and genetics are likely to be beneficial to companies deploying this strategy. Author summary: The growth of human genetics resources has the potential to help us develop better drugs. By looking at whether and how historical drug approvals could have been predicted from our current knowledge of human genetics, we can validate this approach and assess which types of genetic evidence are most likely to be useful in guiding drug discovery. Validation is important because we are often uncertain about the biological mechanisms behind genetic variants linked to disease. Most associated variants do not occur within protein-coding regions of the genome, and it is difficult to tell which of many nearby genes is contributing to disease risk. In this paper, we confirm previous correlations between genetic evidence and historical drug approvals. We find genetic evidence from severe genetic disorders and from genetic variants that alter protein sequence is more strongly associated with historical approvals. We offer statistical approaches for prioritizing new drug candidates based on whether their mechanisms are supported by human genetic evidence. [ABSTRACT FROM AUTHOR]

The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD) which reveals significant clusters with the selected data. [ABSTRACT FROM AUTHOR]

Notch ligands [jagged (JAG) and, delta-like (DLL) families] and receptors [NOTCH family] are key regulators of Notch signaling. NOTCH signaling contributes to vascular development, tissue homeostasis, angiogenesis, and cancer progression. To elucidate the universal functions of the JAG, DLL, and NOTCH families and their connections with various biological functions, we examined 15 types of cancer using The Cancer Genome Atlas clinical database. We selected the differentially expressed genes (DEGs), which were positively correlated to the JAG, DLL, and NOTCH families in each cancer. We selected positive and negative hallmark signatures across cancer types. These indicated biological features associated with angiogenesis, hypoxia, KRAS signaling, cell cycle, and MYC targets by gene ontology and gene set enrichment analyses using DEGs. Furthermore, we analyzed single-cell RNA sequencing data to examine the expression of JAG, DLL, and NOTCH families and enrichment of hallmark signatures. Positive signatures identified using DEGs, such as KRAS signaling and hypoxia, were enriched in clusters with high expression of JAG, DLL, and NOTCH families. We subsequently validated the correlation between the JAG, DLL, and NOTCH families and clinical stages, including treatment response, metastasis, and recurrence. In addition, we performed survival analysis to identify hallmark signatures that critically affect patient survival when combining the expression of JAG, DLL, and NOTCH families. By combining the DEG enrichment and hallmark signature enrichment in survival analysis, we suggested unexplored regulatory functions and synergistic effects causing synthetic lethality. Taken together, our observations demonstrate the functions of JAG, DLL, and NOTCH families in cancer malignancy and provide insights into their molecular regulatory mechanisms. [ABSTRACT FROM AUTHOR]

Extraintestinal pathogenic Escherichia coli (ExPEC), especially uropathogenic E. coli (UPEC) are responsible for urinary tract infections (UTIs), while diarrheagenic E. coli (DEC) cause foodborne illnesses. These pathogenic E. coli are a serious threat to human health and a public concern worldwide. However, the evidence on pork E. coli (PEC) harboring UPEC virulence-associated genes is currently limited. Therefore, this study aimed to determine the phylogroups, virulence genes, and their association between PEC and UPEC from UTI patients. In this study, 330 E. coli were obtained from archived stock culture isolated from pork (PEC; n = 165) and urine of patients with UTIs (UPEC; n = 165) during 2014–2022. Phylogroups, UPEC- and diarrheagenic E. coli (DEC) associated virulence genes were assessed using PCR assays. The results showed that phylogroups A (50.3%), and B1 (32.1%) were commonly found among PEC whereas phylogroups B2 (41.8%), and C (25.5%) were commonly detected in the UPEC. PEC and UPEC carried similar virulence-associated genes with different percentages. The most frequent UPEC virulence-associated gene among UPEC, and PEC strains was fimH, (93.3%, and 92.1%), followed by iucC (55.2%, and 12.7%), papC (21.8%, and 4.2%), afaC (22.4%, and 0%), hlyCA (17%, and 0.6%), cnf (16.4%, and 0.6%), and sfa/focDE (8.5%, and 4.8%). Additionally, 6 of 27 UPEC virulence-associated gene patterns were found in both PEC and UPEC strains regardless of phylogroups. Furthermore, the DEC virulence-associated genes were found in only 3 strains, one from PEC harboring eae, and two from UPEC carried fimH-bfpA or afaC-CVD432 indicating hybrid strains. Cluster analysis showed a relationship between PEC and UPEC strains and demonstrated that PEC harboring UPEC virulence-associated genes in pork may be associated with UPEC in humans. Food safety and hygiene practices during pork production chain are important procedures for minimizing cross-contamination of these strains that could be transmitted to the consumers. [ABSTRACT FROM AUTHOR]

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size, where overdispersion refers to the empirical phenomenon that the variance of read counts is larger than the mean of read counts. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes when the number of replicates is limited as long as the number of conditions is large. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq2 and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments. [ABSTRACT FROM AUTHOR]

The conserved SKN-1A/Nrf1 transcription factor regulates the expression of proteasome subunit genes and is essential for maintenance of adequate proteasome function in animal development, aging, and stress responses. Unusual among transcription factors, SKN-1A/Nrf1 is a glycoprotein synthesized in the endoplasmic reticulum (ER). N-glycosylated SKN-1A/Nrf1 exits the ER and is deglycosylated in the cytosol by the PNG-1/NGLY1 peptide:N-glycanase. Deglycosylation edits the protein sequence of SKN-1A/Nrf1 by converting N-glycosylated asparagine residues to aspartate, which is necessary for SKN-1A/Nrf1 transcriptional activation of proteasome subunit genes. Homozygous loss-of-function mutations in the peptide:N-glycanase (NGLY1) gene cause NGLY1 deficiency, a congenital disorder of deglycosylation. There are no effective treatments for NGLY1 deficiency. Since SKN-1A/Nrf1 is a major client of NGLY1, the resulting proteasome deficit contributes to NGLY1 disease. We sought to identify targets for mitigation of proteasome dysfunction in NGLY1 deficiency that might indicate new avenues for treatment. We isolated mutations that suppress the sensitivity to proteasome inhibitors caused by inactivation of the NGLY1 ortholog PNG-1 in Caenorhabditis elegans. We identified multiple suppressor mutations affecting 3 conserved genes: rsks-1, tald-1, and ent-4. We show that the suppressors act through a SKN-1/Nrf-independent mechanism and confer proteostasis benefits consistent with amelioration of proteasome dysfunction. ent-4 encodes an intestinal nucleoside/nucleotide transporter, and we show that restriction of nucleotide availability is beneficial, whereas a nucleotide-rich diet exacerbates proteasome dysfunction in PNG-1/NGLY1-deficient C. elegans. Our findings suggest that dietary or pharmacological interventions altering nucleotide availability have the potential to mitigate proteasome insufficiency in NGLY1 deficiency and other diseases associated with proteasome dysfunction. Regulation of the proteasome is disrupted in the rare genetic disease known as NGLY1 deficiency. This manuscript finds that proteasome dysfunction in NGLY1-deficient C. elegans depends on nucleotide availability, indicating an unexpected regulatory crosstalk between nucleotide metabolism and protein turnover, and suggesting that interventions to alter nucleotide availability might mitigate proteasome insufficiency in diseases associated with proteasome dysfunction. [ABSTRACT FROM AUTHOR]

Even with advanced plasmid and viral vectors, attaining copy numbers of multiple genes among different transfected cells is challenging. We achieved one gene expression from a single-copy gene in one cell using a transgene competition system, a combination of the Kazusa cDNA clones and our dual recombinase-mediated cassette exchange system. All 48 nuclear receptors were simultaneously expressed in one dish at the same expression level in HEK293 using this system, and the cell proliferation rate was compared. Significant differences were observed between cells transfected with CMV- or EF1 promoter-driven expression of the 48 nuclear receptors after 8 weeks. The EF1-NR1I2 cell line, which exhibited the highest increase from 2 to 8 weeks, showed 1.13-fold higher proliferation than the EF1-DsRed line. On the other hand, the EF1-NR4A1 cell line, which showed the maximum decrease at 8 weeks, showed 0.88-fold lower proliferation than the EF1-DsRed line. The results were confirmed in both our transgene competition system and long-term growth experiments. Our transgene competition system offers a wide-range, simple, and accurate cell competition method. [ABSTRACT FROM AUTHOR]

The symbiosis between rhizobia and legumes is characterized by a complex molecular dialogue in which the bacterial NodD protein plays a major role due to its capacity to activate the expression of the nodulation genes in the presence of appropiate flavonoids. These genes are involved in the synthesis of molecules, the nodulation factors (NF), responsible for launching the nodulation process. Rhizobium tropici CIAT 899, a rhizobial strain that nodulates Phaseolus vulgaris, is characterized by its tolerance to multiple environmental stresses such as high temperatures, acidity or elevated osmolarity. This strain produces nodulation factors under saline stress and the same set of CIAT 899 nodulation genes activated by inducing flavonoids are also up-regulated in a process controlled by the NodD2 protein. In this paper, we have studied the effect of osmotic stress (high mannitol concentrations) on the R. tropici CIAT 899 transcriptomic response. In the same manner as with saline stress, the osmotic stress mediated NF production and export was controlled directly by NodD2. In contrast to previous reports, the nodA2FE operon and the nodA3 and nodD1 genes were up-regulated with mannitol, which correlated with an increase in the production of biologically active NF. Interestingly, in these conditions, this regulatory protein controlled not only the expression of nodulation genes but also the expression of other genes involved in protein folding and synthesis, motility, synthesis of polysaccharides and, surprinsingly, nitrogen fixation. Moreover, the non-metabolizable sugar dulcitol was also able to induce the NF production and the activation of nod genes in CIAT 899. [ABSTRACT FROM AUTHOR]

Background: Microsoft Excel automatically converts certain gene symbols, database accessions, and other alphanumeric text into dates, scientific notation, and other numerical representations. These conversions lead to subsequent, irreversible, corruption of the imported text. A recent survey of popular genomic literature estimates that one-fifth of all papers with supplementary gene lists suffer from this issue., Results: Here, we present an open-source tool, Escape Excel, which prevents these erroneous conversions by generating an escaped text file that can be safely imported into Excel. Escape Excel is implemented in a variety of formats (http://www.github.com/pstew/escape_excel), including a command line based Perl script, a Windows-only Excel Add-In, an OS X drag-and-drop application, a simple web-server, and as a Galaxy web environment interface. Test server implementations are accessible as a Galaxy interface (http://apostl.moffitt.org) and simple non-Galaxy web server (http://apostl.moffitt.org:8000/)., Conclusions: Escape Excel detects and escapes a wide variety of problematic text strings so that they are not erroneously converted into other representations upon importation into Excel. Examples of problematic strings include date-like strings, time-like strings, leading zeroes in front of numbers, and long numeric and alphanumeric identifiers that should not be automatically converted into scientific notation. It is hoped that greater awareness of these potential data corruption issues, together with diligent escaping of text files prior to importation into Excel, will help to reduce the amount of Excel-corrupted data in scientific analyses and publications.

Exonuclease-mediated RNA decay in plants is known to be involved primarily in endogenous RNA degradation, and several RNA decay components have been suggested to attenuate RNA silencing possibly through competing for RNA substrates. In this paper, we report that overexpression of key cytoplasmic 5’–3’ RNA decay pathway gene-encoded proteins (5’RDGs) such as decapping protein 2 (DCP2) and exoribonuclease 4 (XRN4) in Nicotiana benthamiana fails to suppress sense transgene-induced post-transcriptional gene silencing (S-PTGS). On the contrary, knock-down of these 5’RDGs attenuates S-PTGS and supresses the generation of small interfering RNAs (siRNAs). We show that 5’RDGs degrade transgene transcripts via the RNA decay pathway when the S-PTGS pathway is disabled. Thus, RNA silencing and RNA decay degrade exogenous gene transcripts in a hierarchical and coordinated manner. Moreover, we present evidence that infection by turnip mosaic virus (TuMV) activates RNA decay and 5’RDGs also negatively regulate TuMV RNA accumulation. We reveal that RNA silencing and RNA decay can mediate degradation of TuMV RNA in the same way that they target transgene transcripts. Furthermore, we demonstrate that VPg and HC-Pro, the two known viral suppressors of RNA silencing (VSRs) of potyviruses, bind to DCP2 and XRN4, respectively, and the interactions compromise their antiviral function. Taken together, our data highlight the overlapping function of the RNA silencing and RNA decay pathways in plants, as evidenced by their hierarchical and concerted actions against exogenous and viral RNA, and VSRs not only counteract RNA silencing but also subvert RNA decay to promote viral infection. [ABSTRACT FROM AUTHOR]

The MUMmer system and the genome sequence aligner included within it are among the most widely used alignment packages in genomics. Since the last major release of MUMmer version 3 in 2004, it has been applied to many types of problems including aligning whole genome sequences, aligning reads to a reference genome, and comparing different assemblies of the same genome. Despite its broad utility, MUMmer3 has limitations that can make it difficult to use for large genomes and for the very large sequence data sets that are common today. In this paper we describe MUMmer4, a substantially improved version of MUMmer that addresses genome size constraints by changing the 32-bit suffix tree data structure at the core of MUMmer to a 48-bit suffix array, and that offers improved speed through parallel processing of input query sequences. With a theoretical limit on the input size of 141Tbp, MUMmer4 can now work with input sequences of any biologically realistic length. We show that as a result of these enhancements, the program in MUMmer4 is easily able to handle alignments of large genomes; we illustrate this with an alignment of the human and chimpanzee genomes, which allows us to compute that the two species are 98% identical across 96% of their length. With the enhancements described here, MUMmer4 can also be used to efficiently align reads to reference genomes, although it is less sensitive and accurate than the dedicated read aligners. The nucmer aligner in MUMmer4 can now be called from scripting languages such as Perl, Python and Ruby. These improvements make MUMer4 one the most versatile genome alignment packages available. [ABSTRACT FROM AUTHOR]

BRRI31R is one of the Bangladesh's most promising restorer lines due to its abundant pollen producing capacity, strong restoring ability, good combining ability, high outcrossing rate and genetically diverse from cytoplasmic male sterile (CMS) line. But the drawback of this line is that it is highly susceptible to bacterial blight (BB) disease of rice caused by Xanthomonas oryzae pv. oryzae. The present study highlighted the pyramiding of effective BB resistance genes (xa5, xa13 and Xa21) into the background of BRRI31R, through marker–assisted backcrossing (MABC). Backcross progenies were confirmed and advanced based on the foreground selection of target genes. Pyramided lines were used for pathogenicity test against five Bangladeshi Xanthomonas oryzae (BXo) races (BXo93, BXo220, BXo822, BXo826, BXo887) and confirmed the dominant fertility restore genes, Rf3 and Rf4 and further validated against SNP markers for more confirmation of target resistance genes. All pyramided restorer lines consisted of Xa4 (in built), xa5, xa13, Xa21, and Chalk5 with two fertility restorer genes, Rf3, Rf4. and these restorer lines showed intermediate amylose content (<25%). Restorer lines BRRI31R-MASP3 and BRRI31R-MASP4 showed high levels of resistance against five virulent BXo races and SNP genotyping revealed that these lines also contained a blast resistance gene Pita races. Gene pyramided restorer lines, BRRI31R-MASP3 and BRRI31R-MASP4 can directly be used as a male parent for the development of new BB resistant hybrid rice variety or could be used as a replacement of restorer line of BRRI hybrid dhan5 and 7 to enhance the quality of hybrid seeds as well as rice production in Bangladesh. [ABSTRACT FROM AUTHOR]

Although introns are typically tens to thousands of nucleotides, there are notable exceptions. In flies as well as humans, a small number of genes contain introns that are more than 1000 times larger than typical introns, exceeding hundreds of kilobases (kb) to megabases (Mb). It remains unknown why gigantic introns exist and how cells overcome the challenges associated with their transcription and RNA processing. The Drosophila Y chromosome contains some of the largest genes identified to date: multiple genes exceed 4Mb, with introns accounting for over 99% of the gene span. Here we demonstrate that co-transcriptional splicing of these gigantic Y-linked genes is important to ensure successful transcription: perturbation of splicing led to the attenuation of transcription, leading to a failure to produce mature mRNA. Cytologically, defective splicing of the Y-linked gigantic genes resulted in disorganization of transcripts within the nucleus suggestive of entanglement of transcripts, likely resulting from unspliced long RNAs. We propose that co-transcriptional splicing maintains the length of nascent transcripts of gigantic genes under a critical threshold, preventing their entanglement and ensuring proper gene expression. Our study reveals a novel biological significance of co-transcriptional splicing. Author summary: A small number of genes in our genome are characterized by excessively large introns, sometimes exceeding megabases. It remains poorly understood why such large introns exist or how they are processed during gene expression. By examining genes on Drosophila Y chromosome that contain gigantic introns, it is shown that the transcripts of these genes are co-transcriptionally spliced, and co-transcriptional splicing of these genes is critical for proper gene expression. Perturbation of splicing led to attenuation of transcription specifically in genes with gigantic introns, but not genes with average-sized introns. Whereas transcripts of genes with gigantic introns normally form separate nuclear domains, splicing perturbation led to intermingling of these domains, leading to a proposal that co-transcriptional splicing may function to keep transcript length short to avoid entanglement of transcripts, which may stall the progression of RNA polymerase II. Taken together, these results reveal a novel function of co-transcriptional splicing that specifically impacts the expression of genes with gigantic introns. [ABSTRACT FROM AUTHOR]

The blaNDM-1 gene and its variants encode metallo-beta-lactamases that confer resistance to almost all beta-lactam antibiotics. Genes encoding blaNDM-1 and its variants can be found in several Acinetobacter species, and they are usually linked to two different plasmid clades. The plasmids in one of these clades contain a gene encoding a Rep protein of the Rep_3 superfamily. The other clade consists of medium-sized plasmids in which the gene (s) involved in plasmid replication initiation (rep)have not yet been identified. In the present study, we identified the minimal replication region of a blaNDM-1-carrying plasmid of Acinetobacter haemolyticus AN54 (pAhaeAN54e), a member of this second clade. This region of 834 paired bases encodes three small peptides, all of which have roles in plasmid maintenance. The plasmids containing this minimal replication region are closely related; almost all contain blaNDM genes, and they are found in multiple Acinetobacter species, including A. baumannii. None of these plasmids contain an annotated Rep gene, suggesting that their replication relies on the minimal replication region that they share with the plasmid pAhaeAN54e. These observations suggest that this plasmid lineage plays a crucial role in the dissemination of the blaNDM-1 gene and its variants. [ABSTRACT FROM AUTHOR]

Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools. [ABSTRACT FROM AUTHOR]

In the protozoan parasite Leishmania, most genes encoding for ribosomal proteins (RPs) are present as two or more copies in the genome. However, their untranslated regions (UTRs) are predominantly divergent and might be associated with a distinct regulation of the expression of paralogous genes. Herein, we investigated the expression profiles of two RPs (S16 and L13a) encoded by duplicated genes in Leishmania major. The genes encoding for the S16 protein possess identical coding sequences (CDSs) and divergent UTRs, whereas the CDSs of L13a diverge by two amino acids and by their UTRs. Using CRISPR/Cas9 genome editing, we generated knockout (Δ) and endogenously tagged transfectants for each paralog of L13a and S16 genes. Combining tagged and Δ cell lines we found evidence of differential expression of both RPS16 and RPL13a isoforms throughout parasite development, with one isoform consistently more abundant than its respective copy. In addition, compensatory expression was observed for each paralog upon deletion of the corresponding isoform, suggesting functional conservation between these proteins. This differential expression pattern relates to post-translational processes, given compensation occurs at the level of the protein, with no alterations detected at transcript level. Ribosomal profiles for RPL13a indicate a standard behavior for these paralogues suggestive of interaction with heavy RNA-protein complexes, as already reported for other RPs in trypanosomatids. We identified paralog-specific bound to their 3'UTRs which may be influential in regulating paralog expression. In support, we identified conserved cis-elements within the 3'UTRs of RPS16 and RPL13a; cis-elements exclusive to the UTR of the more abundant paralog or to the less abundant ones were identified. [ABSTRACT FROM AUTHOR]

Bean beetle (Callosobruchus maculatus) exhibits clear phenotypic plasticity depending on population density; However, the underlying molecular mechanism remains unknown. Compared to low-density individuals, high-density individuals showed a faster terminal oocyte maturity rate. Four insulin-like peptide (ILP) genes were identified in the bean beetle, which had higher expression levels in the head than in the thorax and abdomen. The population density could regulate the expression levels of CmILP1-3, CmILP2-3, and CmILP1 as well as CmILP3 in the head, thorax, and abdomen, respectively. RNA interference results showed that each CmILP could regulate terminal oocyte maturity rate, indicating that there was functional redundancy among CmILPs. Silencing each CmILP could lead to down-regulation of some other CmILPs, however, CmILP3 was up-regulated in the abdomen after silencing CmILP1 or CmILP2. Compared to single gene silencing, silencing CmILP3 with CmILP1 or CmILP2 at the same time led to more serious retardation in oocyte development, suggesting CmILP3 could be up-regulated to functionally compensate for the down-regulation of CmILP1 and CmILP2. In conclusion, population density-dependent plasticity in terminal oocyte maturity rate of bean beetle was regulated by CmILPs, which exhibited gene redundancy and gene compensation. [ABSTRACT FROM AUTHOR]

Apocynum venetum L. belongs to the Apocynaceae family and is a plant that is highly resistant to stress. It is important in the fields of ecology, feeding, industry and medicine. The molecular mechanism underlying salt tolerance has not been elucidated. In this study, RNA-seq based transcriptome sequencing of A. venetum leaves after 0, 2, 6, 12, 24 and 48 h of treatment with 300 mM NaCl was performed. We conducted a comprehensive analysis of the transcriptome expression profiles of A. venetum under salt stress using the WGCNA method and identified red, black, and brown as the core modules regulating the salt tolerance of A. venetum. A co-expression regulatory network was constructed to identify the core genes in the module according to the correlations between genes. The genes TRINITY_DN102_c0_g1 (serine carboxypeptidase), TRINITY_DN3073_c0_g1 (SOS signaling pathway) and TRINITY_DN6732_c0_g1 (heat shock transcription factor) in the red module were determined to be the core genes. Two core genes in the black module, TRINITY_DN9926_c0_g1 and TRINITY_DN7962_c0_g1, are pioneer candidate salt tolerance-associated genes in A. venetum. The genes in the brown module were mainly enriched in two pathways, namely photosynthesis and osmotic balance. Among them, the TRINITY_DN6321_c0_g2 and TRINITY_DN244_c0_g1 genes encode aquaporin, which is helpful for maintaining the cell water balance and plays a protective role in defending A. venetum under abiotic stress. Our findings contribute to the identification of core genes involved in the response of A. venetum to salt stress. [ABSTRACT FROM AUTHOR]

We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior performance at a lower computational cost relative to other single sample approaches. Author summary: Gene set testing methods are widely used to analyze transcriptomic data with techniques that provide sample level scores increasingly popular given their significant analytical flexibility. For the analysis of single cell data, however, current cell-level methods have several important limitations: poor computational performance, low sensitivity to patterns of differential correlation, and limited support for competitive scenarios that compare set and non-set genes. To address these challenges, we have developed the RESET (Reconstruction Set Test) method that generates overall and cell-level gene set scores using randomized reduced rank reconstruction error. Relative to existing single sample techniques, RESET can more effectively detect patterns of differential abundance and differential correlation under both self-contained and competitive scenarios at a substantially lower computational cost. [ABSTRACT FROM AUTHOR]

Proteins containing domain of unknown function (DUF) are prevalent in eukaryotic genome. The DUF1216 proteins possess a conserved DUF1216 domain resembling to the mediator protein of Arabidopsis RNA polymerase II transcriptional subunit-like protein. The DUF1216 family are specifically existed in Brassicaceae, however, no comprehensive evolutionary analysis of DUF1216 genes have been performed. We performed a first comprehensive genome-wide analysis of DUF1216 proteins in Brassicaceae. Totally 284 DUF1216 genes were identified in 27 Brassicaceae species and classified into four subfamilies on the basis of phylogenetic analysis. The analysis of gene structure and conserved motifs revealed that DUF1216 genes within the same subfamily exhibited similar intron/exon patterns and motif composition. The majority members of DUF1216 genes contain a signal peptide in the N-terminal, and the ninth position of the signal peptide in most DUF1216 is cysteine. Synteny analysis revealed that segmental duplication is a major mechanism for expanding of DUF1216 genes in Brassica oleracea, Brassica juncea, Brassica napus, Lepidium meyneii, and Brassica carinata, while in Arabidopsis thaliana and Capsella rubella, tandem duplication plays a major role in the expansion of the DUF1216 gene family. The analysis of Ka/Ks (non-synonymous substitution rate/synonymous substitution rate) ratios for DUF1216 paralogous indicated that most of gene pairs underwent purifying selection. DUF1216 genes displayed a specifically high expression in reproductive tissues in most Brassicaceae species, while its expression in Brassica juncea was specifically high in root. Our studies offered new insights into the phylogenetic relationships, gene structures and expressional patterns of DUF1216 members in Brassicaceae, which provides a foundation for future functional analysis. [ABSTRACT FROM AUTHOR]

The selection of proper reference genes is critical for accurate gene expression analysis in all fields of biological and medical research, mainly because there are many distinctions between different tissues and specimens. Given this variability, even in known classic reference genes, demands of a comprehensive analysis platform is needed to identify the most suitable genes for each study. For this purpose, we present an analysis tool for assisting in decision-making in the analysis of reverse transcription-quantitative polymerase chain reaction (RT-qPCR) data. EndoGeneAnalyzer, an open-source web tool for reference gene analysis in RT-qPCR studies, was used to compare the groups/conditions under investigation. This interactive application offers an easy-to-use interface that allows efficient exploration of datasets. Through statistical and stability analyses, EndoGeneAnalyzer assists in the select of the most appropriate reference gene or set of genes for each condition. It also allows researchers to identify and remove unwanted outliers. Moreover, EndoGeneAnalyzer provides a graphical interface to compare the evaluated groups, providing a visually informative differential analysis. [ABSTRACT FROM AUTHOR]

Comprehensive understanding prognostic relevance of distinct tumor microenvironment (TME) remained elusive in colon cancer. In this study, we performed in silico analysis of the stromal components of primary colon cancer, with a focus on the markers of cancer-associated fibroblasts (CAF) and tumor-associated endothelia (TAE), as well as immunological infiltrates like tumor-associated myeloid cells (TAMC) and cytotoxic T lymphocytes (CTL). The relevant CAF-associated genes (CAFG)(representing R index = 0.9 or beyond with SPARC) were selected based on stroma specificity (cancer stroma/epithelia, cS/E = 10 or beyond) and expression amounts, which were largely exhibited negative prognostic impacts. CAFG were partially shared with TAE-associated genes (TAEG)(PLAT, ANXA1, and PTRF) and TAMC-associated genes (TAMCG)(NNMT), but not with CTL-associated genes (CTLG). Intriguingly, CAFG were prognostically subclassified in order of fibrosis (representing COL5A2, COL5A1, and COL12A1) followed by exclusive TAEG and TAMCG. Prognosis was independently stratified by CD8A, a CTL marker, in the context of low expression of the strongest negative prognostic CAFG, COL8A1. CTLG were comprehensively identified as IFNG, B2M, and TLR4, in the group of low S/E, representing good prognosis. Our current in silico analysis of the micro-dissected stromal gene signatures with prognostic relevance clarified comprehensive understanding of clinical features of the TME and provides deep insights of the landscape. [ABSTRACT FROM AUTHOR]

Background: Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular process is active only under a subset of conditions. Biclustering finds gene clusters that have similar expression levels across a subset of conditions. This paper proposes a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner. Methods: In order to find the biclusters in a gene expression dataset, we exhaustively select combinations of genes and conditions as seeds to create candidate bicluster tables. The tables have two columns (a) a gene set, and (b) the conditions on which the gene set have dissimilar expression levels to the seed. First, the genes with less than the maximum number of dissimilar conditions are identified and a table of these genes is created. Second, the rows that have the same dissimilar conditions are grouped together. Third, the table is sorted in ascending order based on the number of dissimilar conditions. Finally, beginning with the first row of the table, a test is run repeatedly to determine whether the cardinality of the gene set in the row is greater than the minimum threshold number of genes in a bicluster. If so, a bicluster is outputted and the corresponding row is removed from the table. Repeating this process, all biclusters in the table are systematically identified until the table becomes empty. Conclusions: This paper presents a novel biclustering algorithm for the identification of additive biclusters. Since it involves exhaustively testing combinations of genes and conditions, the additive biclusters can be found more readily. [ABSTRACT FROM AUTHOR]

The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM), as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The method's implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery. [ABSTRACT FROM AUTHOR]

Background: Leishmania major complex is the main causative agent of zoonotic cutaneous leishmaniasis (ZCL) in the Old World. Phlebotomus papatasi and Phlebotomus duboscqi are recognized vectors of L. major complex in Northern and Southern Sahara, respectively. In Mali, ZCL due to L. major is an emerging public health problem, with several cases reported from different parts of the country. The main objective of the present study was to identify the vectors of Leishmania major in the Bandiagara area, in Mali. Methodology/Principal Findings: An entomological survey was carried out in the ZCL foci of Bandiagara area. Sandflies were collected using CDC miniature light traps and sticky papers. In the field, live female Phlebotomine sandflies were identified and examined for the presence of promastigotes. The remaining sandflies were identified morphologically and tested for Leishmania by PCR in the ITS2 gene. The source of blood meal of the engorged females was determined using the cyt-b sequence. Out of the 3,259 collected sandflies, 1,324 were identified morphologically, and consisted of 20 species, of which four belonged to the genus Phlebotomus and 16 to the genus Sergentomyia. Leishmania major DNA was detected by PCR in 7 of the 446 females (1.6%), specifically 2 out of 115 Phlebotomus duboscqi specimens, and 5 from 198 Sergentomyia darlingi specimens. Human DNA was detected in one blood-fed female S. darlingi positive for L. major DNA. Conclusion: Our data suggest the possible involvement of P. duboscqi and potentially S. darlingi in the transmission of ZCL in Mali. [ABSTRACT FROM AUTHOR]

Recent advancements in genomics provide new tools for evolutionary ecological research. The paper wasp genus Polistes is a model for social insect evolution and behavioral ecology. We developed RNA interference (RNAi)-mediated gene silencing to explore proposed connections between expression of hexameric storage proteins and worker vs. gyne (potential future foundress) castes in naturally-founded colonies of P. metricus. We extended four fragments of putative hexamerin-encoding P. metricus transcripts acquired from a previous study and fully sequenced a gene that encodes Hexamerin 2, one of two proposed hexameric storage proteins of P. metricus. MALDI-TOF/TOF, LC-MSMS, deglycosylation, and detection of phosphorylation assays showed that the two putative hexamerins diverge in peptide sequence and biochemistry. We targeted the hexamerin 2 gene in 5th (last)-instar larvae by feeding RNAi-inducing double-stranded hexamerin 2 RNA directly to larvae in naturally-founded colonies in the field. Larval development and adult traits were not significantly altered in hexamerin 2 knockdowns, but there were suggestive trends toward increased developmental time and less developed ovaries, which are gyne characteristics. By demonstrating how data acquisition from 454/Roche pyrosequencing can be combined with biochemical and proteomics assays and how RNAi can be deployed successfully in field experiments on Polistes, our results pave the way for functional genomic research that can contribute significantly to learning the interactions of environment, development, and the roles they play in paper wasp evolution and behavioral ecology. [ABSTRACT FROM AUTHOR]

Recent studies suggest that the deregulation of pathways, rather than individual genes, may be critical in triggering carcinogenesis. The pathway deregulation is often caused by the simultaneous deregulation of more than one gene in the pathway. This suggests that robust gene pair combinations may exploit the underlying bio-molecular reactions that are relevant to the pathway deregulation and thus they could provide better biomarkers for cancer, as compared to individual genes. In order to validate this hypothesis, in this paper, we used gene pair combinations, called doublets, as input to the cancer classification algorithms, instead of the original expression values, and we showed that the classification accuracy was consistently improved across different datasets and classification algorithms. We validated the proposed approach using nine cancer datasets and five classification algorithms including Prediction Analysis for Microarrays (PAM), C4.5 Decision Trees (DT), Naive Bayesian (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN). [ABSTRACT FROM AUTHOR]

Background: Estimating the number of genes in human genome has been long an important problem in computational biology. With the new conception of considering human as a super-organism, it is also interesting to estimate the number of genes in this human super-organism. Principal Findings: We presented our estimation of gene numbers in the human gut bacterial community, the largest microbial community inside the human super-organism. We got 552,700 unique genes from 202 complete human gut bacteria genomes. Then, a novel gene counting model was built to check the total number of genes by combining culture-independent sequence data and those complete genomes. 16S rRNAs were used to construct a three-level tree and different counting methods were introduced for the three levels: strain-to-species, species-to-genus, and genus-and-up. The model estimates that the total number of genes is about 9,000,000 after those with identity percentage of 97% or up were merged. Conclusion: By combining completed genomes currently available and culture-independent sequencing data, we built a model to estimate the number of genes in human gut bacterial community. The total number of genes is estimated to be about 9 million. Although this number is huge, we believe it is underestimated. This is an initial step to tackle this gene counting problem for the human super-organism. It will still be an open problem in the near future. The list of genomes used in this paper can be found in the supplementary table. [ABSTRACT FROM AUTHOR]