Analysis Of Sequence Data Research Articles

Metagenomic Detection of Multiple Viruses in Monk Parakeet (Myiopsitta monachus) in Australia.

Birds are known to harbour many pathogens, including circovirus, herpesviruses, adenoviruses and Chlamydia psittaci. Some of these pose zoonotic risks, while others, such as beak and feather disease virus (BFDV), have a significant impact on the conservation of endangered bird species. This study was aimed to determine the faecal virome of a group of apparently healthy Monk parakeet using high-throughput sequencing. Fresh faecal samples were collected from four Monk parakeets at a pet shop in Melbourne, Australia. Virus enrichment and nucleic acid extraction were performed on the faecal samples, followed by high-throughput sequencing at the Australian Genome Research Facility (AGRF). Utilising an established pipeline for high-throughput sequencing data analysis, this study revealed the presence of three viruses of the families Circoviridae, Parvoviridae and Adenoviridae. Subsequent sequence comparison and phylogenetic analyses further confirmed that the detected viruses belong to the genera Chaphamaparvovirus (unassigned species), Circovirus (species Circovirus parrot) and Siadenovirus (species Siadenovirus viridis). Despite non-pathogenicity, the existence of multiple viruses within a bird species underscores the risk of these viruses spreading into the pet trade. Detection and a better understanding of avian viruses are crucial for the establishment of appropriate management and biosecurity measures in the domestic and international bird trade, which ultimately supports the conservation of vulnerable bird species.

Mcadet: A feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection.

Single-cell RNA sequencing (scRNA-seq) data analysis faces numerous challenges, including high sparsity, a high-dimensional feature space, and biological noise. These challenges hinder downstream analysis, necessitating the use of feature selection methods to identify informative genes, and reduce data dimensionality. However, existing methods for selecting highly variable genes (HVGs) exhibit limited overlap and inconsistent clustering performance across benchmark datasets. Moreover, these methods often struggle to accurately select HVGs from fine-resolution scRNA-seq datasets and minority cell types, which are more difficult to distinguish, raising concerns about the reliability of their results. To overcome these limitations, we propose a novel feature selection framework for scRNA-seq data called Mcadet. Mcadet integrates Multiple Correspondence Analysis (MCA), graph-based community detection, and a novel statistical testing approach. To assess the effectiveness of Mcadet, we conducted extensive evaluations using both simulated and real-world data, employing unbiased metrics for comparison. Our results demonstrate the superior performance of Mcadet in the selection of HVGs in scenarios involving fine-resolution scRNA-seq datasets and datasets containing minority cell populations. Overall, we demonstrate that Mcadet enhances the reliability of selected HVGs, although the impact of HVG selection on various downstream analyses varies and needs to be further investigated.

Blocking the HIF-1α/glycolysis axis inhibits allergic airway inflammation by reducing ILC2 metabolism and function.

The role of lung group 2 innate lymphoid cell (ILC2) activation in allergic asthma is increasingly established. However, the regulatory mechanisms underlying hypoxia-inducible factor-1α (HIF-1α)-mediated glycolysis in ILC2-mediated allergic airway inflammation remain unclear. To investigate the role of the HIF-1α/glycolysis axis in ILC2-mediated allergic airway inflammation. Glycolysis and HIF-1α inhibitors were used to identify their effect on the function and glucose metabolism of mouse and human ILC2s invivo and vitro. Blocking glycolysis and HIF-1α in mice under interleukin-33 (IL-33) stimulation were performed to test ILC2 responses. Conditional HIF-1α-deficient mice were used to confirm the specific role of HIF-1α in ILC2-driven airway inflammation models. Transcriptomic, metabolic, and chromatin immunoprecipitation analyses were performed to elucidate the underlying mechanism. HIF-1α is involved in ILC2 metabolism and is crucial in allergic airway inflammation. Single-cell sequencing data analysis and qPCR confirmation revealed a significant upregulation of glycolysis-related genes, particularly HIF-1α, in murine lung ILC2s after IL-33 intranasal administration or injection. Treatment with the glycolysis inhibitor 2-deoxy-D-glucose (2-DG) and the HIF-1α inhibitor 2-methoxyestradiol (2-ME) abrogated inflammation by suppressing ILC2s function. Conditional HIF-1α-deficient mice showed reduced ILC2 response and airway inflammation induced upon IL-33 or house dust mite (HDM) stimulation. Transcriptome and metabolic analyses revealed significantly impaired glycolysis in lung ILC2s in conditional HIF-1α knockout mice compared to that in their littermate controls. Chromatin immunoprecipitation results confirmed the transcriptional downregulation of glycolysis-related genes in HIF-1α-knockout and 2-DG-treated mice. Furthermore, impaired HIF-1α/glycolysis axis activation is correlated with downregulated ILC2 in patients with asthma. The HIF-1α/glycolysis axis is critical for controlling ILC2 responses in allergic airway inflammation and has potential immunotherapeutic value in asthma.

Comprehensive genome analysis and variant detection at scale using DRAGEN.

Research and medical genomics require comprehensive, scalable methods for the discovery of novel disease targets, evolutionary drivers and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size or location. Here we present DRAGEN, which uses multigenome mapping with pangenome references, hardware acceleration and machine learning-based variant detection to provide insights into individual genomes, with ~30 min of computation time from raw reads to variant detection. DRAGEN outperforms current state-of-the-art methods in speed and accuracy across all variant types (single-nucleotide variations, insertions or deletions, short tandem repeats, structural variations and copy number variations) and incorporates specialized methods for analysis of medically relevant genes. We demonstrate the performance of DRAGEN across 3,202 whole-genome sequencing datasets by generating fully genotyped multisample variant call format files and demonstrate its scalability, accuracy and innovation to further advance the integration of comprehensive genomics. Overall, DRAGEN marks a major milestone in sequencing data analysis and will provide insights across various diseases, including Mendelian and rare diseases, with a highly comprehensive and scalable platform.

Method of moments framework for differential expression analysis of single-cell RNA sequencing data

Differential expression analysis of single-cell RNA sequencing (scRNA-seq) data is central for characterizing how experimental factors affect the distribution of gene expression. However, distinguishing between biological and technical sources of cell-cell variability and assessing the statistical significance of quantitative comparisons between cell groups remain challenging. We introduce Memento, a tool for robust and efficient differential analysis of mean expression, variability, and gene correlation from scRNA-seq data, scalable to millions of cells and thousands of samples. We applied Memento to 70,000 tracheal epithelial cells to identify interferon-responsive genes, 160,000 CRISPR-Cas9 perturbed Tcells to reconstruct gene-regulatory networks, 1.2 million peripheral blood mononuclear cells (PBMCs) to map cell-type-specific quantitative trait loci (QTLs), and the 50-million-cell CELLxGENE Discover corpus to compare arbitrary cell groups. In all cases, Memento identified more significant and reproducible differences in mean expression compared with existing methods. It also identified differences in variability and gene correlation that suggest distinct transcriptional regulation mechanisms imparted by perturbations.

A DNA Polymerase Variant Senses the Epigenetic Marker 5-Methylcytosine by Increased Misincorporation.

Dysregulation of DNA methylation is associated with human disease, particularly cancer, and the assessment of aberrant methylation patterns holds great promise for clinical diagnostics. However, DNA polymerases do not effectively discriminate between processing 5-methylcytosine (5 mC) and unmethylated cytosine, resulting in the silencing of methylation information during amplification or sequencing. As a result, current detection methods require multi-step DNA conversion treatments or careful analysis of sequencing data to decipher individual 5 mC bases. To overcome these challenges, we propose a novel DNA polymerase-mediated 5 mC detection approach. Here, we describe the engineering of a thermostable DNA polymerase variant derived from Thermus aquaticus with altered fidelity towards 5 mC. Using a screening-based evolutionary approach, we have identified a DNA polymerase that exhibits increased misincorporation towards 5 mC during DNA synthesis. This DNA polymerase generates mutation signatures at methylated CpG sites, allowing direct detection of 5 mC by reading an increased error rate after sequencing without prior treatment of the sample DNA.

SpliceTransformer predicts tissue-specific splicing linked to human diseases

We present SpliceTransformer (SpTransformer), a deep-learning framework that predicts tissue-specific RNA splicing alterations linked to human diseases based on genomic sequence. SpTransformer outperforms all previous methods on splicing prediction. Application to approximately 1.3 million genetic variants in the ClinVar database reveals that splicing alterations account for 60% of intronic and synonymous pathogenic mutations, and occur at different frequencies across tissue types. Importantly, tissue-specific splicing alterations match their clinical manifestations independent of gene expression variation. We validate the enrichment in three brain disease datasets involving over 164,000 individuals. Additionally, we identify single nucleotide variations that cause brain-specific splicing alterations, and find disease-associated genes harboring these single nucleotide variations with distinct expression patterns involved in diverse biological processes. Finally, SpTransformer analysis of whole exon sequencing data from blood samples of patients with diabetic nephropathy predicts kidney-specific RNA splicing alterations with 83% accuracy, demonstrating the potential to infer disease-causing tissue-specific splicing events. SpTransformer provides a powerful tool to guide biological and clinical interpretations of human diseases.

Identification of HSPG2 as a bladder pro-tumor protein through NID1/AKT signaling

PurposeHeparan sulfate proteoglycans (HSPGs) are complex molecules found on the cell membrane and within the extracellular matrix, increasingly recognized for their role in tumor progression. This study aimed to investigate the involvement of Heparan sulfate proteoglycan 2 (HSPG2) in the progression of bladder cancer.MethodsWe identified HSPG2 as a promoter of bladder tumor progression using single-cell RNA sequencing and transcriptome analysis of sequencing data from seven patient samples obtained from the Gene Expression Omnibus (GEO) database (GSE135337). Transcript profiles of 28 normal tissues and 407 bladder tumor tissues were analyzed for HSPG2 expression and survival outcomes using the Sanger tools and cBioPortal databases. HSPG2-overexpressing T24 and Biu-87 cell lines were generated, and cell proliferation and migration were assessed using CCK-8 and Transwell assays. Western blotting and immunostaining were performed to evaluate the activation of Nidogen-1 (NID1)/protein kinase B (AKT) signaling. Mouse models with patient-derived tumor organoids (HSPG2high and HSPG2low) were established to assess anticancer effects.ResultsOur results demonstrated a marked upregulation of HSPG2 in malignant bladder tumors, which correlated significantly with poor patient prognosis. HSPG2 overexpression consistently enhanced bladder tumor cell proliferation and conferred chemotherapy resistance, as shown in both in vitro and in vivo experiments. Mechanistically, HSPG2 upregulated NID1 expression, leading to the activation of the AKT pro-survival signaling pathway and promoting sustained tumor growth in bladder cancer.ConclusionThis study highlights the critical pro-tumor role of HSPG2/NID1/AKT signaling in bladder cancer and suggests its potential as a therapeutic target in clinical treatment.

Oxysterols contribute to immune cell recruitment in SLE skin lesions

BackgroundAbnormal oxysterol metabolism has been observed in the peripheral blood of SLE patients, but its role in systemic lupus erythematosus (SLE) skin lesions remains unclear.MethodsTargeted oxidized lipid metabolomics analysis using liquid chromatography-mass spectrometry (LC-MS) was performed to quantify oxysterols in SLE skin lesions. Immunohistochemical staining and single-cell sequencing data analysis confirmed the upregulation of oxysterol-encoding enzymes CH25H and CYP7B1. The impact on fibroblast-mediated PBMCs chemotaxis was assessed using a transwell chamber.ResultsWe identified aberrant oxidized cholesterol metabolism in SLE skin lesions, characterized by elevated levels of 7-ketocholesterol, 5α-6α-cholestane-3β,5α,6β-triol, and so on. Fibroblasts were the primary cells expressing oxysterol-encoding genes, with CH25H and CYP7B1 expression upregulated via the IL-1β-mediated p38 MAPK and NFκB pathways. Notably, IL-1β-stimulated fibroblasts demonstrated enhanced PBMCs recruitment, which was attenuated by a GPR183 inhibitor.ConclusionOur findings reveal a potential mechanism by which fibroblasts contribute to immune cell recruitment in SLE skin lesions by expression of CH25H and CYP7B1. This study underscores the significance of oxysterol metabolism in SLE skin lesion pathogenesis and highlights potential therapeutic targets for SLE skin lesion treatment.Graphical abstract

In vivo CRISPRi screen identified lncRNA portfolio crucial for cutaneous squamous cell carcinoma tumor growth.

Cutaneous squamous cell carcinoma (cSCC) accounts for 20% of all skin cancer deaths globally, making it the second-highest subtype of skin cancer. The prevalence of cSCC in humans, as well as the poor capacity for an efficient prognosis, highlights the need to uncover alternative actors and mechanisms at the foundation of skin cancer development. Significant advances have been made to better understand some key factors in cSCC progression. However, little is known about the role of noncoding RNAs, particularly of a specific category called long noncoding RNA (lncRNA). By performing pseudobulk analysis of single-cell sequencing data from normal and cSCC human skin tissues, we determined a global portfolio of lncRNAs specifically expressed in keratinocyte subpopulations. Integration of CRISPR interference screens in vitro and the xenograft model identified several lncRNAs impacting the growth of cSCC cancer lines both in vitro and in vivo. Among these, we further validated LINC00704 and LINC01116 as proliferation-regulating lncRNAs in cSCC lines and potential biomarkers of cSCC progression. Taken together, our study provides a comprehensive signature of lncRNAs with roles in regulating cSCC progression.

Freshwater Sordariomycetes: a new species Nais submersa and a new record Natantispora unipolaris in Halosphaeriaceae from China

During our ongoing surveys of freshwater fungi in Yunnan Province, two halosphaeriaceous species were collected from submerged wood. Based on their morphological characters and phylogenetic analyses of combined LSU, SSU and ITS sequence data, a new species Nais submersa and a new habitat record Natantispora unipolaris are reported. Descriptions and illustration of two taxa are provided.

Elevated Atmospheric Co2 Levels Impact Soil Protist Functional Core Community Compositions

Protists, known as microeukaryotes, are a significant portion of soil microbial communities. They are crucial predators of bacteria and depend on bacterial community dynamics for the growth and evolution of protistan communities. In parallel, increased levels of atmospheric CO2 significantly impact bacterial metabolic activity in rhizosphere soils. In this study, we investigated the effect of elevated atmospheric CO2 levels on the metabolically active protist community composition and function and their co-occurrences with bacteria from bulk and rhizosphere soils from the Giessen Free-Air CO2 enrichment grassland experiment. Metabarcoding sequencing data analyses of partial 18S rRNA from total soil RNA showed that elevated CO2 concentrations stimulated only a few ASVs of phagotrophic predators of bacteria and other microeukaryotes, affecting protist community composition (P = 0.006, PERMANOVA). In parallel, phagotrophic and parasitic lineages appeared slightly favoured under ambient CO2 conditions, results that were corroborated by microbial signature analyses. Cross-comparisons of protist-bacteria co-occurrences showed mostly negative relations between prokaryotes and microeukaryotes, indicating that the ongoing increase in atmospheric CO2 will lead to changes in microbial soil communities and their interactions, potentially cascading to higher trophic levels in soil systems.

TE-Seq: A Transposable Element Annotation and RNA-Seq Pipeline.

The recognition that transposable elements (TEs) play important roles in many biological processes has elicited growing interest in analyzing sequencing data derived from this 'dark genome'. This is however complicated by the highly repetitive nature of these sequences in genomes, requiring the deployment of several problem-specific tools as well as the curation of appropriate genome annotations. This pipeline aims to make the analysis of TE sequences and their expression more generally accessible. The TE-Seq pipeline conducts an end-to-end analysis of RNA sequencing data, examining both genes and TEs. It implements the most current computational methods tailor- made for TEs, and produces a comprehensive analysis of TE expression at both the level of the individual element and at the TE clade level. Furthermore, if supplied with long-read DNA sequencing data, it is able to assess TE expression from non-reference (polymorphic) loci. As a demonstration, we analyzed proliferating, early senescent, and late senescent lung fibroblast RNA-Seq data, and created a custom reference genome and annotations for this cell strain using Nanopore sequencing data. We found that several retrotransposable element (RTE) clades were upregulated in senescence, which included non-reference, intact, and potentially active elements. TE-Seq is made available as a Snakemake pipeline which can be obtained at https://github.com/maxfieldk/TE-Seq . All software dependencies besides Snakemake and Docker/Singularity are packaged into a container which is automatically built and deployed by the pipeline at runtime.

Copy Number Analysis of Whole Exome Sequencing Data

Abstract Background Genetic alterations implicated in constitutional disorders range in size from single nucleotide substitutions to losses and gains of millions of bases. Whole exome sequencing (WES) captures many clinically relevant variants, but an analytic pipeline optimized for detection of small substitutions, insertions, and deletions may miss pathogenic copy number variants (CNVs). Historically, our laboratory performed separate cytogenetic tests to evaluate for larger genomic imbalances. Addition of copy number analysis to our WES pipeline could improve the assay’s diagnostic yield. This study describes part of our strategy to validate copy number analysis of our constitutional whole exome sequencing data. Methods We identified 18 CNV specimens that underwent both chromosomal microarray analysis (CMA) and WES between 2017 and 2023. Variants included 13 deletions (size range 14Kb-2Mb) and 5 duplications (size range 325Kb-10Mb). The standard method of constitutional copy number analysis in our clinical laboratory is CMA using the Affymetrix Cytoscan HD Array and analysis software. CNVs identified on CMA were reviewed with reference to the Database of Genomic Variants and multiple academic hospital databases. WES was performed on an Illumina sequencing system following capture-based library enrichment with the Integrated DNA Technologies xGen Exome Research, CNV Backbone, Human mtDNA Research, and Human ID Research Panel probes. CNVs identified on WES were called using the Broad Institute’s Genomic Analysis Toolkit (GATK) with a quality score cutoff of 120. Whole exome sequencing data was visualized on the Integrative Genomics Viewer. Results 14 out of 18 CNVs reported on CMA were also detected on WES with a quality score at or above 120 (78%). This includes 10 out of 13 deletions (77%) and 3 out of 5 duplications (60%). For both deletions and duplications, the size ranges of detected variants at or above the cutoff score and undetected variants overlapped. One duplication reported on CMA was detected on WES with a quality score below 120. Visualization of whole exome sequencing data showed variation in probe density that may have affected CNV calling. Conclusions The majority of CNVs reported after CMA (14/18, 78%) were also detected on WES data using a GATK quality score cutoff of 120. The data does not suggest an association between CNV size and detection by WES, although interpretation is limited by the small number of cases. Data visualization may be required to identify some CNVs. Future steps to characterize the limitations of copy number analysis of WES data include assessments of variance in probe density and read depth across the exome. Copy number analysis of WES data from cases with normal CMA results will also be performed for assay validation.

Additions to Lentitheciaceae (Pleosporales, Dothideomycetes): Pseudosetoseptoria jiangxiensis sp. nov. from decaying culms of bamboo in China

In terms of morphology, ecology, and phylogeny, Lentitheciaceae species exhibit an enormous diversity. They have a cosmopolitan distribution worldwide, and lead diverse lifestyles. A novel species in Lentitheciaceae, Pseudosetoseptoria jiangxiensis occurring on decaying culms of bamboo in Jiangxi province, China is described and illustrated. Morphologically, Pseudosetoseptoria jiangxiensis has dark brown to black, semi-immersed, carbonaceous, pycnidial conidiomata and hyaline, obovoid, elliptic, or nearly spherical, aseptate conidia with minute guttules. Maximum likelihood and Bayesian inference analyses of combined ITS, LSU, SSU and tef1-α sequence data were used to clarify the phylogenetic affinities of the species.

Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.

High-throughput sequencing (HTS) technologies have been instrumental in investigating biological questions at the bulk and single-cell levels. Comparative analysis of two HTS data sets often relies on testing the statistical significance for the difference of two negative binomial distributions (DOTNB). Although negative binomial distributions are well studied, the theoretical results for DOTNB remain largely unexplored. Here, we derive basic analytical results for DOTNB and examine its asymptotic properties. As a state-of-the-art application of DOTNB, we introduce DEGage, a computational method for detecting differentially expressed genes (DEGs) in scRNA-seq data. DEGage calculates the mean of the sample-wise differences of gene expression levels as the test statistic and determines significant differential expression by computing the P-value with DOTNB. Extensive validation using simulated and real scRNA-seq data sets demonstrates that DEGage outperforms five popular DEG analysis tools: DEGseq2, DEsingle, edgeR, Monocle3, and scDD. DEGage is robust against high dropout levels and exhibits superior sensitivity when applied to balanced and imbalanced data sets, even with small sample sizes. We utilize DEGage to analyze prostate cancer scRNA-seq data sets and identify marker genes for 17 cell types. Furthermore, we apply DEGage to scRNA-seq data sets of mouse neurons with and without fear memory and reveal eight potential memory-related genes overlooked in previous analyses. The theoretical results and supporting software for DOTNB can be widely applied to comparative analyses of dispersed count data in HTS and broad research questions.

Identification and interaction analysis of molecular markers in myocardial infarction by bioinformatics and next-generation sequencing data analysis

BackgroundCardiovascular diseases are prevalent worldwide with any age, and it is characterized by sudden blockage of blood flow to heart and permanent damage to the heart muscle, whose cause and underlying molecular mechanisms are not fully understood. This investigation aimed to explore and identify essential genes and signaling pathways that contribute to the progression of MI.MethodsThe aim of this investigation was to use bioinformatics and next-generation sequencing (NGS) data analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in MI. NGS dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database. DEGs between MI and normal control samples were identified using the DESeq2 R bioconductor tool. The gene ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using g:Profiler. Next, four kinds of algorithms in the protein–protein interaction (PPI) were performed to identify potential novel biomarkers. Next, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network were constructed by miRNet and NetworkAnalyst database, and Cytoscape software. Finally, the diagnostic effectiveness of hub genes was predicted by receiver operator characteristic curve (ROC) analysis and AUC more than 0.800 was considered as having the capability to diagnose MI with excellent specificity and sensitivity.ResultsA total of 958 DEGs were identified, consisting of 480 up-regulated genes and 478 down-regulated genes. The enriched GO terms and pathways of the DEGs include immune system, neuronal system, response to stimulus and multicellular organismal process. Ten hub genes (namely cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1) were obtained via protein–protein interaction analysis results. MiRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-409-3p, hsa-mir-3200-3p, creb1 and tp63 might play an important role in the MI.ConclusionsAnalysis of next-generation sequencing dataset combined with global network information and validation presents a successful approach to uncover the risk hub genes and prognostic markers of MI. Our investigation identified four risk- and prognostic-related gene signatures, including cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of MI.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq revealed key genes for bone metastasis and chemoresistance in prostate cancer.

Prostate cancer (PCa) is a serious malignancy. The main causes of PCa aggravation and death are unexplained resistance to chemotherapy and bone metastases. This study aimed to investigate the molecular mechanisms associated with the dynamic processes of progression, bone metastasis, and chemoresistance in PCa. Through comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data, Gene Expression Omnibus (GEO) tumor progression and metastasis-related genes were identified. These genes were subjected to lasso regression modeling using the Cancer Genome Atlas (TCGA) database. Tartrate-resistant acid phosphatase (TRAP) staining and real-time quantitative PCR (RT-qPCR) were used to evaluate osteoclast differentiation. CellMiner was used to confirm the effect of LDHA on chemoresistance. Finally, the relationship between LDHA and chemoresistance was verified using doxorubicin-resistant PCa cell lines. 7928 genes were identified as genes related to tumor progression and metastasis. Of these, 7 genes were found to be associated with PCa prognosis. The scRNA-seq and TCGA data showed that the expression of LDHA was higher in tumors and associated with poor prognosis of PCa. In addition, upregulation of LDHA in PCa cells induces osteoclast differentiation. Additionally, high LDHA expression was associated with resistance to Epirubicin, Elliptinium acetate, and doxorubicin. Cellular experiments demonstrated that LDHA knockdown inhibited doxorubicin resistance in PCa cells. LDHA may play a potential contributory role in PCa initiation and development, bone metastasis, and chemoresistance. LDHA is a key target for the treatment of PCa.

ICESp1109, a novel hybrid Integrative Conjugative Element of macrolide-resistant Streptococcus pyogenes serotype M77 collected between 2003 and 2017 in Poland.

Genetic characterization of the antibiotic resistance determinants and associated mobile genetic elements (MGEs) among Streptococcus pyogenes [Group A streptococci (GAS)] clinical isolates of an M77 serotype collected in Poland between 2003 and 2017. The genomes of 136 M77 GAS isolates were sequenced using Illumina, and selected with long-read approach (Oxford Nanopore). Whole genome sequences were analyzed to determine the presence of macrolide resistance determinants, and their genetic context. The strains used in the study were collected in the two multicenter surveys from in- and outpatients. Sequencing data analysis revealed that all strains carried the tet(O) gene (100%, N=136). They were classified as a single sequence type ST63. For erythromycin resistance, the unique determinant was erm(TR) detected in 76.5% (N=104) isolates. A single appearance of tet(M) and erm(B) on Tn3872 was noticed. The mefA, mefE, and msr(D) genes were detected in neither of the genomes. This correlated with the detected strain phenotypes - 11 exhibited cMLSB, 93 - iMLSB, and no M phenotype.The erm(TR) gene was predominantly (N=74) found within a novel hybrid Integrative Conjugative Element composed of the ICESp1108-like sequence and ICESp2906 variant which was then named ICESp1109. However, in strains isolated before 2008, erm(TR) was located within ICESp2905 (N=27). The erm(TR) gene was detected within stand-alone ICESp1108-like sequences in 3 strains. Based on phylogenetic analysis results the clonal dissemination of the macrolide-resistant S. pyogenes M77/ST63 strain with hybrid ICESp1109 was observed between 2008 and 2017. ICESp1109 is the novel hybrid ICE in Gram-positive bacteria.

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

BackgroundEndometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis.MethodsNext-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes.ResultsA total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network.ConclusionsThis investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.