Performance of gene expression analyses using de novo assembled transcripts in polyploid species.

BackgroundEpigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity.ResultsFirst, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we applied nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated data, nonlinear ridge regression attained well-balanced sensitivity, specificity and precision. Marginal model attained the lowest precision and highest sensitivity and was the only algorithm to detect weak signal in real data.ConclusionNonlinear ridge regression performed cell-type-specific association test on bulk omics data with well-balanced performance. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from https://github.com/fumi-github/omicwas

Rationale: Two molecular phenotypes have been identified in acute respiratory distress syndrome (ARDS). In the ROSE (Reevaluation of Systemic Early Neuromuscular Blockade) trial of cisatracurium in moderate to severe ARDS, we addressed three unanswered questions: 1) Do the same phenotypes emerge in a more severe ARDS cohort with earlier recruitment; 2) Do phenotypes respond differently to neuromuscular blockade? and 3) What biological pathways most differentiate inflammatory phenotypes?Methods: We performed latent class analysis in ROSE using preenrollment clinical and protein biomarkers. In a subset of patients (n = 134), we sequenced whole-blood RNA using enrollment and Day 2 samples and performed differential gene expression and pathway analyses. Informed by the differential gene expression analysis, we measured additional plasma proteins and evaluated their abundance relative to gene expression amounts.Measurements and Main Results: In ROSE, we identified the hypoinflammatory (60.4%) and hyperinflammatory (39.6%) phenotypes with similar biological and clinical characteristics as prior studies, including higher mortality at Day 90 for the hyperinflammatory phenotype (30.3% vs. 61.6%; P < 0.0001). We observed no treatment interaction between the phenotypes and randomized groups for mortality. The hyperinflammatory phenotype was enriched for genes associated with innate immune response, tissue remodeling, and zinc metabolism at Day 0 and collagen synthesis and neutrophil degranulation at Day 2. Longitudinal changes in gene expression patterns differed dependent on survivorship. For most highly expressed genes, we observed correlations with their corresponding plasma proteins' abundance. However, for the class-defining plasma proteins in the latent class analysis, no correlation was observed with their corresponding genes' expression.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have different clinical, protein, and dynamic transcriptional characteristics. These findings support the clinical and biological potential of molecular phenotypes to advance precision care in ARDS.

Differential gene expression (DGE) analysis has gained significant attention with the advent of next-generation sequencing technologies, leading to the development of a wide range of methods and tools for DGE analysis. We performed bibliometric analysis using Biblioshiny and VOSviewer software to investigate the trends over the investigated period. Relevant papers with differential gene expression related terms as the subjects from 2005 to 2023 were retrieved from the Web of Science database. Network maps were generated using Biblioshiny and VOSviewer software to illustrate the published trends over the investigated period. A total of 729 studies were examined to reveal trends in the DGE analysis methodologies, tools, and packages. In the analysis, co-authorship, bibliographic coupling, and co-occurrence analyses were conducted for country, institution, source, author, and keyword productivity. It was found that the output and citation numbers increased after 2005. During the study period, the USA and China emerged as the leading contributors to the field. The temporal study revealed a significant increase in publications at certain times, followed by period of slight decrease. The greatest fall was observed between 2008 and 2010. Despite these decreases, DGE analysis remains a critical topic in genomics due to its essential role in understanding the mechanisms of any disease, gene function, and therapeutic targets. This trend suggests that current methods and tools are considered sufficiently powerful for identifying key informative genes associated with diverse diseases.

1024 Background: HER2-low metastatic breast cancer (mBC) has emerged as a clinically significant subgroup since approval of trastuzumab deruxtecan. However, its relevance as a distinct subtype remains debated. We aimed to identify HER2-related gene signatures, examine the molecular characteristics of HER2-low mBC, and investigate the potential for classifying HER2-low mBC into subgroups with profiles resembling either HER2-positive (HER2+) or HER2-negative (HER2-) mBC. Methods: We performed differential gene expression (DGE) analysis using the TCGA-BRCA dataset, comparing HER2+ and HER2- samples to identify a 17-gene HER2 expression signature. The findings were validated on archival samples from a UAE clinical center, where DGE analysis was repeated to compare HER2+ and HER2-zero mBC. HER2-low samples were then classified into two subsets; positive-like and zero-like based on their HER2 signature gene expression level. Lastly, Gene Set Enrichment Analysis (GSEA) was conducted to evaluate the molecular characteristics of these subsets. Results: In the TCGA-BRCA dataset (n = 409; median age 59 years, range 26–90), 20.5% of samples were HER2+, and 79.5% were HER2-. DGE analysis identified a 17-gene HER2 signature that effectively separated HER2+ and HER2- mBC. These 17 genes including GSDMB, ERBB2, and MED1, are associated with HER2 expression. In the UAE cohort (n = 69; median age 52 years, range 24–84), comprising 7.25% HER2+, 60.87% HER2-low, and 31.88% HER2-zero mBC, 7 genes (GSDMB, GRB7, ERBB2, STARD3, PGAP3, MIEN1, TCAP) from the 17-gene HER2 signature were similarly upregulated in the HER2+ samples. We observed an increasing trend in HER2 signature expression across the groups, from HER2-zero to HER2-low and then HER2+ mBC showing the highest expression. This trend was supported by separation across HER2 status with gene-level expression, Gene Set Variation Analysis scores and Principal Component Analysis (PCA). Next, HER2-low mBC were classified into two distinct subgroups based on distance-to-centroid approach with PCA. Based on distance to HER2+ and HER2-zero centroids, HER2-low mBC were classified into positive-like and zero-like subgroups respectively. Separation of the positive-like and zero-like samples were observed with DGE analysis and expression of the HER2 signature genes. GSEA of HER2-low positive-like samples indicate increased activation of ERBB2 oncogenic pathway and suppression of gene sets involved in immune-mediated pathways compared to HER2-low zero-like mBC. Conclusions: This study reveals the potential utility of transcriptomic HER2 signature to characterize HER2-related molecular features in mBC, revealing a gradient of HER2 signature expression across HER2+, HER2-low, and HER2-zero. Identification of suppressed immune-mediated pathways in HER2-low positive-like mBC suggests combination with immune mediators as potential treatment strategy.

Analysis of the antibacterial mechanism of Levan against Escherichia coli using differential gene expression analysis and electron microscopy.

Asthma is a heterogeneous chronic inflammatory disease of the airway, and its development is the result of genetic factors, environmental factors, immune dysfunction, and other factors. This study aimed to identify biomarkers of asthma. A differential gene expression (DGE) analysis and a weighted gene co-expression network analysis (WGCNA) were conducted to identify the asthma-related genes in the GSE67472 dataset, and these genes were intersected with immune genes from the Immuport database to identify the asthma-associated immune genes. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the key asthma-associated immune genes, and receiver operating characteristic (ROC) curves were used to assess the diagnostic potential of these genes. Subsequently, the mechanism of action of the inducible factor interleukin 13 (IL-13) in human bronchial epithelial (HBE) cells and its effect on markers of asthma were verified in vitro. The DGE analysis and WGCNA identified 62 asthma-related genes, and eight asthma-associated immune genes were identified after the intersection of the asthma-related genes and the immune-related genes. Utilizing LASSO regression, five key immune genes linked to asthma were uncovered. These included S100 calcium binding protein A16 (S100A16), Lactotransferrin (LTF), BPI fold containing family A member 1 (BPIFA1), cystatin 4 (CST4), and growth-related protein (GRP). The area under the curve (AUC) values of S100A16 and CST4 were greater than 0.8. The single-cell analysis showed that S100A16 was highly expressed in asthmatic neutrophils. Finally, the results of the in vitro experiments showed that IL-13 not only induced lipid peroxidation to promote iron death in the HBE cells, but also promoted the expression of S100A16. This study identified a new biomarker, S100A16, for the diagnosis of asthma. S100A16 may serve as a new potential target for asthma treatment, and may be an important regulator of ferroptosis.

BackgroundDifferential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.ResultsIn this study, we propose a novel differential expression and feature selection method—GEOlimma—which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.ConclusionsOur results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.

STUDY QUESTIONDoes endometrial gene expression change across consecutive cycles and after endometrial injury?SUMMARY ANSWERAlthough paired endometrial samples collected from the same woman across two consecutive cycles show greater similarity in molecular profile compared to samples from different women, endometrial injury in the preceding cycle did not result in any significant differential gene expression.WHAT IS KNOWN ALREADYUterine receptivity is a crucial factor influencing pregnancy rates in IVF. The potential benefits of endometrial injury in enhancing receptivity, as well as the effectiveness of genomic endometrial receptivity tests in predicting uterine receptivity are subjects of ongoing debate.STUDY DESIGN, SIZE, DURATIONThis was a prospective study, involving endometrial biopsies from 20 women at mid-luteal phase over two consecutive menstrual cycles.PARTICIPANTS/MATERIALS, SETTING, METHODSDifferential gene expression analysis was conducted using a paired design and included estimated menstrual cycle time as a covariate. Pairwise distances of samples and principal component analysis were used to examine sample-to-sample variation.MAIN RESULTS AND THE ROLE OF CHANCEAnalysis of paired endometrial samples revealed no significant difference in differential gene expression before and after endometrial injury (P > 0.05). Sub-analyses of samples from infertile women and those with proven fertility also did not reveal significant changes in gene expression. Although the pairwise distance analysis indicated slightly more similarity between intra-patient samples when compared to inter-patient samples (P = 0.09), PCA did not show clear clustering of these pairs.LARGE SCALE DATAThe RNA-seq data generated in this study have been deposited in the Gene Expression Omnibus database under accession number GSE303049.LIMITATIONS, REASONS FOR CAUTIONThe sample size for this study is relatively small.WIDER IMPLICATIONS OF THE FINDINGSOur findings highlight the high degree of cycle-to-cycle variability in endometrial gene expression, even within the same individual. Importantly, the lack of significant changes in gene expression as a result of endometrial injury in the preceding cycle, indicates the molecular variability reflects inherent biological fluctuations rather than the effect of prior injury. This molecular variability may help explain why implantation does not always occur immediately, even with high-quality embryos, and why interventions such as endometrial injury or molecular receptivity tests have yielded inconsistent clinical outcomes.STUDY FUNDING/COMPETING INTEREST(S)This project was supported in part by National Health and Medical Research Council (NHMRC) Medical Postgraduate Scholarship No. 1055814 and The Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG) Fellows’ Clinical Research Scholarship 2013 (W.T.T.). W.T.T., J.C., and P.R. are named inventors on a patent on the molecular dating of endometrial samples with IP Australia, ID: AU2023287239A1—methods for determining menstrual cycle time point. The patent is held jointly by their employers: University of Melbourne and Royal Women’s Hospital. No payments have been received for this patent to date.TRIAL REGISTRATION NUMBERN/A.

Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis

Excessive intake of dietary fat is known to be a contributing factor in the development of obesity. In this study, we determined the dose-dependent effects of dietary fat on the development of this metabolic condition with a focus on changes in gene expression in the small intestine. C57BL/6J mice were fed diets with either 10, 20, 30 or 45 energy% (E%) derived from fat for four weeks (n = 10 mice/diet). We found a significant higher weight gain in mice fed the 30E% and 45E% fat diet compared to mice on the control diet. These data indicate that the main shift towards an obese phenotype lies between a 20E% and 30E% dietary fat intake. Analysis of differential gene expression in the small intestine showed a fat-dose dependent gradient in differentially expressed genes, with the highest numbers in mice fed the 45E% fat diet. The main shift in fat-induced differential gene expression was found between the 30E% and 45E% fat diet. Furthermore, approximately 70% of the differentially expressed genes were changed in a fat-dose dependent manner. Many of these genes were involved in lipid metabolism-related processes and were already differentially expressed on a 30E% fat diet. Taken together, we conclude that up to 20E% of dietary fat, the small intestine has an effective ‘buffer capacity’ for fat handling. From 30E% of dietary fat, a switch towards an obese phenotype is triggered. We further speculate that especially fat-dose dependently changed lipid metabolism-related genes are involved in development of obesity.

BackgroundThe frontal cortex is critical to many cognitive processes and is vulnerable to pathology and neurodegeneration in Alzheimer’s disease. Cortical dysfunction and impaired connectivity in the frontal cortex have also been observed in “normal” age‐related cognitive decline, suggesting common mechanisms contributing to both age‐related and AD‐related cognitive decline. The divergence that may lead to AD versus normal aging, however, is unclear.MethodHere, we have used genetically diverse mouse populations modeling either AD or normal aging, the AD‐BXDs and their nontransgenic littermates (Neuner et al, Neuron, 2019). We measured cognitive function from early (6mo) to middle (14mo) adulthood to capture presymptomatic and advanced AD stages and early aging processes. We performed bulk RNAseq from the frontal cortex, followed by age‐ and AD‐related differential gene expression analysis and Weighted Gene Coexpression Network Analysis (WGCNA) to identify genes and gene networks whose expression is associated with normal and pathological aging.ResultIn both differential gene expression analysis and WGCNA, we found common mechanisms underlying normal aging and AD (e.g., upregulation of inflammation‐related gene pathways). We also found that downregulation of genes and gene networks associated with synaptic function specifically occurred in AD and not in normal aging, suggesting that early synaptic dysfunction and/or synaptic loss is a unique feature of AD that distinguishes it from normal aging processes. Based on differential gene expression, network analyses, presence of high‐impact variants across the BXD panel, and association with cognitive function, we identified one gene candidate, B230209E15Rik, a lncRNA, as a potential regulator of synaptic gene networks in the frontal cortex that promotes resilience to cognitive decline in AD.ConclusionOverall, our data indicate that cognitive aging and AD may share common neuroimmune activation and dysregulation of neuronal maintenance, but that AD, and particularly late AD, is uniquely characterized by synaptic dysfunction. We nominate an intriguing candidate, B230209E15Rik, as a potentially key driver of synaptic gene regulation in the frontal cortex. In order to elucidate the mechanism by which this gene may promote cognitive resilience in AD, we are currently validating this finding in vivo.

This study aimed to identify transcriptome differences between distinct or transitional stage spherical, ovoid, and tubular porcine blastocysts throughout the initiation of elongation. We performed a global transcriptome analysis of differential gene expression using RNA‐Seq with high temporal resolution between spherical, ovoid, and tubular stage blastocysts at specific sequential stages of development from litters containing conceptus populations of distinct or transitional blastocysts. After RNA‐Seq analysis, significant differentially expressed genes (DEGs) and pathways were identified between distinct morphologies or sequential development stages. Overall, 1898 significant DEGs were identified between distinct spherical and ovoid morphologies, with 311 total DEGs between developmental stages throughout this first morphological transition, while 15 were identified between distinct ovoid and tubular, with eight total throughout these second morphological transition developmental stages. The high quantity of DEGs and pathways between conceptus stages throughout the spherical to ovoid transition suggests the importance of gene regulation during this first morphological transition for initiating elongation. Further, extensive DEG coverage of known elongation signaling pathways was illustrated from spherical to ovoid, and regulation of lipid signaling and membrane/ECM remodeling across these early conceptus stages were implicated as essential to this process, providing novel insights into potential mechanisms governing this rapid morphological change.

Brief isoflurane anaesthesia affects differential gene expression, gene ontology and gene networks in rat brain

Analysis of differential gene expression in Litopenaeus vannamei under High salinity stress

BackgroundNext-generation sequencing is providing researchers with a relatively fast and affordable option for developing genomic resources for organisms that are not among the traditional genetic models. Here we present a de novo assembly of the guppy (Poecilia reticulata) transcriptome using 454 sequence reads, and we evaluate potential uses of this transcriptome, including detection of sex-specific transcripts and deployment as a reference for gene expression analysis in guppies and a related species. Guppies have been model organisms in ecology, evolutionary biology, and animal behaviour for over 100 years. An annotated transcriptome and other genomic tools will facilitate understanding the genetic and molecular bases of adaptation and variation in a vertebrate species with a uniquely well known natural history.ResultsWe generated approximately 336 Mbp of mRNA sequence data from male brain, male body, female brain, and female body. The resulting 1,162,670 reads assembled into 54,921 contigs, creating a reference transcriptome for the guppy with an average read depth of 28×. We annotated nearly 40% of this reference transcriptome by searching protein and gene ontology databases. Using this annotated transcriptome database, we identified candidate genes of interest to the guppy research community, putative single nucleotide polymorphisms (SNPs), and male-specific expressed genes. We also showed that our reference transcriptome can be used for RNA-sequencing-based analysis of differential gene expression. We identified transcripts that, in juveniles, are regulated differently in the presence and absence of an important predator, Rivulus hartii, including two genes implicated in stress response. For each sample in the RNA-seq study, >50% of high-quality reads mapped to unique sequences in the reference database with high confidence. In addition, we evaluated the use of the guppy reference transcriptome for gene expression analyses in a congeneric species, the sailfin molly (Poecilia latipinna). Over 40% of reads from the sailfin molly sample aligned to the guppy transcriptome.ConclusionsWe show that next-generation sequencing provided a reliable and broad reference transcriptome. This resource allowed us to identify candidate gene variants, SNPs in coding regions, and sex-specific gene expression, and permitted quantitative analysis of differential gene expression.