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The common murre (Uria aalge) is a species of seabird particularly vulnerable to several environmental stressors, including parasitic infection and oil contamination. However, the molecular response to these stressors is severely understudied. This study investigated the common murre's transcriptomic responses to these stressors. Blood samples were collected from common murres undergoing rehabilitation at International Bird Rescue in Fairfield, CA. Total RNA was extracted from these samples, followed by library preparation and Illumina sequencing to generate whole transcriptome data. Differential gene expression analysis was conducted using DeSeq2 to identify genes significantly altered in response to oil contamination and parasitic infection. Differential gene expression analysis revealed 194 genes shared between oil-contaminated and infected birds, including key immune-related genes, such as ANXA2, LY96, and LY86. These genes play vital roles inmediating the production of pro-inflammatory cytokines. Gene Set Enrichment Analysis indicated significant alterations in stress, immune, and inflammatory responses, with additional lipid metabolism changes in contaminated birds. Our findings highlight the detrimental effects that these stressors have on wild birds. These findings suggest a generalized stress response and specific metabolic adaptations to oil exposure, providing insights for seabird conservation. [ABSTRACT FROM AUTHOR]

Abstract Parkinson’s disease (PD) is a prevalent neurodegenerative disorder. Currently available drugs for PD, can only relieve the symptoms of PD, but cannot prevent the progression of the disease and have serious side effects. Other new druggable therapeutic targets for PD are needed. First, six GEO datasets with transcriptomic data from the substantia nigra (SN) region of the brain were downloaded to find dysregulated druggable genes in PD. Then, Mendelian randomization (MR) and summary statistics-based MR (SMR) analysis were conducted using eQTL data from both brain tissue and blood to investigate the relationship between gene expression and PD. Next, the association between the expression of candidate druggable targets and disease stage was validated in an additional dataset GSE49036. Finally, a phenome-wide MR analysis was carried out to investigate the potential impact of candidate druggable genes on several other complex diseases or traits. Our study revealed 313 differentially expressed genes that may be directly targetable and have an impact on PD (FDR-p

Abstract A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples. We reproduce the differential expression analysis simulation results and identify a caveat in the data generation process. Data not truly generated under the null hypothesis led to incorrect comparisons of benchmark methods. We provide corrected simulation results that demonstrate the good performance of dearseq and argue against the superiority of the Wilcoxon rank-sum test as suggested in the previous study.

Abstract Missing covariate data is a common problem that has not been addressed in observational studies of gene expression. Here, we present a multiple imputation method that accommodates high dimensional gene expression data by incorporating principal component analysis of the transcriptome into the multiple imputation prediction models to avoid bias. Simulation studies using three datasets show that this method outperforms complete case and single imputation analyses at uncovering true positive differentially expressed genes, limiting false discovery rates, and minimizing bias. This method is easily implemented via an R Bioconductor package, RNAseqCovarImpute that integrates with the limma-voom pipeline for differential expression analysis.

Hyalomma anatolicum is a tick of significant one-health importance due to its role as a vector for various pathogens affecting humans, animals and the environment, such as Theileria annulata, which causes tropical theileriosis in cattle, leading to severe economic losses. When infected with pathogens like T. annulata, the salivary glands of H. anatolicum undergo gene expression changes, secrete modified proteins and activate immune responses, all of which facilitate pathogen survival and transmission by modulating the host immune response and optimizing conditions for pathogen development. Understanding these responses is crucial for developing control strategies for tick-borne diseases. To understand the interaction between H. anatolicum and T. annulata, we performed a differential gene expression analysis of H. anatolicum salivary glands. An average of approximately 25 million raw sequencing reads were generated in each replicate using Illumina Sequencing. The sequenced reads were de novo assembled and the assembled transcriptome yielded approximately 50,231 non-redundant transcripts after clustering with CD-HIT using a sequence identity of 95% and alignment coverage of 90%. The assembly quality was evaluated with BUSCO analysis and found to be 86% complete using the Arachnida dataset and then blasted against non-redundant protein sequence database from NCBI followed by counting of reads and differential expression analysis. Overall, around 2400 and 400 genes were found differentially expressed with logFC > 1 and logFC > 2 respectively at FDR < 0.05. Top up-regulated genes included Calpain, Papilin, Neprilysin, and Ankyrin repeat-containing protein. Top down-regulated genes included Scoloptoxin, and Selenoprotein S and other uncharacterized proteins. Many other up-regulated proteins with high significance were uncharacterized suggesting room for further H. anatolicum functional and structural characterization studies. To our best knowledge, this is the first study of H. anatolicum sialotranscriptome which greatly contributes to sialotranscriptome information not only as sequence database but also indicates the potential targets for development of vaccine against ticks and transmission-blocking vaccines against T. annulata. [ABSTRACT FROM AUTHOR]

Abstract Background Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals. Results We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh’s method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl . Conclusions The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

Abstract The mechanism of spinal cord injury (SCI) is highly complex, and an increasing number of studies have indicated the involvement of pyroptosis in the physiological and pathological processes of secondary SCI. However, there is limited bioinformatics research on pyroptosis-related genes (PRGs) in SCI. This study aims to identify and validate differentially expressed PRGs in the GEO database, perform bioinformatics analysis, and construct regulatory networks to explore potential regulatory mechanisms and therapeutic targets for SCI. We obtained high-throughput sequencing datasets of SCI in rats and mice from the GEO database. Differential analysis was conducted using the “limma” package in R to identify differentially expressed genes (DEGs). These genes were then intersected with previously reported PRGs, resulting in a set of PRGs in SCI. GO and KEGG enrichment analyses, as well as correlation analysis, were performed on the PRGs in both rat and mouse models of SCI. Additionally, a protein–protein interaction (PPI) network was constructed using the STRING website to examine the relationships between proteins. Hub genes were identified using Cytoscape software, and the intersection of the top 5 hub genes in rats and mice were selected for subsequent experimentally validated. Furthermore, a competing endogenous RNA (ceRNA) network was constructed to explore potential regulatory mechanisms. The gene expression profiles of GSE93249, GSE133093, GSE138637, GSE174549, GSE45376, GSE171441_3d and GSE171441_35d were selected in this study. We identified 10 and 12 PRGs in rats and mice datasets respectively. Six common DEGs were identified in the intersection of rats and mice PRGs. Enrichment analysis of these DEGs indicated that GO analysis was mainly focused on inflammation-related factors, while KEGG analysis showed that the most genes were enriched on the NOD-like receptor signaling pathway. We constructed a ceRNA regulatory network that consisted of five important PRGs, as well as 24 miRNAs and 34 lncRNAs. This network revealed potential regulatory mechanisms. Additionally, the three hub genes obtained from the intersection were validated in the rat model, showing high expression of PRGs in SCI. Pyroptosis is involved in secondary SCI and may play a significant role in its pathogenesis. The regulatory mechanisms associated with pyroptosis deserve further in-depth research.

Background: Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals. Results: We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh's method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl. Conclusions: The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity. Key points: RankCompV3 is a method for identifying differentially expressed genes (DEGs) in either bulk or single-cell RNA transcriptomics. It is based on the counts of relative expression orderings (REOs) of gene pairs in the two groups. The contingency tables are tested using McCullagh's method. RankCompV3 has comparable or better performance than that of other conventional methods. It has been shown to be effective in identifying DEGs in both single-cell and pseudo-bulk profiles. Pseudo-bulk method is implemented in RankCompV3, which allows the method to achieve higher computational efficiency and improves the concordance with the bulk ground-truth. RankCompV3 is effective in identifying functionally relevant DEGs in weak-signal datasets. The method is not biased towards highly expressed genes. [ABSTRACT FROM AUTHOR]

[Objective] Long non-coding RNA (lncRNA) is involved in inflammatory responses, however, their specific molecular regulatory mechanism in dairy mastitis remains unclear. In this study, differentially expressed lncRNA (DElncRNA) and its function in mammary tissues of cows with Staphylococcus aureus (S. aureus) -type mastitis were analysed to support subsequent in-depth studies. [Method] Transcriptome sequencing (RNA-Seq) technology and bioinformatics methods were used to carry out lncRNA sequencing, DElncRNA analysis, and GO and KEGG functional enrichment analysis of mammary tissues from healthy cows and cows with S. aureus-induced mastitis. [Result] A total of 1012 lncRNA are detected in the mammary tissues of both groups. Compared to the healthy dairy cows, total 75 lncRNA are up-regulated and 114 down-regulated in cows with S. aureus-induced mastitis. Further analysis reveals that DElncRNA TCONS_00042123, TCONS_00068055, TCONS_00108420 and TCONS_00076361 may be involved in the MAPK, NLR, Jak-STAT and TLR signaling pathways related to inflammation, regulating the occurrence and development of S.aureus-type mastitis in the dairy cows. [Conclusion] 189 DElncRNA were obtained from mammary tissues of cows with S. aureus-induced inflammation, which may regulate the process of onset and progression of S. aureus-type cow mastitis through potential target genes. [ABSTRACT FROM AUTHOR]

Breast cancer has the highest diagnosis rate among all cancers. Tumor budding (TB) is recognized as a recent prognostic marker. Identifying genes specific to high-TB samples is crucial for hindering tumor progression and metastasis. In this study, we utilized an RNA sequencing technique, called TempO-Seq, to profile transcriptomic data from breast cancer samples, aiming to identify biomarkers for high-TB cases. Through differential expression analysis and mutual information, we identified seven genes (NOL4, STAR, C8G, NEIL1, SLC46A3, FRMD6, and SCARF2) that are potential biomarkers in breast cancer. To gain more relevant proteins, further investigation based on a protein–protein interaction network and the network diffusion technique revealed enrichment in the Hippo signaling and Wnt signaling pathways, promoting tumor initiation, invasion, and metastasis in several cancer types. In conclusion, these novel genes, recognized as overexpressed in high-TB samples, along with their associated pathways, offer promising therapeutic targets, thus advancing treatment and diagnosis for breast cancer. [ABSTRACT FROM AUTHOR]

The high incidence of idiopathic recurrent pregnancy loss (iRPL) may stem from the limited research on male contributory factors. Many studies suggest that sperm DNA fragmentation and oxidative stress contribute to iRPL, but their roles are still debated. MicroRNAs (miRNAs) are short non-coding RNAs that regulate various biological processes by modulating gene expression. While differential expression of specific miRNAs has been observed in women suffering from recurrent miscarriages, paternal miRNAs remain unexplored. We hypothesize that analyzing sperm miRNAs can provide crucial insights into the pathophysiology of iRPL. Therefore, this study aims to identify dysregulated miRNAs in the spermatozoa of male partners of iRPL patients. Total mRNA was extracted from sperm samples of iRPL and control groups, followed by miRNA library preparation and high-output miRNA sequencing. Subsequently, raw sequence reads were processed for differential expression analysis, target prediction, and bioinformatics analysis. Twelve differentially expressed miRNAs were identified in the iRPL group, with eight miRNAs upregulated (hsa-miR-4454, hsa-miR-142-3p, hsa-miR-145-5p, hsa-miR-1290, hsa-miR-1246, hsa-miR-7977, hsa-miR-449c-5p, and hsa-miR-92b-3p) and four downregulated (hsa-miR-29c-3p, hsa-miR-30b-5p, hsa-miR-519a-2-5p, and hsa-miR-520b-5p). Functional enrichment analysis revealed that gene targets of the upregulated miRNAs are involved in various biological processes closely associated with sperm quality and embryonic development. [ABSTRACT FROM AUTHOR]

The watermelon (Citrullus lanatus L.) holds substantial economic value as a globally cultivated horticultural crop. However, the genetic architecture of watermelon fruit weight (FW) remains poorly understood. In this study, we used sh14-11 with small fruit and N14 with big fruit to construct 100 recombinant inbred lines (RILs). Based on whole-genome resequencing (WGR), 218,127 single nucleotide polymorphisms (SNPs) were detected to construct a high-quality genetic map. After quantitative trait loci (QTL) mapping, a candidate interval of 31–38 Mb on chromosome 2 was identified for FW. Simultaneously, the bulked segregant analysis (BSA) in the F2 population corroborated the identification of the same interval, encompassing the homologous gene linked to the known FW-related gene fas. Additionally, RNA-seq was carried out across 11 tissues from sh14-11 and N14, revealing expression profiles that identified 1695 new genes and corrected the annotation of 2941 genes. Subsequent differential expression analysis unveiled 8969 differentially expressed genes (DEGs), with 354 of these genes exhibiting significant differences across four key developmental stages. The integration of QTL mapping and differential expression analysis facilitated the identification of 14 FW-related genes, including annotated TGA and NAC transcription factors implicated in fruit development. This combined approach offers valuable insights into the genetic basis of FW, providing crucial resources for enhancing watermelon cultivation. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion The purpose of this study was to investigate the effects and potential mechanisms of ferroptosis‐related gene heat shock protein beta‐1 (HSPB1) on acute myeloid leukemia (AML).The RNA‐seq and clinical data of AML samples were obtained from the Genomic Data Commons database, and the FerrDb database was used to screen the marker, drive and suppressor of ferroptosis. Besides, DESeq2 was applied for differential expression analysis on AML samples and screening for differentially expressed genes (DEGs). The screened DEGs were subjected to the intersection analysis with ferroptosis‐related genes to identify the ferroptosis‐related DEGs. Next, the functional pathways of ferroptosis‐related DEGs were further be discussed by Gene Ontology as well as Kyoto Encyclopedia of Genes and Genomes enrichment analysis of DEGs. Additionally, lasso regression analysis was employed to determine the differential genes related to prognosis in patients with AML and the survival analysis was performed. Subsequently, quantitative real‐time polymerase chain reaction and western blot assay were applied to detect the mRNA and protein expression levels of HSPB1 in normal/AML bone marrow tissues and human normal (HS‐5)/AML (HL‐60) bone marrow cells, respectively. Furthermore, HSPB1 was knocked down to assess the expression changes of glutathione peroxidase 4 and acyl‐CoA synthetase long‐chain family member 4. Ultimately, the viability and oxidative stress levels of HL‐60 were analyzed by Cell Counting Kit‐8 and biochemical detection.A total of 4986 DEGs were identified in AML samples, with 3324 up‐regulated and 1662 down‐regulated. The enrichment analysis illustrated that ferroptosis‐related DEGs were significantly enriched in response to metal irons, oxidative stress, and other pathways. After lasso regression analysis, 17 feature genes related to the prognosis of patients with AML were obtained, with HSPB1 exhibiting a significant correlation. The reliability of our models was verified by Cox regression analysis and survival analysis of the hazard model. Furthermore, the outcomes of quantitative real‐time polymerase chain reaction and western blot showed that mRNA and protein expression levels of HSPB1 were significantly increased in the AML Group and HL‐60 cells. The knockdown of HSPB1 in HL‐60 cells reduced the protein level of glutathione peroxidase 4, increased the protein level of acyl‐CoA synthetase long‐chain family member 4, decreased the cell viability, and aggravated oxidative stress.Ferroptosis‐related gene HSPB1 is highly expressed in patients with AML. In addition, HSPB1 may be involved in the occurrence and development of AML by regulating oxidative stress and ferroptosis‐related pathways. This study provides new clues for further understanding of AML molecular mechanisms. Also, HSPB1 is expected to be a potential therapeutic target for AML in the future. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OC) is a significant contributor to gynecological cancer-related deaths worldwide, with a high mortality rate. Despite several advances in understanding the pathogenesis of OC, the molecular mechanisms underlying its development and prognosis remain poorly understood. Therefore, the current research study aimed to identify hub genes involved in the pathogenesis of OC that could serve as selective diagnostic and therapeutic targets. To achieve this, the dataset GEO2R was used to retrieve differentially expressed genes. The study identified a total of five genes (CDKN1A, DKK1, CYP1B1, NTS, and GDF15) that were differentially expressed in OC. Subsequently, a network analysis was performed using the STRING database, followed by the construction of a network using Cytoscape. The network analyzer tool in Cytoscape predicted 276 upregulated and 269 downregulated genes. Furthermore, KEGG analysis was conducted to identify different pathways related to OC. Subsequently, survival analysis was performed to validate gene expression alterations and predict hub genes, using a p-value of 0.05 as a threshold. Four genes (CDKN1A, DKK1, CYP1B1, and NTS) were predicted as significant hub genes, while one gene (GDF15) was predicted as non-significant. The adjusted P values of said predicted genes are 2.85E − 07, 5.49E − 06, 4.28E − 07, 1.43E − 07, and 3.70E − 07 for CDKN1A, DKK1, NTS, GDF15, and CYP1B1 respectively; additionally 6.08, 5.76, 5.74, 5.01, and 4.9 LogFc values of the said genes were predicted in GEO data set. In a boxplot analysis, the expression of these genes was analyzed in normal and tumor cells. The study found that three genes were highly expressed in tumor cells, while two genes (CDKN1A and DKK1) were more elevated in normal cells. According to the boxplot analysis for CDKN1A, 50% of tumor cells ranged between approx 3.8 and 5, while 50% of normal cells ranged between approx 6.9 and 7.9, which is greater than tumor cells. This shows that in normal cells, the CYP1B1 has a high expression level according to the GEPIA boxplot; addtionally the boxplot for DKK1 indicated that 50% of tumor cells ranged between approx 0 and 0.5, which was less than that of normal cells which ranged between approx 0.3 and 0.9. It shows that DKK1 is highly expressed in normal genes. Overall, the current study provides novel insights into the molecular mechanisms underlying OC. The identified hub genes and drug candidate targets could potentially serve as alternative diagnostic and therapeutic options for OC patients. Further research is needed to investigate the clinical significance of these findings and develop effective interventions that can improve the prognosis of patients with OC. [ABSTRACT FROM AUTHOR]

Abstract Background Plants differ more than threefold in seed oil contents (SOCs). Soybean (Glycine max), cotton (Gossypium hirsutum), rapeseed (Brassica napus), and sesame (Sesamum indicum) are four important oil crops with markedly different SOCs and fatty acid compositions. Results Compared to grain crops like maize and rice, expanded acyl-lipid metabolism genes and relatively higher expression levels of genes involved in seed oil synthesis (SOS) in the oil crops contributed to the oil accumulation in seeds. Here, we conducted comparative transcriptomics on oil crops with two different SOC materials. In common, DIHYDROLIPOAMIDE DEHYDROGENASE, STEAROYL-ACYL CARRIER PROTEIN DESATURASE, PHOSPHOLIPID:DIACYLGLYCEROL ACYLTRANSFERASE, and oil-body protein genes were both differentially expressed between the high- and low-oil materials of each crop. By comparing functional components of SOS networks, we found that the strong correlations between genes in “glycolysis/gluconeogenesis” and “fatty acid synthesis” were conserved in both grain and oil crops, with PYRUVATE KINASE being the common factor affecting starch and lipid accumulation. Network alignment also found a conserved clique among oil crops affecting seed oil accumulation, which has been validated in Arabidopsis. Differently, secondary and protein metabolism affected oil synthesis to different degrees in different crops, and high SOC was due to less competition of the same precursors. The comparison of Arabidopsis mutants and wild type showed that CINNAMYL ALCOHOL DEHYDROGENASE 9, the conserved regulator we identified, was a factor resulting in different relative contents of lignins to oil in seeds. The interconnection of lipids and proteins was common but in different ways among crops, which partly led to differential oil production. Conclusions This study goes beyond the observations made in studies of individual species to provide new insights into which genes and networks may be fundamental to seed oil accumulation from a multispecies perspective.

Crohn’s disease (CD) presents significant diagnostic and therapeutic challenges due to its unclear etiology, frequent relapses, and limited treatment options. Traditional monitoring often relies on invasive and costly gastrointestinal procedures. This study aimed to identify specific diagnostic markers for CD using advanced computational approaches. Four gene expression datasets from the Gene Expression Omnibus (GEO) were analyzed, identifying differentially expressed genes (DEGs) through gene set enrichment analysis in R. Key biomarkers were selected using machine learning algorithms, including LASSO logistic regression, SVM‑RFE, and Random Forest, and their accuracy was assessed using receiver operating characteristic (ROC) curves and nomogram models. Immune cell infiltration was analyzed using the CIBERSORT algorithm, which helped reveal associations between diagnostic markers and immune cell patterns in CD. From a training set of 605 CD samples and 82 normal controls, we identified eight significant biomarkers: LCN2, FOLH1, CXCL1, FPR1, S100P, IGFBP5, CHP2, and AQP9. The diagnostic model showed high predictive power (AUC=0.954) and performed well in external validation (AUC = 1). Immune cell infiltration analysis highlighted various immune cells involved in CD, with all diagnostic markers strongly linked to immune cell interactions. Our findings propose candidate hub genes and present a nomogram for CD diagnosis, providing potential diagnostic biomarkers for clinical applications in CD.

BackgroundRNA-seq followed by de novo transcriptome assembly has been a transformative technique in biological research of non-model organisms, but the computational processing of RNA-seq data entails many different software tools. The complexity of these de novo transcriptomics workflows therefore presents a major barrier for researchers to adopt best-practice methods and up-to-date versions of software.ResultsHere we present a streamlined and universal de novo transcriptome assembly and annotation pipeline, transXpress, implemented in Snakemake. transXpress supports two popular assembly programs, Trinity and rnaSPAdes, and allows parallel execution on heterogeneous cluster computing hardware.ConclusionstransXpress simplifies the use of best-practice methods and up-to-date software for de novo transcriptome assembly, and produces standardized output files that can be mined using SequenceServer to facilitate rapid discovery of new genes and proteins in non-model organisms.

Simple Summary: DExplore is a user-friendly web application addressing challenges in analyzing mRNA microarray experiments for gene expression profiling. For over fifteen years, microarray experiments have played a vital role in understanding gene expression in various conditions. DExplore, built with the R programming language, Shiny package, and Bioconductor, caters to researchers lacking programming skills. It facilitates the detection of differentially expressed genes using data from the NCBI Gene Expression Omnibus (GEO) and integrates with WebGestalt for functional enrichment analysis. DExplore incorporates visualization plots, enhancing the interpretability of analysis results. These plots provide researchers with deeper insights into gene expression patterns, facilitating informed decision-making. The application's versatility extends to a Docker image available on Docker Hub, enabling local usage of data not submitted to GEO. Currently tailored for the Affymetrix platforms, DExplore serves as a powerful application, simplifying the analysis of high-throughput data. This online accessible tool empowers molecular biologists to focus on a smaller number of biologically relevant genes, eliminating complexity and enhancing the utility of publicly available data in advancing gene expression research. DExplore is a free and open-source web application. Microarray experiments, a mainstay in gene expression analysis for nearly two decades, pose challenges due to their complexity. To address this, we introduce DExplore, a user-friendly web application enabling researchers to detect differentially expressed genes using data from NCBI's GEO. Developed with R, Shiny, and Bioconductor, DExplore integrates WebGestalt for functional enrichment analysis. It also provides visualization plots for enhanced result interpretation. With a Docker image for local execution, DExplore accommodates unpublished data. To illustrate its utility, we showcase two case studies on cancer cells treated with chemotherapeutic drugs. DExplore streamlines microarray data analysis, empowering molecular biologists to focus on genes of biological significance. [ABSTRACT FROM AUTHOR]

Subgenomic flaviviral RNAs (sfRNAs) are small non-coding products of the incomplete degradation of viral genomic RNA. They accumulate during flaviviral infection and have been associated with many functional roles inside the host cell. Studies so far have demonstrated that sfRNA plays a crucial role in determining West Nile virus (WNV) pathogenicity. However, its modulatory role on neuronal homeostasis has not been studied in depth. In this study, we investigated the mechanism of sfRNA biosynthesis and its importance for WNV replication in neuronal cells. We found that sfRNA1 is functionally redundant for both replication and translation of WNV. However, the concurrent absence of sfRNA1 and sfRNA2 species is detrimental for the survival of the virus. Differential expression analysis on RNA-seq data from WT and ΔsfRNA replicon cell lines revealed transcriptional changes induced by sfRNA and identified a number of putative targets. Overall, it was shown that sfRNA contributes to the viral evasion by suppressing the interferon-mediated antiviral response. An additional differential expression analysis among replicon and control Neuro2A cells also clarified the transcriptional changes that support WNV replication in neuronal cells. Increased levels of translation and oxidative phosphorylation, post-translational modification processes, and activated DNA repair pathways were observed in replicon cell lines, while developmental processes such as axonal growth were deficient. [ABSTRACT FROM AUTHOR]

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods. [ABSTRACT FROM AUTHOR]

The common murre (Uria aalge) is a species of seabird particularly vulnerable to several environmental stressors, including parasitic infection and oil contamination. However, the molecular response to these stressors is severely understudied. This study investigated the common murre’s transcriptomic responses to these stressors. Blood samples were collected from common murres undergoing rehabilitation at International Bird Rescue in Fairfield, CA. Total RNA was extracted from these samples, followed by library preparation and Illumina sequencing to generate whole transcriptome data. Differential gene expression analysis was conducted using DeSeq2 to identify genes significantly altered in response to oil contamination and parasitic infection. Differential gene expression analysis revealed 194 genes shared between oil-contaminated and infected birds, including key immune-related genes, such as ANXA2, LY96, and LY86. These genes play vital roles in mediating the production of pro-inflammatory cytokines. Gene Set Enrichment Analysis indicated significant alterations in stress, immune, and inflammatory responses, with additional lipid metabolism changes in contaminated birds. Our findings highlight the detrimental effects that these stressors have on wild birds. These findings suggest a generalized stress response and specific metabolic adaptations to oil exposure, providing insights for seabird conservation.

ABSTRACT: Wooden Breast (WB) abnormality represents one of the major challenges that the poultry industry has faced in the last 10 years. Despite the enormous progress in understanding the mechanisms underlying WB, the precise initial causes remain to be clarified. In this scenario, the present research is intended to characterize the gene expression profiles of broiler Pectoralis major muscles affected by WB, comparing them to the unaffected counterpart, to provide new insights into the biological mechanisms underlying this defect and potentially identifying novel genes likely involved in its occurrence. To this purpose, data obtained in a previous study through the RNA-sequencing technology have been used to identify differentially expressed genes (DEGs) between 6 affected and 5 unaffected broilers’ breast muscles, by using the newest reference genome assembly for Gallus gallus (GRCg7b). Also, to deeply investigate molecular and biological pathways involved in the WB progression, pathways analyses have been performed. The results achieved through the differential gene expression analysis mainly evidenced the downregulation of glycogen metabolic processes, gluconeogenesis, and tricarboxylic acid cycle in WB muscles, thus corroborating the evidence of a dysregulated energy metabolism characterizing breasts affected by this abnormality. Also, genes related to hypertrophic muscle growth have been identified as differentially expressed (e.g., WFIKKN1). Together with that, a downregulation of genes involved in mitochondrial biogenesis and functionality has been detected. Among them, PPARGC1A and PPARGC1B chicken genes are particularly noteworthy. These genes not only have essential roles in regulating mitochondrial biogenesis but also play pivotal roles in maintaining glucose and energy homeostasis. In view of that, their downregulation in WB-affected muscle may be considered as potentially related to both the mitochondrial dysfunction and altered glucose metabolism in WB muscles, and their key involvement in the molecular alterations characterizing this muscular abnormality might be hypothesized.

Sarcopenia is a major public health concern among older adults, leading to disabilities, falls, fractures, and mortality. This study aimed to elucidate the pathophysiological mechanisms of sarcopenia and identify potential therapeutic targets using systems biology approaches. RNA-seq data from muscle biopsies of 24 sarcopenic and 29 healthy individuals from a previous cohort were analysed. Differential expression, gene set enrichment, gene co-expression network, and topology analyses were conducted to identify target genes implicated in sarcopenia pathogenesis, resulting in the selection of 6 hub genes (PDHX, AGL, SEMA6C, CASQ1, MYORG, and CCDC69). A drug repurposing approach was then employed to identify new pharmacological treatment options for sarcopenia (clofibric-acid, troglitazone, withaferin-a, palbociclib, MG-132, bortezomib). Finally, validation experiments in muscle cell line (C2C12) revealed MG-132 and troglitazone as promising candidates for sarcopenia treatment. Our approach, based on systems biology and drug repositioning, provides insight into the molecular mechanisms of sarcopenia and offers potential new treatment options using existing drugs.

Abstract Background Wheat streak mosaic virus (WSMV) and Triticum mosaic virus (TriMV) are components of the wheat streak mosaic virus disease complex in the Great Plains region of the U.S.A. and elsewhere. Co-infection of wheat with WSMV and TriMV causes synergistic interaction with more severe disease symptoms compared to single infections. Plants are equipped with multiple antiviral mechanisms, of which regulation of microRNAs (miRNAs) is a potentially effective constituent. In this investigation, we have analyzed the total and relative expression of miRNA transcriptome in two wheat cultivars, Arapahoe (susceptible) and Mace (temperature-sensitive-resistant), that were mock-inoculated or inoculated with WSMV, TriMV, or both at 18 °C and 27 °C. Results Our results showed that the most abundant miRNA family among all the treatments was miRNA166, followed by 159a and 168a, although the order of the latter two changed depending on the infections. When comparing infected and control groups, twenty miRNAs showed significant upregulation, while eight miRNAs were significantly downregulated. Among them, miRNAs 9670-3p, 397-5p, and 5384-3p exhibited the most significant upregulation, whereas miRNAs 319, 9773, and 9774 were the most downregulated. The comparison of infection versus the control group for the cultivar Mace showed temperature-dependent regulation of these miRNAs. The principal component analysis confirmed that less abundant miRNAs among differentially expressed miRNAs were strongly correlated with the inoculated symptomatic wheat cultivars. Notably, miRNAs 397-5p, 398, and 9670-3p were upregulated in response to WSMV and TriMV infections, an observation not yet reported in this context. The significant upregulation of these three miRNAs was further confirmed with RT-qPCR analysis; in general, the RT-qPCR results were in agreement with our computational analysis. Target prediction analysis showed that the miRNAs standing out in our analysis targeted genes involved in defense response and regulation of transcription. Conclusion Investigation into the roles of these miRNAs and their corresponding targets holds promise for advancing our understanding of the mechanisms of virus infection and possible manipulation of these factors for developing durable virus resistance in crop plants.

Abstract Background Among men, prostate cancer (PCa) is the second most common cancer and the second leading cause of cancer death. Etiologic factors associated with both prostate carcinogenesis and somatic alterations in tumors are incompletely understood. While genetic variants associated with PCa have been identified, epigenetic alterations in PCa are relatively understudied. To date, DNA methylation (DNAm) and gene expression (GE) in PCa have been investigated; however, these studies did not correct for cell-type proportions of the tumor microenvironment (TME), which could confound results. Methods The data (GSE183040) consisted of DNAm and GE data from both tumor and adjacent non-tumor prostate tissue of 56 patients who underwent radical prostatectomies prior to any treatment. This study builds upon previous studies that examined methylation patterns and GE in PCa patients by using a novel tumor deconvolution approach to identify and correct for cell-type proportions of the TME in its epigenome-wide association study (EWAS) and differential expression analysis (DEA). Results The inclusion of cell-type proportions in EWASs and DEAs reduced the scope of significant alterations associated with PCa. We identified 2,093 significantly differentially methylated CpGs (DMC), and 51 genes associated with PCa, including PCA3, SPINK1, and AMACR. Conclusions This work illustrates the importance of correcting for cell types of the TME when performing EWASs and DEAs on PCa samples, and establishes a more confounding-adverse methodology. We identified a more tumor-cell-specific set of altered genes and epigenetic marks that can be further investigated as potential biomarkers of disease or potential therapeutic targets.

Background: Wheat streak mosaic virus (WSMV) and Triticum mosaic virus (TriMV) are components of the wheat streak mosaic virus disease complex in the Great Plains region of the U.S.A. and elsewhere. Co-infection of wheat with WSMV and TriMV causes synergistic interaction with more severe disease symptoms compared to single infections. Plants are equipped with multiple antiviral mechanisms, of which regulation of microRNAs (miRNAs) is a potentially effective constituent. In this investigation, we have analyzed the total and relative expression of miRNA transcriptome in two wheat cultivars, Arapahoe (susceptible) and Mace (temperature-sensitive-resistant), that were mock-inoculated or inoculated with WSMV, TriMV, or both at 18 °C and 27 °C. Results: Our results showed that the most abundant miRNA family among all the treatments was miRNA166, followed by 159a and 168a, although the order of the latter two changed depending on the infections. When comparing infected and control groups, twenty miRNAs showed significant upregulation, while eight miRNAs were significantly downregulated. Among them, miRNAs 9670-3p, 397-5p, and 5384-3p exhibited the most significant upregulation, whereas miRNAs 319, 9773, and 9774 were the most downregulated. The comparison of infection versus the control group for the cultivar Mace showed temperature-dependent regulation of these miRNAs. The principal component analysis confirmed that less abundant miRNAs among differentially expressed miRNAs were strongly correlated with the inoculated symptomatic wheat cultivars. Notably, miRNAs 397-5p, 398, and 9670-3p were upregulated in response to WSMV and TriMV infections, an observation not yet reported in this context. The significant upregulation of these three miRNAs was further confirmed with RT-qPCR analysis; in general, the RT-qPCR results were in agreement with our computational analysis. Target prediction analysis showed that the miRNAs standing out in our analysis targeted genes involved in defense response and regulation of transcription. Conclusion: Investigation into the roles of these miRNAs and their corresponding targets holds promise for advancing our understanding of the mechanisms of virus infection and possible manipulation of these factors for developing durable virus resistance in crop plants. [ABSTRACT FROM AUTHOR]

Complex regional pain syndrome (CRPS), a type of primary chronic pain, occurs following trauma or systemic disease and typically affects the limbs. CRPS-induced pain responses result in vascular, cutaneous, and autonomic nerve alterations, seriously impacting the quality of life of affected individuals. We previously identified the involvement of keratinocyte N-methyl-d-asparagic acid (NMDA) receptor subunit 2 B (NR2B) in both peripheral and central sensitizations in CRPS, although the mechanisms whereby NR2B functions following activation remain unclear. Using an in vivo male rat model of chronic post-ischemia pain (CPIP) and an in vitro oxygen–glucose deprivation/reoxygenation (OGD/R) cell model, we discovered that oxidative injury occurs in rat keratinocytes and HaCaT cells, resulting in reduced cell viability, mitochondrial damage, oxidative damage of nucleotides, and increased apoptosis. In HaCaT cells, OGD/R induced increases in intracellular reactive oxygen species levels and disrupted the balance between oxidation and antioxidation by regulating a series of antioxidant genes. The activation of NMDA receptors via NMDA exacerbated these changes, whereas the inhibition of the NR2B subunit alleviated them. Co-administration of ifenprodil (an NR2B antagonist) and NMDA (an NMDA receptor agonist) during the reoxygenation stage did not result in any significant alterations. Furthermore, intraplantar injection of ifenprodil effectively reversed the altered gene expression that was observed in male CPIP rats, thereby revealing the potential mechanisms underlying the therapeutic effects of peripheral ifenprodil administration in CRPS. Collectively, our findings indicate that keratinocytes undergo oxidative injury in CRPS, with NMDA receptors playing regulatory roles. [ABSTRACT FROM AUTHOR]

Background: Among men, prostate cancer (PCa) is the second most common cancer and the second leading cause of cancer death. Etiologic factors associated with both prostate carcinogenesis and somatic alterations in tumors are incompletely understood. While genetic variants associated with PCa have been identified, epigenetic alterations in PCa are relatively understudied. To date, DNA methylation (DNAm) and gene expression (GE) in PCa have been investigated; however, these studies did not correct for cell-type proportions of the tumor microenvironment (TME), which could confound results. Methods: The data (GSE183040) consisted of DNAm and GE data from both tumor and adjacent non-tumor prostate tissue of 56 patients who underwent radical prostatectomies prior to any treatment. This study builds upon previous studies that examined methylation patterns and GE in PCa patients by using a novel tumor deconvolution approach to identify and correct for cell-type proportions of the TME in its epigenome-wide association study (EWAS) and differential expression analysis (DEA). Results: The inclusion of cell-type proportions in EWASs and DEAs reduced the scope of significant alterations associated with PCa. We identified 2,093 significantly differentially methylated CpGs (DMC), and 51 genes associated with PCa, including PCA3, SPINK1, and AMACR. Conclusions: This work illustrates the importance of correcting for cell types of the TME when performing EWASs and DEAs on PCa samples, and establishes a more confounding-adverse methodology. We identified a more tumor-cell-specific set of altered genes and epigenetic marks that can be further investigated as potential biomarkers of disease or potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Rous sarcoma virus (RSV), which is an oncovirus, can cause sarcoma and consequently induce malignant tumours in chicken (Gallus gallus). Research into molecular factors that regulate the tumour-inducing ability are essential to develop prevention and curation methods against RSV. In this study, we aimed to determine candidate genes contributing to the formation of tumours through a transcriptomic analysis in R programming with GSE42516 and GSE15141, which are microarray expression dataset in GEO-NCBI database. We conducted differential expression analysis among a total of 8 metastatic samples and 5 non-metastatic samples, starting from data normalization, then creating model matrixes for pairwise comparisons and using eBayes function to calculate the log fold chance values and significance level of all genes (p-value). As a result, in GSE42516, we identified 295 significant (p-value ≤ 0.05) differentially expressed genes (DEGs), with 195 downregulated genes (logFC ≤ -1) and 190 upregulated genes (logFC ≥ 1). While in GSE15141, a greater list of DEGs was extracted, with 1444 downregulated genes and 1314 upregulated genes. Top 5 DEGs retrieved in GSE42516 were TTC32, DHRS7, RARB, RSPO3, C1QB and RBM24, TOM1L1, LIPI, HINTW, C20orf59 were found in GSE15141. Enrichment GO (in this case, biological process - BP) analysis revealed that the DEGs are mainly enriched in heterochromatin assembly, negative regulation of megakaryocyte differentiation and endocytosis. The identified genes may have a vital role in elucidating the molecular metastasis mechanisms and developing effective strategies against sarcoma virus. [ABSTRACT FROM AUTHOR]

Due to the limitations of the present risk genes in understanding the etiology of amyotrophic lateral sclerosis (ALS), it is necessary to find additional causative genes utilizing novel approaches. In this study, we conducted a two-stage proteome-wide association study (PWAS) using ALS genome-wide association study (GWAS) data (N = 152,268) and two distinct human brain protein quantitative trait loci (pQTL) datasets (ROSMAP N = 376 and Banner N = 152) to identify ALS risk genes and prioritized candidate genes with Mendelian randomization (MR) and Bayesian colocalization analysis. Next, we verified the aberrant expression of risk genes in multiple tissues, including lower motor neurons, skeletal muscle, and whole blood. Six ALS risk genes (SCFD1, SARM1, TMEM175, BCS1L, WIPI2, and DHRS11) were found during the PWAS discovery phase, and SARM1 and BCS1L were confirmed during the validation phase. The following MR (p = 2.10 × 10−7) and Bayesian colocalization analysis (ROSMAP PP4 = 0.999, Banner PP4 = 0.999) confirmed the causal association between SARM1 and ALS. Further differential expression analysis revealed that SARM1 was markedly downregulated in lower motor neurons (p = 7.64 × 10−3), skeletal muscle (p = 9.34 × 10−3), and whole blood (p = 1.94 × 10−3). Our findings identified some promising protein candidates for future investigation as therapeutic targets. The dysregulation of SARM1 in multiple tissues provides a new way to explain ALS pathology. [ABSTRACT FROM AUTHOR]

Introduction: The two-stage molecular profile of the progression of SARS-CoV-2 (SCOV2) infection is explored in terms of five key biological/clinical questions: (a) does SCOV2 exhibits a two-stage infection profile? (b) SARS-CoV-1 (SCOV1) vs. SCOV2: do they differ? (c) does and how SCOV2 differs from Influenza/INFL infection? (d) does low viral-load and (e) does COVID-19 early host response relate to the two-stage SCOV2 infection profile? We provide positive answers to the above questions by analyzing the time-series gene-expression profiles of preserved cell-lines infected with SCOV1/2 or, the gene-expression profiles of infected individuals with different viral-loads levels and different host-response phenotypes. Methods: Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI's gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results. Results: The central finding of our experiments demonstrates the down- regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events. Discussion: The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes. [ABSTRACT FROM AUTHOR]

Dongning Lyu, Guangyu He, Kan Zhou, Jin Xu, Haifei Zeng, Tongyu Li, Ningbo Tang Department of Nephrology Clinic, Guangxi International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of ChinaCorrespondence: Dongning Lyu, Guangxi International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine, No. 8 Qiuyue Road, Liangqing District, Nanning, Guangxi, 530200, People’s Republic of China, Tel +8613377191933, Email tsj757700@163.comPurpose: Uremia, which is characterized by immunodeficiency, is associated with the deterioration of kidney function. Immune-related genes (IRGs) are crucial for uremia progression.Methods: The co-expression network was constructed to identify key modular genes associated with uremia. IRGs were intersected with differentially expressed genes (DEGs) between uremia and control groups and key modular genes to obtain differentially expressed IRGs (DEIRGs). DEIRGs were subjected to functional enrichment analysis. The protein-protein interaction (PPI) network was constructed. The candidate genes were identified using the cytoHubba tool. The biomarkers were identified using various machine learning algorithms. The diagnostic value of the biomarkers was evaluated using receiver operating characteristic (ROC) analysis. The immune infiltration analysis was implemented. The biological pathways of biomarkers were identified using gene set enrichment analysis and ingenuity pathway analysis. The mRNA expression of biomarkers was validated using blood samples of patients with uremia and healthy subjects with quantitative real-time polymerase chain reaction (qRT-PCR).Results: In total, four biomarkers (PDCD1, NGF, PDGFRB, and ZAP70) were identified by machine learning methods. ROC analysis demonstrated that the area under the curve values of individual biomarkers were > 0.9, indicating good diagnostic power. The nomogram model of biomarkers exhibited good predictive power. The proportions of six immune cells significantly varied between the uremia and control groups. ZAP70 expression was positively correlated with the proportions of resting natural killer (NK) cells, naïve B cells, and regulatory T cells. Functional enrichment analysis revealed that the biomarkers were mainly associated with translational function and neuroactive ligand-receptor interaction. ZAP70 regulated NK cell signaling. The PDCD1 and NGF expression levels determined using qRT-PCR were consistent with those determined using bioinformatics analysis.Conclusion: PDCD1, NGF, PDGFRB, and ZAP70 were identified as biomarkers for uremia, providing a theoretical foundation for uremia diagnosis. Keywords: differential expression analysis, WGCNA, immune infiltration, nomogram, diagnosis

Abstract Background Gastric cancer (GC) is a heterogeneous malignancy with variable clinical outcomes. The immune system has been implicated in GC development and progression, highlighting the importance of immune-related gene expression patterns and their prognostic significance. Objective This study aimed to identify differentially expressed immune-related genes (DEIRGs) and establish a prognostic index for GC patients using comprehensive bioinformatic analyses. Methods We integrated RNA sequencing data from multiple databases and identified DEIRGs by overlapping differentially expressed genes with immune-related genes. Functional enrichment analysis was performed to uncover the biological processes and signaling pathways associated with DEIRGs. We conducted a Weighted Gene Co-expression Network Analysis (WGCNA) to identify key gene modules related to with GC. Cox regression analysis was conducted to determine independent prognostic DEIRGs for overall survival prediction. Based on these findings, we developed an immune-related gene prognostic index (IRGPI) based on these findings. The prognostic value of the IRGPI was validated using survival analysis and an independent validation cohort. Functional enrichment analysis, gene mutation analysis, and immune cell profiling were performed to gain insights into the biological functions and immune characteristics associated with the IRGPI-based subgroups. Results We identified 493 DEIRGs significantly enriched in immune-related biological processes and signaling pathways associated with GC. WGCNA analysis revealed a significant module (turquoise module) associated with GC, revealing potential therapeutic targets. Cox regression analysis identified RNASE2, CGB5, CTLA4, and DUSP1 as independent prognostic DEIRGs. The IRGPI, incorporating the expression levels of these genes, demonstrated significant prognostic value in predicting overall survival. The IRGPI-based subgroups exhibited distinct biological functions, genetic alterations, and immune cell compositions. Conclusion Our study identified DEIRGs and established a prognostic index (IRGPI) for GC patients. The IRGPI exhibited promising prognostic potential and provided insights into GC tumor biology and immune characteristics. These findings have implications for guiding therapeutic strategies.

Abstract Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representation and can identify the closest normal representation for a given disease sample. This enables control-free, single-sample differential expression analysis. In breast cancer, we demonstrate how our approach selects marker genes and outperforms a state-of-the-art method. Furthermore, significant genes identified by the model are enriched in driver genes across cancers. Our results show that the in silico closest normal provides a more favorable comparison than control samples.

Abstract Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

Abstract Background Alzheimer’s disease (AD) represents profound degenerative conditions of the brain that cause significant deterioration in memory and cognitive function. Despite extensive research on the significant contribution of lipid metabolism to AD progression, the precise mechanisms remain incompletely understood. Hence, this study aimed to identify key differentially expressed lipid metabolism-related genes (DELMRGs) in AD progression. Methods Comprehensive analyses were performed to determine key DELMRGs in AD compared to controls in GSE122063 dataset from Gene Expression Omnibus. Additionally, the ssGSEA algorithm was utilized for estimating immune cell levels. Subsequently, correlations between key DELMRGs and each immune cell were calculated specifically in AD samples. The key DELMRGs expression levels were validated via two external datasets. Furthermore, gene set enrichment analysis (GSEA) was utilized for deriving associated pathways of key DELMRGs. Additionally, miRNA-TF regulatory networks of the key DELMRGs were constructed using the miRDB, NetworkAnalyst 3.0, and Cytoscape software. Finally, based on key DELMRGs, AD samples were further segmented into two subclusters via consensus clustering, and immune cell patterns and pathway differences between the two subclusters were examined. Results Seventy up-regulated and 100 down-regulated DELMRGs were identified. Subsequently, three key DELMRGs (DLD, PLPP2, and PLAAT4) were determined utilizing three algorithms [(i) LASSO, (ii) SVM-RFE, and (iii) random forest]. Specifically, PLPP2 and PLAAT4 were up-regulated, while DLD exhibited downregulation in AD cerebral cortex tissue. This was validated in two separate external datasets (GSE132903 and GSE33000). The AD group exhibited significantly altered immune cell composition compared to controls. In addition, GSEA identified various pathways commonly associated with three key DELMRGs. Moreover, the regulatory network of miRNA-TF for key DELMRGs was established. Finally, significant differences in immune cell levels and several pathways were identified between the two subclusters. Conclusion This study identified DLD, PLPP2, and PLAAT4 as key DELMRGs in AD progression, providing novel insights for AD prevention/treatment.

Breast cancer has the highest diagnosis rate among all cancers. Tumor budding (TB) is recognized as a recent prognostic marker. Identifying genes specific to high-TB samples is crucial for hindering tumor progression and metastasis. In this study, we utilized an RNA sequencing technique, called TempO-Seq, to profile transcriptomic data from breast cancer samples, aiming to identify biomarkers for high-TB cases. Through differential expression analysis and mutual information, we identified seven genes (NOL4, STAR, C8G, NEIL1, SLC46A3, FRMD6, and SCARF2) that are potential biomarkers in breast cancer. To gain more relevant proteins, further investigation based on a protein–protein interaction network and the network diffusion technique revealed enrichment in the Hippo signaling and Wnt signaling pathways, promoting tumor initiation, invasion, and metastasis in several cancer types. In conclusion, these novel genes, recognized as overexpressed in high-TB samples, along with their associated pathways, offer promising therapeutic targets, thus advancing treatment and diagnosis for breast cancer.

The high incidence of idiopathic recurrent pregnancy loss (iRPL) may stem from the limited research on male contributory factors. Many studies suggest that sperm DNA fragmentation and oxidative stress contribute to iRPL, but their roles are still debated. MicroRNAs (miRNAs) are short non-coding RNAs that regulate various biological processes by modulating gene expression. While differential expression of specific miRNAs has been observed in women suffering from recurrent miscarriages, paternal miRNAs remain unexplored. We hypothesize that analyzing sperm miRNAs can provide crucial insights into the pathophysiology of iRPL. Therefore, this study aims to identify dysregulated miRNAs in the spermatozoa of male partners of iRPL patients. Total mRNA was extracted from sperm samples of iRPL and control groups, followed by miRNA library preparation and high-output miRNA sequencing. Subsequently, raw sequence reads were processed for differential expression analysis, target prediction, and bioinformatics analysis. Twelve differentially expressed miRNAs were identified in the iRPL group, with eight miRNAs upregulated (hsa-miR-4454, hsa-miR-142-3p, hsa-miR-145-5p, hsa-miR-1290, hsa-miR-1246, hsa-miR-7977, hsa-miR-449c-5p, and hsa-miR-92b-3p) and four downregulated (hsa-miR-29c-3p, hsa-miR-30b-5p, hsa-miR-519a-2-5p, and hsa-miR-520b-5p). Functional enrichment analysis revealed that gene targets of the upregulated miRNAs are involved in various biological processes closely associated with sperm quality and embryonic development.

Abstract Background Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis. Results We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance. Conclusions iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

Abstract Background The solubilization of aluminum ions (Al3+) that results from soil acidity (pH