Sperm undergo a series of changes in the epididymis region before acquiring the ability to move and fertilize, and the identification of genes expressed in a region-specific manner in the epididymis provides a valuable insight into functional differences between regions. We collected epididymal tissue from three yaks and cultured epithelial cells from the caput, corpus and cauda regions of the yak epididymis using the tissue block method. RNA sequencing analysis (RNA-seq) technology was used to detect gene expression in yak epididymal caput, corpus and cauda epithelial cells. The results showed that the DEGs were highest in the caput vs. corpus comparison, and lowest in the corpus vs. cauda comparison. Six DEGs were verified by real-time fluorescence quantitative PCR (qRT-PCR), consistent with transcriptome sequencing results. The significantly enriched DNA replication pathway in the caput vs. corpus was coordinated with cell proliferation, while upregulated DEGs such as POLD1 and MCM4 were found in the DNA replication pathway. The AMPK signaling pathway was found significantly enriched in the caput vs cauda, suggesting its involvement in sperm maturation and capacitation. The TGF beta signaling pathway was screened in the corpus vs cauda and is crucial for mammalian reproductive regulation. Upregulated DEGs (TGFB3, INHBA, INHBB) are involved in the TGF beta signaling pathway. This study provides a reference for culturing yak epididymal epithelial cells in vitro, and elucidates the transcriptional profiles of epithelial cells in different segments of the epididymis, revealing the regulatory and functional differences between different segments, providing basic data for exploring the molecular mechanism of yak sperm maturation and improving the reproductive capacity of high-altitude mammals.

The development of next-generation sequencing technology has led to a burst of data in a single assay. Management of a large dataset requires high demands on bioinformatic skills and computing resources. Here we present two popular pipelines for RNA-seq data analysis, using open-source software tools HISAT-StringTie-Ballgown and TopHat-Cufflinks. To meet the need of plant scientist, we describe in detail how to perform such comprehensive analysis beginning with raw RNA-seq reads and available reference genome. It allows biologists to align short reads to a reference genome, measure the transcript abundance, and analyze gene differential expression under two or more conditions. We also discuss other RNA-seq tools that are comparable or alternative to this protocol.

Delta-12 fatty acid desaturases (FAD2s) actively regulate stress responses and cell differentiation in living organisms. In this study, six homologous FAD2 genes were identified based on the genome sequence of Lentinula edodes. Then, the six FAD2 protein sequences were analyzed using bioinformatics tools, including ExPASy ProtParam, SignalP, TMHMM, and TargetP. These analyses were performed to predict the physical and chemical properties, signal peptides, and transmembrane and conserved domains of these proteins. The polypeptide sequences were aligned, and a maximum likelihood phylogenetic tree was constructed using MEGA 7.0 software to elucidate the phylogenetic relationships between homologous FAD2 sequences. The results demonstrated that the FAD2 proteins contained three conserved histidine-rich regions (HXXXH, HXXHH, and HXXHH), which included eight histidine residues. The linoleic acid content and FAD2 enzyme activity were further analyzed, and the levels in the mutagenic heat-tolerant strain 18N44 were lower than those in the wild-type strain 18. Interestingly, the expression levels of the FAD2-2 and FAD2-3 genes under heat stress in strain 18N44 were lower than those in strain 18. These findings indicated that FAD2-2 and FAD2-3 may play major roles in the synthesis of linoleic acid during heat stress.

Phenylpropanoid metabolism plays an important role in cantaloupe ripening and senescence, but the mechanism of ozone regulation on phenylpropanoid metabolism remains unclear. This study investigated how ozone treatment modulates the levels of secondary metabolites associated with phenylpropanoid metabolism, the related enzyme activities, and gene expression in cantaloupe. Treating cantaloupes with 15 mg/m3 of ozone after precooling can help maintain postharvest hardness. This treatment also enhances the production and accumulation of secondary metabolites, such as total phenols, flavonoids, and lignin. These metabolites are essential components of the phenylpropanoid metabolic pathway, activating enzymes like phenylalanine ammonia-lyase, cinnamate 4-hydroxylase, 4CL, chalcone synthase, and chalcone isomerase. The results of the transcriptional expression patterns showed that differential gene expression related to phenylpropanoid metabolism in the peel of ozone-treated cantaloupes was primarily observed during the middle and late storage stages. In contrast, the pulp exhibited significant differential gene expression mainly during the early storage stage. Furthermore, it was observed that the level of gene expression in the peel was generally higher than that in the pulp. The correlation between the relative amount of gene changes in cantaloupe, activity of selected enzymes, and concentration of secondary metabolites could be accompanied by positive regulation of the phenylpropanoid metabolic pathway. Therefore, ozone stress induction positively enhances the biosynthesis of flavonoids in cantaloupes, leading to an increased accumulation of secondary metabolites. Additionally, it also improves the postharvest storage quality of cantaloupes.

The mortality rate associated with cutaneous melanoma (SKCM) remains alarmingly high, highlighting the urgent need for a deeper understanding of its molecular underpinnings. In our study, we leveraged bulk transcriptome sequencing data from the SKCM cohort available in public databases such as TCGA and GEO. We utilized distinct datasets for training and validation purposes and also incorporated mutation and clinical data from TCGA, along with single-cell sequencing data from GEO. Through dimensionality reduction, we annotated cell subtypes within the single-cell data and analyzed the expression of tumor-related pathways across these subtypes. We identified differentially expressed genes (DEGs) in the training set, which were further refined using the Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm, employing tenfold cross-validation. This enabled the construction of a prognostic model, whose diagnostic efficacy we subsequently validated. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on the DEGs, and performed immunological profiling on two risk groups to elucidate the relationship between model genes and the immune responses relevant to SKCM diagnosis, treatment, and prognosis. We also knocked down the GMR6 expression level in the melanoma cells and verified its effect on cancer through multiple experiments. The results indicate that the GMR6 gene plays a role in promoting the proliferation, invasion, and migration of cancer cells in human melanoma. Our findings offer novel insights and a theoretical framework that could enhance prognosis, treatment, and drug development strategies for SKCM, potentially leading to more precise therapeutic interventions.

BACKGROUND: Diabetes mellitus (DM) is a prevalent chronic disease marked by significant metabolic dysfunctions. Understanding its molecular mechanisms is vital for early diagnosis and treatment strategies.
METHODS: We used datasets GSE7014, GSE25724, and GSE156248 from the GEO database to build a diagnostic model for DM using Random Forest (RF) and LASSO regression models. GSE20966 served as a validation cohort. DM patients were classified into two subtypes for functional enrichment analysis. Expression levels of key diagnostic genes were validated using quantitative real-time PCR (qRT-PCR) on Peripheral Blood Mononuclear Cells (PBMCs) from DM patients and healthy controls, focusing on CXCL12 and PPP1R12B with GAPDH as the internal control.
RESULTS: After de-batching the datasets, we identified 131 differentially expressed genes (DEGs) between DM and control groups, with 70 up-regulated and 61 down-regulated. Enrichment analysis revealed significant down-regulation in the IL-12 signaling pathway, JAK signaling post-IL-12 stimulation, and the ferroptosis pathway in DM. Five genes (CXCL12, MXRA5, UCHL1, PPP1R12B, and C7) were identified as having diagnostic value. The diagnostic model showed high accuracy in both the training and validation cohorts. The gene set also enabled the subclassification of DM patients into groups with distinct functional traits. qRT-PCR results confirmed the bioinformatics findings, particularly the up-regulation of CXCL12 and PPP1R12B in DM patients.
CONCLUSIONS: Our study pinpointed seven energy metabolism-related genes differentially expressed in DM and controls, with five holding diagnostic value. Our model accurately diagnosed DM and facilitated patient subclassification, offering new insights into DM pathogenesis.

UNASSIGNED: Pulmonary arterial hypertension (PAH) represents a substantial global risk to human health. This study aims to identify diagnostic biomarkers for PAH and assess their association with the immune microenvironment through the utilization of sophisticated bioinformatics techniques.
UNASSIGNED: Based on two microarray datasets, differentially expressed genes (DEGs) were detected, and hub genes underwent a sequence of machine learning analyses. After pathways associated with PAH were assessed by gene enrichment analysis, the identified genes were validated using external datasets and confirmed in a monocrotaline (MCT)-induced rat model. In addition, three algorithms were employed to estimate the proportions of various immune cell types, and the link between hub genes and immune cells was substantiated.
UNASSIGNED: Using SVM, LASSO, and WGCNA, we identified seven hub genes, including (BPIFA1, HBA2, HBB, LOC441081, PI15, S100A9, and WIF1), of which only BPIFA1 remained stable in the external datasets and was validated in an MCT-induced rat model. Furthermore, the results of the functional enrichment analysis established a link between PAH and both metabolism and the immune system. Correlation assessment showed that BPIFA1 expression in the MCP-counter algorithm was negatively associated with various immune cell types, positively correlated with macrophages in the ssGSEA algorithm, and correlated with M1 and M2 macrophages in the CIBERSORT algorithm.
UNASSIGNED: BPIFA1 serves as a modulator of PAH, with the potential to impact the immune microenvironment and disease progression, possibly through its regulatory influence on both M1 and M2 macrophages.

The rpl1001 gene encodes 60S ribosomal protein L10, which is involved in intracellular protein synthesis and cell growth. However, it is not yet known whether it is involved in the regulation of cell mitosis dynamics. This study focuses on the growth, spore production, cell morphology, the dynamics of microtubules, chromosomes, actin, myosin, and mitochondria of fission yeast (Schizosaccharomyces pombe) to investigate the impact of rpl1001 deletion on cell mitosis. RNA-Seq and bioinformatics analyses were also used to reveal key genes, such as hsp16, mfm1 and isp3, and proteasome pathways. The results showed that rpl1001 deletion resulted in slow cell growth, abnormal spore production, altered cell morphology, and abnormal microtubule number and length during interphase. The cell dynamics of the rpl1001Δ strain showed that the formation of a monopolar spindle leads to abnormal chromosome segregation with increased rate of spindle elongation in anaphase of mitosis, decreased total time of division, prolonged formation time of actin and myosin loops, and increased expression of mitochondrial proteins. Analysis of the RNA-Seq sequencing results showed that the proteasome pathway, up-regulation of isp3, and down-regulation of mfm1 and mfm2 in the rpl1001Δ strain were the main factors underpinning the increased number of spore production. Also, in the rpl1001Δ strain, down-regulation of dis1 caused the abnormal microtubule and chromosome dynamics, and down-regulation of hsp16 and pgk1 were the key genes affecting the delay of actin ring and myosin ring formation. This study reveals the effect and molecular mechanism of rpl1001 gene deletion on cell division, which provides the scientific basis for further clarifying the function of the Rpl1001 protein in cell division.

Colorectal cancer (CRC) is a heterogeneous disease that requires new diagnostic and prognostic markers. Integrated bioinformatics approach to identify novel therapeutic targets associated with CRC. Using GEO2R identified DEGs in CRC, and Funrich software facilitated the visualization of DEGs through Venn diagrams. From a total of 114 enhanced DEGs, potential hub genes were further filtered based on their nodal strength and edges using STRING database. To gain insights into the functional roles of these hub genes, gene ontology and pathway enrichment were conducted thorough g: profiler web server. Subsequently, overall survival plots from GEPIA and oncogenic predictive functions like mRNA expressions for stages and nodal metastasis were employed to identify hub genes in CRC patient samples. Additionally, the cBioPortal and HPA databases also revealed genetic alterations and expression levels in these hub genes in CRC patients, further supporting their involvement in colorectal cancer. Gene expression by RT-PCR shows upregulation of hub genes in HT-29 cells. Finally, our integrated bioinformatic analysis revealed that ABCE1, AURKA, HSPD1, PHKA1, CDK4, and YWHAE as hub genes with potential oncogenic roles in CRC. These genes hold promise as diagnostic and prognostic markers for colorectal tumorigenesis, providing insights into targeted therapies for improved patient outcomes.

Background and Objectives: Osteosarcoma, the most prevalent malignant bone tumor in children and adolescents, presents a complex pathogenesis characterized by various genetic and epigenetic alterations. This study aims to identify key differentially expressed genes (DEGs) in pediatric osteosarcoma, with a focus on those influencing metastasis and patient survival. Materials and Methods: We utilized the GSE33382 dataset from the GEO database for a comprehensive bioinformatic analysis. This included a protein-protein interaction (PPI) network analysis, Cox regression, and Kaplan-Meier survival analysis to identify central DEGs associated with osteosarcoma metastasis and patient survival. Results: Our analysis identified 88 DEGs related to osteosarcoma metastasis. Among them, three survival-related central DEGs-C1QA, CD74, and HLA-DMA-were significantly linked to patient outcomes. Further correlation analysis established a strong relationship between these genes, tumor mutation burden (TMB), immune checkpoint gene expression, and overall survival. Notably, C1QA and CD74 exhibited higher expression in non-metastatic osteosarcoma cases, suggesting a potential role in disease progression. Conclusions: The identified DEGs, particularly C1QA, CD74, and HLA-DMA, may serve as critical biomarkers for pediatric osteosarcoma prognosis and potential targets for immunotherapy. These findings provide a deeper understanding of the molecular landscape of osteosarcoma and open new avenues for therapeutic intervention.