Method For Expression Analysis Research Articles

Plasma Proteomics to Identify Drug Targets and Potential Drugs for Retinal Artery Occlusion: An Integrated Analysis in the UK Biobank.

Retinal artery occlusion (RAO), which is positively correlated with acute ischemic stroke (IS) and results in severe visual impairment, lacks effective intervention drugs. This study aims to perform integrated analysis using UK Biobank plasma proteome data of RAO and IS to identify potential targets and preventive drugs. A total of 7191 participants (22 RAO patients, 1457 IS patients, 8 individuals with both RAO and IS, and 5704 healthy age-gender-matched controls) were included in this study. Unique 1461 protein expression profiles of RAO, IS, and the combined data set, extracted from UK Biobank Plasma proteomics projects, were analyzed using both differential expression analysis and elastic network regression (Enet) methods to identify shared key proteins. Subsequent analyses, including single cell type expression assessment, pathway enrichment, and druggability analysis, were conducted for verifying shared key proteins and discovery of new drugs. Five proteins were found to be shared among the samples, with all of them showing upregulation. Notably, adhesion G-protein coupled receptor G1 (ADGRG1) exhibited high expression in glial cells of the brain and eye tissues. Gene set enrichment analysis revealed pathways associated with lipid metabolism and vascular regulation and inflammation. Druggability analysis unveiled 15 drug candidates targeting ADGRG1, which demonstrated protective effects against RAO, especially troglitazone (-8.5 kcal/mol). Our study identified novel risk proteins and therapeutic drugs associated with the rare disease RAO, providing valuable insights into potential intervention strategies.

Validation of Gene Expression Patterns for Oral Feeding Readiness: Transcriptional Analysis of Set of Genes in Neonatal Salivary Samples.

Neonatal health assessment is crucial for detecting and intervening in various disorders. Traditional gene expression analysis methods often require invasive procedures during sample collection, which may not be feasible or ideal for preterm infants. In recent years, saliva has emerged as a promising noninvasive biofluid for assessing gene expression. Another trend that has been growing is the use of "omics" technologies such as transcriptomics in the analysis of gene expression. The costs for carrying out these analyses and the difficulty of analysis make the detection of candidate genes necessary. These genes act as biomarkers for the maturation stages of the oral feeding issue. Salivary samples (n = 225) were prospectively collected from 45 preterm (<34 gestational age) infants from five predefined feeding stages and submitted to RT-qPCR. A better description of the targeted genes and results from RT-qPCR analyses were included. The six genes previously identified as predictive of feeding success were tested. The genes are AMPK, FOXP2, WNT3, NPHP4, NPY2R, and PLXNA1, along with two reference genes: GAPDH and 18S. RT-qPCR amplification enabled the analysis of the gene expression of AMPK, FOXP2, WNT3, NPHP4, NPY2R, and PLXNA1 in neonatal saliva. Expression results were correlated with the feeding status during sample collection. In summary, the genes AMPK, FOXP2, WNT3, NPHP4, NPY2R, and PLXNA1 play critical roles in regulating oral feeding and the development of premature infants. Understanding the influence of these genes can provide valuable insights for improving nutritional care and support the development of these vulnerable babies. Evidence suggests that saliva-based gene expression analysis in newborns holds great promise for early detection and monitoring of disease and understanding developmental processes. More research and standardization of protocols are needed to fully explore the potential of saliva as a noninvasive biomarker in neonatal care.

Exploring the Roles of Key Mediators IKBKE and HSPA1A in Alzheimer's Disease and Hepatocellular Carcinoma through Bioinformatics Analysis.

Recent studies have hinted at a potential link between Alzheimer's Disease (AD) and cancer. Thus, our study focused on finding genes common to AD and Liver Hepatocellular Carcinoma (LIHC), assessing their promise as diagnostic indicators and guiding future treatment approaches for both conditions. Our research utilized a broad methodology, including differential gene expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), gene enrichment analysis, Receiver Operating Characteristic (ROC) curves, and Kaplan-Meier plots, supplemented with immunohistochemistry data from the Human Protein Atlas (HPA) and machine learning techniques, to identify critical genes and significant pathways shared between AD and LIHC. Through differential gene expression analysis, WGCNA, and machine learning methods, we identified nine key genes associated with AD, which served as entry points for LIHC analysis. Subsequent analyses revealed IKBKE and HSPA1A as shared pivotal genes in patients with AD and LIHC, suggesting these genes as potential targets for intervention in both conditions. Our study indicates that IKBKE and HSPA1A could influence the onset and progression of AD and LIHC by modulating the infiltration levels of immune cells. This lays a foundation for future research into targeted therapies based on their shared mechanisms.

Identification and validation of stable reference genes for RT-qPCR analyses of Kobresia littledalei seedlings

BackgroundKobreisa littledalei, belonging to the Cyperaceae family is the first Kobresia species with a reference genome and the most dominant species in Qinghai-Tibet Plateau alpine meadows. It has several resistance genes which could be used to breed improved crop varieties. Reverse Transcription Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR) is a popular and accurate gene expression analysis method. Its reliability depends on the expression levels of reference genes, which vary by species, tissues and environments. However, K.littledalei lacks a stable and normalized reference gene for RT-qPCR analysis.ResultsThe stability of 13 potential reference genes was tested and the stable reference genes were selected for RT-qPCR normalization for the expression analysis in the different tissues of K. littledalei under two abiotic stresses (salt and drought) and two hormonal treatments (abscisic acid (ABA) and gibberellin (GA)). Five algorithms were used to assess the stability of putative reference genes. The results showed a variation amongst the methods, and the same reference genes showed tissue expression differences under the same conditions. The stability of combining two reference genes was better than a single one. The expression levels of ACTIN were stable in leaves and stems under normal conditions, in leaves under drought stress and in roots under ABA treatment. The expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression was stable in the roots under the control conditions and salt stress and in stems exposed to drought stress. Expression levels of superoxide dismutase (SOD) were stable in stems of ABA-treated plants and in the roots under drought stress. Moreover, RPL6 expression was stable in the leaves and stems under salt stress and in the stems of the GA-treated plants. EF1-alpha expression was stable in leaves under ABA and GA treatments. The expression levels of 28 S were stable in the roots under GA treatment. In general, ACTIN and GAPDH could be employed as housekeeping genes for K. littledalei under different treatments.ConclusionThis study identified the best RT-qPCR reference genes for different K. littledalei tissues under five experimental conditions. ACTIN and GAPDH genes can be employed as the ideal housekeeping genes for expression analysis under different conditions. This is the first study to investigate the stable reference genes for normalized gene expression analysis of K. littledalei under different conditions. The results could aid molecular biology and gene function research on Kobresia and other related species.

A bioinformatics approach of specificity protein transcription factors in head and neck squamous cell carcinoma.

The seventh most common type of cancer with increasing diagnosis rates around the world is head and neck squamous cell carcinoma (HNSCC). Specificity proteins (SPs) have been known for their role in the regulation of cellular division, growth, and apoptotic pathways in various cancers. In this work, we analyzed the expression levels of SPs in HNSCC to assess their diagnostic and prognostic biomarker potential. Differential gene expression and correlation analysis methods were used to determine the top dysregulated genes in HNSCC. Functional enrichment and protein-protein interaction analyses were done with the DAVID database and Cytoscape software to understand their function and biological processes. Receiver operating test, logistic regression, and Cox regression analyses were performed to check SP genes' diagnostic and prognostic potential. SP1 (LogFC = -0.27, P = 0.0013) and SP2 (LogFC = -0.20, P = 0.0019) genes were upregulated in HNSCC samples, while SP8 (LogFC = 2.57, P < 0.001) and SP9 (LogFC = 2.57, P < 0.001) genes were downregulated in cancer samples. A moderate positive correlation was observed among the expression levels of SP1, SP2, and SP3 genes. The SP8 and SP9 genes with AUC values of 0.79 and 0.75 demonstrated diagnostic potential which increased to 0.84 when both genes were assessed by logistic regression test. Also, the SP1 gene held a marginally significant prognostic potential. Our findings clarify the potential of SP transcription factors as candidate diagnostic and prognostic biomarkers for early screening and treatment of HNSCC.

Identification of Hub Genes in Liver Hepatocellular Carcinoma Based on Weighted Gene Co-expression Network Analysis.

Liver hepatocellular carcinoma (LIHC) is a malignant cancer with high incidence and poor prognosis. To investigate the correlation between hub genes and progression of LIHC and to provided potential prognostic markers and therapy targets for LIHC. Our study mainly used The Cancer Genome Atlas (TCGA) LIHC database and the gene expression profiles of GSE54236 from the Gene Expression Omnibus (GEO) to explore the differential co-expression genes between LIHC and normal tissues. The differential co-expression genes were extracted by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. The Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to annotate the function of differential genes. Then the hub genes were validated using protein-protein interaction (PPI) network. And the expression level and prognostic analysis were performed. The probable associations between the expression of hub genes and both tumor purity and infiltration of immune cells were explored by TIMER. A total of 68 differential co-expression genes were extracted. These genes were mainly enriched in complement activation (biological process), collagen trimer (cellular component), carbohydrate binding and receptor ligand activity (molecular function) and cytokine - cytokine receptor interaction. Then we demonstrated that the 10 hub genes (CFP, CLEC1B, CLEC4G, CLEC4M, FCN2, FCN3, PAMR1 and TIMD4) were weakly expressed in LIHC tissues, the qRT-PCR results of clinical samples showed that six genes were significantly downregulated in LIHC patients compared with adjacent tissues. Worse overall survival (OS) and disease-free survival (DFS) in LIHC patients were associated with the lower expression of CFP, CLEC1B, FCN3 and TIMD4. Ten hub genes had positive association with tumor purity. CFP, CLEC1B, FCN3 and TIMD4 could serve as novel potential molecular targets for prognosis prediction in LIHC.

Transcriptome Profiling Unveils Key Genes Regulating the Growth and Development of Yangzhou Goose Knob.

Goose is one of the most economically valuable poultry species and has a distinct appearance due to its possession of a knob. A knob is a hallmark of sexual maturity in goose (Anser cygnoides) and plays crucial roles in artificial selection, health status, social signaling, and body temperature regulation. However, the genetic mechanisms influencing the growth and development of goose knobs remain completely unclear. In this study, histomorphological and transcriptomic analyses of goose knobs in D70, D120, and D300 Yangzhou geese revealed differential changes in tissue morphology during the growth and development of goose knobs and the key core genes that regulate goose knob traits. Observation of tissue sections revealed that as age increased, the thickness of the knob epidermis, cuticle, and spinous cells gradually decreased. Additionally, fat cells in the dermis and subcutaneous connective tissue transitioned from loose to dense. Transcriptome sequencing results, analyzed through differential expression, Weighted Gene Co-expression Network Analysis (WGCNA), and pattern expression analysis methods, showed D70-vs.-D120 (up-regulated: 192; down-regulated: 423), D70-vs.-D300 (up-regulated: 1394; down-regulated: 1893), and D120-vs.-D300 (up-regulated: 1017; down-regulated: 1324). A total of 6243 differentially expressed genes (DEGs) were identified, indicating varied expression levels across the three groups in the knob tissues of D70, D120, and D300 Yangzhou geese. These DEGs are significantly enriched in biological processes (BP) such as skin morphogenesis, the regulation of keratinocyte proliferation, and epidermal cell differentiation. Furthermore, they demonstrate enrichment in pathways related to goose knob development, including ECM-receptor interaction, NF-kappa B, and PPAR signaling. Through pattern expression analysis, three gene expression clusters related to goose knob traits were identified. The joint analysis of candidate genes associated with goose knob development and WGCNA led to the identification of key core genes influencing goose knob development. These core genes comprise WNT4, WNT10A, TCF7L2, GATA3, ADRA2A, CASP3, SFN, KDF1, ERRFI1, SPRY1, and EVPL. In summary, this study provides a reference for understanding the molecular mechanisms of goose knob growth and development and provides effective ideas and methods for the genetic improvement of goose knob traits.

Metabolic Dysregulation and Its Role in Postoperative Pain among Knee Osteoarthritis Patients.

The inflammation observed in joint tissues is linked to pain caused by the production of proinflammatory cytokines. Since the biosynthesis of cytokines requires energy, their production is supported by extensive metabolic conversions of carbohydrates and fatty acids, which could lead to a disruption in cellular homeostasis. Therefore, this study aimed to investigate the association between POP development and disturbances in energy metabolic conversions, focusing on carbohydrate and fatty acid metabolism. Peripheral blood samples were collected from 26 healthy subjects and 50 patients with end-stage OA before joint replacement surgery. All implants were validated by orthopedic surgeons, and patients with OA demonstrated no inherent abnormalities to cause pain from other reasons than OA disease, such as malalignment, aseptic loosening, or excessive bleeding. Pain levels were assessed before surgery using the visual analogue scale (VAS) and neuropathic pain questionnaires, DN4 and PainDETECT. Functional activity was evaluated using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Three and six months after surgery, pain indices according to a VAS of 30 mm or higher were considered. Total RNA isolated from whole blood was analyzed using quantitative real-time RT-PCR (qRT-PCR) for the expression of genes related to carbohydrate and fatty acid metabolism. Protein levels of the examined genes were measured using an ELISA in the peripheral blood mononuclear cells (PBMCs). We used qRT-PCR because it is the most sensitive and reliable method for gene expression analysis, while an ELISA was used to confirm our qRT-PCR results. Among the study cohort, 17 patients who reported POP demonstrated significantly higher (p < 0.05) expressions of the genes PKM2, LDH, SDH, UCP2, CPT1A, and ACLY compared to pain-free patients with KOA. Receiver-operating characteristic (ROC) curve analyses confirmed the association between these gene expressions and pain development post-arthroplasty. A principle component analysis identified the prognostic values of ACLY, CPT1A, AMPK, SDHB, Caspase 3, and IL-1β gene expressions for POP development in the examined subjects. These findings suggest that the disturbances in energy metabolism, as observed in the PBMCs of patients with end-stage KOA before arthroplasty, may contribute to POP development. An understanding of these metabolic processes could provide insights into the pathogenesis of KOA. Additionally, our findings can be used in a clinical setting to predict POP development in end-stage patients with KOA before arthroplasty.

Comparative study on differential expression analysis methods for single-cell RNA sequencing data with small biological replicates: Based on single-cell transcriptional data of PBMCs from COVID-19 severe patients.

Single-cell RNA sequencing (scRNA-seq) is a high-throughput experimental technique for studying gene expression at the single-cell level. As a key component of single-cell data analysis, differential expression analysis (DEA) serves as the foundation for all subsequent secondary studies. Despite the fact that biological replicates are of vital importance in DEA process, small biological replication is still common in sequencing experiment now, which may impose problems to current DEA methods. Therefore, it is necessary to conduct a thorough comparison of various DEA approaches under small biological replications. Here, we compare 6 performance metrics on both simulated and real scRNA-seq datasets to assess the adaptability of 8 DEA approaches, with a particular emphasis on how well they function under small biological replications. Our findings suggest that DEA algorithms extended from bulk RNA-seq are still competitive under small biological replicate conditions, whereas the newly developed method DEF-scRNA-seq which is based on information entropy offers significant advantages. Our research not only provides appropriate suggestions for selecting DEA methods under different conditions, but also emphasizes the application value of machine learning algorithms in this field.

Relative gene expression analysis of catalase in environmental RNA from Japanese medaka exposed to toxic chemicals

AbstractEnvironmental RNA (eRNA) has the potential as a non‐invasive tool for assessing the physiological status of macro‐organisms, yet the quantitative method for relative gene expression analysis is still underexplored. To bridge this gap, this study introduces a relative quantification method for eRNA, employing quantitative PCR (qPCR) to evaluate the expression of the antioxidant gene, catalase (cat), in the Japanese medaka Oryzias latipes exposed to two toxic chemicals for 96 h: chlorpyrifos (CPS) and carbamazepine. Our results showed that, in eRNA, genes frequently used as a reference for tissue or cells in conventional expression analysis correlated with each other. Also, one of them (elfa) exhibited less variability, showing its potential suitability as a reference gene in eRNA analyses. Additionally, cat expression levels in eRNA increased with increasing CPS concentrations, in a concentration‐response manner. These results suggest the promising potential of qPCR applications for eRNA in the monitoring of an organism's health and response to environmental changes. However, we observed disparities in gene expression levels of cat and beta‐actin (actb) between eRNA and whole fish body, indicating eRNA might have a biased origin. Further research is needed to uncover the origin of eRNA and determine the limitations and applicability domain of eRNA analysis.

Single-cell RNA Sequencing Analysis Reveals the Role of Cancer-associated Fibroblasts in Skin Melanoma.

Mechanism of fibroblasts in skin melanoma (SKME) revealed by single-cell RNA sequencing data. SKME is responsible for more than 80% of skin-related cancer deaths. Cancer-associated fibroblasts (CAFs) generate inflammatory factors, growth factors and extracellular matrix proteins to facilitate cancer cell growth, metastasis, drug resistance and immune exclusion. However, molecular mechanisms of CAFs in SKME are still lacking. Our goal was to reveal the role of CAFs in SKME. We downloaded the single-cell RNA sequencing (scRNA-seq) dataset from the Gene Expression Omnibus (GSE215120) database. Then, the Seurat package was applied to analyze the single-cell atlas of SKME data, and cell subsets were annotated with the CellMarker database. The molecular mechanisms of CAFs in SKME were disclosed via differential gene expression and enrichment analysis, Cellchat and SCENIC methods. Using scRNA-seq data, three SKME cases were used and downscaled and clustered to identify 11 cell subgroups and 5 CAF subsets. The enrichment of highly expressed genes among the 5 CAF subsets suggests that cell migration-inducing hyaluronan-binding protein (CEMIP) + fibroblasts and naked cuticle homolog 1 (NKD1) + fibroblasts were closely associated with epithelial to mesenchymal transition. Cellchat analysis revealed that CAF subpopulations promoted melanocyte proliferation through Jagged1 (JAG1)-Notch homolog 1 (NOTCH1), JAG1-NOTCH3 and migration through pleiotrophin (PTN)-syndecan-3 (SDC3) receptor-ligand pairs. The SCENIC analysis identified that most of the transcription factors in each CAF subpopulation played a certain role in the metastasis of melanoma and were highly expressed in metastatic SKME samples. Specifically, we observed that CEMIP+ fibroblasts and NKD1+ fibroblasts had potential roles in participating in immune therapy resistance. Collectively, we uncovered a single-- cell atlas of SKME and revealed the molecular mechanisms of CAFs in SKME development, providing a base for immune therapy and prognosis assessment. Our study reveals that 5 CAFs in SKME have a promoting effect on melanocyte proliferation and metastasis. More importantly, CEMIP+ fibroblasts and NKD1+ fibroblasts displayed close connections with immune therapy resistance. These findings help provide a good basis for future immune therapy and prognosis assessment targeting CAFs in SKME.

Quantitative Real-Time PCR Method to Evaluate Gene Expression in Zebrafish Embryos.

The fast zebrafish embryonic development offers an opportunity for the study of genes crucial for developmental processes. Several genes associated with human diseases have orthologs in zebrafish. Enhanced comprehension of a gene's function can be achieved by knowing when and where it is expressed. This knowledge also makes it possible to alter a gene-by-gene knockdown in a time- and place-specific manner. Moreover, gene expression analyses contribute greatly to teratogenicity studies in zebrafish embryos. Therefore, the importance of examining the differences between the expressions of these genes has increased day by day. The incorporation of reverse transcription (RT) as the initial step prior to thermal cycling in quantitative real-time polymerase chain reaction (RT-qPCR) has made a significant contribution to RNA research. RT-qPCR is the gold standard and an effective method for gene expression analysis. Quick readout, high sensitivity, reproducibility, as well as high potential throughput, along with reliable quantification, are just a few advantages of RT-qPCR. However, there are drawbacks to its application, such as RNA's inherent variability, impurities during RNA extraction, and variations in reverse transcription and PCR efficiencies. Implementing a precise normalization technique is crucial to account for these inaccuracies. In this chapter, the protocol for gene expression analysis by RT-qPCR for zebrafish embryos is explained.

A Novel Method for In Situ RT-PCR Based on Capsules from Centrifuge Tubes, Ideal for Transcripts Detection in Plant Tissues.

In situ RT-PCR presents advantages over other expression analysis methods due to its rapid processing and low-cost equipment. However, this technique is not without its challenges. A protocol based on a capsule made from centrifuge tubes that offers advantages over slides is presented. This capsule protects histological sections from drying out, and its easy assembly reduces time pauses between incubations. In addition, the container size where the sample is deposited allows the addition and withdrawal of the different solutions. The capsule does not need previous sealing after each incubation, and, above all, it is a low-cost and accessible material. A guideline for tissue sectioning using a cryostat that offers advantages over other sectioning methods is also described.

A Novel Method for In Situ RT-PCR Based on Capsules from Centrifuge Tubes, Ideal for Transcripts Detection in Plant Tissues.

In situ RT-PCR presents advantages over other expression analysis methods due to its rapid processing and low-cost equipment. However, this technique is not without its challenges. A protocol based on a capsule made from centrifuge tubes that offers advantages over slides is presented. This capsule protects histological sections from drying out, and its easy assembly reduces time pauses between incubations. In addition, the container size where the sample is deposited allows the addition and withdrawal of the different solutions. The capsule does not need previous sealing after each incubation, and, above all, it is a low-cost and accessible material. A guideline for tissue sectioning using a cryostat that offers advantages over other sectioning methods is also described.

Real-Time Emotion Recognition using Deep Facial Expression Analysis on Mobile Devices

Numerous applications in human-computer interaction, healthcare, and other fields have been made possible by the growth of mobile devices, which has created new opportunities for real-time emotion recognition. This research introduces a novel method for mobile device-based deep facial expression analysis for real-time emotion recognition. In order to execute accurate and efficient emotion recognition directly on the device, our technology makes use of the computational capacity of contemporary smartphones. This eliminates the need for cloud-based processing and ensures user privacy. For mobile platforms, we use a deep learning architecture that is optimised for both speed and accuracy. A lightweight convolutional neural network (CNN) for facial feature extraction and a recurrent neural network (RNN) for temporal emotion modelling are the main elements of our system. Our system accurately detects and categorises a variety of emotions, including joy, sadness, rage, surprise, and more by processing video frames from the device's camera feed in real-time. We carried out extensive trials on a variety of datasets to assess our methodology, attaining state-of-the-art accuracy with minimal processing cost. Through a variety of applications, including emotion-aware virtual assistants, mental health tracking tools, and immersive gaming experiences, we show how useful our technology is. This paper makes a contribution to the burgeoning field of mobile-based emotion recognition by providing a strong and effective solution that enables researchers and developers to produce ground-breaking software that can better comprehend and react to human emotions while protecting data privacy and guaranteeing real-time performance on mobile devices.

ANALISIS GEN KOMPARATIF KARSINOMA SEL SKUAMOSA PARU-PARU ANTARA INDIVIDU MEROKOK DAN TIDAK MEROKOK [Comparative Gene Analysis of Squamous Cell Lung Carcinoma Between Smoking and Non-smoking Individuals

Squamous cell lung carcinoma (SCC) is a form of non-small cell lung cancer that commonly arises in the primary airway. The development of SCC is closely linked to changes in squamous cells that line the airways, primarily caused by exposure to tobacco smoke. To gain insights into SCC, bioinformatics techniques have been employed to detect biomarkers and analyze gene expression patterns, utilizing data from the Cancer Genome Atlas (TCGA) database, which was preprocessed for analysis. By employing DESeq2, a differential gene expression analysis method, identified genes showed significant variations in expression between smoking and non-smoking groups among the 11,530 genes examined. Notably, five genes, namely CT45A1, GCGR, TPTE, ABCC2, and PI16, were found to play a significant role in tumor development and were susceptible to under- or over-expression due to smoking. The majority of these genes were found to be underexpressed rather than overexpressed. These identified genes hold potential as biomarkers for tumor development and exhibit a strong correlation between smoking history and the development of SCC. However, a limitation encountered during this analysis was the unavailability of data from normal non-tumor patients, which could have facilitated a more comprehensive analysis of differential gene expression. Furthermore, this research gives a deeper implementation regarding the molecular mechanisms and genomics underlying SCC development, identifies differentially expressed genes associated with SCC and smoking history, and highlights potential biomarkers that warrant further investigation.

Enhanced clustering-based differential expression analysis method for RNA-seq data

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering has been widely used to classify DEGs with similar expression patterns, but rarely used to identify DEGs themselves. We recently reported that the clustering-based method (called MBCdeg1 and 2) for identifying DEGs has great potential. However, these methods left room for improvement. This study reports on the improvement (named MBCdeg3). We compared a total of six competing methods: three conventional R packages (edgeR, DESeq2, and TCC) and three versions of MBCdeg (i.e., MBCdeg1, 2, and 3) corresponding to three different normalization algorithms. As MBCdeg3 performs well in many simulation scenarios of RNA-seq count data, MBCdeg3 replaces MBCdeg1 and 2 in our previous report.•MBCdeg3 is a method for both identification and classification of DEGs from RNA-seq count data.•MBCdeg3 is available as a function of R, which is common in the field of expression analysis.•MBCdeg3 performs well in a variety of simulation scenarios for RNA-seq count data.

Uncovering hub genes in sepsis through bioinformatics analysis.

In-depth studies on the mechanisms of pathogenesis of sepsis and diagnostic biomarkers in the early stages may be the key to developing individualized and effective treatment strategies. This study aimed to identify sepsis-related hub genes and evaluate their diagnostic reliability. The gene expression profiles of GSE4607 and GSE131761 were obtained from the Gene Expression Omnibus. Differentially co-expressed genes between the sepsis and control groups were screened. Single-sample gene set enrichment analysis and gene set variation analysis were performed to investigate the biological functions of the hub genes. A receiver operating characteristic curve was used to evaluate diagnostic value. Datasets GSE154918 and GSE185263 were used as external validation datasets to verify the reliability of the hub genes. Four differentially co-expressed genes, FAM89A, FFAR3, G0S2, and FGF13, were extracted using a weighted gene co-expression network analysis and differential gene expression analysis methods. These 4 genes were upregulated in the sepsis group and were distinct from those in the controls. Moreover, the receiver operating characteristic curves of the 4 genes exhibited considerable diagnostic value in discriminating septic blood samples from those of the non-septic control group. The reliability and consistency of these 4 genes were externally validated. Single-sample gene set enrichment analysis and gene set variation analysis analyses indicated that the 4 hub genes were significantly correlated with the regulation of immunity and metabolism in sepsis. The identified FAM89A, FFAR3, G0S2, and FGF13 genes may help elucidate the molecular mechanisms underlying sepsis and drive the introduction of new biomarkers to advance diagnosis and treatment.

Decoding glycomics with a suite of methods for differential expression analysis

Glycomics, the comprehensive profiling of all glycan structures in samples, is rapidly expanding to enable insights into physiology and disease mechanisms. However, glycan structure complexity and glycomics data interpretation present challenges, especially for differential expression analysis. Here, we present a framework for differential glycomics expression analysis. Our methodology encompasses specialized and domain-informed methods for data normalization and imputation, glycan motif extraction and quantification, differential expression analysis, motif enrichment analysis, time series analysis, and meta-analytic capabilities, synthesizing results across multiple studies. All methods are integrated into our open-source glycowork package, facilitating performant workflows and user-friendly access. We demonstrate these methods using dedicated simulations and glycomics datasets of N-, O-, lipid-linked, and free glycans. Differential expression tests here focus on human datasets and cancer vs. healthy tissue comparisons. Our rigorous approach allows for robust, reliable, and comprehensive differential expression analyses in glycomics, contributing to advancing glycomics research and its translation to clinical and diagnostic applications.

Identification of Accurate Reference Genes for qRT-PCR Analysis of Gene Expression in Eremochloa ophiuroides under Multiple Stresses of Phosphorus Deficiency and/or Aluminum Toxicity.

Centipedegrass (Eremochloa ophiuroides (Munro.) Hack.) is a species originating in China and is an excellent warm-season turfgrass. As a native species in southern China, it is naturally distributed in the phosphorus-deficient and aluminum-toxic acid soil areas. It is important to research the molecular mechanism of centipedegrass responses to phosphorus-deficiency and/or aluminum-toxicity stress. Quantitative Real-Time PCR (qRT-PCR) is a common method for gene expression analysis, and the accuracy of qRT-PCR results depends heavily on the stability of internal reference genes. However, there are still no reported stable and effective reference genes for qRT-PCR analysis of target genes under the acid-soil-related stresses in different organs of centipedegrass. For scientific rigor, the gene used as a reference for any plant species and/or any stress conditions should be first systematically screened and evaluated. This study is the first to provide a group of reliable reference genes to quantify the expression levels of functional genes of Eremochloa ophiuroides under multiple stresses of P deficiency and/or aluminum toxicity. In this study, centipedegrass seedlings of the acid-soil-resistant strain 'E041' and acid-soil-sensitive strain 'E089' were used for qRT-PCR analysis. A total of 11 candidate reference genes (ACT, TUB, GAPDH, TIP41, CACS, HNR, EP, EF1α, EIF4α, PP2A and actin) were detected by qRT-PCR technology, and the stability of candidate genes was evaluated with the combination of four internal stability analysis software programs. The candidate reference genes exhibited differential stability of expression in roots, stems and leaves under phosphorus-deficiency and/or aluminum-toxicity stress. On the whole, the results showed that GAPDH, TIP41 and HNR were the most stable in the total of samples. In addition, for different tissues under various stresses, the selected reference genes were also different. CACS and PP2A were identified as two stable reference genes in roots through all three stress treatments (phosphate deficiency, aluminum toxicity, and the multiple stress treatment of aluminum toxicity and phosphate deficiency). Moreover, CACS was also stable as a reference gene in roots under each treatment (phosphate deficiency, aluminum toxicity, or multiple stresses of aluminum toxicity and phosphate deficiency). In stems under all three stress treatments, GAPDH and EIF4α were the most stable reference genes; for leaves, PP2A and TIP41 showed the two highest rankings in all three stress treatments. Finally, qRT-PCR analysis of the expression patterns of the target gene ALMT1 was performed to verify the selected reference genes. The application of the reference genes identified as internal controls for qRT-PCR analysis will enable accurate analysis of the target gene expression levels and expression patterns in centipedegrass under acid-soil-related stresses.