Differentially Expressed Genes Research Articles

Transcriptomic Analysis of the CNL Gene Family in the Resistant Rice Cultivar IR28 in Response to Ustilaginoidea virens Infection.

Rice false smut, caused by Ustilaginoidea virens, threatens rice production by reducing yields and contaminating grains with harmful ustiloxins. However, studies on resistance genes are scarce. In this study, the resistance level of IR28 (resistant cultivar) to U. virens was validated through artificial inoculation. Notably, a reactivation of resistance genes after transient down-regulation during the first 3 to 5 dpi was observed in IR28 compared to WX98 (susceptible cultivar). Cluster results of a principal component analysis and hierarchical cluster analysis of differentially expressed genes (DEGs) in the transcriptome exhibited longer expression patterns in the early infection phase of IR28, consistent with its sustained resistance response. Results of GO and KEGG enrichment analyses highlighted the suppression of immune pathways when the hyphae first invade stamen filaments at 5 dpi, but sustained up-regulated DEGs were linked to the 'Plant-pathogen interaction' (osa04626) pathway, notably disease-resistant protein RPM1 (K13457, CNLs, coil-coiled NLR). An analysis of CNLs identified 245 proteins containing Rx-CC and NB-ARC domains in the Oryza sativa Indica genome. Partial candidate CNLs were shown to exhibit up-regulation at both 1 and 5 dpi in IR28. This study provides insights into CNLs' responses to U. virens in IR28, potentially informing resistance mechanisms and genetic breeding targets.

Molecular composition of skeletal muscle in infants and adults: a comparative proteomic and transcriptomic study

To gain a deeper understanding of skeletal muscle function in younger age and aging in elderly, identification of molecular signatures regulating these functions under physiological conditions is needed. Although molecular studies of healthy muscle have been conducted on adults and older subjects, there is a lack of research on infant muscle in terms of combined morphological, transcriptomic and proteomic profiles. To address this gap of knowledge, we performed RNA sequencing (RNA-seq), tandem mass spectrometry (LC–MS/MS), morphometric analysis and assays for mitochondrial maintenance in skeletal muscle biopsies from both, infants aged 4–28 months and adults aged 19–65 years. We identified differently expressed genes (DEGs) and differentially expressed proteins (DEPs) in adults compared to infants. The down-regulated genes in adults were associated with functional terms primarily related to sarcomeres, cellular maintenance, and metabolic, immunological and developmental processes. Thus, our study indicates age-related differences in the molecular signatures and associated functions of healthy skeletal muscle. Moreover, the findings assert that processes previously associated solely with aging are indeed part of development and healthy aging. Hence, combined findings of this study also indicate that age-dependent controls are crucial in muscle disease studies, as otherwise the comparative results may not be reliable.

Identification of 10 differentially expressed genes involved in the tumorigenesis of cervical cancer via next-generation sequencing.

The incidence and mortality of cervical cancer remain high in female malignant tumors worldwide. There is still a lack of diagnostic and prognostic markers for cervical carcinoma. This study aimed to screen differentially expressed genes (DEGs) between normal and cervical cancer tissues to identify candidate genes for further research. Uterine cervical specimens were resected from our clinical patients after radical hysterectomy. Three patients' transcriptomic datasets were built by the next generation sequencing (NGS) results. DEGs were selected through the edgeR and DESeq2 packages in the R environment. Functional enrichment analysis, including GO/DisGeNET/KEGG/Reactome enrichment analysis, was performed. Normal and cervical cancer tissue data from the public databases TCGA and GTEx were collected to compare the expression levels of 10 selected DEGs in tumor and normal tissues. ROC curve and survival analysis were performed to compare the diagnostic and prognostic values of each gene. The expression levels of candidate genes were verified in 15 paired clinical specimens via quantitative real-time polymerase chain reaction. There were 875 up-regulated and 1,482 down-regulated genes in cervical cancer samples compared with the paired adjacent normal cervical tissues according to the NGS analysis. The top 10 DEGs included APOD, MASP1, ACKR1, C1QTNF7, SFRP4, HSPB6, GSTM5, IGFBP6, F10 and DCN. GO, DisGeNET and Reactome analyses revealed that the DEGs were related to extracellular matrix and angiogenesis which might influence tumorigenesis. KEGG enrichment showed that PI3K-Akt signaling pathway might be involved in cervical cancer tumorigenesis and progression. The expression levels of selected genes were decreased in tumors in both the public database and our experimental clinical specimens. All the candidate genes showed excellent diagnostic value, and the AUC values exceeded 0.90. Additionally, APOD, ACKR1 and SFRP4 expression levels could help predict the prognosis of patients with cervical cancer. In this study, we selected the top 10 DEGs which were down-regulated in cervical cancer tissues. All of them had dramatically diagnostic value. APOD, ACKR1 and SFRP4 were associated with the survivals of cervical cancer. C1QTNF7, HSPB6, GSTM5, IGFBP6 and F10 were first reported to be candidate genes of cervical carcinoma.

Integrin subunit beta 6 is a potential diagnostic marker for acute kidney injury in patients with diabetic kidney disease: a single cell sequencing data analysis

Background Diabetic kidney disease (DKD), a prevalent complication of diabetes mellitus, is often associated with acute kidney injury (AKI). Thus, the development of preventive and therapeutic strategies is crucial for delaying the progression of AKI and DKD. Methods The GSE183276 dataset, comprising the data of 20 healthy controls and 12 patients with AKI, was downloaded from the Gene Expression Omnibus (GEO) database to analyze the AKI group. For analyzing the DKD group, the GSE131822 dataset, comprising the data of 3 healthy controls and 3 patients with DKD, was downloaded from the GEO database. The common differentially expressed genes (DEGs) in renal tubular epithelial cells (TECs) were subjected to enrichment analyses. Next, a protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes database to analyze gene-related regulatory networks. Finally, the AKI animal models and the DKD and AKI cell models were established, and the reliability of the identified genes was validated using quantitative real-time polymerase chain reaction analysis. Results Functional analysis was performed with 40 common DEGs in TECs. Eight hub genes were identified using the PPI and gene-related networks. Finally, validation experiments with the in vivo animal model and the in vitro cellular model revealed the four common DEGs. Four DEGs that share molecular mechanisms in the pathogenesis of DKD and AKI were identified. In particular, the expression of Integrin Subunit Beta 6(ITGB6), a hub and commonly upregulated gene, was upregulated in the in vitro models. Conclusion ITGB6 may serve as a biomarker for early AKI diagnosis in patients with DKD and as a target for early intervention therapies.

Androgen receptor regulates the differentiation of myoblasts under cyclic mechanical stretch and its upstream and downstream signals

Our previous studies have demonstrated the important roles of androgen receptor (AR) in myoblast proliferation regulated by 15 % (mimic appropriate exercise) and 20 % (mimic excessive exercise) mechanical stretches. Except for myoblast proliferation, differentiation is also an important factor affecting muscle mass and strength. But the role of AR in stretch-regulated myoblast differentiation and AR's upstream and downstream signals remain unknown. In the present study, firstly the differences of myogenic differentiation between C2C12 (with AR expression) and L6 (without AR expression) myoblasts induced by 15 % and 20 % mechanical stretches were compared; secondly, AR antagonist flutamide and AR agonist GTx-007 were used in 15 % and 20 % stretched myoblasts respectively to confirm AR's roles in stretch-regulated myoblast differentiation; thirdly, RNA-seq, molecular dynamic simulation (MD) and co-immunoprecipitation were performed to screen the downstream and upstream molecules of AR during stretches. We found that (1) 15 % stretch increased while 20 % stretch decreased myotube number in differentiating C2C12 and L6 myoblasts, with more significant changes in C2C12 cells than L6 cells; (2) in stretched C2C12 myoblasts, AR antagonist flutamide inhibited 15 % stretch-promoted differentiation while AR agonist GTx-007 reversed 20 % stretch-inhibited differentiation (reflected by changes in myotube number, MHC contents of fast-twitch and slow-twitch fiber, and the levels of myogenic regulatory factors (MRFs) such as MyoD and myogenin); (3) KEGG analysis of RNA-seq showed that the differently expressed genes (DEGs) in C2C12 cells induced by 15 % stretch were enriched in FoxO and JAK-STAT signaling pathways, while DEGs by 20 % stretch were enriched in FoxO and MAPK signaling pathways; (4) MD and co-immunoprecipitation showed that β1 integrin could interact with AR and influence AR's activity in C2C12 cells. In conclusion, AR plays important roles in myoblast differentiation promoted by 15 % stretch while inhibited by 20 % stretch, which was fulfilled through FoxO-MRFs. In addition, α7β1 integrin may be a bridge linking mechanical stretch and AR. This study is beneficial to deeply understand the roles and mechanisms of AR in stretch-regulated muscle mass and strength; and reports firstly that myoblasts sense mechanical stimulus and transmit into intracellular AR via α7β1 integrin.

Transcriptomic and metabolomic analysis of poplar response to feeding by Hyphantria cunea

Populus cathayana × canadansis ‘Xinlin 1’ (‘P.‘xin lin 1’) with the characteristics of rapid growth and high yield, is frequently attacked by herbivorous insects. However, little is known about how it defenses against Hyphantria cunea (H. cunea) at molecular and biochemical levels. Differences in the transcriptome and metabolome were analyzed after ‘P. ‘xin lin 1’ leaves were fed to H. cunea for 0h, 2h, 4h, 8h, 16h and 24h. In the five comparison groups including 2h vs. CK, 4h vs. CK, 8h vs. CK, 16h vs. CK, and 24h vs. CK, a total of 8925 genes and 842 metabolites were differentially expressed. A total of 825 transcription factors (TFs) were identified, which encoded 56 TF families. The results showed that the top four families with the highest number of TFs were AP2/ERF, MYB, C2C2, bHLH. Analyses of leaves which were fed to H. cunea showed that the differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) were significantly enriched in plant hormone signal transduction pathway, MAPK signaling pathway, flavonoid, flavone and flavonol and anthocyanin biosynthesis pathway. Additionally, there were a number of genes significantly up-regulated in MAPK signaling pathway. Some compounds involved in plant hormone signal transduction and flavonoid/flavone and flavonol/ anthocyanin pathways such as jasmonic acid (JA), jasmonoyl-L-Isoleucine (JA-Ile), kaempferol and cyanidin-3-O-glucoside were induced in infested ‘P.‘xin lin 1’. This study provides a new understanding for exploring the dynamic response mechanism of poplar to the infestation of H. cunea.

Screening and validating genes associated with cuproptosis in systemic lupus erythematosus by expression profiling combined with machine learning.

Cell death has long been a focal point in life sciences research, and recently, scientists have discovered a novel form of cell death induced by copper, termed cuproptosis. This paper aimed to identify genes associated with cuproptosis in Systemic Lupus Erythematosus (SLE) through machine learning, combined with single-cell sequencing (scRNA-seq), to screen and validate related genes. The analytical results were then experimentally verified. Two published microarray gene expression datasets (GSE65391 and GSE61635) from SLE and control peripheral blood samples were downloaded from the GEO database. The GSE65391 dataset was used as the training group, while the GSE61635 dataset served as the validation group. Differentially expressed genes (DEGs) from GSE65391 identified 12 differential genes. Nine diagnostic genes, considered potential biomarkers, were selected using the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analysis. The Receiver Operating Characteristic (ROC) curves for both the training and validation groups were used to calculate the Area Under the Curve (AUC) to assess discriminatory properties. CIBERSORT was used to assess the relationship between these diagnostic genes and a reference set of infiltrating immune cells. Single-cell RNA sequencing data (GSE162577) from SLE patients were also obtained from the GEO database and analyzed. Experimental validation of the most important SLE biomarkers was performed. Twelve significantly different cuproptosis-related genes were identified in the GSE65391 training set. Immune cell analysis revealed 12 immune cell types and identified nine signature genes, including PDHB, GLS, DLAT, LIAS, MTF1, DLST, DLD, LIPT1, and FDX1. In the GSE61635 validation set, seven genes were weakly expressed, and two genes were strongly expressed in the treatment group. According to the ROC curves, PDHB and GLS demonstrated significant diagnostic value. Additionally, correlation analysis was conducted on the nine characteristic genes in relation to immune infiltration. The distribution of key genes in immune cells was determined using single-cell RNA sequencing data. Finally, the mRNA expression of the nine diagnostic genes was validated using qPCR.

KNTC1 functions as a potential biomarker and oncogene regulating proliferation, migration and apoptosis in gastric cancer

BackgroundAs one of the most prevalent cancers, gastric cancer (GC) exhibits a remarkably high morbidity and mortality rate. To date, effective diagnostic and prognostic markers and therapeutic targets for GC are still lacking. Kinetochore associated 1 (KNTC1) is one of the proteins involved in chromosome segregation. However, the diagnostic and prognostic value of KNTC1 and its biological function in GC remain unknown. MethodsIn this study, Gene Expression Omnibus (GEO) datasets were utilized to identify differentially expressed genes (DEGs). Prognostic and diagnostic value were assessed by Kaplan-Meier plotter and receiver operating characteristic (ROC) curve. The expression of KNTC1 was verified by q-PCR, immunohistochemistry (IHC) and Western blotting. Subsequently, KNTC1 knockdown was employed to investigate its effect on GC cells. Gene set enrichment analysis (GSEA) revealed a pathway regulated by KNTC1, which was further verified by Western blotting. ResultsFour highly expressed genes (ESPL1, RAD54L, KNTC1, TACC3) were identified as biomarkers for GC diagnosis and prognosis. Notably, the value of KNTC1 as a biomarker for GC was newly revealed. Single-cell and immune analyses revealed that KNTC1 contributed to the suppression of the GC immune microenvironment. In clinical samples, we demonstrated high expression of KNTC1 in GC tissues. KNTC1 knockdown suppressed proliferation and migration while promoting apoptosis of GC cells. Additionally, KNTC1 may affect GC cells by regulating the PI3K/Akt/mTOR pathway. ConclusionsKNTC1 acts as a potential diagnostic and prognostic marker for GC. It may promote proliferation and migration while inhibiting apoptosis of GC cells via the PI3K/Akt/mTOR pathway.

Unraveling the pathogenic interplay between SARS-CoV-2 and polycystic ovary syndrome using bioinformatics and experimental validation

The prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among polycystic ovary syndrome (PCOS) is significantly higher than in the general population. However, the mechanisms underlying this remain obscure. This study aimed to explore the mechanisms by identifying the genetic signature of SARS-CoV-2 infection in PCOS. In the present study, a total of 27 common differentially expressed genes (DEGs) were selected for subsequent analyses. Functional analyses showed that immunity and hormone-related pathways collectively participated in the development and progression of PCOS and SARS-CoV-2 infection. Under these, 7 significant hub genes were identified, including S100A9, MMP9, TLR2, THBD, ITGB2, ICAM1, and CD86 by using the algorithm in Cytoscape. Furthermore, hub gene expression was confirmed in the validation set, PCOS clinical samples, and mouse model. Immune microenvironment analysis with the CIBERSORTx database demonstrated that the hub genes were significantly correlated with T cells, dendritic cells, mast cells, B cells, NK cells, and eosinophils and positively correlated with immune scores. Among the hub genes, S100A9, MMP9, THBD, ITGB2, CD86, and ICAM1 demonstrated potential as possible diagnostic markers for COVID-19 and PCOS. In addition, we established the interaction networks of ovary-specific genes, transcription factors, miRNAs, drugs, and chemical compounds with hub genes with NetworkAnalyst. This work uncovered the common pathogenesis and genetic signature of PCOS and SARS-CoV-2 infection, which might provide a theoretical basis and innovative ideas for further mechanistic research and drug discovery of the comorbidity of the two diseases.

Exploring novel natural compound-based therapies for Duchenne muscular dystrophy management: insights from network pharmacology, QSAR modeling, molecular dynamics, and free energy calculations.

Muscular dystrophies encompass a heterogeneous group of rare neuromuscular diseases characterized by progressive muscle degeneration and weakness. Among these, Duchenne muscular dystrophy (DMD) stands out as one of the most severe forms. The present study employs an integrative approach combining network pharmacology, quantitative structure-activity relationship (QSAR) modeling, molecular dynamics (MD) simulations, and free energy calculations to identify potential therapeutic targets and natural compounds for DMD. Upon analyzing the GSE38417 dataset, it was found that individuals with DMD exhibited 290 upregulated differentially expressed genes (DEGs) compared to healthy controls. By utilizing gene ontology (GO) and protein-protein interaction (PPI) network analysis, this study provides insights into the functional roles of the identified DEGs, identifying ten hub genes that play a critical role in the pathology of DMD. These key genes include DMD, TTN, PLEC, DTNA, PKP2, SLC24A, FBXO32, SNTA1, SMAD3, and NOS1. Furthermore, through the use of ligand-based pharmacophore modeling and virtual screening, three natural compounds were identified as potential inhibitors. Among these, compounds 3874518 and 12314417 have demonstrated significant promise as an inhibitor of the SMAD3 protein, a crucial factor in the fibrotic and inflammatory mechanisms associated with DMD. The therapeutic potential of the compounds was further supported by molecular dynamics simulation and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis. These findings suggest that the compounds are viable candidates for experimental validation against DMD.

Effects of a Protease Inhibitor Protein on Buchnera aphidicola and Gene Expression in Pea Aphids (Acyrthosiphon pisum)

ABSTRACTAphids are serious insect pests for agricultural and horticultural crops and may cause the major economic losses. Insecticides used to control aphids have caused environmental pollution and the insecticide residues in agricultural products. A new protease inhibitor gene from bacterium Xenorhabdus bovienii (Xbpi‐1) and the protease inhibitor protein expressed by the gene against the pea aphid (Acyrthosiphon pisum Harris; Hemiptera: Aphididae) have been reported, however, effects of the PIP on the symbiotic bacteria, Buchnera aphidicola, and gene expression in pea aphids are unknown. By assessing the quantity of B. aphidicola, the primary symbiotic bacterium, in the aphid fed on an artificial diet containing 100 and 500 μg/mL Xbpi‐1, we observed a substantial reduction in its population by 27% and 46%, respectively, as analysed by real‐time quantitative reverse transcription PCR (qRT‐PCR). Furthermore, the growth of other aphid‐associated bacteria was also significantly inhibited by Xbpi‐1. To elucidate the mechanisms at the gene level, we conducted transcriptome analysis and identified differentially expressed genes (DEGs). Subsequent Gene Ontology (GO) analysis of the 213 DEGs shed light on the impact of Xbpi‐1 on aphid metabolism processes and gene expression. Notably, the results highlighted several aphid nutrient metabolism pathways affected by Xbpi‐1, which are relevant to vector‐borne diseases. These pathways encompass crucial factors such as heat shock proteins, cuticle proteins and proteases. The results from this study revealed that the PIP had a novel mechanism against pea aphids by having adverse effects on the primary symbiotic bacteria in pea aphids and affecting aphid gene expression, showing that the PIP may be a promise bioinsecticide for aphid control in the future.

Exploring the Mechanism of Sempervirine Inhibiting Glioblastoma Invasion Based on Network Pharmacology and Bioinformatics

Background: Invasion is an important characteristic of the malignancy of glioblastoma (GBM) and a significant prognostic factor. Sempervirine (SPV), a yohimbine-type alkaloid, has been proven to inhibit GBM cells proliferation in previous research and found to have a potential effect in anti-invasion, but its mechanism of anti-invasion is still unknown. Methods: To explore its pharmacodynamics in inhibiting GBM cell invasion in this study, we combined network pharmacology and bioinformatics to comprehensive exploratory analysis of SPV and verified the mechanism in vitro. Results: Firstly, targets of SPV and invasion-related genes were collected from public databases. Moreover, GBM samples were obtained to analyze differentially expressed genes (DEGs) from The Cancer Genome Atlas (TCGA). Then, the relevant targets of SPV inhibiting GBM invasion (SIGI) were obtained through the intersection of the three gene sets. Further, GO and KEGG analysis showed that the targets of SIGI were heavily enriched in the AKT signaling pathway. Subsequently, based on the method of machine learning, a clinical prognostic model of the relevant targets of SIGI was constructed using GBM samples from TCGA and the Gene Expression Omnibus (GEO). A four-genes model (DUSP6, BMP2, MMP2, and BMP2) was successfully constructed, and Vina Scores of MMP2 and MMP13 in molecular docking were higher, which may be the main targets of SIGI. Then, the effect of SIGI was confirmed via functional experiments on invasion, migration, and adhesion assay, and the effect involved changes in the expressions of p-AKT, MMP2 and MMP13. Finally, combined with AKT activator (SC79) and inhibitor (MK2206), we further confirmed that SPV inhibits GBM invasion through AKT phosphorylation. Conclusions: This study provides valuable and an expected point of view into the regulation of AKT phosphorylation and inhibition of GBM invasion by SPV.

Integrative transcriptomic analysis reveals a broad range of toxic effects of triclosan on coral Porites lutea

Triclosan (TCS) is an antimicrobial agent commonly used in personal care products. However, little is known about its toxicity to corals. Here, we examined the acute toxic effects (96 h) of TCS at different levels to the coral Porites lutea. Results showed that the bioaccumulation factors (BAFs) of TCS in Porites lutea decreased with increasing TCS exposure levels. Exposure to TCS at the level up to 100 μg/L did not induce bleaching of Porites lutea. However, by the end of the experiment, both the density and chlorophyll a content of the symbiotic zooxanthellae were 19–52 % and 19.9–45.6 % lower in the TCS treatment groups than in the control, respectively. For the coral host, its total antioxidant capacity (T-AOC), superoxide dismutase (SOD) and catalase (CAT) activities were all significantly lower in the TCS treatment groups than the control. Transcriptome analysis showed that 942 and 1077 differentially expressed genes (DEGs) were identified in the coral host in the 0.5 and 100 μg/L TCS treatment groups, respectively. Meanwhile, TCS can interfere with pathways related to immune system and reproductive system in coral host. Overall, our results suggest that environmentally relevant concentrations of TCS can impact both the coral host and the symbiotic zooxanthellae.

Transcriptome signature of juvenile Litopenaeus vannamei cultured under different salinity levels in response to Vibrio harveyi infection

Although the widely cultured Litopenaeus vannamei is highly tolerant to varying salinity levels, the rapid fluctuation of abiotic and biotic factors due to climatic changes may become stressors to the shrimp's physiology, resulting in impaired immunity and increased disease susceptibility. This study aimed to investigate the combined effects of both factors towards L. vannamei at transcriptomic level. L. vannamei were cultured at three different salinity levels (5 ppt, 20 ppt and 30 ppt) for 60 days and were challenged with Vibrio harveyi. RNA-seq analysis was conducted on the hepatopancreas to assess the differential expressed genes (DEGs) in both control and V. harveyi infected groups. Our results revealed 5,725 DEGs was observed in shrimp reared at 5 ppt, 3,643 DEGs at 20 ppt and 1,560 DEGs at 30 ppt. Most DEGs were identified to be associated with osmoregulation and transport activities, immune and stress regulation, nutrient metabolism, growth, chitin binding, gonadal development, and metal ion binding and toxicity. We identified that shrimp cultured at intermediate salinity of 20 ppt exhibits the greatest immunity level after infection by V. harveyi, given that low salinity (5 ppt) may augment the free ion metal toxicity, resulting in higher disease susceptibility. Both V. harveyi and salinity stress function together or separately to activate a wide range of immune and stress regulatory genes, which could be potential candidate markers for future RNAi and knockdown research in developing efficient prophylactic management strategies for L. vannamei.

Bioinformatics analysis of G protein subunit gamma transduction protein 2-autophagy axis in CD11b+ dendritic cells as a potential regulator to skew airway neutrophilic inflammation in asthma endotypes.

Asthma is a heterogeneous inflammatory disease with two main clinical endotypes: type 2 (T2) high and low asthma. The plasticity and autophagy in dendritic cells (DCs) influence T helper (Th)2 or Th17 differentiation to regulate asthma endotypes. Enhanced autophagy in DCs fosters Th2 differentiation in allergic environments, while reduced autophagy favors Th17 cell differentiation in sensitized and infected environments. Autophagy regulation in DCs involves interaction with various pathways like G protein-coupled receptor (GPCR), mammalian target of rapamycin (mTOR), or phosphoinositide 3-kinase (PI3K) pathway. However, specific molecules within DCs influencing asthma endotypes remain unclear. Gene expression data series (GSE) 64896, 6858, 2276, and 55247 were obtained from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between CD103+ and CD11b+ DCs after induction by ovalbumin (OVA) and lipopolysaccharide (LPS) were analyzed using GEO2R. DEGs were examined through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) analyses. The hub gene network was construct with STRING database and Cytoscape. Autophagy differences in DCs and the selected hub gene in GSE6858, GSE2276, and GSE55247 were evaluated using student t tests. Our analysis identified 635 upregulated and 360 downregulated genes in CD11b+ DCs, compared to CD103+ DCs. These DEGs were associated with "PI3K-AKT signaling pathway," "Ras signaling pathway," and so forth. Thirty-five hub genes were identified, in which G protein subunit gamma transduction protein 2 (Gngt2) in CD11b+ DCs exhibited a relatively specific increase in expression associated with autophagy defects under the induction environment similar to T2 low asthma model. No significant difference was found in lung Gngt2 expression between T2 high asthma model and control group. Our analysis suggested Gngt2 acted as an adapter molecule that inhibited autophagy, promoting Th17-mediated airway inflammation via the GPCR pathway in a T2 low asthma mice model. Targeting this pathway provides new asthma treatment strategies in preclinical research.

A transcriptomic analysis of incisional hernia based on high-throughput sequencing technology.

Incisional hernia is a common postoperative complication; however, few transcriptomic studies have been conducted on it. In this study, we used second-generation high-throughput sequencing to explore the pathogenesis and potential therapeutic targets of incisional hernias. Superficial fasciae were collected from 15 patients without hernia and 21 patients with an incisional hernia. High-throughput sequencing of the fascia was performed to generate an expression matrix. We analyzed the matrix to identify differentially expressed genes (DEGs) and performed gene ontology and enrichment analyses of these DEGs. Additionally, an external dataset was utilized to identify key DEGs. We identified 1,823 DEGs closely associated with extracellular matrix (ECM) imbalance, bacterial inflammatory response, and fibrillar collagen trimerization. TNNT3, CMAY5, ATP1B4, ASB5, CILP, SIX4, FBN1 and FNDC5 were identified as key DEGs at the intersection of the two expression matrices. Moreover, non-alcoholic fatty liver disease-related, TNF, and IL-17 signaling pathways were identified as key enrichment pathways. We identified eight key DEGs and three pathways associated with incisional hernias. Our findings offer new insights into the pathogenesis of incisional hernias and highlight potential targets for their prevention and treatment.

Identification of diagnostic candidate genes in COVID-19 patients with sepsis.

Coronavirus Disease 2019 (COVID-19) and sepsis are closely related. This study aims to identify pivotal diagnostic candidate genes in COVID-19 patients with sepsis. We obtained a COVID-19 data set and a sepsis data set from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module genes using the Linear Models for Microarray Data (LIMMA) and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF)) were used to identify candidate hub genes for the diagnosis of COVID-19 patients with sepsis. Receiver operating characteristic (ROC) curves were developed to assess the diagnostic value. Finally, the data set GSE28750 was used to verify the core genes and analyze the immune infiltration. The COVID-19 data set contained 3,438 DEGs， and 595 common genes were screened in sepsis. sepsis DEGs were mainly enriched in immune regulation. The intersection of DEGs for COVID-19 and core genes for sepsis was 329, which were also mainly enriched in the immune system. After developing the PPI network, 17 node genes were filtered and thirteen candidate hub genes were selected for diagnostic value evaluation using machine learning. All thirteen candidate hub genes have diagnostic value, and 8 genes with an Area Under the Curve (AUC) greater than 0.9 were selected as diagnostic genes. Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA-DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID-19 patients with sepsis. This finding contributes to the identification of potential peripheral blood diagnostic candidate genes for COVID-19 patients with sepsis.

Predicting gene signature in breast cancer patients with multiple machine learning models

AimsThe aim of this study was to predict gene signatures in breast cancer patients using multiple machine learning models.MethodsIn this study, we first collated and merged the datasets GSE54002 and GSE22820, obtaining a gene expression matrix comprising 16,820 genes (including 593 breast cancer (BC) samples and 26 normal control (NC) samples). Subsequently, we performed enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO).ResultsWe identified 177 differentially expressed genes (DEGs), including 40 up-regulated and 137 down-regulated genes, through differential expression analysis. The GO enrichment results indicated that these genes are primarily involved in extracellular matrix organization, positive regulation of nervous system development, collagen-containing extracellular matrix, heparin binding, glycosaminoglycan binding, and Wnt protein binding, among others. KEGG enrichment analysis revealed that the DEGs were primarily associated with pathways such as focal adhesion, the PI3K–Akt signaling pathway, and human papillomavirus infection. DO enrichment analysis showed that the DEGs play a significant role in regulating diseases such as intestinal disorders, nephritis, and dermatitis. Further, through LASSO regression analysis and SVM-RFE algorithm analysis, we identified 9 key feature DEGs (CF-DEGs): ANGPTL7, TSHZ2, SDPR, CLCA4, PAMR1, MME, CXCL2, ADAMTS5, and KIT. Additionally, ROC curve analysis demonstrated that these CF-DEGs serve as a reliable diagnostic index. Finally, using the CIBERSORT algorithm, we analyzed the infiltration of immune cells and the associations between CF-DEGs and immune cell infiltration across all samples.ConclusionsOur findings provide new insights into the molecular functions and metabolic pathways involved in breast cancer, potentially aiding in the discovery of new diagnostic and immunotherapeutic biomarkers.

Associations Between Prenatal Vitamin D and Placental Gene Expression

BackgroundVitamin D is a hormone regulating gene transcription. Prenatal vitamin D has been linked to immune and vascular function in the placenta, a key organ of pregnancy. Transcriptome-wide RNA-sequencing can provide a more complete representation of the placental effects of vitamin D. ObjectiveWe investigated the association between prenatal vitamin D levels and placental gene expression in a large, prospective pregnancy cohort. MethodsParticipants were recruited in Shelby County, Tennessee in the Conditions Affecting Neurocognitive Development and Learning in Early childhood (CANDLE) study. Vitamin D (plasma total 25-hydroxyvitatmin D, [25(OH)D]) was measured at mid-pregnancy (16-28 weeks) and delivery. RNA was sequenced from placental samples collected at birth. We identified differentially expressed genes (DEGs) using adjusted linear regression models. We also conducted weighted gene co-expression network analysis (WGCNA). ResultsThe median 25(OH)D of participants was 21.8 ng/mL at mid-pregnancy (N=774, IQR: 15.4-26.5 ng/mL) and 23.6 ng/mL at delivery (N=753, IQR: 16.8-29.1 ng/mL). Placental expression of 17 DEGs was associated with 25(OH)D at mid-pregnancy, but only 1 DEG was associated with 25(OH)D at delivery. DEGs were related to energy metabolism, cytoskeletal function, and transcriptional regulation. We identified 2 WGCNA gene modules whose expression was associated with 25(OH)D at mid-pregnancy and 1 module associated with 25(OH)D at delivery. These modules were enriched for genes related to mitochondrial and cytoskeletal function and were regulated by transcription factors including ARNT2 and FOSL2. We also identified 12 modules associated with 25(OH)D in females and 1 module in males. Conclusions25(OH)D during mid-pregnancy, but not at delivery, is associated with placental gene expression at birth. Future research is needed to investigate a potential role of vitamin D in modulating placental mitochondrial metabolism, intracellular transport, and transcriptional regulation during pregnancy.

Integrated physiological and transcriptome analyses of the effects of water-soluble amino acid fertilizer on plant growth

Tobacco is a key component of China’s economy, serving as a major cash crop. With traditional fertilizers reaching their maximum potential in promoting tobacco growth, the exploration of new fertilizers has emerged as a viable solution for advancing experimental sustainable development. Spraying foliar fertilizer is a key measure to improve tobacco yield and quality. This study employed a combination of field and pot experiments to investigate the effects of applying water-soluble amino acid fertilizers on the growth, development, and quality of tobacco. The results of transcriptome analysis and physiological index measurements indicate that the application of water-soluble amino acid fertilizer can enhance the area of tobacco leaves, promote photosynthesis, and improve the chemical composition of the leaves. This research determined that the optimal concentration for spraying water-soluble amino acid fertilizer is diluted 500 times. Transcriptome analysis identified a total of 6,489 differentially expressed genes (DEGs), including 3,843 genes that were up-regulated and 2,646 genes that were down-regulated. Gene Ontology (GO)analysis demonstrated that these DEGs were significantlyassociatedwithprocessesincludingcell recognition, photosynthesis,thylakoidcomponents, calcium ion binding, and carbohydrate binding.Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis emphasized that the DEGs were largely found in pathwaysrelated tophotosynthesis-antenna proteins, interactions between plants and pathogens, photosynthesisitself, phenylpropanoid biosynthesis, andplant hormone signaling. Further research revealed that a significant number of genes involved in the auxin, gibberellin, salicylic acid, and jasmonic acid signal transduction pathways exhibited varying expression patterns following the application of water-soluble amino acid fertilizers.Additionally, the expression levels of bZIP, MYB, WRKY, bHLH, and AP2/ERF transcription factors exhibited significant variations following the application of water-soluble amino acid fertilizer. These results analyze the mechanisms of action of water-soluble amino acid fertilizers, offering new effective strategies to enhance both the yield and quality of tobacco.