Common Differential Expression Research Articles

Analysis of common differential gene expression in synovial cells of osteoarthritis and rheumatoid arthritis.

To elucidate potential molecular mechanisms differentiating osteoarthritis (OA) and rheumatoid arthritis (RA) through a bioinformatics analysis of differentially expressed genes (DEGs) in patient synovial cells, aiming to provide new insights for clinical treatment strategies. Gene expression datasets GSE1919, GSE82107, and GSE77298 were downloaded from the Gene Expression Omnibus (GEO) database to serve as the training groups, with GSE55235 being used as the validation dataset. The OA and RA data from the GSE1919 dataset were merged with the standardized data from GSE82107 and GSE77298, followed by batch effect removal to obtain the merged datasets of differential expressed genes (DEGs) for OA and RA. Intersection analysis was conducted on the DEGs between the two conditions to identify commonly upregulated and downregulated DEGs. Enrichment analysis was then performed on these common co-expressed DEGs, and a protein-protein interaction (PPI) network was constructed to identify hub genes. These hub genes were further analyzed using the GENEMANIA online platform and subjected to enrichment analysis. Subsequent validation analysis was conducted using the GSE55235 dataset. The analysis of differentially expressed genes in the synovial cells from patients with Osteoarthritis (OA) and Rheumatoid Arthritis (RA), compared to a control group (individuals without OA or RA), revealed significant changes in gene expression patterns. Specifically, the genes APOD, FASN, and SCD were observed to have lower expression levels in the synovial cells of both OA and RA patients, indicating downregulation within the pathological context of these diseases. In contrast, the SDC1 gene was found to be upregulated, displaying higher expression levels in the synovial cells of OA and RA patients compared to normal controls.Additionally, a noteworthy observation was the downregulation of the transcription factor PPARG in the synovial cells of patients with OA and RA. The decrease in expression levels of PPARG further validates the alteration in lipid metabolism and inflammatory processes associated with the pathogenesis of OA and RA. These findings underscore the significance of these genes and the transcription factor not only as biomarkers for differential diagnosis between OA and RA but also as potential targets for therapeutic interventions aimed at modulating their expression to counteract disease progression. The outcomes of this investigation reveal the existence of potentially shared molecular mechanisms within Osteoarthritis (OA) and Rheumatoid Arthritis (RA). The identification of APOD, FASN, SDC1, TNFSF11 as key target genes, along with their downstream transcription factor PPARG, highlights common potential factors implicated in both diseases. A deeper examination and exploration of these findings could pave the way for new candidate targets and directions in therapeutic research aimed at treating both OA and RA. This study underscores the significance of leveraging bioinformatics approaches to unravel complex disease mechanisms, offering a promising avenue for the development of more effective and targeted treatments.

The effects of exosomes originating from different cell sources on the differentiation of bone marrow mesenchymal stem cells into Schwann cells

BackgroundBone marrow mesenchymal stem cells (BMSCs) can differentiate into Schwann cells (SCs) during peripheral nerve injury; in our previous research, we showed that SC-derived exosomes (SC-exos) played a direct induction role while fibroblast-derived exosomes (Fb-exos) had no obvious induction role. The induction role of neural stem cell (NSC)-derived exosomes (NSC-exos) has also been widely confirmed. However, no studies have compared the induction effects of these three types of cells at the same time. Therefore, by investigating the effect of these three cell-derived exosomes upon the induction of BMSCs to differentiate into SCs, this study explored the role of different exosomes in promoting the differentiation of stem cells into SCs cells, and conducted a comparison between the two groups by RNA sequencing to further narrow the range of target genes and related gene pathways in order to study their related mechanisms.Materials and methodsWe extracted exosomes from SCs, fibroblasts (Fb) and neural stem cells (NSC) and then investigated the ability of these exosomes to induce differentiation into BMSCs under different culture conditions. The expression levels of key proteins and gene markers were detected in induced cells by fluorescence immunoassays, western blotting and polymerase chain reaction (PCR); then, we statistically compared the relative induction effects under different conditions. Finally, we analyzed the three types of exosomes by RNA-seq to predict target genes and related gene pathways.ResultsBMSCs were cultured by three media: conventional (no induction), pre-induction or pre-induction + original induction medium (ODM) with exosomes of the same cell origin under different culture conditions. When adding the three different types of exosomes separately, the overall induction of BMSCs to differentiate into SCs was significantly increased (P < 0.05). The induction ability was ranked as follows: pre-induction + ODM + exosome group > pre-induction + exosome group > non-induction + exosome group. Using exosomes from different cell sources under the same culture conditions, we observed the following trends under the three culture conditions: RSC96-exos group ≥ NSC-exos group > Fb-exos group. The overall ability to induce BMSCs into SCs was significantly greater in the RSC96-exos group and the NSC-exos group. Although there was no significant difference in induction efficiency when comparing these two groups, the overall induction ability of the RSC96-exos group was slightly higher than that of the NSC-exos group. By combining the differentiation induction results with the RNA-seq data, the three types of exosomes were divided into three comparative groups: RSC vs. NSC, RSC vs. Fb and NSC vs. Fb. We identified 203 differentially expressed mRNA target genes in these three groups. Two differentially expressed genes were upregulated simultaneously, namely riboflavin kinase (RFK, ENSRNOG00000022273) and ribosomal RNA processing 36 (Rrp36, ENSRNOG00000017836). We did not identify any co-upregulated target genes for the miRNAs, but did identify one target gene of the lncRNAs, namely ENSRNOG00000065005. Analysis identified 90 GO terms related to nerves and axons in the mRNAs; in addition, KEGG enrichment and GASA analysis identified 13 common differential expression pathways in the three groups.ConclusionsOur analysis found that pre-induction + ODM + RSC96/NSC-exos culture conditions were most conducive with regards to induction and differentiation. RSC96-exos and NSC-exos exhibited significantly greater differentiation efficiency of BMSCs into SCs. Although there was no statistical difference, the data indicated a trend for RSC96-exos to be advantageous We identified 203 differentially expressed mRNAs between the three groups and two differentially expressed target mRNAs were upregulated, namely riboflavin kinase (RFK, ENSRNOG00000022273) and ribosomal RNA processing 36 (Rrp36, ENSRNOG00000017836). 90 GO terms were related to nerves and axons. Finally, we identified 13 common differentially expressed pathways across our three types of exosomes. It is hoped that the efficiency of BMSCs induction differentiation into SCs can be improved, bringing hope to patients and more options for clinical treatment.

Abstract 1670: Mechanistic insight into mTOR signaling identifies RNA splicing machinery as a potential therapeutic target for cancer

Abstract Aberrant signal transduction regulating cell growth and proliferation is a hallmark of many cancers. Activation of mTOR signaling is common and is an attractive therapeutic target with mechanistically different mTOR inhibitors in clinical use. One consequence of exposure to the mTOR inhibitor rapamycin is an alteration of gene expression through a global increase in alternative exon inclusion (Cheng et al. 2022, doi.org/10.3390/ijms232012416). To examine the mechanisms underlying this effect, we utilized the TempO-Seq targeted sequencing platform to profile both the whole transcriptome and a selection of alternatively spliced RNA targets in the rapamycin sensitive and resistant breast cancer cell lines MCF-7 and MDA MB 231 following exposure to 13 mTOR inhibitors, some closely related and some from distinct chemical classes. Recent publications have demonstrated that high quality dose response data can be obtained using this sequencing platform (Harrill et al. 2021, doi: 10.1093/toxsci/kfab009; Ramaiahgari et al. 2019, doi: 10.1093/toxsci/kfz065) and TempO-Seq has been widely adopted in toxicology for deriving compound-specific expression signatures to identify mechanisms of action and long-term toxicity. Treated cells were also profiled using TempO-LINC, a novel high-throughput single-cell assay based on the same platform. We observed that certain mTOR inhibitors induced both common and cell-specific differential expression as well as widespread alternative exon inclusion, as expected. Intriguingly, transcripts with the most significant changes in splicing upon treatment were those encoding members of the SR family of splicing factors, often overexpressed or amplified in breast cancers (Park, et al. 2019, doi: 10.1016/j.celrep.2019.10.110). This is significant because SR protein expression is autoregulated by alternative inclusion of an exon containing a premature stop codon in a feedback loop leading to nonsense-mediated decay (NMD) and lowered protein levels. Indeed, NMD is known to be inhibited by rapamycin (Martinez-Nunez, et al. 2017, doi: 10.1093/nar/gkw1109), which would lead to the observed accumulation of these transcripts. Thus, monitoring a targeted selection of alternative splicing events and regulators revealed mTOR regulation of mRNA isoform abundance consistent with regulation of the NMD RNA turnover process and with overexpression of SR proteins in breast cancers. Importantly, targeted bulk and single-cell TempO-Seq assays enable efficient sequencing and simplified data analysis without the expense and bioinformatic burden of RNAseq, making them particularly well-suited for drug discovery. These results support the use of this approach for further characterization of cell-specific effects of mTOR inhibitors and their mechanisms of action. Citation Format: Gaelle Rondeau, Megan Opichka, Kevin White, Joanne M. Yeakley, Dennis J. Eastburn, Bruce Seligmann. Mechanistic insight into mTOR signaling identifies RNA splicing machinery as a potential therapeutic target for cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1670.

Study on Screening Core Biomarkers of Noise and Drug-Induced Hearing Loss Based on Transcriptomics.

Background Noise and drug-induced hearing loss (HL) is becoming more and more serious, but the integration and analysis based on transcriptomics and proteomics are lacking. On the one hand, this study aims to integrate existing public transcriptomic data on noise and gentamicin-induced HL. On the other hand, the study aims to establish the gentamicin and noise-induced HL model of guinea pigs, then to perform the transcriptomic and proteomic analyses. Through comprehensive analysis of the above data, we aim to screen, predict, and preliminarily verify biomarkers closely related to HL. Material and Methods We screened the Gene Expression Omnibus database to obtain transcriptome data expression profiles of HL caused by noise and gentamicin, then constructed the guinea pig HL model and perform the transcriptomic and proteomic analyses. Differential expression and enrichment analysis were performed on public and self-sequenced data, and common differentially expressed genes (DEGs) and signaling pathways were obtained. Finally, we used proteomic data to screen for common differential proteins and validate common differential expression genes for HL. Results By integrating the public data set with self-constructed model data set, we eventually obtained two core biomarkers of HL, which were RSAD2 and matrix metalloproteinase-3 (MMP3). Their main function is to regulate the development of sense organ in the inner ear and they are mainly involved in mitogen-activated protein kinase and phosphoinositol-3 kinase/protein kinase B signaling pathways. Finally, by integrating the proteomic data of the self-constructed model, we also found differential expression of MMP3 protein. This also preliminarily and partially verified the above-mentioned core biomarkers. Conclusion and Significance In this study, public database and transcriptomic data of self-constructed model were integrated, and we screened out two core genes and various signal pathways of HL through differential analysis, enrichment analysis, and other analysis methods. Then, we preliminarily validated the MMP3 by proteomic analysis of self-constructed model. This study pointed out the direction for further laboratory verification of key biomarkers of HL, which is of great significance for revealing the core pathogenic mechanism of HL.

Differences in the transcriptome response in the gills of sea lamprey acutely exposed to 3-trifluoromethyl-4-nitrophenol (TFM), niclosamide or a TFM:niclosamide mixture

Sea lamprey (Petromyzon marinus) control in the Laurentian Great Lakes of North America makes use of two pesticides: 3-trifluoromethyl-4-nitrophenol (TFM) and niclosamide, which are often co-applied. Sea lamprey appear to be vulnerable to these agents resulting from a lack of detoxification responses with evidence suggesting that lampricide mixtures produce a synergistic effect. However, there is a lack of information pertaining to the physiological responses of sea lamprey to niclosamide and TFM:niclosamide mixtures. Here, we characterized the transcriptomic responses of the sea lamprey to TFM, niclosamide, and a TFM:niclosamide (1.5 %) mixture in the gill. Along with a control, larval sea lamprey were exposed to each treatment for 6 h, after which gill tissues were extracted for measuring whole-transcriptome responses using RNA sequencing. Differential gene expression patterns were summarized, which included identifying the broad roles of genes and common expression patterns among the treatments. While niclosamide treatment resulted in no differentially expressed genes, TFM- and mixture-treated fish had several differentially expressed genes that were associated with the cell cycle, DNA damage, metabolism, immune function, and detoxification. However, there was no common differential expression among treatments. For the first time, we characterized the transcriptomic response of sea lamprey to niclosamide and a TFM:niclosamide mixture and identified that these agents impact mRNA transcript abundance of genes associated with the cell cycle and cellular death, and immune function, which are likely mediated through mitochondrial dysregulation. These results may help to inform the production of more targeted and effective lampricides in sea lamprey control efforts.

Novel biomarkers for neoplastic progression from ulcerative colitis to colorectal cancer: a systems biology approach

In recent studies, the void of evaluation and in-depth understanding of unknown clinically relevant potential molecular biomarkers involved in colorectal cancer (CRC) from the inflammatory stage of ulcerative colitis (UC) to CRC metastasis, which can be suitable therapeutic targets, is deeply felt. The regulation and interaction among different cancer-promoting molecules, including messenger RNAs (mRNAs) and micro RNAs (miRNAs) in CRC and its progression, were the aim we pursued in this study. Using microarray data, we investigated the differential expression for five datasets, including mRNA and microRNA samples related to UC, tumor/normal. Then, using robust data analysis, separate lists of differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRNAs) were identified, which were used for robust rank aggregation (RRA) and co-expression network analysis. Then, comprehensive computational systems biology analyses, including gene ontology and Kyoto encyclopedia of genes and genomic pathway enrichment analyses, mRNA-miRNA regulatory network, and survival analysis, were employed to achieve the aim of this study. Finally, we used clinical samples to validate this potential and new target. According to this systems biology approach, a total of 98 DEGs and 8 DEmiRNAs with common differential expression were identified. By combining the distinct results of RRA and network, several potential therapeutic targets, and predictive and prognostic biomarkers for UC and CRC were identified. These targets include six common hub genes, CXCL1, CXCL8, MMP7, SLCA16A9, PLAU, and TIMP1, which are upregulated. Among these, the important and new biomarker SLC16A9 is negatively regulated by hsa-mir-194-5p, and hsa-miR-378a-5p take. The findings of the present study provide new insight into the pathogenesis of CRC in UC. Our study suggests future evaluation of the functional role of SLC16A9 and hsa-mir-194-5p and hsa-miR-378a-5p in CRC development.

Genome-Wide Landscape of mRNAs, lncRNAs, and circRNAs during Testicular Development of Yak.

Testicular development is a tightly regulated process in mammals. Understanding the molecular mechanisms of yak testicular development will benefit the yak breeding industry. However, the roles of different RNAs, such as mRNA, lncRNA, and circRNA in the testicular development of yak, are still largely unclear. In this study, transcriptome analyses were performed on the expression profiles of mRNAs, lncRNAs, and circRNAs in testis tissues of Ashidan yak at different developmental stages, including 6-months-old (M6), 18-months-old (M18), and 30-months-old (M30). A total of 30, 23, and 277 common differentially expressed (DE) mRNAs, lncRNAs, and circRNAs were identified in M6, M18, and M30, respectively. Furthermore, functional enrichment analysis showed that the common DE mRNAs during the entire developmental process were mainly involved in gonadal mesoderm development, cell differentiation, and spermatogenesis processes. Additionally, co-expression network analysis identified the potential lncRNAs related to spermatogenesis, e.g., TCONS_00087394 and TCONS_00012202. Our study provides new information about changes in RNA expression during yak testicular development, which greatly improves our understanding of the molecular mechanisms regulating testicular development in yaks.

Comprehensive bulk and single-cell transcriptome profiling give useful insights into the characteristics of osteoarthritis associated synovial macrophages.

Osteoarthritis (OA) is a common chronic joint disease, but the association between molecular and cellular events and the pathogenic process of OA remains unclear. The study aimed to identify key molecular and cellular events in the processes of immune infiltration of the synovium in OA and to provide potential diagnostic and therapeutic targets. To identify the common differential expression genes and function analysis in OA, we compared the expression between normal and OA samples and analyzed the protein-protein interaction (PPI). Additionally, immune infiltration analysis was used to explore the differences in common immune cell types, and Gene Set Variation Analysis (GSVA) analysis was applied to analyze the status of pathways between OA and normal groups. Furthermore, the optimal diagnostic biomarkers for OA were identified by least absolute shrinkage and selection operator (LASSO) models. Finally, the key role of biomarkers in OA synovitis microenvironment was discussed through single cell and Scissor analysis. A total of 172 DEGs (differentially expressed genes) associated with osteoarticular synovitis were identified, and these genes mainly enriched eight functional categories. In addition, immune infiltration analysis found that four immune cell types, including Macrophage, B cell memory, B cell, and Mast cell were significantly correlated with OA, and LASSO analysis showed that Macrophage were the best diagnostic biomarkers of immune infiltration in OA. Furthermore, using scRNA-seq dataset, we also analyzed the cell communication patterns of Macrophage in the OA synovial inflammatory microenvironment and found that CCL, MIF, and TNF signaling pathways were the mainly cellular communication pathways. Finally, Scissor analysis identified a population of M2-like Macrophages with high expression of CD163 and LYVE1, which has strong anti-inflammatory ability and showed that the TNF gene may play an important role in the synovial microenvironment of OA. Overall, Macrophage is the best diagnostic marker of immune infiltration in osteoarticular synovitis, and it can communicate with other cells mainly through CCL, TNF, and MIF signaling pathways in microenvironment. In addition, TNF gene may play an important role in the development of synovitis.

Expression pattern and diagnostic value of ferroptosis-related genes in acute myocardial infarction.

Ferroptosis is a form of regulatory cell death (RCD) caused by iron-dependent lipid peroxidation. The role of ferroptosis in the process of acute myocardial infarction (AMI) is still unclear and requires further study. Therefore, it is helpful to identify ferroptosis related genes (FRGs) involved in AMI and explore their expression patterns and molecular mechanisms. The AMI-related microarray datasets GSE66360 and GSE61144 were obtained using the Gene Expression Omnibus (GEO) online database. GO annotation, KEGG pathway enrichment analysis and Protein-protein interaction (PPI) analysis were performed for the common significant differential expression genes (CoDEGs) in these two datasets. The FRGs were obtained from the FerrDb V2 and the differentially expressed FRGs were used to identify potential biomarkers by receiver operating characteristic (ROC) analysis. The expression of these FRGs was verified using external dataset GSE60993 and GSE775. Finally, the expression of these FRGs was further verified in myocardial hypoxia model. A total of 131 CoDEGs were identified and these genes were mainly enriched in the pathways of "inflammatory response," "immune response," "plasma membrane," "receptor activity," "protein homodimerization activity," "calcium ion binding," "Phagosome," "Cytokine-cytokine receptor interaction," and "Toll-like receptor signaling pathway." The top 7 hub genes ITGAM, S100A12, S100A9, TLR2, TLR4, TLR8, and TREM1 were identified from the PPI network. 45 and 14 FRGs were identified in GSE66360 and GSE61144, respectively. FRGs ACSL1, ATG7, CAMKK2, GABARAPL1, KDM6B, LAMP2, PANX2, PGD, PTEN, SAT1, STAT3, TLR4, and ZFP36 were significantly differentially expressed in external dataset GSE60993 with AUC ≥ 0.7. Finally, ALOX5, CAMKK2, KDM6B, LAMP2, PTEN, PTGS2, and ULK1 were identified as biomarkers of AMI based on the time-gradient transcriptome dataset of AMI mice and the cellular hypoxia model. In this study, based on the existing datasets, we identified differentially expressed FRGs in blood samples from patients with AMI and further validated these FRGs in the mouse time-gradient transcriptome dataset of AMI and the cellular hypoxia model. This study explored the expression pattern and molecular mechanism of FRGs in AMI, providing a basis for the accurate diagnosis of AMI and the selection of new therapeutic targets.

Bioinformatics Analysis Identified Five Widely Expressed Genes Associated with Prognosis in Sarcoma.

BackgroundHuman sarcomas (SARC) are a group of malignant tumors that originated from mesenchymal lineages with more than 60 subtypes. However, potential biomarkers for the diagnosis and prognosis of SARC remain to be investigated.MethodsWe obtained three GSE raw matrix files (GSE39262, GSE21122, GSE48418) that related to various subtypes of sarcoma from the public GEO database and explored the widely differential expression genes in three obtained GSE files. Then common differential expression genes (CDGEs) were identified. We analyzed the correlation between the expression of the top five interacted genes of CDEGs and genome-wide differences, prognosis, genetic mutation, functional enrichment, immune infiltration, immune checkpoint, and marker genes’ expression of N6-methyladenosine (m6A) modification in SARC patients. Besides, a prognostic nomogram was constructed to predict the survival of SARC patients.ResultsAmong the three GSE files, 42 CDGEs were identified, and the top five interacted genes were ASPM, CCNB2, PRC1, AURKA, and SCM2. The expression levels of the five genes were higher in the SARC group than that in the normal group. The transcriptional level of CCNB2, PRC, and SCM2 was correlated to the worse survival of SARC. The constructed nomogram that combined CNB2, PRC1, and SCM2 showed a fairly good incredibility in predicting the survival of SARC (C-index: 0.711). Furthermore, the five genes were widely involved in immune infiltration, immune checkpoint, and m6A modification. In addition, we found a minor survival-related mutation rate (9%) of the five identified genes in SARC patients (p < 0.05).ConclusionThe results suggested the five identified genes widely participated in the prognosis, immune infiltration, immune checkpoint, and m6A modification of SARC patients. This study provided a theoretical basis for the research about the correlation between the level of five identified genes and sarcoma, but the further mechanism needs to be verified by experiments.

Potentially Functional microRNA-mRNA Regulatory Networks in Intestinal Ischemia-Reperfusion Injury: A Bioinformatics Analysis.

BackgroundIntestinal ischemia-reperfusion (II/R) injury is a common clinical complication associated with high mortality, for which microRNA (miRNA) drives potentially its pathophysiological progression. MiRNAs regulate different messenger RNAs (mRNAs). However, the regulatory network between miRNAs and mRNAs in intestinal ischemia-reperfusion injury is elusive.MethodsWe analyzed the different expression of mRNAs and miRNAs in intestinal tissues from patients from three groups (arterial group (group A), venous group (group V), control group (group C)). Common differentially expressed (Co-DE) miRNAs and differentially expressed mRNAs were acquired via concerned analyses among the three groups. Co-DE mRNAs were shared parts of target mRNAs and differentially expression mRNAs. Cytoscape was employed to construct the regulatory network between miRNAs and mRNAs. Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway depicted the functions and potential pathway associated with Co-DE mRNAs. Using the STRING and Cytoscape, we found critical mRNAs in the protein–protein interaction (PPI) network.ResultsThe miRNA-mRNA network comprised 8 Co-DE miRNAs and 140 Co-DE mRNAs. Of note, 140 Co-DE mRNAs were targets of these 8 miRNAs, and their roles were established through the functional exploration via GO analysis and KEGG analysis. PPI network and Cytoscape revealed COL1A2, THY1, IL10, MMP2, SERPINH1, COL3A1, COL14A1, and P4HA1 as the top 8 key mRNAs.ConclusionThis study has demonstrated a miRNA-mRNA regulatory network in intestinal ischemia-reperfusion injury, and explored the key mRNAs and their potential functions. These findings could provide new insight into prognostic markers and therapeutic targets for patients with intestinal ischemia-reperfusion injury in clinical practice.

An EAV-HP insertion in the promoter region of SLCO1B3 has pleiotropic effects on chicken liver metabolism based on the transcriptome and proteome analysis

Solute carrier organic anion transporter 1B3 (SLCO1B3) is an important liver primarily highly expressed gene, its encoded protein (OATP1B3) involved in the transport of multi-specific endogenous and exogenous substances. We previously reported that an EAV-HP inserted mutation (IM+) in the 5ʹ flanking region of SLCO1B3 was the causative mutation of chicken blue eggs, and a further research showed that IM+ significantly reduced the expression of SLCO1B3 in liver. Herein, we confirmed a cholate response element (IR-1) played an important role in activating SLCO1B3 and in vitro experiments showed that the activation of IR-1 can be significantly reduced by the EAV-HP IM+ . We performed transcriptome and proteomic analysis using the same set of IM+ and IM− liver tissues from Yimeng hens (a Chinese indigenous breed) to study the effect of SLCO1B3 and OATP1B3 expression reduction on chicken liver function. The results showed that common differential expression pathways were screened out from both transcriptome and proteome, in which fatty acid metabolism and drug metabolism—cytochrome P450 were significantly enriched in the KEGG analysis. The lipid-related metabolism was weakened in IM+ group, which was validated by serum biochemical assay. We unexpectedly found that EAV-HP fragment was highly expressed in the liver of the IM+ chickens. We cloned the EAV-HP full-length transcript and obtained the complete open reading frame. It is worth noting that there was some immune related differential expressed genes, such as NFKBIZ, NFKBIA, and IL1RL1, which were higher expressed in the IM+ group, which may due to the high expression of EAV-HP. Our study showed that EAV-HP IM+ reduced the expression of SLCO1B3 in liver, resulting in the decrease of fatty metabolism and exogenous substance transport capacity. The mutation itself also expressed in the liver and may be involved in the immune process. The mechanism needs further study.

Identification of a Potentially Functional microRNA-mRNA Regulatory Network in Lung Adenocarcinoma Using a Bioinformatics Analysis.

BackgroundLung adenocarcinoma (LUAD) is a common lung cancer with a high mortality, for which microRNAs (miRNAs) play a vital role in its regulation. Multiple messenger RNAs (mRNAs) may be regulated by miRNAs, involved in LUAD tumorigenesis and progression. However, the miRNA–mRNA regulatory network involved in LUAD has not been fully elucidated.MethodsDifferentially expressed miRNAs and mRNA were derived from the Cancer Genome Atlas (TCGA) dataset in tissue samples and from our microarray data in plasma (GSE151963). Then, common differentially expressed (Co-DE) miRNAs were obtained through intersected analyses between the above two datasets. An overlap was applied to confirm the Co-DEmRNAs identified both in targeted mRNAs and DEmRNAs in TCGA. A miRNA–mRNA regulatory network was constructed using Cytoscape. The top five miRNA were identified as hub miRNA by degrees in the network. The functions and signaling pathways associated with the hub miRNA-targeted genes were revealed through Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The key mRNAs in the protein–protein interaction (PPI) network were identified using the STRING database and CytoHubba. Survival analyses were performed using Gene Expression Profiling Interactive Analysis (GEPIA).ResultsThe miRNA–mRNA regulatory network consists of 19 Co-DEmiRNAs and 760 Co-DEmRNAs. The five miRNAs (miR-539-5p, miR-656-3p, miR-2110, let-7b-5p, and miR-92b-3p) in the network were identified as hub miRNAs by degrees (>100). The 677 Co-DEmRNAs were targeted mRNAs from the five hub miRNAs, showing the roles in the functional analyses of the GO analysis and KEGG pathways (inclusion criteria: 836 and 48, respectively). The PPI network and Cytoscape analyses revealed that the top ten key mRNAs were NOTCH1, MMP2, IGF1, KDR, SPP1, FLT1, HGF, TEK, ANGPT1, and PDGFB. SPP1 and HGF emerged as hub genes through survival analysis. A high SPP1 expression indicated a poor survival, whereas HGF positively associated with survival outcomes in LUAD.ConclusionThis study investigated a miRNA–mRNA regulatory network associated with LUAD, exploring the hub miRNAs and potential functions of mRNA in the network. These findings contribute to identify new prognostic markers and therapeutic targets for LUAD patients in clinical settings.

Integrated Analysis Reveals ENDOU as a Biomarker in Head and Neck Squamous Cell Carcinoma Progression.

BackgroundHead and neck squamous cell carcinoma (HNSCC) is a leading cancer with high morbidity and mortality worldwide. The aim is to identify genes with clinical significance by integrated bioinformatics analysis and investigate their function in HNSCC.MethodsWe downloaded and analyzed two gene expression datasets of GSE6631 and GSE107591 to screen differentially expressed genes (DEGs) in HNSCC. Common DEGs were functionally analyzed by Gene ontology and KEGG pathway enrichment analysis. Protein-protein interaction (PPI) network was constructed with STRING database and Cytoscape. ENDOU was overexpressed in FaDu and Cal-27 cell lines, and cell proliferation and migration capability were evaluated with MTT, scratch and transwell assay. The prognostic performance of ENDOU and expression correlation with tumor infiltrates in HNSCC were validated with TCGA HNSCC datasets.ResultsNinety-eight genes shared common differential expression in both datasets, with core functions like extracellular matrix organization significantly enriched. 15 genes showed prognostic significance, and COBL and ENDOU serve as independent survival markers in HNSCC. In-vitro ENDOU overexpression inhibited FaDu and Cal-27 cells proliferation and migration, indicating its tumor-suppressing role in HNSCC progression. GSEA analysis indicated ENDOU down-stream pathways like DNA replication, mismatch repair, cell cycle and IL-17 signaling pathway. ENDOU showed relative lower expression in HNSCC, especially HPV-positive HNSCC samples. At last, ENDOU showed negative correlation with tumor purity and tumor infiltrating macrophages, especially M2 macrophages.ConclusionThis study identified ENDOU as a biomarker with prognostic significance in HNSCC progression.

Coupled Transcriptomics for Differential Expression Analysis and Determination of Transcription Start Sites: Design and Bioinformatics.

The term coupled transcriptomics is coined to describe a design of an RNA-seq experiment intended for both differential expression analysis and genome-wide determination of the transcription start sites (TSS). The minimal requirements for the first analysis are two experimental conditions with at least two biological replicates enabling statistical tests. The second analysis involves the bioinformatics comparison of the data generated from a control RNA-seq library with another library enriched in primary transcripts using Terminator™ 5'-phosphate-dependent exonuclease, in an experiment denominated differential RNA-seq (dRNA-seq). Usually, dRNA-seq is carried out with specific protocols for library construction, different of those used for common differential expression analysis. Our experimental design allows to use the same data for both analyses, reducing the number of libraries to be generated and sequenced. This is a guide for designing a coupled transcriptomics experiment and for the subsequent bioinformatics procedures. The proposed methods can be applied to the detection and study of small RNA genes.

Bioinformatics analysis of differentially expressed genes in tumor and paracancerous tissues of patients with lung adenocarcinoma.

BackgroundLung adenocarcinoma is the main pathological type of non-small cell lung cancer (NSCLC). In this study, we analyzed the gene expression profile of lung adenocarcinoma tumor and paracancerous tissues by bioinformatics to assess the genes and signal pathways related to lung adenocarcinoma.MethodsThe expression data of GSE7670, GSE27262, and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database. The three microarray data sets were integrated to obtain common differential expression genes of lung adenocarcinoma tumor and adjacent tissues. The STRING database was used to construct the protein-protein interaction (PPI) network of lung adenocarcinoma and mine the gene modules and core genes in the network, and the online tools, GEPIA and Kaplan-Meier plotter were used to further verify and analyze the core genes.ResultsThere were 109 pairs of lung adenocarcinoma tissues and matched paracancerous normal lung tissues in the three data sets. Eighty-three differentially expressed genes were identified, including 16 up-regulated and 67 down-regulated genes, and 60 differentially expressed genes were successfully incorporated into the PPI network complex. Eleven core genes were identified in the PPI network complex, including three up-regulated (COMP, SPP1, COL1A1) and eight down-regulated genes (CDH5, CAV1, CLDN5, LYVE1, IL6, VWF, TEK, PECAM1). These core genes were verified by the GEPIA tumor database. Survival analysis showed that expression of the core genes was significantly related to the prognosis of lung adenocarcinoma. KEGG pathway analysis of core genes showed six genes (COMP, SPP1, COL1A1, IL6, VWF, TEK) were significantly enriched in the PI3K-Akt signaling-pathway (P=1.62E-06).ConclusionsBy analyzing the differential expression genes of lung adenocarcinoma and paracancerous normal tissues with bioinformatics, 11 genes with significant differential expression and significant influence on prognosis were identified. The findings may provide new concepts for developing diagnosis and treatment targets and prognosis markers for lung adenocarcinoma.

Single-Cell RNA-seq of Human Myeloid-Derived Suppressor Cells in Late Sepsis Reveals Multiple Subsets With Unique Transcriptional Responses: A Pilot Study.

Increased circulating myeloid-derived suppressor cells (MDSCs) are independently associated with poor long-term clinical outcomes in sepsis. Studies implicate subsets of MDSCs having unique roles in lymphocyte suppression; however, characterization of these cells after sepsis remains incomplete. We performed a pilot study to determine the transcriptomic landscape in MDSC subsets in sepsis using single-cell RNAseq (scRNA-seq). A mixture of whole blood myeloid-enriched and Ficoll-enriched PBMCs from two late septic patients on post-sepsis day 21 and two control subjects underwent Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq). We successfully identified the three MDSC subset clusters-granulocytic (G-), monocytic (M-), and early (E-) MDSCs. Sepsis was associated with a greater relative expansion of G-MDSCs versus M-MDSCs at 21 days as compared to control subjects. Genomic analysis between septic patients and control subjects revealed cell-specific and common differential expression of genes in both G-MDSC and M-MDSC subsets. Many of the common genes have previously been associated with MDSC proliferation and immunosuppressive function. Interestingly, there was no differential expression of several genes demonstrated in the literature to be vital to immunosuppression in cancer-induced MDSC. This pilot study successfully demonstrated that MDSCs maintain a transcriptomic profile that is immunosuppressive in late sepsis. Interestingly, the landscape in chronic critical illness is partially dependent on the original septic insult. Preliminary data would also indicate immunosuppressive MDSCs from late sepsis patients appear to have a somewhat unique transcriptome from cancer and/or other inflammatory diseases.

Exosomal microRNAs are novel circulating biomarkers in cigarette, waterpipe smokers, E-cigarette users and dual smokers

BackgroundElectronic cigarettes (e-cigs) vaping, cigarette smoke, and waterpipe tobacco smoking are associated with various cardiopulmonary diseases. microRNAs are present in higher concentration in exosomes that play an important role in various physiological and pathological functions. We hypothesized that the non-coding RNAs transcript may serve as susceptibility to disease biomarkers by smoking and vaping.MethodsPlasma exosomes/EVs from cigarette smokers, waterpipe smokers and dual smokers (cigarette and waterpipe) were characterized for their size, morphology and TEM, Nanosight and immunoblot analysis. Exosomal RNA was used for small RNA library preparation and the library was quantified using the High Sensitivity DNA Analysis on the Agilent 2100 Bioanalyzer system and sequenced using the Illumina NextSeq 500 and were converted to fastq format for mapping genes.ResultsEnrichment of various non-coding RNAs that include microRNAs, tRNAs, piRNAs, snoRNAs, snRNAs, Mt-tRNAs, and other biotypes are shown in exosomes. A comprehensive differential expression analysis of miRNAs, tRNAs and piRNAs showed significant changes across different pairwise comparisons. The seven microRNAs that were common and differentially expressed of when all the smoking and vaping groups were compared with non-smokers (NS) are hsa-let-7a-5p, hsa-miR-21-5p, hsa-miR-29b-3p, hsa-let-7f-5p, hsa-miR-143-3p, hsa-miR-30a-5p and hsa-let-7i-5p. The e-cig vs. NS group has differentially expressed 5 microRNAs (hsa-miR-224-5p, hsa-miR-193b-3p, hsa-miR-30e-5p, hsa-miR-423-3p, hsa-miR-365a-3p, and hsa-miR-365b-3p), which are not expressed in other three groups. Gene set enrichment analysis of microRNAs showed significant changes in the top six enriched functions that consisted of biological pathway, biological process, molecular function, cellular component, site of expression and transcription factor in all the groups. Further, the pairwise comparison of tRNAs and piRNA in all these groups revealed significant changes in their expressions.ConclusionsPlasma exosomes of cigarette smokers, waterpipe smokers, e-cig users and dual smokers have common differential expression of microRNAs which may serve to distinguish smoking and vaping subjects from NS. Among them has-let-7a-5p has high sensitivity and specificity to distinguish NS with the rest of the users, using ROC curve analysis. These findings will pave the way for the utilizing the potential of exosomes/miRNAs as a novel theranostic agents in lung injury and disease caused by tobacco smoking and vaping.

Bioinformatics analysis of a-three-gene signature as an independent prediction of survival in follicular gastritis developing into gastric cancer

Background and aimFrom follicular gastritis (FG) to gastric cancer (GC), the expression of genes has changed and the function of these genes has not been elucidated. Materials and methodsData of FG samples and GC samples were downloaded from GSE106656 and GSE116312 in GEO database. Clinical data of GC were downloaded from TCGA database. The common differential expression genes (DEGs) were enriched and analyzed by GO and KEGG in David. The interaction information of proteins was obtained from the STRING database and the core genes in the Cytoscape software were excavated. After KEGG, PPI and KM survival analysis of DEGs, the significant genes were intersected to fulfill the predicted genes. Univariate/Multivariate Cox regression analysis were used to get the prediction signatures. Results96 up-regulated genes were obtained from the data set. Three genes, CAV1 COL12A1 and ANGPT2, were acquired by survival analysis screening. Age, gender, lymph node status, and the 3-gene signature were selected as independent survival variables in the nomogram. The nomogram showed that C-index was 0.68 (95% CI, 0.60 to 0.75). ConclusionOur study identified the proposed nomogram may be used to predict the prognosis of patients from FG to GC, which may provide a potential useful guidance for preventive and epidemiological applications in GC.

ComBat-seq: batch effect adjustment for RNA-seq count data.

The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch correction method, ComBat-seq, using a negative binomial regression model that retains the integer nature of count data in RNA-seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-seq adjusted data results in better statistical power and control of false positives in differential expression compared to data adjusted by the other available methods. We further demonstrated in a real data example that ComBat-seq successfully removes batch effects and recovers the biological signal in the data.