A Regularized Cox Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies

Background

Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes.

Methods

A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent.

Results

In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level.

Conclusions

The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

Integrated Proteomics and Metabolomics of Safflower Petal Wilting and Seed Development

Safflower (Carthamus tinctorius L.) is an ancient oilseed crop of interest due to its diversity of end-use industrial and food products. Proteomic and metabolomic profiling of its organs during seed development, which can provide further insights on seed quality attributes to assist in variety and product development, has not yet been undertaken. In this study, an integrated proteome and metabolic analysis have shown a high complexity of lipophilic proteins and metabolites differentially expressed across organs and tissues during seed development and petal wilting. We demonstrated that these approaches successfully discriminated safflower reproductive organs and developmental stages with the identification of 2179 unique compounds and 3043 peptides matching 724 unique proteins. A comparison between cotyledon and husk tissues revealed the complementarity of using both technologies, with husks mostly featuring metabolites (99%), while cotyledons predominantly yielded peptides (90%). This provided a more complete picture of mechanisms discriminating the seed envelope from what it protected. Furthermore, we showed distinct molecular signatures of petal wilting and colour transition, seed growth, and maturation. We revealed the molecular makeup shift occurring during petal colour transition and wilting, as well as the importance of benzenoids, phenylpropanoids, flavonoids, and pigments. Finally, our study emphasizes that the biochemical mechanisms implicated in the growing and maturing of safflower seeds are complex and far-reaching, as evidenced by AraCyc, PaintOmics, and MetaboAnalyst mapping capabilities. This study provides a new resource for functional knowledge of safflower seed and potentially further enables the precision development of novel products and safflower varieties with biotechnology and molecular farming applications.

Integrating single-cell RNA sequencing data to genome-wide association analysis data identifies significant cell types in influenza A virus infection and COVID-19

With the global pandemic of COVID-19, the research on influenza virus has entered a new stage, but it is difficult to elucidate the pathogenesis of influenza disease. Genome-wide association studies (GWASs) have greatly shed light on the role of host genetic background in influenza pathogenesis and prognosis, whereas single-cell RNA sequencing (scRNA-seq) has enabled unprecedented resolution of cellular diversity and in vivo following influenza disease. Here, we performed a comprehensive analysis of influenza GWAS and scRNA-seq data to reveal cell types associated with influenza disease and provide clues to understanding pathogenesis. We downloaded two GWAS summary data, two scRNA-seq data on influenza disease. After defining cell types for each scRNA-seq data, we used RolyPoly and LDSC-cts to integrate GWAS and scRNA-seq. Furthermore, we analyzed scRNA-seq data from the peripheral blood mononuclear cells (PBMCs) of a healthy population to validate and compare our results. After processing the scRNA-seq data, we obtained approximately 70 000 cells and identified up to 13 cell types. For the European population analysis, we determined an association between neutrophils and influenza disease. For the East Asian population analysis, we identified an association between monocytes and influenza disease. In addition, we also identified monocytes as a significantly related cell type in a dataset of healthy human PBMCs. In this comprehensive analysis, we identified neutrophils and monocytes as influenza disease-associated cell types. More attention and validation should be given in future studies.

Multi-omic profiling of follicular lymphoma reveals changes in tissue architecture and enhanced stromal remodeling in high-risk patients

Follicular lymphoma (FL) is a generally incurable malignancy that evolves from developmentally blocked germinal center (GC) B cells. To promote survival and immune escape, tumor B cells undergo significant genetic changes and extensively remodel the lymphoid microenvironment. Dynamic interactions between tumor B cells and the tumor microenvironment (TME) are hypothesized to contribute to the broad spectrum of clinical behaviors observed among FL patients. Despite the urgent need, existing clinical tools do not reliably predict disease behavior. Using a multi-modal strategy, we examined cell-intrinsic and -extrinsic factors governing progression and therapeutic outcomes in FL patients enrolled onto a prospective clinical trial. By leveraging the strengths of each platform, we identify several tumor-specific features and microenvironmental patterns enriched in individuals who experience early relapse, the most high-risk FL patients. These features include stromal desmoplasia and changes to the follicular growth pattern present 20 months before first progression and first relapse.

Exploring the allelopathic autotoxicity mechanism of ginsenosides accumulation under ginseng decomposition based on integrated analysis of transcriptomics and metabolomics

Continuous cropping obstacles seriously constrained the sustainable development of the ginseng industry. The allelopathic autotoxicity of ginsenosides is the key "trigger" of continuous cropping obstacles in ginseng. During harvest, the ginseng plants could be broken and remain in the soil. The decomposition of ginseng residue in soil is one of the important release ways of ginsenosides. Therefore, the allelopathic mechanism of ginsenosides through the decomposed release pathway needs an in-depth study. To investigate this allelopathic regulation mechanism, the integrated analysis of transcriptomics and metabolomics was applied. The prototype ginsenosides in ginseng were detected converse to rare ginsenosides during decomposition. The rare ginsenosides caused more serious damage to ginseng hairy root cells and inhibited the growth of ginseng hairy roots more significantly. By high-throughput RNA sequencing gene transcriptomics study, the significantly differential expressed genes (DEGs) were obtained under prototype and rare ginsenoside interventions. These DEGs were mainly enriched in the biosynthesis of secondary metabolites and metabolic pathways, phytohormone signal transduction, and protein processing in endoplasmic reticulum pathways. Based on the functional enrichment of DEGs, the targeted metabolomics analysis based on UPLC-MS/MS determination was applied to screen endogenous differential metabolized phytohormones (DMPs). The influence of prototype and rare ginsenosides on the accumulation of endogenous phytohormones was studied. These were mainly involved in the biosynthesis of diterpenoid, zeatin, and secondary metabolites, phytohormone signal transduction, and metabolic pathways. After integrating the transcriptomics and metabolomics analysis, ginsenosides could regulate the genes in phytohormone signaling pathways to influence the accumulation of JA, ABA, and SA. The conclusion was that the prototype ginsenosides were converted into rare ginsenosides by ginseng decomposition and released into the soil, which aggravated its allelopathic autotoxicity. The allelopathic mechanism was to intervene in the response regulation of genes related to the metabolic accumulation of endogenous phytohormones in ginseng. This result provides a reference for the in-depth study of continuous cropping obstacles of ginseng.

Integrated Transcriptomics-Proteomics Analysis Identifies Molecular Phenotypic Alterations Associated with Colorectal Cancer

Understanding the pathogenesis and finding diagnostic markers for colorectal cancer (CRC) are the key to its diagnosis and treatment. Integrated transcriptomics and proteomics analysis can be used to characterize alterations of molecular phenotypes and reveal the hidden pathogenesis of CRC. This study employed a novel strategy integrating transcriptomics and proteomics to identify pathological molecular pathways and diagnostic biomarkers of CRC. First, differentially expressed proteins and coexpressed genes generated from weighted gene coexpression network analysis (WGCNA) were intersected to obtain key genes of the CRC phenotype. In total, 63 key genes were identified, and pathway enrichment analysis showed that the process of coagulation and peptidase regulator activity could both play important roles in the development of CRC. Second, protein-protein interaction analysis was then conducted on these key genes to find the central genes involved in the metabolic pathways underpinning CRC. Finally, Itih3 and Lrg1 were further screened out as diagnostic biomarkers of CRC by applying statistical analysis on central genes combining transcriptomics and proteomics data. The deep involvement of central genes in tumorigenesis demonstrates the accuracy and reliability of this novel transcriptomics-proteomics integration strategy in biomarker discovery. The identified candidate biomarkers and enriched metabolic pathways provide insights for CRC diagnosis and treatment.

iProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis

Large-scale omics studies have generated a wealth of mass spectrometry-based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature-related, and correlation analyses, including protein-protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein-protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.

Comprehensive Analysis of PANoptosis-Related Gene Signature of Ulcerative Colitis

Accumulating evidence shows that the abnormal increase in the mortality of intestinal epithelial cells (IECs) caused by apoptosis, pyroptosis, and necroptosis is closely related to the function of mucous membrane immunity and barrier function in patients with ulcerative colitis (UC). As a procedural death path that integrates the above-mentioned many deaths, the role of PANoptosis in UC has not been clarified. This study aims to explore the characterization of PANoptosis patterns and determine the potential biomarkers and therapeutic targets. We constructed a PANoptosis gene set and revealed significant activation of PANoptosis in UC patients based on multiple transcriptome profiles of intestinal mucosal biopsies from the GEO database. Comprehensive bioinformatics analysis revealed five key genes (ZBP1, AIM2, CASP1/8, IRF1) of PANoptosome with good diagnostic value and were highly correlated with an increase in pro-inflammatory immune cells and factors. In addition, we established a reliable ceRNA regulatory network of PANoptosis and predicted three potential small-molecule drugs sharing calcium channel blockers that were identified, among which flunarizine exhibited the highest correlation with a high binding affinity to the targets. Finally, we used the DSS-induced colitis model to validate our findings. This study identifies key genes of PANoptosis associated with UC development and hypothesizes that IRF1 as a TF promotes PANoptosome multicomponent expression, activates PANoptosis, and then induces IECs excessive death.

Machine learning identified MDK score has prognostic value for idiopathic pulmonary fibrosis based on integrated bulk and single cell expression data

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease that poses a significant challenge to medical professionals due to its increasing incidence and prevalence coupled with the limited understanding of its underlying molecular mechanisms. In this study, we employed a novel approach by integrating five expression datasets from bulk tissue with single-cell datasets; they underwent pseudotime trajectory analysis, switch gene selection, and cell communication analysis. Utilizing the prognostic information derived from the GSE47460 dataset, we identified 22 differentially expressed switch genes that were correlated with clinical indicators as important genes. Among these genes, we found that the midkine (MDK) gene has the potential to serve as a marker of Idiopathic pulmonary fibrosis because its cellular communicating genes are differentially expressed in the epithelial cells. We then utilized midkine and its cellular communication-related genes to calculate the midkine score. Machine learning models were further constructed through midkine and related genes to predict Idiopathic pulmonary fibrosis disease through the bulk gene expression datasets. The midkine score demonstrated a correlation with clinical indexes, and the machine learning model achieved an AUC of 0.94 and 0.86 in the Idiopathic pulmonary fibrosis classification task based on lung tissue samples and peripheral blood mononuclear cell samples, respectively. Our findings offer valuable insights into the pathogenesis of Idiopathic pulmonary fibrosis, providing new therapeutic directions and target genes for further investigation.

Integrated Analysis Identifies Upregulated SAMD9L as a Potential Biomarker Correlating with the Severity of Primary Sjögren's Syndrome

Background

Primary Sjögren's syndrome (pSS) is an autoimmune disease with lymphocytic infiltration of the salivary and lachrymal glands, whose present disease-specific objective indicators are few and have shortcomings that should be addressed. An integrated analysis of sequencing data from different cohorts has the potential to unveil novel biomarkers in pSS.

Methods

We identified 3 GEO datasets, including gene expression data from minor salivary gland (MSG) biopsy samples of 49 patients with pSS and 31 non-pSS and whole blood cells of 30 pSS patients and 30 healthy controls (HCs). Differentially expressed genes (DEGs) involved in pSS were identified from these datasets. Function Enrichment Analyses of common upregulated DEGs and PPI (protein-protein interaction) networks were performed. Furthermore, we have carried out further analysis of these DEGs to explore their potential clinical significance and diagnostic efficacy as a biomarker for pSS. Sterile Alpha Motif Domain Containing 9 Like (SAMD9L), one of the DEGs, has been identified as a promising candidate biomarker that correlates with the severity of pSS. This has been validated by analyzing local clinical samples from 30 pSS and non-pSS patients' MSG biopsies, as well as serum samples of 18 pSS and HC individuals. Finally, we performed correlation analysis to understand the relationship between SAMD9L and infiltrated immune cells.

Results

We identified 10 common highly expressed DEGs in pSS of different tissues. These genes were mainly involved in virus infection-related pathways and inferno-related pathways. GEO data and our clinical data showed that SAMD9L increases with disease severity. Public and local cohorts showed that SAMD9L has high diagnostic performance (AUC=0.845-0.867) as a biomarker, and its AUC was comparable to the Focus score when combined with RF or SSA.

Conclusion

Up-regulated SAMD9L may serve as a promising novel pSS diagnostic biomarker and have potential value for evaluating the severity of pSS.

Co-expression network analysis for the identification of potential prostate cancer genes and in vitro confirmation of their expression in cell model in the presence of Staphylococcal tsst-1 gene

Prostate cancer has arisen as an important life-threatening malignancy in males worldwide. Therefore, it is important to study underlying molecular pathways to be able to proposed appropriate a novel pathway of apoptosis in prostate cancer. This study aimed to explore the molecular effects of Staphylococcal tsst-1 gene on PC3 cell line apoptosis by in silico and in vitro studies. In this work, the differential expression of genes in prostate cancer was predicted by analyzing the public dataset GSE132063. Then, the pcDNA3.1 (+) vector was used to transfer tsst-1 gene to the PC3 cells and its effects was investigated using flow cytometry and qPCR. Co-expression network analysis indicated that lncRNAs had strong relationship with apoptosis genes in prostate cancer. Results of protein-protein docking indicated that BCL2L11, GRAMD3 and EGR1 interacted with tsst-1. Finally, the flow cytometry results showed that transfection by pcDNA3.1 (+)- tsst-1 could increase cellular death rates (48.15%) compared with the pcDNA3.1 (+) groups (6.35%); and the expression levels of GRAMD3, EGR1, BCL2L11 and PLAC4 were dysregulated in tsst-1 -transfected PC3 compared with empty-transfected PC3 (p < .05). In conclusion, our data will provide a novel landscape to understanding the mechanism of Staphylococcal tsst-1 gene on the PC3 cells apoptosis pathways.

Comprehensive analysis of gut microbiome and host transcriptome in chickens after Eimeria tenella infection

Background

Coccidiosis is an intestinal parasitic disease caused by Eimeria protozoa, which endangers the health and growth of animals, and causes huge economic losses to the poultry industry worldwide every year. Studies have shown that poultry gut microbiota plays an important role in preventing the colonization of pathogens and maintaining the health of the host. Coccidia infection also affects host gene expression. However, the underlying potential relationship between gut microbiome and host transcriptome during E. tenella infection in chickens remain unclear.

Methods

In this study, metagenomic and transcriptome sequencing were applied to identify microbiota and genes in cecal contents and cecal tissues of infected (JS) and control (JC) chickens on day 4.5 postinfection (pi), respectively.

Results

First, microbial sequencing results of cecal contents showed that the abundance of Lactobacillus, Roseburia sp. and Faecalibacterium sp decreased significantly after E. tenella infection (P < 0.05), while the abundance of Alistipes and Prevotella pectinovora increased significantly (P < 0.05). Second, transcriptome sequencing results showed that a total of 434 differentially expressed mRNAs were identified, including 196 up-regulated and 238 down-regulated genes. These differentially expressed genes related to inflammation and immunity, such as GAMA, FABP1, F2RL1 and RSAD2, may play an important role in the process of host resistance to coccidia infection. Functional studies showed that the enriched pathways of differentially expressed genes included the TGF-beta signaling pathway and the ErbB signaling pathways. Finally, the integrated analysis of gut microbiome and host transcriptome suggested that Prevotella pectinovora associated with FABP1, Butyricicoccus porcorum and Colidextribacter sp. associated with RSAD2 were involved in the immune response upon E. tenella infection.

Conclusion

In conclusion, this study provides valuable information on the microbiota and key immune genes after chicken E. tenella infection, with the aim of providing reference for the impact of coccidia infection on cecal microbiome and host.

Establishing Infodemic Management in Germany: A Framework for Social Listening and Integrated Analysis to Report Infodemic Insights at the National Public Health Institute

BACKGROUND

To respond to the need to establish infodemic management functions at the national public health institute in Germany (Robert Koch Institute, RKI), we explored and assessed available data sources, developed a social listening and integrated analysis framework, and defined when infodemic management functions should be activated during emergencies.

OBJECTIVE

We aimed to establish a framework for social listening and integrated analysis for public health in the German context using international examples and technical guidance documents for infodemic management.

METHODS

This study completed the following objectives: identified (potentially) available data sources for social listening and integrated analysis; assessed these data sources for their suitability and usefulness for integrated analysis in addition to an assessment of their risk using the RKI's standardized data protection requirements; developed a framework and workflow to combine social listening and integrated analysis to report back actionable infodemic insights for public health communications by the RKI and stakeholders; and defined criteria for activating integrated analysis structures in the context of a specific health event or health emergency.

RESULTS

We included and classified 38% (16/42) of the identified and assessed data sources for social listening and integrated analysis at the RKI into 3 categories: social media and web-based listening data, RKI-specific data, and infodemic insights. Most data sources can be analyzed weekly to detect current trends and narratives and to inform a timely response by reporting insights that include a risk assessment and scalar judgments of different narratives and themes.

CONCLUSIONS

This study identified, assessed, and prioritized a wide range of data sources for social listening and integrated analysis to report actionable infodemic insights, ensuring a valuable first step in establishing and operationalizing infodemic management at the RKI. This case study also serves as a roadmap for others. Ultimately, once operational, these activities will inform better and targeted public health communication at the RKI and beyond.

Integrated microbiome and metabolome analysis reveals the interaction between intestinal flora and serum metabolites as potential biomarkers in hepatocellular carcinoma patients

Globally, liver cancer poses a serious threat to human health and quality of life. Despite numerous studies on the microbial composition of the gut in hepatocellular carcinoma (HCC), little is known about the interactions of the gut microbiota and metabolites and their role in HCC. This study examined the composition of the gut microbiota and serum metabolic profiles in 68 patients with HCC, 33 patients with liver cirrhosis (LC), and 34 healthy individuals (NC) using a combination of metagenome sequencing and liquid chromatography-mass spectrometry (LC-MS). The composition of the serum metabolites and the structure of the intestinal microbiota were found to be significantly altered in HCC patients compared to non-HCC patients. LEfSe and metabolic pathway enrichment analysis were used to identify two key species (Odoribacter splanchnicus and Ruminococcus bicirculans) and five key metabolites (ouabain, taurochenodeoxycholic acid, glycochenodeoxycholate, theophylline, and xanthine) associated with HCC, which then were combined to create panels for HCC diagnosis. The study discovered that the diagnostic performance of the metabolome was superior to that of the microbiome, and a panel comprised of key species and key metabolites outperformed alpha-fetoprotein (AFP) in terms of diagnostic value. Spearman's rank correlation test was used to determine the relationship between the intestinal flora and serum metabolites and their impact on hepatocarcinogenesis and progression. A random forest model was used to assess the diagnostic performance of the different histologies alone and in combination. In summary, this study describes the characteristics of HCC patients' intestinal flora and serum metabolism, demonstrates that HCC is caused by the interaction of intestinal flora and serum metabolites, and suggests that two key species and five key metabolites may be potential markers for the diagnosis of HCC.

Integrated transcriptome and metabolome analysis to investigate the mechanism of intranasal insulin treatment in a rat model of vascular dementia

Introduction: Insulin has an effect on neurodegenerative diseases. However, the role and mechanism of insulin in vascular dementia (VD) and its underlying mechanism are unknown. In this study, we aimed to investigate the effects and mechanism of insulin on VD. Methods: Experimental rats were randomly assigned to control (CK), Sham, VD, and insulin (INS) + VD groups. Insulin was administered by intranasal spray. Cognitive function was evaluated using the Morris's water maze. Nissl's staining and immunohistochemical staining were used to assess morphological alterations. Apoptosis was evaluated using TUNEL-staining. Transcriptome and metabolome analyses were performed to identify differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs), respectively. Results: Insulin significantly improved cognitive and memory functions in VD model rats (p < 0.05). Compared with the VD group, the insulin + VD group exhibited significantly reduced the number of Nissl's bodies numbers, apoptosis level, GFAP-positive cell numbers, apoptosis rates, and p-tau and tau levels in the hippocampal CA1 region (p < 0.05). Transcriptomic analysis found 1,257 and 938 DEGs in the VD vs. CK and insulin + VD vs. VD comparisons, respectively. The DEGs were mainly enriched in calcium signaling, cAMP signaling, axon guidance, and glutamatergic synapse signaling pathways. In addition, metabolomic analysis identified 1 and 14 DEMs between groups in negative and positive modes, respectively. KEGG pathway analysis indicated that DEGs and DEMs were mostly enriched in metabolic pathway. Conclusion: Insulin could effectively improve cognitive function in VD model rats by downregulating tau and p-tau expression, inhibiting astrocyte inflammation and neuron apoptosis, and regulating genes involved in calcium signaling, cAMP signaling, axon guidance, and glutamatergic synapse pathways, as well as metabolites involved in metabolic pathway.

Integrated multi-omics analysis reveals insights into Chinese forest musk deer (Moschus berezovskii) genome evolution and musk synthesis

Among the artiodactyls, male animals belonging to the Family Moschidae have a unique tissue, the musk gland, with the capability of musk synthesis. However, the genetic basis of musk gland formation and musk production are still poorly understood. Here, musk gland tissues from two juvenile and three adult Chinese forest musk deer (Moschus berezovskii) were utilized to analyze genomic evolution events, evaluate mRNA profiles and investigate cell compositions. By performing genome reannotation and comparison with 11 ruminant genomes, three expanded gene families were identified in the Moschus berezovskii genome. Transcriptional analysis further indicated that the musk gland displayed a prostate-like mRNA expression pattern. Single-cell sequencing revealed that the musk gland is composed of seven distinguishable cell types. Among them, sebaceous gland cells and luminal epithelial cells play important roles in musk synthesis, while endothelial cells master the regulation of cell-to-cell communication. In conclusion, our study provides insights into musk gland formation and the musk-synthesizing process.

Zinc finger and SCAN domain-containing protein 18 is a potential DNA methylation-modified tumor suppressor and biomarker in breast cancer

Introduction

Zinc finger and SCAN domain-containing protein 18 (ZSCAN18) has been investigated as a putative biomarker of multiple human cancers. However, the expression profile, epigenetic modification, prognostic value, transcription regulation, and molecular mechanism of ZSCAN18 in breast cancer (BC) remain unknown.

Methods

In the study, we present an integrated analysis of ZSCAN18 in BC based on public omics datasets with the use of multiple bioinformatics tools. Genes potentially regulated through restoration of ZSCAN18 expression in MDA-MB-231 cells were investigated to identify pathways associated with BC.

Results

We observed that ZSCAN18 was downregulated in BC and mRNA expression was significantly correlated with clinicopathological parameters. Low expression of ZSCAN18 was found in the HER2-positive and TNBC subtypes. High expression of ZSCAN18 was associated with good prognosis. As compared to normal tissues, the extent of ZSCAN18 DNA methylation was greater with fewer genetic alterations in BC tissues. ZSCAN18 was identified as a transcription factor that might be involved in intracellular molecular and metabolic processes. Low ZSCAN18 expression was associated with the cell cycle and glycolysis signaling pathway. Overexpression of ZSCAN18 inhibited mRNA expression of genes associated with the Wnt/β-catenin and glycolysis signaling pathways, including CTNNB1, BCL9, TSC1, and PFKP. ZSCAN18 expression was negatively correlated with infiltrating B cells and dendritic cells (DCs), as determined by the TIMER web server and reference to the TISIDB. ZSCAN18 DNA methylation was positively correlated with activated B cells, activated CD8+ and CD4+ T cells, macrophages, neutrophils, and activated DCs. Moreover, five ZSCAN18-related hub genes (KDM6B, KAT6A, KMT2D, KDM1A, and HSPBP1) were identified. ZSCAN18, ZNF396, and PGBD1 were identified as components of a physical complex.

Conclusion

ZSCAN18 is a potential tumor suppressor in BC, as expression is modified by DNA methylation and associated with patient survival. In addition, ZSCAN18 plays important roles in transcription regulation, the glycolysis signaling pathway, and the tumor immune microenvironment.

PROSE: phenotype-specific network signatures from individual proteomic samples

Proteomic studies characterize the protein composition of complex biological samples. Despite recent advancements in mass spectrometry instrumentation and computational tools, low proteome coverage and interpretability remains a challenge. To address this, we developed Proteome Support Vector Enrichment (PROSE), a fast, scalable and lightweight pipeline for scoring proteins based on orthogonal gene co-expression network matrices. PROSE utilizes simple protein lists as input, generating a standard enrichment score for all proteins, including undetected ones. In our benchmark with 7 other candidate prioritization techniques, PROSE shows high accuracy in missing protein prediction, with scores correlating strongly to corresponding gene expression data. As a further proof-of-concept, we applied PROSE to a reanalysis of the Cancer Cell Line Encyclopedia proteomics dataset, where it captures key phenotypic features, including gene dependency. We lastly demonstrated its applicability on a breast cancer clinical dataset, showing clustering by annotated molecular subtype and identification of putative drivers of triple-negative breast cancer. PROSE is available as a user-friendly Python module from https://github.com/bwbio/PROSE.

miR-34b/c-5p/CXCL10 Axis Induced by RSV Infection Mediates a Mechanism of Airway Hyperresponsive Diseases

Background: RSV is closely correlated with post-infection airway hyperresponsive diseases (AHD), but the mechanism remains unclear. Objective: Due to the pivotal role of miRNAs in AHD, we analyzed the differentially expressed miRNAs (DEmiRs) in RSV-infected patients, asthma patients, and COPD patients from public datasets and explored the mechanisms of association between RSV and AHD. Methods: We obtained miRNA and mRNA databases of patients with RSV infection, as well as miRNA databases of asthma and COPD patients from the GEO database. Through integrated analysis, we screened DEmiRs and DEGs. Further analysis was carried out to obtain the hub genes through the analysis of biological pathways and enrichment pathways of DEGs targeted by DEmiRs and the construction of a protein-protein interaction (PPI) network. Results: The five differential molecules (miR-34b/c-5p, Cd14, Cxcl10, and Rhoh) were verified through in vivo experiments that had the same expression trend in the acute and chronic phases of RSV infection. Following infection of BEAS-2B cells with RSV, we confirmed that RSV infection down-regulated miR-34b/c-5p, and up-regulated the expression levels of CXCL10 and CD14. Furthermore, the results of the dual-luciferase reporter assay showed that CXCL10 was the target of hsa-miR-34c-5p. Conclusions: miR-34b/c-5p/CXCL10 axis mediates a mechanism of AHD.

Anti-Hyperuricemic Effect of Anserine Based on the Gut-Kidney Axis: Integrated Analysis of Metagenomics and Metabolomics

Nowadays, developing effective intervention substances for hyperuricemia has become a public health issue. Herein, the therapeutic ability of anserine, a bioactive peptide, was validated through a comprehensive multiomics analysis of a rat model of hyperuricemia. Anserine was observed to improve liver and kidney function and modulate urate-related transporter expressions in the kidneys. Urine metabolomics showed that 15 and 9 metabolites were significantly increased and decreased, respectively, in hyperuricemic rats after the anserine intervention. Key metabolites such as fructose, xylose, methionine, erythronic acid, glucaric acid, pipecolic acid and trans-ferulic acid were associated with ameliorating kidney injury. Additionally, anserine regularly changed the gut microbiota, thereby ameliorating purine metabolism abnormalities and alleviating inflammatory responses. The integrated multiomics analysis indicated that Saccharomyces, Parasutterella excrementihominis and Emergencia timonensis were strongly associated with key differential metabolites. Therefore, we propose that anserine improved hyperuricemia via the gut-kidney axis, highlighting its potential in preventing and treating hyperuricemia.

Transcriptome and Metabolome Analysis Reveals Salt-Tolerance Pathways in the Leaves and Roots of ZM-4 (Malus zumi) in the Early Stages of Salt Stress

The breeding of salt-tolerant rootstock relies heavily on the availability of salt-tolerant Malus germplasm resources. The first step in developing salt-tolerant resources is to learn their molecular and metabolic underpinnings. Hydroponic seedlings of both ZM-4 (salt-tolerant resource) and M9T337 (salt-sensitive rootstock) were treated with a solution of 75 mM salinity. ZM-4's fresh weight increased, then decreased, and then increased again after being treated with NaCl, whereas M9T337's fresh weight continued to decrease. The results of transcriptome and metabolome after 0 h (CK) and 24 h of NaCl treatment showed that the leaves of ZM-4 had a higher content of flavonoids (phloretinm, naringenin-7-O-glucoside, kaempferol-3-O-galactoside, epiafzelechin, etc.) and the genes (CHI, CYP, FLS, LAR, and ANR) related to the flavonoid synthesis pathway showed up-regulation, suggesting a high antioxidant capacity. In addition to the high polyphenol content (L-phenylalanine, 5-O-p-coumaroyl quinic acid) and the high related gene expression (4CLL9 and SAT), the roots of ZM-4 exhibited a high osmotic adjustment ability. Under normal growing conditions, the roots of ZM-4 contained a higher content of some amino acids (L-proline, tran-4-hydroxy-L-prolin, L-glutamine, etc.) and sugars (D-fructose 6-phosphate, D-glucose 6-phosphate, etc.), and the genes (GLT1, BAM7, INV1, etc.) related to these two pathways were highly expressed. Furthermore, some amino acids (S-(methyl) glutathione, N-methyl-trans-4-hydroxy-L-proline, etc.) and sugars (D-sucrose, maltotriose, etc.) increased and genes (ALD1, BCAT1, AMY1.1, etc.) related to the pathways showed up-regulation under salt stress. This research provided theoretical support for the application of breeding salt-tolerant rootstocks by elucidating the molecular and metabolic mechanisms of salt tolerance during the early stages of salt treatment for ZM-4.

Integrated aqueous humor ceRNA and miRNA-TF-mRNA network analysis reveals potential molecular mechanisms governing primary open-angle glaucoma pathogenesis

Purpose

To conduct an integrated bioinformatics analysis of extant aqueous humor (AH) gene expression datasets in order to identify key genes and the regulatory mechanism governing primary open-angle glaucoma (POAG) progression.

Methods

Two datasets (GSE101727 and GSE105269) were downloaded from the Gene Expression Omnibus, and the messenger RNAs (mRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs) were identified between controls and POAG patients. Differentially expressed (DE) mRNAs and DElncRNAs were then subjected to pathway enrichment analyses, after which a protein-protein interaction (PPI) network was generated. This network was then expanded to establish lncRNA-miRNA-mRNA and miRNA-transcription factor (TF)-mRNA networks.

Results

The GSE101727 dataset was used to identify 2746 DElncRNAs and 2208 DEmRNAs, while the GSE105269 dataset was used to identify 45 DEmiRNAs. We ultimately constructed a competing endogenous RNA (ceRNA) network incorporating 47 lncRNAs, six miRNAs, and 17 mRNAs. The proteins encoded by these 17 hub mRNAs were found to be significantly enriched for activities that may be linked to POAG pathogenesis. In addition, we generated a miRNA-TF-mRNA regulatory network containing two miRNAs (miR-135a-5p and miR-139-5p), five TFs (TGIF2, TCF3, FOS, and so on), and five mRNAs (SHISA7, ST6GAL2, TXNIP, and so on).

Conclusion

The SHISA7, ST6GAL2, TXNIP, FOS, and DCBLD2 genes may be viable therapeutic targets for the prevention or treatment of POAG and are regulated by the TFs (TGIF2, HNF1A, TCF3, and FOS).

The chromosome-level genome of Akebia trifoliata as an important resource to study plant evolution and environmental adaptation in the Cretaceous

The environmental adaptation of eudicots is the most reasonable explanation for why they compose the largest clade of modern plants (>70% of angiosperms), which indicates that the basal eudicots would be valuable and helpful to study their survival and ability to thrive throughout evolutionary processes. Here, we detected two whole-genome duplication (WGD) events in the high-quality assembled Akebia trifoliata genome (652.73 Mb) with 24 138 protein-coding genes based on the evidence of intragenomic and intergenomic collinearity, synonymous substitution rate (K_S ) values and polyploidization and diploidization traces; these events putatively occurred at 85.15 and 146.43 million years ago (Mya). The integrated analysis of 16 species consisting of eight basal and eight core eudicots further revealed that there was a putative ancient WGD at the early stage of eudicots (temporarily designated θ) at 142.72 Mya, similar to the older WGD of Akebia trifoliata, and a putative core eudicot-specific WGD (temporarily designated ω). Functional enrichment analysis of retained duplicate genes following the θ event is suggestive of adaptation to the extreme environment change in both the carbon dioxide concentration and desiccation around the Jurassic-Cretaceous boundary, while the retained duplicate genes following the ω event is suggestive of adaptation to the extreme droughts, possibly leading to the rapid spread of eudicots in the mid-Cretaceous. Collectively, the A. trifoliata genome experienced two WGD events, and the older event may have occurred at the early stage of eudicots, which likely increased plant environmental adaptability and helped them survive in ancient extreme environments.

Construction of m7G subtype classification on heterogeneity of sepsis

Sepsis is a highly heterogeneous disease and a major factor in increasing mortality from infection. N7-Methylguanosine (m7G) is a widely RNA modification in eukaryotes, which involved in regulation of different biological processes. Researchers have found that m7G methylation contributes to a variety of human diseases, but its research in sepsis is still limited. Here, we aim to establish the molecular classification of m7G gene-related sepsis, reveal its heterogeneity and explore the underlying mechanism. We first identified eight m7G related prognostic genes, and identified two different molecular subtypes of sepsis through Consensus Clustering. Among them, the prognosis of C2 subtype is worse than that of C1 subtype. The signal pathways enriched by the two subtypes were analyzed by ssGSEA, and the results showed that the amino acid metabolism activity of C2 subtype was more active than that of C1 subtype. In addition, the difference of immune microenvironment among different subtypes was explored through CIBERSORT algorithm, and the results showed that the contents of macrophages M0 and NK cells activated were significantly increased in C2 subtype, while the content of NK cells resting decreased significantly in C2 subtype. We further explored the relationship between immune regulatory genes and inflammation related genes between C2 subtype and C1 subtype, and found that C2 subtype showed higher expression of immune regulatory genes and inflammation related genes. Finally, we screened the key genes in sepsis by WGCNA analysis, namely NUDT4 and PARN, and verified their expression patterns in sepsis in the datasets GSE131761 and GSE65682. The RT-PCR test further confirmed the increased expression of NUDTA4 in sepsis patients. In conclusion, sepsis clustering based on eight m7G-related genes can well distinguish the heterogeneity of sepsis patients and help guide the personalized treatment of sepsis patients.

Integrated transcriptomic and metabolomic profiling reveals dysregulation of purine metabolism during the acute phase of spinal cord injury in rats

Introduction

Spinal cord injury (SCI) results in drastic dysregulation of microenvironmental metabolism during the acute phase, which greatly affects neural recovery. A better insight into the potential molecular pathways of metabolic dysregulation by multi-omics analysis could help to reveal targets that promote nerve repair and regeneration in the future.

Materials and methods

We established the SCI model and rats were randomly divided into two groups: the acute-phase SCI (ASCI) group (n = 14, 3 days post-SCI) and the sham group with day-matched periods (n = 14, without SCI). In each group, rats were sacrificed at 3 days post-surgery for histology study (n = 3), metabolome sequencing (n = 5), transcriptome sequencing (n = 3), and quantitative real-time polymerase chain reaction (n = 3). The motor function of rats was evaluated by double-blind Basso, Beattie, and Bresnahan (BBB) Locomotor Scores at 0, 1, 2, 3 days post-SCI in an open field area. Then the transcriptomic and metabolomic data were integrated in SCI model of rat to reveal the underlying molecular pathways of microenvironmental metabolic dysregulation.

Results

The histology of the microenvironment was significantly altered in ASCI and the locomotor function was significantly reduced in rats. Metabolomics analysis showed that 360 metabolites were highly altered during the acute phase of SCI, of which 310 were up-regulated and 50 were down-regulated, and bioinformatics analysis revealed that these differential metabolites were mainly enriched in arginine and proline metabolism, D-glutamine and D-glutamate metabolism, purine metabolism, biosynthesis of unsaturated fatty acids. Transcriptomics results showed that 5,963 genes were clearly altered, of which 2,848 genes were up-regulated and 3,115 genes were down-regulated, and these differentially expressed genes were mainly involved in response to stimulus, metabolic process, immune system process. Surprisingly, the Integrative analysis revealed significant dysregulation of purine metabolism at both transcriptome and metabolome levels in the acute phase of SCI, with 48 differential genes and 16 differential metabolites involved. Further analysis indicated that dysregulation of purine metabolism could seriously affect the energy metabolism of the injured microenvironment and increase oxidative stress as well as other responses detrimental to nerve repair and regeneration.

Discussion

On the whole, we have for the first time combined transcriptomics and metabolomics to systematically analyze the potential molecular pathways of metabolic dysregulation in the acute phase of SCI, which will contribute to broaden our understanding of the sophisticated molecular mechanisms of SCI, in parallel with serving as a foundation for future studies of neural repair and regeneration after SCI.

Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets

Background: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. Methods: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. Results: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the “golden ratio” of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. Conclusions: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited.

Integrated analysis identified novel miRNAs and mRNA in endometriosis

OBJECTIVES

Endometriosis is a common gynecological disease that seriously affects women's health and quality of life. However, the pathogenesis of endometriosis remains uncertain. This study aims to find the key microRNAs (miRNAs) and mRNAs and further to elucidate the pathogenesis of endometriosis.

MATERIAL AND METHODS

Differentially expressed mRNAs (DEmRNAs) and the differentially expressed miRNAs (DEmiRNAs) were obtained by Gene Expression Omnibus (GEO) datasets integration analysis. Functional enrichment analysis of DEmRNAs and DEmRNAs targeted by DEmiRNAs was enforced using GeneCodis3. The DEmiRNA-DEmRNA interaction network was built using Cytoscape. The expression of candidate DEmRNA and DEmiRNA was verified using quantitative real time-polymerase chain reaction (QRT-PCR) and online datasets followed by diagnostic and immune cell infiltration analysis.

RESULTS

A total of 835 (327 down-regulated and 508 up-regulated) DEmRNAs and 39 (24 down-regulated and 15 up-regulated) DEmiRNAs were identified between ectopic endometria (EC) group and eutopic endometria (EU) group. DEmRNAs targeted by DEmiRNAs were markedly enriched in cell adhesion molecules, pathways in cancer, leukocyte transendothelial migration, cytokine-cytokine receptor interaction and MAPK signaling pathway. The DEmiRNA-DEmRNA interaction network of up-regulated miRNAs was consisted of 15 miRNAs and 188 corresponding mRNAs. For down-regulated miRNAs, the DEmiRNA-DEmRNA interaction network was consisted of 24 miRNAs and 305 corresponding mRNAs. QRT-PCR validation results of IRF6, PTGER3, NTRK2, hsa-miR-449a and hsa-miR-873-5p were in line with the GEO analysis result. RF6, PTGER3 and NTRK2 had a potential diagnostic value for endometriosis. In addition, the infiltration of macrophages M2 and NK cells activated was the most significantly increased and reduced in ectopic endometrial, respectively.

CONCLUSIONS

These identified DEmRNAs and DEmiRNAs may be may be associated with the pathogenesis of endometriosis. The integrated analysis of miRNA and mRNA expression profiles may provide a new perspective for understanding the mechanisms of endometriosis and developing new treatments.

Genome-wide repertoire of transfer RNA-derived fragments in a mouse model of age-related cataract

Purpose To investigate the roles of tRNA-derived small RNAs (tsRNAs) containing transfer RNA-derived fragments (tRFs) and tRNA halves in age-related cataract (ARC). Methods: Lens capsule tissue from Emory mice at 3 months and 8 months of age were dissected for integrated tsRNA and gene transcriptome sequencing. Quantitative real-time PCR assay (qRT-PCR) was perform for validating sequencing results. Bioinformatics analysis was constructed to reveal the roles of tsRNAs. Results: A total of 422 DE tsRNAs were changed, in which 156 were elevated while 266 were declined in 8-month-old mice. Subsequently, the gene sequencing data exhibited 375 upregulated and 456 downregulated DE genes. Validation by qRT-PCR in 5 selected upregulated tRFs was consistent with tsRNAs sequencing results. Moreover, bioinformatics analysis identified 25 downregulated target genes of the 5 validated tRFs. Furthermore, GO analysis revealed that these target genes were mainly enriched in camera-type eye development, sensory organ development and so on. Conclusion: Our study provide a novel perspective for the role of tsRNAs in pathogenesis of ARC, and thus therapeutic potential targets for ARC.

Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS-EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS-EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS-EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS-EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS-EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS-EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS-EEG data analyses in future research.

Joint Transcriptome and Metabolome Analysis Prevails the Biological Mechanisms Underlying the Pro-Survival Fight in In Vitro Heat-Stressed Granulosa Cells

Previous studies reported the physical, transcriptome, and metabolome changes in in vitro acute heat-stressed (38 °C versus 43 °C for 2 h) bovine granulosa cells. Granulosa cells exhibited transient proliferation senescence, oxidative stress, an increased rate of apoptosis, and a decline in steroidogenic activity. In this study, we performed a joint integration and network analysis of metabolomic and transcriptomic data to further narrow down and elucidate the role of differentially expressed genes, important metabolites, and relevant cellular and metabolic pathways in acute heat-stressed granulosa cells. Among the significant (raw p-value < 0.05) metabolic pathways where metabolites and genes converged, this study found vitamin B6 metabolism, glycine, serine and threonine metabolism, phenylalanine metabolism, arginine biosynthesis, tryptophan metabolism, arginine and proline metabolism, histidine metabolism, and glyoxylate and dicarboxylate metabolism. Important significant convergent biological pathways included ABC transporters and protein digestion and absorption, while functional signaling pathways included cAMP, mTOR, and AMPK signaling pathways together with the ovarian steroidogenesis pathway. Among the cancer pathways, the most important pathway was the central carbon metabolism in cancer. Through multiple analysis queries, progesterone, serotonin, citric acid, pyridoxal, L-lysine, succinic acid, L-glutamine, L-leucine, L-threonine, L-tyrosine, vitamin B6, choline, and CYP1B1, MAOB, VEGFA, WNT11, AOX1, ADCY2, ICAM1, PYGM, SLC2A4, SLC16A3, HSD11B2, and NOS2 appeared to be important enriched metabolites and genes, respectively. These genes, metabolites, and metabolic, cellular, and cell signaling pathways comprehensively elucidate the mechanisms underlying the intricate fight between death and survival in acute heat-stressed bovine granulosa cells and essentially help further our understanding (and will help the future quest) of research in this direction.

What Is Long-Term Survival and Which First-Line Immunotherapy Brings Long-Term Survival for Advanced Wild-Type Non-Small Cell Lung Cancer: A Network Meta-Analysis Based on Integrated Analysis

Background

Immune checkpoint inhibitors (ICIs) have significantly improved survival for advanced wild-type non-small cell lung cancer, but there is no direct comparison to confirm which first-line treatment may lead to the longest overall survival. What qualifies as long-term survival (LS) is even unclear.

Methods

By searching PubMed, Embase, and the Cochrane Central Register of Controlled Trials from January 2005 to December 2020, we included randomized controlled trials (RCTs) of first-line ICI-containing treatments to perform an integrated analysis (IA) to determine the criterion of LS and then screened regimens with LS for network meta-analysis (NMA). The main outcomes for NMA were median overall survival (mOS), 1-year survival rate (1ySR), and 2-year survival rate (2ySR); those for IA were the pooled mOS (POS), 1ySR (P1SR), and 2ySR (P2SR).

Results

By IA of 16 first-line ICIs from 20 RCTs, the POS was 16.20 (95% CI 14.79–17.60) months, with P1SR of 63% (95% CI 59–66%) and P2SR of 37% (33–41%). Thus, we defined LS as mOS ≥ POS (16.20 m) for regimens and screened for RCTs with outcomes meeting this criterion. Eleven ICI-based regimens can bring LS for the overall population, among which ICI with bevacizumab and chemotherapy achieved the longest POS of 19.50 m (16.90–22.10 m) and the highest P1SR (74%, 61%–87%) and P2SR (49%, 38%–61%). Pembrolizumab with chemotherapy ranked first in mOS and 1ySR, while atezolizumab plus bevacizumab and chemotherapy ranked first in 2ySR.

Conclusions

Through the IA of first-line treatment regimens, a POS of 16.20 m can be determined as the LS standard. Further considering 1ySR and 2ySR, atezolizumab combined with bevacizumab and chemotherapy or pembrolizumab plus chemotherapy are likely to bring the longest LS in the overall population, while single ICI may be adequate for patients with a high PD-L1 expression. ICIs with bevacizumab and chemotherapy may be the best combination for LS for its further advantage over time.

Identification of Immune-Related Gene Signature and Prediction of CeRNA Network in Active Ulcerative Colitis

Background

Ulcerative colitis (UC) is an inflammatory disease of the intestinal mucosa, and its incidence is steadily increasing worldwide. Intestinal immune dysfunction has been identified as a central event in UC pathogenesis. However, the underlying mechanisms that regulate dysfunctional immune cells and inflammatory phenotype remain to be fully elucidated.

Methods

Transcriptome profiling of intestinal mucosa biopsies were downloaded from the GEO database. Robust Rank Aggregation (RRA) analysis was performed to identify statistically changed genes and differentially expressed genes (DEGs). Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to explore potential biological mechanisms. CIBERSORT was used to evaluate the proportion of 22 immune cells in biopsies. Weighted co-expression network analysis (WGCNA) was used to determine key module-related clinical traits. Protein-Protein Interaction (PPI) network and Cytoscape were performed to explore protein interaction network and screen hub genes. We used a validation cohort and colitis mouse model to validate hub genes. Several online websites were used to predict competing endogenous RNA (ceRNA) network.

Results

RRA integrated analysis revealed 1838 statistically changed genes from four training cohorts (adj. p-value < 0.05). GSEA showed that statistically changed genes were enriched in the innate immune system. CIBERSORT analysis uncovered an increase in activated dendritic cells (DCs) and M1 macrophages. The red module of WGCNA was considered the most critical module related to active UC. Based on the results of the PPI network and Cytoscape analyses, we identified six critical genes and transcription factor NF-κB. RT-PCR revealed that andrographolide (AGP) significantly inhibited the expression of hub genes. Finally, we identified XIST and three miRNAs (miR-9-5p, miR-129-5p, and miR-340-5p) as therapeutic targets.

Conclusions

Our integrated analysis identified four hub genes (CXCL1, IL1B, MMP1, and MMP10) regulated by NF-κB. We further revealed that AGP decreased the expression of hub genes by inhibiting NF-κB activation. Lastly, we predicted the involvement of ceRNA network in the regulation of NF-κB expression. Collectively, our results provide valuable information in understanding the molecular mechanisms of active UC. Furthermore, we predict the use of AGP and small RNA combination for the treatment of UC.

Integrated co-expression network analysis uncovers novel tissue-specific genes in major depressive disorder and bipolar disorder

Tissue-specific gene expression has been found to be associated with multiple complex diseases including cancer, metabolic disease, aging, etc. However, few studies of brain-tissue-specific gene expression patterns have been reported, especially in psychiatric disorders. In this study, we performed joint analysis on large-scale transcriptome multi-tissue data to investigate tissue-specific expression patterns in major depressive disorder (MDD) and bipolar disorder (BP). We established the strategies of identifying tissues-specific modules, annotated pathways for elucidating biological functions of tissues, and tissue-specific genes based on weighted gene co-expression network analysis (WGCNA) and robust rank aggregation (RRA) with transcriptional profiling data from different human tissues and genome wide association study (GWAS) data, which have been expanded into overlapping tissue-specific modules and genes sharing with MDD and BP. Nine tissue-specific modules were identified and distributed across the four tissues in the MDD and six modules in the BP. In general, the annotated biological functions of differentially expressed genes (DEGs) in blood were mainly involved in MDD and BP progression through immune response, while those in the brain were in neuron and neuroendocrine response. Tissue-specific genes of the prefrontal cortex (PFC) in MDD-, such as IGFBP2 and HTR1A, were involved in disease-related functions, such as response to glucocorticoid, taste transduction, and tissue-specific genes of PFC in BP-, such as CHRM5 and LTB4R2, were involved in neuroactive ligand-receptor interaction. We also found PFC tissue-specific genes including SST and CRHBP were shared in MDD-BP, SST was enriched in neuroactive ligand-receptor interaction, and CRHBP shown was related to the regulation of hormone secretion and hormone transport.

Integration of Urine Proteomic and Metabolomic Profiling Reveals Novel Insights Into Neuroinflammation in Autism Spectrum Disorder

Autism spectrum disorder (ASD) comprises a group of neurodevelopmental disorders whose etiology and pathogenesis are not fully understood. To gain insight into the molecular basis of ASD, we performed comparative integrated proteomic and metabolomic analyses of urine samples from children diagnosed with ASD and healthy children. All 160 samples underwent proteomics analysis and 60 were analyzed by liquid chromatography-mass spectrometry to obtain metabolite profiles. We identified 77 differentially expressed proteins (DEPs; 21 downregulated and 56 upregulated) and 277 differentially expressed metabolites; 31 of the DEPs including glutathione, leukocyte antigens, glycoproteins, neural adhesion factors, and immunoglobulins, have been implicated in neuroinflammation. The proteomic analysis also revealed 8 signaling pathways that were significantly dysregulated in ASD patients; 3 of these (transendothelial leukocyte migration, antigen processing and presentation, and graft vs. host disease) were associated with the neuroimmune response. The metabolism of tryptophan, which is also related to the neuroimmune response, has been found to play a potential role in ASD. Integrated proteome and metabolome analysis showed that 6 signaling pathways were significantly enriched in ASD patients, 3 of which were correlated with impaired neuroinflammation (glutathione metabolism, metabolism of xenobiotics by cytochrome P450 and transendothelial migration of leukocyte). We also found a correlation between prostaglandin (PG) E2 levels and the inflammatory response in ASD. These results underscore the prominent role of the neuroimmune response in ASD and provide potential biomarkers that can be used for diagnosis or as targets for early intervention.

Genome-wide interrogation of transfer RNA-derived small RNAs in a mouse model of traumatic brain injury

Transfer RNA (tRNA)-derived small RNAs (tsRNAs) are a recently established family of regulatory small non-coding RNAs that modulate diverse biological processes. Growing evidence indicates that tsRNAs are involved in neurological disorders and play a role in the pathogenesis of neurodegenerative disease. However, whether tsRNAs are involved in traumatic brain injury-induced secondary injury remains poorly understood. In this study, a mouse controlled cortical impact model of traumatic brain injury was established, and integrated tsRNA and messenger RNA (mRNA) transcriptome sequencing were used. The results revealed that 103 tsRNAs were differentially expressed in the mouse model of traumatic brain injury at 72 hours, of which 56 tsRNAs were upregulated and 47 tsRNAs were downregulated. Based on microRNA-like seed matching and Pearson correlation analysis, 57 differentially expressed tsRNA-mRNA interaction pairs were identified, including 29 tsRNAs and 26 mRNAs. Moreover, Gene Ontology annotation of target genes revealed that the significantly enriched terms were primarily associated with inflammation and synaptic function. Collectively, our findings suggest that tsRNAs may be associated with traumatic brain injury-induced secondary brain injury, and are thus a potential therapeutic target for traumatic brain injury. The study was approved by the Beijing Neurosurgical Institute Animal Care and Use Committee (approval No. 20190411) on April 11, 2019.

Brain and Pituitary Transcriptome Analyses Reveal the Differential Regulation of Reproduction-Related LncRNAs and mRNAs in Cynoglossus semilaevis

Long noncoding RNAs (lncRNAs) have been identified to be involved in half-smooth tongue sole (Cynoglossus semilaevis) reproduction. However, studies of their roles in reproduction have focused mainly on the ovary, and their expression patterns and potential roles in the brain and pituitary are unclear. Thus, to explore the mRNAs and lncRNAs that are closely associated with reproduction in the brain and pituitary, we collected tongue sole brain and pituitary tissues at three stages for RNA sequencing (RNA-seq), the 5,135 and 5,630 differentially expressed (DE) mRNAs and 378 and 532 DE lncRNAs were identified in the brain and pituitary, respectively. The RNA-seq results were verified by RT-qPCR. Moreover, enrichment analyses were performed to analyze the functions of DE mRNAs and lncRNAs. Interestingly, their involvement in pathways related to metabolism, signal transduction and endocrine signaling was revealed. LncRNA-target gene interaction networks were constructed based on antisense, cis and trans regulatory mechanisms. Moreover, we constructed competing endogenous RNA (ceRNA) networks. In summary, this study provides mRNA and lncRNA expression profiles in the brain and pituitary to understand the molecular mechanisms regulating tongue sole reproduction.

Integrative investigation of the TF-miRNA coregulatory network involved in the inhibition of breast cancer cell proliferation by resveratrol

Resveratrol is a polyphenol with antiaging and anticancer effects. Most previous studies used a single analysis to determine the key functions of resveratrol in inhibiting cancer progression. However, most of the signal transmission pathways in biological processes are multilevel. We used bioinformatics to elucidate the mechanism of resveratrol inhibition of breast cancer development. The mRNA expression profile of GSE25412 from the National Center for Biotechnology Information (NCBI) and the microRNA (miRNA) expression profile of PubMed identifier (PMID) 26890143 were integrated. De novo motifs were used to obtain predicted transcription factor (TF) motifs for differentially expressed genes. The regulatory effect of resveratrol on key nodes in the comprehensive analysis results was verified. The TF–miRNA–mRNA interaction network based on the STITCH and miRDB databases showed that resveratrol exerted a dual inhibitory effect by activating inhibitory TFs to block the cell cycle and inhibit miRNAs from upregulating apoptosis. However, these two processes did not work completely independently. TP53 is the dominant hub gene associated with the cell cycle and apoptosis throughout the TF–miRNA network. Kaplan–Meier plotter analysis found that resveratrol‐induced expression changes in key RNAs, such as E2F2, JUN, FOS, BRCA1, CDK1, CDKN1A, TNF, and hsa‐miR‐34a‐5p, significantly improved the prognosis of breast cancer patients, which was further verified using real‐time quantitative PCR (qPCR) and western blotting. This study constructed a TF–miRNA regulatory network with TP53 and E2F as the main central genes to elucidate the molecular mechanism of resveratrol in the treatment of breast cancer.

Bridging the gap between commercial fisheries and survey data to model the spatiotemporal dynamics of marine species

Monitoring and assessment of natural resources often require inputs from multiple data sources. In fisheries science, for example, the inference of a species' abundance distribution relies on two main data sources, namely commercial fisheries and scientific survey data. Despite efforts to combine these data into an integrated statistical model, their coupling is frequently hampered due to differences in their sampling designs, which imposes distinct bias sources in the estimator of the abundance distribution. We developed a flexible species distribution model (SDM) that can integrate both data sources while filtering out their relative bias contributions. We applied the model on three different age groups of the western Baltic cod stock. For each age group, we tested the model on (1) survey data and (2) integrated data (survey + commercial) as a means to compare their differences and investigate how the inclusion of commercial fisheries data improved the spatiotemporal abundance estimator and parameter estimates. Moreover, we proposed a novel validation approach to evaluate whether the inclusion of commercial fisheries data in the integrated model is not in direct contradiction with the survey data. Following our approach, the results indicated that the use of commercial fisheries data is suitable for the integrated model. Across all age groups, our results demonstrated how commercial fisheries supplied additional information on cod's spatiotemporal abundance dynamics, highlighting sometimes abundance hot spots that were not detected by the survey model alone. Additionally, the integrated model provided a reduction of up to 20% and 10% in the uncertainty (SE) of the predicted abundance fields and fixed-effect parameters, respectively. The proposed model represents thus a valuable benchmark for evaluating spatiotemporal dynamics of fish, and strengthens the science-based advice for marine policymakers.

Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle

Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.

Comprehensive Analysis of NAFLD and the Therapeutic Target Identified

Objective: Non-alcoholic fatty liver disease (NAFLD) is a serious health threat worldwide. The aim of this study was to comprehensively describe the metabolic and immunologic characteristics of NAFLD, and to explore potential therapeutic drug targets for NAFLD. Methods: Six NAFLD datasets were downloaded from the Gene Expression Omnibus (GEO) database, including GSE48452, GSE63067, GSE66676, GSE89632, GSE24807, and GSE37031. The datasets we then used to identify and analyze genes that were differentially expressed in samples from patients with NAFLD and normal subjects, followed by analysis of the metabolic and immunologic characteristics of patients with NAFLD. We also identified potential therapeutic drugs for NAFLD using the Connectivity Map (CMAP) database. Moreover, we constructed a prediction model using minimum depth random forest analysis and screened for potential therapeutic targets. Finally, therapeutic targets were verified in a fatty liver model stimulated by palmitic acid (PA). Results: A total of 1,358 differentially expressed genes (DEGs) were obtained, which were mainly enriched in carbohydrate metabolism, lipid metabolism, and other metabolic pathways. Immune infiltration analysis showed that memory B cells, regulatory T cells and M1 macrophage were significantly up-regulated, while T cells follicular helper were down regulated in NAFLD. These may provide a reference for the immune-metabolism interaction in the pathogenesis of NAFLD. Digoxin and helveticoside were identified as potential therapeutic drugs for NAFLD via the CMAP database. In addition, a five-gene prediction model based on minimum depth random forest analysis was constructed, and the receiver operating characteristic (ROC) curves of both training and validation set reached 1. The five candidate therapeutic targets were ENO3, CXCL10, INHBE, LRRC31, and OPTN. Moreover, the efficiency of hepatocyte adipogenesis decreased after OPTN knockout, confirming the potential use of OPTN as a new therapeutic target for NAFLD. Conclusion: This study provides a deeper insight into the molecular pathogenesis of NAFLD. We used five key genes to construct a diagnostic model with a strong predictive effect. Therefore, these five key genes may play an important role in the diagnosis and treatment of NAFLD, particularly those with increased OPTN expression.

Integrated Metabolomic and Transcriptomic Analysis Reveals the Flavonoid Regulatory Network by Eutrema EsMYB90

Flavonoids are representative secondary metabolites with different metabolic functions in plants. Previous study found that ectopic expression of EsMYB90 from Eutremasalsugineum could strongly increase anthocyanin content in transgenic tobacco via regulating the expression of anthocyanin biosynthesis genes. In the present research, metabolome analysis showed that there existed 130 significantly differential metabolites, of which 23 metabolites enhanced more than 1000 times in EsMYB90 transgenic tobacco leaves relative to the control, and the top 10 of the increased metabolites included caffeic acid, cyanidin O-syringic acid, myricetin and naringin. A total of 50 markedly differential flavonoids including flavones (14), flavonols (13), flavone C-glycosides (9), flavanones (7), catechin derivatives (5), anthocyanins (1) and isoflavone (1) were identified, of which 46 metabolites were at a significantly enhanced level. Integrated analysis of metabolome and transcriptome revealed that ectopic expression of EsMYB90 in transgenic tobacco leaves is highly associated with the prominent up-regulation of 16 flavonoid metabolites and the corresponding 42 flavonoid biosynthesis structure genes in phenylpropanoid/flavonoid pathways. Dual luciferase assay documented that EsMYB90 strongly activated the transcription of NtANS and NtDFR genes via improving their promoter activity in transiently expressed tobacco leaves, suggesting that EsMYB90 functions as a key regulator on anthocyanin and flavonoid biosynthesis. Taken together, the crucial regulatory role of EsMYB90 on enhancing many flavonoid metabolite levels is clearly demonstrated via modulating flavonoid biosynthesis gene expression in the leaves of transgenic tobacco, which extends our understanding of the regulating mechanism of MYB transcription factor in the phenylpropanoid/flavonoid pathways and provides a new clue and tool for further investigation and genetic engineering of flavonoid metabolism in plants.

A Potential circRNA-miRNA-mRNA Regulatory Network in Asthmatic Airway Epithelial Cells Identified by Integrated Analysis of Microarray Datasets

Background: Asthma is one of the most prevalent chronic respiratory diseases worldwide. Bronchial epithelial cells play a critical role in the pathogenesis of asthma. Circular RNAs (circRNAs) act as microRNA (miRNA) sponges to regulate downstream gene expression. However, the role of epithelial circRNAs in asthma remains to be investigated. This study aims to explore the potential circRNA-miRNA-messenger RNA (mRNA) regulatory network in asthma by integrated analysis of publicly available microarray datasets.

Methods: Five mRNA microarray datasets derived from bronchial brushing samples from asthma patients and control subjects were downloaded from the Gene Expression Omnibus (GEO) database. The robust rank aggregation (RRA) method was used to identify robust differentially expressed genes (DEGs) in bronchial epithelial cells between asthma patients and controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to annotate the functions of the DEGs. Protein-protein interaction (PPI) analysis was performed to identify hub genes. Three miRNA databases (Targetscan, miRDB, and miRWalk) were used to predict the miRNAs which potentially target the hub genes. A miRNA microarray dataset derived from bronchial brushings was used to validate the miRNA-mRNA relationships. Finally, a circRNA-miRNA-mRNA network was constructed via the ENCORI database.

Results: A total of 127 robust DEGs in bronchial epithelial cells between steroid-naïve asthma patients (n = 272) and healthy controls (n = 165) were identified from five mRNA microarray datasets. Enrichment analyses showed that DEGs were mainly enriched in several biological processes related to asthma, including humoral immune response, salivary secretion, and IL-17 signaling pathway. Nineteen hub genes were identified and were used to construct a potential epithelial circRNA-miRNA-mRNA network. The top 10 competing endogenous RNAs were hsa_circ_0001585, hsa_circ_0078031, hsa_circ_0000552, hsa-miR-30a-3p, hsa-miR-30d-3p, KIT, CD69, ADRA2A, BPIFA1, and GGH.

Conclusion: Our study reveals a potential role for epithelial circRNA-miRNA-mRNA network in the pathogenesis of asthma.

Comprehensive Analysis of Differentially Expressed Genes in Clinically Diagnosed Irreversible Pulpitis by Multiplatform Data Integration Using a Robust Rank Aggregation Approach

INTRODUCTION

Molecular diagnosis may overcome the limitations of clinical and histologic diagnosis in pulpitis, thereby benefiting many treatment techniques, such as vital pulp therapies. In this study, integrated microarray data on pulpitis were used to obtain a list of normalized differentially expressed (DE) genes for analyzing the molecular mechanisms underlying pulpitis and identifying potential diagnostic biomarkers.

METHODS

A systematic search of public microarray and sequencing databases was performed to obtain expression data of pulpitis. Robust rank aggregation (RRA) was used to obtain DE gene lists (RRA_DEmRNAs and RRA_DElncRNAs) between inflamed pulp and normal samples. DE genes were evaluated by functional enrichment analyses, correlation analyses for inflammation-related RRA_DEmRNAs, and protein-protein interaction and competing endogenous RNA network construction. Quantitative real-time polymerase chain reaction validation was applied in snap-frozen pulp tissues.

RESULTS

Using the GSE77459 and GSE92681 data sets, 280 RRA_DEmRNAs and 90 RRA_DElncRNAs were identified. RRA_DEmRNAs were significantly enriched in inflammation-related biological processes and osteoclast differentiation and tumor necrosis factor, chemokine, and B-cell receptor signaling pathways. The molecular complex detection and cytoHubba methods identified 2 clusters and 10 hub genes in the protein-protein interaction network. The competing endogenous RNA network was composed of 2 long noncoding RNAs (ADAMTS9-AS2 and LINC00290), 2 microRNAs (hsa-miR-30a-5p and hsa-miR-128-3p), and 3 messenger RNAs (ABCA1, FBLN5, and SOCS3). The expression between most top inflammation-related RRA_DEmRNAs in pulpitis showed positive correlations. Quantitative real-time polymerase chain reacation validated the expression trends of selected genes, including ITGAX, TREM1, CD86, FCGR2A, ADAMTS9-AS2, LINC00290, hsa-miR-30a-5p, hsa-miR-128-3p, RASGRP3, IL3RA, CCDC178, CRISPLD1, LINC01857, AC007991.2, ARHGEF26-AS1, and AL021408.1.

CONCLUSIONS

The identified biomarkers provide insight into the pathology and may aid in the molecular diagnosis of pulpitis.

Conserved pan-cancer microenvironment subtypes predict response to immunotherapy

The clinical use of molecular targeted therapy is rapidly evolving but has primarily focused on genomic alterations. Transcriptomic analysis offers an opportunity to dissect the complexity of tumors, including the tumor microenvironment (TME), a crucial mediator of cancer progression and therapeutic outcome. TME classification by transcriptomic analysis of >10,000 cancer patients identifies four distinct TME subtypes conserved across 20 different cancers. The TME subtypes correlate with patient response to immunotherapy in multiple cancers, with patients possessing immune-favorable TME subtypes benefiting the most from immunotherapy. Thus, the TME subtypes act as a generalized immunotherapy biomarker across many cancer types due to the inclusion of malignant and microenvironment components. A visual tool integrating transcriptomic and genomic data provides a global tumor portrait, describing the tumor framework, mutational load, immune composition, anti-tumor immunity, and immunosuppressive escape mechanisms. Integrative analyses plus visualization may aid in biomarker discovery and the personalization of therapeutic regimens.

Identification of a Competing Endogenous RNA Network Related to Immune Signature in Lung Adenocarcinoma

Background

The establishment of immunotherapy has led to a new era in oncotherapy. But the signature of immune-related genes (IRGs) in LUAD remains to be elucidated. Here we use integrated analysis to identify IRGs roles in immune signature and detect their relationship with competing endogenous RNA (ceRNA) networks in LUAD progression.

Methods

By analyzing the RNA-seq data from different platforms, we recognized the differentially expressed genes (DEGs) of each platform and screened out the top 20 hub IRGs related to immune responses. Then, we applied the CIBERSORT algorithm to explore the landscape of tumor-infiltrating immune cells (TILs) in LUAD and their connection with hub genes. Next, we predicted and validated the upstream miRNAs and lncRNAs according to their expression and prognostic roles. Finally, we constructed and validated an immune-related ceRNA network by co-expression analysis.

Results

A total of 71 IRGs were identified among 248 DEGs, which play key roles in immune responses. CIBERSORT analysis showed that six hub genes were closely related to TILs, such as SPP1 and naive B cells (R = −0.17), TEK and resting mast cells (R = 0.37). Stepwise prediction and validation from mRNA to lncRNA, including 6 hub genes, 5 miRNAs, and 9 lncRNAs, were applied to construct a ceRNA network. Ultimately, we confirmed the TMPO-AS1/miR-126-5p/SPP1 and CARD8-AS1/miR-21-5p/TEK as immune-related ceRNA networks in LUAD progression.

Conclusion

We elucidated two immune-related ceRNA networks in LUAD progression, which can be considered as immunotherapy targets for this disease.

Differential DNA Methylation and Gene Expression Between ALV-J-Positive and ALV-J-Negative Chickens

Background: Avian leukosis virus subgroup J (ALV-J) is an oncogenic virus that causes serious economic losses in the poultry industry; unfortunately, there is no effective vaccine against ALV-J. DNA methylation plays a crucial role in several biological processes, and an increasing number of diseases have been proven to be related to alterations in DNA methylation. In this study, we screened ALV-J-positive and -negative chickens. Subsequently, we generated and provided the genome-wide gene expression and DNA methylation profiles by MeDIP-seq and RNA-seq of ALV-J-positive and -negative chicken samples; 8,304 differentially methylated regions (DMRs) were identified by MeDIP-seq analysis (p ≤ 0.005) and 515 differentially expressed genes were identified by RNA-seq analysis (p ≤ 0.05). As a result of an integration analysis, we screened six candidate genes to identify ALV-J-negative chickens that possessed differential methylation in the promoter region. Furthermore, TGFB2 played an important role in tumorigenesis and cancer progression, which suggested TGFB2 may be an indicator for identifying ALV-J infections.

Integrated lncRNA and mRNA Transcriptome Analyses in the Ovary of Cynoglossus semilaevis Reveal Genes and Pathways Potentially Involved in Reproduction

Long non-coding RNAs (lncRNAs) have been reported to be involved in multiple biological processes. However, the roles of lncRNAs in the reproduction of half-smooth tongue sole (Cynoglossus semilaevis) are unclear, especially in the molecular regulatory mechanism driving ovarian development and ovulation. Thus, to explore the mRNA and lncRNA mechanisms regulating reproduction, we collected tongue sole ovaries in three stages for RNA sequencing. In stage IV vs. V, we identified 312 differentially expressed (DE) mRNAs and 58 DE lncRNAs. In stage V vs. VI, we identified 1,059 DE mRNAs and 187 DE lncRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DE mRNAs were enriched in ECM-receptor interaction, oocyte meiosis and steroid hormone biosynthesis pathways. Furthermore, we carried out gene set enrichment analysis (GSEA) to identify potential reproduction related-pathways additionally, such as fatty metabolism and retinol metabolism. Based on enrichment analysis, DE mRNAs with a potential role in reproduction were selected and classified into six categories, including signal transduction, cell growth and death, immune response, metabolism, transport and catabolism, and cell junction. The interactions of DE lncRNAs and mRNAs were predicted according to antisense, cis-, and trans-regulatory mechanisms. We constructed a competing endogenous RNA (ceRNA) network. Several lncRNAs were predicted to regulate genes related to reproduction including cyp17a1, cyp19a1, mmp14, pgr, and hsd17b1. The functional enrichment analysis of these target genes of lncRNAs revealed that they were involved in several signaling pathways, such as the TGF-beta, Wnt signaling, and MAPK signaling pathways and reproduction related-pathways such as the progesterone-mediated oocyte maturation, oocyte meiosis, and GnRH signaling pathway. RT-qPCR analysis showed that two lncRNAs (XR_522278.2 and XR_522171.2) were mainly expressed in the ovary. Dual-fluorescence in situ hybridization experiments showed that both XR_522278.2 and XR_522171.2 colocalized with their target genes cyp17a1 and cyp19a1, respectively, in the follicular cell layer. The results further demonstrated that lncRNAs might be involved in the biological processes by modulating gene expression. Taken together, this study provides lncRNA profiles in the ovary of tongue sole and further insight into the role of lncRNA involvement in regulating reproduction in tongue sole.

Comprehensive Gene Expression Analysis Detects Global Reduction of Proteasome Subunits in Schizophrenia

BACKGROUND

The main challenge in the study of schizophrenia is its high heterogeneity. While it is generally accepted that there exist several biological mechanisms that may define distinct schizophrenia subtypes, they have not been identified yet. We performed comprehensive gene expression analysis to search for molecular signals that differentiate schizophrenia patients from healthy controls and examined whether an identified signal was concentrated in a subgroup of the patients.

METHODS

Transcriptome sequencing of 14 superior temporal gyrus (STG) samples of subjects with schizophrenia and 15 matched controls from the Stanley Medical Research Institute (SMRI) was performed. Differential expression and pathway enrichment analysis results were compared to an independent cohort. Replicability was tested on 6 additional independent datasets.

RESULTS

The 2 STG cohorts showed high replicability. Pathway enrichment analysis of the down-regulated genes pointed to proteasome-related pathways. Meta-analysis of differential expression identified down-regulation of 12 of 39 proteasome subunit genes in schizophrenia. The signal of proteasome subunits down-regulation was replicated in 6 additional datasets (overall 8 cohorts with 267 schizophrenia and 266 control samples, from 5 brain regions). The signal was concentrated in a subgroup of patients with schizophrenia.

CONCLUSIONS

We detected global down-regulation of proteasome subunits in a subgroup of patients with schizophrenia. We hypothesize that the down-regulation of proteasome subunits leads to proteasome dysfunction that causes accumulation of ubiquitinated proteins, which has been recently detected in a subgroup of schizophrenia patients. Thus, down-regulation of proteasome subunits might define a biological subtype of schizophrenia.

Integrated analysis of DNA methylation and gene expression profiles identified S100A9 as a potential biomarker in ulcerative colitis

Ulcerative colitis (UC) is a prevalent relapsing-remitting inflammatory bowel disease whose pathogenetic mechanisms remain elusive. In the present study, colonic biopsies samples from three UC patients treated in the Traditional Chinese Medicine Hospital and three healthy controls were obtained. The genome-wide mRNA and lncRNA expression of the samples were profiled through Agilent gene expression microarray. Moreover, the genome-wide DNA methylation dataset of normal and UC colon tissues was also downloaded from GEO for a collaborative analysis. Differential expression of lncRNA (DELs) and mRNAs (DEMs) in UC samples compared with healthy samples were identified by using limma Bioconductor package. Differentially methylated promoters (DMPs) in UC samples compared with controls were obtained through comparing the average methylation level of CpGs located at promoters by using t-test. Functional enrichment analysis was performed by the DAVID. STRING database was applied to the construction of gene functional interaction network. As a result, 2090 DEMs and 1242 DELs were screened out in UC samples that were closely associated with processes related to complement and coagulation cascades, osteoclast differentiation vaccinia, and hemorrhagic diseases. A total of 90 DEMs and 72 DELs were retained for the construction of functional network for the promoters of their corresponding genes were identified as DMPs. S100A9, HECW2, SOD3 and HIX0114733 showed high interaction degrees in the functional network, and expression of S100A9 was confirmed to be significantly elevated in colon tissues of UC patients compared with that of controls by qRT-PCR that was consistent with gene microarray analysis. These indicate that S100A9 could potentially be used as predictive biomarkers in UC.