Differential Co-expression Research Articles

CBDCEM: An effective centrality based differential co-expression method for critical gene finding

Recent years have seen rise in applications of differential co-expression analysis (DCE) for disease biomarker identification. This paper presents a centrality-based hub-gene centric method called Centrality Based Differential Co-Expression Method (CBDCEM), for crucial gene finding for critical diseases. A prominent task of DCE is the identification of hub-gene(s) for each differentially co-expressed module. We propose a consensus-based algorithm that employs seven centrality measures to detect hub-genes. Our method has been validated on three Esophageal Squamous Cell Carcinoma (ESCC) datasets and is found to perform well for denser modules. Through comparison of our proposed hub-gene finding algorithm with 4 other hub-gene finding methods, we have observed that the former was able to identify unique potential biomarkers that were undetected by the rest. CBDCEM is able to identify 15 genes as potential biomarkers for ESCC. Eleven other genes identified by CBDCEM have some evidence of being potential biomarkers for other Squamous Cell Carcinoma and can be considered candidates for ESCC, but require further in-depth validation.

Constructing a prognostic immune-related lncRNA model for colon cancer.

Colon cancer is a common digestive tract tumor. Although many gene prognostic indicators have been used to predict the prognosis of colon cancer patients, the accuracy of these prognostic indicators is still uncertain. Thus, it is necessary to construct a model for the prognostic analysis of colon cancer. We downloaded the original transcriptome data of colon cancer and performed a differential coexpression analysis of immune-related genes to obtain different immune-related long noncoding RNAs, which were paired as differentially expressed immune-related lncRNA pairs (DEirlncRNAPs). Then, the 1-year overall survival rate receiver operating characteristic curve was calculated, and the Akaike information criterion value was evaluated to determine the maximum inflection point, which was used as the cutoff point to identify groups of colon cancer patients at high and low risk for death. Subsequently, the optimal prediction model was established. Finally, we used the patients’ survival times, clinicopathological features, tumor infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers to verify the DEirlncRNAP model. Seventy-one DEirlncRNAPs were obtained to build the risk assessment model. The patients were divided into a high-risk group and a low-risk group according to the cutoff point. Then, the DEirlncRNAP model was verified using patient survival times, clinicopathological features, tumor-infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers. A new DEirlncRNAP model for predicting the prognosis of colon cancer patients was established, which could reveal new insights into the relationships of colon cancer with tumor-infiltrating immune cells and antitumor immunotherapy.

Differential co-expression networks of the gut microbiota are associated with depression and anxiety treatment resistance among psychiatric inpatients

BackgroundComorbid anxiety and depression are common and are associated with greater disease burden than either alone. Our recent efforts have identified an association between gut microbiota dysfunction and severity of anxiety and depression. In this follow-up, we applied Differential Co-Expression Analysis (DiffCoEx) to identify potential gut microbiota biomarker(s) candidates of treatment resistance among psychiatric inpatients. MethodsIn a sample of convenience, 100 psychiatric inpatients provided clinical data at admission and discharge; fecal samples were collected early during the hospitalization. Whole genome shotgun sequencing methods were used to process samples. DiffCoEx was used to identify clusters of microbial features significantly different based on treatment resistance status. Once overlapping features were identified, a knowledge-mining tool was used to review the literature using a list of microbial species/pathways and a select number of medical subject headlines (MeSH) terms relevant for depression, anxiety, and brain-gut-axis dysregulation. Network analysis used overlapping features to identify microbial interactions that could impact treatment resistance. ResultsDiffCoEx analyzed 10,403 bacterial features: 43/44 microbial features associated with depression treatment resistance overlapped with 43/114 microbial features associated with anxiety treatment resistance. Network analysis resulted in 8 biological interactions between 16 bacterial species. Clostridium perfringens evidenced the highest connection strength (0.95). Erysipelotrichaceae bacterium 6_1_45 has been most widely examined, is associated with inflammation and dysbiosis, but has not been associated with depression or anxiety. ConclusionDiffCoEx potentially identified gut bacteria biomarker candidates of depression and anxiety treatment-resistance. Future efforts in psychiatric microbiology should examine the mechanistic relationship of identified pro-inflammatory species, potentially contributing to a biomarker-based algorithm for treatment resistance.

Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome.

Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene–gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene–gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.

Gene Differential Co-Expression Networks Based on RNA-Seq: Construction and Its Applications.

Gene co-expression network (GCN) becomes an increasingly important tool in omics data analysis. A great challenge for GCN construction is that the sample size is far lower than the number of genes. Traditional methods rely on considerable samples. Moreover, association signals are likely weak, nonlinear and stochastic, which are difficult to be identified among thousands of candidates. In this paper, the gray correlation coefficient (GCC) is introduced, and a novel method to construct gene differential co-expression networks (GDCNs) is proposed. Based on the GDCNs, three measures are proposed to explore informative genes. The proposed method can make full use of the information provided by a handful of samples and overcome the shortages of GCNs, which can evaluate the changes of co-expression relationships that are possibly triggered by treatments. Based on RNA-seq data of Brassica napus, GDCNs under multiple experimental conditions are constructed and investigated. It is found that the GCC-based method is very robust to data processing. The GDCNs facilitate the inference of gene functions and the identification of informative genes that are responsible for stress responsiveness. The GDCN-based approaches integrate the 'guilt by association' and the 'guilt by rewiring' rules, which provide alternative tools for omics data analysis.

A Prognostic Ferroptosis-Related lncRNA Model Associated With Immune Infiltration in Colon Cancer.

Colon cancer (CC) is a common malignant tumor worldwide, and ferroptosis plays a vital role in the pathology and progression of CC. Effective prognostic tools are required to guide clinical decision-making in CC. In our study, gene expression and clinical data of CC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed ferroptosis-related lncRNAs using the differential expression and gene co-expression analysis. Then, univariate and multivariate Cox regression analyses were used to identify the effective ferroptosis-related lncRNAs for constructing the prognostic model for CC. Gene set enrichment analysis (GSEA) was conducted to explore the functional enrichment analysis. CIBERSORT and single-sample GSEA were performed to investigate the association between our model and the immune microenvironment. Finally, three ferroptosis-related lncRNAs (XXbac-B476C20.9, TP73-AS1, and SNHG15) were identified to construct the prognostic model. The results of the validation showed that our model was effective in predicting the prognosis of CC patients, which also was an independent prognostic factor for CC. The GSEA analysis showed that several ferroptosis-related pathways were significantly enriched in the low-risk group. Immune infiltration analysis suggested that the level of immune cell infiltration was significantly higher in the high-risk group than that in the low-risk group. In summary, we established a prognostic model based on the ferroptosis-related lncRNAs, which could provide clinical guidance for future laboratory and clinical research on CC.

Identification and validation of a novel cellular senescence-related lncRNA prognostic signature for predicting immunotherapy response in stomach adenocarcinoma

Background: Cellular senescence is a novel hallmark of cancer associated with patient outcomes and tumor immunotherapy. However, the value of cellular senescence-related long non-coding RNAs (lncRNAs) in predicting prognosis and immunotherapy response for stomach adenocarcinoma (STAD) patients needs further investigation. Methods: The transcriptome and corresponding clinical information of STAD and cellular senescence-related genes were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and CellAge databases. Differential expression analysis and coexpression analysis were performed to obtain cellular senescence-related lncRNAs. Univariate regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were conducted to establish the cellular senescence-related lncRNA prognostic signature (CSLPS). Next, the survival curve, ROC curve, and nomogram were developed to assess the capacity of predictive models. Moreover, principal component analysis (PCA), gene set enrichment analysis (GSEA), tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion (TIDE) score analysis were performed between high- and low-risk groups. Results: A novel CSLPS involving fifteen lncRNAs (REPIN1-AS1, AL355574.1, AC104695.3, AL033527.2, AC083902.1, TYMSOS, LINC00460, AC005165.1, AL136115.1, AC007405.2, AL391152.1, SCAT1, AC129507.1, AL121748.1, and ADAMTS9-AS1) was developed. According to the nomogram, the risk model based on the CSLPS was an independent prognostic factor and could predict 1-, 3-, and 5-year overall survival for STAD patients. GSEA suggested that the high-risk group was mainly associated with Toll-like receptor, JAK/STAT, NOD-like receptor, and chemokine signaling pathways. Further analysis revealed that STAD patients in the low-risk group with better clinical outcomes had a higher TMB, higher proportion of high microsatellite instability (MSI-H), better immune infiltration, and lower TIDE scores. Conclusion: A fifteen-CSlncRNA prognostic signature could predict survival outcomes, and patients in the low-risk group may be more sensitive to immunotherapy.

Failure of diet-induced transcriptional adaptations in alpha-synuclein transgenic mice.

Nutritional influences have been discussed as potential modulators of Parkinson's disease (PD) pathology through various epidemiological and physiological studies. In animal models, a high-fat diet (HFD) with greater intake of lipid-derived calories leads to accelerated disease onset and progression. The underlying molecular mechanisms of HFD-induced aggravated pathology, however, remain largely unclear. In this study, we aimed to further illuminate the effects of a fat-enriched diet in PD by examining the brainstem and hippocampal transcriptome of alpha-synuclein transgenic mice exposed to a life-long HFD. Investigating individual transcript isoforms, differential gene expression and co-expression clusters, we observed that transcriptional differences between wild-type (WT) and transgenic animals intensified in both regions under HFD. Both brainstem and hippocampus displayed strikingly similar transcriptomic perturbation patterns. Interestingly, expression differences resulted mainly from responses in WT animals to HFD, while these genes remained largely unchanged or were even slightly oppositely regulated by diet in transgenic animals. Genes and co-expressed gene groups exhibiting this dysregulation were linked to metabolic and mitochondrial pathways. Our findings propose the failure of metabolic adaptions as the potential explanation for accelerated disease unfolding under exposure to HFD. From the identified clusters of co-expressed genes, several candidates lend themselves to further functional investigations.

Regulators of early maize leaf development inferred from transcriptomes of laser capture microdissection (LCM)-isolated embryonic leaf cells

The superior photosynthetic efficiency of C4 leaves over C3 leaves is owing to their unique Kranz anatomy, in which the vein is surrounded by one layer of bundle sheath (BS) cells and one layer of mesophyll (M) cells. Kranz anatomy development starts from three contiguous ground meristem (GM) cells, but its regulators and underlying molecular mechanism are largely unknown. To identify the regulators, we obtained the transcriptomes of 11 maize embryonic leaf cell types from five stages of pre-Kranz cells starting from median GM cells and six stages of pre-M cells starting from undifferentiated cells. Principal component and clustering analyses of transcriptomic data revealed rapid pre-Kranz cell differentiation in the first two stages but slow differentiation in the last three stages, suggesting early Kranz cell fate determination. In contrast, pre-M cells exhibit a more prolonged transcriptional differentiation process. Differential gene expression and coexpression analyses identified gene coexpression modules, one of which included 3 auxin transporter and 18 transcription factor (TF) genes, including known regulators of Kranz anatomy and/or vascular development. In situ hybridization of 11 TF genes validated their expression in early Kranz development. We determined the binding motifs of 15 TFs, predicted TF target gene relationships among the 18 TF and 3 auxin transporter genes, and validated 67 predictions by electrophoresis mobility shift assay. From these data, we constructed a gene regulatory network for Kranz development. Our study sheds light on the regulation of early maize leaf development and provides candidate leaf development regulators for future study.

RETRACTED: hsa-miR-23a∼27a∼24-2 cluster members inhibit aggressiveness of breast cancer cells by commonly targeting NCOA1, NLK and RAP1B

Targeted therapy is receiving considerable attention from the researchers around the globe owing to the increased drug-resistance and incidences of cancer recurrences. MicroRNAs (miRNAs) exhibits tremendous potential as a candidate for molecular targeted therapy in cancer. Unfortunately, majority of research related to microRNAs are focussed on either a particular miRNA or a set of unrelated miRNAs. There is lack of holistic knowledge on differential co-expression of miRNA clusters in regulating the gene expression under physiological conditions. Previously, we reported the cooperative effect of hsa-miR-23a~27a~24-2 cluster in inducing ER (Endoplasmic Reticulum) stress-mediated apoptotic cell death of HEK cells. In the present study, we have investigated the common anti-cancer effects of individual members of this cluster. Our in silico analysis identified twelve common target genes distributed across three independent clusters. Furthermore, we found NCOA1, NLK, and RAP1B to fall in a single cluster with NCOA1 as a central hub molecule. Prognostic analysis showed profound involvement of these three genes in the breast cancer progression and metastasis. We further demonstrated that alteration in the levels of individual members of miR-23a~27a~24-2 cluster commonly regulates the invasive migration of breast cancer cells by modulating EMT and cytoskeletal pathway proteins. Our results reveal a new insight into the therapeutic potential of individual members of the pro-apoptotic hsa-miR-23a~27a~24-2 cluster family against metastatic breast cancer.

Genome-Wide Identification of Brassicaceae Hormone-Related Transcription Factors and Their Roles in Stress Adaptation and Plant Height Regulation in Allotetraploid Rapeseed.

Phytohormone-related transcription factors (TFs) are involved in regulating stress responses and plant growth. However, systematic analysis of these TFs in Brassicaceae is limited, and their functions in stress adaptation and plant height (PH) regulation remain unclear. In this study, 2115 hormone-related TFs were identified in nine Brassicaceae species. Specific domains were found in several Brassicaceae hormone-related TFs, which may be associated with diverse functions. Syntenic analysis indicated that expansion of these genes was mainly caused by segmental duplication, with whole-genome duplication occurring in some species. Differential expression analysis and gene co-expression network analysis identified seven phytohormone-related TFs (BnaWRKY7, 21, 32, 38, 52, BnaGL3-4, and BnaAREB2-5) as possible key genes for cadmium (Cd) toxicity, salinity stress, and potassium (K) and nitrogen (N) deficiencies. Furthermore, BnaWRKY42 and BnaARR21 may play essential roles in plant height. Weighted gene co-expression network analysis (WGCNA) identified 15 phytohormone-related TFs and their potential target genes regulating stress adaptation and plant height. Among the above genes, BnaWRKY56 and BnaWRKY60 responded to four different stresses simultaneously, and BnaWRKY42 was identified in two dwarf rapeseeds. In summary, several candidate genes for stress resistance (BnaWRKY56 and BnaWRKY60) and plant height (BnaWRKY42) were identified. These findings should help elucidate the biological roles of Brassicaceae hormone-related TFs, and the identified candidate genes should provide a genetic resource for the potential development of stress-tolerant and dwarf oilseed plants.

Identification of Pseudo-R genes in Vitis vinifera and characterization of their role as immunomodulators in host-pathogen interactions

IntroductionDuplication events are fundamental to co-evolution in host-pathogen interactions. Pseudogenes (Ψs) are dysfunctional paralogs of functional genes and resistance genes (Rs) in plants are the key to disarming pathogenic invasions. Thus, deciphering the roles of pseudo-R genes in plant defense is momentous. ObjectivesThis study aimed to functionally characterize diverse roles of the resistance Ψs as novel gene footprints and as significant gene regulators in the grapevine genome. MethodsPlantPseudo pipeline and HMM-profiling identified whole-genome duplication-derived (WGD) Ψs associated with resistance genes (Ψ-Rs). Further, novel antifungal and antimicrobial peptides were characterized for fungal associations using protein–protein docking with Erysiphe necator proteins. miRNA and tasiRNA target sites and transcription factor (TF) binding sites were predicted in Ψ-Rs. Finally, differential co-expression patterns in Ψ-Rs-lncRNAs-coding genes were identified using the UPGMA method. Results2,746 Ψ-Rs were identified from 31,032 WGD Ψs in the genome of grapevine. 69-antimicrobial and 81-antifungal novel peptides were generated from Ψ-Rs. The putative genic potential was predicted for five novel antifungal peptides which were further characterized by docking against E. necator proteins. 395 out of 527 resistance loci-specific Ψ-Rs were acting as parental gene mimics. Further, to explore the diverse roles of Ψ-Rs in plant-defense, we identified 37,026 TF-binding sites, 208 miRNA, and 99 tasiRNA targeting sites on these Ψ-Rs. 194 Ψ-Rs were exhibiting tissue-specific expression patterns. The co-expression network analysis between Ψs-lncRNA-genes revealed six out of 79 pathogen-responsive Ψ-Rs as significant during pathogen invasion. ConclusionsOur study provides pathogen responsive Ψ-Rs integral for pathogen invasion, which will offer a useful resource for future experimental validations. In addition, our findings on novel peptide generations from Ψ-Rs offer valuable insights which can serve as a useful resource for predicting novel genes with the futuristic potential of being investigated for their bioactivities in the plant system.

Subtyping and grading of lower-grade gliomas using integrated feature selection and support vector machine.

Classifying lower-grade gliomas (LGGs) is a crucial step for accurate therapeutic intervention. The histopathological classification of various subtypes of LGG, including astrocytoma, oligodendroglioma and oligoastrocytoma, suffers from intraobserver and interobserver variability leading to inaccurate classification and greater risk to patient health. We designed an efficient machine learning-based classification framework to diagnose LGG subtypes and grades using transcriptome data. First, we developed an integrated feature selection method based on correlation and support vector machine (SVM) recursive feature elimination. Then, implementation of the SVM classifier achieved superior accuracy compared with other machine learning frameworks. Most importantly, we found that the accuracy of subtype classification is always high (>90%) in a specific grade rather than in mixed grade (~80%) cancer. Differential co-expression analysis revealed higher heterogeneity in mixed grade cancer, resulting in reduced prediction accuracy. Our findings suggest that it is necessary to identify cancer grades and subtypes to attain a higher classification accuracy. Our six-class classification model efficiently predicts the grades and subtypes with an average accuracy of 91% (±0.02). Furthermore, we identify several predictive biomarkers using co-expression, gene set enrichment and survival analysis, indicating our framework is biologically interpretable and can potentially support the clinician.

Acetylcholine, Fatty Acids, and Lipid Mediators Are Linked to COVID-19 Severity.

Lipid and cholinergic mediators are inflammatory regulators, but their role in the immunopathology of COVID-19 is still unclear. Here, we used human blood and tracheal aspirate (TA) to investigate whether acetylcholine (Ach), fatty acids (FAs), and their derived lipid mediators (LMs) are associated with COVID-19 severity. First, we analyzed the perturbation profile induced by SARS-CoV-2 infection in the transcriptional profile of genes related to the ACh and FA/LM pathways. Blood and TA were used for metabolomic and lipidomic analyses and for quantification of leukocytes, cytokines, and ACh. Differential expression and coexpression gene network data revealed a unique transcriptional profile associated with ACh and FA/LM production, release, and cellular signaling. Transcriptomic data were corroborated by laboratory findings: SARS-CoV-2 infection increased plasma and TA levels of arachidonic acid, 5-hydroxy-6E,8Z,11Z,14Z-eicosatetraenoic acid, 11-hydroxy-5Z,8Z,12E,14Z-eicosatetraenoic acid, and ACh. TA samples also exhibited high levels of PGE2, thromboxane B2, 12-oxo-5Z,8Z,10E,14Z-eicosatetraenoic acid, and 6-trans-leukotriene B4 Bioinformatics and experimental approaches demonstrated robust correlation between transcriptional profile in Ach and FA/LM pathways and parameters of severe COVID-19. As expected, the increased neutrophil-to-lymphocyte ratio, neutrophil counts, and cytokine levels (IL-6, IL-10, IL-1β, and IL-8) correlated with worse clinical scores. Glucocorticoids protected severe and critical patients and correlated with reduced Ach levels in plasma and TA samples. We demonstrated that pulmonary and systemic hyperinflammation in severe COVID-19 are associated with high levels of Ach and FA/LM. Glucocorticoids favored the survival of patients with severe/critical disease, and this effect was associated with a reduction in ACh levels.

Downregulation of the enhancer of zeste homolog 1 transcriptional factor predicts poor prognosis of triple-negative breast cancer patients.

BackgroundTriple-negative breast cancer (TNBC) is the most malignant subtype of breast cancer and lacks effective biomarkers. This study seeks to unravel the expression status and the prospective transcriptional mechanisms of EZH1/EZH2 in TNBC tissue samples. Moreover, another objective of this study is to reveal the prognostic molecular signatures for risk stratification in TNBC patients.MethodsTo determine the expression status of EZH1/EZH2 in TNBC tissue samples, microarray analysis and immunohistochemistry were performed on in house breast cancer tissue samples. External mRNA expression matrices were used to verify its expression patterns. Furthermore, the prospective transcriptional mechanisms of EZH1/EZH2 in TNBC were explored by performing differential expression analysis, co-expression analysis, and chromatin immunoprecipitation sequencing analysis. Kaplan–Meier survival analysis and univariate Cox regression analysis were utilized to detect the prognostic molecular signatures in TNBC patients. Nomogram and time-dependent receiver operating characteristic curves were plotted to predict the risk stratification ability of the prognostic-signatures-based Cox model.ResultsIn-house TMAs (66 TNBC vs. 106 non-TNBC) and external gene microarrays, as well as RNA-seq datasets (1,135 TNBC vs. 6,198 non-TNBC) results, confirmed the downregulation of EZH1 at both the protein and mRNA levels (SMD = −0.59 [−0.80, −0.37]), as is opposite to that of EZH2 (SMD = 0.74 [0.40, 1.08]). The upregulated transcriptional target genes of EZH1 were significantly aggregated in the cell cycle pathway, where CCNA2, CCNB1, MAD2L1, and PKMYT1 were determined as key transcriptional targets. Additionally, the downregulated transcriptional targets of EZH2 were enriched in response to the hormone, where ESR1 was identified as the hub gene. The six-signature-based prognostic model produced an impressive performance in this study, with a training AUC of 0.753, 0.981, and 0.977 at 3-, 5-, and 10-year survival probability, respectively.ConclusionEZH1 downregulation may be a key modulator in the progression of TNBC through negative transcriptional regulation by targeting CCNA2, CCNB1, MAD2L1, and PKMYT1.

Differential Co-Expression Analysis of RNA-Seq Data Reveals Novel Potential Biomarkers of Device-Tissue Interaction.

The biological response to electrodes implanted in the brain has been a long-standing barrier to achieving a stable tissue device-interface. Understanding the mechanisms underlying this response could explain phenomena including recording instability and loss, shifting stimulation thresholds, off-target effects of neuromodulation, and stimulation-induced depression of neural excitability. Our prior work detected differential expression in hundreds of genes following device implantation. Here, we extend upon that work by providing new analyses using differential co-expression analysis, which identifies changes in the correlation structure between groups of genes detected at the interface in comparison to control tissues. We used an "eigengene" approach to identify hub genes associated with each module. Our work adds to a growing body of literature which applies new techniques in molecular biology and computational analysis to long-standing issues surrounding electrode integration with the brain.

Altered Transcriptional Regulation of Glycolysis in Circulating CD8+ T Cells of Rheumatoid Arthritis Patients.

Peripheral T lymphocytes of rheumatoid arthritis (RA) patients show pathological changes in their metabolic pathways, especially glycolysis. These changes may drive the increased proliferation and tissue invasiveness of RA T cells. In order to study the transcriptional regulation underlying these alterations, we analysed publicly available RNA sequencing data from circulating T lymphocyte subsets of healthy individuals, untreated RA patients, and patients undergoing treatment for RA. Differential co-expression networks were created using sample-wise edge weights from an analysis called “linear interpolation to obtain network estimates for single sample” (lionessR), and annotated using the Gene Transcription Regulation Database (GTRD). Genes with high centrality scores were identified. CD8+ effector memory cells (Tem) and CD8+CD45RA+ effector memory cells (Temra) showed large changes in the transcriptional regulation of glycolysis in untreated RA. PFKFB3 and GAPDH were differentially regulated and had high centrality scores in CD8+ Tem cells. PFKFB3 downregulation may be due to HIF1A post transcriptional inhibition. Tocilizumab treatment partially reversed the RA-associated differential expression of several metabolic and regulatory genes. MYC was upregulated and had high centrality scores in RA CD8+ Temra cells; however, its glycolysis targets were unaltered. The upregulation of the PI3K-AKT and mTOR pathways may explain MYC upregulation.

Crystallin Mu in Medial Amygdala Mediates the Effect of Social Experience on Cocaine Seeking in Males but Not in Females

BackgroundSocial experiences influence susceptibility to substance use disorder. The adolescent period is associated with the development of social reward and is exceptionally sensitive to disruptions to reward-associated behaviors by social experiences. Social isolation (SI) during adolescence alters anxiety- and reward-related behaviors in adult males, but little is known about females. The medial amygdala (meA) is a likely candidate for the modulation of social influence on drug reward because it regulates social reward, develops during adolescence, and is sensitive to social stress. However, little is known regarding how the meA responds to drugs of abuse. MethodsWe used adolescent SI coupled with RNA sequencing to better understand the molecular mechanisms underlying meA regulation of social influence on reward. ResultsWe show that SI in adolescence, a well-established preclinical model for addiction susceptibility, enhances preference for cocaine in male but not in female mice and alters cocaine-induced protein and transcriptional profiles within the adult meA particularly in males. To determine whether transcriptional mechanisms within the meA are important for these behavioral effects, we manipulated Crym expression, a sex-specific key driver gene identified through differential gene expression and coexpression network analyses, specifically in meA neurons. Overexpression of Crym, but not another key driver that did not meet our sex-specific criteria, recapitulated the behavioral and transcriptional effects of adolescent SI. ConclusionsThese results show that the meA is essential for modulating the sex-specific effects of social experience on drug reward and establish Crym as a critical mediator of sex-specific behavioral and transcriptional plasticity.

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm.

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.

Small RNA Sequencing in the Tg4-42 Mouse Model Suggests the Involvement of snoRNAs in the Etiology of Alzheimer's Disease.

The Tg4-42 mouse model for sporadic Alzheimer's disease (AD) has unique features, as the neuronal expression of wild type N-truncated Aβ4-42 induces an AD-typical neurological phenotype in the absence of plaques. It is one of the few models developing neuron death in the CA1 region of the hippocampus. As such, it could serve as a powerful tool for preclinical drug testing and identification of the underlying molecular pathways that drive the pathology of AD. The aim of this study was to use a differential co-expression analysis approach for analyzing a small RNA sequencing dataset from a well-established murine model in order to identify potentially new players in the etiology of AD. To investigate small nucleolar RNAs in the hippocampus of Tg4-42 mice, we used RNA-Seq data from this particular tissue and, instead of analyzing the data at single gene level, employed differential co-expression analysis, which takes the comparison to gene pair level and thus affords a new angle to the interpretation of these data. We identified two clusters of differentially correlated small RNAs, including Snord55, Snord57, Snord49a, Snord12, Snord38a, Snord99, Snord87, Mir1981, Mir106b, Mir30d, Mir598, and Mir99b. Interestingly, some of them have been reported to be functionally relevant in AD pathogenesis, as AD biomarkers, regulating tau phosphorylation, TGF-β receptor function or Aβ metabolism. The majority of snoRNAs for which our results suggest a potential role in the etiology of AD were so far not conspicuously implicated in the context of AD pathogenesis and could thus point towards interesting new avenues of research in this field.