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Graph: Figure 4 Correlation of genewise cell type enrichment measures with PubMed text mining results within cell types. Several gene symbols were chosen for highlighting, including gene symbols that have not been mentioned in a PubMed abstract with that cell type to date (labeled red). For each of the six cell types, i.e. the astrocyte (a), endothelial cell (b), microglia (c), neuron (d), oligodendrocyte (e), and oligodendrocyte precursor cell (f), the top 100 gene symbols most enriched in that cell type are plotted against the number of PubMed abstracts that mention both that gene symbol as well as the corresponding cell type. [Extracted from the article]

Motivation Availability of large-scale genomic, epigenetic and proteomic data in complex diseases makes it possible to objectively and comprehensively identify the therapeutic targets that can lead to new therapies. The Connectivity Map has been widely used to explore novel indications of existing drugs. However, the prediction accuracy of the existing methods, such as Kolmogorov–Smirnov statistic remains low. Here we present a novel high-performance drug repositioning approach that improves over the state-of-the-art methods. Results We first designed an expression weighted cosine (EWCos) method to minimize the influence of the uninformative expression changes and then developed an ensemble approach termed ensemble of multiple drug repositioning approaches (EMUDRA) to integrate EWCos and three existing state-of-the-art methods. EMUDRA significantly outperformed individual drug repositioning methods when applied to simulated and independent evaluation datasets. We predicted using EMUDRA and experimentally validated an antibiotic rifabutin as an inhibitor of cell growth in triple negative breast cancer. EMUDRA can identify drugs that more effectively target disease gene signatures and will thus be a useful tool for identifying novel therapies for complex diseases and predicting new indications for existing drugs. Availability and implementation The EMUDRA R package is available at doi: 10.7303/syn11510888. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

During the neonatal period, the ability to generate immune effector and memory responses to vaccines or pathogens is often questioned. This study was undertaken to obtain a global view of the natural differences in the expression of immune genes early in life. Our hypothesis was that transcriptome analyses of peripheral blood mononuclear cells (PBMCs) of foals (on day 1 and day 42 after birth) and adult horses would show differential gene expression profiles that characterize natural immune processes. Gene ontology enrichment analysis provided assessment of biological processes affected by age, and a list of 897 genes with ≥2 fold higher (p<0.01) expression in day 42 when compared to day 1 foal samples. Up-regulated genes included B cell and T cell receptor diversity genes; DNA replication enzymes; natural killer cell receptors; granzyme B and perforin; complement receptors; immunomodulatory receptors; cell adhesion molecules; and cytokines/chemokines and their receptors. The list of 1,383 genes that had higher (p<0.01) expression on day 1 when compared to day 42 foal samples was populated by genes with roles in innate immunity such as antimicrobial proteins; pathogen recognition receptors; cytokines/chemokines and their receptors; cell adhesion molecules; co-stimulatory molecules; and T cell receptor delta chain. Within the 742 genes with increased expression between day 42 foal and adult samples, B cell immunity was the main biological process (p = 2.4E-04). Novel data on markedly low (p<0.0001) TLR3 gene expression, and high (p≤0.01) expression of IL27, IL13RA1, IREM-1, SIRL-1, and SIRPα on day 1 compared to day 42 foal samples point out potential mechanisms of increased susceptibility to pathogens in early life. The results portray a progression from innate immune gene expression predominance early in life to adaptive immune gene expression increasing with age with a putative overlay of immune suppressing genes in the neonatal phase. These results provide insight to the unique attributes of the equine neonatal and young immune system, and offer many avenues of future investigation. [ABSTRACT FROM AUTHOR]

Tri-methylation on histone H3 lysine 4 (H3K4me3) is associated with active gene expression but its regulatory role in transcriptional activation is unclear. Here we used Caenorhabditis elegans to investigate the connection between H3K4me3 and gene expression regulation during aging. We uncovered around 30% of H3K4me3 enriched regions to show significant and reproducible changes with age. We further showed that these age-dynamic H3K4me3 regions largely mark gene-bodies and are acquired during adult stages. We found that these adult-specific age-dynamic H3K4me3 regions are correlated with gene expression changes with age. In contrast, H3K4me3 marking established during developmental stages remained largely stable with age, even when the H3K4me3 associated genes exhibited RNA expression changes during aging. Importantly, the genes associated with changes in H3K4me3 and RNA levels with age are enriched for functional groups commonly implicated in aging biology. Therefore, our findings suggested divergent roles of H3K4me3 in gene expression regulation during aging, with important implications on aging-dependent pathophysiologies. [ABSTRACT FROM AUTHOR]

Background: As one of the most common types of co-regulatory motifs, feed-forward loops (FFLs) control many cell functions and play an important role in human cancers. Therefore, it is crucial to reconstruct and analyze cancer-related FFLs that are controlled by transcription factor (TF) and microRNA (miRNA) simultaneously, in order to find out how miRNAs and TFs cooperate with each other in cancer cells and how they contribute to carcinogenesis. Current FFL studies rely on predicted regulation information and therefore suffer the false positive issue in prediction results. More critically, FFLs generated by existing approaches cannot represent the dynamic and conditional regulation relationship under different experimental conditions. Methodology/Principal Findings: In this study, we proposed a novel filter-wrapper feature selection method to accurately identify co-regulatory mechanism by incorporating prior information from predicted regulatory interactions with parallel miRNA/mRNA expression datasets. By applying this method, we reconstructed 208 and 110 TF-miRNA co-regulatory FFLs from human pan-cancer and prostate datasets, respectively. Further analysis of these cancer-related FFLs showed that the top-ranking TF STAT3 and miRNA hsa-let-7e are key regulators implicated in human cancers, which have regulated targets significantly enriched in cellular process regulations and signaling pathways that are involved in carcinogenesis. Conclusions/Significance: In this study, we introduced an efficient computational approach to reconstruct co-regulatory FFLs by accurately identifying gene co-regulatory interactions. The strength of the proposed feature selection method lies in the fact it can precisely filter out false positives in predicted regulatory interactions by quantitatively modeling the complex co-regulation of target genes mediated by TFs and miRNAs simultaneously. Moreover, the proposed feature selection method can be generally applied to other gene regulation studies using parallel expression data with respect to different biological contexts. [ABSTRACT FROM AUTHOR]

Background: Cerebral amyloidosis, neuroinflammation, and tauopathy are key features of Alzheimer’s disease (AD), but interactions among these features remain poorly understood. Our previous multiscale molecular network models of AD revealed TYROBP as a key driver of an immune- and microglia-specific network that was robustly associated with AD pathophysiology. Recent genetic studies of AD further identified pathogenic mutations in both TREM2 and TYROBP. Methods: In this study, we systematically examined molecular and pathological interactions among Aβ, tau, TREM2, and TYROBP by integrating signatures from transgenic Drosophila models of AD and transcriptome-wide gene co-expression networks from two human AD cohorts. Results: Glial expression of TREM2/TYROBP exacerbated tau-mediated neurodegeneration and synergistically affected pathways underlying late-onset AD pathology, while neuronal Aβ42 and glial TREM2/TYROBP synergistically altered expression of the genes in synaptic function and immune modules in AD. Conclusions: The comprehensive pathological and molecular data generated through this study strongly validate the causal role of TREM2/TYROBP in driving molecular networks in AD and AD-related phenotypes in flies. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a progressive and age-associated neurodegenerative disorder that affects women disproportionally. However, the underlying mechanisms are poorly characterized. Moreover, while the interplay between sex and ApoE genotype in AD has been investigated, multi-omics studies to understand this interaction are limited. Therefore, we applied systems biology approaches to investigate sex-specific molecular networks of AD. Methods: We integrated large-scale human postmortem brain transcriptomic data of AD from two cohorts (MSBB and ROSMAP) via multiscale network analysis and identified key drivers with sexually dimorphic expression patterns and/or different responses to APOE genotypes between sexes. The expression patterns and functional relevance of the top sex-specific network driver of AD were further investigated using postmortem human brain samples and gene perturbation experiments in AD mouse models. Results: Gene expression changes in AD versus control were identified for each sex. Gene co-expression networks were constructed for each sex to identify AD-associated co-expressed gene modules shared by males and females or specific to each sex. Key network regulators were further identified as potential drivers of sex differences in AD development. LRP10 was identified as a top driver of the sex differences in AD pathogenesis and manifestation. Changes of LRP10 expression at the mRNA and protein levels were further validated in human AD brain samples. Gene perturbation experiments in EFAD mouse models demonstrated that LRP10 differentially affected cognitive function and AD pathology in sex- and APOE genotype-specific manners. A comprehensive mapping of brain cells in LRP10 over-expressed (OE) female E4FAD mice suggested neurons and microglia as the most affected cell populations. The female-specific targets of LRP10 identified from the single cell RNA-sequencing (scRNA-seq) data of the LRP10 OE E4FAD mouse brains were significantly enriched in the LRP10-centered subnetworks in female AD subjects, validating LRP10 as a key network regulator of AD in females. Eight LRP10 binding partners were identified by the yeast two-hybrid system screening, and LRP10 over-expression reduced the association of LRP10 with one binding partner CD34. Conclusions: These findings provide insights into key mechanisms mediating sex differences in AD pathogenesis and will facilitate the development of sex- and APOE genotype-specific therapies for AD. [ABSTRACT FROM AUTHOR]

Recent multiscale network analyses of banked brains from subjects who died of late-onset sporadic Alzheimer's disease converged on VGF (non-acronymic) as a key hub or driver. Within this computational VGF network, we identified the dual-specificity protein phosphatase 4 (DUSP4) [also known as mitogen-activated protein kinase (MAPK) phosphatase 2] as an important node. Importantly, DUSP4 gene expression, like that of VGF, is downregulated in postmortem Alzheimer's disease (AD) brains. We investigated the roles that this VGF/DUSP4 network plays in the development of learning behavior impairment and neuropathology in the 5xFAD amyloidopathy mouse model. We found reductions in DUSP4 expression in the hippocampi of male AD subjects, correlating with increased CDR scores, and in 4-month-old female and 12–18-month-old male 5xFAD hippocampi. Adeno-associated virus (AAV5)-mediated overexpression of DUSP4 in 5xFAD mouse dorsal hippocampi (dHc) rescued impaired Barnes maze performance in females but not in males, while amyloid loads were reduced in both females and males. Bulk RNA sequencing of the dHc from 5-month-old mice overexpressing DUSP4, and Ingenuity Pathway and Enrichr analyses of differentially expressed genes (DEGs), revealed that DUSP4 reduced gene expression in female 5xFAD mice in neuroinflammatory, interferon-gamma (IFNγ), programmed cell death protein-ligand 1/programmed cell death protein 1 (PD-L1/PD-1), and extracellular signal-regulated kinase (ERK)/MAPK pathways, via which DUSP4 may modulate AD phenotype with gender-specificity. [ABSTRACT FROM AUTHOR]

Purpose: Osteosarcoma is a type of bone malignancy that mainly occurred in teenagers. This investigation is aimed to clarify the effect of long non-coding RNA (lncRNA) LINC00662 on the proliferation, migration, and invasion in osteosarcoma and explore the underlying action mechanisms. Methods: The mRNA expression of LINC00662 was determined by real-time quantitative polymerase chain reaction. Cell proliferation, migration, and invasion were evaluated by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, wound healing, and transwell assays, respectively. A dual-luciferase reporter assay was used to validate the target relationships Between microRNA (miR)-30b-3p and LINC00662/ ETS domain-containing protein 1 (ELK1). Western blotting was performed to determine the protein expression of ELK1. Xenograft model was established to evaluate the effects of LINC00662 silencing on tumor growth in vivo. Results: LncRNA LINC00662 and ELK1 were significantly increased, while miR-30b-3p was reduced in osteosarcoma tissues. The results of functional experiments indicated that transfection of small hairpin (sh)-LINC00662 and miR-30b-3p mimics repressed the migration, invasion, and proliferation of osteosarcoma cells. LncRNA LINC00662 also appeared to sponge miR-30b-3p in order to affect the expression of ELK1. Simultaneously, there were weak negative correlations between the expression of miR-30b-3p and LINC00662/ELK1 in osteosarcoma tissues. Rescue experiments suggested that ELK1 overexpression and downregulation of miR-30b-3p reversed the suppressive effects of sh-LINC00662 on the cell migration, invasion, and proliferation in osteosarcoma. Conclusions: The current study indicated that knockdown of LINC00662 repressed cell migration, invasion, and proliferation through sponging miR-30b-3p to regulate the expression of ELK1 in osteosarcoma. These results may uncover a promising target for the treatment of osteosarcoma. [ABSTRACT FROM AUTHOR]

Tartaric acid (TA) is a major non-fermentable plant soluble acid that abundantly occur in grapes and wines, imparting low pH and tart flavour to berries thereby regulating numerous quality attributes of wine, such as flavour, microbial stability, and aging potential. Evaluation of acidity in mature fruits of 21 wine grape (Vitis vinifera) varieties revealed significant variation between 'Beichun' and 'Gewürztraminer', which was correlated with TA content. RNA-seq analysis of fruits from the two cultivars at different developmental stages revealed that a transketolase gene, VvTK2 , was significantly dominantly expressed in the high TA phenotype 'Beichun' variety. Subcellular localization assay showed that VvTK2 protein was located in the chloroplast. Virus-induced VvTK2 gene silencing significantly decreased the expression of 2-keto-L-gulonic acid reductase (Vv2-KGR) as well as L-idonate dehydrogenase (VvL-IdnDH3) and inhibited TA accumulation, while its transient over-expression in grape showed the opposite results. Heterologous VvTK2 over-expression in tomato demonstrated its obvious capacity to induce TA synthesis. Overall, these results highlights a novel role of VvTK2 in modulating TA biosynthesis, which could be an excellent strategy for future genetic improvement of grape flavour. [ABSTRACT FROM AUTHOR]

Background and objective Breast cancer is a leading cause of death from cancer for females. The high mortality rate of breast cancer is largely due to the complexity among invasive breast cancer and its significantly varied clinical outcomes. Therefore, improving the accuracy of breast cancer survival prediction has important significance and becomes one of the major research areas. Nowadays many computational models have been proposed for breast cancer survival prediction, however, most of them generate the predictive models by employing only the genomic data information and few of them consider the complementary information from pathological images. Methods In our study, we introduce a novel method called GPMKL based on multiple kernel learning (MKL), which efficiently employs heterogeneous information containing genomic data (gene expression, copy number alteration, gene methylation, protein expression) and pathological images. With above heterogeneous features, GPMKL is proposed to execute feature fusion which is embedded in breast cancer classification. Results Performance analysis of the GPMKL model indicates that the pathological image information plays a critical part in accurately predicting the survival time of breast cancer patients. Furthermore, the proposed method is compared with other existing breast cancer survival prediction methods, and the results demonstrate that the proposed framework with pathological images performs remarkably better than the existing survival prediction methods. Conclusions All results performed in our study suggest that the usefulness and superiority of GPMKL in predicting human breast cancer survival. [ABSTRACT FROM AUTHOR]

Background: Members of the cystatin family have increasingly been proven to be involved in several tumors, including gastric cancer (GC) and colorectal cancer (CRC). Cystatin S (CST4) was found to be upregulated at the gene expression level in GC cells, making it a potential novel biomarker for the early diagnosis of gastrointestinal cancer. Materials and methods: Quantitative real-time polymerase chain reaction and Western blotting analysis were used to explore CST4 expression in gastrointestinal cancer tissues and cell lines. We purified CST4 recombinant protein and generated anti-CST4 monoclonal antibodies to develop an antibody-sandwich enzyme-linked immunosorbent assay (ELISA) analysis system for blood CST4 detection. The performance and clinical efficacy of the detection method were evaluated using a training set and validation set, respectively. Results: According to the quantitative real-time polymerase chain reaction and Western blotting results, CST4-mRNA expression and protein expression were upregulated in gastrointestinal cancer tissues and cell lines. The ELISA detection system for CST4 showed significantly better sensitivities of 69.0% and 69.0% and specificities of 85.6% and 83.6% for GC and CRC, respectively, than other common clinical biomarkers, carcinoembryonic antigen, CA19-9, CA125, and CA72-4. Clinical verification experiments using GC and CRC validation sets also found distinguishable CST4 median concentrations (177.7 pg·mL-1 and 174.2 pg·mL-1 respectively) and high positive detection rates (72.3% and 88.4% respectively), further confirming the specificity and sensitivity of this method. Conclusion: We validated the overexpression of CST4 in gastrointestinal cancer tissues and cell lines and developed an antibody-sandwich ELISA analysis system for blood CST4 detection, which exhibited high specificity and sensitivity. Novel blood biomarkers of CST4 have enormous potential in terms of clinical diagnostic value in GC and CRC. [ABSTRACT FROM AUTHOR]

Conventional genetic approaches and computational strategies have converged on immune-inflammatory pathways as key events in the pathogenesis of late onset sporadic Alzheimer's disease (LOAD). Mutations and/or differential expression of microglial specific receptors such as TREM2, CD33, and CR3 have been associated with strong increased risk for developing Alzheimer's disease (AD). DAP12 (DNAX-activating protein 12)/TYROBP, a molecule localized to microglia, is a direct partner/adapter for TREM2, CD33, and CR3. We and others have previously shown that TYROBP expression is increased in AD patients and in mouse models. Moreover, missense mutations in the coding region of TYROBP have recently been identified in some AD patients. These lines of evidence, along with computational analysis of LOAD brain gene expression, point to DAP12/TYROBP as a potential hub or driver protein in the pathogenesis of AD. Using a comprehensive panel of biochemical, physiological, behavioral, and transcriptomic assays, we evaluated in a mouse model the role of TYROBP in early stage AD. We crossed an Alzheimer's model mutant APP /PSEN1 (APP/PSEN1) mouse model with Tyrobp mice to generate AD model mice deficient or null for TYROBP ( APP/PSEN1; Tyrobp or APP/PSEN1; Tyrobp ). While we observed relatively minor effects of TYROBP deficiency on steady-state levels of amyloid-β peptides, there was an effect of Tyrobp deficiency on the morphology of amyloid deposits resembling that reported by others for Trem2 mice. We identified modulatory effects of TYROBP deficiency on the level of phosphorylation of TAU that was accompanied by a reduction in the severity of neuritic dystrophy. TYROBP deficiency also altered the expression of several AD related genes, including Cd33. Electrophysiological abnormalities and learning behavior deficits associated with APP/PSEN1 transgenes were greatly attenuated on a Tyrobp-null background. Some modulatory effects of TYROBP on Alzheimer's-related genes were only apparent on a background of mice with cerebral amyloidosis due to overexpression of mutant APP/PSEN1. These results suggest that reduction of TYROBP gene expression and/or protein levels could represent an immune-inflammatory therapeutic opportunity for modulating early stage LOAD, potentially leading to slowing or arresting the progression to full-blown clinical and pathological LOAD. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) play important roles in the pathogenesis of many types of cancers by negatively regulating gene expression at posttranscriptional level. Here, we identified that miR-599 is up-regulated in non-small cell lung cancer (NSCLC) patients. It promoted NSCLC cell proliferation by negatively regulating SATB2. In NSCLC cell lines, CCK-8 proliferation assay indicated that the cell proliferation is promoted by miR-599 mimics. Transwell assay showed that miR-599 mimics promoted the invasion and migration of NSCLC cells. Luciferase assays confirmed that miR-599 directly binds to the 3'untranslated region of SATB2, and western blotting showed that miR-599 suppresses the expression of SATB2 at the protein level. This study indicates that miR-599 promotes proliferation and invasion of NSCLC cell lines via SATB2. The miR-599 may represent a potential therapeutic target for NSCLC treatment. [ABSTRACT FROM AUTHOR]

Summary Depression is a complex, heterogeneous disorder and a leading contributor to the global burden of disease. Most previous research has focused on individual brain regions and genes contributing to depression. However, emerging evidence in humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here, we performed RNA sequencing on four brain regions from control animals and those susceptible or resilient to chronic social defeat stress at multiple time points. We employed an integrative network biology approach to identify transcriptional networks and key driver genes that regulate susceptibility to depressive-like symptoms. Further, we validated in vivo several key drivers and their associated transcriptional networks that regulate depression susceptibility and confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our study reveals novel transcriptional networks that control stress susceptibility and offers fundamentally new leads for antidepressant drug discovery. [ABSTRACT FROM AUTHOR]

Recent study shows that long noncoding RNAs (lncRNAs) are participating in diverse biological processes and complex diseases. However, at present the functions of lncRNAs are still rarely known. In this study, we propose a network-based computational method, which is called lncRNA-protein interaction prediction based on Heterogeneous Network Model (LPIHN), to predict the potential lncRNA-protein interactions. First, we construct a heterogeneous network by integrating the lncRNA-lncRNA similarity network, lncRNA-protein interaction network, and protein-protein interaction (PPI) network. Then, a random walk with restart is implemented on the heterogeneous network to infer novel lncRNA-protein interactions. The leave-one-out cross validation test shows that our approach can achieve an AUC value of 96.0%. Some lncRNA-protein interactions predicted by our method have been confirmed in recent research or database, indicating the efficiency of LPIHN to predict novel lncRNA-protein interactions. [ABSTRACT FROM AUTHOR]

Background A major step in the higher plant life cycle is the decision to leave the mitotic cell cycle and begin the progression through the meiotic cell cycle that leads to the formation of gametes. The molecular mechanisms that regulate this transition and early meiosis remain largely unknown. To gain insight into gene expression features during the initiation of meiotic recombination, we profiled early prophase I meiocytes from maize (Zea mays) using capillary collection to isolate meiocytes, followed by RNA-seq. Results We detected ~2,000 genes as preferentially expressed during early meiotic prophase, most of them uncharacterized. Functional analysis uncovered the importance of several cellular processes in early meiosis. Processes significantly enriched in isolated meiocytes included proteolysis, protein targeting, chromatin modification and the regulation of redox homeostasis. The most significantly up-regulated processes in meiocytes were processes involved in carbohydrate metabolism. Consistent with this, many mitochondrial genes were up-regulated in meiocytes, including nuclear- and mitochondrial-encoded genes. The data were validated with real-time PCR and in situ hybridization and also used to generate a candidate maize homologue list of known meiotic genes from Arabidopsis. Conclusions Taken together, we present a high-resolution analysis of the transcriptome landscape in early meiosis of an important crop plant, providing support for choosing genes for detailed characterization of recombination initiation and regulation of early meiosis. Our data also reveal an important connection between meiotic processes and altered/increased energy production. [ABSTRACT FROM AUTHOR]

l A novel method (SGL-SVM) for tumor classification on two-class and multi-class tumor datasets. l Gene primary selection reduces the computational complexity of the selection largely. l Sparse group Lasso considers the group effect variables and select more effective feature genes. l We investigate different feature gene selection methods and classifiers on the results. l The proposed method has better performance than other methods. At present, with the in-depth study of gene expression data, the significant role of tumor classification in clinical medicine has become more apparent. In particular, the sparse characteristics of gene expression data within and between groups. Therefore, this paper focuses on the study of tumor classification based on the sparsity characteristics of genes. On this basis, we propose a new method of tumor classification—Sparse Group Lasso (least absolute shrinkage and selection operator) and Support Vector Machine (SGL-SVM). Firstly, the primary selection of feature genes is performed on the normalized tumor datasets using the Kruskal–Wallis rank sum test. Secondly, using a sparse group Lasso for further selection, and finally, the support vector machine serves as a classifier for classification. We validate proposed method on microarray and NGS datasets respectively. Formerly, on three two-class and five multi-class microarray datasets it is tested by 10-fold cross-validation and compared with other three classifiers. SGL-SVM is then applied on BRCA and GBM datasets and tested by 5-fold cross-validation. Satisfactory accuracy is obtained by above experiments and compared with other proposed methods. The experimental results show that the proposed method achieves a higher classification accuracy and selects fewer feature genes, which can be widely applied in classification for high-dimensional and small-sample tumor datasets. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/SGL-SVM/. [ABSTRACT FROM AUTHOR]