Exercise-Regulated Gene Differential Expression In The Hippocampal Region of C57BL/6J Mice Ameliorates Methamphetamine Dependence

Background: Methamphetamine (METH) is the highly addictive psychoactive drug which could harm to individual health and lead to great social problems. Various approaches have been adopted to address these problems, but relapse rates remain high. Recently, it has been found that comprehensive treatment combined with scientific and appropriate exercise intervention can improve mental state and physical fitness of drug addicts and promote their physical and mental rehabilitation. Long-term regular exercise improves the symptoms of METH withdrawal and reduce METH relapse. This study is to investigate the effects and regulated genes expression related to running exercise in METH addicted mice. Method: We used male C57BL/6J mice to construct METH addiction model and performed running exercise intervention, conditional place preference (CPP) was used to measure the effects of running intervention on METH addict mice. RNA sequencing(RNA-seq) and transcriptome analysis was performed on mice hippocampus, functions and differential expressed genes (DEGs) significantly regulated by exercise intervention in METH addict mice were analysed and noted.Results: The results showed that days of CPP preference was shortened to day 3 in METH addict mice given moderate exercise intervention, compared to preference to day 6 in METH addict mice without exercise. In addition, hippocampal transcriptome analysis revealed 12 DEGs significantly regulated by exercise intervention. By performing Gene ontology and KEGG analysis, function of immune responses was significant enriched in METH addiction mice with exercise. The expression of 12 differential expressed genes was verified by qRT-PCR, which showed that relative mRNA expression of DEGs was consistent with the RNA sequencing results.Conclusion: Running intervention can promote the recovery of METH addiction in mice, and the 12 candidate DEGs from mice hippocampus could use for further research on regulation mechanisms of exercise in METH addiction mice.

BrainEXP-NPD: a database of transcriptomic profiles of human brains of six neuropsychiatric disorders

Background: Spatio-temporal gene expression has been widely used to study gene functions and biological mechanisms in diseases. Numerous microarray and RNA sequencing data focusing on brain transcriptomes in neuropsychiatric disorders have accumulated. However, their consistency, reproducibility has not been properly evaluated. Except for a few psychiatric disorders, like schizophrenia, bipolar disorder and autism, most have not been compared to each other for cross-disorder comparisons. Methods: We organized 48 human brain transcriptome datasets from six sources. The original brain donors include patients with schizophrenia (SCZ, N=427), bipolar disorder (BD, N=312), major depressive disorder (MDD, N=219), autism spectrum disorder (ASD, N=53), Alzheimer's disease (AD, N=765), Parkinson's disease (PD, N=163) as well as controls as unaffected by such disorders (CTRL, N=6,378), making it a total of 8,317 samples. Raw data included multiple brain regions of both sexes, with ages ranging from embryonic to seniors. After standardization, quality control, filtering and removal of known and unknown covariates, we performed comprehensive meta- and mega- analyses, including gene differential expression and gene co-expression network. Results: A total of 6922, 3011, 2703, 4389, 3507, 4279 significantly differentially expressed genes (FDR q < 0.05) were detected in the comparisons of 6 brain regions of SCZ-CTRL, 5 brain regions of BD-CTRL, 6 brain regions of MDD-CTRL, 4 brain regions of ASD-CTRL, 7 brain regions of AD-CTRL, and 6 brain regions of PD-CTRL, respectively. Most differentially expressed genes were brain region-specific and disease-specific. SCZ and BD have a maximal transcriptome similarity in striatum (ρ=0.42) among the four brain regions, as measured by Spearman's correlation of differential expression log2 FC values. SCZ and MDD have a maximal transcriptome similarity in hippocampus (ρ=0.30) among the five brain regions. BD and MDD have a maximal transcriptome similarity in frontal cortex (ρ=0.45) among the five brain regions. Other disease pairs have a less transcriptome similarity (ρ<0.1) in all brain regions. PD is negatively correlated with SCZ, BD, and MDD in cerebellum and striatum. We also performed coexpression network analyses for different disorders and controls separately. We developed a database named BrainEXP-NPD (http://brainexpnpd.org:8088/BrainEXPNPD/), to provide a user-friendly web interface for accessing the data, and analytical results of meta- and mega-analyses, including gene differential expression and gene co-expression networks between cases and controls on different brain regions, sexes and age groups. Discussion: BrainEXP-NPD compiled the largest collection of brain transcriptomic data of major neuropsychiatric disorders and presented lists of differentially expressed genes and coexpression modules in multiple brain regions of six major disorders.

Chronological Gene Expression of Human Gingival Fibroblasts with Low Reactive Level Laser (LLL) Irradiation

Though previously studies have reported that Low reactive Level Laser Therapy (LLLT) promotes wound healing, molecular level evidence was uncleared. The purpose of this study is to examine the temporal molecular processes of human immortalized gingival fibroblasts (HGF) by LLLT by the comprehensive analysis of gene expression. HGF was seeded, cultured for 24 h, and then irradiated with a Nd: YAG laser at 0.5 W for 30 s. After that, gene differential expression analysis and functional analysis were performed with DNA microarray at 1, 3, 6 and 12 h after the irradiation. The number of genes with up- and downregulated differentially expression genes (DEGs) compared to the nonirradiated group was large at 6 and 12 h after the irradiation. From the functional analysis results of DEGs, Biological Process (BP) based Gene Ontology (GO), BP ‘the defense response’ is considered to be an important process with DAVID. Additionally, the results of PPI analysis of DEGs involved in the defense response with STRING, we found that the upregulated DEGs such as CXCL8 and NFKB1, and the downregulated DEGs such as NFKBIA and STAT1 were correlated with multiple genes. We estimate that these genes are key genes on the defense response after LLLT.

Gene Set Analysis Using Spatial Statistics

Gene differential expression consists of the study of the possible association between the gene expression, evaluated using different types of data as DNA microarray or RNA-Seq technologies, and the phenotype. This can be performed marginally for each gene (differential gene expression) or using a gene set collection (gene set analysis). A previous (marginal) per-gene analysis of differential expression is usually performed in order to obtain a set of significant genes or marginal p-values used later in the study of association between phenotype and gene expression. This paper proposes the use of methods of spatial statistics for testing gene set differential expression analysis using paired samples of RNA-Seq counts. This approach is not based on a previous per-gene differential expression analysis. Instead, we compare the paired counts within each sample/control using a binomial test. Each pair per gene will produce a p-value so gene expression profile is transformed into a vector of p-values which will be considered as an event belonging to a point pattern. This would be the first component of a bivariate point pattern. The second component is generated by applying two different randomization distributions to the correspondence between samples and treatment. The self-contained null hypothesis considered in gene set analysis can be formulated in terms of the associated point pattern as a random labeling of the considered bivariate point pattern. The gene sets were defined by the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The proposed methodology was tested in four RNA-Seq datasets of colorectal cancer (CRC) patients and the results were contrasted with those obtained using the edgeR-GOseq pipeline. The proposed methodology has proved to be consistent at the biological and statistical level, in particular using Cuzick and Edwards test with one realization of the second component and between-pair distribution.

Post-transcriptional dysregulation in autism, schizophrenia and bipolar disorder

Post-transcriptional gene regulation (PTGR) contributes to numerous aspects of RNA metabolism. While multiple regulators of PTGR have been associated with the occurrence and development of psychiatric disorders, a systematic investigation of the role of PTGR in the context of neuropsychiatric disorders is still lacking. In this work, we developed a new transcriptome -based algorithm to estimate PTGR and applied it to an RNA-Seq dataset of 2160 brain samples from individuals with autism spectrum disorder (ASD), schizophrenia (SCZ), bipolar disorder (BD) and controls. The results showed that the contribution of PTGR abnormality to gene differential expression between three common psychiatric disorders and controls was about 30% of that of transcriptional gene regulation (TGR) abnormality. Besides, aberrant PTGR tended to decrease RNA stability in SCZ/BD, while increase RNA stability in ASD, implicating contrasting pathologies among diseases. The abnormal alteration of PTGR in SCZ/BD converged on the inhibition of neurogenesis and neural differentiation, whereas dysregulation of PTGR in ASD induced enhanced activity of apoptosis. This suggested that heterogeneity in disease mechanism and clinical manifestation across different psychiatric disorders may be partially attributed to the diverse role of PTGR. Intriguingly, we identified a promising RBP (RNA bind protein) ELAVL3 (ELAV-Like Protein 3) that have a profound role in all three psychiatric disorders. Our systematic study expands the understanding of the link between PTGR and psychiatric disorders and also open a new avenue for deciphering the pathogenesis of psychiatric disorders.

Study on Gene Differential Expression in Tetraploid Populus Leaves

Polyploids exhibit different phenotypes compared to those of diploids in plants, and the important role of polyploids in tree breeding has been widely recognized. The transcriptomes detected by RNA-seq in the Populus triploid by doubling the chromosomes of the female gamete, in the triploid by doubling the chromosomes of somatic cells and the diploid with the parent were compared to reveal the patterns of gene expression of tetraploid leaves and their influence on growth. The results showed that the high expression of GATA and PORA in tetraploid leaves was the reason for the higher chlorophyll content in the leaves than in diploid and triploid leaves. The 11-day-old tetraploid leaves began to enter the aging stage. Compared with that in the diploid, GRF was significantly upregulated, while the amylase genes were downregulated. Compared with those in the triploid, 3 STN7 genes that regulate photosynthetic genes and PGSIP genes which are related to starch synthesis, were significantly downregulated in the tetraploid, and the auxin receptor protein TIR1 was also significantly downregulated. In the tetraploid, auxin-regulating genes such as GH3 and AUX/IAA as well as genes involved in the regulation of leaf senescence, SAG genes and SRG genes were significantly up-regulated, resulting in a decrease in the auxin content. In senescent leaves, CHLD, CHLI1, and CHLM in the early stage of chlorophyll synthesis all began to downregulate their expressions, leading to the downregulation of LHC genes and a decrease in their photosynthetic efficiency, which led to the downregulation of carbon fixation-related genes such as SS genes, thus affecting carbon synthesis and fixation. This finally led to the slow growth of tetraploid plants. These data represent the transcriptome characteristics of tetraploid, and they can be used as a resource for further research on polyploids and provide a reference for further understanding of the function of polyploid vegetative growth-related genes.

Defensive forwards: stress-responsive proteins in cell walls of crop plants

ABSTRACTAs the vital component of plant cell wall, proteins play important roles in stress response through modifying wall structure and involving in wall integrity signaling. However, the potential of cell wall proteins (CWPs) in improvement of crop stress tolerance has probably been underestimated. Recently, we have critically reviewed the predictors, databases and cross-referencing of subcellular locations of possible CWPs in plants (Briefings in Bioinformatics 2018;19:1130-1140). In this study, taking maize (Zea mays) as an example, we retrieved 1873 entries of probable maize CWPs recorded in UniProtKB. As a result, 863 maize CWPs are curated and classified as 59 kinds of protein families. By referring to GO annotation and gene differential expression in Expression Atlas, we highlight the potential of CWPs as defensive forwards in abiotic and biotic stress responses. In particular, several CWPs are found to play key roles in adaptation to many stresses. String analysis also reveals possibly strong interactions among many CWPs, especially those stress-responsive enzymes. The results allow us to narrow down the list of CWPs to a few specific proteins that could be candidates to enhance maize resistance.

scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment

Motivation Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data. Results We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods. Availability and implementation The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts. Supplementary information Supplementary data are available at Bioinformatics online.

Identification of Potentially Therapeutic Target Genes of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a major threat to public health. However, few effective therapeutic strategies exist. We aimed to identify potentially therapeutic target genes of HCC by analyzing three gene expression profiles. Methods: The gene expression profiles were analyzed with GEO2R, an interactive web tool for gene differential expression analysis, to identify common differentially expressed genes (DEGs). Functional enrichment analyses were then conducted followed by a protein-protein interaction (PPI) network construction with the common DEGs. The PPI network was employed to identify hub genes, and the expression level of the hub genes was validated via data mining the Oncomine database. Survival analysis was carried out to assess the prognosis of hub genes in HCC patients. Results: A total of 51 common up-regulated DEGs and 201 down-regulated DEGs were obtained after gene differential expression analysis of the profiles. Functional enrichment analyses indicated that these common DEGs are linked to a series of cancer events. We finally identified 10 hub genes, six of which (OIP5, ASPM, NUSAP1, UBE2C, CCNA2, and KIF20A) are reported as novel HCC hub genes. Data mining the Oncomine database validated that the hub genes have a significant high level of expression in HCC samples compared normal samples (t-test, p < 0.05). Survival analysis indicated that overexpression of the hub genes is associated with a significant reduction (p < 0.05) in survival time in HCC patients. Conclusions: We identified six novel HCC hub genes that might be therapeutic targets for the development of drugs for some HCC patients.