Seqinspector: position-based navigation through the ChIP-seq data landscape to identify gene expression regulators

Loss of full-length dystrophin expression results in major cell-autonomous abnormalities in proliferating myoblasts

Duchenne muscular dystrophy (DMD) affects myofibers and muscle stem cells, causing progressive muscle degeneration and repair defects. It was unknown whether dystrophic myoblasts-the effector cells of muscle growth and regeneration-are affected. Using transcriptomic, genome-scale metabolic modelling and functional analyses, we demonstrate, for the first time, convergent abnormalities in primary mouse and human dystrophic myoblasts. In Dmdmdx myoblasts lacking full-length dystrophin, the expression of 170 genes was significantly altered. Myod1 and key genes controlled by MyoD (Myog, Mymk, Mymx, epigenetic regulators, ECM interactors, calcium signalling and fibrosis genes) were significantly downregulated. Gene ontology analysis indicated enrichment in genes involved in muscle development and function. Functionally, we found increased myoblast proliferation, reduced chemotaxis and accelerated differentiation, which are all essential for myoregeneration. The defects were caused by the loss of expression of full-length dystrophin, as similar and not exacerbated alterations were observed in dystrophin-null Dmdmdx-βgeo myoblasts. Corresponding abnormalities were identified in human DMD primary myoblasts and a dystrophic mouse muscle cell line, confirming the cross-species and cell-autonomous nature of these defects. The genome-scale metabolic analysis in human DMD myoblasts showed alterations in the rate of glycolysis/gluconeogenesis, leukotriene metabolism, and mitochondrial beta-oxidation of various fatty acids. These results reveal the disease continuum: DMD defects in satellite cells, the myoblast dysfunction affecting muscle regeneration, which is insufficient to counteract muscle loss due to myofiber instability. Contrary to the established belief, our data demonstrate that DMD abnormalities occur in myoblasts, making these cells a novel therapeutic target for the treatment of this lethal disease.

Effects of L-DOPA on Gene Expression in the Frontal Cortex of Rats with Unilateral Lesions of Midbrain Dopaminergic Neurons

The development of Parkinson's disease (PD) causes dysfunction of the frontal cortex, which contributes to the hallmark motor symptoms and is regarded as one of the primary causes of the affective and cognitive impairments observed in PD. Treatment with L-3,4-dihydroxyphenylalanine (L-DOPA) alleviates motor symptoms but has mixed efficacy in restoring normal cognitive functions, which is further complicated by the psychoactive effects of the drug. We investigated how L-DOPA affects gene expression in the frontal cortex in an animal model of unilateral PD. We performed RNA sequencing (RNA-Seq) analysis of gene expression in the frontal cortex of rats with 6-hydroxydopamine (6-OHDA)-induced unilateral dopaminergic lesions treated with L-DOPA, for two weeks. The analysis of variance identified 48 genes with a significantly altered transcript abundance after L-DOPA treatment. We also performed a weighted gene coexpression network analysis (WGCNA), which resulted in the detection of five modules consisting of genes with similar expression patterns. The analyses led to three primary observations. First, the changes in gene expression induced by L-DOPA were bilateral, although only one hemisphere was lesioned. Second, the changes were not restricted to neurons but also appeared to affect immune or endothelial cells. Finally, comparisons with databases of drug-induced gene expression signatures revealed multiple nonspecific effects, indicating that a part of the observed response is a common pattern activated by multiple types of drugs in different target tissues. Taken together, our results identify cellular mechanisms in the frontal cortex that are involved in the response to L-DOPA treatment.

Screening candidate microR-15a- IRAK2 regulatory pairs for predicting the response to Staphylococcus aureus-induced mastitis in dairy cows

We established a mastitis model using exogenous infection of the mammary gland of Chinese Holstein cows with Staphylococcus aureus and extracted total RNA from S. aureus-infected and healthy mammary quarters. Differential expression of genes due to mastitis was evaluated using Affymetrix technology and results revealed a total of 1230 differentially expressed mRNAs. A subset of affected genes was verified via Q-PCR and pathway analysis. In addition, Solexa high-throughput sequencing technology was used to analyze profiles of miRNA in infected and healthy quarters. These analyses revealed a total of 52 differentially expressed miRNAs. A subset of those results was verified via Q-PCR. Bioinformatics techniques were used to predict and analyze the correlations among differentially expressed miRNA and mRNA. Results revealed a total of 329 pairs of negatively associated miRNA/mRNA, with 31 upregulated pairs of mRNA and 298 downregulated pairs of mRNA. Differential expression of miR-15a and interleukin-1 receptor-associated kinase-like 2 (IRAK2), were evaluated by western blot and luciferase reporter assays. We conclude that miR-15a and miR-15a target genes (IRAK2) constitute potential miRNA-mRNA regulatory pairs for use as biomarkers to predict a mastitis response.

Expression of alternatively spliced variants of the Dclk1 gene is regulated by psychotropic drugs

Background

The long-term effects of psychotropic drugs are associated with the reversal of disease-related alterations through the reorganization and normalization of neuronal connections. Molecular factors that trigger drug-induced brain plasticity remain only partly understood. Doublecortin-like kinase 1 (Dclk1) possesses microtubule-polymerizing activity during synaptic plasticity and neurogenesis. However, the Dclk1 gene shows a complex profile of transcriptional regulation, with two alternative promoters and exon splicing patterns that suggest the expression of multiple isoforms with different kinase activities.

Results

Here, we applied next-generation sequencing to analyze changes in the expression of Dclk1 gene isoforms in the brain in response to several psychoactive drugs with diverse pharmacological mechanisms of action. We used bioinformatics tools to define the range and levels of Dclk1 transcriptional regulation in the mouse nucleus accumbens and prefrontal cortex. We also sought to investigate the presence of DCLK1-derived peptides using mass spectrometry. We detected 15 transcripts expressed from the Dclk1 locus (FPKM > 1), including 2 drug-regulated variants (fold change > 2). Drugs that act on serotonin receptors (5-HT2A/C) regulate a subset of Dclk1 isoforms in a brain-region-specific manner. The strongest influence was observed for the mianserin-induced expression of an isoform with intron retention. The drug-activated expression of novel alternative Dclk1 isoforms was validated using qPCR. The drug-regulated isoform contains genetic variants of DCLK1 that have been previously associated with schizophrenia and hyperactivity disorder in humans. We identified a short peptide that might originate from the novel DCLK1 protein product. Moreover, protein domains encoded by the regulated variant indicate their potential involvement in the negative regulation of the canonical DCLK1 protein.

Conclusions

In summary, we identified novel isoforms of the neuroplasticity-related gene Dclk1 that are expressed in the brain in response to psychotropic drug treatments.

Electronic supplementary material

The online version of this article (10.1186/s12868-018-0458-4) contains supplementary material, which is available to authorized users.

Decoding the transcriptional programs activated by psychotropic drugs in the brain

Analysis of drug-induced gene expression in the brain has long held the promise of revealing the molecular mechanisms of drug actions as well as predicting their long-term clinical efficacy. However, despite some successes, this promise has yet to be fulfilled. Here, we present an overview of the current state of understanding of drug-induced gene expression in the brain and consider the obstacles to achieving a robust prediction of the properties of psychoactive compounds based on gene expression profiles. We begin with a comprehensive overview of the mechanisms controlling drug-inducible transcription and the complexity resulting from expression of noncoding RNAs and alternative gene isoforms. Particular interest is placed on studies that examine the associations within drug classes with regard to the effects on gene transcription, alterations in cell signaling and neuropharmacological drug properties. While the ability of gene expression signatures to distinguish specific clinical classes of psychotropic and addictive drugs remains unclear, some reports show that under specific constraints, drug properties can be predicted based on gene expression. Such signatures offer a simple and effective way to classify psychotropic drugs and screen novel psychoactive compounds. Finally, we note that the amount of data regarding molecular programs activated in the brain by drug treatment has grown exponentially in recent years and that future advances may therefore come in large part from integrating the currently available high-throughput data sets.

A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction

BACKGROUND

Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building.

RESULTS

In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively.

CONCLUSIONS

We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.

Cell-type-specific gene expression patterns in the knee cartilage in an osteoarthritis rat model

Osteoarthritis (OA) is a chronic degenerative disease that leads to joint failure, pain, and disability. Gene regulation is implicated as a driver of the imbalance between the expression of catabolic and anabolic factors that eventually leads to the degeneration of osteoarthritic cartilage. In our model, knee-joint OA was induced in male Wistar rats by intra-articular sodium monoiodoacetate (MIA) injections. Whole-genome microarrays were used to analyse the alterations in gene expression during the time-course of OA development (at 2, 14, and 28 days post-injection) in rat knee joints. The identified co-expressed groups of genes were analysed for enriched regulatory mechanisms, functional classes, and cell-type-specific expression. This analysis revealed 272 regulated transcripts (ANOVA FDR < 0.1% and fold > 2). Functionally, the five major gene expression patterns (A–E) were connected to PPAR signalling and adipogenesis (in cluster A), WNT signalling (in cluster B), endochondral ossification (in cluster C), matrix metalloproteinases and the ACE/RAGE pathway (in cluster D), and the Toll-like receptor, and IL-1 signalling pathways (in cluster E). Moreover, the dynamic profiles of these transcriptional changes were assigned to cellular compartments of the knee joint. Classifying the molecular processes associated with the development of cartilage degeneration provides novel insight into the OA disease process. Our study identified groups of co-regulated genes that share functional relationships and that may play an important role in the early and intermediate stages of OA.

Transcriptional signatures of steroid hormones in the striatal neurons and astrocytes

Background

The mechanisms of steroids actions in the brain mainly involve the binding and nuclear translocation of specific cytoplasmic receptors. These receptors can act as transcription factors and regulate gene expression. However, steroid-dependent transcriptional regulation in different types of neural cells is not yet fully understood. The aim of this study was to evaluate and compare transcriptional alterations induced by various steroid receptor agonists in primary cultures of astrocytes and neurons from mouse brain.

Results

We utilized whole-genome microarrays (Illumina Mouse WG-6) and quantitative PCR analyses to measure mRNA abundance levels. To stimulate gene expression we treated neuronal and astroglial cultures with dexamethasone (100 nM), aldosterone (200 nM), progesterone (200 nM), 5α-dihydrotestosterone (200 nM) and β-Estradiol (200 nM) for 4 h. Neurons were found to exhibit higher levels of expression of mineralocorticoid receptor, progesterone receptor and estrogen receptor 2 than astrocytes. However, higher mRNA level of glucocorticoid receptor mRNA was observed in astrocytes. We identified 956 genes regulated by steroids. In astrocytes we found 381 genes altered by dexamethasone and 19 altered by aldosterone. Functional classification of the regulated genes indicated their putative involvement in multiple aspects of cell metabolism (up-regulated Slc2a1, Pdk4 and Slc45a3) and the inflammatory response (down-regulated Ccl3, Il1b and Tnf). Progesterone, dihydrotestosterone and estradiol did not change gene expression in astrocytes. We found no significant changes in gene expression in neurons.

Conclusions

The obtained results indicate that glial cells might be the primary targets of transcriptional action of steroids in the central nervous system. Substantial changes in gene expression driven by the glucocorticoid receptor imply an important role for the hypothalamic–pituitary–adrenal axis in the hormone-dependent regulation of brain physiology. This is an in vitro study. Hence, the model may not accurately reflect all the effects of steroids on gene expression in neurons in vivo.

Electronic supplementary material

The online version of this article (doi:10.1186/s12868-017-0352-5) contains supplementary material, which is available to authorized users.

Accurate prediction of protein relative solvent accessibility using a balanced model

BACKGROUND

Protein relative solvent accessibility provides insight into understanding protein structure and function. Prediction of protein relative solvent accessibility is often the first stage of predicting other protein properties. Recent predictors of relative solvent accessibility discriminate against exposed regions as compared with buried regions, resulting in higher prediction accuracy associated with buried regions relative to exposed regions.

METHODS

Here, we propose a more accurate and balanced predictor of protein relative solvent accessibility. First, we collected known proteins in three subsets according to sequence length and constructed a balanced dataset after reducing redundancy within each subset. Next, we measured the performance associated with different variables and variable combinations to determine the best variable combination. Finally, a predictor called BMRSA was constructed for modelling and prediction, which used the balanced set as the training set, the position- specific scoring matrix, predicted secondary structure, buried-exposed profile, and length of a query sequence as variables, and the conditional random field as the machine-learning method.

RESULTS

BMRSA performance on test sets confirmed that our approach improved prediction accuracy relative to state-of-the-art approaches and was balanced in its comparison of buried and exposed regions. Our method is valuable when higher levels of accuracy in predicting exposed-residue states are required. The BMRSA is available at: http://cheminfo.tongji.edu.cn:8080/BMRSA/.