Identification of gene regulatory networks affected across drug-resistant epilepsies.

Epilepsy is a chronic and heterogenous disease characterized by recurrent unprovoked seizures, that are commonly resistant to antiseizure medications. This study applies a transcriptome network-based approach across epilepsies aiming to improve understanding of molecular disease pathobiology, recognize affected biological mechanisms and apply causal reasoning to identify therapeutic hypotheses. This study included the most common drug-resistant epilepsies (DREs), such as temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), and mTOR pathway-related malformations of cortical development (mTORopathies). This systematic comparison characterized the global molecular signature of epilepsies, elucidating the key underlying mechanisms of disease pathology including neurotransmission and synaptic plasticity, brain extracellular matrix and energy metabolism. In addition, specific dysregulations in neuroinflammation and oligodendrocyte function were observed in TLE-HS and mTORopathies, respectively. The aforementioned mechanisms are proposed as molecular hallmarks of DRE with the identified upstream regulators offering opportunities for drug-target discovery and development.

Downregulation of Ubiquitin-Specific Protease 15 (USP15) Does Not Provide Therapeutic Benefit in Experimental Mesial Temporal Lobe Epilepsy.

Structural epilepsies display complex immune activation signatures. However, it is unclear which neuroinflammatory pathways drive pathobiology. Transcriptome studies of brain resections from mesial temporal lobe epilepsy (mTLE) patients revealed a dysregulation of transforming growth factor ß, interferon α/ß, and nuclear factor erythroid 2-related factor 2 pathways. Since these pathways are regulated by ubiquitin-specific proteases (USP), in particular USP15, we hypothesized that USP15 blockade may provide therapeutic relief in treatment-resistant epilepsies. For validation, transgenic mice which either constitutively or inducibly lack Usp15 gene expression underwent intrahippocampal kainate injections to induce mTLE. We show that the severity of status epilepticus is unaltered in mice constitutively lacking Usp15 compared to wild types. Cell death, reactive gliosis, and changes in the inflammatory transcriptome were pronounced at 4 days after kainate injection. However, these brain inflammation signatures did not differ between genotypes. Likewise, induced deletion of Usp15 in chronic epilepsy did not affect seizure generation, cell death, gliosis, or the transcriptome. Concordantly, siRNA-mediated knockdown of Usp15 in a microglial cell line did not impact inflammatory responses in the form of cytokine release. Our data show that a lack of USP15 is insufficient to modulate the expression of relevant neuroinflammatory pathways in an mTLE mouse model and do not support targeting USP15 as a therapeutic approach for pharmacoresistant epilepsy.

Connectivity Mapping Using a Novel sv2a Loss-of-Function Zebrafish Epilepsy Model as a Powerful Strategy for Anti-epileptic Drug Discovery.

Synaptic vesicle glycoprotein 2A (SV2A) regulates action potential-dependent neurotransmitter release and is commonly known as the primary binding site of an approved anti-epileptic drug, levetiracetam. Although several rodent knockout models have demonstrated the importance of SV2A for functional neurotransmission, its precise physiological function and role in epilepsy pathophysiology remains to be elucidated. Here, we present a novel sv2a knockout model in zebrafish, a vertebrate with complementary advantages to rodents. We demonstrated that 6 days post fertilization homozygous sv2a-/- mutant zebrafish larvae, but not sv2a +/- and sv2a+/+ larvae, displayed locomotor hyperactivity and spontaneous epileptiform discharges, however, no major brain malformations could be observed. A partial rescue of this epileptiform brain activity could be observed after treatment with two commonly used anti-epileptic drugs, valproic acid and, surprisingly, levetiracetam. This observation indicated that additional targets, besides Sv2a, maybe are involved in the protective effects of levetiracetam against epileptic seizures. Furthermore, a transcriptome analysis provided insights into the neuropathological processes underlying the observed epileptic phenotype. While gene expression profiling revealed only one differentially expressed gene (DEG) between wildtype and sv2a +/- larvae, there were 4386 and 3535 DEGs between wildtype and sv2a-/- , and sv2a +/- and sv2a-/- larvae, respectively. Pathway and gene ontology (GO) enrichment analysis between wildtype and sv2a-/- larvae revealed several pathways and GO terms enriched amongst up- and down-regulated genes, including MAPK signaling, synaptic vesicle cycle, and extracellular matrix organization, all known to be involved in epileptogenesis and epilepsy. Importantly, we used the Connectivity map database to identify compounds with opposing gene signatures compared to the one observed in sv2a-/- larvae, to finally rescue the epileptic phenotype. Two out of three selected compounds rescued electrographic discharges in sv2a-/- larvae, while negative controls did not. Taken together, our results demonstrate that sv2a deficiency leads to increased seizure vulnerability and provide valuable insight into the functional importance of sv2a in the brain in general. Furthermore, we provided evidence that the concept of connectivity mapping represents an attractive and powerful approach in the discovery of novel compounds against epilepsy.

Deep learning for clustering of multivariate clinical patient trajectories with missing values.

BACKGROUND: Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratification problem translates into a complex problem of clustering multivariate and relatively short time series because (i) these diseases are multifactorial and not well described by single clinical outcome variables and (ii) disease progression needs to be monitored over time. Additionally, clinical data often additionally are hindered by the presence of many missing values, further complicating any clustering attempts. FINDINGS: The problem of clustering multivariate short time series with many missing values is generally not well addressed in the literature. In this work, we propose a deep learning-based method to address this issue, variational deep embedding with recurrence (VaDER). VaDER relies on a Gaussian mixture variational autoencoder framework, which is further extended to (i) model multivariate time series and (ii) directly deal with missing values. We validated VaDER by accurately recovering clusters from simulated and benchmark data with known ground truth clustering, while varying the degree of missingness. We then used VaDER to successfully stratify patients with Alzheimer disease and patients with Parkinson disease into subgroups characterized by clinically divergent disease progression profiles. Additional analyses demonstrated that these clinical differences reflected known underlying aspects of Alzheimer disease and Parkinson disease. CONCLUSIONS: We believe our results show that VaDER can be of great value for future efforts in patient stratification, and multivariate time-series clustering in general.

Pib2-Dependent Feedback Control of the TORC1 Signaling Network by the Npr1 Kinase.

To adjust cell growth and metabolism according to environmental conditions, the conserved TORC1 signaling network controls autophagy, protein synthesis, and turnover. Here, we dissected the signals controlling phosphorylation and activity of the TORC1-effector kinase Npr1, involved in tuning the plasma membrane permeability to nitrogen sources. By evaluating a role of pH as a signal, we show that, although a transient cytosolic acidification accompanies nitrogen source entry and is correlated to a rapid TORC1-dependent phosphorylation of Npr1, a pH drop is not a prerequisite for TORC1 activation. We show that the Gtr1/Gtr2 and Pib2 regulators of TORC1 both independently and differently contribute to regulate Npr1 phosphorylation and activity. Finally, our data reveal that Npr1 mediates nitrogen-dependent phosphorylation of Pib2, as well as a Pib2-dependent inhibition of TORC1. This work highlights a feedback control loop likely enabling efficient downregulation and faster re-activation of TORC1 in response to a novel stimulating signal.

A systems-level framework for drug discovery identifies Csf1R as an anti-epileptic drug target.

The identification of drug targets is highly challenging, particularly for diseases of the brain. To address this problem, we developed and experimentally validated a general computational framework for drug target discovery that combines gene regulatory information with causal reasoning ("Causal Reasoning Analytical Framework for Target discovery"-CRAFT). Using a systems genetics approach and starting from gene expression data from the target tissue, CRAFT provides a predictive framework for identifying cell membrane receptors with a direction-specified influence over disease-related gene expression profiles. As proof of concept, we applied CRAFT to epilepsy and predicted the tyrosine kinase receptor Csf1R as a potential therapeutic target. The predicted effect of Csf1R blockade in attenuating epilepsy seizures was validated in three pre-clinical models of epilepsy. These results highlight CRAFT as a systems-level framework for target discovery and suggest Csf1R blockade as a novel therapeutic strategy in epilepsy. CRAFT is applicable to disease settings other than epilepsy.

BED: a Biological Entity Dictionary based on a graph data model.

The understanding of molecular processes involved in a specific biological system can be significantly improved by combining and comparing different data sets and knowledge resources. However, these information sources often use different identification systems and an identifier conversion step is required before any integration effort. Mapping between identifiers is often provided by the reference information resources and several tools have been implemented to simplify their use. However, most of these tools do not combine the information provided by individual resources to increase the completeness of the mapping process. Also, deprecated identifiers from former versions of databases are not taken into account. Finally, finding automatically the most relevant path to map identifiers from one scope to the other is often not trivial. The Biological Entity Dictionary (BED) addresses these three challenges by relying on a graph data model describing possible relationships between entities and their identifiers. This model has been implemented using Neo4j and an R package provides functions to query the graph but also to create and feed a custom instance of the database. This design combined with a local installation of the graph database and a cache system make BED very efficient to convert large lists of identifiers.

PCAN: phenotype consensus analysis to support disease-gene association.

BACKGROUND: Bridging genotype and phenotype is a fundamental biomedical challenge that underlies more effective target discovery and patient-tailored therapy. Approaches that can flexibly and intuitively, integrate known gene-phenotype associations in the context of molecular signaling networks are vital to effectively prioritize and biologically interpret genes underlying disease traits of interest. RESULTS: We describe Phenotype Consensus Analysis (PCAN); a method to assess the consensus semantic similarity of phenotypes in a candidate gene's signaling neighborhood. We demonstrate that significant phenotype consensus (p < 0.05) is observable for ~67% of 4,549 OMIM disease-gene associations, using a combination of high quality String interactions + Metabase pathways and use Joubert Syndrome to demonstrate the ease with which a significant result can be interrogated to highlight discriminatory traits linked to mechanistically related genes. CONCLUSIONS: We advocate phenotype consensus as an intuitive and versatile method to aid disease-gene association, which naturally lends itself to the mechanistic deconvolution of diverse phenotypes. We provide PCAN to the community as an R package ( http://bioconductor.org/packages/PCAN/ ) to allow flexible configuration, extension and standalone use or integration to supplement existing gene prioritization workflows.

Differential expression of miR-184 in temporal lobe epilepsy patients with and without hippocampal sclerosis - Influence on microglial function.

Epilepsy is one of the most common neurological disorders characterized by recurrent seizures due to neuronal hyperexcitability. Here we compared miRNA expression patterns in mesial temporal lobe epilepsy with and without hippocampal sclerosis (mTLE + HS and mTLE -HS) to investigate the regulatory mechanisms differentiating both patient groups. Whole genome miRNA sequencing in surgically resected hippocampi did not reveal obvious differences in expression profiles between the two groups of patients. However, one microRNA (miR-184) was significantly dysregulated, which was confirmed by qPCR. We observed that overexpression of miR-184 inhibited cytokine release after LPS stimulation in primary microglial cells, while it did not affect the viability of murine primary neurons and primary astrocytes. Pathway analysis revealed that miR-184 is potentially involved in the regulation of inflammatory signal transduction and apoptosis. Dysregulation of some the potential miR-184 target genes was confirmed by qPCR and 3'UTR luciferase reporter assay. The reduced expression of miR-184 observed in patients with mTLE + HS together with its anti-inflammatory effects indicate that miR-184 might be involved in the modulation of inflammatory processes associated with hippocampal sclerosis which warrants further studies elucidating the role of miR-184 in the pathophysiology of mTLE.

Pathway analysis from lists of microRNAs: common pitfalls and alternative strategy.

MicroRNAs (miRNAs) are involved in the regulation of gene expression at a post-transcriptional level. As such, monitoring miRNA expression has been increasingly used to assess their role in regulatory mechanisms of biological processes. In large scale studies, once miRNAs of interest have been identified, the target genes they regulate are often inferred using algorithms or databases. A pathway analysis is then often performed in order to generate hypotheses about the relevant biological functions controlled by the miRNA signature. Here we show that the method widely used in scientific literature to identify these pathways is biased and leads to inaccurate results. In addition to describing the bias and its origin we present an alternative strategy to identify potential biological functions specifically impacted by a miRNA signature. More generally, our study exemplifies the crucial need of relevant negative controls when developing, and using, bioinformatics methods.

Different microRNA profiles in chronic epilepsy versus acute seizure mouse models.

Epilepsy affects around 50 million people worldwide, and in about 65% of patients, the etiology of disease is unknown. MicroRNAs are small non-coding RNAs that have been suggested to play a role in the pathophysiology of epilepsy. Here, we compared microRNA expression patterns in the hippocampus using two chronic models of epilepsy characterised by recurrent spontaneous seizures (pilocarpine and self-sustained status epilepticus (SSSE)) and an acute 6-Hz seizure model. The vast majority of microRNAs deregulated in the acute model exhibited increased expression with 146 microRNAs up-regulated within 6 h after a single seizure. In contrast, in the chronic models, the number of up-regulated microRNAs was similar to the number of down-regulated microRNAs. Three microRNAs-miR-142-5p, miR-331-3p and miR-30a-5p-were commonly deregulated in all three models. However, there is a clear overlap of differentially expressed microRNAs within the chronic models with 36 and 15 microRNAs co-regulated at 24 h and at 28 days following status epilepticus, respectively. Pathway analysis revealed that the altered microRNAs are associated with inflammation, innate immunity and cell cycle regulation. Taken together, the identified microRNAs and the pathways they modulate might represent candidates for novel molecular approaches for the treatment of patients with epilepsy.

Transcriptomic biomarkers of the response of hospitalized geriatric patients admitted with heart failure. Comparison to hospitalized geriatric patients with infectious diseases or hip fracture.

The abundance of a preselection of transcripts involved in inflammation, immunosenescence and stress response was compared between PBMC of healthy aged donors and aged patients in acute phase of heart failure and at recovery. This study identified 22 transcripts differentially abundant in acute phase of heart failure versus healthy aged subjects. Transcripts involved in inflammation and oxidative stress were more abundant. Those associated with T-cell functions were less abundant. The results were compared to two other major acute geriatric issues: infectious diseases and hip fracture. In acute phase, compared to healthy aged subjects, the abundance of 15/22 transcripts was also altered in both geriatric infectious diseases and hip fracture. Many variations had not vanished at the recovery phase. The abundance of CD28, CD69, LCK, HMOX1, TNFRSF1A transcripts, known to be altered in healthy aged versus healthy young subjects, was further affected in acute phase of the three geriatric diseases considered. The transcript levels of BCL2, CASP8, CCL5, DDIT3, EGR3, IL10RB, IL1R2, SERPINB2 and TIMP1 were affected in all three pathological conditions compared to healthy aged, but not versus healthy young subjects. In conclusion, this work provides critical targets for therapeutic research on geriatric heart failure, infectious diseases and hip fracture.

Differentially abundant transcripts in PBMC of hospitalized geriatric patients with hip fracture compared to healthy aged controls.

The abundance of a selection of transcript species involved in inflammation, immunosenescence and stress response was compared between PBMC of 35 geriatric patients with hip fracture in acute phase (days 2-4 after hospitalization) or convalescence phase (days 7-10) and 28 healthy aged controls. Twenty-nine differentially abundant transcripts were identified in acute phase versus healthy ageing. Twelve of these transcripts remained differentially abundant in convalescence phase, and 22 were similarly differentially abundant in acute phase of geriatric infectious diseases. Seven of these 22 transcripts were previously identified as differentially abundant in PBMC of healthy aged versus healthy young controls, with further alteration for CD28, CD69, LCK, CTSD, HMOX1, and TNFRSF1A in acute phase after geriatric hip fracture and infectious diseases. The next question is whether these alterations are common to other geriatric diseases and/or preexist before the clinical onset of the diseases.

Transcriptomic biomarkers of the response of hospitalized geriatric patients with infectious diseases.

BACKGROUND: Infectious diseases are significant causes of morbidity and mortality among elderly populations. However, the relationship between oxidative stress, immune function and inflammatory response in acute phase of the infectious disease is poorly understood. RESULTS: Herein the abundance of a selection of 148 transcripts involved in immunosenescence and stress response was compared in total RNA of PBMC of 28 healthy aged probands and 39 aged patients in acute phase of infectious disease (day 2-4 after hospitalization) or in convalescence phase (day 7-10). This study provides a list of 24 differentially abundant transcript species in the acute phase versus healthy aged. For instance, transcripts associated with inflammatory and anti-inflammatory reactions (TNFRSF1A, IL1R1, IL1R2, IL10RB) and with oxidative stress (HMOX1, GPX1, SOD2, PRDX6) were more abundant while those associated with T-cell functions (CD28, CD69, LCK) were less abundant in acute phase. The abundance of seven of these transcripts (CD28, CD69, LCK, CTSD, HMOX1, TNFRSF1A and PRDX6) was already known to be altered in healthy aged probands compared to healthy young ones and was further affected in aged patients in acute phase, compromising an efficient response. CONCLUSION: This work provides insights of the state of acute phase response to infections in elderly patients and could explain further the lack of appropriate response in the elderly compared to younger persons.

Transcriptomic biomarkers of human ageing in peripheral blood mononuclear cell total RNA.

Age-related changes of gene expression contribute to the physiological alteration observed with human ageing. Herein, the abundance of a selection of 148 transcripts involved in immunosenescence and stress response was compared in total RNA of PBMC of healthy young to middle-age probands (35.0 +/- 6.5 year old) and healthy old probands (82.5 +/- 6.8 year old). This study provides a list of 16 differentially abundant transcripts species in the healthy old probands. Thus, these changes of abundance can be considered as easily accessible biomarkers of ageing. Some of these differential abundances like CD28, CD69, LCK (decreased abundance in old subjects), CD86, Cathepsin D, H and S (increased abundance in old subjects) might explain biochemical and cytochemical changes observed at the protein level in the immune system and thus might correspond to regulatory processes affecting the ageing process. Indeed these changes reflect the low-grade pro-inflammatory status observed in old persons and suggest a hypo-responsiveness of T-cells together with an increase in antigen presentation potential. In addition, among the differentially abundant transcripts were transcripts involved in the oxidative stress response HMOX1 and HSPA6 mRNAs were found as more abundant in PBMC from elderly subjects.

Machine learning techniques to identify putative genes involved in nitrogen catabolite repression in the yeast Saccharomyces cerevisiae.

BACKGROUND: Nitrogen is an essential nutrient for all life forms. Like most unicellular organisms, the yeast Saccharomyces cerevisiae transports and catabolizes good nitrogen sources in preference to poor ones. Nitrogen catabolite repression (NCR) refers to this selection mechanism. All known nitrogen catabolite pathways are regulated by four regulators. The ultimate goal is to infer the complete nitrogen catabolite pathways. Bioinformatics approaches offer the possibility to identify putative NCR genes and to discard uninteresting genes. RESULTS: We present a machine learning approach where the identification of putative NCR genes in the yeast Saccharomyces cerevisiae is formulated as a supervised two-class classification problem. Classifiers predict whether genes are NCR-sensitive or not from a large number of variables related to the GATA motif in the upstream non-coding sequences of the genes. The positive and negative training sets are composed of annotated NCR genes and manually-selected genes known to be insensitive to NCR, respectively. Different classifiers and variable selection methods are compared. We show that all classifiers make significant and biologically valid predictions by comparing these predictions to annotated and putative NCR genes, and by performing several negative controls. In particular, the inferred NCR genes significantly overlap with putative NCR genes identified in three genome-wide experimental and bioinformatics studies. CONCLUSION: These results suggest that our approach can successfully identify potential NCR genes. Hence, the dimensionality of the problem of identifying all genes involved in NCR is drastically reduced.

Expression profiling of senescent-associated genes in human dermis from young and old donors. Proof-of-concept study.

It is often described that it is difficult to really discriminate the cause of intrinsic skin aging. The aim of this study was to compare the profiles of expression of senescence-associated genes in biopsies of dermis from young and old human donors. TGF-beta1 was up-regulated in the dermis of old donors as well as the TGF-beta1-regulated genes. The anti-oxidant enzymes Selenium-dependent Glutathione peroxidase and Glutatione S-Transferase Theta 1 were also up-regulated in old dermis as well as Tumor Necrosis Factor Receptor Superfamily 1A. None of these genes had altered expression level in skin fibroblasts embedded in a collagen matrix and exposed to sublethal doses of UVB, suggesting their involvement in intrinsic aging. This study represents a proof-of-concept of larger whole transcriptome studies where all avenues should be used to subtract changes in gene expression due to extrinsic aging from changes potentially due to intrinsic aging.

Identification of p53-dependent genes potentially involved in UVB-mediated premature senescence of human skin fibroblasts using siRNA technology.

Premature senescence of skin human diploid fibroblasts is induced after a series of 10 sublethal exposures to UVB at 2.5 kJ/m(2) with appearance of several biomarkers of cellular senescence like senescence-associated beta-galactosidase activity (SA beta-gal) and cell cycle arrest. Herein it is shown that the induction of UVB-induced premature senescence is associated with a transient increase of protein abundance and DNA-binding activity of p53. Silencing p53 expression with small interfering RNA (siRNA) affected the basal level of SA beta-gal and proliferative potential, but did not prevent UVB-induced increase of SA beta-gal and decrease of DNA synthesis. We used a senescence-specific low-density DNA array and p53 siRNA to study the mRNA abundance of 240 senescence-related genes and identified several potential p53-dependent genes differentially expressed after the repeated exposures to UVB.

The usefulness of toxicogenomics for predicting acute skin irritation on in vitro reconstructed human epidermis.

In vitro models aiming at replacing the traditional animal test for determining the skin irritation potential of a test substance have been developed, evaluated in prevalidation studies and recently validated by the European Center for the Validation of Alternative Methods (ECVAM). To investigate the usefulness of toxicogenomic technologies to identify novel mechanistic endpoints for skin irritation responses, the present work challenged the human reconstituted epidermis model validated by ECVAM with four irritant chemicals and four non-classified chemicals tested at subcytotoxic concentrations. Using a specifically designed low-density DNA array, about 50 genes out of 240 were found to be significantly and differentially expressed between tissues exposed to irritant and non-irritant chemicals for at least one test chemical when compared to the seven others. These genes are involved in cell signalling, stress response, cell cycle, protein metabolism and cell structure. Among them, 16 are expressed in the same way whatever the irritant compound applied. The differential gene expressions might represent new or additional endpoints useful for the mechanistic understanding and perhaps also the hazard assessment of the skin irritation potential of chemicals and products.

Effect of 21 different nitrogen sources on global gene expression in the yeast Saccharomyces cerevisiae.

We compared the transcriptomes of Saccharomyces cerevisiae cells growing under steady-state conditions on 21 unique sources of nitrogen. We found 506 genes differentially regulated by nitrogen and estimated the activation degrees of all identified nitrogen-responding transcriptional controls according to the nitrogen source. One main group of nitrogenous compounds supports fast growth and a highly active nitrogen catabolite repression (NCR) control. Catabolism of these compounds typically yields carbon derivatives directly assimilable by a cell's metabolism. Another group of nitrogen compounds supports slower growth, is associated with excretion by cells of nonmetabolizable carbon compounds such as fusel oils, and is characterized by activation of the general control of amino acid biosynthesis (GAAC). Furthermore, NCR and GAAC appear interlinked, since expression of the GCN4 gene encoding the transcription factor that mediates GAAC is subject to NCR. We also observed that several transcriptional-regulation systems are active under a wider range of nitrogen supply conditions than anticipated. Other transcriptional-regulation systems acting on genes not involved in nitrogen metabolism, e.g., the pleiotropic-drug resistance and the unfolded-protein response systems, also respond to nitrogen. We have completed the lists of target genes of several nitrogen-sensitive regulons and have used sequence comparison tools to propose functions for about 20 orphan genes. Similar studies conducted for other nutrients should provide a more complete view of alternative metabolic pathways in yeast and contribute to the attribution of functions to many other orphan genes.