Current Genomics - Volume 19, Issue 7, 2018

Preterm Birth (PTB) accounts for approximately 11% of all births worldwide each year and is a profound physiological stressor in early life. The burden of neuropsychiatric and developmental impairment is high, with severity and prevalence correlated with gestational age at delivery. PTB is a major risk factor for the development of cerebral palsy, lower educational attainment and deficits in cognitive functioning, and individuals born preterm have higher rates of schizophrenia, autistic spectrum disorder and attention deficit/hyperactivity disorder. Factors such as gestational age at birth, systemic inflammation, respiratory morbidity, sub-optimal nutrition, and genetic vulnerability are associated with poor outcome after preterm birth, but the mechanisms linking these factors to adverse long term outcome are poorly understood. One potential mechanism linking PTB with neurodevelopmental effects is changes in the epigenome. Epigenetic processes can be defined as those leading to altered gene expression in the absence of a change in the underlying DNA sequence and include DNA methylation/ hydroxymethylation and histone modifications. Such epigenetic modifications may be susceptible to environmental stimuli, and changes may persist long after the stimulus has ceased, providing a mechanism to explain the long-term consequences of acute exposures in early life. Many factors such as inflammation, fluctuating oxygenation and excitotoxicity which are known factors in PTB related brain injury, have also been implicated in epigenetic dysfunction. In this review, we will discuss the potential role of epigenetic dysregulation in mediating the effects of PTB on neurodevelopmental outcome, with specific emphasis on DNA methylation and the α-ketoglutarate dependent dioxygenase family of enzymes.

Involvement of life stress in Late-Onset Alzheimer's Disease (LOAD) has been evinced in longitudinal cohort epidemiological studies, and endocrinologic evidence suggests involvements of catecholamine and corticosteroid systems in LOAD. Early Life Stress (ELS) rodent models have successfully demonstrated sequelae of maternal separation resulting in LOAD-analogous pathology, thereby supporting a role of insulin receptor signalling pertaining to GSK-3beta facilitated tau hyperphosphorylation and amyloidogenic processing. Discussed are relevant ELS studies, and findings from three mitogen-activated protein kinase pathways (JNK/SAPK pathway, ERK pathway, p38/MAPK pathway) relevant for mediating environmental stresses. Further considered were the roles of autophagy impairment, neuroinflammation, and brain insulin resistance. For the meta-analytic evaluation, 224 candidate gene loci were extracted from reviews of animal studies of LOAD pathophysiological mechanisms, of which 60 had no positive results in human LOAD association studies. These loci were combined with 89 gene loci confirmed as LOAD risk genes in previous GWAS and WES. Of the 313 risk gene loci evaluated, there were 35 human reports on epigenomic modifications in terms of methylation or histone acetylation. 64 microRNA gene regulation mechanisms were published for the compiled loci. Genomic association studies support close relations of both noradrenergic and glucocorticoid systems with LOAD. For HPA involvement, a CRHR1 haplotype with MAPT was described, but further association of only HSD11B1 with LOAD found; however, association of FKBP1 and NC3R1 polymorphisms was documented in support of stress influence to LOAD. In the brain insulin system, IGF2R, INSR, INSRR, and plasticity regulator ARC, were associated with LOAD. Pertaining to compromised myelin stability in LOAD, relevant associations were found for BIN1, RELN, SORL1, SORCS1, CNP, MAG, and MOG. Regarding epigenetic modifications, both methylation variability and de-acetylation were reported for LOAD. The majority of up-to-date epigenomic findings include reported modifications in the wellknown LOAD core pathology loci MAPT, BACE1, APP (with FOS, EGR1), PSEN1, PSEN2, and highlight a central role of BDNF. Pertaining to ELS, relevant loci are FKBP5, EGR1, GSK3B; critical roles of inflammation are indicated by CRP, TNFA, NFKB1 modifications; for cholesterol biosynthesis, DHCR24; for myelin stability BIN1, SORL1, CNP; pertaining to (epi)genetic mechanisms, hTERT, MBD2, DNMT1, MTHFR2. Findings on gene regulation were accumulated for BACE1, MAPK signalling, TLR4, BDNF, insulin signalling, with most reports for miR-132 and miR-27. Unclear in epigenomic studies remains the role of noradrenergic signalling, previously demonstrated by neuropathological findings of childhood nucleus caeruleus degeneration for LOAD tauopathy.

System biology problems such as whole-genome network construction from large-scale gene expression data are sophisticated and time-consuming. Therefore, using sequential algorithms are not feasible to obtain a solution in an acceptable amount of time. Today, by using massively parallel computing, it is possible to infer large-scale gene regulatory networks. Recently, establishing gene regulatory networks from large-scale datasets have drawn the noticeable attention of researchers in the field of parallel computing and system biology. In this paper, we attempt to provide a more detailed overview of the recent parallel algorithms for constructing gene regulatory networks. Firstly, fundamentals of gene regulatory networks inference and large-scale datasets challenges are given. Secondly, a detailed description of the four parallel frameworks and libraries including CUDA, OpenMP, MPI, and Hadoop is discussed. Thirdly, parallel algorithms are reviewed. Finally, some conclusions and guidelines for parallel reverse engineering are described.

Background: Cellular homeostasis is regulated by the intricate interplay between a plethora of signaling pathways and “energetic sensors” in organs. In order to maintain energy balance, induction or repression of metabolic pathways must be regulated and act in concert with the energetic demands of the cell at a given point in time. A new class of small noncoding RNAs, the microRNAs (miRNAs), has added yet further complexity to the control of metabolic homeostasis. Objective: Understanding the damages induced by toxins in the liver and the intestine as well as the interplay between the miRNome and transcriptome first requires baseline characterization in these tissues in healthy animals under cellular homeostasis. Methods: The liver (main site for detoxification) and the gut (primary exposure routes for contaminant exposure) were dissected out (wildtype fish), total and small RNA extracted, mRNA and miRNA libraries constructed and subjected to high throughput sequencing. Differential Expression (DE) analysis was performed comparing liver with gut and an “miRNA matrix” that integrates the miRNA-seq and mRNA-seq data was constructed. Results: Both the miRNome and transcriptome of the liver and gut tissues were characterized and putative novel miRNAs were identified. Exploration of the “miRNA matrix” regulatory network revealed that miRNAs uniquely expressed in the liver or gut tissue regulated fundamental cellular processes important for both organs, and that commonly expressed miRNAs in both tissues regulated biological processes that were specific to either the liver or the gut. Conclusion: The result of our analyses revealed new insights into microRNA function in these tissues.

Background: The role of adipose tissue in Insulin resistance (IR) and Type 2 Diabetes (T2D) has well been received in the biomedical community; being a precursor of T2D, identification of the molecular basis of IR is therefore, vital to elucidate T2D- pathogenesis and meta-analysis of previously conducted microarray studies provides an inexpensive approach to achieve this end. Methods: In this study, we have carried out a statistical meta-analysis of 157 microarray datasets from five independent studies and identified a meta-signature of 1,511 genes; their functional meaning was elucidated by integrated pathways-analysis. Further, a protein-protein interaction network was constructed and key genes along with their high confidence transcriptional- and epigenetic-mediators were identified using a network biology approach. Results: Various inflammation- and immune system-related pathways such as TGF-β signaling, IL7 signaling, Neutrophil degranulation, and Chemokine signaling etc. were enriched in sick adipose tissues; identified transcription factors, and microRNAs were also found to regulate processes relevant to IR/T2D pathophysiology. Conclusion: This study endorses the development of effective bioinformatics workflow and further grants an indication for the acceptance of adiposopathy as the root mechanistic pathology that poses risk for development of type 2 diabetes; concept of adipospathy in place of metabolic syndrome will open the possibility to design drugs, those will ameliorate adipose functions and hence proved to be more effective against Type 2 Diabetes.