BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.MethodsRNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19’s main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Genetic and phenotypic parameters for lamb growth traits were estimated for the Shal sheep used as animal model. Data on lamb growth performance were extracted from available performance records at the Shal sheep Station in Qazvin, Iran. Studied traits were the body weight of lambs at birth, at three months of age as weaning weight, the six months weight, nine months weight, yearling weight, average daily gain from birth to weaning and Kleiber ratio from birth to weaning. Significant random effects for each trait were determined by fitting additive direct genetic effects, additive maternal effects, the covariance between additive direct and additive maternal effects, maternal permanent environmental and maternal temporary environmental (common litter) effects of twelve animal models. Univariate analyses were carried out under the most appropriate model, determined by the Akaike information criterion test. Direct heritability estimates for birth weight, weaning weight, average daily gain, Kleiber ratio, six months weight, nine months weight and yearling weight were 0.13, 0.19, 0.18, 0.05, 0.16, 0.18 and 0.19, respectively. Maternal additive genetic effects were fitted only for birth weight and weaning weight. Corresponding estimates of 0.12 and 0.10 were obtained for maternal heritability of birth weight and weaning weight, respectively. Maternal permanent environmental effects have low contribution to the expression of Kleiber ratio and lead to estimates of 0.06 and 0.06 for permanent maternal environmental variance as a proportion of phenotypic variance (c 2 ) of these traits, respectively. All pre-weaning traits, except Kleiber ratio, were affected by litter effects. The magnitude of ratio of common litter variance to phenotypic variance (l 2 ) was 0.05, 0.12 and 0.14 for birth weight, weaning weight and average daily gain, respectively. Direct genetic correlations were positive and ranged from 0.09 for Kleiber ratio-yearling weight to 0.80 for weaning weight-average daily gain. Phenotypic correlations ranged from 0.18 for Kleiber ratio-yearling weight to 0.87 for weaning weight-average daily gain.

The authors assessed the efficacy of bromocriptine in nonfluent aphasia after stroke in a 16-week, randomized, double-blind, placebo-controlled clinical trial conducted from June 2002 to April 2004. In all 38 patients after 4 months of treatment, improvement in both the bromocriptine and placebo treatment groups was observed (p < 0.001). The analysis of repeated-measures analysis of variance revealed bromocriptine did not improve nonfluent aphasia.

At later phases of folliculogenesis, the mammalian ovarian follicle contains layers of granulosa cells surrounding an antral cavity. To better understand the molecular basis of follicular growth and granulosa cell maturation, we study transcriptome profiling of granulosa cells from small (<5 mm) and large (>10 mm) bovine follicles using simultaneous method of Affymetrix microarrays (24,128 probe sets) and RNA-Seq data sets. This study proposes a computational method to discover the functional miRNA-mRNA regulatory modules, that is, groups of miRNAs and their target mRNAs that are believed to take part cooperatively in post-transcriptional gene regulation under specific conditions. The reconstructed network was named Integrated miRNA-mRNA Bipartite Network. 277 genes and 6 key modules were disclosed through clustering for mRNA master list. The 66 genes are among the genes that belong to at least two modules. All these genes, being involved in at least one of the phenomena, namely cell survival, proliferation, metastasis and apoptosis, have an overexpression pattern ( < 0.01). For miRNA master list, a total of 172 sequences were differentially expressed ( < 0.01) between dominant (large) and each of subordinate (small) follicles. Within the follicle, these miRNAs were predominantly expressed in mural granulosa cells. Finally, predicted and validated targets of these miRNAs enriched in dominant (large) follicles were identified, which are mapped to signaling pathways involved in follicular cell proliferation, steroidogenesis, PI3K/AKT/mTOR and Ras/Raf/MEK/ERK. The identification of miRNAs and their target mRNAs and the construction of their regulatory networks may give new insights into biological procedures.

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