Integrative Genomics Analysis Unravels Tissue-Specific Pathways, Networks, and Key Regulators of Blood Pressure Regulation

Blood pressure (BP) is a highly heritable trait and a major cardiovascular disease risk factor. Genome wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, a large portion of the heritability cannot be explained by the top GWAS loci and a comprehensive understanding of the underlying molecular mechanisms is still lacking. Here, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including (a) GWAS for SBP and DBP from the International Consortium for Blood Pressure (ICBP), (b) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to BP, (c) knowledge-driven biological pathways, and (d) data-driven tissue-specific regulatory gene networks. Integration of these multidimensional datasets revealed tens of pathways and gene subnetworks in vascular tissues, liver, adipose, blood, and brain functionally associated with DBP and SBP. Diverse processes such as platelet production, insulin secretion/signaling, protein catabolism, cell adhesion and junction, immune and inflammation, and cardiac/smooth muscle contraction, were shared between DBP and SBP. Furthermore, “Wnt signaling” and “mammalian target of rapamycin (mTOR) signaling” pathways were found to be unique to SBP, while “cytokine network”, and “tryptophan catabolism” to DBP. Incorporation of gene regulatory networks in our analysis informed on key regulator genes that orchestrate tissue-specific subnetworks of genes whose variants together explain ~20% of BP heritability. Our results shed light on the complex mechanisms underlying BP regulation and highlight potential novel targets and pathways for hypertension and cardiovascular diseases

Quantum thermal transport in nanostructures

In this colloquia review we discuss methods for thermal transport calculations for nanojunctions connected to two semi-infinite leads served as heat-baths. Our emphases are on fundamental quantum theory and atomistic models. We begin with an introduction of the Landauer formula for ballistic thermal transport and give its derivation from scattering wave point of view. Several methods (scattering boundary condition, mode-matching, Piccard and Caroli formulas) of calculating the phonon transmission coefficients are given. The nonequilibrium Green's function (NEGF) method is reviewed and the Caroli formula is derived. We also give iterative methods and an algorithm based on a generalized eigenvalue problem for the calculation of surface Green's functions, which are starting point for an NEGF calculation. A systematic exposition for the NEGF method is presented, starting with the fundamental definitions of the Green's functions, and ending with equations of motion for the contour ordered Green's functions and Feynman diagrammatic expansion. In the later part, we discuss the treatments of nonlinear effects in heat conduction, including a phenomenological expression for the transmission, NEGF for phonon-phonon interactions, molecular dynamics (generalized Langevin) with quantum heat-baths, and electron-phonon interactions. Some new results are also shown. We also briefly review the experimental status of the thermal transport measurements in nanostructures.Comment: 24 pages, 10 figures, over 200 reference

Neonatal severe bacterial infection impairment estimates in South Asia, sub-Saharan Africa, and Latin America for 2010.

BACKGROUND: Survivors of neonatal infections are at risk of neurodevelopmental impairment (NDI), a burden not previously systematically quantified and yet important for program priority setting. Systematic reviews and meta-analyses were undertaken and applied in a three-step compartmental model to estimate NDI cases after severe neonatal bacterial infection in South Asia, sub-Saharan Africa, and Latin America in neonates of >32 wk gestation (or >1,500 g). METHODS: We estimated cases of sepsis, meningitis, pneumonia, or no severe bacterial infection from among estimated cases of possible severe bacterial infection ((pSBI) step 1). We applied respective case fatality risks ((CFRs) step 2) and the NDI risk among survivors (step 3). For neonatal tetanus, incidence estimates were based on the estimated deaths, CFRs, and risk of subsequent NDI. RESULTS: For 2010, we estimated 1.7 million (uncertainty range: 1.1-2.4 million) cases of neonatal sepsis, 200,000 (21,000-350,000) cases of meningitis, 510,000 cases (150,000-930,000) of pneumonia, and 79,000 cases (70,000-930,000) of tetanus in neonates >32 wk gestation (or >1,500 g). Among the survivors, we estimated moderate to severe NDI after neonatal meningitis in 23% (95% confidence interval: 19-26%) of survivors, 18,000 (2,700-35,000) cases, and after neonatal tetanus in 16% (6-27%), 4,700 cases (1,700-8,900). CONCLUSION: Data are lacking for impairment after neonatal sepsis and pneumonia, especially among those of >32 wk gestation. Improved recognition and treatment of pSBI will reduce neonatal mortality. Lack of follow-up data for survivors of severe bacterial infections, particularly sepsis, was striking. Given the high incidence of sepsis, even minor NDI would be of major public health importance. Prevention of neonatal infection, improved case management, and support for children with NDI are all important strategies, currently receiving limited policy attention

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Uncovering the Molecular Networks of Metabolic Diseases Using Systems Biology

Common complex metabolic diseases (MetDs) such as obesity, type 2 diabetes (T2D), coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD), impose an unprecedented burden on public health worldwide and demonstrate sex differences. Our general hypothesis is that genetic risk factors perturb set of genes in the form of functional gene networks, which subsequently induces the initiation and progression of MetDs. Following this hypothesis, our research focuses on dissecting the molecular networks that are perturbed by genetic risk factors of MetDs utilizing multiomics systems biology approaches. To address this challenge, I embarked interdisciplinary systems biology studies encompassing the development of an accessible multi-omics integration webserver, elucidation of genetically perturbed tissue networks in numerous MetDs, and uncovering the relative contribution of three sex factors in gene regulation in tissues relevant to MetDs. First, I contributed to the development of a user-friendly webserver for multiomics integration, network modeling, and network-based drug repositioning for complex diseases such as MetDs. Second, I investigated the genetically perturbed gene networks that underly various MetDs, namely, lipid traits, diabetes, CAD, and NAFLD. Third, I employed systems biology approaches to uncover the individual and interactive contribution of three sex factors (sex chromosomes, gonads, and sex hormones) in gene regulation in tissues relevant to MetDs. Completion of these projects offer a user-friendly bioinformatic tool, molecular insights, and drug candidates for MetDs

Integrative Genomics Analysis Reveals Tissue-specific Pathways and Gene Networks for Type 1 Diabetes

Type 1 diabetes (T1D) is a complex disease, involving a genetic predisposition that interacts with environmental triggers, leading to the loss of insulin producing beta cells in the pancreas. However, the molecular cascades underlying T1D are poorly understood and remain to be explored. We hypothesize that genetic risk factors of T1D perturb tissue-specific biological pathways and gene networks, which ultimately leads to the pathogenic end point in beta cells. We sought to identify the gene networks and key regulators for T1D by conducting a comprehensive, data-driven multi-omics analysis that integrates human genome-wide association studies (GWAS) of T1D, tissue-specific genetic regulation of gene expression in the form of expression quantitative trait loci (eQTLs), and tissue-specific gene network models using a computational pipeline Mergeomics. Our integrative genomics approach revealed immune pathways such as adaptive immune system, cytokines and inflammatory response, ZAP70 translocation, primary immunodeficiency and immunoregulatory interactions between a lymphoid and non-lymphoid cell, across various tissues. We also identified tissue-specific signals such as regulation of complement cascade in adipose tissue, macrophages, and monocytes, NOTCH signaling in adipose tissue and macrophages, protein folding, calcium signaling chemotaxis and lysosomal pathways in the pancreas, adipose, and monocytes, and viral infection in macrophages and monocytes. Network modelling of these pathways highlights a number of key regulator genes such as GBP1, USP18, STAT1, RPL17, HLA genes (HLA-A,-B,-C, and –G), and immunomodulatory genes (LCK, VAV1, ZAP-70), each of which has suggestive roles in the pathophysiology of T1D or other autoimmune disorders. Together, our integrative genomics approach offers comprehensive insights into the tissue-specific molecular networks and regulators as well potential between-tissue interactions underlying T1D, with potential for guiding future development of therapeutic strategies targeting the disease