Mapping the genetic architecture of gene expression in human liver

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. © 2008 Schadt et al

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads

Sequence diversity in CYP3A promoters and characterization of the genetic basis of polymorphic CYP3A5 expression

Variation in the CYP3A enzymes, which act in drug metabolism, influences circulating steroid levels and responses to half of all oxidatively metabolized drugs. CYP3A activity is the sum activity of the family of CYP3A genes, including CYP3A5, which is polymorphically expressed at high levels in a minority of Americans of European descent and Europeans (hereafter collectively referred to as 'Caucasians'). Only people with at least one CYP3A5*1 allele express large amounts of CYP3A5. Our findings show that single-nucleotide polymorphisms (SNPs) in CYP3A5*3 and CYP3A5*6 that cause alternative splicing and protein truncation result in the absence of CYP3A5 from tissues of some people. CYP3A5 was more frequently expressed in livers of African Americans (60%) than in those of Caucasians (33%). Because CYP3A5 represents at least 50% of the total hepatic CYP3A content in people polymorphically expressing CYP3A5, CYP3A5 may be the most important genetic contributor to interindividual and interracial differences in CYP3A-dependent drug clearance and in responses to many medicines

Identification of a better Homo sapiens Class II HDAC inhibitor through binding energy calculations and descriptor analysis

Human papillomaviruses (HPVs) are the most common on sexually transmitted viruses in the world. HPVs are responsible for a large spectrum of deseases, both benign and malignant. The certain types of HPV are involved in the development of cervical cancer. In attemps to find additional drugs in the treatment of cervical cancer, inhibitors of the histone deacetylases (HDAC) have received much attention due to their low cytotoxic profiles and the E6/E7 oncogene function of human papilomavirus can be completely by passed by HDAC inhibition. The histone deacetylase inhibitors can induce growth arrest, differentiation and apoptosis of cancer cells. HDAC class I and class II are considered the main targets for cancer. Therefore, the six HDACs class II was modeled and about two inhibitors (SAHA and TSA) were docked using AutoDock4.2, to each of the inhibitor in order to identify the pharmacological properties. Based on the results of docking, SAHA and TSA were able to bind with zinc ion in HDACs models as a drug target. SAHA was satisfied almost all the properties i.e., binding affinity, the Drug-Likeness value and Drug Score with 70% oral bioavailability and the carbonyl group of these compound fits well into the active site of the target where the zinc is present. Hence, SAHA could be developed as potential inhibitors of class II HDACs and valuable cervical cancer drug candidate

Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks

The metabolism of organisms can be studied with comprehensive stoichiometric models of their metabolic networks. Flux balance analysis (FBA) calculates optimal metabolic performance of stoichiometric models. However, detailed biological interpretation of FBA is limited because, in general, a huge number of flux patterns give rise to the same optimal performance. The complete description of the resulting optimal solution spaces was thus far a computationally intractable problem. Here we present CoPE-FBA: Comprehensive Polyhedra Enumeration Flux Balance Analysis, a computational method that solves this problem. CoPE-FBA indicates that the thousands to millions of optimal flux patterns result from a combinatorial explosion of flux patterns in just a few metabolic sub-networks. The entire optimal solution space can now be compactly described in terms of the topology of these sub-networks. CoPE-FBA simplifies the biological interpretation of stoichiometric models of metabolism, and provides a profound understanding of metabolic flexibility in optimal states

Prostaglandin signalling regulates ciliogenesis by modulating intraflagellar transport

Cilia are microtubule-based organelles that mediate signal transduction in a variety of tissues. Despite their importance, the signalling cascades that regulate cilium formation remain incompletely understood. Here we report that prostaglandin signalling affects ciliogenesis by regulating anterograde intraflagellar transport (IFT). Zebrafish leakytail (lkt) mutants show ciliogenesis defects, and the lkt locus encodes an ATP-binding cassette transporter (ABCC4). We show that Lkt/ABCC4 localizes to the cell membrane and exports prostaglandin E2 (PGE2), a function that is abrogated by the Lkt/ABCC4T804M mutant. PGE2 synthesis enzyme cyclooxygenase-1 and its receptor, EP4, which localizes to the cilium and activates the cyclic-AMP-mediated signalling cascade, are required for cilium formation and elongation. Importantly, PGE2 signalling increases anterograde but not retrograde velocity of IFT and promotes ciliogenesis in mammalian cells. These findings lead us to propose that Lkt/ABCC4-mediated PGE2 signalling acts through a ciliary G-protein-coupled receptor, EP4, to upregulate cAMP synthesis and increase anterograde IFT, thereby promoting ciliogenesis

Classification of ductal carcinoma in situ by gene expression profiling

INTRODUCTION: Ductal carcinoma in situ (DCIS) is characterised by the intraductal proliferation of malignant epithelial cells. Several histological classification systems have been developed, but assessing the histological type/grade of DCIS lesions is still challenging, making treatment decisions based on these features difficult. To obtain insight in the molecular basis of the development of different types of DCIS and its progression to invasive breast cancer, we have studied differences in gene expression between different types of DCIS and between DCIS and invasive breast carcinomas. METHODS: Gene expression profiling using microarray analysis has been performed on 40 in situ and 40 invasive breast cancer cases. RESULTS: DCIS cases were classified as well- (n = 6), intermediately (n = 18), and poorly (n = 14) differentiated type. Of the 40 invasive breast cancer samples, five samples were grade I, 11 samples were grade II, and 24 samples were grade III. Using two-dimensional hierarchical clustering, the basal-like type, ERB-B2 type, and the luminal-type tumours originally described for invasive breast cancer could also be identified in DCIS. CONCLUSION: Using supervised classification, we identified a gene expression classifier of 35 genes, which differed between DCIS and invasive breast cancer; a classifier of 43 genes could be identified separating between well- and poorly differentiated DCIS samples

Severity Assessment of Lower Respiratory Tract Infection in Malawi: Derivation of a Novel Index (SWAT-Bp) Which Outperforms CRB-65

OBJECTIVE: To assess the validity of CRB-65 (Confusion, Respiratory rate >30 breaths/min, BP<90/60 mmHg, age >65 years) as a pneumonia severity index in a Malawian hospital population, and determine whether an alternative score has greater accuracy in this setting. DESIGN: Forty three variables were prospectively recorded during the first 48 hours of admission in all patients admitted to Queen Elizabeth Central Hospital, Malawi, for management of lower respiratory tract infection over a two month period (N = 240). Calculation of sensitivity and specificity for CRB-65 in predicting mortality was followed by multivariate modeling to create a score with superior performance in this population. RESULTS: Median age 37, HIV prevalence 79.9%, overall mortality 18.3%. CRB-65 predicted mortality poorly, indicated by the area under the ROC curve of 0.649. Independent predictors of death were: Male sex, “S” (AOR 2.6); Wasting, “W” (AOR 6.6); non-ambulatory, “A” (AOR 2.5); Temp >38°C or <35°C, “T” (AOR 3.2); BP<100/60, “Bp” (AOR 3.7). Combining these factors to form a severity index (SWAT-Bp) predicted mortality with high sensitivity and specificity (AUC: 0.867). Mortality for scores 0–5 was 0%, 3.3%, 7.4%, 29.2%, 61.5% and 87.5% respectively. A score ≥3 was 84% sensitive and 77% specific for mortality prediction, with a negative predictive value of 95.8%. CONCLUSION: CRB-65 performs poorly in this population. The SWAT-Bp score can accurately stratify patients; ≤2 indicates non-severe infection (mortality 4.4%) and ≥3 severe illness (mortality 45%)