predrag@indiana.edu; smooney@buckinstitute.org.

Software is available on request from the authors.

An important challenge in translational bioinformatics is to understand how genetic variation gives rise to molecular changes at the protein level that can precipitate both monogenic and complex disease. To this end, we compiled datasets of human disease-associated amino acid substitutions (AAS) in the contexts of inherited monogenic disease, complex disease, functional polymorphisms with no known disease association, and somatic mutations in cancer, and compared them with respect to predicted functional sites in proteins. Using the sequence homology-based tool SIFT to estimate the proportion of deleterious AAS in each dataset, only complex disease AAS were found to be indistinguishable from neutral polymorphic AAS. Investigation of monogenic disease AAS predicted to be non-deleterious by SIFT were characterized by a significant enrichment for inherited AAS within solvent accessible residues, regions of intrinsic protein disorder, and an association with the loss or gain of various post-translational modifications. Sites of structural and/or functional interest were therefore surmised to constitute useful additional features with which to identify the molecular disruptions caused by deleterious AAS. A range of bioinformatic tools, designed to predict structural and functional sites in protein sequences, were then employed to demonstrate that intrinsic biases exist in terms of the distribution of different types of human AAS with respect to specific structural, functional and pathological features. Our web tool, designed to potentiate the functional profiling of novel AAS, has been made available at http://profile.mutdb.org/.

Summary Maturity-onset diabetes of the young 1 (MODY1) is a monogenic diabetes condition caused by heterozygous HNF4A mutations. We investigate how HNF4A haploinsufficiency from a MODY1/HNF4A mutation influences the development of foregut-derived liver and pancreatic cells through differentiation of human induced pluripotent stem cells from a MODY1 family down the foregut lineage. In MODY1-derived hepatopancreatic progenitors, which expressed reduced HNF4A levels and mislocalized HNF4A, foregut genes were downregulated, whereas hindgut-specifying HOX genes were upregulated. MODY1-derived hepatocyte-like cells were found to exhibit altered morphology. Hepatic and β cell gene signatures were also perturbed in MODY1-derived hepatocyte-like and β-like cells, respectively. As mutant HNF4A (p.Ile271fs) did not undergo complete nonsense-mediated decay or exert dominant negativity, HNF4A-mediated loss of function is likely due to impaired transcriptional activation of target genes. Our results suggest that in MODY1, liver and pancreas development is perturbed early on, contributing to altered hepatic proteins and β cell defects in patients.

Heterozygous HNF1A gene mutations can cause maturity onset diabetes of the young 3 (MODY3), characterized by insulin secretion defects. However, specific mechanisms of MODY3 in humans remain unclear due to lack of access to diseased human pancreatic cells. Here, we utilize MODY3 patient-derived human induced pluripotent stem cells (hiPSCs) to study the effect(s) of a causal HNF1A+/H126D mutation on pancreatic function. Molecular dynamics simulations predict that the H126D mutation could compromise DNA binding and gene target transcription. Genome-wide RNA-Seq and ChIP-Seq analyses on MODY3 hiPSC-derived endocrine progenitors reveal numerous HNF1A gene targets affected by the mutation. We find decreased glucose transporter GLUT2 expression, which is associated with reduced glucose uptake and ATP production in the MODY3 hiPSC-derived β-like cells. Overall, our findings reveal the importance of HNF1A in regulating GLUT2 and several genes involved in insulin secretion that can account for the insulin secretory defect clinically observed in MODY3 patients.

As databases of genome data continue to grow, our understanding of the functional elements of the genome grows as well. Many genetic changes in the genome have now been discovered and characterized, including both disease-causing mutations and neutral polymorphisms. In addition to experimental approaches to characterize specific variants, over the past decade, there has been intense bioinformatic research to understand the molecular effects of these genetic changes. In addition to genomic experimental assays, the bioinformatic efforts have focused on two general areas. First, researchers have annotated genetic variation data with molecular features that are likely to affect function. Second, statistical methods have been developed to predict mutations that are likely to have a molecular effect. In this protocol manuscript, methods for understanding the molecular functions of single nucleotide polymorphisms (SNPs) and mutations are reviewed and described. The intent of this chapter is to provide an introduction to the online tools that are both easy to use and useful.

Asian nonsmoking populations have a higher incidence of lung cancer compared with their European counterparts. There is a long-standing hypothesis that the increase of lung cancer in Asian never-smokers is due to environmental factors such as second-hand smoke. We analyzed whole-genome sequencing of 30 Asian lung cancers. Unsupervised clustering of mutational signatures separated the patients into two categories of either all the never-smokers or all the smokers or ex-smokers. In addition, nearly one third of the ex-smokers and smokers classified with the never-smoker-like cluster. The somatic variant profiles of Asian lung cancers were similar to that of European origin with G.C>T.A being predominant in smokers. We found EGFR and TP53 to be the most frequently mutated genes with mutations in 50% and 27% of individuals, respectively. Among the 16 never-smokers, 69% had an EGFR mutation compared with 29% of 14 smokers/ex-smokers. Asian never-smokers had lung cancer signatures distinct from the smoker signature and their mutation profiles were similar to European never-smokers. The profiles of Asian and European smokers are also similar. Taken together, these results suggested that the same mutational mechanisms underlie the etiology for both ethnic groups. Thus, the high incidence of lung cancer in Asian never-smokers seems unlikely to be due to second-hand smoke or other carcinogens that cause oxidative DNA damage, implying that routine EGFR testing is warranted in the Asian population regardless of smoking status. Cancer Res; 74(21);

Background Target of Rapamycin Complex 1 (TORC1) is a highly conserved eukaryotic protein complex that couples the presence of growth factors and nutrients in the environment with cellular proliferation. TORC1 is primarily implicated in linking amino acid levels with cellular growth in yeast and mammals. Although glucose deprivation has been shown to cause TORC1 inactivation in yeast, the precise role of TORC1 in glucose signaling and the underlying mechanisms remain unclear. Results We demonstrate that the presence of glucose in the growth medium is both necessary and sufficient for TORC1 activation. TORC1 activity increases upon addition of glucose to yeast cells growing in a non-fermentable carbon source. Conversely, shifting yeast cells from glucose to a non-fermentable carbon source reduces TORC1 activity. Analysis of transcriptomic data revealed that glucose and TORC1 co-regulate about 27% (1668/6004) of yeast genes. We demonstrate that TORC1 orchestrates the expression of glucose-responsive genes mainly via the Tap42-Sit4-Rrd1/2 pathway. To confirm TORC1’s function in glucose signaling, we tested its role in spore germination, a glucose-dependent developmental state transition in yeast. TORC1 regulates the glucose-responsive genes during spore germination and inhibition of TORC1 blocks spore germination. Conclusions Our studies indicate that a regulatory loop that involves activation of TORC1 by glucose and regulation of glucose-responsive genes by TORC1, mediates nutritional control of growth and development in yeast.