Proteomic studies related to genetic determinants of variability in protein concentrations

A variation in KCNQ1 gene is associated with repaglinide efficacy on insulin resistance in Chinese Type 2 Diabetes Mellitus Patients

Repaglinide is an insulin secretagogue that often exhibits considerable interindividual variability in therapeutic efficacy. The current study was designed to investigate the impact of KCNQ1 genetic polymorphism on the efficacy of repaglinide and furthermore to identify the potential mechanism of action in patients with type 2 diabetes. A total of 305 patients and 200 healthy subjects were genotyped for the KCNQ1 rs2237892 polymorphism, and 82 patients with T2DM were randomized for the oral administration of repaglinide for 8 weeks. HepG2 cells were incubated with repaglinide in the absence or presence of a KCNQ1 inhibitor or the pcDNA3.1-hKCNQ1 plasmid, after which the levels of Akt, IRS-2 and PI(3)K were determined. Our data showed that repaglinide significantly decreased HOMA-IR in patients with T2DM. Furthermore, the level of HOMA-IR was significantly reduced in those patients with CT or TT genotypes than CC homozygotes. The KCNQ1 inhibitor enhanced repaglinide efficacy on insulin resistance, with IRS-2/PI(3)K/Akt signaling being up-regulated markedly. As in our clinical experiment, these data strongly suggest that KCNQ1 genetic polymorphism influences repaglinide response due to the pivotal role of KCNQ1 in regulating insulin resistance through the IRS-2/PI(3)K/Akt signaling pathway. This study was registered in the Chinese Clinical Trial Register on May 14, 2013. (No. ChiCTR-CCC13003536).

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10-10) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10-392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group.

Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation

Mass spectrometry-based proteomics has emerged as the leading method for detection, quantification, and characterization of proteins. Nearly all proteomic workflows rely on proteomic databases to identify peptides and proteins, but these databases typically contain a generic set of proteins that lack variations unique to a given sample, precluding their detection. Fortunately, proteogenomics enables the detection of such proteomic variations and can be defined, broadly, as the use of nucleotide sequences to generate candidate protein sequences for mass spectrometry database searching. Proteogenomics is experiencing heightened significance due to two developments: (a) advances in DNA sequencing technologies that have made complete sequencing of human genomes and transcriptomes routine, and (b) the unveiling of the tremendous complexity of the human proteome as expressed at the levels of genes, cells, tissues, individuals, and populations. We review here the field of human proteogenomics, with an emphasis on its history, current implementations, the types of proteomic variations it reveals, and several important applications.

MI-PVT: A Tool for Visualizing the Chromosome-Centric Human Proteome

We have developed the web-based Michigan Proteome Visualization Tool (MI-PVT) to visualize and compare protein expression and isoform-level function across human chromosomes and tissues (http://guanlab.ccmb.med.umich.edu/mipvt). As proof of principle, we have populated the tool with Human Proteome Map (HPM) data. We were able to observe many biologically interesting features. From the vantage point of our chromosome 17 team, for example, we found more than 300 proteins from chromosome 17 expressed in each of the 30 tissues and cell types studied, with the highest number of expressed proteins being 685 in testis. Comparisons of expression levels across tissues showed low numbers of proteins expressed in esophagus, but esophagus had 12 cytoskeletal proteins coded on chromosome 17 with very high expression (>1000 spectral counts). This customized MI-PVT should be helpful for biologists to browse and study specific proteins and protein data sets across tissues and chromosomes. Users can upload any data of interest in MI-PVT for visualization. Our aim is to integrate extensive mass-spectrometric proteomic data into the tool to facilitate finding chromosome-centric protein expression and correlation across tissues.

The crop QTLome comes of age

Recent progress in genomics and phenomics allows for a more accurate and comprehensive characterization of the Quantitative Trait Loci (QTLs)—hereafter defined 'QTLome' as a whole—that govern the variation targeted in breeding programs. High-density genotyping now provides unambiguous identification of QTL alleles, and for several traits beneficial alleles at major QTLs have already been deployed in marker-assisted breeding. However, the amount of QTLome information is enormous and approaches to distill and translate this information to breeders remain to be refined. Improved QTL meta-analyses, better estimation of QTL effects, improved crop modelling and full sharing of raw QTL data will enable a more effective exploitation of the QTLome.

Approaches for establishing the function of regulatory genetic variants involved in disease

The diversity of regulatory genetic variants and their mechanisms of action reflect the complexity and context-specificity of gene regulation. Regulatory variants are important in human disease and defining such variants and establishing mechanism is crucial to the interpretation of disease-association studies. This review describes approaches for identifying and functionally characterizing regulatory variants, illustrated using examples from common diseases. Insights from recent advances in resolving the functional epigenomic regulatory landscape in which variants act are highlighted, showing how this has enabled functional annotation of variants and the generation of hypotheses about mechanism of action. The utility of quantitative trait mapping at the transcript, protein and metabolite level to define association of specific genes with particular variants and further inform disease associations are reviewed. Establishing mechanism of action is an essential step in resolving functional regulatory variants, and this review describes how this is being facilitated by new methods for analyzing allele-specific expression, mapping chromatin interactions and advances in genome editing. Finally, integrative approaches are discussed together with examples highlighting how defining the mechanism of action of regulatory variants and identifying specific modulated genes can maximize the translational utility of genome-wide association studies to understand the pathogenesis of diseases and discover new drug targets or opportunities to repurpose existing drugs to treat them.

Genetics of gene expression in immunity to infection

Mapping gene expression as a quantitative trait (eQTL mapping) can reveal local and distant associations with functionally important genetic variation informative for disease. Recent studies are reviewed which have demonstrated that this approach is particularly informative when applied to diverse immune cell populations and situations relevant to infection and immunity. Context-specific eQTL have now been characterised following endotoxin activation, induction with interferons, mycobacteria, and influenza, together with genetic determinants of response to vaccination. The application of genetical genomic approaches offers new opportunities to advance our understanding of gene-environment interactions and fundamental processes in innate and adaptive immunity.