Pingzhao Hu

Global Survey of Organ and Organelle Protein Expression in Mouse: Combined Proteomic and Transcriptomic Profiling

Organs and organelles represent core biological systems in mammals, but the diversity in protein composition remains unclear. Here, we combine subcellular fractionation with exhaustive tandem mass spectrometry-based shotgun sequencing to examine the protein content of four major organellar compartments (cytosol, membranes [microsomes], mitochondria, and nuclei) in six organs (brain, heart, kidney, liver, lung, and placenta) of the laboratory mouse, Mus musculus. Using rigorous statistical filtering and machine-learning methods, the subcellular localization of 3274 of the 4768 proteins identified was determined with high confidence, including 1503 previously uncharacterized factors, while tissue selectivity was evaluated by comparison to previously reported mRNA expression patterns. This molecular compendium, fully accessible via a searchable web-browser interface, serves as a reliable reference of the expressed tissue and organelle proteomes of a leading model mammal.

Gene and miRNA expression profiles in autism spectrum disorders

Accumulating data indicate that there is significant genetic heterogeneity underlying the etiology in individuals diagnosed with autism spectrum disorder (ASD). Some rare and highly-penetrant gene variants and copy number variation (CNV) regions including NLGN3, NLGN4, NRXN1, SHANK2, SHANK3, PTCHD1, 1q21.1, maternally-inherited duplication of 15q11-q13, 16p11.2, amongst others, have been identified to be involved in ASD. Genome-wide association studies have identified other apparently low risk loci and in some other cases, ASD arises as a co-morbid phenotype with other medical genetic conditions (e.g. fragile X). The progress studying the genetics of ASD has largely been accomplished using genomic analyses of germline-derived DNA. Here, we used gene and miRNA expression profiling using cell-line derived total RNA to evaluate possible transcripts and networks of molecules involved in ASD. Our analysis identified several novel dysregulated genes and miRNAs in ASD compared with controls, including HEY1, SOX9, miR-486 and miR-181b. All of these are involved in nervous system development and function and some others, for example, are involved in NOTCH signaling networks (e.g. HEY1). Further, we found significant enrichment in molecules associated with neurological disorders such as Rett syndrome and those associated with nervous system development and function including long-term potentiation. Our data will provide a valuable resource for discovery purposes and for comparison to other gene expression-based, genome-wide DNA studies and other functional data.

NADPH oxidase complex and IBD candidate gene studies: identification of a rare variant in NCF2 that results in reduced binding to RAC2

Objective The NOX2 NADPH oxidase complex produces reactive oxygen species and plays a critical role in the killing of microbes by phagocytes. Genetic mutations in genes encoding components of the complex result in both X-linked and autosomal recessive forms of chronic granulomatous disease (CGD). Patients with CGD often develop intestinal inflammation that is histologically similar to Crohns colitis, suggesting a common aetiology for both diseases. The aim of this study is to determine if polymorphisms in NOX2 NADPH oxidase complex genes that do not cause CGD are associated with the development of inflammatory bowel disease (IBD). Methods Direct sequencing and candidate gene approaches were used to identify susceptibility loci in NADPH oxidase complex genes. Functional studies were carried out on identified variants. Novel findings were replicated in independent cohorts. Results Sequence analysis identified a novel missense variant in the neutrophil cytosolic factor 2 (NCF2) gene that is associated with very early onset IBD (VEO-IBD) and subsequently found in 4% of patients with VEO-IBD compared with 0.2% of controls (p=1.3×10−5, OR 23.8 (95% CI 3.9 to 142.5); Fisher exact test). This variant reduced binding of the NCF2 gene product p67phox to RAC2. This study found a novel genetic association of RAC2 with Crohns disease (CD) and replicated the previously reported association of NCF4 with ileal CD. Conclusion These studies suggest that the rare novel p67phox variant results in partial inhibition of oxidase function and are associated with CD in a subgroup of patients with VEO-IBD; and suggest that components of the NADPH oxidase complex are associated with CD.

Brain-expressed exons under purifying selection are enriched for de novo mutations in autism spectrum disorder

A universal challenge in genetic studies of autism spectrum disorders (ASDs) is determining whether a given DNA sequence alteration will manifest as disease. Among different population controls, we observed, for specific exons, an inverse correlation between exon expression level in brain and burden of rare missense mutations. For genes that harbor de novo mutations predicted to be deleterious, we found that specific critical exons were significantly enriched in individuals with ASD relative to their siblings without ASD (P < 1.13 × 10−38; odds ratio (OR) = 2.40). Furthermore, our analysis of genes with high exonic expression in brain and low burden of rare mutations demonstrated enrichment for known ASD-associated genes (P < 3.40 × 10−11; OR = 6.08) and ASD-relevant fragile-X protein targets (P < 2.91 × 10−157; OR = 9.52). Our results suggest that brain-expressed exons under purifying selection should be prioritized in genotype-phenotype studies for ASD and related neurodevelopmental conditions.

Genome-wide association study of type 2 diabetes in a sample from Mexico City and a meta-analysis of a Mexican-American sample from Starr County, Texas

Aims/hypothesisWe report a genome-wide association study of type 2 diabetes in an admixed sample from Mexico City and describe the results of a meta-analysis of this study and another genome-wide scan in a Mexican-American sample from Starr County, TX, USA. The top signals observed in this meta-analysis were followed up in the Diabetes Genetics Replication and Meta-analysis Consortium (DIAGRAM) and DIAGRAM+ datasets.MethodsWe analysed 967 cases and 343 normoglycaemic controls. The samples were genotyped with the Affymetrix Genome-wide Human SNP array 5.0. Associations of genotyped and imputed markers with type 2 diabetes were tested using a missing data likelihood score test. A fixed-effects meta-analysis including 1,804 cases and 780 normoglycaemic controls was carried out by weighting the effect estimates by their inverse variances.ResultsIn the meta-analysis of the two Hispanic studies, markers showing suggestive associations (p < 10−5) were identified in two known diabetes genes, HNF1A and KCNQ1, as well as in several additional regions. Meta-analysis of the two Hispanic studies and the recent DIAGRAM+ dataset identified genome-wide significant signals (p < 5 × 10−8) within or near the genes HNF1A and CDKN2A/CDKN2B, as well as suggestive associations in three additional regions, IGF2BP2, KCNQ1 and the previously unreported C14orf70.Conclusions/interpretationWe observed numerous regions with suggestive associations with type 2 diabetes. Some of these signals correspond to regions described in previous studies. However, many of these regions could not be replicated in the DIAGRAM datasets. It is critical to carry out additional studies in Hispanic and American Indian populations, which have a high prevalence of type 2 diabetes.

Computational prediction of cancer-gene function

Most cancer genes remain functionally uncharacterized in the physiological context of disease development. High-throughput molecular profiling and interaction studies are increasingly being used to identify clusters of functionally linked gene products related to neoplastic cell processes. However, in vivo determination of cancer-gene function is laborious and inefficient, so accurately predicting cancer-gene function is a significant challenge for oncologists and computational biologists alike. How can modern computational and statistical methods be used to reliably deduce the function(s) of poorly characterized cancer genes from the newly available genomic and proteomic datasets? We explore plausible solutions to this important challenge.

Data Integration in Genetics and Genomics: Methods and Challenges

Due to rapid technological advances, various types of genomic and proteomic data with different sizes, formats, and structures have become available. Among them are gene expression, single nucleotide polymorphism, copy number variation, and protein-protein/gene-gene interactions. Each of these distinct data types provides a different, partly independent and complementary, view of the whole genome. However, understanding functions of genes, proteins, and other aspects of the genome requires more information than provided by each of the datasets. Integrating data from different sources is, therefore, an important part of current research in genomics and proteomics. Data integration also plays important roles in combining clinical, environmental, and demographic data with high-throughput genomic data. Nevertheless, the concept of data integration is not well defined in the literature and it may mean different things to different researchers. In this paper, we first propose a conceptual framework for integrating genetic, genomic, and proteomic data. The framework captures fundamental aspects of data integration and is developed taking the key steps in genetic, genomic, and proteomic data fusion. Secondly, we provide a review of some of the most commonly used current methods and approaches for combining genomic data with focus on the statistical aspects.

Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models

BackgroundWith the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups.ResultsWe extended traditional effect size models to combine information from different microarray datasets by incorporating a quality measure for each gene in each study into the effect size estimation. We illustrated our method by integrating two datasets generated using different Affymetrix oligonucleotide types. Our results indicate that the proposed quality-adjusted weighting strategy for modelling inter-study variation of gene expression profiles not only increases consistency and decreases heterogeneous results between these two datasets, but also identifies many more differentially expressed genes than methods proposed previously.ConclusionData integration and synthesis is becoming increasingly important. We live in a high-throughput era where technologies constantly change leaving behind a trail of data with different forms, shapes and sizes. Statistical and computational methodologies are therefore critical for extracting the most out of these related but not identical sources of data.

Differential gene profiling in acute lung injury identifies injury-specific gene expression.

Objectives:Acute lung injury can result from distinct insults, such as sepsis, ischemia–reperfusion, and ventilator-induced lung injury. Physiologic and morphologic manifestations of disparate forms of injury are often indistinguishable. We sought to demonstrate that acute lung injury resulting from distinct insults may lead to different gene expression profiles. Design:Microarray analysis was used to examine early molecular events in lungs from three rat models of acute lung injury: lipopolysaccharide, hemorrhage shock/resuscitation, and high-volume ventilation. Setting:University laboratory. Subjects:Male Sprague-Dawley rats (body weight, 300–350 g). Interventions:Rats were subjected to hemorrhagic shock or lipopolysaccharide followed by resuscitation or were subjected to sham operation. First hit was followed by ventilation with either low (6 mL/kg) or high (12 mL/kg) tidal volume for 4 hrs. Measurements and Main Results:Physiologic and morphologic variables were assessed. Total RNA was hybridized to Affymetrix chips. Bioconductor was used to identify significantly altered genes. Functional enrichment predictions were performed in Gene Ontology Tree Machine. Confirmation studies included real-time polymerase chain reaction, Western blots, and immunohistochemistry. Physiologic and morphologic variables were noncontributory in determining the cause of acute lung injury. In contrast, molecular analysis revealed unique gene expression patterns that characterized exposure to lipopolysaccharide and high-volume ventilation. We used hypergeometric probability to demonstrate that specific functional enrichment groups were regulated by biochemical vs. biophysical factors. Genes stimulated by lipopolysaccharide were involved in metabolism, defense response, immune cell proliferation, differentiation and migration, and cell death. In contrast, high-volume ventilation led to the regulation of genes involved primarily in organogenesis, morphogenesis, cell cycle, proliferation, and differentiation. Conclusions:These results demonstrate the application of functional genomics to the molecular “fingerprinting” of acute lung injury and the potential for decoupling biophysical from biochemical injury.

IL-23R Polymorphisms in Patients with Ankylosing Spondylitis in Korea

Objective. IL23R polymorphisms have been shown to have a significant association with ankylosing spondylitis (AS). To date, these studies have been restricted to Caucasian patients with AS. Our study addresses this relationship in Korean patients with AS. Methods. A total of 451 patients with AS and 392 ethnically matched healthy controls were enrolled. All patients were native Koreans with AS satisfying the modified New York criteria. In total, 10 single nucleotide polymorphisms (SNP) within the IL-23R gene cluster were genotyped. Results. No IL-23R SNP were found to be associated with AS in Koreans. Conclusion. The association of IL23R and AS that is seen in Caucasian patients with AS is not present in Korean patients with AS.