215 research outputs found

    A metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface

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    Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI. © 2011 Li et al

    Genetic and clinical determinants of abdominal aortic diameter: genome-wide association studies, exome array data and Mendelian randomization study

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    Progressive dilation of the infrarenal aortic diameter is a consequence of the ageing process and is considered the main determinant of abdominal aortic aneurysm (AAA). We aimed to investigate the genetic and clinical determinants of abdominal aortic diameter (AAD). We conducted a meta-analysis of genome-wide association studies in 10 cohorts (n = 13 542) imputed to the 1000 Genome Project reference panel including 12 815 subjects in the discovery phase and 727 subjects [Partners Biobank cohort 1 (PBIO)] as replication. Maximum anterior–posterior diameter of the infrarenal aorta was used as AAD. We also included exome array data (n = 14 480) from seven epidemiologic studies. Single-variant and gene-based associations were done using SeqMeta package. A Mendelian randomization analysis was applied to investigate the causal effect of a number of clinical risk factors on AAD. In genome-wide association study (GWAS) on AAD, rs74448815 in the intronic region of LDLRAD4 reached genome-wide significance (beta = −0.02, SE = 0.004, P-value = 2.10 × 10(−8)). The association replicated in the PBIO1 cohort (P-value = 8.19 × 10(−4)). In exome-array single-variant analysis (P-value threshold = 9 × 10(−7)), the lowest P-value was found for rs239259 located in SLC22A20 (beta = 0.007, P-value = 1.2 × 10(−5)). In the gene-based analysis (P-value threshold = 1.85 × 10(−6)), PCSK5 showed an association with AAD (P-value = 8.03 × 10(−7)). Furthermore, in Mendelian randomization analyses, we found evidence for genetic association of pulse pressure (beta = −0.003, P-value = 0.02), triglycerides (beta = −0.16, P-value = 0.008) and height (beta = 0.03, P-value < 0.0001), known risk factors for AAA, consistent with a causal association with AAD. Our findings point to new biology as well as highlighting gene regions in mechanisms that have previously been implicated in the genetics of other vascular diseases

    A Factor Graph Nested Effects Model To Identify Networks from Genetic Perturbations

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    Complex phenotypes such as the transformation of a normal population of cells into cancerous tissue result from a series of molecular triggers gone awry. We describe a method that searches for a genetic network consistent with expression changes observed under the knock-down of a set of genes that share a common role in the cell, such as a disease phenotype. The method extends the Nested Effects Model of Markowetz et al. (2005) by using a probabilistic factor graph to search for a network representing interactions among these silenced genes. The method also expands the network by attaching new genes at specific downstream points, providing candidates for subsequent perturbations to further characterize the pathway. We investigated an extension provided by the factor graph approach in which the model distinguishes between inhibitory and stimulatory interactions. We found that the extension yielded significant improvements in recovering the structure of simulated and Saccharomyces cerevisae networks. We applied the approach to discover a signaling network among genes involved in a human colon cancer cell invasiveness pathway. The method predicts several genes with new roles in the invasiveness process. We knocked down two genes identified by our approach and found that both knock-downs produce loss of invasive potential in a colon cancer cell line. Nested effects models may be a powerful tool for inferring regulatory connections and genes that operate in normal and disease-related processes

    Defining Natural History: Assessment of the Ability of College Students to Aid in Characterizing Clinical Progression of Niemann-Pick Disease, Type C

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    Niemann-Pick Disease, type C (NPC) is a fatal, neurodegenerative, lysosomal storage disorder. It is a rare disease with broad phenotypic spectrum and variable age of onset. These issues make it difficult to develop a universally accepted clinical outcome measure to assess urgently needed therapies. To this end, clinical investigators have defined emerging, disease severity scales. The average time from initial symptom to diagnosis is approximately 4 years. Further, some patients may not travel to specialized clinical centers even after diagnosis. We were therefore interested in investigating whether appropriately trained, community-based assessment of patient records could assist in defining disease progression using clinical severity scores. In this study we evolved a secure, step wise process to show that pre-existing medical records may be correctly assessed by non-clinical practitioners trained to quantify disease progression. Sixty-four undergraduate students at the University of Notre Dame were expertly trained in clinical disease assessment and recognition of major and minor symptoms of NPC. Seven clinical records, randomly selected from a total of thirty seven used to establish a leading clinical severity scale, were correctly assessed to show expected characteristics of linear disease progression. Student assessment of two new records donated by NPC families to our study also revealed linear progression of disease, but both showed accelerated disease progression, relative to the current severity scale, especially at the later stages. Together, these data suggest that college students may be trained in assessment of patient records, and thus provide insight into the natural history of a disease

    The 5p15.33 Locus Is Associated with Risk of Lung Adenocarcinoma in Never-Smoking Females in Asia

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    Genome-wide association studies of lung cancer reported in populations of European background have identified three regions on chromosomes 5p15.33, 6p21.33, and 15q25 that have achieved genome-wide significance with p-values of 10−7 or lower. These studies have been performed primarily in cigarette smokers, raising the possibility that the observed associations could be related to tobacco use, lung carcinogenesis, or both. Since most women in Asia do not smoke, we conducted a genome-wide association study of lung adenocarcinoma in never-smoking females (584 cases, 585 controls) among Han Chinese in Taiwan and found that the most significant association was for rs2736100 on chromosome 5p15.33 (p = 1.30×10−11). This finding was independently replicated in seven studies from East Asia totaling 1,164 lung adenocarcinomas and 1,736 controls (p = 5.38×10−11). A pooled analysis achieved genome-wide significance for rs2736100. This SNP marker localizes to the CLPTM1L-TERT locus on chromosome 5p15.33 (p = 2.60×10−20, allelic risk = 1.54, 95% Confidence Interval (CI) 1.41–1.68). Risks for heterozygote and homozygote carriers of the minor allele were 1.62 (95% CI; 1.40–1.87), and 2.35 (95% CI: 1.95–2.83), respectively. In summary, our results show that genetic variation in the CLPTM1L-TERT locus of chromosome 5p15.33 is directly associated with the risk of lung cancer, most notably adenocarcinoma
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