Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Effect of surgical experience and spine subspecialty on the reliability of the {AO} Spine Upper Cervical Injury Classification System

OBJECTIVE The objective of this paper was to determine the interobserver reliability and intraobserver reproducibility of the AO Spine Upper Cervical Injury Classification System based on surgeon experience (< 5 years, 5–10 years, 10–20 years, and > 20 years) and surgical subspecialty (orthopedic spine surgery, neurosurgery, and "other" surgery). METHODS A total of 11,601 assessments of upper cervical spine injuries were evaluated based on the AO Spine Upper Cervical Injury Classification System. Reliability and reproducibility scores were obtained twice, with a 3-week time interval. Descriptive statistics were utilized to examine the percentage of accurately classified injuries, and Pearson’s chi-square or Fisher’s exact test was used to screen for potentially relevant differences between study participants. Kappa coefficients (κ) determined the interobserver reliability and intraobserver reproducibility. RESULTS The intraobserver reproducibility was substantial for surgeon experience level (< 5 years: 0.74 vs 5–10 years: 0.69 vs 10–20 years: 0.69 vs > 20 years: 0.70) and surgical subspecialty (orthopedic spine: 0.71 vs neurosurgery: 0.69 vs other: 0.68). Furthermore, the interobserver reliability was substantial for all surgical experience groups on assessment 1 (< 5 years: 0.67 vs 5–10 years: 0.62 vs 10–20 years: 0.61 vs > 20 years: 0.62), and only surgeons with > 20 years of experience did not have substantial reliability on assessment 2 (< 5 years: 0.62 vs 5–10 years: 0.61 vs 10–20 years: 0.61 vs > 20 years: 0.59). Orthopedic spine surgeons and neurosurgeons had substantial intraobserver reproducibility on both assessment 1 (0.64 vs 0.63) and assessment 2 (0.62 vs 0.63), while other surgeons had moderate reliability on assessment 1 (0.43) and fair reliability on assessment 2 (0.36). CONCLUSIONS The international reliability and reproducibility scores for the AO Spine Upper Cervical Injury Classification System demonstrated substantial intraobserver reproducibility and interobserver reliability regardless of surgical experience and spine subspecialty. These results support the global application of this classification system

Genotyping of Toxoplasma gondii isolates from naturally infected Gallus domesticus in Santa Catarina state, Brazil

ABSTRACT Toxoplasmosis is a widespread zoonosis that can infect warm-blooded animals including birds and humans, and chickens are considered to be indicators of environmental contamination. In Brazil, Toxoplasma gondii has a non-clonal population structure composed of three lineages (I, II, and III), presenting high recombination, and resulting in wide genetic diversity. This study aimed to genetically characterize T. gondii isolates from naturally infected chickens (Gallus domesticus) in Santa Catarina state, southern Brazil region. Sera from 133 free-range chickens were analyzed by an immunofluorescence assay (IFA) to detect IgG antibodies against T. gondii. Brain and heart from 30 positive animals, based on IFA (≥ 1:64), were used to isolate the parasite using a mouse bioassay. Strain genotyping was performed by PCR-RFLP using 12 genetic markers (SAG1, 5´-3´SAG2, alt. SAG2, SAG3, BTUB, GRA6, c22-8, c29-2, L358, PK1, Apico, and CS3). The results were classified according to the genotypes based on the ToxoDB (http://toxodb.org/toxo/). Of 133 chicken sera analyzed, 84 (63.16%) were positive, with antibody titers ranging from 16 to 1024. Eleven isolates were obtained from mouse bioassay (Ck3, Ck32, Ck35, Ck56, Ck63, Ck89, Ck102, Ck103, Ck125, Ck127, and Ck128). Genotyping revealed six genotypes; three were classified as #26, #53, and #120, and three (NEO1, NEO2, and NEO3) were had not been previously described. No clonal lineages of type I, II, or III were identified. The present study confirms the high genetic diversity of T. gondii in Brazil

Antibodies against rickettsiae from spotted fever groups in horses from two mesoregions in the state of Santa Catarina, Brazil

Bacteria of the Rickettsia genus are agents of Brazilian Spotted Fever (BSF), a zoonotic disease which is difficult to diagnose, evolves quickly and can result in death. Antibodies against Rickettsia spp. in horses were studied, by means of Indirect Immunofluorescence Assay (IFAT ≥64), in 150 blood samples taken from animals in two Santa Catarina mesoregions (Planalto Serrano and Vale do Itajaí). The overall occurrence of Rickettsia spp. antibodies in horses was 18.66%, with cross-reactivity occurring in all positive samples for at least two of the species tested. Separately, according to the species, 25 (16.66%) samples were positive for R. rickettsii, 15 (10%) for R. parkeri, 22 (14.66%) for R. amblyommii, 23 (15.33%) for R. rhipicephali, 16 (10.66%) for R. bellii and 19 (12.66%) for R. felis. Only two animals resulted in a conclusive serodiagnosis, one for R. bellii and the other for R. rickettsii, at maximum dilutions of 1:4096 and 1:512, respectively. The occurrence of antibodies against Rickettsia spp. in horses from two mesoregions in the state of Santa Catarina indicates the movement of BSF agents in these sentinel animals and confirms the importance of studying spotted fever in the state of Santa Catarina