👤 Chaya Murali

Background Chromosomal microarray analysis (CMA) provides an opportunity to understand genetic causes of congenital heart disease (CHD). The methods for describing cardiac phenotypes in patients with CMA abnormalities have been inconsistent, which may complicate clinical interpretation of abnormal testing results and hinder a more complete understanding of genotype-phenotype relationships. Methods and Results Patients with CHD and abnormal clinical CMA were accrued from 9 pediatric cardiac centers. Highly detailed cardiac phenotypes were systematically classified and analyzed for their association with CMA abnormality. Hierarchical classification of each patient into 1 CHD category facilitated broad analyses. Inclusive classification allowing multiple CHD types per patient provided sensitive descriptions. In 1363 registry patients, 28% had genomic disorders with well-recognized CHD association, 67% had clinically reported copy number variants (CNVs) with rare or no prior CHD association, and 5% had regions of homozygosity without CNV. Hierarchical classification identified expected CHD categories in genomic disorders, as well as uncharacteristic CHDs. Inclusive phenotyping provided sensitive descriptions of patients with multiple CHD types, which occurred commonly. Among CNVs with rare or no prior CHD association, submicroscopic CNVs were enriched for more complex types of CHD compared with large CNVs. The submicroscopic CNVs that contained a curated CHD gene were enriched for left ventricular obstruction or septal defects, whereas CNVs containing a single gene were enriched for conotruncal defects. Neuronal-related pathways were over-represented in single-gene CNVs, including top candidate causative genes Show less

Lung cancer is the leading cause of cancer mortality in the United States (U.S.). Squamous cell carcinoma (SQCC) represents 22.6% of all lung cancers nationally, and 26.4% in Appalachian Kentucky (AppKY), where death from lung cancer is exceptionally high. The Cancer Genome Atlas (TCGA) characterized genetic alterations in lung SQCC, but this cohort did not focus on AppKY residents. Whole-exome sequencing was performed on tumor and normal DNA samples from 51 lung SQCC subjects from AppKY. Somatic genomic alterations were compared between the AppKY and TCGA SQCC cohorts. From this AppKY cohort, we identified an average of 237 nonsilent mutations per patient and, in comparison with TCGA, we found that This study has identified an increased percentage of Our study is the first report to characterize genomic alterations in lung SQCC from AppKY. These findings suggest population differences in the genetics of lung SQCC between AppKY and U.S. populations, highlighting the importance of the relevant population when developing personalized treatment approaches for this disease. Show less

Neonatal heart failure is a rare, poorly-understood presentation of familial dilated cardiomyopathy (DCM). Exome sequencing in a neonate with severe DCM revealed a homozygous nonsense variant in leiomodin 2 ( Show less

The cell cycle is composed of bistable molecular switches that govern the transitions between gap phases (G1 and G2) and the phases in which DNA is replicated (S) and partitioned between daughter cells (M). Many molecular details of the budding yeast G1-S transition (Start) have been elucidated in recent years, especially with regard to its switch-like behavior due to positive feedback mechanisms. These results led us to reevaluate and expand a previous mathematical model of the yeast cell cycle. The new model incorporates Whi3 inhibition of Cln3 activity, Whi5 inhibition of SBF and MBF transcription factors, and feedback inhibition of Whi5 by G1-S cyclins. We tested the accuracy of the model by simulating various mutants not described in the literature. We then constructed these novel mutant strains and compared their observed phenotypes to the model's simulations. The experimental results reported here led to further changes of the model, which will be fully described in a later article. Our study demonstrates the advantages of combining model design, simulation, and testing in a coordinated effort to better understand a complex biological network. Show less

Fungi are the second most abundant type of human pathogens. Invasive fungal pathogens are leading causes of life-threatening infections in clinical settings. Toxicity to the host and drug-resistance are two major deleterious issues associated with existing antifungal agents. Increasing a host's tolerance and/or immunity to fungal pathogens has potential to alleviate these problems. A host's tolerance may be improved by modulating the immune system such that it responds more rapidly and robustly in all facets, ranging from the recognition of pathogens to their clearance from the host. An understanding of biological processes and genes that are perturbed during attempted fungal exposure, colonization, and/or invasion will help guide the identification of endogenous immunomodulators and/or small molecules that activate host-immune responses such as specialized adjuvants. In this study, we present computational techniques and approaches using publicly available transcriptional data sets, to predict immunomodulators that may act against multiple fungal pathogens. Our study analyzed data sets derived from host cells exposed to five fungal pathogens, namely, Alternaria alternata, Aspergillus fumigatus, Candida albicans, Pneumocystis jirovecii, and Stachybotrys chartarum. We observed statistically significant associations between host responses to A. fumigatus and C. albicans. Our analysis identified biological processes that were consistently perturbed by these two pathogens. These processes contained both immune response-inducing genes such as MALT1, SERPINE1, ICAM1, and IL8, and immune response-repressing genes such as DUSP8, DUSP6, and SPRED2. We hypothesize that these genes belong to a pool of common immunomodulators that can potentially be activated or suppressed (agonized or antagonized) in order to render the host more tolerant to infections caused by A. fumigatus and C. albicans. Our computational approaches and methodologies described here can now be applied to newly generated or expanded data sets for further elucidation of additional drug targets. Moreover, identified immunomodulators may be used to generate experimentally testable hypotheses that could help in the discovery of broad-spectrum immunotherapeutic interventions. All of our results are available at the following supplementary website: http://bioinformatics.cs.vt.edu/~murali/supplements/2013-kidane-bmc. Show less