Project description:Antemortem infection is a risk factor for sudden infant death syndrome (SIDS) – the leading postneonatal cause of infant mortality in the developed world. Manifestations of infection and attendant inflammation, however, are not always apparent in clinical settings or by standard autopsy, thus enhanced resolution approaches are needed. Here we screened postmortem SIDS tissues and fluids for inflammatory markers and applied metagenomics and transcriptomics to a subset of cases to look for evidence of occult infection and inflammation.
Project description:Sickle cell disease is associated with systemic complications, many associated with either severity of disease or increased risk of mortality. We sought to identify a circulating gene expression profile whose predictive capacity spanned the spectrum of these poor outcomes in sickle cell disease. The Training cohort consisted of patients with SCD who were prospectively recruited from the University of Illinois. The Testing cohort consisted of a combination of patients prospectively seen at two separate institutions including the University of Chicago and Howard University.
Project description:The aggressive MLL-rearranged leukemias are well-known for their unique gene-expression profiles. The goal of this study was to characterize the MLL-specific DNA methylation profiles in infant acute lymphoblastic leukemia (ALL). Genome-wide DNA methylation profiling was performed on primary infant ALL samples. The majority of infant ALL samples demonstrated severe DNA hypermethylation compared with normal pediatric bone marrows, which implies that targeting of DNA methylation may be an interesting option for future therapeutic strategies in MLL-rearranged infant ALL. Using ALL cell lines carrying the MLL translocation t(4;11) (SEMK2 and RS4;11) as a model for the patient cells, we demonstrated that the hypermethylated genes are sensitive to demethylation.
Project description:Capture Hi-C (CHi-C) is a state-of-the art method for profiling chromosomal interactions involving targeted regions of interest (such as gene promoters) globally and at high resolution. Signal detection in CHi-C data involves a number of statistical challenges that are not observed when using other Hi-C-like techniques. We present a background model, and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C experiments, in which many spatially dispersed regions are captured, such as in Promoter CHi-C. We implement these procedures in CHiCAGO (http://regulatorygenomicsgroup.org/chicago), an open-source package for robust interaction detection in CHi-C. We validate CHiCAGO by showing that promoter-interacting regions detected with this method are enriched for regulatory features and disease-associated SNPs.
