The past decade has seen the creation and maturation of a number of new technologies designed to study life on a genome-wide scale. However, the sheer volume of data generated by these methods surpasses the analytical and critical abilities of a single researcher. For this reason, it is necessary to create new computational methods to assist in the analysis of these new sources of data. Both yeast-two hybrid and co-immunoprecipitation followed by mass spectrometry allow the determination of binding interactions between proteins. Functional (genetic) interactions are determined via SGA (Synthetic Genetic Array) and E-MAP (Epistasis Mini-array Profile). In Chapters 2 and 3, we develop algorithms to integrate these two types of interactions together for the purpose of biological pathway discovery. Moreover, our approaches create maps of genetic interactions that provide a picture of the global organization of pathways and complexes within the cell. Expression arrays are a genome-wide quantitative assay for mRNA levels within the cell. Using fluorescent dyes, two different biological samples can be directly compared on a single array slide. In Chapter 4, we identify a gene-specific dye bias in this type of expression array data. We improve upon a maximum likelihood method in order to remove the effect of this bias. Using novel control experiments, we show that this enhanced analysis yields results that more reproducible. Complementary to expression profiling, deletion fitness profiling quantifies the relative fitness defect of every deletion strain in Saccharomyces cerevisiae under a particular stress. In Chapters 5 and 6, we discuss how to use the type of pathway information uncovered in Chapters 2 and 3 to improve the analysis of both expression and deletion fitness profiling datasets. We apply these methods to the study of two different cellular stresses in Saccharomyces cerevisiae, arsenic exposure and adaptation to oxidative stress

A search for new physics is performed using events with isolated same-sign leptons and jets in the final state. Results are based on the full sample of proton-proton collisions collected from the Large Hadron Collider at a center-of-mass energy of 8 TeV with the CMS detector and corresponding to an integrated luminosity of 19.5 fb⁻¹. No excess above the standard model background is observed and constraints on a number of new physics models are set

OBJECTIVE: Bipolar disorder (BD) commonly begins during adolescence and may continue into adulthood. Studies in adults with BD suggest that disruptions in amygdalar neural circuitry explain the pathophysiology underlying the disorder. Importantly, however, amygdala subregion networks have not yet been examined in youth close to mania onset. The goal of this study was to compare resting state functional connectivity patterns in amygdala subregions in youth with bipolar I disorder with patterns in healthy controls. METHOD: Centromedial, laterobasal, and superficial amygdala subdivisions were assessed during rest and examined in relation to clinical measures of mania in youth (14-20 years old) with bipolar I disorder who experienced only a single episode of mania (BD; n = 20) and age-matched healthy comparison youth without any personal or family history of DSM-IV Axis I disorders (HC; n = 23). RESULTS: Relative to HC youth, youth with BD exhibited decreased connectivity between the laterobasal subdivision of the amygdala and the hippocampus and precentral gyrus, and increased connectivity between the laterobasal subdivision and the precuneus. Connectivity between the right laterobasal amygdala and right hippocampus was positively correlated with levels of anxiety in BD but not in HC youth, and connectivity between the right laterobasal amygdala and right precuneus was negatively correlated with insight about bipolar illness. CONCLUSION: Youth with BD have abnormal amygdala resting state network connections to regions that are critical for emotional processing and self-awareness. Longitudinal studies are needed to determine whether these aberrant patterns in youth with BD can be altered with intervention and can influence the course of disorder.

Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.

Abstract TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.

Brain imaging studies in Alzheimer's disease research have demonstrated structural and functional perturbations in the hippocampus and default mode network (DMN). Additional evidence suggests risk for pathological brain aging in association with insulin resistance (IR). This study piloted investigation of associations of IR with DMN-hippocampal functional connectivity among postmenopausal women at risk for Alzheimer's disease. Twenty middle-aged women underwent resting state functional magnetic resonance imaging. Subjects were dichotomized relative to fasting plasma insulin levels (i.e., > 8 μIU/mL [n = 10] and < 8 μIU/mL [n = 10]), and functional connectivity analysis contrasted their respective blood oxygen level-dependent signal correlation between DMN and hippocampal regions. Higher-insulin women had significantly reduced positive associations between the medial prefrontal cortex and bilateral parahippocampal regions extending to the right hippocampus, and conversely, between the left and right hippocampus and medial prefrontal cortex. Neuropsychological data (all within normal ranges) also showed significant differences with respect to executive functioning and global intelligence. The results provide further evidence of deleterious effects of IR on the hippocampus and cognition. Further imaging studies of the IR-related perturbations in DMN-hippocampal functional connectivity are needed.

OBJECTIVES: Bipolar disorder (BD) has been associated with dysfunctional brain connectivity and with family chaos. It is not known whether aberrant connectivity occurs before illness onset, representing vulnerability for developing BD amidst family chaos. We used resting-state functional magnetic resonance imaging (fMRI) to examine neural network dysfunction in healthy offspring living with parents with BD and healthy comparison youth. METHODS: Using two complementary methodologies [data-driven independent component analysis (ICA) and hypothesis-driven region-of-interest (ROI)-based intrinsic connectivity], we examined resting-state fMRI data in 8-17-year-old healthy offspring of a parent with BD (n = 24; high risk) and age-matched healthy youth without any personal or family psychopathology (n = 25; low risk). RESULTS: ICA revealed that, relative to low-risk youth, high-risk youth showed increased connectivity in the ventrolateral prefrontal cortex (VLPFC) subregion of the left executive control network (ECN), which includes frontoparietal regions important for emotion regulation. ROI-based analyses revealed that high-risk versus low-risk youth had decreased connectivities between the left amygdala and pregenual cingulate, between the subgenual cingulate and supplementary motor cortex, and between the left VLPFC and left caudate. High-risk youth showed stronger connections in the VLPFC with age and higher functioning, which may be neuroprotective, and weaker connections between the left VLPFC and caudate with more family chaos, suggesting an environmental influence on frontostriatal connectivity. CONCLUSIONS: Healthy offspring of parents with BD show atypical patterns of prefrontal and subcortical intrinsic connectivity that may be early markers of resilience to or vulnerability for developing BD. Longitudinal studies are needed to determine whether these patterns predict outcomes.

We wished to determine whether decreases in N-acetyl aspartate (NAA) and increases in myoinositol (mI) concentrations as a ratio of creatine (Cr) occurred in the dorsolateral prefrontal cortex (DLPFC) of pediatric offspring of parents with bipolar disorder (BD) and a healthy comparison group (HC) over a 5-year period using proton magnetic resonance spectroscopy ((1)H-MRS). Paticipants comprised 64 offspring (9-18 years old) of parents with BD (36 with established BD, and 28 offspring with symptoms subsyndromal to mania) and 28 HCs, who were examined for group differences in NAA/Cr and mI/Cr in the DLPFC at baseline and follow-up at either 8, 10, 12, 52, 104, 156, 208, or 260 weeks. No significant group differences were found in metabolite concentrations at baseline or over time. At baseline, BD offspring had trends for higher mI/Cr concentrations in the right DLPFC than the HC group. mI/Cr concentrations increased with age, but no statistically significant group differences were found between groups on follow-up. It may be the case that with intervention youth at risk for BD are normalizing otherwise potentially aberrant neurochemical trajectories in the DLPFC. A longer period of follow-up may be required before observing any group differences.