Aimee Teo Broman

Energy Metabolic Reprogramming in the Hypertrophied and Early Stage Failing Heart A Multisystems Approach

Background—An unbiased systems approach was used to define energy metabolic events that occur during the pathological cardiac remodeling en route to heart failure (HF). Methods and Results—Combined myocardial transcriptomic and metabolomic profiling were conducted in a well-defined mouse model of HF that allows comparative assessment of compensated and decompensated (HF) forms of cardiac hypertrophy because of pressure overload. The pressure overload data sets were also compared with the myocardial transcriptome and metabolome for an adaptive (physiological) form of cardiac hypertrophy because of endurance exercise training. Comparative analysis of the data sets led to the following conclusions: (1) expression of most genes involved in mitochondrial energy transduction were not significantly changed in the hypertrophied or failing heart, with the notable exception of a progressive downregulation of transcripts encoding proteins and enzymes involved in myocyte fatty acid transport and oxidation during the development of HF; (2) tissue metabolite profiles were more broadly regulated than corresponding metabolic gene regulatory changes, suggesting significant regulation at the post-transcriptional level; (3) metabolomic signatures distinguished pathological and physiological forms of cardiac hypertrophy and served as robust markers for the onset of HF; and (4) the pattern of metabolite derangements in the failing heart suggests bottlenecks of carbon substrate flux into the Krebs cycle. Conclusions—Mitochondrial energy metabolic derangements that occur during the early development of pressure overload–induced HF involve both transcriptional and post-transcriptional events. A subset of the myocardial metabolomic profile robustly distinguished pathological and physiological cardiac remodeling.

Energy Metabolic Reprogramming in the Hypertrophied and Early Stage Failing HeartCLINICAL PERSPECTIVE

Background—An unbiased systems approach was used to define energy metabolic events that occur during the pathological cardiac remodeling en route to heart failure (HF). Methods and Results—Combined myocardial transcriptomic and metabolomic profiling were conducted in a well-defined mouse model of HF that allows comparative assessment of compensated and decompensated (HF) forms of cardiac hypertrophy because of pressure overload. The pressure overload data sets were also compared with the myocardial transcriptome and metabolome for an adaptive (physiological) form of cardiac hypertrophy because of endurance exercise training. Comparative analysis of the data sets led to the following conclusions: (1) expression of most genes involved in mitochondrial energy transduction were not significantly changed in the hypertrophied or failing heart, with the notable exception of a progressive downregulation of transcripts encoding proteins and enzymes involved in myocyte fatty acid transport and oxidation during the development of HF; (2) tissue metabolite profiles were more broadly regulated than corresponding metabolic gene regulatory changes, suggesting significant regulation at the post-transcriptional level; (3) metabolomic signatures distinguished pathological and physiological forms of cardiac hypertrophy and served as robust markers for the onset of HF; and (4) the pattern of metabolite derangements in the failing heart suggests bottlenecks of carbon substrate flux into the Krebs cycle. Conclusions—Mitochondrial energy metabolic derangements that occur during the early development of pressure overload–induced HF involve both transcriptional and post-transcriptional events. A subset of the myocardial metabolomic profile robustly distinguished pathological and physiological cardiac remodeling.

Genetic Architecture of Ethanol-Responsive Transcriptome Variation in Saccharomyces Cerevisiae Strains

Natural variation in gene expression is pervasive within and between species, and it likely explains a significant fraction of phenotypic variation between individuals. Phenotypic variation in acute systemic responses can also be leveraged to reveal physiological differences in how individuals perceive and respond to environmental perturbations. We previously found extensive variation in the transcriptomic response to acute ethanol exposure in two wild isolates and a common laboratory strain of Saccharomyces cerevisiae. Many expression differences persisted across several modules of coregulated genes, implicating trans-acting systemic differences in ethanol sensing and/or response. Here, we conducted expression QTL mapping of the ethanol response in two strain crosses to identify the genetic basis for these differences. To understand systemic differences, we focused on “hotspot” loci that affect many transcripts in trans. Candidate causal regulators contained within hotspots implicate upstream regulators as well as downstream effectors of the ethanol response. Overlap in hotspot targets revealed additive genetic effects of trans-acting loci as well as “epi-hotspots,” in which epistatic interactions between two loci affected the same suites of downstream targets. One epi-hotspot implicated interactions between Mkt1p and proteins linked to translational regulation, prompting us to show that Mkt1p localizes to P bodies upon ethanol stress in a strain-specific manner. Our results provide a glimpse into the genetic architecture underlying natural variation in a stress response and present new details on how yeast respond to ethanol stress.

Resident Self-Assessment and Self-Reflection: University of Wisconsin-Madison’s Five-Year Study

BACKGROUNDChart review represents a critical cornerstone for practice-based learning and improvement in our internal medicine residency program.OBJECTIVETo document residents’ performance monitoring and improvement skills in their continuity clinics, their satisfaction with practice-based learning and improvement, and their ability to self-reflect on their performance.DESIGNRetrospective longitudinal design with repeated measures.PARTICIPANTSEighty Internal Medicine residents abstracted data for 3 consecutive years from the medical records of their 4,390 patients in the University of Wisconsin-Madison (UW) Hospital and Clinics and William S. Middleton Veterans Administration (VA) outpatient clinics.MEASUREMENTLogistic modeling was used to determine the effect of postgraduate year, resident sex, graduation cohort, and clinic setting on residents’ “compliance rate” on 17 nationally recognized health screening and chronic disease management parameters from 2003 to 2007.RESULTSResidents’ adherence to national preventive and chronic disease standards increased significantly from intern to subsequent years for administering immunizations, screening for diabetes, cholesterol, cancer, and behavioral risks, and for management of diabetes. Of the residents, 92% found the chart review exercise beneficial, with 63% reporting gains in understanding about their medical practices, 26% reflecting on specific gaps in their practices, and 8% taking critical action to improve their patient outcomes.CONCLUSIONSThis paper provides support for the feasibility and practicality of this limited-cost method of chart review. It also directs our residency program’s attention in the continuity clinic to a key area important to internal medicine training programs by highlighting the potential benefit of enhancing residents’ self-reflection skills.

Modeling Causality for Pairs of Phenotypes in System Genetics

Current efforts in systems genetics have focused on the development of statistical approaches that aim to disentangle causal relationships among molecular phenotypes in segregating populations. Reverse engineering of transcriptional networks plays a key role in the understanding of gene regulation. However, transcriptional regulation is only one possible mechanism, as methylation, phosphorylation, direct protein–protein interaction, transcription factor binding, etc., can also contribute to gene regulation. These additional modes of regulation can be interpreted as unobserved variables in the transcriptional gene network and can potentially affect its reconstruction accuracy. We develop tests of causal direction for a pair of phenotypes that may be embedded in a more complicated but unobserved network by extending Vuong’s selection tests for misspecified models. Our tests provide a significance level, which is unavailable for the widely used AIC and BIC criteria. We evaluate the performance of our tests against the AIC, BIC, and a recently published causality inference test in simulation studies. We compare the precision of causal calls using biologically validated causal relationships extracted from a database of 247 knockout experiments in yeast. Our model selection tests are more precise, showing greatly reduced false-positive rates compared to the alternative approaches. In practice, this is a useful feature since follow-up studies tend to be time consuming and expensive and, hence, it is important for the experimentalist to have causal predictions with low false-positive rates.

Identification of the Bile Acid Transporter Slco1a6 as a Candidate Gene that Broadly Affects Gene Expression in Mouse Pancreatic Islets

We surveyed gene expression in six tissues in an F2 intercross between mouse strains C57BL/6J (abbreviated B6) and BTBR T+ tf/J (abbreviated BTBR) made genetically obese with the Leptinob mutation. We identified a number of expression quantitative trait loci (eQTL) affecting the expression of numerous genes distal to the locus, called trans-eQTL hotspots. Some of these trans-eQTL hotspots showed effects in multiple tissues, whereas some were specific to a single tissue. An unusually large number of transcripts (∼8% of genes) mapped in trans to a hotspot on chromosome 6, specifically in pancreatic islets. By considering the first two principal components of the expression of genes mapping to this region, we were able to convert the multivariate phenotype into a simple Mendelian trait. Fine mapping the locus by traditional methods reduced the QTL interval to a 298-kb region containing only three genes, including Slco1a6, one member of a large family of organic anion transporters. Direct genomic sequencing of all Slco1a6 exons identified a nonsynonymous coding SNP that converts a highly conserved proline residue at amino acid position 564 to serine. Molecular modeling suggests that Pro564 faces an aqueous pore within this 12-transmembrane domain-spanning protein. When transiently overexpressed in HEK293 cells, BTBR organic anion transporting polypeptide (OATP)1A6-mediated cellular uptake of the bile acid taurocholic acid (TCA) was enhanced compared to B6 OATP1A6. Our results suggest that genetic variation in Slco1a6 leads to altered transport of TCA (and potentially other bile acids) by pancreatic islets, resulting in broad gene regulation.

Fungal Morphology, Iron Homeostasis, and Lipid Metabolism Regulated by a GATA Transcription Factor in Blastomyces dermatitidis.

In response to temperature, Blastomyces dermatitidis converts between yeast and mold forms. Knowledge of the mechanism(s) underlying this response to temperature remains limited. In B. dermatitidis, we identified a GATA transcription factor, SREB, important for the transition to mold. Null mutants (SREBΔ) fail to fully complete the conversion to mold and cannot properly regulate siderophore biosynthesis. To capture the transcriptional response regulated by SREB early in the phase transition (0–48 hours), gene expression microarrays were used to compare SREB∆ to an isogenic wild type isolate. Analysis of the time course microarray data demonstrated SREB functioned as a transcriptional regulator at 37°C and 22°C. Bioinformatic and biochemical analyses indicated SREB was involved in diverse biological processes including iron homeostasis, biosynthesis of triacylglycerol and ergosterol, and lipid droplet formation. Integration of microarray data, bioinformatics, and chromatin immunoprecipitation identified a subset of genes directly bound and regulated by SREB in vivo in yeast (37°C) and during the phase transition to mold (22°C). This included genes involved with siderophore biosynthesis and uptake, iron homeostasis, and genes unrelated to iron assimilation. Functional analysis suggested that lipid droplets were actively metabolized during the phase transition and lipid metabolism may contribute to filamentous growth at 22°C. Chromatin immunoprecipitation, RNA interference, and overexpression analyses suggested that SREB was in a negative regulatory circuit with the bZIP transcription factor encoded by HAPX. Both SREB and HAPX affected morphogenesis at 22°C; however, large changes in transcript abundance by gene deletion for SREB or strong overexpression for HAPX were required to alter the phase transition.

Tumoricidal Effects of Macrophage-Activating Immunotherapy in a Murine Model of Relapsed/Refractory Multiple Myeloma.

Jensen and colleagues report that inhibition of innate immunity checkpoint TPL2 kinase signaling potentiates the efficacy of anti–CD40-based immunotherapy, which expands M1-polarized macrophages in the bone marrow, prolonging survival in an immunocompetent, transplant-based preclinical model of relapsed/refractory multiple myeloma. Myeloma remains a virtually incurable malignancy. The inevitable evolution of multidrug-resistant clones and widespread clonal heterogeneity limit the potential of traditional and novel therapies to eliminate minimal residual disease (MRD), a reliable harbinger of relapse. Here, we show potent anti-myeloma activity of macrophage-activating immunotherapy (αCD40+CpG) that resulted in prolongation of progression-free survival (PFS) and overall survival (OS) in an immunocompetent, preclinically validated, transplant-based model of multidrug-resistant, relapsed/refractory myeloma (t-Vκ*MYC). αCD40+CpG was effective in vivo in the absence of cytolytic natural killer, T, or B cells and resulted in expansion of M1-polarized (cytolytic/tumoricidal) macrophages in the bone marrow. Moreover, we show that concurrent loss/inhibition of Tpl2 kinase (Cot, Map3k8), a MAP3K that is recruited to activated CD40 complex and regulates macrophage activation/cytokine production, potentiated direct, ex vivo anti-myeloma tumoricidal activity of αCD40+CpG–activated macrophages, promoted production of antitumor cytokine IL12 in vitro and in vivo, and synergized with αCD40+CpG to further prolong PFS and OS in vivo. Our results support the combination of αCD40-based macrophage activation and TPL2 inhibition for myeloma immunotherapy. We propose that αCD40-mediated activation of innate antitumor immunity may be a promising approach to control/eradicate MRD following cytoreduction with traditional or novel anti-myeloma therapies. Cancer Immunol Res; 3(8); 881–90. ©2015 AACR.

Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study.

In a mouse intercross with more than 500 animals and genome-wide gene expression data on six tissues, we identified a high proportion (18%) of sample mix-ups in the genotype data. Local expression quantitative trait loci (eQTL; genetic loci influencing gene expression) with extremely large effect were used to form a classifier to predict an individual’s eQTL genotype based on expression data alone. By considering multiple eQTL and their related transcripts, we identified numerous individuals whose predicted eQTL genotypes (based on their expression data) did not match their observed genotypes, and then went on to identify other individuals whose genotypes did match the predicted eQTL genotypes. The concordance of predictions across six tissues indicated that the problem was due to mix-ups in the genotypes (although we further identified a small number of sample mix-ups in each of the six panels of gene expression microarrays). Consideration of the plate positions of the DNA samples indicated a number of off-by-one and off-by-two errors, likely the result of pipetting errors. Such sample mix-ups can be a problem in any genetic study, but eQTL data allow us to identify, and even correct, such problems. Our methods have been implemented in an R package, R/lineup.

The Dissection of Expression Quantitative Trait Locus Hotspots

Studies of the genetic loci that contribute to variation in gene expression frequently identify loci with broad effects on gene expression: expression quantitative trait locus hotspots. We describe a set of exploratory graphical methods as well as a formal likelihood-based test for assessing whether a given hotspot is due to one or multiple polymorphisms. We first look at the pattern of effects of the locus on the expression traits that map to the locus: the direction of the effects and the degree of dominance. A second technique is to focus on the individuals that exhibit no recombination event in the region, apply dimensionality reduction (e.g., with linear discriminant analysis), and compare the phenotype distribution in the nonrecombinant individuals to that in the recombinant individuals: if the recombinant individuals display a different expression pattern than the nonrecombinant individuals, this indicates the presence of multiple causal polymorphisms. In the formal likelihood-based test, we compare a two-locus model, with each expression trait affected by one or the other locus, to a single-locus model. We apply our methods to a large mouse intercross with gene expression microarray data on six tissues.