Relationship of disease-associated gene expression to cardiac phenotype is buffered by genetic diversity and chromatin regulation.

Expression of a cohort of disease-associated genes, some of which are active in fetal myocardium, is considered a hallmark of transcriptional change in cardiac hypertrophy models. How this transcriptome remodeling is affected by the common genetic variation present in populations is unknown. We examined the role of genetics, as well as contributions of chromatin proteins, to regulate cardiac gene expression and heart failure susceptibility. We examined gene expression in 84 genetically distinct inbred strains of control and isoproterenol-treated mice, which exhibited varying degrees of disease. Unexpectedly, fetal gene expression was not correlated with hypertrophic phenotypes. Unbiased modeling identified 74 predictors of heart mass after isoproterenol-induced stress, but these predictors did not enrich for any cardiac pathways. However, expanded analysis of fetal genes and chromatin remodelers as groups correlated significantly with individual systemic phenotypes. Yet, cardiac transcription factors and genes shown by gain-/loss-of-function studies to contribute to hypertrophic signaling did not correlate with cardiac mass or function in disease. Because the relationship between gene expression and phenotype was strain specific, we examined genetic contribution to expression. Strikingly, strains with similar transcriptomes in the basal heart did not cluster together in the isoproterenol state, providing comprehensive evidence that there are different genetic contributors to physiological and pathological gene expression. Furthermore, the divergence in transcriptome similarity versus genetic similarity between strains is organ specific and genome-wide, suggesting chromatin is a critical buffer between genetics and gene expression.

"Good enough solutions" and the genetics of complex diseases.

In this Emerging Science Review, we discuss a systems genetics strategy, which we call gene module association study (GMAS), as a novel approach complementing genome-wide association studies (GWAS), to understand complex diseases by focusing on how genes work together in groups rather than singly. The first step is to characterize phenotypic differences among a genetically diverse population. The second step is to use gene expression microarray (or other high-throughput) data from the population to construct gene coexpression networks. Coexpression analysis typically groups 20 000 genes into 20 to 30 modules containing tens to hundreds of genes, whose aggregate behavior can be represented by the module's "eigengene." The third step is to correlate expression patterns with phenotype, as in GWAS, only applied to eigengenes instead of single nucleotide polymorphisms. The goal of the GMAS approach is to identify groups of coregulated genes that explain complex traits from a systems perspective. From an evolutionary standpoint, we hypothesize that variability in eigengene patterns reflects the "good enough solution" concept, that biological systems are sufficiently complex so that many possible combinations of the same elements (in this case eigengenes) can produce an equivalent output, that is, a "good enough solution" to accomplish normal biological functions. However, when faced with environmental stresses, some "good enough solutions" adapt better than others, explaining individual variability to disease and drug susceptibility. If validated, GMAS may imply that common polygenic diseases are related as much to group interactions between normal genes, as to multiple gene mutations.

Temporal expression of cytokines and B-cell phenotypes during mechanical circulatory support.

BACKGROUND: Allosensitization during mechanical circulatory support (MCS) is a well-described phenomenon, although its mechanism remains unknown. Although immune-mediated interactions from devices or blood transfusions have been proposed, the role of inflammation in this development is less clear. This study was undertaken to further investigate the temporal association of cytokines and B-cell phenotypes in the MCS population. METHODS: Adult patients who received the Heartmate II (Thoratec, Pleasanton, Calif) at our center between September 2012 and March 2015 were prospectively followed after device implantation. Blood draws for anti-human leukocyte antigen (HLA) antibody, cytokine expression, and B-cell immunophenotyping were performed before implantation and for 3 weeks postoperatively. Time courses for cytokines and B-cell subsets were expressed using visual representations of median levels as heat maps, and mixed modeling analysis was used to model changes with time and patient factors. RESULTS: Twenty patients who received the Heartmate II (Thoratec) were analyzed during the study period. Four patients showed measureable levels of anti-HLA antibody during the follow-up period, although 3 of these had evidence of antibodies preoperatively. Analysis of cytokine trends revealed early (interleukin [IL]-6, IL-8, and IL-10) and late peaking (IL-3, IL-4, fibroblast growth factor 2, and CD40L) patterns. Upregulation of switched memory, transitional, and plasma blast B cells occurred over time. Right ventricular assist device use and low Interagency Registry for Mechanically Assisted Circulatory Support score were associated with decreased mature naive and increased antibody-secreting cells. CONCLUSIONS: MCS device implantation was associated with increased inflammatory cytokines and maturation of B-cell phenotypes. No patients developed de novo HLA antibodies, whereas several showed increases in anti-HLA antibody levels detected before implantation. This suggests that inflammation and maturation of existing sensitized B cells might play an important role in the pathogenesis of allosensitization in MCS.

T cell dysfunction and patient age are associated with poor outcomes after mechanical circulatory support device implantation.

Immunologic impairment may contribute to poor outcomes after implantation of mechanical circulatory support device (MCSD), with infection often as a terminal event. The study of immune dysfunction is of special relevance given the growing numbers of older patients with heart disease. The aim of the study was to define which immunologic characteristics are associated with development of adverse clinical outcomes after MCSD implantation. We isolated peripheral blood mononuclear cells (PBMC) from patients pre- and up to 20 days post-MCSD implantation and analyzed them by multiparameter flow cytometry for T cell dysfunction, including terminal differentiation, exhaustion, and senescence. We used MELD-XI and SOFA scores measured at each time point as surrogate markers of clinical outcome. Older patients demonstrated increased frequencies of terminally differentiated T cells as well as NKT cells. Increased frequency of terminally differentiated and immune senescent T cells were associated with worse clinical outcome as measured by MELD-XI and SOFA scores, and with progression to infection and death. In conclusion, our data suggest that T cell dysfunction, independently from age, is associated with poor outcomes after MCSD implantation, providing a potential immunologic mechanism behind patient vulnerability to multiorgan dysfunction and death. This noninvasive approach to PBMC evaluation holds promise for candidate evaluation and patient monitoring.

Haploinsufficiency leads to neurodegeneration in C9ORF72 ALS/FTD human induced motor neurons.

An intronic GGGGCC repeat expansion in C9ORF72 is the most common cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), but the pathogenic mechanism of this repeat remains unclear. Using human induced motor neurons (iMNs), we found that repeat-expanded C9ORF72 was haploinsufficient in ALS. We found that C9ORF72 interacted with endosomes and was required for normal vesicle trafficking and lysosomal biogenesis in motor neurons. Repeat expansion reduced C9ORF72 expression, triggering neurodegeneration through two mechanisms: accumulation of glutamate receptors, leading to excitotoxicity, and impaired clearance of neurotoxic dipeptide repeat proteins derived from the repeat expansion. Thus, cooperativity between gain- and loss-of-function mechanisms led to neurodegeneration. Restoring C9ORF72 levels or augmenting its function with constitutively active RAB5 or chemical modulators of RAB5 effectors rescued patient neuron survival and ameliorated neurodegenerative processes in both gain- and loss-of-function C9ORF72 mouse models. Thus, modulating vesicle trafficking was able to rescue neurodegeneration caused by the C9ORF72 repeat expansion. Coupled with rare mutations in ALS2, FIG4, CHMP2B, OPTN and SQSTM1, our results reveal mechanistic convergence on vesicle trafficking in ALS and FTD.

Integrative model of leukocyte genomics and organ dysfunction in heart failure patients requiring mechanical circulatory support: a prospective observational study.

BACKGROUND: The implantation of mechanical circulatory support devices in heart failure patients is associated with a systemic inflammatory response, potentially leading to death from multiple organ dysfunction syndrome. Previous studies point to the involvement of many mechanisms, but an integrative hypothesis does not yet exist. Using time-dependent whole-genome mRNA expression in circulating leukocytes, we constructed a systems-model to improve mechanistic understanding and prediction of adverse outcomes. METHODS: We sampled peripheral blood mononuclear cells from 22 consecutive patients undergoing mechanical circulatory support device (MCS) surgery, at 5 timepoints: day -1 preoperative, and postoperative days 1, 3, 5, and 8. Clinical phenotyping was performed using 12 clinical parameters, 2 organ dysfunction scoring systems, and survival outcomes. We constructed a strictly phenotype-driven time-dependent non-supervised systems-representation using weighted gene co-expression network analysis, and annotated eigengenes using gene ontology, pathway, and transcription factor binding site enrichment analyses. Genes and eigengenes were mapped to the clinical phenotype using a linear mixed-effect model, with Cox models also fit at each timepoint to survival outcomes. RESULTS: We inferred a 19-module network, in which most module eigengenes correlated with at least one aspect of the clinical phenotype. We observed a response of advanced heart failure patients to surgery orchestrated into stages: first, activation of the innate immune response, followed by anti-inflammation, and finally reparative processes such as mitosis, coagulation, and apoptosis. Eigengenes related to red blood cell production and extracellular matrix degradation became predictors of survival late in the timecourse corresponding to multiorgan dysfunction and disseminated intravascular coagulation. CONCLUSIONS: Our model provides an integrative representation of leukocyte biology during the systemic inflammatory response following MCS device implantation. It demonstrates consistency with previous hypotheses, identifying a number of known mechanisms. At the same time, it suggests novel hypotheses about time-specific targets.

Comparison of whole blood and peripheral blood mononuclear cell gene expression for evaluation of the perioperative inflammatory response in patients with advanced heart failure.

BACKGROUND: Heart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction (MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition of MOD allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from patients' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined. METHODS: We collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25-75% IQR 7-14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (≤ 4 points), intermediate (5-11), and high (≥ 12) risk categories correlating with GEP. RESULTS: Results indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups. CONCLUSION: GEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD.

Maximal information component analysis: a novel non-linear network analysis method.

BACKGROUND: Network construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems. RESULTS: We have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case. CONCLUSIONS: In making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions.

Linear invariant tensor interpolation applied to cardiac diffusion tensor MRI.

PURPOSE: Various methods exist for interpolating diffusion tensor fields, but none of them linearly interpolate tensor shape attributes. Linear interpolation is expected not to introduce spurious changes in tensor shape. METHODS: Herein we define a new linear invariant (LI) tensor interpolation method that linearly interpolates components of tensor shape (tensor invariants) and recapitulates the interpolated tensor from the linearly interpolated tensor invariants and the eigenvectors of a linearly interpolated tensor. The LI tensor interpolation method is compared to the Euclidean (EU), affine-invariant Riemannian (AI), log-Euclidean (LE) and geodesic-loxodrome (GL) interpolation methods using both a synthetic tensor field and three experimentally measured cardiac DT-MRI datasets. RESULTS: EU, AI, and LE introduce significant microstructural bias, which can be avoided through the use of GL or LI. CONCLUSION: GL introduces the least microstructural bias, but LI tensor interpolation performs very similarly and at substantially reduced computational cost.