Nicholas Wisniewski

Microstructural remodeling in the post-infarct porcine heart measured by diffusion tensor MRI and T1-weighted late gadolinium enhancement MRI

The objective of this study was to quantify microstructural remodeling in peri-infarcted and infarcted porcine myocardium using diffusion tensor MRI (DT-MRI) for the first time. High resolution ex vivo late gadolinium enhanced (LGE) MRI was used to segment the DT-MRI data into normal, peri-infarct and infarcted myocardium. LGE-MRI based segmentation produces regions with significantly different microstructural remodeling.

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.

Reduced HLA Class II antibody response to proteasome inhibition in heart transplantation

Antibody-mediated rejection (AMR) has become an increasingly recognized entity in heart transplantation linked to poor outcomes. Treatment algorithms for AMR vary from institution to institution, and therapeutic options remain limited. Responses to standard therapies, such as plasmapheresis, intravenous immunoglobulin, corticosteroids, and rituximab, are not always apparent. Bortezomib, a first-in-class 26S proteasome inhibitor that was approved by the U.S. Food and Drug Administration for multiple myeloma, is emerging as an alternative therapy option for AMR. Proteasome inhibition results in depletion of plasma cells, a primary source of antibody production. Additionally, a major function of the 26S proteasome is antigen processing for HLA Class I presentation. We postulated that compared with Class II responses, bortezomib would be more effective in preventing de novo Class I antibody production. To date, there have not been published reports of the variable immunologic responses of specific HLA antibody types to bortezomib in heart transplantation. Here we report the first case series, to our knowledge, of the differential effect on donor-specific and third-party antibodies in heart transplant recipients treated with bortezomib. Institutional review board approval was obtained. A retrospective review was conducted of all adult patients receiving bortezomib for biopsy-proven or clinically suspected AMR from August 2010 to January 2012. Diagnosis of AMR consisted of positive endomyocardial biopsy by histology and immunohistochemistry, hemodynamic changes, or significant changes in donor-specific antibody (DSA) mean fluorescence intensity (MFI). An antibody MFI cutoff of Z5,000 was used, and a Z50% change in MFI was considered significant. Treatment consisted of plasmapheresis followed by bortezomib (0.7–1 mg/m intravenous push) on Day 1, 4, 7, and 10 followed by a 3-day course of plasmapheresis beginning 72 hours after the last bortezomib dose. Treatment efficacy was determined by clinical or histologic improvement or a significant change in DSA. Antibody MFI was determined via single-antigen Luminex assay (Luminex (R), One Lambda Inc., Canoga, Park, CA). Antibody samples were obtained on Day 5, 11, 14, and 30 and at each clinical follow-up. To study the antibody response to treatment, we constructed a matrix of data of all tested antibodies and used hierarchical clustering based on the Pearson correlation to generate a clustered heat map. The Pearson correlation is a standardized measure of linear dependency between 2 variables and was used because of its independence of scale, allowing for the identification of variables undergoing similar relative changes without dependence on absolute magnitudes or units. Wilcoxon signed rank test was used to calculate statistical significance of MFI changes. Statistical analysis was done using R (R Foundation for Statistical Computing, Vienna, Austria).

Challenges and Solutions in the Development of Genomic Biomarker Panels: A Systematic Phased Approach

In the post-genome era, high throughput gene expression profiling has been successfully used to develop genomic biomarker panels (GBP) that can be integrated into clinical decision making. The development of GBPs in the context of personalized medicine is a scientifically challenging and resource-intense process. It needs to be accomplished in a systematic phased approach to address biological variation related to a clinical phenotype (e.g. disease etiology, gender, etc.) and minimize technical variation (noise). Here we present the methodological aspects of GBP development based on the experience of the Cardiac Allograft Rejection Gene Expression Observation (CARGO) study, a study that lead to the development of a molecular classifier for rejection screening in heart transplant patients.

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.

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

BackgroundThe 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.MethodsWe 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.ResultsWe 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.ConclusionsOur 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.

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.

An integrative model of leukocyte genomics and organ dysfunction in heart failure patients requiring mechanical circulatory support

Background The implantation of mechanical circulatory support (MCS) 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 MCS surgery, at 5 timepoints: day -1 preoperative, and days 1, 3, 5, and 8 postoperative. Phenotyping was performed using 12 clinical parameters, 2 organ dysfunction scoring systems, and survival outcomes. We constructed a 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. Finally, we selected top genes associated with survival across all timepoints, and trained a penalized Cox model, based on day -1 data, to predict mortality risk thereafter. 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 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, consistent with organ failure due to disseminated coagulopathy. Our final predictive model consisted of 10 genes: IL2RA, HSPA7, AFAP1, SYNJ2, LOC653406, GAPDHP35, MGC12916, ZRSR2, and two currently unidentified genes, warranting further investigation. Conclusion 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.