Mario C. Deng

Background: Statin-associated muscle symptoms (SAMS) are a significant clinical issue, and their exact cause is not well understood. Immunological mechanisms have been suggested but have not been confirmed. This study is a rare, longitudinal case-based analysis that uses transcriptomics to explore immune-related gene expression changes in peripheral blood mononuclear cells (PBMCs) in response to exercise before, during, and after the onset and resolution of SAMS. Methods: A healthy volunteer (HV1) enrolled in an exercise immuno-fitness study underwent cardiopulmonary exercise testing (CPX) with blood collected at three timepoints: pre-exercise (TP1), peak exercise (TP2), and 1 hour post-exercise (TP3). After baseline testing (Visit 1), the participant began statin therapy on their own, developed SAMS, and had repeat CPX testing during the symptomatic phase (Visit 2) and partial recovery phase (Visit 3). RNA was extracted from PBMCs and analyzed using next-generation RNA sequencing. The data were evaluated using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Pathway and gene ontology enrichment were used to identify immunologic signatures associated with SAMS. Results: The PBMC gene expression profiles showed distinct changes during SAMS compared to the baseline and recovery phases. WGCNA identified 39 co-expression modules. Several modules had high expression at peak exercise in the healthy state (V1), which was attenuated in SAMS (V2) and partially restored in recovery (V3). Gene ontology and Reactome analyses of key modules identified 16 genes that were differentially expressed at peak exercise and may be involved in specific immune pathways in SAMS pathogenesis. Conclusion: This case study suggests that profiling the exercise-induced immune transcriptome can reveal dynamic immunological changes related to statin-induced myopathy. These findings support the hypothesis of an immune-mediated component in SAMS and provide a basis for future studies to validate transcriptomic biomarkers for the early detection and management of SAMS.

Background: Cardiorespiratory fitness positively correlates with longevity and immune health. Regular exercise may provide health benefits by reducing systemic inflammation. In chronic disease conditions, such as chronic heart failure and chronic fatigue syndrome, mechanistic links have been postulated between inflammation, muscle weakness, frailty, catabolic/anabolic imbalance, and aberrant chronic activation of immunity with monocyte upregulation. We hypothesize that (1) temporal changes in transcriptome profiles of peripheral blood mononuclear cells during strenuous acute bouts of exercise using cardiopulmonary exercise testing are present in adult subjects, (2) these temporal dynamic changes are different between healthy persons and heart failure patients and correlate with clinical exercise-parameters and (3) they portend prognostic information. Methods: In total, 16 Heart Failure (HF) patients and 4 healthy volunteers (HV) were included in our proof-of-concept study. All participants underwent upright bicycle cardiopulmonary exercise testing. Blood samples were collected at three time points (TP) (TP1: 30 min before, TP2: peak exercise, TP3: 1 h after peak exercise). We divided 20 participants into 3 clinically relevant groups of cardiorespiratory fitness, defined by peak VO₂: HV (n = 4, VO₂ ≥ 22 mL/kg/min), mild HF (HF1) (n = 7, 14 < VO₂ < 22 mL/kg/min), and severe HF (HF2) (n = 9, VO₂ ≤ 14 mL/kg/min). Results: Based on the statistical analysis with 20–100% restriction, FDR correction (p-value 0.05) and 2.0-fold change across the three time points (TP1, TP2, TP3) criteria, we obtained 11 differentially expressed genes (DEG). Out of these 11 genes, the median Gene Expression Profile value decreased from TP1 to TP2 in 10 genes. The only gene that did not follow this pattern was CCDC181. By performing 1-way ANOVA, we identified 8/11 genes in each of the two groups (HV versus HF) while 5 of the genes (TTC34, TMEM119, C19orf33, ID1, TKTL2) overlapped between the two groups. We found 265 genes which are differentially expressed between those who survived and those who died. Conclusions: From our proof-of-concept heart failure study, we conclude that gene expression correlates with VO₂ peak in both healthy individuals and HF patients, potentially by regulating various physiological processes involved in oxygen uptake and utilization during exercise. Multi-omics profiling may help identify novel biomarkers for assessing exercise capacity and prognosis in HF patients, as well as potential targets for therapeutic intervention to improve VO₂ peak and quality of life. We anticipate that our results will provide a novel metric for classifying immune health.

Background: Over the last decade, expanding use of molecular diagnostics in heart transplantation has allowed implementation of non-invasive surveillance strategies for monitoring allograft health. The commercially available HeartCare platform combines the AlloMap gene expression profiling assay and the AlloSure donor-derived cell-free DNA test (dd-cfDNA). Beyond their established use for assessment of rejection, evidence is building for predictive utility, with the longitudinal AlloMap Variability score previously shown to correlate with the risk of future rejection, graft dysfunction, re-transplantation, or death. In this single-center, retrospective pilot study, we evaluated the performance of a novel AlloSure Variability metric in predicting mortality in a cohort of heart transplant recipients.Methods: Seventy-two adult heart transplant recipients with at least 3 concurrent AlloMap/AlloSure results were included. Demographic, clinical, imaging, and laboratory parameters were captured. Variability was defined as the standard deviation of longitudinal AlloMap/AlloSure results. A Cox multivariable adjusted proportional hazards model was used to evaluate the variability metrics as predictors of mortality. Associations between AlloMap/AlloSure variability and donor specific antibody (DSA) status were also assessed.Results: A total of 5 patients (6.9%) died during a median follow-up of 480 days. In a univariate Cox proportional hazards model, higher AlloSure variability (HR 1.66, 95%CI 1.14 – 2.41), but not AlloMap variability or the cross-sectional AlloSure/AlloMap results was associated with increased mortality risk. Longitudinal AlloSure variability was also higher among patients with both preformed DSA and those developing de novo DSA.Conclusion: Our results suggest that increased variability of dd-cfDNA in heart transplant patients is associated with both mortality risk and the presence of donor specific antibodies. These findings highlight the added value of longitudinal data in the interpretation of AlloMap/AlloSure scores in this population and open the door to larger studies investigating the utility of these metrics in shaping post-transplant clinical care paradigms.

CMV causes mostly asymptomatic but lifelong infection. Primary infection or reactivation in immunocompromised individuals can be life-threatening. CMV viremia often occurs in solid organ transplant recipients and associates with decreased graft survival and higher mortality. Furthering understanding of impaired immunity that allows CMV reactivation is critical to guiding antiviral therapy and examining the effect of CMV on solid organ transplant outcomes. This study characterized longitudinal immune responses to CMV in 31 kidney transplant recipients with CMV viremia and matched, nonviremic recipients. Recipients were sampled 3 and 12 months after transplant, with additional samples 1 week and 1 month after viremia. PBMCs were stained for NK and T cell markers. PBMC transcriptomes were characterized by RNA-Seq. Plasma proteins were quantified by Luminex. CD8 ⁺ T cell transcriptomes were characterized by single-cell RNA-Seq. Before viremia, patients had high levels of IL-15 with concurrent expansion of immature CD56 ^bright NK cells. After viremia, mature CD56 ^dim NK cells and CD28 ^– CD8 ⁺ T cells upregulating inhibitory and NK-associated receptors were expanded. Memory NK cells and NK-like CD28 ^– CD8 ⁺ T cells were associated with control of viremia. These findings suggest that signatures of innate activation may be prognostic for CMV reactivation after transplant, while CD8 ⁺ T cell functionality is critical for effective control of CMV.

STEM disciplines are the dominant culture in K-12 education. With its study of organs and diseases that afflict patients’ bodies, Western evidence-based medicine is seen and understood in the modern cultural paradigm as a science and as the practice in which a subject, the doctor, acts on an object; the patient’s body—a dominant culture in the patient’s journey. However, with the continually evolving high-technological and medical knowledge, life-saving therapeutic options are life-changing. They can range from changes in the diet, requiring structural and cultural changes in family life, to changes related to the experiences of learning to live tethered to a machine that is partly inside and partly outside one’s body or with somebody else’s heart. In this article, we show how competing needs to personalize care for the patient as a person forcefully emerge in response to evidence-based medicine’s global cultural dominance. We highlight two fundamental issues emerging in decision-making processes: (1) Framing evidence-based knowledge, uncertainties of the course of the disease and options, and (2) working with different, equally important, and often at odds conceptions of time in the care for the Other. Through the longitudinal analysis of moment-to-moment interactions in high-tech medicine encounters of a patient, his family, and the team caring for them, we show how framing and different conceptions of time emerge as issues, are profoundly interconnected, and are addressed by participants to care for a patient confronting existential decisions.

Biological determinants of survival in advanced heart failure (AdHF) are linked to systems biological properties of disease severity including age, comorbidities, and frailty. We hypothesize that a...

In parallel to the clinical maturation of heart transplantation over the last 50 years, rejection testing has been revolutionized within the systems biology paradigm triggered by the Human Genome P...

COVID-19 is an evolutionarily unprecedented natural experiment testing human immunological fitness and the potential to recover from a virus-activated ‘cytokine storm syndrome’ [1], a manifestation...

In preparation to create a clinical assay that predicts 1-year survival status of advanced heart failure (AdHF) patients before surgical/interventional therapies and to select the appropriate clinical assay platform for the future assay, we compared the properties of next generation sequencing (NGS) used in the gene discovery phase to the NanoString platform used in the clinical assay development phase. In 25 AdHF patients in a tertiary academic medical center from 2015 to 2016, PBMC samples were collected and aliquoted for NGS RNA whole transcriptome sequencing and compared to 770 genes represented on NanoString’s PanCancer IO 360 Gene Expression research panel. Prior to statistical analysis, NanoString and NGS expression values were log transformed. We computed Pearson correlation coefficients for each sample, comparing gene expression values between NanoString and NGS across the set of matched genes and for each of the matched genes across the set of samples. Genes were grouped by average NGS expression, and the NanoString-NGS correlation for each group was computed. Out of 770 genes from the NanoString panel, 734 overlapped between both platforms and showed high intrasample correlation. Within an individual sample, there was an expression-level dependent correlation between both platforms. The low- vs. intermediate/high-expression groups showed NGS average correlation 0.21 vs. 0.58–0.68, respectively, and NanoString average correlation 0.07–0.34 vs. 0.59–0.70, respectively. NanoString demonstrated high reproducibility (R2 > 0.99 for 100 ng input), sensitivity (probe counts between 100 and 500 detected and quantified), and robustness (similar gene signature scores across different RNA input concentrations, cartridges, and outcomes). Data from NGS and NanoString were highly correlated. These platforms play a meaningful, complementary role in the biomarker development process.