Publications

The DNA-damaging agent N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) causes cardiomyocyte death as a result of energy loss from excessive activation of poly-(ADP) ribose polymerase-1 (PARP-1) resulting in depletion of its substrates nicotinamide adenine dinucleotide (NAD) and ATP. Previously we showed that the chemotherapeutic agent vincristine (VCR) is cardioprotective. Here we tested the hypothesis that VCR inhibits MNNG-induced PARP activation. Adult mouse cardiomyocytes were incubated with 100 micromol/L MNNG with or without concurrent VCR (20 micromol/L) for 2 to 4 hours. Cardiomyocyte survival was measured using the trypan blue exclusion assay. Western blots were used to measure signaling responses. MNNG-induced cardiomyocyte damage was time- and concentration-dependent. MNNG activated PARP-1 and depleted NAD and ATP. VCR completely protected cardiomyocytes from MNNG-induced cell damage and maintained intracellular levels of NAD and ATP. VCR increased phosphorylation of the prosurvival signals Akt, GSK-3beta, Erk1/2, and p70S6 kinase. VCR delayed PARP activation as evidenced by Western blot and by immunofluorescence staining of poly (ADP)-ribose, but without directly inhibiting PARP-1 itself. Known PARP-1 inhibitors also protected cardiomyocytes from MNNG-induced death. Repletion of ATP, NAD, pyruvate, and glutamine had effects similar to PARP-1 inhibitors. We conclude that VCR protects cardiomyocytes from MNNG toxicity by regulating PARP-1 activation, intracellular energy metabolism, and prosurvival signaling.

In 1980 the American Psychiatric Association (APA), faced with increased professional competition, revised the Diagnostic and Statistical Manual of Mental Disorders (DSM). Psychiatric expertise was redefined along a biomedical model via a standardised nosology. While they were an integral part of capturing professional authority, the revisions demystified psychiatric expertise, leaving psychiatrists vulnerable to infringements upon their autonomy by institutions adopting the DSM literally. This research explores the tensions surrounding standardisation in psychiatry. Drawing on in-depth interviews with psychiatrists, I explore the 'sociological ambivalence' psychiatrists feel towards the DSM, which arises from the tension between the desire for autonomy in practice and the professional goal of legitimacy within the system of mental health professions. To carve a space for autonomy for their practice, psychiatrists develop 'workarounds' that undermine the DSM in practice. These workarounds include employing alternative diagnostic typologies, fudging the numbers (or codes) on official paperwork and negotiating diagnoses with patients. In creating opportunities for patient input and resistance to fixed diagnoses, the varied use of the DSM raises fundamental questions for psychiatrists about the role of the biomedical model of mental illness, especially its particular manifestation in the DSM.

Heart transplant recipients who experience humoral rejection are at risk for hemodynamic instability. We report a case of a 64-year-old male with cardiogenic shock due to allograft rejection requiring mechanical support while undergoing intense immunosuppression. He underwent implantation of a micro-axial endovascular pump (Impella). To our knowledge, this is the first reported case of successful Impella device deployment as a bridge-to-recovery strategy.

Inference of biological networks from high-throughput data is a central problem in bioinformatics. Particularly powerful for network reconstruction is data collected by recent studies that contain both genetic variation information and gene expression profiles from genetically distinct strains of an organism. Various statistical approaches have been applied to these data to tease out the underlying biological networks that govern how individual genetic variation mediates gene expression and how genes regulate and interact with each other. Extracting meaningful causal relationships from these networks remains a challenging but important problem. In this article, we use causal inference techniques to infer the presence or absence of causal relationships between yeast gene expressions in the framework of graphical causal models. We evaluate our method using a well studied dataset consisting of both genetic variations and gene expressions collected over randomly segregated yeast strains. Our predictions of causal regulators, genes that control the expression of a large number of target genes, are consistent with previously known experimental evidence. In addition, our method can detect the absence of causal relationships and can distinguish between direct and indirect effects of variation on a gene expression level.