Anshu Sinha

Time-series clustering of gene expression in irradiated and bystander fibroblasts: an application of FBPA clustering

BackgroundThe radiation bystander effect is an important component of the overall biological response of tissues and organisms to ionizing radiation, but the signaling mechanisms between irradiated and non-irradiated bystander cells are not fully understood. In this study, we measured a time-series of gene expression after α-particle irradiation and applied the Feature Based Partitioning around medoids Algorithm (FBPA), a new clustering method suitable for sparse time series, to identify signaling modules that act in concert in the response to direct irradiation and bystander signaling. We compared our results with those of an alternate clustering method, Short Time series Expression Miner (STEM).ResultsWhile computational evaluations of both clustering results were similar, FBPA provided more biological insight. After irradiation, gene clusters were enriched for signal transduction, cell cycle/cell death and inflammation/immunity processes; but only FBPA separated clusters by function. In bystanders, gene clusters were enriched for cell communication/motility, signal transduction and inflammation processes; but biological functions did not separate as clearly with either clustering method as they did in irradiated samples. Network analysis confirmed p53 and NF-κB transcription factor-regulated gene clusters in irradiated and bystander cells and suggested novel regulators, such as KDM5B/JARID1B (lysine (K)-specific demethylase 5B) and HDACs (histone deacetylases), which could epigenetically coordinate gene expression after irradiation.ConclusionsIn this study, we have shown that a new time series clustering method, FBPA, can provide new leads to the mechanisms regulating the dynamic cellular response to radiation. The findings implicate epigenetic control of gene expression in addition to transcription factor networks.

New-onset graft dysfunction after heart transplantation—incidence and mechanism-related outcomes

BACKGROUND Graft dysfunction (GD) after heart transplantation (HTx) is a major cause of morbidity and mortality. The impact of different pathophysiologic mechanisms on outcome is unknown. In this large, single-center study we aimed to assess the incidence of GD and compare the outcomes with different histopathologic mechanisms of rejection. METHODS We analyzed a data set of 1,099 consecutive patients after their HTx at Columbia University Medical Center between January 1994 and March 2008, and identified all patients hospitalized with new-onset GD. Based on the histopathologic data, patients were divided into GD-unexplained (Group-GD-U), GD-antibody-mediated rejection (Group-GD-AMR), GD-cardiac allograft vasculopathy (Group-GD-CAV) and GD-acute cellular rejection (Group-GD-ACR) groups. We compared the in-hospital and 3-, 6- and 12-month mortality across these groups using the chi-square test. We also compared the 3-, 6- and 12-month survival curves across groups using the log-rank test. RESULTS Of 126 patients (12%) identified with GD, complete histology data were available for 100 patients. There were 21, 20, 27 and 32 patients identified in Group-GD-U, Group-GD-AMR, Group-GD-CAV and Group-GD-ACR, respectively. The in-hospital mortality rates were 52%, 20%, 15% and 6%, respectively. The in-hospital mortality rate was significantly higher in Group-GD-U compared with all other groups (p = 0.0006). The 3-, 6- and 12-month survival rate was also significantly lower in Group-GD-U compared with all other groups. CONCLUSION A significant proportion of patients presenting with new-onset GD have unexplained histopathology. Unexplained GD is associated with a significantly higher mortality rate. New diagnostic tools are necessary to better understand and detect/predict this malignant phenotype.

A Platform for Processing Expression of Short Time Series (PESTS)

BackgroundTime course microarray profiles examine the expression of genes over a time domain. They are necessary in order to determine the complete set of genes that are dynamically expressed under given conditions, and to determine the interaction between these genes. Because of cost and resource issues, most time series datasets contain less than 9 points and there are few tools available geared towards the analysis of this type of data.ResultsTo this end, we introduce a platform for Processing Expression of Short Time Series (PESTS). It was designed with a focus on usability and interpretability of analyses for the researcher. As such, it implements several standard techniques for comparability as well as visualization functions. However, it is designed specifically for the unique methods we have developed for significance analysis, multiple test correction and clustering of short time series data. The central tenet of these methods is the use of biologically relevant features for analysis. Features summarize short gene expression profiles, inherently incorporate dependence across time, and allow for both full description of the examined curve and missing data points.ConclusionsPESTS is fully generalizable to other types of time series analyses. PESTS implements novel methods as well as several standard techniques for comparability and visualization functions. These features and functionality make PESTS a valuable resource for a researchers toolkit. PESTS is available to download for free to academic and non-profit users at http://www.mailman.columbia.edu/academic-departments/biostatistics/research-service/software-development.

Gene Expression Profiles of Patients With Antibody-Mediated Rejection After Cardiac Transplantation

ntibody-mediated rejection (AMR) is characterized y interstitial edema, prominent endothelial cell damge, occasional inflammatory cells, donor-specific ntibodies and C4d deposition, and may cause acute raft loss after heart transplantation. Unfortuately, there is no non-invasive method to accurately redict or diagnose AMR. Peripheral blood mononuclear cell (PBMC) gene ignatures allow for identification of patients at risk of ejection. We conducted a pilot study to test the ypothesis that patients with AMR show specific PBMC ene expression profiles. We included all patients at our center who were part f the Cardiac Allograft Rejection Gene expression bservational (CARGO) study and evaluated with gene icroarrays. Gene probes with expression values resent in 70% of the samples were filtered retaining ,688 probes of the original 7,370. AMR was defined as ew-onset graft dysfunction in the absence of cellular ejection, with light-microscopic criteria of endothelial welling, requiring specific treatment according to our nstitutional practice. Repeat samples from the same atients were averaged. Candidate genes were identied by Significance Analysis of Microarrays (SAM). unctional analysis was performed with High Throughut GOminer (HTGM) and Gene Set Enrichment Analsis (GSEA). Clinical variables were compared using a -test or chi-square test when appropriate.

Drawing networks of rejection - a systems biological approach to the identification of candidate genes in heart transplantation

Technological development led to an increased interest in systems biological approaches to characterize disease mechanisms and candidate genes relevant to specific diseases. We suggested that the human peripheral blood mononuclear cells (PBMC) network can be delineated by cellular reconstruction to guide identification of candidate genes. Based on 285 microarrays (7370 genes) from 98 heart transplant patients enrolled in the Cardiac Allograft Rejection Gene Expression Observational study, we used an information‐theoretic, reverse‐engineering algorithm called ARACNe (algorithm for the reconstruction of accurate cellular networks) and chromatin immunoprecipitation assay to reconstruct and validate a putative gene PBMC interaction network. We focused our analysis on transcription factor (TF) genes and developed a priority score to incorporate aspects of network dynamics and information from published literature to supervise gene discovery. ARACNe generated a cellular network and predicted interactions for each TF during rejection and quiescence. Genes ranked highest by priority score included those related to apoptosis, humoural and cellular immune response such as GA binding protein transcription factor (GABP), nuclear factor of κ light polypeptide gene enhancer in B‐cells (NFκB), Fas (TNFRSF6)‐associated via death domain (FADD) and c‐AMP response element binding protein. We used the TF CREB to validate our network. ARACNe predicted 29 putative first‐neighbour genes of CREB. Eleven of these (37%) were previously reported. Out of the 18 unknown predicted interactions, 14 primers were identified and 11 could be immunoprecipitated (78.6%). Overall, 75% (n= 22) inferred CREB targets were validated, a significantly higher fraction than randomly expected (P < 0.001, Fisher’s exact test). Our results confirm the accuracy of ARACNe to reconstruct the PBMC transcriptional network and show the utility of systems biological approaches to identify possible molecular targets and biomarkers.

Gene Expression Signatures of Peripheral Blood Mononuclear Cells during the Early Post-Transplant Period in Patients Developing Cardiac Allograft Vasculopathy

Background. Cardiac allograft vasculopathy (CAV) is a major cause of graft loss and death after heart transplantation. Currently, no diagnostic methods are available during the early post-transplant period to accurately identify patients at risk of CAV. We hypothesized that PBMC gene expression profiles (GEP) can identify patients at risk of CAV. Methods. We retrospectively analyzed a limited set of whole-genome PBMC microarrays from 10 post-transplant patients who did (n = 3) or did not (n = 7) develop advanced grade CAV during their long-term follow-up. We used significance analysis of microarrays to identify differentially expressed genes and High-Throughput GoMiner to assess gene ontology (GO) categories. We corroborated our findings by retrospective analysis of PBMC real-time PCR data from 33 patients. Results. Over 300 genes were differentially expressed (FDR < 5%), and 18 GO-categories including “macrophage activation”, “Interleukin-6 pathway”, “NF-KappaB cascade”, and “response to virus” were enriched by these genes (FDR < 5%). Out of 8 transcripts available for RT-PCR analysis, we confirmed 6 transcripts (75.0%) including FPRL1, S100A9, CXCL10, PRO1073, and MMP9 (P < .05). Conclusion. Our pilot data suggest that GEP of PBMC may become a valuable tool in the evaluation of patients at risk of CAV. Larger prospectively designed studies are needed to corroborate our hypothesis.

Relationship between a validated molecular cardiac transplant rejection classifier and routine organ function parameters

Cadeiras M, Shahzad K, John MM, Gruber D, von Bayern M, Auerbach S, Sinha A, Latif F, Unniachan S, Memon S, Mital S, Restaino S, Marboe CC, Addonizio LJ, Deng MC. Relationship between a validated molecular cardiac transplant rejection classifier and routine organ function parameters.
Clin Transplant 2010: 24: 321–327.

Cardiac Support and Multiorgan Dysfunction Syndrome

Approximately 5 million Americans suffer from heart failure (HF), the burden of which will grow exponentially over the next 50 years. HF currently results in 3.5 million hospitalizations and 20% of all hospital admissions among individuals >65 years of age. Surgical interventions for HF include cardiac repair (coronary artery bypass grafting, valve repair or replacement), cardiac support (mechanical circulatory support devices) and cardiac replacement (heart transplantation). These modern interventions of cardiac surgery and critical care medicine dramatically improved outcomes. They are offered to patients with increasingly high-risk clinical profiles and a higher likelihood of complications. The development of vital-organ support therapies (respirator, dialysis, transfusion, etc) in the intensive care units (ICUs) increased the survival of critically ill patients. However, despite these organ-saving therapies, up to 15% of these patients have an unfavourable perioperative course. Frequently, more than one organ system becomes dysfunctional, leading to progressive multiorgan dysfunction (MOD) (Lietz et al., 2007). The hallmark of MOD is the development of progressive physiologic dysfunction in two or more organ systems after an acute threat to systemic homeostasis. MOD is the leading cause of morbidity and mortality in the ICUs and after mechanical circulatory support device (MCSD) implantation (Deng et al., 2005).