Manoj Kandpal

Comparative evaluation of isoform-level gene expression estimation algorithms for RNA-seq and exon-array platforms

Abstract Given that the majority of multi-exon genes generate diverse functional products, it is important to evaluate expression at the isoform level. Previous studies have demonstrated strong gene-level correlations between RNA sequencing (RNA-seq) and microarray platforms, but have not studied their concordance at the isoform level. We performed transcript abundance estimation on raw RNA-seq and exon-array expression profiles available for common glioblastoma multiforme samples from The Cancer Genome Atlas using different analysis pipelines, and compared both the isoform- and gene-level expression estimates between programs and platforms. The results showed better concordance between RNA-seq/exon-array and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) platforms for fold change estimates than for raw abundance estimates, suggesting that fold change normalization against a control is an important step for integrating expression data across platforms. Based on RT-qPCR validations, eXpress and Multi-Mapping Bayesian Gene eXpression (MMBGX) programs achieved the best performance for RNA-seq and exon-array platforms, respectively, for deriving the isoform-level fold change values. While eXpress achieved the highest correlation with the RT-qPCR and exon-array (MMBGX) results overall, RSEM was more highly correlated with MMBGX for the subset of transcripts that are highly variable across the samples. eXpress appears to be most successful in discriminating lowly expressed transcripts, but IsoformEx and RSEM correlate more strongly with MMBGX for highly expressed transcripts. The results also reinforce how potentially important isoform-level expression changes can be masked by gene-level estimates, and demonstrate that exon arrays yield comparable results to RNA-seq for evaluating isoform-level expression changes.

Metastatic cancers promote cachexia through ZIP14 upregulation in skeletal muscle

Patients with metastatic cancer experience a severe loss of skeletal muscle mass and function known as cachexia. Cachexia is associated with poor prognosis and accelerated death in patients with cancer, yet its underlying mechanisms remain poorly understood. Here, we identify the metal-ion transporter ZRT- and IRT-like protein 14 (ZIP14) as a critical mediator of cancer-induced cachexia. ZIP14 is upregulated in cachectic muscles of mice and in patients with metastatic cancer and can be induced by TNF-α and TGF-β cytokines. Strikingly, germline ablation or muscle-specific depletion of Zip14 markedly reduces muscle atrophy in metastatic cancer models. We find that ZIP14-mediated zinc uptake in muscle progenitor cells represses the expression of MyoD and Mef2c and blocks muscle-cell differentiation. Importantly, ZIP14-mediated zinc accumulation in differentiated muscle cells induces myosin heavy chain loss. These results highlight a previously unrecognized role for altered zinc homeostasis in metastatic cancer–induced muscle wasting and implicate ZIP14 as a therapeutic target for its treatment.Accumulation of zinc in muscle cells resulting from transcriptional upregulation of metal transporter ZIP14 causes muscle atrophy and promotes cachexia in metastatic cancer.

Building Genomic Analysis Pipelines in a Hackathon Setting with Bioinformatician Teams: DNA-seq, Epigenomics, Metagenomics and RNA-seq

We assembled teams of genomics professionals to assess whether we could rapidly develop pipelines to answer biological questions commonly asked by biologists and others new to bioinformatics by facilitating analysis of high-throughput sequencing data. In January 2015, teams were assembled on the National Institutes of Health (NIH) campus to address questions in the DNA-seq, epigenomics, metagenomics and RNA-seq subfields of genomics. The only two rules for this hackathon were that either the data used were housed at the National Center for Biotechnology Information (NCBI) or would be submitted there by a participant in the next six months, and that all software going into the pipeline was open-source or open-use. Questions proposed by organizers, as well as suggested tools and approaches, were distributed to participants a few days before the event and were refined during the event. Pipelines were published on GitHub, a web service providing publicly available, free-usage tiers for collaborative software development (https://github.com/features/). The code was published at https://github.com/DCGenomics/ with separate repositories for each team, starting with hackathon_v001.

Multi-Criteria Optimization Based Experimental Design for Parameter Estimation of a Double Feedback Gene Switching Model

Despite the rapid increase in quantity and quality of experimental data in many fields of engineering and science, quantitative measurements of many cellular components are still relatively scarce. This work deals with estimating the parameters of a double feedback gene-switching model. To achieve the goal, a model-based design of experiment (MBDOE) approach for parameter estimation is employed. To overcome the problem of convergence in parameter estimation step (due to correlation among the parameters), a non-dominated sorting genetic algorithm (NSGA-II) based, multi-objective optimization (MOO) based MBDOE has been used. The parameter estimates obtained through the MOO based DOE as well as a standard alphabetical DOE technique are then compared with the known true values from the literature to highlight the efficacy of the MOO-MBDOE technique.

Data driven fault detection using multi-block PLS based path modeling approach

Abstract Early detection of process faults (while the plant is still operating in a controllable region) can save billions of dollars and enhance safety by minimizing the loss of productivity and preventing the occurrence of process mishaps. This has encouraged researchers to develop methods for improved monitoring of industrial units. This work is based on a pathway modeling approach using multi-block PLS as the mathematical machinery. The proposed approach is a multivariate data analysis procedure which divides the process data into different blocks, determines relationships among the blocks, which are then used for fault detection. In the developed methodology, an extension of multi-block PLS, a special modification of the PLS technique is realized by incorporating the H-Principle in the algorithm. This renders it different from PLS as the analysis is done in steps, maximizing the product of size of improvement of fit and associated precision at each step. The T 2 statistic is primarily used as an indicator of normalcy or fault in the system. This new technique is illustrated via application to two industrial-scale, high-fidelity simulated systems namely the Tennessee Eastman Process (TEP) and a Depropanizer Process (DPP).

Association of increased primary breast tumor AGR2 with decreased disease-specific survival

Objective Tumor expression of Anterior Gradient 2 (AGR2), an endoplasmic reticulum protein disulfide isomerase, was associated with decreased breast cancer survival. We aimed to validate the association of tumor AGR2 mRNA expression with disease-specific survival (DSS) and identify differentially expressed signaling pathways between high and low AGR2 expression tumor groups. Methods Primary tumor mRNA expression data from the METABRIC study was used to evaluate AGR2 expression as a prognostic factor for DSS while adjusting for survival-determining confounders using Cox proportional-hazards regression. Differentially expressed genes and signaling pathway differences between high and low AGR2 groups were determined by modular enrichment analyses using DAVID and Ingenuity Pathway Analysis. Results Increased tumor AGR2 mRNA expression was associated with decreased DSS among 1,341 women (per each standard deviation increase of AGR2 expression: HR 1.14, 95% CI: 1.01-1.29, P = 0.03). Pathway analyses supported prior experimental studies showing that estrogen receptor 1 (ESR1) regulated AGR2 expression. Canonical signaling pathways significantly differentially represented between high and low AGR2 groups included those involved in inflammation and immunity. Conclusion Increased primary tumor AGR2 expression was associated with decreased DSS. Pathway analyses suggested that increased AGR2 was associated with endoplasmic reticular homeostasis, possibly allowing tumor cells to overcome hypoxic stress and meet the increased protein demand of tumorigenesis, thereby preventing unfolded protein response-mediated apoptosis.

Development and clinical validity of a novel blood-based molecular biomarker for subclinical acute rejection following kidney transplant

Noninvasive biomarkers are needed to monitor stable patients after kidney transplant (KT), because subclinical acute rejection (subAR), currently detectable only with surveillance biopsies, can lead to chronic rejection and graft loss. We conducted a multicenter study to develop a blood‐based molecular biomarker for subAR using peripheral blood paired with surveillance biopsies and strict clinical phenotyping algorithms for discovery and validation. At a predefined threshold, 72% to 75% of KT recipients achieved a negative biomarker test correlating with the absence of subAR (negative predictive value: 78%‐88%), while a positive test was obtained in 25% to 28% correlating with the presence of subAR (positive predictive value: 47%‐61%). The clinical phenotype and biomarker independently and statistically correlated with a composite clinical endpoint (renal function, biopsy‐proved acute rejection, ≥grade 2 interstitial fibrosis, and tubular atrophy), as well as with de novo donor‐specific antibodies. We also found that <50% showed histologic improvement of subAR on follow‐up biopsies despite treatment and that the biomarker could predict this outcome. Our data suggest that a blood‐based biomarker that reduces the need for the indiscriminate use of invasive surveillance biopsies and that correlates with transplant outcomes could be used to monitor KT recipients with stable renal function, including after treatment for subAR, potentially improving KT outcomes.