Gourab Mukherjee

Single-Cell Mass Cytometry Analysis of Human Tonsil T Cell Remodeling by Varicella Zoster Virus

Although pathogens must infect differentiated host cells that exhibit substantial diversity, documenting the consequences of infection against this heterogeneity is challenging. Single-cell mass cytometry permits deep profiling based on combinatorial expression of surface and intracellular proteins. We used this method to investigate varicella-zoster virus (VZV) infection of tonsil T cells, which mediate viral transport to skin. Our results indicate that VZV induces a continuum of changes regardless of basal phenotypic and functional T cell characteristics. Contrary to the premise that VZV selectively infects T cells with skin trafficking profiles, VZV infection altered T cell surface proteins to enhance or induce these properties. Zap70 and Akt signaling pathways that trigger such surface changes were activated in VZV-infected naive and memory cells by a T cell receptor (TCR)-independent process. Single-cell mass cytometry is likely to be broadly relevant for demonstrating how intracellular pathogens modulate differentiated cells to support pathogenesis in the natural host.

Innate immune response to homologous rotavirus infection in the small intestinal villous epithelium at single-cell resolution

“Bulk” measurements of antiviral innate immune responses from pooled cells yield averaged signals and do not reveal underlying signaling heterogeneity in infected and bystander single cells. We examined such heterogeneity in the small intestine during rotavirus (RV) infection. Murine RV EW robustly activated type I IFNs and several antiviral genes (IFN-stimulated genes) in the intestine by bulk analysis, the source of induced IFNs primarily being hematopoietic cells. Flow cytometry and microfluidics-based single-cell multiplex RT-PCR allowed dissection of IFN responses in single RV-infected and bystander intestinal epithelial cells (IECs). EW replicates in IEC subsets differing in their basal type I IFN transcription and induces IRF3-dependent and IRF3-augmented transcription, but not NF-κB–dependent or type I IFN transcripts. Bystander cells did not display enhanced type I IFN transcription but had elevated levels of certain IFN-stimulated genes, presumably in response to exogenous IFNs secreted from immune cells. Comparison of IRF3 and NF-κB induction in STAT1−/− mice revealed that murine but not simian RRV mediated accumulation of IkB-α protein and decreased transcription of NF-κB–dependent genes. RRV replication was significantly rescued in IFN types I and II, as well as STAT1 (IFN types I, II, and III) deficient mice in contrast to EW, which was only modestly sensitive to IFNs I and II. Resolution of “averaged” innate immune responses in single IECs thus revealed unexpected heterogeneity in both the induction and subversion of early host antiviral immunity, which modulated host range.

Rotavirus NSP1 Protein Inhibits Interferon-Mediated STAT1 Activation

ABSTRACT Rotavirus (RV) replicates efficiently in intestinal epithelial cells (IECs) in vivo despite the activation of a local host interferon (IFN) response. Previously, we demonstrated that homologous RV efficiently inhibits IFN induction in single infected and bystander villous IECs in vivo. Paradoxically, RV also induces significant type I IFN expression in the intestinal hematopoietic cell compartment in a relatively replication-independent manner. This suggests that RV replication and spread in IECs must occur despite exogenous stimulation of the STAT1-mediated IFN signaling pathway. Here we report that RV inhibits IFN-mediated STAT1 tyrosine 701 phosphorylation in human IECs in vitro and identify RV NSP1 as a direct inhibitor of the pathway. Infection of human HT29 IECs with simian (RRV) or porcine (SB1A or OSU) RV strains, which inhibit IFN induction by targeting either IFN regulatory factor 3 (IRF3) or NF-κB, respectively, resulted in similar regulation of IFN secretion. By flow cytometric analysis at early times during infection, neither RRV nor SB1A effectively inhibited the activation of Y701-STAT1 in response to exogenously added IFN. However, at later times during infection, both RV strains efficiently inhibited IFN-mediated STAT1 activation within virus-infected cells, indicating that RV encodes inhibitors of IFN signaling targeting STAT1 phosphorylation. Expression of RV NSP1 in the absence of other viral proteins resulted in blockage of exogenous IFN-mediated STAT1 phosphorylation, and this function was conserved in NSP1 from simian, bovine, and murine RV strains. Analysis of NSP1 determinants responsible for the inhibition of IFN induction and signaling pathways revealed that these determinants are encoded on discrete domains of NSP1. Finally, we observed that at later times during infection with SB1A, there was almost complete inhibition of IFN-mediated Y701-STAT1 in bystander cells staining negative for viral antigen. This property segregated with the NSP1 gene and was observed in a simian SA11 monoreassortant that encoded porcine OSU NSP1 but not in wild-type SA11 or a reassortant encoding simian RRV NSP1.

Exact minimax estimation of the predictive density in sparse Gaussian models

We consider estimating the predictive density under Kullback-Leibler loss in an ℓ0 sparse Gaussian sequence model. Explicit expressions of the first order minimax risk along with its exact constant, asymptotically least favorable priors and optimal predictive density estimates are derived. Compared to the sparse recovery results involving point estimation of the normal mean, new decision theoretic phenomena are seen. Suboptimal performance of the class of plug-in density estimates reflects the predictive nature of the problem and optimal strategies need diversification of the future risk. We find that minimax optimal strategies lie outside the Gaussian family but can be constructed with threshold predictive density estimates. Novel minimax techniques involving simultaneous calibration of the sparsity adjustment and the risk diversification mechanisms are used to design optimal predictive density estimates.

Empirical Bayes estimates for a two-way cross-classified model

We develop an empirical Bayes procedure for estimating the cell means in an unbalanced, two-way additive model with fixed effects. We employ a hierarchical model, which reflects exchangeability of the effects within treatment and within block but not necessarily between them, as suggested before by Lindley and Smith [J. R. Stat. Soc., B 34 (1972) 1–41]. The hyperparameters of this hierarchical model, instead of considered fixed, are to be substituted with data-dependent values in such a way that the point risk of the empirical Bayes estimator is small. Our method chooses the hyperparameters by minimizing an unbiased risk estimate and is shown to be asymptotically optimal for the estimation problem defined above, under suitable conditions. The usual empirical Best Linear Unbiased Predictor (BLUP) is shown to be substantially different from the proposed method in the unbalanced case and, therefore, performs suboptimally. Our estimator is implemented through a computationally tractable algorithm that is scalable to work under large designs. The case of missing cell observations is treated as well.

Feature screening in large scale cluster analysis

Abstract We propose a novel methodology for feature screening in the clustering of massive datasets, in which both the number of features and the number of observations can potentially be very large. Taking advantage of a fusion penalization based convex clustering criterion, we propose a highly scalable screening procedure that efficiently discards non-informative features by first computing a clustering score corresponding to the clustering tree constructed for each feature, and then thresholding the resulting values. We provide theoretical support for our approach by establishing uniform non-asymptotic bounds on the clustering scores of the “noise” features. These bounds imply perfect screening of non-informative features with high probability and are derived via careful analysis of the empirical processes corresponding to the clustering trees that are constructed for each of the features by the associated clustering procedure. Through extensive simulation experiments, we compare the performance of our proposed method with other screening approaches popularly used in cluster analysis and obtain encouraging results. We demonstrate empirically that our method is applicable to cluster analysis of big datasets arising in single-cell gene expression studies.