Maria Chikina

A Temporal Switch in the Germinal Center Determines Differential Output of Memory B and Plasma Cells

There is little insight into or agreement about the signals that control differentiation of memory B cells (MBCs) and long-lived plasma cells (LLPCs). By performing BrdU pulse-labeling studies, we found that MBC formation preceded the formation of LLPCs in an adoptive transfer immunization system, which allowed for a synchronized Ag-specific response with homogeneous Ag-receptor, yet at natural precursor frequencies. We confirmed these observations in wild-type (WT) mice and extended them with germinal center (GC) disruption experiments and variable region gene sequencing. We thus show that the GC response undergoes a temporal switch in its output as it matures, revealing that the reaction engenders both MBC subsets with different immune effector function and, ultimately, LLPCs at largely separate points in time. These data demonstrate the kinetics of the formation of the cells that provide stable humoral immunity and therefore have implications for autoimmunity, for vaccine development, and for understanding long-term pathogen resistance.

Global prediction of tissue-specific gene expression and context-dependent gene networks in Caenorhabditis elegans.

Tissue-specific gene expression plays a fundamental role in metazoan biology and is an important aspect of many complex diseases. Nevertheless, an organism-wide map of tissue-specific expression remains elusive due to difficulty in obtaining these data experimentally. Here, we leveraged existing whole-animal Caenorhabditis elegans microarray data representing diverse conditions and developmental stages to generate accurate predictions of tissue-specific gene expression and experimentally validated these predictions. These patterns of tissue-specific expression are more accurate than existing high-throughput experimental studies for nearly all tissues; they also complement existing experiments by addressing tissue-specific expression present at particular developmental stages and in small tissues. We used these predictions to address several experimentally challenging questions, including the identification of tissue-specific transcriptional motifs and the discovery of potential miRNA regulation specific to particular tissues. We also investigate the role of tissue context in gene function through tissue-specific functional interaction networks. To our knowledge, this is the first study producing high-accuracy predictions of tissue-specific expression and interactions for a metazoan organism based on whole-animal data.

The Sleipnir library for computational functional genomics

Motivation: Biological data generation has accelerated to the point where hundreds or thousands of whole-genome datasets of various types are available for many model organisms. This wealth of data can lead to valuable biological insights when analyzed in an integrated manner, but the computational challenge of managing such large data collections is substantial. In order to mine these data efficiently, it is necessary to develop methods that use storage, memory and processing resources carefully. Results: The Sleipnir C++ library implements a variety of machine learning and data manipulation algorithms with a focus on heterogeneous data integration and efficiency for very large biological data collections. Sleipnir allows microarray processing, functional ontology mining, clustering, Bayesian learning and inference and support vector machine tasks to be performed for heterogeneous data on scales not previously practical. In addition to the library, which can easily be integrated into new computational systems, prebuilt tools are provided to perform a variety of common tasks. Many tools are multithreaded for parallelization in desktop or high-throughput computing environments, and most tasks can be performed in minutes for hundreds of datasets using a standard personal computer. Availability: Source code (C++) and documentation are available at http://function.princeton.edu/sleipnir and compiled binaries are available from the authors on request. Contact: ogt@princeton.edu

An effective statistical evaluation of ChIPseq dataset similarity

MOTIVATION ChIPseq is rapidly becoming a common technique for investigating protein-DNA interactions. However, results from individual experiments provide a limited understanding of chromatin structure, as various chromatin factors cooperate in complex ways to orchestrate transcription. In order to quantify chromtain interactions, it is thus necessary to devise a robust similarity metric applicable to ChIPseq data. Unfortunately, moving past simple overlap calculations to give statistically rigorous comparisons of ChIPseq datasets often involves arbitrary choices of distance metrics, with significance being estimated by computationally intensive permutation tests whose statistical power may be sensitive to non-biological experimental and post-processing variation. RESULTS We show that it is in fact possible to compare ChIPseq datasets through the efficient computation of exact P-values for proximity. Our method is insensitive to non-biological variation in datasets such as peak width, and can rigorously model peak location biases by evaluating similarity conditioned on a restricted set of genomic regions (such as mappable genome or promoter regions). Applying our method to the well-studied dataset of Chen et al. (2008), we elucidate novel interactions which conform well with our biological understanding. By comparing ChIPseq data in an asymmetric way, we are able to observe clear interaction differences between cofactors such as p300 and factors that bind DNA directly. AVAILABILITY Source code is available for download at http://sonorus.princeton.edu/IntervalStats/IntervalStats.tar.gz. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

CellCODE: A robust latent variable approach to differential expression analysis for heterogeneous cell populations

MOTIVATION Identifying alterations in gene expression associated with different clinical states is important for the study of human biology. However, clinical samples used in gene expression studies are often derived from heterogeneous mixtures with variable cell-type composition, complicating statistical analysis. Considerable effort has been devoted to modeling sample heterogeneity, and presently, there are many methods that can estimate cell proportions or pure cell-type expression from mixture data. However, there is no method that comprehensively addresses mixture analysis in the context of differential expression without relying on additional proportion information, which can be inaccurate and is frequently unavailable. RESULTS In this study, we consider a clinically relevant situation where neither accurate proportion estimates nor pure cell expression is of direct interest, but where we are rather interested in detecting and interpreting relevant differential expression in mixture samples. We develop a method, Cell-type COmputational Differential Estimation (CellCODE), that addresses the specific statistical question directly, without requiring a physical model for mixture components. Our approach is based on latent variable analysis and is computationally transparent; it requires no additional experimental data, yet outperforms existing methods that use independent proportion measurements. CellCODE has few parameters that are robust and easy to interpret. The method can be used to track changes in proportion, improve power to detect differential expression and assign the differentially expressed genes to the correct cell type.

Involvement of Histone Demethylase LSD1 in Short-Time-Scale Gene Expression Changes during Cell Cycle Progression in Embryonic Stem Cells

ABSTRACT The histone demethylase LSD1, a component of the CoREST (corepressor for element 1-silencing transcription factor) corepressor complex, plays an important role in the downregulation of gene expression during development. However, the activities of LSD1 in mediating short-time-scale gene expression changes have not been well understood. To reveal the mechanisms underlying these two distinct functions of LSD1, we performed genome-wide mapping and cellular localization studies of LSD1 and its dimethylated histone 3 lysine 4 (substrate H3K4me2) in mouse embryonic stem cells (ES cells). Our results showed an extensive overlap between the LSD1 and H3K4me2 genomic regions and a correlation between the genomic levels of LSD1/H3K4me2 and gene expression, including many highly expressed ES cell genes. LSD1 is recruited to the chromatin of cells in the G1/S/G2 phases and is displaced from the chromatin of M-phase cells, suggesting that LSD1 or H3K4me2 alternatively occupies LSD1 genomic regions during cell cycle progression. LSD1 knockdown by RNA interference or its displacement from the chromatin by antineoplastic agents caused an increase in the levels of a subset of LSD1 target genes. Taken together, these results suggest that cell cycle-dependent association and dissociation of LSD1 with chromatin mediates short-time-scale gene expression changes during embryonic stem cell cycle progression.

Modeling a human hepatocellular carcinoma subset in mice through coexpression of met and point‐mutant β‐catenin

Hepatocellular cancer (HCC) remains a significant therapeutic challenge due to its poorly understood molecular basis. In the current study, we investigated two independent cohorts of 249 and 194 HCC cases for any combinatorial molecular aberrations. Specifically we assessed for simultaneous HMET expression or hMet activation and catenin β1 gene (CTNNB1) mutations to address any concomitant Met and Wnt signaling. To investigate cooperation in tumorigenesis, we coexpressed hMet and β‐catenin point mutants (S33Y or S45Y) in hepatocytes using sleeping beauty transposon/transposase and hydrodynamic tail vein injection and characterized tumors for growth, signaling, gene signatures, and similarity to human HCC. Missense mutations in exon 3 of CTNNB1 were identified in subsets of HCC patients. Irrespective of amino acid affected, all exon 3 mutations induced similar changes in gene expression. Concomitant HMET overexpression or hMet activation and CTNNB1 mutations were evident in 9%‐12.5% of HCCs. Coexpression of hMet and mutant‐β‐catenin led to notable HCC in mice. Tumors showed active Wnt and hMet signaling with evidence of glutamine synthetase and cyclin D1 positivity and mitogen‐activated protein kinase/extracellular signal‐regulated kinase, AKT/Ras/mammalian target of rapamycin activation. Introduction of dominant‐negative T‐cell factor 4 prevented tumorigenesis. The gene expression of mouse tumors in hMet‐mutant β‐catenin showed high correlation, with subsets of human HCC displaying concomitant hMet activation signature and CTNNB1 mutations. Conclusion: We have identified cooperation of hMet and β‐catenin activation in a subset of HCC patients and modeled this human disease in mice with a significant transcriptomic intersection; this model will provide novel insight into the biology of this tumor and allow us to evaluate novel therapies as a step toward precision medicine. (Hepatology 2016;64:1587‐1605)

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes.

The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP‐IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen‐stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14‐color staining panel. Two approaches (FlowReMi.1 and flowDensity‐flowType‐RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.

Misfolded proteins inhibit proliferation and promote stress-induced death in SV40-transformed mammalian cells

Protein misfolding is implicated in neurodegenerative diseases and occurs in aging. However, the contribution of the misfolded ensembles to toxicity remains largely unknown. Here we introduce 2 primate cell models of destabilized proteins devoid of specific cellular functions and interactors, as bona fide mis‐folded proteins, allowing us to isolate the gain‐of‐function of non‐native structures. Both GFP‐degron and a mutant chloramphenicol‐acetyltransferase fused to GFP (GFP‐Δ9CAT) form perinuclear aggregates, are degraded by the proteasome, and colocalize with and induce the chaperone Hsp70 (HSPA1A/B) in COS‐7 cells. We find that misfolded proteins neither significantly compromise chaperone‐mediated folding capacity nor induce cell death. However, they do induce growth arrest in cells that are unable to degrade them and promote stress‐induced death upon proteasome inhibition by MG‐132 and heat shock. Finally, we show that overexpression of all heat‐shock factor‐1 (HSF1) and Hsp70 proteins, as well as wild‐type and deacetylase‐deficient (H363Y) SIRT1, rescue survival upon stress, implying a noncatalytic action of SIRT1 in response to protein misfolding. Our study establishes a novel model and extends our knowledge on the mechanism of the function‐independent proteotoxicity of misfolded proteins in dividing cells.—Arslan, M. A., Chikina, M., Csermely, P., Sőti, C. Misfolded proteins inhibit proliferation and promote stress‐induced death in SV40‐transformed mammalian cells. FASEB J. 26, 766–777 (2012). www.fasebj.org

Accurate Quantification of Functional Analogy among Close Homologs

Correctly evaluating functional similarities among homologous proteins is necessary for accurate transfer of experimental knowledge from one organism to another, and is of particular importance for the development of animal models of human disease. While the fact that sequence similarity implies functional similarity is a fundamental paradigm of molecular biology, sequence comparison does not directly assess the extent to which two proteins participate in the same biological processes, and has limited utility for analyzing families with several parologous members. Nevertheless, we show that it is possible to provide a cross-organism functional similarity measure in an unbiased way through the exclusive use of high-throughput gene-expression data. Our methodology is based on probabilistic cross-species mapping of functionally analogous proteins based on Bayesian integrative analysis of gene expression compendia. We demonstrate that even among closely related genes, our method is able to predict functionally analogous homolog pairs better than relying on sequence comparison alone. We also demonstrate that the landscape of functional similarity is often complex and that definitive “functional orthologs” do not always exist. Even in these cases, our method and the online interface we provide are designed to allow detailed exploration of sources of inferred functional similarity that can be evaluated by the user.