Sushmita Roy

Identification of functional elements and regulatory circuits by Drosophila modENCODE

From Genome to Regulatory Networks For biologists, having a genome in hand is only the beginning—much more investigation is still needed to characterize how the genome is used to help to produce a functional organism (see the Perspective by Blaxter). In this vein, Gerstein et al. (p. 1775) summarize for the Caenorhabditis elegans genome, and The modENCODE Consortium (p. 1787) summarize for the Drosophila melanogaster genome, full transcriptome analyses over developmental stages, genome-wide identification of transcription factor binding sites, and high-resolution maps of chromatin organization. Both studies identified regions of the nematode and fly genomes that show highly occupied targets (or HOT) regions where DNA was bound by more than 15 of the transcription factors analyzed and the expression of related genes were characterized. Overall, the studies provide insights into the organization, structure, and function of the two genomes and provide basic information needed to guide and correlate both focused and genome-wide studies. The Drosophila modENCODE project demonstrates the functional regulatory network of flies. To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation.

Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures

Sequencing of multiple related species followed by comparative genomics analysis constitutes a powerful approach for the systematic understanding of any genome. Here, we use the genomes of 12 Drosophila species for the de novo discovery of functional elements in the fly. Each type of functional element shows characteristic patterns of change, or ‘evolutionary signatures’, dictated by its precise selective constraints. Such signatures enable recognition of new protein-coding genes and exons, spurious and incorrect gene annotations, and numerous unusual gene structures, including abundant stop-codon readthrough. Similarly, we predict non-protein-coding RNA genes and structures, and new microRNA (miRNA) genes. We provide evidence of miRNA processing and functionality from both hairpin arms and both DNA strands. We identify several classes of pre- and post-transcriptional regulatory motifs, and predict individual motif instances with high confidence. We also study how discovery power scales with the divergence and number of species compared, and we provide general guidelines for comparative studies.

Calorie Restriction and SIRT3 Trigger Global Reprogramming of the Mitochondrial Protein Acetylome

Calorie restriction (CR) extends life span in diverse species. Mitochondria play a key role in CR adaptation; however, the molecular details remain elusive. We developed and applied a quantitative mass spectrometry method to probe the liver mitochondrial acetyl-proteome during CR versus control diet in mice that were wild-type or lacked the protein deacetylase SIRT3. Quantification of 3,285 acetylation sites-2,193 from mitochondrial proteins-rendered a comprehensive atlas of the acetyl-proteome and enabled global site-specific, relative acetyl occupancy measurements between all four experimental conditions. Bioinformatic and biochemical analyses provided additional support for the effects of specific acetylation on mitochondrial protein function. Our results (1) reveal widespread reprogramming of mitochondrial protein acetylation in response to CR and SIRT3, (2) identify three biochemically distinct classes of acetylation sites, and (3) provide evidence that SIRT3 is a prominent regulator in CR adaptation by coordinately deacetylating proteins involved in diverse pathways of metabolism and mitochondrial maintenance.

Comparative Functional Genomics of the Fission Yeasts

A combined analysis of genome sequence, structure, and expression gives insights into fission yeast biology. The fission yeast clade—comprising Schizosaccharomyces pombe, S. octosporus, S. cryophilus, and S. japonicus—occupies the basal branch of Ascomycete fungi and is an important model of eukaryote biology. A comparative annotation of these genomes identified a near extinction of transposons and the associated innovation of transposon-free centromeres. Expression analysis established that meiotic genes are subject to antisense transcription during vegetative growth, which suggests a mechanism for their tight regulation. In addition, trans-acting regulators control new genes within the context of expanded functional modules for meiosis and stress response. Differences in gene content and regulation also explain why, unlike the budding yeast of Saccharomycotina, fission yeasts cannot use ethanol as a primary carbon source. These analyses elucidate the genome structure and gene regulation of fission yeast and provide tools for investigation across the Schizosaccharomyces clade.

Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum

SUMMARY Using replicated human serum samples, we applied an error model for proteomic differential expression profiling for a high-resolution liquid chromatography-mass spectrometry (LC-MS) platform. The detailed noise analysis presented here uses an experimental design that separates variance caused by sample preparation from variance due to analytical equipment. An analytic approach based on a two-component error model was applied, and in combination with an existing data driven technique that utilizes local sample averaging, we characterized and quantified the noise variance as a function of mean peak intensity. The results indicate that for processed LC-MS data a constant coefficient of variation is dominant for high intensities, whereas a model for low intensities explains Poisson-like variations. This result leads to a quadratic variance model which is used for the estimation of sample preparation noise present in LC-MS data.

Characterization of Differentiated Quiescent and Nonquiescent Cells in Yeast Stationary-Phase Cultures

Cells in glucose-limited Saccharomyces cerevisiae cultures differentiate into quiescent (Q) and nonquiescent (NQ) fractions before entering stationary phase. To understand this differentiation, Q and NQ cells from 101 deletion-mutant strains were tested for viability and reproductive capacity. Eleven mutants that affected one or both phenotypes in Q or NQ fractions were identified. NQ fractions exhibit a high level of petite colonies, and nine mutants affecting this phenotype were identified. Microarray analysis revealed >1300 mRNAs distinguished Q from NQ fractions. Q cell-specific mRNAs encode proteins involved in membrane maintenance, oxidative stress response, and signal transduction. NQ-cell mRNAs, consistent with apoptosis in these cells, encode proteins involved in Ty-element transposition and DNA recombination. More than 2000 protease-released mRNAs were identified only in Q cells, consistent with these cells being physiologically poised to respond to environmental changes. Our results indicate that Q and NQ cells differentiate significantly, with Q cells providing genomic stability and NQ cells providing nutrients to Q cells and a regular source of genetic diversity through mutation and transposition. These studies are relevant to chronological aging, cell cycle, and genome evolution, and they provide insight into complex responses that even simple organisms have to starvation.

Multivariate curve resolution of time course microarray data.

BackgroundModeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data.ResultsIn this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data.ConclusionProfiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements.

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

The proteomics of quiescent and nonquiescent cell differentiation in yeast stationary-phase cultures

As yeast cultures enter stationary phase in rich, glucose-based medium, differentiation of two major subpopulations of cells, termed quiescent and nonquiescent, is observed. Differences in mRNA abundance between exponentially growing and stationary-phase cultures and quiescent and nonquiescent cells are known, but little was known about protein abundance in these cells. To measure protein abundance in exponential and stationary-phase cultures, the yeast GFP-fusion library (4159 strains) was examined during exponential and stationary phases, using high-throughput flow cytometry (HyperCyt). Approximately 5% of proteins in the library showed twofold or greater changes in median fluorescence intensity (abundance) between the two conditions. We examined 38 strains exhibiting two distinct fluorescence-intensity peaks in stationary phase and determined that the two fluorescence peaks distinguished quiescent and nonquiescent cells, the two major subpopulations of cells in stationary-phase cultures. GFP-fusion proteins in this group were more abundant in quiescent cells, and half were involved in mitochondrial function, consistent with the sixfold increase in respiration observed in quiescent cells and the relative absence of Cit1p:GFP in nonquiescent cells. Finally, examination of quiescent cell-specific GFP-fusion proteins revealed symmetry in protein accumulation in dividing quiescent and nonquiescent cells after glucose exhaustion, leading to a new model for the differentiation of these cells.

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

Background Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information. Results AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins. Conclusion AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains.