Vineeth Surendranath

Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry

Although the transcriptome, proteome, and interactome of several eukaryotic model organisms have been described in detail, lipidomes remain relatively uncharacterized. Using Saccharomyces cerevisiae as an example, we demonstrate that automated shotgun lipidomics analysis enabled lipidome-wide absolute quantification of individual molecular lipid species by streamlined processing of a single sample of only 2 million yeast cells. By comparative lipidomics, we achieved the absolute quantification of 250 molecular lipid species covering 21 major lipid classes. This analysis provided ≈95% coverage of the yeast lipidome achieved with 125-fold improvement in sensitivity compared with previous approaches. Comparative lipidomics demonstrated that growth temperature and defects in lipid biosynthesis induce ripple effects throughout the molecular composition of the yeast lipidome. This work serves as a resource for molecular characterization of eukaryotic lipidomes, and establishes shotgun lipidomics as a powerful platform for complementing biochemical studies and other systems-level approaches.

Human protein reference database as a discovery resource for proteomics

The rapid pace at which genomic and proteomic data is being generated necessitates the development of tools and resources for managing data that allow integration of information from disparate sources. The Human Protein Reference Database (http://www.hprd.org) is a web-based resource based on open source technologies for protein information about several aspects of human proteins including protein-protein interactions, post-translational modifications, enzyme-substrate relationships and disease associations. This information was derived manually by a critical reading of the published literature by expert biologists and through bioinformatics analyses of the protein sequence. This database will assist in biomedical discoveries by serving as a resource of genomic and proteomic information and providing an integrated view of sequence, structure, function and protein networks in health and disease.

Genome-scale RNAi profiling of cell division in human tissue culture cells

Cell division is fundamental for all organisms. Here we report a genome-scale RNA-mediated interference screen in HeLa cells designed to identify human genes that are important for cell division. We have used a library of endoribonuclease-prepared short interfering RNAs for gene silencing and have used DNA content analysis to identify genes that induced cell cycle arrest or altered ploidy on silencing. Validation and secondary assays were performed to generate a nine-parameter loss-of-function phenoprint for each of the genes. These phenotypic signatures allowed the assignment of genes to specific functional classes by combining hierarchical clustering, cross-species analysis and proteomic data mining. We highlight the richness of our dataset by ascribing novel functions to genes in mitosis and cytokinesis. In particular, we identify two evolutionarily conserved transcriptional regulatory networks that govern cytokinesis. Our work provides an experimental framework from which the systematic analysis of novel genes necessary for cell division in human cells can begin.

Genome-wide resources of endoribonuclease-prepared short interfering RNAs for specific loss-of-function studies.

RNA interference (RNAi) has become an important technique for loss-of-gene-function studies in mammalian cells. To achieve reliable results in an RNAi experiment, efficient and specific silencing triggers are required. Here we present genome-wide data sets for the production of endoribonuclease-prepared short interfering RNAs (esiRNAs) for human, mouse and rat. We used an algorithm to predict the optimal region for esiRNA synthesis for every protein-coding gene of these three species. We created a database, RiDDLE, for retrieval of target sequences and primer information. To test this in silico resource experimentally, we generated 16,242 esiRNAs that can be used for RNAi screening in human cells. Comparative analyses with chemically synthesized siRNAs demonstrated a high silencing efficacy of esiRNAs and a 12-fold reduction of downregulated off-target transcripts as detected by microarray analysis. Hence, the presented esiRNA libraries offer an efficient, cost-effective and specific alternative to presently available mammalian RNAi resources.

Systematic imaging reveals features and changing localization of mRNAs in Drosophila development

mRNA localization is critical for eukaryotic cells and affects numerous transcripts, yet how cells regulate distribution of many mRNAs to their subcellular destinations is still unknown. We combined transcriptomics and systematic imaging to determine the tissue-specific expression and subcellular distribution of 5862 mRNAs during Drosophila oogenesis. mRNA localization is widespread in the ovary and detectable in all of its cell types—the somatic epithelial, the nurse cells, and the oocyte. Genes defined by a common RNA localization share distinct gene features and differ in expression level, 3′UTR length and sequence conservation from unlocalized mRNAs. Comparison of mRNA localizations in different contexts revealed that localization of individual mRNAs changes over time in the oocyte and between ovarian and embryonic cell types. This genome scale image-based resource (Dresden Ovary Table, DOT, http://tomancak-srv1.mpi-cbg.de/DOT/main.html) enables the transition from mechanistic dissection of singular mRNA localization events towards global understanding of how mRNAs transcribed in the nucleus distribute in cells. DOI: http://dx.doi.org/10.7554/eLife.05003.001

Combined RNAi and localization for functionally dissecting long noncoding RNAs

Whereas methods to comprehensively study cellular roles of protein-coding genes are available, techniques to systematically investigate long noncoding RNAs (lncRNAs), which have been implicated in diverse biological pathways, are limited. Here we report combined knockdown and localization analysis of noncoding RNAs (c-KLAN) that merges functional characterization and localization approaches to study lncRNAs. Using this technique we identified transcripts that regulate mouse embryonic stem cell identity.

Separating the Wheat from the Chaff: Unbiased Filtering of Background Tandem Mass Spectra Improves Protein Identification

Only a small fraction of spectra acquired in LC-MS/MS runs matches peptides from target proteins upon database searches. The remaining, operationally termed background, spectra originate from a variety of poorly controlled sources and affect the throughput and confidence of database searches. Here, we report an algorithm and its software implementation that rapidly removes background spectra, regardless of their precise origin. The method estimates the dissimilarity distance between screened MS/MS spectra and unannotated spectra from a partially redundant background library compiled from several control and blank runs. Filtering MS/MS queries enhanced the protein identification capacity when searches lacked spectrum to sequence matching specificity. In sequence-similarity searches it reduced by, on average, 30-fold the number of orphan hits, which were not explicitly related to background protein contaminants and required manual validation. Removing high quality background MS/MS spectra, while preserving in the data set the genuine spectra from target proteins, decreased the false positive rate of stringent database searches and improved the identification of low-abundance proteins.

Revisiting the Yeast PPR Proteins—Application of an Iterative Hidden Markov Model Algorithm Reveals New Members of the Rapidly Evolving Family

Pentatricopeptide repeat (PPR) proteins are the largest known RNA-binding protein family, and are found in all eukaryotes, being particularly abundant in higher plants. PPR proteins localize mostly to mitochondria and chloroplasts, and many were shown to modulate organellar genome expression on the posttranscriptional level. Although the genomes of land plants encode hundreds of PPR proteins, only a few have been identified in Fungi and Metazoa. As the current PPR motif profiles are built mainly on the basis of the predominant plant sequences, they are unlikely to be optimal for detecting fungal and animal members of the family, and many putative PPR proteins in these genomes may remain undetected. In order to verify this hypothesis, we designed a hidden Markov model-based bioinformatic tool called Supervised Clustering-based Iterative Phylogenetic Hidden Markov Model algorithm for the Evaluation of tandem Repeat motif families (SCIPHER) using sequence data from orthologous clusters from available yeast genomes. This approach allowed us to assign 12 new proteins in Saccharomyces cerevisiae to the PPR family. Similarly, in other yeast species, we obtained a 5-fold increase in the detection of PPR motifs, compared with the previous tools. All the newly identified S. cerevisiae PPR proteins localize in the mitochondrion and are a part of the RNA processing interaction network. Furthermore, the yeast PPR proteins seem to undergo an accelerated divergent evolution. Analysis of single and double amino acid substitutions in the Dmr1 protein of S. cerevisiae suggests that cooperative interactions between motifs and pseudoreversion could be the force driving this rapid evolution.

Vika/vox, a novel efficient and specific Cre/loxP-like site-specific recombination system

Targeted genome engineering has become an important research area for diverse disciplines, with site-specific recombinases (SSRs) being among the most popular genome engineering tools. Their ability to trigger excision, integration, inversion and translocation has made SSRs an invaluable tool to manipulate DNA in vitro and in vivo. However, sophisticated strategies that combine different SSR systems are ever increasing. Hence, the demand for additional precise and efficient recombinases is dictated by the increasing complexity of the genetic studies. Here, we describe a novel site-specific recombination system designated Vika/vox. Vika originates from a degenerate bacteriophage of Vibrio coralliilyticus and shares low sequence similarity to other tyrosine recombinases, but functionally carries out a similar type of reaction. We demonstrate that Vika is highly specific in catalyzing vox recombination without recombining target sites from other SSR systems. We also compare the recombination activity of Vika/vox with other SSR systems, providing a guideline for deciding on the most suitable enzyme for a particular application and demonstrate that Vika expression does not cause cytotoxicity in mammalian cells. Our results show that Vika/vox is a novel powerful and safe instrument in the ‘genetic toolbox’ that can be used alone or in combination with other SSRs in heterologous hosts.

SeLOX—a locus of recombination site search tool for the detection and directed evolution of site-specific recombination systems

Site-specific recombinases have become a resourceful tool for genome engineering, allowing sophisticated in vivo DNA modifications and rearrangements, including the precise removal of integrated retroviruses from host genomes. In a recent study, a mutant form of Cre recombinase has been used to excise the provirus of a specific HIV-1 strain from the human genome. To achieve provirus excision, the Cre recombinase had to be evolved to recombine an asymmetric locus of recombination (lox)-like sequence present in the long terminal repeat (LTR) regions of a HIV-1 strain. One pre-requisite for this type of work is the identification of degenerate lox-like sites in genomic sequences. Given their nature—two inverted repeats flanking a spacer of variable length—existing search tools like BLAST or RepeatMasker perform poorly. To address this lack of available algorithms, we have developed the web-server SeLOX, which can identify degenerate lox-like sites within genomic sequences. SeLOX calculates a position weight matrix based on lox-like sequences, which is used to search genomic sequences. For computational efficiency, we transform sequences into binary space, which allows us to use a bit-wise AND Boolean operator for comparisons. Next to finding lox-like sites for Cre type recombinases in HIV LTR sequences, we have used SeLOX to identify lox-like sites in HIV LTRs for six yeast recombinases. We finally demonstrate the general usefulness of SeLOX in identifying lox-like sequences in large genomes by searching Cre type recombination sites in the entire human genome. SeLOX is freely available at http://selox.mpi-cbg.de/cgi-bin/selox/index.