Knowledge based identification of essential signaling from genome-scale siRNA experiments

<p>Abstract</p> <p>Background</p> <p>A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.</p> <p>Results</p> <p>We use PIPA to analyze genome-scale siRNA cell growth screens performed in HeLa and TOV cell lines. First we show that interacting gene pair siRNA hits are more reproducible than single gene hits. Using protein interactions, PIPA identifies enriched pathways not found using the standard Hypergeometric analysis including the FAK <it>cytoskeletal remodeling pathway</it>. Different branches of the <it>FAK </it>pathway are distinctly essential in HeLa versus TOV cell lines while other portions are uneffected by siRNA perturbations. Enriched hits belong to protein interactions associated with cell cycle regulation, anti-apoptosis, and signal transduction.</p> <p>Conclusion</p> <p>PIPA provides an analytical framework to interpret siRNA screen data by merging biologically annotated gene sets with the human interactome. As a result we identify pathways and signaling hypotheses that are statistically enriched to effect cell growth in human cell lines. This method provides a complementary approach to standard gene set enrichment that utilizes the additional knowledge of specific interactions within biological gene sets. </p

Developing microRNA screening as a functional genomics tool for disease research

Originally discovered as regulators of developmental timing in C. elegans, microRNAs (miRNAs) have emerged as modulators of nearly every cellular process, from normal development to pathogenesis. With the advent of whole genome libraries of miRNA mimics suitable for high throughput screening, it is possible to comprehensively evaluate the function of each member of the miRNAome in cell-based assays. Since the relatively few microRNAs in the genome are thought to directly regulate a large portion of the proteome, miRNAome screening, coupled with the identification of the regulated proteins, might be a powerful new approach to gaining insight into complex biological processes

Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia

Data integration stands to improve interpretation of RNAi screens which, as a result of off-target effects, typically yield numerous gene hits of which only a few validate. These off-target effects can result from seed matches to unintended gene targets (reagent-based) or cellular pathways, which can compensate for gene perturbations (biology-based). We focus on the biology-based effects and use network modeling tools to discover pathways de novo around RNAi hits. By looking at hits in a functional context, we can uncover novel biology not identified from any individual ‘omics measurement. We leverage multiple ‘omic measurements using the Simultaneous Analysis of Multiple Networks (SAMNet) computational framework to model a genome scale shRNA screen investigating Acute Lymphoblastic Leukemia (ALL) progression in vivo. Our network model is enriched for cellular processes associated with hematopoietic differentiation and homeostasis even though none of the individual ‘omic sets showed this enrichment. The model identifies genes associated with the TGF-beta pathway and predicts a role in ALL progression for many genes without this functional annotation. We further experimentally validate the hidden genes – Wwp1, a ubiquitin ligase, and Hgs, a multi-vesicular body associated protein – for their role in ALL progression. Our ALL pathway model includes genes with roles in multiple types of leukemia and roles in hematological development. We identify a tumor suppressor role for Wwp1 in ALL progression. This work demonstrates that network integration approaches can compensate for off-target effects, and that these methods can uncover novel biology retroactively on existing screening data. We anticipate that this framework will be valuable to multiple functional genomic technologies – siRNA, shRNA, and CRISPR – generally, and will improve the utility of functional genomic studies.National Institutes of Health (U.S.) (Grants U01-CA155758, U54-CA112967, U01-CA184898, and U01-CA155758)National Science Foundation (U.S.). Graduate Research Fellowship ProgramDavid H. Koch Institute for Integrative Cancer Research at MIT (Graduate Fellowship

A Network of Conserved Damage Survival Pathways Revealed by a Genomic RNAi Screen

Damage initiates a pleiotropic cellular response aimed at cellular survival when appropriate. To identify genes required for damage survival, we used a cell-based RNAi screen against the Drosophila genome and the alkylating agent methyl methanesulphonate (MMS). Similar studies performed in other model organisms report that damage response may involve pleiotropic cellular processes other than the central DNA repair components, yet an intuitive systems level view of the cellular components required for damage survival, their interrelationship, and contextual importance has been lacking. Further, by comparing data from different model organisms, identification of conserved and presumably core survival components should be forthcoming. We identified 307 genes, representing 13 signaling, metabolic, or enzymatic pathways, affecting cellular survival of MMS–induced damage. As expected, the majority of these pathways are involved in DNA repair; however, several pathways with more diverse biological functions were also identified, including the TOR pathway, transcription, translation, proteasome, glutathione synthesis, ATP synthesis, and Notch signaling, and these were equally important in damage survival. Comparison with genomic screen data from Saccharomyces cerevisiae revealed no overlap enrichment of individual genes between the species, but a conservation of the pathways. To demonstrate the functional conservation of pathways, five were tested in Drosophila and mouse cells, with each pathway responding to alkylation damage in both species. Using the protein interactome, a significant level of connectivity was observed between Drosophila MMS survival proteins, suggesting a higher order relationship. This connectivity was dramatically improved by incorporating the components of the 13 identified pathways within the network. Grouping proteins into “pathway nodes” qualitatively improved the interactome organization, revealing a highly organized “MMS survival network.” We conclude that identification of pathways can facilitate comparative biology analysis when direct gene/orthologue comparisons fail. A biologically intuitive, highly interconnected MMS survival network was revealed after we incorporated pathway data in our interactome analysis

STRING v9.1: protein-protein interaction networks, with increased coverage and integration

Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made—particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their network

A cell spot microarray method for high-throughput biology

High-throughput screening of cellular effects of RNA interference (RNAi) libraries is now being increasingly applied to explore the role of genes in specific cell biological processes and disease states. However, the technology is still limited to specialty laboratories, due to the requirements for robotic infrastructure, access to expensive reagent libraries, expertise in high-throughput screening assay development, standardization, data analysis and applications. In the future, alternative screening platforms will be required to expand functional large-scale experiments to include more RNAi constructs, allow combinatorial loss-of-function analyses (e.g. genegene or gene-drug interaction), gain-of-function screens, multi-parametric phenotypic readouts or comparative analysis of many different cell types. Such comprehensive perturbation of gene networks in cells will require a major increase in the flexibility of the screening platforms, throughput and reduction of costs. As an alternative for the conventional multi-well based high-throughput screening -platforms, here the development of a novel cell spot microarray method for production of high density siRNA reverse transfection arrays is described. The cell spot microarray platform is distinguished from the majority of other transfection cell microarray techniques by the spatially confined array layout that allow highly parallel screening of large-scale RNAi reagent libraries with assays otherwise difficult or not applicable to high-throughput screening. This study depicts the development of the cell spot microarray method along with biological application examples of high-content immunofluorescence and phenotype based cancer cell biological analyses focusing on the regulation of prostate cancer cell growth, maintenance of genomic integrity in breast cancer cells, and functional analysis of integrin protein-protein interactions in situ.RNA-interferenssin (RNAi) käyttö geenituotteiden toimintojen tutkimuksessa on vuosikymmenessä kehittynyt solubiologisen tutkimuksen merkittävimpien teknologioiden joukkoon. RNAi-menetelmät ovat mahdollistaneet myös eri solubiologisten signaalivälitysreittien kartoittamisen koko perimänlaajuisten geenitoimintojen suurtehoseulonnan avulla. Teknologian nopeasta kehityksestä ja laajamittaisesta hyödyntämisestä huolimatta, RNAi-menetelmien käyttö suurtehoseulontaan rajoittuu edelleen erikoislaboratorioihin huomattavien laite-, reagenssi- sekä tietotaitovaatimusten johdosta. Lisäksi yleisimmin käytetyt kuoppalevypohjaiset seulonta-menetelmät rajoittavat seulontatutkimusten laajuutta, eri analyysimenetelmien käyttöä sekä useiden mallisolulinjojen rinnakkaista seulontaa suuren reagenssikulutuksen takia. RNAi-suurtehoseulonnan kustannuksien ja laiteriippuvuuden alentamiseksi sekä monipuolisuuden lisäämiseksi tulevaisuudessa tarvitaan uusia vaihtoehtoisia seulontamenetelmiä. Tämän väitöskirjatyön lähtökohtana oli uuden kuoppalevyillä tehtäviä seulontamenetelmiä edullisemman ja monipuolisemman RNAi-suurtehoseulontamenetelmän kehittäminen. Tutkimuksessa kehitettiin solujen siRNA käänteistransfektioon perustuva solumikrosirumenetelmä, joka tarkasti rajattujen siRNA näytepisteiden avulla mahdollistaa jopa koko ihmisen perimänlaajuisten siRNA näytekirjastojen seulonnan yksittäisellä mittasirulla. Menetelmän suuri näytetiheys sekä sirujen pieni pinta-ala mahdollistavat myös erikoismittamenetelmien käytön suurtehoseulonnassa, jotka teknisistä tai kustannussyistä eivät sovellu käytettäväksi suurtehoseulontaan kuoppalevyillä. Väitöskirjan ensimmäinen osatyö kuvaa menetelmän kehittämistyön. Väitöskirjan toisessa, kolmannessa ja neljännessä osatyössä menetelmää on hyödynnetty syöpäbiologisissa seulontatutkimuksissa selvitettäessä geenejä jotka vaikuttavat eturauhassyöpäsolujen kasvuun, rintasyöpäsolujen solunjakautumiseen sekä integriini tarttumisreseptorien aktiivisuuden säätelyyn.Siirretty Doriast

BIASES AND BLIND-SPOTS IN GENOME-WIDE CRISPR-CAS9 KNOCKOUT SCREENS

Adaptation of the bacterial CRISPR-Cas9 system to mammalian cells revolutionized the field of functional genomics, enabling genome-scale genetic perturbations to study essential genes, whose loss of function results in a severe fitness defect. There are two types of essential genes in a cell. Core essential genes are absolutely required for growth and proliferation in every cell type. On the other hand, context-dependent essential genes become essential in an environmental or genetic context. The concept of context-dependent gene essentiality is particularly important in cancer, since killing cancer cells selectively without harming surrounding healthy tissue remains a major challenge. The toxicity of traditional cancer treatment protocols to the normal cells stresses the need for new strategies that can identify and address the weaknesses specific to cancer cells. Studies showed that CRISPR monogenic knockout screens can identify specific processes that cells rely on for growth and proliferation, which is a crucial step in identifying candidate cancer-specific therapeutic targets. While it is widely accepted that CRISPR screening is both more specific and more sensitive than previously established methods, the limitations of this technology have not been systematically investigated. In this dissertation, through several lines of integrated analysis of CRISPR screen data in cancer cell lines from the Cancer Dependency Map initiative, I will describe several computational approaches to demonstrate that CRISPR screens are not saturating. In fact, a typical screen has a ~20% false-negative rate, saturating coverage requires multiple repeats and false negatives are more prevalent among moderately expressed genes. I will then introduce a solution to the false negative problem and describe another method that provides a cleaner analysis of the data, rescuing the false negatives observed in these screens. Moreover, I will show that half of all constitutively expressed genes are never observed as essential in any CRISPR screen. Notably, these never-essentials are highly enriched for paralogs, suggesting that functional redundancy masks the detection of a substantial number of genes. Finally, I will describe our efforts to investigate functional buffering among approximately 400 candidate paralog pairs using CRISPR/enCas12a dual-gene knockout screening technology and discuss the paralog synthetic lethal interactions that we have identified, which have escaped detection in monogenic CRISPR-Cas9 knockout screens. Collectively, these observations reveal significant biases and blind-spots in the analysis of CRISPR-based functional genomics approaches and offer new opportunities for the discovery of novel candidate drug targets