Recent

Preprints

Authors: IGVF Consortium. The Impact of Genomic Variation on Function (IGVF) Consortium.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402186/

Harley O'Connor Mount, Malene L Urbanus, Dayag Sheykhkarimli, Atina G Cote, Florent Laval, Georges Coppin, Nishka Kishore, Roujia Li, Kerstin Spirohn-Fitzgerald, Morgan O Petersen, Jennifer J Knapp, Dae-Kyum Kim, Jean-Claude Twizere, Michael A Calderwood, Marc Vidal, Frederick P Roth, Alexander W Ensminger. A comprehensive two-hybrid analysis to explore the L. pneumophila effector-effector interactome.
doi: https://doi.org/10.1101/2024.03.29.587239

Luke Lambourne, Kaia Mattioli, Clarissa Santoso, Gloria Sheynkman, Sachi Inukai, Babita Kaundal, Anna Berenson, Kerstin Spirohn-Fitzgerald, Anukana Bhattacharjee, Elisabeth Rothman, Shaleen Shrestha, Florent Laval, Zhipeng Yang, Deepa Bisht, Jared A. Sewell, Guangyuan Li, Anisa Prasad, Sabrina Phanor, Ryan Lane, Devlin M. Campbell, Toby Hunt, Dawit Balcha, Marinella Gebbia, Jean-Claude Twizere, Tong Hao, Adam, Frankish, Josh A. Riback, Nathan Salomonis, Michael A. Calderwood, David E. Hill, Nidhi Sahni, Marc Vidal, Martha L. Bulyk, Juan I. Fuxman Bass. Widespread variation in molecular interactions and regulatory properties amongtranscription factor isoforms.
doi: https://doi.org/10.1101/2024.03.12.584681

Jessica Lacoste, Marzieh Haghighi, Shahan Haider, Zhen-Yuan Lin, Dmitri Segal, Chloe Reno, Wesley Wei Qian, Xueting Xiong, Hamdah Shafqat-Abbasi, Pearl V Ryder, Rebecca Senft, Beth A Cimini, Frederick P Roth, Michael Calderwood, David Hill, Marc Vidal, S Stephen Yi, Nidhi Sahni, Jian Peng, Anne-Claude Gingras, Shantanu Singh, Anne E Carpenter, Mikko Taipale. Pervasive mislocalization of pathogenic coding variants underlying human disorders.
doi: https://doi.org/10.1101/2023.09.05.556368

Julien Olivet, Soon Gang, Salvador Sierra, Tina M. O’Grady, Mario de la Fuente Revenga, Florent Laval, Vladimir V. Botchkarev Jr., Christoph Gorgulla, Paul W. Coote, Jérémy Blavier, Ezekiel A. Geffken, Jimit Lakhani, Kijun Song, Zoe C. Yeoh, Bin Hu, Anthony C. Varca, Jonathan Bruyr, Samira Ibrahim, Tasneem Jivanjee, Joshua D. Bromley, Sarah K. Nyquist, Aaron Richardson, Hong Yue, Yang Wang, Natalia Calonghi, Alessandra Stefan, Kerstin Spirohn, Didier Vertommen, Maria F. Baietti, Irma Lemmens, Hyuk-Soo Seo, Mikhail G. Dozmorov, Luc Willems, Jan Tavernier, Kalyan Das, Eleonora Leucci, Alejandro Hochkoeppler, Zhen-Yu Jim Sun, Michael A. Calderwood, Tong Hao, Alex K. Shalek, David E. Hill, Andras Boeszoermenyi, Haribabu Arthanari, Sara J. Buhrlage, Sirano Dhe-Paganon, Javier González-Maeso, Franck Dequiedt, Jean-Claude Twizere, Marc Vidal. Expanding the HDAC druggable landscape beyond enzymatic activity.
https://doi.org/10.1101/2022.12.07.519454

Philipp Trepte, Christopher Secker, Soon Gang Choi, Julien Olivet, Eduardo Silva Ramos, Patricia Cassonnet, Sabrina Golusik, Martina Zenkner, Stephanie Beetz, Marcel Sperling, Yang Wang, Tong Hao, Kerstin Spirohn, Jean-Claude Twizere, Michael A Calderwood, David E Hill, Yves Jacob, Marc Vidal, Erich E Wanker. A quantitative mapping approach to identify direct interactions within complexomes.
bioRxiv 2021.08.25.457734; doi: https://doi.org/10.1101/2021.08.25.457734

Luke Lambourne, Anupama Yadav, Yang Wang, Alice Desbuleux, Dae-Kyum Kim, Tiziana Cafarelli, Carles Pons, István A. Kovács, Noor Jailkhani, Sadie Schlabach, David De Ridder, Katja Luck, Wenting Bian, Yun Shen, Miles Mee, Yves Jacob, Irma Lemmens, Thomas Rolland, Jan Tavernier, Kerstin Spirohn, Quan Zhong, Patrick Aloy, Tong Hao, Benoit Charloteaux, Frederick P. Roth, David E. Hill, Michael A. Calderwood, Jean-Claude Twizere, Marc Vidal. Binary Interactome Models of Inner- Versus Outer-Complexome Organization.
bioRxiv 2021.03.16.435663; doi: https://doi.org/10.1101/2021.03.16.435663

Estelle M.N. Laurent, Yorgos Sofianatos, Anastassia Komarova, Jean-Pascal Gimeno, Payman Samavarchi Tehrani, Dae-Kyum Kim, Hala Abdouni, Marie Duhamel, Patricia Cassonnet, Jennifer J. Knapp, Da Kuang, Aditya Chawla, Dayag Sheykhkarimli, Ashyad Rayhan, Roujia Li, Oxana Pogoutse, David E. Hill, Michael A. Calderwood, Pascal Falter-Braun, Patrick Aloy, Ulrich Stelzl, Marc Vidal, Anne-Claude Gingras, Georgios A. Pavlopoulos, Sylvie Van Der Werf, Isabelle Fournier, Frederick P. Roth, Michel Salzet, Caroline Demeret, Yves Jacob, Etienne Coyaud. Global BioID-based SARS-CoV-2 proteins proximal interactome unveils novel ties between viral polypeptides and host factors involved in multiple COVID19-associated mechanisms.
bioRxiv 2020.08.28.272955; doi: https://doi.org/10.1101/2020.08.28.272955

Recent Publications

Copyrighted text is provided here only to permit fair use.

IGVF Consortium. Deciphering the impact of genomic variation on function. Nature 2024, https://doi.org/10.1038/s41586-024-07510-0. PDF

Abstract
Our genomes influence nearly every aspect of human biology—from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

Philipp Trepte, Christopher Secker, Julien Olivet, Jeremy Blavier, Simona Kostova, Sibusiso Maseko, Igor Minia, Eduardo Silva Ramos, Patricia Cassonnet, Sabrina Golusik, Martina Zenkner, Stephanie Beetz, Mara Liebich, Nadine Scharek, Anja Schuetz, Marcel Sperling, Michael Lisurek, Yang Wang, Kerstin Spirohn, Tong Hao, Michael Calderwood, David Hill, Markus Landthaler, Soon Gang Choi, Jean-claude Twizere, Marc Vidal, and Erich Wanker. AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-CoV-2 inhibitor. Mol Syst Biol. 2024, https://doi.org/10.1038/s44320-024-00019-8. PDF

Abstract
Protein–protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

Anna Berenson, Ryan Lane, Luis F Soto-Ugaldi, Mahir Patel, Cosmin Ciausu , Zhaorong Li, Yilin Chen, Sakshi Shah, Clarissa Santoso, Xing Liu, Kerstin Spirohn, Tong Hao, David E Hill, Marc Vidal, Juan I Fuxman Bass. Paired yeast one-hybrid assays to detect DNA-binding cooperativity and antagonism across transcription factors. Nature Communications. 2023 Oct 18;14(1):6570. doi: 10.1038/s41467-023-42445-6 PDF

Abstract
Cooperativity and antagonism between transcription factors (TFs) can drastically modify their binding to regulatory DNA elements. While mapping these relationships between TFs is important for understanding their context-specific functions, existing approaches either rely on DNA binding motif predictions, interrogate one TF at a time, or study individual TFs in parallel. Here, we introduce paired yeast one-hybrid (pY1H) assays to detect cooperativity and antagonism across hundreds of TF-pairs at DNA regions of interest. We provide evidence that a wide variety of TFs are subject to modulation by other TFs in a DNA region-specific manner. We also demonstrate that TF-TF relationships are often affected by alternative isoform usage and identify cooperativity and antagonism between human TFs and viral proteins from human papillomaviruses, Epstein-Barr virus, and other viruses. Altogether, pY1H assays provide a broadly applicable framework to study how different functional relationships affect protein occupancy at regulatory DNA regions.

Florent Laval, Georges Coppin, Jean-Claude Twizere, Marc Vidal. Homo cerevisiae - Leveraging Yeast for Investigating Protein–Protein Interactions and Their Role in Human Disease. International Journal of Molecular Science. 2023, 24(11), 9179; https://doi.org/10.3390/ijms24119179 PDF

Abstract
Understanding how genetic variation affects phenotypes represents a major challenge, particularly in the context of human disease. Although numerous disease-associated genes have been identified, the clinical significance of most human variants remains unknown. Despite unparalleled advances in genomics, functional assays often lack sufficient throughput, hindering efficient variant functionalization. There is a critical need for the development of more potent, high-throughput methods for characterizing human genetic variants. Here, we review how yeast helps tackle this challenge, both as a valuable model organism and as an experimental tool for investigating the molecular basis of phenotypic perturbation upon genetic variation. In systems biology, yeast has played a pivotal role as a highly scalable platform which has allowed us to gain extensive genetic and molecular knowledge, including the construction of comprehensive interactome maps at the proteome scale for various organisms. By leveraging interactome networks, one can view biology from a systems perspective, unravel the molecular mechanisms underlying genetic diseases, and identify therapeutic targets. The use of yeast to assess the molecular impacts of genetic variants, including those associated with viral interactions, cancer, and rare and complex diseases, has the potential to bridge the gap between genotype and phenotype, opening the door for precision medicine approaches and therapeutic development.

Hong-Wen Tang, Kerstin Spirohn, Yanhui Hu, Tong Hao, Istvan A Kovacs, Yue Gao, Richard Binari, Donghui Yang-Zhou, Kenneth H. Wan, Joel S. Bader, Dawit Balcha, Wenting Bian, Benjamin W. Booth, Atina G. Cote, Steffi De Rouck, Alice Desbuleux, Dae-Kyum Kim, Jennifer J. Knapp, Wen Xing Lee, Irma Lemmens, Cathleen Li, Mian Li, Roujia Li, Hyobin Lim, Katja Luck, Dylan Markey, Carl Pollis, Sudharshan Rangarajan, Jonathan Rodiger, Sadie Schlabach, Yun Shen, Bridget TeeKing, Frederick P. Roth, Jan Tavernier, Mike Calderwood, David E Hill, Susan E Celniker, Marc Vidal, Norbert Perrimon, Stephanie Mohr. Next-generation large-scale binary protein interaction network for Drosophila. Nature Communications. 2023, https://doi.org/10.1038/s41467-023-37876-0 PDF

Abstract
Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the ‘FlyBi’ dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Xu-Wen Wang, Lorenzo Madeddu, Kerstin Spirohn, Leonardo Martini, Adriano Fazzone, Luca Becchetti, Thomas P. Wytock, István A. Kovács, Olivér M. Balogh, Bettina Benczik, Mátyás Pétervári, Bence Ágg, Péter Ferdinandy, Loan Vulliard, Jörg Menche, Stefania Colonnese, Manuela Petti, Gaetano Scarano, Francesca Cuomo, Tong Hao, Florent Laval, Luc Willems, Jean-Claude Twizere, Marc Vidal, Michael A. Calderwood, Enrico Petrillo, Albert-László Barabási, Edwin K. Silverman, Joseph Loscalzo, Paola Velardi & Yang-Yu Liu. Assessment of community efforts to advance network-based prediction of protein–protein interactions. Nature Communications. 2023, https://doi.org/10.1038/s41467-023-37079-7 PDF

Abstract
Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S.cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Dae-Kyum Kim, Benjamin Weller, Chung-Wen Lin, Dayag Sheykhkarimli, Jennifer J. Knapp, Guillaume Dugied, Andreas Zanzoni, Carles Pons, Marie J. Tofaute, Sibusiso B. Maseko, Kerstin Spirohn, Florent Laval, Luke Lambourne, Nishka Kishore, Ashyad Rayhan, Mayra Sauer, Veronika Young, Hridi Halder, Nora Marín-de la Rosa, Oxana Pogoutse, Alexandra Strobel, Patrick Schwehn, Roujia Li, Simin T. Rothballer, Melina Altmann, Patricia Cassonnet, Atina G. Coté, Lena Elorduy Vergara, Isaiah Hazelwood, Betty B. Liu, Maria Nguyen, Ramakrishnan Pandiarajan, Bushra Dohai, Patricia A. Rodriguez Coloma6, Juline Poirson, Paolo Giuliana, Luc Willems, Mikko Taipale, Yves Jacob, Tong Hao, David E. Hill, Christine Brun, Jean-Claude Twizere, Daniel Krappmann, Matthias Heinig, Claudia Falter, Patrick Aloy, Caroline Demeret, Marc Vidal, Michael A. Calderwood, Frederick P. Roth, Pascal Falter-Braun. A proteome-scale map of the SARS-CoV-2–human contactome. Nature Biotechnology. 2022, https://doi.org/10.1038/s41587-022-01475-z PDF

Abstract
Understanding the mechanisms of coronavirus disease 2019 (COVID-19) disease severity to efficiently design therapies for emerging virus variants remains an urgent challenge of the ongoing pandemic. Infection and immune reactions are mediated by direct contacts between viral moleculesand the host proteome, and the vast majority of these virus–host contacts (the ‘contactome’) have not been identified. Here, we present a systematic contactome map of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with the human host encompassing more than 200 binary virus–host and intraviral protein–protein interactions. We find that host proteins genetically associated with comorbidities of severe illness and long COVID are enriched in SARS-CoV-2 targeted network communities. Evaluating contactome-derived hypotheses, we demonstrate that viral NSP14 activates nuclear factor κB (NF-κB)-dependent transcription, even in the presence of cytokine signaling. Moreover, for several tested host proteins, genetic knock-down substantially reduces viral replication. Additionally, we show for USP25 that this effect is phenocopied by the small-molecule inhibitor AZ1. Our results connect viral proteins to human genetic architecture for COVID-19 severity and offer potential therapeutic targets.

Mohamed Helmy, Miles Mee, Aniket Ranjan, Tong Hao, Marc Vidal, Michael A. Calderwood, Katja Luck, Gary D. Bader. OpenPIP: An Open-source Platform for Hosting, Visualizing and Analyzing Protein Interaction Data. Journal of Molecular Biology. 2022, https://www.sciencedirect.com/science/article/pii/S0022283622001838?via%3Dihub, PDF

Abstract
Knowing which proteins interact with each other is essential information for understanding how most biological processes at the cellular and organismal level operate and how their perturbation can cause disease. Continuous technical and methodological advances over the last two decades have led to many genome-wide systematically-generated protein–protein interaction (PPI) maps. To help store, visualize, analyze and disseminate these specialized experimental datasets via the web, we developed the freely-available Open-source Protein Interaction Platform (openPIP) as a customizable web portal designed to host experimental PPI maps. Such a portal is often required to accompany a paper describing the experimental data set, in addition to depositing the data in a standard repository. No coding skills are required to set up and customize the database and web portal. OpenPIP has been used to build the databases and web portals of two major protein interactome maps, the Human and Yeast Reference Protein Interactome maps (HuRI and YeRI, respectively). OpenPIP is freely available as a ready-to-use Docker container for hosting and sharing PPI data with the scientific community at http://openpip.baderlab.org/ and the source code can be downloaded from https://github.com/BaderLab/openPIP/.

Charlotte Vandermeulen, Tina O’Grady, Jerome Wayet, Bartimee Galvan, Sibusiso Maseko, Majid Cherkaoui, Alice Desbuleux, Georges Coppin, Julien Olivet, Lamya Ben Ameur, Keisuke Kataoka, Seishi Ogawa, Olivier Hermine, Ambroise Marcais, Marc Thiry, Franck Mortreux, Michael A. Calderwood, Johan Van Weyenbergh, Jean-Marie Peloponese, Benoit Charloteaux, Anne Van den Broeke, David E. Hill, Marc Vidal, Franck Dequiedt, Jean-Claude Twizere. The HTLV-1 viral oncoproteins Tax and HBZ reprogram the cellular mRNA splicing landscape. PLOS Pathogens. 2021, https://doi.org/10.1371/journal.ppat.1009919. PDF

Abstract
Viral infections are known to hijack the transcription and translation of the host cell. However, the extent to which viral proteins coordinate these perturbations remains unclear. Here we used a model system, the human T-cell leukemia virus type 1 (HTLV-1), and systematically analyzed the transcriptome and interactome of key effectors oncoviral proteins Tax and HBZ. We showed that Tax and HBZ target distinct but also common transcription factors. Unexpectedly, we also uncovered a large set of interactions with RNA-binding proteins, including the U2 auxiliary factor large subunit (U2AF2), a key cellular regulator of pre-mRNA splicing. We discovered that Tax and HBZ perturb the splicing landscape by altering cassette exons in opposing manners, with Tax inducing exon inclusion while HBZ induces exon exclusion. Among Tax- and HBZ-dependent splicing changes, we identify events that are also altered in Adult T cell leukemia/lymphoma (ATLL) samples from two independent patient cohorts, and in well-known cancer census genes. Our interactome mapping approach, applicable to other viral oncogenes, has identified spliceosome perturbation as a novel mechanism coordinated by Tax and HBZ to reprogram the transcriptome.

Alex H. M. Ng, Parastoo Khoshakhlagh, Jesus Eduardo Rojo Arias, Giovanni Pasquini, Kai Wang, Anka Swiersy, Seth L. Shipman, Evan Appleton, Kiavash Kiaee, Richie E. Kohman, Andyna Vernet, Matthew Dysart, Kathleen Leeper, Wren Saylor, Jeremy Y. Huang, Amanda Graveline, Jussi Taipale, David E. Hill, Marc Vidal, Juan M. Melero-Martin, Volker Busskamp & George M. Church. A comprehensive library of human transcription factors for cell fate engineering. Nat Biotechnol. 2021 Apr;39(4):510-519. doi: 10.1038/s41587-020-0742-6. PDF

Abstract
Human pluripotent stem cells (hPSCs) offer an unprecedented opportunity to model diverse cell types and tissues. To enable systematic exploration of the programming landscape mediated by transcription factors (TFs), we present the Human TFome, a comprehensive library containing 1,564 TF genes and 1,732 TF splice isoforms. By screening the library in three hPSC lines, we discovered 290 TFs, including 241 that were previously unreported, that induce differentiation in 4 days without alteration of external soluble or biomechanical cues. We used four of the hits to program hPSCs into neurons, fibroblasts, oligodendrocytes and vascular endothelial-like cells that have molecular and functional similarity to primary cells. Our cell-autonomous approach enabled parallel programming of hPSCs into multiple cell types simultaneously. We also demonstrated orthogonal programming by including oligodendrocyte-inducible hPSCs with unmodified hPSCs to generate cerebral organoids, which expedited in situ myelination. Large-scale combinatorial screening of the Human TFome will complement other strategies for cell engineering based on developmental biology and computational systems biology.

Feixiong Cheng, Junfei Zhao, Yang Wang, Weiqiang Lu, Zehui Liu, Yadi Zhou, William R. Martin, Ruisheng Wang, Jin Huang, Tong Hao, Hong Yue, Jing Ma, Yuan Hou, Jessica A. Castrillon, Jiansong Fang, Justin D. Lathia, Ruth A. Keri, Felice C. Lightstone, Elliott Marshall Antman, Raul Rabadan, David E. Hill, Charis Eng, Marc Vidal and Joseph Loscalzo. Comprehensive characterization of protein–protein interactions perturbed by disease mutations. Nat Genet 2021, https://doi.org/10.1038/s41588-020-00774-y. PDF

Abstract
Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein–protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

En-Ching Luo, Jason L Nathanson, Frederick E Tan, Joshua L Schwartz, Jonathan C Schmok, Archana Shankar, Sebastian Markmiller, Brian A Yee, Shashank Sathe, Gabriel A Pratt, Duy B Scaletta, Yuanchi Ha, David E Hill, Stefan Aigner, Gene W Yeo. Large-scale tethered function assays identify factors that regulate mRNA stability and translation. Nat Struct Mol Biol. 2020 Oct;27(10):989-1000.doi: 10.1038/s41594-020-0477-6. PDF

Abstract
The molecular functions of the majority of RNA-binding proteins (RBPs) remain unclear, highlighting a major bottleneck to a full understanding of gene expression regulation. Here, we develop a plasmid resource of 690 human RBPs that we subject to luciferase-based 3'-untranslated-region tethered function assays to pinpoint RBPs that regulate RNA stability or translation. Enhanced UV-cross-linking and immunoprecipitation of these RBPs identifies thousands of endogenous mRNA targets that respond to changes in RBP level, recapitulating effects observed in tethered function assays. Among these RBPs, the ubiquitin-associated protein 2-like (UBAP2L) protein interacts with RNA via its RGG domain and cross-links to mRNA and rRNA. Fusion of UBAP2L to RNA-targeting CRISPR-Cas9 demonstrates programmable translational enhancement. Polysome profiling indicates that UBAP2L promotes translation of target mRNAs, particularly global regulators of translation. Our tethering survey allows rapid assignment of the molecular activity of proteins, such as UBAP2L, to specific steps of mRNA metabolism.

Kim JH, Seo Y, Jo M, Jeon H, Kim YS, Kim EJ, Seo D, Lee WH, Kim SR, Yachie N, Zhong Q, Vidal M, Roth FP, Suk K. Interrogation of kinase genetic interactions provides a global view of PAK1-mediated signal transduction pathways. J Biol Chem. 2020 Oct 15:jbc.RA120.014831. doi: 10.1074/jbc.RA120.014831. Online ahead of print. PDF

Abstract
Kinases are critical components of intracellular signaling pathways and have been extensively investigated in regards to their roles in cancer. p21-activated kinase-1 (PAK1) is a serine/threonine kinase that has been previously implicated in numerous biological processes, such as cell migration, cell cycle progression, cell motility, invasion, and angiogenesis, in glioma and other cancers. However, the signaling network linked to PAK1 is not fully defined. We previously reported a large-scale yeast genetic interaction screen using toxicity as a readout to identify candidate PAK1 genetic interactions. En masse transformation of the PAK1 gene into 4,653 homozygous diploid S. cerevisiae yeast deletion mutants identified approximately 400 candidates that suppressed yeast toxicity. Here we selected 19 candidate PAK1 genetic interactions that had human orthologs and were expressed in glioma for further examination in mammalian cells, brain slice cultures, and orthotopic glioma models. RNAi and pharmacological inhibition of potential PAK1 interactors confirmed that DPP4, KIF11, mTOR, PKM2, SGPP1, TTK, and YWHAE regulate PAK1-induced cell migration, and revealed the importance of genes related to the mitotic spindle, proteolysis, autophagy, and metabolism in PAK1-mediated glioma cell migration, drug resistance, and proliferation. AKT1 was further identified as a downstream mediator of the PAK1-TTK genetic interaction. Taken together, these data provide a global view of PAK1-mediated signal transduction pathways and point to potential new drug targets for glioma therapy.

Wierbowski SD, Vo TV, Falter-Braun P, Jobe TO, Kruse LH, Wei X, Liang J, Meyer MJ, Akturk N, Rivera-Erick CA, Cordero NA, Paramo MI, Shayhidin EE, Bertolotti M, Tippens ND, Akther K, Sharma R, Katayose Y, Salehi-Ashtiani K, Hao T, Ronald PC, Ecker JR, Schweitzer PA, Kikuchi S, Mizuno H, Hill DE, Vidal M, Moghe GD, McCouch SR, Yu H. A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice. Proc Natl Acad Sci U S A. 2020 May 26;117(21):11836-11842. doi: 10.1073/pnas.1918068117. Epub 2020 May 12. PDF

Abstract
Systematic mappings of protein interactome networks have provided invaluable functional information for numerous model organisms. Here we develop PCR-mediated Linkage of barcoded Adapters To nucleic acid Elements for sequencing (PLATE-seq) that serves as a general tool to rapidly sequence thousands of DNA elements. We validate its utility by generating the ORFeome for Oryza sativa covering 2,300 genes and constructing a high-quality protein-protein interactome map consisting of 322 interactions between 289 proteins, expanding the known interactions in rice by roughly 50%. Our work paves the way for high-throughput profiling of protein-protein interactions in a wide range of organisms.

Sheynkman GM, Tuttle KS, Laval F, Tseng E, Underwood JG, Yu L, Dong D, Smith ML, Sebra R, Willems L, Hao T, Calderwood MA, Hill DE, Vidal M. ORF Capture-Seq as a versatile method for targeted identification of full-length isoforms. Nat Commun. 2020 May 11;11(1):2326. doi: 10.1038/s41467-020-16174-z. PDF

Abstract
Most human protein-coding genes are expressed as multiple isoforms, which greatly expands the functional repertoire of the encoded proteome. While at least one reliable open reading frame (ORF) model has been assigned for every coding gene, the majority of alternative isoforms remains uncharacterized due to (i) vast differences of overall levels between different isoforms expressed from common genes, and (ii) the difficulty of obtaining full-length transcript sequences. Here, we present ORF Capture-Seq (OCS), a flexible method that addresses both challenges for targeted full-length isoform sequencing applications using collections of cloned ORFs as probes. As a proof-of-concept, we show that an OCS pipeline focused on genes coding for transcription factors increases isoform detection by an order of magnitude when compared to unenriched samples. In short, OCS enables rapid discovery of isoforms from custom-selected genes and will accelerate mapping of the human transcriptome.

Kim JH, Seo Y, Jo M, Jeon H, Lee WH, Yachie N, Zhong Q, Vidal M, Roth FP, Suk K. Yeast-Based Genetic Interaction Analysis of Human Kinome. Cells. 2020 May 7;9(5):1156. doi: 10.3390/cells9051156. PDF

Abstract:
Kinases are critical intracellular signaling proteins. To better understand kinase-mediated signal transduction, a large-scale human-yeast genetic interaction screen was performed. Among 597 human kinase genes tested, 28 displayed strong toxicity in yeast when overexpressed. En masse transformation of these toxic kinase genes into 4653 homozygous diploid yeast deletion mutants followed by barcode sequencing identified yeast toxicity modifiers and thus their human orthologs. Subsequent network analyses and functional grouping revealed that the 28 kinases and their 676 interaction partners (corresponding to a total of 969 genetic interactions) are enriched in cell death and survival (34%), small-molecule biochemistry (18%) and molecular transport (11%), among others. In the subnetwork analyses, a few kinases were commonly associated with glioma, cell migration and cell death/survival. Our analysis enabled the creation of a first draft of the kinase genetic interactome network and identified multiple drug targets for inflammatory diseases and cancer, in which deregulated kinase signaling plays a pathogenic role.

Luck K, Kim DK, Lambourne L, Spirohn K, Begg BE, Bian W, Brignall R, Cafarelli T, Campos-Laborie FJ, Charloteaux B, Choi D, Coté AG, Daley M, Deimling S, Desbuleux A, Dricot A, Gebbia M, Hardy MF, Kishore N, Knapp JJ, Kovács IA, Lemmens I, Mee MW, Mellor JC, Pollis C, Pons C, Richardson AD, Schlabach S, Teeking B, Yadav A, Babor M, Balcha D, Basha O, Bowman-Colin C, Chin SF, Choi SG, Colabella C, Coppin G, D'Amata C, De Ridder D, De Rouck S, Duran-Frigola M, Ennajdaoui H, Goebels F, Goehring L, Gopal A, Haddad G, Hatchi E, Helmy M, Jacob Y, Kassa Y, Landini S, Li R, van Lieshout N, MacWilliams A, Markey D, Paulson JN, Rangarajan S, Rasla J, Rayhan A, Rolland T, San-Miguel A, Shen Y, Sheykhkarimli D, Sheynkman GM, Simonovsky E, Taşan M, Tejeda A, Tropepe V, Twizere JC, Wang Y, Weatheritt RJ, Weile J, Xia Y, Yang X, Yeger-Lotem E, Zhong Q, Aloy P, Bader GD, De Las Rivas J, Gaudet S, Hao T, Rak J, Tavernier J, Hill DE, Vidal M, Roth FP, Calderwood MA. A reference map of the human binary protein interactome. Nature. 2020 Apr;580(7803):402-408. doi: 10.1038/s41586-020-2188-x. Epub 2020 Apr 8. PDF

Abstract
Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype–phenotype relationships. Here we present a human ‘all-by-all’ reference interactome map of human binary protein interactions, or ‘HuRI’. With approximately 53,000 protein–protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome, transcriptome and proteome data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein–protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes.

Yadav A, Vidal M, Luck K. Precision medicine - networks to the rescue. Curr Opin Biotechnol. 2020 Mar 18;63:177-189. doi: 10.1016/j.copbio.2020.02.005. PDF

Abstract
Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.

Song Sun, Jochen Weile, Marta Verby, Yingzhou Wu, Yang Wang, Atina G. Cote, Iosifina Fotiadou, Julia Kitaygorodsky, Marc Vidal, Jasper Rine, Pavel Ješina, Viktor Kožich and Frederick P. Roth. A proactive genotype-to-patient-phenotype map for cystathionine beta-synthase. Genome Med. 2020 Jan 30. doi: 10.1186/s13073-020-0711-1. PDF

Background:
Rapid development of high-throughput sequencing technology has made it feasible to sequence the genome of every human. However, for personalized diagnostic surveillance and therapy, timely and accurate methods to interpret the clinical impact of genetic variants are needed. Over 138,000 exomes have been collected in the Genome Aggregation Database (gnomAD) [1, 2] and 4.6 million coding variants have been discovered. Among these discovered coding variants, 99% are rare, having a minor allele frequency (MAF) below 0.5%. Although statistical association methods have identified many common variants that correlate with (and in some cases cause) human disease, correlational methods are typically futile for rare variants. In ClinVar [3], the majority of interpreted missense variants are annotated as “variants of uncertain significance” (VUS) [4, 5].

Choi SG, Olivet J, Cassonnet P, Vidalain PO, Luck K, Lambourne L, Spirohn K, Lemmens I, Dos Santos M, Demeret C, Jones L, Rangarajan S, Bian W, Coutant EP, Janin YL, van der Werf S, Trepte P, Wanker EE, De Las Rivas J, Tavernier J, Twizere JC, Hao T, Hill DE, Vidal M, Calderwood MA, Jacob Y. Maximizing binary interactome mapping with a minimal number of assays. Nat Commun. 2019 Aug 29;10(1):3907. doi: 10.1038/s41467-019-11809-2. PDF

Abstract
Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose a framework for binary protein-protein interaction (PPI) mapping based on optimally combining assays and/or assay versions to maximize detection of true positive interactions, while avoiding detection of random protein pairs. We have engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions, differing by protein expression systems and tagging configurations. The resulting union of N2H versions recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our findings should be applicable to systematic mapping of other biological landscapes.

Cheng F, Lu W, Liu C, Fang J, Hou Y, Handy DE, Wang R, Zhao Y, Yang Y, Huang J, Hill DE, Vidal M, Eng C, Loscalzo J. A genome-wide positioning systems network algorithm for in silico drug repurposing. Nat Commun. 2019 Aug 2;10(1):3476. doi: 10.1038/s41467-019-10744-6. PDF

Abstract
Recent advances in DNA/RNA sequencing have made it possible to identify new targets rapidly and to repurpose approved drugs for treating heterogeneous diseases by the 'precise' targeting of individualized disease modules. In this study, we develop a Genome-wide Positioning Systems network (GPSnet) algorithm for drug repurposing by specifically targeting disease modules derived from individual patient's DNA and RNA sequencing profiles mapped to the human protein-protein interactome network. We investigate whole-exome sequencing and transcriptome profiles from ~5,000 patients across 15 cancer types from The Cancer Genome Atlas. We show that GPSnet-predicted disease modules can predict drug responses and prioritize new indications for 140 approved drugs. Importantly, we experimentally validate that an approved cardiac arrhythmia and heart failure drug, ouabain, shows potential antitumor activities in lung adenocarcinoma by uniquely targeting a HIF1α/LEO1-mediated cell metabolism pathway. In summary, GPSnet offers a network-based, in silico drug repurposing framework for more efficacious therapeutic selections.

Lee EJ, Kim N, Park JW, Kang KH, Kim WI, Sim NS, Jeong CS, Blackshaw S, Vidal M, Huh SO, Kim D, Lee JH, Kim JW. Global Analysis of Intercellular Homeodomain Protein Transfer. Cell Rep. 2019 Jul 16;28(3):712-722.e3. doi: 10.1016/j.celrep.2019.06.056. PDF

Abstract
The homeodomain is found in hundreds of transcription factors that play roles in fate determination via cell-autonomous regulation of gene expression. However, some homeodomain-containing proteins (HPs) are thought to be secreted and penetrate neighboring cells to affect the recipient cell fate. To determine whether this is a general characteristic of HPs, we carried out a large-scale validation for intercellular transfer of HPs. Our screening reveals that intercellular transfer is a general feature of HPs, but it occurs in a cell-context-sensitive manner. We also found the secretion is not solely a function of the homeodomain, but it is supported by external motifs containing hydrophobic residues. Thus, mutations of hydrophobic residues of HPs abrogate secretion and consequently interfere with HP function in recipient cells. Collectively, our study proposes that HP transfer is an intercellular communication method that couples the functions of interacting cells.

Kovács IA, Luck K, Spirohn K, Wang Y, Pollis C, Schlabach S, Bian W, Kim DK, Kishore N, Hao T, Calderwood MA, Vidal M, Barabási AL. Network-based prediction of protein interactions. Nat Commun. 2019 Mar 18;10(1):1240. doi: 10.1038/s41467-019-09177-y. PDF

Abstract
Despite exceptional experimental efforts to map out the human interactome, the continued data incompleteness limits our ability to understand the molecular roots of human disease. Computational tools offer a promising alternative, helping identify biologically significant, yet unmapped protein-protein interactions (PPIs). While link prediction methods connect proteins on the basis of biological or network-based similarity, interacting proteins are not necessarily similar and similar proteins do not necessarily interact. Here, we offer structural and evolutionary evidence that proteins interact not if they are similar to each other, but if one of them is similar to the other's partners. This approach, that mathematically relies on network paths of length three (L3), significantly outperforms all existing link prediction methods. Given its high accuracy, we show that L3 can offer mechanistic insights into disease mechanisms and can complement future experimental efforts to complete the human interactome.

Alonso-López D, Campos-Laborie FJ, Gutiérrez MA, Lambourne L, Calderwood MA, Vidal M, De Las Rivas J, 2019. APID database: redefining protein–protein interaction experimental evidences and binary interactomes, Database, Volume 2019, 1 January 2019, doi:https://doi.org/10.1093/database/baz005. PDF

Abstract
The collection and integration of all the known protein–protein physical interactions within a proteome framework are critical to allow proper exploration of the protein interaction networks that drive biological processes in cells at molecular level. APID Interactomes is a public resource of biological data (http://apid.dep.usal.es) that provides a comprehensive and curated collection of `protein interactomes’ for more than 1100 organisms, including 30 species with more than 500 interactions, derived from the integration of experimentally detected protein-to-protein physical interactions (PPIs). We have performed an update of APID database including a redefinition of several key properties of the PPIs to provide a more precise data integration and to avoid false duplicated records. This includes the unification of all the PPIs from five primary databases of molecular interactions (BioGRID, DIP, HPRD, IntAct and MINT), plus the information from two original systematic sources of human data and from experimentally resolved 3D structures (i.e. PDBs, Protein Data Bank files, where more than two distinct proteins have been identified). Thus, APID provides PPIs reported in published research articles (with traceable PMIDs) and detected by valid experimental interaction methods that give evidences about such protein interactions (following the `ontology and controlled vocabulary’: www.ebi.ac.uk/ols/ontologies/mi; developed by `HUPO PSI-MI’). Within this data mining framework, all interaction detection methods have been grouped into two main types: (i) `binary’ physical direct detection methods and (ii) `indirect’ methods. As a result of these redefinitions, APID provides unified protein interactomes including the specific `experimental evidences’ that support each PPI, indicating whether the interactions can be considered `binary’ (i.e. supported by at least one binary detection method) or not.

Fessenden, M., 2017. Protein maps chart the causes of disease. Nature 549, 293–295. doi:10.1038/549293a PDF

Abstract
Improvements in mapping protein–protein interactions are allowing researchers to deconstruct the delicate mechanics of cells.

Ghadie, M.A., Lambourne, L., Vidal, M., Xia, Y., 2017. Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing. PLOS Computational Biology 13, e1005717. doi:10.1371/journal.pcbi.1005717 PDF

Abstract
Alternative splicing is known to remodel protein-protein interaction networks (“interac- tomes”), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform interactome from the reference interactome. First, we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction frame- work is of high-quality when assessed by experimental data. The predicted human isoform interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demon- strates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing.

Yang, F., Sun, S., Tan, G., Costanzo, M., Hill, D.E., Vidal, M., Andrews, B.J., Boone, C., Roth, F.P., 2017. Identifying pathogenicity of human variants via paralog-based yeast complementation. PLoS Genet 13. doi:10.1371/journal.pgen.1006779. PDF

Abstract
To better understand the health implications of personal genomes, we now face a largely unmet challenge to identify functional variants within disease-associated genes. Functional variants can be identified by trans-species complementation, e.g., by failure to rescue a yeast strain bearing a mutation in an orthologous human gene. Although orthologous complementation assays are powerful predictors of pathogenic variation, they are available for only a few percent of human disease genes. Here we systematically examine the question of whether complementation assays based on paralogy relationships can expand the num- ber of human disease genes with functional variant detection assays. We tested over 1,000 paralogous human-yeast gene pairs for complementation, yielding 34 complementation relationships, of which 33 (97%) were novel. We found that paralog-based assays identified disease variants with success on par with that of orthology-based assays. Combining all homology-based assay results, we found that complementation can often identify patho- genic variants outside the homologous sequence region, presumably because of global effects on protein folding or stability. Within our search space, paralogy-based complemen- tation more than doubled the number of human disease genes with a yeast-based comple- mentation assay for disease variation.

Jo, M., Chung, A.Y., Yachie, N., Seo, M., Jeon, H., Nam, Y., Seo, Y., Kim, E., Zhong, Q., Vidal, M., Hae Chul Park, Roth, F., Suk, K., 2017. Yeast genetic interaction screen of human genes associated with amyotrophic lateral sclerosis: identification of MAP2K5 kinase as a potential drug target. Genome Res. doi:10.1101/gr.211649.116 PDF

Abstract
To understand disease mechanisms, a large-scale analysis of human-yeast genetic interactions was performed. Of 1,305 human disease genes assayed, 20 genes exhibited strong toxicity in yeast. Human-yeast genetic interactions were identified by en masse transformation of the human disease genes into a pool of 4,653 homozygous diploid yeast deletion mutants with unique barcode sequences, followed by multiplexed barcode sequencing of yeast toxicity modifiers. Subsequent network analyses focusing on amyotrophic lateral sclerosis (ALS)-associated genes, such as optineurin (OPTN) and angiogenin (ANG), showed that the human orthologs of the yeast toxicity modifiers of these ALS genes are enriched for several biological processes, such as cell death, lipid metabolism, and molecular transport. When yeast genetic interaction partners held in common between human OPTN and ANG were validated in mammalian cells and zebrafish, MAP2K5 emerged as a potential drug target for ALS therapy. The toxicity modifiers identified in this study may deepen our understanding of the pathogenic mechanisms of ALS and other devastating diseases.

TM Cafarelli, A Desbuleux, Y Wang, SG Choi, D De Ridder and M Vidal. "Mapping, modeling, and characterization of protein–protein interactions on a proteomic scale." Current Opinion in Structural Biology June 2017, no. 44: 201-210. doi.org/10.1016/j.sbi.2017.05.003. PDF

Abstract
Proteins effect a number of biological functions, from cellular signaling, organization, mobility, and transport to catalyzing biochemical reactions and coordinating an immune response. These varied functions are often dependent upon macromolecular interactions, particularly with other proteins. Small-scale studies in the scientific literature report protein– protein interactions (PPIs), but slowly and with bias towards well-studied proteins. In an era where genomic sequence is readily available, deducing genotype–phenotype relationships requires an understanding of protein connectivity at proteome- scale. A proteome-scale map of the protein–protein interaction network provides a global view of cellular organization and function. Here, we discuss a summary of methods for building proteome-scale interactome maps and the current status and implications of mapping achievements. Not only do interactome maps serve as a reference, detailing global cellular function and organization patterns, but they can also reveal the mechanisms altered by disease alleles, highlight the patterns of interaction rewiring across evolution, and help pinpoint biologically and therapeutically relevant proteins. Despite the considerable strides made in proteome-wide mapping, several technical challenges persist. Therefore, future considerations that impact current mapping efforts are also discussed.

Yao, Chen, Roby Joehanes, Andrew D. Johnson, Tianxiao Huan, Chunyu Liu, Jane E. Freedman, Peter J. Munson, David E. Hill, Marc Vidal, and Daniel Levy. “Dynamic Role of Trans Regulation of Gene Expression in Relation to Complex Traits.” The American Journal of Human Genetics 100, no. 4 (April 6, 2017): 571–80. doi:10.1016/j.ajhg.2017.02.003. PDF

Abstract
Identifying causal genetic variants and understanding their mechanisms of effect on traits remains a challenge in genome-wide association studies (GWASs). In particular, how genetic variants (i.e., trans-eQTLs) affect expression of remote genes (i.e., trans-eGenes) remains unknown.We hypothesized that some trans-eQTLs regulate expression of distant genes by altering the expression of nearby genes (cis-eGenes). Using published GWAS datasets with 39,165 single-nucleotide polymorphisms (SNPs) associated with 1,960 traits, we explored whole blood gene expression associations of trait-associated SNPs in 5,257 individuals from the Framingham Heart Study.We identified 2,350 trans-eQTLs (at p<107 );more than 80% of them were found to have cis-associated eGenes.Mediation testing suggested that for 35% oftrans-eQTL-transeGene pairs in different chromosomes and 90% pairs in the same chromosome, the disease-associated SNP may alter expression of the transeGene via cis-eGene expression. In addition, we identified 13 trans-eQTL hotspots, affecting from ten to hundreds of genes, suggesting the existence of master genetic regulators. Using causal inference testing, we searched causal variants across eight cardiometabolic traits (BMI, systolic and diastolic blood pressure, LDL cholesterol, HDL cholesterol, total cholesterol, triglycerides, and fasting blood glucose) and identified several cis-eGenes (ALDH2 for systolic and diastolic blood pressure, MCM6 and DARS for total cholesterol, and TRIB1 for triglycerides) that were causal mediators for the corresponding traits, as well as examples of trans-mediators (TAGAP for LDL cholesterol). The finding of extensive evidence of genome-wide mediation effects suggests a critical role of cryptic gene regulation underlying many disease traits.

Karras, Georgios I., Song Yi, Nidhi Sahni, Máté Fischer, Jenny Xie, Marc Vidal, Alan D. D’Andrea, Luke Whitesell, and Susan Lindquist. “HSP90 Shapes the Consequences of Human Genetic Variation.” Cell 168, no. 5 (n.d.): 856–66.e12. doi:10.1016/j.cell.2017.01.023. PDF

Abstract
HSP90 acts as a protein-folding buffer that shapes the manifestations of genetic variation in model organisms. Whether HSP90 influences the consequences of mutations in humans, potentially modifying the clinical course of genetic diseases, remains unknown. By mining data for >1,500 disease-causing mutants, we found a strong correlation between reduced phenotypic severity and a dominant (HSP90 ≥ HSP70) increase in mutant engagement by HSP90. Examining the cancer predisposition syndrome Fanconi anemia in depth revealed that mutant FANCA proteins engaged predominantly by HSP70 had severely compromised function. In contrast, the function of less severe mutants was preserved by a dominant increase in HSP90 binding. Reducing HSP90's buffering capacity with inhibitors or febrile temperatures destabilized HSP90-buffered mutants, exacerbating FA-related chemosensitivities. Strikingly, a compensatory FANCA somatic mutation from an "experiment of nature" in monozygotic twins both prevented anemia and reduced HSP90 binding. These findings provide one plausible mechanism for the variable expressivity and environmental sensitivity of genetic diseases.

Luck, K., Sheynkman, G.M., Zhang, I., Vidal, M. (2017). Proteome-Scale Human Interactomics. Trends in Biochemical Sciences. 2017 Mar 8. http://dx.doi.org/10.1016/j.tibs.2017.02.006 PDF

Abstract
Cellular functions are mediated by complex interactome networks of physical, biochemical, and functional interactions between DNA sequences, RNA mol- ecules, proteins, lipids, and small metabolites. A thorough understanding of cellular organization requires accurate and relatively complete models of inter- actome networks at proteome scale. The recent publication of four human protein–protein interaction (PPI) maps represents a technological breakthrough and an unprecedented resource for the scientific community, heralding a new era of proteome-scale human interactomics. Our knowledge gained from these and complementary studies provides fresh insights into the opportunities and challenges when analyzing systematically generated interactome data, defines a clear roadmap towards the generation of a first reference interactome, and reveals new perspectives on the organization of cellular life.

Chung, C., Khurana, V., Yi, S., Sahni, N., Loh, K., Auluck, P., Baru, V., Udeshi, N., Freyzon, Y., Carr, S., Hill, D., Vidal, M., Ting, A. and Lindquist, S. (2017). In Situ Peroxidase Labeling and Mass-Spectrometry Connects Alpha-Synuclein Directly to Endocytic Trafficking and mRNA Metabolism in Neurons. Cell Systems. 2017 Feb 22;4, 1-9. http://dx.doi.org/10.1016/j.cels.2017.01.002 PDF

Abstract
Synucleinopathies, including Parkinson’s disease (PD), are associated with the misfolding and mis- trafficking of alpha-synuclein (a-syn). Here, using an ascorbate peroxidase (APEX)-based labeling method combined with mass spectrometry, we defined a network of proteins in the immediate vicinity of a-syn in living neurons to shed light on a-syn function. This approach identified 225 proteins, including synaptic proteins, proteins involved in endocytic vesicle trafficking, the retromer complex, phosphatases and mRNA binding proteins. Many were in complexes with a-syn, and some were encoded by genes known to be risk factors for PD and other neurodegenerative diseases. Endocytic trafficking and mRNA translation proteins within this spatial a-syn map overlapped with genetic modifiers of a-syn toxicity, developed in an accompanying study (Khurana et al., this issue of Cell Systems). Our data suggest that perturbation of these particular pathways is directly related to the spatial localization of a-syn within the cell. These approaches provide new avenues to systematically examine protein function and pathology in living cells.

Khurana, V., Peng, J., Chung, C., Auluck, P., Fanning, S., Tardiff, D., Bartels, T., Koeva, M., Eichhorn, S., Benyamini, H., Lou, Y., Nutter-Upham, A., Baru, V., Freyzon, Y., Tuncbag, N., Costanzo, M., San Luis, B., Schöndorf, D., Barrasa, M., Ehsani, S., Sanjana, N., Zhong, Q., Gasser, T., Bartel, D., Vidal, M., Deleidi, M., Boone, C., Fraenkel, E., Berger, B. and Lindquist, S. (2017). Genome-Scale Networks Link Neurodegenerative Disease Genes to α-Synuclein through Specific Molecular Pathways. Cell Systems. 2017 Feb 22; 4 1-14. http://dx.doi.org/10.1016/j.cels.2016.12.011 PDF

Abstract
Numerous genes and molecular pathways are implicated in neurodegenerative proteinopathies, but their inter-relationships are poorly understood. We systematically mapped molecular pathways underlying the toxicity of alpha-synuclein (a-syn), a protein central to Parkinson’s disease. Genome-wide screens in yeast identified 332 genes that impact a-syn toxicity. To ‘‘humanize’’ this molecular network, we developed a computational method, TransposeNet. This integrates a Steiner prize-collecting approach with homology assignment through sequence, structure, and interaction topology. TransposeNet linked a-syn to multiple parkinsonism genes and druggable targets through perturbed protein trafficking and ER quality control as well as mRNA metabolism and translation. A calcium signaling hub linked these processes to perturbed mitochondrial quality control and function, metal ion transport, transcriptional regulation, and signal transduction. Parkinsonism gene interaction profiles spatially opposed in the network (ATP13A2/PARK9 and VPS35/PARK17) were highly distinct, and network relationships for specific genes (LRRK2/PARK8, ATXN2, and EIF4G1/PARK18) were confirmed in patient induced pluripotent stem cell (iPSC)-derived neurons. This cross-species platform connected diverse neurodegenerative genes to proteinopathy through specific mechanisms and may facilitate patient stratification for targeted therapy.

Kitsak, M., Sharma, A., Menche, J., Guney, E., Ghiassian, S.D., Loscalzo, J. and Barabási, A.-L.
Tissue specificity of human disease module. Scientific Reports. 2016 Oct 17;6, p. 35241. doi: 10.1038/srep35241. PDF

Abstract
Genes carrying mutations associated with genetic diseases are present in all human cells; yet, clinical manifestations of genetic diseases are usually highly tissue-specific. Although some disease genes are expressed only in selected tissues, the expression patterns of disease genes alone cannot explain the observed tissue specificity of human diseases. Here we hypothesize that for a disease to manifest itself in a particular tissue, a whole functional subnetwork of genes (disease module) needs to be expressed in that tissue. Driven by this hypothesis, we conducted a systematic study of the expression patterns of disease genes within the human interactome. We find that genes expressed in a specific tissue tend to be localized in the same neighborhood of the interactome. By contrast, genes expressed in different tissues are segregated in distinct network neighborhoods. Most important, we show that it is the integrity and the completeness of the expression of the disease module that determines disease manifestation in selected tissues. This approach allows us to construct a disease-tissue network that confirms known and predicts unexpected disease-tissue associations.

Vidal M. Playing Hide-and-Seek with Yeast. Cell. 2016 Aug 25;166(5):1069-73. doi: 10.1016/j.cell.2016.08.018. PDF

Abstract
I like to think of the late 80s and early 90s as extremely exciting times in my career. I was working as a visiting graduate student in Rick Gaber’s laboratory at Northwestern University, and some of the work I did during those years turned out to be seminal for the then-fledgling field of epigenetics. Together with other scientists, I provided long-awaited in vivo functional evidence for a model proposed by Vince Allfrey in 1964, postulating that post-translational modifications of histones, particularly acetylation and deacetylation, are crucial for transcriptional regulation.

Chaiboonchoa A, Ghamsari L, Dohai B, Ng P, Khraiwesh B, Jaiswal A, Jijakli K, Koussa J, Nelson DR, Cai H, Yang X, Chang RL, Papin J, Yu H, Balaji S, Salehi-Ashtiani K. Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation. Mol Biosyst. 2016 Jul 19;12(8):2394-407. doi: 10.1039/c6mb00237d PDF

Abstract
Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii’s network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

Junshi Yazaki, Mary Galli, Alice Y. Kim, Kazumasa Nito, Fernando Aleman, Katherine N. Chang, Anne-Ruxandra Carvunis, Rosa Quan, Hien Nguyen, Liang Song, José M. Alvarez, Shao-shan Carol Huang, Huaming Chen, Niroshan Ramachandran, Stefan Altmann, Rodrigo A. Gutiérrez, David E. Hill, Julian I. Schroeder, Joanne Chory, Joshua LaBaer, Marc Vidal, Pascal Braun, and Joseph R. Ecker. Mapping transcription factor interactome networks using HaloTag protein arrays. PNAS. 2016 Jul 19;113(29):E4238-47. doi:10.1073/pnas.1603229113. PDF

Abstract
Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we cre- ated high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein–protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thou- sands of novel interactions. Validation in a benchmarked in vitro pull-down assay revealed that a random subset of TF-NAPPA validated at the same rate of 64% as a positive reference set of literature- curated interactions. Moreover, using a bimolecular fluorescence com- plementation (BiFC) assay, we confirmed in planta several interactions of biological interest and determined the interaction localizations for seven pairs. The application of HaloTag-NAPPA technology to plant hormone signaling pathways allowed the identification of many novel transcription factor–protein interactions and led to the devel- opment of a proteome-wide plant hormone TF interactome network.

Rambout X, Detiffe C, Bruyr J, Mariavelle E, Cherkaoui M, Brohée S, Demoitié P, Lebrun M, Soin R, Lesage B, Guedri K, Beullens M, Bollen M, Farazi TA, Kettmann R, Struman I, Hill DE, Vidal M, Kruys V, Simonis N, Twizere JC, Dequiedt F. The transcription factor ERG recruits CCR4-NOT to control mRNA decay and mitotic progression. Nat Struct Mol Biol. 2016 June 6. doi: 1038/nsmb.3243. PDF

Abstract
Control of mRNA levels, a fundamental aspect in the regulation of gene expression, is achieved through a balance between mRNA synthesis and decay. E26-related gene (Erg) proteins are canonical transcription factors whose previously described functions are confined to the control of mRNA synthesis. Here, we report that ERG also regulates gene expression by affecting mRNA stability and identify the molecular mechanisms underlying this function in human cells. ERG is recruited to mRNAs via interaction with the RNA-binding protein RBPMS, and it promotes mRNA decay by binding CNOT2, a component of the CCR4–NOT deadenylation complex. Transcriptome-wide mRNA stability analysis revealed that ERG controls the degradation of a subset of mRNAs highly connected to Aurora signaling, whose decay during S phase is necessary for mitotic progression. Our data indicate that control of gene expression by mammalian transcription factors may follow a more complex scheme than previously anticipated, integrating mRNA synthesis and degradation.