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Non-linear Deep Neural Network for Rapid and Accurate Prediction of Phenotypic Responses to Kinase Inhibitors

ISCIENCE 2020年第5期23卷 101129-101129页

作者： Vijay, Siddharth Gujral, Taranjit S. Fred Hutchinson Canc Res Ctr Div Human Biol 1124 Columbia St Seattle WA 98104 USA Univ Washington Dept Pharmacol Seattle WA 98195 USA

Protein kinase inhibitors are one of the most successful targeted therapies to date. Despite this progress, additional kinase inhibitors are needed to expand the target space as well as overcome drug resistance that has emerged in clinical setting. Here, we developed KiDNN (Kinase inhibitor prediction using Deep Neural Networks). KiDNN utilizes non-linear, multilayer feedforward network that mimics complex and dynamic kinase-driven signaling pathways. We used KiDNN to predict the effect of similar to 200 kinase inhibitors on migration of breast and liver cancer cells. We show that the prediction accuracy of KiDNN outperformed other prediction tools based on linear models. We validated that an inhibitor of tyrosine kinase receptors, and an inhibitor of Src family kinases, decreased migration of triple-negative breast cancer cells, consistent with the role of these kinases in driving motility. Overall, we show that non-linear, DNN-based models provide a powerful approach to in silico screen hundreds of kinase inhibitors.

关键词： bioinformatics Cancer computational bioinformatics Neural Networks

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Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity

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ISCIENCE 2020年第1期23卷 100801-100801页

作者： Demeter, Damion, V Engelhardt, Laura E. Mallett, Remington Gordon, Evan M. Nugiel, Tehila Harden, K. Paige Tucker-Drob, Elliot M. Lewis-Peacock, Jarrod A. Church, Jessica A. Univ Texas Austin Dept Psychol Austin TX 78712 USA Univ Texas Austin Populat Res Ctr Austin TX 78712 USA Univ Texas Austin Biomed Imaging Ctr Austin TX 78712 USA VISN 17 Ctr Excellence Res Returning War Vet Waco TX 76711 USA Univ Texas Dallas Ctr Vital Longev Sch Behav & Brain Sci Dallas TX 75235 USA Baylor Univ Dept Psychol & Neurosci Waco TX 76789 USA

Distinguishing individuals from brain connectivity, and studying the genetic influences on that identification across different ages, improves our basic understanding of functional brain network organization. We applied support vector machine classifiers to two datasets of twins (adult, pediatric) and two datasets of repeat-scan individuals (adult, pediatric). Classifiers were trained on resting state functional connectivity magnetic resonance imaging (rs-fcMRI) data and used to predict individuals and co-twin pairs from independent data. The classifiers successfully identified individuals from a previous scan with 100% accuracy, even when scans were separated by months. In twin samples, classifier accuracy decreased as genetic similarity decreased. Our results demonstrate that classification is stable within individuals, similar within families, and contains similar representations of functional connections over a few decades of life. Moreover, the degree to which these patterns of connections predict siblings' data varied by genetic relatedness, suggesting that genetic influences on rs-fcMRI connectivity are established early in life.

关键词： Biological Sciences computational bioinformatics Neuroscience

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Modularization of grid cells constrained by the pyramidal patch lattice

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ISCIENCE 2021年第4期24卷 102301页

作者： Wang, Tao Yang, Fan Wang, Ziqun Zhang, Bing Wang, Wei Liu, Feng Nanjing Univ Collaborat Innovat Ctr Adv Microstruct Dept Phys Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China Nanjing Univ Med Sch Drum Tower Hosp Dept Radiol Nanjing 210008 Peoples R China Nanjing Univ Inst Brain Sci Nanjing 210023 Peoples R China

Grid cells provide a metric representation of self-location. They are organized into modules, showing discretized scales of grid spacing, but the underlying mechanism remains elusive. In this modeling study, we propose that the hexagonal lattice of pyramidal cell patches may underlie the discretization of grid spacing and orientation. In the continuous attractor network composed of interneurons, stellate and pyramidal cells, the hexagonal lattice of bump attractors is specifically aligned to the patch lattice under 22 conditions determined by the geometry of the patch lattice, while pyramidal cells exhibit synchrony to diverse extents. Given the bump attractor lattice in each module originates from those 22 scenarios, the experimental data on the grid spacing ratio and orientation difference between modules can be reproduced. This work recapitulates the patterns of grid spacing versus orientation in individual animals and reveals the correlation between microstructures and firing fields, providing a systems-level mechanism for grid modularity.

关键词： Biophysics computational bioinformatics Neuroscience

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Machine learning workflows identify a microRNA signature of insulin transcription in human tissues

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ISCIENCE 2021年第4期24卷 102379页

作者： Wong, Wilson K. M. Joglekar, Mugdha, V Saini, Vijit Jiang, Guozhi Dong, Charlotte X. Chaitarvornkit, Alissa Maciag, Grzegorz J. Gerace, Dario Farr, Ryan J. Satoor, Sarang N. Sahu, Subhshri Sharangdhar, Tejaswini Ahmed, Asma S. Chew, Yi Vee Liuwantara, David Heng, Benjamin Lim, Chai K. Hunter, Julie Januszewski, Andrzej S. Sorensen, Anja E. Akil, Ammira S. A. Gamble, Jennifer R. Loudovaris, Thomas Kay, Thomas W. Thomas, Helen E. O'Connell, Philip J. Guillemin, Gilles J. Martin, David Simpson, Ann M. Hawthorne, Wayne J. Dalgaard, Louise T. Ma, Ronald C. W. Hardikar, Anandwardhan A. Western Sydney Univ Sch Med Diabet & Islet Biol Grp Narellan Rd & Gilchrist Dr Campbelltown NSW 2560 Australia Univ Sydney Fac Med & Hlth Diabet & Islet Biol Grp 92-94 Parramatta Rd Camperdown NSW 2050 Australia Univ Technol Sydney Sch Life Sci 15 Broadway Ultimo NSW 2007 Australia Univ Technol Sydney Ctr Hlth Technol 15 Broadway Ultimo NSW 2007 Australia Chinese Univ Hong Kong Prince Wales Hosp Dept Med & Therapeut Hong Kong Peoples R China Chinese Univ Hong Kong Prince Wales Hosp Hong Kong Inst Diabet & Obes Hong Kong Peoples R China Chinese Univ Hong Kong Prince Wales Hosp Li Ka Shing Inst Hlth Sci Hong Kong Peoples R China Roskilde Univ Dept Sci & Environm Univ Vej 1 DK-4000 Roskilde Denmark Univ Sydney Ctr Transplant & Renal Res Westmead Inst Med Res 176 Hawkesbury Rd Westmead NSW 2145 Australia Macquarie Univ Fac Med Hlth & Human Sci Sydney NSW 2019 Australia Univ Sydney Med Sch Centenary Inst Ctr EndotheliumVasc Biol Program Locked Bag 6 Newtown NSW 2042 Australia Univ Sydney NHMRC Clin Trials Ctr 92-94 Parramatta Rd Camperdown NSW 2050 Australia Sidra Med Dept Human Genet Precis Med Program POB 26999 Doha Qatar St Vincents Inst 9 Princes St Fitzroy Vic Australia Univ Melbourne Dept Med 9 Princes St Fitzroy Vic Australia Strathfield Hosp Upper GI Surg 2-3 Everton Rd Strathfield NSW 2135 Australia

Dicer knockout mouse models demonstrated a key role for microRNAs in pancreatic beta-cell function. Studies to identify specific microRNA( s) associated with human (pro-)endocrine gene expression are needed. We profiled microRNAs and key pancreatic genes in 353 human tissue samples. Machine learning workflows identified microRNAs associated with (pro-)insulin transcripts in a discovery set of islets (n = 30) and insulin-negative tissues (n = 62). This microRNA signature was validated in remaining 261 tissues that include nine islet samples from individuals with type 2 diabetes. Top eight microRNAs (miR-183-5p, -375-3p, 216b-5p, 183-3p, -7-5p, -217-5p, -7-2-3p, and -429-3p) were confirmed to be associated with and predictive of (pro-)insulin transcript levels. Use of doxycycline-inducible microRNA-overexpressing human pancreatic duct cell lines confirmed the regulatory roles of thesemicroRNAs in (pro-)endocrine gene expression. Knockdown of these microRNAs in human islet cells reduced (pro-)insulin transcript abundance. Our data provide specific microRNAs to further study microRNA-mRNA interactions in regulating insulin transcription.

关键词： Pathophysiology computational bioinformatics Transcriptomics

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Characterization of mechanisms positioning costimulatory complexes in immune synapses

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ISCIENCE 2021年第10期24卷 103100页

作者： Siokis, Anastasios Robert, Philippe A. Demetriou, Philippos Kvalvaag, Audun Valvo, Salvatore Mayya, Viveka Dustin, Michael L. Meyer-Hermann, Michael Helmholtz Ctr Infect Res Dept Syst Immunol D-38106 Braunschweig Germany Helmholtz Ctr Infect Res Braunschweig Integrated Ctr Syst Biol D-38106 Braunschweig Germany Tech Univ Carolo Wilhelmina Braunschweig Inst Biochem Biotechnol & Bioinformat D-38106 Braunschweig Germany Univ Oxford Nuffield Dept Orthopaed Rheumatol & Musculoskelet Kennedy Inst Rheumatol Oxford OX3 7FY England Oslo Univ Hosp Inst Canc Res Dept Mol Cell Biol N-0379 Oslo Norway Univ Oslo Dept Immunol N-0372 Oslo Norway

Small immunoglobulin superfamily (sIGSF) adhesion complexes form a corolla of microdomains around an integrin ring and secretory core during immunological synapse (IS) formation. The corolla recruits and retains major costimulatory/ checkpoint complexes, such as CD28, making forces that govern corolla formation of particular interest. Here, we investigated the mechanisms underlying molecular reorganization of CD2, an adhesion and costimulatory molecule of the sIGSF family during IS formation. Computer simulations showed passive distal exclusion of CD2 complexes under weak interactions with the ramified F-actin transport network. Attractive forces between CD2 and CD28 complexes relocate CD28 from the IS center to the corolla. Size-based sorting interactions with large glycocalyx components, such as CD45, or short-range CD2 self-attraction successfully explain the corolla 'petals.' This establishes a general simulation framework for complex pattern formation observed in cell-bilayer and cell-cell interfaces, and the suggestion of new therapeutic targets, where boosting or impairing characteristic pattern formation can be pivotal.

关键词： Cell biology computational bioinformatics computational molecular modelling Immunology Molecular biology

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Automated design of CRISPR prime editors for 56,000 human pathogenic variants

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ISCIENCE 2021年第11期24卷 103380页

作者： Morris, John A. Rahman, Jahan A. Guo, Xinyi Sanjana, Neville E. New York Genome Ctr New York NY 10013 USA NYU Dept Biol New York NY 10003 USA

Prime editors (PEs) are clustered regularly interspaced short palindromic repeats (CRISPR)-based genome engineering tools that can introduce precise base-pair edits. We developed an automated pipeline to correct (therapeutic editing) or introduce (disease modeling) human pathogenic variants from ClinVar that optimizes the design of several RNA constructs required for prime editing and avoids predicted off-targets in the human genome. However, using optimal PE design criteria, we find that only a small fraction of these pathogenic variants can be targeted. Through the use of alternative Cas9 enzymes and extended templates, we increase the number of targetable pathogenic variants from 32,000 to 56,000 variants and make these pre-designed PE constructs accessible through a web-based portal (http://***). Given the tremendous potential for therapeutic gene editing, we also assessed the possibility of developing universal PE constructs, finding that common genetic variants impact only a small minority of designed PEs.

关键词： Biotechnology computational bioinformatics

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DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding

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ISCIENCE 2021年第6期24卷 102455页

作者： Ji, Bo-Ya You, Zhu-Hong Wang, Yi Li, Zheng-Wei Wong, Leon Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Xinjiang Lab Minor Speech & Language Informat Pro Urumqi 830011 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China

Predicting the microRNA-disease associations by using computational methods is conductive to the efficiency of costly and laborious traditional bio-experiments. In this study, we propose a computational machine learning-based method (DANE-MDA) that preserves integrated structure and attribute features via deep attributed network embedding to predict potential miRNA-disease associations. Specifically, the integrated features are extracted by using deep stacked auto-encoder on the diverse orders of matrixes containing structure and attribute information and are then trained by using random forest classifier. Under 5-fold cross-validation experiments, DANE-MDA yielded average accuracy, sensitivity, and AUC at 85.59%, 84.23%, and 0.9264 in term of HMDD v3.0 dataset, and 83.21%, 80.39%, and 0.9113 in term of HMDD v2.0 dataset, respectively. Additionally, case studies on breast, colon, and lung neoplasms related disease show that 47, 47, and 46 of the top 50 miRNAs can be predicted and retrieved in the other database.

关键词： computational bioinformatics Systems biology Cancer

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MicroRNA function can be reversed by altering target gene expression levels

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ISCIENCE 2021年第9期24卷 103208页

作者： Svoronos, Alexander A. Campbell, Stuart G. Engelman, Donald M. Yale Univ Dept Biomed Engn New Haven CT 06520 USA Yale Univ Dept Mol Biophys & Biochem 266 Whitney AvePOB 208114 New Haven CT 06520 USA

Paradoxically, many microRNAs appear to exhibit entirely opposite functions when placed in different contexts. For example, miR-125b has been shown to be pro-apoptotic in some studies, but anti-apoptotic in others. To investigate this phenomenon, we combine computational modeling with experimental approaches to examine how the function of miR-125b in apoptosis varies with respect to the expression levels of its pro-apoptotic and anti-apoptotic targets. In doing so, we elucidate a general trend that miR-125b is more pro-apoptotic when its anti-apoptotic targets are overexpressed, whereas it is more anti-apoptotic when its pro-apoptotic targets are overexpressed. We show that it is possible to completely reverse miR-125b's function in apoptosis by modifying the expression levels of its target genes. Furthermore, miR-125b's function may also be altered by the presence of anticancer drugs. These results suggest that the function of a microRNA can vary substantially and is dependent on its target gene expression levels.

关键词： Molecular biology Molecular mechanism of gene regulation Cell biology computational bioinformatics

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Prediction and identification of synergistic compound combinations against pancreatic cancer cells

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ISCIENCE 2021年第9期24卷 103080页

作者： KalantarMotamedi, Yasaman Choi, Ran Joo Koh, Siang-Boon Bramhall, Jo L. Fan, Tai-Ping Bender, Andreas Univ Cambridge Ctr Mol Informat Dept Chem Lensfield Rd Cambridge CB2 1EW England Univ Cambridge Dept Pharmacol Tennis Court Rd Cambridge CB2 1PD England Univ Cambridge Canc Res UK Cambridge Inst Cambridge CB2 0RE England Yonsei Univ 50 Yonsei Ro Seoul South Korea

Resistance to current therapies is common for pancreatic cancer and hence novel treatment options are urgently needed. In this work, we developed and validated a computational method to select synergistic compound combinations based on transcriptomic profiles from both the disease and compound side, combined with a pathway scoring system, which was then validated prospectively by testing 30 compounds (and their combinations) on PANC-1 cells. Some compounds selected as single agents showed lower GI50 values than the standard of care, gemcitabine. Compounds suggested as combination agents with standard therapy gemcitabine based on the best performing scoring system showed on average 2.82-5.18 times higher synergies compared to compounds that were predicted to be active as single agents. Examples of highly synergistic in vitro validated compound pairs include gemcitabine combined with Entinostat, thioridazine, loperamide, scriptaid and Saracatinib. Hence, the computational approach presented here was able to identify synergistic compound combinations against pancreatic cancer cells.

关键词： Molecular biology computational bioinformatics Cancer systems biology

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Rapid methods for the evaluation of fluorescent reporters in tissue clearing and the segmentation of large vascular structures

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ISCIENCE 2021年第6期24卷 102650页

作者： Kirschnick, Nils Drees, Dominik Redder, Esther Erapaneedi, Raghu da Graca, Abel Pereira Schafers, Michael Jiang, Xiaoyi Kiefer, Friedemann Univ Munster European Inst Mol Imaging Waldeyerstr 15 D-48149 Munster Germany Univ Munster Inst Comp Sci Einsteinstr 62 D-48149 Munster Germany Carl Zeiss Microscopy GmbH Zeiss Res Microscopy Solut Zeiss Microscopy Customer Ctr Europe Rudolf Eber Str 2 D-73447 Oberkochen Germany

Light sheet fluorescence microscopy (LSFM) of large tissue samples does not require mechanical sectioning and allows efficient visualization of spatially complex or rare structures. Therefore, LSFMhas become invaluable in developmental and biomedical research. Because sample size may limit whole-mount staining, LSFM benefits from transgenic reporter organisms expressing fluorescent proteins (FPs) and, however, requires optical clearing and computational data visualization and analysis. The former often interferes with FPs, while the latter requires massive computing resources. Here, we describe 3D-polymerized cell dispersions, a rapid and straightforward method, based on recombinant FP expression in freely selectable tester cells, to evaluate and compare fluorescence retention in different tissue-clearing protocols. For the analysis of large LSFM data, which usually requires huge computing resources, we introduce a refined, interactive, hierarchical random walker approach that is capable of efficient segmentation of the vasculature in data sets even on a consumer grade PC.

关键词： Optical imaging computational bioinformatics

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