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Genome-wide transcription factor-binding maps reveal cell-specific changes in the regulatory architecture of human HSPCs

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BLOOD 2023年第17期142卷 1448-1462页

作者： Subramanian, Shruthi Thoms, Julie A. I. Huang, Yizhou Cornejo-Paramo, Paola Koch, Forrest C. Jacquelin, Sebastien Shen, Sylvie Song, Emma Joshi, Swapna Brownlee, Chris Woll, Petter S. Chacon-Fajardo, Diego Beck, Dominik Curtis, David J. Yehson, Kenneth Antonenas, Vicki O'Brien, Tracey Trickett, Annette Powell, Jason A. Lewis, Ian D. Pitson, Stuart M. Gandhi, Maher K. Lane, Steven W. Vafaee, Fatemeh Wong, Emily S. Goettgens, Berthold Alinejad-Rokny, Hamid Wong, Jason W. H. Pimanda, John E. Univ New South Wales Sch Clin Med Sydney Australia Univ New South Wales Sch Biomed Sci Sydney Australia Univ Technol Sydney Sch Biomed Engn Ctr Hlth Technol Sydney Australia Victor Chang Cardiac Res Inst Sydney Australia Univ New South Wales Fac Sci Sch Biotechnol & Biomol Sci Sydney Australia Macrophage Biol Lab Mater Res Brisbane Australia Prince Wales Hosp Bone Marrow Transplant Lab NSW Hlth Pathol Randwick NSW Australia Univ New South Wales Mark Wainwright Analyt Ctr Sydney Australia Karolinska Inst Ctr Hematol & Regenerat Med Dept Med Huddinge Sweden Monash Univ Australian Ctr Blood Dis Melbourne Vic Australia NSW Hlth Pathol Blood Transplant & Cell Therapies Lab Westmead NSW Australia Sydney Childrens Hosp Sydney Australia Univ South Australia Ctr Canc Biol SA Pathol Adelaide Australia Univ Adelaide Adelaide Med Sch Adelaide Australia Univ Queensland Blood Canc Res Grp Mater Res Brisbane Qld Australia QIMR Berghofer Med Res Canc Program Brisbane Australia Univ New South Wales UNSW Data Sci Hub Sydney Australia Wellcome MRC Cambridge Stem Cell Inst Cambridge England Univ New South Wales Grad Sch Biomed Engn Biomed Machine Learning Lab Sydney Australia Univ Hong Kong Li Ka Shing Fac Med Sch Biomed Sci Hong Kong Peoples R China Prince Wales Hosp Haematol Dept Sydney Australia Univ New South Wales Lowy Canc Res Ctr Level 2 Sydney NSW 2052 Australia Pokfulam L1-51Lab Block 21 Sassoon Rd Hong Kong Peoples R China

Hematopoietic stem and progenitor cells (HSPCs) rely on a complex interplay among transcription factors (TFs) to regulate differentiation into mature blood cells. A heptad of TFs (FLI1, ERG, GATA2, RUNX1, TAL1, LYL1, LMO2) bind regulatory elements in bulk CD34(+) HSPCs. However, whether specific heptad-TF combinations have distinct roles in regulating hematopoietic differentiation remains unknown. We mapped genome-wide chromatin contacts (HiC, H3K27ac, HiChIP), chromatin modifications (H3K4me3, H3K27ac, H3K27me3) and 10 TF binding profiles (heptad, PU.1, CTCF, STAG2) in HSPC subsets (stem/multipotent progenitors plus common myeloid, granulocyte macrophage, and megakaryocyte erythrocyte progenitors) and found TF occupancy and enhancer-promoter interactions varied significantly across cell types and were associated with cell-type-specific gene expression. Distinct regulatory elements were enriched with specific heptad-TF combinations, including stem-cell-specific elements with ERG, and myeloid-and erythroid-specific elements with combinations of FLI1, RUNX1, GATA2, TAL1, LYL1, and LMO2. Furthermore, heptad-occupied regions in HSPCs were subsequently bound by lineage-defining TFs, including PU.1 and GATA1, suggesting that heptad factors may prime regulatory elements for use in mature cell types. We also found that enhancers with cell-type-specific heptad occupancy shared a common grammar with respect to TF binding motifs, suggesting that combinatorial binding of TF complexes was at least partially regulated by features encoded in DNA sequence motifs. Taken together, this study comprehensively characterizes the gene regulatory landscape in rare subpopulations of human HSPCs. The accompanying data sets should serve as a valuable resource for understanding adult hematopoiesis and a framework for analyzing aberrant regulatory networks in leukemic cells.

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A deep-learning algorithm to classify skin lesions from mpox virus infection

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NATURE MEDICINE 2023年第3期29卷 738-+页

作者： Thieme, Alexander H. H. Zheng, Yuanning Machiraju, Gautam Sadee, Chris Mittermaier, Mirja Gertler, Maximilian Salinas, Jorge L. Srinivasan, Krithika Gyawali, Prashnna Carrillo-Perez, Francisco Capodici, Angelo Uhlig, Maximilian Habenicht, Daniel Loeser, Anastassia Kohler, Maja Schuessler, Maximilian Kaul, David Gollrad, Johannes Ma, Jackie Lippert, Christoph Billick, Kendall Bogoch, Isaac Hernandez-Boussard, Tina Geldsetzer, Pascal Gevaert, Olivier Stanford Univ Dept Med Stanford CA 94305 USA Stanford Univ Stanford Ctr Biomed Informat Res BMIR Dept Biomed Data Sci Stanford CA 94305 USA Charite Univ Med Berlin Dept Radiat Oncol Berlin Germany Charite Univ Med Berlin Berlin Inst Hlth BIH Biomed Innovat Acad BIH Charite Digital Clinician Scientist Program Berlin Germany Stanford Univ Dept Biomed Data Sci Stanford CA USA Charite Univ Med Berlin Dept Infect Dis & Resp Med Berlin Germany Charite Univ Med Berlin Inst Trop Med & Int Hlth Berlin Germany Stanford Univ Dept Med Div Infect Dis & Geog Med Stanford CA USA Univ Granada Dept Architecture & Comp Technol ATC Granada Spain Univ Bologna Dept Biomed & Neuromotor Sci Alma Mater Studiorum Bologna Italy Justus Liebig Univ Giessen Dept Med Giessen Germany Tech Univ Berlin Berlin Germany Univ Med Ctr Schleswig Holstein Dept Radiotherapy Lubeck Germany Heidelberg Univ Hosp Heidelberg Inst Global Hlth Heidelberg Germany Univ Basel Ctr Cognit & Decis Sci Dept Psychol Basel Switzerland Fraunhofer Heinrich Hertz Inst Dept Artificial Intelligence Berlin Germany Univ Potsdam Hasso Plattner Inst Digital Hlth & Machine Learning Potsdam Germany Icahn Sch Med Mt Sinai Hasso Plattner Inst Digital Hlth Mt Sinai New York NY USA Univ Hlth Network Toronto Western Hosp Div Dermatol Toronto ON Canada Univ Hlth Network Toronto Gen Hosp Div Infect Dis Toronto ON Canada Stanford Univ Dept Surg Stanford CA USA Stanford Univ Dept Med Div Primary Care & Populat Hlth Stanford CA USA Chan Zuckerberg Biohub San Francisco CA USA

A deep-learning algorithm was developed to identify skin lesions caused by the mpox virus and was then implemented in a web-based app designed for patient use. Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation.

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Optimal multiparametric set-up modelled for best survival outcomes in palliative treatment of liver malignancies: unsupervised machine learning and 3 PM recommendations

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EPMA JOURNAL 2020年第3期11卷 505-515页

作者： Goldstein, Elisha Yeghiazaryan, Kristina Ahmad, Ashar Giordano, Frank A. Froehlich, Holger Golubnitschaja, Olga Weizmann Inst Sci Dept Bioinformat Machine Learning Res Grp Rehovot Israel Rheinische Friedrich Wilhelms Univ Bonn State NRW Israel Program Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Univ Hosp Bonn IT Dept Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat AI & Data Sci D-53754 St Augustin Germany Rheinische Friedrich Wilhelms Univ Bonn Bonn Aachen Int Ctr IT D-53115 Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Univ Hosp Bonn Dept Radiat Oncol Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Univ Hosp Bonn Dept Radiat Oncol Predict Prevent & Personalised 3P Med Venusberg Campus 1 D-53127 Bonn Germany

Over the last decade, a rapid rise in deaths due to liver disease has been observed especially amongst young people. Nowadays liver disease accounts for approximately 2 million deaths per year worldwide: 1 million due to complications of cirrhosis and 1 million due to viral hepatitis and hepatocellular carcinoma. Besides primary liver malignancies, almost all solid tumours are capable to spread metastases to the liver, in particular, gastrointestinal cancers, breast and genitourinary cancers, lung cancer, melanomas and sarcomas. A big portion of liver malignancies undergo palliative care. To this end, the paradigm of the palliative care in the liver cancer management is evolving from "just end of the life" care to careful evaluation of all aspects relevant for the survivorship. In the presented study, an evidence-based approach has been taken to target molecular pathways and subcellular components for modelling most optimal conditions with the longest survival rates for patients diagnosed with advanced liver malignancies who underwent palliative treatments. We developed an unsupervised machine learning (UML) approach to robustly identify patient subgroups based on estimated survival curves for each individual patient and each individual potential biomarker. UML using consensus hierarchical clustering of biomarker derived risk profiles resulted into 3 stable patient subgroups. There were no significant differences in age, gender, therapy, diagnosis or comorbidities across clusters. Survival times across clusters differed significantly. Furthermore, several of the biomarkers demonstrated highly significant pairwise differences between clusters after correction for multiple testing, namely, "comet assay" patterns of classes I, III, IV and expression rates of calgranulin A (S100), SOD2 and profilin-all measured ex vivo in circulating leucocytes. Considering worst, intermediate and best survival curves with regard to identified clusters and corresponding patterns of para

关键词： Predictive preventive personalised medicine (PPPM 3 PM) Liver diseases Liver malignancy Palliative medicine Unsupervised machine learning Multiparametric modelling Patient stratification Hepatocellular carcinoma Colorectal cancer Breast cancer Prostate cancer Metastasis Liquid biopsy Ex vivo Circulating leucocytes Multi-omics Biomarker patterns Multi-level diagnostics Survival Prognosis Metalloproteinase Comet assay Calgranulin A S100 Catalase Superoxide-dismutase 2 SOD-2 Profilin Rho A Thioredoxin Scavenger Antioxidant compounds ROS inhibition Selective internal radiation therapy (SIRT) Trans-arterial chemo-embolisation (TACE) Individual outcomes Covid-19 Viral infection Hepatitis Hypoxia Inflammation Detoxification Impairments Individualised patient profile Redox status Genoprotection Phytochemicals Redox-based therapy Risk mitigation

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BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences

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CELL SYSTEMS 2023年第6期14卷 525-+页

作者： Valeri, Jacqueline A. Soenksen, Luis R. Collins, Katherine M. Ramesh, Pradeep Cai, George Powers, Rani Angenent-Mari, Nicolaas M. Camacho, Diogo M. Wong, Felix Lu, Timothy K. Collins, James J. MIT Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA MIT Inst Med Engn & Sci 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Wyss Inst Biolog Inspired Engn Boston MA 02115 USA Broad Inst & Harvard Cambridge MA 02142 USA MIT Dept Mech Engn 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Brain & Cognit Sci 77 Massachusetts Ave Cambridge MA 02139 USA Univ Cambridge Dept Engn Trumpington St Cambridge CB2 1PZ England Pluto Biosci Golden CO 80402 USA MIT Res Lab Elect Synthet Biol Grp Cambridge MA 02139 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA MIT Abdul Latif Jameel Clin Machine Learning Hlth Cambridge MA 02139 USA

The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) and cannot be easily compared with other AutoML algorithms. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automat-ically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation, and designs optimized synthetic biology components, revealing salient sequence characteristics. By auto-mating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.

关键词： automated machine learning architecture search hyperparameter optimization biological sequences

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DeePN2: A deep learning-based non-Newtonian hydrodynamic model

arXiv

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arXiv 2021年

作者： Fang, Lidong Ge, Pei Zhang, Lei Weinan, E. Lei, Huan Department of Computational Mathematics Science and Engineering Michigan State University MI48824 United States School of Mathematical Sciences Institute of Natural Sciences and MOE-LSC Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing100871 China AI for Science Institute Beijing100080 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University NJ08544 United States Department of Statistics and Probability Michigan State University MI48824 United States

A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully encode the underlying micro-scale polymer dynamics. The main complication arises from the long polymer relaxation time, the complex molecular structure and heterogeneous interaction. DeePN2, a deep learning-based non-Newtonian hydrodynamic model, has been proposed and has shown some success in systematically passing the micro-scale structural mechanics information to the macro-scale hydrodynamics for suspensions with simple polymer conformation and bond potential. The model retains a multi-scaled nature by mapping the polymer configurations into a set of symmetry-preserving macro-scale features. The extended constitutive laws for these macro-scale features can be directly learned from the kinetics of their micro-scale counterparts. In this paper, we develop DeePN2 using more complex micro-structural models. We show that DeePN2 can faithfully capture the broadly overlooked viscoelastic differences arising from the specific molecular structural mechanics without human intervention. © 2021, CC BY.

关键词： Non Newtonian flow

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers machine learning Random Forest

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Increasing digital mental health reach and uptake via youth partnerships

Npj mental health research

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Npj mental health research 2023年第1期2卷 9-9页

作者： Colleen Stiles-Shields Giovanni Ramos Adrian Ortega Alexandra M Psihogios Institute for Juvenile Research Department of Psychiatry College of Medicine University of Illinois Chicago Chicago IL USA. Center for Health Equity using Machine Learning & Artificial Intelligence College of Medicine University of Illinois Chicago Chicago IL USA. Montefiore Medical Center University of California Los Angeles Los Angeles CA USA. Clinical Child Psychology Program University of Kansas Lawrence KS USA. Department of Medical Social Sciences Northwestern University Chicago IL USA.

Youth in the United States are facing an unprecedented mental health crisis. Yet, brick-and-mortar mental healthcare, such as face-to-face therapy, is overwhelmingly inaccessible to youth despite research advances in youth mental health. Digital Mental Health tools (DMH), the use of technologies to deliver mental health assessments and interventions, may help to increase mental healthcare accessibility. However, for a variety of reasons, evidence-based DMH have not been successful in reaching youth in real-world settings, particularly those who are most encumbered with access barriers to mental healthcare. This Comment therefore focuses on increasing DMH reach and uptake by young people, particularly among minoritized youth, by engaging in community-based youth partnerships. This idea recognizes and grows from decades' worth of community-based participatory research and youth partnerships successfully conducted by other disciplines (e.g., social work, public health, urban planning, education). Increasing uptake and engagement is an issue that is unlikely to be solved by adult-driven theory and design. As such, we emphasize the necessity of reframing youth input into DMH design and deployment from one-time participants to integral community-based partners. Indeed, recognizing and valuing their expertise to equitably address DMH implementation challenges, youth should help to pose the very questions that they will help to answer throughout the design and implementation planning for DMH moving forward.

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Somatic point mutations are enriched in non-coding RNAs with possible regulatory function in breast cancer

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COMMUNICATIONS BIOLOGY 2022年第1期5卷 1-13页

作者： Rezaie, Narges Bayati, Masroor Hamidi, Mehrab Tahaei, Maedeh Sadat Khorasani, Sadegh Lovell, Nigel H. Breen, James Rabiee, Hamid R. Alinejad-Rokny, Hamid Univ Calif Irvine Ctr Complex Biol Syst Irvine CA 92697 USA Sharif Univ Technol Dept Comp Engn Bioinformat & Computat Biol Lab Tehran 11365 Iran UNSW Sydney Tyree Inst Hlth Engn Sydney NSW 2052 Australia UNSW Sydney Grad Sch Biomed Engn Sydney NSW 2052 Australia South Australian Hlth & Med Res Inst Adelaide SA 5000 Australia Univ Adelaide Robinson Res Inst Adelaide SA 5006 Australia Univ Adelaide Bioinformat Hub Adelaide SA 5006 Australia UNSW Sydney Grad Sch Biomed Engn BioMed Machine Learning Lab BML Sydney NSW 2052 Australia Univ New South Wales UNSW Sydney UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ Hlth Data Analyt Program Al Eenabled Proc AIP Res Ctr Sydney NSW 2109 Australia

Non-coding RNAs (ncRNAs) form a large portion of the mammalian genome. However, their biological functions are poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we analyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancer samples. We identify 1030 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs significantly enrich active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 1030 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other cancers. Using breast cell line related Hi-C data, we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role of candidate ncRNA genes in the activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource (https://www. ***/research) to communicate our results with the research community. Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used to analyze somatic mutations in all cancers.

关键词： Data integration Breast cancer

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Deep learning identifies synergistic drug combinations for treating COVID-19

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2021年第39期118卷 e2105070118页

作者： Jin, Wengong Stokes, Jonathan M. Eastman, Richard T. Itkin, Zina Zakharov, Alexey, V Collins, James J. Jaakkola, Tommi S. Barzilay, Regina MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA MIT Inst Med Engn & Sci Synthet Biol Ctr Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA Natl Ctr Adv Translat Sci Div Preclin Innovat Rockville MD 20850 USA MIT Abdul Latif Jameel Clin Machine Learning Hlth 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug-target interaction and drug-drug synergy. The model consists of two parts: a drug-target interaction module and a target-disease association module. This design enables the model to utilize drug-target interaction data and single agent antiviral activity data, in addition to available drug-drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical-chemical combination data exists.

关键词： deep learning drug discovery drug synergy SARS-CoV-2

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Bacterial Metabolism and Antibiotic Efficacy

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CELL METABOLISM 2019年第2期30卷 251-259页

作者： Stokes, Jonathan M. Lopatkin, Allison J. Lobritz, Michael A. Collins, James J. MIT Dept Biol Engn Inst Med Engn & Sci 77 Massachusetts Ave Cambridge MA 02139 USA MIT Synthet Biol Ctr 77 Massachusetts Ave Cambridge MA 02139 USA Broad Inst MIT & Harvard Infect Dis & Microbiome Program Cambridge MA 02142 USA MIT Machine Learning Pharmaceut Discovery & Synth Con 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA Roche Innovat Ctr Basel Roche Pharma Res & Early Dev CH-4070 Basel Switzerland Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA

Antibiotics target energy-consuming processes. As such, perturbations to bacterial metabolic homeostasis are significant consequences of treatment. Here, we describe three postulates that collectively define antibiotic efficacy in the context of bacterial metabolism: (1) antibiotics alter the metabolic state of bacteria, which contributes to the resulting death or stasis;(2) the metabolic state of bacteria influences their susceptibility to antibiotics;and (3) antibiotic efficacy can be enhanced by altering the metabolic state of bacteria. Altogether, we aim to emphasize the close relationship between bacterial metabolism and antibiotic efficacy as well as propose areas of exploration to develop novel antibiotics that optimally exploit bacterial metabolic networks.

关键词： antibiotic adjuvants antibiotic mechanism antibiotic tolerance bacterial metabolism

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