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Helical structure motifs made searchable for functional peptide design

NATURE COMMUNICATIONS 2022年第1期13卷 1-14页

作者： Tsai, Cheng-Yu Salawu, Emmanuel Oluwatobi Li, Hongchun Lin, Guan-Yu Kuo, Ting-Yu Voon, Liyin Sharma, Adarsh Hu, Kai-Di Cheng, Yi-Yun Sahoo, Sobha Stuart, Lutimba Chen, Chih-Wei Chang, Yuan-Yu Lu, Yu-Lin Ke, Simai Ortiz, Christopher Llynard D. Fang, Bai-Shan Wu, Chen-Chi Lan, Chung-Yu Fu, Hua-Wen Yang, Lee-Wei Natl Tsing Hua Univ Inst Bioinformat & Struct Biol Hsinchu 300044 Taiwan Natl Taiwan Univ Grad Inst Med Genom & Prote Coll Med Taipei 100025 Taiwan Natl Taiwan Univ Hosp Dept Otolaryngol Taipei 100225 Taiwan Acad Sinica Inst Informat Sci Bioinformat Program Taipei 115201 Taiwan Amazon Web Serv AWS Machine Learning Solut Lab Herndon VA USA Chinese Acad Sci Shenzhen Inst Adv Technol Res Ctr Comp Aided Drug Discovery Shenzhen 518055 Peoples R China Xiamen Univ Coll Chem & Chem Engn Xiamen 361005 Peoples R China Univ Pittsburgh Sch Med Dept Computat & Syst Biol Pittsburgh PA 15213 USA Natl Tsing Hua Univ Inst Mol & Cellular Biol Hsinchu 300044 Taiwan Praexisio Taiwan Inc New Taipei 221425 Taiwan Acad Sinica Inst Biol Chem Chem Biol & Mol Biophys Program Taipei 115201 Taiwan Natl Tsing Hua Univ Dept Chem Hsinchu 300044 Taiwan Xiamen Univ Key Lab Synthet Biotechnol Xiamen City Key Lab Chem Biol Fujian Prov Xiamen 361005 Peoples R China Natl Taiwan Univ Hosp Dept Med Res Hsin Chu Branch Hsinchu 302058 Taiwan Natl Tsing Hua Univ Dept Life Sci Hsinchu 300044 Taiwan Natl Ctr Theoret Sci Div Phys Taipei 106319 Taiwan Natl Tsing Hua Univ PhD Program Biomed Artificial Intelligence Hsinchu 300044 Taiwan

The systematic design of functional peptides has technological and therapeutic applications. However, there is a need for pattern-based search engines that help locate desired functional motifs in primary sequences regardless of their evolutionary conservation. Existing databases such as The Protein Secondary Structure database (PSS) no longer serves the community, while the Dictionary of Protein Secondary Structure (DSSP) annotates the secondary structures when tertiary structures of proteins are provided. Here, we extract 1.7 million helices from the PDB and compile them into a database (Therapeutic Peptide Design database;TP-DB) that allows queries of compounded patterns to facilitate the identification of sequence motifs of helical structures. We show how TP-DB helps us identify a known purification-tag-specific antibody that can be repurposed into a diagnostic kit for Helicobacter pylori. We also show how the database can be used to design a new antimicrobial peptide that shows better Candida albicans clearance and lower hemolysis than its template homologs. Finally, we demonstrate how TP-DB can suggest point mutations in helical peptide blockers to prevent a targeted tumorigenic protein-protein interaction. TP-DB is made available at http://***/design/.

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Comparative Analysis of Genome-Scale Gene Regulatory Networks in Human Hematopoietic Stem and Myeloid Progenitor Fractions

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BLOOD 2022年 140卷 2846-2848页

作者： Subramanian, Shruthi Thoms, Julie A., I Huang, Yizhou Jacquelin, Sebastien Shen, Sylvie Song, Emma Joshi, Swapna Brownlee, Chris Woll, Petter S. Fajardo, Diego Chacon Beck, Dominik Curtis, David J. Yehson, Kenneth Antonenas, Vicki O' Brien, Tracey Trickett, Annette Powell, Jason Pitson, Stuart M. Gandhi, Maher K. Cornejo, Paola Wong, Emily Lane, Steven W. Gottgens, Bertie Rokny, Hamid Alinejad Wong, Jason W. H. Pimanda, John E. Univ New South wales Sch Clin Med Sydney NSW Australia Univ New South Wales Sch Med Sci Randwick NSW Australia Univ Technol Sydney Ctr Hlth Technol Sydney NSW Australia Univ Technol Sydney Sch Biomed Engn Sydney NSW Australia Mater Res Macrophage Biol Lab Brisbane Qld Australia Sydney Childrens Hosp Sydney NSW Australia Prince Wales Hosp Bone Marrow Transplant Lab NSW Hlth Pathol Sydney NSW Australia Univ New South Wales Mark Wainwright Analyt Ctr Sydney NSW Australia Karolinska Inst Ctr Hematol & Regenerat Med Dept Med MedH Huddinge Sweden Monash Univ Australian Ctr Blood Dis Melbourne Vic Australia Westmead Hosp Sydney NSW Australia Westmead Hosp Sydney Cellular Therapies Lab Sydney NSW Australia Univ South Australia Ctr Canc Biol SA Pathol Adelaide SA Australia Univ Queensland Mater Res Brisbane Qld Australia Victor Chang Cardiac Res Inst Sydney NSW Australia Queensland Inst Med Res Canc Program Brisbane Qld Australia Univ Cambridge Stem Cell Inst Cambridge England Univ New South Wales Grad Sch Biomed Engn BioMed Machine Learning Lab Sydney NSW Australia Univ Hong Kong Li Ka Shing Fac Med Sch Biomed Sci Hong Kong Peoples R China Prince Wales Hosp Haematol Dept Sydney NSW Australia Univ New South Wales Sch Med Sci Sydney NSW Australia

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Accelerated chemical science with AI

DIGITAL DISCOVERY

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DIGITAL DISCOVERY 2024年第1期3卷 23-33页

作者： Back, Seoin Aspuru-Guzik, Alan Ceriotti, Michele Gryn'ova, Ganna Grzybowski, Bartosz Gu, Geun Ho Hein, Jason Hippalgaonkar, Kedar Hormazabal, Rodrigo Jung, Yousung Kim, Seonah Kim, Woo Youn Moosavi, Seyed Mohamad Noh, Juhwan Park, Changyoung Schrier, Joshua Schwaller, Philippe Tsuda, Koji Vegge, Tejs von Lilienfeld, O. Anatole Walsh, Aron Sogang Univ Inst Emergent Mat Dept Chem & Biomol Engn Seoul South Korea Univ Toronto Dept Chem Comp Sci St George Campus Toronto ON Canada Accelerat Consortium Toronto ON M5S 1M1 Canada Vector Inst Articial Intelligence Toronto ON M5S 1M1 Canada Ecole Polytech Fed Lausanne Lab Computat Sci & Modeling COSMO Lausanne Switzerland Heidelberg Inst Theoret Studies HITS gGmbH D-69118 Heidelberg Germany Heidelberg Univ Interdisciplinary Ctr Sci Comp D-69120 Heidelberg Germany Inst Basic Sci IBS Ctr Algorithm & Robotized Synth CARS Ulsan South Korea Polish Acad Sci Inst Organ Chem Warsaw Poland Ulsan Natl Inst Sci & Technol Dept Chem Ulsan South Korea Korea Inst Energy Technol KENTECH Dept Energy Engn Naju 58330 South Korea Univ British Columbia Dept Chem Vancouver BC V6T 1Z1 Canada Nanyang Technol Univ Sch Mat Sci & Engn 50 Nanyang Ave Singapore 639798 Singapore Agcy Sci Technol & Res Inst Mat Res & Engn 2 Fusionopolis Way08-03 Singapore 138634 Singapore LG AI Res Seoul South Korea Korea Adv Inst Sci & Technol Dept Chem & Biomol Engn Daejeon South Korea Seoul Natl Univ Sch Chem & Biol Engn Interdisciplinary Program Artificial Intelligence 1 Gwanak Ro Seoul 08826 South Korea Colorado State Univ Dept Chem 1301 Ctr Ave Ft Collins CO 80523 USA Korea Adv Inst Sci & Technol Dept Chem Daejeon South Korea Univ Toronto Chem Engn & Appl Chem Toronto ON M5S 3E5 Canada Korea Res Inst Chem Technol Chem Data Driven Res Ctr Daejeon 34114 South Korea Fordham Univ Dept Chem The Bronx NY 10458 USA Ecole Polytech Fed Lausanne Lab Artificial Chem Intelligence LIAC Lausanne Switzerland Ecole Polytech Fed Lausanne Natl Ctr Competence Res NCCR Catalysis Lausanne Switzerland Univ Tokyo Grad Sch Frontier Sci Kashiwa Chiba 2778561 Japan Natl Inst Mat Sci Ctr Basic Res Mat Tsukuba Ibaraki 3050044 Japan RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Tech Univ Denmark Dept Energy Convers & Storage 301 Anker Engelunds Vej DK-2800 Copenhagen Denmar

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions. The ASLLA Symposium focused on accelerating chemical science with AI. Discussions on data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, and academic bodies were provided.

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Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study

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SCIENTIFIC REPORTS 2021年第1期11卷 1-9页

作者： Cao, Bo Liu, Yang S. Selvitella, Alessandro Librenza-Garcia, Diego Passos, Ives Cavalcante Sawalha, Jeffrey Ballester, Pedro Chen, Jianshan Dong, Shimiao Wang, Fei Kapczinski, Flavio Dursun, Serdar M. Li, Xin-Min Greiner, Russell Greenshaw, Andrew Univ Alberta Fac Med & Dent Dept Psychiat Edmonton AB Canada Univ Alberta Fac Sci Dept Comp Sci Edmonton AB Canada Purdue Univ Dept Math Sci Ft Wayne IN USA Univ Washington US eSci Inst Seattle WA 98195 USA McMaster Univ Dept Psychiat & Behav Neurosci Hamilton ON Canada Univ Fed Rio Grande do Sul Hosp Clin Porto Alegre Lab Mol Psychiat Programa Posgrad Psiquiatria & Ciencias Comportam Porto Alegre RS Brazil McMaster Univ Neurosci Grad Program Hamilton ON Canada Nanjing Med Univ Dept Psychiat Early Intervent Unit Affiliated Brain Hosp Nanjing Jiangsu Peoples R China Amii Alberta Machine Learning Inst Edmonton AB Canada

The placebo effect across psychiatric disorders is still not well understood. In the present study, we conducted meta-analyses including meta-regression, and machine learning analyses to investigate whether the power of placebo effect depends on the types of psychiatric disorders. We included 108 clinical trials (32,035 participants) investigating pharmacological intervention effects on major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SCZ). We developed measures based on clinical rating scales and Clinical Global Impression scores to compare placebo effects across these disorders. We performed meta-analysis including meta-regression using sample-size weighted bootstrapping techniques, and machine learning analysis to identify the disorder type included in a trial based on the placebo response. Consistently through multiple measures and analyses, we found differential placebo effects across the three disorders, and found lower placebo effect in SCZ compared to mood disorders. The differential placebo effects could also distinguish the condition involved in each trial between SCZ and mood disorders with machine learning. Our study indicates differential placebo effect across MDD, BD, and SCZ, which is important for future neurobiological studies of placebo effects across psychiatric disorders and may lead to potential therapeutic applications of placebo on disorders more responsive to placebo compared to other conditions.

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An engineered protein-phosphorylation toggle network with implications for endogenous network discovery

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SCIENCE 2021年第6550期373卷 eaav0780页

作者： Mishra, Deepak Bepler, Tristan Teague, Brian Berger, Bonnie Broach, Jim Weiss, Ron MIT Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA MIT Synthet Biol Ctr 77 Massachusetts Ave Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA MIT Computat & Syst Biol Grad Program 77 Massachusetts Ave Cambridge MA 02139 USA New York Struct Biol Ctr Simons Machine Learning Ctr New York NY USA Univ Wisconsin Dept Biol Stout WI USA MIT Dept Math Cambridge MA 02139 USA Penn State Coll Med Dept Biochem & Mol Biol Hershey PA USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA

Synthetic biological networks comprising fast, reversible reactions could enable engineering of new cellular behaviors that are not possible with slower regulation. Here, we created a bistable toggle switch in Saccharomyces cerevisiae using a cross-repression topology comprising 11 protein-protein phosphorylation elements. The toggle is ultrasensitive, can be induced to switch states in seconds, and exhibits long-term bistability. Motivated by our toggle's architecture and size, we developed a computational framework to search endogenous protein pathways for other large and similar bistable networks. Our framework helped us to identify and experimentally verify five formerly unreported endogenous networks that exhibit bistability. Building synthetic protein-protein networks will enable bioengineers to design fast sensing and processing systems, allow sophisticated regulation of cellular processes, and aid discovery of endogenous networks with particular functions.

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Minimally instrumented SHERLOCK (miSHERLOCK) for CRISPR-based point-of-care diagnosis of SARS-CoV-2 and emerging variants

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SCIENCE ADVANCES 2021年第32期7卷 eabh2944页

作者： de Puig, Helena Lee, Rose A. Najjar, Devora Tan, Xiao Soekensen, Luis R. Angenent-Mari, Nicolaas M. Donghia, Nina M. Weckman, Nicole E. Ory, Audrey Ng, Carlos F. Nguyen, Peter Q. Mao, Angelo S. Ferrante, Thomas C. Lansberry, Geoffrey Sallum, Hani Niemi, James Collins, James J. Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA MIT Dept Biol Engn Inst Med Engn & Sci 77 Massachusetts Ave Cambridge MA 02139 USA Broad Inst MIT & Harvard Infect Dis & Microbiome Program Cambridge MA 02142 USA Boston Childrens Hosp Dept Pediat Div Infect Dis Boston MA 02115 USA Beth Israel Deaconess Med Ctr Dept Med Div Infect Dis Boston MA 02215 USA Harvard Med Sch Boston MA 02115 USA MIT Media Lab Cambridge MA 02139 USA Massachusetts Gen Hosp Div Gastroenterol Boston MA 02114 USA MIT Abdul Latif Jameel Clin Machine Learning Hlth 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Coll Arts & Sci Cambridge MA 02138 USA MIT Synthet Biol Ctr 77 Massachusetts Ave Cambridge MA 02139 USA MIT Harvard MIT Program Hlth Sci & Technol 77 Massachusetts Ave Cambridge MA 02139 USA

The COVID-19 pandemic highlights the need for diagnostics that can be rapidly adapted and deployed in a variety of settings. Several SARS-CoV-2 variants have shown worrisome effects on vaccine and treatment efficacy, but no current point-of-care (POC) testing modality allows their specific identification. We have developed miSHERLOCK, a low-cost, CRISPR-based POC diagnostic platform that takes unprocessed patient saliva;extracts, purifies, and concentrates viral RNA;performs amplification and detection reactions;and provides fluorescent visual output with only three user actions and 1 hour from sample input to answer out. miSHERLOCK achieves highly sensitive multiplexed detection of SARS-CoV-2 and mutations associated with variants B.1.1.7, B.1.351, and P.1. Our modular system enables easy exchange of assays to address diverse user needs and can be rapidly reconfigured to detect different viruses and variants of concern. An adjunctive smartphone application enables output quantification, automated interpretation, and the possibility of remote, distributed result reporting.

关键词： Coronavirus

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Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate

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NATURE MICROBIOLOGY 2019年第12期4卷 2109-2117页

作者： Lopatkin, Allison J. Stokes, Jonathan M. Zheng, Erica J. Yang, Jason H. Takahashi, Melissa K. You, Lingchong Collins, James J. MIT Inst Med Engn & Sci 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA Broad Inst MIT & Harvard Infect Dis & Microbiome Program Cambridge MA 02142 USA Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA MIT Machine Learning Pharmaceut Discovery & Synth Con 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Program Chem Biol Cambridge MA 02138 USA Calif State Univ Northridge Dept Biol Northridge CA 91330 USA Duke Univ Dept Biomed Engn Durham NC 27706 USA MIT Synthet Biol Ctr 77 Massachusetts Ave Cambridge MA 02139 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA

Growth rate and metabolic state of bacteria have been separately shown to affect antibiotic efficacy(1-3). However, the two are interrelated as bacterial growth inherently imposes a metabolic burden(4);thus, determining individual contributions from each is challenging(5,6). Indeed, faster growth is often correlated with increased antibiotic efficacy(7,8);however, the concurrent role of metabolism in that relationship has not been well characterized. As a result, a clear understanding of the interdependence between growth and metabolism, and their implications for antibiotic efficacy, are lacking(9). Here, we measured growth and metabolism in parallel across a broad range of coupled and uncoupled conditions to determine their relative contribution to antibiotic lethality. We show that when growth and metabolism are uncoupled, antibiotic lethality uniformly depends on the bacterial metabolic state at the time of treatment, rather than growth rate. We further reveal a critical metabolic threshold below which antibiotic lethality is negligible. These findings were general for a wide range of conditions, including nine representative bactericidal drugs and a diverse range of Gram-positive and Gram-negative species (Escherichia coli, Acinetobacter baumannii and Staphylococcus aureus). This study provides a cohesive metabolic-dependent basis for antibiotic-mediated cell death, with implications for current treatment strategies and future drug development.

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LtU-ILI: AN ALL-IN-ONE FRAMEWORK FOR IMPLICIT INFERENCE IN ASTROPHYSICS AND COSMOLOGY

arXiv

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arXiv 2024年

作者： Ho, Matthew Bartlett, Deaglan J. Chartier, Nicolas Cuesta-Lazaro, Carolina Ding, Simon Lapel, Axel Lemos, Pablo Lovell, Christopher C. Makinen, T. Lucas Modi, Chirag Pandya, Viraj Pandey, Shivam Perez, Lucia A. Wandelt, Benjamin Bryan, Greg L. UMR 7095 98 bis bd Arago ParisF-75014 France Center for the Gravitational-Wave Universe Astronomy Program Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Center for Astrophysics Harvard & Smithsonian 60 Garden St CambridgeMA02138 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Institute of Cosmology and Gravitation University of Portsmouth Burnaby Road PortsmouthPO1 3FX United Kingdom & Astrophysics Group Imperial College London Blackett Laboratory Prince Consort Road LondonSW7 2AZ United Kingdom Center for Computational Mathematics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States 4 place Jussieu ParisF-75252 Cedex 5 France

This paper presents the learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schemata, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable and is designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry;inferring cosmology from matter power spectra and halo point clouds;characterizing progenitors in gravitational wave signals;capturing physical dust parameters from galaxy colors and luminosities;and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://***/maho3/ltu-ili. © 2024, CC BY.

关键词： Cosmology

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In the Era of Deep learning, Why Reconstruct an Image at All?

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JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY 2021年第1期18卷 170-173页

作者： Chung, Caroline Kalpathy-Cramer, Jayashree Knopp, Michael V. Jaffray, David A. Univ Texas MD Anderson Canc Ctr Imaging Technol & Innovat Radiat Oncol Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Data Governance Program Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr MR Coordinating Comm Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dynam Contrast Enhanced MRI Comm & Quantitat Imag Houston TX 77030 USA Massachusetts Gen Hosp Dept Radiol Athinoula A Martinos Ctr Biomed Imaging Quantitat Translat Imaging Med Lab Charlestown MA USA Massachusetts Gen Hosp Dept Radiol Athinoula A Martinos Ctr Biomed Imaging Ctr Machine Learning Charlestown MA USA Ohio State Univ Wexner Med Ctr Wright Ctr Innovat Biomed Imaging ResDiv Imaging SciDept Radiol Columbus OH 43210 USA Ohio State Univ Wexner Med Ctr Wright Ctr Innovat Biomed Imaging Columbus OH 43210 USA Ohio State Univ Wexner Med Ctr Wright Ctr Innovat Biomed Imaging ImagingDiv Imaging SciDept Radiol Columbus OH 43210 USA Ohio State Univ Wright Ctr Innovat Biomed Imaging Alliance Clin Trials Oncol Imaging CommDiv Imaging SciDept RadiolWexner M Columbus OH 43210 USA

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Longitudinal clinical and functional outcome in distinct cognitive subgroups of first-episode psychosis: a cluster analysis

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PSYCHOLOGICAL MEDICINE 2023年第6期53卷 2317-2327页

作者： Oomen, Priscilla P. Begemann, Marieke J. H. Brand, Bodyl A. de Haan, Lieuwe Veling, Wim Koops, Sanne van Os, Jim Smit, Filip Bakker, P. Roberto van Beveren, Nico Boonstra, Nynke Guloksuz, Sinan Kikkert, Martijn Lokkerbol, Joran Marcelis, Machteld Rosema, Bram-Sieben de Beer, Franciska Gangadin, Shiral S. Geraets, Chris N. W. van't Hag, Erna Haveman, Yudith van der Heijden, Inge Voppel, Alban E. Willemse, Elske van Amelsvoort, Therese Bak, Maarten Batalla, Albert Been, Agaath van den Bosch, Marinte van den Brink, Truus Faber, Gunnar Grootens, Koen P. de Jonge, Martin Knegtering, Rikus Kurkamp, Jorg Mahabir, Amrita Pijnenborg, Gerdina H. M. Staring, Tonnie Veen, Natalie Veerman, Selene Wiersma, Sybren Graveland, Ellen Hoornaar, Joelle Sommer, Iris E. C. Univ Groningen Univ Med Ctr Groningen Dept Biomed Sci Cells & Syst Groningen Netherlands Univ Groningen Univ Med Ctr Groningen Dept Psychiat Groningen Netherlands Amsterdam UMC Dept Early Psychosis Acad Med Ctr Amsterdam Netherlands Univ Groningen Univ Med Ctr Groningen Dept Psychiat Groningen Netherlands Univ Med Ctr Utrecht Dept Psychiat UMC Utrecht Brain Ctr Utrecht Netherlands Maastricht Univ Sch Mental Hlth & Neurosci MheNS Dept Psychiat & Neuropsychol Med Ctr Maastricht Netherlands Kings Coll London Kings Hlth Partners Dept Psychosis Studies Inst Psychiat Psychol & Neurosci London England Univ Amsterdam Amsterdam Publ Hlth Res Inst Dept Epidemiol & Biostat Med Ctr Locat VUmc Amsterdam Netherlands Vrije Univ Amsterdam Publ Hlth Res Inst Dept Clin Neuro & Dev Psychol Amsterdam Netherlands Trimbos Inst Netherlands Inst Mental Hlth Ctr Econ Evaluat & Machine Learning Utrecht Netherlands Arkin Mental Hlth Care Dept Res Amsterdam Netherlands Antes Ctr Mental Hlth Care Rotterdam Netherlands Erasmus MC Dept Neurosci Rotterdam Netherlands Erasmus MC Dept Psychiat Rotterdam Netherlands Univ Appl Sci NHL Stenden Leeuwarden Netherlands KieN VIP Mental Hlth Care Serv Leeuwarden Netherlands Yale Univ Dept Psychiat Sch Med New Haven CT USA Maastricht Univ Med Ctr Sch Mental Hlth & Neurosci Dept Psychiat & Neuropsychol EURON Maastricht Netherlands Inst Mental Hlth Care Eindhoven GGzE Eindhoven Netherlands Janssen Cilag BV Breda Netherlands Mondriaan Mental Hlth Care Heerlen Netherlands Dimence Inst Mental Hlth Deventer Netherlands GGZ Centraal Early Intervent Team Amersfoort Netherlands Mental Hlth Inst Yulius Dordrecht Netherlands Reinier Arkel Inst Mental Hlth Care sHertogenbosch Netherlands Pro Persona Mental Hlth Program Psychosis & Severe Mental Illness Wolfheze Netherlands Lentis Psychiat Inst Lentis Res Groningen Netherlands Mediant ABC Twente Ctr Youth Psychosis Enschede Netherlands GGNet Early Psychosis Team Apeldoorn N

Background Cognitive deficits may be characteristic for only a subgroup of first-episode psychosis (FEP) and the link with clinical and functional outcomes is less profound than previously thought. This study aimed to identify cognitive subgroups in a large sample of FEP using a clustering approach with healthy controls as a reference group, subsequently linking cognitive subgroups to clinical and functional outcomes. Methods 204 FEP patients were included. Hierarchical cluster analysis was performed using baseline brief assessment of cognition in schizophrenia (BACS). Cognitive subgroups were compared to 40 controls and linked to longitudinal clinical and functional outcomes (PANSS, GAF, self-reported WHODAS 2.0) up to 12-month follow-up. Results Three distinct cognitive clusters emerged: relative to controls, we found one cluster with preserved cognition (n = 76), one moderately impaired cluster (n = 74) and one severely impaired cluster (n = 54). Patients with severely impaired cognition had more severe clinical symptoms at baseline, 6- and 12-month follow-up as compared to patients with preserved cognition. General functioning (GAF) in the severely impaired cluster was significantly lower than in those with preserved cognition at baseline and showed trend-level effects at 6- and 12-month follow-up. No significant differences in self-reported functional outcome (WHODAS 2.0) were present. Conclusions Current results demonstrate the existence of three distinct cognitive subgroups, corresponding with clinical outcome at baseline, 6- and 12-month follow-up. Importantly, the cognitively preserved subgroup was larger than the severely impaired group. Early identification of discrete cognitive profiles can offer valuable information about the clinical outcome but may not be relevant in predicting self-reported functional outcomes.

关键词： Clustering cognition FEP functional outcome global functioning psychosis

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