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Sikirzhytskaya A, Tyagin I, Sutton SS, Wyatt MD, Safro I, Shtutman M. AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597745. [PMID: 38895485 PMCID: PMC11185689 DOI: 10.1101/2024.06.06.597745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing. Author Summary This manuscript outlines our project involving the application of AGATHA, an AI-based literature mining tool, to discover drugs with the potential for repurposing in the context of neurocognitive disorders. The primary objective is to identify connections between approved medications and specific health conditions through advanced statistical analysis, including techniques like Partial Least Squares Discriminant Analysis (PLSDA) and unsupervised clustering. The methodology involves grouping scientific terms related to different health conditions and genes, followed by building discrimination models to extract lists of disease-specific genes. These genes are then analyzed through pathway analysis to select candidates for drug repurposing.
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Hwang Y, Cornman AL, Kellogg EH, Ovchinnikov S, Girguis PR. Genomic language model predicts protein co-regulation and function. Nat Commun 2024; 15:2880. [PMID: 38570504 PMCID: PMC10991518 DOI: 10.1038/s41467-024-46947-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 03/13/2024] [Indexed: 04/05/2024] Open
Abstract
Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function paradigm from massive protein sequence datasets. However, to date, limited attempts have been made in extending this continuum to include higher order genomic context information. Evolutionary processes dictate the specificity of genomic contexts in which a gene is found across phylogenetic distances, and these emergent genomic patterns can be leveraged to uncover functional relationships between gene products. Here, we train a genomic language model (gLM) on millions of metagenomic scaffolds to learn the latent functional and regulatory relationships between genes. gLM learns contextualized protein embeddings that capture the genomic context as well as the protein sequence itself, and encode biologically meaningful and functionally relevant information (e.g. enzymatic function, taxonomy). Our analysis of the attention patterns demonstrates that gLM is learning co-regulated functional modules (i.e. operons). Our findings illustrate that gLM's unsupervised deep learning of the metagenomic corpus is an effective and promising approach to encode functional semantics and regulatory syntax of genes in their genomic contexts and uncover complex relationships between genes in a genomic region.
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Affiliation(s)
- Yunha Hwang
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
| | | | - Elizabeth H Kellogg
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Peter R Girguis
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
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3
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Xu J, Xu X, Jiang Y, Fu Y, Shen C. Waste to resource: Mining antimicrobial peptides in sludge from metagenomes using machine learning. ENVIRONMENT INTERNATIONAL 2024; 186:108574. [PMID: 38507933 DOI: 10.1016/j.envint.2024.108574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/26/2024] [Accepted: 03/09/2024] [Indexed: 03/22/2024]
Abstract
The emergence of antibiotic-resistant bacteria poses a huge threat to the treatment of infections. Antimicrobial peptides are a class of short peptides that widely exist in organisms and are considered as potential substitutes for traditional antibiotics. Here, we use metagenomics combined with machine learning to find antimicrobial peptides from environmental metagenomes and successfully obtained 16,044,909 predicted AMPs. We compared the abundance of potential antimicrobial peptides in natural environments and engineered environments, and found that engineered environments also have great potential. Further, we chose sludge as a typical engineered environmental sample, and tried to mine antimicrobial peptides from it. Through metaproteome analysis and correlation analysis, we mined 27 candidate AMPs from sludge. We successfully synthesized 25 peptides by chemical synthesis, and experimentally verified that 21 peptides had antibacterial activity against the 4 strains tested. Our work highlights the potential for mining new antimicrobial peptides from engineered environments and demonstrates the effectiveness of mining antimicrobial peptides from sludge.
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Affiliation(s)
- Jiaqi Xu
- Department of Environmental Engineering, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory for Water Pollution Control and Environmental Safety, Hangzhou, China
| | - Xin Xu
- Department of Environmental Engineering, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory for Water Pollution Control and Environmental Safety, Hangzhou, China
| | - Yunhan Jiang
- Department of Environmental Engineering, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory for Water Pollution Control and Environmental Safety, Hangzhou, China
| | - Yulong Fu
- Department of Environmental Engineering, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China; Innovation Center of Yangtze River Delta, Zhejiang University, China
| | - Chaofeng Shen
- Department of Environmental Engineering, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China; Innovation Center of Yangtze River Delta, Zhejiang University, China.
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4
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Dotan E, Jaschek G, Pupko T, Belinkov Y. Effect of tokenization on transformers for biological sequences. Bioinformatics 2024; 40:btae196. [PMID: 38608190 PMCID: PMC11055402 DOI: 10.1093/bioinformatics/btae196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 02/20/2024] [Accepted: 04/11/2024] [Indexed: 04/14/2024] Open
Abstract
MOTIVATION Deep-learning models are transforming biological research, including many bioinformatics and comparative genomics algorithms, such as sequence alignments, phylogenetic tree inference, and automatic classification of protein functions. Among these deep-learning algorithms, models for processing natural languages, developed in the natural language processing (NLP) community, were recently applied to biological sequences. However, biological sequences are different from natural languages, such as English, and French, in which segmentation of the text to separate words is relatively straightforward. Moreover, biological sequences are characterized by extremely long sentences, which hamper their processing by current machine-learning models, notably the transformer architecture. In NLP, one of the first processing steps is to transform the raw text to a list of tokens. Deep-learning applications to biological sequence data mostly segment proteins and DNA to single characters. In this work, we study the effect of alternative tokenization algorithms on eight different tasks in biology, from predicting the function of proteins and their stability, through nucleotide sequence alignment, to classifying proteins to specific families. RESULTS We demonstrate that applying alternative tokenization algorithms can increase accuracy and at the same time, substantially reduce the input length compared to the trivial tokenizer in which each character is a token. Furthermore, applying these tokenization algorithms allows interpreting trained models, taking into account dependencies among positions. Finally, we trained these tokenizers on a large dataset of protein sequences containing more than 400 billion amino acids, which resulted in over a 3-fold decrease in the number of tokens. We then tested these tokenizers trained on large-scale data on the above specific tasks and showed that for some tasks it is highly beneficial to train database-specific tokenizers. Our study suggests that tokenizers are likely to be a critical component in future deep-network analysis of biological sequence data. AVAILABILITY AND IMPLEMENTATION Code, data, and trained tokenizers are available on https://github.com/technion-cs-nlp/BiologicalTokenizers.
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Affiliation(s)
- Edo Dotan
- The Henry and Marilyn Taub Faculty of Computer Science, Technion – Israel Institute of Technology, Haifa 3200003, Israel
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Gal Jaschek
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, United States
| | - Tal Pupko
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yonatan Belinkov
- The Henry and Marilyn Taub Faculty of Computer Science, Technion – Israel Institute of Technology, Haifa 3200003, Israel
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Ke S, Pan Y, Jin Y, Meng J, Xiao Y, Chen S, Zhang Z, Li R, Tong F, Jiang B, Song Z, Zhu M, Ye C. Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons. ACS APPLIED MATERIALS & INTERFACES 2024; 16:13989-13996. [PMID: 38441421 DOI: 10.1021/acsami.3c19261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spiking neural networks (SNNs) are becoming a potentially energy-efficient replacement for conventional artificial neural networks. However, neuromorphic devices used to construct SNNs persistently result in considerable energy consumption owing to the absence of sufficient biological parallels. Drawing inspiration from the transport nature of Na+ and K+ in synapses, here, a Li-based memristor (LixAlOy) was proposed to emulate the biological synapse, leveraging the similarity of Li as a homologous main group element to Na and K. The Li-based memristor exhibits ∼8 ns ultrafast operating speed, 1.91 and 0.72 linearity conductance modulation, and reproducible switching behavior, enabled by lithium vacancies forming a conductive filament mechanism. Moreover, these memristors are capable of simulating fundamental behaviors of a biological synapse, including long-term potentiation and long-term depression behaviors. Most importantly, a threshold-tunable leaky integrate-and-fire (TT-LIF) neuron is built using LixAlOy memristors, successfully integrating synaptic signals from both temporal and spatial levels and achieving an optimal threshold of SNNs. A computationally efficient TT-LIF-based SNN algorithm is also implemented for image recognition schemes, featuring a high recognition rate of 90.1% and an ultralow firing rate of 0.335%, which is 4 times lower than those of other memristor-based SNNs. Our studies reveal the ion dynamics mechanism of the LixAlOy memristor and confirm its potential in rapid switching and the construction of SNNs.
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Affiliation(s)
- Shanwu Ke
- School of Microelectronics, Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Hubei University, Wuhan 430062, China
| | - Yanqin Pan
- School of Microelectronics, Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Hubei University, Wuhan 430062, China
| | - Yaoyao Jin
- School of Microelectronics, Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Hubei University, Wuhan 430062, China
| | - Jiahao Meng
- School of Microelectronics, Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Hubei University, Wuhan 430062, China
| | - Yongyue Xiao
- School of Microelectronics, Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Hubei University, Wuhan 430062, China
| | - Siqi Chen
- School of Microelectronics, Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Hubei University, Wuhan 430062, China
| | - Zihao Zhang
- School of Microelectronics, Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Hubei University, Wuhan 430062, China
| | - Ruiqi Li
- School of Microelectronics, Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Hubei University, Wuhan 430062, China
| | - Fangjiu Tong
- School of Microelectronics, Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Hubei University, Wuhan 430062, China
| | - Bei Jiang
- School of Microelectronics, Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Hubei University, Wuhan 430062, China
| | - Zhitang Song
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
| | - Min Zhu
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
| | - Cong Ye
- School of Microelectronics, Key Laboratory of Intelligent Sensing System and Security, Ministry of Education, Hubei University, Wuhan 430062, China
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Chen W, Miao C, Zhang Z, Fung CSH, Wang R, Chen Y, Qian Y, Cheng L, Yip KY, Tsui SKW, Cao Q. Commonly used software tools produce conflicting and overly-optimistic AUPRC values. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578654. [PMID: 38370825 PMCID: PMC10871236 DOI: 10.1101/2024.02.02.578654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
The precision-recall curve (PRC) and the area under it (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluated 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in >3,000 published studies. We found the AUPRC values computed by the tools rank classifiers differently and some tools produce overly-optimistic results.
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Affiliation(s)
- Wenyu Chen
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Chen Miao
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Zhenghao Zhang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Cathy Sin-Hang Fung
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Ran Wang
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Yizhen Chen
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Yan Qian
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lixin Cheng
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Kevin Y. Yip
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Stephen Kwok-Wing Tsui
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Qin Cao
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
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7
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Miller D, Arias O, Burstein D. GeNLP: a web tool for NLP-based exploration and prediction of microbial gene function. Bioinformatics 2024; 40:btae034. [PMID: 38291951 PMCID: PMC10868303 DOI: 10.1093/bioinformatics/btae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 12/13/2023] [Accepted: 01/29/2024] [Indexed: 02/01/2024] Open
Abstract
SUMMARY GeNLP is a web application that enables exploring microbial gene "semantics" and predictions of uncharacterized gene families based on their genomic context. It utilizes a pre-trained language model to uncover gene relationships and allows users to access and utilize the data as well as make their own predictions through an interactive interface. AVAILABILITY AND IMPLEMENTATION The web application is accessible from all browsers at: http://gnlp.bursteinlab.org/. All source codes are freely available from GitHub under the MIT license here: https://github.com/burstein-lab/genomic-nlp-server.
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Affiliation(s)
- Danielle Miller
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv 6997801, Israel
| | - Ofir Arias
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv 6997801, Israel
| | - David Burstein
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv 6997801, Israel
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8
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Rodríguez Del Río Á, Giner-Lamia J, Cantalapiedra CP, Botas J, Deng Z, Hernández-Plaza A, Munar-Palmer M, Santamaría-Hernando S, Rodríguez-Herva JJ, Ruscheweyh HJ, Paoli L, Schmidt TSB, Sunagawa S, Bork P, López-Solanilla E, Coelho LP, Huerta-Cepas J. Functional and evolutionary significance of unknown genes from uncultivated taxa. Nature 2024; 626:377-384. [PMID: 38109938 PMCID: PMC10849945 DOI: 10.1038/s41586-023-06955-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 12/08/2023] [Indexed: 12/20/2023]
Abstract
Many of the Earth's microbes remain uncultured and understudied, limiting our understanding of the functional and evolutionary aspects of their genetic material, which remain largely overlooked in most metagenomic studies1. Here we analysed 149,842 environmental genomes from multiple habitats2-6 and compiled a curated catalogue of 404,085 functionally and evolutionarily significant novel (FESNov) gene families exclusive to uncultivated prokaryotic taxa. All FESNov families span multiple species, exhibit strong signals of purifying selection and qualify as new orthologous groups, thus nearly tripling the number of bacterial and archaeal gene families described to date. The FESNov catalogue is enriched in clade-specific traits, including 1,034 novel families that can distinguish entire uncultivated phyla, classes and orders, probably representing synapomorphies that facilitated their evolutionary divergence. Using genomic context analysis and structural alignments we predicted functional associations for 32.4% of FESNov families, including 4,349 high-confidence associations with important biological processes. These predictions provide a valuable hypothesis-driven framework that we used for experimental validatation of a new gene family involved in cell motility and a novel set of antimicrobial peptides. We also demonstrate that the relative abundance profiles of novel families can discriminate between environments and clinical conditions, leading to the discovery of potentially new biomarkers associated with colorectal cancer. We expect this work to enhance future metagenomics studies and expand our knowledge of the genetic repertory of uncultivated organisms.
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Affiliation(s)
- Álvaro Rodríguez Del Río
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Joaquín Giner-Lamia
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain
- Departamento de Bioquímica Vegetal y Biología Molecular, Facultad de Biología, Instituto de Bioquímica Vegetal y Fotosíntesis (IBVF), Universidad de Sevilla-CSIC, Seville, Spain
| | - Carlos P Cantalapiedra
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Jorge Botas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Ziqi Deng
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Ana Hernández-Plaza
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Martí Munar-Palmer
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Saray Santamaría-Hernando
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - José J Rodríguez-Herva
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Hans-Joachim Ruscheweyh
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland
| | - Lucas Paoli
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland
| | - Thomas S B Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Shinichi Sunagawa
- Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Emilia López-Solanilla
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Luis Pedro Coelho
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Shanghai, China
- Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Translational Research Institute, Woolloongabba, Queensland, Australia
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain.
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Wu S, Feng T, Tang W, Qi C, Gao J, He X, Wang J, Zhou H, Fang Z. metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model. Brief Bioinform 2024; 25:bbae085. [PMID: 38487846 PMCID: PMC10940841 DOI: 10.1093/bib/bbae085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/26/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
Beneficial bacteria remain largely unexplored. Lacking systematic methods, understanding probiotic community traits becomes challenging, leading to various conclusions about their probiotic effects among different publications. We developed language model-based metaProbiotics to rapidly detect probiotic bins from metagenomes, demonstrating superior performance in simulated benchmark datasets. Testing on gut metagenomes from probiotic-treated individuals, it revealed the probioticity of intervention strains-derived bins and other probiotic-associated bins beyond the training data, such as a plasmid-like bin. Analyses of these bins revealed various probiotic mechanisms and bai operon as probiotic Ruminococcaceae's potential marker. In different health-disease cohorts, these bins were more common in healthy individuals, signifying their probiotic role, but relevant health predictions based on the abundance profiles of these bins faced cross-disease challenges. To better understand the heterogeneous nature of probiotics, we used metaProbiotics to construct a comprehensive probiotic genome set from global gut metagenomic data. Module analysis of this set shows that diseased individuals often lack certain probiotic gene modules, with significant variation of the missing modules across different diseases. Additionally, different gene modules on the same probiotic have heterogeneous effects on various diseases. We thus believe that gene function integrity of the probiotic community is more crucial in maintaining gut homeostasis than merely increasing specific gene abundance, and adding probiotics indiscriminately might not boost health. We expect that the innovative language model-based metaProbiotics tool will promote novel probiotic discovery using large-scale metagenomic data and facilitate systematic research on bacterial probiotic effects. The metaProbiotics program can be freely downloaded at https://github.com/zhenchengfang/metaProbiotics.
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Affiliation(s)
- Shufang Wu
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Tao Feng
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Waijiao Tang
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Cancan Qi
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jie Gao
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Department of Gastroenterology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaolong He
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jiaxuan Wang
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Hongwei Zhou
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zhencheng Fang
- Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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Harari S, Miller D, Fleishon S, Burstein D, Stern A. Using big sequencing data to identify chronic SARS-Coronavirus-2 infections. Nat Commun 2024; 15:648. [PMID: 38245511 PMCID: PMC10799923 DOI: 10.1038/s41467-024-44803-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/04/2024] [Indexed: 01/22/2024] Open
Abstract
The evolution of SARS-Coronavirus-2 (SARS-CoV-2) has been characterized by the periodic emergence of highly divergent variants. One leading hypothesis suggests these variants may have emerged during chronic infections of immunocompromised individuals, but limited data from these cases hinders comprehensive analyses. Here, we harnessed millions of SARS-CoV-2 genomes to identify potential chronic infections and used language models (LM) to infer chronic-associated mutations. First, we mined the SARS-CoV-2 phylogeny and identified chronic-like clades with identical metadata (location, age, and sex) spanning over 21 days, suggesting a prolonged infection. We inferred 271 chronic-like clades, which exhibited characteristics similar to confirmed chronic infections. Chronic-associated mutations were often high-fitness immune-evasive mutations located in the spike receptor-binding domain (RBD), yet a minority were unique to chronic infections and absent in global settings. The probability of observing high-fitness RBD mutations was 10-20 times higher in chronic infections than in global transmission chains. The majority of RBD mutations in BA.1/BA.2 chronic-like clades bore predictive value, i.e., went on to display global success. Finally, we used our LM to infer hundreds of additional chronic-like clades in the absence of metadata. Our approach allows mining extensive sequencing data and providing insights into future evolutionary patterns of SARS-CoV-2.
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Affiliation(s)
- Sheri Harari
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
| | - Danielle Miller
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
| | - Shay Fleishon
- Israeli Health Intelligence Agency, Public Health Division, Ministry of Health, Jerusalem, Israel
| | - David Burstein
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
| | - Adi Stern
- The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel.
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel.
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Egli A. ChatGPT, GPT-4, and Other Large Language Models: The Next Revolution for Clinical Microbiology? Clin Infect Dis 2023; 77:1322-1328. [PMID: 37399030 PMCID: PMC10640689 DOI: 10.1093/cid/ciad407] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/16/2023] [Accepted: 06/30/2023] [Indexed: 07/04/2023] Open
Abstract
ChatGPT, GPT-4, and Bard are highly advanced natural language process-based computer programs (chatbots) that simulate and process human conversation in written or spoken form. Recently released by the company OpenAI, ChatGPT was trained on billions of unknown text elements (tokens) and rapidly gained wide attention for its ability to respond to questions in an articulate manner across a wide range of knowledge domains. These potentially disruptive large language model (LLM) technologies have a broad range of conceivable applications in medicine and medical microbiology. In this opinion article, I describe how chatbot technologies work and discuss the strengths and weaknesses of ChatGPT, GPT-4, and other LLMs for applications in the routine diagnostic laboratory, focusing on various use cases for the pre- to post-analytical process.
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Affiliation(s)
- Adrian Egli
- Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland
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Aguirre-Sánchez JR, Quiñones B, Ortiz-Muñoz JA, Prieto-Alvarado R, Vega-López IF, Martínez-Urtaza J, Lee BG, Chaidez C. Comparative Genomic Analyses of Virulence and Antimicrobial Resistance in Citrobacter werkmanii, an Emerging Opportunistic Pathogen. Microorganisms 2023; 11:2114. [PMID: 37630674 PMCID: PMC10457828 DOI: 10.3390/microorganisms11082114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/11/2023] [Accepted: 08/13/2023] [Indexed: 08/27/2023] Open
Abstract
Citrobacter werkmanii is an emerging and opportunistic human pathogen found in developing countries and is a causative agent of wound, urinary tract, and blood infections. The present study conducted comparative genomic analyses of a C. werkmanii strain collection from diverse geographical locations and sources to identify the relevant virulence and antimicrobial resistance genes. Pangenome analyses divided the examined C. werkmanii strains into five distinct clades; the subsequent classification identified genes with functional roles in carbohydrate and general metabolism for the core genome and genes with a role in secretion, adherence, and the mobilome for the shell and cloud genomes. A maximum-likelihood phylogenetic tree with a heatmap, showing the virulence and antimicrobial genes' presence or absence, demonstrated the presence of genes with functional roles in secretion systems, adherence, enterobactin, and siderophore among the strains belonging to the different clades. C. werkmanii strains in clade V, predominantly from clinical sources, harbored genes implicated in type II and type Vb secretion systems as well as multidrug resistance to aminoglycoside, beta-lactamase, fluoroquinolone, phenicol, trimethoprim, macrolides, sulfonamide, and tetracycline. In summary, these comparative genomic analyses have demonstrated highly pathogenic and multidrug-resistant genetic profiles in C. werkmanii strains, indicating a virulence potential for this commensal and opportunistic human pathogen.
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Affiliation(s)
- José R. Aguirre-Sánchez
- Laboratorio Nacional para la Investigación en Inocuidad Alimentaria, Centro de Investigación en Alimentación y Desarrollo A.C. (CIAD), Coordinación Regional Culiacán, Culiacan 80110, Mexico;
| | - Beatriz Quiñones
- Produce Safety and Microbiology Research Unit, Western Regional Research Center, Agricultural Research Service, U.S. Department of Agriculture, Albany, CA 94710, USA; (B.Q.); (B.G.L.)
| | - José A. Ortiz-Muñoz
- Parque de Innovación Tecnológica de la Universidad Autónoma de Sinaloa, Culiacan 80040, Mexico; (J.A.O.-M.); (R.P.-A.); (I.F.V.-L.)
| | - Rogelio Prieto-Alvarado
- Parque de Innovación Tecnológica de la Universidad Autónoma de Sinaloa, Culiacan 80040, Mexico; (J.A.O.-M.); (R.P.-A.); (I.F.V.-L.)
| | - Inés F. Vega-López
- Parque de Innovación Tecnológica de la Universidad Autónoma de Sinaloa, Culiacan 80040, Mexico; (J.A.O.-M.); (R.P.-A.); (I.F.V.-L.)
| | - Jaime Martínez-Urtaza
- Departament de Genètica i de Microbiologia, Universitat Autờnoma de Barcelona, 08193 Bellaterra, Spain;
| | - Bertram G. Lee
- Produce Safety and Microbiology Research Unit, Western Regional Research Center, Agricultural Research Service, U.S. Department of Agriculture, Albany, CA 94710, USA; (B.Q.); (B.G.L.)
| | - Cristóbal Chaidez
- Laboratorio Nacional para la Investigación en Inocuidad Alimentaria, Centro de Investigación en Alimentación y Desarrollo A.C. (CIAD), Coordinación Regional Culiacán, Culiacan 80110, Mexico;
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de Lorenzo V. Innovation versus novelty in microbial systems. Environ Microbiol 2023; 25:167-170. [PMID: 36335556 PMCID: PMC10098617 DOI: 10.1111/1462-2920.16278] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 11/02/2022] [Indexed: 11/08/2022]
Affiliation(s)
- Víctor de Lorenzo
- Systems Biology Department, Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain
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