PLoS Computational BiologyPub Date : 2024-09-23eCollection Date: 2024-09-01DOI: 10.1371/journal.pcbi.1012428
Nafiseh Atapour, Marcello G P Rosa, Shi Bai, Sylwia Bednarek, Agata Kulesza, Gabriela Saworska, Sadaf Teymornejad, Katrina H Worthy, Piotr Majka
{"title":"Distribution of calbindin-positive neurons across areas and layers of the marmoset cerebral cortex.","authors":"Nafiseh Atapour, Marcello G P Rosa, Shi Bai, Sylwia Bednarek, Agata Kulesza, Gabriela Saworska, Sadaf Teymornejad, Katrina H Worthy, Piotr Majka","doi":"10.1371/journal.pcbi.1012428","DOIUrl":"10.1371/journal.pcbi.1012428","url":null,"abstract":"<p><p>The diversity of the mammalian cerebral cortex demands technical approaches to map the spatial distribution of neurons with different biochemical identities. This issue is magnified in the case of the primate cortex, characterized by a large number of areas with distinctive cytoarchitectures. To date, no full map of the distribution of cells expressing a specific protein has been reported for the cortex of any primate. Here we have charted the 3-dimensional distribution of neurons expressing the calcium-binding protein calbindin (CB+ neurons) across the entire marmoset cortex, using a combination of immunohistochemistry, automated cell identification, computerized reconstruction, and cytoarchitecture-aware registration. CB+ neurons formed a heterogeneous population, which together corresponded to 10-20% of the cortical neurons. They occurred in higher proportions in areas corresponding to low hierarchical levels of processing, such as sensory cortices. Although CB+ neurons were concentrated in the supragranular and granular layers, there were clear global trends in their laminar distribution. For example, their relative density in infragranular layers increased with hierarchical level along sensorimotor processing streams, and their density in layer 4 was lower in areas involved in sensorimotor integration, action planning and motor control. These results reveal new quantitative aspects of the cytoarchitectural organization of the primate cortex, and demonstrate an approach to mapping the full distribution of neurochemically distinct cells throughout the brain which is readily applicable to most other mammalian species.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11495585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142308437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PLoS Computational BiologyPub Date : 2024-09-23eCollection Date: 2024-09-01DOI: 10.1371/journal.pcbi.1012452
Matt J Keeling, Louise Dyson
{"title":"A retrospective assessment of forecasting the peak of the SARS-CoV-2 Omicron BA.1 wave in England.","authors":"Matt J Keeling, Louise Dyson","doi":"10.1371/journal.pcbi.1012452","DOIUrl":"10.1371/journal.pcbi.1012452","url":null,"abstract":"<p><p>We discuss the invasion of the Omicron BA.1 variant into England as a paradigm for real-time model fitting and projection. Here we use a mixture of simple SIR-type models, analysis of the early data and a more complex age-structure model fit to the outbreak to understand the dynamics. In particular, we highlight that early data shows that the invading Omicron variant had a substantial growth advantage over the resident Delta variant. However, early data does not allow us to reliably infer other key epidemiological parameters-such as generation time and severity-which influence the expected peak hospital numbers. With more complete epidemic data from January 2022 are we able to capture the true scale of the epidemic in terms of both infections and hospital admissions, driven by different infection characteristics of Omicron compared to Delta and a substantial shift in estimated precautionary behaviour during December. This work highlights the challenges of real time forecasting, in a rapidly changing environment with limited information on the variant's epidemiological characteristics.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11449292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142308435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PLoS Computational BiologyPub Date : 2024-09-23eCollection Date: 2024-09-01DOI: 10.1371/journal.pcbi.1012447
Juan Pablo Franco, Peter Bossaerts, Carsten Murawski
{"title":"The neural dynamics associated with computational complexity.","authors":"Juan Pablo Franco, Peter Bossaerts, Carsten Murawski","doi":"10.1371/journal.pcbi.1012447","DOIUrl":"10.1371/journal.pcbi.1012447","url":null,"abstract":"<p><p>Many everyday tasks require people to solve computationally complex problems. However, little is known about the effects of computational hardness on the neural processes associated with solving such problems. Here, we draw on computational complexity theory to address this issue. We performed an experiment in which participants solved several instances of the 0-1 knapsack problem, a combinatorial optimization problem, while undergoing ultra-high field (7T) functional magnetic resonance imaging (fMRI). Instances varied in computational hardness. We characterize a network of brain regions whose activation was correlated with computational complexity, including the anterior insula, dorsal anterior cingulate cortex and the intra-parietal sulcus/angular gyrus. Activation and connectivity changed dynamically as a function of complexity, in line with theoretical computational requirements. Overall, our results suggest that computational complexity theory provides a suitable framework to study the effects of computational hardness on the neural processes associated with solving complex cognitive tasks.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11449275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142308439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PLoS Computational BiologyPub Date : 2024-09-19eCollection Date: 2024-09-01DOI: 10.1371/journal.pcbi.1012431
Diego A Forero, Walter H Curioso, Wei Wang
{"title":"Ten simple rules for successfully carrying out funded research projects.","authors":"Diego A Forero, Walter H Curioso, Wei Wang","doi":"10.1371/journal.pcbi.1012431","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012431","url":null,"abstract":"","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11412653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PLoS Computational BiologyPub Date : 2024-09-19eCollection Date: 2024-09-01DOI: 10.1371/journal.pcbi.1012402
Wilson Wen Bin Goh, Mohammad Neamul Kabir, Sehwan Yoo, Limsoon Wong
{"title":"Ten quick tips for ensuring machine learning model validity.","authors":"Wilson Wen Bin Goh, Mohammad Neamul Kabir, Sehwan Yoo, Limsoon Wong","doi":"10.1371/journal.pcbi.1012402","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012402","url":null,"abstract":"<p><p>Artificial Intelligence (AI) and Machine Learning (ML) models are increasingly deployed on biomedical and health data to shed insights on biological mechanism, predict disease outcomes, and support clinical decision-making. However, ensuring model validity is challenging. The 10 quick tips described here discuss useful practices on how to check AI/ML models from 2 perspectives-the user and the developer.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11412509/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Eric Bastien, Rachel N. Cable, Cecelia Batterbee, A. J. Wing, Luis Zaman, Melissa B. Duhaime
{"title":"Virus-host interactions predictor (VHIP): Machine learning approach to resolve microbial virus-host interaction networks","authors":"G. Eric Bastien, Rachel N. Cable, Cecelia Batterbee, A. J. Wing, Luis Zaman, Melissa B. Duhaime","doi":"10.1371/journal.pcbi.1011649","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1011649","url":null,"abstract":"Viruses of microbes are ubiquitous biological entities that reprogram their hosts’ metabolisms during infection in order to produce viral progeny, impacting the ecology and evolution of microbiomes with broad implications for human and environmental health. Advances in genome sequencing have led to the discovery of millions of novel viruses and an appreciation for the great diversity of viruses on Earth. Yet, with knowledge of only <jats:italic>“who is there</jats:italic>?<jats:italic>”</jats:italic> we fall short in our ability to infer the impacts of viruses on microbes at population, community, and ecosystem-scales. To do this, we need a more explicit understanding <jats:italic>“who do they infect</jats:italic>?<jats:italic>”</jats:italic> Here, we developed a novel machine learning model (ML), Virus-Host Interaction Predictor (VHIP), to predict virus-host interactions (infection/non-infection) from input virus and host genomes. This ML model was trained and tested on a high-value manually curated set of 8849 virus-host pairs and their corresponding sequence data. The resulting dataset, ‘Virus Host Range network’ (VHRnet), is core to VHIP functionality. Each data point that underlies the VHIP training and testing represents a lab-tested virus-host pair in VHRnet, from which meaningful signals of viral adaptation to host were computed from genomic sequences. VHIP departs from existing virus-host prediction models in its ability to predict multiple interactions rather than predicting a single most likely host or host clade. As a result, VHIP is able to infer the complexity of virus-host networks in natural systems. VHIP has an 87.8% accuracy rate at predicting interactions between virus-host pairs at the species level and can be applied to novel viral and host population genomes reconstructed from metagenomic datasets.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PLoS Computational BiologyPub Date : 2024-09-18eCollection Date: 2024-09-01DOI: 10.1371/journal.pcbi.1012454
Kiesha Prem, Kevin van Zandvoort, Petra Klepac, Rosalind M Eggo, Nicholas G Davies
{"title":"Correction: Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era.","authors":"Kiesha Prem, Kevin van Zandvoort, Petra Klepac, Rosalind M Eggo, Nicholas G Davies","doi":"10.1371/journal.pcbi.1012454","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012454","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1371/journal.pcbi.1009098.].</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11410204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hannah Zoller, Carlos Garcia Perez, Javier Betel Geijo Fernández, Wolfgang zu Castell
{"title":"Measuring and understanding information storage and transfer in a simulated human gut microbiome","authors":"Hannah Zoller, Carlos Garcia Perez, Javier Betel Geijo Fernández, Wolfgang zu Castell","doi":"10.1371/journal.pcbi.1012359","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012359","url":null,"abstract":"Considering biological systems as information processing entities and analyzing their organizational structure via information-theoretic measures has become an established approach in life sciences. We transfer this framework to a field of broad general interest, the human gut microbiome. We use BacArena, a software combining agent-based modelling and flux-balance analysis, to simulate a simplified human intestinal microbiome (SIHUMI). In a first step, we derive information theoretic measures from the simulated abundance data, and, in a second step, relate them to the metabolic processes underlying the abundance data. Our study provides further evidence on the role of active information storage as an indicator of unexpected structural change in the observed system. Besides, we show that information transfer reflects coherent behavior in the microbial community, both as a reaction to environmental changes and as a result of direct effective interaction. In this sense, purely abundance-based information theoretic measures can provide meaningful insight on metabolic interactions within bacterial communities. Furthermore, we shed light on the important however little noticed technical aspect of distinguishing immediate and delayed effects in the interpretation of local information theoretical measures.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PLoS Computational BiologyPub Date : 2024-09-17eCollection Date: 2024-09-01DOI: 10.1371/journal.pcbi.1012469
Ke Li, Chrispin Chaguza, Julian Stamp, Yi Ting Chew, Nicholas F G Chen, David Ferguson, Sameer Pandya, Nick Kerantzas, Wade Schulz, Anne M Hahn, C Brandon Ogbunugafor, Virginia E Pitzer, Lorin Crawford, Daniel M Weinberger, Nathan D Grubaugh
{"title":"Genome-wide association study between SARS-CoV-2 single nucleotide polymorphisms and virus copies during infections.","authors":"Ke Li, Chrispin Chaguza, Julian Stamp, Yi Ting Chew, Nicholas F G Chen, David Ferguson, Sameer Pandya, Nick Kerantzas, Wade Schulz, Anne M Hahn, C Brandon Ogbunugafor, Virginia E Pitzer, Lorin Crawford, Daniel M Weinberger, Nathan D Grubaugh","doi":"10.1371/journal.pcbi.1012469","DOIUrl":"10.1371/journal.pcbi.1012469","url":null,"abstract":"<p><p>Significant variations have been observed in viral copies generated during SARS-CoV-2 infections. However, the factors that impact viral copies and infection dynamics are not fully understood, and may be inherently dependent upon different viral and host factors. Here, we conducted virus whole genome sequencing and measured viral copies using RT-qPCR from 9,902 SARS-CoV-2 infections over a 2-year period to examine the impact of virus genetic variation on changes in viral copies adjusted for host age and vaccination status. Using a genome-wide association study (GWAS) approach, we identified multiple single-nucleotide polymorphisms (SNPs) corresponding to amino acid changes in the SARS-CoV-2 genome associated with variations in viral copies. We further applied a marginal epistasis test to detect interactions among SNPs and identified multiple pairs of substitutions located in the spike gene that have non-linear effects on viral copies. We also analyzed the temporal patterns and found that SNPs associated with increased viral copies were predominantly observed in Delta and Omicron BA.2/BA.4/BA.5/XBB infections, whereas those associated with decreased viral copies were only observed in infections with Omicron BA.1 variants. Our work showcases how GWAS can be a useful tool for probing phenotypes related to SNPs in viral genomes that are worth further exploration. We argue that this approach can be used more broadly across pathogens to characterize emerging variants and monitor therapeutic interventions.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11432881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The neglected giants: Uncovering the prevalence and functional groups of huge proteins in proteomes.","authors":"Anibal S Amaral, Damien P Devos","doi":"10.1371/journal.pcbi.1012459","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012459","url":null,"abstract":"<p><p>An often-overlooked aspect of biology is formed by the outliers of the protein length distribution, specifically those proteins with more than 5000 amino acids, which we refer to as huge proteins (HPs). By examining UniprotKB, we discovered more than 41 000 HPs throughout the tree of life, with the majority found in eukaryotes. Notably, the phyla with the highest propensity for huge proteins are Apicomplexa and Fornicata. Moreover, we observed that certain bacteria, such as Elusomicrobia or Planctomycetota, have a higher tendency for encoding huge proteins, even more than the average eukaryote. To investigate if these macro-polypeptides represent \"real\" proteins, we explored several indirect metrics. Additionally, orthology analyses reveals thousands of clusters of homologous sequences of HPs, revealing functional groups related to key cellular processes such as cytoskeleton organization and functioning as chaperones or as E3-ubiquitin ligases in eukaryotes. In the case of bacteria, the major clusters have functions related to Non-Ribosomomal peptide synthesis/Polyketide synthesis, followed by pathogen-host attachment or recognition surface proteins. Further exploration of the annotations for each HPs supported the previously identified functional groups. These findings underscore the need for further investigation of the cellular and ecological roles of these HPs and their potential impact on biology and biotechnology.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142293730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}