Pub Date : 2024-05-15DOI: 10.1016/j.cels.2024.04.004
Rory J Maizels, Daniel M Snell, James Briscoe
The snapshot nature of single-cell transcriptomics presents a challenge for studying the dynamics of cell fate decisions. Metabolic labeling and splicing can provide temporal information at single-cell level, but current methods have limitations. Here, we present a framework that overcomes these limitations: experimentally, we developed sci-FATE2, an optimized method for metabolic labeling with increased data quality, which we used to profile 45,000 embryonic stem (ES) cells differentiating into neural tube identities. Computationally, we developed a two-stage framework for dynamical modeling: VelvetVAE, a variational autoencoder (VAE) for velocity inference that outperforms all other tools tested, and VelvetSDE, a neural stochastic differential equation (nSDE) framework for simulating trajectory distributions. These recapitulate underlying dataset distributions and capture features such as decision boundaries between alternative fates and fate-specific gene expression. These methods recast single-cell analyses from descriptions of observed data to models of the dynamics that generated them, providing a framework for investigating developmental fate decisions.
{"title":"Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics.","authors":"Rory J Maizels, Daniel M Snell, James Briscoe","doi":"10.1016/j.cels.2024.04.004","DOIUrl":"https://doi.org/10.1016/j.cels.2024.04.004","url":null,"abstract":"<p><p>The snapshot nature of single-cell transcriptomics presents a challenge for studying the dynamics of cell fate decisions. Metabolic labeling and splicing can provide temporal information at single-cell level, but current methods have limitations. Here, we present a framework that overcomes these limitations: experimentally, we developed sci-FATE2, an optimized method for metabolic labeling with increased data quality, which we used to profile 45,000 embryonic stem (ES) cells differentiating into neural tube identities. Computationally, we developed a two-stage framework for dynamical modeling: VelvetVAE, a variational autoencoder (VAE) for velocity inference that outperforms all other tools tested, and VelvetSDE, a neural stochastic differential equation (nSDE) framework for simulating trajectory distributions. These recapitulate underlying dataset distributions and capture features such as decision boundaries between alternative fates and fate-specific gene expression. These methods recast single-cell analyses from descriptions of observed data to models of the dynamics that generated them, providing a framework for investigating developmental fate decisions.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"15 5","pages":"411-424.e9"},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.1016/j.cels.2024.04.005
Rohit Singh, Alexander P Wu, Anish Mudide, Bonnie Berger
Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.
{"title":"Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors.","authors":"Rohit Singh, Alexander P Wu, Anish Mudide, Bonnie Berger","doi":"10.1016/j.cels.2024.04.005","DOIUrl":"https://doi.org/10.1016/j.cels.2024.04.005","url":null,"abstract":"<p><p>Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"15 5","pages":"462-474.e5"},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1016/j.cels.2024.04.004
R. Maizels, Daniel M Snell, James Briscoe
{"title":"Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics.","authors":"R. Maizels, Daniel M Snell, James Briscoe","doi":"10.1016/j.cels.2024.04.004","DOIUrl":"https://doi.org/10.1016/j.cels.2024.04.004","url":null,"abstract":"","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"5 2","pages":"411-424.e9"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141025250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1016/j.cels.2024.04.006
Emily Laubscher, X. Wang, Nitzan Razin, Tom Dougherty, Rosalind J. Xu, Lincoln Ombelets, Edward Pao, William Graf, Jeffrey R. Moffitt, Yisong Yue, David Van Valen
{"title":"Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.","authors":"Emily Laubscher, X. Wang, Nitzan Razin, Tom Dougherty, Rosalind J. Xu, Lincoln Ombelets, Edward Pao, William Graf, Jeffrey R. Moffitt, Yisong Yue, David Van Valen","doi":"10.1016/j.cels.2024.04.006","DOIUrl":"https://doi.org/10.1016/j.cels.2024.04.006","url":null,"abstract":"","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"5 4","pages":"475-482.e6"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141048562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-01DOI: 10.1016/j.cels.2024.04.007
Van M. Savage
{"title":"Multilevel relations among plankton stitched together with an eco-evolutionary needle.","authors":"Van M. Savage","doi":"10.1016/j.cels.2024.04.007","DOIUrl":"https://doi.org/10.1016/j.cels.2024.04.007","url":null,"abstract":"","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":"21 12","pages":"409-410"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141030660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17Epub Date: 2024-03-26DOI: 10.1016/j.cels.2024.03.002
Anthony T Meger, Matthew A Spence, Mahakaran Sandhu, Dana Matthews, Jackie Chen, Colin J Jackson, Srivatsan Raman
How a protein's function influences the shape of its fitness landscape, smooth or rugged, is a fundamental question in evolutionary biochemistry. Smooth landscapes arise when incremental mutational steps lead to a progressive change in function, as commonly seen in enzymes and binding proteins. On the other hand, rugged landscapes are poorly understood because of the inherent unpredictability of how sequence changes affect function. Here, we experimentally characterize the entire sequence phylogeny, comprising 1,158 extant and ancestral sequences, of the DNA-binding domain (DBD) of the LacI/GalR transcriptional repressor family. Our analysis revealed an extremely rugged landscape with rapid switching of specificity, even between adjacent nodes. Further, the ruggedness arises due to the necessity of the repressor to simultaneously evolve specificity for asymmetric operators and disfavors potentially adverse regulatory crosstalk. Our study provides fundamental insight into evolutionary, molecular, and biophysical rules of genetic regulation through the lens of fitness landscapes.
蛋白质的功能如何影响其适应性景观的形状(平滑或崎岖),这是生物化学进化中的一个基本问题。当增量突变步骤导致功能逐渐改变时,就会出现平滑的景观,这在酶和结合蛋白中很常见。另一方面,由于序列变化对功能的影响具有固有的不可预测性,人们对崎岖地貌的了解甚少。在这里,我们通过实验描述了 LacI/GalR 转录抑制因子家族 DNA 结合域 (DBD) 的整个序列系统发育,包括 1,158 个现存序列和祖先序列。我们的分析表明,即使在相邻的节点之间,特异性也会快速转换,从而形成一个极其崎岖不平的景观。此外,这种崎岖是由于抑制因子必须同时进化出对非对称操作者的特异性,并且不利于潜在的不利调控串扰。我们的研究通过适合度景观的视角,对遗传调控的进化、分子和生物物理规则提供了基本的见解。
{"title":"Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors.","authors":"Anthony T Meger, Matthew A Spence, Mahakaran Sandhu, Dana Matthews, Jackie Chen, Colin J Jackson, Srivatsan Raman","doi":"10.1016/j.cels.2024.03.002","DOIUrl":"10.1016/j.cels.2024.03.002","url":null,"abstract":"<p><p>How a protein's function influences the shape of its fitness landscape, smooth or rugged, is a fundamental question in evolutionary biochemistry. Smooth landscapes arise when incremental mutational steps lead to a progressive change in function, as commonly seen in enzymes and binding proteins. On the other hand, rugged landscapes are poorly understood because of the inherent unpredictability of how sequence changes affect function. Here, we experimentally characterize the entire sequence phylogeny, comprising 1,158 extant and ancestral sequences, of the DNA-binding domain (DBD) of the LacI/GalR transcriptional repressor family. Our analysis revealed an extremely rugged landscape with rapid switching of specificity, even between adjacent nodes. Further, the ruggedness arises due to the necessity of the repressor to simultaneously evolve specificity for asymmetric operators and disfavors potentially adverse regulatory crosstalk. Our study provides fundamental insight into evolutionary, molecular, and biophysical rules of genetic regulation through the lens of fitness landscapes.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"374-387.e6"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11299162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140308242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17Epub Date: 2024-03-19DOI: 10.1016/j.cels.2024.02.005
Katherine B McCauley, Kalki Kukreja, Alfredo E Tovar Walker, Aron B Jaffe, Allon M Klein
Receptor-mediated signaling plays a central role in tissue regeneration, and it is dysregulated in disease. Here, we build a signaling-response map for a model regenerative human tissue: the airway epithelium. We analyzed the effect of 17 receptor-mediated signaling pathways on organotypic cultures to determine changes in abundance and phenotype of epithelial cell types. This map recapitulates the gamut of known airway epithelial signaling responses to these pathways. It defines convergent states induced by multiple ligands and diverse, ligand-specific responses in basal cell and secretory cell metaplasia. We show that loss of canonical differentiation induced by multiple pathways is associated with cell-cycle arrest, but that arrest is not sufficient to block differentiation. Using the signaling-response map, we show that a TGFB1-mediated response underlies specific aberrant cells found in multiple lung diseases and identify interferon responses in COVID-19 patient samples. Thus, we offer a framework enabling systematic evaluation of tissue signaling responses. A record of this paper's transparent peer review process is included in the supplemental information.
{"title":"A map of signaling responses in the human airway epithelium.","authors":"Katherine B McCauley, Kalki Kukreja, Alfredo E Tovar Walker, Aron B Jaffe, Allon M Klein","doi":"10.1016/j.cels.2024.02.005","DOIUrl":"10.1016/j.cels.2024.02.005","url":null,"abstract":"<p><p>Receptor-mediated signaling plays a central role in tissue regeneration, and it is dysregulated in disease. Here, we build a signaling-response map for a model regenerative human tissue: the airway epithelium. We analyzed the effect of 17 receptor-mediated signaling pathways on organotypic cultures to determine changes in abundance and phenotype of epithelial cell types. This map recapitulates the gamut of known airway epithelial signaling responses to these pathways. It defines convergent states induced by multiple ligands and diverse, ligand-specific responses in basal cell and secretory cell metaplasia. We show that loss of canonical differentiation induced by multiple pathways is associated with cell-cycle arrest, but that arrest is not sufficient to block differentiation. Using the signaling-response map, we show that a TGFB1-mediated response underlies specific aberrant cells found in multiple lung diseases and identify interferon responses in COVID-19 patient samples. Thus, we offer a framework enabling systematic evaluation of tissue signaling responses. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"307-321.e10"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11031335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140178236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17Epub Date: 2024-03-29DOI: 10.1016/j.cels.2024.03.001
Eric Wilson, John Kevin Cava, Diego Chowell, Remya Raja, Kiran K Mangalaparthi, Akhilesh Pandey, Marion Curtis, Karen S Anderson, Abhishek Singharoy
Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.
大分子识别和蛋白质互补的预测模型是生物物理科学的基石之一。然而,分子界面相互作用的组合复杂性往往阻碍了此类模型的建立。这一问题的典型例子是高度多态的主要组织相容性复合体 I 类(MHC-I)分子呈现肽,这是免疫识别的主要组成部分。我们开发了人类白细胞抗原(HLA)-Inception,这是一种深度生物物理卷积神经网络,它整合了分子静电学,捕捉非键式相互作用,用于预测 5,821 个 MHC-I 等位基因的肽结合主题。这些预测生成的主题与实验肽结合和呈现数据密切相关。除了分子相互作用外,该研究还展示了预测基团在分析 MHC-I 等位基因与 HIV 疾病进展和患者对免疫检查点抑制剂的反应之间的关联方面的应用。补充信息中包含了这篇论文透明的同行评审过程记录。
{"title":"The electrostatic landscape of MHC-peptide binding revealed using inception networks.","authors":"Eric Wilson, John Kevin Cava, Diego Chowell, Remya Raja, Kiran K Mangalaparthi, Akhilesh Pandey, Marion Curtis, Karen S Anderson, Abhishek Singharoy","doi":"10.1016/j.cels.2024.03.001","DOIUrl":"10.1016/j.cels.2024.03.001","url":null,"abstract":"<p><p>Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"362-373.e7"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140330355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20Epub Date: 2024-02-27DOI: 10.1016/j.cels.2024.02.002
Gwanggyu Sun, Mialy M DeFelice, Taryn E Gillies, Travis A Ahn-Horst, Cecelia J Andrews, Markus Krummenacker, Peter D Karp, Jerry H Morrison, Markus W Covert
Many bacteria use operons to coregulate genes, but it remains unclear how operons benefit bacteria. We integrated E. coli's 788 polycistronic operons and 1,231 transcription units into an existing whole-cell model and found inconsistencies between the proposed operon structures and the RNA-seq read counts that the model was parameterized from. We resolved these inconsistencies through iterative, model-guided corrections to both datasets, including the correction of RNA-seq counts of short genes that were misreported as zero by existing alignment algorithms. The resulting model suggested two main modes by which operons benefit bacteria. For 86% of low-expression operons, adding operons increased the co-expression probabilities of their constituent proteins, whereas for 92% of high-expression operons, adding operons resulted in more stable expression ratios between the proteins. These simulations underscored the need for further experimental work on how operons reduce noise and synchronize both the expression timing and the quantity of constituent genes. A record of this paper's transparent peer review process is included in the supplemental information.
{"title":"Cross-evaluation of E. coli's operon structures via a whole-cell model suggests alternative cellular benefits for low- versus high-expressing operons.","authors":"Gwanggyu Sun, Mialy M DeFelice, Taryn E Gillies, Travis A Ahn-Horst, Cecelia J Andrews, Markus Krummenacker, Peter D Karp, Jerry H Morrison, Markus W Covert","doi":"10.1016/j.cels.2024.02.002","DOIUrl":"10.1016/j.cels.2024.02.002","url":null,"abstract":"<p><p>Many bacteria use operons to coregulate genes, but it remains unclear how operons benefit bacteria. We integrated E. coli's 788 polycistronic operons and 1,231 transcription units into an existing whole-cell model and found inconsistencies between the proposed operon structures and the RNA-seq read counts that the model was parameterized from. We resolved these inconsistencies through iterative, model-guided corrections to both datasets, including the correction of RNA-seq counts of short genes that were misreported as zero by existing alignment algorithms. The resulting model suggested two main modes by which operons benefit bacteria. For 86% of low-expression operons, adding operons increased the co-expression probabilities of their constituent proteins, whereas for 92% of high-expression operons, adding operons resulted in more stable expression ratios between the proteins. These simulations underscored the need for further experimental work on how operons reduce noise and synchronize both the expression timing and the quantity of constituent genes. A record of this paper's transparent peer review process is included in the supplemental information.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"227-245.e7"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10957310/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139992088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-20Epub Date: 2024-02-23DOI: 10.1016/j.cels.2024.02.001
Xiaoli Chen, Miaoxiao Wang, Laipeng Luo, Liyun An, Xiaonan Liu, Yuan Fang, Ting Huang, Yong Nie, Xiao-Lei Wu
Unraveling the mechanisms governing the diversity of ecological communities is a central goal in ecology. Although microbial dispersal constitutes an important ecological process, the effect of dispersal on microbial diversity is poorly understood. Here, we sought to fill this gap by combining a generalized Lotka-Volterra model with experimental investigations. Our model showed that emigration increases the diversity of the community when the immigration rate crosses a defined threshold, which we identified as Ineutral. We also found that at high immigration rates, emigration weakens the relative abundance of fast-growing species and thus enhances the mass effect and increases the diversity. We experimentally confirmed this finding using co-cultures of 20 bacterial strains isolated from the soil. Our model further showed that Ineutral decreases with the increase of species pool size, growth rate, and interspecies interaction. Our work deepens the understanding of the effects of dispersal on the diversity of natural communities.
{"title":"High immigration rates critical for establishing emigration-driven diversity in microbial communities.","authors":"Xiaoli Chen, Miaoxiao Wang, Laipeng Luo, Liyun An, Xiaonan Liu, Yuan Fang, Ting Huang, Yong Nie, Xiao-Lei Wu","doi":"10.1016/j.cels.2024.02.001","DOIUrl":"10.1016/j.cels.2024.02.001","url":null,"abstract":"<p><p>Unraveling the mechanisms governing the diversity of ecological communities is a central goal in ecology. Although microbial dispersal constitutes an important ecological process, the effect of dispersal on microbial diversity is poorly understood. Here, we sought to fill this gap by combining a generalized Lotka-Volterra model with experimental investigations. Our model showed that emigration increases the diversity of the community when the immigration rate crosses a defined threshold, which we identified as I<sub>neutral</sub>. We also found that at high immigration rates, emigration weakens the relative abundance of fast-growing species and thus enhances the mass effect and increases the diversity. We experimentally confirmed this finding using co-cultures of 20 bacterial strains isolated from the soil. Our model further showed that I<sub>neutral</sub> decreases with the increase of species pool size, growth rate, and interspecies interaction. Our work deepens the understanding of the effects of dispersal on the diversity of natural communities.</p>","PeriodicalId":93929,"journal":{"name":"Cell systems","volume":" ","pages":"275-285.e4"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139944789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}