Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. Utilizing a dataset of whole-slide images from low-, intermediate-, and high-risk prostate cancer patients, we manually annotated axons to train our model, achieving significant accuracy in detecting axonal structures that were previously hard to segment. Our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories, providing insights into the correlation between tumor innervation and cancer progression. Our paper suggests the potential utility of neuronal markers in the prognostic assessment of prostate cancer in aiding the pathologist's assessment of tumor sections and advancing our understanding of neurosignaling in the tumor microenvironment.
{"title":"AxonFinder: Automated segmentation of tumor innervating neuronal fibers","authors":"Kaoutar Ait-Ahmad, Cigdem Ak, Guillaume Thibault, Young Hwan Chang, Sebnem Ece Eksi","doi":"10.1101/2024.09.03.611089","DOIUrl":"https://doi.org/10.1101/2024.09.03.611089","url":null,"abstract":"Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. Utilizing a dataset of whole-slide images from low-, intermediate-, and high-risk prostate cancer patients, we manually annotated axons to train our model, achieving significant accuracy in detecting axonal structures that were previously hard to segment. Our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories, providing insights into the correlation between tumor innervation and cancer progression. Our paper suggests the potential utility of neuronal markers in the prognostic assessment of prostate cancer in aiding the pathologist's assessment of tumor sections and advancing our understanding of neurosignaling in the tumor microenvironment.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202745","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 : 2024-09-06DOI: 10.1101/2024.09.03.611014
Wen Luo, Ioannis P Androulakis
This study discusses an efficient approach to characterizing chronotypes using Symbolic Aggregate approXimation (SAX) on actigraphy data. Actigraphy, a non-invasive monitoring of human rest/activity cycles, provides valuable insights into sleep-wake behaviors and circadian rhythms. However, the high dimensionality of actigraphy data poses significant challenges in storage, processing, and analysis. To address these challenges, we applied the SAX algorithm to transform continuous time-series actigraphy data into a symbolic representation, enabling dimensionality reduction while preserving essential patterns. We analyzed actigraphy data from the National Health and Nutrition Examination Survey (NHANES) database, covering over 10,000 individuals, and used unsupervised clustering to identify distinct chronotype patterns. The SAX transformation facilitated the application of machine learning techniques, revealing five chronotype clusters characterized by differences in activity onset, resolution, and intensity. Age distribution analysis showed biases towards specific age groups within the clusters, highlighting the relationship between age and chronotype. Key findings include age-related Chronotype variations with younger individuals exhibiting delayed chronotypes with significant differences in sleep onset (SOT) and wake time (WT) compared to older adults, suggesting a phase delay in sleep patterns as age decreases and activity transition dynamics where clusters showed distinct patterns in winding up and winding down periods, providing insights into the dynamics of activity transitions. This study demonstrates the efficiency and effectiveness of SAX in processing large-scale actigraphy data, enabling robust chronotype characterization that can inform personalized healthcare and public health initiatives. Further exploration of SAX integration with other biometric measures could deepen our understanding of human circadian biology and its impact on health and behavior.
{"title":"Characterization of Chronotypes Using the Symbolic Aggregate approXimation (SAX) on Actigraphy Data","authors":"Wen Luo, Ioannis P Androulakis","doi":"10.1101/2024.09.03.611014","DOIUrl":"https://doi.org/10.1101/2024.09.03.611014","url":null,"abstract":"This study discusses an efficient approach to characterizing chronotypes using Symbolic Aggregate approXimation (SAX) on actigraphy data. Actigraphy, a non-invasive monitoring of human rest/activity cycles, provides valuable insights into sleep-wake behaviors and circadian rhythms. However, the high dimensionality of actigraphy data poses significant challenges in storage, processing, and analysis. To address these challenges, we applied the SAX algorithm to transform continuous time-series actigraphy data into a symbolic representation, enabling dimensionality reduction while preserving essential patterns. We analyzed actigraphy data from the National Health and Nutrition Examination Survey (NHANES) database, covering over 10,000 individuals, and used unsupervised clustering to identify distinct chronotype patterns. The SAX transformation facilitated the application of machine learning techniques, revealing five chronotype clusters characterized by differences in activity onset, resolution, and intensity. Age distribution analysis showed biases towards specific age groups within the clusters, highlighting the relationship between age and chronotype. Key findings include age-related Chronotype variations with younger individuals exhibiting delayed chronotypes with significant differences in sleep onset (SOT) and wake time (WT) compared to older adults, suggesting a phase delay in sleep patterns as age decreases and activity transition dynamics where clusters showed distinct patterns in winding up and winding down periods, providing insights into the dynamics of activity transitions. This study demonstrates the efficiency and effectiveness of SAX in processing large-scale actigraphy data, enabling robust chronotype characterization that can inform personalized healthcare and public health initiatives. Further exploration of SAX integration with other biometric measures could deepen our understanding of human circadian biology and its impact on health and behavior.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202747","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 : 2024-09-06DOI: 10.1101/2024.09.03.611021
Zhiyuan Zhang, Guozhong Chen, Erguang Li
Bacteria play a crucial role in environmental conservation, human health, and medicine. Whether in the gut or the soil, bacterial genomes are rich repositories of resources, such as exploring potential drugs and biopesticides. However, our ability to develop new therapies and deepen our understanding of the bacterial world is hindered by the largely unknown functions of bacterial genes. In this study, we proposed a method of gene network construction and analysis based on a Gaussian Graphical Model (GGM) and random sampling strategy to infer direct interactions at the genomic level in bacteria. Using Vibrio cholerae and Staphylococcus aureus as examples, we integrated partial correlation-based gene co-expression data with gene regulatory and essentiality information extracted from public databases to construct more comprehensive gene networks. Networks built upon bacterial different phenotypes, such as biofilm formation, flagellar assembly, and stress response, demonstrate the effectiveness of this method in deciphering unknown gene functions, uncovering new phenotype-associated factors, and identifying their corresponding interactions, thus providing new targets for experimental validation by researchers. Additionally, we extended this method to 14 bacteria, including 13 pathogens, supporting the investigation of gene functions and pathways at the genomic level in these bacteria. More importantly, for other species, this method of gene network construction can be easily implemented, provided that sufficient transcriptome sequencing samples are available.
{"title":"A statistical-based method for the construction and analysis of gene network：application to bacteria","authors":"Zhiyuan Zhang, Guozhong Chen, Erguang Li","doi":"10.1101/2024.09.03.611021","DOIUrl":"https://doi.org/10.1101/2024.09.03.611021","url":null,"abstract":"Bacteria play a crucial role in environmental conservation, human health, and medicine. Whether in the gut or the soil, bacterial genomes are rich repositories of resources, such as exploring potential drugs and biopesticides. However, our ability to develop new therapies and deepen our understanding of the bacterial world is hindered by the largely unknown functions of bacterial genes. In this study, we proposed a method of gene network construction and analysis based on a Gaussian Graphical Model (GGM) and random sampling strategy to infer direct interactions at the genomic level in bacteria. Using Vibrio cholerae and Staphylococcus aureus as examples, we integrated partial correlation-based gene co-expression data with gene regulatory and essentiality information extracted from public databases to construct more comprehensive gene networks. Networks built upon bacterial different phenotypes, such as biofilm formation, flagellar assembly, and stress response, demonstrate the effectiveness of this method in deciphering unknown gene functions, uncovering new phenotype-associated factors, and identifying their corresponding interactions, thus providing new targets for experimental validation by researchers. Additionally, we extended this method to 14 bacteria, including 13 pathogens, supporting the investigation of gene functions and pathways at the genomic level in these bacteria. More importantly, for other species, this method of gene network construction can be easily implemented, provided that sufficient transcriptome sequencing samples are available.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202746","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 : 2024-09-03DOI: 10.1101/2024.09.03.609973
Uriel Urquiza-Garcia, Nacho Molina, Karen J. Halliday, Andrew J Millar
Understanding the biochemistry behind whole-organism traits such as flowering time is a longstanding challenge, where mathematical models are critical. Very few models of plant gene circuits use the absolute units required for comparison to biochemical data. We refactor two detailed models of the plant circadian clock from relative to absolute units. Using absolute RNA quantification, a simple model predicted abundant clock protein levels in Arabidopsis thaliana, up to 100,000 proteins per cell. NanoLUC reporter protein fusions validated the predicted levels of clock proteins in vivo. Recalibrating the detailed models to these protein levels estimated their DNA-binding dissociation constants (Kd). We estimate the same Kd from multiple results in vitro, extending the method to any promoter sequence. The detailed models simulated the Kd range estimated from LUX DNA-binding in vitro but departed from the data for CCA1 binding, pointing to further circadian mechanisms. Our analytical and experimental methods should transfer to understand other plant gene regulatory networks, potentially including the natural sequence variation that contributes to evolutionary adaptation.
了解整个生物体性状（如开花时间）背后的生物化学是一项长期挑战，其中数学模型至关重要。很少有植物基因回路模型使用与生化数据比较所需的绝对单位。我们将植物昼夜节律时钟的两个详细模型从相对单位重构为绝对单位。使用绝对 RNA 定量，一个简单的模型预测了拟南芥中丰富的时钟蛋白水平，每个细胞高达 100,000 个蛋白。NanoLUC 报告蛋白融合验证了预测的体内时钟蛋白水平。根据这些蛋白质水平重新校准详细模型，估算出了它们的 DNA 结合解离常数（Kd）。我们从体外的多个结果中估算出了相同的 Kd，从而将该方法扩展到了任何启动子序列。详细模型模拟了体外 LUX DNA 结合估计的 Kd 范围，但与 CCA1 结合的数据有偏差，这表明存在进一步的昼夜节律机制。我们的分析和实验方法可用于了解其他植物基因调控网络，可能包括有助于进化适应的自然序列变异。
{"title":"Abundant clock proteins point to missing molecular regulation in the plant circadian clock","authors":"Uriel Urquiza-Garcia, Nacho Molina, Karen J. Halliday, Andrew J Millar","doi":"10.1101/2024.09.03.609973","DOIUrl":"https://doi.org/10.1101/2024.09.03.609973","url":null,"abstract":"Understanding the biochemistry behind whole-organism traits such as flowering time is a longstanding challenge, where mathematical models are critical. Very few models of plant gene circuits use the absolute units required for comparison to biochemical data. We refactor two detailed models of the plant circadian clock from relative to absolute units. Using absolute RNA quantification, a simple model predicted abundant clock protein levels in Arabidopsis thaliana, up to 100,000 proteins per cell. NanoLUC reporter protein fusions validated the predicted levels of clock proteins in vivo. Recalibrating the detailed models to these protein levels estimated their DNA-binding dissociation constants (Kd). We estimate the same Kd from multiple results in vitro, extending the method to any promoter sequence. The detailed models simulated the Kd range estimated from LUX DNA-binding in vitro but departed from the data for CCA1 binding, pointing to further circadian mechanisms. Our analytical and experimental methods should transfer to understand other plant gene regulatory networks, potentially including the natural sequence variation that contributes to evolutionary adaptation.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"106 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202748","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 : 2024-09-03DOI: 10.1101/2024.09.01.610667
Benjamin Planterose Jiménez, Alexander R Blackwell, João J Ramalho, Sander van den Heuvel, Kirsten ten Tusscher, Erika Tsingos
The invariant lineages of C. elegans provide tractable cell fate models to study how developing organisms robustly integrate spatial signals at the single-cell level via gene regulatory networks. For instance, during postembryonic development, a mesoderm lineage arises through a sequence of oriented cell divisions from a single progenitor. This mesoblast initially gives rise to 18 cells with three distinct fates – 14 body wall muscles (BWMs), 2 coelomocytes (CCs; dorsal), and 2 sex myoblasts (SMs; ventral). The latter cells migrate and then proliferate to contribute 16 smooth muscles to the nematode's reproductive organs. Prior work identified key symmetry breaking cues: i) ventrally restricted activation of the LIN-12 Notch pathway promoting SM over CC fate and ii) asymmetric re-distribution of SYS-1 β-catenin and POP-1 TCF among daughter cells along the anteroposterior (A-P) axis, i.e. the Wnt/β-catenin asymmetry pathway. However, it remains unclear whether these pathways are sufficient to specify all cell fates accordingly or whether additional symmetry breaking cues are necessary. In this study, we use quantitative modelling to better understand fate specification in the postembryonic M lineage. Specifically, we focus on the anteroposterior symmetry break by creating increasingly complex models towards robustly reproducing fate specification in wild type larvae and mutants. This iterative process resulted in two alternative models that explain the experimental observations by either introducing an additional spatial (spatial symmetry break) or temporal cue (temporal symmetry break). Finally, we evaluate their plausibility and propose a series of experiments to provide support for alternative models. Overall, our study highlights how a quantitative examination of mechanistic ideas can identify knowledge gaps and guide experimental follow-up.
秀丽隐杆线虫的不变系为研究发育中生物如何通过基因调控网络在单细胞水平上稳健地整合空间信号提供了可行的细胞命运模型。例如，在胚后发育过程中，一个中胚层系是通过从单个祖细胞定向分裂的序列产生的。这种中胚层细胞最初产生 18 个具有三种不同命运的细胞--14 个体壁肌（BWMs）、2 个腹腔细胞（CCs；背侧）和 2 个性肌细胞（SMs；腹侧）。后一种细胞迁移并增殖，为线虫的生殖器官提供 16 块平滑肌。先前的工作确定了打破对称的关键线索：i）LIN-12 Notch 通路的腹侧限制性激活，促进 SM 的命运，而不是 CC 的命运；ii）SYS-1 β-catenin 和 POP-1 TCF 沿前胸（A-P）轴在子细胞中的不对称再分布，即 Wnt/β-catenin 不对称通路。然而，目前仍不清楚这些途径是否足以相应地指定所有细胞的命运，或者是否还需要其他打破对称的线索。在本研究中，我们利用定量建模来更好地理解胚后 M 系的命运规范。具体来说，我们通过建立越来越复杂的模型，在野生型幼虫和突变体中稳健地再现命运规格，重点研究前后对称性断裂。这一反复过程产生了两个替代模型，通过引入额外的空间线索（空间对称性断裂）或时间线索（时间对称性断裂）来解释实验观察结果。最后，我们对它们的合理性进行了评估，并提出了一系列实验来为替代模型提供支持。总之，我们的研究强调了对机理观点的定量研究如何能够发现知识差距并指导后续实验。
{"title":"Quantitative modelling of fate specification in the C. elegans postembryonic M lineage reveals a missing spatiotemporal signal","authors":"Benjamin Planterose Jiménez, Alexander R Blackwell, João J Ramalho, Sander van den Heuvel, Kirsten ten Tusscher, Erika Tsingos","doi":"10.1101/2024.09.01.610667","DOIUrl":"https://doi.org/10.1101/2024.09.01.610667","url":null,"abstract":"The invariant lineages of <em>C. elegans</em> provide tractable cell fate models to study how developing organisms robustly integrate spatial signals at the single-cell level via gene regulatory networks. For instance, during postembryonic development, a mesoderm lineage arises through a sequence of oriented cell divisions from a single progenitor. This mesoblast initially gives rise to 18 cells with three distinct fates – 14 body wall muscles (BWMs), 2 coelomocytes (CCs; dorsal), and 2 sex myoblasts (SMs; ventral). The latter cells migrate and then proliferate to contribute 16 smooth muscles to the nematode's reproductive organs. Prior work identified key symmetry breaking cues: i) ventrally restricted activation of the LIN-12 Notch pathway promoting SM over CC fate and ii) asymmetric re-distribution of SYS-1 β-catenin and POP-1 TCF among daughter cells along the anteroposterior (A-P) axis, i.e. the Wnt/β-catenin asymmetry pathway. However, it remains unclear whether these pathways are sufficient to specify all cell fates accordingly or whether additional symmetry breaking cues are necessary. In this study, we use quantitative modelling to better understand fate specification in the postembryonic M lineage. Specifically, we focus on the anteroposterior symmetry break by creating increasingly complex models towards robustly reproducing fate specification in wild type larvae and mutants. This iterative process resulted in two alternative models that explain the experimental observations by either introducing an additional spatial (spatial symmetry break) or temporal cue (temporal symmetry break). Finally, we evaluate their plausibility and propose a series of experiments to provide support for alternative models. Overall, our study highlights how a quantitative examination of mechanistic ideas can identify knowledge gaps and guide experimental follow-up.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202750","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 : 2024-09-03DOI: 10.1101/2024.09.02.610818
Devlina Sarkar, Sudip Kundu
CAM (Crassulacean Acid Metabolism) plants reduce the water loss through transpiration in arid environments (Wickell et al., 2021), using an alternative pathway of carbon assimilation. To ensure food security, engineering CAM into C3 plants can be achieved by inverting the stomatal rhythm and the timing of major CO2 uptake from day to night. Identification of the metabolic enzymes and intra-cellular transporters, present in both C3 and CAM but having different differential temporal activities throughout the diel cycle (Yang et al., 2015; Heyduk et al., 2019) and quantitative estimations of the flux distributions along the biochemical trajectory of C3-to-CAM transition may help us to achieve the goal. Here, we simulate a constraint-based combined metabolic model of guard cell (GC) and mesophyll cell (MC), linking temporal fluctuations of temperature (T) and relative humidity (RH) throughout the diurnal cycle with osmolyte accumulation dependent stomatal opening, CO2 uptake and transpirational water loss. Starting with C3 metabolism, gradual increase in water-use efficiency (WUE) captures several known and new differential activities of metabolic enzymes, transporters, sugar-malate cycle etc. in GC and MC during C3-to-CAM transition.
{"title":"Water-saving GC-MC model captures temporally differential enzymatic and transporter activities during C3-CAM transition","authors":"Devlina Sarkar, Sudip Kundu","doi":"10.1101/2024.09.02.610818","DOIUrl":"https://doi.org/10.1101/2024.09.02.610818","url":null,"abstract":"CAM (Crassulacean Acid Metabolism) plants reduce the water loss through transpiration in arid environments (Wickell et al., 2021), using an alternative pathway of carbon assimilation. To ensure food security, engineering CAM into C3 plants can be achieved by inverting the stomatal rhythm and the timing of major CO2 uptake from day to night. Identification of the metabolic enzymes and intra-cellular transporters, present in both C3 and CAM but having different differential temporal activities throughout the diel cycle (Yang et al., 2015; Heyduk et al., 2019) and quantitative estimations of the flux distributions along the biochemical trajectory of C3-to-CAM transition may help us to achieve the goal. Here, we simulate a constraint-based combined metabolic model of guard cell (GC) and mesophyll cell (MC), linking temporal fluctuations of temperature (T) and relative humidity (RH) throughout the diurnal cycle with osmolyte accumulation dependent stomatal opening, CO2 uptake and transpirational water loss. Starting with C3 metabolism, gradual increase in water-use efficiency (WUE) captures several known and new differential activities of metabolic enzymes, transporters, sugar-malate cycle etc. in GC and MC during C3-to-CAM transition.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202749","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 : 2024-09-02DOI: 10.1101/2024.09.01.610678
Kathleen Wilson Kurgan, Freddie James Oliver Martin, William Michael Dawson, Thomas Brunnock, Andrew J Orr-Ewing, Dek N Woolfson
De novo protein design is delivering new peptide and protein structures at a rapid pace. Many of these synthetic polypeptides form well-defined and hyperthermal-stable structures. Generally, however, less is known about the dynamic properties of the de novo designed structures. Here, we explore one aspect of dynamics in a series of de novo coiled-coil peptide assemblies: namely, peptide exchange within and between different oligomers from dimers through to heptamers. First, we develop a fluorescence-based reporter assay for peptide exchange that is straightforward to implement, and, thus, would be useful to others examining similar systems. We apply this assay to explore both homotypic exchange within single species, and heterotypic exchange between coiled coils of different coiled-coil oligomer states. For the former, we provide detailed study for the dimeric coiled coil CC-Di finding a half-life for exchange of 4.2 plus or minus 0.3 minutes when the concentration of CC-Di is 200 micro molar. Interestingly, more broadly when assessing exchange across all of the oligomeric states, we find that some of the designs are faithful and only undergo homotypic strand exchange, whereas others are promiscuous and exchange to form unexpected hetero-oligomers. Finally, we develop two design strategies to improve the orthogonality of the different oligomers: (i) using alternate positioning of salt bridge interactions; and (ii) incorporating of non-canonical repeats into the designed sequences. In so doing, we reconcile the promiscuity and deliver a set of faithful homo-oligomeric de novo coiled-coil peptides. Our findings have implications for the application of these and other coiled coils as modules in chemical and synthetic biology.
{"title":"Exchange, promiscuity, and orthogonality in de novo designed coiled-coil peptide assemblies","authors":"Kathleen Wilson Kurgan, Freddie James Oliver Martin, William Michael Dawson, Thomas Brunnock, Andrew J Orr-Ewing, Dek N Woolfson","doi":"10.1101/2024.09.01.610678","DOIUrl":"https://doi.org/10.1101/2024.09.01.610678","url":null,"abstract":"De novo protein design is delivering new peptide and protein structures at a rapid pace. Many of these synthetic polypeptides form well-defined and hyperthermal-stable structures. Generally, however, less is known about the dynamic properties of the de novo designed structures. Here, we explore one aspect of dynamics in a series of de novo coiled-coil peptide assemblies: namely, peptide exchange within and between different oligomers from dimers through to heptamers. First, we develop a fluorescence-based reporter assay for peptide exchange that is straightforward to implement, and, thus, would be useful to others examining similar systems. We apply this assay to explore both homotypic exchange within single species, and heterotypic exchange between coiled coils of different coiled-coil oligomer states. For the former, we provide detailed study for the dimeric coiled coil CC-Di finding a half-life for exchange of 4.2 plus or minus 0.3 minutes when the concentration of CC-Di is 200 micro molar. Interestingly, more broadly when assessing exchange across all of the oligomeric states, we find that some of the designs are faithful and only undergo homotypic strand exchange, whereas others are promiscuous and exchange to form unexpected hetero-oligomers. Finally, we develop two design strategies to improve the orthogonality of the different oligomers: (i) using alternate positioning of salt bridge interactions; and (ii) incorporating of non-canonical repeats into the designed sequences. In so doing, we reconcile the promiscuity and deliver a set of faithful homo-oligomeric de novo coiled-coil peptides. Our findings have implications for the application of these and other coiled coils as modules in chemical and synthetic biology.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202751","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 : 2024-09-01DOI: 10.1101/2024.08.30.610498
Daniel Ramirez, Mingyang Lu
Understanding cell state transitions and their governing regulatory mechanisms remains one of the fundamental questions in biology. We develop a computational method, state transition inference using cross-cell correlations (STICCC), for predicting reversible and irreversible cell state transitions at single-cell resolution by using gene expression data and a set of gene regulatory interactions. The method is inspired by the fact that the gene expression time delays between regulators and targets can be exploited to infer past and future gene expression states. From applications to both simulated and experimental single-cell gene expression data, we show that STICCC-inferred vector fields capture basins of attraction and irreversible fluxes. By connecting regulatory information with systems' dynamical behaviors, STICCC reveals how network interactions influence reversible and irreversible state transitions. Compared to existing methods that infer pseudotime and RNA velocity, STICCC provides complementary insights into the gene regulation of cell state transitions.
{"title":"Dissecting reversible and irreversible single cell state transitions from gene regulatory networks","authors":"Daniel Ramirez, Mingyang Lu","doi":"10.1101/2024.08.30.610498","DOIUrl":"https://doi.org/10.1101/2024.08.30.610498","url":null,"abstract":"Understanding cell state transitions and their governing regulatory mechanisms remains one of the fundamental questions in biology. We develop a computational method, state transition inference using cross-cell correlations (STICCC), for predicting reversible and irreversible cell state transitions at single-cell resolution by using gene expression data and a set of gene regulatory interactions. The method is inspired by the fact that the gene expression time delays between regulators and targets can be exploited to infer past and future gene expression states. From applications to both simulated and experimental single-cell gene expression data, we show that STICCC-inferred vector fields capture basins of attraction and irreversible fluxes. By connecting regulatory information with systems' dynamical behaviors, STICCC reveals how network interactions influence reversible and irreversible state transitions. Compared to existing methods that infer pseudotime and RNA velocity, STICCC provides complementary insights into the gene regulation of cell state transitions.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202752","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 : 2024-08-30DOI: 10.1101/2024.08.29.610284
Achuthan Ambat, Naomi Iris van den Berg, Francisco Zorrilla, Shruti Menon, Abhijit Maji, Arianna Basile, Sudeep Ghimire, Lajos Kalmar, Kiran Raosaheb Patil, Joy Scaria
Commensal gut bacteria are key contributors to the resilience against pathogen invasion. This is exemplified by the success of fecal microbiota transplantation in treating recurrent Clostridioides difficile infection. Yet, characteristics of communities that can confer colonization resistance and the underlying mechanisms remain largely unknown. Here we use a synthetic community of 14 commensal gut bacteria to uncover inter-species interactions and metabolic pathways underpinning the emergent resilience against C. difficile invasion. We challenged this synthetic community as well as fecal-matter-derived communities with antibiotic treatment and C. difficile in a continuous flow bioreactor. Using generalized Lotka-Volterra and genome-scale metabolic modelling, we identified interactions between Escherichia coli and Bacteroides/Phocaeicola sp. as key to the pathogen's suppression. Metabolomics analysis further revealed that fructooligosaccharide metabolism, vitamin B3 biosynthesis, and competition for Stickland metabolism precursors contribute to suppression. Analysis of metagenomics data from patient cohorts and clinical trials attested the in vivo relevance of the identified metabolic pathways and the ratio between Bacteroides and Escherichia in successful colonization resistance. The latter was found to be a much stronger discriminator than commonly used alpha diversity metrics. Our study uncovers emergent microbial interactions in pathogen resistance with implications for rational design of bacteriotherapies.
{"title":"Emergent metabolic interactions in resistance to Clostridioides difficile invasion","authors":"Achuthan Ambat, Naomi Iris van den Berg, Francisco Zorrilla, Shruti Menon, Abhijit Maji, Arianna Basile, Sudeep Ghimire, Lajos Kalmar, Kiran Raosaheb Patil, Joy Scaria","doi":"10.1101/2024.08.29.610284","DOIUrl":"https://doi.org/10.1101/2024.08.29.610284","url":null,"abstract":"Commensal gut bacteria are key contributors to the resilience against pathogen invasion. This is exemplified by the success of fecal microbiota transplantation in treating recurrent <em>Clostridioides difficile</em> infection. Yet, characteristics of communities that can confer colonization resistance and the underlying mechanisms remain largely unknown. Here we use a synthetic community of 14 commensal gut bacteria to uncover inter-species interactions and metabolic pathways underpinning the emergent resilience against <em>C. difficile</em> invasion. We challenged this synthetic community as well as fecal-matter-derived communities with antibiotic treatment and <em>C. difficile</em> in a continuous flow bioreactor. Using generalized Lotka-Volterra and genome-scale metabolic modelling, we identified interactions between <em>Escherichia coli</em> and <em>Bacteroides/Phocaeicola sp.</em> as key to the pathogen's suppression. Metabolomics analysis further revealed that fructooligosaccharide metabolism, vitamin B3 biosynthesis, and competition for Stickland metabolism precursors contribute to suppression. Analysis of metagenomics data from patient cohorts and clinical trials attested the <em>in vivo</em> relevance of the identified metabolic pathways and the ratio between <em>Bacteroides</em> and <em>Escherichia</em> in successful colonization resistance. The latter was found to be a much stronger discriminator than commonly used alpha diversity metrics. Our study uncovers emergent microbial interactions in pathogen resistance with implications for rational design of bacteriotherapies.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202758","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 : 2024-08-30DOI: 10.1101/2024.08.28.610203
He-Gang Chen, XIONGHUI ZHOU
Combination therapy, which can improve therapeutic efficacy and reduce side effects, plays an important role in the treatment of multiple complex diseases. Yet, the design principles of molecular combinations remain unclear. In addition, the huge search space of candidate drug combinations and the numerous heterogeneous data has brought us a big challenge. Here, we proposed a Friendship based Method (FSM), which integrates diverse drug-to-drug information to predict drug combinations for specific diseases. By quantifying the friendship-based relationship between drugs, we found that there is a moderate similarity between the drugs of effective drug combinations in a high-dimensional, heterogeneous feature space. Following this discovery, FSM applied a two-step strategy to predict clinically efficacious drug combinations for specific diseases. First, our method employs the friendship features to evaluate whether each drug is combinable. Then, the synergistic potential of combinable drugs was further evaluated. FSM was validated on two types of disease. The results show that FSM achieves substantial performance improvement over other state-of-the-art methods and tends to have low toxicity. These results indicate that our model could potentially offer a generic, powerful strategy to identify efficacious combination therapies in the vast search space.
{"title":"Prediction and principle discovery of drug combination based on multimodal friendship features","authors":"He-Gang Chen, XIONGHUI ZHOU","doi":"10.1101/2024.08.28.610203","DOIUrl":"https://doi.org/10.1101/2024.08.28.610203","url":null,"abstract":"Combination therapy, which can improve therapeutic efficacy and reduce side effects, plays an important role in the treatment of multiple complex diseases. Yet, the design principles of molecular combinations remain unclear. In addition, the huge search space of candidate drug combinations and the numerous heterogeneous data has brought us a big challenge. Here, we proposed a Friendship based Method (FSM), which integrates diverse drug-to-drug information to predict drug combinations for specific diseases. By quantifying the friendship-based relationship between drugs, we found that there is a moderate similarity between the drugs of effective drug combinations in a high-dimensional, heterogeneous feature space. Following this discovery, FSM applied a two-step strategy to predict clinically efficacious drug combinations for specific diseases. First, our method employs the friendship features to evaluate whether each drug is combinable. Then, the synergistic potential of combinable drugs was further evaluated. FSM was validated on two types of disease. The results show that FSM achieves substantial performance improvement over other state-of-the-art methods and tends to have low toxicity. These results indicate that our model could potentially offer a generic, powerful strategy to identify efficacious combination therapies in the vast search space.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202754","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}
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