1
|
Goonewardena SN, Chen Q, Tate AM, Grushko OG, Damodaran D, Blakely PK, Hayek SS, Pinsky DJ, Rosenson RS. Monocyte-Mediated Thrombosis Linked to Circulating Tissue Factor and Immune Paralysis in COVID-19. Arterioscler Thromb Vasc Biol 2024; 44:1124-1134. [PMID: 38511328 DOI: 10.1161/atvbaha.122.318721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/29/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND SARS-CoV-2 infections cause COVID-19 and are associated with inflammation, coagulopathy, and high incidence of thrombosis. Myeloid cells help coordinate the initial immune response in COVID-19. Although we appreciate that myeloid cells lie at the nexus of inflammation and thrombosis, the mechanisms that unite the two in COVID-19 remain largely unknown. METHODS In this study, we used systems biology approaches including proteomics, transcriptomics, and mass cytometry to define the circulating proteome and circulating immune cell phenotypes in subjects with COVID-19. RESULTS In a cohort of subjects with COVID-19 (n=35), circulating markers of inflammation (CCL23 [C-C motif chemokine ligand 23] and IL [interleukin]-6) and vascular dysfunction (ACE2 [angiotensin-converting enzyme 2] and TF [tissue factor]) were elevated in subjects with severe compared with mild COVID-19. Additionally, although the total white blood cell counts were similar between COVID-19 groups, CD14+ (cluster of differentiation) monocytes from subjects with severe COVID-19 expressed more TF. At baseline, transcriptomics demonstrated increased IL-6, CCL3, ACOD1 (aconitate decarboxylase 1), C5AR1 (complement component 5a receptor), C5AR2, and TF in subjects with severe COVID-19 compared with controls. Using stress transcriptomics, we found that circulating immune cells from subjects with severe COVID-19 had evidence of profound immune paralysis with greatly reduced transcriptional activation and release of inflammatory markers in response to TLR (Toll-like receptor) activation. Finally, sera from subjects with severe (but not mild) COVID-19 activated human monocytes and induced TF expression. CONCLUSIONS Taken together, these observations further elucidate the pathological mechanisms that underlie immune dysfunction and coagulation abnormalities in COVID-19, contributing to our growing understanding of SARS-CoV-2 infections that could also be leveraged to develop novel diagnostic and therapeutic strategies.
Collapse
Affiliation(s)
- Sascha N Goonewardena
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor (S.N.G., A.M.T., O.G.G., D.D., P.K.B., S.S.H., D.J.P.)
| | - Qinzhong Chen
- Metabolism and Lipids Unit, Cardiovascular Institute, Marie-Josee and Henry R. Kravis Center for Cardiovascular Health, Icahn School of Medicine at Mount Sinai, New York, NY (Q.C., R.S.R.)
| | - Ashley M Tate
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor (S.N.G., A.M.T., O.G.G., D.D., P.K.B., S.S.H., D.J.P.)
| | - Olga G Grushko
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor (S.N.G., A.M.T., O.G.G., D.D., P.K.B., S.S.H., D.J.P.)
| | - Dilna Damodaran
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor (S.N.G., A.M.T., O.G.G., D.D., P.K.B., S.S.H., D.J.P.)
| | - Pennelope K Blakely
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor (S.N.G., A.M.T., O.G.G., D.D., P.K.B., S.S.H., D.J.P.)
| | - Salim S Hayek
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor (S.N.G., A.M.T., O.G.G., D.D., P.K.B., S.S.H., D.J.P.)
| | - David J Pinsky
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor (S.N.G., A.M.T., O.G.G., D.D., P.K.B., S.S.H., D.J.P.)
| | - Robert S Rosenson
- Metabolism and Lipids Unit, Cardiovascular Institute, Marie-Josee and Henry R. Kravis Center for Cardiovascular Health, Icahn School of Medicine at Mount Sinai, New York, NY (Q.C., R.S.R.)
| |
Collapse
|
2
|
Nguyen V, Li Y, Lu T. Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae. ACS Synth Biol 2024. [PMID: 38657170 DOI: 10.1021/acssynbio.3c00542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. This study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.
Collapse
Affiliation(s)
- Viviana Nguyen
- Department of Physics, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States
| | - Yifei Li
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Ting Lu
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801-3028, United States
- Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- Carl R Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
- National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| |
Collapse
|
3
|
Xue G, Zhang X, Li W, Zhang L, Zhang Z, Zhou X, Zhang D, Zhang L, Li Z. A logic-incorporated gene regulatory network deciphers principles in cell fate decisions. eLife 2024; 12:RP88742. [PMID: 38652107 PMCID: PMC11037919 DOI: 10.7554/elife.88742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024] Open
Abstract
Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.
Collapse
Affiliation(s)
- Gang Xue
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaoyi Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Wanqi Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Zongxu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaolin Zhou
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Di Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lei Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Beijing International Center for Mathematical Research, Center for Machine Learning Research, Peking UniversityBeijingChina
| | - Zhiyuan Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| |
Collapse
|
4
|
Tarandovskiy ID, Surov SS, Parunov LA, Liang Y, Jankowski W, Sauna ZE, Ovanesov MV. Investigation of thrombin concentration at the time of clot formation in simultaneous thrombin and fibrin generation assays. Sci Rep 2024; 14:9225. [PMID: 38649717 PMCID: PMC11035586 DOI: 10.1038/s41598-023-47694-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/16/2023] [Indexed: 04/25/2024] Open
Abstract
Thrombin generation (TG) and fibrin clot formation represent the central process of blood coagulation. Up to 95% of thrombin is considered to be generated after the clot is formed. However, this was not investigated in depth. In this study, we conducted a quantitative analysis of the Thrombin at Clot Time (TCT) parameter in 5758 simultaneously recorded TG and clot formation assays using frozen plasma samples from commercial sources under various conditions of activation. These samples were supplemented with clotting factor concentrates, procoagulant lipid vesicles and a fluorogenic substrate and triggered with tissue factor (TF). We found that TCT is often close to a 10% of thrombin peak height (TPH) yet it can be larger or smaller depending on whether the sample has low or high TPH value. In general, the samples with high TPH are associated with elevated TCT. TCT appeared more sensitive to some procoagulant phenotypes than other commonly used parameters such as clotting time, TPH or Thrombin Production Rate (TPR). In a minority of cases, TCT were not predicted from TG parameters. For example, elevated TCT (above 15% of TPH) was associated with either very low or very high TPR values. We conclude that clotting and TG assays may provide complementary information about the plasma sample, and that the TCT parameter may serve as an additional marker for the procoagulant potential in plasma sample.
Collapse
Affiliation(s)
- Ivan D Tarandovskiy
- U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - Stepan S Surov
- U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - Leonid A Parunov
- U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - Yideng Liang
- U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - Wojciech Jankowski
- U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - Zuben E Sauna
- U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - Mikhail V Ovanesov
- U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
| |
Collapse
|
5
|
Song W, Shi Y, Lin GN. Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits. eLife 2024; 12:RP92574. [PMID: 38639992 PMCID: PMC11031082 DOI: 10.7554/elife.92574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024] Open
Abstract
We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.
Collapse
Affiliation(s)
- Weichen Song
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Bioengineering, Shanghai Jiao Tong UniversityShanghaiChina
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong UniversityShanghaiChina
- Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X12 Institutes), Qingdao UniversityQingdaoChina
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Bioengineering, Shanghai Jiao Tong UniversityShanghaiChina
| |
Collapse
|
6
|
Zimmermann J, Piecyk A, Sieber M, Petersen C, Johnke J, Moitinho-Silva L, Künzel S, Bluhm L, Traulsen A, Kaleta C, Schulenburg H. Gut-associated functions are favored during microbiome assembly across a major part of C. elegans life. mBio 2024:e0001224. [PMID: 38634692 DOI: 10.1128/mbio.00012-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024] Open
Abstract
The microbiome expresses a variety of functions that influence host biology. The range of functions depends on the microbiome's composition, which can change during the host's lifetime due to neutral assembly processes, host-mediated selection, and environmental conditions. To date, the exact dynamics of microbiome assembly, the underlying determinants, and the effects on host-associated functions remain poorly understood. Here, we used the nematode Caenorhabditis elegans and a defined community of fully sequenced, naturally associated bacteria to study microbiome dynamics and functions across a major part of the worm's lifetime of hosts under controlled experimental conditions. Bacterial community composition initially shows strongly declining levels of stochasticity, which increases during later time points, suggesting selective effects in younger animals as opposed to more random processes in older animals. The adult microbiome is enriched in genera Ochrobactrum and Enterobacter compared to the direct substrate and a host-free control environment. Using pathway analysis, metabolic, and ecological modeling, we further find that the lifetime assembly dynamics increase competitive strategies and gut-associated functions in the host-associated microbiome, indicating that the colonizing bacteria benefit the worm. Overall, our study introduces a framework for studying microbiome assembly dynamics based on stochastic, ecological, and metabolic models, yielding new insights into the processes that determine host-associated microbiome composition and function. IMPORTANCE The microbiome plays a crucial role in host biology. Its functions depend on the microbiome composition that can change during a host's lifetime. To date, the dynamics of microbiome assembly and the resulting functions still need to be better understood. This study introduces a new approach to characterize the functional consequences of microbiome assembly by modeling both the relevance of stochastic processes and metabolic characteristics of microbial community changes. The approach was applied to experimental time-series data obtained for the microbiome of the nematode Caenorhabditis elegans across the major part of its lifetime. Stochastic processes played a minor role, whereas beneficial bacteria as well as gut-associated functions enriched in hosts. This indicates that the host might actively shape the composition of its microbiome. Overall, this study provides a framework for studying microbiome assembly dynamics and yields new insights into C. elegans microbiome functions.
Collapse
Affiliation(s)
- Johannes Zimmermann
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
- Max Planck Fellow Group Antibiotic Resistance Evolution, Max Planck Institute for Evolutionary Biology, Ploen, Germany
- Research Group Medical Systems Biology, Institute of Experimental Medicine, Kiel University, Kiel, Germany
| | - Agnes Piecyk
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
| | - Michael Sieber
- Department for Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Ploen, Germany
| | - Carola Petersen
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
| | - Julia Johnke
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
| | - Lucas Moitinho-Silva
- 5Institute of Clinical Molecular Biology, Christian-Albrechts University, Kiel, Germany
| | - Sven Künzel
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Ploen, Germany
| | - Lena Bluhm
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
| | - Arne Traulsen
- Department for Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Ploen, Germany
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute of Experimental Medicine, Kiel University, Kiel, Germany
| | - Hinrich Schulenburg
- Research Group Evolutionary Ecology and Genetics, Zoological Institute, Kiel University, Kiel, Germany
- Max Planck Fellow Group Antibiotic Resistance Evolution, Max Planck Institute for Evolutionary Biology, Ploen, Germany
| |
Collapse
|
7
|
Hickey JW, Agmon E, Horowitz N, Tan TK, Lamore M, Sunwoo JB, Covert MW, Nolan GP. Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component of therapeutic T cell efficacy. Cell Syst 2024; 15:322-338.e5. [PMID: 38636457 PMCID: PMC11030795 DOI: 10.1016/j.cels.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/07/2023] [Accepted: 03/19/2024] [Indexed: 04/20/2024]
Abstract
Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here, we integrated CODEX multiplexed tissue imaging with multiscale modeling software to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface. A record of this paper's transparent peer review process is included in the supplemental information.
Collapse
Affiliation(s)
- John W Hickey
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Eran Agmon
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Center for Cell Analysis and Modeling, University of Connecticut Health, Farmington, CT 06032, USA
| | - Nina Horowitz
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Tze-Kai Tan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Matthew Lamore
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - John B Sunwoo
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Otolaryngology, Head and Neck Surgery, Stanford Cancer Institute Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
| |
Collapse
|
8
|
Yuan Q, Tian C, Yang Y. Genome-scale annotation of protein binding sites via language model and geometric deep learning. eLife 2024; 13:RP93695. [PMID: 38630609 PMCID: PMC11023698 DOI: 10.7554/elife.93695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven't fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.
Collapse
Affiliation(s)
- Qianmu Yuan
- School of Computer Science and Engineering, Sun Yat-sen UniversityGuangzhouChina
| | - Chong Tian
- School of Computer Science and Engineering, Sun Yat-sen UniversityGuangzhouChina
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen UniversityGuangzhouChina
| |
Collapse
|
9
|
Xie L, Yu W, Gao J, Wang H, Zhou YJ. Ogataea polymorpha as a next-generation chassis for industrial biotechnology. Trends Biotechnol 2024:S0167-7799(24)00086-6. [PMID: 38622041 DOI: 10.1016/j.tibtech.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/15/2024] [Accepted: 03/18/2024] [Indexed: 04/17/2024]
Abstract
Ogataea (Hansenula) polymorpha is a nonconventional yeast with some unique characteristics, including fast growth, thermostability, and broad substrate spectrum. Other than common applications for protein production, O. polymorpha is attracting interest for chemical and protein production from methanol; a promising feedstock for the next-generation biomanufacturing due to its abundant sources and excellent characteristics. Benefiting from the development of synthetic biology, it has been engineered to produce value-added chemicals by extensively rewiring cellular metabolism. This Review discusses recently developed synthetic biology tools of O. polymorpha. The advances of chemicals production and systems biology were reviewed comprehensively. Finally, we look ahead to the developments of biomanufacturing in O. polymorpha to make an overall understanding of this chassis for academia and industry.
Collapse
Affiliation(s)
- Linfeng Xie
- Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Yu
- Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Dalian Key Laboratory of Energy Biotechnology, Dalian Institute of Chemical Physics, CAS, 457 Zhongshan Road, Dalian 116023, China
| | - Jiaoqi Gao
- Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Dalian Key Laboratory of Energy Biotechnology, Dalian Institute of Chemical Physics, CAS, 457 Zhongshan Road, Dalian 116023, China
| | - Haoyu Wang
- Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongjin J Zhou
- Division of Biotechnology, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; Dalian Key Laboratory of Energy Biotechnology, Dalian Institute of Chemical Physics, CAS, 457 Zhongshan Road, Dalian 116023, China.
| |
Collapse
|
10
|
Liu Y, Cheng YY, Thompson J, Zhou Z, Vivas EI, Warren MF, Rey FE, Anantharaman K, Venturelli OS. Shaping human gut community assembly and butyrate production by controlling the arginine dihydrolase pathway. bioRxiv 2024:2023.01.10.523442. [PMID: 37986770 PMCID: PMC10659395 DOI: 10.1101/2023.01.10.523442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The arginine dihydrolase pathway (arc operon) present in a subset of diverse human gut species enables arginine catabolism. This specialized metabolic pathway can alter environmental pH and nitrogen availability, which in turn could shape gut microbiota inter-species interactions. By exploiting synthetic control of gene expression, we investigated the role of the arc operon in probiotic Escherichia coli Nissle 1917 on human gut community assembly and health-relevant metabolite profiles in vitro and in the murine gut. By stabilizing environmental pH, the arc operon reduced variability in community composition across different initial pH perturbations. The abundance of butyrate producing bacteria were altered in response to arc operon activity and butyrate production was enhanced in a physiologically relevant pH range. While the presence of the arc operon altered community dynamics, it did not impact production of short chain fatty acids. Dynamic computational modeling of pH-mediated interactions reveals the quantitative contribution of this mechanism to community assembly. In sum, our framework to quantify the contribution of molecular pathways and mechanism modalities on microbial community dynamics and functions could be applied more broadly.
Collapse
Affiliation(s)
- Yiyi Liu
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison WI 53706
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706
| | - Yu-Yu Cheng
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706
| | - Jaron Thompson
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison WI 53706
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706
| | - Zhichao Zhou
- Department of Bacteriology, University of Wisconsin-Madison, WI 53706
| | - Eugenio I. Vivas
- Department of Bacteriology, University of Wisconsin-Madison, WI 53706
- Gnotobiotic Animal Core Facility, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Matthew F. Warren
- Department of Bacteriology, University of Wisconsin-Madison, WI 53706
| | - Federico E. Rey
- Department of Bacteriology, University of Wisconsin-Madison, WI 53706
| | | | - Ophelia S. Venturelli
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison WI 53706
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706
- Department of Bacteriology, University of Wisconsin-Madison, WI 53706
| |
Collapse
|
11
|
Bleker C, Ramšak Ž, Bittner A, Podpečan V, Zagorščak M, Wurzinger B, Baebler Š, Petek M, Križnik M, van Dieren A, Gruber J, Afjehi-Sadat L, Weckwert W, Županič A, Teige M, Vothknecht UC, Gruden K. Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses. Plant Commun 2024:100920. [PMID: 38616489 DOI: 10.1016/j.xplc.2024.100920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/28/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
Stress Knowledge Map (SKM, https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs describing current knowledge of biochemical, signalling, and regulatory molecular interactions in plants: a highly curated model of plant stress signalling (PSS, 543 reactions) and a large comprehensive knowledge network (CKN, 488,390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entrypoint for plant stress response investigations and the related growth trade-offs as well as interactive exploration of current knowledge. PSS is also formulated as qualitative and quantitative models for systems biology, and thus represents a starting point of a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation, design of validation experiments, or to gain new insights into experimental observations in plant biology.
Collapse
Affiliation(s)
- Carissa Bleker
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, SI-1000 Ljubljana, Slovenia.
| | - Živa Ramšak
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, SI-1000 Ljubljana, Slovenia
| | - Andras Bittner
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, D-53115 Bonn, Germany
| | - Vid Podpečan
- Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
| | - Maja Zagorščak
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, SI-1000 Ljubljana, Slovenia
| | - Bernhard Wurzinger
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, AT-1030 Vienna, Austria
| | - Špela Baebler
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, SI-1000 Ljubljana, Slovenia
| | - Marko Petek
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, SI-1000 Ljubljana, Slovenia
| | - Maja Križnik
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, SI-1000 Ljubljana, Slovenia
| | - Annelotte van Dieren
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, D-53115 Bonn, Germany
| | - Juliane Gruber
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, AT-1030 Vienna, Austria
| | - Leila Afjehi-Sadat
- Mass spectrometry unit, Core Facility Shared Services, University of Vienna, Djerassiplatz 1, AT-1030 Vienna, Austria
| | - Wolfram Weckwert
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, AT-1030 Vienna, Austria
| | - Anže Županič
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, SI-1000 Ljubljana, Slovenia
| | - Markus Teige
- Department of Functional & Evolutionary Ecology, University of Vienna, Djerassiplatz 1, AT-1030 Vienna, Austria
| | - Ute C Vothknecht
- Plant Cell Biology, Institute of Cellular and Molecular Botany, University of Bonn, Kirschallee 1, D-53115 Bonn, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, SI-1000 Ljubljana, Slovenia.
| |
Collapse
|
12
|
Grandits T, Augustin CM, Haase G, Jost N, Mirams GR, Niederer SA, Plank G, Varró A, Virág L, Jung A. Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies. eLife 2024; 12:RP91911. [PMID: 38598284 PMCID: PMC11006416 DOI: 10.7554/elife.91911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024] Open
Abstract
Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.
Collapse
Affiliation(s)
- Thomas Grandits
- Department of Mathematics and Scientific Computing, University of GrazGrazAustria
- NAWI Graz, University of GrazGrazAustria
| | - Christoph M Augustin
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Medical Physics and Biophysics, Medical University of GrazGrazAustria
- BioTechMed-GrazGrazAustria
| | - Gundolf Haase
- Department of Mathematics and Scientific Computing, University of GrazGrazAustria
| | - Norbert Jost
- Department of Pharmacology and Pharmacotherapy, University of SzegedSzegedHungary
- HUN-REN-TKI, Research Group of PharmacologyBudapestHungary
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of NottinghamNottinghamUnited Kingdom
| | - Steven A Niederer
- Division of Imaging Sciences & Biomedical Engineering, King’s College LondonLondonUnited Kingdom
| | - Gernot Plank
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Medical Physics and Biophysics, Medical University of GrazGrazAustria
- BioTechMed-GrazGrazAustria
| | - András Varró
- Department of Pharmacology and Pharmacotherapy, University of SzegedSzegedHungary
- HUN-REN-TKI, Research Group of PharmacologyBudapestHungary
| | - László Virág
- Department of Pharmacology and Pharmacotherapy, University of SzegedSzegedHungary
| | - Alexander Jung
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Medical Physics and Biophysics, Medical University of GrazGrazAustria
| |
Collapse
|
13
|
French S, Guo ABY, Ellis MJ, Deisinger JP, Johnson JW, Rachwalski K, Piquette ZA, Lluka T, Zary M, Gamage S, Magolan J, Brown ED. A platform for predicting mechanism of action based on bacterial transcriptional responses identifies an unusual DNA gyrase inhibitor. Cell Rep 2024; 43:114053. [PMID: 38578824 DOI: 10.1016/j.celrep.2024.114053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 02/02/2024] [Accepted: 03/19/2024] [Indexed: 04/07/2024] Open
Abstract
In the search for much-needed new antibacterial chemical matter, a myriad of compounds have been reported in academic and pharmaceutical screening endeavors. Only a small fraction of these, however, are characterized with respect to mechanism of action (MOA). Here, we describe a pipeline that categorizes transcriptional responses to antibiotics and provides hypotheses for MOA. 3D-printed imaging hardware PFIboxes) profiles responses of Escherichia coli promoter-GFP fusions to more than 100 antibiotics. Notably, metergoline, a semi-synthetic ergot alkaloid, mimics a DNA replication inhibitor. In vitro supercoiling assays confirm this prediction, and a potent analog thereof (MLEB-1934) inhibits growth at 0.25 μg/mL and is highly active against quinolone-resistant strains of methicillin-resistant Staphylococcus aureus. Spontaneous suppressor mutants map to a seldom explored allosteric binding pocket, suggesting a mechanism distinct from DNA gyrase inhibitors used in the clinic. In all, the work highlights the potential of this platform to rapidly assess MOA of new antibacterial compounds.
Collapse
Affiliation(s)
- Shawn French
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Amelia Bing Ya Guo
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Michael J Ellis
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Julia P Deisinger
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Jarrod W Johnson
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Kenneth Rachwalski
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Zoë A Piquette
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Telmah Lluka
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Miranda Zary
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Sineli Gamage
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Jakob Magolan
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada
| | - Eric D Brown
- McMaster University, Department of Biochemistry and Biomedical Sciences and Michael G. DeGroote Institute for Infectious Disease Research, Hamilton, ON L8S 4L8, Canada.
| |
Collapse
|
14
|
Zhang Y, Zhao L, Sun Y. Using single-sample networks to identify the contrasting patterns of gene interactions and reveal the radiation dose-dependent effects in multiple tissues of spaceflight mice. NPJ Microgravity 2024; 10:45. [PMID: 38575629 PMCID: PMC10995210 DOI: 10.1038/s41526-024-00383-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/08/2024] [Indexed: 04/06/2024] Open
Abstract
Transcriptome profiles are sensitive to space stressors and serve as valuable indicators of the biological effects during spaceflight. Herein, we transformed the expression profiles into gene interaction patterns by single-sample networks (SSNs) and performed the integrated analysis on the 301 spaceflight and 290 ground control samples, which were obtained from the GeneLab platform. Specifically, an individual SSN was established for each sample. Based on the topological structures of 591 SSNs, the differentially interacted genes (DIGs) were identified between spaceflights and ground controls. The results showed that spaceflight disrupted the gene interaction patterns in mice and resulted in significant enrichment of biological processes such as protein/amino acid metabolism and nucleic acid (DNA/RNA) metabolism (P-value < 0.05). We observed that the mice exposed to radiation doses within the three intervals (4.66-7.14, 7.592-8.295, 8.49-22.099 mGy) exhibited similar gene interaction patterns. Low and medium doses resulted in changes to the circadian rhythm, while the damaging effects on genetic material became more pronounced in higher doses. The gene interaction patterns in response to space stressors varied among different tissues, with the spleen, lung, and skin being the most responsive to space radiation (P-value < 0.01). The changes observed in gene networks during spaceflight conditions might contribute to the development of various diseases, such as mental disorders, depression, and metabolic disorders, among others. Additionally, organisms activated specific gene networks in response to virus reactivation. We identified several hub genes that were associated with circadian rhythms, suggesting that spaceflight could lead to substantial circadian rhythm dysregulation.
Collapse
Affiliation(s)
- Yan Zhang
- Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University, 116026, Dalian, Liaoning, China
| | - Lei Zhao
- Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University, 116026, Dalian, Liaoning, China.
| | - Yeqing Sun
- Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University, 116026, Dalian, Liaoning, China.
| |
Collapse
|
15
|
Hamant O. Debunking the idea of biological optimisation: quantitative biology to the rescue. Quant Plant Biol 2024; 5:e3. [PMID: 38617131 PMCID: PMC11016357 DOI: 10.1017/qpb.2024.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 03/11/2024] [Indexed: 04/16/2024]
Abstract
The idea that plants would be efficient, frugal or optimised echoes the recurrent semantics of 'blueprint' and 'program' in molecular genetics. However, when analysing plants with quantitative approaches and systems thinking, we instead find that plants are the results of stochastic processes with many inefficiencies, incoherence or delays fuelling their robustness. If one had to highlight the main value of quantitative biology, this could be it: plants are robust systems because they are not efficient. Such systemic insights extend to the way we conduct plant research and opens plant science publication to a much broader framework.
Collapse
Affiliation(s)
- Olivier Hamant
- Laboratoire de Reproduction et Développement des Plantes, Université de Lyon, ENS de Lyon, UCBL, INRAE, CNRS, INRIA 46 Allée d’Italie, Lyon, France
| |
Collapse
|
16
|
Tarzi C, Zampieri G, Sullivan N, Angione C. Emerging methods for genome-scale metabolic modeling of microbial communities. Trends Endocrinol Metab 2024:S1043-2760(24)00062-6. [PMID: 38575441 DOI: 10.1016/j.tem.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.
Collapse
Affiliation(s)
- Chaimaa Tarzi
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK
| | - Guido Zampieri
- Department of Biology, University of Padova, Padova, 35122, Veneto, Italy
| | - Neil Sullivan
- Complement Genomics Ltd, Station Rd, Lanchester, Durham, DH7 0EX, County Durham, UK
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; Centre for Digital Innovation, Teesside University, Southfield Rd, Middlesbrough, TS1 3BX, North Yorkshire, UK; National Horizons Centre, Teesside University, 38 John Dixon Ln, Darlington, DL1 1HG, North Yorkshire, UK.
| |
Collapse
|
17
|
Si Y, Yan C. Protein language model-embedded geometric graphs power inter-protein contact prediction. eLife 2024; 12:RP92184. [PMID: 38564241 PMCID: PMC10987090 DOI: 10.7554/elife.92184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024] Open
Abstract
Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, there is still a large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter, by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.
Collapse
Affiliation(s)
- Yunda Si
- School of Physics, Huazhong University of Science and TechnologyWuhanChina
| | - Chengfei Yan
- School of Physics, Huazhong University of Science and TechnologyWuhanChina
| |
Collapse
|
18
|
Latini S, Venafra V, Massacci G, Bica V, Graziosi S, Pugliese GM, Iannuccelli M, Frioni F, Minnella G, Marra JD, Chiusolo P, Pepe G, Helmer Citterich M, Mougiakakos D, Böttcher M, Fischer T, Perfetto L, Sacco F. Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction. eLife 2024; 12:RP90532. [PMID: 38564252 PMCID: PMC10987088 DOI: 10.7554/elife.90532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024] Open
Abstract
Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, and patient-specific genomic and transcriptomic data with a prior knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.
Collapse
Affiliation(s)
- Sara Latini
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | - Veronica Venafra
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | | | - Valeria Bica
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | - Simone Graziosi
- Cellular and Molecular Biology, Department of Biology, University of Rome Tor VergataRomeItaly
| | | | | | - Filippo Frioni
- Sezione di Ematologia, Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro CuoreRomeItaly
| | - Gessica Minnella
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico A. Gemelli IRCCSRomeItaly
| | - John Donald Marra
- Sezione di Ematologia, Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro CuoreRomeItaly
| | - Patrizia Chiusolo
- Sezione di Ematologia, Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro CuoreRomeItaly
| | - Gerardo Pepe
- Department of Biology, University of Rome Tor VergataRomeItaly
| | | | - Dimitros Mougiakakos
- Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of MagdeburgMagdeburgGermany
- Department of Hematology and Oncology, Otto-von-Guericke University of MagdeburgMagdeburgGermany
| | - Martin Böttcher
- Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of MagdeburgMagdeburgGermany
- Department of Hematology and Oncology, Otto-von-Guericke University of MagdeburgMagdeburgGermany
| | - Thomas Fischer
- Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of MagdeburgMagdeburgGermany
- Institute of Molecular and Clinical Immunology, Otto-von-Guericke University of MagdeburgMagdeburgGermany
| | - Livia Perfetto
- Department of Biology, University of Rome Tor VergataRomeItaly
- Department of Biology, Fondazione Human TechnopoleMilanItaly
| | - Francesca Sacco
- Department of Biology, University of Rome Tor VergataRomeItaly
- Telethon Institute of Genetics and Medicine (TIGEM)PozzuoliItaly
| |
Collapse
|
19
|
Bunyavanich S, Becker PM, Altman MC, Lasky-Su J, Ober C, Zengler K, Berdyshev E, Bonneau R, Chatila T, Chatterjee N, Chung KF, Cutcliffe C, Davidson W, Dong G, Fang G, Fulkerson P, Himes BE, Liang L, Mathias RA, Ogino S, Petrosino J, Price ND, Schadt E, Schofield J, Seibold MA, Steen H, Wheatley L, Zhang H, Togias A, Hasegawa K. Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop. J Allergy Clin Immunol 2024; 153:954-968. [PMID: 38295882 PMCID: PMC10999353 DOI: 10.1016/j.jaci.2024.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 02/29/2024]
Abstract
Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.
Collapse
Affiliation(s)
| | - Patrice M Becker
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | | | - Jessica Lasky-Su
- Brigham & Women's Hospital and Harvard Medical School, Boston, Mass
| | | | | | | | | | - Talal Chatila
- Boston Children's Hospital and Harvard Medical School, Boston, Mass
| | | | | | | | - Wendy Davidson
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Gang Dong
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Gang Fang
- Icahn School of Medicine at Mount Sinai, New York, NY
| | - Patricia Fulkerson
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | | | - Liming Liang
- Harvard T. H. Chan School of Public Health, Boston, Mass
| | | | - Shuji Ogino
- Brigham & Women's Hospital and Harvard Medical School, Boston, Mass; Harvard T. H. Chan School of Public Health, Boston, Mass; Broad Institute of MIT and Harvard, Boston, Mass
| | | | | | - Eric Schadt
- Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Max A Seibold
- National Jewish Health, Denver, Colo; University of Colorado School of Medicine, Aurora, Colo
| | - Hanno Steen
- Boston Children's Hospital and Harvard Medical School, Boston, Mass
| | - Lisa Wheatley
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Hongmei Zhang
- School of Public Health, University of Memphis, Memphis, Tenn
| | - Alkis Togias
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Md
| | - Kohei Hasegawa
- Massachusetts General Hospital and Harvard Medical School, Boston, Mass
| |
Collapse
|
20
|
Künzel SE, Pompös IM, Flesch LTM, Frentzel DP, Knecht VA, Winkler S, Skosyrski S, Rübsam A, Dreher F, Kociok N, Schütte M, Dubrac A, Lange B, Yaspo ML, Lehrach H, Strauß O, Joussen AM, Zeitz O. Exploring the Impact of Saccharin on Neovascular Age-Related Macular Degeneration: A Comprehensive Study in Patients and Mice. Invest Ophthalmol Vis Sci 2024; 65:5. [PMID: 38558091 PMCID: PMC10996979 DOI: 10.1167/iovs.65.4.5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 02/11/2024] [Indexed: 04/04/2024] Open
Abstract
Purpose We aimed to determine the impact of artificial sweeteners (AS), especially saccharin, on the progression and treatment efficacy of patients with neovascular age-related macular degeneration (nAMD) under anti-vascular endothelial growth factor (anti-VEGF-A) treatment. Methods In a cross-sectional study involving 46 patients with nAMD undergoing intravitreal anti-VEGF therapy, 6 AS metabolites were detected in peripheral blood using liquid chromatography - tandem mass spectrometry (LC-MS/MS). Disease features were statistically tested against these metabolite levels. Additionally, a murine choroidal neovascularization (CNV) model, induced by laser, was used to evaluate the effects of orally administered saccharin, assessing both imaging outcomes and gene expression patterns. Polymerase chain reaction (PCR) methods were used to evaluate functional expression of sweet taste receptors in a retinal pigment epithelium (RPE) cell line. Results Saccharin levels in blood were significantly higher in patients with well-controlled CNV activity (P = 0.004) and those without subretinal hyper-reflective material (P = 0.015). In the murine model, saccharin-treated mice exhibited fewer leaking laser scars, lesser occurrence of bleeding, smaller fibrotic areas (P < 0.05), and a 40% decrease in mononuclear phagocyte accumulation (P = 0.06). Gene analysis indicated downregulation of inflammatory and VEGFR-1 response genes in the treated animals. Human RPE cells expressed taste receptor type 1 member 3 (TAS1R3) mRNA and reacted to saccharin stimulation with changes in mRNA expression. Conclusions Saccharin appears to play a protective role in patients with nAMD undergoing intravitreal anti-VEGF treatment, aiding in better pathological lesion control and scar reduction. The murine study supports this observation, proposing saccharin's potential in mitigating pathological VEGFR-1-induced immune responses potentially via the RPE sensing saccharin in the blood stream.
Collapse
Affiliation(s)
- Steffen E. Künzel
- Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt–Universität zu Berlin, Department of Ophthalmology, Hindenburgdamm 30, Berlin, Germany
| | - Inga-Marie Pompös
- Experimental Ophthalmology, Department of Ophthalmology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität, Berlin Institute of Health, Humboldt-University, Berlin, Germany
| | - Leonie T. M. Flesch
- Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt–Universität zu Berlin, Department of Ophthalmology, Hindenburgdamm 30, Berlin, Germany
| | - Dominik P. Frentzel
- Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt–Universität zu Berlin, Department of Ophthalmology, Hindenburgdamm 30, Berlin, Germany
| | - Vitus A. Knecht
- Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt–Universität zu Berlin, Department of Ophthalmology, Hindenburgdamm 30, Berlin, Germany
| | - Silvia Winkler
- Experimental Ophthalmology, Department of Ophthalmology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität, Berlin Institute of Health, Humboldt-University, Berlin, Germany
| | - Sergej Skosyrski
- Experimental Ophthalmology, Department of Ophthalmology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität, Berlin Institute of Health, Humboldt-University, Berlin, Germany
| | - Anne Rübsam
- Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt–Universität zu Berlin, Department of Ophthalmology, Hindenburgdamm 30, Berlin, Germany
| | - Felix Dreher
- Alacris Theranostics, Max-Planck-Straße 3, Berlin, Germany
| | - Norbert Kociok
- Experimental Ophthalmology, Department of Ophthalmology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität, Berlin Institute of Health, Humboldt-University, Berlin, Germany
| | - Moritz Schütte
- Alacris Theranostics, Max-Planck-Straße 3, Berlin, Germany
| | - Alexandre Dubrac
- Département de Pathologie et Biologie Cellulaire, Université de Montréal, Montréal, Quebec, Canada
| | - Bodo Lange
- Alacris Theranostics, Max-Planck-Straße 3, Berlin, Germany
| | - Marie-Laure Yaspo
- Max-Planck-Institute for Molecular Genetics, Ihnestrasse 63-73, Berlin, Germany
| | - Hans Lehrach
- Max-Planck-Institute for Molecular Genetics, Ihnestrasse 63-73, Berlin, Germany
| | - Olaf Strauß
- Experimental Ophthalmology, Department of Ophthalmology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität, Berlin Institute of Health, Humboldt-University, Berlin, Germany
| | - Antonia M. Joussen
- Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt–Universität zu Berlin, Department of Ophthalmology, Hindenburgdamm 30, Berlin, Germany
| | - Oliver Zeitz
- Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt–Universität zu Berlin, Department of Ophthalmology, Hindenburgdamm 30, Berlin, Germany
| |
Collapse
|
21
|
Gupta N, Somayajulu M, Gurdziel K, LoGrasso G, Aziz H, Rosati R, McClellan S, Pitchaikannu A, Santra M, Shukkur MFA, Stemmer P, Hazlett LD, Xu S. The miR-183/96/182 cluster regulates sensory innervation, resident myeloid cells and functions of the cornea through cell type-specific target genes. Sci Rep 2024; 14:7676. [PMID: 38561433 PMCID: PMC10985120 DOI: 10.1038/s41598-024-58403-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/28/2024] [Indexed: 04/04/2024] Open
Abstract
The conserved miR-183/96/182 cluster (miR-183C) is expressed in both corneal resident myeloid cells (CRMCs) and sensory nerves (CSN) and modulates corneal immune/inflammatory responses. To uncover cell type-specific roles of miR-183C in CRMC and CSN and their contributions to corneal physiology, myeloid-specific miR-183C conditional knockout (MS-CKO), and sensory nerve-specific CKO (SNS-CKO) mice were produced and characterized in comparison to the conventional miR-183C KO. Immunofluorescence and confocal microscopy of flatmount corneas, corneal sensitivity, and tear volume assays were performed in young adult naïve mice; 3' RNA sequencing (Seq) and proteomics in the trigeminal ganglion (TG), cornea and CRMCs. Our results showed that, similar to conventional KO mice, the numbers of CRMCs were increased in both MS-CKO and SNS-CKO vs age- and sex-matched WT control littermates, suggesting intrinsic and extrinsic regulations of miR-183C on CRMCs. The number of CRMCs was increased in male vs female MS-CKO mice, suggesting sex-dependent regulation of miR-183C on CRMCs. In the miR-183C KO and SNS-CKO, but not the MS-CKO mice, CSN density was decreased in the epithelial layer of the cornea, but not the stromal layer. Functionally, corneal sensitivity and basal tear volume were reduced in the KO and SNS-CKO, but not the MS-CKO mice. Tear volume in males is consistently higher than female WT mice. Bioinformatic analyses of the transcriptomes revealed a series of cell-type specific target genes of miR-183C in TG sensory neurons and CRMCs. Our data elucidate that miR-183C imposes intrinsic and extrinsic regulation on the establishment and function of CSN and CRMCs by cell-specific target genes. miR-183C modulates corneal sensitivity and tear production through its regulation of corneal sensory innervation.
Collapse
Affiliation(s)
- Naman Gupta
- Department of Ophthalmology, Visual and Anatomical Sciences, School of Medicine, Wayne State University, 540 E Canfield Street, Detroit, MI, 48201, USA
| | - Mallika Somayajulu
- Department of Ophthalmology, Visual and Anatomical Sciences, School of Medicine, Wayne State University, 540 E Canfield Street, Detroit, MI, 48201, USA
| | | | - Giovanni LoGrasso
- Department of Ophthalmology, Visual and Anatomical Sciences, School of Medicine, Wayne State University, 540 E Canfield Street, Detroit, MI, 48201, USA
| | - Haidy Aziz
- School of Biological Sciences, Wayne State University, Detroit, MI, USA
| | - Rita Rosati
- Institute of Environmental Health Sciences, Wayne State University, Detroit, MI, USA
| | - Sharon McClellan
- Department of Ophthalmology, Visual and Anatomical Sciences, School of Medicine, Wayne State University, 540 E Canfield Street, Detroit, MI, 48201, USA
| | - Ahalya Pitchaikannu
- Department of Ophthalmology, Visual and Anatomical Sciences, School of Medicine, Wayne State University, 540 E Canfield Street, Detroit, MI, 48201, USA
| | - Manoranjan Santra
- Department of Ophthalmology, Visual and Anatomical Sciences, School of Medicine, Wayne State University, 540 E Canfield Street, Detroit, MI, 48201, USA
| | - Muhammed Farooq Abdul Shukkur
- Department of Ophthalmology, Visual and Anatomical Sciences, School of Medicine, Wayne State University, 540 E Canfield Street, Detroit, MI, 48201, USA
| | - Paul Stemmer
- Institute of Environmental Health Sciences, Wayne State University, Detroit, MI, USA
| | - Linda D Hazlett
- Department of Ophthalmology, Visual and Anatomical Sciences, School of Medicine, Wayne State University, 540 E Canfield Street, Detroit, MI, 48201, USA
| | - Shunbin Xu
- Department of Ophthalmology, Visual and Anatomical Sciences, School of Medicine, Wayne State University, 540 E Canfield Street, Detroit, MI, 48201, USA.
| |
Collapse
|
22
|
Dekker PM, Boeren S, Saccenti E, Hettinga KA. Network analysis of the proteome and peptidome sheds light on human milk as a biological system. Sci Rep 2024; 14:7569. [PMID: 38555284 PMCID: PMC10981717 DOI: 10.1038/s41598-024-58127-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 03/26/2024] [Indexed: 04/02/2024] Open
Abstract
Proteins and peptides found in human milk have bioactive potential to benefit the newborn and support healthy development. Research has been carried out on the health benefits of proteins and peptides, but many questions still need to be answered about the nature of these components, how they are formed, and how they end up in the milk. This study explored and elucidated the complexity of the human milk proteome and peptidome. Proteins and peptides were analyzed with non-targeted nanoLC-Orbitrap-MS/MS in a selection of 297 milk samples from the CHILD Cohort Study. Protein and peptide abundances were determined, and a network was inferred using Gaussian graphical modeling (GGM), allowing an investigation of direct associations. This study showed that signatures of (1) specific mechanisms of transport of different groups of proteins, (2) proteolytic degradation by proteases and aminopeptidases, and (3) coagulation and complement activation are present in human milk. These results show the value of an integrated approach in evaluating large-scale omics data sets and provide valuable information for studies that aim to associate protein or peptide profiles from biofluids such as milk with specific physiological characteristics.
Collapse
Affiliation(s)
- Pieter M Dekker
- Food Quality and Design Group, Wageningen University and Research, Wageningen, 6708 WE, The Netherlands
- Laboratory of Biochemistry, Wageningen University and Research, Wageningen, 6708 WE, The Netherlands
| | - Sjef Boeren
- Laboratory of Biochemistry, Wageningen University and Research, Wageningen, 6708 WE, The Netherlands
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, 6708 WE, The Netherlands
| | - Kasper A Hettinga
- Food Quality and Design Group, Wageningen University and Research, Wageningen, 6708 WE, The Netherlands.
| |
Collapse
|
23
|
Kim Y, Choi SR, Cho KH. Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance. Cancers (Basel) 2024; 16:1337. [PMID: 38611015 PMCID: PMC11010870 DOI: 10.3390/cancers16071337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/20/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.
Collapse
Affiliation(s)
| | | | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-Inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; (Y.K.); (S.R.C.)
| |
Collapse
|
24
|
McMillen P, Levin M. Collective intelligence: A unifying concept for integrating biology across scales and substrates. Commun Biol 2024; 7:378. [PMID: 38548821 PMCID: PMC10978875 DOI: 10.1038/s42003-024-06037-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 03/11/2024] [Indexed: 04/01/2024] Open
Abstract
A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.
Collapse
Affiliation(s)
- Patrick McMillen
- Department of Biology, Tufts University, Medford, MA, 02155, USA
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
| | - Michael Levin
- Department of Biology, Tufts University, Medford, MA, 02155, USA.
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
| |
Collapse
|
25
|
Przanowska RK, Labban N, Przanowski P, Hawes RB, Atkins KA, Showalter SL, Janes KA. Patient-derived response estimates from zero-passage organoids of luminal breast cancer. bioRxiv 2024:2024.03.24.586432. [PMID: 38585922 PMCID: PMC10996455 DOI: 10.1101/2024.03.24.586432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Background Primary luminal breast cancer cells lose their identity rapidly in standard tissue culture, which is problematic for testing hormone interventions and molecular pathways specific to the luminal subtype. Breast cancer organoids are thought to retain tumor characteristics better, but long-term viability of luminal-subtype cases is a persistent challenge. Our goal was to adapt short-term organoids of luminal breast cancer for parallel testing of genetic and pharmacologic perturbations. Methods We freshly isolated patient-derived cells from luminal tumor scrapes, miniaturized the organoid format into 5 μl replicates for increased throughput, and set an endpoint of 14 days to minimize drift. Therapeutic hormone targeting was mimicked in these "zero-passage" organoids by withdrawing β-estradiol and adding 4-hydroxytamoxifen. We also examined sulforaphane as an electrophilic stress and commercial neutraceutical with reported anti-cancer properties. Downstream mechanisms were tested genetically by lentiviral transduction of two complementary sgRNAs and Cas9 stabilization for the first week of organoid culture. Transcriptional changes were measured by RT-qPCR or RNA sequencing, and organoid phenotypes were quantified by serial brightfield imaging, digital image segmentation, and regression modeling of cellular doubling times. Results We achieved >50% success in initiating luminal breast cancer organoids from tumor scrapes and maintaining them to the 14-day zero-passage endpoint. Success was mostly independent of clinical parameters, supporting general applicability of the approach. Abundance of ESR1 and PGR in zero-passage organoids consistently remained within the range of patient variability at the endpoint. However, responsiveness to hormone withdrawal and blockade was highly variable among luminal breast cancer cases tested. Combining sulforaphane with knockout of NQO1 (a phase II antioxidant response gene and downstream effector of sulforaphane) also yielded a breadth of organoid growth phenotypes, including growth inhibition with sulforaphane, growth promotion with NQO1 knockout, and growth antagonism when combined. Conclusions Zero-passage organoids are a rapid and scalable way to interrogate properties of luminal breast cancer cells from patient-derived material. This includes testing drug mechanisms of action in different clinical cohorts. A future goal is to relate inter-patient variability of zero-passage organoids to long-term outcomes.
Collapse
Affiliation(s)
- Róża K Przanowska
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Najwa Labban
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Piotr Przanowski
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Russell B Hawes
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Kristen A Atkins
- Department of Pathology, University of Virginia, Charlottesville, VA 22908, USA
| | - Shayna L Showalter
- Department of Surgery, University of Virginia Health System, Charlottesville, VA 22908, USA
- University of Virginia Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22908, USA
| | - Kevin A Janes
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
- University of Virginia Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| |
Collapse
|
26
|
Wang S, Lee D. Community cohesion looseness in gene networks reveals individualized drug targets and resistance. Brief Bioinform 2024; 25:bbae175. [PMID: 38622359 PMCID: PMC11018546 DOI: 10.1093/bib/bbae175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/19/2024] [Accepted: 04/02/2024] [Indexed: 04/17/2024] Open
Abstract
Community cohesion plays a critical role in the determination of an individual's health in social science. Intriguingly, a community structure of gene networks indicates that the concept of community cohesion could be applied between the genes as well to overcome the limitations of single gene-based biomarkers for precision oncology. Here, we develop community cohesion scores which precisely quantify the community ability to retain the interactions between the genes and their cellular functions in each individualized gene network. Using breast cancer as a proof-of-concept study, we measure the community cohesion score profiles of 950 case samples and predict the individualized therapeutic targets in 2-fold. First, we prioritize them by finding druggable genes present in the community with the most and relatively decreased scores in each individual. Then, we pinpoint more individualized therapeutic targets by discovering the genes which greatly contribute to the community cohesion looseness in each individualized gene network. Compared with the previous approaches, the community cohesion scores show at least four times higher performance in predicting effective individualized chemotherapy targets based on drug sensitivity data. Furthermore, the community cohesion scores successfully discover the known breast cancer subtypes and we suggest new targeted therapy targets for triple negative breast cancer (e.g. KIT and GABRP). Lastly, we demonstrate that the community cohesion scores can predict tamoxifen responses in ER+ breast cancer and suggest potential combination therapies (e.g. NAMPT and RXRA inhibitors) to reduce endocrine therapy resistance based on individualized characteristics. Our method opens new perspectives for the biomarker development in precision oncology.
Collapse
Affiliation(s)
- Seunghyun Wang
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| |
Collapse
|
27
|
Shuey MM, Wang Y, Xiang RR, Zou A, Rahman P, Fabbri D, Beckman JA, Jaffe I, Wells QS. Aggregation and Contextualization of Murine Investigations Improves Discovery of Significant Human Atherosclerotic Cardiovascular Disease Associations. Circulation 2024; 149:1056-1058. [PMID: 38527133 PMCID: PMC10965229 DOI: 10.1161/circulationaha.123.067510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Affiliation(s)
- Megan M. Shuey
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Yihua Wang
- Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA
| | - Rachel R. Xiang
- Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA
| | - Aaron Zou
- Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA
| | - Protiva Rahman
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Daniel Fabbri
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Joshua A. Beckman
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Iris Jaffe
- Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA
| | - Quinn S. Wells
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN
| |
Collapse
|
28
|
Li B, Kugeratski FG, Kalluri R. A novel machine learning algorithm selects proteome signature to specifically identify cancer exosomes. eLife 2024; 12:RP90390. [PMID: 38529947 PMCID: PMC10965221 DOI: 10.7554/elife.90390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024] Open
Abstract
Non-invasive early cancer diagnosis remains challenging due to the low sensitivity and specificity of current diagnostic approaches. Exosomes are membrane-bound nanovesicles secreted by all cells that contain DNA, RNA, and proteins that are representative of the parent cells. This property, along with the abundance of exosomes in biological fluids makes them compelling candidates as biomarkers. However, a rapid and flexible exosome-based diagnostic method to distinguish human cancers across cancer types in diverse biological fluids is yet to be defined. Here, we describe a novel machine learning-based computational method to distinguish cancers using a panel of proteins associated with exosomes. Employing datasets of exosome proteins from human cell lines, tissue, plasma, serum, and urine samples from a variety of cancers, we identify Clathrin Heavy Chain (CLTC), Ezrin, (EZR), Talin-1 (TLN1), Adenylyl cyclase-associated protein 1 (CAP1), and Moesin (MSN) as highly abundant universal biomarkers for exosomes and define three panels of pan-cancer exosome proteins that distinguish cancer exosomes from other exosomes and aid in classifying cancer subtypes employing random forest models. All the models using proteins from plasma, serum, or urine-derived exosomes yield AUROC scores higher than 0.91 and demonstrate superior performance compared to Support Vector Machine, K Nearest Neighbor Classifier and Gaussian Naive Bayes. This study provides a reliable protein biomarker signature associated with cancer exosomes with scalable machine learning capability for a sensitive and specific non-invasive method of cancer diagnosis.
Collapse
Affiliation(s)
- Bingrui Li
- Department of Cancer Biology, University of Texas MD Anderson Cancer CenterHoustonUnited States
| | - Fernanda G Kugeratski
- Department of Cancer Biology, University of Texas MD Anderson Cancer CenterHoustonUnited States
| | - Raghu Kalluri
- Department of Cancer Biology, University of Texas MD Anderson Cancer CenterHoustonUnited States
- Department of Bioengineering, Rice UniversityHoustonUnited States
- Department of Molecular and Cellular Biology, Baylor College of MedicineHoustonUnited States
| |
Collapse
|
29
|
Youssef A, Paul I, Crovella M, Emili A. DESP demixes cell-state profiles from dynamic bulk molecular measurements. Cell Rep Methods 2024; 4:100729. [PMID: 38490205 PMCID: PMC10985230 DOI: 10.1016/j.crmeth.2024.100729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 12/22/2023] [Accepted: 02/16/2024] [Indexed: 03/17/2024]
Abstract
Understanding the dynamic expression of proteins and other key molecules driving phenotypic remodeling in development and pathobiology has garnered widespread interest, yet the exploration of these systems at the foundational resolution of the underlying cell states has been significantly limited by technical constraints. Here, we present DESP, an algorithm designed to leverage independent estimates of cell-state proportions, such as from single-cell RNA sequencing, to resolve the relative contributions of cell states to bulk molecular measurements, most notably quantitative proteomics, recorded in parallel. We applied DESP to an in vitro model of the epithelial-to-mesenchymal transition and demonstrated its ability to accurately reconstruct cell-state signatures from bulk-level measurements of both the proteome and transcriptome, providing insights into transient regulatory mechanisms. DESP provides a generalizable computational framework for modeling the relationship between bulk and single-cell molecular measurements, enabling the study of proteomes and other molecular profiles at the cell-state level using established bulk-level workflows.
Collapse
Affiliation(s)
- Ahmed Youssef
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Indranil Paul
- Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Mark Crovella
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Computer Science Department, Boston University, Boston, MA, USA; Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.
| | - Andrew Emili
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA; Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA.
| |
Collapse
|
30
|
Questa M, Weimer BC, Fiehn O, Chow B, Hill SL, Ackermann MR, Lidbury JA, Steiner JM, Suchodolski JS, Marsilio S. Unbiased serum metabolomic analysis in cats with naturally occurring chronic enteropathies before and after medical intervention. Sci Rep 2024; 14:6939. [PMID: 38521833 PMCID: PMC10960826 DOI: 10.1038/s41598-024-57004-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/13/2024] [Indexed: 03/25/2024] Open
Abstract
Chronic enteropathies (CE) are common disorders in cats and the differentiation between the two main underlying diseases, inflammatory bowel disease (IBD) and low-grade intestinal T-cell lymphoma (LGITL), can be challenging. Characterization of the serum metabolome could provide further information on alterations of disease-associated metabolic pathways and may identify diagnostic or therapeutic targets. Unbiased metabolomics analysis of serum from 28 cats with CE (14 cats with IBD, 14 cats with LGITL) and 14 healthy controls identified 1,007 named metabolites, of which 129 were significantly different in cats with CE compared to healthy controls at baseline. Random Forest analysis revealed a predictive accuracy of 90% for differentiating controls from cats with chronic enteropathy. Metabolic pathways found to be significantly altered included phospholipids, amino acids, thiamine, and tryptophan metabolism. Several metabolites were found to be significantly different between cats with IBD versus LGITL, including several sphingolipids, phosphatidylcholine 40:7, uridine, pinitol, 3,4-dihydroxybenzoic acid, and glucuronic acid. However, random forest analysis revealed a poor group predictive accuracy of 60% for the differentiation of IBD from LGITL. Of 129 compounds found to be significantly different between healthy cats and cats with CE at baseline, 58 remained different following treatment.
Collapse
Affiliation(s)
- Maria Questa
- Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA
| | - Bart C Weimer
- Department of Population Health and Reproduction, 100K Pathogen Genome Project, University of California School of Veterinary Medicine, University of California, Davis, Davis, CA, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis, Davis, CA, USA
| | - Betty Chow
- VCA Animal Specialty & Emergency Center, Los Angeles, CA, USA
| | - Steve L Hill
- Veterinary Specialty Hospital, San Diego, CA, USA
| | - Mark R Ackermann
- US Department of Agriculture, National Animal Disease Center, Ames, IA, USA
| | - Jonathan A Lidbury
- Gastrointestinal Laboratory, Texas A&M University, College Station, TX, USA
| | - Joerg M Steiner
- Gastrointestinal Laboratory, Texas A&M University, College Station, TX, USA
| | - Jan S Suchodolski
- Gastrointestinal Laboratory, Texas A&M University, College Station, TX, USA
| | - Sina Marsilio
- Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA.
| |
Collapse
|
31
|
Blanot M, Casaroli-Marano RP, Mondéjar-Medrano J, Sallén T, Ramírez E, Segú-Vergés C, Artigas L. Aflibercept Off-Target Effects in Diabetic Macular Edema: An In Silico Modeling Approach. Int J Mol Sci 2024; 25:3621. [PMID: 38612432 PMCID: PMC11011561 DOI: 10.3390/ijms25073621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/08/2024] [Accepted: 03/13/2024] [Indexed: 04/14/2024] Open
Abstract
Intravitreal aflibercept injection (IAI) is a treatment for diabetic macular edema (DME), but its mechanism of action (MoA) has not been completely elucidated. Here, we aimed to explore IAI's MoA and its multi-target nature in DME pathophysiology with an in silico (computer simulation) disease model. We used the Therapeutic Performance Mapping System (Anaxomics Biotech property) to generate mathematical models based on the available scientific knowledge at the time of the study, describing the relationship between the modulation of vascular endothelial growth factor receptors (VEGFRs) by IAI and DME pathophysiological processes. We also undertook an enrichment analysis to explore the processes modulated by IAI, visualized the effectors' predicted protein activity, and specifically evaluated the role of VEGFR1 pathway inhibition on DME treatment. The models simulated the potential pathophysiology of DME and the likely IAI's MoA by inhibiting VEGFR1 and VEGFR2 signaling. The action of IAI through both signaling pathways modulated the identified pathophysiological processes associated with DME, with the strongest effects in angiogenesis, blood-retinal barrier alteration and permeability, and inflammation. VEGFR1 inhibition was essential to modulate inflammatory protein effectors. Given the role of VEGFR1 signaling on the modulation of inflammatory-related pathways, IAI may offer therapeutic advantages for DME through sustained VEGFR1 pathway inhibition.
Collapse
Affiliation(s)
- Morgane Blanot
- Anaxomics Biotech S.L., 08007 Barcelona, Spain; (M.B.); (E.R.); (C.S.-V.); (L.A.)
| | - Ricardo Pedro Casaroli-Marano
- Department of Surgery (FMCS), Universitat de Barcelona, 08007 Barcelona, Spain
- Hospital Clínic de Barcelona (IDIBAPS), Universitat de Barcelona, 08007 Barcelona, Spain
| | | | - Thaïs Sallén
- Bayer Hispania S.L., 08970 Sant Joan Despí, Spain; (J.M.-M.); (T.S.)
| | - Esther Ramírez
- Anaxomics Biotech S.L., 08007 Barcelona, Spain; (M.B.); (E.R.); (C.S.-V.); (L.A.)
| | - Cristina Segú-Vergés
- Anaxomics Biotech S.L., 08007 Barcelona, Spain; (M.B.); (E.R.); (C.S.-V.); (L.A.)
- Research Programme on Biomedical Informatics (GRIB), Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, 08002 Barcelona, Spain
| | - Laura Artigas
- Anaxomics Biotech S.L., 08007 Barcelona, Spain; (M.B.); (E.R.); (C.S.-V.); (L.A.)
| |
Collapse
|
32
|
Owen MJ, Wright JR, Tuddenham EGD, King JR, Goodall AH, Dunster JL. Mathematical models of coagulation-are we there yet? J Thromb Haemost 2024:S1538-7836(24)00167-3. [PMID: 38521192 DOI: 10.1016/j.jtha.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/24/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Mathematical models of coagulation have been developed to mirror thrombin generation in plasma, with the aim of investigating how variation in coagulation factor levels regulates hemostasis. However, current models vary in the reactions they capture and the reaction rates used, and their validation is restricted by a lack of large coherent datasets, resulting in questioning of their utility. OBJECTIVES To address this debate, we systematically assessed current models against a large dataset, using plasma coagulation factor levels from 348 individuals with normal hemostasis to identify the causes of these variations. METHODS We compared model predictions with measured thrombin generation, quantifying and comparing the ability of each model to predict thrombin generation, the contributions of the individual reactions, and their dependence on reaction rates. RESULTS We found that no current model predicted the hemostatic response across the whole cohort and all produced thrombin generation curves that did not resemble those obtained experimentally. Our analysis has identified the key reactions that lead to differential model predictions, where experimental uncertainty leads to variability in predictions, and we determined reactions that have a high influence on measured thrombin generation, such as the contribution of factor XI. CONCLUSION This systematic assessment of models of coagulation, using large dataset inputs, points to ways in which these models can be improved. A model that accurately reflects the effects of the multiple subtle variations in an individual's hemostatic profile could be used for assessing antithrombotics or as a tool for precision medicine.
Collapse
Affiliation(s)
- Matt J Owen
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom. https://twitter.com/MattJOwen_
| | - Joy R Wright
- Department of Cardiovascular Sciences, University of Leicester, Clinical Sciences Wing, Glenfield Hospital, Leicester, United Kingdom; National Institute for Healthcare Research, Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Edward G D Tuddenham
- Royal Free Hospital Haemophilia Centre, University College London, London, United Kingdom
| | - John R King
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Alison H Goodall
- Department of Cardiovascular Sciences, University of Leicester, Clinical Sciences Wing, Glenfield Hospital, Leicester, United Kingdom; National Institute for Healthcare Research, Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Joanne L Dunster
- Institute for Cardiovascular and Metabolic Research, School of Biological Sciences, University of Reading, Reading, United Kingdom.
| |
Collapse
|
33
|
Josephs-Spaulding J, Rajput A, Hefner Y, Szubin R, Balasubramanian A, Li G, Zielinski DC, Jahn L, Sommer M, Phaneuf P, Palsson BO. Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning. mSystems 2024; 9:e0125723. [PMID: 38349131 PMCID: PMC10949432 DOI: 10.1128/msystems.01257-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 01/09/2024] [Indexed: 03/20/2024] Open
Abstract
Limosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied the independent component analysis (ICA) to 117 RNA-seq data sets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. Our findings indicate that the ICA provides a qualitative advancement and captures nuanced relationships within gene clusters that other methods may miss. This study uncovers the fundamental properties of L. reuteri's TRN and deepens our understanding of its arginine metabolism and the co-regulation of riboflavin metabolism and fatty acid conversion. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and allows for the speculation of the potential role of isoprenoid biosynthesis in L. reuteri's adaptive response to environmental changes. By integrating transcriptomics and machine learning, we provide a system-level understanding of L. reuteri's response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production. IMPORTANCE We have studied Limosilactobacillus reuteri, a beneficial probiotic microbe that plays a significant role in our health and production of sustainable foods, a type of foods that are nutritionally dense and healthier and have low-carbon emissions compared to traditional foods. Similar to how humans adapt their lifestyles to different environments, this microbe adjusts its behavior by modulating the expression of genes. We applied machine learning to analyze large-scale data sets on how these genes behave across diverse conditions. From this, we identified 35 unique patterns demonstrating how L. reuteri adjusts its genes based on 50 unique environmental conditions (such as various sugars, salts, microbial cocultures, human milk, and fruit juice). This research helps us understand better how L. reuteri functions, especially in processes like breaking down certain nutrients and adapting to stressful changes. More importantly, with our findings, we become closer to using this knowledge to improve how we produce more sustainable and healthier foods with the help of microbes.
Collapse
Affiliation(s)
- Jonathan Josephs-Spaulding
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark
| | - Akanksha Rajput
- Department of Bioengineering, University of California, San Diego, California, USA
| | - Ying Hefner
- Department of Bioengineering, University of California, San Diego, California, USA
| | - Richard Szubin
- Department of Bioengineering, University of California, San Diego, California, USA
| | | | - Gaoyuan Li
- Department of Bioengineering, University of California, San Diego, California, USA
| | - Daniel C. Zielinski
- Department of Bioengineering, University of California, San Diego, California, USA
| | - Leonie Jahn
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark
| | - Morten Sommer
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark
| | - Patrick Phaneuf
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark
| | - Bernhard O. Palsson
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark
- Department of Bioengineering, University of California, San Diego, California, USA
| |
Collapse
|
34
|
de Lope EG, Loo RTJ, Rauschenberger A, Ali M, Pavelka L, Marques TM, Gomes CPC, Krüger R, Glaab E. Comprehensive blood metabolomics profiling of Parkinson's disease reveals coordinated alterations in xanthine metabolism. NPJ Parkinsons Dis 2024; 10:68. [PMID: 38503737 PMCID: PMC10951366 DOI: 10.1038/s41531-024-00671-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 02/26/2024] [Indexed: 03/21/2024] Open
Abstract
Parkinson's disease (PD) is a highly heterogeneous disorder influenced by several environmental and genetic factors. Effective disease-modifying therapies and robust early-stage biomarkers are still lacking, and an improved understanding of the molecular changes in PD could help to reveal new diagnostic markers and pharmaceutical targets. Here, we report results from a cohort-wide blood plasma metabolic profiling of PD patients and controls in the Luxembourg Parkinson's Study to detect disease-associated alterations at the level of systemic cellular process and network alterations. We identified statistically significant changes in both individual metabolite levels and global pathway activities in PD vs. controls and significant correlations with motor impairment scores. As a primary observation when investigating shared molecular sub-network alterations, we detect pronounced and coordinated increased metabolite abundances in xanthine metabolism in de novo patients, which are consistent with previous PD case/control transcriptomics data from an independent cohort in terms of known enzyme-metabolite network relationships. From the integrated metabolomics and transcriptomics network analysis, the enzyme hypoxanthine phosphoribosyltransferase 1 (HPRT1) is determined as a potential key regulator controlling the shared changes in xanthine metabolism and linking them to a mechanism that may contribute to pathological loss of cellular adenosine triphosphate (ATP) in PD. Overall, the investigations revealed significant PD-associated metabolome alterations, including pronounced changes in xanthine metabolism that are mechanistically congruent with alterations observed in independent transcriptomics data. The enzyme HPRT1 may merit further investigation as a main regulator of these network alterations and as a potential therapeutic target to address downstream molecular pathology in PD.
Collapse
Affiliation(s)
- Elisa Gómez de Lope
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Rebecca Ting Jiin Loo
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Armin Rauschenberger
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Muhammad Ali
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Lukas Pavelka
- Parkinson's Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Tainá M Marques
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Clarissa P C Gomes
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Rejko Krüger
- Parkinson's Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Enrico Glaab
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
| |
Collapse
|
35
|
Pulfer A, Pizzagalli DU, Gagliardi PA, Hinderling L, Lopez P, Zayats R, Carrillo-Barberà P, Antonello P, Palomino-Segura M, Grädel B, Nicolai M, Giusti A, Thelen M, Gambardella LM, Murooka TT, Pertz O, Krause R, Gonzalez SF. Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging. eLife 2024; 12:RP90502. [PMID: 38497754 PMCID: PMC10948145 DOI: 10.7554/elife.90502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024] Open
Abstract
Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.
Collapse
Affiliation(s)
- Alain Pulfer
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, USILuganoSwitzerland
- Department of Information Technology and Electrical Engineering, ETH ZurichZürichSwitzerland
| | - Diego Ulisse Pizzagalli
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, USILuganoSwitzerland
- Euler Institute, USILuganoSwitzerland
| | | | | | | | | | - Pau Carrillo-Barberà
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, USILuganoSwitzerland
- Instituto de Biotecnología y Biomedicina (BioTecMed), Universitat de ValènciaValenciaSpain
| | - Paola Antonello
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, USILuganoSwitzerland
- Institute of Cell Biology, University of BernBernSwitzerland
| | | | - Benjamin Grädel
- Institute of Cell Biology, University of BernBernSwitzerland
| | | | - Alessandro Giusti
- Dalle Molle Institute for Artificial Intelligence, IDSIALuganoSwitzerland
| | - Marcus Thelen
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, USILuganoSwitzerland
| | | | | | - Olivier Pertz
- Institute of Cell Biology, University of BernBernSwitzerland
| | | | | |
Collapse
|
36
|
Johnson SR, Peshwa M, Sun Z. Sensitive remote homology search by local alignment of small positional embeddings from protein language models. eLife 2024; 12:RP91415. [PMID: 38488154 PMCID: PMC10942778 DOI: 10.7554/elife.91415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024] Open
Abstract
Accurately detecting distant evolutionary relationships between proteins remains an ongoing challenge in bioinformatics. Search methods based on primary sequence struggle to accurately detect homology between sequences with less than 20% amino acid identity. Profile- and structure-based strategies extend sensitive search capabilities into this twilight zone of sequence similarity but require slow pre-processing steps. Recently, whole-protein and positional embeddings from deep neural networks have shown promise for providing sensitive sequence comparison and annotation at long evolutionary distances. Embeddings are generally faster to compute than profiles and predicted structures but still suffer several drawbacks related to the ability of whole-protein embeddings to discriminate domain-level homology, and the database size and search speed of methods using positional embeddings. In this work, we show that low-dimensionality positional embeddings can be used directly in speed-optimized local search algorithms. As a proof of concept, we use the ESM2 3B model to convert primary sequences directly into the 3D interaction (3Di) alphabet or amino acid profiles and use these embeddings as input to the highly optimized Foldseek, HMMER3, and HH-suite search algorithms. Our results suggest that positional embeddings as small as a single byte can provide sufficient information for dramatically improved sensitivity over amino acid sequence searches without sacrificing search speed.
Collapse
Affiliation(s)
| | | | - Zhiyi Sun
- New England Biolabs IncIpswichUnited States
| |
Collapse
|
37
|
Litsios A, Grys BT, Kraus OZ, Friesen H, Ross C, Masinas MPD, Forster DT, Couvillion MT, Timmermann S, Billmann M, Myers C, Johnsson N, Churchman LS, Boone C, Andrews BJ. Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle. Cell 2024; 187:1490-1507.e21. [PMID: 38452761 PMCID: PMC10947830 DOI: 10.1016/j.cell.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/01/2023] [Accepted: 02/12/2024] [Indexed: 03/09/2024]
Abstract
Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.
Collapse
Affiliation(s)
- Athanasios Litsios
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Benjamin T Grys
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Oren Z Kraus
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Helena Friesen
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Catherine Ross
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Myra Paz David Masinas
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Duncan T Forster
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Mary T Couvillion
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Stefanie Timmermann
- Institute of Molecular Genetics and Cell Biology, Department of Biology, Ulm University, Ulm 89081, Germany
| | - Maximilian Billmann
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA; Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Chad Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Nils Johnsson
- Institute of Molecular Genetics and Cell Biology, Department of Biology, Ulm University, Ulm 89081, Germany
| | | | - Charles Boone
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; RIKEN Center for Sustainable Resource Science, Wako 351-0198 Saitama, Japan.
| | - Brenda J Andrews
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
| |
Collapse
|
38
|
Tays GD, Hupfeld KE, McGregor HR, Beltran NE, De Dios YE, Mulder E, Bloomberg JJ, Mulavara AP, Wood SJ, Seidler RD. Daily artificial gravity partially mitigates vestibular processing changes associated with head-down tilt bedrest. NPJ Microgravity 2024; 10:27. [PMID: 38472244 PMCID: PMC10933323 DOI: 10.1038/s41526-024-00367-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 02/15/2024] [Indexed: 03/14/2024] Open
Abstract
Microgravity alters vestibular signaling and reduces body loading, driving sensory reweighting. The unloading effects can be modelled using head-down tilt bedrest (HDT). Artificial gravity (AG) has been hypothesized to serve as an integrated countermeasure for the declines associated with HDT and spaceflight. Here, we examined the efficacy of 30 min of daily AG to counteract brain and behavior changes from 60 days of HDT. Two groups received 30 min of AG delivered via short-arm centrifuge daily (n = 8 per condition), either in one continuous bout, or in 6 bouts of 5 min. To improve statistical power, we combined these groups (AG; n = 16). Another group served as controls in HDT with no AG (CTRL; n = 8). We examined how HDT and AG affect vestibular processing by collecting fMRI scans during vestibular stimulation. We collected these data prior to, during, and post-HDT. We assessed brain activation initially in 12 regions of interest (ROIs) and then conducted an exploratory whole brain analysis. The AG group showed no changes in activation during vestibular stimulation in a cerebellar ROI, whereas the CTRL group showed decreased activation specific to HDT. Those that received AG and showed little pre- to post-HDT changes in left vestibular cortex activation had better post-HDT balance performance. Whole brain analyses identified increased pre- to during-HDT activation in CTRLs in the right precentral gyrus and right inferior frontal gyrus, whereas AG maintained pre-HDT activation levels. These results indicate that AG could mitigate activation changes in vestibular processing that is associated with better balance performance.
Collapse
Affiliation(s)
- G D Tays
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA
| | - K E Hupfeld
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA
| | - H R McGregor
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA
| | | | | | - E Mulder
- German Aerospace Center (DLR), Cologne, Germany
| | | | | | - S J Wood
- NASA Johnson Space Center, Houston, TX, USA
| | - R D Seidler
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA.
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA.
| |
Collapse
|
39
|
Simons BD, Karin O. Tuning of plasma cell lifespan by competition explains the longevity and heterogeneity of antibody persistence. Immunity 2024; 57:600-611.e6. [PMID: 38447570 DOI: 10.1016/j.immuni.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 01/12/2024] [Accepted: 02/07/2024] [Indexed: 03/08/2024]
Abstract
Plasma cells that emerge after infection or vaccination exhibit heterogeneous lifespans; most survive for days to months, whereas others persist for decades, providing antigen-specific long-term protection. We developed a mathematical framework to explore the dynamics of plasma cell removal and its regulation by survival factors. Analyses of antibody persistence following hepatitis A and B and HPV vaccination revealed specific patterns of longevity and heterogeneity within and between responses, implying that this process is fine-tuned near a critical "flat" state between two dynamic regimes. This critical state reflects the tuning of rates of the underlying regulatory network and is highly sensitive to variation in parameters, which amplifies lifespan differences between cells. We propose that fine-tuning is the generic outcome of competition over shared survival signals, with a competition-based mechanism providing a unifying explanation for a wide range of experimental observations, including the dynamics of plasma cell accumulation and the effects of survival factor deletion. Our theory is testable, and we provide specific predictions.
Collapse
Affiliation(s)
- Benjamin D Simons
- Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Cambridge CB3 0WA, UK; Wellcome Trust, Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge CB2 1QN, UK; Wellcome Trust-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB2 0AW, UK
| | - Omer Karin
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK.
| |
Collapse
|
40
|
Liu S, Ezran C, Wang MFZ, Li Z, Awayan K, Long JZ, De Vlaminck I, Wang S, Epelbaum J, Kuo CS, Terrien J, Krasnow MA, Ferrell JE. An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome. Nat Commun 2024; 15:2188. [PMID: 38467625 PMCID: PMC10928088 DOI: 10.1038/s41467-024-46070-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 02/07/2024] [Indexed: 03/13/2024] Open
Abstract
Hormones mediate long-range cell communication and play vital roles in physiology, metabolism, and health. Traditionally, endocrinologists have focused on one hormone or organ system at a time. Yet, hormone signaling by its very nature connects cells of different organs and involves crosstalk of different hormones. Here, we leverage the organism-wide single cell transcriptional atlas of a non-human primate, the mouse lemur (Microcebus murinus), to systematically map source and target cells for 84 classes of hormones. This work uncovers previously-uncharacterized sites of hormone regulation, and shows that the hormonal signaling network is densely connected, decentralized, and rich in feedback loops. Evolutionary comparisons of hormonal genes and their expression patterns show that mouse lemur better models human hormonal signaling than mouse, at both the genomic and transcriptomic levels, and reveal primate-specific rewiring of hormone-producing/target cells. This work complements the scale and resolution of classical endocrine studies and sheds light on primate hormone regulation.
Collapse
Affiliation(s)
- Shixuan Liu
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford, CA, USA
| | - Camille Ezran
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford, CA, USA
| | - Michael F Z Wang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Zhengda Li
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kyle Awayan
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Jonathan Z Long
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Sarafan ChEM-H, Stanford, CA, USA
| | - Iwijn De Vlaminck
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Sheng Wang
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Jacques Epelbaum
- Adaptive Mechanisms and Evolution (MECADEV), UMR 7179, National Center for Scientific Research, National Museum of Natural History, Brunoy, France
| | - Christin S Kuo
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jérémy Terrien
- Adaptive Mechanisms and Evolution (MECADEV), UMR 7179, National Center for Scientific Research, National Museum of Natural History, Brunoy, France
| | - Mark A Krasnow
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford, CA, USA.
| | - James E Ferrell
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA.
| |
Collapse
|
41
|
Osborne AJ, Bierzynska A, Colby E, Andag U, Kalra PA, Radresa O, Skroblin P, Taal MW, Welsh GI, Saleem MA, Campbell C. Multivariate canonical correlation analysis identifies additional genetic variants for chronic kidney disease. NPJ Syst Biol Appl 2024; 10:28. [PMID: 38459044 PMCID: PMC10924093 DOI: 10.1038/s41540-024-00350-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/20/2024] [Indexed: 03/10/2024] Open
Abstract
Chronic kidney diseases (CKD) have genetic associations with kidney function. Univariate genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN), two complementary kidney function markers. However, it is unknown whether additional SNPs for kidney function can be identified by multivariate statistical analysis. To address this, we applied canonical correlation analysis (CCA), a multivariate method, to two individual-level CKD genotype datasets, and metaCCA to two published GWAS summary statistics datasets. We identified SNPs previously associated with kidney function by published univariate GWASs with high replication rates, validating the metaCCA method. We then extended discovery and identified previously unreported lead SNPs for both kidney function markers, jointly. These showed expression quantitative trait loci (eQTL) colocalisation with genes having significant differential expression between CKD and healthy individuals. Several of these identified lead missense SNPs were predicted to have a functional impact, including in SLC14A2. We also identified previously unreported lead SNPs that showed significant correlation with both kidney function markers, jointly, in the European ancestry CKDGen, National Unified Renal Translational Research Enterprise (NURTuRE)-CKD and Salford Kidney Study (SKS) datasets. Of these, rs3094060 colocalised with FLOT1 gene expression and was significantly more common in CKD cases in both NURTURE-CKD and SKS, than in the general population. Overall, by using multivariate analysis by CCA, we identified additional SNPs and genes for both kidney function and CKD, that can be prioritised for further CKD analyses.
Collapse
Affiliation(s)
- Amy J Osborne
- Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1TW, UK.
| | - Agnieszka Bierzynska
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Elizabeth Colby
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Uwe Andag
- Department of Metabolic and Renal Diseases, Evotec International GmbH, Marie-Curie-Strasse 7, 37079, Göttingen, Germany
| | - Philip A Kalra
- Department of Renal Medicine, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Stott Lane, Salford, M6 8HD, UK
| | - Olivier Radresa
- Department of Metabolic and Renal Diseases, Evotec International GmbH, Marie-Curie-Strasse 7, 37079, Göttingen, Germany
| | - Philipp Skroblin
- Department of Metabolic and Renal Diseases, Evotec International GmbH, Marie-Curie-Strasse 7, 37079, Göttingen, Germany
| | - Maarten W Taal
- Centre for Kidney Research and Innovation, University of Nottingham, Derby, UK
| | - Gavin I Welsh
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Moin A Saleem
- Bristol Renal, University of Bristol and Bristol Royal Hospital for Children, Bristol, BS1 3NY, UK
| | - Colin Campbell
- Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1TW, UK.
| |
Collapse
|
42
|
Leite de Castro D, Aroso M, Aguiar AP, Grayden DB, Aguiar P. Disrupting abnormal neuronal oscillations with adaptive delayed feedback control. eLife 2024; 13:e89151. [PMID: 38450635 PMCID: PMC10987087 DOI: 10.7554/elife.89151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 03/05/2024] [Indexed: 03/08/2024] Open
Abstract
Closed-loop neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson's disease. However, at the moment, standard stimulation protocols rely on continuous open-loop stimulation and the design of adaptive controllers is an active field of research. Delayed feedback control (DFC), a popular method used to control chaotic systems, has been proposed as a closed-loop technique for desynchronisation of neuronal populations but, so far, was only tested in computational studies. We implement DFC for the first time in neuronal populations and access its efficacy in disrupting unwanted neuronal oscillations. To analyse in detail the performance of this activity control algorithm, we used specialised in vitro platforms with high spatiotemporal monitoring/stimulating capabilities. We show that the conventional DFC in fact worsens the neuronal population oscillatory behaviour, which was never reported before. Conversely, we present an improved control algorithm, adaptive DFC (aDFC), which monitors the ongoing oscillation periodicity and self-tunes accordingly. aDFC effectively disrupts collective neuronal oscillations restoring a more physiological state. Overall, these results support aDFC as a better candidate for therapeutic closed-loop brain stimulation.
Collapse
Affiliation(s)
- Domingos Leite de Castro
- Neuroengineering and Computational Neuroscience Lab, i3S - Instituto de Investigação e Inovação em Saúde, Universidade do PortoPortoPortugal
- Faculdade de Engenharia, Universidade do PortoPortoPortugal
| | - Miguel Aroso
- Neuroengineering and Computational Neuroscience Lab, i3S - Instituto de Investigação e Inovação em Saúde, Universidade do PortoPortoPortugal
| | - A Pedro Aguiar
- Faculdade de Engenharia, Universidade do PortoPortoPortugal
| | - David B Grayden
- Department of Biomedical Engineering, University of MelbourneMelbourneAustralia
| | - Paulo Aguiar
- Neuroengineering and Computational Neuroscience Lab, i3S - Instituto de Investigação e Inovação em Saúde, Universidade do PortoPortoPortugal
| |
Collapse
|
43
|
Garge RK, Geck RC, Armstrong JO, Dunn B, Boutz DR, Battenhouse A, Leutert M, Dang V, Jiang P, Kwiatkowski D, Peiser T, McElroy H, Marcotte EM, Dunham MJ. Systematic profiling of ale yeast protein dynamics across fermentation and repitching. G3 (Bethesda) 2024; 14:jkad293. [PMID: 38135291 PMCID: PMC10917522 DOI: 10.1093/g3journal/jkad293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/28/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023]
Abstract
Studying the genetic and molecular characteristics of brewing yeast strains is crucial for understanding their domestication history and adaptations accumulated over time in fermentation environments, and for guiding optimizations to the brewing process itself. Saccharomyces cerevisiae (brewing yeast) is among the most profiled organisms on the planet, yet the temporal molecular changes that underlie industrial fermentation and beer brewing remain understudied. Here, we characterized the genomic makeup of a Saccharomyces cerevisiae ale yeast widely used in the production of Hefeweizen beers, and applied shotgun mass spectrometry to systematically measure the proteomic changes throughout 2 fermentation cycles which were separated by 14 rounds of serial repitching. The resulting brewing yeast proteomics resource includes 64,740 protein abundance measurements. We found that this strain possesses typical genetic characteristics of Saccharomyces cerevisiae ale strains and displayed progressive shifts in molecular processes during fermentation based on protein abundance changes. We observed protein abundance differences between early fermentation batches compared to those separated by 14 rounds of serial repitching. The observed abundance differences occurred mainly in proteins involved in the metabolism of ergosterol and isobutyraldehyde. Our systematic profiling serves as a starting point for deeper characterization of how the yeast proteome changes during commercial fermentations and additionally serves as a resource to guide fermentation protocols, strain handling, and engineering practices in commercial brewing and fermentation environments. Finally, we created a web interface (https://brewing-yeast-proteomics.ccbb.utexas.edu/) to serve as a valuable resource for yeast geneticists, brewers, and biochemists to provide insights into the global trends underlying commercial beer production.
Collapse
Affiliation(s)
- Riddhiman K Garge
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Renee C Geck
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Joseph O Armstrong
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Barbara Dunn
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Daniel R Boutz
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
- Antibody Discovery and Accelerated Protein Therapeutics, Department of Pathology and Genomic Medicine, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Anna Battenhouse
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Mario Leutert
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Institute of Molecular Systems Biology, ETH Zürich, Zürich 8049, Switzerland
| | - Vy Dang
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Pengyao Jiang
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | | | | | | | - Edward M Marcotte
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Maitreya J Dunham
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| |
Collapse
|
44
|
Khandibharad S, Singh S. Single-cell ATAC sequencing identifies sleepy macrophages during reciprocity of cytokines in L. major infection. Microbiol Spectr 2024; 12:e0347823. [PMID: 38299832 PMCID: PMC10913457 DOI: 10.1128/spectrum.03478-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 12/31/2023] [Indexed: 02/02/2024] Open
Abstract
The hallmark characteristic of macrophages lies in their inherent plasticity, allowing them to adapt to dynamic microenvironments. Leishmania strategically modulates the phenotypic plasticity of macrophages, creating a favorable environment for intracellular survival and persistent infection through regulatory cytokine such as interleukin (IL)-10. Nevertheless, these effector cells can counteract infection by modulating crucial cytokines like IL-12 and key components involved in its production. Using sophisticated tool of single-cell assay for transposase accessible chromatin (ATAC) sequencing, we systematically examined the regulatory axis of IL-10 and IL-12 in a time-dependent manner during Leishmania major infection in macrophages Our analysis revealed the cellular heterogeneity post-infection with the regulators of IL-10 and IL-12, unveiling a reciprocal relationship between these cytokines. Notably, our significant findings highlighted the presence of sleepy macrophages and their pivotal role in mediating reciprocity between IL-10 and IL-12. To summarize, the roles of cytokine expression, transcription factors, cell cycle, and epigenetics of host cell machinery were vital in identification of sleepy macrophages, which is a transient state where transcription factors controlled the epigenetic remodeling and expression of genes involved in pro-inflammatory cytokine expression and recruitment of immune cells.IMPORTANCELeishmaniasis is an endemic affecting 99 countries and territories globally, as outlined in the 2022 World Health Organization report. The disease's severity is compounded by compromised host immune systems, emphasizing the pivotal role of the interplay between parasite and host immune factors in disease regulation. In instances of cutaneous leishmaniasis induced by L. major, macrophages function as sentinel cells. Our findings indicate that the plasticity and phenotype of macrophages can be modulated to express a cytokine profile involving IL-10 and IL-12, mediated by the regulation of transcription factors and their target genes post-L. major infection in macrophages. Employing sophisticated methodologies such as single-cell ATAC sequencing and computational genomics, we have identified a distinctive subset of macrophages termed "sleepy macrophages." These macrophages exhibit downregulated housekeeping genes while expressing a unique set of variable features. This data set constitutes a valuable resource for comprehending the intricate host-parasite interplay during L. major infection.
Collapse
Affiliation(s)
- Shweta Khandibharad
- Systems Medicine Lab, National Centre for Cell Science, SP Pune University Campus, Pune, India
| | - Shailza Singh
- Systems Medicine Lab, National Centre for Cell Science, SP Pune University Campus, Pune, India
| |
Collapse
|
45
|
Jensen MF, Nielsen M. Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration. eLife 2024; 12:RP93934. [PMID: 38437160 PMCID: PMC10942633 DOI: 10.7554/elife.93934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024] Open
Abstract
Predicting the interaction between Major Histocompatibility Complex (MHC) class I-presented peptides and T-cell receptors (TCR) holds significant implications for vaccine development, cancer treatment, and autoimmune disease therapies. However, limited paired-chain TCR data, skewed towards well-studied epitopes, hampers the development of pan-specific machine-learning (ML) models. Leveraging a larger peptide-TCR dataset, we explore various alterations to the ML architectures and training strategies to address data imbalance. This leads to an overall improved performance, particularly for peptides with scant TCR data. However, challenges persist for unseen peptides, especially those distant from training examples. We demonstrate that such ML models can be used to detect potential outliers, which when removed from training, leads to augmented performance. Integrating pan-specific and peptide-specific models alongside with similarity-based predictions, further improves the overall performance, especially when a low false positive rate is desirable. In the context of the IMMREP22 benchmark, this modeling framework attained state-of-the-art performance. Moreover, combining these strategies results in acceptable predictive accuracy for peptides characterized with as little as 15 positive TCRs. This observation places great promise on rapidly expanding the peptide covering of the current models for predicting TCR specificity. The NetTCR 2.2 model incorporating these advances is available on GitHub (https://github.com/mnielLab/NetTCR-2.2) and as a web server at https://services.healthtech.dtu.dk/services/NetTCR-2.2/.
Collapse
Affiliation(s)
- Mathias Fynbo Jensen
- Department of Health Technology, Section for Bioinformatics, Technical University of DenmarkLyngbyDenmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of DenmarkLyngbyDenmark
| |
Collapse
|
46
|
Alawam AS, Alwethaynani MS. Construction of an aerolysin-based multi-epitope vaccine against Aeromonas hydrophila: an in silico machine learning and artificial intelligence-supported approach. Front Immunol 2024; 15:1369890. [PMID: 38495891 PMCID: PMC10940347 DOI: 10.3389/fimmu.2024.1369890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 02/14/2024] [Indexed: 03/19/2024] Open
Abstract
Aeromonas hydrophila, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic arthritis, diarrhea (traveler's diarrhea), gastroenteritis, skin and wound infections, meningitis, fulminating septicemia, enterocolitis, peritonitis, and endocarditis. It frequently occurs in aquatic environments and readily contacts humans, leading to high infection rates. This bacterium has exhibited resistance to numerous commercial antibiotics, and no vaccine has yet been developed. Aiming to combat the alarmingly high infection rate, this study utilizes in silico techniques to design a multi-epitope vaccine (MEV) candidate against this bacterium based on its aerolysin toxin, which is the most toxic and highly conserved virulence factor among the Aeromonas species. After retrieval, aerolysin was processed for B-cell and T-cell epitope mapping. Once filtered for toxicity, antigenicity, allergenicity, and solubility, the chosen epitopes were combined with an adjuvant and specific linkers to create a vaccine construct. These linkers and the adjuvant enhance the MEV's ability to elicit robust immune responses. Analyses of the predicted and improved vaccine structure revealed that 75.5%, 19.8%, and 1.3% of its amino acids occupy the most favored, additional allowed, and generously allowed regions, respectively, while its ERRAT score reached nearly 70%. Docking simulations showed the MEV exhibiting the highest interaction and binding energies (-1,023.4 kcal/mol, -923.2 kcal/mol, and -988.3 kcal/mol) with TLR-4, MHC-I, and MHC-II receptors. Further molecular dynamics simulations demonstrated the docked complexes' remarkable stability and maximum interactions, i.e., uniform RMSD, fluctuated RMSF, and lowest binding net energy. In silico models also predict the vaccine will stimulate a variety of immunological pathways following administration. These analyses suggest the vaccine's efficacy in inducing robust immune responses against A. hydrophila. With high solubility and no predicted allergic responses or toxicity, it appears safe for administration in both healthy and A. hydrophila-infected individuals.
Collapse
Affiliation(s)
- Abdullah S. Alawam
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Maher S. Alwethaynani
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Shaqra University, Al-Quwayiyah, Saudi Arabia
| |
Collapse
|
47
|
Andrews SS, Wiley HS, Sauro HM. Design patterns of biological cells. Bioessays 2024; 46:e2300188. [PMID: 38247191 PMCID: PMC10922931 DOI: 10.1002/bies.202300188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 12/03/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.
Collapse
Affiliation(s)
- Steven S. Andrews
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - H. Steven Wiley
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| |
Collapse
|
48
|
Pranavathiyani G, Pan A. Prediction of Essential Proteins of Klebsiella pneumoniae using Integrative Bioinformatics and Systems Biology Approach: Unveiling New Avenues for Drug Discovery. OMICS 2024; 28:138-147. [PMID: 38478777 DOI: 10.1089/omi.2024.0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Klebsiella pneumoniae is an opportunistic multidrug-resistant bacterial pathogen responsible for various health care-associated infections. The prediction of proteins that are essential for the survival of bacterial pathogens can greatly facilitate the drug development and discovery pipeline toward target identification. To this end, the present study reports a comprehensive computational approach integrating bioinformatics and systems biology-based methods to identify essential proteins of K. pneumoniae involved in vital processes. From the proteome of this pathogen, we predicted a total of 854 essential proteins based on sequence, protein-protein interaction (PPI) and genome-scale metabolic model methods. These predicted essential proteins are involved in vital processes for cellular regulation such as translation, metabolism, and biosynthesis of essential factors, among others. Cluster analysis of the PPI network revealed the highly connected modules involved in the basic functionality of the organism. Further, the predicted consensus set of essential proteins of K. pneumoniae was evaluated by comparing them with existing resources (NetGenes and PATHOgenex) and literature. The findings of this study offer guidance toward understanding cell functionality, thereby facilitating the understanding of pathogen systems and providing a way forward to shortlist potential therapeutic candidates for developing novel antimicrobial agents against K. pneumoniae. In addition, the research strategy presented herein is a fusion of sequence and systems biology-based approaches that offers prospects as a model to predict essential proteins for other pathogens.
Collapse
Affiliation(s)
- Gnanasekar Pranavathiyani
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry, India
| | - Archana Pan
- Department of Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry, India
| |
Collapse
|
49
|
Soubrier C, Foxall E, Ciandrini L, Dao Duc K. Optimal control of ribosome population for gene expression under periodic nutrient intake. J R Soc Interface 2024; 21:20230652. [PMID: 38442858 PMCID: PMC10914516 DOI: 10.1098/rsif.2023.0652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 02/05/2024] [Indexed: 03/07/2024] Open
Abstract
Translation of proteins is a fundamental part of gene expression that is mediated by ribosomes. As ribosomes significantly contribute to both cellular mass and energy consumption, achieving efficient management of the ribosome population is also crucial to metabolism and growth. Inspired by biological evidence for nutrient-dependent mechanisms that control both ribosome-active degradation and genesis, we introduce a dynamical model of protein production, that includes the dynamics of resources and control over the ribosome population. Under the hypothesis that active degradation and biogenesis are optimal for maximizing and maintaining protein production, we aim to qualitatively reproduce empirical observations of the ribosome population dynamics. Upon formulating the associated optimization problem, we first analytically study the stability and global behaviour of solutions under constant resource input, and characterize the extent of oscillations and convergence rate to a global equilibrium. We further use these results to simplify and solve the problem under a quasi-static approximation. Using biophysical parameter values, we find that optimal control solutions lead to both control mechanisms and the ribosome population switching between periods of feeding and fasting, suggesting that the intense regulation of ribosome population observed in experiments allows to maximize and maintain protein production. Finally, we find some range for the control values over which such a regime can be observed, depending on the intensity of fasting.
Collapse
Affiliation(s)
- Clément Soubrier
- Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4
| | - Eric Foxall
- Department of Mathematics, University of British Columbia Okanagan, Kelowna, British Columbia, Canada V1V 1V7
| | - Luca Ciandrini
- Centre de Biochimie Structurale (CBS), INSERM U154, CNRS UMR5048, University of Montpellier, Montpellier, France
- Institut Universitaire de France (IUF), Paris, France
| | - Khanh Dao Duc
- Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4
| |
Collapse
|
50
|
Pensotti A, Bizzarri M, Bertolaso M. The phenotypic reversion of cancer: Experimental evidences on cancer reversibility through epigenetic mechanisms (Review). Oncol Rep 2024; 51:48. [PMID: 38275101 PMCID: PMC10835663 DOI: 10.3892/or.2024.8707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024] Open
Abstract
Different experimental models reveal that malignant cancer cells can be induced to change their phenotype into a benign one. This phenotypic transformation, confirmed both in vitro and in vivo, currently is known as 'tumor reversion'. This evidence raises a radical question among current cancer models: Is cancer reversible? How do genetic and epigenetic alterations hierarchically relate? Understanding the mechanisms of 'tumor reversion' represents a key point in order to evolve the actual cancer models and develop new heuristic models that can possibly lead to drugs that target epigenetic mechanisms, for example epigenetic drugs. Even though evidence of tumor reversion dates back to the 1950s, this remains a completely new field of research recently re‑discovered thanks to the interest in cell reprogramming research, developmental biology and the increasing understanding of epigenetic mechanisms. In the current review, a comprehensive review of all the main experimental models on tumor reversion was presented.
Collapse
Affiliation(s)
- Andrea Pensotti
- Research Unit of Philosophy of Science and Human Development, University Campus Bio‑Medico of Rome, I‑00128 Rome, Italy
| | - Mariano Bizzarri
- Systems Biology Group Lab, Department of Experimental Medicine, Sapienza University, I‑00185 Rome, Italy
| | - Marta Bertolaso
- Research Unit of Philosophy of Science and Human Development, University Campus Bio‑Medico of Rome, I‑00128 Rome, Italy
| |
Collapse
|