1
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Najm M, Cornet M, Albergante L, Zinovyev A, Sermet-Gaudelus I, Stoven V, Calzone L, Martignetti L. Representation and quantification of module activity from omics data with rROMA. NPJ Syst Biol Appl 2024; 10:8. [PMID: 38242871 PMCID: PMC10799004 DOI: 10.1038/s41540-024-00331-x] [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: 05/31/2023] [Accepted: 01/03/2024] [Indexed: 01/21/2024] Open
Abstract
The efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. The rROMA package incorporates significant improvements in the calculation algorithm, along with the implementation of several functions for statistical analysis and visualizing results. These additions greatly expand the package's capabilities and offer valuable tools for data analysis and interpretation. It is an open-source package available on github at: www.github.com/sysbio-curie/rROMA . Based on publicly available transcriptomic datasets, we applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Results indicate that rROMA can detect disease-related active signaling pathways using transcriptomic and proteomic data. The results notably identified a significant mechanism relevant to cystic fibrosis, raised awareness of a possible bias related to cell culture, and uncovered an intriguing gene that warrants further investigation.
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Affiliation(s)
- Matthieu Najm
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Matthieu Cornet
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Luca Albergante
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Andrei Zinovyev
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Isabelle Sermet-Gaudelus
- Faculté de Médecine, Université de Paris, Paris, France
- Institut Necker Enfants Malades, INSERM U1151, Paris, France
- AP-HP. Centre - Université Paris Cité; Hôpital Necker Enfants Malades, Centre de Référence Maladie Rare - Mucoviscidose, Paris, France
| | - Véronique Stoven
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Laurence Calzone
- INSERM U900, 75428, Paris, France
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France
- Institut Curie, PSL Research University, 75248, Paris, France
| | - Loredana Martignetti
- INSERM U900, 75428, Paris, France.
- Center for Computational Biology, Mines ParisTech, PSL Research University, 75006, Paris, France.
- Institut Curie, PSL Research University, 75248, Paris, France.
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2
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Abstract
In order to maintain functional robustness and species integrity, organisms must ensure high fidelity of the genome duplication process. This is particularly true during early development, where cell division is often occurring both rapidly and coherently. By studying the extreme limits of suppressing DNA replication failure due to double fork stall errors, we uncover a fundamental constant that describes a trade-off between genome size and architectural complexity of the developing organism. This constant has the approximate value N U ≈ 3 × 1012 basepairs, and depends only on two highly conserved molecular properties of DNA biology. We show that our theory is successful in interpreting a diverse range of data across the Eukaryota.
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Affiliation(s)
- M Al Mamun
- School of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom. CIB-CSIC, Madrid 28040, Spain
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3
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Kieffer Y, Hocine HR, Gentric G, Pelon F, Bernard C, Bourachot B, Lameiras S, Albergante L, Bonneau C, Guyard A, Tarte K, Zinovyev A, Baulande S, Zalcman G, Vincent-Salomon A, Mechta-Grigoriou F. Single-Cell Analysis Reveals Fibroblast Clusters Linked to Immunotherapy Resistance in Cancer. Cancer Discov 2020; 10:1330-1351. [PMID: 32434947 DOI: 10.1158/2159-8290.cd-19-1384] [Citation(s) in RCA: 373] [Impact Index Per Article: 93.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/26/2020] [Accepted: 05/15/2020] [Indexed: 11/16/2022]
Abstract
A subset of cancer-associated fibroblasts (FAP+/CAF-S1) mediates immunosuppression in breast cancers, but its heterogeneity and its impact on immunotherapy response remain unknown. Here, we identify 8 CAF-S1 clusters by analyzing more than 19,000 single CAF-S1 fibroblasts from breast cancer. We validate the five most abundant clusters by flow cytometry and in silico analyses in other cancer types, highlighting their relevance. Myofibroblasts from clusters 0 and 3, characterized by extracellular matrix proteins and TGFβ signaling, respectively, are indicative of primary resistance to immunotherapies. Cluster 0/ecm-myCAF upregulates PD-1 and CTLA4 protein levels in regulatory T lymphocytes (Tregs), which, in turn, increases CAF-S1 cluster 3/TGFβ-myCAF cellular content. Thus, our study highlights a positive feedback loop between specific CAF-S1 clusters and Tregs and uncovers their role in immunotherapy resistance. SIGNIFICANCE: Our work provides a significant advance in characterizing and understanding FAP+ CAF in cancer. We reached a high resolution at single-cell level, which enabled us to identify specific clusters associated with immunosuppression and immunotherapy resistance. Identification of cluster-specific signatures paves the way for therapeutic options in combination with immunotherapies.This article is highlighted in the In This Issue feature, p. 1241.
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Affiliation(s)
- Yann Kieffer
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, Paris, France.,Inserm, U830, Paris, France
| | - Hocine R Hocine
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, Paris, France.,Inserm, U830, Paris, France
| | - Géraldine Gentric
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, Paris, France.,Inserm, U830, Paris, France
| | - Floriane Pelon
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, Paris, France.,Inserm, U830, Paris, France
| | - Charles Bernard
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, Paris, France.,Inserm, U830, Paris, France
| | - Brigitte Bourachot
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, Paris, France.,Inserm, U830, Paris, France
| | - Sonia Lameiras
- ICGex Next-Generation Sequencing Platform, Institut Curie, SIRIC, Paris, France
| | - Luca Albergante
- Institut Curie, Inserm, U900, PSL Research University, Paris, France.,Mines ParisTech, CBIO-Centre for Computational Biology, Paris, France
| | - Claire Bonneau
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, Paris, France.,Inserm, U830, Paris, France.,Department of Surgery, Institut Curie Hospital Group, Saint-Cloud, France
| | - Alice Guyard
- Department of Pathology Bichat Claude Bernard Hospital Group, Paris Diderot University, Paris, France
| | - Karin Tarte
- UMR U1236-MICMAC, Immunology and Cell Therapy Lab, Rennes University, Rennes, France
| | - Andrei Zinovyev
- Institut Curie, Inserm, U900, PSL Research University, Paris, France.,Mines ParisTech, CBIO-Centre for Computational Biology, Paris, France
| | - Sylvain Baulande
- ICGex Next-Generation Sequencing Platform, Institut Curie, SIRIC, Paris, France
| | - Gerard Zalcman
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, Paris, France.,Inserm, U830, Paris, France.,Thoracic Oncology Department, CIC 1425-CLIP2, Bichat Claude Bernard Hospital Group, Paris Diderot University, Paris, France
| | - Anne Vincent-Salomon
- Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital Group, Paris, France
| | - Fatima Mechta-Grigoriou
- Institut Curie, Stress and Cancer Laboratory, Equipe labélisée par la Ligue Nationale contre le Cancer, PSL Research University, Paris, France. .,Inserm, U830, Paris, France
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4
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Albergante L, Mirkes E, Bac J, Chen H, Martin A, Faure L, Barillot E, Pinello L, Gorban A, Zinovyev A. Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph. Entropy (Basel) 2020; 22:E296. [PMID: 33286070 PMCID: PMC7516753 DOI: 10.3390/e22030296] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 02/26/2020] [Accepted: 03/02/2020] [Indexed: 12/19/2022]
Abstract
Multidimensional datapoint clouds representing large datasets are frequently characterized by non-trivial low-dimensional geometry and topology which can be recovered by unsupervised machine learning approaches, in particular, by principal graphs. Principal graphs approximate the multivariate data by a graph injected into the data space with some constraints imposed on the node mapping. Here we present ElPiGraph, a scalable and robust method for constructing principal graphs. ElPiGraph exploits and further develops the concept of elastic energy, the topological graph grammar approach, and a gradient descent-like optimization of the graph topology. The method is able to withstand high levels of noise and is capable of approximating data point clouds via principal graph ensembles. This strategy can be used to estimate the statistical significance of complex data features and to summarize them into a single consensus principal graph. ElPiGraph deals efficiently with large datasets in various fields such as biology, where it can be used for example with single-cell transcriptomic or epigenomic datasets to infer gene expression dynamics and recover differentiation landscapes.
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Affiliation(s)
- Luca Albergante
- Institut Curie, PSL Research University, 75005 Paris, France; (J.B.); (A.M.); (L.F.); (E.B.)
- INSERM U900, 75248 Paris, France
- CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75006 Paris, France
- Sensyne Health, Oxford OX4 4GE, UK
| | - Evgeny Mirkes
- Center for Mathematical Modeling, University of Leicester, Leicester LE1 7RH, UK; (E.M.); (A.G.)
- Lobachevsky University, 603000 Nizhny Novgorod, Russia
| | - Jonathan Bac
- Institut Curie, PSL Research University, 75005 Paris, France; (J.B.); (A.M.); (L.F.); (E.B.)
- INSERM U900, 75248 Paris, France
- CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75006 Paris, France
- Centre de Recherches Interdisciplinaires, Université de Paris, F-75000 Paris, France
| | - Huidong Chen
- Molecular Pathology Unit & Cancer Center, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, MA 02114, USA; (H.C.); (L.P.)
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alexis Martin
- Institut Curie, PSL Research University, 75005 Paris, France; (J.B.); (A.M.); (L.F.); (E.B.)
- INSERM U900, 75248 Paris, France
- CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75006 Paris, France
- ECE Paris, F-75015 Paris, France
| | - Louis Faure
- Institut Curie, PSL Research University, 75005 Paris, France; (J.B.); (A.M.); (L.F.); (E.B.)
- INSERM U900, 75248 Paris, France
- CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75006 Paris, France
- Center for Brain Research, Medical University of Vienna, 22180 Vienna, Austria
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, 75005 Paris, France; (J.B.); (A.M.); (L.F.); (E.B.)
- INSERM U900, 75248 Paris, France
- CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75006 Paris, France
| | - Luca Pinello
- Molecular Pathology Unit & Cancer Center, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, MA 02114, USA; (H.C.); (L.P.)
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alexander Gorban
- Center for Mathematical Modeling, University of Leicester, Leicester LE1 7RH, UK; (E.M.); (A.G.)
- Lobachevsky University, 603000 Nizhny Novgorod, Russia
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, 75005 Paris, France; (J.B.); (A.M.); (L.F.); (E.B.)
- INSERM U900, 75248 Paris, France
- CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75006 Paris, France
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5
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Chen H, Albergante L, Hsu JY, Lareau CA, Lo Bosco G, Guan J, Zhou S, Gorban AN, Bauer DE, Aryee MJ, Langenau DM, Zinovyev A, Buenrostro JD, Yuan GC, Pinello L. Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nat Commun 2019; 10:1903. [PMID: 31015418 PMCID: PMC6478907 DOI: 10.1038/s41467-019-09670-4] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 03/22/2019] [Indexed: 12/14/2022] Open
Abstract
Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data. We have tested STREAM on several synthetic and real datasets generated with different single-cell technologies. We further demonstrate its utility for understanding myoblast differentiation and disentangling known heterogeneity in hematopoiesis for different organisms. STREAM is an open-source software package.
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Affiliation(s)
- Huidong Chen
- Molecular Pathology Unit & Cancer Center, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, MA, 02114, USA
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- Department of Computer Science and Technology, Tongji University, 201804, Shanghai, China
| | - Luca Albergante
- Institut Curie, PSL Research University, F-75005, Paris, France
- INSERM, U900, F-75005, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006, Paris, France
| | - Jonathan Y Hsu
- Molecular Pathology Unit & Cancer Center, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, MA, 02114, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Caleb A Lareau
- Molecular Pathology Unit & Cancer Center, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, MA, 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Giosuè Lo Bosco
- Department of Mathematics and Computer Science, University of Palermo, 90123, Palermo, Italy
- Department of Sciences for technological innovation, Euro-Mediterranean Institute of Science and Technology, 90139, Palermo, Italy
| | - Jihong Guan
- Department of Computer Science and Technology, Tongji University, 201804, Shanghai, China
| | - Shuigeng Zhou
- Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, 200433, Shanghai, China
| | - Alexander N Gorban
- Department of Mathematics, University of Leicester, University Road, Leicester, LE1 7RH, UK
- Lobachevsky University, Nizhni Novgorod, 603022, Russia
| | - Daniel E Bauer
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, 02215, USA
| | - Martin J Aryee
- Molecular Pathology Unit & Cancer Center, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, MA, 02114, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - David M Langenau
- Molecular Pathology Unit & Cancer Center, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, F-75005, Paris, France
- INSERM, U900, F-75005, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006, Paris, France
- Lobachevsky University, Nizhni Novgorod, 603022, Russia
| | - Jason D Buenrostro
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Harvard Society of Fellows, Harvard University, Cambridge, MA, 02138, USA
| | - Guo-Cheng Yuan
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA.
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA.
| | - Luca Pinello
- Molecular Pathology Unit & Cancer Center, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, MA, 02114, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
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6
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Liu D, Albergante L, Newman TJ, Horn D. Faster growth with shorter antigens can explain a VSG hierarchy during African trypanosome infections: a feint attack by parasites. Sci Rep 2018; 8:10922. [PMID: 30026531 PMCID: PMC6053454 DOI: 10.1038/s41598-018-29296-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 07/09/2018] [Indexed: 11/22/2022] Open
Abstract
The parasitic African trypanosome, Trypanosoma brucei, evades the adaptive host immune response by a process of antigenic variation that involves the clonal switching of variant surface glycoproteins (VSGs). The VSGs that come to dominate in vivo during an infection are not entirely random, but display a hierarchical order. How this arises is not fully understood. Combining available genetic data with mathematical modelling, we report a VSG-length-dependent hierarchical timing of clonal VSG dominance in a mouse model, consistent with an inverse correlation between VSG length and trypanosome growth-rate. Our analyses indicate that, among parasites switching to new VSGs, those expressing shorter VSGs preferentially accumulate to a detectable level that is sufficient to trigger a targeted immune response. This may be due to the increased metabolic cost of producing longer VSGs. Subsequent elimination of faster-growing parasites then allows slower-growing parasites with longer VSGs to accumulate. This interaction between the host and parasite is able to explain the temporal distribution of VSGs observed in vivo. Thus, our findings reveal a length-dependent hierarchy that operates during T. brucei infection. This represents a 'feint attack' diversion tactic utilised by these persistent parasites to out-maneuver the host adaptive immune system.
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Affiliation(s)
- Dianbo Liu
- School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.
- The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA.
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, 02139, USA.
| | - Luca Albergante
- School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK
- Institut Curie, PLS Research University, Mines Paris Tech, Inserm U900, F-75005, Paris, France
| | - T J Newman
- School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK
- Solaravus, PO Box 29476, Cupar, KY15 9AS, UK
| | - David Horn
- School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.
- Wellcome Trust Centre for Anti-Infectives Research, School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.
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7
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Abstract
For many cancer types, incidence rises rapidly with age as an apparent power law, supporting the idea that cancer is caused by a gradual accumulation of genetic mutations. Similarly, the incidence of many infectious diseases strongly increases with age. Here, combining data from immunology and epidemiology, we show that many of these dramatic age-related increases in incidence can be modeled based on immune system decline, rather than mutation accumulation. In humans, the thymus atrophies from infancy, resulting in an exponential decline in T cell production with a half-life of ∼16 years, which we use as the basis for a minimal mathematical model of disease incidence. Our model outperforms the power law model with the same number of fitting parameters in describing cancer incidence data across a wide spectrum of different cancers, and provides excellent fits to infectious disease data. This framework provides mechanistic insight into cancer emergence, suggesting that age-related decline in T cell output is a major risk factor.
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Affiliation(s)
- Sam Palmer
- School of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom;
- School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, 62200 Putrajaya, Malaysia
| | - Luca Albergante
- School of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom
- Institut Curie, Université de Recherche Paris Sciences et Lettres, Mines ParisTech, INSERM U900, F-75005 Paris, France
| | - Clare C Blackburn
- Medical Research Council Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biological Sciences, University of Edinburgh, EH16 4UU Edinburgh, United Kingdom
| | - T J Newman
- School of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom;
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8
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Liu D, Albergante L, Newman TJ. Universal attenuators and their interactions with feedback loops in gene regulatory networks. Nucleic Acids Res 2017; 45:7078-7093. [PMID: 28575450 PMCID: PMC5499555 DOI: 10.1093/nar/gkx485] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 05/29/2017] [Indexed: 12/18/2022] Open
Abstract
Using a combination of mathematical modelling, statistical simulation and large-scale data analysis we study the properties of linear regulatory chains (LRCs) within gene regulatory networks (GRNs). Our modelling indicates that downstream genes embedded within LRCs are highly insulated from the variation in expression of upstream genes, and thus LRCs act as attenuators. This observation implies a progressively weaker functionality of LRCs as their length increases. When analyzing the preponderance of LRCs in the GRNs of Escherichia coli K12 and several other organisms, we find that very long LRCs are essentially absent. In both E. coli and M. tuberculosis we find that four-gene LRCs are intimately linked to identical feedback loops that are involved in potentially chaotic stress response, indicating that the dynamics of these potentially destabilising motifs are strongly restrained under homeostatic conditions. The same relationship is observed in a human cancer cell line (K562), and we postulate that four-gene LRCs act as ‘universal attenuators’. These findings suggest a role for long LRCs in dampening variation in gene expression, thereby protecting cell identity, and in controlling dramatic shifts in cell-wide gene expression through inhibiting chaos-generating motifs.
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Affiliation(s)
- Dianbo Liu
- School of Life sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK.,The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.,Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA 02139, USA
| | - Luca Albergante
- School of Life sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK.,Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, F-75005 Paris, France
| | - Timothy J Newman
- School of Life sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK
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9
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Abstract
We discuss an overtly "simple approach" to complex biological systems borrowing selectively from theoretical physics. The approach is framed by three maxims, and we show examples of its success in two different applications: investigating cellular robustness at the level of gene regulatory networks and quantifying rare events of DNA replication errors.
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Affiliation(s)
- L Albergante
- School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK
| | - D Liu
- School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK
| | - S Palmer
- School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK
| | - T J Newman
- School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.
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10
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Dayal JHS, Albergante L, Newman TJ, South AP. Quantitation of multiclonality in control and drug-treated tumour populations using high-throughput analysis of karyotypic heterogeneity. Converg Sci Phys Oncol 2015. [DOI: 10.1088/2057-1739/1/2/025001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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11
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Albergante L, Blow JJ, Newman TJ. Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks. eLife 2014; 3:e02863. [PMID: 25182846 PMCID: PMC4151086 DOI: 10.7554/elife.02863] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 08/08/2014] [Indexed: 01/30/2023] Open
Abstract
The gene regulatory network (GRN) is the central decision-making module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and large-scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation.
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Affiliation(s)
- Luca Albergante
- College of Life Sciences, University of Dundee, Dundee, United Kingdom
| | - J Julian Blow
- College of Life Sciences, University of Dundee, Dundee, United Kingdom
| | - Timothy J Newman
- College of Life Sciences, University of Dundee, Dundee, United Kingdom School of Engineering, Physics and Mathematics, University of Dundee, Dundee, United Kingdom
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Tinti M, Dissanayake K, Synowsky S, Albergante L, MacKintosh C. Identification of 2R-ohnologue gene families displaying the same mutation-load skew in multiple cancers. Open Biol 2014; 4:140029. [PMID: 24806839 PMCID: PMC4042849 DOI: 10.1098/rsob.140029] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The complexity of signalling pathways was boosted at the origin of the vertebrates, when two rounds of whole genome duplication (2R-WGD) occurred. Those genes and proteins that have survived from the 2R-WGD-termed 2R-ohnologues-belong to families of two to four members, and are enriched in signalling components relevant to cancer. Here, we find that while only approximately 30% of human transcript-coding genes are 2R-ohnologues, they carry 42-60% of the gene mutations in 30 different cancer types. Across a subset of cancer datasets, including melanoma, breast, lung adenocarcinoma, liver and medulloblastoma, we identified 673 2R-ohnologue families in which one gene carries mutations at multiple positions, while sister genes in the same family are relatively mutation free. Strikingly, in 315 of the 322 2R-ohnologue families displaying such a skew in multiple cancers, the same gene carries the heaviest mutation load in each cancer, and usually the second-ranked gene is also the same in each cancer. Our findings inspire the hypothesis that in certain cancers, heterogeneous combinations of genetic changes impair parts of the 2R-WGD signalling networks and force information flow through a limited set of oncogenic pathways in which specific non-mutated 2R-ohnologues serve as effectors. The non-mutated 2R-ohnologues are therefore potential therapeutic targets. These include proteins linked to growth factor signalling, neurotransmission and ion channels.
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Affiliation(s)
- Michele Tinti
- Division of Cell and Developmental Biology, College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK
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