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Thurell J, Manouchehri N, Fredriksson I, Wilking U, Bergh J, Ryden L, Koppert LB, Karsten MM, Kiani NA, Hedayati E. Risk-adjusted benchmarking of long-term overall survival in patients with HER2-positive early-stage Breast cancer: A Swedish retrospective cohort study. Breast 2023; 70:18-24. [PMID: 37295176 DOI: 10.1016/j.breast.2023.05.008] [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: 05/25/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
AIM The main objective of the current study was to explore the value of risk-adjustment when comparing (i.e. benchmarking) long-term overall survival (OS) in breast cancer (BC) between Swedish regions. We performed risk-adjusted benchmarking of 5- and 10-year OS after HER2-positive early BC diagnosis between Sweden's two largest healthcare regions, constituting approximately a third of the total population in Sweden. METHODS All patients diagnosed with HER2-positive early-stage BC between 01-01-2009 and 31-12-2016 in healthcare regions Stockholm-Gotland and Skane were included in the study. Cox proportional hazards model was used for risk-adjustment. Unadjusted (i.e. crude) and adjusted 5- and 10-year OS was benchmarked between the two regions. RESULTS The crude 5-year OS was 90.3% in the Stockholm-Gotland region and 87.8% in the Skane region. The crude 10-year OS was 81.7% in the Stockholm-Gotland region and 77.3% in the Skane region. However, when adjusted for age, menopausal status and tumour biology, there was no significant OS disparity between the regions, neither at the 5-year nor 10-year follow-up. CONCLUSION This study showed that risk-adjustment is relevant when benchmarking OS in BC, even when comparing regions from the same country that share the same national treatment guidelines. This is, to our knowledge, the first published risk-adjusted benchmarking of OS in HER2-positive BC.
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Affiliation(s)
- Jacob Thurell
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Breast Cancer Center, Department of Breast, Endocrine Tumours and Sarcoma, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden.
| | - Narges Manouchehri
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Algorithmic Dynamics Lab, Center of Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Irma Fredriksson
- Breast Cancer Center, Department of Breast, Endocrine Tumours and Sarcoma, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden; Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Ulla Wilking
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Jonas Bergh
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Breast Cancer Center, Department of Breast, Endocrine Tumours and Sarcoma, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Lisa Ryden
- Department of Clinical Sciences Lund, Division of Surgery, Lund University, Lund, Sweden; Department of Surgery, Skane University Hospital, Malmö, Sweden
| | - Linetta B Koppert
- Erasmus MC Cancer Institute, Dept of Surgery, Rotterdam, the Netherlands
| | - Maria M Karsten
- Charité - Universitätsmedizin Berlin, Department of Gynecology with Breast Center, Berlin, Germany
| | - Narsis A Kiani
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Algorithmic Dynamics Lab, Center of Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Elham Hedayati
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Breast Cancer Center, Department of Breast, Endocrine Tumours and Sarcoma, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden
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Johnsson A, Kiani NA, Gernaat SAM, Wilking U, Shabo I, Hedayati E. Planning for return to work during the first year after breast cancer metastasis: A Swedish cohort study. Cancer Med 2023; 12:10840-10850. [PMID: 36880198 DOI: 10.1002/cam4.5752] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/22/2023] [Accepted: 02/17/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND Planning for return to work (RTW) is relevant among sub-groups of metastatic breast cancer (mBC) survivors. RTW and protective factors for RTW in patients with mBC were determined. METHODS Patients with mBC, ages 18-63 years, were identified in Swedish registers, and data were collected starting 1 year before their mBC diagnosis. The prevalence of working net days (WNDs) (>90 and >180) during the year after mBC diagnosis (y1) was determined. Factors associated with RTW were assessed using regression analysis. The impact of contemporary oncological treatment of mBC on RTW and 5-year mBC-specific survival was compared between those diagnosed in 1997-2002 and 2003-2011. RESULTS Of 490 patients, 239 (48.8%) and 189 (36.8%) had >90 and >180 WNDs, respectively, during y1. Adjusted odds ratios (AORs) of WNDs >90 or >180 during y1 were significantly higher for patients with age ≤50 years (AOR180 = 1.54), synchronous metastasis (AOR90 = 1.68, AOR180 = 1.67), metastasis within 24 months (AOR180 = 1.51), soft tissue, visceral, brain as first metastatic site (AOR90 = 1.47) and sickness absence <90 net days in the year before mBC diagnosis, suggesting limited comorbidities (AOR90 = 1.28, AOR180 = 2.00), respectively. Mean (standard deviation) WNDs were 134.9 (140.1) and 161.3 (152.4) for patients diagnosed with mBC in 1997-2002 and 2003-2011, respectively (p = 0.046). Median (standard error) mBC-specific survivals were 41.0 (2.5) and 62.0 (9.6) months for patients diagnosed with mBC in 1997-2002 and 2003-2011, respectively (p < 0.001). CONCLUSIONS RTW of more than 180 WNDs was associated with younger age, early development of metastases and limited comorbidities during the year before the diagnosis of mBC. Patients diagnosed with mBC in 2003 or later had more WNDs and better survival than those diagnosed earlier.
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Affiliation(s)
- Aina Johnsson
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Narsis A Kiani
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Sofie A M Gernaat
- Division of Clinical Epidemiology, Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - Ulla Wilking
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Ivan Shabo
- Breast Cancer Centre, Cancer Theme, Karolinska University Hospital, Karolinska CCC, Stockholm, Sweden.,Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
| | - Elham Hedayati
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden.,Breast Cancer Centre, Cancer Theme, Karolinska University Hospital, Karolinska CCC, Stockholm, Sweden
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Ruffini G, Damiani G, Lozano-Soldevilla D, Deco N, Rosas FE, Kiani NA, Ponce-Alvarez A, Kringelbach ML, Carhart-Harris R, Deco G. LSD-induced increase of Ising temperature and algorithmic complexity of brain dynamics. PLoS Comput Biol 2023; 19:e1010811. [PMID: 36735751 PMCID: PMC9943020 DOI: 10.1371/journal.pcbi.1010811] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 02/21/2023] [Accepted: 12/11/2022] [Indexed: 02/04/2023] Open
Abstract
A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e., from criticality. Here we aim to characterize two brain states (psychedelics-induced and placebo) as captured by functional magnetic resonance imaging (fMRI), with features derived from the Ising spin model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. We hypothesized, along the lines of the entropic brain hypothesis, that psychedelics drive brain dynamics into a more disordered state at a higher Ising temperature and increased complexity. We analyze resting state blood-oxygen-level-dependent (BOLD) fMRI data collected in an earlier study from fifteen subjects in a control condition (placebo) and during ingestion of lysergic acid diethylamide (LSD). Working with the automated anatomical labeling (AAL) brain parcellation, we first create "archetype" Ising models representative of the entire dataset (global) and of the data in each condition. Remarkably, we find that such archetypes exhibit a strong correlation with an average structural connectome template obtained from dMRI (r = 0.6). We compare the archetypes from the two conditions and find that the Ising connectivity in the LSD condition is lower than in the placebo one, especially in homotopic links (interhemispheric connectivity), reflecting a significant decrease of homotopic functional connectivity in the LSD condition. The global archetype is then personalized for each individual and condition by adjusting the system temperature. The resulting temperatures are all near but above the critical point of the model in the paramagnetic (disordered) phase. The individualized Ising temperatures are higher in the LSD condition than in the placebo condition (p = 9 × 10-5). Next, we estimate the Lempel-Ziv-Welch (LZW) complexity of the binarized BOLD data and the synthetic data generated with the individualized model using the Metropolis algorithm for each participant and condition. The LZW complexity computed from experimental data reveals a weak statistical relationship with condition (p = 0.04 one-tailed Wilcoxon test) and none with Ising temperature (r(13) = 0.13, p = 0.65), presumably because of the limited length of the BOLD time series. Similarly, we explore complexity using the block decomposition method (BDM), a more advanced method for estimating algorithmic complexity. The BDM complexity of the experimental data displays a significant correlation with Ising temperature (r(13) = 0.56, p = 0.03) and a weak but significant correlation with condition (p = 0.04, one-tailed Wilcoxon test). This study suggests that the effects of LSD increase the complexity of brain dynamics by loosening interhemispheric connectivity-especially homotopic links. In agreement with earlier work using the Ising formalism with BOLD data, we find the brain state in the placebo condition is already above the critical point, with LSD resulting in a shift further away from criticality into a more disordered state.
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Affiliation(s)
- Giulio Ruffini
- Neuroelectrics Barcelona, Barcelona, Spain
- Starlab Barcelona, Barcelona, Spain
- Haskins Laboratories, New Haven, Connecticut, United States of America
- * E-mail:
| | | | | | | | - Fernando E. Rosas
- Department of Informatics, University of Sussex, Brighton, United Kingdom
- Centre For Psychedelic Research (Department of Brain Science), Imperial College London, London, United Kingdom
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
| | - Narsis A. Kiani
- Algorithmic Dynamics Lab, Center of Molecular Medicine, Karolinksa Institutet, Stockholm, Sweden
- Oncology and Pathology Department, Karolinksa Institutet, Stockholm, Sweden
| | - Adrián Ponce-Alvarez
- Computational Neuroscience Group, Center for Brain and Cognition (Department of Information and Communication Technologies), Universitat Pompeu Fabra, Barcelona, Spain
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Robin Carhart-Harris
- Centre For Psychedelic Research (Department of Brain Science), Imperial College London, London, United Kingdom
- Psychedelics Division - Neuroscape, University of California San Francisco, San Francisco, California, United States of America
| | - Gustavo Deco
- The Catalan Institution for Research and Advanced Studies (ICREA), Universitat Pompeu Fabra, Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Australia
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Altena R, Gernaat SAM, Wilking U, Kiani NA, Johnsson A, Hedayati E. Use of sickness benefits by patients with metastatic breast cancer-A Swedish cohort study. Eur J Cancer Care (Engl) 2022; 31:e13626. [PMID: 35621269 PMCID: PMC9541357 DOI: 10.1111/ecc.13626] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 05/05/2022] [Accepted: 05/16/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE The objective of this study is to determine the prevalence and predictors of sickness absence (SA) and disability pension (DP) in women with metastatic breast cancer (mBC). METHODS Data were obtained from Swedish registers concerning 1,240 adult women diagnosed 1997-2011 with mBC, from 1 year before (y-1) to 2 (y1) and 2 (y2) years after diagnosis. SA and DP prevalence was calculated. Odds ratios (AOR) were determined for factors associated with using long-term (SA > 180 days or DP > 0 days) sickness benefits. RESULTS Prevalence of SA and DP was 56.0% and 24.8% during y-1, 69.9% and 28.9% during y1, and 64.0% and 34.7% during y2, respectively. Odds of using long-term sickness benefits were higher y1 and y2 in patients using long-term sickness benefits the year before diagnosis (AOR = 3.82, 95% CI 2.91-5.02; AOR = 4.31, 95% CI 2.96-6.29, respectively) and y2 in patients with mBC diagnosis 1997-2000 (AOR = 1.84, 95% CI 1.10-3.08) and using long-term sickness benefits the year after diagnosis (AOR = 22.10, 95% CI 14.33-34.22). CONCLUSIONS The prevalence of sickness benefit utilisation was high and increased after mBC diagnosis, particularly for patients using long-term sickness benefits prior to diagnosis. Additional study is needed to determine factors that might reduce the need for sickness benefits and enhance work ability in these patients.
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Affiliation(s)
- Renske Altena
- Department of Oncology-Pathology, Bioclinicum, Karolinska Institutet, Stockholm, Sweden.,Breast Cancer Center, Cancer Theme, Karolinska University Hospital and Karolinska CCC, Stockholm, Sweden
| | - Sofie A M Gernaat
- Department of Medicine, Division of Clinical Epidemiology, Karolinska Institute, Stockholm, Sweden
| | - Ulla Wilking
- Department of Oncology-Pathology, Bioclinicum, Karolinska Institutet, Stockholm, Sweden
| | - Narsis A Kiani
- Department of Oncology-Pathology, Center of Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.,Algorithmic Dynamics Lab, Center of Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Aina Johnsson
- Department of Oncology-Pathology, Bioclinicum, Karolinska Institutet, Stockholm, Sweden.,Department of Neurobiology, Care Science and Society, Karolinska Institutet, Huddinge, Sweden.,Department of Oncology, South Hospital, Stockholm, Sweden
| | - Elham Hedayati
- Department of Oncology-Pathology, Bioclinicum, Karolinska Institutet, Stockholm, Sweden.,Breast Cancer Center, Cancer Theme, Karolinska University Hospital and Karolinska CCC, Stockholm, Sweden
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5
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Altena R, Hubbert L, Kiani NA, Wengström Y, Bergh J, Hedayati E. Evidence-based prediction and prevention of cardiovascular morbidity in adults treated for cancer. Cardiooncology 2021; 7:20. [PMID: 34049593 PMCID: PMC8161987 DOI: 10.1186/s40959-021-00105-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/04/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Cancer treatment-related morbidity relevantly compromises health status in cancer survivors, and efforts to optimise health-related outcomes in this population are vital to maximising healthy survivorship. A pre-treatment assessment - and possibly preventive management strategies - of cancer patients at increased risk for cardiovascular disease (CVD) seems a rational approach in this regard. Definitive evidence for such strategies is largely lacking, thereby impeding the formulation of firm recommendations. RESULTS The current scoping review aims to summarise and grade the evidence regarding strategies for prediction and prevention of CVD in adults in relation to oncological treatments. We conducted a scoping literature search for different strategies for primary prevention, such as medical and lifestyle interventions, as well as the use of predictive risk scores. We identified studies with moderate to good strength and up to now limited evidence to recommend primary preventive strategies in unselected patients treated with potentially cardiotoxic oncologic therapies. CONCLUSION Efforts to minimize the CVD burden in cancer survivors are needed to accomplish healthy survivorship. This can be done by means of robust models predictive for CVD events or application of interventions during or after oncological treatments. Up to now there is insufficient evidence to implement preventive strategies in an unselected group of patients treated with potential cardiotoxic oncological treatments. We conclude that randomised controlled trials are needed that evaluate medical and lifestyle interventions in groups at increased risk for complications, in order to be able to influence chronic illness risks, such as cardiovascular complications, for cancer survivors.
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Affiliation(s)
- Renske Altena
- Department of Oncology and Pathology Cancer Center Karolinska, Karolinska Institutet, Stockholm, Sweden.
- Medical Unit breast, endocrine tumours and sarcoma, Theme Cancer, Karolinska University Hospital Stockholm, Solna, Sweden.
| | - Laila Hubbert
- Department of Cardiology and Department of Health, Medicine and Caring Sciences, Linköping University, Norrköping, Sweden
| | - Narsis A Kiani
- Department of Oncology and Pathology Cancer Center Karolinska, Karolinska Institutet, Stockholm, Sweden
| | - Yvonne Wengström
- Department of Neurobiology, Care Sciences and Society, Division of Nursing, Karolinska Institutet, Stockholm, Sweden
| | - Jonas Bergh
- Department of Oncology and Pathology Cancer Center Karolinska, Karolinska Institutet, Stockholm, Sweden
- Medical Unit breast, endocrine tumours and sarcoma, Theme Cancer, Karolinska University Hospital Stockholm, Solna, Sweden
| | - Elham Hedayati
- Department of Oncology and Pathology Cancer Center Karolinska, Karolinska Institutet, Stockholm, Sweden
- Medical Unit breast, endocrine tumours and sarcoma, Theme Cancer, Karolinska University Hospital Stockholm, Solna, Sweden
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Hernández-Orozco S, Zenil H, Riedel J, Uccello A, Kiani NA, Tegnér J. Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces. Front Artif Intell 2021; 3:567356. [PMID: 33733213 PMCID: PMC7944352 DOI: 10.3389/frai.2020.567356] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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] [Received: 05/29/2020] [Accepted: 11/19/2020] [Indexed: 11/20/2022] Open
Abstract
We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research. We show that this model-driven approach may require less training data and can potentially be more generalizable as it shows greater resilience to random attacks. In an algorithmic space the order of its element is given by its algorithmic probability, which arises naturally from computable processes. We investigate the shape of a discrete algorithmic space when performing regression or classification using a loss function parametrized by algorithmic complexity, demonstrating that the property of differentiation is not required to achieve results similar to those obtained using differentiable programming approaches such as deep learning. In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that 1) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; 2) that solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; 3) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; 4) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges.
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Affiliation(s)
- Santiago Hernández-Orozco
- Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico.,Oxford Immune Algorithmics, Oxford, United Kingdom
| | - Hector Zenil
- Oxford Immune Algorithmics, Oxford, United Kingdom.,Algorithmic Dynamics Lab, Unit of Computational Medicine, Karolinska Institutet, Solna, Sweden.,Algorithmic Nature Group, LABORES, Paris, France.,King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering, Thuwal, Saudi Arabia
| | - Jürgen Riedel
- Oxford Immune Algorithmics, Oxford, United Kingdom.,Algorithmic Nature Group, LABORES, Paris, France
| | - Adam Uccello
- Algorithmic Nature Group, LABORES, Paris, France
| | - Narsis A Kiani
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Karolinska Institutet, Solna, Sweden.,Algorithmic Nature Group, LABORES, Paris, France
| | - Jesper Tegnér
- King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering, Thuwal, Saudi Arabia
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Sumida N, Sifakis EG, Kiani NA, Ronnegren AL, Scholz BA, Vestlund J, Gomez-Cabrero D, Tegner J, Göndör A, Ohlsson R. MYC as a driver of stochastic chromatin networks: implications for the fitness of cancer cells. Nucleic Acids Res 2020; 48:10867-10876. [PMID: 33051686 PMCID: PMC7641766 DOI: 10.1093/nar/gkaa817] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [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: 07/09/2020] [Revised: 09/09/2020] [Accepted: 10/11/2020] [Indexed: 11/20/2022] Open
Abstract
The relationship between stochastic transcriptional bursts and dynamic 3D chromatin states is not well understood. Using an innovated, ultra-sensitive technique, we address here enigmatic features underlying the communications between MYC and its enhancers in relation to the transcriptional process. MYC thus interacts with its flanking enhancers in a mutually exclusive manner documenting that enhancer hubs impinging on MYC detected in large cell populations likely do not exist in single cells. Dynamic encounters with pathologically activated enhancers responsive to a range of environmental cues, involved <10% of active MYC alleles at any given time in colon cancer cells. Being the most central node of the chromatin network, MYC itself likely drives its communications with flanking enhancers, rather than vice versa. We submit that these features underlie an acquired ability of MYC to become dynamically activated in response to a diverse range of environmental cues encountered by the cell during the neoplastic process.
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Affiliation(s)
- Noriyuki Sumida
- Department of Oncology-Pathology, Karolinska Institutet, Karolinska University Hospital, Z1:00, SE-171 76 Stockholm, Sweden
| | - Emmanouil G Sifakis
- Department of Oncology-Pathology, Karolinska Institutet, Karolinska University Hospital, Z1:00, SE-171 76 Stockholm, Sweden
| | - Narsis A Kiani
- Department of Oncology-Pathology, Karolinska Institutet, Karolinska University Hospital, Z1:00, SE-171 76 Stockholm, Sweden
| | - Anna Lewandowska Ronnegren
- Department of Oncology-Pathology, Karolinska Institutet, Karolinska University Hospital, Z1:00, SE-171 76 Stockholm, Sweden
| | - Barbara A Scholz
- Department of Oncology-Pathology, Karolinska Institutet, Karolinska University Hospital, Z1:00, SE-171 76 Stockholm, Sweden
| | - Johanna Vestlund
- Department of Oncology-Pathology, Karolinska Institutet, Karolinska University Hospital, Z1:00, SE-171 76 Stockholm, Sweden.,Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, L8:05, SE-171 76, Stockholm, Sweden
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, L8:05, SE-171 76, Stockholm, Sweden.,Mucosal and Salivary Biology Division, King's College London Dental Institute, London SE1 9RT, UK
| | - Jesper Tegner
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, L8:05, SE-171 76, Stockholm, Sweden.,Science for Life Laboratory, Tomtebodavägen 23A, SE-17165, Solna, Sweden.,Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Anita Göndör
- Department of Oncology-Pathology, Karolinska Institutet, Karolinska University Hospital, Z1:00, SE-171 76 Stockholm, Sweden
| | - Rolf Ohlsson
- Department of Oncology-Pathology, Karolinska Institutet, Karolinska University Hospital, Z1:00, SE-171 76 Stockholm, Sweden
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8
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Rad Pour S, Morikawa H, Kiani NA, Gomez-Cabrero D, Hayes A, Zheng X, Pernemalm M, Lehtiö J, Mole DJ, Hansson J, Eriksson H, Tegnér J. Immunometabolic Network Interactions of the Kynurenine Pathway in Cutaneous Malignant Melanoma. Front Oncol 2020; 10:51. [PMID: 32117720 PMCID: PMC7017805 DOI: 10.3389/fonc.2020.00051] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 01/13/2020] [Indexed: 12/16/2022] Open
Abstract
Dysregulation of the kynurenine pathway has been regarded as a mechanism of tumor immune escape by the enzymatic activity of indoleamine 2, 3 dioxygenase and kynurenine production. However, the immune-modulatory properties of other kynurenine metabolites such as kynurenic acid, 3-hydroxykynurenine, and anthranilic acid are poorly understood. In this study, plasma from patients diagnosed with metastatic cutaneous malignant melanoma (CMM) was obtained before (PRE) and during treatment (TRM) with inhibitors of mitogen-activated protein kinase pathway (MAPKIs). Immuno-oncology related protein profile and kynurenine metabolites were analyzed by proximity extension assay (PEA) and LC/MS-MS, respectively. Correlation network analyses of the data derived from PEA and LC/MS-MS identified a set of proteins that modulate the differentiation of Th1 cells, which is linked to 3-hydroxykynurenine levels. Moreover, MAPKIs treatments are associated with alteration of 3-hydroxykynurenine and 3hydroxyanthranilic acid (3HAA) concentrations and led to higher "CXCL11," and "KLRD1" expression that are involved in T and NK cells activation. These findings imply that the kynurenine pathway is pathologically relevant in patients with CMM.
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Affiliation(s)
- Soudabeh Rad Pour
- Unit of Computational Medicine, Department of Medicine, Centre for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
| | - Hiromasa Morikawa
- Biological and Environmental Sciences and Engineering Division (BESE), Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Narsis A. Kiani
- Unit of Computational Medicine, Department of Medicine, Centre for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
- Unit of Computational Medicine, Algorithmic Dynamics Lab, Department of Medicine Solna, Centre for Molecular Medicine, Karolinska Institute and SciLifeLab, Stockholm, Sweden
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Centre for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
- Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Sweden
| | - Alistair Hayes
- MRC Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Xiaozhong Zheng
- MRC Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Maria Pernemalm
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Janne Lehtiö
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
| | - Damian J. Mole
- MRC Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Johan Hansson
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
- Department of Oncology/Skin Cancer Center, Theme Cancer, Karolinska University Hospital, Stockholm, Sweden
| | - Hanna Eriksson
- Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
- Department of Oncology/Skin Cancer Center, Theme Cancer, Karolinska University Hospital, Stockholm, Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Centre for Molecular Medicine, Karolinska Institute, Stockholm, Sweden
- Biological and Environmental Sciences and Engineering Division (BESE), Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Unit of Computational Medicine, Algorithmic Dynamics Lab, Department of Medicine Solna, Centre for Molecular Medicine, Karolinska Institute and SciLifeLab, Stockholm, Sweden
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9
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Zenil H, Kiani NA, Marabita F, Deng Y, Elias S, Schmidt A, Ball G, Tegnér J. An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems. iScience 2019; 19:1160-1172. [PMID: 31541920 PMCID: PMC6831824 DOI: 10.1016/j.isci.2019.07.043] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [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] [Received: 06/03/2018] [Revised: 04/27/2019] [Accepted: 07/26/2019] [Indexed: 12/26/2022] Open
Abstract
We introduce and develop a method that demonstrates that the algorithmic information content of a system can be used as a steering handle in the dynamical phase space, thus affording an avenue for controlling and reprogramming systems. The method consists of applying a series of controlled interventions to a networked system while estimating how the algorithmic information content is affected. We demonstrate the method by reconstructing the phase space and their generative rules of some discrete dynamical systems (cellular automata) serving as controlled case studies. Next, the model-based interventional or causal calculus is evaluated and validated using (1) a huge large set of small graphs, (2) a number of larger networks with different topologies, and finally (3) biological networks derived from a widely studied and validated genetic network (E. coli) as well as on a significant number of differentiating (Th17) and differentiated human cells from a curated biological network data.
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Affiliation(s)
- Hector Zenil
- Algorithmic Dynamics Lab, Center for Molecular Medicine, Karolinska Institutet, Stockholm 171 76, Sweden; Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Oxford Immune Algorithmics, Reading RG1 3EU, UK; Science for Life Laboratory, Solna 171 65, Sweden; Algorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris 75006, France.
| | - Narsis A Kiani
- Algorithmic Dynamics Lab, Center for Molecular Medicine, Karolinska Institutet, Stockholm 171 76, Sweden; Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden; Algorithmic Nature Group, LABORES for the Natural and Digital Sciences, Paris 75006, France
| | - Francesco Marabita
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden
| | - Yue Deng
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden
| | - Szabolcs Elias
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden
| | - Angelika Schmidt
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden
| | - Gordon Ball
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Solna, Stockholm 171 76, Sweden; Science for Life Laboratory, Solna 171 65, Sweden; Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
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10
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Rad Pour S, Morikawa H, Kiani NA, Yang M, Azimi A, Shafi G, Shang M, Baumgartner R, Ketelhuth DFJ, Kamleh MA, Wheelock CE, Lundqvist A, Hansson J, Tegnér J. Exhaustion of CD4+ T-cells mediated by the Kynurenine Pathway in Melanoma. Sci Rep 2019; 9:12150. [PMID: 31434983 PMCID: PMC6704156 DOI: 10.1038/s41598-019-48635-x] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [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: 03/12/2019] [Accepted: 08/08/2019] [Indexed: 12/15/2022] Open
Abstract
Kynurenine pathway (KP) activation by the enzymatic activity of indoleamine 2,3-dioxygenase1 (IDO1) and kynurenine (KYN) production represents an attractive target for reducing tumour progression and improving anti-tumour immunity in multiple cancers. However, immunomodulatory properties of other KP metabolites such as 3-hydroxy kynurenine (3-HK) and kynurenic acid (KYNA) are poorly understood. The association of the kynurenine metabolic pathway with T-cell status in the tumour microenvironment were characterized, using gene expression data of 368 cutaneous skin melanoma (SKCM) patients from the TCGA cohort. Based on the identified correlations, we characterized the production of KYN, 3-HK, and KYNA in vitro using melanoma-derived cell lines and primary CD4+ CD25- T-cells. Activation of the CD4+ T-cells produced IFNγ, which yielded increased levels of KYN and KYNA. Concurrently, kynurenine 3-monooxygenase (KMO) expression and proliferation of CD4+ T-cells were reduced, whereas exhaustion markers such as PD-L1, AHR, FOXP3, and CTLA4 were increased. Additionally, an analysis of the correlation network reconstructed using TCGA-SKCM emphasized KMO and KYNU with high variability among BRAF wild-type compared with V600E, which underscored their role in distinct CD4+ T-cell behavior in tumour immunity. Our results suggest that, in addition to IDO1, there is an alternative immune regulatory mechanism associated with the lower KMO expression and the higher KYNA production, which contributes to dysfunctional effector CD4+ T-cell response.
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Affiliation(s)
- Soudabeh Rad Pour
- Unit of Computational Medicine, Department of Medicine, Centre for Molecular Medicine, Karolinska Institute, SE-171 76, Stockholm, Sweden.
| | - Hiromasa Morikawa
- Biological and Environmental Sciences and Engineering Division (BESE), Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Narsis A Kiani
- Unit of Computational Medicine, Department of Medicine, Centre for Molecular Medicine, Karolinska Institute, SE-171 76, Stockholm, Sweden
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Centre for Molecular Medicine, Karolinska Institute and SciLifeLab, SE-171 77, Stockholm, Sweden
| | - Muyi Yang
- Department of Oncology-Pathology, Karolinska University Hospital, Stockholm, Sweden
| | - Alireza Azimi
- Department of Immunology, Genetics & Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Gowhar Shafi
- Department of Genomics and Bioinformatics, Positive Bioscience, Mumbai, -400 002, India
| | - Mingmei Shang
- Unit of Computational Medicine, Department of Medicine, Centre for Molecular Medicine, Karolinska Institute, SE-171 76, Stockholm, Sweden
| | - Roland Baumgartner
- Experimental Cardiovascular Research Group, Cardiovascular Medicine Unit, Centre for Molecular Medicine, Department of Medicine, Karolinska Institute, Karolinska University Hospital, SE-171 76, Stockholm, Sweden
| | - Daniel F J Ketelhuth
- Experimental Cardiovascular Research Group, Cardiovascular Medicine Unit, Centre for Molecular Medicine, Department of Medicine, Karolinska Institute, Karolinska University Hospital, SE-171 76, Stockholm, Sweden
| | - Muhammad Anas Kamleh
- Division of Physiological Chemistry II, Department of Medical Biochemistry and Biophysics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Craig E Wheelock
- Division of Physiological Chemistry II, Department of Medical Biochemistry and Biophysics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Andreas Lundqvist
- Department of Oncology-Pathology, Karolinska University Hospital, Stockholm, Sweden
| | - Johan Hansson
- Department of Oncology-Pathology, Karolinska University Hospital, Stockholm, Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Centre for Molecular Medicine, Karolinska Institute, SE-171 76, Stockholm, Sweden
- Biological and Environmental Sciences and Engineering Division (BESE), Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
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11
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Zenil H, Kiani NA, Tegnér J. The Thermodynamics of Network Coding, and an Algorithmic Refinement of the Principle of Maximum Entropy. Entropy (Basel) 2019; 21:e21060560. [PMID: 33267274 PMCID: PMC7515049 DOI: 10.3390/e21060560] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [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: 04/09/2019] [Revised: 05/17/2019] [Accepted: 05/20/2019] [Indexed: 12/03/2022]
Abstract
The principle of maximum entropy (Maxent) is often used to obtain prior probability distributions as a method to obtain a Gibbs measure under some restriction giving the probability that a system will be in a certain state compared to the rest of the elements in the distribution. Because classical entropy-based Maxent collapses cases confounding all distinct degrees of randomness and pseudo-randomness, here we take into consideration the generative mechanism of the systems considered in the ensemble to separate objects that may comply with the principle under some restriction and whose entropy is maximal but may be generated recursively from those that are actually algorithmically random offering a refinement to classical Maxent. We take advantage of a causal algorithmic calculus to derive a thermodynamic-like result based on how difficult it is to reprogram a computer code. Using the distinction between computable and algorithmic randomness, we quantify the cost in information loss associated with reprogramming. To illustrate this, we apply the algorithmic refinement to Maxent on graphs and introduce a Maximal Algorithmic Randomness Preferential Attachment (MARPA) Algorithm, a generalisation over previous approaches. We discuss practical implications of evaluation of network randomness. Our analysis provides insight in that the reprogrammability asymmetry appears to originate from a non-monotonic relationship to algorithmic probability. Our analysis motivates further analysis of the origin and consequences of the aforementioned asymmetries, reprogrammability, and computation.
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Affiliation(s)
- Hector Zenil
- Algorithmic Dynamics Lab, Karolinska Institute, 17177 Stockholm, Sweden
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska Institute, 17177 Stockholm, Sweden
- Algorithmic Nature Group, Laboratory of Scientific Research (LABORES) for the Natural and Digital Sciences, 75006 Paris, France
- Oxford Immune Algorithmics, Oxford University Innovation, Reading RG1 7TT, UK
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
- Correspondence:
| | - Narsis A. Kiani
- Algorithmic Dynamics Lab, Karolinska Institute, 17177 Stockholm, Sweden
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska Institute, 17177 Stockholm, Sweden
- Algorithmic Nature Group, Laboratory of Scientific Research (LABORES) for the Natural and Digital Sciences, 75006 Paris, France
| | - Jesper Tegnér
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska Institute, 17177 Stockholm, Sweden
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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12
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Joshi RN, Fernandes SJ, Shang MM, Kiani NA, Gomez-Cabrero D, Tegnér J, Schmidt A. Phosphatase inhibitor PPP1R11 modulates resistance of human T cells toward Treg-mediated suppression of cytokine expression. J Leukoc Biol 2019; 106:413-430. [PMID: 30882958 PMCID: PMC6850362 DOI: 10.1002/jlb.2a0618-228r] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [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] [Received: 06/18/2018] [Revised: 01/15/2019] [Accepted: 03/07/2019] [Indexed: 12/17/2022] Open
Abstract
Regulatory T cells (Tregs) act as indispensable unit for maintaining peripheral immune tolerance mainly by regulating effector T cells. T cells resistant to suppression by Tregs pose therapeutic challenges in the treatment of autoimmune diseases, while augmenting susceptibility to suppression may be desirable for cancer therapy. To understand the cell intrinsic signals in T cells during suppression by Tregs, we have previously performed a global phosphoproteomic characterization. We revealed altered phosphorylation of protein phosphatase 1 regulatory subunit 11 (PPP1R11; Inhibitor‐3) in conventional T cells upon suppression by Tregs. Here, we show that silencing of PPP1R11 renders T cells resistant toward Treg‐mediated suppression of TCR‐induced cytokine expression. Furthermore, whole‐transcriptome sequencing revealed that PPP1R11 differentially regulates not only the expression of specific T cell stimulation‐induced cytokines but also other molecules and pathways in T cells. We further confirmed the target of PPP1R11, PP1, to augment TCR‐induced cytokine expression. In conclusion, we present PPP1R11 as a novel negative regulator of T cell activation‐induced cytokine expression. Targeting PPP1R11 may have therapeutic potential to regulate the T cell activation status including modulating the susceptibility of T cells toward Treg‐mediated suppression, specifically altering the stimulation‐induced T cell cytokine milieu.
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Affiliation(s)
- Rubin N Joshi
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Sunjay Jude Fernandes
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Ming-Mei Shang
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden.,Division of Rheumatology, Department of Medicine Solna, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Narsis A Kiani
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden.,Mucosal and Salivary Biology Division, King's College London Dental Institute, London, United Kingdom.,Translational Bioinformatics Unit, NavarraBiomed, Departamento de Salud-Universidad Pública de Navarra, Pamplona, Navarra, Spain
| | - Jesper Tegnér
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden.,Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Angelika Schmidt
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
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13
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14
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Zenil H, Hernández-Orozco S, Kiani NA, Soler-Toscano F, Rueda-Toicen A, Tegnér J. A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic Complexity. Entropy (Basel) 2018; 20:e20080605. [PMID: 33265694 PMCID: PMC7513128 DOI: 10.3390/e20080605] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [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: 04/28/2018] [Revised: 06/18/2018] [Accepted: 07/31/2018] [Indexed: 12/28/2022]
Abstract
We investigate the properties of a Block Decomposition Method (BDM), which extends the power of a Coding Theorem Method (CTM) that approximates local estimations of algorithmic complexity based on Solomonoff–Levin’s theory of algorithmic probability providing a closer connection to algorithmic complexity than previous attempts based on statistical regularities such as popular lossless compression schemes. The strategy behind BDM is to find small computer programs that produce the components of a larger, decomposed object. The set of short computer programs can then be artfully arranged in sequence so as to produce the original object. We show that the method provides efficient estimations of algorithmic complexity but that it performs like Shannon entropy when it loses accuracy. We estimate errors and study the behaviour of BDM for different boundary conditions, all of which are compared and assessed in detail. The measure may be adapted for use with more multi-dimensional objects than strings, objects such as arrays and tensors. To test the measure we demonstrate the power of CTM on low algorithmic-randomness objects that are assigned maximal entropy (e.g., π) but whose numerical approximations are closer to the theoretical low algorithmic-randomness expectation. We also test the measure on larger objects including dual, isomorphic and cospectral graphs for which we know that algorithmic randomness is low. We also release implementations of the methods in most major programming languages—Wolfram Language (Mathematica), Matlab, R, Perl, Python, Pascal, C++, and Haskell—and an online algorithmic complexity calculator.
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Affiliation(s)
- Hector Zenil
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institute and SciLifeLab, SE-171 77 Stockholm, Sweden
- Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
- Correspondence:
| | - Santiago Hernández-Orozco
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institute and SciLifeLab, SE-171 77 Stockholm, Sweden
- Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
- Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México (UNAM), Mexico City 04510, Mexico
| | - Narsis A. Kiani
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institute and SciLifeLab, SE-171 77 Stockholm, Sweden
- Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
| | | | - Antonio Rueda-Toicen
- Algorithmic Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institute and SciLifeLab, SE-171 77 Stockholm, Sweden
- Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
- Instituto Nacional de Bioingeniería, Universidad Central de Venezuela, Caracas 1051, Venezuela
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, SciLifeLab and Karolinska Institute, Stockholm SE-171 77, Sweden
- Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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15
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Hernández-Orozco S, Kiani NA, Zenil H. Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity. R Soc Open Sci 2018; 5:180399. [PMID: 30225028 PMCID: PMC6124114 DOI: 10.1098/rsos.180399] [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] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 07/20/2018] [Indexed: 05/07/2023]
Abstract
Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied formal explanations when based on random (uniformly distributed) mutations. Here, we investigate the application of a simplicity bias based on a natural but algorithmic distribution of mutations (no recombination) in various examples, particularly binary matrices, in order to compare evolutionary convergence rates. Results both on synthetic and on small biological examples indicate an accelerated rate when mutations are not statistically uniform but algorithmically uniform. We show that algorithmic distributions can evolve modularity and genetic memory by preservation of structures when they first occur sometimes leading to an accelerated production of diversity but also to population extinctions, possibly explaining naturally occurring phenomena such as diversity explosions (e.g. the Cambrian) and massive extinctions (e.g. the End Triassic) whose causes are currently a cause for debate. The natural approach introduced here appears to be a better approximation to biological evolution than models based exclusively upon random uniform mutations, and it also approaches a formal version of open-ended evolution based on previous formal results. These results validate some suggestions in the direction that computation may be an equally important driver of evolution. We also show that inducing the method on problems of optimization, such as genetic algorithms, has the potential to accelerate convergence of artificial evolutionary algorithms.
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Affiliation(s)
- Santiago Hernández-Orozco
- Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México (UNAM), Mexico
- Algorithmic Dynamics Lab, Unit of Computational Medicine, SciLifeLab, Department of Medicine Solna, Centre for Molecular Medicine, Stockholm, Sweden
- Algorithmic Nature Group, LABORES, Paris, France
| | - Narsis A. Kiani
- Algorithmic Dynamics Lab, Unit of Computational Medicine, SciLifeLab, Department of Medicine Solna, Centre for Molecular Medicine, Stockholm, Sweden
- Algorithmic Nature Group, LABORES, Paris, France
| | - Hector Zenil
- Algorithmic Dynamics Lab, Unit of Computational Medicine, SciLifeLab, Department of Medicine Solna, Centre for Molecular Medicine, Stockholm, Sweden
- Algorithmic Nature Group, LABORES, Paris, France
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16
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Zenil H, Kiani NA, Tegnér J. A Review of Graph and Network Complexity from an Algorithmic Information Perspective. Entropy (Basel) 2018; 20:e20080551. [PMID: 33265640 PMCID: PMC7513075 DOI: 10.3390/e20080551] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [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: 06/21/2018] [Revised: 07/18/2018] [Accepted: 07/20/2018] [Indexed: 11/22/2022]
Abstract
Information-theoretic-based measures have been useful in quantifying network complexity. Here we briefly survey and contrast (algorithmic) information-theoretic methods which have been used to characterize graphs and networks. We illustrate the strengths and limitations of Shannon’s entropy, lossless compressibility and algorithmic complexity when used to identify aspects and properties of complex networks. We review the fragility of computable measures on the one hand and the invariant properties of algorithmic measures on the other demonstrating how current approaches to algorithmic complexity are misguided and suffer of similar limitations than traditional statistical approaches such as Shannon entropy. Finally, we review some current definitions of algorithmic complexity which are used in analyzing labelled and unlabelled graphs. This analysis opens up several new opportunities to advance beyond traditional measures.
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Affiliation(s)
- Hector Zenil
- Algorithmic Dynamics Lab, Centre for Molecular Medicine, Karolinska Institute, 171 77 Stockholm, Sweden
- Unit of Computational Medicine, Department of Medicine, Karolinska Institute, 171 77 Stockholm, Sweden
- Science for Life Laboratory (SciLifeLab), 171 77 Stockholm, Sweden
- Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
- Correspondence: or
| | - Narsis A. Kiani
- Algorithmic Dynamics Lab, Centre for Molecular Medicine, Karolinska Institute, 171 77 Stockholm, Sweden
- Unit of Computational Medicine, Department of Medicine, Karolinska Institute, 171 77 Stockholm, Sweden
- Science for Life Laboratory (SciLifeLab), 171 77 Stockholm, Sweden
- Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Karolinska Institute, 171 77 Stockholm, Sweden
- Science for Life Laboratory (SciLifeLab), 171 77 Stockholm, Sweden
- Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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Abstract
Drug discovery is complex and expensive. Numerous drug candidates fail late in clinical trials or even after being released to the market. These failures are not only due to commercial considerations and less optimal drug efficacies but, adverse reactions originating from toxic effects also constitute a major challenge. During the last two decades, significant advances have been made enabling the early prediction of toxic effects using in silico techniques. However, by design, these essentially statistical techniques have not taken the disease driving pathophysiological mechanisms into account. The complexity of such mechanisms in combination with their interactions with drugspecific properties and environmental and life-style related factors renders the task of predicting toxicity on a purely statistical basis which is an insurmountable challenge. In response to this situation, an interdisciplinary field has developed, referred to as systems toxicology, where the notion of a network is used to integrate and model different types of information to better predict drug toxicity. In this study, we briefly review the merits and limitations of such recent promising predictive approaches integrating molecular networks, chemical compound networks, and protein drug association networks.
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Affiliation(s)
- Narsis A Kiani
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Science for Life Laboratories, Stockholm, Sweden
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18
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Kotelnikova E, Kiani NA, Abad E, Martinez-Lapiscina EH, Andorra M, Zubizarreta I, Pulido-Valdeolivas I, Pertsovskaya I, Alexopoulos LG, Olsson T, Martin R, Paul F, Tegnér J, Garcia-Ojalvo J, Villoslada P. Dynamics and heterogeneity of brain damage in multiple sclerosis. PLoS Comput Biol 2017; 13:e1005757. [PMID: 29073203 PMCID: PMC5657613 DOI: 10.1371/journal.pcbi.1005757] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [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: 12/15/2016] [Accepted: 08/31/2017] [Indexed: 11/24/2022] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune disease driving inflammatory and degenerative processes that damage the central nervous system (CNS). However, it is not well understood how these events interact and evolve to evoke such a highly dynamic and heterogeneous disease. We established a hypothesis whereby the variability in the course of MS is driven by the very same pathogenic mechanisms responsible for the disease, the autoimmune attack on the CNS that leads to chronic inflammation, neuroaxonal degeneration and remyelination. We propose that each of these processes acts more or less severely and at different times in each of the clinical subgroups. To test this hypothesis, we developed a mathematical model that was constrained by experimental data (the expanded disability status scale [EDSS] time series) obtained from a retrospective longitudinal cohort of 66 MS patients with a long-term follow-up (up to 20 years). Moreover, we validated this model in a second prospective cohort of 120 MS patients with a three-year follow-up, for which EDSS data and brain volume time series were available. The clinical heterogeneity in the datasets was reduced by grouping the EDSS time series using an unsupervised clustering analysis. We found that by adjusting certain parameters, albeit within their biological range, the mathematical model reproduced the different disease courses, supporting the dynamic CNS damage hypothesis to explain MS heterogeneity. Our analysis suggests that the irreversible axon degeneration produced in the early stages of progressive MS is mainly due to the higher rate of myelinated axon degeneration, coupled to the lower capacity for remyelination. However, and in agreement with recent pathological studies, degeneration of chronically demyelinated axons is not a key feature that distinguishes this phenotype. Moreover, the model reveals that lower rates of axon degeneration and more rapid remyelination make relapsing MS more resilient than the progressive subtype. Therefore, our results support the hypothesis of a common pathogenesis for the different MS subtypes, even in the presence of genetic and environmental heterogeneity. Hence, MS can be considered as a single disease in which specific dynamics can provoke a variety of clinical outcomes in different patient groups. These results have important implications for the design of therapeutic interventions for MS at different stages of the disease. Multiple Sclerosis (MS) is an autoimmune disease in which inflammatory and degenerative processes damage the brain. We tested the hypothesis that the variability in disease progression and the clinical heterogeneity observed in MS is driven by a single mechanism, namely the autoimmune attack on the CNS that provokes this chronic inflammation and degeneration. As such, it is the difference in the intensity of these processes at distinct times that is responsible for establishing each of the disease subtypes defined to date. Mathematical models of brain damage and disease course were generated that were fitted to clinical data. We found that the phenotypes of the different MS subtypes were reproduced by the model, supporting the concept of a common pathogenesis and thus, that of a single disease in which specific dynamics can produce a variety of clinical outcomes in different groups of patients. These results are likely to be helpful when designing new therapies for this disease.
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Affiliation(s)
- Ekaterina Kotelnikova
- Center for Neuroimmunology, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Narsis A. Kiani
- Unit of Computational Medicine, Department of Medicine & Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Elena Abad
- Center for Neuroimmunology, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Elena H. Martinez-Lapiscina
- Center for Neuroimmunology, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Magi Andorra
- Center for Neuroimmunology, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Irati Zubizarreta
- Center for Neuroimmunology, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Irene Pulido-Valdeolivas
- Center for Neuroimmunology, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Inna Pertsovskaya
- Center for Neuroimmunology, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | | | - Tomas Olsson
- Unit of Neuroimmunology, Karolinska Institute, Stockholm, Sweden
| | - Roland Martin
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital, University Zurich, Zurich, Switzerland
| | - Friedemann Paul
- NeuroCure Clinical Research Center, and the Experimental and Clinical Research Center, Charité Universitätsmedizin Berlin and Max Delbrueck Center for Molecular Medicine Berlin, Berlin, Germany
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine & Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
- Biological and Environmental Sciences and Engineering Division & Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | | | - Pablo Villoslada
- Center for Neuroimmunology, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
- University of California, San Francisco, United States of America
- * E-mail:
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Deng Y, Zenil H, Tegnér J, Kiani NA. HiDi: an efficient reverse engineering schema for large-scale dynamic regulatory network reconstruction using adaptive differentiation. Bioinformatics 2017; 33:3964-3972. [DOI: 10.1093/bioinformatics/btx501] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 08/05/2017] [Indexed: 11/14/2022] Open
Affiliation(s)
- Yue Deng
- Algorithmic Dynamics Lab, Karolinska Institute, Stockholm, Sweden
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna and Science for Life Laboratory (SciLifeLab), Karolinska Institute, Stockholm, Sweden
| | - Hector Zenil
- Algorithmic Dynamics Lab, Karolinska Institute, Stockholm, Sweden
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna and Science for Life Laboratory (SciLifeLab), Karolinska Institute, Stockholm, Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna and Science for Life Laboratory (SciLifeLab), Karolinska Institute, Stockholm, Sweden
- Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Narsis A Kiani
- Algorithmic Dynamics Lab, Karolinska Institute, Stockholm, Sweden
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna and Science for Life Laboratory (SciLifeLab), Karolinska Institute, Stockholm, Sweden
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20
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Abstract
In estimating the complexity of objects, in particular, of graphs, it is common practice to rely on graph- and information-theoretic measures. Here, using integer sequences with properties such as Borel normality, we explain how these measures are not independent of the way in which an object, such as a graph, can be described or observed. From observations that can reconstruct the same graph and are therefore essentially translations of the same description, we see that when applying a computable measure such as the Shannon entropy, not only is it necessary to preselect a feature of interest where there is one, and to make an arbitrary selection where there is not, but also more general properties, such as the causal likelihood of a graph as a measure (opposed to randomness), can be largely misrepresented by computable measures such as the entropy and entropy rate. We introduce recursive and nonrecursive (uncomputable) graphs and graph constructions based on these integer sequences, whose different lossless descriptions have disparate entropy values, thereby enabling the study and exploration of a measure's range of applications and demonstrating the weaknesses of computable measures of complexity.
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Affiliation(s)
- Hector Zenil
- Information Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, SciLifeLab, Karolinska Institute, Stockholm 171 76, Sweden; Department of Computer Science, University of Oxford, Oxford OX1 3QD, United Kingdom; and Algorithmic Nature Group, LABoRES, Paris 75006, France
| | - Narsis A Kiani
- Information Dynamics Lab, Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, SciLifeLab, Karolinska Institute, Stockholm 171 76, Sweden and Algorithmic Nature Group, LABoRES, Paris 75006, France
| | - Jesper Tegnér
- Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955 - 6900, Kingdom of Saudi Arabia and Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, SciLifeLab, Karolinska Institute, Stockholm 171 76, Sweden
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21
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Kannan V, Kiani NA, Piehl F, Tegner J. A minimal unified model of disease trajectories captures hallmarks of multiple sclerosis. Math Biosci 2017; 289:1-8. [PMID: 28365299 DOI: 10.1016/j.mbs.2017.03.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 03/04/2017] [Accepted: 03/16/2017] [Indexed: 01/04/2023]
Abstract
Multiple Sclerosis (MS) is an autoimmune disease targeting the central nervous system (CNS) causing demyelination and neurodegeneration leading to accumulation of neurological disability. Here we present a minimal, computational model involving the immune system and CNS that generates the principal subtypes of the disease observed in patients. The model captures several key features of MS, especially those that distinguish the chronic progressive phase from that of the relapse-remitting. In addition, a rare subtype of the disease, progressive relapsing MS naturally emerges from the model. The model posits the existence of two key thresholds, one in the immune system and the other in the CNS, that separate dynamically distinct behavior of the model. Exploring the two-dimensional space of these thresholds, we obtain multiple phases of disease evolution and these shows greater variation than the clinical classification of MS, thus capturing the heterogeneity that is manifested in patients.
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Affiliation(s)
- Venkateshan Kannan
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet 17176, Sweden
| | - Narsis A Kiani
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet 17176, Sweden
| | - Fredrik Piehl
- Unit of NeuroImmunology, Center for Molecular Medicine, Department of Clinical Neuroscience, Karolinska University Hospital L8 17176, Stockholm, Sweden
| | - Jesper Tegner
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet 17176, Sweden; Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
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22
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Tegnér J, Zenil H, Kiani NA, Ball G, Gomez-Cabrero D. A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference. Philos Trans A Math Phys Eng Sci 2016; 374:rsta.2016.0144. [PMID: 27698038 PMCID: PMC5052728 DOI: 10.1098/rsta.2016.0144] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [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] [Accepted: 08/15/2016] [Indexed: 05/06/2023]
Abstract
Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.
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Affiliation(s)
- Jesper Tegnér
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - Hector Zenil
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - Narsis A Kiani
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - Gordon Ball
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden
| | - David Gomez-Cabrero
- Department of Medicine, Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden Center for Molecular Medicine, Karolinska Institutet, L8:05, 171 76 Stockholm, Sweden Department of Medicine, Unit of Clinical Epidemiology, Karolinska University Hospital L8, 17176 Stockholm, Sweden Science for Life Laboratory, Stockholm, Sweden Mucosal and Salivary Biology Division, King's College London Dental Institute, London SE1 9RT, UK
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23
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Kannan V, Swartz F, Kiani NA, Silberberg G, Tsipras G, Gomez-Cabrero D, Alexanderson K, Tegnèr J. Conditional Disease Development extracted from Longitudinal Health Care Cohort Data using Layered Network Construction. Sci Rep 2016; 6:26170. [PMID: 27211115 PMCID: PMC4876508 DOI: 10.1038/srep26170] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [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: 10/23/2015] [Accepted: 04/27/2016] [Indexed: 11/16/2022] Open
Abstract
Health care data holds great promise to be used in clinical decision support systems. However, frequent near-synonymous diagnoses recorded separately, as well as the sheer magnitude and complexity of the disease data makes it challenging to extract non-trivial conclusions beyond confirmatory associations from such a web of interactions. Here we present a systematic methodology to derive statistically valid conditional development of diseases. To this end we utilize a cohort of 5,512,469 individuals followed over 13 years at inpatient care, including data on disability pension and cause of death. By introducing a causal information fraction measure and taking advantage of the composite structure in the ICD codes, we extract an effective directed lower dimensional network representation (100 nodes and 130 edges) of our cohort. Unpacking composite nodes into bipartite graphs retrieves, for example, that individuals with behavioral disorders are more likely to be followed by prescription drug poisoning episodes, whereas women with leiomyoma were more likely to subsequently experience endometriosis. The conditional disease development represent putative causal relations, indicating possible novel clinical relationships and pathophysiological associations that have not been explored yet.
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Affiliation(s)
- Venkateshan Kannan
- Computational Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, SE-17176, Stockholm, Sweden.,Center for Molecular Medicine, L8:05, SE-17176, Stockholm, Karolinska Institutet, Sweden
| | - Fredrik Swartz
- Computational Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, SE-17176, Stockholm, Sweden.,Center for Molecular Medicine, L8:05, SE-17176, Stockholm, Karolinska Institutet, Sweden.,Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, SE-17177 Stockholm, Sweden
| | - Narsis A Kiani
- Computational Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, SE-17176, Stockholm, Sweden.,Center for Molecular Medicine, L8:05, SE-17176, Stockholm, Karolinska Institutet, Sweden
| | - Gilad Silberberg
- Computational Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, SE-17176, Stockholm, Sweden.,Center for Molecular Medicine, L8:05, SE-17176, Stockholm, Karolinska Institutet, Sweden
| | - Giorgos Tsipras
- Computational Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, SE-17176, Stockholm, Sweden.,Center for Molecular Medicine, L8:05, SE-17176, Stockholm, Karolinska Institutet, Sweden
| | - David Gomez-Cabrero
- Computational Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, SE-17176, Stockholm, Sweden.,Center for Molecular Medicine, L8:05, SE-17176, Stockholm, Karolinska Institutet, Sweden
| | - Kristina Alexanderson
- Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, SE-17177 Stockholm, Sweden
| | - Jesper Tegnèr
- Computational Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, SE-17176, Stockholm, Sweden.,Center for Molecular Medicine, L8:05, SE-17176, Stockholm, Karolinska Institutet, Sweden.,Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital L8, SE-17176, Stockholm, Sweden.,Science for Life Laboratory, Stockholm, Sweden
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24
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Zenil H, Kiani NA, Tegnér J. Methods of information theory and algorithmic complexity for network biology. Semin Cell Dev Biol 2016; 51:32-43. [PMID: 26802516 DOI: 10.1016/j.semcdb.2016.01.011] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [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: 08/16/2015] [Accepted: 01/07/2016] [Indexed: 10/22/2022]
Abstract
We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdös-Rényi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity.
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Affiliation(s)
- Hector Zenil
- Unit of Computational Medicine, Department of Medicine, Karolinska Institute & Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden.
| | - Narsis A Kiani
- Unit of Computational Medicine, Department of Medicine, Karolinska Institute & Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Karolinska Institute & Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
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25
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Gomez-Cabrero D, Menche J, Cano I, Abugessaisa I, Huertas-Migueláñez M, Tenyi A, Marin de Mas I, Kiani NA, Marabita F, Falciani F, Burrowes K, Maier D, Wagner P, Selivanov V, Cascante M, Roca J, Barabási AL, Tegnér J. Systems Medicine: from molecular features and models to the clinic in COPD. J Transl Med 2014; 12 Suppl 2:S4. [PMID: 25471042 PMCID: PMC4255907 DOI: 10.1186/1479-5876-12-s2-s4] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Background and hypothesis Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice. Objective and method Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework. Results In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice. Conclusions The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.
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Kotelnikova E, Bernardo-Faura M, Silberberg G, Kiani NA, Messinis D, Melas IN, Artigas L, Schwartz E, Mazo I, Masso M, Alexopoulos LG, Mas JM, Olsson T, Tegner J, Martin R, Zamora A, Paul F, Saez-Rodriguez J, Villoslada P. Signaling networks in MS: a systems-based approach to developing new pharmacological therapies. Mult Scler 2014; 21:138-46. [PMID: 25112814 DOI: 10.1177/1352458514543339] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [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: 12/22/2022]
Abstract
The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.
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Affiliation(s)
- Ekaterina Kotelnikova
- Institute Biomedical Research August Pi Sunyer (IDIBAPS) - Hospital Clinic of Barcelona, Spain/Personal Biomedicine ZAO, and A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Russia
| | | | - Gilad Silberberg
- Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Sweden
| | - Narsis A Kiani
- Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Sweden
| | | | - Ioannis N Melas
- European Molecular Biology Laboratory, European Bioinformatics Institute, UK/ProtATonce Ltd, Greece/National Technical University of Athens, Greece
| | | | | | | | | | | | | | | | - Jesper Tegner
- Unit of Computational Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Sweden
| | | | | | - Friedemann Paul
- NeuroCure Clinical Research Center and Department of Neurology, Charité University Medicine Berlin, Germany
| | | | - Pablo Villoslada
- Institute Biomedical Research August Pi Sunyer (IDIBAPS) - Hospital Clinic of Barcelona, Spain/Personal Biomedicine ZAO, and A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Russia
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27
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Kiani NA, Kaderali L. Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data. BMC Bioinformatics 2014; 15:250. [PMID: 25047753 PMCID: PMC4133630 DOI: 10.1186/1471-2105-15-250] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [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: 07/09/2013] [Accepted: 07/15/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system's response after systematic perturbations are available. RESULTS We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway. CONCLUSIONS Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway.
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Affiliation(s)
- Narsis A Kiani
- Technische Universität Dresden, Medical Faculty Carl Gustav Carus, Institute for Medical Informatics and Biometry, Fetscherstr, 74, 01307 Dresden, Germany.
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Amberkar S, Kiani NA, Bartenschlager R, Alvisi G, Kaderali L. High-throughput RNA interference screens integrative analysis: Towards a comprehensive understanding of the virus-host interplay. World J Virol 2013; 2:18-31. [PMID: 24175227 PMCID: PMC3785050 DOI: 10.5501/wjv.v2.i2.18] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2012] [Revised: 02/15/2013] [Accepted: 03/15/2013] [Indexed: 02/05/2023] Open
Abstract
Viruses are extremely heterogeneous entities; the size and the nature of their genetic information, as well as the strategies employed to amplify and propagate their genomes, are highly variable. However, as obligatory intracellular parasites, replication of all viruses relies on the host cell. Having co-evolved with their host for several million years, viruses have developed very sophisticated strategies to hijack cellular factors that promote virus uptake, replication, and spread. Identification of host cell factors (HCFs) required for these processes is a major challenge for researchers, but it enables the identification of new, highly selective targets for anti viral therapeutics. To this end, the establishment of platforms enabling genome-wide high-throughput RNA interference (HT-RNAi) screens has led to the identification of several key factors involved in the viral life cycle. A number of genome-wide HT-RNAi screens have been performed for major human pathogens. These studies enable first inter-viral comparisons related to HCF requirements. Although several cellular functions appear to be uniformly required for the life cycle of most viruses tested (such as the proteasome and the Golgi-mediated secretory pathways), some factors, like the lipid kinase Phosphatidylinositol 4-kinase IIIα in the case of hepatitis C virus, are selectively required for individual viruses. However, despite the amount of data available, we are still far away from a comprehensive understanding of the interplay between viruses and host factors. Major limitations towards this goal are the low sensitivity and specificity of such screens, resulting in limited overlap between different screens performed with the same virus. This review focuses on how statistical and bioinformatic analysis methods applied to HT-RNAi screens can help overcoming these issues thus increasing the reliability and impact of such studies.
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Knapp B, Rebhan I, Kumar A, Matula P, Kiani NA, Binder M, Erfle H, Rohr K, Eils R, Bartenschlager R, Kaderali L. Normalizing for individual cell population context in the analysis of high-content cellular screens. BMC Bioinformatics 2011; 12:485. [PMID: 22185194 PMCID: PMC3259109 DOI: 10.1186/1471-2105-12-485] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [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: 09/14/2011] [Accepted: 12/20/2011] [Indexed: 12/21/2022] Open
Abstract
Background High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology. Results We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach. Conclusions Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.
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Affiliation(s)
- Bettina Knapp
- Heidelberg University, ViroQuant Research Group Modeling, BioQuant BQ26, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
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