Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 20 de 10.686
Filter
1.
Proc Natl Acad Sci U S A ; 119(32): e2204967119, 2022 Aug 09.
Article in English | MEDLINE | ID: mdl-35914142

ABSTRACT

Networks are fundamental for our understanding of complex systems. The study of networks has uncovered common principles that underlie the behavior of vastly different fields of study, including physics, biology, sociology, and engineering. One of these common principles is the existence of network motifs-small recurrent patterns that can provide certain features that are important for the specific network. However, it remains unclear how network motifs are joined in real networks to make larger circuits and what properties emerge from interactions between network motifs. Here, we develop a framework to explore the mesoscale-level behavior of complex networks. Considering network motifs as hypernodes, we define the rules for their interaction at the network's next level of organization. We develop a method to infer the favorable arrangements of interactions between network motifs into hypermotifs from real evolved and designed network data. We mathematically explore the emergent properties of these higher-order circuits and their relations to the properties of the individual minimal circuit components they combine. We apply this framework to biological, neuronal, social, linguistic, and electronic networks and find that network motifs are not randomly distributed in real networks but are combined in a way that both maintains autonomy and generates emergent properties. This framework provides a basis for exploring the mesoscale structure and behavior of complex systems where it can be used to reveal intermediate patterns in complex networks and to identify specific nodes and links in the network that are the key drivers of the network's emergent properties.


Subject(s)
Models, Theoretical , Biology , Engineering , Feedback , Linguistics , Models, Biological , Neurons/physiology , Physics , Sociology , Systems Biology
2.
Brief Bioinform ; 23(4)2022 07 18.
Article in English | MEDLINE | ID: mdl-35804437

ABSTRACT

Current tailored-therapy efforts in cancer are largely focused on a small number of highly recurrently mutated driver genes but therapeutic targeting of these oncogenes remains challenging. However, the vast number of genes mutated infrequently across cancers has received less attention, in part, due to a lack of understanding of their biological significance. We present SYSMut, an extendable systems biology platform that can robustly infer the biologic consequences of somatic mutations by integrating routine multiomics profiles in primary tumors. We establish SYSMut's improved performance vis-à-vis state-of-the-art driver gene identification methodologies by recapitulating the functional impact of known driver genes, while additionally identifying novel functionally impactful mutated genes across 29 cancers. Subsequent application of SYSMut on low-frequency gene mutations in head and neck squamous cell (HNSC) cancers, followed by molecular and pharmacogenetic validation, revealed the lipidogenic network as a novel therapeutic vulnerability in aggressive HNSC cancers. SYSMut is thus a robust scalable framework that enables the discovery of new targetable avenues in cancer.


Subject(s)
Neoplasms , Humans , Mutation , Neoplasms/genetics , Oncogenes , Systems Biology
3.
Clin Transl Med ; 12(7): e898, 2022 Jul.
Article in English | MEDLINE | ID: mdl-35904141

ABSTRACT

Increasing efforts points to the understanding of how to maximize the capabilities of the adaptive immune system to fight against the development of immune and inflammatory disorders. Here we focus on the role of T cells as immune cells which subtype imbalance may lead to disease onset. Specifically, we propose that autoimmune disorders may develop as a consequence of a metabolic imbalance that modulates switching between T cell phenotypes. We highlight a Systems Biology strategy that integrates computational metabolic modelling with experimental data to investigate the metabolic requirements of T cell phenotypes, and to predict metabolic genes that may be targeted in autoimmune inflammatory diseases. Thus, we propose a new perspective of targeting T cell metabolism to modulate the immune response and prevent T cell phenotype imbalance, which may help to repurpose already existing drugs targeting metabolism for therapeutic treatment.


Subject(s)
Autoimmune Diseases , T-Lymphocytes , Autoimmune Diseases/drug therapy , Autoimmune Diseases/genetics , Humans , Immunity , Phenotype , Systems Biology , T-Lymphocytes/metabolism
4.
World J Microbiol Biotechnol ; 38(9): 153, 2022 Jul 05.
Article in English | MEDLINE | ID: mdl-35788443

ABSTRACT

In recent decades, antimicrobial resistance has been augmented as a global concern to public health owing to the global spread of multidrug-resistant strains from different ESKAPE pathogens. This alarming trend and the lack of new antibiotics with novel modes of action in the pipeline necessitate the development of non-antibiotic ways to treat illnesses caused by these isolates. In molecular biology, computational approaches have become crucial tools, particularly in one of the most challenging areas of multidrug resistance. The rapid advancements in bioinformatics have led to a plethora of computational approaches involving genomics, systems biology, and structural biology currently gaining momentum among molecular biologists since they can be useful and provide valuable information on the complex mechanisms of AMR research in ESKAPE pathogens. These computational approaches would be helpful in elucidating the AMR mechanisms, identifying important hub genes/proteins, and their promising targets together with their interactions with important drug targets, which is a crucial step in drug discovery. Therefore, the present review aims to provide holistic information on currently employed bioinformatic tools and their application in the discovery of multifunctional novel therapeutic drugs to combat the current problem of AMR in ESKAPE pathogens. The review also summarizes the recent advancement in the AMR research in ESKAPE pathogens utilizing the in silico approaches.


Subject(s)
Anti-Bacterial Agents , Systems Biology , Anti-Bacterial Agents/pharmacology , Computational Biology , Drug Resistance, Bacterial/genetics , Genomics
5.
Viruses ; 14(7)2022 06 23.
Article in English | MEDLINE | ID: mdl-35891354

ABSTRACT

More than two years on, the COVID-19 pandemic continues to wreak havoc around the world and has battle-tested the pandemic-situation responses of all major global governments. Two key areas of investigation that are still unclear are: the molecular mechanisms that lead to heterogenic patient outcomes, and the causes of Post COVID condition (AKA Long-COVID). In this paper, we introduce the HYGIEIA project, designed to respond to the enormous challenges of the COVID-19 pandemic through a multi-omic approach supported by network medicine. It is hoped that in addition to investigating COVID-19, the logistics deployed within this project will be applicable to other infectious agents, pandemic-type situations, and also other complex, non-infectious diseases. Here, we first look at previous research into COVID-19 in the context of the proteome, metabolome, transcriptome, microbiome, host genome, and viral genome. We then discuss a proposed methodology for a large-scale multi-omic longitudinal study to investigate the aforementioned biological strata through high-throughput sequencing (HTS) and mass-spectrometry (MS) technologies. Lastly, we discuss how a network medicine approach can be used to analyze the data and make meaningful discoveries, with the final aim being the translation of these discoveries into the clinics to improve patient care.


Subject(s)
COVID-19 , Communicable Diseases , COVID-19/complications , COVID-19/epidemiology , Communicable Diseases/epidemiology , Humans , Longitudinal Studies , Metabolomics/methods , Pandemics , Systems Biology/methods
6.
Rehabilitación (Madr., Ed. impr.) ; 56(3): 226-236, Jul - Sep 2022. graf
Article in Spanish | IBECS | ID: ibc-VR-100

ABSTRACT

El objetivo principal del estudio es analizar la evolución científica del campo de investigación de la fibromialgia y la biomecánica. Para ello, se llevó a cabo una búsqueda en Web of Science, desde 1985 hasta 2021, con cuyos resultados se creó un mapa bibliométrico de palabras clave con VOSviewer. También se realizó un de mapeo científico y análisis del rendimiento mediante SciMAT. Se analizaron 233 artículos de todo el mundo, destacando la producción de EE. UU. y España. Los resultados muestran una gran diversidad temática con 54 temáticas diferentes y 33 palabras clave. Si bien la mayoría de temas no están muy desarrollados salvo la actividad física y la sintomatología. En conclusión, el estudio de la fibromialgia y la biomecánica ha crecido de forma general a lo largo del tiempo.(AU)


The main objective of the study is to analyse the scientific evolution of the research field of fibromyalgia and biomechanics. A search was carried out in Web of Science, from 1985 to 2021. With those results, a bibliometric map of keywords was created with VOSviewer. On top of that, scientific mapping and performance analysis were also conducted using SciMAT. A total of 233 articles from around the world were analysed, highlighting the production of the USA and Spain. The results show great diversity in topics with 54 different topics and 33 keywords. Although most of the topics found are not widely developed except the topics of physical activity and symptomatology. In conclusion, the study of fibromyalgia and biomechanics has generally grown over time.(AU)


Subject(s)
Humans , Male , Female , Bibliometric Indicators , Bibliometrics , Fibromyalgia , Systems Biology , Chronic Pain , Musculoskeletal Pain , Pain Management , Physical and Rehabilitation Medicine
7.
Malar J ; 21(1): 177, 2022 Jun 07.
Article in English | MEDLINE | ID: mdl-35672852

ABSTRACT

"The Primate Malarias" book has been a uniquely important resource for multiple generations of scientists, since its debut in 1971, and remains pertinent to the present day. Indeed, nonhuman primates (NHPs) have been instrumental for major breakthroughs in basic and pre-clinical research on malaria for over 50 years. Research involving NHPs have provided critical insights and data that have been essential for malaria research on many parasite species, drugs, vaccines, pathogenesis, and transmission, leading to improved clinical care and advancing research goals for malaria control, elimination, and eradication. Whilst most malaria scientists over the decades have been studying Plasmodium falciparum, with NHP infections, in clinical studies with humans, or using in vitro culture or rodent model systems, others have been dedicated to advancing research on Plasmodium vivax, as well as on phylogenetically related simian species, including Plasmodium cynomolgi, Plasmodium coatneyi, and Plasmodium knowlesi. In-depth study of these four phylogenetically related species over the years has spawned the design of NHP longitudinal infection strategies for gathering information about ongoing infections, which can be related to human infections. These Plasmodium-NHP infection model systems are reviewed here, with emphasis on modern systems biological approaches to studying longitudinal infections, pathogenesis, immunity, and vaccines. Recent discoveries capitalizing on NHP longitudinal infections include an advanced understanding of chronic infections, relapses, anaemia, and immune memory. With quickly emerging new technological advances, more in-depth research and mechanistic discoveries can be anticipated on these and additional critical topics, including hypnozoite biology, antigenic variation, gametocyte transmission, bone marrow dysfunction, and loss of uninfected RBCs. New strategies and insights published by the Malaria Host-Pathogen Interaction Center (MaHPIC) are recapped here along with a vision that stresses the importance of educating future experts well trained in utilizing NHP infection model systems for the pursuit of innovative, effective interventions against malaria.


Subject(s)
Malaria , Plasmodium cynomolgi , Plasmodium knowlesi , Animals , Malaria/parasitology , Primates , Systems Biology
8.
Sci Adv ; 8(22): eabm2510, 2022 06 03.
Article in English | MEDLINE | ID: mdl-35648852

ABSTRACT

Despite the availability of highly efficacious vaccines, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lacks effective drug treatment, which results in a high rate of mortality. To address this therapeutic shortcoming, we applied a systems biology approach to the study of patients hospitalized with severe COVID. We show that, at the time of hospital admission, patients who were equivalent on the clinical ordinal scale displayed significant differential monocyte epigenetic and transcriptomic attributes between those who would survive and those who would succumb to COVID-19. We identified messenger RNA metabolism, RNA splicing, and interferon signaling pathways as key host responses overactivated by patients who would not survive. Those pathways are prime drug targets to reduce mortality of critically ill patients with COVID-19, leading us to identify tacrolimus, zotatifin, and nintedanib as three strong candidates for treatment of severely ill patients at the time of hospital admission.


Subject(s)
COVID-19 , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Humans , SARS-CoV-2 , Systems Biology
9.
Nat Commun ; 13(1): 3682, 2022 06 27.
Article in English | MEDLINE | ID: mdl-35760776

ABSTRACT

The bacterial respiratory electron transport system (ETS) is branched to allow condition-specific modulation of energy metabolism. There is a detailed understanding of the structural and biochemical features of respiratory enzymes; however, a holistic examination of the system and its plasticity is lacking. Here we generate four strains of Escherichia coli harboring unbranched ETS that pump 1, 2, 3, or 4 proton(s) per electron and characterized them using a combination of synergistic methods (adaptive laboratory evolution, multi-omic analyses, and computation of proteome allocation). We report that: (a) all four ETS variants evolve to a similar optimized growth rate, and (b) the laboratory evolutions generate specific rewiring of major energy-generating pathways, coupled to the ETS, to optimize ATP production capability. We thus define an Aero-Type System (ATS), which is a generalization of the aerobic bioenergetics and is a metabolic systems biology description of respiration and its inherent plasticity.


Subject(s)
Escherichia coli , Systems Biology , Electron Transport/genetics , Escherichia coli/metabolism , Proteome/metabolism , Respiratory System
10.
Heart Fail Clin ; 18(3): 335-347, 2022 Jul.
Article in English | MEDLINE | ID: mdl-35718410

ABSTRACT

The development of human-induced pluripotent stem cell-derived cardiac cell types has created a new paradigm in assessing drug-induced cardiotoxicity. Advances in genomics and epigenomics have also implicated several genomic loci and biological pathways that may contribute to susceptibility to cancer therapies. In this review, we first provide a brief overview of the cardiotoxicity associated with chemotherapy. We then provide a detailed summary of systems biology approaches being applied to elucidate potential molecular mechanisms involved in cardiotoxicity. Finally, we discuss combining systems biology approaches with iPSC technology to help discover molecular mechanisms associated with cardiotoxicity.


Subject(s)
Induced Pluripotent Stem Cells , Neoplasms , Cardiotoxicity/etiology , Humans , Induced Pluripotent Stem Cells/metabolism , Myocytes, Cardiac/metabolism , Neoplasms/drug therapy , Neoplasms/metabolism , Systems Biology
11.
Philos Trans A Math Phys Eng Sci ; 380(2229): 20210201, 2022 Aug 08.
Article in English | MEDLINE | ID: mdl-35719075

ABSTRACT

We present a machine learning framework (GP-NODE) for Bayesian model discovery from partial, noisy and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo sampling and Gaussian Process priors over the observed system states. This allows us to exploit temporal correlations in the observed data, and efficiently infer posterior distributions over plausible models with quantified uncertainty. The use of the Finnish Horseshoe as a sparsity-promoting prior for free model parameters also enables the discovery of parsimonious representations for the latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed GP-NODE method including predator-prey systems, systems biology and a 50-dimensional human motion dynamical system. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.


Subject(s)
Nonlinear Dynamics , Systems Biology , Bayes Theorem , Humans , Monte Carlo Method , Normal Distribution , Systems Biology/methods
12.
J Chem Phys ; 156(21): 214103, 2022 Jun 07.
Article in English | MEDLINE | ID: mdl-35676145

ABSTRACT

In this paper, we consider the problem of quantifying parametric uncertainty in classical empirical interatomic potentials (IPs) using both Bayesian (Markov Chain Monte Carlo) and frequentist (profile likelihood) methods. We interface these tools with the Open Knowledgebase of Interatomic Models and study three models based on the Lennard-Jones, Morse, and Stillinger-Weber potentials. We confirm that IPs are typically sloppy, i.e., insensitive to coordinated changes in some parameter combinations. Because the inverse problem in such models is ill-conditioned, parameters are unidentifiable. This presents challenges for traditional statistical methods, as we demonstrate and interpret within both Bayesian and frequentist frameworks. We use information geometry to illuminate the underlying cause of this phenomenon and show that IPs have global properties similar to those of sloppy models from fields, such as systems biology, power systems, and critical phenomena. IPs correspond to bounded manifolds with a hierarchy of widths, leading to low effective dimensionality in the model. We show how information geometry can motivate new, natural parameterizations that improve the stability and interpretation of uncertainty quantification analysis and further suggest simplified, less-sloppy models.


Subject(s)
Systems Biology , Bayes Theorem , Markov Chains , Monte Carlo Method , Uncertainty
13.
Bioinformatics ; 38(Suppl 1): i369-i377, 2022 06 24.
Article in English | MEDLINE | ID: mdl-35758789

ABSTRACT

MOTIVATION: A factory in a metabolic network specifies how to produce target molecules from source compounds through biochemical reactions, properly accounting for reaction stoichiometry to conserve or not deplete intermediate metabolites. While finding factories is a fundamental problem in systems biology, available methods do not consider the number of reactions used, nor address negative regulation. METHODS: We introduce the new problem of finding optimal factories that use the fewest reactions, for the first time incorporating both first- and second-order negative regulation. We model this problem with directed hypergraphs, prove it is NP-complete, solve it via mixed-integer linear programming, and accommodate second-order negative regulation by an iterative approach that generates next-best factories. RESULTS: This optimization-based approach is remarkably fast in practice, typically finding optimal factories in a few seconds, even for metabolic networks involving tens of thousands of reactions and metabolites, as demonstrated through comprehensive experiments across all instances from standard reaction databases. AVAILABILITY AND IMPLEMENTATION: Source code for an implementation of our new method for optimal factories with negative regulation in a new tool called Odinn, together with all datasets, is available free for non-commercial use at http://odinn.cs.arizona.edu.


Subject(s)
Algorithms , Metabolic Networks and Pathways , Programming, Linear , Software , Systems Biology
14.
PLoS Comput Biol ; 18(6): e1010183, 2022 06.
Article in English | MEDLINE | ID: mdl-35731728

ABSTRACT

Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions into reaction rate functions from covariability data between pairs of components. This promises to turn a globally intractable problem into a sequence of solvable inference problems to quantify complex interaction networks from incomplete snapshots of their stochastic fluctuations.


Subject(s)
Models, Biological , Systems Biology , Cell Physiological Phenomena , Models, Statistical , Stochastic Processes
15.
PLoS Comput Biol ; 18(6): e1009657, 2022 06.
Article in English | MEDLINE | ID: mdl-35666771

ABSTRACT

Model-checking is a methodology developed in computer science to automatically assess the dynamics of discrete systems, by checking if a system modelled as a state-transition graph satisfies a dynamical property written as a temporal logic formula. The dynamics of ecosystems have been drawn as state-transition graphs for more than a century, ranging from state-and-transition models to assembly graphs. Model-checking can provide insights into both empirical data and theoretical models, as long as they sum up into state-transition graphs. While model-checking proved to be a valuable tool in systems biology, it remains largely underused in ecology apart from precursory applications. This article proposes to address this situation, through an inventory of existing ecological STGs and an accessible presentation of the model-checking methodology. This overview is illustrated by the application of model-checking to assess the dynamics of a vegetation pathways model. We select management scenarios by model-checking Computation Tree Logic formulas representing management goals and built from a proposed catalogue of patterns. In discussion, we sketch bridges between existing studies in ecology and available model-checking frameworks. In addition to the automated analysis of ecological state-transition graphs, we believe that defining ecological concepts with temporal logics could help clarify and compare them.


Subject(s)
Ecosystem , Systems Biology , Logic , Models, Theoretical , Problem Solving
16.
Biochem J ; 479(12): 1361-1374, 2022 06 30.
Article in English | MEDLINE | ID: mdl-35748700

ABSTRACT

In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.


Subject(s)
Models, Biological , Systems Biology , Liver , Models, Theoretical , Signal Transduction/physiology
17.
Sci Rep ; 12(1): 9099, 2022 06 01.
Article in English | MEDLINE | ID: mdl-35650297

ABSTRACT

Gastric cancer is the fourth cause of cancer death globally, and gastric adenocarcinoma is its most common type. Efforts for the treatment of gastric cancer have increased its median survival rate by only seven months. Due to the relatively low response of gastric cancer to surgery and adjuvant therapy, as well as the complex role of risk factors in its incidences, such as protein-pomp inhibitors (PPIs) and viral and bacterial infections, we aimed to study the pathological pathways involved in gastric cancer development and investigate possible medications by systems biology and bioinformatics tools. In this study, the protein-protein interaction network was analyzed based on microarray data, and possible effective compounds were discovered. Non-coding RNA versus coding RNA interaction network and gene-disease network were also reconstructed to better understand the underlying mechanisms. It was found that compounds such as amiloride, imatinib, omeprazole, troglitazone, pantoprazole, and fostamatinib might be effective in gastric cancer treatment. In a gene-disease network, it was indicated that diseases such as liver carcinoma, breast carcinoma, liver fibrosis, prostate cancer, ovarian carcinoma, and lung cancer were correlated with gastric adenocarcinoma through specific genes, including hgf, mt2a, mmp2, fbn1, col1a1, and col1a2. It was shown that signaling pathways such as cell cycle, cell division, and extracellular matrix organization were overexpressed, while digestion and ion transport pathways were underexpressed. Based on a multilevel systems biology analysis, hub genes in gastric adenocarcinoma showed participation in the pathways such as focal adhesion, platelet activation, gastric acid secretion, HPV infection, and cell cycle. PPIs are hypothesized to have a therapeutic effect on patients with gastric cancer. Fostamatinib seems a potential therapeutic drug in gastric cancer due to its inhibitory effect on two survival genes. However, these findings should be confirmed through experimental investigations.


Subject(s)
Adenocarcinoma , Stomach Neoplasms , Adenocarcinoma/genetics , Gene Expression Profiling , Gene Expression Regulation, Neoplastic , Humans , Male , Stomach Neoplasms/drug therapy , Stomach Neoplasms/genetics , Systems Biology
18.
Brief Bioinform ; 23(4)2022 07 18.
Article in English | MEDLINE | ID: mdl-35671510

ABSTRACT

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.


Subject(s)
Computational Biology , Systems Biology , Computer Simulation , Reproducibility of Results
19.
Appl Microbiol Biotechnol ; 106(9-10): 3507-3530, 2022 May.
Article in English | MEDLINE | ID: mdl-35575915

ABSTRACT

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.


Subject(s)
Genomics , Systems Biology , Computational Biology/methods , Genomics/methods , Machine Learning , Metabolomics/methods , Plants/genetics , Proteomics/methods , Systems Biology/methods
20.
Adv Sci (Weinh) ; 9(21): e2200978, 2022 07.
Article in English | MEDLINE | ID: mdl-35585676

ABSTRACT

Graft-versus-host disease (GVHD) is a major life-threatening complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). Inflammatory signaling pathways promote T-cell activation and are involved in the pathogenesis of GVHD. Suppressor of cytokine signaling 1 (SOCS1) is a critical negative regulator for several inflammatory cytokines. However, its regulatory role in T-cell activation and GVHD has not been elucidated. Multiomics analysis of the transcriptome and chromatin structure of granulocyte-colony-stimulating-factor (G-CSF)-administered hyporesponsive T cells from healthy donors reveal that G-CSF upregulates SOCS1 by reorganizing the chromatin structure around the SOCS1 locus. Parallel in vitro and in vivo analyses demonstrate that SOCS1 is critical for restraining T cell activation. Loss of Socs1 in T cells exacerbates GVHD pathogenesis and diminishes the protective role of G-CSF in GVHD mouse models. Further analysis shows that SOCS1 inhibits T cell activation not only by inhibiting the colony-stimulating-factor 3 receptor (CSF3R)/Janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) pathway, but also by restraining activation of the inflammasome signaling pathway. Moreover, high expression of SOCS1 in T cells from patients correlates with low acute GVHD occurrence after HSCT. Overall, these findings identify that SOCS1 is critical for inhibiting T cell activation and represents a potential target for the attenuation of GVHD.


Subject(s)
Graft vs Host Disease , Suppressor of Cytokine Signaling 1 Protein , T-Lymphocytes , Animals , Chromatin , Graft vs Host Disease/etiology , Graft vs Host Disease/genetics , Granulocyte Colony-Stimulating Factor/pharmacology , Mice , Suppressor of Cytokine Signaling 1 Protein/genetics , Systems Biology/methods , T-Lymphocytes/metabolism , Transplantation, Homologous/adverse effects
SELECTION OF CITATIONS
SEARCH DETAIL
...