1
|
Aplakidou E, Vergoulidis N, Chasapi M, Venetsianou NK, Kokoli M, Panagiotopoulou E, Iliopoulos I, Karatzas E, Pafilis E, Georgakopoulos-Soares I, Kyrpides NC, Pavlopoulos GA, Baltoumas FA. Visualizing metagenomic and metatranscriptomic data: A comprehensive review. Comput Struct Biotechnol J 2024; 23:2011-2033. [PMID: 38765606 PMCID: PMC11101950 DOI: 10.1016/j.csbj.2024.04.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024] Open
Abstract
The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.
Collapse
Affiliation(s)
- Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Nikolaos Vergoulidis
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Maria Chasapi
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Nefeli K. Venetsianou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Maria Kokoli
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Eleni Panagiotopoulou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Ioannis Iliopoulos
- Department of Basic Sciences, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece
| | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Nikos C. Kyrpides
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Center of New Biotechnologies & Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Greece
- Hellenic Army Academy, 16673 Vari, Greece
| | - Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| |
Collapse
|
2
|
Zhang M, Li X, Oladeinde A, Rothrock M, Pokoo-Aikins A, Zock G. A Novel Slope-Matrix-Graph Algorithm to Analyze Compositional Microbiome Data. Microorganisms 2024; 12:1866. [PMID: 39338540 DOI: 10.3390/microorganisms12091866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2024] [Revised: 09/06/2024] [Accepted: 09/07/2024] [Indexed: 09/30/2024] Open
Abstract
Networks are widely used to represent relationships between objects, including microorganisms within ecosystems, based on high-throughput sequencing data. However, challenges arise with appropriate statistical algorithms, handling of rare taxa, excess zeros in compositional data, and interpretation. This work introduces a novel Slope-Matrix-Graph (SMG) algorithm to identify microbiome correlations primarily based on slope-based distance calculations. SMG effectively handles any proportion of zeros in compositional data and involves: (1) searching for correlated relationships (e.g., positive and negative directions of changes) based on a "target of interest" within a setting, and (2) quantifying graph changes via slope-based distances between objects. Evaluations on simulated datasets demonstrated SMG's ability to accurately cluster microbes into distinct positive/negative correlation groups, outperforming methods like Bray-Curtis and SparCC in both sensitivity and specificity. Moreover, SMG demonstrated superior accuracy in detecting differential abundance (DA) compared to ZicoSeq and ANCOM-BC2, making it a robust tool for microbiome analysis. A key advantage is SMG's natural capacity to analyze zero-inflated compositional data without transformations. Overall, this simple yet powerful algorithm holds promise for diverse microbiome analysis applications.
Collapse
Affiliation(s)
- Meng Zhang
- Department of Mathematics, University of North Georgia, 82 College Cir, Dahlonega, GA 30597, USA
| | - Xiang Li
- U.S. National Poultry Research Center, Egg & Poultry Production Safety Research Unit, Agricultural Research Service, U.S. Department of Agriculture, 950 College Station Road, Athens, GA 30605, USA
| | - Adelumola Oladeinde
- U.S. National Poultry Research Center, Egg & Poultry Production Safety Research Unit, Agricultural Research Service, U.S. Department of Agriculture, 950 College Station Road, Athens, GA 30605, USA
| | - Michael Rothrock
- U.S. National Poultry Research Center, Egg & Poultry Production Safety Research Unit, Agricultural Research Service, U.S. Department of Agriculture, 950 College Station Road, Athens, GA 30605, USA
| | - Anthony Pokoo-Aikins
- U.S. National Poultry Research Center, Toxicology & Mycotoxin Research Unit, Agricultural Research Service, U.S. Department of Agriculture, 950 College Station Road, Athens, GA 30605, USA
| | - Gregory Zock
- U.S. National Poultry Research Center, Egg & Poultry Production Safety Research Unit, Agricultural Research Service, U.S. Department of Agriculture, 950 College Station Road, Athens, GA 30605, USA
| |
Collapse
|
3
|
Krishnan SN, Ji S, Elhossiny AM, Rao A, Frankel TL, Rao A. Proximogram-A multi-omics network-based framework to capture tissue heterogeneity integrating single-cell omics and spatial profiling. Comput Biol Med 2024; 182:109082. [PMID: 39255657 DOI: 10.1016/j.compbiomed.2024.109082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 08/26/2024] [Accepted: 08/27/2024] [Indexed: 09/12/2024]
Abstract
The increasing availability of patient-derived multimodal biological data for various diseases has opened up avenues for finding the optimal methods for jointly leveraging the information extracted in a customizable and scalable manner. Here, we propose the Proximogram, a graph-based representation that provides a joint construct for embedding independently obtained omics and spatial data. To evaluate the representation, we generated proximograms from 2 distinct biological sources, namely, multiplexed immunofluorescence images and single-cell RNA-seq data obtained from patients across two pancreatic diseases that include normal and chronic Pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC). The generated proximograms were used as inputs to 2 distinct graph deep-learning models. The improved classification results over simpler spatial-data-based input graphs point to the increased discriminatory power obtained by integrating structural information from single-cell ligand-receptor signaling data and the spatial architecture of cells in each disease class, which can help point to markers of high diagnostic significance.
Collapse
Affiliation(s)
- Santhoshi N Krishnan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Sunjong Ji
- Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA
| | - Ahmed M Elhossiny
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA; Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|
4
|
Agrawal M, Mani A. Integrative in silico approaches to analyse microRNA-mediated responses in human diseases. J Gene Med 2024; 26:e3734. [PMID: 39197943 DOI: 10.1002/jgm.3734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 07/23/2024] [Accepted: 08/13/2024] [Indexed: 09/01/2024] Open
Abstract
Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although, there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.
Collapse
Affiliation(s)
- Meghna Agrawal
- Department of Biotechnology, Motilal Nehru Institute of Technology Allahabad, Prayagraj, India
| | - Ashutosh Mani
- Department of Biotechnology, Motilal Nehru Institute of Technology Allahabad, Prayagraj, India
| |
Collapse
|
5
|
Lu Y, Li Q, Li T. A novel hierarchical network-based approach to unveil the complexity of functional microbial genome. BMC Genomics 2024; 25:786. [PMID: 39138557 PMCID: PMC11323692 DOI: 10.1186/s12864-024-10692-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 08/07/2024] [Indexed: 08/15/2024] Open
Abstract
Biological networks serve a crucial role in elucidating intricate biological processes. While interspecies environmental interactions have been extensively studied, the exploration of gene interactions within species, particularly among individual microorganisms, is less developed. The increasing amount of microbiome genomic data necessitates a more nuanced analysis of microbial genome structures and functions. In this context, we introduce a complex structure using higher-order network theory, "Solid Motif Structures (SMS)", via a hierarchical biological network analysis of genomes within the same genus, effectively linking microbial genome structure with its function. Leveraging 162 high-quality genomes of Microcystis, a key freshwater cyanobacterium within microbial ecosystems, we established a genome structure network. Employing deep learning techniques, such as adaptive graph encoder, we uncovered 27 critical functional subnetworks and their associated SMSs. Incorporating metagenomic data from seven geographically distinct lakes, we conducted an investigation into Microcystis' functional stability under varying environmental conditions, unveiling unique functional interaction models for each lake. Our work compiles these insights into an extensive resource repository, providing novel perspectives on the functional dynamics within Microcystis. This research offers a hierarchical network analysis framework for understanding interactions between microbial genome structures and functions within the same genus.
Collapse
Affiliation(s)
- Yuntao Lu
- University of Michigan, Ann Arbor, USA
| | - Qi Li
- The State Key Laboratory of Freshwater Ecology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China.
| | - Tao Li
- The State Key Laboratory of Freshwater Ecology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China.
| |
Collapse
|
6
|
Jia C, Grima R. Holimap: an accurate and efficient method for solving stochastic gene network dynamics. Nat Commun 2024; 15:6557. [PMID: 39095346 PMCID: PMC11297302 DOI: 10.1038/s41467-024-50716-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 07/13/2024] [Indexed: 08/04/2024] Open
Abstract
Gene-gene interactions are crucial to the control of sub-cellular processes but our understanding of their stochastic dynamics is hindered by the lack of simulation methods that can accurately and efficiently predict how the distributions of gene product numbers vary across parameter space. To overcome these difficulties, here we present Holimap (high-order linear-mapping approximation), an approach that approximates the protein or mRNA number distributions of a complex gene regulatory network by the distributions of a much simpler reaction system. We demonstrate Holimap's computational advantages over conventional methods by applying it to predict the stochastic time-dependent dynamics of various gene networks, including transcriptional networks ranging from simple autoregulatory loops to complex randomly connected networks, post-transcriptional networks, and post-translational networks. Holimap is ideally suited to study how the intricate network of gene-gene interactions results in precise coordination and control of gene expression.
Collapse
Affiliation(s)
- Chen Jia
- Applied and Computational Mathematics Division, Beijing Computational Science Research Center, Beijing, China
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
| |
Collapse
|
7
|
Venot T, Desbois A, Corsi MC, Hugueville L, Saint-Bauzel L, De Vico Fallani F. Intentional binding for noninvasive BCI control. J Neural Eng 2024; 21:046026. [PMID: 38996409 DOI: 10.1088/1741-2552/ad628c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 07/12/2024] [Indexed: 07/14/2024]
Abstract
Objective. Noninvasive brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation.Approach. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood. To address this question, we performed a hybrid EEG-based BCI training involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm.Main results. Showed that the hand grasping motor imagery timing significantly affects the BCI accuracy evolution as well as the spatiotemporal brain dynamics. Larger accuracy improvement was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions.Significance. Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.
Collapse
Affiliation(s)
- Tristan Venot
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013 Paris, France
| | - Arthur Desbois
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013 Paris, France
| | - Marie Constance Corsi
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013 Paris, France
| | - Laurent Hugueville
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013 Paris, France
| | - Ludovic Saint-Bauzel
- Sorbonne Université, Institut des Systèmes Intelligents et de Robotiques ISIR, F-75005 Paris, France
| | - Fabrizio De Vico Fallani
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013 Paris, France
| |
Collapse
|
8
|
Morang S, Bisht M, Upadhyay V, Thapliyal S, Handu S. S1P Signaling Genes as Prominent Drivers of BCR-ABL1-Independent Imatinib Resistance and Six Herbal Compounds as Potential Drugs for Chronic Myeloid Leukemia. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:367-376. [PMID: 38986084 DOI: 10.1089/omi.2024.0074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Imatinib (IM), a breakthrough in chronic myeloid leukemia (CML) treatment, is accompanied by discontinuation challenges owing to drug intolerance. Although BCR-ABL1 mutation is a key cause of CML resistance, understanding mechanisms independent of BCR-ABL1 is also important. This study investigated the sphingosine-1-phosphate (S1P) signaling-associated genes (SphK1 and S1PRs) and their role in BCR-ABL1-independent resistant CML, an area currently lacking investigation. Through comprehensive transcriptomic analysis of IM-sensitive and IM-resistant CML groups, we identified the differentially expressed genes and found a notable upregulation of SphK1, S1PR2, and S1PR5 in IM-resistant CML. Functional annotation revealed their roles in critical cellular processes such as proliferation and GPCR activity. Their network analysis uncovered significant clusters, emphasizing the interconnectedness of the S1P signaling genes. Further, we identified interactors such as BIRC3, TRAF6, and SRC genes, with potential implications for IM resistance. Additionally, receiver operator characteristic curve analysis suggested these genes' potential as biomarkers for predicting IM resistance. Network pharmacology analysis identified six herbal compounds-ampelopsin, ellagic acid, colchicine, epigallocatechin-3-gallate, cucurbitacin B, and evodin-as potential drug candidates targeting the S1P signaling genes. In summary, this study contributes to efforts to better understand the molecular mechanisms underlying BCR-ABL1-independent CML resistance. Moreover, the S1P signaling genes are promising therapeutic targets and plausible new innovation avenues to combat IM resistance in cancer clinical care in the future.
Collapse
MESH Headings
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/genetics
- Leukemia, Myelogenous, Chronic, BCR-ABL Positive/metabolism
- Humans
- Drug Resistance, Neoplasm/genetics
- Drug Resistance, Neoplasm/drug effects
- Imatinib Mesylate/pharmacology
- Imatinib Mesylate/therapeutic use
- Fusion Proteins, bcr-abl/genetics
- Fusion Proteins, bcr-abl/metabolism
- Signal Transduction/drug effects
- Lysophospholipids/metabolism
- Gene Expression Profiling/methods
- Antineoplastic Agents/pharmacology
- Antineoplastic Agents/therapeutic use
- Female
- Sphingosine/analogs & derivatives
Collapse
Affiliation(s)
- Sikha Morang
- Department of Pharmacology, All India Institute of Medical Sciences, Rishikesh, India
| | - Manisha Bisht
- Department of Pharmacology, All India Institute of Medical Sciences, Rishikesh, India
| | - Vikas Upadhyay
- Department of AYUSH, All India Institute of Medical Sciences, Rishikesh, India
| | | | - Shailendra Handu
- Department of Pharmacology, All India Institute of Medical Sciences, Rishikesh, India
| |
Collapse
|
9
|
Cassan O, Lecellier CH, Martin A, Bréhélin L, Lèbre S. Optimizing data integration improves gene regulatory network inference in Arabidopsis thaliana. Bioinformatics 2024; 40:btae415. [PMID: 38913855 PMCID: PMC11227367 DOI: 10.1093/bioinformatics/btae415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 06/12/2024] [Accepted: 06/21/2024] [Indexed: 06/26/2024] Open
Abstract
MOTIVATIONS Gene regulatory networks (GRNs) are traditionally inferred from gene expression profiles monitoring a specific condition or treatment. In the last decade, integrative strategies have successfully emerged to guide GRN inference from gene expression with complementary prior data. However, datasets used as prior information and validation gold standards are often related and limited to a subset of genes. This lack of complete and independent evaluation calls for new criteria to robustly estimate the optimal intensity of prior data integration in the inference process. RESULTS We address this issue for two regression-based GRN inference models, a weighted random forest (weigthedRF) and a generalized linear model estimated under a weighted LASSO penalty with stability selection (weightedLASSO). These approaches are applied to data from the root response to nitrate induction in Arabidopsis thaliana. For each gene, we measure how the integration of transcription factor binding motifs influences model prediction. We propose a new approach, DIOgene, that uses model prediction error and a simulated null hypothesis in order to optimize data integration strength in a hypothesis-driven, gene-specific manner. This integration scheme reveals a strong diversity of optimal integration intensities between genes, and offers good performance in minimizing prediction error as well as retrieving experimental interactions. Experimental results show that DIOgene compares favorably against state-of-the-art approaches and allows to recover master regulators of nitrate induction. AVAILABILITY AND IMPLEMENTATION The R code and notebooks demonstrating the use of the proposed approaches are available in the repository https://github.com/OceaneCsn/integrative_GRN_N_induction.
Collapse
Affiliation(s)
- Océane Cassan
- LIRMM, Univ Montpellier, CNRS, Montpellier, 34095, France
| | - Charles-Henri Lecellier
- LIRMM, Univ Montpellier, CNRS, Montpellier, 34095, France
- IGMM, Univ Montpellier, CNRS, Montpellier, 34090, France
| | - Antoine Martin
- IPSIM, CNRS, INRAE, Institut Agro, Univ Montpellier, 34060, Montpellier, France
| | | | - Sophie Lèbre
- LIRMM, Univ Montpellier, CNRS, Montpellier, 34095, France
- IMAG, Univ Montpellier, CNRS, Montpellier, 34090, France
- Université Paul-Valéry-Montpellier 3, Montpellier, 34090, France
| |
Collapse
|
10
|
Nietz AK, Popa LS, Carter RE, Gerhart ML, Manikonda K, Ranum LP, Ebner TJ. Cerebral cortical functional hyperconnectivity in a mouse model of spinocerebellar ataxia type 8 (SCA8). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.20.599947. [PMID: 38948725 PMCID: PMC11212952 DOI: 10.1101/2024.06.20.599947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Spinocerebellar Ataxia Type 8 (SCA8) is an inherited neurodegenerative disease caused by a bidirectionally expressed CTG●CAG expansion mutation in the ATXN-8 and ATXN8-OS genes. While primarily a motor disorder, psychiatric and cognitive symptoms have been reported. It is difficult to elucidate how the disease alters brain function in areas with little or no degeneration producing both motor and cognitive symptoms. Using transparent polymer skulls and CNS-wide GCaMP6f expression, we studied neocortical networks throughout SCA8 progression using wide-field Ca2+ imaging in a transgenic mouse model of SCA8. We observed that neocortical networks in SCA8+ mice were hyperconnected globally which led to network configurations with increased global efficiency and centrality. At the regional level, significant network changes occurred in nearly all cortical regions, however mainly involved sensory and association cortices. Changes in functional connectivity in anterior motor regions worsened later in the disease. Near perfect decoding of animal genotype was obtained using a generalized linear model based on canonical correlation strengths between activity in cortical regions. The major contributors to decoding were concentrated in the somatosensory, higher visual and retrosplenial cortices and occasionally extended into the motor regions, demonstrating that the areas with the largest network changes are predictive of disease state.
Collapse
Affiliation(s)
- Angela K. Nietz
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Laurentiu S. Popa
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Russell E. Carter
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Morgan L Gerhart
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Keerthi Manikonda
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Laura P.W. Ranum
- Department of Molecular Genetics & Microbiology, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Timothy J. Ebner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA
| |
Collapse
|
11
|
Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
Collapse
Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
| |
Collapse
|
12
|
Stock M, Popp N, Fiorentino J, Scialdone A. Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data. Bioinformatics 2024; 40:btae267. [PMID: 38627250 PMCID: PMC11096270 DOI: 10.1093/bioinformatics/btae267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 02/28/2024] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
MOTIVATION In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs. RESULTS To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest. AVAILABILITY AND IMPLEMENTATION STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.
Collapse
Affiliation(s)
- Marco Stock
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich 85354, Germany
| | - Niclas Popp
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| | - Jonathan Fiorentino
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| | - Antonio Scialdone
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Munich 85764, Germany
| |
Collapse
|
13
|
Williams A. Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont. FEMS Microbiol Ecol 2024; 100:fiae058. [PMID: 38653719 PMCID: PMC11067971 DOI: 10.1093/femsec/fiae058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 03/25/2024] [Accepted: 04/22/2024] [Indexed: 04/25/2024] Open
Abstract
Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.
Collapse
Affiliation(s)
- Amanda Williams
- Microbial Biology Graduate Program, Rutgers University, 76 Lipman Drive, New Brunswick, NJ 08901, United States
- Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Drive, New Brunswick, NJ 08901, United States
| |
Collapse
|
14
|
Barakat A, Munro G, Heegaard AM. Finding new analgesics: Computational pharmacology faces drug discovery challenges. Biochem Pharmacol 2024; 222:116091. [PMID: 38412924 DOI: 10.1016/j.bcp.2024.116091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/10/2024] [Accepted: 02/23/2024] [Indexed: 02/29/2024]
Abstract
Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.
Collapse
Affiliation(s)
- Ahmed Barakat
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Assiut University, Assiut, Egypt.
| | | | - Anne-Marie Heegaard
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
15
|
Shin J, Miaskowski C, Wong ML, Yates P, Olshen AB, Roy R, Dokiparthi V, Cooper B, Paul S, Conley YP, Levine JD, Hammer MJ, Kober K. Perturbations in inflammatory pathways are associated with shortness of breath profiles in oncology patients receiving chemotherapy. Support Care Cancer 2024; 32:250. [PMID: 38532105 DOI: 10.1007/s00520-024-08446-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 03/18/2024] [Indexed: 03/28/2024]
Abstract
PURPOSE One plausible mechanistic hypothesis is the potential contribution of inflammatory mechanisms to shortness of breath. This study was aimed to evaluate for associations between the occurrence of shortness of breath and perturbations in inflammatory pathways. METHODS Patients with cancer reported the occurrence of shortness of breath six times over two cycles of chemotherapy. Latent class analysis was used to identify subgroups of patients with distinct shortness of breath occurrence profiles (i.e., none (70.5%), decreasing (8.2%), increasing (7.8%), high (13.5%)). Using an extreme phenotype approach, whole transcriptome differential gene expression and pathway impact analyses were performed to evaluate for perturbed signaling pathways associated with shortness of breath between the none and high classes. Two independent samples (RNA-sequencing (n = 293) and microarray (n = 295) methodologies) were evaluated. Fisher's combined probability method was used to combine these results to obtain a global test of the null hypothesis. In addition, an unweighted knowledge network was created using the specific pathway maps to evaluate for interconnections among these pathways. RESULTS Twenty-nine Kyoto Encyclopedia of Genes and Genomes inflammatory signaling pathways were perturbed. The mitogen-activated protein kinase signaling pathway node had the highest closeness, betweenness, and degree scores. In addition, five common respiratory disease-related pathways, that may share mechanisms with cancer-related shortness of breath, were perturbed. CONCLUSIONS Findings provide preliminary support for the hypothesis that inflammation contribute to the occurrence of shortness of breath in patients with cancer. In addition, the mechanisms that underlie shortness of breath in oncology patients may be similar to other respiratory diseases.
Collapse
Affiliation(s)
- Joosun Shin
- School of Nursing, University of California, 2 Koret Way - N631Y, San Francisco, CA, 94143-0610, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Christine Miaskowski
- School of Nursing, University of California, 2 Koret Way - N631Y, San Francisco, CA, 94143-0610, USA
- School of Medicine, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Melisa L Wong
- School of Medicine, University of California, San Francisco, CA, USA
| | - Patsy Yates
- Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Adam B Olshen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Ritu Roy
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA
| | - Vasuda Dokiparthi
- School of Nursing, University of California, 2 Koret Way - N631Y, San Francisco, CA, 94143-0610, USA
| | - Bruce Cooper
- School of Nursing, University of California, 2 Koret Way - N631Y, San Francisco, CA, 94143-0610, USA
| | - Steven Paul
- School of Nursing, University of California, 2 Koret Way - N631Y, San Francisco, CA, 94143-0610, USA
| | - Yvette P Conley
- School of Nursing, Univeristy of Pittsburgh, 3500 Victoria St, Pittsburgh, 15213, PA, USA
| | - Jon D Levine
- School of Medicine, University of California, San Francisco, CA, USA
| | | | - Kord Kober
- School of Nursing, University of California, 2 Koret Way - N631Y, San Francisco, CA, 94143-0610, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.
| |
Collapse
|
16
|
Cain JY, Evarts JI, Yu JS, Bagheri N. Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence. Bioinformatics 2024; 40:btae131. [PMID: 38444088 PMCID: PMC10957516 DOI: 10.1093/bioinformatics/btae131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 02/08/2024] [Accepted: 03/01/2024] [Indexed: 03/07/2024] Open
Abstract
MOTIVATION Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. RESULTS Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. AVAILABILITY AND IMPLEMENTATION All source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.
Collapse
Affiliation(s)
- Jason Y Cain
- Department of Chemical Engineering, University of Washington, Seattle, WA 98195, United States
| | - Jacob I Evarts
- Department of Biology, University of Washington, Seattle, WA 98195, United States
| | - Jessica S Yu
- Department of Biology, University of Washington, Seattle, WA 98195, United States
| | - Neda Bagheri
- Department of Chemical Engineering, University of Washington, Seattle, WA 98195, United States
- Department of Biology, University of Washington, Seattle, WA 98195, United States
| |
Collapse
|
17
|
Kellmann AJ, Postema M, de Keijser J, Svetachov P, Wilson RC, van Enckevort EJ, Swertz MA. Visualization and exploration of linked data using virtual reality. Database (Oxford) 2024; 2024:baae008. [PMID: 38554132 PMCID: PMC11184448 DOI: 10.1093/database/baae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/18/2023] [Accepted: 01/25/2024] [Indexed: 04/01/2024]
Abstract
In this report, we analyse the use of virtual reality (VR) as a method to navigate and explore complex knowledge graphs. Over the past few decades, linked data technologies [Resource Description Framework (RDF) and Web Ontology Language (OWL)] have shown to be valuable to encode such graphs and many tools have emerged to interactively visualize RDF. However, as knowledge graphs get larger, most of these tools struggle with the limitations of 2D screens or 3D projections. Therefore, in this paper, we evaluate the use of VR to visually explore SPARQL Protocol and RDF Query Language (SPARQL) (construct) queries, including a series of tutorial videos that demonstrate the power of VR (see Graph2VR tutorial playlist: https://www.youtube.com/playlist?list=PLRQCsKSUyhNIdUzBNRTmE-_JmuiOEZbdH). We first review existing methods for Linked Data visualization and then report the creation of a prototype, Graph2VR. Finally, we report a first evaluation of the use of VR for exploring linked data graphs. Our results show that most participants enjoyed testing Graph2VR and found it to be a useful tool for graph exploration and data discovery. The usability study also provides valuable insights for potential future improvements to Linked Data visualization in VR.
Collapse
Affiliation(s)
- Alexander J Kellmann
- Department of Genetics, University of Groningen, Antonius Deusinglaan 1, Groningen, Groningen 9713 AV, The Netherlands
- Department of Genetics, University Medical Center Groningen, Antonius Deusinglaan 1, Groningen, Groningen 9713 AV, The Netherlands
| | - Max Postema
- Department of Genetics, University Medical Center Groningen, Antonius Deusinglaan 1, Groningen, Groningen 9713 AV, The Netherlands
| | - Joris de Keijser
- Department of Genetics, University Medical Center Groningen, Antonius Deusinglaan 1, Groningen, Groningen 9713 AV, The Netherlands
| | - Pjotr Svetachov
- Center of information technology, University of Groningen, Nettelbosje 1, Groningen, Groningen 9747 AJ, The Netherlands
| | - Rebecca C Wilson
- Public Health, Policy & Systems, University of Liverpool, Block B, 1st Floor, Waterhouse Building, 1-5 Dover Street, Liverpool L69 3GL, United Kingdom
| | - Esther J van Enckevort
- Department of Genetics, University of Groningen, Antonius Deusinglaan 1, Groningen, Groningen 9713 AV, The Netherlands
- Department of Genetics, University Medical Center Groningen, Antonius Deusinglaan 1, Groningen, Groningen 9713 AV, The Netherlands
| | - Morris A Swertz
- Department of Genetics, University of Groningen, Antonius Deusinglaan 1, Groningen, Groningen 9713 AV, The Netherlands
- Department of Genetics, University Medical Center Groningen, Antonius Deusinglaan 1, Groningen, Groningen 9713 AV, The Netherlands
| |
Collapse
|
18
|
Kole A, Bag AK, Pal AJ, De D. Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology. BMC Bioinformatics 2024; 25:74. [PMID: 38365632 PMCID: PMC10874019 DOI: 10.1186/s12859-024-05690-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/02/2024] [Indexed: 02/18/2024] Open
Abstract
PURPOSE Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. METHODS To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. RESULTS We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. CONCLUSION Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.
Collapse
Affiliation(s)
- Arnab Kole
- Department of Computer Application, The Heritage Academy, Kolkata, W.B., 700107, India.
| | - Arup Kumar Bag
- Beckman Research Institute of City of Hope, Duarte, CA, 91010, USA
| | | | - Debashis De
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Nadia, W.B., 741249, India
| |
Collapse
|
19
|
Kundu S. A mathematically rigorous algorithm to define, compute and assess relevance of the probable dissociation constants in characterizing a biochemical network. Sci Rep 2024; 14:3507. [PMID: 38347039 PMCID: PMC10861591 DOI: 10.1038/s41598-024-53231-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
Metabolism results from enzymatic- and non-enzymatic interactions of several molecules, is easily parameterized with the dissociation constant and occurs via biochemical networks. The dissociation constant is an empirically determined parameter and cannot be used directly to investigate in silico models of biochemical networks. Here, we develop and present an algorithm to define, compute and assess the relevance of the probable dissociation constant for every reaction of a biochemical network. The reactants and reactions of this network are modelled by a stoichiometry number matrix. The algorithm computes the null space and then serially generates subspaces by combinatorially summing the spanning vectors that are non-trivial and unique. This is done until the terms of each row either monotonically diverge or form an alternating sequence whose terms can be partitioned into subsets with almost the same number of oppositely signed terms. For a selected null space-generated subspace the algorithm utilizes several statistical and mathematical descriptors to select and bin terms from each row into distinct outcome-specific subsets. The terms of each subset are summed, mapped to the real-valued open interval [Formula: see text] and used to populate a reaction-specific outcome vector. The p1-norm for this vector is then the probable dissociation constant for this reaction. These steps are continued until every reaction of a modelled network is unambiguously annotated. The assertions presented are complemented by computational studies of a biochemical network for aerobic glycolysis. The fundamental premise of this work is that every row of a null space-generated subspace is a valid reaction and can therefore, be modelled as a reaction-specific sequence vector with a dimension that corresponds to the cardinality of the subspace after excluding all trivial- and redundant-vectors. A major finding of this study is that the row-wise sum or the sum of the terms contained in each reaction-specific sequence vector is mapped unambiguously to a positive real number. This means that the probable dissociation constants, for all reactions, can be directly computed from the stoichiometry number matrix and are suitable indicators of outcome for every reaction of the modelled biochemical network. Additionally, we find that the unambiguous annotation for a biochemical network will require a minimum number of iterations and will determine computational complexity.
Collapse
Affiliation(s)
- Siddhartha Kundu
- Department of Biochemistry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India.
| |
Collapse
|
20
|
Barrera-Vázquez OS, Hernández-González O. Structural and Pharmacological Network Focused on MiRNAs Involved in Rheumatoid Arthritis: A Systematic Review. Curr Mol Med 2024; 24:599-609. [PMID: 37185324 DOI: 10.2174/1566524023666230423144114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/14/2023] [Accepted: 02/17/2023] [Indexed: 05/17/2023]
Abstract
BACKGROUND Rheumatoid Arthritis (RA) is a chronic autoimmune disease that has a prevalence of over one percent of the world population, causing substantial pain, joint deformity, and functional disability in patients. The identification and measurement of miRNAs are relatively easy to perform. Future studies will corroborate if miRNAs can fulfill their roles as biomarkers with either predictive or diagnostic evaluation of treatment potential and provide actual clinical utility. METHODS In the last decade, various advances have been made regarding the identification of the origin and exact functions of miRNAs, allowing us to have a potential use both in the research and clinical fields. OBJECTIVE This systematic review aimed to collect, analyze, and improve the current understanding of RA-related miRNAs and their applicability in therapeutics. A bibliographic search of the miRNAs involved in RA was carried out, and through the use of databases, their target genes and small molecules that had some relationship with their expression were searched. The analysis of these data was done through structural network analysis. RESULTS During the network analysis, miR-30a, miR-30c, let-7a, miR-144, miR-17-5p, miR-124, miR -23b, miR-23, miR-15a, miR-16 were the most connected, which could be used as possible biomarkers or be candidates for further analysis due to their interaction with other miRNAs and genes. CONCLUSION Additionally, this is the first systematic review, in which we proposed that small compounds like toxicants and drugs could have a potential role within RA because they regulate the expression of miRNAs involved in this pathology. Some of these compounds are commonly found as environmental contaminants, and others as drugs. These ideas open a new panorama of understanding RA, proposing possible causes or treatments against this pathology. Therefore, these small molecules would give us some indication of a relationship with RA, thereby helping in seeking causes, treatment, or prevention of this disease. CONCLUSION This is the first time it is intended to use structural network analysis to determine possible biomarkers of AR for diagnosis and prognosis through the expression of these miRNAs and their relationship with compounds of daily life.
Collapse
Affiliation(s)
| | - Olivia Hernández-González
- Laboratorio de Microscopia Electrónica, Instituto Nacional de Rehabilitación, Mexico City, 14389, Mexico
| |
Collapse
|
21
|
Chen HM, Liu JX, Liu D, Hao GF, Yang GF. Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection. Rev Med Virol 2024; 34:e2517. [PMID: 38282401 DOI: 10.1002/rmv.2517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 09/12/2023] [Accepted: 01/16/2024] [Indexed: 01/30/2024]
Abstract
Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.
Collapse
Affiliation(s)
- Hui-Min Chen
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Jia-Xin Liu
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Di Liu
- CAS Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
| | - Guang-Fu Yang
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| |
Collapse
|
22
|
Wang L, Sesachalam PV, Chua R, Ghosh S. Interactome Analysis of Visceral Adipose Tissue Elucidates Gene Regulatory Networks and Novel Gene Candidates in Obesity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.21.572734. [PMID: 38187694 PMCID: PMC10769441 DOI: 10.1101/2023.12.21.572734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Objective Visceral adiposity is associated with increased proinflammatory activity, insulin resistance, diabetes risk and mortality rate. Numerous individual genes have been associated with obesity, but studies investigating gene-regulatory networks in human visceral obesity are lacking. Methods We analyzed gene-regulatory networks in human visceral adipose tissue (VAT) from 48 obese and 11 non-obese Chinese subjects using gene co-expression and network construction with RNA-sequencing data. We also conducted RNA interference-based tests on selected genes for adipocyte differentiation effects. Results A scale-free gene co-expression network was constructed from 360 differentially expressed genes between obese and non-obese VAT (absolute log fold-change >1, FDR<0.05) with edge probability >0.8. Gene regulatory network analysis identified candidate transcription factors associated with differentially expressed genes. Fifteen subnetworks (communities) displayed altered connectivity patterns between obese and non-obese networks. Genes in pro-inflammatory pathways showed increased network connectivities in obese VAT whereas the oxidative phosphorylation pathway displayed reduced connections (enrichment FDR<0.05). Functional screening via RNA interference identified SOX30 and OSBPL3 as potential network-derived gene candidates influencing adipocyte differentiation. Conclusions This interactome-based approach highlights the network architecture, identifies novel candidate genes, and leads to new hypotheses regarding network-assisted gene regulation in obese vs. non-obese VAT.What is already known about this subject?: Visceral adipose tissue (VAT) is associated with increased levels of proinflammatory activity, insulin resistance, diabetes risk and mortality rate.Gene expression studies have identified candidate genes associated with proinflammatory function in VAT.What are the new findings in your manuscript?: Using integrative network-science, we identified co-expression and gene regulatory networks that are differentially regulated in VAT samples from subjects with and without obesityWe used functional testing (adipocyte differentiation) to validate a subset of novel candidate genes with minimal prior reported associations to obesityHow might your results change the direction of research or the focus of clinical practice: Network biology-based investigation provides a new avenue to our understanding of gene function in visceral adiposityFunctional validation screen allows for the identification of novel gene candidates that may be targeted for the treatment of adipose tissue dysfunction in obesity.
Collapse
|
23
|
Qi B, Trakadis YJ. Advancing Clinical Psychiatry: Integration of Clinical and Omics Data Using Machine Learning. Biol Psychiatry 2023; 94:908-909. [PMID: 37968028 DOI: 10.1016/j.biopsych.2023.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 09/05/2023] [Indexed: 11/17/2023]
Affiliation(s)
- Bill Qi
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Yannis J Trakadis
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada; Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Center, Montreal, QC, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
| |
Collapse
|
24
|
Li S, Nakayama H, Sinha NR. How to utilize comparative transcriptomics to dissect morphological diversity in plants. CURRENT OPINION IN PLANT BIOLOGY 2023; 76:102474. [PMID: 37804608 DOI: 10.1016/j.pbi.2023.102474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/09/2023]
Abstract
Comparative transcriptomics has emerged as a powerful approach that allows us to unravel the genetic basis of organ morphogenesis and its diversification processes during evolution. However, the application of comparative transcriptomics in studying plant morphological diversity addresses challenges such as identifying homologous gene pairs, selecting appropriate developmental stages for comparison, and extracting biologically meaningful networks. Methods such as phylostratigraphy, clustering, and gene co-expression networks are explored to identify functionally equivalent genes, align developmental stages, and uncover gene regulatory relationships. In the current review, we highlight the importance of these approaches in overcoming the complexity of plant genomes, the impact of heterochrony on stage alignment, and the integration of gene networks with additional data for a comprehensive understanding of morphological evolution.
Collapse
Affiliation(s)
- Siyu Li
- Department of Plant Biology, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Hokuto Nakayama
- Department of Plant Biology, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA; Graduate School of Science, Department of Biological Sciences, The University of Tokyo, Science Build. #2, 7-3-1 Hongo Bunkyo-ku Tokyo, 113-0033, Japan
| | - Neelima R Sinha
- Department of Plant Biology, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA.
| |
Collapse
|
25
|
Briggs JK, Gresch A, Marinelli I, Dwulet JM, Albers DJ, Kravets V, Benninger RKP. β-cell intrinsic dynamics rather than gap junction structure dictates subpopulations in the islet functional network. eLife 2023; 12:e83147. [PMID: 38018905 PMCID: PMC10803032 DOI: 10.7554/elife.83147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/27/2023] [Indexed: 11/30/2023] Open
Abstract
Diabetes is caused by the inability of electrically coupled, functionally heterogeneous β-cells within the pancreatic islet to provide adequate insulin secretion. Functional networks have been used to represent synchronized oscillatory [Ca2+] dynamics and to study β-cell subpopulations, which play an important role in driving islet function. The mechanism by which highly synchronized β-cell subpopulations drive islet function is unclear. We used experimental and computational techniques to investigate the relationship between functional networks, structural (gap junction) networks, and intrinsic β-cell dynamics in slow and fast oscillating islets. Highly synchronized subpopulations in the functional network were differentiated by intrinsic dynamics, including metabolic activity and KATP channel conductance, more than structural coupling. Consistent with this, intrinsic dynamics were more predictive of high synchronization in the islet functional network as compared to high levels of structural coupling. Finally, dysfunction of gap junctions, which can occur in diabetes, caused decreases in the efficiency and clustering of the functional network. These results indicate that intrinsic dynamics rather than structure drive connections in the functional network and highly synchronized subpopulations, but gap junctions are still essential for overall network efficiency. These findings deepen our interpretation of functional networks and the formation of functional subpopulations in dynamic tissues such as the islet.
Collapse
Affiliation(s)
- Jennifer K Briggs
- Department of Bioengineering, University of Colorado Anschutz Medical CampusAuroraUnited States
| | - Anne Gresch
- Department of Bioengineering, University of Colorado Anschutz Medical CampusAuroraUnited States
- Barbara Davis Center for Childhood Diabetes, University of Colorado Anschutz Medical CampusAuroraUnited States
| | - Isabella Marinelli
- Centre for Systems Modelling and Quantitative Biomedicine, University of BirminghamBirminghamUnited Kingdom
| | - JaeAnn M Dwulet
- Department of Bioengineering, University of Colorado Anschutz Medical CampusAuroraUnited States
| | - David J Albers
- Department of Bioengineering, University of Colorado Anschutz Medical CampusAuroraUnited States
- Department of Biomedical Informatics, University of Colorado Anschutz Medical CampusAuroraUnited States
| | - Vira Kravets
- Department of Bioengineering, University of Colorado Anschutz Medical CampusAuroraUnited States
| | - Richard KP Benninger
- Department of Bioengineering, University of Colorado Anschutz Medical CampusAuroraUnited States
- Barbara Davis Center for Childhood Diabetes, University of Colorado Anschutz Medical CampusAuroraUnited States
| |
Collapse
|
26
|
Hu S, Zhang Y, Yi Q, Yang C, Liu Y, Bai Y. Time-resolved proteomic profiling reveals compositional and functional transitions across the stress granule life cycle. Nat Commun 2023; 14:7782. [PMID: 38012130 PMCID: PMC10682001 DOI: 10.1038/s41467-023-43470-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/09/2023] [Indexed: 11/29/2023] Open
Abstract
Stress granules (SGs) are dynamic, membrane-less organelles. With their formation and disassembly processes characterized, it remains elusive how compositional transitions are coordinated during prolonged stress to meet changing functional needs. Here, using time-resolved proteomic profiling of the acute to prolonged heat-shock SG life cycle, we identify dynamic SG proteins, further segregated into early and late proteins. Comparison of different groups of SG proteins suggests that their biochemical properties help coordinate SG compositional and functional transitions. In particular, early proteins, with high phase-separation-propensity, drive the rapid formation of the initial SG platform, while late proteins are subsequently recruited as discrete modules to further functionalize SGs. This model, supported by immunoblotting and immunofluorescence imaging, provides a conceptual framework for the compositional transitions throughout the acute to prolonged SG life cycle. Additionally, an early SG constituent, non-muscle myosin II, is shown to promote SG formation by increasing SG fusion, underscoring the strength of this dataset in revealing the complexity of SG regulation.
Collapse
Affiliation(s)
- Shuyao Hu
- School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China.
| | - Yufeng Zhang
- School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Qianqian Yi
- School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Cuiwei Yang
- School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China
| | - Yanfen Liu
- School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China.
| | - Yun Bai
- School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China.
| |
Collapse
|
27
|
Guan Y, Cheng CH, Bellomo LI, Narain S, Bigornia SJ, Garelnabi MO, Scott T, Ordovás JM, Tucker KL, Bhadelia R, Koo BB. APOE4 allele-specific associations between diet, multimodal biomarkers, and cognition among Puerto Rican adults in Massachusetts. Front Aging Neurosci 2023; 15:1285333. [PMID: 38035273 PMCID: PMC10684694 DOI: 10.3389/fnagi.2023.1285333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 10/25/2023] [Indexed: 12/02/2023] Open
Abstract
Background Apolipoprotein E (APOE) is the strongest genetic risk factor for sporadic Alzheimer's Disease (AD), and the ε4 allele (APOE4) may interact with lifestyle factors that relate to brain structural changes, underlying the increased risk of AD. However, the exact role of APOE4 in mediating interactions between the peripheral circulatory system and the central nervous system, and how it may link to brain and cognitive aging requires further elucidation. In this analysis, we investigated the association between APOE4 carrier status and multimodal biomarkers (diet, blood markers, clinical diagnosis, brain structure, and cognition) in the context of gene-environment interactions. Methods Participants were older adults from a longitudinal observational study, the Boston Puerto Rican Health Study (BPRHS), who self-identified as of Puerto Rican descent. Demographics, APOE genotype, diet, blood, and clinical data were collected at baseline and at approximately 12th year, with the addition of multimodal brain magnetic resonance imaging (MRI) (T1-weighted and diffusion) and cognitive testing acquired at 12-year. Measures were compared between APOE4 carriers and non-carriers, and associations between multimodal variables were examined using correlation and multivariate network analyses within each group. Results A total of 156 BPRHS participants (mean age at imaging = 68 years, 77% female, mean follow-up 12.7 years) with complete multimodal data were included in the current analysis. APOE4 carriers (n = 43) showed reduced medial temporal lobe (MTL) white matter (WM) microstructural integrity and lower mini-mental state examination (MMSE) score than non-carriers (n = 113). This pattern was consistent with an independent sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI) of n = 283 non-Hispanic White adults without dementia (mean age = 75, 40% female). Within BPRHS, carriers showed distinct connectivity patterns between multimodal biomarkers, characterized by stronger direct network connections between baseline diet/blood markers with 12-year blood/clinical measures, and between blood markers (especially lipids and cytokines) and WM. Cardiovascular burden (i.e., hypertension and diabetes status) was associated with WM integrity for both carriers and non-carriers. Conclusion APOE4 carrier status affects interactions between dietary factors, multimodal blood biomarkers, and MTL WM integrity across ~12 years of follow-up, which may reflect increased peripheral-central systems crosstalk following blood-brain barrier breakdown in carriers.
Collapse
Affiliation(s)
- Yi Guan
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Chia Hsin Cheng
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Luis I. Bellomo
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Sriman Narain
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Sherman J. Bigornia
- Department of Agriculture, Nutrition, and Food Systems, College of Life Sciences and Agriculture, University of New Hampshire, Durham, NH, United States
| | - Mahdi O. Garelnabi
- Department of Public Health, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA, United States
| | - Tammy Scott
- School of Medicine, Tufts University, Boston, MA, United States
| | - José M. Ordovás
- Nutrition and Genomics Laboratory, J.M.-US Department of Agriculture Human Nutrition Research Center on Aging at Tufts University, Boston, MA, United States
- IMDEA Alimentacion, Madrid, Spain
- CIBER Fisiopatologia de la Obesidad y la Nutricion (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Katherine L. Tucker
- Department of Biomedical and Nutritional Sciences, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA, United States
- Center for Population Health, University of Massachusetts Lowell, Lowell, MA, United States
| | - Rafeeque Bhadelia
- Neuroradiology Section, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Bang-Bon Koo
- Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| |
Collapse
|
28
|
Li X, Cong J, Liu K, Wang P, Sun M, Wei B. Aberrant intrinsic functional brain topology in methamphetamine-dependent individuals after six-months of abstinence. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:19565-19583. [PMID: 38052615 DOI: 10.3934/mbe.2023867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Our aim was to explore the aberrant intrinsic functional topology in methamphetamine-dependent individuals after six months of abstinence using resting-state functional magnetic imaging (rs-fMRI). Eleven methamphetamines (MA) abstainers who have abstained for six months and eleven healthy controls (HC) were recruited for rs-fMRI examination. The graph theory and functional connectivity (FC) analysis were employed to investigate the aberrant intrinsic functional brain topology between the two groups at multiple levels. Compared with the HC group, the characteristic shortest path length ($ {L}_{p} $) showed a significant decrease at the global level, while the global efficiency ($ {E}_{glob} $) and local efficiency ($ {E}_{loc} $) showed an increase considerably. After FDR correction, we found significant group differences in nodal degree and nodal efficiency at the regional level in the ventral attentional network (VAN), dorsal attentional network (DAN), somatosensory network (SMN), visual network (VN) and default mode network (DMN). In addition, the NBS method presented the aberrations in edge-based FC, including frontoparietal network (FPN), subcortical network (SCN), VAN, DAN, SMN, VN and DMN. Moreover, the FC of large-scale functional brain networks revealed a decrease within the VN and SCN and between the networks. These findings suggest that some functions, e.g., visual processing skills, object recognition and memory, may not fully recover after six months of withdrawal. This leads to the possibility of relapse behavior when confronted with MA-related cues, which may contribute to explaining the relapse mechanism. We also provide an imaging basis for revealing the neural mechanism of MA-dependency after six months of abstinence.
Collapse
Affiliation(s)
- Xiang Li
- First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Jinyu Cong
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Kunmeng Liu
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Pingping Wang
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| | - Min Sun
- Shandong Detoxification Monitoring and Treatment Institute, Zibo 255311, China
| | - Benzheng Wei
- Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
- Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao 266112, China
| |
Collapse
|
29
|
Kundu S. ReDirection: an R-package to compute the probable dissociation constant for every reaction of a user-defined biochemical network. Front Mol Biosci 2023; 10:1206502. [PMID: 37942290 PMCID: PMC10628733 DOI: 10.3389/fmolb.2023.1206502] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 09/14/2023] [Indexed: 11/10/2023] Open
Abstract
Biochemical networks integrate enzyme-mediated substrate conversions with non-enzymatic complex formation and disassembly to accomplish complex biochemical and physiological functions. The choice of parameters and constraints used in most of these studies is numerically motivated and network-specific. Although sound in theory, the outcomes that result depart significantly from the intracellular milieu and are less likely to retain relevance in a clinical setting. There is a need for a computational tool which is biochemically relevant, mathematically rigorous, and unbiased, and can ascribe functionality to and generate potentially testable hypotheses for a user-defined biochemical network. Here, we present "ReDirection," an R-package which computes the probable dissociation constant for every reaction of a biochemical network directly from a null space-generated subspace of the stoichiometry number matrix of the modeled network. "ReDirection" delineates this subspace by excluding all trivial and redundant or duplicate occurrences of non-trivial vectors, combinatorially summing the vectors that remain and verifying that the upper or lower bounds of the sequence of terms formed by each row of this subspace belong to the open real-valued intervals - ∞ , - 1 or 1 , ∞ or whether the number of terms that are differently signed are almost equal. "ReDirection" iterates these steps until these bounds are consistent and unambiguous for all reactions of the modeled biochemical network. Thereafter, "ReDirection" filters the terms from each row of this subspace, bins them to outcome-specific subsets, sums and maps this to an outcome-specific reaction vector, and computes the p1-norm, which is the probable dissociation constant for a reaction. "ReDirection" works on first principles, does not discriminate between enzymatic and non-enzymatic reactions, offers a biochemically relevant and mathematically rigorous environment to explore user-defined biochemical networks under baseline and perturbed conditions, and can be used to address empirically intractable biochemical problems. The utility and relevance of "ReDirection" are highlighted by numerical studies on stoichiometric number models of biochemical networks of galactose metabolism and heme and cholesterol biosynthesis. "ReDirection" is freely available and accessible from the comprehensive R archive network (CRAN) with the URL (https://cran.r-project.org/package=ReDirection).
Collapse
Affiliation(s)
- Siddhartha Kundu
- Department of Biochemistry, All India Institute of Medical Sciences, New Delhi, India
| |
Collapse
|
30
|
Khilwani R, Singh S. Systems Biology and Cytokines Potential Role in Lung Cancer Immunotherapy Targeting Autophagic Axis. Biomedicines 2023; 11:2706. [PMID: 37893079 PMCID: PMC10604646 DOI: 10.3390/biomedicines11102706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/29/2023] Open
Abstract
Lung cancer accounts for the highest number of deaths among men and women worldwide. Although extensive therapies, either alone or in conjunction with some specific drugs, continue to be the principal regimen for evolving lung cancer, significant improvements are still needed to understand the inherent biology behind progressive inflammation and its detection. Unfortunately, despite every advancement in its treatment, lung cancer patients display different growth mechanisms and continue to die at significant rates. Autophagy, which is a physiological defense mechanism, serves to meet the energy demands of nutrient-deprived cancer cells and sustain the tumor cells under stressed conditions. In contrast, autophagy is believed to play a dual role during different stages of tumorigenesis. During early stages, it acts as a tumor suppressor, degrading oncogenic proteins; however, during later stages, autophagy supports tumor cell survival by minimizing stress in the tumor microenvironment. The pivotal role of the IL6-IL17-IL23 signaling axis has been observed to trigger autophagic events in lung cancer patients. Since the obvious roles of autophagy are a result of different immune signaling cascades, systems biology can be an effective tool to understand these interconnections and enhance cancer treatment and immunotherapy. In this review, we focus on how systems biology can be exploited to target autophagic processes that resolve inflammatory responses and contribute to better treatment in carcinogenesis.
Collapse
Affiliation(s)
| | - Shailza Singh
- Systems Medicine Laboratory, National Centre for Cell Science, SPPU Campus, Ganeshkhind Road, Pune 411007, India;
| |
Collapse
|
31
|
Vicencio E, Nuñez-Belmar J, Cardenas JP, Cortés BI, Martin AJM, Maracaja-Coutinho V, Rojas A, Cafferata EA, González-Osuna L, Vernal R, Cortez C. Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis. Int J Mol Sci 2023; 24:14835. [PMID: 37834287 PMCID: PMC10573220 DOI: 10.3390/ijms241914835] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
Periodontitis is a chronic inflammatory disease characterized by the progressive and irreversible destruction of the periodontium. Its aetiopathogenesis lies in the constant challenge of the dysbiotic biofilm, which triggers a deregulated immune response responsible for the disease phenotype. Although the molecular mechanisms underlying periodontitis have been extensively studied, the regulatory mechanisms at the transcriptional level remain unclear. To generate transcriptomic data, we performed RNA shotgun sequencing of the oral mucosa of periodontitis-affected mice. Since genes are not expressed in isolation during pathological processes, we disclose here the complete repertoire of differentially expressed genes (DEG) and co-expressed modules to build Gene Regulatory Networks (GRNs) and identify the Master Transcriptional Regulators of periodontitis. The transcriptional changes revealed 366 protein-coding genes and 42 non-coding genes differentially expressed and enriched in the immune response. Furthermore, we found 13 co-expression modules with different representation degrees and gene expression levels. Our GRN comprises genes from 12 gene clusters, 166 nodes, of which 33 encode Transcription Factors, and 201 connections. Finally, using these strategies, 26 master regulators of periodontitis were identified. In conclusion, combining the transcriptomic analyses with the regulatory network construction represents a powerful and efficient strategy for identifying potential periodontitis-therapeutic targets.
Collapse
Affiliation(s)
- Emiliano Vicencio
- Escuela de Tecnología Médica, Facultad de Ciencias, Pontificia Universidad Católica de Valparaíso, Valparaíso 2373223, Chile;
| | - Josefa Nuñez-Belmar
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago 8580745, Chile; (J.N.-B.); (J.P.C.)
| | - Juan P. Cardenas
- Centro de Genómica y Bioinformática, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago 8580745, Chile; (J.N.-B.); (J.P.C.)
- Escuela de Biotecnología, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago 8580745, Chile
| | - Bastian I. Cortés
- Departamento de Biología Celular y Molecular, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | - Alberto J. M. Martin
- Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Santiago 7780272, Chile;
- Escuela de Ingeniería, Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago 8420524, Chile
| | - Vinicius Maracaja-Coutinho
- Centro de Modelamiento Molecular, Biofísica y Bioinformática, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago 8380492, Chile; (V.M.-C.); (A.R.)
- Advanced Center for Chronic Diseases—ACCDiS, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago 8380492, Chile
| | - Adolfo Rojas
- Centro de Modelamiento Molecular, Biofísica y Bioinformática, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago 8380492, Chile; (V.M.-C.); (A.R.)
| | - Emilio A. Cafferata
- Laboratorio de Biología Periodontal, Facultad de Odontología, Universidad de Chile, Santiago 8380492, Chile; (E.A.C.); (L.G.-O.); (R.V.)
| | - Luis González-Osuna
- Laboratorio de Biología Periodontal, Facultad de Odontología, Universidad de Chile, Santiago 8380492, Chile; (E.A.C.); (L.G.-O.); (R.V.)
| | - Rolando Vernal
- Laboratorio de Biología Periodontal, Facultad de Odontología, Universidad de Chile, Santiago 8380492, Chile; (E.A.C.); (L.G.-O.); (R.V.)
| | - Cristian Cortez
- Escuela de Tecnología Médica, Facultad de Ciencias, Pontificia Universidad Católica de Valparaíso, Valparaíso 2373223, Chile;
| |
Collapse
|
32
|
Hakobyan S, Stepanyan A, Nersisyan L, Binder H, Arakelyan A. PSF toolkit: an R package for pathway curation and topology-aware analysis. Front Genet 2023; 14:1264656. [PMID: 37680201 PMCID: PMC10482229 DOI: 10.3389/fgene.2023.1264656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 08/09/2023] [Indexed: 09/09/2023] Open
Abstract
Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.
Collapse
Affiliation(s)
- Siras Hakobyan
- Bioinformatics Group, Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia
- Armenian Bioinformatics Institute (ABI), Yerevan, Armenia
| | | | | | - Hans Binder
- Armenian Bioinformatics Institute, Yerevan, Armenia
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany
| | - Arsen Arakelyan
- Bioinformatics Group, Institute of Molecular Biology, Armenian National Academy of Sciences, Yerevan, Armenia
- Russian-Armenian University, Yerevan, Armenia
| |
Collapse
|
33
|
Karatzas E, Baltoumas FA, Aplakidou E, Kontou PI, Stathopoulos P, Stefanis L, Bagos PG, Pavlopoulos GA. Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources. Bioinformatics 2023; 39:btad490. [PMID: 37540207 PMCID: PMC10423032 DOI: 10.1093/bioinformatics/btad490] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/31/2023] [Accepted: 08/03/2023] [Indexed: 08/05/2023] Open
Abstract
Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms. AVAILABILITY AND IMPLEMENTATION Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame.
Collapse
Affiliation(s)
- Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Fotis A Baltoumas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
| | - Panagiota I Kontou
- Department of Mathematics, University of Thessaly, Lamia, 35100, Greece
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, 35131, Greece
| | - Panos Stathopoulos
- 1st Department of Neurology, Eginition Hospital, Athens, 11528, Greece
- School of Medicine, National and Kapodistrian University of Athens, Athens, 11527, Greece
| | - Leonidas Stefanis
- 1st Department of Neurology, Eginition Hospital, Athens, 11528, Greece
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, 35131, Greece
| | - Georgios A Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari (Athens), 16672, Greece
- Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, 11527, Greece
- Hellenic Army Academy, Vari, 16673, Greece
| |
Collapse
|
34
|
Caetano-Anollés G. Agency in evolution of biomolecular communication. Ann N Y Acad Sci 2023; 1525:88-103. [PMID: 37219369 DOI: 10.1111/nyas.15005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Biomolecular communication demands that interactions between parts of a molecular system act as scaffolds for message transmission. It also requires an organized system of signs-a communicative agency-for creating and transmitting meaning. The emergence of agency, the capacity to act in a given context and generate end-directed behaviors, has baffled evolutionary biologists for centuries. Here, I explore its emergence with knowledge grounded in over two decades of evolutionary genomic and bioinformatic exploration. Biphasic processes of growth and diversification exist that generate hierarchy and modularity in biological systems at widely ranging time scales. Similarly, a biphasic process exists in communication that constructs a message before it can be transmitted for interpretation. Transmission dissipates matter-energy and information and involves computation. Agency emerges when molecular machinery generates hierarchical layers of vocabularies in an entangled communication network clustered around the universal Turing machine of the ribosome. Computations canalize biological systems to perform biological functions in a dissipative quest to structure long-lived occurrents. This occurs within the confines of a "triangle of persistence" that maximizes invariance with trade-offs between economy, flexibility, and robustness. Thus, learning from previous historical and circumstantial experiences unifies modules in a hierarchy that expands the agency of systems.
Collapse
Affiliation(s)
- Gustavo Caetano-Anollés
- Evolutionary Bioinformatics Laboratory, Department of Crop Sciences and C. R. Woese Institute for Genomic Biology, University of Illinois, Urbana, Illinois, USA
| |
Collapse
|
35
|
Ferreira-Martins AJ, Castaldoni R, Alencar BM, Ferreira MV, Nogueira T, Rios RA, Lopes TJS. Full-scale network analysis reveals properties of the FV protein structure organization. Sci Rep 2023; 13:9546. [PMID: 37308572 DOI: 10.1038/s41598-023-36528-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/05/2023] [Indexed: 06/14/2023] Open
Abstract
Blood coagulation is a vital process for humans and other species. Following an injury to a blood vessel, a cascade of molecular signals is transmitted, inhibiting and activating more than a dozen coagulation factors and resulting in the formation of a fibrin clot that ceases the bleeding. In this process, the Coagulation factor V (FV) is a master regulator, coordinating critical steps of this process. Mutations to this factor result in spontaneous bleeding episodes and prolonged hemorrhage after trauma or surgery. Although the role of FV is well characterized, it is unclear how single-point mutations affect its structure. In this study, to understand the effect of mutations, we created a detailed network map of this protein, where each node is a residue, and two residues are connected if they are in close proximity in the three-dimensional structure. Overall, we analyzed 63 point-mutations from patients and identified common patterns underlying FV deficient phenotypes. We used structural and evolutionary patterns as input to machine learning algorithms to anticipate the effects of mutations and anticipated FV-deficiency with fair accuracy. Together, our results demonstrate how clinical features, genetic data and in silico analysis are converging to enhance treatment and diagnosis of coagulation disorders.
Collapse
Affiliation(s)
| | | | - Brenno M Alencar
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Marcos V Ferreira
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Tatiane Nogueira
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Ricardo A Rios
- Institute of Computing, Federal University of Bahia, Salvador, Brazil
| | - Tiago J S Lopes
- Center for Regenerative Medicine, National Centre for Child Health and Development Research Institute, 2-10-1 Okura, Setagaya, Tokyo, 157-8535, Japan.
| |
Collapse
|
36
|
Kokoli M, Karatzas E, Baltoumas FA, Schneider R, Pafilis E, Paragkamian S, Doncheva NT, Jensen L, Pavlopoulos G. Arena3D web: interactive 3D visualization of multilayered networks supporting multiple directional information channels, clustering analysis and application integration. NAR Genom Bioinform 2023; 5:lqad053. [PMID: 37260509 PMCID: PMC10227371 DOI: 10.1093/nargab/lqad053] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/25/2023] [Accepted: 05/18/2023] [Indexed: 06/02/2023] Open
Abstract
Arena3Dweb is an interactive web tool that visualizes multi-layered networks in 3D space. In this update, Arena3Dweb supports directed networks as well as up to nine different types of connections between pairs of nodes with the use of Bézier curves. It comes with different color schemes (light/gray/dark mode), custom channel coloring, four node clustering algorithms which one can run on-the-fly, visualization in VR mode and predefined layer layouts (zig-zag, star and cube). This update also includes enhanced navigation controls (mouse orbit controls, layer dragging and layer/node selection), while its newly developed API allows integration with external applications as well as saving and loading of sessions in JSON format. Finally, a dedicated Cytoscape app has been developed, through which users can automatically send their 2D networks from Cytoscape to Arena3Dweb for 3D multi-layer visualization. Arena3Dweb is accessible at http://arena3d.pavlopouloslab.info or http://arena3d.org.
Collapse
Affiliation(s)
| | | | - Fotis A Baltoumas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari16672, Greece
| | - Reinhard Schneider
- University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Bioinformatics Core, Esch-sur-Alzette, Luxembourg
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes, Heraklion 71003, Greece
| | - Savvas Paragkamian
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Former U.S. Base of Gournes, Heraklion 71003, Greece
- Department of Biology, University of Crete, Voutes University Campus, P.O. Box 2208, 70013 Heraklion, Crete, Greece
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen N DK-2200, Denmark
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen N DK-2200, Denmark
| | | |
Collapse
|
37
|
Muniyappan S, Rayan AXA, Varrieth GT. DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:9530-9571. [PMID: 37161255 DOI: 10.3934/mbe.2023419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
MOTIVATION In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the drug for a particular disease by identifying how the drug binds to the target for treating that disease. Hence, DTI plays a major role in drug discovery. Many computational methods have been developed for DTI prediction. However, the existing methods have limitations in terms of capturing the interactions via multiple semantics between drug and target nodes in a heterogeneous biological network (HBN). METHODS In this paper, we propose a DTiGNN framework for identifying unknown drug-target pairs. The DTiGNN first calculates the similarity between the drug and target from multiple perspectives. Then, the features of drugs and targets from each perspective are learned separately by using a novel method termed an information entropy-based random walk. Next, all of the learned features from different perspectives are integrated into a single drug and target similarity network by using a multi-view convolutional neural network. Using the integrated similarity networks, drug interactions, drug-disease associations, protein interactions and protein-disease association, the HBN is constructed. Next, a novel embedding algorithm called a meta-graph guided graph neural network is used to learn the embedding of drugs and targets. Then, a convolutional neural network is employed to infer new DTIs after balancing the sample using oversampling techniques. RESULTS The DTiGNN is applied to various datasets, and the result shows better performance in terms of the area under receiver operating characteristic curve (AUC) and area under precision-recall curve (AUPR), with scores of 0.98 and 0.99, respectively. There are 23,739 newly predicted DTI pairs in total.
Collapse
Affiliation(s)
- Saranya Muniyappan
- Computer Science and Engineering, CEG Campus, Anna University, Tamil Nadu, India
| | | | | |
Collapse
|
38
|
Weaver DT, Scott JG. Crosstalkr: An open-source R package to facilitate drug target identification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.531526. [PMID: 36945602 PMCID: PMC10028947 DOI: 10.1101/2023.03.07.531526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
In the last few decades, interest in graph-based analysis of biological networks has grown substantially. Protein-protein interaction networks are one of the most common biological networks, and represent the molecular relationships between every known protein and every other known protein. Integration of these interactomic data into bioinformatic pipelines may increase the translational potential of discoveries made through analysis of multi-omic datasets. Crosstalkr provides a unified toolkit for drug target and disease subnetwork identification, two of the most common uses of protein protein interaction networks. First, crosstalkr enables users to download and leverage high-quality protein-protein interaction networks from online repositories. Users can then filter these large networks into manageable subnetworks using a variety of methods. For example, network filtration can be done using random walks with restarts, starting at the user-provided seed proteins. Affinity scores from a given random walk with restarts are compared to a bootstrapped null distribution to assess statistical significance. Random walks are implemented using sparse matrix multiplication to facilitate fast execution. Next, users can perform in-silico repression experiments to assess the relative importance of nodes in their network. At this step, users can supply protein or gene expression data to make node rankings more meaningful. The default behavior evaluates the human interactome. However, users can evaluate more than 1000 non-human protein-protein interaction networks as a result of integration with StringDB. It is a free, open-source R package designed to allow users to integrate functional analysis using the protein-protein interaction network into existing bioinformatic pipelines. A beta version of crosstalkr available on CRAN (https://cran.rstudio.com/web/packages/crosstalkr/index.html).
Collapse
Affiliation(s)
- Davis T. Weaver
- Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Translational Hematology Oncology Research, Cleveland Clinic, Cleveland OH, 44106, USA
| | - Jacob G. Scott
- Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
- Translational Hematology Oncology Research, Cleveland Clinic, Cleveland OH, 44106, USA
- Department of Physics, Case Western Reserve University, Cleveland, OH, 44106, USA
| |
Collapse
|
39
|
Baltoumas FA, Karatzas E, Paez-Espino D, Venetsianou NK, Aplakidou E, Oulas A, Finn RD, Ovchinnikov S, Pafilis E, Kyrpides NC, Pavlopoulos GA. Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters. FRONTIERS IN BIOINFORMATICS 2023; 3:1157956. [PMID: 36959975 PMCID: PMC10029925 DOI: 10.3389/fbinf.2023.1157956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023] Open
Abstract
Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.
Collapse
Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - David Paez-Espino
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
| | - Nefeli K. Venetsianou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Anastasis Oulas
- The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Robert D. Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, United States
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece
| | - Nikos C. Kyrpides
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
- Center of New Biotechnologies and Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Athens, Greece
- Hellenic Army Academy, Vari, Greece
| |
Collapse
|
40
|
Wang J, Xue Y, He Y, Quan H, Zhang J, Gao YQ. Characterization of network hierarchy reflects cell state specificity in genome organization. Genome Res 2023; 33:247-260. [PMID: 36828586 PMCID: PMC10069467 DOI: 10.1101/gr.277206.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/31/2023] [Indexed: 02/26/2023]
Abstract
Dynamic chromatin structure acts as the regulator of transcription program in crucial processes including cancer and cell development, but a unified framework for characterizing chromatin structural evolution remains to be established. Here, we performed graph inferences on Hi-C data sets and derived the chromatin contact networks. We discovered significant decreases in information transmission efficiencies in chromatin of colorectal cancer (CRC) and T-cell acute lymphoblastic leukemia (T-ALL) compared to corresponding normal controls through graph statistics. Using network embedding in the Poincaré disk, the hierarchy depths of chromatin from CRC and T-ALL patients were found to be significantly shallower compared to their normal controls. A reverse trend of change in chromatin structure was observed during early embryo development. We found tissue-specific conservation of hierarchy order in chromatin contact networks. Our findings reveal the top-down hierarchy of chromatin organization, which is significantly attenuated in cancer.
Collapse
Affiliation(s)
- Jingyao Wang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Yue Xue
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Yueying He
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Hui Quan
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Jun Zhang
- Changping Laboratory, Beijing, 102206, China
| | - Yi Qin Gao
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China; .,Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, 100871, China.,Changping Laboratory, Beijing, 102206, China
| |
Collapse
|
41
|
Tanglay O, Dadario NB, Chong EHN, Tang SJ, Young IM, Sughrue ME. Graph Theory Measures and Their Application to Neurosurgical Eloquence. Cancers (Basel) 2023; 15:556. [PMID: 36672504 PMCID: PMC9857081 DOI: 10.3390/cancers15020556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/04/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Improving patient safety and preserving eloquent brain are crucial in neurosurgery. Since there is significant clinical variability in post-operative lesions suffered by patients who undergo surgery in the same areas deemed compensable, there is an unknown degree of inter-individual variability in brain 'eloquence'. Advances in connectomic mapping efforts through diffusion tractography allow for utilization of non-invasive imaging and statistical modeling to graphically represent the brain. Extending the definition of brain eloquence to graph theory measures of hubness and centrality may help to improve our understanding of individual variability in brain eloquence and lesion responses. While functional deficits cannot be immediately determined intra-operatively, there has been potential shown by emerging technologies in mapping of hub nodes as an add-on to existing surgical navigation modalities to improve individual surgical outcomes. This review aims to outline and review current research surrounding novel graph theoretical concepts of hubness, centrality, and eloquence and specifically its relevance to brain mapping for pre-operative planning and intra-operative navigation in neurosurgery.
Collapse
Affiliation(s)
- Onur Tanglay
- UNSW School of Clinical Medicine, Faulty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
| | - Nicholas B. Dadario
- Robert Wood Johnson Medical School, Rutgers University, 125 Paterson St, New Brunswick, NJ 08901, USA
| | - Elizabeth H. N. Chong
- Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore 117597, Singapore
| | - Si Jie Tang
- School of Medicine, University of California Davis, Sacramento, CA 95817, USA
| | - Isabella M. Young
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
| | - Michael E. Sughrue
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW 2000, Australia
| |
Collapse
|
42
|
Costa MDOCE, do Nascimento APB, Martins YC, dos Santos MT, Figueiredo AMDS, Perez-Rueda E, Nicolás MF. The gene regulatory network of Staphylococcus aureus ST239-SCC mecIII strain Bmb9393 and assessment of genes associated with the biofilm in diverse backgrounds. Front Microbiol 2023; 13:1049819. [PMID: 36704545 PMCID: PMC9871828 DOI: 10.3389/fmicb.2022.1049819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 12/19/2022] [Indexed: 01/12/2023] Open
Abstract
Introduction Staphylococcus aureus is one of the most prevalent and relevant pathogens responsible for a wide spectrum of hospital-associated or community-acquired infections. In addition, methicillin-resistant Staphylococcus aureus may display multidrug resistance profiles that complicate treatment and increase the mortality rate. The ability to produce biofilm, particularly in device-associated infections, promotes chronic and potentially more severe infections originating from the primary site. Understanding the complex mechanisms involved in planktonic and biofilm growth is critical to identifying regulatory connections and ways to overcome the global health problem of multidrug-resistant bacteria. Methods In this work, we apply literature-based and comparative genomics approaches to reconstruct the gene regulatory network of the high biofilm-producing strain Bmb9393, belonging to one of the highly disseminating successful clones, the Brazilian epidemic clone. To the best of our knowledge, we describe for the first time the topological properties and network motifs for the Staphylococcus aureus pathogen. We performed this analysis using the ST239-SCCmecIII Bmb9393 strain. In addition, we analyzed transcriptomes available in the literature to construct a set of genes differentially expressed in the biofilm, covering different stages of the biofilms and genetic backgrounds of the strains. Results and discussion The Bmb9393 gene regulatory network comprises 1,803 regulatory interactions between 64 transcription factors and the non-redundant set of 1,151 target genes with the inclusion of 19 new regulons compared to the N315 transcriptional regulatory network published in 2011. In the Bmb9393 network, we found 54 feed-forward loop motifs, where the most prevalent were coherent type 2 and incoherent type 2. The non-redundant set of differentially expressed genes in the biofilm consisted of 1,794 genes with functional categories relevant for adaptation to the variable microenvironments established throughout the biofilm formation process. Finally, we mapped the set of genes with altered expression in the biofilm in the Bmb9393 gene regulatory network to depict how different growth modes can alter the regulatory systems. The data revealed 45 transcription factors and 876 shared target genes. Thus, the gene regulatory network model provided represents the most up-to-date model for Staphylococcus aureus, and the set of genes altered in the biofilm provides a global view of their influence on biofilm formation from distinct experimental perspectives and different strain backgrounds.
Collapse
Affiliation(s)
| | - Ana Paula Barbosa do Nascimento
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, Brazil
| | | | | | - Agnes Marie de Sá Figueiredo
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Unidad Académica Yucatán, Merida, Mexico
| | - Ernesto Perez-Rueda
- Laboratório de Biologia Molecular de Bactérias, Instituto de Microbiologia Paulo de Goés, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil,*Correspondence: Ernesto Perez-Rueda ✉
| | - Marisa Fabiana Nicolás
- Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil,Marisa Fabiana Nicolás ✉
| |
Collapse
|
43
|
Li C, Dou P, Wang T, Lu X, Xu G, Lin X. Defining disease-related modules based on weighted miRNA synergistic network. Comput Biol Med 2023; 152:106382. [PMID: 36493730 DOI: 10.1016/j.compbiomed.2022.106382] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/04/2022] [Accepted: 11/28/2022] [Indexed: 11/30/2022]
Abstract
MicroRNAs (miRNAs) play an important role in the biological process. Their expression and functional changes have been observed in most cancers. Meanwhile, there exists cooperative regulation among miRNAs which is important for studying the mechanisms of complex post-transcriptional regulations. Hence, studying miRNA synergy and identifying miRNA synergistic modules can help understand the development and progression of complex diseases, such as cancers. This work studies miRNA synergy and proposes a new method for defining disease-related modules (DDRM) by combining the knowledge databases and miRNA data. DDRM measures the miRNA synergy not only by the co-regulating target subset but also by the non-common target set to construct the weighted miRNA synergistic network (WMSN). The experiments on twelve the cancer genome atlas (TCGA) datasets showed that the important modules identified by DDRM can well distinguish the cancer samples from the normal samples, and DDRM performed better than the previous method in most cases. An external dataset of prostate cancer was applied to validate the module biomarkers determined by DDRM on the prostate cancer data of TCGA. The area under the receiver operating characteristic curve (AUC) value is 0.92 and the performance is superior. Hence, combining the miRNA synergy networks from the knowledge databases and the miRNA data can determine the important functional modules related to diseases, which is of great significance to the study of disease mechanism.
Collapse
Affiliation(s)
- Chao Li
- School of Computer Science & Technology, Dalian University of Technology, 116024, Dalian, China; CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Peng Dou
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Tianxiang Wang
- School of Computer Science & Technology, Dalian University of Technology, 116024, Dalian, China
| | - Xin Lu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; Liaoning Province Key Laboratory of Metabolomics, Dalian, China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China; Liaoning Province Key Laboratory of Metabolomics, Dalian, China
| | - Xiaohui Lin
- School of Computer Science & Technology, Dalian University of Technology, 116024, Dalian, China.
| |
Collapse
|
44
|
Bard JB. Modelling speciation: Problems and implications. In Silico Biol 2023; 15:23-42. [PMID: 36502315 PMCID: PMC10741375 DOI: 10.3233/isb-220253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Darwin's and Wallace's 1859 explanation that novel speciation resulted from natural variants that had been subjected to selection was refined over the next 150 years as genetic inheritance and the importance of mutation-induced change were discovered, the quantitative theory of evolutionary population genetics was produced, the speed of genetic change in small populations became apparent and the ramifications of the DNA revolution became clear. This paper first discusses the modern view of speciation in its historical context. It then uses systems-biology approaches to consider the many complex processes that underpin the production of a new species; these extend in scale from genes to populations with the processes of variation, selection and speciation being affected by factors that range from mutation to climate change. Here, events at a particular scale level (e.g. protein network activity) are activated by the output of the level immediately below (i.e. gene expression) and generate a new output that activates the layer above (e.g. embryological development), with this change often being modulated by feedback from higher and lower levels. The analysis shows that activity at each level in the evolution of a new species is marked by stochastic activity, with mutation of course being the key step for variation. The paper examines events at each of these scale levels and particularly considers how the pathway by which mutation leads to phenotypic variants and the wide range of factors that drive selection can be investigated computationally. It concludes that, such is the complexity of speciation, most steps in the process are currently difficult to model and that predictions about future speciation will, apart from a few special cases, be hard to make. The corollary is that opportunities for novel variants to form are maximised.
Collapse
|
45
|
Ginsberg SD, Sharma S, Norton L, Chiosis G. Targeting stressor-induced dysfunctions in protein-protein interaction networks via epichaperomes. Trends Pharmacol Sci 2023; 44:20-33. [PMID: 36414432 PMCID: PMC9789192 DOI: 10.1016/j.tips.2022.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/31/2022] [Accepted: 10/31/2022] [Indexed: 11/21/2022]
Abstract
Diseases are manifestations of complex changes in protein-protein interaction (PPI) networks whereby stressors, genetic, environmental, and combinations thereof, alter molecular interactions and perturb the individual from the level of cells and tissues to the entire organism. Targeting stressor-induced dysfunctions in PPI networks has therefore become a promising but technically challenging frontier in therapeutics discovery. This opinion provides a new framework based upon disrupting epichaperomes - pathological entities that enable dysfunctional rewiring of PPI networks - as a mechanism to revert context-specific PPI network dysfunction to a normative state. We speculate on the implications of recent research in this area for a precision medicine approach to detecting and treating complex diseases, including cancer and neurodegenerative disorders.
Collapse
Affiliation(s)
- Stephen D Ginsberg
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY 10962, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA; NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Sahil Sharma
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY 10065, USA
| | - Larry Norton
- Breast Cancer Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Gabriela Chiosis
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY 10065, USA; Breast Cancer Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
| |
Collapse
|
46
|
Lanciano T, Savino A, Porcu F, Cittaro D, Bonchi F, Provero P. Contrast subgraphs allow comparing homogeneous and heterogeneous networks derived from omics data. Gigascience 2022; 12:giad010. [PMID: 36852877 PMCID: PMC9972522 DOI: 10.1093/gigascience/giad010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/30/2022] [Accepted: 02/08/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Biological networks are often used to describe the relationships between relevant entities, particularly genes and proteins, and are a powerful tool for functional genomics. Many important biological problems can be investigated by comparing biological networks between different conditions or networks obtained with different techniques. FINDINGS We show that contrast subgraphs, a recently introduced technique to identify the most important structural differences between 2 networks, provide a versatile tool for comparing gene and protein networks of diverse origin. We demonstrate the use of contrast subgraphs in the comparison of coexpression networks derived from different subtypes of breast cancer, coexpression networks derived from transcriptomic and proteomic data, and protein-protein interaction networks assayed in different cell lines. CONCLUSIONS These examples demonstrate how contrast subgraphs can provide new insight in functional genomics by extracting the gene/protein modules whose connectivity is most altered between 2 conditions or experimental techniques.
Collapse
Affiliation(s)
| | - Aurora Savino
- Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Turin, Turin 10126, Italy
| | | | - Davide Cittaro
- Center for Omics Sciences, San Raffaele Scientific Institute IRCSS, Milan 20132, Italy
| | | | - Paolo Provero
- Center for Omics Sciences, San Raffaele Scientific Institute IRCSS, Milan 20132, Italy
- Department of Neurosciences “Rita Levi Montalcini,” University of Turin, Turin 10126, Italy
| |
Collapse
|
47
|
Li MM, Huang K, Zitnik M. Graph representation learning in biomedicine and healthcare. Nat Biomed Eng 2022; 6:1353-1369. [PMID: 36316368 PMCID: PMC10699434 DOI: 10.1038/s41551-022-00942-x] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 08/09/2022] [Indexed: 11/11/2022]
Abstract
Networks-or graphs-are universal descriptors of systems of interacting elements. In biomedicine and healthcare, they can represent, for example, molecular interactions, signalling pathways, disease co-morbidities or healthcare systems. In this Perspective, we posit that representation learning can realize principles of network medicine, discuss successes and current limitations of the use of representation learning on graphs in biomedicine and healthcare, and outline algorithmic strategies that leverage the topology of graphs to embed them into compact vectorial spaces. We argue that graph representation learning will keep pushing forward machine learning for biomedicine and healthcare applications, including the identification of genetic variants underlying complex traits, the disentanglement of single-cell behaviours and their effects on health, the assistance of patients in diagnosis and treatment, and the development of safe and effective medicines.
Collapse
Affiliation(s)
- Michelle M Li
- Bioinformatics and Integrative Genomics Program, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kexin Huang
- Health Data Science Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Data Science Initiative, Cambridge, MA, USA.
| |
Collapse
|
48
|
Wysmolek PM, Kiessler FD, Salbaum KA, Shelton ER, Sonntag SM, Serwane F. A minimal-complexity light-sheet microscope maps network activity in 3D neuronal systems. Sci Rep 2022; 12:20420. [PMID: 36443413 PMCID: PMC9705530 DOI: 10.1038/s41598-022-24350-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/14/2022] [Indexed: 11/29/2022] Open
Abstract
In vitro systems mimicking brain regions, brain organoids, are revolutionizing the neuroscience field. However, characterization of their electrical activity has remained a challenge as it requires readout at millisecond timescale in 3D at single-neuron resolution. While custom-built microscopes used with genetically encoded sensors are now opening this door, a full 3D characterization of organoid neural activity has not been performed yet, limited by the combined complexity of the optical and the biological system. Here, we introduce an accessible minimalistic light-sheet microscope to the neuroscience community. Designed as an add-on to a standard inverted microscope it can be assembled within one day. In contrast to existing simplistic setups, our platform is suited to record volumetric calcium traces. We successfully extracted 4D calcium traces at high temporal resolution by using a lightweight piezo stage to allow for 5 Hz volumetric scanning combined with a processing pipeline for true 3D neuronal trace segmentation. As a proof of principle, we created a 3D connectivity map of a stem cell derived neuron spheroid by imaging its activity. Our fast, low complexity setup empowers researchers to study the formation of neuronal networks in vitro for fundamental and neurodegeneration research.
Collapse
Affiliation(s)
- Paulina M. Wysmolek
- grid.414703.50000 0001 2202 0959Max Planck Institute for Medical Research, Heidelberg, Germany
| | - Filippo D. Kiessler
- grid.5252.00000 0004 1936 973XFaculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Katja A. Salbaum
- grid.5252.00000 0004 1936 973XFaculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Munich, Germany ,Graduate School of Systemic Neuroscience (GSN), Munich, Germany
| | - Elijah R. Shelton
- grid.5252.00000 0004 1936 973XFaculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Selina M. Sonntag
- grid.5252.00000 0004 1936 973XFaculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Friedhelm Serwane
- grid.5252.00000 0004 1936 973XFaculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Munich, Germany ,Graduate School of Systemic Neuroscience (GSN), Munich, Germany ,grid.452617.3Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| |
Collapse
|
49
|
Kober KM, Harris C, Conley YP, Dhruva A, Dokiparthi V, Hammer MJ, Levine JD, Oppegaard K, Paul S, Shin J, Sucher A, Wright F, Yuen B, Olshen AB, Miaskowski C. Perturbations in common and distinct inflammatory pathways associated with morning and evening fatigue in outpatients receiving chemotherapy. Cancer Med 2022; 12:7369-7380. [PMID: 36373573 PMCID: PMC10067125 DOI: 10.1002/cam4.5435] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 10/24/2022] [Accepted: 10/29/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Moderate to severe fatigue occurs in up to 94% of patients with cancer. Recent evidence suggests that morning and evening fatigue are distinct dimensions of physical fatigue. The purposes of this study were to evaluate the transcriptome for common and distinct perturbed inflammatory pathways in patients receiving chemotherapy who reported low versus high levels of morning or low versus high levels of evening cancer-related fatigue. METHODS Patients completed questionnaires during the week prior to their chemotherapy treatment. Severity of morning and evening fatigue was evaluated using the Lee Fatigue Scale. Gene expression and pathway impact analyses (PIA) were performed in two independent samples using RNA-sequencing (n = 357) and microarray (n = 360). Patterns of interactions between and among these perturbed pathways were evaluated using a knowledge network (KN). RESULTS Across the PIA, nine perturbed pathways (FDR < 0.025) were common to both morning and evening fatigue, six were distinct for morning fatigue, and four were distinct for evening fatigue. KN (19 nodes, 39 edges) identified the phosphatidylinositol 3-kinase (PI3K)-Akt pathway node (perturbed in evening fatigue) with the highest betweenness (0.255) and closeness (0.255) centrality indices. The next highest betweenness centrality indices were seen in pathways perturbed in evening fatigue (i.e., nuclear factor kappa B: 0.200, natural killer cell-mediated cytotoxicity: 0.178, mitogen-activated protein kinase: 0.175). CONCLUSIONS This study describes perturbations in common and distinct inflammatory pathways associated with morning and/or evening fatigue. PI3K-Akt was identified as a bottleneck pathway. The analysis identified potential targets for therapeutic interventions for this common and devastating clinical problem.
Collapse
Affiliation(s)
- Kord M. Kober
- School of Nursing University of California San Francisco California USA
- Helen Diller Family Comprehensive Cancer Center University of California San Francisco California USA
| | - Carolyn Harris
- School of Nursing University of California San Francisco California USA
| | - Yvette P. Conley
- School of Nursing University of Pittsburg Pittsburg Pennsylvania USA
| | - Anand Dhruva
- School of Medicine University of California San Francisco California USA
| | - Vasuda Dokiparthi
- School of Nursing University of California San Francisco California USA
| | | | - Jon D. Levine
- School of Medicine University of California San Francisco California USA
| | - Kate Oppegaard
- School of Nursing University of California San Francisco California USA
| | - Steven Paul
- School of Nursing University of California San Francisco California USA
| | - Joosun Shin
- School of Nursing University of California San Francisco California USA
| | - Anatol Sucher
- School of Nursing University of California San Francisco California USA
| | - Fay Wright
- Rory Meyers College of Nursing, New York University New York New York USA
| | - Brian Yuen
- School of Nursing University of California San Francisco California USA
| | - Adam B. Olshen
- Helen Diller Family Comprehensive Cancer Center University of California San Francisco California USA
- School of Nursing University of Pittsburg Pittsburg Pennsylvania USA
| | - Christine Miaskowski
- School of Nursing University of California San Francisco California USA
- Helen Diller Family Comprehensive Cancer Center University of California San Francisco California USA
- School of Medicine University of California San Francisco California USA
| |
Collapse
|
50
|
Nath SS. Thermodynamic Consistency and Kinetic Mechanism of a Complex Reaction Pathway by Analysis of Its Constituent Cyclic Networks. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c02906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Sarang S. Nath
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800Lyngby, Denmark
| |
Collapse
|