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Kassis G, Palshikar MG, Hilchey SP, Zand MS, Thakar J. Discrete-state models identify pathway specific B cell states across diseases and infections at single-cell resolution. J Theor Biol 2024; 583:111769. [PMID: 38423206 PMCID: PMC11046450 DOI: 10.1016/j.jtbi.2024.111769] [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: 05/28/2023] [Revised: 02/10/2024] [Accepted: 02/17/2024] [Indexed: 03/02/2024]
Abstract
Oxygen (O2) regulated pathways modulate B cell activation, migration and proliferation during infection, vaccination, and other diseases. Modeling these pathways in health and disease is critical to understand B cell states and ways to mediate them. To characterize B cells by their activation of O2 regulated pathways we develop pathway specific discrete state models using previously published single-cell RNA-sequencing (scRNA-seq) datasets from isolated B cells. Specifically, Single Cell Boolean Omics Network Invariant-Time Analysis (scBONITA) was used to infer logic gates for known pathway topologies. The simplest inferred set of logic gates that maximized the number of "OR" interactions between genes was used to simulate B cell networks involved in oxygen sensing until they reached steady network states (attractors). By focusing on the attractors that best represented sequenced cells, we identified genes critical in determining pathway specific cellular states that corresponded to diseased and healthy B cell phenotypes. Specifically, we investigate the transendothelial migration, regulation of actin cytoskeleton, HIF1A, and Citrate Cycle pathways. Our analysis revealed attractors that resembled the state of B cell exhaustion in HIV+ patients as well as attractors that promoted anerobic metabolism, angiogenesis, and tumorigenesis in breast cancer patients, which were eliminated after neoadjuvant chemotherapy (NACT). Finally, we investigated the attractors to which the Azimuth-annotated B cells mapped and found that attractors resembling B cells from HIV+ patients encompassed a significantly larger number of atypical memory B cells than HIV- attractors. Meanwhile, attractors resembling B cells from breast cancer patients post NACT encompassed a reduced number of atypical memory B cells compared to pre-NACT attractors.
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Affiliation(s)
- George Kassis
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, USA
| | - Mukta G Palshikar
- Biophysics, Structural, and Computational Biology Program, University of Rochester School of Medicine and Dentistry, Rochester, USA
| | - Shannon P Hilchey
- Department of Medicine, Division of Nephrology, University of Rochester Medical Center, Rochester, NY, USA
| | - Martin S Zand
- Department of Medicine, Division of Nephrology, University of Rochester Medical Center, Rochester, NY, USA
| | - Juilee Thakar
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, USA; Biophysics, Structural, and Computational Biology Program, University of Rochester School of Medicine and Dentistry, Rochester, USA; Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, USA; Department of Biomedical Genetics, University of Rochester School of Medicine and Dentistry, Rochester, USA.
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Palshikar MG, Min X, Crystal A, Meng J, Hilchey SP, Zand MS, Thakar J. Executable Network Models of Integrated Multiomics Data. J Proteome Res 2023; 22:1546-1556. [PMID: 37000949 PMCID: PMC10167691 DOI: 10.1021/acs.jproteome.2c00730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Multiomics profiling provides a holistic picture of a condition being examined and captures the complexity of signaling events, beginning from the original cause (environmental or genetic), to downstream functional changes at multiple molecular layers. Pathway enrichment analysis has been used with multiomics data sets to characterize signaling mechanisms. However, technical and biological variability between these layered data limit an integrative computational analyses. We present a Boolean network-based method, multiomics Boolean Omics Network Invariant-Time Analysis (mBONITA), to integrate omics data sets that quantify multiple molecular layers. mBONITA utilizes prior knowledge networks to perform topology-based pathway analysis. In addition, mBONITA identifies genes that are consistently modulated across molecular measurements by combining observed fold-changes and variance, with a measure of node (i.e., gene or protein) influence over signaling, and a measure of the strength of evidence for that gene across data sets. We used mBONITA to integrate multiomics data sets from RAMOS B cells treated with the immunosuppressant drug cyclosporine A under varying O2 tensions to identify pathways involved in hypoxia-mediated chemotaxis. We compare mBONITA's performance with 6 other pathway analysis methods designed for multiomics data and show that mBONITA identifies a set of pathways with evidence of modulation across all omics layers. mBONITA is freely available at https://github.com/Thakar-Lab/mBONITA.
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Palshikar MG, Palli R, Tyrell A, Maggirwar S, Schifitto G, Singh MV, Thakar J. Executable models of immune signaling pathways in HIV-associated atherosclerosis. NPJ Syst Biol Appl 2022; 8:35. [PMID: 36131068 PMCID: PMC9492768 DOI: 10.1038/s41540-022-00246-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 09/01/2022] [Indexed: 11/09/2022] Open
Abstract
Atherosclerosis (AS)-associated cardiovascular disease is an important cause of mortality in an aging population of people living with HIV (PLWH). This elevated risk has been attributed to viral infection, anti-retroviral therapy, chronic inflammation, and lifestyle factors. However, the rates at which PLWH develop AS vary even after controlling for length of infection, treatment duration, and for lifestyle factors. To investigate the molecular signaling underlying this variation, we sequenced 9368 peripheral blood mononuclear cells (PBMCs) from eight PLWH, four of whom have atherosclerosis (AS+). Additionally, a publicly available dataset of PBMCs from persons before and after HIV infection was used to investigate the effect of acute HIV infection. To characterize dysregulation of pathways rather than just measuring enrichment, we developed the single-cell Boolean Omics Network Invariant Time Analysis (scBONITA) algorithm. scBONITA infers executable dynamic pathway models and performs a perturbation analysis to identify high impact genes. These dynamic models are used for pathway analysis and to map sequenced cells to characteristic signaling states (attractor analysis). scBONITA revealed that lipid signaling regulates cell migration into the vascular endothelium in AS+ PLWH. Pathways implicated included AGE-RAGE and PI3K-AKT signaling in CD8+ T cells, and glucagon and cAMP signaling pathways in monocytes. Attractor analysis with scBONITA facilitated the pathway-based characterization of cellular states in CD8+ T cells and monocytes. In this manner, we identify critical cell-type specific molecular mechanisms underlying HIV-associated atherosclerosis using a novel computational method.
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Affiliation(s)
- Mukta G Palshikar
- Biophysics, Structural, and Computational Biology Program, University of Rochester School of Medicine and Dentistry, Rochester, USA
| | - Rohith Palli
- Medical Scientist Training Program, University of Rochester School of Medicine and Dentistry, Rochester, USA
| | - Alicia Tyrell
- University of Rochester Clinical & Translational Science Institute, Rochester, USA
| | - Sanjay Maggirwar
- Department of Microbiology, Immunology and Tropical Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Giovanni Schifitto
- Department of Neurology, University of Rochester School of Medicine and Dentistry, Rochester, USA
- Department of Imaging Sciences, University of Rochester School of Medicine and Dentistry, Rochester, USA
| | - Meera V Singh
- Department of Neurology, University of Rochester School of Medicine and Dentistry, Rochester, USA
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, USA
| | - Juilee Thakar
- Biophysics, Structural, and Computational Biology Program, University of Rochester School of Medicine and Dentistry, Rochester, USA.
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, USA.
- Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, USA.
- Department of Biomedical Genetics, University of Rochester School of Medicine and Dentistry, Rochester, USA.
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Sanders AL, Hermanson JN, Samuels DC, Plate L, Sanders CR. Compendium of proteins containing segments that exhibit
zero‐tolerance
to amino acid variation in humans. Protein Sci 2022; 31:e4408. [PMID: 36040257 PMCID: PMC9387208 DOI: 10.1002/pro.4408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 06/11/2022] [Accepted: 07/08/2022] [Indexed: 11/08/2022]
Affiliation(s)
- Adam L. Sanders
- Department of Biochemistry Vanderbilt University School of Medicine—Basic Sciences Nashville Tennessee USA
| | - Jake N. Hermanson
- Quantitative Chemical and Physical Biology Graduate Program Vanderbilt University School of Medicine—Basic Sciences Nashville Tennessee USA
| | - David C. Samuels
- Department of Molecular Physiology and Biophysics Vanderbilt University School of Medicine Nashville Tennessee USA
| | - Lars Plate
- Departments of Chemistry and Biological Sciences Vanderbilt University Nashville Tennessee USA
| | - Charles R. Sanders
- Department of Biochemistry Vanderbilt University School of Medicine—Basic Sciences Nashville Tennessee USA
- Center for Structural Biology Vanderbilt University School of Medicine—Basic Sciences Nashville Tennessee USA
- Department of Medicine Vanderbilt University School of Medicine Nashville Tennessee USA
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Catozzi S, Ternet C, Gourrege A, Wynne K, Oliviero G, Kiel C. Reconstruction and analysis of a large-scale binary Ras-effector signaling network. Cell Commun Signal 2022; 20:24. [PMID: 35246154 PMCID: PMC8896392 DOI: 10.1186/s12964-022-00823-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 12/18/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Ras is a key cellular signaling hub that controls numerous cell fates via multiple downstream effector pathways. While pathways downstream of effectors such as Raf, PI3K and RalGDS are extensively described in the literature, how other effectors signal downstream of Ras is often still enigmatic. METHODS A comprehensive and unbiased Ras-effector network was reconstructed downstream of 43 effector proteins (converging onto 12 effector classes) using public pathway and protein-protein interaction (PPI) databases. The output is an oriented graph of pairwise interactions defining a 3-layer signaling network downstream of Ras. The 2290 proteins comprising the network were studied for their implication in signaling crosstalk and feedbacks, their subcellular localizations, and their cellular functions. RESULTS The final Ras-effector network consists of 2290 proteins that are connected via 19,080 binary PPIs, increasingly distributed across the downstream layers, with 441 PPIs in layer 1, 1660 in layer 2, and 16,979 in layer 3. We identified a high level of crosstalk among proteins of the 12 effector classes. A class-specific Ras sub-network was generated in CellDesigner (.xml file) and a functional enrichment analysis thereof shows that 58% of the processes have previously been associated to a respective effector pathway, with the remaining providing insights into novel and unexplored functions of specific effector pathways. CONCLUSIONS Our large-scale and cell general Ras-effector network is a crucial steppingstone towards defining the network boundaries. It constitutes a 'reference interactome' and can be contextualized for specific conditions, e.g. different cell types or biopsy material obtained from cancer patients. Further, it can serve as a basis for elucidating systems properties, such as input-output relationships, crosstalk, and pathway redundancy. Video Abstract.
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Affiliation(s)
- Simona Catozzi
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Camille Ternet
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Alize Gourrege
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Kieran Wynne
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Giorgio Oliviero
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Christina Kiel
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland. .,UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland. .,Department of Molecular Medicine, University of Pavia, 27100, Pavia, Italy.
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