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Wu F, Sun G, Nai Y, Shi X, Ma Y, Cao H. NUP43 promotes PD-L1/nPD-L1/PD-L1 feedback loop via TM4SF1/JAK/STAT3 pathway in colorectal cancer progression and metastatsis. Cell Death Discov 2024; 10:241. [PMID: 38762481 PMCID: PMC11102480 DOI: 10.1038/s41420-024-02025-z] [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: 03/11/2024] [Revised: 05/05/2024] [Accepted: 05/10/2024] [Indexed: 05/20/2024] Open
Abstract
Programmed cell death-ligand 1 (PD-L1) has a significant role in tumor progression and metastasis, facilitating tumor cell evasion from immune surveillance. PD-L1 can be detected in the tumor cell nucleus and exert an oncogenic effect by nuclear translocation. Colorectal cancer (CRC) progression and liver metastasis (CCLM) are among the most lethal diseases worldwide, but the mechanism of PD-L1 nuclear translocation in CRC and CCLM remains to be fully understood. In this study, using CRISPR-Cas9-based genome-wide screening combined with RNA-seq, we found that the oncogenic factor NUP43 impacted the process of PD-L1 nuclear translocation by regulating the expression level of the PD-L1 chaperone protein IPO5. Subsequent investigation revealed that this process could stimulate the expression of tumor-promoting factor TM4SF1 and further activate the JAK/STAT3 signaling pathway, which ultimately enhanced the transcription of PD-L1, thus establishing a PD-L1-nPD-L1-PD-L1 feedback loop that ultimately promoted CRC progression and CCLM. In conclusion, our study reveals a novel role for nPD-L1 in CRC, identifies the PD-L1-nPD-L1-PD-L1 feedback loop in CRC, and provides a therapeutic strategy for CRC patients.
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Affiliation(s)
- Fan Wu
- Department of General Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Guoqiang Sun
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, China
| | - Yongjun Nai
- Department of General Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xuesong Shi
- Department of General Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Yong Ma
- Department of General Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Hongyong Cao
- Department of General Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
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2
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Khullar S, Huang X, Ramesh R, Svaren J, Wang D. NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.25.563769. [PMID: 37961577 PMCID: PMC10634989 DOI: 10.1101/2023.10.25.563769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Transcription factor (TF) coordination plays a key role in target gene (TG) regulation via protein-protein interactions (PPIs) and DNA co-binding to regulatory elements. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF coordination and TG regulation of various cell types remains unclear. To address this, we have developed a novel computational approach, Network Regression Embeddings (NetREm), to reveal cell-type TF-TF coordination activities for TG regulation. NetREm leverages network-constrained regularization using prior knowledge of direct and/or indirect PPIs among TFs to analyze single-cell gene expression data. We test NetREm by simulation data and benchmark its performance in 4 real-world applications that have gold standard TF-TG networks available: mouse (mESCs) and simulated human (hESCs) embryonic stem (ESCs), human hematopoietic stem (HSCs), and mouse dendritic (mDCs) cells. Further, we use NetREm to prioritize valid novel TF-TF coordination links in human Peripheral Blood Mononuclear cell (PBMC) sub-types. We apply NetREm to analyze various cell types in both central (CNS) and peripheral (PNS) nerve system (NS) (e.g. neuronal, glial, Schwann cells (SCs)) as well as in Alzheimers disease (AD). Our findings uncover cell-type coordinating TFs and identify new TF-TG candidate links. We validate our top predictions using Cut&Run and knockout loss-of-function expression data in rat/mouse models and compare results with additional functional genomic data, including expression quantitative trait loci (eQTL) and Genome-Wide Association Studies (GWAS) to link genetic variants (single nucleotide polymorphisms (SNPs)) to TF coordination.
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3
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Wright SN, Colton S, Schaffer LV, Pillich RT, Churas C, Pratt D, Ideker T. State of the Interactomes: an evaluation of molecular networks for generating biological insights. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.587073. [PMID: 38746239 PMCID: PMC11092493 DOI: 10.1101/2024.04.26.587073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.
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Ghosh S, Mitra P. MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107955. [PMID: 38064959 DOI: 10.1016/j.cmpb.2023.107955] [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: 09/07/2023] [Revised: 11/09/2023] [Accepted: 11/26/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND AND OBJECTIVE Protein-protein interaction (PPI) is a vital process in all living cells, controlling essential cell functions such as cell cycle regulation, signal transduction, and metabolic processes with broad applications that include antibody therapeutics, vaccines, and drug discovery. The problem of sequence-based PPI prediction has been a long-standing issue in computational biology. METHODS We introduce MaTPIP, a cutting-edge deep-learning framework for predicting PPI. MaTPIP stands out due to its innovative design, fusing pre-trained Protein Language Model (PLM)-based features with manually curated protein sequence attributes, emphasizing the part-whole relationship by incorporating two-dimensional granular part (amino-acid) level features and one-dimensional whole-level (protein) features. What sets MaTPIP apart is its ability to integrate these features across three different input terminals seamlessly. MatPIP also includes a distinctive configuration of Convolutional Neural Network (CNN) with Transformer components for concurrent utilization of CNN and sequential characteristics in each iteration and a one-dimensional to two-dimensional converter followed by a unified embedding. The statistical significance of this classifier is validated using McNemar's test. RESULTS MaTPIP outperformed the existing methods on both the Human PPI benchmark and cross-species PPI testing datasets, demonstrating its immense generalization capability for PPI prediction. We used seven diverse datasets with varying PPI target class distributions. Notably, within the novel PPI scenario, the most challenging category for Human PPI Benchmark, MaTPIP improves the existing state-of-the-art score from 74.1% to 78.6% (measured in Area under ROC Curve), from 23.2% to 32.8% (in average precision) and from 4.9% to 9.5% (in precision at 3% recall) for 50%, 10% and 0.3% target class distributions, respectively. In cross-species PPI evaluation, hybrid MaTPIP establishes a new benchmark score (measured in Area Under precision-recall curve) of 81.1% from the previous 60.9% for Mouse, 80.9% from 56.2% for Fly, 78.1% from 55.9% for Worm, 59.9% from 41.7% for Yeast, and 66.2% from 58.8% for E.coli. Our eXplainable AI-based assessment reveals an average contribution of different feature families per prediction on these datasets. CONCLUSIONS MaTPIP mixes manually curated features with the feature extracted from the pre-trained PLM to predict sequence-based protein-protein association. Furthermore, MaTPIP demonstrates strong generalization capabilities for cross-species PPI predictions.
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Affiliation(s)
- Shubhrangshu Ghosh
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, India; TCS Research, Tata Consultancy Services Limited, Kolkata, West Bengal, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, India.
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5
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Gaikani HK, Stolar M, Kriti D, Nislow C, Giaever G. From beer to breadboards: yeast as a force for biological innovation. Genome Biol 2024; 25:10. [PMID: 38178179 PMCID: PMC10768129 DOI: 10.1186/s13059-023-03156-9] [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: 02/14/2023] [Accepted: 12/21/2023] [Indexed: 01/06/2024] Open
Abstract
The history of yeast Saccharomyces cerevisiae, aka brewer's or baker's yeast, is intertwined with our own. Initially domesticated 8,000 years ago to provide sustenance to our ancestors, for the past 150 years, yeast has served as a model research subject and a platform for technology. In this review, we highlight many ways in which yeast has served to catalyze the fields of functional genomics, genome editing, gene-environment interaction investigation, proteomics, and bioinformatics-emphasizing how yeast has served as a catalyst for innovation. Several possible futures for this model organism in synthetic biology, drug personalization, and multi-omics research are also presented.
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Affiliation(s)
- Hamid Kian Gaikani
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada
- Department of Chemistry, University of British Columbia, Vancouver, BC, Canada
| | - Monika Stolar
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Divya Kriti
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Corey Nislow
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada.
| | - Guri Giaever
- Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada
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6
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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.
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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
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7
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Brixi G, Ye T, Hong L, Wang T, Monticello C, Lopez-Barbosa N, Vincoff S, Yudistyra V, Zhao L, Haarer E, Chen T, Pertsemlidis S, Palepu K, Bhat S, Christopher J, Li X, Liu T, Zhang S, Petersen L, DeLisa MP, Chatterjee P. SaLT&PepPr is an interface-predicting language model for designing peptide-guided protein degraders. Commun Biol 2023; 6:1081. [PMID: 37875551 PMCID: PMC10598214 DOI: 10.1038/s42003-023-05464-z] [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: 07/05/2023] [Accepted: 10/13/2023] [Indexed: 10/26/2023] Open
Abstract
Protein-protein interactions (PPIs) are critical for biological processes and predicting the sites of these interactions is useful for both computational and experimental applications. We present a Structure-agnostic Language Transformer and Peptide Prioritization (SaLT&PepPr) pipeline to predict interaction interfaces from a protein sequence alone for the subsequent generation of peptidic binding motifs. Our model fine-tunes the ESM-2 protein language model (pLM) with a per-position prediction task to identify PPI sites using data from the PDB, and prioritizes motifs which are most likely to be involved within inter-chain binding. By only using amino acid sequence as input, our model is competitive with structural homology-based methods, but exhibits reduced performance compared with deep learning models that input both structural and sequence features. Inspired by our previous results using co-crystals to engineer target-binding "guide" peptides, we curate PPI databases to identify partners for subsequent peptide derivation. Fusing guide peptides to an E3 ubiquitin ligase domain, we demonstrate degradation of endogenous β-catenin, 4E-BP2, and TRIM8, and highlight the nanomolar binding affinity, low off-targeting propensity, and function-altering capability of our best-performing degraders in cancer cells. In total, our study suggests that prioritizing binders from natural interactions via pLMs can enable programmable protein targeting and modulation.
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Affiliation(s)
- Garyk Brixi
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Tianzheng Ye
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA
| | - Lauren Hong
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Tian Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Connor Monticello
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Natalia Lopez-Barbosa
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA
| | - Sophia Vincoff
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Vivian Yudistyra
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Lin Zhao
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Elena Haarer
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Tianlai Chen
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Kalyan Palepu
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Suhaas Bhat
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Xinning Li
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Tong Liu
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Sue Zhang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Lillian Petersen
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Matthew P DeLisa
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
- Cornell Institute of Biotechnology, Cornell University, Ithaca, NY, USA
| | - Pranam Chatterjee
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Department of Computer Science, Duke University, Durham, NC, USA.
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
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8
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Peng R, Deng M. Mapping the protein-protein interactome in the tumor immune microenvironment. Antib Ther 2023; 6:311-321. [PMID: 38098892 PMCID: PMC10720949 DOI: 10.1093/abt/tbad026] [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: 06/06/2023] [Revised: 10/01/2023] [Accepted: 11/02/2023] [Indexed: 12/17/2023] Open
Abstract
The cell-to-cell communication primarily occurs through cell-surface and secreted proteins, which form a sophisticated network that coordinates systemic immune function. Uncovering these protein-protein interactions (PPIs) is indispensable for understanding the molecular mechanism and elucidating immune system aberrances under diseases. Traditional biological studies typically focus on a limited number of PPI pairs due to the relative low throughput of commonly used techniques. Encouragingly, classical methods have advanced, and many new systems tailored for large-scale protein-protein screening have been developed and successfully utilized. These high-throughput PPI investigation techniques have already made considerable achievements in mapping the immune cell interactome, enriching PPI databases and analysis tools, and discovering therapeutic targets for cancer and other diseases, which will definitely bring unprecedented insight into this field.
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Affiliation(s)
- Rui Peng
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing 100191, PR China
- School of Basic Medical Sciences, Health Science Center, Peking University, Beijing 100191, PR China
| | - Mi Deng
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing 100191, PR China
- School of Basic Medical Sciences, Health Science Center, Peking University, Beijing 100191, PR China
- Peking University Cancer Hospital and Institute, Peking University, Beijing 100142, PR China
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9
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Wang C, Xu S, Sun D, Liu ZP. ActivePPI: quantifying protein-protein interaction network activity with Markov random fields. Bioinformatics 2023; 39:btad567. [PMID: 37698984 PMCID: PMC10516639 DOI: 10.1093/bioinformatics/btad567] [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: 05/18/2023] [Revised: 08/11/2023] [Accepted: 09/11/2023] [Indexed: 09/14/2023] Open
Abstract
MOTIVATION Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning. RESULTS To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena. AVAILABILITY AND IMPLEMENTATION All source code and data are freely available at https://github.com/zpliulab/ActivePPI.
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Affiliation(s)
- Chuanyuan Wang
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Shiyu Xu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Duanchen Sun
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
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10
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Zaied RE, Fadason T, O'Sullivan JM. De novo identification of complex traits associated with asthma. Front Immunol 2023; 14:1231492. [PMID: 37680636 PMCID: PMC10480836 DOI: 10.3389/fimmu.2023.1231492] [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: 05/30/2023] [Accepted: 08/02/2023] [Indexed: 09/09/2023] Open
Abstract
Introduction Asthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships. Methods Here, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0-4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level. Results We identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand. Discussion Our discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available.
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Affiliation(s)
- Roan E Zaied
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Tayaza Fadason
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Justin M O'Sullivan
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
- Garvan Institute of Medical Research, Sydney, NSW, Australia
- Medical Research Council (MRC) Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
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11
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Ma D, Liu S, He Q, Kong L, Liu K, Xiao L, Xin Q, Bi Y, Wu J, Jiang C. A novel approach for the analysis of single-cell RNA sequencing identifies TMEM14B as a novel poor prognostic marker in hepatocellular carcinoma. Sci Rep 2023; 13:10508. [PMID: 37380717 DOI: 10.1038/s41598-023-36650-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/07/2023] [Indexed: 06/30/2023] Open
Abstract
A fundamental goal in cancer-associated genome sequencing is to identify the key genes. Protein-protein interactions (PPIs) play a crucially important role in this goal. Here, human reference interactome (HuRI) map was generated and 64,006 PPIs involving 9094 proteins were identified. Here, we developed a physical link and co-expression combinatory network construction (PLACE) method for genes of interest, which provides a rapid way to analyze genome sequencing datasets. Next, Kaplan‒Meier survival analysis, CCK8 assays, scratch wound assays and Transwell assays were applied to confirm the results. In this study, we selected single-cell sequencing data from patients with hepatocellular carcinoma (HCC) in GSE149614. The PLACE method constructs a protein connection network for genes of interest, and a large fraction (80%) of the genes (screened by the PLACE method) were associated with survival. Then, PLACE discovered that transmembrane protein 14B (TMEM14B) was the most significant prognostic key gene, and target genes of TMEM14B were predicted. The TMEM14B-target gene regulatory network was constructed by PLACE. We also detected that TMEM14B-knockdown inhibited proliferation and migration. The results demonstrate that we proposed a new effective method for identifying key genes. The PLACE method can be used widely and make outstanding contributions to the tumor research field.
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Affiliation(s)
- Ding Ma
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing University, 22 Hankou Road, Nanjing, 210093, Jiangsu, China
- Jinan Microecological Biomedicine Shandong Laboratory, Shounuo City Light West Block, Qingdao Road 3716#, Huaiyin District, Jinan City, Shandong Province, China
- Department of Gastroenterology, Third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shuwen Liu
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing University, 22 Hankou Road, Nanjing, 210093, Jiangsu, China
- Jinan Microecological Biomedicine Shandong Laboratory, Shounuo City Light West Block, Qingdao Road 3716#, Huaiyin District, Jinan City, Shandong Province, China
| | - Qinyu He
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing University, 22 Hankou Road, Nanjing, 210093, Jiangsu, China
- Jinan Microecological Biomedicine Shandong Laboratory, Shounuo City Light West Block, Qingdao Road 3716#, Huaiyin District, Jinan City, Shandong Province, China
| | - Lingkai Kong
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing University, 22 Hankou Road, Nanjing, 210093, Jiangsu, China
- Jinan Microecological Biomedicine Shandong Laboratory, Shounuo City Light West Block, Qingdao Road 3716#, Huaiyin District, Jinan City, Shandong Province, China
| | - Kua Liu
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing University, 22 Hankou Road, Nanjing, 210093, Jiangsu, China
- Jinan Microecological Biomedicine Shandong Laboratory, Shounuo City Light West Block, Qingdao Road 3716#, Huaiyin District, Jinan City, Shandong Province, China
| | - Lingjun Xiao
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing University, 22 Hankou Road, Nanjing, 210093, Jiangsu, China
- Jinan Microecological Biomedicine Shandong Laboratory, Shounuo City Light West Block, Qingdao Road 3716#, Huaiyin District, Jinan City, Shandong Province, China
| | - Qilei Xin
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing University, 22 Hankou Road, Nanjing, 210093, Jiangsu, China
| | - Yanyu Bi
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing University, 22 Hankou Road, Nanjing, 210093, Jiangsu, China
| | - Junhua Wu
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing University, 22 Hankou Road, Nanjing, 210093, Jiangsu, China.
- Jinan Microecological Biomedicine Shandong Laboratory, Shounuo City Light West Block, Qingdao Road 3716#, Huaiyin District, Jinan City, Shandong Province, China.
| | - Chunping Jiang
- State Key Laboratory of Pharmaceutical Biotechnology, National Institute of Healthcare Data Science at Nanjing University, Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing University, 22 Hankou Road, Nanjing, 210093, Jiangsu, China.
- Jinan Microecological Biomedicine Shandong Laboratory, Shounuo City Light West Block, Qingdao Road 3716#, Huaiyin District, Jinan City, Shandong Province, China.
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12
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Jaros RK, Fadason T, Cameron-Smith D, Golovina E, O'Sullivan JM. Comorbidity genetic risk and pathways impact SARS-CoV-2 infection outcomes. Sci Rep 2023; 13:9879. [PMID: 37336921 DOI: 10.1038/s41598-023-36900-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 06/12/2023] [Indexed: 06/21/2023] Open
Abstract
Understanding the genetic risk and mechanisms through which SARS-CoV-2 infection outcomes and comorbidities interact to impact acute and long-term sequelae is essential if we are to reduce the ongoing health burdens of the COVID-19 pandemic. Here we use a de novo protein diffusion network analysis coupled with tissue-specific gene regulatory networks, to examine putative mechanisms for associations between SARS-CoV-2 infection outcomes and comorbidities. Our approach identifies a shared genetic aetiology and molecular mechanisms for known and previously unknown comorbidities of SARS-CoV-2 infection outcomes. Additionally, genomic variants, genes and biological pathways that provide putative causal mechanisms connecting inherited risk factors for SARS-CoV-2 infection and coronary artery disease and Parkinson's disease are identified for the first time. Our findings provide an in depth understanding of genetic impacts on traits that collectively alter an individual's predisposition to acute and post-acute SARS-CoV-2 infection outcomes. The existence of complex inter-relationships between the comorbidities we identify raises the possibility of a much greater post-acute burden arising from SARS-CoV-2 infection if this genetic predisposition is realised.
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Affiliation(s)
- Rachel K Jaros
- The Liggins Institute, The University of Auckland, Auckland, 1023, New Zealand
| | - Tayaza Fadason
- The Liggins Institute, The University of Auckland, Auckland, 1023, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, 1010, New Zealand
| | - David Cameron-Smith
- College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, 2308, Australia
| | - Evgeniia Golovina
- The Liggins Institute, The University of Auckland, Auckland, 1023, New Zealand
| | - Justin M O'Sullivan
- The Liggins Institute, The University of Auckland, Auckland, 1023, New Zealand.
- Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Auckland, 1010, New Zealand.
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
- Australian Parkinson's Mission, Garvan Institute of Medical Research, Sydney, NSW, Australia.
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13
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Gokulu IS, Banta S. Biotechnology applications of proteins functionalized with DNA oligonucleotides. Trends Biotechnol 2023; 41:575-585. [PMID: 36115723 DOI: 10.1016/j.tibtech.2022.08.004] [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: 07/11/2022] [Revised: 08/16/2022] [Accepted: 08/18/2022] [Indexed: 10/14/2022]
Abstract
The functionalization of proteins with DNA through the formation of covalent bonds enables a wide range of biotechnology advancements. For example, single-molecule analytical methods rely on bioconjugated DNA as elastic biolinkers for protein immobilization. Labeling proteins with DNA enables facile protein identification, as well as spatial and temporal organization and control of protein within DNA-protein networks. Bioconjugation reactions can target native, engineered, and non-canonical amino acids (NCAAs) within proteins. In addition, further protein engineering via the incorporation of peptide tags and self-labeling proteins can also be used for conjugation reactions. The selection of techniques will depend on application requirements such as yield, selectivity, conjugation position, potential for steric hindrance, cost, commercial availability, and potential impact on protein function.
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Affiliation(s)
- Ipek Simay Gokulu
- Department of Chemical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, USA
| | - Scott Banta
- Department of Chemical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, USA.
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14
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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.
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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
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15
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Zambo B, Morlet B, Negroni L, Trave G, Gogl G. Native holdup (nHU) to measure binding affinities from cell extracts. SCIENCE ADVANCES 2022; 8:eade3828. [PMID: 36542723 PMCID: PMC9770967 DOI: 10.1126/sciadv.ade3828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Characterizing macromolecular interactions is essential for understanding cellular processes, yet most methods currently used to detect protein interactions from cells are qualitative. Here, we introduce the native holdup (nHU) approach to estimate equilibrium binding constants of protein interactions directly from cell extracts. Compared to other pull-down-based assays, nHU requires less sample preparation and can be coupled to any analytical methods as readouts, such as Western blotting or mass spectrometry. We use nHU to explore interactions of SNX27, a cargo adaptor of the retromer complex and find good agreement between in vitro affinities and those measured directly from cell extracts using nHU. We discuss the strengths and limitations of nHU and provide simple protocols that can be implemented in most laboratories.
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Affiliation(s)
- Boglarka Zambo
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
| | - Bastien Morlet
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
| | - Luc Negroni
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
| | - Gilles Trave
- Équipe Labellisée Ligue 2015, Département de Biologie Structurale Intégrative, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
- Corresponding author. (G.T.); (G.G.)
| | - Gergo Gogl
- Équipe Labellisée Ligue 2015, Département de Biologie Structurale Intégrative, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM U1258/CNRS UMR 7104/Université de Strasbourg, 1 rue Laurent Fries, BP 10142, Illkirch F-67404, France
- Corresponding author. (G.T.); (G.G.)
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16
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Holguin-Cruz JA, Foster LJ, Gsponer J. Where protein structure and cell diversity meet. Trends Cell Biol 2022; 32:996-1007. [PMID: 35537902 DOI: 10.1016/j.tcb.2022.04.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 01/21/2023]
Abstract
Protein-protein interaction networks - interactomes - are charted with the hope to understand how phenotypes emerge and how they are altered in disease states. Early efforts to map interactomes have focused on the assembly of context agnostic, reference networks. However, recent studies have mapped interactomes across different cell lines and tissues, finding highly variable interactomes due to the rewiring of protein-protein interactions in different contexts. Increasing evidence points to significant links between protein structure and interactome diversity seen across cell types and tissues. We discuss how recent findings support the key role of alternative splicing and phosphorylation, two well-established regulators of protein structural and functional diversity, in defining cell type- and tissue-specific interactomes. Moreover, we show that intrinsically disordered protein regions are most favorably equipped to support interactome rewiring by acting as hubs of protein structure and function regulation.
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Affiliation(s)
- Jorge A Holguin-Cruz
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada
| | - Jörg Gsponer
- Michael Smith Laboratories, Department of Biochemistry and Molecular Biology, The University of British Columbia, Vancouver, Canada.
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17
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Deciphering Spatial Protein-Protein Interactions in Brain Using Proximity Labeling. Mol Cell Proteomics 2022; 21:100422. [PMID: 36198386 PMCID: PMC9650050 DOI: 10.1016/j.mcpro.2022.100422] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 01/18/2023] Open
Abstract
Cellular biomolecular complexes including protein-protein, protein-RNA, and protein-DNA interactions regulate and execute most biological functions. In particular in brain, protein-protein interactions (PPIs) mediate or regulate virtually all nerve cell functions, such as neurotransmission, cell-cell communication, neurogenesis, synaptogenesis, and synaptic plasticity. Perturbations of PPIs in specific subsets of neurons and glia are thought to underly a majority of neurobiological disorders. Therefore, understanding biological functions at a cellular level requires a reasonably complete catalog of all physical interactions between proteins. An enzyme-catalyzed method to biotinylate proximal interacting proteins within 10 to 300 nm of each other is being increasingly used to characterize the spatiotemporal features of complex PPIs in brain. Thus, proximity labeling has emerged recently as a powerful tool to identify proteomes in distinct cell types in brain as well as proteomes and PPIs in structures difficult to isolate, such as the synaptic cleft, axonal projections, or astrocyte-neuron junctions. In this review, we summarize recent advances in proximity labeling methods and their application to neurobiology.
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18
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- *Correspondence: Arnaud Droit,
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19
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Oss-Ronen L, Sarusi T, Cohen I. Histone Mono-Ubiquitination in Transcriptional Regulation and Its Mark on Life: Emerging Roles in Tissue Development and Disease. Cells 2022; 11:cells11152404. [PMID: 35954248 PMCID: PMC9368181 DOI: 10.3390/cells11152404] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/26/2022] [Accepted: 08/02/2022] [Indexed: 02/06/2023] Open
Abstract
Epigenetic regulation plays an essential role in driving precise transcriptional programs during development and homeostasis. Among epigenetic mechanisms, histone mono-ubiquitination has emerged as an important post-transcriptional modification. Two major histone mono-ubiquitination events are the mono-ubiquitination of histone H2A at lysine 119 (H2AK119ub), placed by Polycomb repressive complex 1 (PRC1), and histone H2B lysine 120 mono-ubiquitination (H2BK120ub), placed by the heteromeric RNF20/RNF40 complex. Both of these events play fundamental roles in shaping the chromatin epigenetic landscape and cellular identity. In this review we summarize the current understandings of molecular concepts behind histone mono-ubiquitination, focusing on their recently identified roles in tissue development and pathologies.
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Affiliation(s)
| | | | - Idan Cohen
- Correspondence: ; Tel.: +972-8-6477593; Fax: +972-8-6477626
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20
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Fernandez G, Yubero D, Palau F, Armstrong J. Molecular Modelling Hurdle in the Next-Generation Sequencing Era. Int J Mol Sci 2022; 23:ijms23137176. [PMID: 35806177 PMCID: PMC9266691 DOI: 10.3390/ijms23137176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 12/10/2022] Open
Abstract
There are challenges in the genetic diagnosis of rare diseases, and pursuing an optimal strategy to identify the cause of the disease is one of the main objectives of any clinical genomics unit. A range of techniques are currently used to characterize the genomic variability within the human genome to detect causative variants of specific disorders. With the introduction of next-generation sequencing (NGS) in the clinical setting, geneticists can study single-nucleotide variants (SNVs) throughout the entire exome/genome. In turn, the number of variants to be evaluated per patient has increased significantly, and more information has to be processed and analyzed to determine a proper diagnosis. Roughly 50% of patients with a Mendelian genetic disorder are diagnosed using NGS, but a fair number of patients still suffer a diagnostic odyssey. Due to the inherent diversity of the human population, as more exomes or genomes are sequenced, variants of uncertain significance (VUSs) will increase exponentially. Thus, assigning relevance to a VUS (non-synonymous as well as synonymous) in an undiagnosed patient becomes crucial to assess the proper diagnosis. Multiple algorithms have been used to predict how a specific mutation might affect the protein’s function, but they are far from accurate enough to be conclusive. In this work, we highlight the difficulties of genomic variability determined by NGS that have arisen in diagnosing rare genetic diseases, and how molecular modelling has to be a key component to elucidate the relevance of a specific mutation in the protein’s loss of function or malfunction. We suggest that the creation of a multi-omics data model should improve the classification of pathogenicity for a significant amount of the detected genomic variability. Moreover, we argue how it should be incorporated systematically in the process of variant evaluation to be useful in the clinical setting and the diagnostic pipeline.
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Affiliation(s)
- Guerau Fernandez
- Department of Genetic and Molecular Medicine—IPER, Hospital Sant Joan de Déu, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (G.F.); (F.P.); (J.A.)
- Center for Biomedical Research Network on Rare Diseases (CIBERER), ISCIII, 08950 Barcelona, Spain
| | - Dèlia Yubero
- Department of Genetic and Molecular Medicine—IPER, Hospital Sant Joan de Déu, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (G.F.); (F.P.); (J.A.)
- Center for Biomedical Research Network on Rare Diseases (CIBERER), ISCIII, 08950 Barcelona, Spain
- Correspondence: ; Tel.: +34-93-600-9451; Fax: +34-93-600-9760
| | - Francesc Palau
- Department of Genetic and Molecular Medicine—IPER, Hospital Sant Joan de Déu, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (G.F.); (F.P.); (J.A.)
- Center for Biomedical Research Network on Rare Diseases (CIBERER), ISCIII, 08950 Barcelona, Spain
- Division of Pediatrics, University of Barcelona School of Medicine and Health Sciences, 08007 Barcelona, Spain
| | - Judith Armstrong
- Department of Genetic and Molecular Medicine—IPER, Hospital Sant Joan de Déu, Institut de Recerca Sant Joan de Déu, 08950 Barcelona, Spain; (G.F.); (F.P.); (J.A.)
- Center for Biomedical Research Network on Rare Diseases (CIBERER), ISCIII, 08950 Barcelona, Spain
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21
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Shah PS, Beesabathuni NS, Fishburn AT, Kenaston MW, Minami SA, Pham OH, Tucker I. Systems Biology of Virus-Host Protein Interactions: From Hypothesis Generation to Mechanisms of Replication and Pathogenesis. Annu Rev Virol 2022; 9:397-415. [PMID: 35576593 PMCID: PMC10150767 DOI: 10.1146/annurev-virology-100520-011851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
As obligate intracellular parasites, all viruses must co-opt cellular machinery to facilitate their own replication. Viruses often co-opt these cellular pathways and processes through physical interactions between viral and host proteins. In addition to facilitating fundamental aspects of virus replication cycles, these virus-host protein interactions can also disrupt physiological functions of host proteins, causing disease that can be advantageous to the virus or simply a coincidence. Consequently, unraveling virus-host protein interactions can serve as a window into molecular mechanisms of virus replication and pathogenesis. Identifying virus-host protein interactions using unbiased systems biology approaches provides an avenue for hypothesis generation. This review highlights common systems biology approaches for identification of virus-host protein interactions and the mechanistic insights revealed by these methods. We also review conceptual innovations using comparative and integrative systems biology that can leverage global virus-host protein interaction data sets to more rapidly move from hypothesis generation to mechanism. Expected final online publication date for the Annual Review of Virology, Volume 9 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Priya S Shah
- Department of Microbiology and Molecular Genetics, University of California, Davis, California, USA; .,Department of Chemical Engineering, University of California, Davis, California, USA
| | - Nitin S Beesabathuni
- Department of Chemical Engineering, University of California, Davis, California, USA
| | - Adam T Fishburn
- Department of Microbiology and Molecular Genetics, University of California, Davis, California, USA;
| | - Matthew W Kenaston
- Department of Microbiology and Molecular Genetics, University of California, Davis, California, USA;
| | - Shiaki A Minami
- Department of Chemical Engineering, University of California, Davis, California, USA
| | - Oanh H Pham
- Department of Microbiology and Molecular Genetics, University of California, Davis, California, USA;
| | - Inglis Tucker
- Department of Microbiology and Molecular Genetics, University of California, Davis, California, USA;
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22
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Dunham B, Ganapathiraju MK. Benchmark Evaluation of Protein-Protein Interaction Prediction Algorithms. Molecules 2021; 27:41. [PMID: 35011283 PMCID: PMC8746451 DOI: 10.3390/molecules27010041] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/23/2021] [Indexed: 11/16/2022] Open
Abstract
Protein-protein interactions (PPIs) perform various functions and regulate processes throughout cells. Knowledge of the full network of PPIs is vital to biomedical research, but most of the PPIs are still unknown. As it is infeasible to discover all of them experimentally due to technical and resource limitations, computational prediction of PPIs is essential and accurately assessing the performance of algorithms is required before further application or translation. However, many published methods compose their evaluation datasets incorrectly, using a higher proportion of positive class data than occuring naturally, leading to exaggerated performance. We re-implemented various published algorithms and evaluated them on datasets with realistic data compositions and found that their performance is overstated in original publications; with several methods outperformed by our control models built on 'illogical' and random number features. We conclude that these methods are influenced by an over-characterization of some proteins in the literature and due to scale-free nature of PPI network and that they fail when tested on all possible protein pairs. Additionally, we found that sequence-only-based algorithms performed worse than those that employ functional and expression features. We present a benchmark evaluation of many published algorithms for PPI prediction. The source code of our implementations and the benchmark datasets created here are made available in open source.
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23
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de la Fuente J, Contreras M. Vaccinomics: a future avenue for vaccine development against emerging pathogens. Expert Rev Vaccines 2021; 20:1561-1569. [PMID: 34582295 DOI: 10.1080/14760584.2021.1987222] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Vaccines are a major achievement in medical sciences, but the development of more effective vaccines against infectious diseases is essential for prevention and control of emerging pathogens worldwide. The application of omics technologies has advanced vaccinology through the characterization of host-vector-pathogen molecular interactions and the identification of candidate protective antigens. However, major challenges such as host immunity, pathogen and environmental factors, vaccine efficacy and safety need to be addressed. Vaccinomics provides a platform to address these challenges and improve vaccine efficacy and safety. AREAS COVERED In this review, we summarize current information on vaccinomics and propose quantum vaccinomics approaches to further advance vaccine development through the identification and combination of antigen protective epitopes, the immunological quantum. The COVID-19 pandemic caused by SARS-CoV-2 is an example of emerging infectious diseases with global impact on human health. EXPERT OPINION Vaccines are required for the effective and environmentally sustainable intervention for the control of emerging infectious diseases worldwide. Recent advances in vaccinomics provide a platform to address challenges in improving vaccine efficacy and implementation. As proposed here, quantum vaccinomics will contribute to vaccine development, efficacy, and safety by facilitating antigen combinations to target pathogen infection and transmission in emerging infectious diseases.
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Affiliation(s)
- José de la Fuente
- SaBio, Instituto De Investigación En Recursos Cinegéticos Irec-csic-uclm-jccm, Ciudad Real, Spain.,Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK, USA
| | - Marinela Contreras
- Interdisciplinary Laboratory of Clinical Analysis, Interlab-UMU, Regional Campus of International Excellence Campus Mare Nostrum, University of Murcia, Espinardo, Spain
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