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Kuang N, Ma Q, Zheng X, Meng X, Zhai Z, Li Q, Pan J. GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns. Comput Struct Biotechnol J 2024; 23:2488-2496. [PMID: 38939556 PMCID: PMC11208770 DOI: 10.1016/j.csbj.2024.06.003] [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: 01/15/2024] [Revised: 05/29/2024] [Accepted: 06/04/2024] [Indexed: 06/29/2024] Open
Abstract
Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.
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Affiliation(s)
- Ni Kuang
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Qinfeng Ma
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Xiao Zheng
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Xuehang Meng
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Zhaoyu Zhai
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Qiang Li
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
| | - Jianbo Pan
- Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, China
- Precision Medicine Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
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2
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Hasanaj E, Mathur S, Bar-Joseph Z. Integrating patients in time series clinical transcriptomics data. Bioinformatics 2024; 40:i151-i159. [PMID: 38940139 DOI: 10.1093/bioinformatics/btae241] [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] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Analysis of time series transcriptomics data from clinical trials is challenging. Such studies usually profile very few time points from several individuals with varying response patterns and dynamics. Current methods for these datasets are mainly based on linear, global orderings using visit times which do not account for the varying response rates and subgroups within a patient cohort. RESULTS We developed a new method that utilizes multi-commodity flow algorithms for trajectory inference in large scale clinical studies. Recovered trajectories satisfy individual-based timing restrictions while integrating data from multiple patients. Testing the method on multiple drug datasets demonstrated an improved performance compared to prior approaches suggested for this task, while identifying novel disease subtypes that correspond to heterogeneous patient response patterns. AVAILABILITY AND IMPLEMENTATION The source code and instructions to download the data have been deposited on GitHub at https://github.com/euxhenh/Truffle.
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Affiliation(s)
- Euxhen Hasanaj
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Sachin Mathur
- R&D Data and Computational Sciences, Sanofi, Cambridge, MA 02141, United States
| | - Ziv Bar-Joseph
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
- R&D Data and Computational Sciences, Sanofi, Cambridge, MA 02141, United States
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
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3
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Magni S, Sawlekar R, Capelle CM, Tslaf V, Baron A, Zeng N, Mombaerts L, Yue Z, Yuan Y, Hefeng FQ, Gonçalves J. Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data. NPJ Syst Biol Appl 2024; 10:59. [PMID: 38811598 PMCID: PMC11137136 DOI: 10.1038/s41540-024-00387-9] [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: 02/08/2024] [Accepted: 05/10/2024] [Indexed: 05/31/2024] Open
Abstract
The discovery of upstream regulatory genes of a gene of interest still remains challenging. Here we applied a scalable computational method to unbiasedly predict candidate regulatory genes of critical transcription factors by searching the whole genome. We illustrated our approach with a case study on the master regulator FOXP3 of human primary regulatory T cells (Tregs). While target genes of FOXP3 have been identified, its upstream regulatory machinery still remains elusive. Our methodology selected five top-ranked candidates that were tested via proof-of-concept experiments. Following knockdown, three out of five candidates showed significant effects on the mRNA expression of FOXP3 across multiple donors. This provides insights into the regulatory mechanisms modulating FOXP3 transcriptional expression in Tregs. Overall, at the genome level this represents a high level of accuracy in predicting upstream regulatory genes of key genes of interest.
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Affiliation(s)
- Stefano Magni
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Rucha Sawlekar
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
- Robotics and Artificial Intelligence, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden
| | - Christophe M Capelle
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-Sur-Alzette, Luxembourg
- Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Vera Tslaf
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-Sur-Alzette, Luxembourg
- Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Alexandre Baron
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-Sur-Alzette, Luxembourg
| | - Ni Zeng
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-Sur-Alzette, Luxembourg
| | - Laurent Mombaerts
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Zuogong Yue
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ye Yuan
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Q Hefeng
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-Sur-Alzette, Luxembourg.
| | - Jorge Gonçalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
- Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.
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4
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Wan R, Zhang Y, Peng Y, Tian F, Gao G, Tang F, Jia J, Ge H. Unveiling gene regulatory networks during cellular state transitions without linkage across time points. Sci Rep 2024; 14:12355. [PMID: 38811747 PMCID: PMC11137113 DOI: 10.1038/s41598-024-62850-1] [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: 01/24/2024] [Accepted: 05/22/2024] [Indexed: 05/31/2024] Open
Abstract
Time-stamped cross-sectional data, which lack linkage across time points, are commonly generated in single-cell transcriptional profiling. Many previous methods for inferring gene regulatory networks (GRNs) driving cell-state transitions relied on constructing single-cell temporal ordering. Introducing COSLIR (COvariance restricted Sparse LInear Regression), we presented a direct approach to reconstructing GRNs that govern cell-state transitions, utilizing only the first and second moments of samples between two consecutive time points. Simulations validated COSLIR's perfect accuracy in the oracle case and demonstrated its robust performance in real-world scenarios. When applied to single-cell RT-PCR and RNAseq datasets in developmental biology, COSLIR competed favorably with existing methods. Notably, its running time remained nearly independent of the number of cells. Therefore, COSLIR emerges as a promising addition to GRN reconstruction methods under cell-state transitions, bypassing the single-cell temporal ordering to enhance accuracy and efficiency in single-cell transcriptional profiling.
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Affiliation(s)
- Ruosi Wan
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Yuhao Zhang
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
| | - Yongli Peng
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Feng Tian
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
| | - Ge Gao
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Fuchou Tang
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China
| | - Jinzhu Jia
- School of Public Health and Center for Statistical Science, Peking University, Beijing, China.
| | - Hao Ge
- Beijing International Center for Mathematical Research, Peking University, Beijing, China.
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China.
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5
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Zhang D, Gao S, Liu ZP, Gao R. LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data. Brief Bioinform 2024; 25:bbae286. [PMID: 38886006 PMCID: PMC11182660 DOI: 10.1093/bib/bbae286] [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: 03/30/2024] [Revised: 05/09/2024] [Accepted: 06/06/2024] [Indexed: 06/20/2024] Open
Abstract
Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.
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Affiliation(s)
- Dezhen Zhang
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Shuhua Gao
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Zhi-Ping Liu
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Rui Gao
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
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6
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Chen Y, Mao R, Xu J, Huang Y, Xu J, Cui S, Zhu Z, Ji X, Huang S, Huang Y, Huang HY, Yen SC, Lin YCD, Huang HD. A Causal Regulation Modeling Algorithm for Temporal Events with Application to Escherichia coli's Aerobic to Anaerobic Transition. Int J Mol Sci 2024; 25:5654. [PMID: 38891842 PMCID: PMC11171773 DOI: 10.3390/ijms25115654] [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: 04/13/2024] [Revised: 05/10/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
Time-series experiments are crucial for understanding the transient and dynamic nature of biological phenomena. These experiments, leveraging advanced classification and clustering algorithms, allow for a deep dive into the cellular processes. However, while these approaches effectively identify patterns and trends within data, they often need to improve in elucidating the causal mechanisms behind these changes. Building on this foundation, our study introduces a novel algorithm for temporal causal signaling modeling, integrating established knowledge networks with sequential gene expression data to elucidate signal transduction pathways over time. Focusing on Escherichia coli's (E. coli) aerobic to anaerobic transition (AAT), this research marks a significant leap in understanding the organism's metabolic shifts. By applying our algorithm to a comprehensive E. coli regulatory network and a time-series microarray dataset, we constructed the cross-time point core signaling and regulatory processes of E. coli's AAT. Through gene expression analysis, we validated the primary regulatory interactions governing this process. We identified a novel regulatory scheme wherein environmentally responsive genes, soxR and oxyR, activate fur, modulating the nitrogen metabolism regulators fnr and nac. This regulatory cascade controls the stress regulators ompR and lrhA, ultimately affecting the cell motility gene flhD, unveiling a novel regulatory axis that elucidates the complex regulatory dynamics during the AAT process. Our approach, merging empirical data with prior knowledge, represents a significant advance in modeling cellular signaling processes, offering a deeper understanding of microbial physiology and its applications in biotechnology.
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Affiliation(s)
- Yigang Chen
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Runbo Mao
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
| | - Jiatong Xu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Yixian Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Jingyi Xu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
| | - Shidong Cui
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Zihao Zhu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Xiang Ji
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Shenghan Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Yanzhe Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
| | - Hsi-Yuan Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Shih-Chung Yen
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Yang-Chi-Duang Lin
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Hsien-Da Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
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7
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Loubaton R, Champagnat N, Vallois P, Vallat L. MultiRNAflow: integrated analysis of temporal RNA-seq data with multiple biological conditions. Bioinformatics 2024; 40:btae315. [PMID: 38810104 PMCID: PMC11139518 DOI: 10.1093/bioinformatics/btae315] [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: 09/21/2023] [Revised: 04/04/2024] [Accepted: 05/28/2024] [Indexed: 05/31/2024] Open
Abstract
MOTIVATION The dynamic transcriptional mechanisms that govern eukaryotic cell function can now be analyzed by RNA sequencing. However, the packages currently available for the analysis of raw sequencing data do not provide automatic analysis of complex experimental designs with multiple biological conditions and multiple analysis time-points. RESULTS The MultiRNAflow suite combines several packages in a unified framework allowing exploratory and supervised statistical analyses of temporal data for multiple biological conditions. AVAILABILITY AND IMPLEMENTATION The R package MultiRNAflow is freely available on Bioconductor (https://bioconductor.org/packages/MultiRNAflow/), and the latest version of the source code is available on a GitHub repository (https://github.com/loubator/MultiRNAflow).
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Affiliation(s)
| | | | - Pierre Vallois
- University of Lorraine, CNRS, Inria, IECL, F-54000 Nancy, France
| | - Laurent Vallat
- University of Strasbourg, CNRS, UMR-7242 Biotechnology and Cell Signaling, F-67400 Illkirch, France
- Department of Molecular Genetic of Cancers, Strasbourg University Hospital, F-67200 Strasbourg, France
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8
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Zhang J, Li Y, Zhu F, Guo X, Huang Y. Time-/dose- series transcriptome data analysis and traditional Chinese medicine treatment of pneumoconiosis. Int J Biol Macromol 2024; 267:131515. [PMID: 38614165 DOI: 10.1016/j.ijbiomac.2024.131515] [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: 02/03/2024] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 04/15/2024]
Abstract
Pneumoconiosis' pathogenesis is still unclear and specific drugs for its treatment are lacking. Analysis of series transcriptome data often uses a single comparison method, and there are few reports on using such data to predict the treatment of pneumoconiosis with traditional Chinese medicine (TCM). Here, we proposed a new method for analyzing series transcriptomic data, series difference analysis (SDA), and applied it to pneumoconiosis. By comparison with 5 gene sets including existing pneumoconiosis-related genes and gene set functional enrichment analysis, we demonstrated that the new method was not inferior to two existing traditional analysis methods. Furthermore, based on the TCM-drug target interaction network, we predicted the TCM corresponding to the common pneumoconiosis-related genes obtained by multiple methods, and combined them with the high-frequency TCM for its treatment obtained through literature mining to form a new TCM formula for it. After feeding it to pneumoconiosis modeling mice for two months, compared with the untreated group, the coat color, mental state and tissue sections of the mice in the treated group were markedly improved, indicating that the new TCM formula has a certain efficacy. Our study provides new insights into method development for series transcriptomic data analysis and treatment of pneumoconiosis.
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Affiliation(s)
- Jifeng Zhang
- Key Laboratory of Industrial Dust Prevention and Control & Occupational Health and Safety, Ministry of Education, Anhui University of Science and Technology, Huainan, Anhui 232001, China; School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
| | - Yaobin Li
- Key Laboratory of Industrial Dust Prevention and Control & Occupational Health and Safety, Ministry of Education, Anhui University of Science and Technology, Huainan, Anhui 232001, China.
| | - Fenglin Zhu
- Key Laboratory of Industrial Dust Prevention and Control & Occupational Health and Safety, Ministry of Education, Anhui University of Science and Technology, Huainan, Anhui 232001, China
| | - Xiaodi Guo
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
| | - Yuqing Huang
- School of Biological Engineering & Institute of Digital Ecology and Health, Huainan Normal University, Huainan, China
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9
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Rosebrock D, Vingron M, Arndt PF. Modeling gene expression cascades during cell state transitions. iScience 2024; 27:109386. [PMID: 38500834 PMCID: PMC10946328 DOI: 10.1016/j.isci.2024.109386] [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: 07/18/2023] [Revised: 12/14/2023] [Accepted: 02/27/2024] [Indexed: 03/20/2024] Open
Abstract
During cellular processes such as differentiation or response to external stimuli, cells exhibit dynamic changes in their gene expression profiles. Single-cell RNA sequencing (scRNA-seq) can be used to investigate these dynamic changes. To this end, cells are typically ordered along a pseudotemporal trajectory which recapitulates the progression of cells as they transition from one cell state to another. We infer transcriptional dynamics by modeling the gene expression profiles in pseudotemporally ordered cells using a Bayesian inference approach. This enables ordering genes along transcriptional cascades, estimating differences in the timing of gene expression dynamics, and deducing regulatory gene interactions. Here, we apply this approach to scRNA-seq datasets derived from mouse embryonic forebrain and pancreas samples. This analysis demonstrates the utility of the method to derive the ordering of gene dynamics and regulatory relationships critical for proper cellular differentiation and maturation across a variety of developmental contexts.
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Affiliation(s)
- Daniel Rosebrock
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Martin Vingron
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Peter F. Arndt
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
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10
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Fan Y, Li L, Sun S. Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq. Genome Biol 2024; 25:96. [PMID: 38622747 PMCID: PMC11020788 DOI: 10.1186/s13059-024-03237-3] [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: 09/22/2023] [Accepted: 04/03/2024] [Indexed: 04/17/2024] Open
Abstract
We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.
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Affiliation(s)
- Yue Fan
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi, 710061, People's Republic of China
| | - Lei Li
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China
| | - Shiquan Sun
- Center for Single-Cell Omics and Health, School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China.
- Collaborative Innovation Center of Endemic Diseases and Health Promotion in Silk Road Region; NHC Key Laboratory of Environment and Endemic Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, People's Republic of China.
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi, 710061, People's Republic of China.
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, Shaanxi, 710061, People's Republic of China.
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11
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Cheemalavagu N, Shoger KE, Cao YM, Michalides BA, Botta SA, Faeder JR, Gottschalk RA. Predicting gene-level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework. Cell Syst 2024; 15:37-48.e4. [PMID: 38198893 PMCID: PMC10812086 DOI: 10.1016/j.cels.2023.12.006] [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: 05/01/2023] [Revised: 09/30/2023] [Accepted: 12/15/2023] [Indexed: 01/12/2024]
Abstract
The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational framework to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to interleukin (IL)-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified cytokine-specific genes associated with late pSTAT3 time frames and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Neha Cheemalavagu
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Karsen E Shoger
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yuqi M Cao
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brandon A Michalides
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Samuel A Botta
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - James R Faeder
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Rachel A Gottschalk
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA.
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12
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Chaturvedi A, Som A. Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data. Methods Mol Biol 2024; 2719:51-77. [PMID: 37803112 DOI: 10.1007/978-1-0716-3461-5_4] [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: 10/08/2023]
Abstract
Growth is regulated by gene expression variation at different developmental stages of biological processes such as cell differentiation, disease progression, or drug response. In cancer, a stage-specific regulatory model constructed to infer the dynamic expression changes in genes contributing to tissue growth or proliferation is referred as a dynamic growth regulatory network (dGRN). Over the past decade, gene expression data has been widely used for reconstructing dGRN by computing correlations between the differentially expressed genes (DEGs). A wide variety of pipelines are available to construct the GRNs using DEGs and the choice of a particular method or tool depends on the nature of the study. In this protocol, we have outlined a step-by-step guide for the analysis of DEGs using RNA-Seq data, beginning from data acquisition, pre-processing, mapping to reference genome, and construction of a correlation-based co-expression network to further downstream analysis. We have also outlined the steps for the inclusion of publicly available interaction/regulation information into the dGRN followed by relevant topological inferences. This tutorial has been designed in a way that early researchers can refer to for an easy and comprehensive glimpse of methodologies used in the inference of dGRN using transcriptomics data.
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Affiliation(s)
- Aparna Chaturvedi
- Centre of Bioinformatics, Institute of Interdisciplinary Studies, University of Allahabad, Prayagraj, India
| | - Anup Som
- Centre of Bioinformatics, Institute of Interdisciplinary Studies, University of Allahabad, Prayagraj, India
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13
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Wang J, Ye H, Li X, Lv X, Lou J, Chen Y, Yu S, Zhang L. Genome-Wide Analysis of the MADS-Box Gene Family in Hibiscus syriacus and Their Role in Floral Organ Development. Int J Mol Sci 2023; 25:406. [PMID: 38203576 PMCID: PMC10779063 DOI: 10.3390/ijms25010406] [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: 10/31/2023] [Revised: 12/16/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
Hibiscus syriacus belongs to the Malvaceae family, and is a plant with medicinal, edible, and greening values. MADS-box transcription factor is a large family of regulatory factors involved in a variety of biological processes in plants. Here, we performed a genome-wide characterization of MADS-box proteins in H. syriacus and investigated gene structure, phylogenetics, cis-acting elements, three-dimensional structure, gene expression, and protein interaction to identify candidate MADS-box genes that mediate petal developmental regulation in H. syriacus. A total of 163 candidate MADS-box genes were found and classified into type I (Mα, Mβ, and Mγ) and type II (MIKC and Mδ). Analysis of cis-acting elements in the promoter region showed that most elements were correlated to plant hormones. The analysis of nine HsMADS expressions of two different H. syriacus cultivars showed that they were differentially expressed between two type flowers. The analysis of protein interaction networks also indicated that MADS proteins played a crucial role in floral organ identification, inflorescence and fruit development, and flowering time. This research is the first to analyze the MADS-box family of H. syriacus and provides an important reference for further study of the biological functions of the MADS-box, especially in flower organ development.
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Affiliation(s)
- Jie Wang
- College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China; (J.W.); (H.Y.); (X.L.); (J.L.); (Y.C.)
| | - Heng Ye
- College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China; (J.W.); (H.Y.); (X.L.); (J.L.); (Y.C.)
| | - Xiaolong Li
- College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, China;
| | - Xue Lv
- College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China; (J.W.); (H.Y.); (X.L.); (J.L.); (Y.C.)
| | - Jiaqi Lou
- College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China; (J.W.); (H.Y.); (X.L.); (J.L.); (Y.C.)
| | - Yulu Chen
- College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China; (J.W.); (H.Y.); (X.L.); (J.L.); (Y.C.)
| | - Shuhan Yu
- College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China; (J.W.); (H.Y.); (X.L.); (J.L.); (Y.C.)
| | - Lu Zhang
- College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China; (J.W.); (H.Y.); (X.L.); (J.L.); (Y.C.)
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14
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Liu J, Chen PJ, Mehta S, Dutra EH, Yadav S. Dynamic changes in transcriptome during orthodontic tooth movement. Orthod Craniofac Res 2023; 26 Suppl 1:73-81. [PMID: 36891648 DOI: 10.1111/ocr.12650] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/09/2023] [Accepted: 02/13/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVES The objective of this study was to determine global changes in gene expression with next generation sequencing (NGS) in order to assess the biological effects of orthodontic tooth movement (OTM) on alveolar bone in a rat model. MATERIALS AND METHODS Thirty-five Wistar rats (age 14 weeks) were used in the study. The OTM was performed using closed coil Nickel-Titanium spring to apply a mesial force on maxillary first molars of 8-10 g. Three hours, 1, 3, 7 and 14 days after the placement of the appliance, rats were killed at each time point respectively. The alveolar bone, around left maxillary first molar, were excised on compression side. The samples were immediately frozen in liquid nitrogen for subsequent RNA extraction. Total RNA samples were prepared for mRNA sequencing using the Illumina kit. RNA-Seq reads were aligned to the rat genomes using the STAR Aligner and bioinformatic analysis was performed. RESULTS A total of 18 192 genes were determined. Day 1 has the highest number of differentially expressed genes (DEGs) observed with more upregulated than downregulated genes. A total of 2719 DEGs were identified to use as input for the algorithm. Six distinct clusters of temporal patterns were observed representing proteins that were differentially regulated indicating different expression kinetics. Principal component analysis (PCA) showed distinct clustering by time points and days 3, 7 and 14 share similar gene expression pattern. CONCLUSIONS Distinct gene expression pattern was observed at different time points studied. Hypoxia, inflammation and bone remodelling pathways are major mechanisms behind OTM.
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Affiliation(s)
- Jia Liu
- Private Practice, Boston, Massachusetts, USA
| | - Po-Jung Chen
- Section of Orthodontics, Department of Growth and Development, University of Nebraska Medical Center, Lincoln, Nebraska, USA
| | - Shivam Mehta
- Department of Developmental Sciences/Orthodontics, Marquette University School of Dentistry, Milwaukee, Wisconsin, USA
| | - Eliane H Dutra
- Division of Orthodontics, University of Connecticut Health, Farmington, Connecticut, USA
| | - Sumit Yadav
- Department of Growth and Development, University of Nebraska Medical Center, Lincoln, Nebraska, USA
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15
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Li S, Liu Q, Wang E, Wang J. Global quantitative understanding of non-equilibrium cell fate decision-making in response to pheromone. iScience 2023; 26:107885. [PMID: 37766979 PMCID: PMC10520453 DOI: 10.1016/j.isci.2023.107885] [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: 05/23/2023] [Revised: 08/09/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Cell-cycle arrest and polarized growth are commonly used to characterize the response of yeast to pheromone. However, the quantitative decision-making processes underlying time-dependent changes in cell fate remain unclear. In this study, we conducted single-cell level experiments to observe multidimensional responses, uncovering diverse fates of yeast cells. Multiple states are revealed, along with the kinetic switching rates and pathways among them, giving rise to a quantitative landscape of mating response. To quantify the experimentally observed cell fates, we developed a theoretical framework based on non-equilibrium landscape and flux theory. Additionally, we performed stochastic simulations of biochemical reactions to elucidate signal transduction and cell growth. Notably, our experimental findings have provided the first global quantitative evidence of the real-time synchronization between intracellular signaling, physiological growth, and morphological functions. These results validate the proposed underlying mechanism governing the emergence of multiple cell fate states. This study introduces an emerging mechanistic approach to understand non-equilibrium cell fate decision-making in response to pheromone.
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Affiliation(s)
- Sheng Li
- College of Chemistry, Jilin University, Changchun, Jilin 130012, China
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
| | - Qiong Liu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
| | - Erkang Wang
- College of Chemistry, Jilin University, Changchun, Jilin 130012, China
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
| | - Jin Wang
- Department of Chemistry and of Physics and Astronomy, State University of New York at Stony Brook, Stony Brook, NY 11794-3400, USA
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16
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Velten B, Stegle O. Principles and challenges of modeling temporal and spatial omics data. Nat Methods 2023; 20:1462-1474. [PMID: 37710019 DOI: 10.1038/s41592-023-01992-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/31/2023] [Indexed: 09/16/2023]
Abstract
Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.
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Affiliation(s)
- Britta Velten
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Centre for Organismal Studies (COS) and Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
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17
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Steinhart MR, van der Valk WH, Osorio D, Serdy SA, Zhang J, Nist-Lund C, Kim J, Moncada-Reid C, Sun L, Lee J, Koehler KR. Mapping oto-pharyngeal development in a human inner ear organoid model. Development 2023; 150:dev201871. [PMID: 37796037 DOI: 10.1242/dev.201871] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/08/2023] [Indexed: 10/06/2023]
Abstract
Inner ear development requires the coordination of cell types from distinct epithelial, mesenchymal and neuronal lineages. Although we have learned much from animal models, many details about human inner ear development remain elusive. We recently developed an in vitro model of human inner ear organogenesis using pluripotent stem cells in a 3D culture, fostering the growth of a sensorineural circuit, including hair cells and neurons. Despite previously characterizing some cell types, many remain undefined. This study aimed to chart the in vitro development timeline of the inner ear organoid to understand the mechanisms at play. Using single-cell RNA sequencing at ten stages during the first 36 days of differentiation, we tracked the evolution from pluripotency to various ear cell types after exposure to specific signaling modulators. Our findings showcase gene expression that influences differentiation, identifying a plethora of ectodermal and mesenchymal cell types. We also discern aspects of the organoid model consistent with in vivo development, while highlighting potential discrepancies. Our study establishes the Inner Ear Organoid Developmental Atlas (IODA), offering deeper insights into human biology and improving inner ear tissue differentiation.
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Affiliation(s)
- Matthew R Steinhart
- Department of Otolaryngology, Boston Children's Hospital, Boston, MA 02115, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
- Department of Otolaryngology-Head and Neck Surgery, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Medical Neuroscience Graduate Program, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Wouter H van der Valk
- Department of Otolaryngology, Boston Children's Hospital, Boston, MA 02115, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA 02115, USA
- OtoBiology Leiden, Department of Otorhinolaryngology and Head & Neck Surgery; Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
- The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW); Leiden University Medical Center, Leiden, 2333 ZA, the Netherlands
| | - Daniel Osorio
- Research Computing, Department of Information Technology; Boston Children's Hospital, Boston, MA 02115, USA
| | - Sara A Serdy
- Department of Otolaryngology, Boston Children's Hospital, Boston, MA 02115, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
| | - Jingyuan Zhang
- Department of Otolaryngology, Boston Children's Hospital, Boston, MA 02115, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA 02115, USA
| | - Carl Nist-Lund
- Department of Otolaryngology, Boston Children's Hospital, Boston, MA 02115, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
- Program in Neuroscience, Harvard Medical School, Boston, MA 02115, USA
| | - Jin Kim
- Department of Otolaryngology, Boston Children's Hospital, Boston, MA 02115, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA 02115, USA
- Department of Plastic and Oral Surgery, Boston Children's Hospital, Boston, MA 02115, USA
| | - Cynthia Moncada-Reid
- Speech and Hearing Bioscience and Technology (SHBT) Graduate Program, Harvard Medical School, Boston, MA 02115, USA
| | - Liang Sun
- Research Computing, Department of Information Technology; Boston Children's Hospital, Boston, MA 02115, USA
| | - Jiyoon Lee
- Department of Otolaryngology, Boston Children's Hospital, Boston, MA 02115, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA 02115, USA
- Department of Plastic and Oral Surgery, Boston Children's Hospital, Boston, MA 02115, USA
| | - Karl R Koehler
- Department of Otolaryngology, Boston Children's Hospital, Boston, MA 02115, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA 02115, USA
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA 02115, USA
- Department of Plastic and Oral Surgery, Boston Children's Hospital, Boston, MA 02115, USA
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18
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Jackson CA, Beheler-Amass M, Tjärnberg A, Suresh I, Hickey ASM, Bonneau R, Gresham D. Simultaneous estimation of gene regulatory network structure and RNA kinetics from single cell gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.21.558277. [PMID: 37790443 PMCID: PMC10542544 DOI: 10.1101/2023.09.21.558277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Cells respond to environmental and developmental stimuli by remodeling their transcriptomes through regulation of both mRNA transcription and mRNA decay. A central goal of biology is identifying the global set of regulatory relationships between factors that control mRNA production and degradation and their target transcripts and construct a predictive model of gene expression. Regulatory relationships are typically identified using transcriptome measurements and causal inference algorithms. RNA kinetic parameters are determined experimentally by employing run-on or metabolic labeling (e.g. 4-thiouracil) methods that allow transcription and decay rates to be separately measured. Here, we develop a deep learning model, trained with single-cell RNA-seq data, that both infers causal regulatory relationships and estimates RNA kinetic parameters. The resulting in silico model predicts future gene expression states and can be perturbed to simulate the effect of transcription factor changes. We acquired model training data by sequencing the transcriptomes of 175,000 individual Saccharomyces cerevisiae cells that were subject to an external perturbation and continuously sampled over a one hour period. The rate of change for each transcript was calculated on a per-cell basis to estimate RNA velocity. We then trained a deep learning model with transcriptome and RNA velocity data to calculate time-dependent estimates of mRNA production and decay rates. By separating RNA velocity into transcription and decay rates, we show that rapamycin treatment causes existing ribosomal protein transcripts to be rapidly destabilized, while production of new transcripts gradually slows over the course of an hour. The neural network framework we present is designed to explicitly model causal regulatory relationships between transcription factors and their genes, and shows superior performance to existing models on the basis of recovery of known regulatory relationships. We validated the predictive power of the model by perturbing transcription factors in silico and comparing transcriptome-wide effects with experimental data. Our study represents the first step in constructing a complete, predictive, biophysical model of gene expression regulation.
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Affiliation(s)
- Christopher A Jackson
- Center For Genomics and Systems Biology, New York University, New York, NY, USA
- Department of Biology, New York University, New York, NY, USA
| | - Maggie Beheler-Amass
- Center For Genomics and Systems Biology, New York University, New York, NY, USA
- Department of Biology, New York University, New York, NY, USA
| | - Andreas Tjärnberg
- Center For Genomics and Systems Biology, New York University, New York, NY, USA
- Department of Biology, New York University, New York, NY, USA
| | - Ina Suresh
- Center For Genomics and Systems Biology, New York University, New York, NY, USA
- Department of Biology, New York University, New York, NY, USA
| | - Angela Shang-mei Hickey
- Center For Genomics and Systems Biology, New York University, New York, NY, USA
- Department of Biology, New York University, New York, NY, USA
| | | | - David Gresham
- Center For Genomics and Systems Biology, New York University, New York, NY, USA
- Department of Biology, New York University, New York, NY, USA
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19
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Liu Y, Min Q, Tang J, Yang L, Meng X, Peng T, Jiang M. Transcriptome profiling in rumen, reticulum, omasum, and abomasum tissues during the developmental transition of pre-ruminant to the ruminant in yaks. Front Vet Sci 2023; 10:1204706. [PMID: 37808112 PMCID: PMC10556492 DOI: 10.3389/fvets.2023.1204706] [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: 04/12/2023] [Accepted: 08/29/2023] [Indexed: 10/10/2023] Open
Abstract
The development of the four stomachs of yak is closely related to its health and performance, however the underlying molecular mechanisms are largely unknown. Here, we systematically analyzed mRNAs of four stomachs in five growth time points [0 day, 20 days, 60 days, 15 months and 3 years (adult)] of yaks. Overall, the expression patterns of DEmRNAs were unique at 0 d, similar at 20 d and 60 d, and similar at 15 m and adult in four stomachs. The expression pattern in abomasum was markedly different from that in rumen, reticulum and omasum. Short Time-series Expression Miner (STEM) analysis demonstrated that multi-model spectra are drastically enriched over time in four stomachs. All the identified mRNAs in rumen, reticulum, omasum and abomasum were classified into 6, 4, 7, and 5 cluster profiles, respectively. Modules 9, 38, and 41 were the most significant three colored modules. By weighted gene co-expression network analysis (WGCNA), a total of 5,486 genes were categorized into 10 modules. CCKBR, KCNQ1, FER1L6, and A4GNT were the hub genes of the turquoise module, and PAK6, TRIM29, ADGRF4, TGM1, and TMEM79 were the hub genes of the blue module. Furthermore, functional KEGG enrichment analysis suggested that the turquoise module was involved in gastric acid secretion, sphingolipid metabolism, ether lipid metabolism, etc., and the blue module was enriched in pancreatic secretion, pantothenate and CoA biosynthesis, and starch and sucrose metabolism, etc. Our study aims to lay a molecular basis for the study of the physiological functions of rumen, reticulum, omasum and abomasum in yaks. It can further elucidate the important roles of these mRNAs in regulation of growth, development and metabolism in yaks, and to provide a theoretical basis for age-appropriate weaning and supplementary feeding in yaks.
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Affiliation(s)
- Yili Liu
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation, College of Animal and Veterinary Sciences, Southwest Minzu University, Chengdu, China
| | - Qi Min
- Institute of Qinghai-Tibetan Plateau, Southwest Minzu University, Chengdu, China
| | - Jiao Tang
- Institute of Qinghai-Tibetan Plateau, Southwest Minzu University, Chengdu, China
| | - Lu Yang
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation, College of Animal and Veterinary Sciences, Southwest Minzu University, Chengdu, China
| | - Xinxin Meng
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation, College of Animal and Veterinary Sciences, Southwest Minzu University, Chengdu, China
| | - Tao Peng
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation, College of Animal and Veterinary Sciences, Southwest Minzu University, Chengdu, China
| | - Mingfeng Jiang
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation, College of Animal and Veterinary Sciences, Southwest Minzu University, Chengdu, China
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20
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Klumpe HE, Lugagne JB, Khalil AS, Dunlop MJ. Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes. ACS Synth Biol 2023; 12:2367-2381. [PMID: 37467372 DOI: 10.1021/acssynbio.3c00203] [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: 07/21/2023]
Abstract
Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single-cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.
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Affiliation(s)
- Heidi E Klumpe
- Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
| | - Jean-Baptiste Lugagne
- Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
| | - Ahmad S Khalil
- Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115, United States
| | - Mary J Dunlop
- Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
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21
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Marku M, Pancaldi V. From time-series transcriptomics to gene regulatory networks: A review on inference methods. PLoS Comput Biol 2023; 19:e1011254. [PMID: 37561790 PMCID: PMC10414591 DOI: 10.1371/journal.pcbi.1011254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023] Open
Abstract
Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.
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Affiliation(s)
- Malvina Marku
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
| | - Vera Pancaldi
- CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
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22
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Zhou Y, Tao L, Zhu Y. TempShift Reveals the Sequential Development of Human Neocortex and Skewed Developmental Timing of Down Syndrome Brains. Brain Sci 2023; 13:1070. [PMID: 37509002 PMCID: PMC10377154 DOI: 10.3390/brainsci13071070] [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: 05/31/2023] [Revised: 07/01/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Development is a complex process involving precise regulation. Developmental regulation may vary in tissues and individuals, and is often altered in disorders. Currently, the regulation of developmental timing across neocortical areas and developmental changes in Down syndrome (DS) brains remain unclear. The changes in regulation are often accompanied by changes in the gene expression trajectories, which can be divided into two scenarios: (1) changes of gene expression trajectory shape that reflect changes in cell type composition or altered molecular machinery; (2) temporal shift of gene expression trajectories that indicate different regulation of developmental timing. Therefore, we developed an R package TempShift to separates these two scenarios and demonstrated that TempShift can distinguish temporal shift from different shape (DiffShape) of expression trajectories, and can accurately estimate the time difference between multiple trajectories. We applied TempShift to identify sequential gene expression across 11 neocortical areas, which suggested sequential occurrence of synapse formation and axon guidance, as well as reconstructed interneuron migration pathways within neocortex. Comparison between healthy and DS brains revealed increased microglia, shortened neuronal migration process, and delayed synaptogenesis and myelination in DS. These applications also demonstrate the potential of TempShift in understanding gene expression temporal dynamics during different biological processes.
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Affiliation(s)
- Yuqiu Zhou
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
| | - Li Tao
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
| | - Ying Zhu
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200032, China
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23
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Kravtsova N, McGee II RL, Dawes AT. Scalable Gromov-Wasserstein Based Comparison of Biological Time Series. Bull Math Biol 2023; 85:77. [PMID: 37415049 PMCID: PMC10326159 DOI: 10.1007/s11538-023-01175-y] [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: 12/27/2022] [Accepted: 05/30/2023] [Indexed: 07/08/2023]
Abstract
A time series is an extremely abundant data type arising in many areas of scientific research, including the biological sciences. Any method that compares time series data relies on a pairwise distance between trajectories, and the choice of distance measure determines the accuracy and speed of the time series comparison. This paper introduces an optimal transport type distance for comparing time series trajectories that are allowed to lie in spaces of different dimensions and/or with differing numbers of points possibly unequally spaced along each trajectory. The construction is based on a modified Gromov-Wasserstein distance optimization program, reducing the problem to a Wasserstein distance on the real line. The resulting program has a closed-form solution and can be computed quickly due to the scalability of the one-dimensional Wasserstein distance. We discuss theoretical properties of this distance measure, and empirically demonstrate the performance of the proposed distance on several datasets with a range of characteristics commonly found in biologically relevant data. We also use our proposed distance to demonstrate that averaging oscillatory time series trajectories using the recently proposed Fused Gromov-Wasserstein barycenter retains more characteristics in the averaged trajectory when compared to traditional averaging, which demonstrates the applicability of Fused Gromov-Wasserstein barycenters for biological time series. Fast and user friendly software for computing the proposed distance and related applications is provided. The proposed distance allows fast and meaningful comparison of biological time series and can be efficiently used in a wide range of applications.
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Affiliation(s)
- Natalia Kravtsova
- Department of Mathematics, The Ohio State University, 231 West 18th Avenue, Columbus, OH 43210 USA
| | - Reginald L. McGee II
- Department of Mathematics and Computer Science, College of the Holy Cross, 1 College Street, Worcester, MA 01609 USA
| | - Adriana T. Dawes
- Department of Mathematics, The Ohio State University, 231 West 18th Avenue, Columbus, OH 43210 USA
- Department of Molecular Genetics, The Ohio State University, 484 West 12th Avenue, Columbus, OH 43210 USA
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24
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Ismailov ZB, Belykh ES, Chernykh AA, Udoratina AM, Kazakov DV, Rybak AV, Kerimova SN, Velegzhaninov IO. Systematic review of comparative transcriptomic studies of cellular resistance to genotoxic stress. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2023; 792:108467. [PMID: 37657754 DOI: 10.1016/j.mrrev.2023.108467] [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: 01/07/2023] [Revised: 08/19/2023] [Accepted: 08/28/2023] [Indexed: 09/03/2023]
Abstract
The development of resistance by tumor cells to various types of therapy is a significant problem that decreases the effectiveness of oncology treatments. For more than two decades, comparative transcriptomic studies of tumor cells with different sensitivities to ionizing radiation and chemotherapeutic agents have been conducted in order to identify the causes and mechanisms underlying this phenomenon. However, the results of such studies have little in common and often contradict each other. We have assumed that a systematic analysis of a large number of such studies will provide new knowledge about the mechanisms of development of therapeutic resistance in tumor cells. Our comparison of 123 differentially expressed gene (DEG) lists published in 98 papers suggests a very low degree of consistency between the study results. Grouping the data by type of genotoxic agent and tumor type did not increase the similarity. The most frequently overexpressed genes were found to be those encoding the transport protein ABCB1 and the antiviral defense protein IFITM1. We put forward a hypothesis that the role played by the overexpression of the latter in the development of resistance may be associated not only with the stimulation of proliferation, but also with the limitation of exosomal communication and, as a result, with a decrease in the bystander effect. Among down regulated DEGs, BNIP3 was observed most frequently. The expression of BNIP3, together with BNIP3L, is often suppressed in cells resistant to non-platinum genotoxic chemotherapeutic agents, whereas it is increased in cells resistant to ionizing radiation. These observations are likely to be mediated by the binary effects of these gene products on survival, and regulation of apoptosis and autophagy. The combined data also show that even such obvious mechanisms as inhibition of apoptosis and increase of proliferation are not universal but show multidirectional changes.
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Affiliation(s)
- Z B Ismailov
- Institute of Biology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences, 28b Kommunisticheskaya St., Syktyvkar 167982, Russia
| | - E S Belykh
- Institute of Biology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences, 28b Kommunisticheskaya St., Syktyvkar 167982, Russia
| | - A A Chernykh
- Institute of Physiology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences, 50 Pervomaiskaya St., Syktyvkar 167982, Russia
| | - A M Udoratina
- Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, Nizhny Novgorod 603022, Russia
| | - D V Kazakov
- Institute of Physics and Mathematics of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences, 4 Oplesnina St., Syktyvkar 167982, Russia
| | - A V Rybak
- Institute of Biology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences, 28b Kommunisticheskaya St., Syktyvkar 167982, Russia
| | - S N Kerimova
- State Medical Institution Komi Republican Oncology Center, 46 Nyuvchimskoe highway, Syktyvkar 167904, Russia
| | - I O Velegzhaninov
- Institute of Biology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences, 28b Kommunisticheskaya St., Syktyvkar 167982, Russia.
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25
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Madsen SF, Sand JMB, Juhl P, Karsdal M, Thudium CS, Siebuhr AS, Bay-Jensen AC. Fibroblasts are not just fibroblasts: clear differences between dermal and pulmonary fibroblasts' response to fibrotic growth factors. Sci Rep 2023; 13:9411. [PMID: 37296166 PMCID: PMC10256773 DOI: 10.1038/s41598-023-36416-6] [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: 02/20/2023] [Accepted: 06/03/2023] [Indexed: 06/12/2023] Open
Abstract
Systemic Sclerosis (SSc) hallmark is skin fibrosis, but up to 80% of the patients have fibrotic involvement in the pulmonary system. Antifibrotic drugs which have failed in a general SSc population have now been approved in patients with SSc-associated interstitial lung disease (ILD). This indicates that the fibrotic progression and regulation of fibroblasts likely depend on local factors specific to the tissue type. This study investigated the difference between dermal and pulmonary fibroblasts in a fibrotic setting, mimicking the extracellular matrix. Primary healthy fibroblasts were grown in a crowded environment and stimulated with TGF-β1 and PDGF-AB. The viability, morphology, migration capacity, extracellular matrix formation, and gene expression were assessed: TGF-β1 only increased the viability in the dermal fibroblasts. PDGF-AB increased the migration capacity of dermal fibroblasts while the pulmonary fibroblasts fully migrated. The morphology of the fibroblasts was different without stimulation. TGF-β1 increased the formation of type III collagen in pulmonary fibroblasts, while PDGF-AB increased it in dermal fibroblasts. The gene expression trend of type VI collagen was the opposite after PDGF-AB stimulation. The fibroblasts exhibit different response profiles to TGF-β1 and PDGF-AB; this suggests that drivers of fibrosis are tissue-dependent, which needs to be considered in drug development.
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Affiliation(s)
- Sofie Falkenløve Madsen
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Immunoscience, Nordic Bioscience, Herlev, Denmark.
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26
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Huang K, Zhang Y, Gong H, Qiao Z, Wang T, Zhao W, Huang L, Zhou X. Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression. PLoS Comput Biol 2023; 19:e1011122. [PMID: 37228122 DOI: 10.1371/journal.pcbi.1011122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
Lung adenocarcinoma (LUAD) is a deadly tumor with dynamic evolutionary process. Although much endeavors have been made in identifying the temporal patterns of cancer progression, it remains challenging to infer and interpret the molecular alterations associated with cancer development and progression. To this end, we developed a computational approach to infer the progression trajectory based on cross-sectional transcriptomic data. Analysis of the LUAD data using our approach revealed a linear trajectory with three different branches for malignant progression, and the results showed consistency in three independent cohorts. We used the progression model to elucidate the potential molecular events in LUAD progression. Further analysis showed that overexpression of BUB1B, BUB1 and BUB3 promoted tumor cell proliferation and metastases by disturbing the spindle assembly checkpoint (SAC) in the mitosis. Aberrant mitotic spindle checkpoint signaling appeared to be one of the key factors promoting LUAD progression. We found the inferred cancer trajectory allows to identify LUAD susceptibility genetic variations using genome-wide association analysis. This result shows the opportunity for combining analysis of candidate genetic factors with disease progression. Furthermore, the trajectory showed clear evident mutation accumulation and clonal expansion along with the LUAD progression. Understanding how tumors evolve and identifying mutated genes will help guide cancer management. We investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. Validation of the model in multiple independent data sets and correlation analysis with clinical results demonstrate that our method is effective and unbiased.
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Affiliation(s)
- Kexin Huang
- School of Life Science and Technology, Xidian University, Xi'an, China
- West China Biomedical Big Data Centre, West China Hospital of Sichuan University, Chengdu, China
| | - Yun Zhang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Haoran Gong
- West China Biomedical Big Data Centre, West China Hospital of Sichuan University, Chengdu, China
| | - Zhengzheng Qiao
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Tiangang Wang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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27
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Cheemalavagu N, Shoger KE, Cao YM, Michalides BA, Botta SA, Faeder JR, Gottschalk RA. Predicting gene level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.19.541151. [PMID: 37292918 PMCID: PMC10245690 DOI: 10.1101/2023.05.19.541151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The JAK-STAT pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational workflow to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to IL-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified select cytokine-induced gene sets associated with late pSTAT3 timeframes and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying dynamically regulated genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems.
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Affiliation(s)
- Neha Cheemalavagu
- University of Pittsburgh, Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA USA
- University of Pittsburgh, Department of Immunology, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA USA
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA USA
| | - Karsen E. Shoger
- University of Pittsburgh, Department of Immunology, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA USA
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA USA
| | - Yuqi M. Cao
- University of Pittsburgh, Department of Immunology, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA USA
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA USA
| | - Brandon A. Michalides
- University of Pittsburgh, Department of Immunology, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA USA
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA USA
| | - Samuel A. Botta
- University of Pittsburgh, Department of Immunology, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA USA
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA USA
| | - James R. Faeder
- University of Pittsburgh, Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA USA
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA USA
| | - Rachel A. Gottschalk
- University of Pittsburgh, Department of Immunology, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA USA
- Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA USA
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28
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Cai H, Des Marais DL. Revisiting regulatory coherence: accounting for temporal bias in plant gene co-expression analyses. THE NEW PHYTOLOGIST 2023; 238:16-24. [PMID: 36617750 DOI: 10.1111/nph.18720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Affiliation(s)
- Haoran Cai
- Department of Civil and Environmental Engineering, MIT, 15 Vassar St., Cambridge, MA, 02139, USA
| | - David L Des Marais
- Department of Civil and Environmental Engineering, MIT, 15 Vassar St., Cambridge, MA, 02139, USA
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29
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Reagor CC, Velez-Angel N, Hudspeth AJ. Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference. PNAS NEXUS 2023; 2:pgad113. [PMID: 37113980 PMCID: PMC10129065 DOI: 10.1093/pnasnexus/pgad113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 03/21/2023] [Accepted: 03/23/2023] [Indexed: 04/29/2023]
Abstract
Identifying the causal interactions in gene-regulatory networks requires an accurate understanding of the time-lagged relationships between transcription factors and their target genes. Here we describe DELAY (short for Depicting Lagged Causality), a convolutional neural network for the inference of gene-regulatory relationships across pseudotime-ordered single-cell trajectories. We show that combining supervised deep learning with joint probability matrices of pseudotime-lagged trajectories allows the network to overcome important limitations of ordinary Granger causality-based methods, for example, the inability to infer cyclic relationships such as feedback loops. Our network outperforms several common methods for inferring gene regulation and, when given partial ground-truth labels, predicts novel regulatory networks from single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data sets. To validate this approach, we used DELAY to identify important genes and modules in the regulatory network of auditory hair cells, as well as likely DNA-binding partners for two hair cell cofactors (Hist1h1c and Ccnd1) and a novel binding sequence for the hair cell-specific transcription factor Fiz1. We provide an easy-to-use implementation of DELAY under an open-source license at https://github.com/calebclayreagor/DELAY.
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Affiliation(s)
| | - Nicolas Velez-Angel
- Howard Hughes Medical Institute and Laboratory of Sensory Neuroscience, The Rockefeller University, New York, NY 10065, USA
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30
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Li A, Xiong S, Li J, Mallik S, Liu Y, Fei R, Zhou H, Liu G. AngClust: Angle Feature-Based Clustering for Short Time Series Gene Expression Profiles. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1574-1580. [PMID: 35853049 DOI: 10.1109/tcbb.2022.3192306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
When clustering gene expression, it is expected that correlation coefficients of genes in the same clusters are high, and that gene ontology (GO) enrichment analysis of most clusters will be significant. However, existing short-term gene expression clustering algorithms have limitations. To address this problem, we proposed a novel clustering process based on angular features for short-term gene expression. Our method (named AngClust) uses angular features to indicate the change of trend in gene expression levels at two neighboring time points. The changes of angles at multiple time points reflects the change of trend of the overall expression levels. Such changes are used to measure whether the expression trends of different genes are similar. To obtain functionally significant clusters from the clustering results, we evaluated numbers of genes in clusters, average correlation coefficient, fluctuation, and their correlation with GO term enrichment. The efficacy of AngClust outperform two other measures, Euclidean distance (ED) and dynamic time warping of correlation (DTW), on a dataset of yeast gene expression. The ratios of GO and pathway term-enriched of clusters of AngClust is higher than or equal to that of STEM and TMixClust on human, mouse, and yeast time series of gene expression.
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31
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Fujita S, Karasawa Y, Hironaka KI, Taguchi YH, Kuroda S. Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome. PLoS One 2023; 18:e0281594. [PMID: 36791130 PMCID: PMC9931158 DOI: 10.1371/journal.pone.0281594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/27/2023] [Indexed: 02/16/2023] Open
Abstract
High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion of 75 g oral glucose. We characterized each molecule by individual dependence (constant or variable) and time dependence (later peak or early peak). Three of the four features extracted by TD were characterized by our previous hypothesis-driven study, indicating that TD can extract some of the same features obtained by hypothesis-driven analysis in a non-biased manner. In contrast to the years taken for our previous hypothesis-driven analysis, the data-driven analysis in this study took days, indicating that TD can extract biological features in a non-biased manner without the time-consuming process of hypothesis generation.
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Affiliation(s)
- Suguru Fujita
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Yasuaki Karasawa
- Department of Neurosurgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ken-ichi Hironaka
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Y.-h. Taguchi
- Department of Physics, Chuo University, Tokyo, Japan
| | - Shinya Kuroda
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
- * E-mail:
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32
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Harder AV, Terwindt GM, Nyholt DR, van den Maagdenberg AM. Migraine genetics: Status and road forward. Cephalalgia 2023; 43:3331024221145962. [PMID: 36759319 DOI: 10.1177/03331024221145962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
BACKGROUND Migraine is considered a multifactorial genetic disorder. Different platforms and methods are used to unravel the genetic basis of migraine. Initially, linkage analysis in multigenerational families followed by Sanger sequencing of protein-coding parts (exons) of genes in the genomic region shared by affected family members identified high-effect risk DNA mutations for rare Mendelian forms of migraine, foremost hemiplegic migraine. More recently, genome-wide association studies testing millions of DNA variants in large groups of patients and controls have proven successful in identifying many dozens of low-effect risk DNA variants for the more common forms of migraine with the number of associated DNA variants increasing steadily with larger sample sizes. Currently, next-generation sequencing, utilising whole exome and whole genome sequence data, and other omics data are being used to facilitate their functional interpretation and the discovery of additional risk factors. Various methods and analysis tools, such as genetic correlation and causality analysis, are used to further characterise genetic risk factors. FINDINGS We describe recent findings in genome-wide association studies and next-generation sequencing analysis in migraine. We show that the combined results of the two most recent and most powerful migraine genome-wide association studies have identified a total of 178 LD-independent (r2 < 0.1) genome-wide significant single nucleotide polymorphisms (SNPs), of which 99 were unique to Hautakangas et al., 11 were unique to Choquet et al., and 68 were identified by both studies. When considering that Choquet et al. also identified three SNPs in a female-specific genome-wide association studies then these two recent studies identified 181 independent SNPs robustly associated with migraine. Cross-trait and causal analyses are beginning to identify and characterise specific biological factors that contribute to migraine risk and its comorbid conditions. CONCLUSION This review provides a timely update and overview of recent genetic findings in migraine.
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Affiliation(s)
- Aster Ve Harder
- Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands.,Department of Human Genetics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Gisela M Terwindt
- Department of Human Genetics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Dale R Nyholt
- School of Biomedical Sciences, Faculty of Health, and Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Australia
| | - Arn Mjm van den Maagdenberg
- Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands.,Department of Human Genetics, Leiden University Medical Centre, Leiden, The Netherlands
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Alharbi F, Vakanski A. Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review. Bioengineering (Basel) 2023; 10:bioengineering10020173. [PMID: 36829667 PMCID: PMC9952758 DOI: 10.3390/bioengineering10020173] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.
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Time-resolved RNA signatures of CD4+ T cells in Parkinson's disease. Cell Death Dis 2023; 9:18. [PMID: 36681665 PMCID: PMC9867723 DOI: 10.1038/s41420-023-01333-0] [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: 11/07/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/22/2023]
Abstract
Parkinson's disease (PD) emerges as a complex, multifactorial disease. While there is increasing evidence that dysregulated T cells play a central role in PD pathogenesis, elucidation of the pathomechanical changes in related signaling is still in its beginnings. We employed time-resolved RNA expression upon the activation of peripheral CD4+ T cells to track and functionally relate changes on cellular signaling in representative cases of patients at different stages of PD. While only few miRNAs showed time-course related expression changes in PD, we identified groups of genes with significantly altered expression for each different time window. Towards a further understanding of the functional consequences, we highlighted pathways with decreased or increased activity in PD, including the most prominent altered IL-17 pathway. Flow cytometric analyses showed not only an increased prevalence of Th17 cells but also a specific subtype of IL-17 producing γδ-T cells, indicating a previously unknown role in PD pathogenesis.
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Lin Z, Ou-Yang L. Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning. Brief Bioinform 2023; 24:6965907. [PMID: 36585783 DOI: 10.1093/bib/bbac586] [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: 10/10/2022] [Revised: 11/28/2022] [Accepted: 11/29/2022] [Indexed: 01/01/2023] Open
Abstract
The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.
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Affiliation(s)
- Zerun Lin
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, and Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Le Ou-Yang
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, and Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China
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Jiang X, Liu K, Peng H, Fang J, Zhang A, Han Y, Zhang X. Comparative network analysis reveals the dynamics of organic acid diversity during fruit ripening in peach (Prunus persica L. Batsch). BMC PLANT BIOLOGY 2023; 23:16. [PMID: 36617558 PMCID: PMC9827700 DOI: 10.1186/s12870-023-04037-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND Organic acids are important components that determine the fruit flavor of peach (Prunus persica L. Batsch). However, the dynamics of organic acid diversity during fruit ripening and the key genes that modulate the organic acids metabolism remain largely unknown in this kind of fruit tree which yield ranks sixth in the world. RESULTS In this study, we used 3D transcriptome data containing three dimensions of information, namely time, phenotype and gene expression, from 5 different varieties of peach to construct gene co-expression networks throughout fruit ripening of peach. With the network inferred, the time-ordered network comparative analysis was performed to select high-acid specific gene co-expression network and then clarify the regulatory factors controlling organic acid accumulation. As a result, network modules related to organic acid synthesis and metabolism under high-acid and low-acid comparison conditions were identified for our following research. In addition, we obtained 20 candidate genes as regulatory factors related to organic acid metabolism in peach. CONCLUSIONS The study provides new insights into the dynamics of organic acid accumulation during fruit ripening, complements the results of classical co-expression network analysis and establishes a foundation for key genes discovery from time-series multiple species transcriptome data.
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Affiliation(s)
- Xiaohan Jiang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kangchen Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Huixiang Peng
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jing Fang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Aidi Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
| | - Yuepeng Han
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China.
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China.
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China.
- Center of Economic Botany, Core Botanical Gardens, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China.
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Sun Q, Zhao T, Li B, Li M, Luo P, Zhang C, Chen G, Cao Z, Li Y, Du M, He H. FTO/RUNX2 signaling axis promotes cementoblast differentiation under normal and inflammatory condition. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2022; 1869:119358. [PMID: 36084732 DOI: 10.1016/j.bbamcr.2022.119358] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/30/2022] [Accepted: 09/03/2022] [Indexed: 06/15/2023]
Abstract
N6-methyladenosine (m6A) is the most prevalent mRNA modification which plays crucial roles in various biological processes, but its role in cementogenesis remains largely unknown. Here, using time-series transcriptomic analysis, we reveal that mRNA m6A demethylase Fat mass and obesity-associated protein (FTO) is involved in cementogenesis. Knocking down FTO decreases cementoblast differentiation and mineralization in both OCCM-30 cellular model and murine ectopic bone formation model. Mechanistically, we find that FTO directly binds Runt-related transcription factor 2 (Runx2) mRNA, an important cementogenesis factor, thus protecting it from YTH domain-containing family protein 2 (YTHDF2) mediated degradation, when cementoblasts are differentiating. Knocking down YTHDF2 restores the expression of Runx2 in FTO-knockdown cells. Moreover, under inflammatory conditions, TNF-α inhibits cementoblast differentiation and mineralization partly through FTO/RUNX2 axis. Collectively, our study reveals an important regulatory role of FTO/RUNX2 axis in normal and pathological cementogenesis.
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Affiliation(s)
- Qiao Sun
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei- MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Tingting Zhao
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei- MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Biao Li
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei- MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Mengying Li
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei- MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Ping Luo
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei- MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Chen Zhang
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei- MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Gang Chen
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei- MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Zhengguo Cao
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei- MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Periodontology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Yicun Li
- Department of Oral and Maxillofacial Surgery, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Guangdong province, China
| | - Mingyuan Du
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei- MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China.
| | - Hong He
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei- MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
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Zhao X, Wang Z, Ji X, Bu S, Fang P, Wang Y, Wang M, Yang Y, Zhang W, Leung AY, Shi P. Discrete single-cell microRNA analysis for phenotyping the heterogeneity of acute myeloid leukemia. Biomaterials 2022; 291:121869. [DOI: 10.1016/j.biomaterials.2022.121869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/14/2022] [Accepted: 10/19/2022] [Indexed: 11/28/2022]
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Lu X, Sheng Y, Xiao Y, Wang W. Stakeholder relationships and corporate social goal orientation: Implications for entrepreneurial psychology. Front Psychol 2022; 13:942294. [PMID: 36389547 PMCID: PMC9649820 DOI: 10.3389/fpsyg.2022.942294] [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: 05/12/2022] [Accepted: 09/27/2022] [Indexed: 11/03/2023] Open
Abstract
As the sensitivity to corporate social responsibility (CSR) continues to grow, the goal of enterprises has expanded beyond the sole pursuit of economic value. Corporate social goal orientation has therefore come to occupy a central position in entrepreneurs' psychology and the transition away from a market-only economy. This study uses secondary data from 4,288 samples of 725 Chinese-listed companies from 2009 to 2020 to explore the driving factors in social goal orientation based on the characteristics of sample companies and their industry groups from the perspective of stakeholder relationships. The results can be summarized as follows: (1) there is an inverted U-shaped relationship between government stakeholder relationships and social goal orientation, and there is a significant positive relationship between financial stakeholder relationships, market stakeholder relationships, and corporate social goal orientation. (2) The correlation between single-dual stakeholder relationships and social goal orientation is not consistent. In light of the nature of the roles of government and the market, the correlation between the government-market dual relationship and corporate social goal orientation is not significant. However, there is a significant correlation between the finance-government dual stakeholder relationship and social goal orientation; that is, the dual stakeholder relationship maintains the existence of non-institutional capital and corporate financial capital. Moreover, there is no significant correlation between the market-finance dual relationship and corporate social goal orientation, and there is substitutability between market and financial stakeholder relationships. With the deepening of our understanding of CSR, the core goal of enterprises is no longer confined to the pursuit of economic value, and their social goal orientation has come to be regarded as a major driving force in sustainable development. This study enriches the research on the relationship between stakeholder relationships and shows that stakeholder relationships also have important significance to both achieving corporate goals and shaping entrepreneurs' psychology.
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Affiliation(s)
- Xiaowei Lu
- College of Economics and Management, Zhejiang A&F University, Hangzhou, China
| | - Ya Sheng
- School of Business Administration, Zhejiang Gongshang University, Hangzhou, China
| | - Yao Xiao
- School of Business Administration, Zhejiang Gongshang University, Hangzhou, China
| | - Wei Wang
- School of International Studies, Zhejiang Business College, Hangzhou, China
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40
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Cheng CH, Lai PY. Efficient reconstruction of directed networks from noisy dynamics using stochastic force inference. Phys Rev E 2022; 106:034302. [PMID: 36266821 DOI: 10.1103/physreve.106.034302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 08/19/2022] [Indexed: 06/16/2023]
Abstract
We consider coupled network dynamics under uncorrelated noises that fluctuate about the noise-free long-time asymptotic state. Our goal is to reconstruct the directed network only from the time-series data of the dynamics of the nodes. By using the stochastic force inference method with a simple natural choice of linear polynomial basis, we derive a reconstruction scheme of the connection weights and the noise strength of each node. Explicit simulations for directed and undirected random networks with various node dynamics are carried out to demonstrate the good accuracy and high efficiency of the reconstruction scheme. We further consider the case when only a subset of the network and its node dynamics can be observed, and it is demonstrated that the directed weighted connections among the observed nodes can be easily and faithfully reconstructed. In addition, we propose a scheme to infer the number of hidden nodes and their effects on each observed node. The accuracy of these results is illustrated by simulations.
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Affiliation(s)
- Chi-Ho Cheng
- Department of Physics, National Changhua University of Education, Changhua 500, Taiwan, Republic of China and Department of Physics and Center for Complex Systems, National Central University, Chung-Li District, Taoyuan City 320, Taiwan, Republic of China
| | - Pik-Yin Lai
- Department of Physics, National Changhua University of Education, Changhua 500, Taiwan, Republic of China and Department of Physics and Center for Complex Systems, National Central University, Chung-Li District, Taoyuan City 320, Taiwan, Republic of China
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Gupta A, Martin-Rufino JD, Jones TR, Subramanian V, Qiu X, Grody EI, Bloemendal A, Weng C, Niu SY, Min KH, Mehta A, Zhang K, Siraj L, Al' Khafaji A, Sankaran VG, Raychaudhuri S, Cleary B, Grossman S, Lander ES. Inferring gene regulation from stochastic transcriptional variation across single cells at steady state. Proc Natl Acad Sci U S A 2022; 119:e2207392119. [PMID: 35969771 PMCID: PMC9407670 DOI: 10.1073/pnas.2207392119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/20/2022] [Indexed: 12/24/2022] Open
Abstract
Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.
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Affiliation(s)
- Anika Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
| | - Jorge D. Martin-Rufino
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
| | | | | | - Xiaojie Qiu
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
- HHMI, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | | | - Chen Weng
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
| | | | - Kyung Hoi Min
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Arnav Mehta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Dana-Farber Cancer Institute, Boston, MA 02215
- Department of Medicine, Massachusetts General Hospital, Boston, MA 02114
| | - Kaite Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Layla Siraj
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Vijay G. Sankaran
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
| | - Soumya Raychaudhuri
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA 02115
| | - Brian Cleary
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Eric S. Lander
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115
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Bar N, Nikparvar B, Jayavelu ND, Roessler FK. Constrained Fourier estimation of short-term time-series gene expression data reduces noise and improves clustering and gene regulatory network predictions. BMC Bioinformatics 2022; 23:330. [PMID: 35945515 PMCID: PMC9364503 DOI: 10.1186/s12859-022-04839-z] [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: 11/25/2021] [Accepted: 07/12/2022] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Biological data suffers from noise that is inherent in the measurements. This is particularly true for time-series gene expression measurements. Nevertheless, in order to to explore cellular dynamics, scientists employ such noisy measurements in predictive and clustering tools. However, noisy data can not only obscure the genes temporal patterns, but applying predictive and clustering tools on noisy data may yield inconsistent, and potentially incorrect, results. RESULTS To reduce the noise of short-term (< 48 h) time-series expression data, we relied on the three basic temporal patterns of gene expression: waves, impulses and sustained responses. We constrained the estimation of the true signals to these patterns by estimating the parameters of first and second-order Fourier functions and using the nonlinear least-squares trust-region optimization technique. Our approach lowered the noise in at least 85% of synthetic time-series expression data, significantly more than the spline method ([Formula: see text]). When the data contained a higher signal-to-noise ratio, our method allowed downstream network component analyses to calculate consistent and accurate predictions, particularly when the noise variance was high. Conversely, these tools led to erroneous results from untreated noisy data. Our results suggest that at least 5-7 time points are required to efficiently de-noise logarithmic scaled time-series expression data. Investing in sampling additional time points provides little benefit to clustering and prediction accuracy. CONCLUSIONS Our constrained Fourier de-noising method helps to cluster noisy gene expression and interpret dynamic gene networks more accurately. The benefit of noise reduction is large and can constitute the difference between a successful application and a failing one.
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Affiliation(s)
- Nadav Bar
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
| | - Bahareh Nikparvar
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
| | - Naresh Doni Jayavelu
- grid.34477.330000000122986657Division of Medical Genetics, Department of Medicine, University of Washington Seattle, Seattle, WA 98195-7720 USA
| | - Fabienne Krystin Roessler
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
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Lattin CR, Kelly TR, Kelly MW, Johnson KM. Constitutive gene expression differs in three brain regions important for cognition in neophobic and non-neophobic house sparrows (Passer domesticus). PLoS One 2022; 17:e0267180. [PMID: 35536842 PMCID: PMC9089922 DOI: 10.1371/journal.pone.0267180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 04/04/2022] [Indexed: 12/13/2022] Open
Abstract
Neophobia (aversion to new objects, food, and environments) is a personality trait that affects the ability of wildlife to adapt to new challenges and opportunities. Despite the ubiquity and importance of this trait, the molecular mechanisms underlying repeatable individual differences in neophobia in wild animals are poorly understood. We evaluated wild-caught house sparrows (Passer domesticus) for neophobia in the lab using novel object tests. We then selected a subset of neophobic and non-neophobic individuals (n = 3 of each, all females) and extracted RNA from four brain regions involved in learning, memory, threat perception, and executive function: striatum, caudal dorsomedial hippocampus, medial ventral arcopallium, and caudolateral nidopallium (NCL). Our analysis of differentially expressed genes (DEGs) used 11,889 gene regions annotated in the house sparrow reference genome for which we had an average of 25.7 million mapped reads/sample. PERMANOVA identified significant effects of brain region, phenotype (neophobic vs. non-neophobic), and a brain region by phenotype interaction. Comparing neophobic and non-neophobic birds revealed constitutive differences in DEGs in three of the four brain regions examined: hippocampus (12% of the transcriptome significantly differentially expressed), striatum (4%) and NCL (3%). DEGs included important known neuroendocrine mediators of learning, memory, executive function, and anxiety behavior, including serotonin receptor 5A, dopamine receptors 1, 2 and 5 (downregulated in neophobic birds), and estrogen receptor beta (upregulated in neophobic birds). These results suggest that some of the behavioral differences between phenotypes may be due to underlying gene expression differences in the brain. The large number of DEGs in neophobic and non-neophobic birds also implies that there are major differences in neural function between the two phenotypes that could affect a wide variety of behavioral traits beyond neophobia.
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Affiliation(s)
- Christine R. Lattin
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America
- * E-mail:
| | - Tosha R. Kelly
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America
| | - Morgan W. Kelly
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America
| | - Kevin M. Johnson
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States of America
- Center for Coastal Marine Sciences, California Polytechnic State University, San Luis Obispo, CA, United States of America
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Passemiers A, Moreau Y, Raimondi D. Fast and accurate inference of gene regulatory networks through robust precision matrix estimation. Bioinformatics 2022; 38:2802-2809. [PMID: 35561176 PMCID: PMC9113237 DOI: 10.1093/bioinformatics/btac178] [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: 10/06/2021] [Revised: 03/14/2022] [Accepted: 03/22/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Transcriptional regulation mechanisms allow cells to adapt and respond to external stimuli by altering gene expression. The possible cell transcriptional states are determined by the underlying gene regulatory network (GRN), and reliably inferring such network would be invaluable to understand biological processes and disease progression. RESULTS In this article, we present a novel method for the inference of GRNs, called PORTIA, which is based on robust precision matrix estimation, and we show that it positively compares with state-of-the-art methods while being orders of magnitude faster. We extensively validated PORTIA using the DREAM and MERLIN+P datasets as benchmarks. In addition, we propose a novel scoring metric that builds on graph-theoretical concepts. AVAILABILITY AND IMPLEMENTATION The code and instructions for data acquisition and full reproduction of our results are available at https://github.com/AntoinePassemiers/PORTIA-Manuscript. PORTIA is available on PyPI as a Python package (portia-grn). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Saremi M, Amirmazlaghani M. Reconstruction of Gene Regulatory Networks Using Multiple Datasets. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1827-1839. [PMID: 33539303 DOI: 10.1109/tcbb.2021.3057241] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
MOTIVATION Laboratory gene regulatory data for a species are sporadic. Despite the abundance of gene regulatory network algorithms that employ single data sets, few algorithms can combine the vast but disperse sources of data and extract the potential information. With a motivation to compensate for this shortage, we developed an algorithm called GENEREF that can accumulate information from multiple types of data sets in an iterative manner, with each iteration boosting the performance of the prediction results. RESULTS The algorithm is examined extensively on data extracted from the quintuple DREAM4 networks and DREAM5's Escherichia coli and Saccharomyces cerevisiae networks and sub-networks. Many single-dataset and multi-dataset algorithms were compared to test the performance of the algorithm. Results show that GENEREF surpasses non-ensemble state-of-the-art multi-perturbation algorithms on the selected networks and is competitive to present multiple-dataset algorithms. Specifically, it outperforms dynGENIE3 and is on par with iRafNet. Also, we argued that a scoring method solely based on the AUPR criterion would be more trustworthy than the traditional score. AVAILABILITY The Python implementation along with the data sets and results can be downloaded from github.com/msaremi/GENEREF.
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Profiling and Functional Analysis of mRNAs during Skeletal Muscle Differentiation in Goats. Animals (Basel) 2022; 12:ani12081048. [PMID: 35454294 PMCID: PMC9024908 DOI: 10.3390/ani12081048] [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: 03/01/2022] [Revised: 04/10/2022] [Accepted: 04/12/2022] [Indexed: 02/01/2023] Open
Abstract
Skeletal myogenesis is a complicated biological event that involves a succession of tightly controlled gene expressions. In order to identify novel regulators of this process, we performed mRNA-Seq studies of goat skeletal muscle satellite cells (MuSCs) cultured under proliferation (GM) and differentiation (DM1/DM5) conditions. A total of 19,871 goat genes were expressed during these stages, 198 of which represented novel transcripts. Notably, in pairwise comparisons at the different stages, 2551 differentially expressed genes (DEGs) were identified (p < 0.05), including 1560 in GM vs. DM1, 1597 in GM vs. DM5, and 959 in DM1 vs. DM5 DEGs. The time-series expression profile analysis clustered the DEGs into eight gene groups, three of which had significantly upregulated and downregulated patterns (p < 0.05). Functional enrichment analysis showed that DEGs were enriched for essential biological processes such as muscle structure development, muscle contraction, muscle cell development, striated muscle cell differentiation, and myofibril assembly, and were involved in pathways such as the MAPK, Wnt and PPAR signaling pathways. Moreover, the expression of eight DEGs (MYL2, DES, MYOG, FAP, PLK2, ADAM, WWC1, and PRDX1) was validated. These findings offer novel insights into the transcriptional regulation of skeletal myogenesis in goats.
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Hsieh HC, Lin PT, Sung KB. Characterization and identification of cell death dynamics by quantitative phase imaging. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:046502. [PMID: 35484694 PMCID: PMC9047449 DOI: 10.1117/1.jbo.27.4.046502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/05/2022] [Indexed: 06/14/2023]
Abstract
SIGNIFICANCE Investigating cell death dynamics at the single-cell level plays an essential role in biological research. Quantitative phase imaging (QPI), a label-free method without adverse effects of exogenous labels, has been widely used to image many types of cells under various conditions. However, the dynamics of QPI features during cell death have not been thoroughly characterized. AIM We aim to develop a label-free technique to quantitatively characterize single-cell dynamics of cellular morphology and intracellular mass distribution of cells undergoing apoptosis and necrosis. APPROACH QPI was used to capture time-lapse phase images of apoptotic, necrotic, and normal cells. The dynamics of morphological and QPI features during cell death were fitted by a sigmoid function to quantify both the extent and rate of changes. RESULTS The two types of cell death mainly differed from normal cells in the lower phase of the central region and differed from each other in the sharp nuclear boundary shown in apoptotic cells. CONCLUSIONS The proposed method characterizes the dynamics of cellular morphology and intracellular mass distributions, which could be applied to studying cells undergoing state transition such as drug response.
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Affiliation(s)
- Huai-Ching Hsieh
- National Taiwan University, Department of Life Science, Taipei, Taiwan
- National Taiwan University, Department of Electrical Engineering, Taipei, Taiwan
| | - Po-Ting Lin
- National Taiwan University, Graduate Institute of Biomedical Electronics and Bioinformatics, Taipei, Taiwan
| | - Kung-Bin Sung
- National Taiwan University, Department of Electrical Engineering, Taipei, Taiwan
- National Taiwan University, Graduate Institute of Biomedical Electronics and Bioinformatics, Taipei, Taiwan
- National Taiwan University, Molecular Imaging Center, Taipei, Taiwan
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Periyasamy S, Mowry B. BrainDevo: Spatio-Temporal Gene Regulation Repository of Brain Development. Front Mol Neurosci 2022; 15:799801. [PMID: 35392271 PMCID: PMC8981586 DOI: 10.3389/fnmol.2022.799801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 02/09/2022] [Indexed: 11/16/2022] Open
Affiliation(s)
- Sathish Periyasamy
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Sathish Periyasamy
| | - Bryan Mowry
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The University of Queensland, Brisbane, QLD, Australia
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Fuchs P, Adrion F, Shafiullah AZM, Bruckmaier RM, Umstätter C. Detecting Ultra- and Circadian Activity Rhythms of Dairy Cows in Automatic Milking Systems Using the Degree of Functional Coupling—A Pilot Study. FRONTIERS IN ANIMAL SCIENCE 2022. [DOI: 10.3389/fanim.2022.839906] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Ultra- and circadian activity rhythms of animals can provide important insights into animal welfare. The consistency of behavioral patterns is characteristic of healthy organisms, while changes in the regularity of behavioral rhythms may indicate health and stress-related challenges. This pilot study aimed to examine whether dairy cows in free-stall barns with an automatic milking system (AMS) and free cow traffic can develop ultra- and circadian activity rhythms. On 4 dairy farms, pedometers recorded the activity of 10 cows each over 28 days. Based on time series calculation, the Degree of Functional Coupling (DFC) was used to determine the cows' activity rhythms. The DFC identified significant rhythmic patterns in sliding 7-day periods and indicated the percentage of activity (0–100%) that was synchronized with the 24-h day-night rhythm. As light is the main factor influencing the sleep-wake cycle of organisms, light intensity was recorded in the AMS, at the feed alley and in the barn of each farm. In addition, feeding and milking management were considered as part of the environmental context. Saliva samples of each cow were taken every 3 h for 1 day to determine the melatonin concentration. The DFC approach was successfully used to detect activity rhythms of dairy cows in commercial housing systems. However, large inter- and intra-individual variations were observed. Due to a high frequency of 0 and 100%, a median split was used to dichotomize into “low” (<72.34%) and “high” (≥72.34%) DFC. Forty percent of the sliding 7-day periods corresponded to a low DFC and 50% to a high DFC. No DFC could be calculated for 10% of the periods, as the cows' activity was not synchronized to 24 h. A generalized linear mixed-effects model revealed that the DFC levels were positively associated with a longer milking interval and a higher amount of daytime activity and negatively associated with higher number of lactations. The DFC is a novel approach to animal behavior monitoring. Due to its automation capability, it represents a promising tool in its further development for the purpose of longitudinal monitoring of animal welfare.
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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