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Xu C. The Oryza sativa transcriptome responds spatiotemporally to polystyrene nanoplastic stress. Sci Total Environ 2024; 928:172449. [PMID: 38615784 DOI: 10.1016/j.scitotenv.2024.172449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/20/2024] [Accepted: 04/10/2024] [Indexed: 04/16/2024]
Abstract
Nanoplastic represents an emerging abiotic stress facing modern agriculture, impacting global crop production. However, the molecular response of crop plants to this stress remains poorly understood at a spatiotemporal resolution. We therefore used RNA sequencing to profile the transcriptome expressed in rice (Oryza sativa) root and leaf organs at 1, 2, 4, and 8 d post exposure with nanoplastic. We revealed a striking similarity between the rice biomass dynamics in aboveground parts to that in belowground parts during nanoplastic stress, but transcriptome did not. At the global transcriptomic level, a total of 2332 differentially expressed genes were identified, with the majority being spatiotemporal specific, reflecting that nanoplastics predominantly regulate three processes in rice seedlings: (1) down-regulation of chlorophyll biosynthesis, photosynthesis, and starch, sucrose and nitrogen metabolism, (2) activation of defense responses such as brassinosteroid biosynthesis and phenylpropanoid biosynthesis, and (3) modulation of jasmonic acid and cytokinin signaling pathways by transcription factors. Notably, the genes involved in plant-pathogen interaction were shown to be successively modulated by both root and leaf organs, particularly plant disease defense genes (OsWRKY24, OsWRKY53, Os4CL3, OsPAL4, and MPK5), possibly indicating that nanoplastics affect rice growth indirectly through other biota. Finally, we associated biomass phenotypes with the temporal reprogramming of rice transcriptome by weighted gene co-expression network analysis, noting a significantly correlation with photosynthesis, carbon metabolism, and phenylpropanoid biosynthesis that may reflect the mechanisms of biomass reduction. Functional analysis further identified PsbY, MYB, cytochrome P450, and AP2/ERF as hub genes governing these pathways. Overall, our work provides the understanding of molecular mechanisms of rice in response to nanoplastics, which in turn suggests how rice might behave in a nanoplastic pollution scenario.
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Affiliation(s)
- Chanchan Xu
- Research Center for Eco-Environmental Engineering, Dongguan University of Technology, Dongguan 523808, China; Institute of Environmental Research at Greater Bay Area, Guangzhou University, Guangzhou 510006, China.
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2
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Xie X, Wang F, Wang G, Zhu W, Du X, Wang H. Learning the cellular activity representation based on gene regulatory networks for prediction of tumor response to drugs. Artif Intell Med 2024; 152:102864. [PMID: 38640702 DOI: 10.1016/j.artmed.2024.102864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 01/28/2024] [Accepted: 03/30/2024] [Indexed: 04/21/2024]
Abstract
Predicting the response of tumor cells to anti-tumor drugs is critical to realizing cancer precision medicine. Currently, most existing methods ignore the regulatory relationships between genes and thus have unsatisfactory predictive performance. In this paper, we propose to predict anti-tumor drug efficacy via learning the activity representation of tumor cells based on a priori knowledge of gene regulation networks (GRNs). Specifically, the method simulates the cellular biosystem by synthesizing a cell-gene activity network and then infers a new low-dimensional activity representation for tumor cells from the raw high-dimensional expression profile. The simulated cell-gene network mainly comprises known gene regulatory networks collected from multiple resources and fuses tumor cells by linking them to hotspot genes that are over- or under-expressed in them. The resulting activity representation could not only reflect the shallow expression profile (hotspot genes) but also mines in-depth information of gene regulation activity in tumor cells before treatment. Finally, we build deep learning models on the activity representation for predicting drug efficacy in tumor cells. Experimental results on the benchmark GDSC dataset demonstrate the superior performance of the proposed method over SOTA methods with the highest AUC of 0.954 in the efficacy label prediction and the best R2 of 0.834 in the regression of half maximal inhibitory concentration (IC50) values, suggesting the potential value of the proposed method in practice.
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Affiliation(s)
- Xinping Xie
- School of mathematics and physics, Anhui Jianzhu University, Hefei, China
| | - Fengting Wang
- School of mathematics and physics, Anhui Jianzhu University, Hefei, China; Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, Hefei, China
| | - Guanfu Wang
- School of mathematics and physics, Anhui Jianzhu University, Hefei, China
| | - Weiwei Zhu
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, Hefei, China; Zhongqi AI Lab, Hefei, China
| | - Xiaodong Du
- Experimental Teaching Center, Hefei University, Hefei, China
| | - Hongqiang Wang
- Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, Hefei, China; Zhongqi AI Lab, Hefei, China.
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3
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Ma X, Li Z, Du Z, Xu Y, Chen Y, Zhuo L, Fu X, Liu R. Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction. Comput Biol Med 2024; 174:108484. [PMID: 38643595 DOI: 10.1016/j.compbiomed.2024.108484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/18/2024] [Accepted: 04/15/2024] [Indexed: 04/23/2024]
Abstract
Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.
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Affiliation(s)
- Xinqian Ma
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325027, Wenzhou, China
| | - Zhen Li
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, China; Institute of Computational Science and Technology, Guangzhou University, 510000, Guangzhou, China
| | - Zhenya Du
- Guangzhou Xinhua University, 510520, Guangzhou, China
| | - Yan Xu
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325027, Wenzhou, China
| | - Yifan Chen
- College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, 410004, China
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, 325027, Wenzhou, China.
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, 410012, Changsha, China
| | - Ruijun Liu
- School of Software, Beihang University, Beijing, China.
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4
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Li F, Zhu Y, Wang T, Tang J, Huang Y, Gu J, Mai Y, Wang M, Zhang Z, Ning J, Kang B, Wang J, Zhou T, Cui Y, Pan G. Characterization of gene regulatory networks underlying key properties in human hematopoietic stem cell ontogeny. Cell Regen 2024; 13:9. [PMID: 38630195 PMCID: PMC11024070 DOI: 10.1186/s13619-024-00192-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 03/23/2024] [Indexed: 04/20/2024]
Abstract
Human hematopoiesis starts at early yolk sac and undergoes site- and stage-specific changes over development. The intrinsic mechanism underlying property changes in hematopoiesis ontogeny remains poorly understood. Here, we analyzed single-cell transcriptome of human primary hematopoietic stem/progenitor cells (HSPCs) at different developmental stages, including yolk-sac (YS), AGM, fetal liver (FL), umbilical cord blood (UCB) and adult peripheral blood (PB) mobilized HSPCs. These stage-specific HSPCs display differential intrinsic properties, such as metabolism, self-renewal, differentiating potentialities etc. We then generated highly co-related gene regulatory network (GRNs) modules underlying the differential HSC key properties. Particularly, we identified GRNs and key regulators controlling lymphoid potentiality, self-renewal as well as aerobic respiration in human HSCs. Introducing selected regulators promotes key HSC functions in HSPCs derived from human pluripotent stem cells. Therefore, GRNs underlying key intrinsic properties of human HSCs provide a valuable guide to generate fully functional HSCs in vitro.
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Affiliation(s)
- Fei Li
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Yanling Zhu
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China.
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, GIBH-CUHK Joint Research Laboratory On Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
| | - Tianyu Wang
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, GIBH-CUHK Joint Research Laboratory On Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Jun Tang
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
| | - Yuhua Huang
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Jiaming Gu
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Yuchan Mai
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Mingquan Wang
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, GIBH-CUHK Joint Research Laboratory On Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Zhishuai Zhang
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Jiaying Ning
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Baoqiang Kang
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Junwei Wang
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Tiancheng Zhou
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Yazhou Cui
- Key Lab for Rare & Uncommon Diseases of Shandong Province, Biomedical Sciences College & Shandong Medicinal Biotechnology Centre, Shandong First Medical University & Shandong Academy of Medical Sciences, Ji'nan, 250117, Shandong, China
| | - Guangjin Pan
- Key Laboratory of Immune Response and Immunotherapy, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences; Guangzhou Medical University, Guangzhou, 510530, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong, China.
- Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangdong-Hong Kong Joint Laboratory for Stem Cell and Regenerative Medicine, Center for Cell Lineage and Cell Therapy, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, GIBH-CUHK Joint Research Laboratory On Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
- Key Lab for Rare & Uncommon Diseases of Shandong Province, Biomedical Sciences College & Shandong Medicinal Biotechnology Centre, Shandong First Medical University & Shandong Academy of Medical Sciences, Ji'nan, 250117, Shandong, China.
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5
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Mitra S, Sil P, Subbaroyan A, Martin OC, Samal A. Preponderance of generalized chain functions in reconstructed Boolean models of biological networks. Sci Rep 2024; 14:6734. [PMID: 38509145 PMCID: PMC10954731 DOI: 10.1038/s41598-024-57086-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 03/14/2024] [Indexed: 03/22/2024] Open
Abstract
Boolean networks (BNs) have been extensively used to model gene regulatory networks (GRNs). The dynamics of BNs depend on the network architecture and regulatory logic rules (Boolean functions (BFs)) associated with nodes. Nested canalyzing functions (NCFs) have been shown to be enriched among the BFs in the large-scale studies of reconstructed Boolean models. The central question we address here is whether that enrichment is due to certain sub-types of NCFs. We build on one sub-type of NCFs, the chain functions (or chain-0 functions) proposed by Gat-Viks and Shamir. First, we propose two other sub-types of NCFs, namely, the class of chain-1 functions and generalized chain functions, the union of the chain-0 and chain-1 types. Next, we find that the fraction of NCFs that are chain-0 (also holds for chain-1) functions decreases exponentially with the number of inputs. We provide analytical treatment for this and other observations on BFs. Then, by analyzing three different datasets of reconstructed Boolean models we find that generalized chain functions are significantly enriched within the NCFs. Lastly we illustrate that upon imposing the constraints of generalized chain functions on three different GRNs we are able to obtain biologically viable Boolean models.
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Affiliation(s)
- Suchetana Mitra
- Indian Institute of Science Education and Research (IISER) Mohali, Manauli, Punjab, 140306, India
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
| | - Priyotosh Sil
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
| | - Ajay Subbaroyan
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
| | - Olivier C Martin
- Université Paris-Saclay, CNRS, INRAE, Univ Evry, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France.
- Université Paris-Cité, CNRS, INRAE, Institute of Plant Sciences Paris-Saclay (IPS2), 91405, Orsay, France.
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, 600113, India.
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India.
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6
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Vahab N, Bonu T, Kuhlmann L, Ramialison M, Tyagi S. Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data. Comput Biol Med 2024; 171:108068. [PMID: 38354497 DOI: 10.1016/j.compbiomed.2024.108068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/30/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024]
Abstract
The availability of large-scale epigenomic data from various cell types and conditions has yielded valuable insights for evaluating and learning features predicting the co-binding of transcription factors (TF). However, prior attempts to develop models predicting motif co-occurrence lacked scalability for globally analyzing any motif combination or making cross-species predictions. Moreover, mapping co-regulatory modules (CRM) to gene regulatory networks (GRN) is crucial for understanding underlying function. Currently, no comprehensive pipeline exists for large-scale, rapid, and accurate CRM and GRN identification. In this study, we analyzed and evaluated different TF binding characteristics facilitating biologically significant co-binding to identify all potential clusters of co-binding TFs. We curated the UniBind database, containing ChIP-Seq data from over 1983 samples and 232 TFs, and implemented two machine learning models to predict CRMs and the potential regulatory networks they operate on. Two machine learning models, Convolution Neural Networks (CNN) and Random Forest Classifier(RFC), used to predict co-binding between TFs, were compared using precision-recall Receiver Operating Characteristic (ROC) curves. CNN outperformed RFC (AUC 0.94 vs. 0.88) and achieved higher F1 scores (0.938 vs. 0.872). The CRMs generated by the clustering algorithm were validated against ChipAtlas and MCOT, revealing additional motifs forming CRMs. We predicted 200k CRMs for 50k+ human genes, validated against recent CRM prediction methods with 100% overlap. Further, we narrowed our focus to study heart-related regulatory motifs, filtering the generated CRMs to report 1784 Cardiac CRMs containing at least four cardiac TFs. Identified cardiac CRMs revealed potential novel regulators like ARID3A and RXRB for SCAD, including known TFs like PPARG for F11R. Our findings highlight the importance of the NKX family of transcription factors in cardiac development and provide potential targets for further investigation in cardiac disease.
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Affiliation(s)
- Naima Vahab
- School of Computational Technologies, RMIT University, Melbourne VIC 3000, Australia; Department of Infectious Diseases, Alfred Hospital, Prahran VIC 3008, Australia
| | - Tarun Bonu
- Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Levin Kuhlmann
- Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | | | - Sonika Tyagi
- School of Computational Technologies, RMIT University, Melbourne VIC 3000, Australia; Department of Infectious Diseases, Alfred Hospital, Prahran VIC 3008, Australia.
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Wu S, Jin K, Tang M, Xia Y, Gao W. Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs. Interdiscip Sci 2024:10.1007/s12539-024-00604-3. [PMID: 38342857 DOI: 10.1007/s12539-024-00604-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/26/2023] [Accepted: 01/03/2024] [Indexed: 02/13/2024]
Abstract
Since gene regulation is a complex process in which multiple genes act simultaneously, accurately inferring gene regulatory networks (GRNs) is a long-standing challenge in systems biology. Although graph neural networks can formally describe intricate gene expression mechanisms, current GRN inference methods based on graph learning regard only transcription factor (TF)-target gene interactions as pairwise relationships, and cannot model the many-to-many high-order regulatory patterns that prevail among genes. Moreover, these methods often rely on limited prior regulatory knowledge, ignoring the structural information of GRNs in gene expression profiles. Therefore, we propose a multi-view hierarchical hypergraphs GRN (MHHGRN) inference model. Specifically, multiple heterogeneous biological information is integrated to construct multi-view hierarchical hypergraphs of TFs and target genes, using hypergraph convolution networks to model higher order complex regulatory relationships. Meanwhile, the coupled information diffusion mechanism and the cross-domain messaging mechanism facilitate the information sharing between genes to optimise gene embedding representations. Finally, a unique channel attention mechanism is used to adaptively learn feature representations from multiple views for GRN inference. Experimental results show that MHHGRN achieves better results than the baseline methods on the E. coli and S. cerevisiae benchmark datasets of the DREAM5 challenge, and it has excellent cross-species generalization, achieving comparable or better performance on scRNA-seq datasets from five mouse and two human cell lines.
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Affiliation(s)
- Songyang Wu
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
| | - Kui Jin
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
| | - Mingjing Tang
- School of Life Science, Yunnan Normal University, Kunming, 650500, China.
- Engineering Research Center of Sustainable Development and Utilization of Biomass Energy, Ministry of Education, Yunnan Normal University, Kunming, 650500, China.
| | - Yuelong Xia
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
| | - Wei Gao
- School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China
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Vághy MA, Otero-Muras I, Pájaro M, Szederkényi G. A Kinetic Finite Volume Discretization of the Multidimensional PIDE Model for Gene Regulatory Networks. Bull Math Biol 2024; 86:22. [PMID: 38253903 PMCID: PMC10803439 DOI: 10.1007/s11538-023-01251-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 12/22/2023] [Indexed: 01/24/2024]
Abstract
In this paper, a finite volume discretization scheme for partial integro-differential equations (PIDEs) describing the temporal evolution of protein distribution in gene regulatory networks is proposed. It is shown that the obtained set of ODEs can be formally represented as a compartmental kinetic system with a strongly connected reaction graph. This allows the application of the theory of nonnegative and compartmental systems for the qualitative analysis of the approximating dynamics. In this framework, it is straightforward to show the existence, uniqueness and stability of equilibria. Moreover, the computation of the stationary probability distribution can be traced back to the solution of linear equations. The discretization scheme is presented for one and multiple dimensional models separately. Illustrative computational examples show the precision of the approach, and good agreement with previous results in the literature.
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Affiliation(s)
- Mihály A Vághy
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/a, Budapest, 1083, Hungary.
| | - Irene Otero-Muras
- Institute for Integrative Systems Biology, Spanish Council for Scientific Research, Carrer del Catedràtic Agustín Escardino Benlloch, 46980, Valencia, Spain
| | - Manuel Pájaro
- Department of Mathematics, Escola Superior de Enxeñaría Informática, University of Vigo, Campus Ourense, 32004, Ourense, Spain
| | - Gábor Szederkényi
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/a, Budapest, 1083, Hungary
- Systems and Control Laboratory, ELKH Institute for Computer Science and Control (SZTAKI), Kende u. 13-17, Budapest, 1111, Hungary
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Gutierrez-Tordera L, Papandreou C, Novau-Ferré N, García-González P, Rojas M, Marquié M, Chapado LA, Papagiannopoulos C, Fernàndez-Castillo N, Valero S, Folch J, Ettcheto M, Camins A, Boada M, Ruiz A, Bulló M. Exploring small non-coding RNAs as blood-based biomarkers to predict Alzheimer's disease. Cell Biosci 2024; 14:8. [PMID: 38229129 PMCID: PMC10790437 DOI: 10.1186/s13578-023-01190-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/27/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Alzheimer's disease (AD) diagnosis relies on clinical symptoms complemented with biological biomarkers, the Amyloid Tau Neurodegeneration (ATN) framework. Small non-coding RNA (sncRNA) in the blood have emerged as potential predictors of AD. We identified sncRNA signatures specific to ATN and AD, and evaluated both their contribution to improving AD conversion prediction beyond ATN alone. METHODS This nested case-control study was conducted within the ACE cohort and included MCI patients matched by sex. Patients free of type 2 diabetes underwent cerebrospinal fluid (CSF) and plasma collection and were followed-up for a median of 2.45-years. Plasma sncRNAs were profiled using small RNA-sequencing. Conditional logistic and Cox regression analyses with elastic net penalties were performed to identify sncRNA signatures for A+(T|N)+ and AD. Weighted scores were computed using cross-validation, and the association of these scores with AD risk was assessed using multivariable Cox regression models. Gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) enrichment analysis of the identified signatures were performed. RESULTS The study sample consisted of 192 patients, including 96 A+(T|N)+ and 96 A-T-N- patients. We constructed a classification model based on a 6-miRNAs signature for ATN. The model could classify MCI patients into A-T-N- and A+(T|N)+ groups with an area under the curve of 0.7335 (95% CI, 0.7327 to 0.7342). However, the addition of the model to conventional risk factors did not improve the prediction of AD beyond the conventional model plus ATN status (C-statistic: 0.805 [95% CI, 0.758 to 0.852] compared to 0.829 [95% CI, 0.786, 0.872]). The AD-related 15-sncRNAs signature exhibited better predictive performance than the conventional model plus ATN status (C-statistic: 0.849 [95% CI, 0.808 to 0.890]). When ATN was included in this model, the prediction further improved to 0.875 (95% CI, 0.840 to 0.910). The miRNA-target interaction network and functional analysis, including GO and KEGG pathway enrichment analysis, suggested that the miRNAs in both signatures are involved in neuronal pathways associated with AD. CONCLUSIONS The AD-related sncRNA signature holds promise in predicting AD conversion, providing insights into early AD development and potential targets for prevention.
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Affiliation(s)
- Laia Gutierrez-Tordera
- Nutrition and Metabolic Health Research Group, Department of Biochemistry and Biotechnology, Rovira i Virgili University (URV), 43201, Reus, Spain
- Institute of Health Pere Virgili (IISPV), 43204, Reus, Spain
- Center of Environmental, Food and Toxicological Technology-TecnATox, Rovira i Virgili University, 43201, Reus, Spain
| | - Christopher Papandreou
- Nutrition and Metabolic Health Research Group, Department of Biochemistry and Biotechnology, Rovira i Virgili University (URV), 43201, Reus, Spain.
- Institute of Health Pere Virgili (IISPV), 43204, Reus, Spain.
- Center of Environmental, Food and Toxicological Technology-TecnATox, Rovira i Virgili University, 43201, Reus, Spain.
| | - Nil Novau-Ferré
- Nutrition and Metabolic Health Research Group, Department of Biochemistry and Biotechnology, Rovira i Virgili University (URV), 43201, Reus, Spain
- Institute of Health Pere Virgili (IISPV), 43204, Reus, Spain
- Center of Environmental, Food and Toxicological Technology-TecnATox, Rovira i Virgili University, 43201, Reus, Spain
| | - Pablo García-González
- ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08028, Barcelona, Spain
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Carlos III Health Institute, 28031, Madrid, Spain
| | - Melina Rojas
- Nutrition and Metabolic Health Research Group, Department of Biochemistry and Biotechnology, Rovira i Virgili University (URV), 43201, Reus, Spain
- Institute of Health Pere Virgili (IISPV), 43204, Reus, Spain
- Center of Environmental, Food and Toxicological Technology-TecnATox, Rovira i Virgili University, 43201, Reus, Spain
| | - Marta Marquié
- ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08028, Barcelona, Spain
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Carlos III Health Institute, 28031, Madrid, Spain
| | - Luis A Chapado
- Laboratory of Epigenetics of Lipid Metabolism, Instituto Madrileño de Estudios Avanzados (IMDEA)-Alimentación, CEI UAM+CSIC, 28049, Madrid, Spain
| | - Christos Papagiannopoulos
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, 45500, Ioannina, Greece
| | - Noèlia Fernàndez-Castillo
- Department de Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, 08007, Barcelona, Spain
| | - Sergi Valero
- ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08028, Barcelona, Spain
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Carlos III Health Institute, 28031, Madrid, Spain
| | - Jaume Folch
- Nutrition and Metabolic Health Research Group, Department of Biochemistry and Biotechnology, Rovira i Virgili University (URV), 43201, Reus, Spain
- Institute of Health Pere Virgili (IISPV), 43204, Reus, Spain
- Center of Environmental, Food and Toxicological Technology-TecnATox, Rovira i Virgili University, 43201, Reus, Spain
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Carlos III Health Institute, 28031, Madrid, Spain
| | - Miren Ettcheto
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Carlos III Health Institute, 28031, Madrid, Spain
- Department of Pharmacology, Toxicology and Therapeutic Chemistry, Faculty of Pharmacy and Food Science, Universitat de Barcelona, 08028, Barcelona, Spain
- Institute of Neuroscience, Universitat de Barcelona, 08035, Barcelona, Spain
| | - Antoni Camins
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Carlos III Health Institute, 28031, Madrid, Spain
- Department of Pharmacology, Toxicology and Therapeutic Chemistry, Faculty of Pharmacy and Food Science, Universitat de Barcelona, 08028, Barcelona, Spain
- Institute of Neuroscience, Universitat de Barcelona, 08035, Barcelona, Spain
| | - Mercè Boada
- ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08028, Barcelona, Spain
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Carlos III Health Institute, 28031, Madrid, Spain
| | - Agustín Ruiz
- ACE Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08028, Barcelona, Spain
- Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), Carlos III Health Institute, 28031, Madrid, Spain
| | - Mònica Bulló
- Nutrition and Metabolic Health Research Group, Department of Biochemistry and Biotechnology, Rovira i Virgili University (URV), 43201, Reus, Spain.
- Institute of Health Pere Virgili (IISPV), 43204, Reus, Spain.
- Center of Environmental, Food and Toxicological Technology-TecnATox, Rovira i Virgili University, 43201, Reus, Spain.
- CIBER Physiology of Obesity and Nutrition (CIBEROBN), Carlos III Health Institute, 28029, Madrid, Spain.
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Manosalva Pérez N, Ferrari C, Engelhorn J, Depuydt T, Nelissen H, Hartwig T, Vandepoele K. MINI-AC: inference of plant gene regulatory networks using bulk or single-cell accessible chromatin profiles. Plant J 2024; 117:280-301. [PMID: 37788349 DOI: 10.1111/tpj.16483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/13/2023] [Accepted: 09/16/2023] [Indexed: 10/05/2023]
Abstract
Gene regulatory networks (GRNs) represent the interactions between transcription factors (TF) and their target genes. Plant GRNs control transcriptional programs involved in growth, development, and stress responses, ultimately affecting diverse agricultural traits. While recent developments in accessible chromatin (AC) profiling technologies make it possible to identify context-specific regulatory DNA, learning the underlying GRNs remains a major challenge. We developed MINI-AC (Motif-Informed Network Inference based on Accessible Chromatin), a method that combines AC data from bulk or single-cell experiments with TF binding site (TFBS) information to learn GRNs in plants. We benchmarked MINI-AC using bulk AC datasets from different Arabidopsis thaliana tissues and showed that it outperforms other methods to identify correct TFBS. In maize, a crop with a complex genome and abundant distal AC regions, MINI-AC successfully inferred leaf GRNs with experimentally confirmed, both proximal and distal, TF-target gene interactions. Furthermore, we showed that both AC regions and footprints are valid alternatives to infer AC-based GRNs with MINI-AC. Finally, we combined MINI-AC predictions from bulk and single-cell AC datasets to identify general and cell-type specific maize leaf regulators. Focusing on C4 metabolism, we identified diverse regulatory interactions in specialized cell types for this photosynthetic pathway. MINI-AC represents a powerful tool for inferring accurate AC-derived GRNs in plants and identifying known and novel candidate regulators, improving our understanding of gene regulation in plants.
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Affiliation(s)
- Nicolás Manosalva Pérez
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium
- Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium
| | - Camilla Ferrari
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium
- Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium
| | - Julia Engelhorn
- Molecular Physiology Department, Heinrich-Heine University, 40225, Düsseldorf, Germany
- Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany
| | - Thomas Depuydt
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium
- Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium
| | - Hilde Nelissen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium
- Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium
| | - Thomas Hartwig
- Molecular Physiology Department, Heinrich-Heine University, 40225, Düsseldorf, Germany
- Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany
- Cluster of Excellence on Plant Sciences, Düsseldorf, Germany
| | - Klaas Vandepoele
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium
- Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, 9052, Ghent, Belgium
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11
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Fox J, Cummins B, Moseley RC, Gameiro M, Haase SB. A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets. Math Biosci 2024; 367:109102. [PMID: 37939998 PMCID: PMC10842220 DOI: 10.1016/j.mbs.2023.109102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 09/13/2023] [Accepted: 10/27/2023] [Indexed: 11/10/2023]
Abstract
Modeling biological systems holds great promise for speeding up the rate of discovery in systems biology by predicting experimental outcomes and suggesting targeted interventions. However, this process is dogged by an identifiability issue, in which network models and their parameters are not sufficiently constrained by coarse and noisy data to ensure unique solutions. In this work, we evaluated the capability of a simplified yeast cell-cycle network model to reproduce multiple observed transcriptomic behaviors under genomic mutations. We matched time-series data from both cycling and checkpoint arrested cells to model predictions using an asynchronous multi-level Boolean approach. We showed that this single network model, despite its simplicity, is capable of exhibiting dynamical behavior similar to the datasets in most cases, and we demonstrated the drop in severity of the identifiability issue that results from matching multiple datasets.
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Affiliation(s)
- Julian Fox
- Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA
| | - Breschine Cummins
- Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA.
| | | | - Marcio Gameiro
- Department of Mathematics, Rutgers University, New Brunswick, NJ, USA
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12
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Kim H, Choi H, Lee D, Kim J. A review on gene regulatory network reconstruction algorithms based on single cell RNA sequencing. Genes Genomics 2024; 46:1-11. [PMID: 38032470 DOI: 10.1007/s13258-023-01473-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND Understanding gene regulatory networks (GRNs) is essential for unraveling the molecular mechanisms governing cellular behavior. With the advent of high-throughput transcriptome measurement technology, researchers have aimed to reverse engineer the biological systems, extracting gene regulatory rules from their outputs, which represented by gene expression data. Bulk RNA sequencing, a widely used method for measuring gene expression, has been employed for GRN reconstruction. However, it falls short in capturing dynamic changes in gene expression at the level of individual cells since it averages gene expression across mixed cell populations. OBJECTIVE In this review, we provide an overview of 15 GRN reconstruction tools and discuss their respective strengths and limitations, particularly in the context of single cell RNA sequencing (scRNA-seq). METHODS Recent advancements in scRNA-seq break new ground of GRN reconstruction. They offer snapshots of the individual cell transcriptomes and capturing dynamic changes. We emphasize how these technological breakthroughs have enhanced GRN reconstruction. CONCLUSION GRN reconstructors can be classified based on their requirement for cellular trajectory, which represents a dynamical cellular process including differentiation, aging, or disease progression. Benchmarking studies support the superiority of GRN reconstructors that do not require trajectory analysis in identifying regulator-target relationships. However, methods equipped with trajectory analysis demonstrate better performance in identifying key regulatory factors. In conclusion, researchers should select a suitable GRN reconstructor based on their specific research objectives.
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Affiliation(s)
- Hyeonkyu Kim
- School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea
| | - Hwisoo Choi
- School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea
| | - Daewon Lee
- School of Art and Technology, Chung-Ang University, 4726 Seodong-Daero, Anseong-Si, Gyeonggi-Do, 17546, Republic of Korea.
| | - Junil Kim
- School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea.
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13
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Cheng J, Cheng M, Lusis AJ, Yang X. Gene Regulatory Networks in Coronary Artery Disease. Curr Atheroscler Rep 2023; 25:1013-1023. [PMID: 38008808 DOI: 10.1007/s11883-023-01170-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2023] [Indexed: 11/28/2023]
Abstract
PURPOSE OF REVIEW Coronary artery disease is a complex disorder and the leading cause of mortality worldwide. As technologies for the generation of high-throughput multiomics data have advanced, gene regulatory network modeling has become an increasingly powerful tool in understanding coronary artery disease. This review summarizes recent and novel gene regulatory network tools for bulk tissue and single cell data, existing databases for network construction, and applications of gene regulatory networks in coronary artery disease. RECENT FINDINGS New gene regulatory network tools can integrate multiomics data to elucidate complex disease mechanisms at unprecedented cellular and spatial resolutions. At the same time, updates to coronary artery disease expression data in existing databases have enabled researchers to build gene regulatory networks to study novel disease mechanisms. Gene regulatory networks have proven extremely useful in understanding CAD heritability beyond what is explained by GWAS loci and in identifying mechanisms and key driver genes underlying disease onset and progression. Gene regulatory networks can holistically and comprehensively address the complex nature of coronary artery disease. In this review, we discuss key algorithmic approaches to construct gene regulatory networks and highlight state-of-the-art methods that model specific modes of gene regulation. We also explore recent applications of these tools in coronary artery disease patient data repositories to understand disease heritability and shared and distinct disease mechanisms and key driver genes across tissues, between sexes, and between species.
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Grants
- DK120342, HL148577, and HL147883 (AJL). NS111378, NS117148, HL147883 (XY) NIH HHS
- DK120342, HL148577, and HL147883 (AJL). NS111378, NS117148, HL147883 (XY) NIH HHS
- DK120342, HL148577, and HL147883 (AJL). NS111378, NS117148, HL147883 (XY) NIH HHS
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Affiliation(s)
- Jenny Cheng
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA
- Molecular, Cellular and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA
| | - Michael Cheng
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA
| | - Aldons J Lusis
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, 650 Charles E Young Drive South, Los Angeles, CA, 90095, USA.
- Departments of Human Genetics & Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, 650 Charles E. Young Drive South, Los Angeles, CA, 90095, USA.
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA.
- Molecular, Cellular and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA.
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA.
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA.
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14
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Hsiao YC, Dutta A. Nonlinear control designs and their application to cancer differentiation therapy. Math Biosci 2023; 366:109105. [PMID: 37944795 DOI: 10.1016/j.mbs.2023.109105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/29/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
We designed three new controllers: a sigmoid-based controller, a polynomial dynamic inversion-based controller, and a proportional-integral-derivative (PID) impulsive controller for cancer differentiation therapy. We compared these three controllers to existing control strategies to show the improvement in performance and compare their robustness. The sigmoid-based controller adds a sigmoid term associated with the error of the controlled state and a selected observed state. The sigmoid term is multiplied by a control gain, thereby decreasing the control effort for state transition. The polynomial dynamic inversion-based controller adds a cubic error term in the error dynamic aiming to achieve a shorter convergence time to the desired value of the controlled state. The PID impulsive controller considers the accumulated controlled state error and the rate of change of the controlled state error, thereby forcing the controlled state to converge to the desired value and alleviating the damping effect in the steady state. For the considered cancer network, the 3 new cancer control strategies exhibit superior and robust performance. The PID impulsive controller has a significant improvement in robustness compared to the impulsive controller and has greater potential for cancer differentiation therapy.
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Affiliation(s)
- Yen-Che Hsiao
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, 06269, CT, USA.
| | - Abhishek Dutta
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, 06269, CT, USA
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15
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Cingiz MÖ. k- Strong Inference Algorithm: A Hybrid Information Theory Based Gene Network Inference Algorithm. Mol Biotechnol 2023:10.1007/s12033-023-00929-2. [PMID: 37950851 DOI: 10.1007/s12033-023-00929-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 10/05/2023] [Indexed: 11/13/2023]
Abstract
Gene networks allow researchers to understand the underlying mechanisms between diseases and genes while reducing the need for wet lab experiments. Numerous gene network inference (GNI) algorithms have been presented in the literature to infer accurate gene networks. We proposed a hybrid GNI algorithm, k-Strong Inference Algorithm (ksia), to infer more reliable and robust gene networks from omics datasets. To increase reliability, ksia integrates Pearson correlation coefficient (PCC) and Spearman rank correlation coefficient (SCC) scores to determine mutual information scores between molecules to increase diversity of relation predictions. To infer a more robust gene network, ksia applies three different elimination steps to remove redundant and spurious relations between genes. The performance of ksia was evaluated on microbe microarrays database in the overlap analysis with other GNI algorithms, namely ARACNE, C3NET, CLR, and MRNET. Ksia inferred less number of relations due to its strict elimination steps. However, ksia generally performed better on Escherichia coli (E.coli) and Saccharomyces cerevisiae (yeast) gene expression datasets due to F- measure and precision values. The integration of association estimator scores and three elimination stages slightly increases the performance of ksia based gene networks. Users can access ksia R package and user manual of package via https://github.com/ozgurcingiz/ksia .
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Affiliation(s)
- Mustafa Özgür Cingiz
- Computer Engineering Department, Faculty of Engineering and Natural Sciences, Bursa Technical University, Mimar Sinan Campus, Yildirim, 16310, Bursa, Turkey.
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16
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Pulver C, Grun D, Duc J, Sheppard S, Planet E, Coudray A, de Fondeville R, Pontis J, Trono D. Statistical learning quantifies transposable element-mediated cis-regulation. Genome Biol 2023; 24:258. [PMID: 37950299 PMCID: PMC10637000 DOI: 10.1186/s13059-023-03085-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/09/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Transposable elements (TEs) have colonized the genomes of most metazoans, and many TE-embedded sequences function as cis-regulatory elements (CREs) for genes involved in a wide range of biological processes from early embryogenesis to innate immune responses. Because of their repetitive nature, TEs have the potential to form CRE platforms enabling the coordinated and genome-wide regulation of protein-coding genes by only a handful of trans-acting transcription factors (TFs). RESULTS Here, we directly test this hypothesis through mathematical modeling and demonstrate that differences in expression at protein-coding genes alone are sufficient to estimate the magnitude and significance of TE-contributed cis-regulatory activities, even in contexts where TE-derived transcription fails to do so. We leverage hundreds of overexpression experiments and estimate that, overall, gene expression is influenced by TE-embedded CREs situated within approximately 500 kb of promoters. Focusing on the cis-regulatory potential of TEs within the gene regulatory network of human embryonic stem cells, we find that pluripotency-specific and evolutionarily young TE subfamilies can be reactivated by TFs involved in post-implantation embryogenesis. Finally, we show that TE subfamilies can be split into truly regulatorily active versus inactive fractions based on additional information such as matched epigenomic data, observing that TF binding may better predict TE cis-regulatory activity than differences in histone marks. CONCLUSION Our results suggest that TE-embedded CREs contribute to gene regulation during and beyond gastrulation. On a methodological level, we provide a statistical tool that infers TE-dependent cis-regulation from RNA-seq data alone, thus facilitating the study of TEs in the next-generation sequencing era.
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Affiliation(s)
- Cyril Pulver
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Delphine Grun
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Julien Duc
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Shaoline Sheppard
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Evarist Planet
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Alexandre Coudray
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Raphaël de Fondeville
- Swiss Data Science Center, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
| | - Julien Pontis
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
- SOPHiA GENETICS SA, La Pièce 12, CH-1180, Rolle, Switzerland.
| | - Didier Trono
- School of Life Sciences, Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
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Xu B, Hwangbo DS, Saurabh S, Rosensweig C, Allada R, Kath WL, Braun R. Temperature-driven coordination of circadian transcriptome regulation. bioRxiv 2023:2023.10.27.563979. [PMID: 37961403 PMCID: PMC10634908 DOI: 10.1101/2023.10.27.563979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The circadian rhythm is an evolutionarily-conserved molecular oscillator that enables species to anticipate rhythmic changes in their environment. At a molecular level, the core clock genes induce a circadian oscillation in thousands of genes in a tissue-specific manner, orchestrating myriad biological processes. While studies have investigated how the core clock circuit responds to environmental perturbations such as temperature, the downstream effects of such perturbations on circadian regulation remain poorly understood. By analyzing bulk-RNA sequencing of Drosophila fat bodies harvested from flies subjected to different environmental conditions, we demonstrate a highly condition-specific circadian transcriptome. Further employing a reference-based gene regulatory network (Reactome), we find evidence of increased gene-gene coordination at low temperatures and synchronization of rhythmic genes that are network neighbors. Our results point to the mechanisms by which the circadian clock mediates the fly's response to seasonal changes in temperature.
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Affiliation(s)
- Bingxian Xu
- Department of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
| | - Dae-Sung Hwangbo
- Department of Biology, University of Louisville, Louisville, KY 40292, USA
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Sumit Saurabh
- Department of Biology, Loyola University, Chicago, IL 60660, USA
| | - Clark Rosensweig
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Ravi Allada
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - William L Kath
- Department of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA
| | - Rosemary Braun
- Department of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA
- NSF-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL 60208, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA
- Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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18
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Nemati Bajestan M, Piroozkhah M, Chaleshi V, Ghiasi NE, Jamshidi N, Mirfakhraie R, Balaii H, Shahrokh S, Asadzadeh Aghdaei H, Salehi Z, Nazemalhosseini Mojarad E. Expression Analysis of Long Noncoding RNA-MALAT1 and Interleukin-6 in Inflammatory Bowel Disease Patients. IJAAI 2023; 22:482-494. [PMID: 38085149 DOI: 10.18502/ijaai.v22i5.13997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 07/29/2023] [Indexed: 12/18/2023]
Abstract
Inflammatory bowel disease (IBD) manifests as chronic inflammation within the gastrointestinal tract. The study focuses on a long noncoding RNA (lncRNA) known as Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1). MALAT1's misregulation has been linked with various autoimmune diseases and regulates proinflammatory cytokines. The role of IL6 in immune-triggered conditions, including IBD, is another focal point. In this research, the expression of MALAT1 and IL6 in IBD patients was meticulously analyzed to uncover potential interactions. The study involved 33 IBD patients (13 with Crohn's disease and 20 with ulcerative colitis) and 20 healthy counterparts. Quantitative real-time polymerase chain reaction determined the MALAT1 and IL6 gene expression levels. The competitive endogenous RNA (ceRNA) regulatory network was constructed using several tools, including LncRRIsearch and Cytoscape. A deep dive into the Inflammatory Bowel Disease database was undertaken to understand IL6's role in IBD. Drugs potentially targeting these genes were also pinpointed using DGIdb. Results indicated a notable elevation in the expression levels of MALAT1 and IL6 in IBD patients versus healthy controls. MALAT1 and IL6 did not show a direct linear correlation, but IL6 could serve as MALAT1's target. Analyses unveiled interactions between MALAT1 and IL6, regulated by hsa-miR-202-3p, hsa-miR-1-3p, and has-miR-9-5p. IL6's pivotal role in IBD-associated inflammation, likely interacting with other cytokines, was accentuated. Moreover, potential drugs like CILOBRADINE for MALAT1 and SILTUXIMAB for IL6 were identified. This research underscored MALAT1 and IL6's potential value as targets in diagnosis and treatment for IBD patients.
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Affiliation(s)
- Mohsen Nemati Bajestan
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Centre, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Moein Piroozkhah
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Centre, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Vahid Chaleshi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Centre, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran..
| | - Naser Elmi Ghiasi
- 2 Laboratory of Biological Complex Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Negar Jamshidi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Centre, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Reza Mirfakhraie
- Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Hedieh Balaii
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Centre, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Shabnam Shahrokh
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Centre, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Hamid Asadzadeh Aghdaei
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Centre, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Zahra Salehi
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran.
| | - Ehsan Nazemalhosseini Mojarad
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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19
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Ovadia S, Cui G, Elkon R, Cohen-Gulkar M, Zuk-Bar N, Tuoc T, Jing N, Ashery-Padan R. SWI/SNF complexes are required for retinal pigmented epithelium differentiation and for the inhibition of cell proliferation and neural differentiation programs. Development 2023; 150:dev201488. [PMID: 37522516 PMCID: PMC10482007 DOI: 10.1242/dev.201488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 07/14/2023] [Indexed: 08/01/2023]
Abstract
During embryonic development, tissue-specific transcription factors and chromatin remodelers function together to ensure gradual, coordinated differentiation of multiple lineages. Here, we define this regulatory interplay in the developing retinal pigmented epithelium (RPE), a neuroectodermal lineage essential for the development, function and maintenance of the adjacent retina. We present a high-resolution spatial transcriptomic atlas of the developing mouse RPE and the adjacent ocular mesenchyme obtained by geographical position sequencing (Geo-seq) of a single developmental stage of the eye that encompasses young and more mature ocular progenitors. These transcriptomic data, available online, reveal the key transcription factors and their gene regulatory networks during RPE and ocular mesenchyme differentiation. Moreover, conditional inactivation followed by Geo-seq revealed that this differentiation program is dependent on the activity of SWI/SNF complexes, shown here to control the expression and activity of RPE transcription factors and, at the same time, inhibit neural progenitor and cell proliferation genes. The findings reveal the roles of the SWI/SNF complexes in controlling the intersection between RPE and neural cell fates and the coupling of cell-cycle exit and differentiation.
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Affiliation(s)
- Shai Ovadia
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Guizhong Cui
- Guangzhou National Laboratory, Department of Basic Research, Guangzhou 510005, China
| | - Ran Elkon
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Mazal Cohen-Gulkar
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Nitay Zuk-Bar
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tran Tuoc
- Department of Human Genetics, Ruhr University of Bochum, 44791 Bochum, Germany
| | - Naihe Jing
- Guangzhou National Laboratory, Department of Basic Research, Guangzhou 510005, China
| | - Ruth Ashery-Padan
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
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20
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Owen LJ, Rainger J, Bengani H, Kilanowski F, FitzPatrick DR, Papanastasiou AS. Characterization of an eye field-like state during optic vesicle organoid development. Development 2023; 150:dev201432. [PMID: 37306293 PMCID: PMC10445745 DOI: 10.1242/dev.201432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/02/2023] [Indexed: 06/13/2023]
Abstract
Specification of the eye field (EF) within the neural plate marks the earliest detectable stage of eye development. Experimental evidence, primarily from non-mammalian model systems, indicates that the stable formation of this group of cells requires the activation of a set of key transcription factors. This crucial event is challenging to probe in mammals and, quantitatively, little is known regarding the regulation of the transition of cells to this ocular fate. Using optic vesicle organoids to model the onset of the EF, we generate time-course transcriptomic data allowing us to identify dynamic gene expression programmes that characterize this cellular-state transition. Integrating this with chromatin accessibility data suggests a direct role of canonical EF transcription factors in regulating these gene expression changes, and highlights candidate cis-regulatory elements through which these transcription factors act. Finally, we begin to test a subset of these candidate enhancer elements, within the organoid system, by perturbing the underlying DNA sequence and measuring transcriptomic changes during EF activation.
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Affiliation(s)
- Liusaidh J. Owen
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Jacqueline Rainger
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Hemant Bengani
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Fiona Kilanowski
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - David R. FitzPatrick
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Andrew S. Papanastasiou
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
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21
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Wang XM, Ming K, Wang S, Wang J, Li PL, Tian RF, Liu SY, Cheng X, Chen Y, Shi W, Wan J, Hu M, Tian S, Zhang X, She ZG, Li H, Ding Y, Zhang XJ. Network-based analysis identifies key regulatory transcription factors involved in skin aging. Exp Gerontol 2023; 178:112202. [PMID: 37178875 DOI: 10.1016/j.exger.2023.112202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/07/2023] [Accepted: 05/08/2023] [Indexed: 05/15/2023]
Abstract
Skin aging is a complex process involving intricate genetic and environmental factors. In this study, we performed a comprehensive analysis of the transcriptional regulatory landscape of skin aging in canines. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify aging-related gene modules. We subsequently validated the expression changes of these module genes in single-cell RNA sequencing (scRNA-seq) data of human aging skin. Notably, basal cell (BC), spinous cell (SC), mitotic cell (MC), and fibroblast (FB) were identified as the cell types with the most significant gene expression changes during aging. By integrating GENIE3 and RcisTarget, we constructed gene regulation networks (GRNs) for aging-related modules and identified core transcription factors (TFs) by intersecting significantly enriched TFs within the GRNs with hub TFs from WGCNA analysis, revealing key regulators of skin aging. Furthermore, we demonstrated the conserved role of CTCF and RAD21 in skin aging using an H2O2-stimulated cell aging model in HaCaT cells. Our findings provide new insights into the transcriptional regulatory landscape of skin aging and unveil potential targets for future intervention strategies against age-related skin disorders in both canines and humans.
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Affiliation(s)
- Xiao-Ming Wang
- School of Basic Medical Sciences, Wuhan University, Wuhan 430071, China; Institute of Model Animal, Wuhan University, Wuhan 430071, China
| | - Ke Ming
- School of Life Sciences, Hubei University, Wuhan 430062, China
| | - Shuang Wang
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China
| | - Jia Wang
- Institute of Model Animal, Wuhan University, Wuhan 430071, China; Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Peng-Long Li
- School of Basic Medical Sciences, Wuhan University, Wuhan 430071, China; Institute of Model Animal, Wuhan University, Wuhan 430071, China
| | - Rui-Feng Tian
- Institute of Model Animal, Wuhan University, Wuhan 430071, China; Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Shuai-Yang Liu
- School of Basic Medical Sciences, Wuhan University, Wuhan 430071, China; Institute of Model Animal, Wuhan University, Wuhan 430071, China
| | - Xu Cheng
- Gannan Innovation and Translational Medicine Research Institute, Gannan Medical University, Ganzhou 341000, China; Key Laboratory of Cardiovascular Disease Prevention and Control, Ministry of Education, First Affiliated Hospital, Gannan Medical University, Ganzhou 341000, China
| | - Yun Chen
- Department of Cardiology, Huanggang Central Hospital, Huanggang 438000, China
| | - Wei Shi
- School of Basic Medical Sciences, Wuhan University, Wuhan 430071, China; Institute of Model Animal, Wuhan University, Wuhan 430071, China
| | - Juan Wan
- Gannan Innovation and Translational Medicine Research Institute, Gannan Medical University, Ganzhou 341000, China
| | - Manli Hu
- Gannan Innovation and Translational Medicine Research Institute, Gannan Medical University, Ganzhou 341000, China; Key Laboratory of Cardiovascular Disease Prevention and Control, Ministry of Education, First Affiliated Hospital, Gannan Medical University, Ganzhou 341000, China
| | - Song Tian
- School of Basic Medical Sciences, Wuhan University, Wuhan 430071, China; Institute of Model Animal, Wuhan University, Wuhan 430071, China
| | - Xin Zhang
- Gannan Innovation and Translational Medicine Research Institute, Gannan Medical University, Ganzhou 341000, China; Key Laboratory of Cardiovascular Disease Prevention and Control, Ministry of Education, First Affiliated Hospital, Gannan Medical University, Ganzhou 341000, China
| | - Zhi-Gang She
- Institute of Model Animal, Wuhan University, Wuhan 430071, China; Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Hongliang Li
- School of Basic Medical Sciences, Wuhan University, Wuhan 430071, China; Institute of Model Animal, Wuhan University, Wuhan 430071, China; Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan 430060, China; Gannan Innovation and Translational Medicine Research Institute, Gannan Medical University, Ganzhou 341000, China; Medical Science Research Center, Zhongnan Hospital of Wuhan University, Wuhan 430071, China.
| | - Yi Ding
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China.
| | - Xiao-Jing Zhang
- School of Basic Medical Sciences, Wuhan University, Wuhan 430071, China; Institute of Model Animal, Wuhan University, Wuhan 430071, China.
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22
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Massri AJ, McDonald B, Wray GA, McClay DR. Feedback circuits are numerous in embryonic gene regulatory networks and offer a stabilizing influence on evolution of those networks. EvoDevo 2023; 14:10. [PMID: 37322563 DOI: 10.1186/s13227-023-00214-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 06/06/2023] [Indexed: 06/17/2023] Open
Abstract
The developmental gene regulatory networks (dGRNs) of two sea urchin species, Lytechinus variegatus (Lv) and Strongylocentrotus purpuratus (Sp), have remained remarkably similar despite about 50 million years since a common ancestor. Hundreds of parallel experimental perturbations of transcription factors with similar outcomes support this conclusion. A recent scRNA-seq analysis suggested that the earliest expression of several genes within the dGRNs differs between Lv and Sp. Here, we present a careful reanalysis of the dGRNs in these two species, paying close attention to timing of first expression. We find that initial expression of genes critical for cell fate specification occurs during several compressed time periods in both species. Previously unrecognized feedback circuits are inferred from the temporally corrected dGRNs. Although many of these feedbacks differ in location within the respective GRNs, the overall number is similar between species. We identify several prominent differences in timing of first expression for key developmental regulatory genes; comparison with a third species indicates that these heterochronies likely originated in an unbiased manner with respect to embryonic cell lineage and evolutionary branch. Together, these results suggest that interactions can evolve even within highly conserved dGRNs and that feedback circuits may buffer the effects of heterochronies in the expression of key regulatory genes.
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Affiliation(s)
| | - Brennan McDonald
- Department of Biology, Duke University, Box 90338, Durham, NC, 27708, USA
| | - Gregory A Wray
- Department of Biology, Duke University, Box 90338, Durham, NC, 27708, USA
| | - David R McClay
- Department of Biology, Duke University, Box 90338, Durham, NC, 27708, USA.
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23
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Rivera-Rivera CJ, Grbic D. CastNet: a systems-level sequence evolution simulator. BMC Bioinformatics 2023; 24:247. [PMID: 37308829 DOI: 10.1186/s12859-023-05366-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 05/26/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Simulating DNA evolution has been done through coevolution-agnostic probabilistic frameworks for the past 3 decades. The most common implementation is by using the converse of the probabilistic approach used to infer phylogenies which, in the simplest form, simulates a single sequence at a time. However, biological systems are multi-genic, and gene products can affect each other's evolutionary paths through coevolution. These crucial evolutionary dynamics still remain to be simulated, and we believe that modelling them can lead to profound insights for comparative genomics. RESULTS Here we present CastNet, a genome evolution simulator that assumes each genome is a collection of genes with constantly evolving regulatory interactions in between them. The regulatory interactions produce a phenotype in the form of gene expression profiles, upon which fitness is calculated. A genetic algorithm is then used to evolve a population of such entities through a user-defined phylogeny. Importantly, the regulatory mutations are a response to sequence mutations, thus making a 1-1 relationship between the rate of evolution of sequences and of regulatory parameters. This is, to our knowledge, the first time the evolution of sequences and regulation have been explicitly linked in a simulation, despite there being a multitude of sequence evolution simulators, and a handful of models to simulate Gene Regulatory Network (GRN) evolution. In our test runs, we see a coevolutionary signal among genes that are active in the GRN, and neutral evolution in genes that are not included in the network, showing that selective pressures imposed on the regulatory output of the genes are reflected in their sequences. CONCLUSION We believe that CastNet represents a substantial step for developing new tools to study genome evolution, and more broadly, coevolutionary webs and complex evolving systems. This simulator also provides a new framework to study molecular evolution where sequence coevolution has a leading role.
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Affiliation(s)
| | - Djordje Grbic
- IT-University of Copenhagen, Rued Langgaards Vej 7, 2300, Copenhagen, Denmark
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24
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Aygün N, Liang D, Crouse WL, Keele GR, Love MI, Stein JL. Inferring cell-type-specific causal gene regulatory networks during human neurogenesis. Genome Biol 2023; 24:130. [PMID: 37254169 PMCID: PMC10230710 DOI: 10.1186/s13059-023-02959-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 05/05/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND Genetic variation influences both chromatin accessibility, assessed in chromatin accessibility quantitative trait loci (caQTL) studies, and gene expression, assessed in expression QTL (eQTL) studies. Genetic variants can impact either nearby genes (cis-eQTLs) or distal genes (trans-eQTLs). Colocalization between caQTL and eQTL, or cis- and trans-eQTLs suggests that they share causal variants. However, pairwise colocalization between these molecular QTLs does not guarantee a causal relationship. Mediation analysis can be applied to assess the evidence supporting causality versus independence between molecular QTLs. Given that the function of QTLs can be cell-type-specific, we performed mediation analyses to find epigenetic and distal regulatory causal pathways for genes within two major cell types of the developing human cortex, progenitors and neurons. RESULTS We find that the expression of 168 and 38 genes is mediated by chromatin accessibility in progenitors and neurons, respectively. We also find that the expression of 11 and 12 downstream genes is mediated by upstream genes in progenitors and neurons. Moreover, we discover that a genetic locus associated with inter-individual differences in brain structure shows evidence for mediation of SLC26A7 through chromatin accessibility, identifying molecular mechanisms of a common variant association to a brain trait. CONCLUSIONS In this study, we identify cell-type-specific causal gene regulatory networks whereby the impacts of variants on gene expression were mediated by chromatin accessibility or distal gene expression. Identification of these causal paths will enable identifying and prioritizing actionable regulatory targets perturbing these key processes during neurodevelopment.
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Affiliation(s)
- Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Dan Liang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Wesley L Crouse
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | - Gregory R Keele
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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25
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Guo PC, Zuo J, Huang KK, Lai GY, Zhang X, An J, Li JX, Li L, Wu L, Lin YT, Wang DY, Xu JS, Hao SJ, Wang Y, Li RH, Ma W, Song YM, Liu C, Liu CY, Dai Z, Xu Y, Sharma AD, Ott M, Ou-Yang Q, Huo F, Fan R, Li YY, Hou JL, Volpe G, Liu LQ, Esteban MA, Lai YW. Cell atlas of CCl 4-induced progressive liver fibrosis reveals stage-specific responses. Zool Res 2023; 44:451-466. [PMID: 36994536 PMCID: PMC10236302 DOI: 10.24272/j.issn.2095-8137.2023.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/11/2023] [Indexed: 03/12/2023] Open
Abstract
Chronic liver injury leads to progressive liver fibrosis and ultimately cirrhosis, a major cause of morbidity and mortality worldwide. However, there are currently no effective anti-fibrotic therapies available, especially for late-stage patients, which is partly attributed to the major knowledge gap regarding liver cell heterogeneity and cell-specific responses in different fibrosis stages. To reveal the multicellular networks regulating mammalian liver fibrosis from mild to severe phenotypes, we generated a single-nucleus transcriptomic atlas encompassing 49 919 nuclei corresponding to all main liver cell types at different stages of murine carbon tetrachloride (CCl 4)-induced progressive liver fibrosis. Integrative analysis distinguished the sequential responses to injury of hepatocytes, hepatic stellate cells and endothelial cells. Moreover, we reconstructed cell-cell interactions and gene regulatory networks implicated in these processes. These integrative analyses uncovered previously overlooked aspects of hepatocyte proliferation exhaustion and disrupted pericentral metabolic functions, dysfunction for clearance by apoptosis of activated hepatic stellate cells, accumulation of pro-fibrotic signals, and the switch from an anti-angiogenic to a pro-angiogenic program during CCl 4-induced progressive liver fibrosis. Our dataset thus constitutes a useful resource for understanding the molecular basis of progressive liver fibrosis using a relevant animal model.
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Affiliation(s)
- Peng-Cheng Guo
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, Jilin 130062, China
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Jing Zuo
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Ke-Ke Huang
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510799, China
| | - Guang-Yao Lai
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
- Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical University, Guangzhou, Guangdong 510530, China
| | - Xiao Zhang
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, Jilin 130062, China
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Juan An
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jin-Xiu Li
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li Li
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Liang Wu
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Yi-Ting Lin
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Dong-Ye Wang
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Jiang-Shan Xu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Shi-Jie Hao
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Wang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Rong-Hai Li
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Wen Ma
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Yu-Mo Song
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Chang Liu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Chuan-Yu Liu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Zhen Dai
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Yan Xu
- Biotherapy Centre, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Amar Deep Sharma
- Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover 30625, Germany
| | - Michael Ott
- Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover 30625, Germany
| | - Qing Ou-Yang
- Department of Hepatobiliary Surgery and Liver Transplant Center, General Hospital of Southern Theater Command, Guangzhou, Guangdong 510010, China
| | - Feng Huo
- Department of Hepatobiliary Surgery and Liver Transplant Center, General Hospital of Southern Theater Command, Guangzhou, Guangdong 510010, China
| | - Rong Fan
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangzhou, Guangdong 510515, China
| | - Yong-Yin Li
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangzhou, Guangdong 510515, China
| | - Jin-Lin Hou
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangzhou, Guangdong 510515, China
| | - Giacomo Volpe
- Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori 'Giovanni Paolo II', Bari 70124, Italy
| | - Long-Qi Liu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Miguel A Esteban
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, Jilin 130062, China
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510799, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
- Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical University, Guangzhou, Guangdong 510530, China
- Institute of Experimental Hematology, Hannover Medical School, Hannover 30625, Germany. E-mail:
| | - Yi-Wei Lai
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China. E-mail:
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26
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Ruz GA, Goles E. Gene regulatory networks with binary weights. Biosystems 2023; 227-228:104902. [PMID: 37080282 DOI: 10.1016/j.biosystems.2023.104902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/08/2023] [Indexed: 04/22/2023]
Abstract
An evolutionary computation framework to learn binary threshold networks is presented. Inspired by the recent trend of binary neural networks, where weights and activation thresholds are represented using 1 and -1 such that they can be stored in 1-bit instead of full precision, we explore this approach for gene regulatory network modeling. We test our method by inferring binary threshold networks of two regulatory network models: Quorum sensing systems in bacterium Paraburkholderia phytofirmans PsJN and the fission yeast cell-cycle. We considered differential evolution and particle swarm optimization for the simulations. Results for weights having only 1 and -1 values, and different activation thresholds are presented. Full binary threshold networks were found with minimum error (2 bits), whereas when the binary restriction is relaxed for the activation thresholds, networks with 0 bit error were found. .
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Affiliation(s)
- Gonzalo A Ruz
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, 7941169, Chile; Center of Applied Ecology and Sustainability (CAPES), Santiago, 8331150, Chile; Data Observatory Foundation, Santiago, 7941169, Chile.
| | - Eric Goles
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, 7941169, Chile.
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27
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Babal YK, Sonmez E, Aksan Kurnaz I. Nervous system-related gene regulatory networks and functional evolution of ETS proteins across species. Biosystems 2023; 227-228:104891. [PMID: 37030605 DOI: 10.1016/j.biosystems.2023.104891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 03/21/2023] [Accepted: 04/05/2023] [Indexed: 04/10/2023]
Abstract
The ETS domain transcription factor family is one of the major transcription factor superfamilies that play regulatory roles in development, cell growth, and cancer progression. Although different functions of ETS member proteins in the nervous system have been demonstrated in various studies, their role in neuronal cell differentiation and the evolutionary conservation of its target genes have not yet been extensively studied. In this study, we focused on the regulatory role of ETS transcription factors in neuronal differentiation and their functional evolution by comparative transcriptomics. In order to investigate the regulatory role of ETS transcription factors in neuronal differentiation across species, transcriptional profiles of ETS members and their target genes were investigated by comparing differentially expressed genes and gene regulatory networks, which were analyzed using human, gorilla, mouse, fruit fly and worm transcriptomics datasets. Bioinformatics approaches to examine the evolutionary conservation of ETS transcription factors during neuronal differentiation have shown that ETS member proteins regulate genes associated with neuronal differentiation, nervous system development, axon, and synaptic regulation in different organisms. This study is a comparative transcriptomic study of ETS transcription factors in terms of neuronal differentiation using a gene regulatory network inference algorithm. Overall, a comparison of gene regulation networks revealed that ETS members are indeed evolutionarily conserved in the regulation of neuronal differentiation. Nonetheless, ETS, PEA3, and ELF subfamilies were found to be relatively more active transcription factors in the transcriptional regulation of neuronal differentiation.
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Affiliation(s)
- Yigit Koray Babal
- Gebze Technical University, Institute of Biotechnology, 41400, Gebze Kocaeli, Turkey.
| | - Ekin Sonmez
- Gebze Technical University, Institute of Biotechnology, 41400, Gebze Kocaeli, Turkey
| | - Isil Aksan Kurnaz
- Gebze Technical University, Institute of Biotechnology, 41400, Gebze Kocaeli, Turkey; Gebze Technical University, Dept Molecular Biology and Genetics, 41400, Gebze Kocaeli, Turkey
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28
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Karaaslanli A, Saha S, Maiti T, Aviyente S. Kernelized multiview signed graph learning for single-cell RNA sequencing data. BMC Bioinformatics 2023; 24:127. [PMID: 37016281 PMCID: PMC10071725 DOI: 10.1186/s12859-023-05250-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 03/22/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND Characterizing the topology of gene regulatory networks (GRNs) is a fundamental problem in systems biology. The advent of single cell technologies has made it possible to construct GRNs at finer resolutions than bulk and microarray datasets. However, cellular heterogeneity and sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing GRNs. Additionally, most GRN reconstruction approaches estimate a single network for the entire data. This could cause potential loss of information when single cell datasets are generated from multiple treatment conditions/disease states. RESULTS To better characterize single cell GRNs under different but related conditions, we propose the joint estimation of multiple networks using multiple signed graph learning (scMSGL). The proposed method is based on recently developed graph signal processing (GSP) based graph learning, where GRNs and gene expressions are modeled as signed graphs and graph signals, respectively. scMSGL learns multiple GRNs by optimizing the total variation of gene expressions with respect to GRNs while ensuring that the learned GRNs are similar to each other through regularization with respect to a learned signed consensus graph. We further kernelize scMSGL with the kernel selected to suit the structure of single cell data. CONCLUSIONS scMSGL is shown to have superior performance over existing state of the art methods in GRN recovery on simulated datasets. Furthermore, scMSGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma.
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Affiliation(s)
- Abdullah Karaaslanli
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA.
| | - Satabdi Saha
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tapabrata Maiti
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA
| | - Selin Aviyente
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA
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29
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Shen B, Coruzzi G, Shasha D. EnsInfer: a simple ensemble approach to network inference outperforms any single method. BMC Bioinformatics 2023; 24:114. [PMID: 36964499 PMCID: PMC10037858 DOI: 10.1186/s12859-023-05231-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/15/2023] [Indexed: 03/26/2023] Open
Abstract
This study evaluates both a variety of existing base causal inference methods and a variety of ensemble methods. We show that: (i) base network inference methods vary in their performance across different datasets, so a method that works poorly on one dataset may work well on another; (ii) a non-homogeneous ensemble method in the form of a Naive Bayes classifier leads overall to as good or better results than using the best single base method or any other ensemble method; (iii) for the best results, the ensemble method should integrate all methods that satisfy a statistical test of normality on training data. The resulting ensemble model EnsInfer easily integrates all kinds of RNA-seq data as well as new and existing inference methods. The paper categorizes and reviews state-of-the-art underlying methods, describes the EnsInfer ensemble approach in detail, and presents experimental results. The source code and data used will be made available to the community upon publication.
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Affiliation(s)
- Bingran Shen
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, New York, 10012, USA
| | - Gloria Coruzzi
- Department of Biology, Center for Genomics and Systems Biology, New York University, 12 Waverly Pl, New York, 10003, USA
| | - Dennis Shasha
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, New York, 10012, USA.
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Segura-Ortiz A, García-Nieto J, Aldana-Montes JF, Navas-Delgado I. GENECI: A novel evolutionary machine learning consensus-based approach for the inference of gene regulatory networks. Comput Biol Med 2023; 155:106653. [PMID: 36803795 DOI: 10.1016/j.compbiomed.2023.106653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/09/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023]
Abstract
Gene regulatory networks define the interactions between DNA products and other substances in cells. Increasing knowledge of these networks improves the level of detail with which the processes that trigger different diseases are described and fosters the development of new therapeutic targets. These networks are usually represented by graphs, and the primary sources for their correct construction are usually time series from differential expression data. The inference of networks from this data type has been approached differently in the literature. Mostly, computational learning techniques have been implemented, which have finally shown some specialization in specific datasets. For this reason, the need arises to create new and more robust strategies for reaching a consensus based on previous results to gain a particular capacity for generalization. This paper presents GENECI (GEne NEtwork Consensus Inference), an evolutionary machine learning approach that acts as an organizer for constructing ensembles to process the results of the main inference techniques reported in the literature and to optimize the consensus network derived from them, according to their confidence levels and topological characteristics. After its design, the proposal was confronted with datasets collected from academic benchmarks (DREAM challenges and IRMA network) to quantify its accuracy. Subsequently, it was applied to a real-world biological network of melanoma patients whose results could be contrasted with medical research collected in the literature. Finally, it has been proved that its ability to optimize the consensus of several networks leads to outstanding robustness and accuracy, gaining a certain generalization capacity after facing the inference of multiple datasets. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a python package available at PyPI: https://pypi.org/project/geneci/.
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Affiliation(s)
- Adrián Segura-Ortiz
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain
| | - José García-Nieto
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain.
| | - José F Aldana-Montes
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
| | - Ismael Navas-Delgado
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
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31
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White M, Arif-Pardy J, Connor KL. Identification of novel nutrient-sensitive gene regulatory networks in amniocytes from fetuses with spina bifida. Reprod Toxicol 2023; 116:108333. [PMID: 36584796 DOI: 10.1016/j.reprotox.2022.12.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/14/2022] [Accepted: 12/26/2022] [Indexed: 12/28/2022]
Abstract
Neural tube defects (NTDs) remain among the most common congenital anomalies. Contributing risk factors include genetics and nutrient deficiencies, however, a comprehensive assessment of nutrient-gene interactions in NTDs is lacking. We applied a nutrient-focused gene expression analysis pipeline to identify nutrient-sensitive gene regulatory networks in amniocyte gene expression data (GSE4182) from fetuses with NTDs (cases; n = 3) and fetuses with no congenital anomalies (controls; n = 5). Differentially expressed genes (DEGs) were screened for having nutrient cofactors. Nutrient-dependent transcriptional regulators (TRs) that regulated DEGs, and nutrient-sensitive miRNAs with a previous link to NTDs, were identified. Of the 880 DEGs in cases, 10% had at least one nutrient cofactor. DEG regulatory network analysis revealed that 39% and 52% of DEGs in cases were regulated by 22 nutrient-sensitive miRNAs and 10 nutrient-dependent TRs, respectively. Zinc- and B vitamin-dependent gene regulatory networks (Zinc: 10 TRs targeting 50.6% of DEGs; B vitamins: 4 TRs targeting 37.7% of DEGs, 9 miRNAs targeting 17.6% of DEGs) were dysregulated in cases. We identified novel, nutrient-sensitive gene regulatory networks not previously linked to NTDs, which may indicate new targets to explore for NTD prevention or to optimise fetal development.
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Affiliation(s)
- Marina White
- Health Sciences, Carleton University, 1125 Colonel By Dr, Ottawa K1S 5B6, ON, Canada
| | - Jayden Arif-Pardy
- Health Sciences, Carleton University, 1125 Colonel By Dr, Ottawa K1S 5B6, ON, Canada
| | - Kristin L Connor
- Health Sciences, Carleton University, 1125 Colonel By Dr, Ottawa K1S 5B6, ON, Canada.
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32
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Su Y, Xu C, Shea J, DeStephanis D, Su Z. Transcriptomic changes in single yeast cells under various stress conditions. BMC Genomics 2023; 24:88. [PMID: 36829151 PMCID: PMC9960639 DOI: 10.1186/s12864-023-09184-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND The stress response of Saccharomyces cerevisiae has been extensively studied in the past decade. However, with the advent of recent technology in single-cell transcriptome profiling, there is a new opportunity to expand and further understanding of the yeast stress response with greater resolution on a system level. To understand transcriptomic changes in baker's yeast S. cerevisiae cells under stress conditions, we sequenced 117 yeast cells under three stress treatments (hypotonic condition, glucose starvation and amino acid starvation) using a full-length single-cell RNA-Seq method. RESULTS We found that though single cells from the same treatment showed varying degrees of uniformity, technical noise and batch effects can confound results significantly. However, upon careful selection of samples to reduce technical artifacts and account for batch-effects, we were able to capture distinct transcriptomic signatures for different stress conditions as well as putative regulatory relationships between transcription factors and target genes. CONCLUSION Our results show that a full-length single-cell based transcriptomic analysis of the yeast may help paint a clearer picture of how the model organism responds to stress than do bulk cell population-based methods.
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Affiliation(s)
- Yangqi Su
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, 28223, Charlotte, NC, USA
| | - Chen Xu
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, 28223, Charlotte, NC, USA
| | - Jonathan Shea
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, 28223, Charlotte, NC, USA
| | - Darla DeStephanis
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, 28223, Charlotte, NC, USA
| | - Zhengchang Su
- Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, 28223, Charlotte, NC, USA.
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33
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Díaz-Valenzuela E, Hernández-Ríos D, Cibrián-Jaramillo A. The role of non-additive gene action on gene expression variation in plant domestication. EvoDevo 2023; 14:3. [PMID: 36765382 DOI: 10.1186/s13227-022-00206-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 12/05/2022] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Plant domestication is a remarkable example of rapid phenotypic transformation of polygenic traits, such as organ size. Evidence from a handful of study cases suggests this transformation is due to gene regulatory changes that result in non-additive phenotypes. Employing data from published genetic crosses, we estimated the role of non-additive gene action in the modulation of transcriptional landscapes in three domesticated plants: maize, sunflower, and chili pepper. Using A. thaliana, we assessed the correlation between gene regulatory network (GRN) connectivity properties, transcript abundance variation, and gene action. Finally, we investigated the propagation of non-additive gene action in GRNs. RESULTS We compared crosses between domesticated plants and their wild relatives to a set of control crosses that included a pair of subspecies evolving under natural selection and a set of inbred lines evolving under domestication. We found abundance differences on a higher portion of transcripts in crosses between domesticated-wild plants relative to the control crosses. These transcripts showed non-additive gene action more often in crosses of domesticated-wild plants than in our control crosses. This pattern was strong for genes associated with cell cycle and cell fate determination, which control organ size. We found weak but significant negative correlations between the number of targets of trans-acting genes (Out-degree) and both the magnitude of transcript abundance difference a well as the absolute degree of dominance. Likewise, we found that the number of regulators that control a gene's expression (In-degree) is weakly but negatively correlated with the magnitude of transcript abundance differences. We observed that dominant-recessive gene action is highly propagable through GRNs. Finally, we found that transgressive gene action is driven by trans-acting regulators showing additive gene action. CONCLUSIONS Our study highlights the role of non-additive gene action on modulating domestication-related traits, such as organ size via regulatory divergence. We propose that GRNs are shaped by regulatory changes at genes with modest connectivity, which reduces the effects of antagonistic pleiotropy. Finally, we provide empirical evidence of the propagation of non-additive gene action in GRNs, which suggests a transcriptional epistatic model for the control of polygenic traits, such as organ size.
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34
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Cardozo MJ, Sánchez-Bustamante E, Bovolenta P. Optic cup morphogenesis across species and related inborn human eye defects. Development 2023; 150:286775. [PMID: 36714981 PMCID: PMC10110496 DOI: 10.1242/dev.200399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
The vertebrate eye is shaped as a cup, a conformation that optimizes vision and is acquired early in development through a process known as optic cup morphogenesis. Imaging living, transparent teleost embryos and mammalian stem cell-derived organoids has provided insights into the rearrangements that eye progenitors undergo to adopt such a shape. Molecular and pharmacological interference with these rearrangements has further identified the underlying molecular machineries and the physical forces involved in this morphogenetic process. In this Review, we summarize the resulting scenarios and proposed models that include common and species-specific events. We further discuss how these studies and those in environmentally adapted blind species may shed light on human inborn eye malformations that result from failures in optic cup morphogenesis, including microphthalmia, anophthalmia and coloboma.
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Affiliation(s)
- Marcos J Cardozo
- Centro de Biología Molecular Severo Ochoa, Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid, c/ Nicolás Cabrera 1, Cantoblanco, Madrid 28049, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), c/ Nicolás Cabrera 1, Cantoblanco, Madrid 28049, Spain
| | - Elena Sánchez-Bustamante
- Centro de Biología Molecular Severo Ochoa, Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid, c/ Nicolás Cabrera 1, Cantoblanco, Madrid 28049, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), c/ Nicolás Cabrera 1, Cantoblanco, Madrid 28049, Spain
| | - Paola Bovolenta
- Centro de Biología Molecular Severo Ochoa, Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid, c/ Nicolás Cabrera 1, Cantoblanco, Madrid 28049, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), c/ Nicolás Cabrera 1, Cantoblanco, Madrid 28049, Spain
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35
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Lau V, Provart NJ. AGENT for Exploring and Analyzing Gene Regulatory Networks from Arabidopsis. Methods Mol Biol 2023; 2698:351-360. [PMID: 37682484 DOI: 10.1007/978-1-0716-3354-0_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Gene regulatory networks (GRNs) are important for determining how an organism develops and how it responds to external stimuli. In the case of Arabidopsis thaliana, several GRNs have been identified covering many important biological processes. We present AGENT, the Arabidopsis GEne Network Tool, for exploring and analyzing published GRNs. Using tools in AGENT, regulatory motifs such as feed-forward loops can be easily identified. Nodes with high centrality-and hence importance-can likewise be identified. Gene expression data can also be overlaid onto GRNs to help discover subnetworks acting in specific tissues or under certain conditions.
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Affiliation(s)
- Vincent Lau
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada
| | - Nicholas J Provart
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada.
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Abstract
MicroRNAs exert their effects in the context of gene regulatory networks. The recent development of high-throughput experimental approaches and the growing availability of gene expression data have permitted comprehensive functional studies of miRNAs. However, the data interpretation is often challenging due to the fact that miRNAs not only act cooperatively with other miRNAs but also participate in complex networks by interacting with other functional elements, including non-coding RNAs or transcription factors that often have extensive effects on cell biology. This chapter provides detailed practical procedures on how to use miRNet 2.0 ( https://www.mirnet.ca ) to perform miRNA regulatory network analytics to gain functional insights.
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Affiliation(s)
- Le Chang
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Jianguo Xia
- Department of Human Genetics, McGill University, Montreal, QC, Canada.
- Institute of Parasitology, McGill University, Montreal, QC, Canada.
- Department of Animal Science, McGill University, Montreal, QC, Canada.
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37
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Ikonomou L, Yampolskaya M, Mehta P. Multipotent Embryonic Lung Progenitors: Foundational Units of In Vitro and In Vivo Lung Organogenesis. Adv Exp Med Biol 2023; 1413:49-70. [PMID: 37195526 PMCID: PMC10351616 DOI: 10.1007/978-3-031-26625-6_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Transient, tissue-specific, embryonic progenitors are important cell populations in vertebrate development. In the course of respiratory system development, multipotent mesenchymal and epithelial progenitors drive the diversification of fates that results to the plethora of cell types that compose the airways and alveolar space of the adult lungs. Use of mouse genetic models, including lineage tracing and loss-of-function studies, has elucidated signaling pathways that guide proliferation and differentiation of embryonic lung progenitors as well as transcription factors that underlie lung progenitor identity. Furthermore, pluripotent stem cell-derived and ex vivo expanded respiratory progenitors offer novel, tractable, high-fidelity systems that allow for mechanistic studies of cell fate decisions and developmental processes. As our understanding of embryonic progenitor biology deepens, we move closer to the goal of in vitro lung organogenesis and resulting applications in developmental biology and medicine.
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Affiliation(s)
- Laertis Ikonomou
- Department of Oral Biology, University at Buffalo, The State University of New York, Buffalo, NY, USA.
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University at Buffalo, The State University of New York, Buffalo, NY, USA.
- Cell, Gene and Tissue Engineering Center, University at Buffalo, The State University of New York, Buffalo, NY, USA.
| | | | - Pankaj Mehta
- Department of Physics, Boston University, Boston, MA, USA
- Faculty of Computing and Data Science, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
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John M, Grimm D, Korte A. Predicting Gene Regulatory Interactions Using Natural Genetic Variation. Methods Mol Biol 2023; 2698:301-322. [PMID: 37682482 DOI: 10.1007/978-1-0716-3354-0_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Genome-wide association studies (GWAS) are a powerful tool to elucidate the genotype-phenotype map. Although GWAS are usually used to assess simple univariate associations between genetic markers and traits of interest, it is also possible to infer the underlying genetic architecture and to predict gene regulatory interactions. In this chapter, we describe the latest methods and tools to perform GWAS by calculating permutation-based significance thresholds. For this purpose, we first provide guidelines on univariate GWAS analyses that are extended in the second part of this chapter to more complex models that enable the inference of gene regulatory networks and how these networks vary.
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Affiliation(s)
- Maura John
- Technical University of Munich & Weihenstephan-Triesdorf University of Applied Sciences, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany
| | - Dominik Grimm
- Technical University of Munich & Weihenstephan-Triesdorf University of Applied Sciences, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany
| | - Arthur Korte
- Center for Computational and Theoretical Biology, University of Würzburg, Würzburg, Germany.
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Clark NM, Hurgobin B, Kelley DR, Lewsey MG, Walley JW. A Practical Guide to Inferring Multi-Omics Networks in Plant Systems. Methods Mol Biol 2023; 2698:233-257. [PMID: 37682479 DOI: 10.1007/978-1-0716-3354-0_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
The inference of gene regulatory networks can reveal molecular connections underlying biological processes and improve our understanding of complex biological phenomena in plants. Many previous network studies have inferred networks using only one type of omics data, such as transcriptomics. However, given more recent work applying multi-omics integration in plant biology, such as combining (phospho)proteomics with transcriptomics, it may be advantageous to integrate multiple omics data types into a comprehensive network prediction. Here, we describe a state-of-the-art approach for integrating multi-omics data with gene regulatory network inference to describe signaling pathways and uncover novel regulators. We detail how to download and process transcriptomics and (phospho)proteomics data for network inference, using an example dataset from the plant hormone signaling field. We provide a step-by-step protocol for inference, visualization, and analysis of an integrative multi-omics network using currently available methods. This chapter serves as an accessible guide for novice and intermediate bioinformaticians to analyze their own datasets and reanalyze published work.
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Affiliation(s)
- Natalie M Clark
- Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Bhavna Hurgobin
- Australian Research Council Research Hub for Medicinal Agriculture, La Trobe University, Bundoora, VIC, Australia
- La Trobe Institute for Sustainable Agriculture and Food, Department of Animal, Plant and Soil Sciences, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
| | - Dior R Kelley
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA
| | - Mathew G Lewsey
- Australian Research Council Research Hub for Medicinal Agriculture, La Trobe University, Bundoora, VIC, Australia
- La Trobe Institute for Sustainable Agriculture and Food, Department of Animal, Plant and Soil Sciences, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
- Australian Research Council Centre of Excellence in Plants for Space, AgriBio Building, La Trobe University, Bundoora, VIC, Australia
| | - Justin W Walley
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, USA
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Saberi F, Dehghan Z, Noori E, Taheri Z, Sameni M, Zali H. Identification of Critical Molecular Factors and Side Effects Underlying the Response to Thalicthuberine in Prostate Cancer: A Systems Biology Approach. Avicenna J Med Biotechnol 2023; 15:53-64. [PMID: 36789117 PMCID: PMC9895985 DOI: 10.18502/ajmb.v15i1.11425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 12/05/2022] [Indexed: 12/27/2022] Open
Abstract
Background Uncontrolled mitosis of cancer cells and resistance cells to chemotherapy drugs are the challenges of prostate cancer. Thalicthuberine causes a mitotic arrest and a reduction of the effects of drug resistance, resulting in cell death. In this study, we applied bioinformatics and computational biology methods to identify functional pathways and side effects in response to Thalicthuberine in prostate cancer patients. Methods Microarray data were retrieved from Gene Expression Omnibus (GEO), and protein-protein interactions and gene regulatory networks were constructed, using the Cytoscape software. The critical genes and molecular mechanisms in response to Thalicthuberine and its side effects were identified, using the Cytoscape software and WebGestalt server, respectively. Finally, GEPIA2 was used to predict the relationship between critical genes and prostate cancer. Results The POLQ, EGR1, CDKN1A, FOS, MDM2, CDC20, CCNB1, and CCNB2 were identified as critical genes in response to this drug. The functional mechanisms of Thalicthuberine include a response to oxygen levels, toxic substances and immobilization stress, cell cycle regulation, regeneration, the p53 signaling pathway, the action of the parathyroid hormone, and the FoxO signaling pathway. Besides, the drug has side effects including muscle cramping, abdominal pains, paresthesia, and metabolic diseases. Conclusion Our model suggested newly predicted crucial genes, molecular mechanisms, and possible side effects of this drug. However, further studies are required.
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Affiliation(s)
- Fatemeh Saberi
- Student Research Committee, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zeinab Dehghan
- Department of Comparative Biomedical Sciences, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Effat Noori
- Student Research Committee, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zahra Taheri
- Department of Biology and Biotechnology, Pavia University, Pavia, Italy
| | - Marzieh Sameni
- Student Research Committee, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hakimeh Zali
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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41
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Ratnapriya R. The Role of Gene Expression Regulation on Genetic Risk of Age-Related Macular Degeneration. Adv Exp Med Biol 2023; 1415:61-66. [PMID: 37440015 DOI: 10.1007/978-3-031-27681-1_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
Age-related macular degeneration (AMD) is a major cause of irreversible vision loss in the elderly. It is a complex multifactorial disease that is caused by the cumulative impact of genetic predisposition, environmental stress, and advanced aging. Knowledge of genetic risk factors underlying AMD susceptibility has advanced rapidly in the past decade that can be largely credited to genome-wide association studies (GWAS) and next-generation sequencing (NGS) efforts. GWAS have identified 34 genetic risk loci for AMD; the majority of which are in the noncoding genome. Several lines of evidence suggest that a complex trait-associated variant is likely to regulate the gene expression (acting as expression quantitative trait loci (eQTLs)), and there is a significant enrichment of GWAS-associated variants within eQTLs. In the last two years, eQTL studies in AMD-relevant tissues have provided functional interpretation of several AMD-GWAS loci. This review highlights the knowledge gained to date and discusses future directions to bridge the gap between genetic predisposition and biological mechanisms to reap the full benefits of GWAS findings.
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Affiliation(s)
- Rinki Ratnapriya
- Department of Ophthalmology, Baylor College of Medicine, Houston, TX, USA.
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Su K, Katebi A, Kohar V, Clauss B, Gordin D, Qin ZS, Karuturi RKM, Li S, Lu M. NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity. Genome Biol 2022; 23:270. [PMID: 36575445 DOI: 10.1186/s13059-022-02835-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 12/05/2022] [Indexed: 12/28/2022] Open
Abstract
A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.
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Abstract
Plasticity-led evolution is a form of evolution where a change in the environment induces novel traits via phenotypic plasticity, after which the novel traits are genetically accommodated over generations under the novel environment. This mode of evolution is expected to resolve the problem of gradualism (i.e., evolution by the slow accumulation of mutations that induce phenotypic variation) implied by the Modern Evolutionary Synthesis, in the face of a large environmental change. While experimental works are essential for validating that plasticity-led evolution indeed happened, we need computational models to gain insight into its underlying mechanisms and make qualitative predictions. Such computational models should include the developmental process and gene-environment interactions in addition to genetics and natural selection. We point out that gene regulatory network models can incorporate all the above notions. In this review, we highlight results from computational modelling of gene regulatory networks that consolidate the criteria of plasticity-led evolution. Since gene regulatory networks are mathematically equivalent to artificial recurrent neural networks, we also discuss their analogies and discrepancies, which may help further understand the mechanisms underlying plasticity-led evolution.
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Affiliation(s)
- Eden Tian Hwa Ng
- Department of Mathematics, Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE1410 Brunei Darussalam
| | - Akira R. Kinjo
- Department of Mathematics, Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE1410 Brunei Darussalam
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Loers JU, Vermeirssen V. SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks. BMC Bioinformatics 2022; 23:363. [PMID: 36064320 PMCID: PMC9442970 DOI: 10.1186/s12859-022-04908-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/24/2022] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Representing the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators. RESULTS SUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein-protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels. CONCLUSIONS We developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/CBIGR/SUBATOMIC .
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Affiliation(s)
- Jens Uwe Loers
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium.,Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Vanessa Vermeirssen
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium. .,Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium. .,Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
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Ye Q, Bhojwani A, Hu JK. Understanding the development of oral epithelial organs through single cell transcriptomic analysis. Development 2022; 149:dev200539. [PMID: 35831953 PMCID: PMC9481975 DOI: 10.1242/dev.200539] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 07/07/2022] [Indexed: 01/29/2023]
Abstract
During craniofacial development, the oral epithelium begins as a morphologically homogeneous tissue that gives rise to locally complex structures, including the teeth, salivary glands and taste buds. How the epithelium is initially patterned and specified to generate diverse cell types remains largely unknown. To elucidate the genetic programs that direct the formation of distinct oral epithelial populations, we mapped the transcriptional landscape of embryonic day 12 mouse mandibular epithelia at single cell resolution. Our analysis identified key transcription factors and gene regulatory networks that define different epithelial cell types. By examining the spatiotemporal patterning process along the oral-aboral axis, our results propose a model in which the dental field is progressively confined to its position by the formation of the aboral epithelium anteriorly and the non-dental oral epithelium posteriorly. Using our data, we also identified Ntrk2 as a proliferation driver in the forming incisor, contributing to its invagination. Together, our results provide a detailed transcriptional atlas of the embryonic mandibular epithelium, and unveil new genetic markers and regulators that are present during the specification of various oral epithelial structures.
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Affiliation(s)
- Qianlin Ye
- School of Dentistry, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Arshia Bhojwani
- School of Dentistry, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jimmy K Hu
- School of Dentistry, University of California Los Angeles, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA 90095, USA
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Pušnik Ž, Mraz M, Zimic N, Moškon M. Review and assessment of Boolean approaches for inference of gene regulatory networks. Heliyon 2022; 8:e10222. [PMID: 36033302 PMCID: PMC9403406 DOI: 10.1016/j.heliyon.2022.e10222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/22/2022] [Accepted: 08/03/2022] [Indexed: 10/25/2022] Open
Abstract
Boolean descriptions of gene regulatory networks can provide an insight into interactions between genes. Boolean networks hold predictive power, are easy to understand, and can be used to simulate the observed networks in different scenarios. We review fundamental and state-of-the-art methods for inference of Boolean networks. We introduce a methodology for a straightforward evaluation of Boolean inference approaches based on the generation of evaluation datasets, application of selected inference methods, and evaluation of performance measures to guide the selection of the best method for a given inference problem. We demonstrate this procedure on inference methods REVEAL (REVerse Engineering ALgorithm), Best-Fit Extension, MIBNI (Mutual Information-based Boolean Network Inference), GABNI (Genetic Algorithm-based Boolean Network Inference) and ATEN (AND/OR Tree ENsemble algorithm), which infers Boolean descriptions of gene regulatory networks from discretised time series data. Boolean inference approaches tend to perform better in terms of dynamic accuracy, and slightly worse in terms of structural correctness. We believe that the proposed methodology and provided guidelines will help researchers to develop Boolean inference approaches with a good predictive capability while maintaining structural correctness and biological relevance.
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Affiliation(s)
- Žiga Pušnik
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Miha Mraz
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Nikolaj Zimic
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
| | - Miha Moškon
- University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, SI-1000, Slovenia
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Bulbul Ahmed M, Humayan Kabir A. Understanding of the various aspects of gene regulatory networks related to crop improvement. Gene 2022; 833:146556. [PMID: 35609798 DOI: 10.1016/j.gene.2022.146556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/14/2022] [Accepted: 05/06/2022] [Indexed: 12/30/2022]
Abstract
The hierarchical relationship between transcription factors, associated proteins, and their target genes is defined by a gene regulatory network (GRN). GRNs allow us to understand how the genotype and environment of a plant are incorporated to control the downstream physiological responses. During plant growth or environmental acclimatization, GRNs are diverse and can be differently regulated across tissue types and organs. An overview of recent advances in the development of GRN that speed up basic and applied plant research is given here. Furthermore, the overview of genome and transcriptome involving GRN research along with the exciting advancement and application are discussed. In addition, different approaches to GRN predictions were elucidated. In this review, we also describe the role of GRN in crop improvement, crop plant manipulation, stress responses, speed breeding and identifying genetic variations/locus. Finally, the challenges and prospects of GRN in plant biology are discussed.
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Affiliation(s)
- Md Bulbul Ahmed
- Plant Science Department, McGill University, 21111 lakeshore Road, Ste. Anne de Bellevue H9X3V9, Quebec, Canada; Institut de Recherche en Biologie Végétale (IRBV), University of Montreal, Montréal, Québec H1X 2B2, Canada.
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Gill J, Chetty M, Shatte A, Hallinan J. Combining kinetic orders for efficient S-System modelling of gene regulatory network. Biosystems 2022;:104736. [PMID: 35863700 DOI: 10.1016/j.biosystems.2022.104736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 07/10/2022] [Accepted: 07/10/2022] [Indexed: 11/21/2022]
Abstract
S-System models, non-linear differential equation models, are widely used for reconstructing gene regulatory networks from temporal gene expression data. An S-System model involves two states, generation and degeneration, and uses the kinetic parameters gij and hij, to represent the direction, nature, and intensity of the genetic interactions. The need for learning a large number of model parameters results in increased computational expense. Previously, we improved the performance of the algorithm using dynamic allocation of the maximum in-degree for each gene. While the method was effective for smaller networks, a large amount of computation was still needed for larger networks. This problem arose mainly due to the increased occurrence of invalid networks during optimization, primarily because the two kinetic parameters (gij and hij) of the S-System model converge independently during optimization. Being independent, these two parameters can converge to values that can indicate contradictory gene interactions, specifically inhibition or activation. In this study, to address this major challenge in S-System modelling, we developed a novel method that includes two features: a penalty term that penalizes those networks with invalid kinetic orders, and a parameter, wij, derived by combining the kinetic parameters gij and hij. The novel penalty term was used for candidate selection during the process of optimizing the DRNI (Dynamically Regulated Network Initialization) algorithm. Rather than remaining constant, it is dynamic, with its magnitude dependent on the number of invalid interactions in the given network. This approach encourages the generation of valid candidate solutions, and eliminates invalid networks in a systematic manner. The previous DRNI method, a two-stage approach which uses dynamic allocation of the maximum in-degree for each gene, was further improved by adding a third stage which applies the proposed wij to handle the invalid regulations that may still exist in that candidate solutions. The method was tested on different gene expression datasets, and was able to reduce the number of iterations and produce improved network accuracies. For a 20 gene network, the number of generations required for convergence was reduced by 300, and the F-score improved by 0.05 compared to our previously reported DRNI approach. For the well-known 10 gene networks of the DREAM challenge, our method produced an improvement in the average area under the ROC curve of the DREAM4 10 gene networks.
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Abstract
A gene regulatory network summarizes the interactions between a set of genes and regulatory gene products. These interactions include transcriptional regulation, protein activity regulation, and regulation of the transport of proteins between cellular compartments. DSGRN is a network modeling approach that builds on traditions of discrete-time Boolean models and continuous-time switching system models. When all interactions are transcriptional, DSGRN uses a combinatorial approximation to describe the entire range of dynamics that is compatible with network structure. Here we present an extension of the DGSRN approach to transport regulation across a boundary between compartments, such as a cellular membrane. We illustrate our approach by searching a model of the p53-Mdm2 network for the potential to admit two experimentally observed distinct stable periodic cycles.
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Affiliation(s)
- Erika Fox
- Department of Mathematics, University of Nevada, Reno, NV, USA
| | - Bree Cummins
- Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA
| | - William Duncan
- Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA
| | - Tomáš Gedeon
- Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA.
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50
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Lasri A, Shahrezaei V, Sturrock M. Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation. BMC Bioinformatics 2022; 23:236. [PMID: 35715748 PMCID: PMC9204969 DOI: 10.1186/s12859-022-04778-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 05/31/2022] [Indexed: 11/30/2022] Open
Abstract
Background Single cell RNA-sequencing (scRNA-seq) has very rapidly become the new workhorse of modern biology providing an unprecedented global view on cellular diversity and heterogeneity. In particular, the structure of gene-gene expression correlation contains information on the underlying gene regulatory networks. However, interpretation of scRNA-seq data is challenging due to specific experimental error and biases that are unique to this kind of data including drop-out (or technical zeros). Methods To deal with this problem several methods for imputation of zeros for scRNA-seq have been developed. However, it is not clear how these processing steps affect inference of genetic networks from single cell data. Here, we introduce Biomodelling.jl, a tool for generation of synthetic scRNA-seq data using multiscale modelling of stochastic gene regulatory networks in growing and dividing cells. Results Our tool produces realistic transcription data with a known ground truth network topology that can be used to benchmark different approaches for gene regulatory network inference. Using this tool we investigate the impact of different imputation methods on the performance of several network inference algorithms. Conclusions Biomodelling.jl provides a versatile and useful tool for future development and benchmarking of network inference approaches using scRNA-seq data. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04778-9
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Affiliation(s)
- Ayoub Lasri
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Marc Sturrock
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland.
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