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Omelyanchuk NA, Lavrekha VV, Bogomolov AG, Dolgikh VA, Sidorenko AD, Zemlyanskaya EV. Computational Reconstruction of the Transcription Factor Regulatory Network Induced by Auxin in Arabidopsis thaliana L. PLANTS (BASEL, SWITZERLAND) 2024; 13:1905. [PMID: 39065433 PMCID: PMC11280061 DOI: 10.3390/plants13141905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Revised: 07/05/2024] [Accepted: 07/06/2024] [Indexed: 07/28/2024]
Abstract
In plant hormone signaling, transcription factor regulatory networks (TFRNs), which link the master transcription factors to the biological processes under their control, remain insufficiently characterized despite their crucial function. Here, we identify a TFRN involved in the response to the key plant hormone auxin and define its impact on auxin-driven biological processes. To reconstruct the TFRN, we developed a three-step procedure, which is based on the integrated analysis of differentially expressed gene lists and a representative collection of transcription factor binding profiles. Its implementation is available as a part of the CisCross web server. With the new method, we distinguished two transcription factor subnetworks. The first operates before auxin treatment and is switched off upon hormone application, the second is switched on by the hormone. Moreover, we characterized the functioning of the auxin-regulated TFRN in control of chlorophyll and lignin biosynthesis, abscisic acid signaling, and ribosome biogenesis.
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Affiliation(s)
- Nadya A. Omelyanchuk
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
| | - Viktoriya V. Lavrekha
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Anton G. Bogomolov
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
| | - Vladislav A. Dolgikh
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Aleksandra D. Sidorenko
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Elena V. Zemlyanskaya
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia; (N.A.O.); (V.V.L.); (A.G.B.); (V.A.D.); (A.D.S.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
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2
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Abid D, Brent MR. NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration. Bioinformatics 2023; 39:7000334. [PMID: 36692138 PMCID: PMC9912366 DOI: 10.1093/bioinformatics/btad038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 01/11/2023] [Accepted: 01/18/2023] [Indexed: 01/25/2023] Open
Abstract
MOTIVATION Many methods have been proposed for mapping the targets of transcription factors (TFs) from gene expression data. It is known that combining outputs from multiple methods can improve performance. To date, outputs have been combined by using either simplistic formulae, such as geometric mean, or carefully hand-tuned formulae that may not generalize well to new inputs. Finally, the evaluation of accuracy has been challenging due to the lack of genome-scale, ground-truth networks. RESULTS We developed NetProphet3, which combines scores from multiple analyses automatically, using a tree boosting algorithm trained on TF binding location data. We also developed three independent, genome-scale evaluation metrics. By these metrics, NetProphet3 is more accurate than other commonly used packages, including NetProphet 2.0, when gene expression data from direct TF perturbations are available. Furthermore, its integration mode can forge a consensus network from gene expression data and TF binding location data. AVAILABILITY AND IMPLEMENTATION All data and code are available at https://zenodo.org/record/7504131#.Y7Wu3i-B2x8. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dhoha Abid
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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3
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Chauhan JS, Hölzel M, Lambert JP, Buffa FM, Goding CR. The MITF regulatory network in melanoma. Pigment Cell Melanoma Res 2022; 35:517-533. [PMID: 35771179 PMCID: PMC9545041 DOI: 10.1111/pcmr.13053] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 06/09/2022] [Accepted: 06/28/2022] [Indexed: 12/02/2022]
Abstract
Bidirectional interactions between plastic tumor cells and the microenvironment critically impact tumor evolution and metastatic dissemination by enabling cancer cells to adapt to microenvironmental stresses by switching phenotype. In melanoma, a key determinant of phenotypic identity is the microphthalmia‐associated transcription factor MITF that promotes proliferation, suppresses senescence, and anticorrelates with immune infiltration and therapy resistance. What determines whether MITF can activate or repress genes associated with specific phenotypes, or how signaling regulating MITF might impact immune infiltration is poorly understood. Here, we find that MITF binding to genes associated with high MITF is via classical E/M‐box motifs, but genes downregulated when MITF is high contain FOS/JUN/AP1/ATF3 sites. Significantly, the repertoire of MITF‐interacting factors identified here includes JUN and ATF3 as well as many previously unidentified interactors. As high AP1 activity is a hallmark of MITFLow, invasive, slow‐cycling, therapy resistant cells, the ability of MITF to repress AP1‐regulated genes provides an insight into how MITF establishes and maintains a pro‐proliferative phenotype. Moreover, although β‐catenin has been linked to immune exclusion, many Hallmark β‐catenin signaling genes are associated with immune infiltration. Instead, low MITF together with Notch signaling is linked to immune infiltration in both mouse and human melanoma tumors.
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Affiliation(s)
- Jagat S Chauhan
- Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Michael Hölzel
- Institute of Experimental Oncology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Jean-Philippe Lambert
- Department of Molecular Medicine and Cancer Research Centre, Université Laval, Quebec, Canada.,Endocrinology - Nephrology Axis, CHU de Québec - Université Laval Research Center, QC, Canada.,CHU de Québec Research Center, CHUL, 2705 Boulevard Laurier, Quebec, Canada
| | - Francesca M Buffa
- Department of Oncology, University of Oxford, Headington, Oxford, UK
| | - Colin R Goding
- Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
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4
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Gan Y, Qi X, Lin Y, Guo Y, Zhang Y, Wang Q. A Hierarchical Transcriptional Regulatory Network Required for Long-Term Thermal Stress Tolerance in an Industrial Saccharomyces cerevisiae Strain. Front Bioeng Biotechnol 2022; 9:826238. [PMID: 35118059 PMCID: PMC8804346 DOI: 10.3389/fbioe.2021.826238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 12/30/2021] [Indexed: 11/13/2022] Open
Abstract
Yeast cells suffer from continuous and long-term thermal stress during high-temperature ethanol fermentation. Understanding the mechanism of yeast thermotolerance is important not only for studying microbial stress biology in basic research but also for developing thermotolerant strains for industrial application. Here, we compared the effects of 23 transcription factor (TF) deletions on high-temperature ethanol fermentation and cell survival after heat shock treatment and identified three core TFs, Sin3p, Srb2p and Mig1p, that are involved in regulating the response to long-term thermotolerance. Further analyses of comparative transcriptome profiling of the core TF deletions and transcription regulatory associations revealed a hierarchical transcriptional regulatory network centered on these three TFs. This global transcriptional regulatory network provided a better understanding of the regulatory mechanism behind long-term thermal stress tolerance as well as potential targets for transcriptome engineering to improve the performance of high-temperature ethanol fermentation by an industrial Saccharomyces cerevisiae strain.
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Affiliation(s)
- Yuman Gan
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Marine Drugs, Guangxi University of Chinese Medicine, Nanning, China
| | - Xianni Qi
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
| | - Yuping Lin
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- University of Chinese Academy of Sciences, Beijing, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
- *Correspondence: Qinhong Wang, ; Yuping Lin,
| | - Yufeng Guo
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
| | - Yuanyuan Zhang
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
| | - Qinhong Wang
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- University of Chinese Academy of Sciences, Beijing, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
- *Correspondence: Qinhong Wang, ; Yuping Lin,
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5
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Conard AM, Goodman N, Hu Y, Perrimon N, Singh R, Lawrence C, Larschan E. TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data. Nucleic Acids Res 2021; 49:W641-W653. [PMID: 34125906 PMCID: PMC8262710 DOI: 10.1093/nar/gkab384] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/13/2021] [Accepted: 04/28/2021] [Indexed: 01/17/2023] Open
Abstract
Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods integrating ordered RNA-seq data with protein–DNA binding data to distinguish direct from indirect interactions are urgently needed. We present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time-series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein–DNA binding data, and protein-protein interaction networks. TIMEOR’s user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git and http://timeor.brown.edu.
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Affiliation(s)
- Ashley Mae Conard
- Computer Science Department, Brown University, Providence, RI 02912, USA.,Center for Computational and Molecular Biology, Brown University, Providence, RI 02912, USA
| | - Nathaniel Goodman
- Computer Science Department, Brown University, Providence, RI 02912, USA
| | - Yanhui Hu
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Director of Bioinformatics DRSC/TRiP Functional Genomics Resources, Harvard Medical School, Boston, MA 02115, USA
| | - Norbert Perrimon
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Ritambhara Singh
- Computer Science Department, Brown University, Providence, RI 02912, USA.,Center for Computational and Molecular Biology, Brown University, Providence, RI 02912, USA
| | - Charles Lawrence
- Center for Computational and Molecular Biology, Brown University, Providence, RI 02912, USA.,Applied Math Department, Brown University, Providence, RI 02912, USA
| | - Erica Larschan
- Center for Computational and Molecular Biology, Brown University, Providence, RI 02912, USA.,Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI 02912, USA
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6
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Ma CZ, Brent MR. Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data. Bioinformatics 2021; 37:1234-1245. [PMID: 33135076 PMCID: PMC8189679 DOI: 10.1093/bioinformatics/btaa947] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/26/2020] [Accepted: 10/27/2020] [Indexed: 12/20/2022] Open
Abstract
Motivation The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now. Results We systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2. Availability and implementation Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cynthia Z Ma
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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7
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Melkus G, Rucevskis P, Celms E, Čerāns K, Freivalds K, Kikusts P, Lace L, Opmanis M, Rituma D, Viksna J. Network motif-based analysis of regulatory patterns in paralogous gene pairs. J Bioinform Comput Biol 2021; 18:2040008. [PMID: 32698721 DOI: 10.1142/s0219720020400089] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Current high-throughput experimental techniques make it feasible to infer gene regulatory interactions at the whole-genome level with reasonably good accuracy. Such experimentally inferred regulatory networks have become available for a number of simpler model organisms such as S. cerevisiae, and others. The availability of such networks provides an opportunity to compare gene regulatory processes at the whole genome level, and in particular, to assess similarity of regulatory interactions for homologous gene pairs either from the same or from different species. We present here a new technique for analyzing the regulatory interaction neighborhoods of paralogous gene pairs. Our central focus is the analysis of S. cerevisiae gene interaction graphs, which are of particular interest due to the ancestral whole-genome duplication (WGD) that allows to distinguish between paralogous transcription factors that are traceable to this duplication event and other paralogues. Similar analysis is also applied to E. coli and C. elegans networks. We compare paralogous gene pairs according to the presence and size of bi-fan arrays, classically associated in the literature with gene duplication, within other network motifs. We further extend this framework beyond transcription factor comparison to obtain topology-based similarity metrics based on the overlap of interaction neighborhoods applicable to most genes in a given organism. We observe that our network divergence metrics show considerably larger similarity between paralogues, especially those traceable to WGD. This is the case for both yeast and C. elegans, but not for E. coli regulatory network. While there is no obvious cross-species link between metrics, different classes of paralogues show notable differences in interaction overlap, with traceable duplications tending toward higher overlap compared to genes with shared protein families. Our findings indicate that divergence in paralogous interaction networks reflects a shared genetic origin, and that our approach may be useful for investigating structural similarity in the interaction networks of paralogous genes.
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Affiliation(s)
- Gatis Melkus
- Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV-1459, Latvia
| | - Peteris Rucevskis
- Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV-1459, Latvia
| | - Edgars Celms
- Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV-1459, Latvia
| | - Kārlis Čerāns
- Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV-1459, Latvia
| | - Karlis Freivalds
- Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV-1459, Latvia
| | - Paulis Kikusts
- Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV-1459, Latvia
| | - Lelde Lace
- Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV-1459, Latvia
| | - Mārtiņš Opmanis
- Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV-1459, Latvia
| | - Darta Rituma
- Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV-1459, Latvia
| | - Juris Viksna
- Institute of Mathematics and Computer Science, University of Latvia, Rainis blvd. 29, Riga, LV-1459, Latvia
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8
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Runx1 and Runx3 drive progenitor to T-lineage transcriptome conversion in mouse T cell commitment via dynamic genomic site switching. Proc Natl Acad Sci U S A 2021; 118:2019655118. [PMID: 33479171 DOI: 10.1073/pnas.2019655118] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Runt domain-related (Runx) transcription factors are essential for early T cell development in mice from uncommitted to committed stages. Single and double Runx knockouts via Cas9 show that target genes responding to Runx activity are not solely controlled by the dominant factor, Runx1. Instead, Runx1 and Runx3 are coexpressed in single cells; bind to highly overlapping genomic sites; and have redundant, collaborative functions regulating genes pivotal for T cell development. Despite stable combined expression levels across pro-T cell development, Runx1 and Runx3 preferentially activate and repress genes that change expression dynamically during lineage commitment, mostly activating T-lineage genes and repressing multipotent progenitor genes. Furthermore, most Runx target genes are sensitive to Runx perturbation only at one stage and often respond to Runx more for expression transitions than for maintenance. Contributing to this highly stage-dependent gene regulation function, Runx1 and Runx3 extensively shift their binding sites during commitment. Functionally distinct Runx occupancy sites associated with stage-specific activation or repression are also distinguished by different patterns of partner factor cobinding. Finally, Runx occupancies change coordinately at numerous clustered sites around positively or negatively regulated targets during commitment. This multisite binding behavior may contribute to a developmental "ratchet" mechanism making commitment irreversible.
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9
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Dynamic proteomic profiling of human periodontal ligament stem cells during osteogenic differentiation. Stem Cell Res Ther 2021; 12:98. [PMID: 33536073 PMCID: PMC7860046 DOI: 10.1186/s13287-020-02123-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 12/25/2020] [Indexed: 01/07/2023] Open
Abstract
Background Human periodontal ligament stem cells (hPDLSCs) are ideal seed cells for periodontal regeneration. A greater understanding of the dynamic protein profiles during osteogenic differentiation contributed to the improvement of periodontal regeneration tissue engineering. Methods Tandem Mass Tag quantitative proteomics was utilized to reveal the temporal protein expression pattern during osteogenic differentiation of hPDLSCs on days 0, 3, 7 and 14. Differentially expressed proteins (DEPs) were clustered and functional annotated by Gene Ontology (GO) terms. Pathway enrichment analysis was performed based on the Kyoto Encyclopedia of Genes and Genomes database, followed by the predicted activation using Ingenuity Pathway Analysis software. Interaction networks of redox-sensitive signalling pathways and oxidative phosphorylation (OXPHOS) were conducted and the hub protein SOD2 was validated with western blotting. Results A total of 1024 DEPs were identified and clustered in 5 distinctive clusters representing dynamic tendencies. The GO enrichment results indicated that proteins with different tendencies show different functions. Pathway enrichment analysis found that OXPHOS was significantly involved, which further predicted continuous activation. Redox-sensitive signalling pathways with dynamic activation status showed associations with OXPHOS to various degrees, especially the sirtuin signalling pathway. SOD2, an important component of the sirtuin pathway, displays a persistent increase during osteogenesis. Data are available via ProteomeXchange with identifier PXD020908. Conclusion This is the first in-depth dynamic proteomic analysis of osteogenic differentiation of hPDLSCs. It demonstrated a dynamic regulatory mechanism of hPDLSC osteogenesis and might provide a new perspective for research on periodontal regeneration. Graphical abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s13287-020-02123-6.
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Zanin M, Aitya NA, Basilio J, Baumbach J, Benis A, Behera CK, Bucholc M, Castiglione F, Chouvarda I, Comte B, Dao TT, Ding X, Pujos-Guillot E, Filipovic N, Finn DP, Glass DH, Harel N, Iesmantas T, Ivanoska I, Joshi A, Boudjeltia KZ, Kaoui B, Kaur D, Maguire LP, McClean PL, McCombe N, de Miranda JL, Moisescu MA, Pappalardo F, Polster A, Prasad G, Rozman D, Sacala I, Sanchez-Bornot JM, Schmid JA, Sharp T, Solé-Casals J, Spiwok V, Spyrou GM, Stalidzans E, Stres B, Sustersic T, Symeonidis I, Tieri P, Todd S, Van Steen K, Veneva M, Wang DH, Wang H, Wang H, Watterson S, Wong-Lin K, Yang S, Zou X, Schmidt HH. An Early Stage Researcher's Primer on Systems Medicine Terminology. NETWORK AND SYSTEMS MEDICINE 2021; 4:2-50. [PMID: 33659919 PMCID: PMC7919422 DOI: 10.1089/nsm.2020.0003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2020] [Indexed: 12/19/2022] Open
Abstract
Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.
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Affiliation(s)
- Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Nadim A.A. Aitya
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - José Basilio
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Jan Baumbach
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Arriel Benis
- Faculty of Technology Management, Holon Institute of Technology (HIT), Holon, Israel
| | - Chandan K. Behera
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Ioanna Chouvarda
- Lab of Computing, Medical Informatics, and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Blandine Comte
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Tien-Tuan Dao
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Xuemei Ding
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nenad Filipovic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - David H. Glass
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Nissim Harel
- Faculty of Sciences, Holon Institute of Technology (HIT), Holon, Israel
| | - Tomas Iesmantas
- Department of Mathematics and Natural Sciences, Kaunas University of Technology, Kaunas, Lithuania
| | - Ilinka Ivanoska
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, Macedonia
| | - Alok Joshi
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Karim Zouaoui Boudjeltia
- Laboratory of Experimental Medicine (ULB 222), Medicine Faculty, Université libre de Bruxelles, CHU de Charleroi, Charleroi, Belgium
| | - Badr Kaoui
- Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne, Compiègne, France
- Labex MS2T “Control of Technological Systems-of-Systems,” CNRS and Université de Technologie de Compiègne, Compiègne, France
| | - Daman Kaur
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Ulster, United Kingdom
| | - Niamh McCombe
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - João Luís de Miranda
- Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre, Portalegre, Portugal
- Centro de Recursos Naturais e Ambiente (CERENA), Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | | | | | - Annikka Polster
- Centre for Molecular Medicine Norway (NCMM), Forskningparken, Oslo, Norway
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Ioan Sacala
- Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Jose M. Sanchez-Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Johannes A. Schmid
- Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of Vienna, Vienna, Austria
| | - Trevor Sharp
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic–Central University of Catalonia, Vic, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Vojtěch Spiwok
- Department of Biochemistry and Microbiology, University of Chemistry and Technology, Prague, Czech Republic
| | - George M. Spyrou
- The Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Egils Stalidzans
- Computational Systems Biology Group, Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Blaž Stres
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Tijana Sustersic
- Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia
- Steinbeis Advanced Risk Technologies Institute doo Kragujevac, Kragujevac, Serbia
| | - Ioannis Symeonidis
- Center for Research and Technology Hellas, Hellenic Institute of Transport, Thessaloniki, Greece
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Altnagelvin, United Kingdom
| | - Kristel Van Steen
- BIO3-Systems Genetics, GIGA-R, University of Liege, Liege, Belgium
- BIO3-Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Da-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and School of Systems Science, Beijing Normal University, Beijing, China
| | - Haiying Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Hui Wang
- School of Computing, Ulster University, Ulster, United Kingdom
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Ulster University, Londonderry, United Kingdom
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Su Yang
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, United Kingdom
| | - Xin Zou
- Shanghai Centre for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Harald H.H.W. Schmidt
- Faculty of Health, Medicine & Life Science, Maastricht University, Maastricht, The Netherlands
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11
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Li S, Xue T, He F, Liu Z, Ouyang S, Cao D, Wu J. A time-resolved proteomic analysis of transcription factors regulating adipogenesis of human adipose derived stem cells. Biochem Biophys Res Commun 2019; 511:855-861. [PMID: 30850164 DOI: 10.1016/j.bbrc.2019.02.134] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 02/25/2019] [Indexed: 12/30/2022]
Abstract
Adipogenesis is one of the key processes during obesity development. Better understanding of this process could advance our knowledge on obesity and its treatment. Transcription factors (TFs) are master regulators during adipogenesis, however, a system-wide analysis of TFs dynamic proteome during adipogenesis is lacking. Here, we profiled 472 TFs and systematically elucidated their roles during the first 7 days of adipogenesis of human adipose-derived stem cells (hADSCs) on proteome scale. We identified two main and four sub-phases during adipogenesis. The commitment phase (0 h-8 h) mainly mediated stem cell proliferation, differentiation and chromatin remodeling, in which proteins of SWI/SNF family are the key centroid nodes. The determination phase (1D-7D) predominately regulated fat cell differentiation and response to lipid and oxygen, which could be associated with terminal differentiation of adipocyte and responsible for maturation. PPARγ, CREB1 and MYC are the centroid nodes of this phase. Remarkably, we identified and verified three TFs (BATF3, MAFF and MXD4) as novel regulators of adipogenesis, whose over-expression could inhibit adipogenesis of hADSCs in vitro. Overall, our study provided a valuable TFs resource to understand the complex process of adipogenesis.
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Affiliation(s)
- Sen Li
- Department of Biochemistry & Immunology, Capital Institute of Pediatrics-Peking University Teaching Hospital, NO. 2, Yabao Road, Chaoyang District, Beijing, 100020, China.
| | - Ting Xue
- Omicsolution Co, Ltd, Shanghai, 201101, China.
| | - Feng He
- Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Institute of Pediatrics, NO. 2, Yabao Road, Chaoyang District, Beijing, 100020, China.
| | - Zhuo Liu
- Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Institute of Pediatrics, NO. 2, Yabao Road, Chaoyang District, Beijing, 100020, China.
| | - Shengrong Ouyang
- Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Institute of Pediatrics, NO. 2, Yabao Road, Chaoyang District, Beijing, 100020, China.
| | - Dingding Cao
- Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Institute of Pediatrics, NO. 2, Yabao Road, Chaoyang District, Beijing, 100020, China.
| | - Jianxin Wu
- Department of Biochemistry & Immunology, Capital Institute of Pediatrics-Peking University Teaching Hospital, NO. 2, Yabao Road, Chaoyang District, Beijing, 100020, China; Beijing Municipal Key Laboratory of Child Development and Nutriomics, Capital Institute of Pediatrics, NO. 2, Yabao Road, Chaoyang District, Beijing, 100020, China.
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12
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Abstract
The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types) of the molecular networks, for example, genome-scale metabolic network (GMN), transcriptional regulatory network (TRN), and signal transduction network (STN). It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling) of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.
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Affiliation(s)
- Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Dan Wu
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Lingxuan Zhao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Qian Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Edwin Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Tianjin Bohai Fisheries Research Institute, Tianjin, China
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13
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Fu X, He F, Li Y, Shahveranov A, Hutchins AP. Genomic and molecular control of cell type and cell type conversions. CELL REGENERATION 2017; 6:1-7. [PMID: 29348912 PMCID: PMC5769489 DOI: 10.1016/j.cr.2017.09.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 09/06/2017] [Accepted: 09/18/2017] [Indexed: 12/17/2022]
Abstract
Organisms are made of a limited number of cell types that combine to form higher order tissues and organs. Cell types have traditionally been defined by their morphologies or biological activity, yet the underlying molecular controls of cell type remain unclear. The onset of single cell technologies, and more recently genomics (particularly single cell genomics), has substantially increased the understanding of the concept of cell type, but has also increased the complexity of this understanding. These new technologies have added a new genome wide molecular dimension to the description of cell type, with genome-wide expression and epigenetic data acting as a cell type ‘fingerprint’ to describe the cell state. Using these genomic fingerprints cell types are being increasingly defined based on specific genomic and molecular criteria, without necessarily a distinct biological function. In this review, we will discuss the molecular definitions of cell types and cell type control, and particularly how endogenous and exogenous transcription factors can control cell types and cell type conversions.
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14
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Wilkinson AC, Nakauchi H, Göttgens B. Mammalian Transcription Factor Networks: Recent Advances in Interrogating Biological Complexity. Cell Syst 2017; 5:319-331. [PMID: 29073372 PMCID: PMC5928788 DOI: 10.1016/j.cels.2017.07.004] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 06/29/2017] [Accepted: 07/20/2017] [Indexed: 12/11/2022]
Abstract
Transcription factor (TF) networks are a key determinant of cell fate decisions in mammalian development and adult tissue homeostasis and are frequently corrupted in disease. However, our inability to experimentally resolve and interrogate the complexity of mammalian TF networks has hampered the progress in this field. Recent technological advances, in particular large-scale genome-wide approaches, single-cell methodologies, live-cell imaging, and genome editing, are emerging as important technologies in TF network biology. Several recent studies even suggest a need to re-evaluate established models of mammalian TF networks. Here, we provide a brief overview of current and emerging methods to define mammalian TF networks. We also discuss how these emerging technologies facilitate new ways to interrogate complex TF networks, consider the current open questions in the field, and comment on potential future directions and biomedical applications.
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Affiliation(s)
- Adam C Wilkinson
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, Stanford, CA 94305, USA
| | - Hiromitsu Nakauchi
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, Stanford, CA 94305, USA; Division of Stem Cell Therapy, Center for Stem Cell Biology and Regenerative Medicine, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Berthold Göttgens
- Department of Haematology, Cambridge Institute for Medical Research and Wellcome Trust and MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0XY, UK.
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15
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Kang Y, Liow HH, Maier EJ, Brent MR. NetProphet 2.0: mapping transcription factor networks by exploiting scalable data resources. Bioinformatics 2017; 34:249-257. [PMID: 28968736 PMCID: PMC5860202 DOI: 10.1093/bioinformatics/btx563] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 03/14/2017] [Accepted: 09/11/2017] [Indexed: 11/15/2022] Open
Abstract
Motivation Cells process information, in part, through transcription factor (TF) networks, which control the rates at which individual genes produce their products. A TF network map is a graph that indicates which TFs bind and directly regulate each gene. Previous work has described network mapping algorithms that rely exclusively on gene expression data and ‘integrative’ algorithms that exploit a wide range of data sources including chromatin immunoprecipitation sequencing (ChIP-seq) of many TFs, genome-wide chromatin marks, and binding specificities for many TFs determined in vitro. However, such resources are available only for a few major model systems and cannot be easily replicated for new organisms or cell types. Results We present NetProphet 2.0, a ‘data light’ algorithm for TF network mapping, and show that it is more accurate at identifying direct targets of TFs than other, similarly data light algorithms. In particular, it improves on the accuracy of NetProphet 1.0, which used only gene expression data, by exploiting three principles. First, combining multiple approaches to network mapping from expression data can improve accuracy relative to the constituent approaches. Second, TFs with similar DNA binding domains bind similar sets of target genes. Third, even a noisy, preliminary network map can be used to infer DNA binding specificities from promoter sequences and these inferred specificities can be used to further improve the accuracy of the network map. Availability and implementation Source code and comprehensive documentation are freely available at https://github.com/yiming-kang/NetProphet_2.0. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yiming Kang
- Department of Computer Science and Engineering and Center for Genome Sciences and Systems Biology, Washington University, Saint Louis, MO, USA
| | - Hien-Haw Liow
- Department of Mathematics, Washington University, Saint Louis, MO, USA
| | - Ezekiel J Maier
- Department of Computer Science and Engineering and Center for Genome Sciences and Systems Biology, Washington University, Saint Louis, MO, USA
| | - Michael R Brent
- Department of Computer Science and Engineering and Center for Genome Sciences and Systems Biology, Washington University, Saint Louis, MO, USA
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16
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Peng G, Tam PPL, Jing N. Lineage specification of early embryos and embryonic stem cells at the dawn of enabling technologies. Natl Sci Rev 2017. [DOI: 10.1093/nsr/nwx093] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Abstract
Establishment of progenitor cell populations and lineage diversity during embryogenesis and the differentiation of pluripotent stem cells is a fascinating and intricate biological process. Conceptually, an understanding of this developmental process provides a framework to integrate stem-cell pluripotency, cell competence and differentiating potential with the activity of extrinsic and intrinsic molecular determinants. The recent advent of enabling technologies of high-resolution transcriptome analysis at the cellular, population and spatial levels proffers the capability of gaining deeper insights into the attributes of the gene regulatory network and molecular signaling in lineage specification and differentiation. In this review, we provide a snapshot of the emerging enabling genomic technologies that contribute to the study of development and stem-cell biology.
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Affiliation(s)
- Guangdun Peng
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Patrick P. L. Tam
- Embryology Unit, Children's Medical Research Institute, School of Medical Sciences, Sydney Medical School, University of Sydney, NSW 2145, Australia
| | - Naihe Jing
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
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17
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Birkenbihl RP, Liu S, Somssich IE. Transcriptional events defining plant immune responses. CURRENT OPINION IN PLANT BIOLOGY 2017; 38:1-9. [PMID: 28458046 DOI: 10.1016/j.pbi.2017.04.004] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 04/06/2017] [Accepted: 04/10/2017] [Indexed: 05/20/2023]
Abstract
Rapid and massive transcriptional reprogramming upon pathogen recognition is the decisive step in plant-phytopathogen interactions. Plant transcription factors (TFs) are key players in this process but they require a suite of other context-specific co-regulators to establish sensory transcription regulatory networks to bring about host immunity. Molecular, genetic and biochemical studies, particularly in the model plants Arabidopsis and rice, are continuously uncovering new components of the transcriptional machinery that can selectively impact host resistance toward a diverse range of pathogens. Moreover, detailed studies on key immune regulators, such as WRKY TFs and NPR1, are beginning to reveal the underlying mechanisms by which defense hormones influence the function of these factors. Here we provide a short update on such recent developments.
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Affiliation(s)
- Rainer P Birkenbihl
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Carl-von-Linné Weg 10, 50829 Koeln, Germany.
| | - Shouan Liu
- College of Plant Sciences, Jilin University, 130062 Changchun, China.
| | - Imre E Somssich
- Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Carl-von-Linné Weg 10, 50829 Koeln, Germany.
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18
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Aschenbrenner AC, Bassler K, Brondolin M, Bonaguro L, Carrera P, Klee K, Ulas T, Schultze JL, Hoch M. A cross-species approach to identify transcriptional regulators exemplified for Dnajc22 and Hnf4a. Sci Rep 2017; 7:4056. [PMID: 28642491 PMCID: PMC5481429 DOI: 10.1038/s41598-017-04370-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 05/05/2017] [Indexed: 12/03/2022] Open
Abstract
There is an enormous need to make better use of the ever increasing wealth of publicly available genomic information and to utilize the tremendous progress in computational approaches in the life sciences. Transcriptional regulation of protein-coding genes is a major mechanism of controlling cellular functions. However, the myriad of transcription factors potentially controlling transcription of any given gene makes it often difficult to quickly identify the biological relevant transcription factors. Here, we report on the identification of Hnf4a as a major transcription factor of the so far unstudied DnaJ heat shock protein family (Hsp40) member C22 (Dnajc22). We propose an approach utilizing recent advances in computational biology and the wealth of publicly available genomic information guiding the identification of potential transcription factor candidates together with wet-lab experiments validating computational models. More specifically, the combined use of co-expression analyses based on self-organizing maps with sequence-based transcription factor binding prediction led to the identification of Hnf4a as the potential transcriptional regulator for Dnajc22 which was further corroborated using publicly available datasets on Hnf4a. Following this procedure, we determined its functional binding site in the murine Dnajc22 locus using ChIP-qPCR and luciferase assays and verified this regulatory loop in fruitfly, zebrafish, and humans.
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Affiliation(s)
- A C Aschenbrenner
- Developmental Genetics & Molecular Physiology, Life & Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany.
| | - K Bassler
- Genomics and Immunoregulation, Life & Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - M Brondolin
- Developmental Genetics & Molecular Physiology, Life & Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
- Department of Craniofacial Development and Stem Cell Biology, Dental Institute, King's College London, SE1 9RT, London, United Kingdom
| | - L Bonaguro
- Developmental Genetics & Molecular Physiology, Life & Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - P Carrera
- Developmental Genetics & Molecular Physiology, Life & Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - K Klee
- Genomics and Immunoregulation, Life & Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - T Ulas
- Genomics and Immunoregulation, Life & Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - J L Schultze
- Genomics and Immunoregulation, Life & Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
- Single Cell Genomics and Epigenomics Unit at the German Center for Neurodegenerative Diseases and the University of Bonn, 53175, Bonn, Germany
| | - M Hoch
- Developmental Genetics & Molecular Physiology, Life & Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
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19
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Developmental Control of NRAMP1 (SLC11A1) Expression in Professional Phagocytes. BIOLOGY 2017; 6:biology6020028. [PMID: 28467369 PMCID: PMC5485475 DOI: 10.3390/biology6020028] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 04/25/2017] [Accepted: 04/25/2017] [Indexed: 12/11/2022]
Abstract
NRAMP1 (SLC11A1) is a professional phagocyte membrane importer of divalent metals that contributes to iron recycling at homeostasis and to nutritional immunity against infection. Analyses of data generated by several consortia and additional studies were integrated to hypothesize mechanisms restricting NRAMP1 expression to mature phagocytes. Results from various epigenetic and transcriptomic approaches were collected for mesodermal and hematopoietic cell types and compiled for combined analysis with results of genetic studies associating single nucleotide polymorphisms (SNPs) with variations in NRAMP1 expression (eQTLs). Analyses establish that NRAMP1 is part of an autonomous topologically associated domain delimited by ubiquitous CCCTC-binding factor (CTCF) sites. NRAMP1 locus contains five regulatory regions: a predicted super-enhancer (S-E) key to phagocyte-specific expression; the proximal promoter; two intronic areas, including 3' inhibitory elements that restrict expression during development; and a block of upstream sites possibly extending the S-E domain. Also the downstream region adjacent to the 3' CTCF locus boundary may regulate expression during hematopoiesis. Mobilization of the locus 14 predicted transcriptional regulatory elements occurs in three steps, beginning with hematopoiesis; at the onset of myelopoiesis and through myelo-monocytic differentiation. Basal expression level in mature phagocytes is further influenced by genetic variation, tissue environment, and in response to infections that induce various epigenetic memories depending on microorganism nature. Constitutively associated transcription factors (TFs) include CCAAT enhancer binding protein beta (C/EBPb), purine rich DNA binding protein (PU.1), early growth response 2 (EGR2) and signal transducer and activator of transcription 1 (STAT1) while hypoxia-inducible factors (HIFs) and interferon regulatory factor 1 (IRF1) may stimulate iron acquisition in pro-inflammatory conditions. Mouse orthologous locus is generally conserved; chromatin patterns typify a de novo myelo-monocytic gene whose expression is tightly controlled by TFs Pu.1, C/ebps and Irf8; Irf3 and nuclear factor NF-kappa-B p 65 subunit (RelA) regulate expression in inflammatory conditions. Functional differences in the determinants identified at these orthologous loci imply that species-specific mechanisms control gene expression.
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20
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Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast. Proc Natl Acad Sci U S A 2016; 113:E7428-E7437. [PMID: 27810962 DOI: 10.1073/pnas.1603577113] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed an algorithm, NetSurgeon, which uses genome-wide gene-regulatory networks to identify interventions that force a cell toward a desired expression state. We first validated NetSurgeon extensively on existing datasets. Next, we used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in Saccharomyces cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional state of cells using xylose toward that of cells producing large amounts of ethanol from glucose might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism.
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