1
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Jin L, Zhang S, Song Z, Heng X, Chen SJ. Kinetic pathway of HIV-1 TAR cotranscriptional folding. Nucleic Acids Res 2024; 52:6066-6078. [PMID: 38738640 PMCID: PMC11162800 DOI: 10.1093/nar/gkae362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/09/2024] [Accepted: 04/24/2024] [Indexed: 05/14/2024] Open
Abstract
The Trans-Activator Receptor (TAR) RNA, located at the 5'-end untranslated region (5' UTR) of the human immunodeficiency virus type 1 (HIV-1), is pivotal in the virus's life cycle. As the initial functional domain, it folds during the transcription of viral mRNA. Although TAR's role in recruiting the Tat protein for trans-activation is established, the detailed kinetic mechanisms at play during early transcription, especially at points of temporary transcriptional pausing, remain elusive. Moreover, the precise physical processes of transcriptional pause and subsequent escape are not fully elucidated. This study focuses on the folding kinetics of TAR and the biological implications by integrating computer simulations of RNA folding during transcription with nuclear magnetic resonance (NMR) spectroscopy data. The findings reveal insights into the folding mechanism of a non-native intermediate that triggers transcriptional pause, along with different folding pathways leading to transcriptional pause and readthrough. The profiling of the cotranscriptional folding pathway and identification of kinetic structural intermediates reveal a novel mechanism for viral transcriptional regulation, which could pave the way for new antiviral drug designs targeting kinetic cotranscriptional folding pathways in viral RNAs.
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Affiliation(s)
- Lei Jin
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Sicheng Zhang
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Zhenwei Song
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
| | - Xiao Heng
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
| | - Shi-Jie Chen
- Department of Physics and Institute of Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA
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2
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Johnson GA, Gould SI, Sánchez-Rivera FJ. Deconstructing cancer with precision genome editing. Biochem Soc Trans 2024; 52:803-819. [PMID: 38629716 PMCID: PMC11088927 DOI: 10.1042/bst20230984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/25/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
Recent advances in genome editing technologies are allowing investigators to engineer and study cancer-associated mutations in their endogenous genetic contexts with high precision and efficiency. Of these, base editing and prime editing are quickly becoming gold-standards in the field due to their versatility and scalability. Here, we review the merits and limitations of these precision genome editing technologies, their application to modern cancer research, and speculate how these could be integrated to address future directions in the field.
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Affiliation(s)
- Grace A. Johnson
- Department of Biology, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
| | - Samuel I. Gould
- Department of Biology, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
| | - Francisco J. Sánchez-Rivera
- Department of Biology, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge 02142, MA, U.S.A
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3
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Coté A, O'Farrell A, Dardani I, Dunagin M, Coté C, Wan Y, Bayatpour S, Drexler HL, Alexander KA, Chen F, Wassie AT, Patel R, Pham K, Boyden ES, Berger S, Phillips-Cremins J, Churchman LS, Raj A. Post-transcriptional splicing can occur in a slow-moving zone around the gene. eLife 2024; 12:RP91357. [PMID: 38577979 PMCID: PMC10997330 DOI: 10.7554/elife.91357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024] Open
Abstract
Splicing is the stepwise molecular process by which introns are removed from pre-mRNA and exons are joined together to form mature mRNA sequences. The ordering and spatial distribution of these steps remain controversial, with opposing models suggesting splicing occurs either during or after transcription. We used single-molecule RNA FISH, expansion microscopy, and live-cell imaging to reveal the spatiotemporal distribution of nascent transcripts in mammalian cells. At super-resolution levels, we found that pre-mRNA formed clouds around the transcription site. These clouds indicate the existence of a transcription-site-proximal zone through which RNA move more slowly than in the nucleoplasm. Full-length pre-mRNA undergo continuous splicing as they move through this zone following transcription, suggesting a model in which splicing can occur post-transcriptionally but still within the proximity of the transcription site, thus seeming co-transcriptional by most assays. These results may unify conflicting reports of co-transcriptional versus post-transcriptional splicing.
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Affiliation(s)
- Allison Coté
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Aoife O'Farrell
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Ian Dardani
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Margaret Dunagin
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Chris Coté
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Yihan Wan
- School of Life Sciences, Westlake UniversityHangzhouChina
| | - Sareh Bayatpour
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Heather L Drexler
- Department of Genetics, Blavatnik Institute, Harvard Medical SchoolBostonUnited States
| | - Katherine A Alexander
- Department of Cell and Developmental Biology, Penn Institute of Epigenetics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Fei Chen
- Broad Institute of MIT and HarvardCambridgeUnited States
| | - Asmamaw T Wassie
- Department of Cell and Molecular Biology, University of PennsylvaniaPhiladelphiaUnited States
| | - Rohan Patel
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Kenneth Pham
- Department of Cell and Molecular Biology, University of PennsylvaniaPhiladelphiaUnited States
| | - Edward S Boyden
- Departments of Biological Engineering and Brain and Cognitive Sciences, Media Lab and McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Shelly Berger
- Department of Cell and Developmental Biology, Penn Institute of Epigenetics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | | | - L Stirling Churchman
- Department of Genetics, Blavatnik Institute, Harvard Medical SchoolBostonUnited States
| | - Arjun Raj
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
- Department of Genetics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
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4
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Kocanova S, Raynal F, Goiffon I, Oksuz BA, Baú D, Kamgoué A, Cantaloube S, Zhan Y, Lajoie B, Marti-Renom MA, Dekker J, Bystricky K. Enhancer-driven 3D chromatin domain folding modulates transcription in human mammary tumor cells. Life Sci Alliance 2024; 7:e202302154. [PMID: 37989525 PMCID: PMC10663337 DOI: 10.26508/lsa.202302154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 11/03/2023] [Accepted: 11/06/2023] [Indexed: 11/23/2023] Open
Abstract
The genome is organized in functional compartments and structural domains at the sub-megabase scale. How within these domains interactions between numerous cis-acting enhancers and promoters regulate transcription remains an open question. Here, we determined chromatin folding and composition over several hundred kb around estrogen-responsive genes in human breast cancer cell lines after hormone stimulation. Modeling of 5C data at 1.8 kb resolution was combined with quantitative 3D analysis of multicolor FISH measurements at 100 nm resolution and integrated with ChIP-seq data on transcription factor binding and histone modifications. We found that rapid estradiol induction of the progesterone gene expression occurs in the context of preexisting, cell type-specific chromosomal architectures encompassing the 90 kb progesterone gene coding region and an enhancer-spiked 5' 300 kb upstream genomic region. In response to estradiol, interactions between estrogen receptor α (ERα) bound regulatory elements are reinforced. Whereas initial enhancer-gene contacts coincide with RNA Pol 2 binding and transcription initiation, sustained hormone stimulation promotes ERα accumulation creating a regulatory hub stimulating transcript synthesis. In addition to implications for estrogen receptor signaling, we uncover that preestablished chromatin architectures efficiently regulate gene expression upon stimulation without the need for de novo extensive rewiring of long-range chromatin interactions.
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Affiliation(s)
- Silvia Kocanova
- Molecular, Cellular and Developmental Biology Unit (MCD), Centre de Biologie Integrative (CBI), University of Toulouse, UPS, CNRS, Toulouse, France
| | - Flavien Raynal
- Molecular, Cellular and Developmental Biology Unit (MCD), Centre de Biologie Integrative (CBI), University of Toulouse, UPS, CNRS, Toulouse, France
| | - Isabelle Goiffon
- Molecular, Cellular and Developmental Biology Unit (MCD), Centre de Biologie Integrative (CBI), University of Toulouse, UPS, CNRS, Toulouse, France
| | - Betul Akgol Oksuz
- https://ror.org/0464eyp60 Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Davide Baú
- Centre Nacional d'Anàlisi Genòmica (CNAG), Barcelona, Spain
| | - Alain Kamgoué
- Molecular, Cellular and Developmental Biology Unit (MCD), Centre de Biologie Integrative (CBI), University of Toulouse, UPS, CNRS, Toulouse, France
| | - Sylvain Cantaloube
- Molecular, Cellular and Developmental Biology Unit (MCD), Centre de Biologie Integrative (CBI), University of Toulouse, UPS, CNRS, Toulouse, France
| | - Ye Zhan
- https://ror.org/0464eyp60 Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Bryan Lajoie
- https://ror.org/0464eyp60 Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Marc A Marti-Renom
- Centre Nacional d'Anàlisi Genòmica (CNAG), Barcelona, Spain
- Genome Biology Program, Centre de Regulació Genòmica (CRG), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Job Dekker
- https://ror.org/0464eyp60 Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Kerstin Bystricky
- Molecular, Cellular and Developmental Biology Unit (MCD), Centre de Biologie Integrative (CBI), University of Toulouse, UPS, CNRS, Toulouse, France
- Institut Universitaire de France (IUF), Paris, France
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5
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Millius A, Yamada RG, Fujishima H, Maeda K, Standley DM, Sumiyama K, Perrin D, Ueda HR. Circadian ribosome profiling reveals a role for the Period2 upstream open reading frame in sleep. Proc Natl Acad Sci U S A 2023; 120:e2214636120. [PMID: 37769257 PMCID: PMC10556633 DOI: 10.1073/pnas.2214636120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 08/29/2023] [Indexed: 09/30/2023] Open
Abstract
Many mammalian proteins have circadian cycles of production and degradation, and many of these rhythms are altered posttranscriptionally. We used ribosome profiling to examine posttranscriptional control of circadian rhythms by quantifying RNA translation in the liver over a 24-h period from circadian-entrained mice transferred to constant darkness conditions and by comparing ribosome binding levels to protein levels for 16 circadian proteins. We observed large differences in ribosome binding levels compared to protein levels, and we observed delays between peak ribosome binding and peak protein abundance. We found extensive binding of ribosomes to upstream open reading frames (uORFs) in circadian mRNAs, including the core clock gene Period2 (Per2). An increase in the number of uORFs in the 5'UTR was associated with a decrease in ribosome binding in the main coding sequence and a reduction in expression of synthetic reporter constructs. Mutation of the Per2 uORF increased luciferase and fluorescence reporter expression in 3T3 cells and increased luciferase expression in PER2:LUC MEF cells. Mutation of the Per2 uORF in mice increased Per2 mRNA expression, enhanced ribosome binding on Per2, and reduced total sleep time compared to that in wild-type mice. These results suggest that uORFs affect mRNA posttranscriptionally, which can impact physiological rhythms and sleep.
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Affiliation(s)
- Arthur Millius
- Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center, Suita, Osaka565-0871, Japan
- Laboratory for Host Defense, Immunology Frontier Research Center, Suita, Osaka565-0871, Japan
- Laboratory for Systems Immunology, Immunology Frontier Research Center, Suita, Osaka565-0871, Japan
| | - Rikuhiro G. Yamada
- Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center, Suita, Osaka565-0871, Japan
| | - Hiroshi Fujishima
- Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center, Suita, Osaka565-0871, Japan
| | - Kazuhiko Maeda
- Laboratory for Host Defense, Immunology Frontier Research Center, Suita, Osaka565-0871, Japan
| | - Daron M. Standley
- Laboratory for Systems Immunology, Immunology Frontier Research Center, Suita, Osaka565-0871, Japan
| | - Kenta Sumiyama
- Laboratory of Animal Genetics and Breeding, Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya464-8601, Japan
| | - Dimitri Perrin
- School of Computer Science, Queensland University of Technology, BrisbaneQLD 4000, Australia
- Centre for Data Science, Queensland University of Technology, BrisbaneQLD 4000, Australia
| | - Hiroki R. Ueda
- Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center, Suita, Osaka565-0871, Japan
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo113-0033, Japan
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6
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Piryaei Z, Salehi Z, Ebrahimie E, Ebrahimi M, Kavousi K. Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer. BMC Med Genomics 2023; 16:219. [PMID: 37715225 PMCID: PMC10503144 DOI: 10.1186/s12920-023-01655-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 09/04/2023] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND The largest group of patients with breast cancer are estrogen receptor-positive (ER+) type. The estrogen receptor acts as a transcription factor and triggers cell proliferation and differentiation. Hence, investigating ER-DNA interaction genomic regions can help identify genes directly regulated by ER and understand the mechanism of ER action in cancer progression. METHODS In the present study, we employed a workflow to do a meta-analysis of ChIP-seq data of ER+ cell lines stimulated with 10 nM and 100 nM of E2. All publicly available data sets were re-analyzed with the same platform. Then, the known and unknown batch effects were removed. Finally, the meta-analysis was performed to obtain meta-differentially bound sites in estrogen-treated MCF7 cell lines compared to vehicles (as control). Also, the meta-analysis results were compared with the results of T47D cell lines for more precision. Enrichment analyses were also employed to find the functional importance of common meta-differentially bound sites and associated genes among both cell lines. RESULTS Remarkably, POU5F1B, ZNF662, ZNF442, KIN, ZNF410, and SGSM2 transcription factors were recognized in the meta-analysis but not in individual studies. Enrichment of the meta-differentially bound sites resulted in the candidacy of pathways not previously reported in breast cancer. PCGF2, HNF1B, and ZBED6 transcription factors were also predicted through the enrichment analysis of associated genes. In addition, comparing the meta-analysis results of both ChIP-seq and RNA-seq data showed that many transcription factors affected by ER were up-regulated. CONCLUSION The meta-analysis of ChIP-seq data of estrogen-treated MCF7 cell line leads to the identification of new binding sites of ER that have not been previously reported. Also, enrichment of the meta-differentially bound sites and their associated genes revealed new terms and pathways involved in the development of breast cancer which should be examined in future in vitro and in vivo studies.
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Affiliation(s)
- Zeynab Piryaei
- Department of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Zahra Salehi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Esmaeil Ebrahimie
- Genomics Research Platform, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, VIC, Australia
| | - Mansour Ebrahimi
- School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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7
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Cavallaro M, Wang Y, Hebenstreit D, Dutta R. Bayesian inference of polymerase dynamics over the exclusion process. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221469. [PMID: 37538742 PMCID: PMC10394410 DOI: 10.1098/rsos.221469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 07/12/2023] [Indexed: 08/05/2023]
Abstract
Transcription is a complex phenomenon that permits the conversion of genetic information into phenotype by means of an enzyme called RNA polymerase, which erratically moves along and scans the DNA template. We perform Bayesian inference over a paradigmatic mechanistic model of non-equilibrium statistical physics, i.e. the asymmetric exclusion processes in the hydrodynamic limit, assuming a Gaussian process prior for the polymerase progression rate as a latent variable. Our framework allows us to infer the speed of polymerases during transcription given their spatial distribution, while avoiding the explicit inversion of the system's dynamics. The results, which show processing rates strongly varying with genomic position and minor role of traffic-like congestion, may have strong implications for the understanding of gene expression.
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Affiliation(s)
- Massimo Cavallaro
- Mathematics Institute, University of Warwick, Coventry, UK
- School of Life Sciences, University of Warwick, Coventry, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Yuexuan Wang
- Institute of Applied Statistics, Johannes Kepler Universität, Linz, Austria
| | | | - Ritabrata Dutta
- Department of Statistics, University of Warwick, Coventry, UK
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8
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Forbes Beadle L, Love JC, Shapovalova Y, Artemev A, Rattray M, Ashe HL. Combined modelling of mRNA decay dynamics and single-molecule imaging in the Drosophila embryo uncovers a role for P-bodies in 5' to 3' degradation. PLoS Biol 2023; 21:e3001956. [PMID: 36649329 PMCID: PMC9882958 DOI: 10.1371/journal.pbio.3001956] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 01/27/2023] [Accepted: 12/13/2022] [Indexed: 01/18/2023] Open
Abstract
Regulation of mRNA degradation is critical for a diverse array of cellular processes and developmental cell fate decisions. Many methods for determining mRNA half-lives rely on transcriptional inhibition or metabolic labelling. Here, we use a non-invasive method for estimating half-lives for hundreds of mRNAs in the early Drosophila embryo. This approach uses the intronic and exonic reads from a total RNA-seq time series and Gaussian process regression to model the dynamics of premature and mature mRNAs. We show how regulation of mRNA stability is used to establish a range of mature mRNA dynamics during embryogenesis, despite shared transcription profiles. Using single-molecule imaging, we provide evidence that, for the mRNAs tested, there is a correlation between short half-life and mRNA association with P-bodies. Moreover, we detect an enrichment of mRNA 3' ends in P-bodies in the early embryo, consistent with 5' to 3' degradation occurring in P-bodies for at least a subset of mRNAs. We discuss our findings in relation to recently published data suggesting that the primary function of P-bodies in other biological contexts is mRNA storage.
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Affiliation(s)
- Lauren Forbes Beadle
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Jennifer C. Love
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Yuliya Shapovalova
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Artem Artemev
- Department of Computing, Imperial College London, London, United Kingdom
| | - Magnus Rattray
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- * E-mail: (MR); (HLA)
| | - Hilary L. Ashe
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
- * E-mail: (MR); (HLA)
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9
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Bar N, Nikparvar B, Jayavelu ND, Roessler FK. Constrained Fourier estimation of short-term time-series gene expression data reduces noise and improves clustering and gene regulatory network predictions. BMC Bioinformatics 2022; 23:330. [PMID: 35945515 PMCID: PMC9364503 DOI: 10.1186/s12859-022-04839-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 07/12/2022] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Biological data suffers from noise that is inherent in the measurements. This is particularly true for time-series gene expression measurements. Nevertheless, in order to to explore cellular dynamics, scientists employ such noisy measurements in predictive and clustering tools. However, noisy data can not only obscure the genes temporal patterns, but applying predictive and clustering tools on noisy data may yield inconsistent, and potentially incorrect, results. RESULTS To reduce the noise of short-term (< 48 h) time-series expression data, we relied on the three basic temporal patterns of gene expression: waves, impulses and sustained responses. We constrained the estimation of the true signals to these patterns by estimating the parameters of first and second-order Fourier functions and using the nonlinear least-squares trust-region optimization technique. Our approach lowered the noise in at least 85% of synthetic time-series expression data, significantly more than the spline method ([Formula: see text]). When the data contained a higher signal-to-noise ratio, our method allowed downstream network component analyses to calculate consistent and accurate predictions, particularly when the noise variance was high. Conversely, these tools led to erroneous results from untreated noisy data. Our results suggest that at least 5-7 time points are required to efficiently de-noise logarithmic scaled time-series expression data. Investing in sampling additional time points provides little benefit to clustering and prediction accuracy. CONCLUSIONS Our constrained Fourier de-noising method helps to cluster noisy gene expression and interpret dynamic gene networks more accurately. The benefit of noise reduction is large and can constitute the difference between a successful application and a failing one.
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Affiliation(s)
- Nadav Bar
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
| | - Bahareh Nikparvar
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
| | - Naresh Doni Jayavelu
- grid.34477.330000000122986657Division of Medical Genetics, Department of Medicine, University of Washington Seattle, Seattle, WA 98195-7720 USA
| | - Fabienne Krystin Roessler
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
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10
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The distributed delay rearranges the bimodal distribution at protein level. J Taiwan Inst Chem Eng 2022. [DOI: 10.1016/j.jtice.2022.104436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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11
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Xu X, Jin L, Xie L, Chen SJ. Landscape Zooming toward the Prediction of RNA Cotranscriptional Folding. J Chem Theory Comput 2022; 18:2002-2015. [PMID: 35133833 DOI: 10.1021/acs.jctc.1c01233] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
RNA molecules fold as they are transcribed. Cotranscriptional folding of RNA plays a critical role in RNA functions in vivo. Present computational strategies focus on simulations where large structural changes may not be completely sampled. Here, we describe an alternative approach to predicting cotranscriptional RNA folding by zooming in and out of the RNA folding energy landscape. By classifying the RNA structural ensemble into "partitions" based on long, stable helices, we zoom out of the landscape and predict the overall slow folding kinetics from the interpartition kinetic network, and for each interpartition transition, we zoom in on the landscape to simulate the kinetics. Applications of the model to the 117-nucleotide E. coli SRP RNA and the 59-nucleotide HIV-1 TAR RNA show agreements with the experimental data and new structural and kinetic insights into biologically significant conformational switches and pathways for these important systems. This approach, by zooming in/out of an RNA folding landscape at different resolutions, might allow us to treat large RNAs in vivo with transcriptional pause, transcription speed, and other in vivo effects.
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Affiliation(s)
- Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, China
| | - Lei Jin
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
| | - Liangxu Xie
- Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou, Jiangsu 213001, China
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry, and Institute of Data Science and Informatics, University of Missouri, Columbia, Missouri 65211, United States
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12
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Shirokikh NE. Translation complex stabilization on messenger RNA and footprint profiling to study the RNA responses and dynamics of protein biosynthesis in the cells. Crit Rev Biochem Mol Biol 2021; 57:261-304. [PMID: 34852690 DOI: 10.1080/10409238.2021.2006599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
During protein biosynthesis, ribosomes bind to messenger (m)RNA, locate its protein-coding information, and translate the nucleotide triplets sequentially as codons into the corresponding sequence of amino acids, forming proteins. Non-coding mRNA features, such as 5' and 3' untranslated regions (UTRs), start sites or stop codons of different efficiency, stretches of slower or faster code and nascent polypeptide interactions can alter the translation rates transcript-wise. Most of the homeostatic and signal response pathways of the cells converge on individual mRNA control, as well as alter the global translation output. Among the multitude of approaches to study translational control, one of the most powerful is to infer the locations of translational complexes on mRNA based on the mRNA fragments protected by these complexes from endonucleolytic hydrolysis, or footprints. Translation complex profiling by high-throughput sequencing of the footprints allows to quantify the transcript-wise, as well as global, alterations of translation, and uncover the underlying control mechanisms by attributing footprint locations and sizes to different configurations of the translational complexes. The accuracy of all footprint profiling approaches critically depends on the fidelity of footprint generation and many methods have emerged to preserve certain or multiple configurations of the translational complexes, often in challenging biological material. In this review, a systematic summary of approaches to stabilize translational complexes on mRNA for footprinting is presented and major findings are discussed. Future directions of translation footprint profiling are outlined, focusing on the fidelity and accuracy of inference of the native in vivo translation complex distribution on mRNA.
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Affiliation(s)
- Nikolay E Shirokikh
- Division of Genome Sciences and Cancer, The John Curtin School of Medical Research, The Australian National University, Canberra, Australia
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13
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Burton J, Manning CS, Rattray M, Papalopulu N, Kursawe J. Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data. J R Soc Interface 2021; 18:20210393. [PMID: 34583566 PMCID: PMC8479358 DOI: 10.1098/rsif.2021.0393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/26/2021] [Indexed: 11/19/2022] Open
Abstract
Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time-series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.
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Affiliation(s)
- Joshua Burton
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Oxford Road, Manchester M13 9PT, UK
| | - Cerys S. Manning
- Division of Developmental Biology and Medicine, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester M13 9PT, UK
| | - Magnus Rattray
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Oxford Road, Manchester M13 9PT, UK
| | - Nancy Papalopulu
- Division of Developmental Biology and Medicine, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester M13 9PT, UK
| | - Jochen Kursawe
- School of Mathematics and Statistics, University of St Andrews, North Haugh, St Andrews, KY16 9SS, UK
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14
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Yang M, Lee JH, Zhang Z, De La Rosa R, Bi M, Tan Y, Liao Y, Hong J, Du B, Wu Y, Scheirer J, Hong T, Li W, Fei T, Hsieh CL, Liu Z, Li W, Rosenfeld MG, Xu K. Enhancer RNAs Mediate Estrogen-Induced Decommissioning of Selective Enhancers by Recruiting ERα and Its Cofactor. Cell Rep 2021; 31:107803. [PMID: 32579929 PMCID: PMC8564762 DOI: 10.1016/j.celrep.2020.107803] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 03/26/2020] [Accepted: 06/02/2020] [Indexed: 12/14/2022] Open
Abstract
The function of enhancer RNAs (eRNAs) in transcriptional regulation remains obscure. By analyzing the genome-wide nascent transcript profiles in breast cancer cells, we identify a special group of eRNAs that are essential for estrogen-induced transcriptional repression. Using eRNAs of TM4SF1 and EFEMP1 as the paradigms, we find that these RNA molecules not only stabilize promoter-enhancer interactions but also recruit liganded estrogen receptor α (ERα) to particular enhancer regions, facilitate the formation of a functional transcriptional complex, and cause gene silencing. Interestingly, ERα is shown to directly bind with eRNAs by its DNA-binding domain. These eRNAs help with the formation of a specific ERα-centered transcriptional complex and promote the association of the histone demethylase KDM2A, which dismisses RNA polymerase II from designated enhancers and suppresses the transcription of target genes. Our work demonstrates a complete mechanism underlying the action of eRNAs in modulating and refining the locus-specific transcriptional program. Yang et al. identified a group of eRNAs that are essential for estrogen-induced transcriptional repression by assisting with the chromatin recruitment of ERα through binding to its DNA-binding domain and facilitating the interaction of ERα with its cofactors, which leads to the dismissal of RNA polymerase II.
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Affiliation(s)
- Mei Yang
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Ji Hoon Lee
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Zhao Zhang
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Richard De La Rosa
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Mingjun Bi
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Yuliang Tan
- Howard Hughes Medical Institute, Department of Medicine, University of California, San Diego, CA 92093, USA
| | - Yiji Liao
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Juyeong Hong
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Baowen Du
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Yanming Wu
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Jessica Scheirer
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Tao Hong
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA; Xiangya School of Medicine, Central South University, Changsha 410008, China
| | - Wei Li
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA 92697, USA; Division of Biostatistics, Dan L. Duncan Comprehensive Cancer Center and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Teng Fei
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, China
| | - Chen-Lin Hsieh
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Zhijie Liu
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Wenbo Li
- Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77030, USA; Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center and UTHealth, Houston, TX 77030, USA
| | - Michael G Rosenfeld
- Howard Hughes Medical Institute, Department of Medicine, University of California, San Diego, CA 92093, USA
| | - Kexin Xu
- Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
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15
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Lopez-Lopera AF, Durrande N, Alvarez MA. Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:656-666. [PMID: 31144643 DOI: 10.1109/tcbb.2019.2918774] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The regulatory process of Drosophila is thoroughly studied for understanding a great variety of biological principles. While pattern-forming gene networks are analyzed in the transcription step, post-transcriptional events (e.g., translation, protein processing) play an important role in establishing protein expression patterns and levels. Since the post-transcriptional regulation of Drosophila depends on spatiotemporal interactions between mRNAs and gap proteins, proper physically-inspired stochastic models are required to study the link between both quantities. Previous research attempts have shown that using Gaussian processes (GPs) and differential equations lead to promising predictions when analyzing regulatory networks. Here, we aim at further investigating two types of physically-inspired GP models based on a reaction-diffusion equation where the main difference lies in where the prior is placed. While one of them has been studied previously using protein data only, the other is novel and yields a simple approach requiring only the differentiation of kernel functions. In contrast to other stochastic frameworks, discretizing the spatial space is not required here. Both GP models are tested under different conditions depending on the availability of gap gene mRNA expression data. Finally, their performances are assessed on a high-resolution dataset describing the blastoderm stage of the early embryo of Drosophila melanogaster.
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16
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Meisig J, Dreser N, Kapitza M, Henry M, Rotshteyn T, Rahnenführer J, Hengstler J, Sachinidis A, Waldmann T, Leist M, Blüthgen N. Kinetic modeling of stem cell transcriptome dynamics to identify regulatory modules of normal and disturbed neuroectodermal differentiation. Nucleic Acids Res 2020; 48:12577-12592. [PMID: 33245762 PMCID: PMC7736781 DOI: 10.1093/nar/gkaa1089] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 10/21/2020] [Accepted: 10/27/2020] [Indexed: 12/22/2022] Open
Abstract
Thousands of transcriptome data sets are available, but approaches for their use in dynamic cell response modelling are few, especially for processes affected simultaneously by two orthogonal influencing variables. We approached this problem for neuroepithelial development of human pluripotent stem cells (differentiation variable), in the presence or absence of valproic acid (signaling variable). Using few basic assumptions (sequential differentiation states of cells; discrete on/off states for individual genes in these states), and time-resolved transcriptome data, a comprehensive model of spontaneous and perturbed gene expression dynamics was developed. The model made reliable predictions (average correlation of 0.85 between predicted and subsequently tested expression values). Even regulations predicted to be non-monotonic were successfully validated by PCR in new sets of experiments. Transient patterns of gene regulation were identified from model predictions. They pointed towards activation of Wnt signaling as a candidate pathway leading to a redirection of differentiation away from neuroepithelial cells towards neural crest. Intervention experiments, using a Wnt/beta-catenin antagonist, led to a phenotypic rescue of this disturbed differentiation. Thus, our broadly applicable model allows the analysis of transcriptome changes in complex time/perturbation matrices.
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Affiliation(s)
- Johannes Meisig
- Institute of Pathology, Charité-Universitätsmedizin, 10117 Berlin, Germany
- IRI Life Sciences, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Nadine Dreser
- In Vitro Toxicology and Biomedicine, Dept inaugurated by the Doerenkamp-Zbinden Chair foundation, University of Konstanz, 78457 Konstanz, Germany
| | - Marion Kapitza
- In Vitro Toxicology and Biomedicine, Dept inaugurated by the Doerenkamp-Zbinden Chair foundation, University of Konstanz, 78457 Konstanz, Germany
| | - Margit Henry
- Faculty of Medicine, Institute of Neurophysiology, University of Cologne, 50931 Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany
| | - Tamara Rotshteyn
- Faculty of Medicine, Institute of Neurophysiology, University of Cologne, 50931 Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany
| | - Jörg Rahnenführer
- Department of Statistics, TU Dortmund University, 44221 Dortmund, Germany
| | - Jan G Hengstler
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), TU Dortmund University, 44139 Dortmund, Germany
| | - Agapios Sachinidis
- Faculty of Medicine, Institute of Neurophysiology, University of Cologne, 50931 Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany
| | - Tanja Waldmann
- In Vitro Toxicology and Biomedicine, Dept inaugurated by the Doerenkamp-Zbinden Chair foundation, University of Konstanz, 78457 Konstanz, Germany
| | - Marcel Leist
- In Vitro Toxicology and Biomedicine, Dept inaugurated by the Doerenkamp-Zbinden Chair foundation, University of Konstanz, 78457 Konstanz, Germany
| | - Nils Blüthgen
- Institute of Pathology, Charité-Universitätsmedizin, 10117 Berlin, Germany
- IRI Life Sciences, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
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17
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Chen BR, You CX, Shu CC. The common misuse of noise decomposition as applied to genetic systems. Biosystems 2020; 198:104269. [PMID: 33038463 DOI: 10.1016/j.biosystems.2020.104269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 09/22/2020] [Accepted: 10/02/2020] [Indexed: 10/23/2022]
Abstract
The noise-decomposition technique is applied in several fields, including genetic systems, optical images, recording, and navigation. In genetic systems, noise decomposition is usually achieved by using two reporters [Elowitz M.B., Levine A.J., Siggia E.D., Swain P·S., 2002. Stochastic gene expression in a single cell. Science 297, 1183-6.]. A reporter is a protein with fluorescence, an RNA hybridized with a fluorescent probe, or any other detectable intracellular component. If a reporter is constructed in addition to the original reporter, the system's stochasticity may change. Such phenomena became severe for genes in plasmids with a high copy number. By SSA (stochastic simulation algorithm), we observed an approximately 50% increment in the coefficient of variation while introducing additional reporters. Besides, if two reporters respond to the upstream element at a different time, the trunk noise (or extrinsic noise) cannot be accurately determined. This is because the "calculative trunk noise" changes along with the delay, though the real trunk noise does not. For RNA reporters, a 5-min transcriptional delay caused a calculative trunk noise that was 90% less than the real trunk noise. Fortunately, this problem is negligible when the degradation rate constant is low, and it is usually true in the case of the protein reporters. One can check the lifespan of the reporter before applying the noise-decomposition technique.
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Affiliation(s)
- Bo-Ren Chen
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taiwan
| | - Chao-Xuan You
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taiwan
| | - Che-Chi Shu
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taiwan.
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18
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Sheng XY, Wang CH, Wang CF, Xu HY. Long-Chain Non-Coding SOX21-AS1 Promotes Proliferation and Migration of Breast Cancer Cells Through the PI3K/AKT Signaling Pathway. Cancer Manag Res 2020; 12:11005-11014. [PMID: 33173334 PMCID: PMC7648155 DOI: 10.2147/cmar.s270464] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 09/23/2020] [Indexed: 12/18/2022] Open
Abstract
Aim This study aimed to investigate the effect of long-chain non-coding SOX21-AS1 on the proliferation and migration of breast cancer (BC) cells through the PI3K/AKT signaling pathway. Methods Eighty-eight BC and adjacent tissues were collected, and BC cells and normal breast epithelial cells were purchased. LncRNA SOX21-AS1 expression in tissues and cells was detected by RT-PCR. miR-NC, si-SOX21-AS1, and Sh-SOX21-AS1 were transfected into BC cells. The PI3K/AKT signaling pathway was interfered with L740Y-P (activator of the PI3K/AKT pathway) and LY294002 (inhibitor of the PI3K/AKT pathway) in BC cells. The SOX21-AS1 expression in BC tissues and cells was tested by qRT-PCR, and the expression levels of p-PI3K, p-AKT, N-cadherin, E-cadherin, and vimentin were detected by WB. Results SOX21-AS1 was highly expressed in BC, and the p-PI3K and p-AKT levels were also high. Cell experiments showed that inhibiting SOX21-AS1 expression could inhibit the proliferation, invasion, migration, and EMT of BC cells, and up-regulating its expression could promote the proliferation, invasion, migration, and EMT of ovarian cancer cells. The tumor-forming experiment in nude mice was consistent with the results in vitro. 740Y-P intervention could reverse the inhibition effect of Si-SOX21-AS1 on BC cell proliferation, invasion, migration, and EMT, while LY294002 intervention could reverse the promotion effect of Sh-SOX21-AS1 on BC cell proliferation, invasion, migration, and EMT. Conclusion SOX21-AS1 is highly expressed in BC tissues. Silencing BC expression can inhibit the proliferation, invasion, migration, and EMT of cells by inhibiting the PI3K/AKT signaling pathway, which may be a new target for diagnosis and treatment.
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Affiliation(s)
- Xiu-Yun Sheng
- Department of Hematology and Oncology, The Second People's Hospital of Liaocheng, Liaocheng, Shandong Province, 252600, People's Republic of China
| | - Cheng-Hong Wang
- Department of Radiotherapy, The Second People's Hospital of Liaocheng, Liaocheng 252600, Shandong Province, People's Republic of China
| | - Chun-Feng Wang
- Department of Thyroid and Breast Surgery, The Second People's Hospital of Liaocheng, Liaocheng 252600, Shandong Province, People's Republic of China
| | - Hong-Yan Xu
- Department of Oncology, The Second People's Hospital of Liaocheng, Liaocheng 252600, Shandong Province, People's Republic of China
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19
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Yan F, Liu L, Wang Q. Combinatorial dynamics of protein synthesis time delay and negative feedback loop in NF- κB signalling pathway. IET Syst Biol 2020; 14:284-291. [PMID: 33095749 PMCID: PMC8687223 DOI: 10.1049/iet-syb.2020.0034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/12/2020] [Accepted: 07/15/2020] [Indexed: 11/19/2022] Open
Abstract
The transcription factor NF-κB links immune response and inflammatory reaction and its different oscillation patterns determine different cell fates. In this study, a mathematical model with IκBα protein synthesis time delay is developed based on the experimental evidences. The results show that time delay has the ability to drive oscillation of NF-κB via Hopf bifurcation. Meanwhile, the amplitude and period are sensitive to the time delay. Moreover, the time delay threshold is a function of four parameters characterising the negative feedback loop. Likewise, the parameters also have effects on the amplitude and period of NF-κB oscillation induced by time delay. Therefore, the oscillation patterns of NF-κB are collaborative results of time delay coupled with the negative feedback loop. These results not only enhance the understanding of NF-κB biological oscillation but also provide clues for the development of anti-inflammatory or anti-cancer drugs.
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Affiliation(s)
- Fang Yan
- Department of Mathematics, Yunnan Normal University, Kunming 650500, People's Republic of China
| | - Li Liu
- Department of Mathematics, Yunnan Normal University, Kunming 650500, People's Republic of China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing 100191, People's Republic of China.
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20
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Cell type specific gene expression profiling reveals a role for complement component C3 in neutrophil responses to tissue damage. Sci Rep 2020; 10:15716. [PMID: 32973200 PMCID: PMC7518243 DOI: 10.1038/s41598-020-72750-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 08/27/2020] [Indexed: 01/09/2023] Open
Abstract
Tissue damage induces rapid recruitment of leukocytes and changes in the transcriptional landscape that influence wound healing. However, the cell-type specific transcriptional changes that influence leukocyte function and tissue repair have not been well characterized. Here, we employed translating ribosome affinity purification (TRAP) and RNA sequencing, TRAP-seq, in larval zebrafish to identify genes differentially expressed in neutrophils, macrophages, and epithelial cells in response to wounding. We identified the complement pathway and c3a.1, homologous to the C3 component of human complement, as significantly increased in neutrophils in response to wounds. c3a.1−/− zebrafish larvae have impaired neutrophil directed migration to tail wounds with an initial lag in recruitment early after wounding. Moreover, c3a.1−/− zebrafish larvae have impaired recruitment to localized bacterial infections and reduced survival that is, at least in part, neutrophil mediated. Together, our findings support the power of TRAP-seq to identify cell type specific changes in gene expression that influence neutrophil behavior in response to tissue damage.
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21
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Zhang Y, Liu H, Li Z, Miao Z, Zhou J. Oscillatory Dynamics of p53-Mdm2 Circuit in Response to DNA Damage Caused by Ionizing Radiation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1703-1713. [PMID: 30762566 DOI: 10.1109/tcbb.2019.2899574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Although the dynamical behavior of the p53-Mdm2 loop has been extensively studied, the understanding of the mechanism underlying the regulation of this pathway still remains limited. Herein, we developed an integrated model with five basic components and three ubiquitous time delays for the p53-Mdm2 interaction in response to DNA damage following ionizing radiation (IR). We showed that a sufficient amount of activated ATM level can initiate the p53 oscillations with nearly the same amplitude over a wide range of the ATM level; a proper range of p53 level is also required for generating the oscillations, for too high or too low levels it would fail to generate the oscillations; and increased Mdm2 level leads to decreased amplitude of the p53 oscillation and reduced expression of the p53 activity. Moreover, we found that the negative feedback loop formed between p53 and nuclear Mdm2 plays a dominant role in determining the p53 dynamics, whereas when interaction strength of the negative feedback loop becomes weaker, the positive feedback loop formed between p53 and cytoplasmatic Mdm2 can induce different types of dynamics. Furthermore, we demonstrated that the total time delay required for protein production and nuclear translocation of Mdm2 can induce p53 oscillations even when the p53 level is at a certain stable high steady state or at a certain stable low steady state. In addition, the two important features of the oscillatory dynamics-amplitude and period-can be controlled by such time delay. These results are in agreement with multiple experimental observations and may enrich our understanding of the dynamics of the p53 network.
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22
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Gao C, Liu H, Liu Z, Zhang Y, Yan F. Oscillatory behavior of p53-Mdm2 system driven by transcriptional and translational time delays. INT J BIOMATH 2020. [DOI: 10.1142/s1793524520500345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Biological experiments clarify that p53-Mdm2 module is the core of tumor network and p53 oscillation plays an important role in determining the tumor cell fate. In this paper, we investigate the effect of time delay on the oscillatory behavior induced by Hopf bifurcation in p53-Mdm2 system. First, the stability of the unique positive equilibrium point and the existence of Hopf bifurcation are investigated by using the time delay as the bifurcation parameter and by applying the bifurcation theory. Second, the explicit criteria determining the direction of Hopf bifurcation and the stability of bifurcating periodic solutions are developed based on the normal form theory and the center manifold theorem. In addition, the combination of numerical simulation results and theoretical calculation results indicates that time delays in p53-Mdm2 system are critical for p53 oscillations. The results may help us to better understand the biological functions of p53 pathway and provide clues for treatment of cancer.
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Affiliation(s)
- Chunyan Gao
- Department of Mathematics, Yunnan Normal University, No. 768, Juxian Street, Chenggong District, Kunming, P. R. China
| | - Haihong Liu
- Department of Mathematics, Yunnan Normal University, No. 768, Juxian Street, Chenggong District, Kunming, P. R. China
| | - Zengrong Liu
- Department of Mathematics, Yunnan Normal University, No. 768, Juxian Street, Chenggong District, Kunming, P. R. China
| | - Yuan Zhang
- School of Mathematics and Information Technology, Yuxi Normal University, No. 134, Fenghuang Road, Yuxi City, Yunnan Province, P. R. China
| | - Fang Yan
- Department of Mathematics, Yunnan Normal University, No. 768, Juxian Street, Chenggong District, Kunming, P. R. China
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23
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Vanhatalo J, Li Z, Sillanpää MJ. A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotypic data. Bioinformatics 2020; 35:3684-3692. [PMID: 30850830 PMCID: PMC6761969 DOI: 10.1093/bioinformatics/btz164] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 12/05/2018] [Accepted: 03/06/2019] [Indexed: 12/22/2022] Open
Abstract
Motivation Recent advances in high dimensional phenotyping bring time as an extra dimension into the phenotypes. This promotes the quantitative trait locus (QTL) studies of function-valued traits such as those related to growth and development. Existing approaches for analyzing functional traits utilize either parametric methods or semi-parametric approaches based on splines and wavelets. However, very limited choices of software tools are currently available for practical implementation of functional QTL mapping and variable selection. Results We propose a Bayesian Gaussian process (GP) approach for functional QTL mapping. We use GPs to model the continuously varying coefficients which describe how the effects of molecular markers on the quantitative trait are changing over time. We use an efficient gradient based algorithm to estimate the tuning parameters of GPs. Notably, the GP approach is directly applicable to the incomplete datasets having even larger than 50% missing data rate (among phenotypes). We further develop a stepwise algorithm to search through the model space in terms of genetic variants, and use a minimal increase of Bayesian posterior probability as a stopping rule to focus on only a small set of putative QTL. We also discuss the connection between GP and penalized B-splines and wavelets. On two simulated and three real datasets, our GP approach demonstrates great flexibility for modeling different types of phenotypic trajectories with low computational cost. The proposed model selection approach finds the most likely QTL reliably in tested datasets. Availability and implementation Software and simulated data are available as a MATLAB package ‘GPQTLmapping’, and they can be downloaded from GitHub (https://github.com/jpvanhat/GPQTLmapping). Real datasets used in case studies are publicly available at QTL Archive. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jarno Vanhatalo
- Department of Mathematics and Statistics and Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
| | - Zitong Li
- CSIRO Agriculture & Food, GPO Box 1600, Canberra, ACT 2601, Australia
| | - Mikko J Sillanpää
- Department of Mathematical Sciences, Biocenter Oulu and Infotech Oulu University of Oulu, Oulu FI-90014, Finland
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Dai CY, Liu HH, Liu HH. The role of time delays in P53 gene regulatory network stimulated by growth factor. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 17:3794-3835. [PMID: 32987556 DOI: 10.3934/mbe.2020213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this paper, a delayed mathematical model for the P53-Mdm2 network is developed. The P53-Mdm2 network we study is triggered by growth factor instead of DNA damage and the amount of DNA damage is regarded as zero. We study the influences of time delays, growth factor and other important chemical reaction rates on the dynamic behaviors in the system. It is shown that the time delay is a critical factor and its length determines the period, amplitude and stability of the P53 oscillation. Furthermore, as for some important chemical reaction rates, we also obtain some interesting results through numerical simulation. Especially, S (growth factor), k3 (rate constant for Mdm2p dephosphorylation), k10 (basal expression of PTEN) and k14 (Rate constant for PTEN-induced Akt dephosphorylation) could undermine the dynamic behavior of the system in different degree. These findings are expected to understand the mechanisms of action of several carcinogenic and tumor suppressor factors in humans under normal conditions.
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Affiliation(s)
- Chang Yong Dai
- Department of Mathematics, Yunnan Normal University, Kunming, 650500, China
| | - Hai Hong Liu
- Department of Mathematics, Yunnan Normal University, Kunming, 650500, China
| | - Hai Hong Liu
- Department of Mathematics, Yunnan Normal University, Kunming, 650500, China
- Department of Dynamics and Control, Beihang University, Beijing 100191, China
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25
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Nutrient dose-responsive transcriptome changes driven by Michaelis-Menten kinetics underlie plant growth rates. Proc Natl Acad Sci U S A 2020; 117:12531-12540. [PMID: 32414922 PMCID: PMC7293603 DOI: 10.1073/pnas.1918619117] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
An increase in nutrient dose leads to proportional increases in crop biomass and agricultural yield. However, the molecular underpinnings of this nutrient dose-response are largely unknown. To investigate, we assayed changes in the Arabidopsis root transcriptome to different doses of nitrogen (N)-a key plant nutrient-as a function of time. By these means, we found that rate changes of genome-wide transcript levels in response to N-dose could be explained by a simple kinetic principle: the Michaelis-Menten (MM) model. Fitting the MM model allowed us to estimate the maximum rate of transcript change (V max), as well as the N-dose at which one-half of V max was achieved (K m) for 1,153 N-dose-responsive genes. Since transcription factors (TFs) can act in part as the catalytic agents that determine the rates of transcript change, we investigated their role in regulating N-dose-responsive MM-modeled genes. We found that altering the abundance of TGA1, an early N-responsive TF, perturbed the maximum rates of N-dose transcriptomic responses (V max), K m, as well as the rate of N-dose-responsive plant growth. We experimentally validated that MM-modeled N-dose-responsive genes included both direct and indirect TGA1 targets, using a root cell TF assay to detect TF binding and/or TF regulation genome-wide. Taken together, our results support a molecular mechanism of transcriptional control that allows an increase in N-dose to lead to a proportional change in the rate of genome-wide expression and plant growth.
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Chen X, Gu J, Neuwald AF, Hilakivi-Clarke L, Clarke R, Xuan J. BICORN: An R package for integrative inference of de novo cis-regulatory modules. Sci Rep 2020; 10:7960. [PMID: 32409786 PMCID: PMC7224214 DOI: 10.1038/s41598-020-63043-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 01/15/2020] [Indexed: 12/18/2022] Open
Abstract
Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites based on inferred cis-regulatory modules (CRMs). CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html.
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Affiliation(s)
- Xi Chen
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA
| | - Jinghua Gu
- Baylor Research Institute, 3310 Live Oak St, Dallas, TX, 75204, USA
| | - Andrew F Neuwald
- Institute for Genome Sciences and Department Biochemistry & Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Leena Hilakivi-Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, 20057, USA
| | - Robert Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, 20057, USA
| | - Jianhua Xuan
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA.
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Gorin G, Wang M, Golding I, Xu H. Stochastic simulation and statistical inference platform for visualization and estimation of transcriptional kinetics. PLoS One 2020; 15:e0230736. [PMID: 32214380 PMCID: PMC7098607 DOI: 10.1371/journal.pone.0230736] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 03/06/2020] [Indexed: 12/20/2022] Open
Abstract
Recent advances in single-molecule fluorescent imaging have enabled quantitative measurements of transcription at a single gene copy, yet an accurate understanding of transcriptional kinetics is still lacking due to the difficulty of solving detailed biophysical models. Here we introduce a stochastic simulation and statistical inference platform for modeling detailed transcriptional kinetics in prokaryotic systems, which has not been solved analytically. The model includes stochastic two-state gene activation, mRNA synthesis initiation and stepwise elongation, release to the cytoplasm, and stepwise co-transcriptional degradation. Using the Gillespie algorithm, the platform simulates nascent and mature mRNA kinetics of a single gene copy and predicts fluorescent signals measurable by time-lapse single-cell mRNA imaging, for different experimental conditions. To approach the inverse problem of estimating the kinetic parameters of the model from experimental data, we develop a heuristic optimization method based on the genetic algorithm and the empirical distribution of mRNA generated by simulation. As a demonstration, we show that the optimization algorithm can successfully recover the transcriptional kinetics of simulated and experimental gene expression data. The platform is available as a MATLAB software package at https://data.caltech.edu/records/1287.
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Affiliation(s)
- Gennady Gorin
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Mengyu Wang
- Department of Physics, Grainger College of Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Ido Golding
- Department of Physics, Grainger College of Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Heng Xu
- School of Physics and Astronomy, Shanghai Jiao Tong University, Minhang District, Shanghai, China
- Institute of Natural Sciences, Shanghai Jiao Tong University, Minhang District, Shanghai, China
- * E-mail:
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28
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Lo SC, You CX, Shu CC. A Practicable Method of Tuning the Noise Intensity at Protein Level. J Comput Biol 2020; 27:1452-1460. [PMID: 32058806 DOI: 10.1089/cmb.2018.0151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The expression of genes is inevitably subject to intracellular noise. Noise, for some regulatory networks, is constructive but detrimental to many others. The intensity of the noise is a determinant factor and the method of tuning it is of great value. In this study, we illustrated that the transcriptional delay in an incoherent feedforward loop (FFL) grants the target protein modulation the intensity of noise. Remarkably, for a wide range, the coefficient of variation (COV) of the target protein appeared to be about linear to the time span of the transcriptional delay. Without a noise-buffering method, the COV of the target protein is 0.455. While applying incoherent FFL, the COV reduced to 0.236. Then, it changed from 0.236 to 0.630 as the transcriptional delay raised from 0 to 1000 seconds. If we further increased the delay out of the linear range, the COV finally reached 0.779. In addition, we incorporated the distribution of the transcriptional delay in the delay stochastic simulation algorithm. This distribution is based on the experimental observation in the literature. The outcome suggested that the distributed delay slightly improved the ability of tuning noise. In conclusion, we demonstrated a noise-tuning method that altered only the intensity of noise without changing the deterministic steady-state behaviors. It is ready to be applied to various systems in the field of synthetic biology.
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Affiliation(s)
- Shih-Chiang Lo
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei City, Taiwan
| | - Chao-Xuan You
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei City, Taiwan
| | - Che-Chi Shu
- Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei City, Taiwan
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29
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Xu T, Zhang R, Dong M, Zhang Z, Li H, Zhan C, Li X. Osteoglycin (OGN) Inhibits Cell Proliferation and Invasiveness in Breast Cancer via PI3K/Akt/mTOR Signaling Pathway. Onco Targets Ther 2019; 12:10639-10650. [PMID: 31824171 PMCID: PMC6900314 DOI: 10.2147/ott.s222967] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 11/18/2019] [Indexed: 12/24/2022] Open
Abstract
Introduction Previous studies have indicated that the small leucine-rich proteoglycan (SLR) osteoglycin (OGN) is downregulated in various cancers, including squamous cervical carcinoma, gastric cancer, and colorectal adenoma, indicating that OGN is a putative tumor suppressor. However, its exact role in the pathology of human cancers, especially breast cancer (BC), is not clear. Methods The expression of OGN in BC tissues was examined using qRT-PCR. Online databases were employed to analyze the correlation between OGN expression and clinicopathological characteristics. CCK-8 assay, colony formation assay, transwell migration and invasion assays were applied to detect cell proliferation, colony formation, migration and invasion of BC cells, respectively. Xenograft tumor models were constructed to explore the role of OGN on tumor growth in vivo. Results OGN expression was reduced in 24 paired BC samples compared with normal tissue. Decreased expression of OGN was correlated with greater pathological grade, a more aggressive tumor subtype, and poor overall survival. In vitro experiments showed that OGN overexpressed by plasmid transfection significantly inhibited cell proliferation, colony formation, migration, and invasion of BC cell lines. In xenograft tumor models, overexpression of OGN repressed the growth of MCF-7 cells in vivo and alleviated the compression of the tumor on surrounding structures. We also observed that OGN expression reversed EMT via repressing the PI3K/Akt/mTOR pathway. Conclusion This study revealed that OGN could function as a tumor suppressor during breast carcinogenesis, and we contribute new evidence to the body of research on the SLRP family.
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Affiliation(s)
- Tao Xu
- Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430030, People's Republic of China
| | - Rui Zhang
- Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430030, People's Republic of China
| | - Menglu Dong
- Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430030, People's Republic of China
| | - Zeyu Zhang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, People's Republic of China
| | - Hanning Li
- Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430030, People's Republic of China
| | - Chenao Zhan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, People's Republic of China
| | - Xingrui Li
- Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei 430030, People's Republic of China
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30
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Gao C, Ji J, Yan F, Liu H. Oscillation induced by Hopf bifurcation in the p53-Mdm2 feedback module. IET Syst Biol 2019; 13:251-259. [PMID: 31538959 PMCID: PMC8687385 DOI: 10.1049/iet-syb.2018.5092] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Revised: 03/15/2019] [Accepted: 06/20/2019] [Indexed: 11/19/2022] Open
Abstract
This study develops an integrated model of the p53-Mdm2 interaction composed of five basic components and time delay in the DNA damage response based on the existing research work. Some critical factors, including time delay, system parameters, and their interactions in the p53-Mdm2 system are investigated to examine their effects on the oscillatory behaviour induced by Hopf bifurcation. It is shown that the positive feedback formed between p53 and the activity of Mdm2 in the cytoplasm can cause a slight decrease in the amplitude of the p53 oscillation. The length of the time delay plays an important role in determining the amplitude and period of the oscillation and can significantly extend the parameter range for the system to demonstrate oscillatory behaviour. The numerical simulation results are found to be in good agreement with the published experimental observation. It is expected that the results of this research would be helpful to better understand the biological functions of p53 pathway and provide some clues in the treatment of cancer.
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Affiliation(s)
- Chunyan Gao
- Department of Mathematical, Yunnan Normal University, Kunming, People's Republic of China
| | - Jinchen Ji
- School of Mechanical and Mechatronic Engineering, University of Technology Sydney, NSW 2007, Australia
| | - Fang Yan
- Department of Mathematical, Yunnan Normal University, Kunming, People's Republic of China
| | - Haihong Liu
- School of Mechanical and Mechatronic Engineering, University of Technology Sydney, NSW 2007, Australia.
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31
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Dvinge H, Guenthoer J, Porter PL, Bradley RK. RNA components of the spliceosome regulate tissue- and cancer-specific alternative splicing. Genome Res 2019; 29:1591-1604. [PMID: 31434678 PMCID: PMC6771400 DOI: 10.1101/gr.246678.118] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 08/15/2019] [Indexed: 01/08/2023]
Abstract
Alternative splicing of pre-mRNAs plays a pivotal role during the establishment and maintenance of human cell types. Characterizing the trans-acting regulatory proteins that control alternative splicing has therefore been the focus of much research. Recent work has established that even core protein components of the spliceosome, which are required for splicing to proceed, can nonetheless contribute to splicing regulation by modulating splice site choice. We here show that the RNA components of the spliceosome likewise influence alternative splicing decisions. Although these small nuclear RNAs (snRNAs), termed U1, U2, U4, U5, and U6 snRNA, are present in equal stoichiometry within the spliceosome, we found that their relative levels vary by an order of magnitude during development, across tissues, and across cancer samples. Physiologically relevant perturbation of individual snRNAs drove widespread gene-specific differences in alternative splicing but not transcriptome-wide splicing failure. Genes that were particularly sensitive to variations in snRNA abundance in a breast cancer cell line model were likewise preferentially misspliced within a clinically diverse cohort of invasive breast ductal carcinomas. As aberrant mRNA splicing is prevalent in many cancers, we propose that a full understanding of such dysregulated pre-mRNA processing requires study of snRNAs, as well as protein splicing factors. Together, our data show that the RNA components of the spliceosome are not merely basal factors, as has long been assumed. Instead, these noncoding RNAs constitute a previously uncharacterized layer of regulation of alternative splicing, and contribute to the establishment of global splicing programs in both healthy and malignant cells.
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Affiliation(s)
- Heidi Dvinge
- Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.,Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Jamie Guenthoer
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Peggy L Porter
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Robert K Bradley
- Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.,Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
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32
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Zhou Y, Gerrard DL, Wang J, Li T, Yang Y, Fritz AJ, Rajendran M, Fu X, Stein G, Schiff R, Lin S, Frietze S, Jin VX. Temporal dynamic reorganization of 3D chromatin architecture in hormone-induced breast cancer and endocrine resistance. Nat Commun 2019; 10:1522. [PMID: 30944316 PMCID: PMC6447566 DOI: 10.1038/s41467-019-09320-9] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 02/27/2019] [Indexed: 01/01/2023] Open
Abstract
Recent studies have demonstrated that chromatin architecture is linked to the progression of cancers. However, the roles of 3D structure and its dynamics in hormone-dependent breast cancer and endocrine resistance are largely unknown. Here we report the dynamics of 3D chromatin structure across a time course of estradiol (E2) stimulation in human estrogen receptor α (ERα)-positive breast cancer cells. We identified subsets of temporally highly dynamic compartments predominantly associated with active open chromatin and found that these highly dynamic compartments showed higher alteration in tamoxifen-resistant breast cancer cells. Remarkably, these compartments are characterized by active chromatin states, and enhanced ERα binding but decreased transcription factor CCCTC-binding factor (CTCF) binding. We finally identified a set of ERα-bound promoter-enhancer looping genes enclosed within altered domains that are enriched with cancer invasion, aggressiveness or metabolism signaling pathways. This large-scale analysis expands our understanding of high-order temporal chromatin reorganization underlying hormone-dependent breast cancer.
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Affiliation(s)
- Yufan Zhou
- Department of Molecular Medicine, University of Texas Health San Antonio, San Antonio, TX, 78229, USA
| | - Diana L Gerrard
- MLRS Department, University of Vermont, Burlington, VT, 05405, USA
| | - Junbai Wang
- Department of Pathology, Oslo University Hospital-Norwegian Radium Hospital, 0310, Montebello, Oslo, Norway
| | - Tian Li
- Department of Molecular Medicine, University of Texas Health San Antonio, San Antonio, TX, 78229, USA
| | - Yini Yang
- Department of Molecular Medicine, University of Texas Health San Antonio, San Antonio, TX, 78229, USA
| | - Andrew J Fritz
- Department of Biochemistry, University of Vermont, Burlington, VT, 05405, USA
| | - Mahitha Rajendran
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA.,Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.,Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Xiaoyong Fu
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA.,Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.,Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Gary Stein
- Department of Surgery, University of Vermont Larner College of Medicine, 89 Beaumont Avenue, Given C401, Burlington, Vermont, 05405, USA
| | - Rachel Schiff
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA.,Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.,Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Shili Lin
- Department of Statistics, The Ohio State University, Columbus, OH, 43210, USA
| | - Seth Frietze
- MLRS Department, University of Vermont, Burlington, VT, 05405, USA.
| | - Victor X Jin
- Department of Molecular Medicine, University of Texas Health San Antonio, San Antonio, TX, 78229, USA.
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Wang C, Yan F, Liu H, Zhang Y. Theoretical study on the oscillation mechanism of p53-Mdm2 network. INT J BIOMATH 2019. [DOI: 10.1142/s1793524518501127] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, a delayed mathematical model was developed based on experimental data to understand how the time delays required for transcription and translation in Mdm2 gene expression affect the kinetic behavior of the p53-Mdm2 network. Taking the time delays as the main research parameters, the stability of the system at the positive equilibrium was studied by using the theoretical method of delay differential equation. We found that such delays can induce oscillations by undergoing a supercritical Hopf bifurcation. Then, we used the normal form theory and the center manifold reduction to study the direction and stability of the bifurcation in detail. Furthermore, we also studied the effects of the length of time delays and the model parameters by numerical simulations. We found that time delays in Mdm2 synthesis are required for p53 oscillations and the length of such delays can determine the amplitude and period of the oscillations. In addition, the model parameters can also change the stability of the system. These results illustrate that the repair process after DNA damage can be regulated by varying time delays and the model parameters.
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Affiliation(s)
- Conghua Wang
- Department of Mathematics, Yunnan Normal University, Kunming, Yunnan 650500, P. R. China
| | - Fang Yan
- Department of Mathematics, Yunnan Normal University, Kunming, Yunnan 650500, P. R. China
| | - Haihong Liu
- Department of Mathematics, Yunnan Normal University, Kunming, Yunnan 650500, P. R. China
| | - Yuan Zhang
- Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai 200072, P. R. China
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Holding AN, Cullen AE, Markowetz F. Genome-wide Estrogen Receptor-α activation is sustained, not cyclical. eLife 2018; 7:e40854. [PMID: 30457555 PMCID: PMC6287946 DOI: 10.7554/elife.40854] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 11/16/2018] [Indexed: 01/24/2023] Open
Abstract
Estrogen Receptor-alpha (ER) drives 75% of breast cancers. Stimulation of the ER by estra-2-diol forms a transcriptionally-active chromatin-bound complex. Previous studies reported that ER binding follows a cyclical pattern. However, most studies have been limited to individual ER target genes and without replicates. Thus, the robustness and generality of ER cycling are not well understood. We present a comprehensive genome-wide analysis of the ER after activation, based on 6 replicates at 10 time-points, using our method for precise quantification of binding, Parallel-Factor ChIP-seq. In contrast to previous studies, we identified a sustained increase in affinity, alongside a class of estra-2-diol independent binding sites. Our results are corroborated by quantitative re-analysis of multiple independent studies. Our new model reconciles the conflicting studies into the ER at the TFF1 promoter and provides a detailed understanding in the context of the ER's role as both the driver and therapeutic target of breast cancer.
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Affiliation(s)
- Andrew N Holding
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUnited Kingdom
| | - Amy E Cullen
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUnited Kingdom
| | - Florian Markowetz
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUnited Kingdom
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35
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Topa H, Honkela A. GPrank: an R package for detecting dynamic elements from genome-wide time series. BMC Bioinformatics 2018; 19:367. [PMID: 30286713 PMCID: PMC6172792 DOI: 10.1186/s12859-018-2370-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 09/11/2018] [Indexed: 01/30/2023] Open
Abstract
Background Genome-wide high-throughput sequencing (HTS) time series experiments are a powerful tool for monitoring various genomic elements over time. They can be used to monitor, for example, gene or transcript expression with RNA sequencing (RNA-seq), DNA methylation levels with bisulfite sequencing (BS-seq), or abundances of genetic variants in populations with pooled sequencing (Pool-seq). However, because of high experimental costs, the time series data sets often consist of a very limited number of time points with very few or no biological replicates, posing challenges in the data analysis. Results Here we present the GPrank R package for modelling genome-wide time series by incorporating variance information obtained during pre-processing of the HTS data using probabilistic quantification methods or from a beta-binomial model using sequencing depth. GPrank is well-suited for analysing both short and irregularly sampled time series. It is based on modelling each time series by two Gaussian process (GP) models, namely, time-dependent and time-independent GP models, and comparing the evidence provided by data under two models by computing their Bayes factor (BF). Genomic elements are then ranked by their BFs, and temporally most dynamic elements can be identified. Conclusions Incorporating the variance information helps GPrank avoid false positives without compromising computational efficiency. Fitted models can be easily further explored in a browser. Detection and visualisation of temporally most active dynamic elements in the genome can provide a good starting point for further downstream analyses for increasing our understanding of the studied processes.
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Affiliation(s)
- Hande Topa
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, 00014, Finland. .,Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, 00076, Finland.
| | - Antti Honkela
- Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, 00014, Finland.,Department of Public Health, University of Helsinki, Helsinki, 00014, Finland
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36
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Shi X, Wang X, Wang TL, Hilakivi-Clarke L, Clarke R, Xuan J. SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data. Bioinformatics 2018; 34:56-63. [PMID: 28968634 DOI: 10.1093/bioinformatics/btx557] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2017] [Accepted: 09/02/2017] [Indexed: 01/01/2023] Open
Abstract
Motivation Recent advances in high-throughput RNA sequencing (RNA-seq) technologies have made it possible to reconstruct the full transcriptome of various types of cells. It is important to accurately assemble transcripts or identify isoforms for an improved understanding of molecular mechanisms in biological systems. Results We have developed a novel Bayesian method, SparseIso, to reliably identify spliced isoforms from RNA-seq data. A spike-and-slab prior is incorporated into the Bayesian model to enforce the sparsity for isoform identification, effectively alleviating the problem of overfitting. A Gibbs sampling procedure is further developed to simultaneously identify and quantify transcripts from RNA-seq data. With the sampling approach, SparseIso estimates the joint distribution of all candidate transcripts, resulting in a significantly improved performance in detecting lowly expressed transcripts and multiple expressed isoforms of genes. Both simulation study and real data analysis have demonstrated that the proposed SparseIso method significantly outperforms existing methods for improved transcript assembly and isoform identification. Availability and implementation The SparseIso package is available at http://github.com/henryxushi/SparseIso. Contact xuan@vt.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xu Shi
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Xiao Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Tian-Li Wang
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA
| | - Leena Hilakivi-Clarke
- Department of Oncology and Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA
| | - Robert Clarke
- Department of Oncology and Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA
| | - Jianhua Xuan
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
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37
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Fichtner A, Simutė S. Hamiltonian Monte Carlo Inversion of Seismic Sources in Complex Media. JOURNAL OF GEOPHYSICAL RESEARCH. SOLID EARTH 2018; 123:2984-2999. [PMID: 30034980 PMCID: PMC6049980 DOI: 10.1002/2017jb015249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 03/05/2018] [Accepted: 03/13/2018] [Indexed: 06/08/2023]
Abstract
We present a probabilistic seismic point source inversion, taking into account 3-D heterogeneous Earth structure. Our method rests on (1) reciprocity and numerical wavefield simulations in complex media and (2) Hamiltonian Monte Carlo sampling that requires only a small amount of test models to provide reliable uncertainty information on the timing, location, and mechanism of the source. Using spectral element simulations of 3-D, viscoelastic, anisotropic wave propagation, we precompute receiver side strain tensors in time and space. This enables the fast computation of synthetic seismograms for any hypothetical source within the volume of interest, and thus a Bayesian solution of the inverse problem. To improve efficiency, we developed a variant of Hamiltonian Monte Carlo sampling. Taking advantage of easily computable derivatives, numerical examples indicate that Hamiltonian Monte Carlo can converge to the posterior probability density with orders of magnitude less samples than the derivative-free Metropolis-Hastings algorithm, which we use for benchmarking. Exact numbers depend on observational errors and the quality of the prior. We apply our method to the Japanese Islands region where we previously constrained 3-D structure of the crust and upper mantle using full-waveform inversion with a minimum period of 15 s.
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Affiliation(s)
| | - Saule Simutė
- Institute of GeophysicsETH ZurichZurichSwitzerland
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Rezaei M, Eskandari F, Mohammadpour-Gharehbagh A, Harati-Sadegh M, Teimoori B, Salimi S. Hypomethylation of the miRNA-34a gene promoter is associated with Severe Preeclampsia. Clin Exp Hypertens 2018; 41:1-5. [PMID: 29557690 DOI: 10.1080/10641963.2018.1451534] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 02/26/2018] [Accepted: 02/26/2018] [Indexed: 01/08/2023]
Abstract
PURPOSE PE is a pregnancy-specific complication, which genetic and epigenetic factors play key roles in its pathogenesis. DNA methylation is a main epigenetic alteration with important roles in gene regulation. Micro RNAs (miRNAs) as another member of epigenetic machinery regulate the gene expression and involve in different biological pathways including apoptosis and placental development. Therefore, the present study performed to assess the association between miRNA-34a promoter methylation and PE susceptibility. METHODS The placenta of 104 PE pregnant women and 119 normotensive pregnant women were collected after delivery. The methylation status of the miRNA-34a promoter was assessed using Methylation Specific PCR (MSP). RESULTS The frequency of the hemi-methylated (MU) miR-34a promoter was significantly lower in PE women compared to the controls (17.3 vs. 29.4%) (OR, 0.45 [95% CI, 0.2-0.9], P = 0.016). The overall methylation rate was 23.1% in PE women and 41.2% in the control group and was significantly lower in PE women (OR, 0.4 [95% CI, 0.2-0.8], P = 0.004). The frequency of hemi-methylated (MU) and overall methylated (MU+MM) promoter of miR-34a gene was significantly lower in severe PE but not in mild PE women compared to the controls [(OR, 0.3 [95% CI, 0.1-0.8], P = 0.02) and (OR, 0.3 [95% CI, 0.1-0.7], P = 0.009), respectively]. There was an association between hemi-methylated (MU) and overall methylated (MU+MM) promoter and late onset PE [(OR, 0.4 [95% CI, 0.2-0.9], P = 0.03) and (OR, 0.4 [95% CI, 0.2-0.8], P = 0.01), respectively]. CONCLUSIONS An association was found between hypo-methylation of the miR-34a promoter and PE and PE severity.
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Affiliation(s)
- Mahnaz Rezaei
- a Department of Clinical Biochemistry, School of Medicine , Zahedan University of Medical Sciences , Zahedan , Iran
- b Cellular and Molecular Research Center , Zahedan University of Medical Sciences , Zahedan , Iran
| | - Fatemeh Eskandari
- a Department of Clinical Biochemistry, School of Medicine , Zahedan University of Medical Sciences , Zahedan , Iran
- b Cellular and Molecular Research Center , Zahedan University of Medical Sciences , Zahedan , Iran
| | - Abbas Mohammadpour-Gharehbagh
- a Department of Clinical Biochemistry, School of Medicine , Zahedan University of Medical Sciences , Zahedan , Iran
- b Cellular and Molecular Research Center , Zahedan University of Medical Sciences , Zahedan , Iran
| | - Mahdiyeh Harati-Sadegh
- c Department of Genetics, Fars Science and Research Branch , Islamic Azad University , Marvdasht , Iran
- d Department of Genetics, Marvdasht Branch , Islamic Azad University , Marvdasht , Iran
| | - Batool Teimoori
- e Department of Obstetrics and Gynecology, School of Medicine , Zahedan University of Medical Sciences , Zahedan , Iran
| | - Saeedeh Salimi
- a Department of Clinical Biochemistry, School of Medicine , Zahedan University of Medical Sciences , Zahedan , Iran
- b Cellular and Molecular Research Center , Zahedan University of Medical Sciences , Zahedan , Iran
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Miano V, Ferrero G, Rosti V, Manitta E, Elhasnaoui J, Basile G, De Bortoli M. Luminal lncRNAs Regulation by ERα-Controlled Enhancers in a Ligand-Independent Manner in Breast Cancer Cells. Int J Mol Sci 2018; 19:E593. [PMID: 29462945 PMCID: PMC5855815 DOI: 10.3390/ijms19020593] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 02/07/2018] [Accepted: 02/14/2018] [Indexed: 12/12/2022] Open
Abstract
Estrogen receptor-α (ERα) is a ligand-inducible protein which mediates estrogenic hormones signaling and defines the luminal BC phenotype. Recently, we demonstrated that even in absence of ligands ERα (apoERα) binds chromatin sites where it regulates transcription of several protein-coding and lncRNA genes. Noteworthy, apoERα-regulated lncRNAs marginally overlap estrogen-induced transcripts, thus representing a new signature of luminal BC genes. By the analysis of H3K27ac enrichment in hormone-deprived MCF-7 cells, we defined a set of Super Enhancers (SEs) occupied by apoERα, including one mapped in proximity of the DSCAM-AS1 lncRNA gene. This represents a paradigm of apoERα activity since its expression is largely unaffected by estrogenic treatment, despite the fact that E2 increases ERα binding on DSCAM-AS1 promoter. We validated the enrichment of apoERα, p300, GATA3, FoxM1 and CTCF at both DSCAM-AS1 TSS and at its associated SE by ChIP-qPCR. Furthermore, by analyzing MCF-7 ChIA-PET data and by 3C assays, we confirmed long range chromatin interaction between the SE and the DSCAM-AS1 TSS. Interestingly, CTCF and p300 binding showed an enrichment in hormone-depleted medium and in the presence of ERα, elucidating the dynamics of the estrogen-independent regulation of DSCAM-AS1 expression. The analysis of this lncRNA provides a paradigm of transcriptional regulation of a luminal specific apoERα regulated lncRNA.
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Affiliation(s)
- Valentina Miano
- Center for Molecular Systems Biology, University of Turin, Orbassano, 10043 Turin, Italy.
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy.
| | - Giulio Ferrero
- Center for Molecular Systems Biology, University of Turin, Orbassano, 10043 Turin, Italy.
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy.
- Department of Computer Science, University of Turin, 10149 Turin, Italy.
| | - Valentina Rosti
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy.
| | - Eleonora Manitta
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy.
| | - Jamal Elhasnaoui
- Center for Molecular Systems Biology, University of Turin, Orbassano, 10043 Turin, Italy.
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy.
| | - Giulia Basile
- Italian Institute for Genomic Medicine (IIGM), 10126 Turin, Italy.
| | - Michele De Bortoli
- Center for Molecular Systems Biology, University of Turin, Orbassano, 10043 Turin, Italy.
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, 10043 Turin, Italy.
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40
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Reduction and Stability Analysis of a Transcription–Translation Model of RNA Polymerase. Bull Math Biol 2017; 80:294-318. [DOI: 10.1007/s11538-017-0372-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 11/22/2017] [Indexed: 01/28/2023]
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41
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Dzida T, Iqbal M, Charapitsa I, Reid G, Stunnenberg H, Matarese F, Grote K, Honkela A, Rattray M. Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data. PeerJ 2017; 5:e3742. [PMID: 28970965 PMCID: PMC5623311 DOI: 10.7717/peerj.3742] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 08/07/2017] [Indexed: 12/31/2022] Open
Abstract
We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα), RNA polymerase (Pol II) and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.
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Affiliation(s)
- Tomasz Dzida
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Mudassar Iqbal
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Iryna Charapitsa
- Chemical Biology Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - George Reid
- Chemical Biology Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Henk Stunnenberg
- Department of Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Filomena Matarese
- Department of Molecular Life Sciences, Radboud University Nijmegen, Nijmegen, Netherlands
| | | | - Antti Honkela
- Helsinki Institute for InformationTechnology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Magnus Rattray
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
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42
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Zhang Y, Liu H, Yan F, Zhou J. Oscillatory dynamics of p38 activity with transcriptional and translational time delays. Sci Rep 2017; 7:11495. [PMID: 28904347 PMCID: PMC5597677 DOI: 10.1038/s41598-017-11149-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 07/31/2017] [Indexed: 01/30/2023] Open
Abstract
Recent experimental evidence reports that oscillations of p38 MAPK (p38) activity would efficiently induce pro-inflammatory gene expression, which might be deleterious to immune systems and may even cause cellular damage and apoptosis. It is widely accepted now that transcriptional and translational delays are ubiquitous in gene expression, which can typically result in oscillatory responses of gene regulations. Consequently, delay-driven sustained oscillations in p38 activity (p38*) could in principle be commonplace. Nevertheless, so far the studies of the impact of such delays on p38* have been lacking both experimentally and theoretically. Here, we use experimental data to develop a delayed mathematical model, with the aim of understanding how such delays affect oscillatory behaviour on p38*. We analyze the stability and oscillation of the model with and without explicit time delays. We show that a sufficiently input stimulation strength is prerequisite for generating p38* oscillations, and that an optimal rate of model parameters is also essential to these oscillations. Moreover, we find that the time delays required for transcription and translation in mitogen-activated protein kinase phosphatase-1 (MKP-1) gene expression can drive p38* to be oscillatory even when the concentration of p38* level is at a stable state. Furthermore, the length of these delays can determine the amplitude and period of the oscillations and can enormously extend the oscillatory ranges of model parameters. These results indicate that time delays in MKP-1 synthesis are required, albeit not sufficient, for p38* oscillations, which may lead to new insights related to p38 oscillations.
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Affiliation(s)
- Yuan Zhang
- Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai, 200072, China
| | - Haihong Liu
- Department of mathematics, Yunnan Normal University, Kunming, 650092, China
| | - Fang Yan
- Department of mathematics, Yunnan Normal University, Kunming, 650092, China
| | - Jin Zhou
- Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai, 200072, China.
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Topa H, Honkela A. Analysis of differential splicing suggests different modes of short-term splicing regulation. Bioinformatics 2017; 32:i147-i155. [PMID: 27307611 PMCID: PMC4908367 DOI: 10.1093/bioinformatics/btw283] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Motivation: Alternative splicing is an important mechanism in which the regions of pre-mRNAs are differentially joined in order to form different transcript isoforms. Alternative splicing is involved in the regulation of normal physiological functions but also linked to the development of diseases such as cancer. We analyse differential expression and splicing using RNA-sequencing time series in three different settings: overall gene expression levels, absolute transcript expression levels and relative transcript expression levels. Results: Using estrogen receptor α signaling response as a model system, our Gaussian process-based test identifies genes with differential splicing and/or differentially expressed transcripts. We discover genes with consistent changes in alternative splicing independent of changes in absolute expression and genes where some transcripts change whereas others stay constant in absolute level. The results suggest classes of genes with different modes of alternative splicing regulation during the experiment. Availability and Implementation: R and Matlab codes implementing the method are available at https://github.com/PROBIC/diffsplicing. An interactive browser for viewing all model fits is available at http://users.ics.aalto.fi/hande/splicingGP/ Contact:hande.topa@helsinki.fi or antti.honkela@helsinki.fi Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hande Topa
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo 00076, Finland Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki 00014, Finland
| | - Antti Honkela
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki 00014, Finland
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44
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Associating transcription factors and conserved RNA structures with gene regulation in the human brain. Sci Rep 2017; 7:5776. [PMID: 28720872 PMCID: PMC5516038 DOI: 10.1038/s41598-017-06200-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Accepted: 06/20/2017] [Indexed: 02/06/2023] Open
Abstract
Anatomical subdivisions of the human brain can be associated with different neuronal functions. This functional diversification is reflected by differences in gene expression. By analyzing post-mortem gene expression data from the Allen Brain Atlas, we investigated the impact of transcription factors (TF) and RNA secondary structures on the regulation of gene expression in the human brain. First, we modeled the expression of a gene as a linear combination of the expression of TFs. We devised an approach to select robust TF-gene interactions and to determine localized contributions to gene expression of TFs. Among the TFs with the most localized contributions, we identified EZH2 in the cerebellum, NR3C1 in the cerebral cortex and SRF in the basal forebrain. Our results suggest that EZH2 is involved in regulating ZIC2 and SHANK1 which have been linked to neurological diseases such as autism spectrum disorder. Second, we associated enriched regulatory elements inside differentially expressed mRNAs with RNA secondary structure motifs. We found a group of purine-uracil repeat RNA secondary structure motifs plus other motifs in neuron related genes such as ACSL4 and ERLIN2.
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45
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van Nues R, Schweikert G, de Leau E, Selega A, Langford A, Franklin R, Iosub I, Wadsworth P, Sanguinetti G, Granneman S. Kinetic CRAC uncovers a role for Nab3 in determining gene expression profiles during stress. Nat Commun 2017; 8:12. [PMID: 28400552 PMCID: PMC5432031 DOI: 10.1038/s41467-017-00025-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 02/20/2017] [Indexed: 02/07/2023] Open
Abstract
RNA-binding proteins play a key role in shaping gene expression profiles during stress, however, little is known about the dynamic nature of these interactions and how this influences the kinetics of gene expression. To address this, we developed kinetic cross-linking and analysis of cDNAs (χCRAC), an ultraviolet cross-linking method that enabled us to quantitatively measure the dynamics of protein-RNA interactions in vivo on a minute time-scale. Here, using χCRAC we measure the global RNA-binding dynamics of the yeast transcription termination factor Nab3 in response to glucose starvation. These measurements reveal rapid changes in protein-RNA interactions within 1 min following stress imposition. Changes in Nab3 binding are largely independent of alterations in transcription rate during the early stages of stress response, indicating orthogonal transcriptional control mechanisms. We also uncover a function for Nab3 in dampening expression of stress-responsive genes. χCRAC has the potential to greatly enhance our understanding of in vivo dynamics of protein-RNA interactions.Protein RNA interactions are dynamic and regulated in response to environmental changes. Here the authors describe 'kinetic CRAC', an approach that allows time resolved analyses of protein RNA interactions with minute time point resolution and apply it to gain insight into the function of the RNA-binding protein Nab3.
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Affiliation(s)
- Rob van Nues
- Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Edinburgh, EH9 3BF, UK.,Institute of Cell Biology, University of Edinburgh, Edinburgh, EH9 3FF, UK
| | | | - Erica de Leau
- Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Edinburgh, EH9 3BF, UK.,Institute for Molecular Plant Sciences, University of Edinburgh, Edinburgh, EH9 3BF, UK
| | - Alina Selega
- School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Andrew Langford
- UVO3 Ltd, Unit 25 Stephenson Road, St Ives, Cambridgeshire, PE27 3WJ, UK
| | - Ryan Franklin
- UVO3 Ltd, Unit 25 Stephenson Road, St Ives, Cambridgeshire, PE27 3WJ, UK
| | - Ira Iosub
- Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Edinburgh, EH9 3BF, UK
| | - Peter Wadsworth
- UVO3 Ltd, Unit 25 Stephenson Road, St Ives, Cambridgeshire, PE27 3WJ, UK
| | - Guido Sanguinetti
- Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Edinburgh, EH9 3BF, UK.,School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Sander Granneman
- Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Edinburgh, EH9 3BF, UK.
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46
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Zhang Y, Liu H, Yan F, Zhou J. Oscillatory Behaviors in Genetic Regulatory Networks Mediated by MicroRNA With Time Delays and Reaction-Diffusion Terms. IEEE Trans Nanobioscience 2017; 16:166-176. [DOI: 10.1109/tnb.2017.2675446] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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47
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Lohmann J, D'Huys O, Haynes ND, Schöll E, Gauthier DJ. Transient dynamics and their control in time-delay autonomous Boolean ring networks. Phys Rev E 2017; 95:022211. [PMID: 28297900 DOI: 10.1103/physreve.95.022211] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Indexed: 01/08/2023]
Abstract
Biochemical systems with switch-like interactions, such as gene regulatory networks, are well modeled by autonomous Boolean networks. Specifically, the topology and logic of gene interactions can be described by systems of continuous piecewise-linear differential equations, enabling analytical predictions of the dynamics of specific networks. However, most models do not account for time delays along links associated with spatial transport, mRNA transcription, and translation. To address this issue, we have developed an experimental test bed to realize a time-delay autonomous Boolean network with three inhibitory nodes, known as a repressilator, and use it to study the dynamics that arise as time delays along the links vary. We observe various nearly periodic oscillatory transient patterns with extremely long lifetime, which emerge in small network motifs due to the delay, and which are distinct from the eventual asymptotically stable periodic attractors. For repeated experiments with a given network, we find that stochastic processes give rise to a broad distribution of transient times with an exponential tail. In some cases, the transients are so long that it is doubtful the attractors will ever be approached in a biological system that has a finite lifetime. To counteract the long transients, we show experimentally that small, occasional perturbations applied to the time delays can force the trajectories to rapidly approach the attractors.
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Affiliation(s)
- Johannes Lohmann
- Department of Physics, Duke University, Durham, North Carolina 27708, USA.,Institut für Theoretische Physik, Technische Universität Berlin, 10623 Berlin, Germany
| | - Otti D'Huys
- Department of Physics, Duke University, Durham, North Carolina 27708, USA
| | - Nicholas D Haynes
- Department of Physics, Duke University, Durham, North Carolina 27708, USA
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, 10623 Berlin, Germany
| | - Daniel J Gauthier
- Department of Physics, Duke University, Durham, North Carolina 27708, USA.,Department of Physics, The Ohio State University, Columbus, Ohio 43210, USA
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48
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Tsigkinopoulou A, Baker SM, Breitling R. Respectful Modeling: Addressing Uncertainty in Dynamic System Models for Molecular Biology. Trends Biotechnol 2017; 35:518-529. [PMID: 28094080 DOI: 10.1016/j.tibtech.2016.12.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 12/05/2016] [Accepted: 12/15/2016] [Indexed: 10/20/2022]
Abstract
Although there is still some skepticism in the biological community regarding the value and significance of quantitative computational modeling, important steps are continually being taken to enhance its accessibility and predictive power. We view these developments as essential components of an emerging 'respectful modeling' framework which has two key aims: (i) respecting the models themselves and facilitating the reproduction and update of modeling results by other scientists, and (ii) respecting the predictions of the models and rigorously quantifying the confidence associated with the modeling results. This respectful attitude will guide the design of higher-quality models and facilitate the use of models in modern applications such as engineering and manipulating microbial metabolism by synthetic biology.
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Affiliation(s)
- Areti Tsigkinopoulou
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
| | - Syed Murtuza Baker
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
| | - Rainer Breitling
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
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49
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Vaiman D. Genes, epigenetics and miRNA regulation in the placenta. Placenta 2016; 52:127-133. [PMID: 28043658 DOI: 10.1016/j.placenta.2016.12.026] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Revised: 10/24/2016] [Accepted: 12/23/2016] [Indexed: 12/19/2022]
Abstract
This text reviews briefly the context in which epigenetics regulate gene expression in trophoblast development and function. It is an attempt to focus on a limited number of recent papers that, according to the author, shed new light on placental development, and constitute possible trails for improving knowledge and women follow-up in pathological pregnancies.
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Affiliation(s)
- Daniel Vaiman
- Institut Cochin, INSERM U1016, CNRS UMR8104, Université Paris-Descartes, 24, rue du Faubourg St-Jacques, 75014, Paris, France.
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50
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Alpert T, Herzel L, Neugebauer KM. Perfect timing: splicing and transcription rates in living cells. WILEY INTERDISCIPLINARY REVIEWS-RNA 2016; 8. [PMID: 27873472 DOI: 10.1002/wrna.1401] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 09/12/2016] [Accepted: 09/26/2016] [Indexed: 12/27/2022]
Abstract
An important step toward understanding gene regulation is the elucidation of the time necessary for the completion of individual steps. Measurement of reaction rates can reveal potential nodes for regulation. For example, measurements of in vivo transcription elongation rates reveal regulation by DNA sequence, gene architecture, and chromatin. Pre-mRNA splicing is regulated by transcription elongation rates and vice versa, yet the rates of RNA processing reactions remain largely elusive. Since the 1980s, numerous model systems and approaches have been used to determine the precise timing of splicing in vivo. Because splicing can be co-transcriptional, the position of Pol II when splicing is detected has been used as a proxy for time by some investigators. In addition to these 'distance-based' measurements, 'time-based' measurements have been possible through live cell imaging, metabolic labeling of RNA, and gene induction. Yet splicing rates can be convolved by the time it takes for transcription, spliceosome assembly and spliceosome disassembly. The variety of assays and systems used has, perhaps not surprisingly, led to reports of widely differing splicing rates in vivo. Recently, single molecule RNA-seq has indicated that splicing occurs more quickly than previously deduced. Here we comprehensively review these findings and discuss evidence that splicing and transcription rates are closely coordinated, facilitating the efficiency of gene expression. On the other hand, introduction of splicing delays through as yet unknown mechanisms provide opportunity for regulation. More work is needed to understand how cells optimize the rates of gene expression for a range of biological conditions. WIREs RNA 2017, 8:e1401. doi: 10.1002/wrna.1401 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Tara Alpert
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Lydia Herzel
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Karla M Neugebauer
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
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