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Mellis IA, Bodkin N, Melzer ME, Goyal Y. Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.14.553318. [PMID: 37645989 PMCID: PMC10462021 DOI: 10.1101/2023.08.14.553318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates important model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.
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Affiliation(s)
- Ian A. Mellis
- Department of Pathology and Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Nicholas Bodkin
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Madeline E. Melzer
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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2
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Pillai M, Hojel E, Jolly MK, Goyal Y. Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools. NATURE COMPUTATIONAL SCIENCE 2023; 3:301-313. [PMID: 38177938 DOI: 10.1038/s43588-023-00427-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 03/03/2023] [Indexed: 01/06/2024]
Abstract
Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.
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Affiliation(s)
- Maalavika Pillai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Emilia Hojel
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India.
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA.
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3
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Beck LE, Lee J, Coté C, Dunagin MC, Lukonin I, Salla N, Chang MK, Hughes AJ, Mornin JD, Gartner ZJ, Liberali P, Raj A. Systematically quantifying morphological features reveals constraints on organoid phenotypes. Cell Syst 2022; 13:547-560.e3. [PMID: 35705097 PMCID: PMC9350855 DOI: 10.1016/j.cels.2022.05.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/15/2022] [Accepted: 05/26/2022] [Indexed: 01/25/2023]
Abstract
Organoids recapitulate complex 3D organ structures and represent a unique opportunity to probe the principles of self-organization. While we can alter an organoid's morphology by manipulating the culture conditions, the morphology of an organoid often resembles that of its original organ, suggesting that organoid morphologies are governed by a set of tissue-specific constraints. Here, we establish a framework to identify constraints on an organoid's morphological features by quantifying them from microscopy images of organoids exposed to a range of perturbations. We apply this framework to Madin-Darby canine kidney cysts and show that they obey a number of constraints taking the form of scaling relationships or caps on certain parameters. For example, we found that the number, but not size, of cells increases with increasing cyst size. We also find that these constraints vary with cyst age and can be altered by varying the culture conditions. We observed similar sets of constraints in intestinal organoids. This quantitative framework for identifying constraints on organoid morphologies may inform future efforts to engineer organoids.
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Affiliation(s)
- Lauren E. Beck
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Jasmine Lee
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher Coté
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Margaret C. Dunagin
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA,Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya Lukonin
- Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland,University of Basel, Basel, Switzerland
| | - Nikkita Salla
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Marcello K. Chang
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Alex J. Hughes
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA,Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Zev J. Gartner
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA,Center for Cellular Construction, University of California, San Francisco, San Francisco, CA, USA,Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Prisca Liberali
- Friedrich Miescher Institute for Biomedical Research (FMI), Basel, Switzerland,University of Basel, Basel, Switzerland
| | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA,Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA,Lead contact,Correspondence:
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4
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Vu T, Vallmitjana A, Gu J, La K, Xu Q, Flores J, Zimak J, Shiu J, Hosohama L, Wu J, Douglas C, Waterman ML, Ganesan A, Hedde PN, Gratton E, Zhao W. Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis. Nat Commun 2022; 13:169. [PMID: 35013281 PMCID: PMC8748653 DOI: 10.1038/s41467-021-27798-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/15/2021] [Indexed: 12/14/2022] Open
Abstract
Multiplexed mRNA profiling in the spatial context provides new information enabling basic research and clinical applications. Unfortunately, existing spatial transcriptomics methods are limited due to either low multiplexing or complexity. Here, we introduce a spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA and protein markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based decoding. We demonstrate MOSAICA's multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of five fluorophores with facile error-detection and removal of autofluorescence. MOSAICA's analysis is strongly correlated with sequencing data (Pearson's r = 0.96) and was further benchmarked using RNAscopeTM and LGC StellarisTM. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues. We finally demonstrate simultaneous co-detection of protein and mRNA in cancer cells.
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Affiliation(s)
- Tam Vu
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Alexander Vallmitjana
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
- Laboratory for Fluorescence Dynamics, University of California, Irvine, Irvine, CA, 92697, USA
| | - Joshua Gu
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
| | - Kieu La
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
| | - Qi Xu
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
| | - Jesus Flores
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA
- CIRM Stem Cell Research Biotechnology Training Program at California State University, Long Beach, Long Beach, CA, 90840, USA
| | - Jan Zimak
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA
| | - Jessica Shiu
- Department of Dermatology, University of California, Irvine, Irvine, CA, 92697, USA
| | - Linzi Hosohama
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA, 92697, USA
| | - Jie Wu
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Christopher Douglas
- Department of Pathology & Laboratory Medicine, University of California, Irvine, Irvine, CA, 92617, USA
| | - Marian L Waterman
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA, 92697, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Anand Ganesan
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Dermatology, University of California, Irvine, Irvine, CA, 92697, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Per Niklas Hedde
- Laboratory for Fluorescence Dynamics, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA
- Beckman Laser Institute & Medical Clinic, University of California, Irvine, Irvine, CA, 92697, USA
| | - Enrico Gratton
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA.
- Laboratory for Fluorescence Dynamics, University of California, Irvine, Irvine, CA, 92697, USA.
- Beckman Laser Institute & Medical Clinic, University of California, Irvine, Irvine, CA, 92697, USA.
| | - Weian Zhao
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA.
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA.
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA.
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA.
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA.
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5
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Mellis IA, Edelstein HI, Truitt R, Goyal Y, Beck LE, Symmons O, Dunagin MC, Linares Saldana RA, Shah PP, Pérez-Bermejo JA, Padmanabhan A, Yang W, Jain R, Raj A. Responsiveness to perturbations is a hallmark of transcription factors that maintain cell identity in vitro. Cell Syst 2021; 12:885-899.e8. [PMID: 34352221 PMCID: PMC8522198 DOI: 10.1016/j.cels.2021.07.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/27/2020] [Accepted: 07/09/2021] [Indexed: 02/07/2023]
Abstract
Identifying the particular transcription factors that maintain cell type in vitro is important for manipulating cell type. Identifying such transcription factors by their cell-type-specific expression or their involvement in developmental regulation has had limited success. We hypothesized that because cell type is often resilient to perturbations, the transcriptional response to perturbations would reveal identity-maintaining transcription factors. We developed perturbation panel profiling (P3) as a framework for perturbing cells across many conditions and measuring gene expression responsiveness transcriptome-wide. In human iPSC-derived cardiac myocytes, P3 showed that transcription factors important for cardiac myocyte differentiation and maintenance were among the most frequently upregulated (most responsive). We reasoned that one function of responsive genes may be to maintain cellular identity. We identified responsive transcription factors in fibroblasts using P3 and found that suppressing their expression led to enhanced reprogramming. We propose that responsiveness to perturbations is a property of transcription factors that help maintain cellular identity in vitro. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Ian A Mellis
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Genomics and Computational Biology Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hailey I Edelstein
- Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rachel Truitt
- Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogesh Goyal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren E Beck
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Orsolya Symmons
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Margaret C Dunagin
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Ricardo A Linares Saldana
- Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Parisha P Shah
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Arun Padmanabhan
- Gladstone Institute of Cardiovascular Disease, San Francisco, CA, USA; Division of Cardiology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Wenli Yang
- Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rajan Jain
- Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Arjun Raj
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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6
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Gómez-Schiavon M, El-Samad H. Complexity-aware simple modeling. Curr Opin Microbiol 2018; 45:47-52. [PMID: 29494832 DOI: 10.1016/j.mib.2018.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 01/07/2018] [Indexed: 11/19/2022]
Abstract
Mathematical models continue to be essential for deepening our understanding of biology. On one extreme, simple or small-scale models help delineate general biological principles. However, the parsimony of detail in these models as well as their assumption of modularity and insulation make them inaccurate for describing quantitative features. On the other extreme, large-scale and detailed models can quantitatively recapitulate a phenotype of interest, but have to rely on many unknown parameters, making them often difficult to parse mechanistically and to use for extracting general principles. We discuss some examples of a new approach-complexity-aware simple modeling-that can bridge the gap between the small-scale and large-scale approaches.
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Affiliation(s)
- Mariana Gómez-Schiavon
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco CA 94158, United States
| | - Hana El-Samad
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco CA 94158, United States; Chan Zuckerberg Biohub, San Francisco, CA 94158, United States.
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7
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Symmons O, Raj A. What's Luck Got to Do with It: Single Cells, Multiple Fates, and Biological Nondeterminism. Mol Cell 2017; 62:788-802. [PMID: 27259209 DOI: 10.1016/j.molcel.2016.05.023] [Citation(s) in RCA: 131] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
The field of single-cell biology has morphed from a philosophical digression at its inception, to a playground for quantitative biologists, to a major area of biomedical research. The last several years have witnessed an explosion of new technologies, allowing us to apply even more of the modern molecular biology toolkit to single cells. Conceptual progress, however, has been comparatively slow. Here, we provide a framework for classifying both the origins of the differences between individual cells and the consequences of those differences. We discuss how the concept of "random" differences is context dependent, and propose that rigorous definitions of inputs and outputs may bring clarity to the discussion. We also categorize ways in which probabilistic behavior may influence cellular function, highlighting studies that point to exciting future directions in the field.
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Affiliation(s)
- Orsolya Symmons
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Arjun Raj
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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8
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Techniques for Single-Molecule mRNA Imaging in Living Cells. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 978:425-441. [DOI: 10.1007/978-3-319-53889-1_22] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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9
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Zallot R, Harrison KJ, Kolaczkowski B, de Crécy-Lagard V. Functional Annotations of Paralogs: A Blessing and a Curse. Life (Basel) 2016; 6:life6030039. [PMID: 27618105 PMCID: PMC5041015 DOI: 10.3390/life6030039] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 08/29/2016] [Accepted: 09/02/2016] [Indexed: 12/15/2022] Open
Abstract
Gene duplication followed by mutation is a classic mechanism of neofunctionalization, producing gene families with functional diversity. In some cases, a single point mutation is sufficient to change the substrate specificity and/or the chemistry performed by an enzyme, making it difficult to accurately separate enzymes with identical functions from homologs with different functions. Because sequence similarity is often used as a basis for assigning functional annotations to genes, non-isofunctional gene families pose a great challenge for genome annotation pipelines. Here we describe how integrating evolutionary and functional information such as genome context, phylogeny, metabolic reconstruction and signature motifs may be required to correctly annotate multifunctional families. These integrative analyses can also lead to the discovery of novel gene functions, as hints from specific subgroups can guide the functional characterization of other members of the family. We demonstrate how careful manual curation processes using comparative genomics can disambiguate subgroups within large multifunctional families and discover their functions. We present the COG0720 protein family as a case study. We also discuss strategies to automate this process to improve the accuracy of genome functional annotation pipelines.
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Affiliation(s)
- Rémi Zallot
- Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
| | - Katherine J Harrison
- Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
| | - Bryan Kolaczkowski
- Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
| | - Valérie de Crécy-Lagard
- Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA.
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10
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Atay O, Doncic A, Skotheim JM. Switch-like Transitions Insulate Network Motifs to Modularize Biological Networks. Cell Syst 2016; 3:121-132. [PMID: 27453443 DOI: 10.1016/j.cels.2016.06.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 05/06/2016] [Accepted: 06/20/2016] [Indexed: 01/27/2023]
Abstract
Cellular decisions are made by complex networks that are difficult to analyze. Although it is common to analyze smaller sub-networks known as network motifs, it is unclear whether this is valid, because these motifs are embedded in complex larger networks. Here, we address the general question of modularity by examining the S. cerevisiae pheromone response. We demonstrate that the feedforward motif controlling the cell-cycle inhibitor Far1 is insulated from cell-cycle dynamics by the positive feedback switch that drives reentry to the cell cycle. Before cells switch on positive feedback, the feedforward motif model predicts the behavior of the larger network. Conversely, after the switch, the feedforward motif is dismantled and has no discernable effect on the cell cycle. When insulation is broken, the feedforward motif no longer predicts network behavior. This work illustrates how, despite the interconnectivity of networks, the activity of motifs can be insulated by switches that generate well-defined cellular states.
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Affiliation(s)
- Oguzhan Atay
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Andreas Doncic
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Jan M Skotheim
- Department of Biology, Stanford University, Stanford, CA 94305, USA.
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11
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Hilfinger A, Norman TM, Paulsson J. Exploiting Natural Fluctuations to Identify Kinetic Mechanisms in Sparsely Characterized Systems. Cell Syst 2016; 2:251-9. [PMID: 27135537 DOI: 10.1016/j.cels.2016.04.002] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 02/26/2016] [Accepted: 04/06/2016] [Indexed: 11/29/2022]
Abstract
From biochemistry to ecology, many biological systems are stochastic, complex, and sparsely characterized. In such systems, each component may respond to changes in any directly or indirectly connected components, thus requiring knowledge of the whole to predict the dynamics of the parts. Here, we address this challenge by deriving relations between properties of fluctuations that only reflect local interactions between a subset of components but are invariant to all indirectly connected dynamics. This greatly reduces the number of assumptions when evaluating dynamic models experimentally. We illustrate the approach by revisiting systematic single-cell gene expression data, and we show that the observed fluctuations contradict the assumptions made in most published models of stochastic gene expression, even when accounting for the possibility of systematic experimental artifacts.
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Affiliation(s)
- Andreas Hilfinger
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA.
| | - Thomas M Norman
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Johan Paulsson
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA.
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12
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Stoeger T, Battich N, Pelkmans L. Passive Noise Filtering by Cellular Compartmentalization. Cell 2016; 164:1151-1161. [DOI: 10.1016/j.cell.2016.02.005] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Indexed: 12/30/2022]
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