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Ahi EP. Fish Evo-Devo: Moving Toward Species-Specific and Knowledge-Based Interactome. JOURNAL OF EXPERIMENTAL ZOOLOGY. PART B, MOLECULAR AND DEVELOPMENTAL EVOLUTION 2025; 344:158-168. [PMID: 40170296 DOI: 10.1002/jez.b.23287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2024] [Revised: 12/13/2024] [Accepted: 01/12/2025] [Indexed: 04/03/2025]
Abstract
A knowledge-based interactome maps interactions among proteins and molecules within a cell using experimental data, computational predictions, and literature mining. These interactomes are vital for understanding cellular functions, pathways, and the evolutionary conservation of protein interactions. They reveal how interactions regulate growth, differentiation, and development. Transitioning to functionally validated interactomes is crucial in evolutionary developmental biology (Evo-Devo), especially for non-model species, to uncover unique regulatory networks, evolutionary novelties, and reliable gene interaction models. This enhances our understanding of complex trait evolution across species. The European Evo-Devo 2024 conference in Helsinki hosted the first fish-specific Evo-Devo symposium, highlighting the growing interest in fish models. Advances in genome annotation, genome editing, imaging, and molecular screening are expanding fish Evo-Devo research. High-throughput molecular data have enabled the deduction of gene regulatory networks. The next steps involve creating species-specific interactomes, validating them functionally, and integrating additional molecular data to deepen the understanding of complex regulatory interactions in fish Evo-Devo. This short review aims to address the logical steps for this transition, as well as the necessities and limitations of this journey.
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Affiliation(s)
- Ehsan Pashay Ahi
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
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2
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Gil-Gomez A, Rest JS. Wiring Between Close Nodes in Molecular Networks Evolves More Quickly Than Between Distant Nodes. Mol Biol Evol 2024; 41:msae098. [PMID: 38768245 PMCID: PMC11136681 DOI: 10.1093/molbev/msae098] [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: 12/21/2023] [Revised: 04/14/2024] [Accepted: 05/15/2024] [Indexed: 05/22/2024] Open
Abstract
As species diverge, a wide range of evolutionary processes lead to changes in protein-protein interaction (PPI) networks and metabolic networks. The rate at which molecular networks evolve is an important question in evolutionary biology. Previous empirical work has focused on interactomes from model organisms to calculate rewiring rates, but this is limited by the relatively small number of species and sparse nature of network data across species. We present a proxy for variation in network topology: variation in drug-drug interactions (DDIs), obtained by studying drug combinations (DCs) across taxa. Here, we propose the rate at which DDIs change across species as an estimate of the rate at which the underlying molecular network changes as species diverge. We computed the evolutionary rates of DDIs using previously published data from a high-throughput study in gram-negative bacteria. Using phylogenetic comparative methods, we found that DDIs diverge rapidly over short evolutionary time periods, but that divergence saturates over longer time periods. In parallel, we mapped drugs with known targets in PPI and cofunctional networks. We found that the targets of synergistic DDIs are closer in these networks than other types of DCs and that synergistic interactions have a higher evolutionary rate, meaning that nodes that are closer evolve at a faster rate. Future studies of network evolution may use DC data to gain larger-scale perspectives on the details of network evolution within and between species.
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Affiliation(s)
- Alejandro Gil-Gomez
- Department of Ecology and Evolution, Laufer Center for Physical and Quantitative Biology, Stony Brook University, 650 Life Sciences, Stony Brook, NY 11794-4254, USA
| | - Joshua S Rest
- Department of Ecology and Evolution, Laufer Center for Physical and Quantitative Biology, Stony Brook University, 650 Life Sciences, Stony Brook, NY 11794-4254, USA
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3
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Zhang S, Pyne S, Pietrzak S, Halberg S, McCalla SG, Siahpirani AF, Sridharan R, Roy S. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 2023; 14:3064. [PMID: 37244909 PMCID: PMC10224950 DOI: 10.1038/s41467-023-38637-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 05/10/2023] [Indexed: 05/29/2023] Open
Abstract
Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.
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Affiliation(s)
- Shilu Zhang
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - Saptarshi Pyne
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - Stefan Pietrzak
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI, USA
| | - Spencer Halberg
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Sunnie Grace McCalla
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Alireza Fotuhi Siahpirani
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Rupa Sridharan
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI, USA
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
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4
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Siahpirani AF, Knaack S, Chasman D, Seirup M, Sridharan R, Stewart R, Thomson J, Roy S. Dynamic regulatory module networks for inference of cell type-specific transcriptional networks. Genome Res 2022; 32:1367-1384. [PMID: 35705328 PMCID: PMC9341506 DOI: 10.1101/gr.276542.121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 06/02/2022] [Indexed: 11/25/2022]
Abstract
Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic data sets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type-specific regulatory networks is a major challenge. We present dynamic regulatory module networks (DRMNs), a novel approach to infer cell type-specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state, and accessibility to predict cis-regulators of context-specific expression, where context can be cell type, developmental stage, or time point, and uses multitask learning to capture network dynamics across linearly and hierarchically related contexts. We applied DRMNs to study regulatory network dynamics in three developmental processes, each showing different temporal relationships and measuring a different combination of regulatory genomic data sets: cellular reprogramming, liver dedifferentiation, and forward differentiation. DRMN identified known and novel regulators driving cell type-specific expression patterns, showing its broad applicability to examine dynamics of gene regulatory networks from linearly and hierarchically related multi-omic data sets.
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Affiliation(s)
- Alireza Fotuhi Siahpirani
- Wisconsin Institute for Discovery, University of Wisconsin, Madison, Wisconsin 53715, USA
- Department of Computer Sciences, University of Wisconsin, Madison, Wisconsin 53715, USA
| | - Sara Knaack
- Wisconsin Institute for Discovery, University of Wisconsin, Madison, Wisconsin 53715, USA
| | - Deborah Chasman
- Wisconsin Institute for Discovery, University of Wisconsin, Madison, Wisconsin 53715, USA
| | - Morten Seirup
- Morgridge Institute for Research, Madison, Wisconsin 53715, USA
- Molecular and Environmental Toxicology Program, University of Wisconsin, Madison, Wisconsin 53715, USA
| | - Rupa Sridharan
- Wisconsin Institute for Discovery, University of Wisconsin, Madison, Wisconsin 53715, USA
- Department of Cell and Regenerative Biology, University of Wisconsin, Madison, Wisconsin 53715, USA
| | - Ron Stewart
- Morgridge Institute for Research, Madison, Wisconsin 53715, USA
| | - James Thomson
- Morgridge Institute for Research, Madison, Wisconsin 53715, USA
- Department of Cell and Regenerative Biology, University of Wisconsin, Madison, Wisconsin 53715, USA
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, California 93117, USA
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin, Madison, Wisconsin 53715, USA
- Department of Computer Sciences, University of Wisconsin, Madison, Wisconsin 53715, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53715, USA
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5
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Zhang S, Knaack S, Roy S. Enabling Studies of Genome-Scale Regulatory Network Evolution in Large Phylogenies with MRTLE. Methods Mol Biol 2022; 2477:439-455. [PMID: 35524131 PMCID: PMC9794031 DOI: 10.1007/978-1-0716-2257-5_24] [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: 05/17/2023]
Abstract
Transcriptional regulatory networks specify context-specific patterns of genes and play a central role in how species evolve and adapt. Inferring genome-scale regulatory networks in non-model species is the first step for examining patterns of conservation and divergence of regulatory networks. Transcriptomic data obtained under varying environmental stimuli in multiple species are becoming increasingly available, which can be used to infer regulatory networks. However, inference and analysis of multiple gene regulatory networks in a phylogenetic setting remains challenging. We developed an algorithm, Multi-species Regulatory neTwork LEarning (MRTLE), to facilitate such studies of regulatory network evolution. MRTLE is a probabilistic graphical model-based algorithm that uses phylogenetic structure, transcriptomic data for multiple species, and sequence-specific motifs in each species to simultaneously infer genome-scale regulatory networks across multiple species. We applied MRTLE to study regulatory network evolution across six ascomycete yeasts using transcriptomic measurements collected across different stress conditions. MRTLE networks recapitulated experimentally derived interactions in the model organism S. cerevisiae as well as non-model species, and it was more beneficial for network inference than methods that do not use phylogenetic information. We examined the regulatory networks across species and found that regulators associated with significant expression and network changes are involved in stress-related processes. MTRLE and its associated downstream analysis provide a scalable and principled framework to examine evolutionary dynamics of transcriptional regulatory networks across multiple species in a large phylogeny.
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Affiliation(s)
- Shilu Zhang
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - Sara Knaack
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
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6
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Naseri A, Sharghi M, Hasheminejad SMH. Enhancing gene regulatory networks inference through hub-based data integration. Comput Biol Chem 2021; 95:107589. [PMID: 34673384 DOI: 10.1016/j.compbiolchem.2021.107589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/11/2021] [Accepted: 10/04/2021] [Indexed: 12/09/2022]
Abstract
One of the main research topics in computational biology is Gene Regulatory Network (GRN) reconstruction that refers to inferring the relationships between genes involved in regulating cell conditions in response to internal or external stimuli. To this end, most computational methods use only transcriptional gene expression data to reconstruct gene regulatory networks, but recent studies suggest that gene expression data must be integrated with other types of data to obtain more accurate models predicting real relationships between genes. In this study, a diffusion-based method is enhanced to integrate biological data of network types besides structural prior knowledge. The Random Walk with Restart algorithm (RWR) with an emphasis on hub nodes is executed separately on each network, and then jointly optimizes low-dimensional feature vectors for network nodes by diffusion component analysis. Next, these feature vectors are used to infer gene regulatory networks. Fourteen centrality measures are studied for the detection of hub nodes to be used in the RWR algorithm, and the best centrality measure having the greatest effect on the improvement of gene network inference is selected. A case study for the Saccharomyces cerevisiae and E. coli networks shows that using the proposed features in comparison with gene expression data alone results in 0.02-0.08 units improvement in Area Under Receiver Characteristic Operator (AUROC) criteria across different gene regulatory network inference methods. Furthermore, the proposed method was applied to the esophageal cancer data to infer its gene regulatory network. The proposed framework substantially improves accuracy and scalability of GRN inference. The fused features and the best centrality measure detected can be used to provide functional insights about genes or proteins in various biological applications. Moreover, it can be served as a general framework for network data and structural data integration and analysis problems in various scientific disciplines including biology.
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Affiliation(s)
- Atefeh Naseri
- Department of Computer Engineering, Alzahra University, Tehran, Iran.
| | - Mehran Sharghi
- Department of Computer Engineering, Alzahra University, Tehran, Iran.
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7
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Ovens K, Eames BF, McQuillan I. Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution. Front Genet 2021; 12:695399. [PMID: 34484293 PMCID: PMC8414652 DOI: 10.3389/fgene.2021.695399] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.
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Affiliation(s)
- Katie Ovens
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - B. Frank Eames
- Department of Anatomy, Physiology, & Pharmacology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ian McQuillan
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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8
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Wu AP, Peng J, Berger B, Cho H. Bayesian information sharing enhances detection of regulatory associations in rare cell types. Bioinformatics 2021; 37:i349-i357. [PMID: 34252956 PMCID: PMC8275330 DOI: 10.1093/bioinformatics/btab269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies promise to enable the study of gene regulatory associations at unprecedented resolution in diverse cellular contexts. However, identifying unique regulatory associations observed only in specific cell types or conditions remains a key challenge; this is particularly so for rare transcriptional states whose sample sizes are too small for existing gene regulatory network inference methods to be effective. RESULTS We present ShareNet, a Bayesian framework for boosting the accuracy of cell type-specific gene regulatory networks by propagating information across related cell types via an information sharing structure that is adaptively optimized for a given single-cell dataset. The techniques we introduce can be used with a range of general network inference algorithms to enhance the output for each cell type. We demonstrate the enhanced accuracy of our approach on three benchmark scRNA-seq datasets. We find that our inferred cell type-specific networks also uncover key changes in gene associations that underpin the complex rewiring of regulatory networks across cell types, tissues and dynamic biological processes. Our work presents a path toward extracting deeper insights about cell type-specific gene regulation in the rapidly growing compendium of scRNA-seq datasets. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. AVAILABILITY AND IMPLEMENTATION The code for ShareNet is available at http://sharenet.csail.mit.edu and https://github.com/alexw16/sharenet.
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Affiliation(s)
- Alexander P Wu
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Jian Peng
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.,Department of Mathematics, MIT, Cambridge, MA 02139, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hyunghoon Cho
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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9
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Mehta TK, Koch C, Nash W, Knaack SA, Sudhakar P, Olbei M, Bastkowski S, Penso-Dolfin L, Korcsmaros T, Haerty W, Roy S, Di-Palma F. Evolution of regulatory networks associated with traits under selection in cichlids. Genome Biol 2021; 22:25. [PMID: 33419455 PMCID: PMC7791837 DOI: 10.1186/s13059-020-02208-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 11/18/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Seminal studies of vertebrate protein evolution speculated that gene regulatory changes can drive anatomical innovations. However, very little is known about gene regulatory network (GRN) evolution associated with phenotypic effect across ecologically diverse species. Here we use a novel approach for comparative GRN analysis in vertebrate species to study GRN evolution in representative species of the most striking examples of adaptive radiations, the East African cichlids. We previously demonstrated how the explosive phenotypic diversification of East African cichlids can be attributed to diverse molecular mechanisms, including accelerated regulatory sequence evolution and gene expression divergence. RESULTS To investigate these mechanisms across species at a genome-wide scale, we develop a novel computational pipeline that predicts regulators for co-extant and ancestral co-expression modules along a phylogeny, and candidate regulatory regions associated with traits under selection in cichlids. As a case study, we apply our approach to a well-studied adaptive trait-the visual system-for which we report striking cases of network rewiring for visual opsin genes, identify discrete regulatory variants, and investigate their association with cichlid visual system evolution. In regulatory regions of visual opsin genes, in vitro assays confirm that transcription factor binding site mutations disrupt regulatory edges across species and segregate according to lake species phylogeny and ecology, suggesting GRN rewiring in radiating cichlids. CONCLUSIONS Our approach reveals numerous novel potential candidate regulators and regulatory regions across cichlid genomes, including some novel and some previously reported associations to known adaptive evolutionary traits.
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Affiliation(s)
| | - Christopher Koch
- Department of Biostatistics and Medical Informatics, UW Madison, Madison, USA
| | | | - Sara A Knaack
- Wisconsin Institute for Discovery (WID), Madison, USA
| | | | - Marton Olbei
- Earlham Institute (EI), Norwich, UK
- Quadram Institute, Norwich, UK
| | - Sarah Bastkowski
- Earlham Institute (EI), Norwich, UK
- Quadram Institute, Norwich, UK
| | | | - Tamas Korcsmaros
- Earlham Institute (EI), Norwich, UK
- Quadram Institute, Norwich, UK
| | | | - Sushmita Roy
- Department of Biostatistics and Medical Informatics, UW Madison, Madison, USA.
- Wisconsin Institute for Discovery (WID), Madison, USA.
- Department of Computer Sciences, UW Madison, Madison, USA.
| | - Federica Di-Palma
- Earlham Institute (EI), Norwich, UK.
- Norwich Medical School, University of East Anglia, Norwich, UK.
- School of Biological Sciences, University of East Anglia, Norwich, UK.
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10
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Bapteste E, Papale F. Modeling the evolution of interconnected processes: It is the song and the singers: Tracking units of selection with interaction networks. Bioessays 2020; 43:e2000077. [PMID: 33165956 DOI: 10.1002/bies.202000077] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 09/18/2020] [Accepted: 09/24/2020] [Indexed: 01/04/2023]
Abstract
Recently, Doolittle and Inkpen formulated a thought provoking theory, asserting that evolution by natural selection was responsible for the sideways evolution of two radically different kinds of selective units (also called Domains). The former entities, termed singers, correspond to the usual objects studied by evolutionary biologists (gene, genomes, individuals, species, etc.), whereas the later, termed songs, correspond to re-produced biological and ecosystemic functions, processes, information, and memes. Singers perform songs through selected patterns of interactions, meaning that a wealth of critical phenomena might receive novel evolutionary explanations. However, this theory did not provide an empirical approach to study evolution in such a broadened context. Here, we show that analyzing songs and singers, using patterns of interaction networks as a common ontology for both, offers a novel, actionable, inclusive and mathematical way to analyze not only the re-production but also the evolution and fitness of biological and ecosystemic interconnected processes.
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Affiliation(s)
- Eric Bapteste
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Sorbonne Université, CNRS, Museum National d'Histoire Naturelle, EPHE, Université des Antilles, 7, quai Saint Bernard, Bâtiment A 4ème étage, pièce 427, Paris, 75005, France
| | - François Papale
- Departement of Philosophy, University of Montreal, 2910 Édouard-Montpetit blvd, Montréal, QC, H3C 3J7, Canada
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Lenz AR, Galán-Vásquez E, Balbinot E, de Abreu FP, Souza de Oliveira N, da Rosa LO, de Avila e Silva S, Camassola M, Dillon AJP, Perez-Rueda E. Gene Regulatory Networks of Penicillium echinulatum 2HH and Penicillium oxalicum 114-2 Inferred by a Computational Biology Approach. Front Microbiol 2020; 11:588263. [PMID: 33193246 PMCID: PMC7652724 DOI: 10.3389/fmicb.2020.588263] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 09/23/2020] [Indexed: 11/29/2022] Open
Abstract
Penicillium echinulatum 2HH and Penicillium oxalicum 114-2 are well-known cellulase fungal producers. However, few studies addressing global mechanisms for gene regulation of these two important organisms are available so far. A recent finding that the 2HH wild-type is closely related to P. oxalicum leads to a combined study of these two species. Firstly, we provide a global gene regulatory network for P. echinulatum 2HH and P. oxalicum 114-2, based on TF-TG orthology relationships, considering three related species with well-known regulatory interactions combined with TFBSs prediction. The network was then analyzed in terms of topology, identifying TFs as hubs, and modules. Based on this approach, we explore numerous identified modules, such as the expression of cellulolytic and xylanolytic systems, where XlnR plays a key role in positive regulation of the xylanolytic system. It also regulates positively the cellulolytic system by acting indirectly through the cellodextrin induction system. This remarkable finding suggests that the XlnR-dependent cellulolytic and xylanolytic regulatory systems are probably conserved in both P. echinulatum and P. oxalicum. Finally, we explore the functional congruency on the genes clustered in terms of communities, where the genes related to cellular nitrogen, compound metabolic process and macromolecule metabolic process were the most abundant. Therefore, our approach allows us to confer a degree of accuracy regarding the existence of each inferred interaction.
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Affiliation(s)
- Alexandre Rafael Lenz
- Unidad Académica Yucatán, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de Mexico, Mérida, Mexico
- Laboratório de Bioinformática e Biologia Computacional, Instituto de Biotecnologia, Universidade de Caxias do Sul, Caxias do Sul, Brazil
- Departamento de Ciências Exatas e da Terra, Universidade do Estado da Bahia, Salvador, Brazil
| | - Edgardo Galán-Vásquez
- Departamento de Ingeniería de Sistemas Computacionales y Automatización, Instituto de Investigaciones en Matemàticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de Mexico, Ciudad Universitaria, Mexico
| | - Eduardo Balbinot
- Laboratório de Bioinformática e Biologia Computacional, Instituto de Biotecnologia, Universidade de Caxias do Sul, Caxias do Sul, Brazil
| | - Fernanda Pessi de Abreu
- Laboratório de Bioinformática e Biologia Computacional, Instituto de Biotecnologia, Universidade de Caxias do Sul, Caxias do Sul, Brazil
| | - Nikael Souza de Oliveira
- Laboratório de Bioinformática e Biologia Computacional, Instituto de Biotecnologia, Universidade de Caxias do Sul, Caxias do Sul, Brazil
- Laboratório de Enzimas e Biomassas, Instituto de Biotecnologia, Universidade de Caxias do Sul, Caxias do Sul, Brazil
| | - Letícia Osório da Rosa
- Laboratório de Enzimas e Biomassas, Instituto de Biotecnologia, Universidade de Caxias do Sul, Caxias do Sul, Brazil
| | - Scheila de Avila e Silva
- Laboratório de Bioinformática e Biologia Computacional, Instituto de Biotecnologia, Universidade de Caxias do Sul, Caxias do Sul, Brazil
| | - Marli Camassola
- Laboratório de Enzimas e Biomassas, Instituto de Biotecnologia, Universidade de Caxias do Sul, Caxias do Sul, Brazil
| | - Aldo José Pinheiro Dillon
- Laboratório de Enzimas e Biomassas, Instituto de Biotecnologia, Universidade de Caxias do Sul, Caxias do Sul, Brazil
| | - Ernesto Perez-Rueda
- Unidad Académica Yucatán, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de Mexico, Mérida, Mexico
- Facultad de Ciencias, Centro de Genómica y Bioinformática, Universidad Mayor, Santiago, Chile
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12
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Watson A, Habib M, Bapteste E. Phylosystemics: Merging Phylogenomics, Systems Biology, and Ecology to Study Evolution. Trends Microbiol 2020; 28:176-190. [DOI: 10.1016/j.tim.2019.10.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 10/21/2019] [Accepted: 10/22/2019] [Indexed: 11/28/2022]
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13
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Papale F, Saget J, Bapteste É. Networks Consolidate the Core Concepts of Evolution by Natural Selection. Trends Microbiol 2019; 28:254-265. [PMID: 31866140 DOI: 10.1016/j.tim.2019.11.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 11/12/2019] [Accepted: 11/18/2019] [Indexed: 02/07/2023]
Abstract
Microbiology has unraveled rich evidence of ongoing reticulate evolutionary processes and complex interactions both within and between cells. These phenomena feature real biological networks, which can logically be analyzed using network-based tools. It is thus not surprising that network sciences, a field independent from evolutionary biology and microbiology, have recently pervasively infused their methods into both fields. Importantly, network tools bring forward observations enhancing the understanding of three core evolutionary concepts: variation, fitness, and heredity. Consequently, our work shows how network sciences can enhance evolutionary theory by explaining the evolution by natural selection of a broad diversity of units of selection, while updating the popular figure of Darwin's tree of life with a comprehensive sketch of the networks of evolution.
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Affiliation(s)
- François Papale
- Departement of Philosophy, University of Montreal, Montréal, QC, H3C 3J7, Canada; Institut de Systématique, Evolution, Biodiversité (ISYEB), Sorbonne Université, CNRS, Museum National d'Histoire Naturelle, EPHE, Université des Antilles, 75005 Paris, France
| | - Jordane Saget
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Sorbonne Université, CNRS, Museum National d'Histoire Naturelle, EPHE, Université des Antilles, 75005 Paris, France
| | - Éric Bapteste
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Sorbonne Université, CNRS, Museum National d'Histoire Naturelle, EPHE, Université des Antilles, 75005 Paris, France.
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14
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Mercatelli D, Scalambra L, Triboli L, Ray F, Giorgi FM. Gene regulatory network inference resources: A practical overview. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2019; 1863:194430. [PMID: 31678629 DOI: 10.1016/j.bbagrm.2019.194430] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 09/06/2019] [Accepted: 09/09/2019] [Indexed: 02/08/2023]
Abstract
Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.
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Affiliation(s)
- Daniele Mercatelli
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Laura Scalambra
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Luca Triboli
- Centre for Integrative Biology (CIBIO), University of Trento, Italy
| | - Forest Ray
- Department of Systems Biology, Columbia University Medical Center, New York, NY, United States
| | - Federico M Giorgi
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
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15
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Liebeskind BJ, Aldrich RW, Marcotte EM. Ancestral reconstruction of protein interaction networks. PLoS Comput Biol 2019; 15:e1007396. [PMID: 31658251 PMCID: PMC6837550 DOI: 10.1371/journal.pcbi.1007396] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/07/2019] [Accepted: 09/11/2019] [Indexed: 11/19/2022] Open
Abstract
The molecular and cellular basis of novelty is an active area of research in evolutionary biology. Until very recently, the vast majority of cellular phenomena were so difficult to sample that cross-species studies of biochemistry were rare and comparative analysis at the level of biochemical systems was almost impossible. Recent advances in systems biology are changing what is possible, however, and comparative phylogenetic methods that can handle this new data are wanted. Here, we introduce the term "phylogenetic latent variable models" (PLVMs, pronounced "plums") for a class of models that has recently been used to infer the evolution of cellular states from systems-level molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification mass spectrometry experiments. Based on these data, we estimate ancestral interactions across unikonts, broadly recovering protein complexes involved in translation, transcription, proteostasis, transport, and membrane trafficking. Using these results, we predict an ancient core of the Commander complex made up of CCDC22, CCDC93, C16orf62, and DSCR3, with more recent additions of COMMD-containing proteins in tetrapods. We also use simulations to develop model fitting strategies and discuss future model developments.
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Affiliation(s)
- Benjamin J. Liebeskind
- Center for Systems and Synthetic Biology, Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas, United States of America
- Department of Neuroscience, University of Texas at Austin, Austin, Texas, United States of America
| | - Richard W. Aldrich
- Department of Neuroscience, University of Texas at Austin, Austin, Texas, United States of America
| | - Edward M. Marcotte
- Center for Systems and Synthetic Biology, Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas, United States of America
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16
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Hackley RK, Schmid AK. Global Transcriptional Programs in Archaea Share Features with the Eukaryotic Environmental Stress Response. J Mol Biol 2019; 431:4147-4166. [PMID: 31437442 PMCID: PMC7419163 DOI: 10.1016/j.jmb.2019.07.029] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 07/18/2019] [Accepted: 07/18/2019] [Indexed: 01/06/2023]
Abstract
The environmental stress response (ESR), a global transcriptional program originally identified in yeast, is characterized by a rapid and transient transcriptional response composed of large, oppositely regulated gene clusters. Genes induced during the ESR encode core components of stress tolerance, macromolecular repair, and maintenance of homeostasis. In this review, we investigate the possibility for conservation of the ESR across the eukaryotic and archaeal domains of life. We first re-analyze existing transcriptomics data sets to illustrate that a similar transcriptional response is identifiable in Halobacterium salinarum, an archaeal model organism. To substantiate the archaeal ESR, we calculated gene-by-gene correlations, gene function enrichment, and comparison of temporal dynamics. We note reported examples of variation in the ESR across fungi, then synthesize high-level trends present in expression data of other archaeal species. In particular, we emphasize the need for additional high-throughput time series expression data to further characterize stress-responsive transcriptional programs in the Archaea. Together, this review explores an open question regarding features of global transcriptional stress response programs shared across domains of life.
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Affiliation(s)
- Rylee K Hackley
- Department of Biology, Duke University, Durham, NC 27708, USA; University Program in Genetics and Genomics, Duke University, Durham, NC 27708, USA
| | - Amy K Schmid
- Department of Biology, Duke University, Durham, NC 27708, USA; University Program in Genetics and Genomics, Duke University, Durham, NC 27708, USA; Center for Genomics and Computational Biology, Duke University, Durham, NC 27708, USA.
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17
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Chasman D, Iyer N, Fotuhi Siahpirani A, Estevez Silva M, Lippmann E, McIntosh B, Probasco MD, Jiang P, Stewart R, Thomson JA, Ashton RS, Roy S. Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development. Cell Syst 2019; 9:167-186.e12. [PMID: 31302154 DOI: 10.1016/j.cels.2019.05.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 05/05/2019] [Accepted: 05/30/2019] [Indexed: 12/19/2022]
Abstract
Neuroepithelial stem cells (NSC) from different anatomical regions of the embryonic neural tube's rostrocaudal axis can differentiate into diverse central nervous system tissues, but the transcriptional regulatory networks governing these processes are incompletely understood. Here, we measure region-specific NSC gene expression along the rostrocaudal axis in a human pluripotent stem cell model of early central nervous system development over a 72-h time course, spanning the hindbrain to cervical spinal cord. We introduce Escarole, a probabilistic clustering algorithm for non-stationary time series, and combine it with prior-based regulatory network inference to identify genes that are regulated dynamically and predict their upstream regulators. We identify known regulators of patterning and neural development, including the HOX genes, and predict a direct regulatory connection between the transcription factor POU3F2 and target gene STMN2. We demonstrate that POU3F2 is required for expression of STMN2, suggesting that this regulatory connection is important for region specificity of NSCs.
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Affiliation(s)
- Deborah Chasman
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA
| | - Nisha Iyer
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Alireza Fotuhi Siahpirani
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Maria Estevez Silva
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Ethan Lippmann
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Brian McIntosh
- Regenerative Biology Theme, Morgridge Institute for Research, Madison, WI 53715, USA
| | - Mitchell D Probasco
- Regenerative Biology Theme, Morgridge Institute for Research, Madison, WI 53715, USA
| | - Peng Jiang
- Regenerative Biology Theme, Morgridge Institute for Research, Madison, WI 53715, USA
| | - Ron Stewart
- Regenerative Biology Theme, Morgridge Institute for Research, Madison, WI 53715, USA
| | - James A Thomson
- Regenerative Biology Theme, Morgridge Institute for Research, Madison, WI 53715, USA
| | - Randolph S Ashton
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA.
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18
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Verd B, Monk NAM, Jaeger J. Modularity, criticality, and evolvability of a developmental gene regulatory network. eLife 2019; 8:e42832. [PMID: 31169494 PMCID: PMC6645726 DOI: 10.7554/elife.42832] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 06/05/2019] [Indexed: 01/16/2023] Open
Abstract
The existence of discrete phenotypic traits suggests that the complex regulatory processes which produce them are functionally modular. These processes are usually represented by networks. Only modular networks can be partitioned into intelligible subcircuits able to evolve relatively independently. Traditionally, functional modularity is approximated by detection of modularity in network structure. However, the correlation between structure and function is loose. Many regulatory networks exhibit modular behaviour without structural modularity. Here we partition an experimentally tractable regulatory network-the gap gene system of dipteran insects-using an alternative approach. We show that this system, although not structurally modular, is composed of dynamical modules driving different aspects of whole-network behaviour. All these subcircuits share the same regulatory structure, but differ in components and sensitivity to regulatory interactions. Some subcircuits are in a state of criticality, while others are not, which explains the observed differential evolvability of the various expression features in the system.
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Affiliation(s)
- Berta Verd
- EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu Fabra (UPF)BarcelonaSpain
- Konrad Lorenz Institute for Evolution and Cognition Research (KLI)KlosterneuburgAustria
- Department of GeneticsUniversity of CambridgeCambridgeUnited Kingdom
| | - Nicholas AM Monk
- School of Mathematics and StatisticsUniversity of SheffieldSheffieldUnited States
| | - Johannes Jaeger
- EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu Fabra (UPF)BarcelonaSpain
- Konrad Lorenz Institute for Evolution and Cognition Research (KLI)KlosterneuburgAustria
- School of Mathematics and StatisticsUniversity of SheffieldSheffieldUnited States
- Wissenschaftskolleg zu BerlinBerlinGermany
- Center for Systems Biology Dresden (CSBD)DresdenGermany
- Complexity Science Hub (CSH)ViennaAustria
- Centre de Recherches Interdisciplinaires (CRI)ParisFrance
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19
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Han SK, Kong J, Kim S, Lee JH, Han DH. Exomic and transcriptomic alterations of hereditary gingival fibromatosis. Oral Dis 2019; 25:1374-1383. [PMID: 30907493 DOI: 10.1111/odi.13093] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 02/18/2019] [Accepted: 02/25/2019] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Hereditary gingival fibromatosis (HGF) is a rare oral disease characterized by either localized or generalized gradual, benign, non-hemorrhagic enlargement of gingivae. Although several genetic causes of HGF are known, the genetic etiology of HGF as a non-syndromic and idiopathic entity remains uncertain. SUBJECTS AND METHODS We performed exome and RNA-seq of idiopathic HGF patients and controls, and then devised a computational framework that specifies exomic/transcriptomic alterations interconnected by a regulatory network to unravel genetic etiology of HGF. Moreover, given the lack of animal model or large-scale cohort data of HGF, we developed a strategy to cross-check their clinical relevance through in silico gene-phenotype mapping with biomedical literature mining and semantic analysis of disease phenotype similarities. RESULTS Exomic variants and differentially expressed genes of HGF were connected by members of TGF-β/SMAD signaling pathway and craniofacial development processes, accounting for the molecular mechanism of fibroblast overgrowth mimicking HGF. Our cross-check supports that genes derived from the regulatory network analysis have pathogenic roles in fibromatosis-related diseases. CONCLUSIONS The computational approach of connecting exomic and transcriptomic alterations through regulatory networks is applicable in the clinical interpretation of genetic variants in HGF patients.
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Affiliation(s)
- Seong Kyu Han
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea
| | - Jungho Kong
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea.,Department of Prosthodontics, College of Dentistry, Yonsei University, Seoul, Korea
| | - Jae-Hoon Lee
- Department of Prosthodontics, College of Dentistry, Yonsei University, Seoul, Korea
| | - Dong-Hoo Han
- Department of Prosthodontics, College of Dentistry, Yonsei University, Seoul, Korea
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20
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Castro DM, de Veaux NR, Miraldi ER, Bonneau R. Multi-study inference of regulatory networks for more accurate models of gene regulation. PLoS Comput Biol 2019; 15:e1006591. [PMID: 30677040 PMCID: PMC6363223 DOI: 10.1371/journal.pcbi.1006591] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 02/05/2019] [Accepted: 10/23/2018] [Indexed: 12/16/2022] Open
Abstract
Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets. Due to increasing availability of biological data, methods to properly integrate data generated across the globe become essential for extracting reproducible insights into relevant research questions. In this work, we developed a framework to reconstruct gene regulatory networks from expression datasets generated in separate studies—and thus, because of technical variation (different dates, handlers, laboratories, protocols etc…), challenging to integrate. Since regulatory mechanisms are often shared across conditions, we hypothesized that drawing conclusions from various data sources would improve performance of gene regulatory network inference. By transferring knowledge among regulatory models, our method is able to detect weaker patterns that are conserved across datasets, while also being able to detect dataset-unique interactions. We also allow incorporation of prior knowledge on network structure to favor models that are somewhat similar to the prior itself. Using two model organisms, we show that joint network inference outperforms inference from a single dataset. We also demonstrate that our method is robust to false edges in the prior and to low condition overlap across datasets, and that it can outperform current data integration strategies.
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Affiliation(s)
| | - Nicholas R de Veaux
- Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
| | - Emily R Miraldi
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA.,Divisions of Immunobiology & Biomedical Informatics, Cincinnati Children's Hospital, Cincinnati, OH 45229, USA
| | - Richard Bonneau
- New York University, New York, NY 10003, USA.,Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA
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21
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Çakır T, Kökrek E, Avşar G, Abdik E, Pir P. Next-Generation Genome-Scale Models Incorporating Multilevel 'Omics Data: From Yeast to Human. Methods Mol Biol 2019; 2049:347-363. [PMID: 31602621 DOI: 10.1007/978-1-4939-9736-7_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Genome-scale modelling in eukaryotes has been pioneered by the yeast Saccharomyces cerevisiae. Early metabolic networks have been reconstructed based on genome sequence and information accumulated in the literature on biochemical reactions. Protein-protein interaction networks have been constructed based on experimental observations such as yeast-2-hybrid method. Gene regulatory networks were based on a variety of data types, including information on TF-promoter binding and gene coexpression. The aforementioned networks have been improved gradually, and methods for their integration were developed. Incorporation of omics data including genomics, metabolomics, transcriptomics, fluxome, and phosphoproteome led to next-generation genome-scale models. The methods tested on yeast have later been implemented in human, further, cellular components found to be important in yeast physiology under (ab)normal conditions, and (dis)regulation mechanisms in yeast shed light to the healthy and disease states in human. This chapter provides a historical perspective on next-generation genome-scale models incorporating multilevel 'omics data, from yeast to human.
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Affiliation(s)
- Tunahan Çakır
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Emel Kökrek
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Gülben Avşar
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Ecehan Abdik
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Pınar Pir
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey.
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22
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Abstract
The classic Darwinian theory and the Synthetic evolutionary theory and their linear models, while invaluable to study the origins and evolution of species, are not primarily designed to model the evolution of organisations, typically that of ecosystems, nor that of processes. How could evolutionary theory better explain the evolution of biological complexity and diversity? Inclusive network-based analyses of dynamic systems could retrace interactions between (related or unrelated) components. This theoretical shift from a Tree of Life to a Dynamic Interaction Network of Life, which is supported by diverse molecular, cellular, microbiological, organismal, ecological and evolutionary studies, would further unify evolutionary biology.
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Affiliation(s)
- Eric Bapteste
- Sorbonne Universités, UPMC Université Paris 06, Institut de Biologie Paris-Seine (IBPS), F-75005 Paris, France
- CNRS, UMR7138, Institut de Biologie Paris-Seine, F-75005 Paris, France
| | - Philippe Huneman
- Institut d’Histoire et de Philosophie des Sciences et des Techniques (CNRS / Paris I Sorbonne), F-75006 Paris, France
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23
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Benchouaia M, Ripoche H, Sissoko M, Thiébaut A, Merhej J, Delaveau T, Fasseu L, Benaissa S, Lorieux G, Jourdren L, Le Crom S, Lelandais G, Corel E, Devaux F. Comparative Transcriptomics Highlights New Features of the Iron Starvation Response in the Human Pathogen Candida glabrata. Front Microbiol 2018; 9:2689. [PMID: 30505294 PMCID: PMC6250833 DOI: 10.3389/fmicb.2018.02689] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 10/22/2018] [Indexed: 11/21/2022] Open
Abstract
In this work, we used comparative transcriptomics to identify regulatory outliers (ROs) in the human pathogen Candida glabrata. ROs are genes that have very different expression patterns compared to their orthologs in other species. From comparative transcriptome analyses of the response of eight yeast species to toxic doses of selenite, a pleiotropic stress inducer, we identified 38 ROs in C. glabrata. Using transcriptome analyses of C. glabrata response to five different stresses, we pointed out five ROs which were more particularly responsive to iron starvation, a process which is very important for C. glabrata virulence. Global chromatin Immunoprecipitation and gene profiling analyses showed that four of these genes are actually new targets of the iron starvation responsive Aft2 transcription factor in C. glabrata. Two of them (HBS1 and DOM34b) are required for C. glabrata optimal growth in iron limited conditions. In S. cerevisiae, the orthologs of these two genes are involved in ribosome rescue by the NO GO decay (NGD) pathway. Hence, our results suggest a specific contribution of NGD co-factors to the C. glabrata adaptation to iron starvation.
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Affiliation(s)
- Médine Benchouaia
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Hugues Ripoche
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Mariam Sissoko
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Antonin Thiébaut
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Jawad Merhej
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Thierry Delaveau
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Laure Fasseu
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Sabrina Benaissa
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Geneviève Lorieux
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Laurent Jourdren
- École Normale Supérieure, PSL Research University, CNRS, Inserm U1024, Institut de Biologie de l’École Normale Supérieure, Plateforme Génomique, Paris, France
| | - Stéphane Le Crom
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7138, Évolution, Paris, France
| | - Gaëlle Lelandais
- UMR 9198, Institute for Integrative Biology of the Cell, CEA, CNRS, Université Paris-Sud, UPSay, Gif-sur-Yvette, France
| | - Eduardo Corel
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7138, Évolution, Paris, France
| | - Frédéric Devaux
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
- *Correspondence: Frédéric Devaux,
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24
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Gordon JL, Gallone B, Maere S, Verstrepen KJ. Evolutionary Context Improves Regulatory Network Predictions. Cell Syst 2017; 4:478-479. [PMID: 28544878 DOI: 10.1016/j.cels.2017.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
A novel algorithm harnesses phylogenetic information and facilitates a better understanding of the evolutionary divergence of gene regulation between species.
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Affiliation(s)
- Jonathan L Gordon
- VIB Center for Microbiology and Laboratory for Genetics and Genomics, Department M2S, KU Leuven, Bio-Incubator, Gaston Geenslaan 1, 3001 Leuven, Belgium
| | - Brigida Gallone
- VIB Center for Microbiology and Laboratory for Genetics and Genomics, Department M2S, KU Leuven, Bio-Incubator, Gaston Geenslaan 1, 3001 Leuven, Belgium; VIB Center for Plant Systems Biology and Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Gent, Belgium
| | - Steven Maere
- VIB Center for Plant Systems Biology and Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Gent, Belgium
| | - Kevin J Verstrepen
- VIB Center for Microbiology and Laboratory for Genetics and Genomics, Department M2S, KU Leuven, Bio-Incubator, Gaston Geenslaan 1, 3001 Leuven, Belgium.
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