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Akeju OJ, Cope AL. Re-examining Correlations Between Synonymous Codon Usage and Protein Bond Angles in Escherichia coli. Genome Biol Evol 2024; 16:evae080. [PMID: 38619010 PMCID: PMC11077309 DOI: 10.1093/gbe/evae080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 04/05/2024] [Accepted: 04/10/2024] [Indexed: 04/16/2024] Open
Abstract
Rosenberg AA, Marx A, Bronstein AM (Codon-specific Ramachandran plots show amino acid backbone conformation depends on identity of the translated codon. Nat Commun. 2022:13:2815) recently found a surprising correlation between synonymous codon usage and the dihedral bond angles of the resulting amino acid. However, their analysis did not account for the strongest known correlate of codon usage: gene expression. We re-examined the relationship between bond angles and codon usage by applying the approach of Rosenberg et al. to simulated protein-coding sequences that (i) have random codon usage, (ii) codon usage determined by mutation biases, and (iii) maintain the general relationship between codon usage and gene expression via the assumption of selection-mutation-drift equilibrium. We observed correlations between dihedral bond angle and codon usage when codon usage is entirely random, indicating possible conflation of noise with differences in bond angle distributions between synonymous codons. More relevant to the general analysis of codon usage patterns, we found surprisingly good agreement between the analysis of the real sequences and the analysis of sequences simulated assuming selection-mutation-drift equilibrium, with 91% of significant synonymous codon pairs detected in the former were also detected in the latter. We believe the correlation between codon usage and dihedral bond angles resulted from the variation in codon usage across genes due to the interplay between mutation bias, natural selection for translation efficiency, and gene expression, further underscoring these factors must be controlled for when looking for novel patterns related to codon usage.
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Affiliation(s)
| | - Alexander L Cope
- Department of Genetics, Rutgers University, Piscataway, New Jersey, USA
- Human Genetics Institute of New Jersey, Rutgers University, Piscataway, New Jersey, USA
- Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA
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2
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W B Jr M, A S R, P M, F B. Cellular and Natural Viral Engineering in Cognition-Based Evolution. Commun Integr Biol 2023; 16:2196145. [PMID: 37153718 PMCID: PMC10155641 DOI: 10.1080/19420889.2023.2196145] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/23/2023] [Indexed: 05/10/2023] Open
Abstract
Neo-Darwinism conceptualizes evolution as the continuous succession of predominately random genetic variations disciplined by natural selection. In that frame, the primary interaction between cells and the virome is relegated to host-parasite dynamics governed by selective influences. Cognition-Based Evolution regards biological and evolutionary development as a reciprocating cognition-based informational interactome for the protection of self-referential cells. To sustain cellular homeorhesis, cognitive cells collaborate to assess the validity of ambiguous biological information. That collective interaction involves coordinate measurement, communication, and active deployment of resources as Natural Cellular Engineering. These coordinated activities drive multicellularity, biological development, and evolutionary change. The virome participates as the vital intercessory among the cellular domains to ensure their shared permanent perpetuation. The interactions between the virome and the cellular domains represent active virocellular cross-communications for the continual exchange of resources. Modular genetic transfers between viruses and cells carry bioactive potentials. Those exchanges are deployed as nonrandom flexible tools among the domains in their continuous confrontation with environmental stresses. This alternative framework fundamentally shifts our perspective on viral-cellular interactions, strengthening established principles of viral symbiogenesis. Pathogenesis can now be properly appraised as one expression of a range of outcomes between cells and viruses within a larger conceptual framework of Natural Viral Engineering as a co-engineering participant with cells. It is proposed that Natural Viral Engineering should be viewed as a co-existent facet of Natural Cellular Engineering within Cognition-Based Evolution.
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Affiliation(s)
- Miller W B Jr
- Banner Health Systems - Medicine, Paradise Valley, Arizona, AZ, USA
| | - Reber A S
- Department of Psychology, University of British Columbia, Vancouver, BC, Canada
| | - Marshall P
- Department of Engineering, Evolution 2.0, Oak Park, IL, USA
| | - Baluška F
- Institute of Cellular and Molecular Botany, University of Bonn, Bonn, Germany
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3
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Sloan DB, Warren JM, Williams AM, Kuster SA, Forsythe ES. Incompatibility and Interchangeability in Molecular Evolution. Genome Biol Evol 2023; 15:evac184. [PMID: 36583227 PMCID: PMC9839398 DOI: 10.1093/gbe/evac184] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 12/31/2022] Open
Abstract
There is remarkable variation in the rate at which genetic incompatibilities in molecular interactions accumulate. In some cases, minor changes-even single-nucleotide substitutions-create major incompatibilities when hybridization forces new variants to function in a novel genetic background from an isolated population. In other cases, genes or even entire functional pathways can be horizontally transferred between anciently divergent evolutionary lineages that span the tree of life with little evidence of incompatibilities. In this review, we explore whether there are general principles that can explain why certain genes are prone to incompatibilities while others maintain interchangeability. We summarize evidence pointing to four genetic features that may contribute to greater resistance to functional replacement: (1) function in multisubunit enzyme complexes and protein-protein interactions, (2) sensitivity to changes in gene dosage, (3) rapid rate of sequence evolution, and (4) overall importance to cell viability, which creates sensitivity to small perturbations in molecular function. We discuss the relative levels of support for these different hypotheses and lay out future directions that may help explain the striking contrasts in patterns of incompatibility and interchangeability throughout the history of molecular evolution.
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Affiliation(s)
- Daniel B Sloan
- Department of Biology, Colorado State University, Fort Collins, Colorado
| | - Jessica M Warren
- Center for Mechanisms of Evolution, Biodesign Institute and School of Life Sciences, Arizona State University, Tempe, Arizona
| | - Alissa M Williams
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee
| | - Shady A Kuster
- Department of Biology, Colorado State University, Fort Collins, Colorado
| | - Evan S Forsythe
- Department of Biology, Colorado State University, Fort Collins, Colorado
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4
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Caldu-Primo JL, Verduzco-Martínez JA, Alvarez-Buylla ER, Davila-Velderrain J. In vivo and in vitro human gene essentiality estimations capture contrasting functional constraints. NAR Genom Bioinform 2021; 3:lqab063. [PMID: 34268495 PMCID: PMC8276763 DOI: 10.1093/nargab/lqab063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/18/2021] [Accepted: 07/07/2021] [Indexed: 11/28/2022] Open
Abstract
Gene essentiality estimation is a popular empirical approach to link genotypes to phenotypes. In humans, essentiality is estimated based on loss-of-function (LoF) mutation intolerance, either from population exome sequencing (in vivo) data or CRISPR-based in vitro perturbation experiments. Both approaches identify genes presumed to have detrimental consequences on the organism upon mutation. Are these genes constrained by having key cellular/organismal roles? Do in vivo and in vitro estimations equally recover these constraints? Insights into these questions have important implications in generalizing observations from cell models and interpreting disease risk genes. To empirically address these questions, we integrate genome-scale datasets and compare structural, functional and evolutionary features of essential genes versus genes with extremely high mutational tolerance. We found that essentiality estimates do recover functional constraints. However, the organismal or cellular context of estimation leads to functionally contrasting properties underlying the constraint. Our results suggest that depletion of LoF mutations in human populations effectively captures organismal-level functional constraints not experimentally accessible through CRISPR-based screens. Finally, we identify a set of genes (OrgEssential), which are mutationally intolerant in vivo but highly tolerant in vitro. These genes drive observed functional constraint differences and have an unexpected preference for nervous system expression.
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Affiliation(s)
- Jose Luis Caldu-Primo
- Instituto de Ecología, Universidad Nacional Autónoma de México, Cd. Universitaria, CDMX., 04510, México
| | - Jorge Armando Verduzco-Martínez
- Departamento de Biología Celular y Genética, Facultad de Ciencias Biológicas, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Nuevo León, 66400, México
| | - Elena R Alvarez-Buylla
- Instituto de Ecología, Universidad Nacional Autónoma de México, Cd. Universitaria, CDMX., 04510, México
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5
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Chang AYF, Liao BY. Reduced Translational Efficiency of Eukaryotic Genes after Duplication Events. Mol Biol Evol 2021; 37:1452-1461. [PMID: 31904835 DOI: 10.1093/molbev/msz309] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Control of gene expression has been found to be predominantly determined at the level of protein translation. However, to date, reduced expression from duplicated genes in eukaryotes for dosage maintenance has only been linked to transcriptional control involving epigenetic mechanisms. Here, we hypothesize that dosage maintenance following gene duplication also involves regulation at the protein level. To test this hypothesis, we compared transcriptome and proteome data of yeast models, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and worm models, Caenorhabditis elegans and Caenorhabditis briggsae, to investigate lineage-specifically duplicated genes. Duplicated genes in both eukaryotic models exhibited a reduced protein-to-mRNA abundance ratio. Moreover, dosage sensitive genes, represented by genes encoding protein complex subunits, reduced their protein-to-mRNA abundance ratios more significantly than the other genes after duplication events. An analysis of ribosome profiling (Ribo-Seq) data further showed that reduced translational efficiency was more prominent for dosage sensitive genes than for the other genes. Meanwhile, no difference in protein degradation rate was associated with duplication events. Translationally repressed duplicated genes were also more likely to be inhibited at the level of transcription. Taken together, these results suggest that translation-mediated dosage control is partially contributed by natural selection and it enhances transcriptional control in maintaining gene dosage after gene duplication events during eukaryotic genome evolution.
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Affiliation(s)
- Andrew Ying-Fei Chang
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan, Republic of China
| | - Ben-Yang Liao
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan, Republic of China
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6
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Swamy KBS, Schuyler SC, Leu JY. Protein Complexes Form a Basis for Complex Hybrid Incompatibility. Front Genet 2021; 12:609766. [PMID: 33633780 PMCID: PMC7900514 DOI: 10.3389/fgene.2021.609766] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 01/20/2021] [Indexed: 12/20/2022] Open
Abstract
Proteins are the workhorses of the cell and execute many of their functions by interacting with other proteins forming protein complexes. Multi-protein complexes are an admixture of subunits, change their interaction partners, and modulate their functions and cellular physiology in response to environmental changes. When two species mate, the hybrid offspring are usually inviable or sterile because of large-scale differences in the genetic makeup between the two parents causing incompatible genetic interactions. Such reciprocal-sign epistasis between inter-specific alleles is not limited to incompatible interactions between just one gene pair; and, usually involves multiple genes. Many of these multi-locus incompatibilities show visible defects, only in the presence of all the interactions, making it hard to characterize. Understanding the dynamics of protein-protein interactions (PPIs) leading to multi-protein complexes is better suited to characterize multi-locus incompatibilities, compared to studying them with traditional approaches of genetics and molecular biology. The advances in omics technologies, which includes genomics, transcriptomics, and proteomics can help achieve this end. This is especially relevant when studying non-model organisms. Here, we discuss the recent progress in the understanding of hybrid genetic incompatibility; omics technologies, and how together they have helped in characterizing protein complexes and in turn multi-locus incompatibilities. We also review advances in bioinformatic techniques suitable for this purpose and propose directions for leveraging the knowledge gained from model-organisms to identify genetic incompatibilities in non-model organisms.
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Affiliation(s)
- Krishna B. S. Swamy
- Division of Biological and Life Sciences, School of Arts and Sciences, Ahmedabad University, Ahmedabad, India
| | - Scott C. Schuyler
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Division of Head and Neck Surgery, Department of Otolaryngology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Jun-Yi Leu
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
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7
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Aligning functional network constraint to evolutionary outcomes. BMC Evol Biol 2020; 20:58. [PMID: 32448114 PMCID: PMC7245893 DOI: 10.1186/s12862-020-01613-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 04/15/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Functional constraint through genomic architecture is suggested to be an important dimension of genome evolution, but quantitative evidence for this idea is rare. In this contribution, existing evidence and discussions on genomic architecture as constraint for convergent evolution, rapid adaptation, and genic adaptation are summarized into alternative, testable hypotheses. Network architecture statistics from protein-protein interaction networks are then used to calculate differences in evolutionary outcomes on the example of genomic evolution in yeast, and the results are used to evaluate statistical support for these longstanding hypotheses. RESULTS A discriminant function analysis lent statistical support to classifying the yeast interactome into hub, intermediate and peripheral nodes based on network neighborhood connectivity, betweenness centrality, and average shortest path length. Quantitative support for the existence of genomic architecture as a mechanistic basis for evolutionary constraint is then revealed through utilizing these statistical parameters of the protein-protein interaction network in combination with estimators of protein evolution. CONCLUSIONS As functional genetic networks are becoming increasingly available, it will now be possible to evaluate functional genetic network constraint against variables describing complex phenotypes and environments, for better understanding of commonly observed deterministic patterns of evolution in non-model organisms. The hypothesis framework and methodological approach outlined herein may help to quantify the extrinsic versus intrinsic dimensions of evolutionary constraint, and result in a better understanding of how fast, effectively, or deterministically organisms adapt.
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8
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V K MA, Chandrasekaran VM, Pandurangan S. Protein Domain Level Cancer Drug Targets in the Network of MAPK Pathways. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:2057-2065. [PMID: 29993692 DOI: 10.1109/tcbb.2018.2829507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Proteins in the MAPK pathways considered as potential drug targets for cancer treatment. Pathways along with the cross-talks increase their scope to view them as a network of MAPK pathways. Side effect causing targeted domains act as a proxy for drug targets due to its structural similarity and frequent reuse of their variants. We proposed to identify non-repeatable protein domains as the drug targets to disrupt the signal transduction than targeting the whole protein. Network based approach is used to understand the contribution of 52 domains in non-hub, non-essential, and intra-pathway cancerous nodes and to identify potential drug target domains. 34 distinct domains in the cancerous proteins are playing vital roles in making cancer as a complex disease and pose challenges to identify potential drug targets. Distribution of domain families follows the power law in the network. Single promiscuous domains are contributing to the formation of hubs like Pkinease, Pkinease Tyr, and Ras. Hub nodes are positively correlated with the domain coverage and targeting them would disrupt functional properties of the proteins. EIF 4EBP, alpha Kinase, Sel1, ROKNT, and KH 1 are the domains identified as potential domain targets for the disruption of the signaling mechanism involved in cancer.
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9
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Santos-Otte P, Leysen H, van Gastel J, Hendrickx JO, Martin B, Maudsley S. G Protein-Coupled Receptor Systems and Their Role in Cellular Senescence. Comput Struct Biotechnol J 2019; 17:1265-1277. [PMID: 31921393 PMCID: PMC6944711 DOI: 10.1016/j.csbj.2019.08.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 08/20/2019] [Accepted: 08/21/2019] [Indexed: 12/17/2022] Open
Abstract
Aging is a complex biological process that is inevitable for nearly all organisms. Aging is the strongest risk factor for development of multiple neurodegenerative disorders, cancer and cardiovascular disorders. Age-related disease conditions are mainly caused by the progressive degradation of the integrity of communication systems within and between organs. This is in part mediated by, i) decreased efficiency of receptor signaling systems and ii) an increasing inability to cope with stress leading to apoptosis and cellular senescence. Cellular senescence is a natural process during embryonic development, more recently it has been shown to be also involved in the development of aging disorders and is now considered one of the major hallmarks of aging. G-protein-coupled receptors (GPCRs) comprise a superfamily of integral membrane receptors that are responsible for cell signaling events involved in nearly every physiological process. Recent advances in the molecular understanding of GPCR signaling complexity have expanded their therapeutic capacity tremendously. Emerging data now suggests the involvement of GPCRs and their associated proteins in the development of cellular senescence. With the proven efficacy of therapeutic GPCR targeting, it is reasonable to now consider GPCRs as potential platforms to control cellular senescence and the consequently, age-related disorders.
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Key Words
- ADP-ribosylation factor GTPase-activating protein, (Arf-GAP)
- AT1R blockers, (ARB)
- Aging
- Angiotensin II, (Ang II)
- Ataxia telangiectasia mutated, (ATM)
- Cellular senescence
- G protein-coupled receptor kinase interacting protein 2 (GIT2)
- G protein-coupled receptor kinase interacting protein 2, (GIT2)
- G protein-coupled receptor kinase, (GRK)
- G protein-coupled receptors (GPCRs)
- G protein-coupled receptors, (GPCRs)
- Hutchinson–Gilford progeria syndrome, (HGPS)
- Lysophosphatidic acid, (LPA)
- Regulator of G-protein signaling, (RGS)
- Relaxin family receptor 3, (RXFP3)
- active state, (R*)
- angiotensin type 1 receptor, (AT1R)
- angiotensin type 2 receptor, (AT2R)
- beta2-adrenergic receptor, (β2AR)
- cyclin-dependent kinase 2, (CDK2)
- cyclin-dependent kinase inhibitor 1, (cdkn1A/p21)
- endothelial cell differentiation gene, (Edg)
- inactive state, (R)
- latent semantic indexing, (LSI)
- mitogen-activated protein kinase, (MAPK)
- nuclear factor kappa-light-chain-enhancer of activated B cells, (NF- κβ)
- protein kinases, (PK)
- purinergic receptors family, (P2Y)
- renin-angiotensin system, (RAS)
- retinoblastoma, (RB)
- senescence associated secretory phenotype, (SASP)
- stress-induced premature senescence, (SIPS)
- transcription factor E2F3, (E2F3)
- transmembrane, (TM)
- tumor suppressor gene PTEN, (PTEN)
- tumor suppressor protein 53, (p53)
- vascular smooth muscle cells, (VSMC)
- β-Arrestin
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Affiliation(s)
- Paula Santos-Otte
- Center for Molecular and Cellular Bioengineering (CMCB), Technische Universität Dresden, 01062 Dresden, Germany
| | - Hanne Leysen
- Receptor Biology Lab, University of Antwerp, 2610 Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, 2610 Antwerp, Belgium
| | - Jaana van Gastel
- Receptor Biology Lab, University of Antwerp, 2610 Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, 2610 Antwerp, Belgium
| | - Jhana O. Hendrickx
- Receptor Biology Lab, University of Antwerp, 2610 Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, 2610 Antwerp, Belgium
| | - Bronwen Martin
- Receptor Biology Lab, University of Antwerp, 2610 Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Lab, University of Antwerp, 2610 Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, 2610 Antwerp, Belgium
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van Gastel J, Boddaert J, Jushaj A, Premont RT, Luttrell LM, Janssens J, Martin B, Maudsley S. GIT2-A keystone in ageing and age-related disease. Ageing Res Rev 2018; 43:46-63. [PMID: 29452267 DOI: 10.1016/j.arr.2018.02.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 02/06/2018] [Accepted: 02/08/2018] [Indexed: 12/15/2022]
Abstract
Since its discovery, G protein-coupled receptor kinase-interacting protein 2, GIT2, and its family member, GIT1, have received considerable interest concerning their potential key roles in regulating multiple inter-connected physiological and pathophysiological processes. GIT2 was first identified as a multifunctional protein that is recruited to G protein-coupled receptors (GPCRs) during the process of receptor internalization. Recent findings have demonstrated that perhaps one of the most important effects of GIT2 in physiology concerns its role in controlling multiple aspects of the complex ageing process. Ageing can be considered the most prevalent pathophysiological condition in humans, affecting all tissue systems and acting as a driving force for many common and intractable disorders. The ageing process involves a complex interplay among various deleterious activities that profoundly disrupt the body's ability to cope with damage, thus increasing susceptibility to pathophysiologies such as neurodegeneration, central obesity, osteoporosis, type 2 diabetes mellitus and atherosclerosis. The biological systems that control ageing appear to function as a series of interconnected complex networks. The inter-communication among multiple lower-complexity signaling systems within the global ageing networks is likely coordinated internally by keystones or hubs, which regulate responses to dynamic molecular events through protein-protein interactions with multiple distinct partners. Multiple lines of research have suggested that GIT2 may act as one of these network coordinators in the ageing process. Identifying and targeting keystones, such as GIT2, is thus an important approach in our understanding of, and eventual ability to, medically ameliorate or interdict age-related progressive cellular and tissue damage.
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MTGO: PPI Network Analysis Via Topological and Functional Module Identification. Sci Rep 2018; 8:5499. [PMID: 29615773 PMCID: PMC5882952 DOI: 10.1038/s41598-018-23672-0] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 02/28/2018] [Indexed: 11/08/2022] Open
Abstract
Protein-protein interaction (PPI) networks are viable tools to understand cell functions, disease machinery, and drug design/repositioning. Interpreting a PPI, however, it is a particularly challenging task because of network complexity. Several algorithms have been proposed for an automatic PPI interpretation, at first by solely considering the network topology, and later by integrating Gene Ontology (GO) terms as node similarity attributes. Here we present MTGO - Module detection via Topological information and GO knowledge, a novel functional module identification approach. MTGO let emerge the bimolecular machinery underpinning PPI networks by leveraging on both biological knowledge and topological properties. In particular, it directly exploits GO terms during the module assembling process, and labels each module with its best fit GO term, easing its functional interpretation. MTGO shows largely better results than other state of the art algorithms (including recent GO-based ones) when searching for small or sparse functional modules, while providing comparable or better results all other cases. MTGO correctly identifies molecular complexes and literature-consistent processes in an experimentally derived PPI network of Myocardial infarction. A software version of MTGO is available freely for non-commercial purposes at https://gitlab.com/d1vella/MTGO .
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12
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Non-Pleiotropic Coupling of Daily and Seasonal Temporal Isolation in the European Corn Borer. Genes (Basel) 2018; 9:genes9040180. [PMID: 29587435 PMCID: PMC5924522 DOI: 10.3390/genes9040180] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 03/15/2018] [Accepted: 03/20/2018] [Indexed: 01/22/2023] Open
Abstract
Speciation often involves the coupling of multiple isolating barriers to produce reproductive isolation, but how coupling is generated among different premating barriers is unknown. We measure the degree of coupling between the daily mating time and seasonal mating time between strains of European corn borer (Ostrinia nubilalis) and evaluate the hypothesis that the coupling of different forms of allochrony is due to a shared genetic architecture, involving genes with pleiotropic effects on both timing phenotypes. We measure differences in gene expression at peak mating times and compare these genes to previously identified candidates that are associated with changes in seasonal mating time between the corn borer strains. We find that the E strain, which mates earlier in the season, also mates 2.7 h earlier in the night than the Z strain. Earlier daily mating is correlated with the differences in expression of the circadian clock genes cycle, slimb, and vrille. However, different circadian clock genes associate with daily and seasonal timing, suggesting that the coupling of timing traits is maintained by natural selection rather than pleiotropy. Juvenile hormone gene expression was associated with both types of timing, suggesting that circadian genes activate common downstream modules that may impose constraint on future evolution of these traits.
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13
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Beyond Royalactin and a master inducer explanation of phenotypic plasticity in honey bees. Commun Biol 2018; 1:8. [PMID: 30271895 PMCID: PMC6123742 DOI: 10.1038/s42003-017-0004-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 11/06/2017] [Indexed: 12/26/2022] Open
Abstract
Distinct female castes produced from one genotype are the trademark of a successful evolutionary invention in eusocial insects known as reproductive division of labour. In honey bees, fertile queens develop from larvae fed a complex diet called royal jelly. Recently, one protein in royal jelly, dubbed Royalactin, was deemed to be the exclusive driver of queen bee determination. However, this notion has not been universally accepted. Here I critically evaluate this line of research and argue that the sheer complexity of creating alternate phenotypes from one genotype cannot be reduced to a single dietary component. An acceptable model of environmentally driven caste differentiation should include the facets of dynamic thinking, such as the concepts of attractor states and genetic hierarchical networks. In honeybees, genotypically identical females develop into queens or sterile workers, depending on their diets. In this review, Ryszard Maleszka discusses the controversial role of the royal jelly protein Royalactin in caste determination and provides a framework for moving beyond the master inducer concept.
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Biswas K, Acharya D, Podder S, Ghosh TC. Evolutionary rate heterogeneity between multi- and single-interface hubs across human housekeeping and tissue-specific protein interaction network: Insights from proteins' and its partners' properties. Genomics 2017; 110:283-290. [PMID: 29198610 DOI: 10.1016/j.ygeno.2017.11.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 11/10/2017] [Accepted: 11/29/2017] [Indexed: 12/12/2022]
Abstract
Integrating gene expression into protein-protein interaction network (PPIN) leads to the construction of tissue-specific (TS) and housekeeping (HK) sub-networks, with distinctive TS- and HK-hubs. All such hub proteins are divided into multi-interface (MI) hubs and single-interface (SI) hubs, where MI hubs evolve slower than SI hubs. Here we explored the evolutionary rate difference between MI and SI proteins within TS- and HK-PPIN and observed that this difference is present only in TS, but not in HK-class. Next, we explored whether proteins' own properties or its partners' properties are more influential in such evolutionary discrepancy. Statistical analyses revealed that this evolutionary rate correlates negatively with protein's own properties like expression level, miRNA count, conformational diversity and functional properties and with its partners' properties like protein disorder and tissue expression similarity. Moreover, partial correlation and regression analysis revealed that both proteins' and its partners' properties have independent effects on protein evolutionary rate.
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Affiliation(s)
- Kakali Biswas
- Bioinformatics Centre, Bose Institute, P-1/12, C.I.T. Scheme VII M, Kolkata 700 054, India
| | - Debarun Acharya
- Bioinformatics Centre, Bose Institute, P-1/12, C.I.T. Scheme VII M, Kolkata 700 054, India
| | - Soumita Podder
- Bioinformatics Centre, Bose Institute, P-1/12, C.I.T. Scheme VII M, Kolkata 700 054, India; Department of Microbiology, Raiganj University, Raiganj, Uttar Dinajpur 733134, India
| | - Tapash Chandra Ghosh
- Bioinformatics Centre, Bose Institute, P-1/12, C.I.T. Scheme VII M, Kolkata 700 054, India.
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15
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Turmo A, Gonzalez-Esquer CR, Kerfeld CA. Carboxysomes: metabolic modules for CO2 fixation. FEMS Microbiol Lett 2017; 364:4082729. [DOI: 10.1093/femsle/fnx176] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Accepted: 08/12/2017] [Indexed: 11/13/2022] Open
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16
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Lechno-Yossef S, Melnicki MR, Bao H, Montgomery BL, Kerfeld CA. Synthetic OCP heterodimers are photoactive and recapitulate the fusion of two primitive carotenoproteins in the evolution of cyanobacterial photoprotection. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2017; 91:646-656. [PMID: 28503830 DOI: 10.1111/tpj.13593] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 04/25/2017] [Accepted: 05/03/2017] [Indexed: 06/07/2023]
Abstract
The orange carotenoid protein (OCP) governs photoprotection in the majority of cyanobacteria. It is structurally and functionally modular, comprised of a C-terminal regulatory domain (CTD), an N-terminal effector domain (NTD) and a ketocarotenoid; the chromophore spans the two domains in the ground state and translocates fully into the NTD upon illumination. Using both the canonical OCP1 from Fremyella diplosiphon and the presumably more primitive OCP2 paralog from the same organism, we show that an NTD-CTD heterodimer forms when the domains are expressed as separate polypeptides. The carotenoid is required for the heterodimeric association, assembling an orange complex which is stable in the dark. Both OCP1 and OCP2 heterodimers are photoactive, undergoing light-driven heterodimer dissociation, but differ in their ability to reassociate in darkness, setting the stage for bioengineering photoprotection in cyanobacteria as well as for developing new photoswitches for biotechnology. Additionally, we reveal that homodimeric CTD can bind carotenoid in the absence of NTD, and name this truncated variant the C-terminal domain-like carotenoid protein (CCP). This finding supports the hypothesis that the OCP evolved from an ancient fusion event between genes for two different carotenoid-binding proteins ancestral to the NTD and CTD. We suggest that the CCP and its homologs constitute a new family of carotenoproteins within the NTF2-like superfamily found across all kingdoms of life.
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Affiliation(s)
- Sigal Lechno-Yossef
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA
| | - Matthew R Melnicki
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Han Bao
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA
| | - Beronda L Montgomery
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, 48824, USA
| | - Cheryl A Kerfeld
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, 94720, USA
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17
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Will T, Helms V. Rewiring of the inferred protein interactome during blood development studied with the tool PPICompare. BMC SYSTEMS BIOLOGY 2017; 11:44. [PMID: 28376810 PMCID: PMC5379774 DOI: 10.1186/s12918-017-0400-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 01/26/2017] [Indexed: 12/24/2022]
Abstract
BACKGROUND Differential analysis of cellular conditions is a key approach towards understanding the consequences and driving causes behind biological processes such as developmental transitions or diseases. The progress of whole-genome expression profiling enabled to conveniently capture the state of a cell's transcriptome and to detect the characteristic features that distinguish cells in specific conditions. In contrast, mapping the physical protein interactome for many samples is experimentally infeasible at the moment. For the understanding of the whole system, however, it is equally important how the interactions of proteins are rewired between cellular states. To overcome this deficiency, we recently showed how condition-specific protein interaction networks that even consider alternative splicing can be inferred from transcript expression data. Here, we present the differential network analysis tool PPICompare that was specifically designed for isoform-sensitive protein interaction networks. RESULTS Besides detecting significant rewiring events between the interactomes of grouped samples, PPICompare infers which alterations to the transcriptome caused each rewiring event and what is the minimal set of alterations necessary to explain all between-group changes. When applied to the development of blood cells, we verified that a reasonable amount of rewiring events were reported by the tool and found that differential gene expression was the major determinant of cellular adjustments to the interactome. Alternative splicing events were consistently necessary in each developmental step to explain all significant alterations and were especially important for rewiring in the context of transcriptional control. CONCLUSIONS Applying PPICompare enabled us to investigate the dynamics of the human protein interactome during developmental transitions. A platform-independent implementation of the tool PPICompare is available at https://sourceforge.net/projects/ppicompare/ .
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Affiliation(s)
- Thorsten Will
- Center for Bioinformatics, Saarland University, Campus E2.1, Saarbrücken, 66123 Germany
- Graduate School of Computer Science, Saarland University, Campus E1.3, Saarbrücken, 66123 Germany
| | - Volkhard Helms
- Center for Bioinformatics, Saarland University, Campus E2.1, Saarbrücken, 66123 Germany
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18
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Modos D, Brooks J, Fazekas D, Ari E, Vellai T, Csermely P, Korcsmaros T, Lenti K. Identification of critical paralog groups with indispensable roles in the regulation of signaling flow. Sci Rep 2016; 6:38588. [PMID: 27922122 PMCID: PMC5138592 DOI: 10.1038/srep38588] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 11/11/2016] [Indexed: 01/21/2023] Open
Abstract
Extensive cross-talk between signaling pathways is required to integrate the myriad of extracellular signal combinations at the cellular level. Gene duplication events may lead to the emergence of novel functions, leaving groups of similar genes - termed paralogs - in the genome. To distinguish critical paralog groups (CPGs) from other paralogs in human signaling networks, we developed a signaling network-based method using cross-talk annotation and tissue-specific signaling flow analysis. 75 CPGs were found with higher degree, betweenness centrality, closeness, and ‘bowtieness’ when compared to other paralogs or other proteins in the signaling network. CPGs had higher diversity in all these measures, with more varied biological functions and more specific post-transcriptional regulation than non-critical paralog groups (non-CPG). Using TGF-beta, Notch and MAPK pathways as examples, SMAD2/3, NOTCH1/2/3 and MEK3/6-p38 CPGs were found to regulate the signaling flow of their respective pathways. Additionally, CPGs showed a higher mutation rate in both inherited diseases and cancer, and were enriched in drug targets. In conclusion, the results revealed two distinct types of paralog groups in the signaling network: CPGs and non-CPGs. Thus highlighting the importance of CPGs as compared to non-CPGs in drug discovery and disease pathogenesis.
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Affiliation(s)
- Dezso Modos
- Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary.,Department of Genetics, Eotvos Lorand University, Budapest, Hungary.,Earlham Institute, Norwich Research Park, Norwich, UK
| | - Johanne Brooks
- Gut Health and Food Safety Programme, Institute of Food Research, Norwich Research Park, Norwich, UK.,Faculty of Medicine and Health, University of East Anglia, Norwich, UK.,Department of Gastroenterology, Norfolk and Norwich University Hospitals, Norwich, UK
| | - David Fazekas
- Department of Genetics, Eotvos Lorand University, Budapest, Hungary
| | - Eszter Ari
- Department of Genetics, Eotvos Lorand University, Budapest, Hungary.,Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary
| | - Tibor Vellai
- Department of Genetics, Eotvos Lorand University, Budapest, Hungary
| | - Peter Csermely
- Department of Medical Chemistry, Semmelweis University, Budapest, Hungary
| | - Tamas Korcsmaros
- Department of Genetics, Eotvos Lorand University, Budapest, Hungary.,Earlham Institute, Norwich Research Park, Norwich, UK.,Gut Health and Food Safety Programme, Institute of Food Research, Norwich Research Park, Norwich, UK
| | - Katalin Lenti
- Department of Morphology and Physiology, Faculty of Health Sciences, Semmelweis University, Budapest, Hungary
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19
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Patra B, Kon Y, Yadav G, Sevold AW, Frumkin JP, Vallabhajosyula RR, Hintze A, Østman B, Schossau J, Bhan A, Marzolf B, Tamashiro JK, Kaur A, Baliga NS, Grayhack EJ, Adami C, Galas DJ, Raval A, Phizicky EM, Ray A. A genome wide dosage suppressor network reveals genomic robustness. Nucleic Acids Res 2016; 45:255-270. [PMID: 27899637 PMCID: PMC5224485 DOI: 10.1093/nar/gkw1148] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 10/17/2016] [Accepted: 11/07/2016] [Indexed: 01/17/2023] Open
Abstract
Genomic robustness is the extent to which an organism has evolved to withstand the effects of deleterious mutations. We explored the extent of genomic robustness in budding yeast by genome wide dosage suppressor analysis of 53 conditional lethal mutations in cell division cycle and RNA synthesis related genes, revealing 660 suppressor interactions of which 642 are novel. This collection has several distinctive features, including high co-occurrence of mutant-suppressor pairs within protein modules, highly correlated functions between the pairs and higher diversity of functions among the co-suppressors than previously observed. Dosage suppression of essential genes encoding RNA polymerase subunits and chromosome cohesion complex suggests a surprising degree of functional plasticity of macromolecular complexes, and the existence of numerous degenerate pathways for circumventing the effects of potentially lethal mutations. These results imply that organisms and cancer are likely able to exploit the genomic robustness properties, due the persistence of cryptic gene and pathway functions, to generate variation and adapt to selective pressures.
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Affiliation(s)
- Biranchi Patra
- Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA
| | - Yoshiko Kon
- Department of Biochemistry, University of Rochester School of Medicine, Rochester, NY 14627, USA
| | - Gitanjali Yadav
- Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA.,National Institute of Plant Genome Research (NIPGR), Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Anthony W Sevold
- Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA
| | - Jesse P Frumkin
- Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA
| | | | - Arend Hintze
- Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA
| | - Bjørn Østman
- Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA
| | - Jory Schossau
- Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA
| | - Ashish Bhan
- Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA
| | - Bruz Marzolf
- Institute for Systems Biology, 1441 N 34th St, Seattle, WA 98103, USA
| | | | - Amardeep Kaur
- Institute for Systems Biology, 1441 N 34th St, Seattle, WA 98103, USA
| | - Nitin S Baliga
- Institute for Systems Biology, 1441 N 34th St, Seattle, WA 98103, USA
| | - Elizabeth J Grayhack
- Department of Biochemistry, University of Rochester School of Medicine, Rochester, NY 14627, USA
| | - Christoph Adami
- Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA
| | - David J Galas
- Institute for Systems Biology, 1441 N 34th St, Seattle, WA 98103, USA
| | - Alpan Raval
- Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA.,Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA 91711, USA
| | - Eric M Phizicky
- Department of Biochemistry, University of Rochester School of Medicine, Rochester, NY 14627, USA
| | - Animesh Ray
- Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA .,Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
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20
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Chesmore KN, Bartlett J, Cheng C, Williams SM. Complex Patterns of Association between Pleiotropy and Transcription Factor Evolution. Genome Biol Evol 2016; 8:3159-3170. [PMID: 27635052 PMCID: PMC5174740 DOI: 10.1093/gbe/evw228] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Pleiotropy has been claimed to constrain gene evolution but specific mechanisms and extent of these constraints have been difficult to demonstrate. The expansion of molecular data makes it possible to investigate these pleiotropic effects. Few classes of genes have been characterized as intensely as human transcription factors (TFs). We therefore analyzed the evolutionary rates of full TF proteins, along with their DNA binding domains and protein-protein interacting domains (PID) in light of the degree of pleiotropy, measured by the number of TF-TF interactions, or the number of DNA-binding targets. Data were extracted from the ENCODE Chip-Seq dataset, the String v 9.2 database, and the NHGRI GWAS catalog. Evolutionary rates of proteins and domains were calculated using the PAML CodeML package. Our analysis shows that the numbers of TF-TF interactions and DNA binding targets associated with constrained gene evolution; however, the constraint caused by the number of DNA binding targets was restricted to the DNA binding domains, whereas the number of TF-TF interactions constrained the full protein and did so more strongly. Additionally, we found a positive correlation between the number of protein-PIDs and the evolutionary rates of the protein-PIDs. These findings show that not only does pleiotropy associate with constrained protein evolution but the constraint differs by domain function. Finally, we show that GWAS associated TF genes are more highly pleiotropic : The GWAS data illustrates that mutations in highly pleiotropic genes are more likely to be associated with disease phenotypes.
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Affiliation(s)
- Kevin N Chesmore
- Department of Genetics, Geisel School of Medicine, Dartmouth College, Hanover, NH
| | - Jacquelaine Bartlett
- Department of Genetics, Geisel School of Medicine, Dartmouth College, Hanover, NH
| | - Chao Cheng
- Department of Genetics, Geisel School of Medicine, Dartmouth College, Hanover, NH
| | - Scott M Williams
- Department of Genetics, Geisel School of Medicine, Dartmouth College, Hanover, NH
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21
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Morrison ES, Badyaev AV. Structuring evolution: biochemical networks and metabolic diversification in birds. BMC Evol Biol 2016; 16:168. [PMID: 27561312 PMCID: PMC5000421 DOI: 10.1186/s12862-016-0731-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Accepted: 08/01/2016] [Indexed: 12/17/2022] Open
Abstract
Background Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a “global” carotenoid network – comprising of all known enzymatic reactions among naturally occurring carotenoids – with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds. Results We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network – compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution. Conclusions The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution. Electronic supplementary material The online version of this article (doi:10.1186/s12862-016-0731-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Erin S Morrison
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA.
| | - Alexander V Badyaev
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
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22
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Montgomery BL, Lechno-Yossef S, Kerfeld CA. Interrelated modules in cyanobacterial photosynthesis: the carbon-concentrating mechanism, photorespiration, and light perception. JOURNAL OF EXPERIMENTAL BOTANY 2016; 67:2931-2940. [PMID: 27117337 DOI: 10.1093/jxb/erw162] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Here we consider the cyanobacterial carbon-concentrating mechanism (CCM) and photorespiration in the context of the regulation of light harvesting, using a conceptual framework borrowed from engineering: modularity. Broadly speaking, biological 'modules' are semi-autonomous functional units such as protein domains, operons, metabolic pathways, and (sub)cellular compartments. They are increasingly recognized as units of both evolution and engineering. Modules may be connected by metabolites, such as NADPH, ATP, and 2PG. While the Calvin-Benson-Bassham Cycle and photorespiratory salvage pathways can be considered as metabolic modules, the carboxysome, the core of the cyanobacterial CCM, is both a structural and a metabolic module. In photosynthetic organisms, which use light cues to adapt to the external environment and which tune the photosystems to provide the ATP and reducing power for carbon fixation, light-regulated modules are critical. The primary enzyme of carbon fixation, RuBisCO, uses CO2 as a substrate, which is accumulated via the CCM. However RuBisCO also has a secondary reaction in which it utilizes O2, a by-product of the photochemical modules, which leads to photorespiration. A complete understanding of the interplay among CCM and photorespiration is predicated on uncovering their connections to the light reactions and the regulatory factors and pathways that tune these modules to external cues. We probe this connection by investigating light inputs into the CCM and photorespiratory pathways in the chromatically acclimating cyanobacterium Fremyella diplosiphon.
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Affiliation(s)
- Beronda L Montgomery
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Sigal Lechno-Yossef
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA
| | - Cheryl A Kerfeld
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
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23
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Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks. BIOMED RESEARCH INTERNATIONAL 2016; 2016:4658506. [PMID: 27119079 PMCID: PMC4826914 DOI: 10.1155/2016/4658506] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2015] [Accepted: 03/07/2016] [Indexed: 01/28/2023]
Abstract
Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins) are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons) tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes' adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.
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24
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Kiran M, Nagarajaram HA. Interaction and localization diversities of global and local hubs in human protein–protein interaction networks. MOLECULAR BIOSYSTEMS 2016; 12:2875-82. [DOI: 10.1039/c6mb00104a] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Hubs, the highly connected nodes in protein–protein interaction networks (PPINs), are associated with several characteristic properties and are known to perform vital roles in cells.
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Affiliation(s)
- M. Kiran
- Laboratory of Computational Biology
- Centre for DNA Fingerprinting and Diagnostics
- Gruhakalpa
- Hyderabad 500 001
- India
| | - H. A. Nagarajaram
- Laboratory of Computational Biology
- Centre for DNA Fingerprinting and Diagnostics
- Gruhakalpa
- Hyderabad 500 001
- India
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25
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Gu X, Tang W. Model parameters of molecular evolution explain genomic correlations. Brief Bioinform 2015; 18:37-42. [PMID: 26628558 DOI: 10.1093/bib/bbv098] [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: 08/25/2015] [Revised: 10/01/2015] [Indexed: 11/13/2022] Open
Abstract
One long-standing research focus in evolutionary genomics is trying to resolve how biological variables (expression, essentiality, protein-protein interaction, structural stability, etc.) determine the rate of protein evolution. While these studies have considerably deepened our understanding of molecular evolution, many issues remain unsolved. In this opinion article, after having a brief survey of literatures, we establish relationships between model parameters of molecular evolution and genomic variables, based on which, most-observed genomic correlations and confounds can be explained by model parameter combinations under different conditions, which include the strength of stabilizing selection, mutational variance, expression sufficiency, gene pleiotropy, as well as the effective population size. We suggest that the problem to discern biological variable(s) that may determine the rate of protein evolution can be tackled at two levels. The first level, as discussed here, is to demonstrate how the model of molecular evolution can predict potential genomic correlations under various conditions. And the second level is to estimate genome-wide variations of model parameters (or combinations) that help to identify canonical biological variables that may underlie the rate variation among genes that ranges up to at least three magnitudes.
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26
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Rangarajan N, Kulkarni P, Hannenhalli S. Evolutionarily conserved network properties of intrinsically disordered proteins. PLoS One 2015; 10:e0126729. [PMID: 25974317 PMCID: PMC4431869 DOI: 10.1371/journal.pone.0126729] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Accepted: 04/07/2015] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Intrinsically disordered proteins (IDPs) lack a stable tertiary structure in isolation. Remarkably, however, a substantial portion of IDPs undergo disorder-to-order transitions upon binding to their cognate partners. Structural flexibility and binding plasticity enable IDPs to interact with a broad range of partners. However, the broader network properties that could provide additional insights into the functional role of IDPs are not known. RESULTS Here, we report the first comprehensive survey of network properties of IDP-induced sub-networks in multiple species from yeast to human. Our results show that IDPs exhibit greater-than-expected modularity and are connected to the rest of the protein interaction network (PIN) via proteins that exhibit the highest betweenness centrality and connect to fewer-than-expected IDP communities, suggesting that they form critical communication links from IDP modules to the rest of the PIN. Moreover, we found that IDPs are enriched at the top level of regulatory hierarchy. CONCLUSION Overall, our analyses reveal coherent and remarkably conserved IDP-centric network properties, namely, modularity in IDP-induced network and a layer of critical nodes connecting IDPs with the rest of the PIN.
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Affiliation(s)
| | - Prakash Kulkarni
- Institute for Bioscience & Biotechnology Research, University of Maryland, Rockville, Maryland, United States of America
| | - Sridhar Hannenhalli
- Center for Bioinformatics and Computational Biology, Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
- * E-mail:
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27
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Zoller S, Boskova V, Anisimova M. Maximum-Likelihood Tree Estimation Using Codon Substitution Models with Multiple Partitions. Mol Biol Evol 2015; 32:2208-16. [PMID: 25911229 DOI: 10.1093/molbev/msv097] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Many protein sequences have distinct domains that evolve with different rates, different selective pressures, or may differ in codon bias. Instead of modeling these differences by more and more complex models of molecular evolution, we present a multipartition approach that allows maximum-likelihood phylogeny inference using different codon models at predefined partitions in the data. Partition models can, but do not have to, share free parameters in the estimation process. We test this approach with simulated data as well as in a phylogenetic study of the origin of the leucin-rich repeat regions in the type III effector proteins of the pythopathogenic bacteria Ralstonia solanacearum. Our study does not only show that a simple two-partition model resolves the phylogeny better than a one-partition model but also gives more evidence supporting the hypothesis of lateral gene transfer events between the bacterial pathogens and its eukaryotic hosts.
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Affiliation(s)
- Stefan Zoller
- Computational Biochemistry Research Group, ETH Zürich, Zürich, Switzerland Swiss Institute of Bioinformatics, Switzerland
| | - Veronika Boskova
- Computational Biochemistry Research Group, ETH Zürich, Zürich, Switzerland
| | - Maria Anisimova
- Institute of Applied Simulations, School of Life Sciences and Facility Management, Zurich University of Applied Sciences, Wädenswil, Switzerland
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28
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Lumba S, Toh S, Handfield LF, Swan M, Liu R, Youn JY, Cutler SR, Subramaniam R, Provart N, Moses A, Desveaux D, McCourt P. A mesoscale abscisic acid hormone interactome reveals a dynamic signaling landscape in Arabidopsis. Dev Cell 2014; 29:360-72. [PMID: 24823379 DOI: 10.1016/j.devcel.2014.04.004] [Citation(s) in RCA: 89] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2013] [Revised: 02/21/2014] [Accepted: 04/01/2014] [Indexed: 12/26/2022]
Abstract
The sesquiterpenoid abscisic acid (ABA) mediates an assortment of responses across a variety of kingdoms including both higher plants and animals. In plants, where most is known, a linear core ABA signaling pathway has been identified. However, the complexity of ABA-dependent gene expression suggests that ABA functions through an intricate network. Here, using systems biology approaches that focused on genes transcriptionally regulated by ABA, we defined an ABA signaling network of over 500 interactions among 138 proteins. This map greatly expanded ABA core signaling but was still manageable for systematic analysis. For example, functional analysis was used to identify an ABA module centered on two sucrose nonfermenting (SNF)-like kinases. We also used coexpression analysis of interacting partners within the network to uncover dynamic subnetwork structures in response to different abiotic stresses. This comprehensive ABA resource allows for application of approaches to understanding ABA functions in higher plants.
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Affiliation(s)
- Shelley Lumba
- Cell & Systems Biology, University of Toronto and the Centre for The Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada
| | - Shigeo Toh
- Cell & Systems Biology, University of Toronto and the Centre for The Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada
| | | | - Michael Swan
- Cell & Systems Biology, University of Toronto and the Centre for The Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada
| | - Raymond Liu
- Cell & Systems Biology, University of Toronto and the Centre for The Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada
| | - Ji-Young Youn
- Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Sean R Cutler
- Botany and Plant Sciences, Chemistry Genomics Building, University of California, Riverside, Riverside, CA 92521, USA
| | | | - Nicholas Provart
- Cell & Systems Biology, University of Toronto and the Centre for The Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada
| | - Alan Moses
- Cell & Systems Biology, University of Toronto and the Centre for The Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Darrell Desveaux
- Cell & Systems Biology, University of Toronto and the Centre for The Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada.
| | - Peter McCourt
- Cell & Systems Biology, University of Toronto and the Centre for The Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON M5S 3B2, Canada.
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Srivastava A, Kumar S, Ramaswamy R. Two-layer modular analysis of gene and protein networks in breast cancer. BMC SYSTEMS BIOLOGY 2014; 8:81. [PMID: 24997799 PMCID: PMC4105126 DOI: 10.1186/1752-0509-8-81] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2014] [Accepted: 06/26/2014] [Indexed: 02/05/2023]
Abstract
Background Genomic, proteomic and high-throughput gene expression data, when integrated, can be used to map the interaction networks between genes and proteins. Different approaches have been used to analyze these networks, especially in cancer, where mutations in biologically related genes that encode mutually interacting proteins are believed to be involved. This system of integrated networks as a whole exhibits emergent biological properties that are not obvious at the individual network level. We analyze the system in terms of modules, namely a set of densely interconnected nodes that can be further divided into submodules that are expected to participate in multiple biological activities in coordinated manner. Results In the present work we construct two layers of the breast cancer network: the gene layer, where the correlation network of breast cancer genes is analyzed to identify gene modules, and the protein layer, where each gene module is extended to map out the network of expressed proteins and their interactions in order to identify submodules. Each module and its associated submodules are analyzed to test the robustness of their topological distribution. The constituent biological phenomena are explored through the use of the Gene Ontology. We thus construct a “network of networks”, and demonstrate that both the gene and protein interaction networks are modular in nature. By focusing on the ontological classification, we are able to determine the entire GO profiles that are distributed at different levels of hierarchy. Within each submodule most of the proteins are biologically correlated, and participate in groups of distinct biological activities. Conclusions The present approach is an effective method for discovering coherent gene modules and protein submodules. We show that this also provides a means of determining biological pathways (both novel and as well those that have been reported previously) that are related, in the present instance, to breast cancer. Similar strategies are likely to be useful in the analysis of other diseases as well.
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Affiliation(s)
- Alok Srivastava
- C R RAO Advanced Institute of Mathematics, Statistics and Computer Science, University of Hyderabad Campus, Hyderabad 500046, India.
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Singh PK, Shakya M. Comparative evolutionary analysis of cell cycle proteins networks in fission and budding yeast. Cell Biochem Biophys 2014; 70:1167-75. [PMID: 24906232 DOI: 10.1007/s12013-014-0037-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Fission yeast and budding yeast are the two distantly related species with common ancestors. Various studies have shown significant differences in metabolic networks and regulatory networks. Cell cycle regulatory proteins in both species have differences in structural as well as in functional organization. Orthologous proteins in cell cycle regulatory protein networks seem to play contemporary role in both species during the evolution but little is known about non-orthologous proteins. Here, we used system biology approach to compare topological parameters of orthologous and non-orthologous proteins to find their contributions during the evolution to make an efficient cell cycle regulation. Observed results have shown a significant role of non-orthologous proteins in fission yeast in maintaining the efficiency of cell cycle regulation with less number of proteins as compared to budding yeast.
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Affiliation(s)
- Praveen K Singh
- Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal, India,
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31
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Papakostas S, Vøllestad LA, Bruneaux M, Aykanat T, Vanoverbeke J, Ning M, Primmer CR, Leder EH. Gene pleiotropy constrains gene expression changes in fish adapted to different thermal conditions. Nat Commun 2014; 5:4071. [PMID: 24892934 PMCID: PMC4059932 DOI: 10.1038/ncomms5071] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Accepted: 05/08/2014] [Indexed: 02/07/2023] Open
Abstract
Understanding the factors that shape the evolution of gene expression is a central goal in biology, but the molecular mechanisms behind this remain controversial. A related major goal is ascertaining how such factors may affect the adaptive potential of a species or population. Here we demonstrate that temperature-driven gene expression changes in fish adapted to differing thermal environments are constrained by the level of gene pleiotropy estimated by either the number of protein interactions or gene biological processes. Genes with low pleiotropy levels were the main drivers of both plastic and evolutionary global expression profile changes, while highly pleiotropic genes had limited expression response to temperature treatment. Our study provides critical insights into the molecular mechanisms by which natural populations can adapt to changing environments. In addition to having important implications for climate change adaptation, these results suggest that gene pleiotropy should be considered more carefully when interpreting expression profiling data. The factors that shape the evolution of gene expression and their role in adaptation are poorly understood. Here, Papakostas et al. show that gene pleiotropy constrains protein expression evolution in freshwater salmonids adapted to different temperatures.
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Affiliation(s)
- Spiros Papakostas
- Division of Genetics and Physiology, Department of Biology, University of Turku, Pharmacity, Itäinen Pitkäkatu 4, 20520 Turku, Finland
| | - L Asbjørn Vøllestad
- Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, P.O. Box 1066 Blindern, NO-0316 Oslo, Norway
| | - Matthieu Bruneaux
- Division of Genetics and Physiology, Department of Biology, University of Turku, Pharmacity, Itäinen Pitkäkatu 4, 20520 Turku, Finland
| | - Tutku Aykanat
- Division of Genetics and Physiology, Department of Biology, University of Turku, Pharmacity, Itäinen Pitkäkatu 4, 20520 Turku, Finland
| | - Joost Vanoverbeke
- Laboratory of Aquatic Ecology, Evolution and Conservation, Department of Biology, KU Leuven, Ch. Deberiotstraat 32, 3000 Leuven, Belgium
| | - Mei Ning
- 1] Division of Genetics and Physiology, Department of Biology, University of Turku, Pharmacity, Itäinen Pitkäkatu 4, 20520 Turku, Finland [2]
| | - Craig R Primmer
- 1] Division of Genetics and Physiology, Department of Biology, University of Turku, Pharmacity, Itäinen Pitkäkatu 4, 20520 Turku, Finland [2]
| | - Erica H Leder
- 1] Division of Genetics and Physiology, Department of Biology, University of Turku, Pharmacity, Itäinen Pitkäkatu 4, 20520 Turku, Finland [2]
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Feiglin A, Ashkenazi S, Schlessinger A, Rost B, Ofran Y. Co-expression and co-localization of hub proteins and their partners are encoded in protein sequence. MOLECULAR BIOSYSTEMS 2014; 10:787-94. [PMID: 24457447 DOI: 10.1039/c3mb70411d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Spatiotemporal coordination is a critical factor in biological processes. Some hubs in protein-protein interaction networks tend to be co-expressed and co-localized with their partners more strongly than others, a difference which is arguably related to functional differences between the hubs. Based on numerous analyses of yeast hubs, it has been suggested that differences in co-expression and co-localization are reflected in the structural and molecular characteristics of the hubs. We hypothesized that if indeed differences in co-expression and co-localization are encoded in the molecular characteristics of the protein, it may be possible to predict the tendency for co-expression and co-localization of human hubs based on features learned from systematically characterized yeast hubs. Thus, we trained a prediction algorithm on hubs from yeast that were classified as either strongly or weakly co-expressed and co-localized with their partners, and applied the trained model to 800 human hub proteins. We found that the algorithm significantly distinguishes between human hubs that are co-expressed and co-localized with their partners and hubs that are not. The prediction is based on sequence derived features such as "stickiness", i.e. the existence of multiple putative binding sites that enable multiple simultaneous interactions, "plasticity", i.e. the existence of predicted structural disorder which conjecturally allows for multiple consecutive interactions with the same binding site and predicted subcellular localization. These results suggest that spatiotemporal dynamics is encoded, at least in part, in the amino acid sequence of the protein and that this encoding is similar in yeast and in human.
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Affiliation(s)
- Ariel Feiglin
- The Goodman faculty of life sciences, Bar Ilan University, Ramat Gan 52900, Israel.
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Hasan MM, Brocca S, Sacco E, Spinelli M, Papaleo E, Lambrughi M, Alberghina L, Vanoni M. A comparative study of Whi5 and retinoblastoma proteins: from sequence and structure analysis to intracellular networks. Front Physiol 2014; 4:315. [PMID: 24478706 PMCID: PMC3897220 DOI: 10.3389/fphys.2013.00315] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Accepted: 10/13/2013] [Indexed: 11/18/2022] Open
Abstract
Cell growth and proliferation require a complex series of tight-regulated and well-orchestrated events. Accordingly, proteins governing such events are evolutionary conserved, even among distant organisms. By contrast, it is more singular the case of “core functions” exerted by functional analogous proteins that are not homologous and do not share any kind of structural similarity. This is the case of proteins regulating the G1/S transition in higher eukaryotes–i.e., the retinoblastoma (Rb) tumor suppressor Rb—and budding yeast, i.e., Whi5. The interaction landscape of Rb and Whi5 is quite large, with more than one hundred proteins interacting either genetically or physically with each protein. The Whi5 interactome has been used to construct a concept map of Whi5 function and regulation. Comparison of physical and genetic interactors of Rb and Whi5 allows highlighting a significant core of conserved, common functionalities associated with the interactors indicating that structure and function of the network—rather than individual proteins—are conserved during evolution. A combined bioinformatics and biochemical approach has shown that the whole Whi5 protein is highly disordered, except for a small region containing the protein family signature. The comparison with Whi5 homologs from Saccharomycetales has prompted the hypothesis of a modular organization of structural disorder, with most evolutionary conserved regions alternating with highly variable ones. The finding of a consensus sequence points to the conservation of a specific phosphorylation rhythm along with two disordered sequence motifs, probably acting as phosphorylation-dependent seeds in Whi5 folding/unfolding. Thus, the widely disordered Whi5 appears to act as a hierarchical, “date hub” that has evolutionary assayed an original way of modular organization before being supplanted by the globular, multi-domain structured Rb, more suitable to cover the role of a “party hub”.
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Affiliation(s)
- Md Mehedi Hasan
- SYSBIO Centre for Systems Biology Milano, Italy ; Department of Biotechnology and Biosciences, University of Milano-Bicocca Milano, Italy
| | - Stefania Brocca
- SYSBIO Centre for Systems Biology Milano, Italy ; Department of Biotechnology and Biosciences, University of Milano-Bicocca Milano, Italy
| | - Elena Sacco
- SYSBIO Centre for Systems Biology Milano, Italy ; Department of Biotechnology and Biosciences, University of Milano-Bicocca Milano, Italy
| | - Michela Spinelli
- SYSBIO Centre for Systems Biology Milano, Italy ; Department of Biotechnology and Biosciences, University of Milano-Bicocca Milano, Italy
| | - Elena Papaleo
- Department of Biotechnology and Biosciences, University of Milano-Bicocca Milano, Italy
| | - Matteo Lambrughi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca Milano, Italy
| | - Lilia Alberghina
- SYSBIO Centre for Systems Biology Milano, Italy ; Department of Biotechnology and Biosciences, University of Milano-Bicocca Milano, Italy
| | - Marco Vanoni
- SYSBIO Centre for Systems Biology Milano, Italy ; Department of Biotechnology and Biosciences, University of Milano-Bicocca Milano, Italy
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Wodak SJ, Vlasblom J, Turinsky AL, Pu S. Protein–protein interaction networks: the puzzling riches. Curr Opin Struct Biol 2013; 23:941-53. [DOI: 10.1016/j.sbi.2013.08.002] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2013] [Revised: 07/14/2013] [Accepted: 08/08/2013] [Indexed: 12/13/2022]
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Chang X, Xu T, Li Y, Wang K. Dynamic modular architecture of protein-protein interaction networks beyond the dichotomy of 'date' and 'party' hubs. Sci Rep 2013; 3:1691. [PMID: 23603706 PMCID: PMC3631766 DOI: 10.1038/srep01691] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2012] [Accepted: 03/26/2013] [Indexed: 02/07/2023] Open
Abstract
The protein-protein interaction (PPI) networks are dynamically organized as modules, and are typically described by hub dichotomy: 'party' hubs act as intramodule hubs and are coexpressed with their partners, yet 'date' hubs act as coordinators among modules and are incoherently expressed with their partners. However, there remains skepticism about the existence of hub dichotomy. Since different algorithms and data sets were used in previous studies to test the model of hub classification, the conclusions may be largely influenced by the potential inherent biases. In this study, we evaluated two data sets of yeast interactome, and systematically investigated the behavior of hubs from multiple perspectives including co-expression patterns, topological roles and functional classifications. Our results revealed consistency between the two data sets, confirming the presence of hub dichotomy. Furthermore, we analyzed a human interactome data set, and demonstrated that the modular architecture of the PPI networks was more complicated than hub dichotomy.
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Affiliation(s)
- Xiao Chang
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, USA
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36
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Simple topological features reflect dynamics and modularity in protein interaction networks. PLoS Comput Biol 2013; 9:e1003243. [PMID: 24130468 PMCID: PMC3794914 DOI: 10.1371/journal.pcbi.1003243] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Accepted: 08/14/2013] [Indexed: 11/30/2022] Open
Abstract
The availability of large-scale protein-protein interaction networks for numerous organisms provides an opportunity to comprehensively analyze whether simple properties of proteins are predictive of the roles they play in the functional organization of the cell. We begin by re-examining an influential but controversial characterization of the dynamic modularity of the S. cerevisiae interactome that incorporated gene expression data into network analysis. We analyse the protein-protein interaction networks of five organisms, S. cerevisiae, H. sapiens, D. melanogaster, A. thaliana, and E. coli, and confirm significant and consistent functional and structural differences between hub proteins that are co-expressed with their interacting partners and those that are not, and support the view that the former tend to be intramodular whereas the latter tend to be intermodular. However, we also demonstrate that in each of these organisms, simple topological measures are significantly correlated with the average co-expression of a hub with its partners, independent of any classification, and therefore also reflect protein intra- and inter- modularity. Further, cross-interactomic analysis demonstrates that these simple topological characteristics of hub proteins tend to be conserved across organisms. Overall, we give evidence that purely topological features of static interaction networks reflect aspects of the dynamics and modularity of interactomes as well as previous measures incorporating expression data, and are a powerful means for understanding the dynamic roles of hubs in interactomes. A better understanding of protein interaction networks would be a great aid in furthering our knowledge of the molecular biology of the cell. Towards this end, large-scale protein-protein physical interaction data have been determined for organisms across the evolutionary spectrum. However, the resulting networks give a static view of interactomes, and our knowledge about protein interactions is rarely time or context specific. A previous prominent but controversial attempt to characterize the dynamic modularity of the interactome was based on integrating physical interaction data with gene activity measurements from transcript expression data. This analysis distinguished between proteins that are co-expressed with their interacting partners and those that are not, and argued that the former are intramodular and the latter are intermodular. By analyzing the interactomes of five organisms, we largely confirm the biological significance of this characterization through a variety of statistical tests and computational experiments. Surprisingly, however, we find that similar results can be obtained using just network information without additionally integrating expression data, suggesting that purely topological characteristics of interaction networks strongly reflect certain aspects of the dynamics and modularity of interactomes.
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Gyurkó DM, Veres DV, Módos D, Lenti K, Korcsmáros T, Csermely P. Adaptation and learning of molecular networks as a description of cancer development at the systems-level: Potential use in anti-cancer therapies. Semin Cancer Biol 2013; 23:262-9. [DOI: 10.1016/j.semcancer.2013.06.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Chen G, Chen J, Shi C, Shi L, Tong W, Shi T. Dissecting the Characteristics and Dynamics of Human Protein Complexes at Transcriptome Cascade Using RNA-Seq Data. PLoS One 2013; 8:e66521. [PMID: 23824284 PMCID: PMC3688907 DOI: 10.1371/journal.pone.0066521] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Accepted: 05/06/2013] [Indexed: 11/19/2022] Open
Abstract
Human protein complexes play crucial roles in various biological processes as the functional module. However, the expression features of human protein complexes at the transcriptome cascade are poorly understood. Here, we used the RNA-Seq data from 16 disparate tissues and four types of human cancers to explore the characteristics and dynamics of human protein complexes. We observed that many individual components of human protein complexes can be generated by multiple distinct transcripts. Similar with yeast, the human protein complex constituents are inclined to co-express in diverse tissues. The dominant isoform of the genes involved in protein complexes tend to encode the complex constituents in each tissue. Our results indicate that the protein complex dynamics not only correlate with the presence or absence of complexes, but may also be related to the major isoform switching for complex subunits. Between any two cancers of breast, colon, lung and prostate, we found that only a few of the differentially expressed transcripts associated with complexes were identical, but 5-10 times more protein complexes involved in differentially expressed transcripts were common. Collectively, our study reveals novel properties and dynamics of human protein complexes at the transcriptome cascade in diverse normal tissues and different cancers.
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Affiliation(s)
- Geng Chen
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Jiwei Chen
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Caiping Shi
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Leming Shi
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, United States of America
| | - Weida Tong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, United States of America
| | - Tieliu Shi
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
- * E-mail:
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Alvarez-Ponce D, Fares MA. Evolutionary rate and duplicability in the Arabidopsis thaliana protein-protein interaction network. Genome Biol Evol 2013; 4:1263-74. [PMID: 23160177 PMCID: PMC3542556 DOI: 10.1093/gbe/evs101] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Genes show a bewildering variation in their patterns of molecular evolution, as a result of the action of different levels and types of selective forces. The factors underlying this variation are, however, still poorly understood. In the last decade, the position of proteins in the protein-protein interaction network has been put forward as a determinant factor of the evolutionary rate and duplicability of their encoding genes. This conclusion, however, has been based on the analysis of the limited number of microbes and animals for which interactome-level data are available (essentially, Escherichia coli, yeast, worm, fly, and humans). Here, we study, for the first time, the relationship between the position of proteins in the high-density interactome of a plant (Arabidopsis thaliana) and the patterns of molecular evolution of their encoding genes. We found that genes whose encoded products act at the center of the network are more evolutionarily constrained than those acting at the network periphery. This trend remains significant when potential confounding factors (gene expression level and breadth, duplicability, function, and length of the encoded products) are controlled for. Even though the correlation between centrality measures and rates of evolution is generally weak, for some functional categories, it is comparable in strength to (or even stronger than) the correlation between evolutionary rates and expression levels or breadths. In addition, genes encoding interacting proteins in the network evolve at relatively similar rates. Finally, Arabidopsis proteins encoded by duplicated genes are more highly connected than those encoded by singleton genes. This observation is in agreement with the patterns observed in humans, but in contrast with those observed in E. coli, yeast, worm, and fly (whose duplicated genes tend to act at the periphery of the network), implying that the relationship between duplicability and centrality inverted at least twice during eukaryote evolution. Taken together, these results indicate that the structure of the A. thaliana network constrains the evolution of its components at multiple levels.
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Affiliation(s)
- David Alvarez-Ponce
- Department of Abiotic Stress, Integrative and Systems Biology Laboratory, Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicias (CSIC-UPV), Valencia, Spain.
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Bertolazzi P, Bock ME, Guerra C. On the functional and structural characterization of hubs in protein–protein interaction networks. Biotechnol Adv 2013; 31:274-86. [DOI: 10.1016/j.biotechadv.2012.12.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2012] [Revised: 11/13/2012] [Accepted: 12/01/2012] [Indexed: 01/07/2023]
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Ferreira RM, Rybarczyk-Filho JL, Dalmolin RJS, Castro MAA, Moreira JCF, Brunnet LG, de Almeida RMC. Preferential duplication of intermodular hub genes: an evolutionary signature in eukaryotes genome networks. PLoS One 2013; 8:e56579. [PMID: 23468868 PMCID: PMC3582557 DOI: 10.1371/journal.pone.0056579] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2012] [Accepted: 01/14/2013] [Indexed: 12/31/2022] Open
Abstract
Whole genome protein-protein association networks are not random and their topological properties stem from genome evolution mechanisms. In fact, more connected, but less clustered proteins are related to genes that, in general, present more paralogs as compared to other genes, indicating frequent previous gene duplication episodes. On the other hand, genes related to conserved biological functions present few or no paralogs and yield proteins that are highly connected and clustered. These general network characteristics must have an evolutionary explanation. Considering data from STRING database, we present here experimental evidence that, more than not being scale free, protein degree distributions of organisms present an increased probability for high degree nodes. Furthermore, based on this experimental evidence, we propose a simulation model for genome evolution, where genes in a network are either acquired de novo using a preferential attachment rule, or duplicated with a probability that linearly grows with gene degree and decreases with its clustering coefficient. For the first time a model yields results that simultaneously describe different topological distributions. Also, this model correctly predicts that, to produce protein-protein association networks with number of links and number of nodes in the observed range for Eukaryotes, it is necessary 90% of gene duplication and 10% of de novo gene acquisition. This scenario implies a universal mechanism for genome evolution.
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Affiliation(s)
- Ricardo M. Ferreira
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Rodrigo J. S. Dalmolin
- Departamento de Bioquímica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Mauro A. A. Castro
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- National Institute of Science and Technology for Complex Systems, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - José C. F. Moreira
- Departamento de Bioquímica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Leonardo G. Brunnet
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Rita M. C. de Almeida
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- National Institute of Science and Technology for Complex Systems, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- * E-mail:
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Liu J, Zhao L, Li G, Xiong S, Nan J, Li J, Yuan K, von Deneen KM, Liang F, Qin W, Tian J. Hierarchical alteration of brain structural and functional networks in female migraine sufferers. PLoS One 2012; 7:e51250. [PMID: 23227257 PMCID: PMC3515541 DOI: 10.1371/journal.pone.0051250] [Citation(s) in RCA: 84] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2012] [Accepted: 10/30/2012] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Little is known about the changes of brain structural and functional connectivity networks underlying the pathophysiology in migraine. We aimed to investigate how the cortical network reorganization is altered by frequent cortical overstimulation associated with migraine. METHODOLOGY/PRINCIPAL FINDINGS Gray matter volumes and resting-state functional magnetic resonance imaging signal correlations were employed to construct structural and functional networks between brain regions in 43 female patients with migraine (PM) and 43 gender-matched healthy controls (HC) by using graph theory-based approaches. Compared with the HC group, the patients showed abnormal global topology in both structural and functional networks, characterized by higher mean clustering coefficients without significant change in the shortest absolute path length, which indicated that the PM lost optimal topological organization in their cortical networks. Brain hubs related to pain-processing revealed abnormal nodal centrality in both structural and functional networks, including the precentral gyrus, orbital part of the inferior frontal gyrus, parahippocampal gyrus, anterior cingulate gyrus, thalamus, temporal pole of the middle temporal gyrus and the inferior parietal gyrus. Negative correlations were found between migraine duration and regions with abnormal centrality. Furthermore, the dysfunctional connections in patients' cortical networks formed into a connected component and three dysregulated modules were identified involving pain-related information processing and motion-processing visual networks. CONCLUSIONS Our results may reflect brain alteration dynamics resulting from migraine and suggest that long-term and high-frequency headache attacks may cause both structural and functional connectivity network reorganization. The disrupted information exchange between brain areas in migraine may be reshaped into a hierarchical modular structure progressively.
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Affiliation(s)
- Jixin Liu
- School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Ling Zhao
- The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Guoying Li
- School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Shiwei Xiong
- School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Jiaofen Nan
- School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Jing Li
- School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Kai Yuan
- School of Life Sciences and Technology, Xidian University, Xi'an, China
| | | | - Fanrong Liang
- The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Wei Qin
- School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Jie Tian
- School of Life Sciences and Technology, Xidian University, Xi'an, China
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Panda A, Begum T, Ghosh TC. Insights into the evolutionary features of human neurodegenerative diseases. PLoS One 2012; 7:e48336. [PMID: 23118989 PMCID: PMC3484049 DOI: 10.1371/journal.pone.0048336] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2012] [Accepted: 09/24/2012] [Indexed: 02/06/2023] Open
Abstract
Comparative analyses between human disease and non-disease genes are of great interest in understanding human disease gene evolution. However, the progression of neurodegenerative diseases (NDD) involving amyloid formation in specific brain regions is still unknown. Therefore, in this study, we mainly focused our analysis on the evolutionary features of human NDD genes with respect to non-disease genes. Here, we observed that human NDD genes are evolutionarily conserved relative to non-disease genes. To elucidate the conserved nature of NDD genes, we incorporated the evolutionary attributes like gene expression level, number of regulatory miRNAs, protein connectivity, intrinsic disorder content and relative aggregation propensity in our analysis. Our studies demonstrate that NDD genes have higher gene expression levels in favor of their lower evolutionary rates. Additionally, we observed that NDD genes have higher number of different regulatory miRNAs target sites and also have higher interaction partners than the non-disease genes. Moreover, miRNA targeted genes are known to have higher disorder content. In contrast, our analysis exclusively established that NDD genes have lower disorder content. In favor of our analysis, we found that NDD gene encoded proteins are enriched with multi interface hubs (party hubs) with lower disorder contents. Since, proteins with higher disorder content need to adapt special structure to reduce their aggregation propensity, NDD proteins found to have elevated relative aggregation propensity (RAP) in support of their lower disorder content. Finally, our categorical regression analysis confirmed the underlined relative dominance of protein connectivity, 3'UTR length, RAP, nature of hubs (singlish/multi interface) and disorder content for such evolutionary rates variation between human NDD genes and non-disease genes.
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Affiliation(s)
- Arup Panda
- Bioinformatics Centre, Bose Institute, Kolkata, India
| | - Tina Begum
- Bioinformatics Centre, Bose Institute, Kolkata, India
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Rorick M. Quantifying protein modularity and evolvability: a comparison of different techniques. Biosystems 2012; 110:22-33. [PMID: 22796584 DOI: 10.1016/j.biosystems.2012.06.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2011] [Revised: 06/20/2012] [Accepted: 06/27/2012] [Indexed: 10/28/2022]
Abstract
Modularity increases evolvability by reducing constraints on adaptation and by allowing preexisting parts to function in new contexts for novel uses. Protein evolution provides an excellent context to study the causes and consequences of biological modularity. In order to address such questions, however, an index for protein modularity is necessary. This paper proposes a simple index for protein modularity-"module density"-which is the number of evolutionarily independent modules that compose a protein divided by the number of amino acids in the protein. The decomposition of proteins into constituent modules can be accomplished by either of two classes of methods. The first class of methods relies on "suppositional" criteria to assign amino acids to modules, whereas the second class of methods relies on "coevolutionary" criteria for this task. One simple and practical method from the first class consists of approximating the number of modules in a protein as the number of regular secondary structure elements (i.e., helices and sheets). Methods based on coevolutionary criteria require more elaborate data, but they have the advantage of being able to specify modules without prior assumptions about why they exist. Given the increasing availability of datasets sampling protein mutational spectra (e.g., from comparative genomics, experimental evolution, and computational prediction), methods based on coevolutionary criteria will likely become more promising in the near future. The ability to meaningfully quantify protein modularity via simple indices has the potential to aid future efforts to understand protein evolutionary rate determinants, improve molecular evolution models and engineer novel proteins.
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Affiliation(s)
- Mary Rorick
- University of Michigan, Department of Ecology and Evolutionary Biology, Ann Arbor, MI 48109-1048, United States.
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Affiliation(s)
- Ian W. Taylor
- Samuel Lunenfeld Research Institute; Mount Sinai Hospital; Toronto Ontario Canada
- Department of Molecular Genetics; University of Toronto; Toronto Ontario Canada
| | - Jeffrey L. Wrana
- Samuel Lunenfeld Research Institute; Mount Sinai Hospital; Toronto Ontario Canada
- Department of Molecular Genetics; University of Toronto; Toronto Ontario Canada
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Li W, Wang R, Bai L, Yan Z, Sun Z. Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level. BMC SYSTEMS BIOLOGY 2012; 6:64. [PMID: 22691569 PMCID: PMC3443057 DOI: 10.1186/1752-0509-6-64] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2012] [Accepted: 06/12/2012] [Indexed: 02/04/2023]
Abstract
BACKGROUND Identification of driver mutations among numerous genomic alternations remains a critical challenge to the elucidation of the underlying mechanisms of cancer. Because driver mutations by definition are associated with a greater number of cancer phenotypes compared to other mutations, we hypothesized that driver mutations could more easily be identified once the genotype-phenotype correlations are detected across tumor samples. RESULTS In this study, we describe a novel network analysis to identify the driver mutation through integrating both cancer genomes and transcriptomes. Our method successfully identified a significant genotype-phenotype change correlation in all six solid tumor types and revealed core modules that contain both significantly enriched somatic mutations and aberrant expression changes specific to tumor development. Moreover, we found that the majority of these core modules contained well known cancer driver mutations, and that their mutated genes tended to occur at hub genes with central regulatory roles. In these mutated genes, the majority were cancer-type specific and exhibited a closer relationship within the same cancer type rather than across cancer types. The remaining mutated genes that exist in multiple cancer types led to two cancer type clusters, one cluster consisted of three neural derived or related cancer types, and the other cluster consisted of two adenoma cancer types. CONCLUSIONS Our approach can successfully identify the candidate drivers from the core modules. Comprehensive network analysis on the core modules potentially provides critical insights into convergent cancer development in different organs.
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Affiliation(s)
- Wenting Li
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Institute of Bioinformatics and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
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Chen FC, Liao BY, Pan CL, Lin HY, Chang AYF. Assessing determinants of exonic evolutionary rates in mammals. Mol Biol Evol 2012; 29:3121-9. [PMID: 22504521 DOI: 10.1093/molbev/mss116] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
From studies investigating the differences in evolutionary rates between genes, gene compactness and gene expression level have been identified as important determinants of gene-level protein evolutionary rate, as represented by nonsynonymous to synonymous substitution rate (d(N)/d(S)) ratio. However, the causes of exon-level variances in d(N)/d(S) are less understood. Here, we use principal component regression to examine to what extent 13 exon features explain the variance in d(N), d(S), and the d(N)/d(S) ratio of human-rhesus macaque or human-mouse orthologous exons. The exon features were grouped into six functional categories: expression features, mRNA splicing features, structural-functional features, compactness features, exon duplicability, and other features, including G + C content and exon length. Although expression features are important for determining d(N) and d(N)/d(S) between exons of different genes, structural-functional features and splicing features explained more of the variance for exons of the same genes. Furthermore, we show that compactness features can explain only a relatively small percentage of variance in exon-level d(N) or d(N)/d(S) in either between-gene or within-gene comparison. By contrast, d(S) yielded inconsistent results in the human-mouse comparison and the human-rhesus macaque comparison. This inconsistency may suggest rapid evolutionary changes of the mutation landscape in mammals. Our results suggest that between-gene and within-gene variation in d(N)/d(S) (and d(N)) are driven by different evolutionary forces and that the role of mRNA splicing in causing the variation in evolutionary rates of coding sequences may be underappreciated.
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Affiliation(s)
- Feng-Chi Chen
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli County, Taiwan, Republic of China.
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Musso G, Emili A, Zhang Z. Characterization and evolutionary analysis of protein-protein interaction networks. Methods Mol Biol 2012; 856:363-380. [PMID: 22399467 DOI: 10.1007/978-1-61779-585-5_15] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
While researchers have known the importance of the protein-protein interaction for decades, recent innovations in large-scale screening techniques have caused a shift in the paradigm of protein function analysis. Where the focus was once on the individual protein, attention is now directed to the surrounding network of protein associations. As protein interaction networks can provide useful insights into the potential function of and phenotypes associated with proteins, the increasing availability of large-scale protein interaction data suggests that molecular biologists can extract more meaningful hypotheses through examination of these large networks. Further, increasing availability of high-quality protein interaction data in multiple species has allowed interpretation of the properties of networks (i.e., the presence of hubs and modularity) from an evolutionary perspective. In this chapter, we discuss major previous findings derived from analyses of large-scale protein interaction data, focusing on approaches taken by landmark assays in evaluating the structure and evolution of these networks. We then outline basic techniques for protein interaction network analysis with the goal of pointing out the benefits and potential limitations of these approaches. As the majority of large-scale protein interaction data has been generated in budding yeast, literature described here focuses on this important model organism with references to other species included where possible.
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Affiliation(s)
- Gabriel Musso
- Cardiovascular Division, Brigham & Women's Hospital, Boston, MA, USA.
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Building Synthetic Systems to Learn Nature’s Design Principles. EVOLUTIONARY SYSTEMS BIOLOGY 2012; 751:411-29. [DOI: 10.1007/978-1-4614-3567-9_19] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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