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Vila JA. Analysis of proteins in the light of mutations. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2024:10.1007/s00249-024-01714-y. [PMID: 38955858 DOI: 10.1007/s00249-024-01714-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/23/2024] [Accepted: 06/18/2024] [Indexed: 07/04/2024]
Abstract
Proteins have evolved through mutations-amino acid substitutions-since life appeared on Earth, some 109 years ago. The study of these phenomena has been of particular significance because of their impact on protein stability, function, and structure. This study offers a new viewpoint on how the most recent findings in these areas can be used to explore the impact of mutations on protein sequence, stability, and evolvability. Preliminary results indicate that: (1) mutations can be viewed as sensitive probes to identify 'typos' in the amino-acid sequence, and also to assess the resistance of naturally occurring proteins to unwanted sequence alterations; (2) the presence of 'typos' in the amino acid sequence, rather than being an evolutionary obstacle, could promote faster evolvability and, in turn, increase the likelihood of higher protein stability; (3) the mutation site is far more important than the substituted amino acid in terms of the marginal stability changes of the protein, and (4) the unpredictability of protein evolution at the molecular level-by mutations-exists even in the absence of epistasis effects. Finally, the Darwinian concept of evolution "descent with modification" and experimental evidence endorse one of the results of this study, which suggests that some regions of any protein sequence are susceptible to mutations while others are not. This work contributes to our general understanding of protein responses to mutations and may spur significant progress in our efforts to develop methods to accurately forecast changes in protein stability, their propensity for metamorphism, and their ability to evolve.
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Affiliation(s)
- Jorge A Vila
- IMASL-CONICET, Universidad Nacional de San Luis, Ejército de los Andes 950, 5700, San Luis, Argentina.
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2
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Schmidlin K, Ogbunugafor CB, Geiler-Samerotte K. Environment by environment interactions (ExE) differ across genetic backgrounds (ExExG). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593194. [PMID: 38766025 PMCID: PMC11100745 DOI: 10.1101/2024.05.08.593194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
While the terms "gene-by-gene interaction" (GxG) and "gene-by-environment interaction" (GxE) are commonplace within the fields of quantitative and evolutionary genetics, "environment-by-environment interaction" (ExE) is a term used less often. In this study, we find that environment-by-environment interactions are a meaningful driver of phenotypes, and that they differ across different genotypes (suggestive of ExExG). To reach this conclusion, we analyzed a large dataset of roughly 1,000 mutant yeast strains with varying degrees of resistance to different antifungal drugs. We show that the effectiveness of a drug combination, relative to single drugs, often varies across different drug resistant mutants. Even mutants that differ by only a single nucleotide change can have dramatically different drug x drug (ExE) interactions. We also introduce a new framework that better predicts the direction and magnitude of ExE interactions for some mutants. Studying how ExE interactions change across genotypes (ExExG) is not only important when modeling the evolution of pathogenic microbes, but also for broader efforts to understand the cell biology underlying these interactions and to resolve the source of phenotypic variance across populations. The relevance of ExExG interactions have been largely omitted from canon in evolutionary and population genetics, but these fields and others stand to benefit from perspectives that highlight how interactions between external forces craft the complex behavior of living systems.
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Zhang F, Naeem M, Yu B, Liu F, Ju J. Improving the enzymatic activity and stability of N-carbamoyl hydrolase using deep learning approach. Microb Cell Fact 2024; 23:164. [PMID: 38834993 PMCID: PMC11151596 DOI: 10.1186/s12934-024-02439-5] [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: 02/14/2024] [Accepted: 05/24/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Optically active D-amino acids are widely used as intermediates in the synthesis of antibiotics, insecticides, and peptide hormones. Currently, the two-enzyme cascade reaction is the most efficient way to produce D-amino acids using enzymes DHdt and DCase, but DCase is susceptible to heat inactivation. Here, to enhance the enzymatic activity and thermal stability of DCase, a rational design software "Feitian" was developed based on kcat prediction using the deep learning approach. RESULTS According to empirical design and prediction of "Feitian" software, six single-point mutants with high kcat value were selected and successfully constructed by site-directed mutagenesis. Out of six, three mutants (Q4C, T212S, and A302C) showed higher enzymatic activity than the wild-type. Furthermore, the combined triple-point mutant DCase-M3 (Q4C/T212S/A302C) exhibited a 4.25-fold increase in activity (29.77 ± 4.52 U) and a 2.25-fold increase in thermal stability as compared to the wild-type, respectively. Through the whole-cell reaction, the high titer of D-HPG (2.57 ± 0.43 mM) was produced by the mutant Q4C/T212S/A302C, which was about 2.04-fold of the wild-type. Molecular dynamics simulation results showed that DCase-M3 significantly enhances the rigidity of the catalytic site and thus increases the activity of DCase-M3. CONCLUSIONS In this study, an efficient rational design software "Feitian" was successfully developed with a prediction accuracy of about 50% in enzymatic activity. A triple-point mutant DCase-M3 (Q4C/T212S/A302C) with enhanced enzymatic activity and thermostability was successfully obtained, which could be applied to the development of a fully enzymatic process for the industrial production of D-HPG.
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Affiliation(s)
- Fa Zhang
- College of Life Science, Hebei Normal University, Shijiazhuang, 050024, China
- Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Muhammad Naeem
- College of Life Science, Hebei Normal University, Shijiazhuang, 050024, China
| | - Bo Yu
- Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Feixia Liu
- Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jiansong Ju
- College of Life Science, Hebei Normal University, Shijiazhuang, 050024, China.
- Hebei Collaborative Innovation Center for Eco-Environment, Shijiazhuang, 050024, China.
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4
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Martin NS, Schaper S, Camargo CQ, Louis AA. Non-Poissonian Bursts in the Arrival of Phenotypic Variation Can Strongly Affect the Dynamics of Adaptation. Mol Biol Evol 2024; 41:msae085. [PMID: 38693911 PMCID: PMC11156200 DOI: 10.1093/molbev/msae085] [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: 11/08/2023] [Revised: 03/01/2024] [Accepted: 04/17/2024] [Indexed: 05/03/2024] Open
Abstract
Modeling the rate at which adaptive phenotypes appear in a population is a key to predicting evolutionary processes. Given random mutations, should this rate be modeled by a simple Poisson process, or is a more complex dynamics needed? Here we use analytic calculations and simulations of evolving populations on explicit genotype-phenotype maps to show that the introduction of novel phenotypes can be "bursty" or overdispersed. In other words, a novel phenotype either appears multiple times in quick succession or not at all for many generations. These bursts are fundamentally caused by statistical fluctuations and other structure in the map from genotypes to phenotypes. Their strength depends on population parameters, being highest for "monomorphic" populations with low mutation rates. They can also be enhanced by additional inhomogeneities in the mapping from genotypes to phenotypes. We mainly investigate the effect of bursts using the well-studied genotype-phenotype map for RNA secondary structure, but find similar behavior in a lattice protein model and in Richard Dawkins's biomorphs model of morphological development. Bursts can profoundly affect adaptive dynamics. Most notably, they imply that fitness differences play a smaller role in determining which phenotype fixes than would be the case for a Poisson process without bursts.
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Affiliation(s)
- Nora S Martin
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, UK
| | - Steffen Schaper
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, UK
| | - Chico Q Camargo
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, UK
- Faculty of Environment, Science and Economy, University of Exeter, Exeter EX4 4QF, UK
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, UK
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5
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Vargas-Pinilla P, S Oliveira Fam B, Medina Tavares G, Lima T, Landau L, Paré P, de Cássia Aleixo Tostes R, Pissinatti A, Falótico T, Costa-Neto C, Maestri R, Bortolini MC. From molecular variations to behavioral adaptations: Unveiling adaptive epistasis in primate oxytocin system. AMERICAN JOURNAL OF BIOLOGICAL ANTHROPOLOGY 2024:e24947. [PMID: 38783700 DOI: 10.1002/ajpa.24947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 04/12/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE Our primary objective was to investigate the variability of oxytocin (OT) and the GAMEN binding motif within the LNPEP oxytocinase in primates. MATERIALS AND METHODS We sequenced the LNPEP segment encompassing the GAMEN motif in 34 Platyrrhini species, with 21 of them also sequenced for the OT gene. Our dataset was supplemented with primate sequences of LNPEP, OT, and the oxytocin receptor (OTR) sourced from public databases. Evolutionary analysis and coevolution predictions were made followed by the macroevolution analysis of relevant amino acids associated with phenotypic traits, such as mating systems, parental care, and litter size. To account for phylogenetic structure, we utilized two distinct statistical tests. Additionally, we calculated binding energies focusing on the interaction between Callithtrix jacchus VAMEN and Pro8OT. RESULTS We identified two novel motifs (AAMEN and VAMEN), challenging the current knowledge of motif conservation in placental mammals. Coevolution analysis demonstrated a correlation between GAMEN, AAMEN, and VAMEN and their corresponding OTs and OTRs. Callithrix jacchus exhibited a higher binding energy between VAMEN and Pro8OT than orthologous molecules found in humans (GAMEN and Leu8OT). DISCUSSION The coevolution of AAMEN and VAMEN with their corresponding OTs and OTRs suggests a functional relationship that could have contributed to specific reproductive and adaptive behaviors, including paternal care, social monogamy, and twin births, prominent traits in Cebidae species, such as marmosets and tamarins. Our findings underscore the coevolution of taxon-specific amino acids among the three studied molecules, shedding light on the oxytocinergic system as an adaptive epistatic repertoire in primates.
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Affiliation(s)
- Pedro Vargas-Pinilla
- Laboratory of Human and Molecular Evolution, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Departamento de Farmacologia, Faculdade de Medicina, Universidade de São Paulo, Ribeirão Preto, Brazil
| | - Bibiana S Oliveira Fam
- Laboratory of Human and Molecular Evolution, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Laboratório de Medicina Genômica, Centro de Pesquisa Experimental (CPE), Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Gustavo Medina Tavares
- Laboratory of Human and Molecular Evolution, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Thaynara Lima
- Laboratory of Human and Molecular Evolution, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Luane Landau
- Laboratory of Human and Molecular Evolution, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Department of Biological Sciences, University at Buffalo, Buffalo, New York, USA
| | - Pâmela Paré
- Laboratory of Human and Molecular Evolution, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | | | - Tiago Falótico
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo, São Paulo, Brazil
| | - Cláudio Costa-Neto
- Departamento de Bioquímica e Imunologia, Faculdade de Medicina, Universidade de São Paulo, Ribeirão Preto, Brazil
| | - Renan Maestri
- Laboratório de Ecomorfologia e Macroevolução, Departamento de Ecologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Maria Cátira Bortolini
- Laboratory of Human and Molecular Evolution, Departamento de Genética, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
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6
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Metzger BPH, Park Y, Starr TN, Thornton JW. Epistasis facilitates functional evolution in an ancient transcription factor. eLife 2024; 12:RP88737. [PMID: 38767330 PMCID: PMC11105156 DOI: 10.7554/elife.88737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024] Open
Abstract
A protein's genetic architecture - the set of causal rules by which its sequence produces its functions - also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest - excluding the vast majority of possible genotypes and evolutionary trajectories - and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor's specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor's capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.
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Affiliation(s)
- Brian PH Metzger
- Department of Ecology and Evolution, University of ChicagoChicagoUnited States
| | - Yeonwoo Park
- Program in Genetics, Genomics, and Systems Biology, University of ChicagoChicagoUnited States
| | - Tyler N Starr
- Department of Biochemistry and Molecular Biophysics, University of ChicagoChicagoUnited States
| | - Joseph W Thornton
- Department of Ecology and Evolution, University of ChicagoChicagoUnited States
- Department of Human Genetics, University of ChicagoChicagoUnited States
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7
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Hsiung CCS, Wilson CM, Sambold NA, Dai R, Chen Q, Teyssier N, Misiukiewicz S, Arab A, O'Loughlin T, Cofsky JC, Shi J, Gilbert LA. Engineered CRISPR-Cas12a for higher-order combinatorial chromatin perturbations. Nat Biotechnol 2024:10.1038/s41587-024-02224-0. [PMID: 38760567 DOI: 10.1038/s41587-024-02224-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 03/28/2024] [Indexed: 05/19/2024]
Abstract
Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting one to three genomic sites per cell. We engineer an Acidaminococcus Cas12a (AsCas12a) variant, multiplexed transcriptional interference AsCas12a (multiAsCas12a), that incorporates R1226A, a mutation that stabilizes the ribonucleoprotein-DNA complex via DNA nicking. The multiAsCas12a-KRAB fusion improves CRISPRi activity over DNase-dead AsCas12a-KRAB fusions, often rescuing the activities of lentivirally delivered CRISPR RNAs (crRNA) that are inactive when used with the latter. multiAsCas12a-KRAB supports CRISPRi using 6-plex crRNA arrays in high-throughput pooled screens. Using multiAsCas12a-KRAB, we discover enhancer elements and dissect the combinatorial function of cis-regulatory elements in human cells. These results instantiate a group testing framework for efficiently surveying numerous combinations of chromatin perturbations for biological discovery and engineering.
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Affiliation(s)
- C C-S Hsiung
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
| | - C M Wilson
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
- Tetrad Graduate Program, University of California, San Francisco, CA, USA
| | | | - R Dai
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Arc Institute, Palo Alto, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Q Chen
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - N Teyssier
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - S Misiukiewicz
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - A Arab
- Arc Institute, Palo Alto, CA, USA
| | - T O'Loughlin
- Department of Urology, University of California, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - J C Cofsky
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - J Shi
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - L A Gilbert
- Department of Urology, University of California, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
- Arc Institute, Palo Alto, CA, USA.
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8
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Faure AJ, Lehner B, Miró Pina V, Serrano Colome C, Weghorn D. An extension of the Walsh-Hadamard transform to calculate and model epistasis in genetic landscapes of arbitrary shape and complexity. PLoS Comput Biol 2024; 20:e1012132. [PMID: 38805561 PMCID: PMC11161127 DOI: 10.1371/journal.pcbi.1012132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 06/07/2024] [Accepted: 05/04/2024] [Indexed: 05/30/2024] Open
Abstract
Accurate models describing the relationship between genotype and phenotype are necessary in order to understand and predict how mutations to biological sequences affect the fitness and evolution of living organisms. The apparent abundance of epistasis (genetic interactions), both between and within genes, complicates this task and how to build mechanistic models that incorporate epistatic coefficients (genetic interaction terms) is an open question. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values (known as genetical, biological or physiological epistasis), and can therefore be used to address fundamental questions related to sequence-to-function encodings. However, one of its main limitations is that it can only accommodate two alleles (amino acid or nucleotide states) per sequence position. In this paper we provide an extension of the Walsh-Hadamard transform that allows the calculation and modeling of background-averaged epistasis (also known as ensemble epistasis) in genetic landscapes with an arbitrary number of states per position (20 for amino acids, 4 for nucleotides, etc.). We also provide a recursive formula for the inverse matrix and then derive formulae to directly extract any element of either matrix without having to rely on the computationally intensive task of constructing or inverting large matrices. Finally, we demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic fitness landscapes, revealing that both pairwise and higher-order genetic interactions are enriched between physically interacting positions.
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Affiliation(s)
- Andre J. Faure
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain
| | - Ben Lehner
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- ICREA, Pg. Lluis Companys 23, Barcelona 08010, Spain
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Verónica Miró Pina
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain
| | - Claudia Serrano Colome
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain
| | - Donate Weghorn
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
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9
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Nguyen TN, Ingle C, Thompson S, Reynolds KA. The genetic landscape of a metabolic interaction. Nat Commun 2024; 15:3351. [PMID: 38637543 PMCID: PMC11026382 DOI: 10.1038/s41467-024-47671-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 04/09/2024] [Indexed: 04/20/2024] Open
Abstract
While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focus on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We use deep mutational scanning to quantify the growth rate effect of 2696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.
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Affiliation(s)
- Thuy N Nguyen
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- The Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- The Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Form Bio, Dallas, TX, 75226, USA
| | - Christine Ingle
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- The Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- The Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Samuel Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, 94158, USA
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Kimberly A Reynolds
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
- The Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
- The Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
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10
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Li J, Bank C. Dominance and multi-locus interaction. Trends Genet 2024; 40:364-378. [PMID: 38453542 DOI: 10.1016/j.tig.2023.12.003] [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: 07/10/2023] [Revised: 12/03/2023] [Accepted: 12/04/2023] [Indexed: 03/09/2024]
Abstract
Dominance is usually considered a constant value that describes the relative difference in fitness or phenotype between heterozygotes and the average of homozygotes at a focal polymorphic locus. However, the observed dominance can vary with the genetic background of the focal locus. Here, alleles at other loci modify the observed phenotype through position effects or dominance modifiers that are sometimes associated with pathogen resistance, lineage, sex, or mating type. Theoretical models have illustrated how variable dominance appears in the context of multi-locus interaction (epistasis). Here, we review empirical evidence for variable dominance and how the observed patterns may be captured by proposed epistatic models. We highlight how integrating epistasis and dominance is crucial for comprehensively understanding adaptation and speciation.
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Affiliation(s)
- Juan Li
- Institute of Ecology and Evolution, University of Bern, Bern, Switzerland; Swiss Institute for Bioinformatics, Lausanne, Switzerland.
| | - Claudia Bank
- Institute of Ecology and Evolution, University of Bern, Bern, Switzerland; Swiss Institute for Bioinformatics, Lausanne, Switzerland
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11
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Seitz EE, McCandlish DM, Kinney JB, Koo PK. Interpreting cis-regulatory mechanisms from genomic deep neural networks using surrogate models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.14.567120. [PMID: 38013993 PMCID: PMC10680760 DOI: 10.1101/2023.11.14.567120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Deep neural networks (DNNs) have greatly advanced the ability to predict genome function from sequence. Interpreting genomic DNNs in terms of biological mechanisms, however, remains difficult. Here we introduce SQUID, a genomic DNN interpretability framework based on surrogate modeling. SQUID approximates genomic DNNs in user-specified regions of sequence space using surrogate models, i.e., simpler models that are mechanistically interpretable. Importantly, SQUID removes the confounding effects that nonlinearities and heteroscedastic noise in functional genomics data can have on model interpretation. Benchmarking analysis on multiple genomic DNNs shows that SQUID, when compared to established interpretability methods, identifies motifs that are more consistent across genomic loci and yields improved single-nucleotide variant-effect predictions. SQUID also supports surrogate models that quantify epistatic interactions within and between cis-regulatory elements. SQUID thus advances the ability to mechanistically interpret genomic DNNs.
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Affiliation(s)
- Evan E Seitz
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - David M McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Justin B Kinney
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Peter K Koo
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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12
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Santiago-Collazo G, Brown PJB, Randich AM. The divergent early divisome: is there a functional core? Trends Microbiol 2024; 32:231-240. [PMID: 37741788 DOI: 10.1016/j.tim.2023.08.010] [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: 06/29/2023] [Revised: 08/21/2023] [Accepted: 08/23/2023] [Indexed: 09/25/2023]
Abstract
The bacterial divisome is a complex nanomachine that drives cell division and separation. The essentiality of these processes leads to the assumption that proteins with core roles will be strictly conserved across all bacterial genomes. However, recent studies in diverse proteobacteria have revealed considerable variation in the early divisome compared with Escherichia coli. While some proteins are highly conserved, their specific functions and interacting partners vary. Meanwhile, different subphyla use clade-specific proteins with analogous functions. Thus, instead of focusing on gene conservation, we must also explore how key functions are maintained during early division by diverging protein networks. An enhanced awareness of these complex genetic networks will clarify the physical and evolutionary constraints of bacterial division.
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Affiliation(s)
- Gustavo Santiago-Collazo
- Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri-Columbia, Columbia, MO, USA
| | - Pamela J B Brown
- Division of Biological Sciences, College of Arts and Sciences, University of Missouri-Columbia, Columbia, MO, USA
| | - Amelia M Randich
- Department of Biology, College of Arts and Sciences, University of Scranton, Scranton, PA, USA.
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13
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Thean LF, Wong M, Lo M, Tan I, Wong E, Gao F, Tan E, Tang CL, Cheah PY. Functional annotation with expression validation identifies novel metastasis-relevant genes from post-GWAS risk loci in sporadic colorectal carcinomas. J Med Genet 2024; 61:276-283. [PMID: 37890997 DOI: 10.1136/jmg-2023-109517] [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: 07/17/2023] [Accepted: 09/25/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND Colorectal cancer (CRC) is the third highest incidence cancer and is the leading cause of cancer mortality worldwide. Metastasis to distal organ is the major cause of cancer mortality. However, the underlying genetic factors are unclear. This study aimed to identify metastasis-relevant genes and pathways for better management of metastasis-prone patients. METHODS A case-case genome-wide association study comprising 2677 sporadic Chinese CRC cases (1282 metastasis-positive vs 1395 metastasis-negative) was performed using the Human SNP6 microarray platform and analysed with the correlation/trend test based on the additive model. SNP variants with association testing -log10 p value ≥5 were imported into Functional Mapping and Annotation (FUMA) for functional annotation. RESULTS Glycolysis was uncovered as the top hallmark gene set. Transcripts from two of the five genes profiled, hematopoietic substrate 1 associated protein X 1 (HAX1) and hyaluronan-mediatedmotility receptor (HMMR), were significantly upregulated in the metastasis-positive tumours. In contrast to disease-risk variants, HAX1 appeared to act synergistically with HMMR in significantly impacting metastasis-free survival. Examining the subtype datasets with FUMA and Ingenuity Pathway Analysis (IPA) identified distinct pathways demonstrating sexual dimorphism in CRC metastasis. CONCLUSIONS Combining genome-wide association testing with in silico functional annotation and wet-bench validation identified metastasis-relevant genes that could serve as features to develop subtype-specific metastasis-risk signatures for tailored management of patients with stage I-III CRC.
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Affiliation(s)
- Lai Fun Thean
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
| | - Michelle Wong
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
| | - Michelle Lo
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
| | - Iain Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Evelyn Wong
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Fei Gao
- National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Emile Tan
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
| | - Choong Leong Tang
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
| | - Peh Yean Cheah
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
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14
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Otto RM, Turska-Nowak A, Brown PM, Reynolds KA. A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment. Cell Syst 2024; 15:134-148.e7. [PMID: 38340730 PMCID: PMC10885703 DOI: 10.1016/j.cels.2024.01.003] [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: 05/04/2023] [Revised: 10/13/2023] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Ryan M Otto
- Green Center for Systems Biology - Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA
| | - Agata Turska-Nowak
- Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA
| | - Philip M Brown
- Green Center for Systems Biology - Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA
| | - Kimberly A Reynolds
- Green Center for Systems Biology - Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA; Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA.
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15
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Hsiung CC, Wilson CM, Sambold NA, Dai R, Chen Q, Misiukiewicz S, Arab A, Teyssier N, O'Loughlin T, Cofsky JC, Shi J, Gilbert LA. Higher-order combinatorial chromatin perturbations by engineered CRISPR-Cas12a for functional genomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.18.558350. [PMID: 37781594 PMCID: PMC10541102 DOI: 10.1101/2023.09.18.558350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting 1-3 genomic sites per cell. To develop a tool for higher-order ( > 3) combinatorial targeting of genomic sites with CRISPRi in functional genomics screens, we engineered an Acidaminococcus Cas12a variant -- referred to as mul tiplexed transcriptional interference AsCas12a (multiAsCas12a). multiAsCas12a incorporates a key mutation, R1226A, motivated by the hypothesis of nicking-induced stabilization of the ribonucleoprotein:DNA complex for improving CRISPRi activity. multiAsCas12a significantly outperforms prior state-of-the-art Cas12a variants in combinatorial CRISPRi targeting using high-order multiplexed arrays of lentivirally transduced CRISPR RNAs (crRNA), including in high-throughput pooled screens using 6-plex crRNA array libraries. Using multiAsCas12a CRISPRi, we discover new enhancer elements and dissect the combinatorial function of cis-regulatory elements. These results instantiate a group testing framework for efficiently surveying potentially numerous combinations of chromatin perturbations for biological discovery and engineering.
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16
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Duan B, Qiu C, Lockless SW, Sze SH, Kaplan CD. Higher-order epistasis within Pol II trigger loop haplotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576280. [PMID: 38293233 PMCID: PMC10827151 DOI: 10.1101/2024.01.20.576280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
RNA polymerase II (Pol II) has a highly conserved domain, the trigger loop (TL), that controls transcription fidelity and speed. We previously probed pairwise genetic interactions between residues within and surrounding the TL and identified widespread incompatibility between TLs of different species when placed in the Saccharomyces cerevisiae Pol II context, indicating epistasis between the TL and its surrounding context. We sought to understand the nature of this incompatibility and probe higher order epistasis internal to the TL. We have employed deep mutational scanning with selected natural TL variants ("haplotypes"), and all possible intermediate substitution combinations between them and the yeast Pol II TL. We identified both positive and negative higher-order residue interactions within example TL haplotypes. Intricate higher-order epistasis formed by TL residues was sometimes only apparent from analysis of intermediate genotypes, emphasizing complexity of epistatic interactions. Furthermore, we distinguished TL substitutions with distinct classes of epistatic patterns, suggesting specific TL residues that potentially influence TL evolution. Our examples of complex residue interactions suggest possible pathways for epistasis to facilitate Pol II evolution.
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Affiliation(s)
- Bingbing Duan
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260
| | - Chenxi Qiu
- Department of Genetics, Harvard Medical School, Boston, MA 02215
| | - Steve W Lockless
- Department of Biology, Texas A&M University, College Station, TX 77843
| | - Sing-Hoi Sze
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX 77843
- Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX 77843
| | - Craig D Kaplan
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260
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Irvine EB, Reddy ST. Advancing Antibody Engineering through Synthetic Evolution and Machine Learning. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2024; 212:235-243. [PMID: 38166249 DOI: 10.4049/jimmunol.2300492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/20/2023] [Indexed: 01/04/2024]
Abstract
Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the number of Ab clones able to be experimentally screened for binding. Moreover, the burgeoning integration of high-throughput screening, deep sequencing, and machine learning has further augmented in vitro Ab optimization, promising to accelerate the design process and massively expand the Ab sequence space interrogated. In this Brief Review, we discuss the experimental and computational tools employed in synthetic Ab engineering and optimization. We also explore the therapeutic challenges posed by developing Abs for infectious diseases, and the prospects for leveraging machine learning-guided protein engineering to prospectively design Abs resistant to viral escape.
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Affiliation(s)
- Edward B Irvine
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
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18
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Ciesielski TH, Sirugo G, Iyengar SK, Williams SM. Characterizing the pathogenicity of genetic variants: the consequences of context. NPJ Genom Med 2024; 9:3. [PMID: 38195641 PMCID: PMC10776585 DOI: 10.1038/s41525-023-00386-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 12/15/2023] [Indexed: 01/11/2024] Open
Affiliation(s)
- Timothy H Ciesielski
- The Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine, Cleveland, OH, USA.
- Mary Ann Swetland Center for Environmental Health at Case Western Reserve University School of Medicine, Cleveland, OH, USA.
- Ronin Institute, Montclair, NJ, USA.
| | - Giorgio Sirugo
- The Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Institute of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sudha K Iyengar
- The Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine, Cleveland, OH, USA
- The Department of Genetics and Genome Sciences at Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Cleveland Institute for Computational Biology, Cleveland, OH, USA
| | - Scott M Williams
- The Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine, Cleveland, OH, USA
- The Department of Genetics and Genome Sciences at Case Western Reserve University School of Medicine, Cleveland, OH, USA
- Cleveland Institute for Computational Biology, Cleveland, OH, USA
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19
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Periyasamy S, Youssef P, John S, Thara R, Mowry BJ. Genetic interactions of schizophrenia using gene-based statistical epistasis exclusively identify nervous system-related pathways and key hub genes. Front Genet 2024; 14:1301150. [PMID: 38259618 PMCID: PMC10800577 DOI: 10.3389/fgene.2023.1301150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024] Open
Abstract
Background: The relationship between genotype and phenotype is governed by numerous genetic interactions (GIs), and the mapping of GI networks is of interest for two main reasons: 1) By modelling biological robustness, GIs provide a powerful opportunity to infer compensatory biological mechanisms via the identification of functional relationships between genes, which is of interest for biological discovery and translational research. Biological systems have evolved to compensate for genetic (i.e., variations and mutations) and environmental (i.e., drug efficacy) perturbations by exploiting compensatory relationships between genes, pathways and biological processes; 2) GI facilitates the identification of the direction (alleviating or aggravating interactions) and magnitude of epistatic interactions that influence the phenotypic outcome. The generation of GIs for human diseases is impossible using experimental biology approaches such as systematic deletion analysis. Moreover, the generation of disease-specific GIs has never been undertaken in humans. Methods: We used our Indian schizophrenia case-control (case-816, controls-900) genetic dataset to implement the workflow. Standard GWAS sample quality control procedure was followed. We used the imputed genetic data to increase the SNP coverage to analyse epistatic interactions across the genome comprehensively. Using the odds ratio (OR), we identified the GIs that increase or decrease the risk of a disease phenotype. The SNP-based epistatic results were transformed into gene-based epistatic results. Results: We have developed a novel approach by conducting gene-based statistical epistatic analysis using an Indian schizophrenia case-control genetic dataset and transforming these results to infer GIs that increase the risk of schizophrenia. There were ∼9.5 million GIs with a p-value ≤ 1 × 10-5. Approximately 4.8 million GIs showed an increased risk (OR > 1.0), while ∼4.75 million GIs had a decreased risk (OR <1.0) for schizophrenia. Conclusion: Unlike model organisms, this approach is specifically viable in humans due to the availability of abundant disease-specific genome-wide genotype datasets. The study exclusively identified brain/nervous system-related processes, affirming the findings. This computational approach fills a critical gap by generating practically non-existent heritable disease-specific human GIs from human genetic data. These novel datasets can train innovative deep-learning models, potentially surpassing the limitations of conventional GWAS.
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Affiliation(s)
- Sathish Periyasamy
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
| | - Pierre Youssef
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Sujit John
- Schizophrenia Research Foundation, Chennai, Tamil Nadu, India
| | | | - Bryan J. Mowry
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
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20
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Matkarimov BT, Saparbaev MK. Chargaff's second parity rule lies at the origin of additive genetic interactions in quantitative traits to make omnigenic selection possible. PeerJ 2023; 11:e16671. [PMID: 38107580 PMCID: PMC10725672 DOI: 10.7717/peerj.16671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/22/2023] [Indexed: 12/19/2023] Open
Abstract
Background Francis Crick's central dogma provides a residue-by-residue mechanistic explanation of the flow of genetic information in living systems. However, this principle may not be sufficient for explaining how random mutations cause continuous variation of quantitative highly polygenic complex traits. Chargaff's second parity rule (CSPR), also referred to as intrastrand DNA symmetry, defined as near-exact equalities G ≈ C and A ≈ T within a single DNA strand, is a statistical property of cellular genomes. The phenomenon of intrastrand DNA symmetry was discovered more than 50 years ago; at present, it remains unclear what its biological role is, what the mechanisms are that force cellular genomes to comply strictly with CSPR, and why genomes of certain noncellular organisms have broken intrastrand DNA symmetry. The present work is aimed at studying a possible link between intrastrand DNA symmetry and the origin of genetic interactions in quantitative traits. Methods Computational analysis of single-nucleotide polymorphisms in human and mouse populations and of nucleotide composition biases at different codon positions in bacterial and human proteomes. Results The analysis of mutation spectra inferred from single-nucleotide polymorphisms observed in murine and human populations revealed near-exact equalities of numbers of reverse complementary mutations, indicating that random genetic variations obey CSPR. Furthermore, nucleotide compositions of coding sequences proved to be statistically interwoven via CSPR because pyrimidine bias at the 3rd codon position compensates purine bias at the 1st and 2nd positions. Conclusions According to Fisher's infinitesimal model, we propose that accumulation of reverse complementary mutations results in a continuous phenotypic variation due to small additive effects of statistically interwoven genetic variations. Therefore, additive genetic interactions can be inferred as a statistical entanglement of nucleotide compositions of separate genetic loci. CSPR challenges the neutral theory of molecular evolution-because all random mutations participate in variation of a trait-and provides an alternative solution to Haldane's dilemma by making a gene function diffuse. We propose that CSPR is symmetry of Fisher's infinitesimal model and that genetic information can be transferred in an implicit contactless manner.
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Affiliation(s)
- Bakhyt T. Matkarimov
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
- L.N.Gumilev Eurasian National University, Astana, Kazakhstan
| | - Murat K. Saparbaev
- Groupe «Mechanisms of DNA Repair and Carcinogenesis», CNRS UMR9019, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- Al-Farabi Kazakh National University, Almaty, Kazakhstan
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21
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Flores SV, Roco-Videla Á, Maureira-Carsalade N, Olguín-Barraza M. [Statistical association between genetic markers and food addiction: methodological considerations]. NUTR HOSP 2023; 40:1306-1307. [PMID: 37929828 DOI: 10.20960/nh.04871] [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] [Indexed: 11/07/2023] Open
Abstract
Introduction
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Affiliation(s)
- Sergio V Flores
- Facultad de Ciencias de la Salud. Universidad Autónoma de Chile. Chile. Departamento de Ingeniería Industrial. Facultad de Ingeniería y Tecnología. Universidad San Sebastián
| | - Ángel Roco-Videla
- Facultad de Medicina. Universidad Católica de la Santísima Concepción
| | - Nelson Maureira-Carsalade
- Departamento de Ingeniería Civil. Facultad de Ingeniería. Universidad Católica de la Santísima Concepción
| | - Mariela Olguín-Barraza
- Facultad de Ciencias de Salud. Programa de Magister en Ciencias Químico-Biológicas. Universidad Bernardo O'Higgins
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22
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Santorsola M, Lescai F. The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI. N Biotechnol 2023; 77:1-11. [PMID: 37329982 DOI: 10.1016/j.nbt.2023.06.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/01/2023] [Accepted: 06/14/2023] [Indexed: 06/19/2023]
Abstract
Deep learning has already revolutionised the way a wide range of data is processed in many areas of daily life. The ability to learn abstractions and relationships from heterogeneous data has provided impressively accurate prediction and classification tools to handle increasingly big datasets. This has a significant impact on the growing wealth of omics datasets, with the unprecedented opportunity for a better understanding of the complexity of living organisms. While this revolution is transforming the way these data are analyzed, explainable deep learning is emerging as an additional tool with the potential to change the way biological data is interpreted. Explainability addresses critical issues such as transparency, so important when computational tools are introduced especially in clinical environments. Moreover, it empowers artificial intelligence with the capability to provide new insights into the input data, thus adding an element of discovery to these already powerful resources. In this review, we provide an overview of the transformative effects explainable deep learning is having on multiple sectors, ranging from genome engineering and genomics, from radiomics to drug design and clinical trials. We offer a perspective to life scientists, to better understand the potential of these tools, and a motivation to implement them in their research, by suggesting learning resources they can use to move their first steps in this field.
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Affiliation(s)
| | - Francesco Lescai
- Department of Biology and Biotechnology, University of Pavia, Pavia, Italy.
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23
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Fam BSDO, Vargas-Pinilla P, Paré P, Landau L, Viscardi LH, Pissinatti A, Falótico T, Maestri R, Bortolini MC. Exploring the diversity of AVPR2 in Primates and its evolutionary implications. Genet Mol Biol 2023; 46:e20230045. [PMID: 37930141 PMCID: PMC10626583 DOI: 10.1590/1678-4685-gmb-2023-0045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 09/09/2023] [Indexed: 11/07/2023] Open
Abstract
The current study focuses on the investigation of AVPR2 (VTR2C) protein-coupled receptor variants specific to different primate taxa. AVPR2 is activated by the neurohormone AVP, which modulates physiological processes, including water homeostasis. Our findings reveal positive selection at three AVPR2 sites at positions 190, 250, and 346. Variation at position 250 is associated with human Congenital Nephrogenic Diabetes Insipidus (cNDI), a condition characterized by excessive water loss. Other 13 functional sites with potential adaptive relevance include positions 185, 202, 204, and 252 associated with cNDI. We identified SH3-binding motifs in AVPR2's ICL3 and N-terminus domains, with some losses observed in clades of Cercopithecidae, Callitrichinae, and Atelidae. SH3-binding motifs are crucial in regulating cellular physiology, indicating that the differences may be adaptive. Co-evolution was found between AVPR2 residues and those in the AVP signal peptide/Neurophysin-2 and AQP2, other molecules in the same signaling cascade. No significant correlation was found between these Primates' taxon-specific variants and the bioclimatic variables of the areas where they live. Distinct co-evolving amino acid sequences in functional sites were found in Platyrrhini and Catarrhini, which may have adaptive implications involving glucocorticoid hormones, suggesting varied selective pressures. Further studies are required to confirm these results.
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Affiliation(s)
- Bibiana Sampaio de Oliveira Fam
- Universidade Federal do Rio Grande do Sul, Departamento de Genética,
Laboratório de Evolução Humana e Molecular, Porto Alegre, RS, Brazil
| | - Pedro Vargas-Pinilla
- Universidade Federal do Rio Grande do Sul, Departamento de Genética,
Laboratório de Evolução Humana e Molecular, Porto Alegre, RS, Brazil
- Universidade de São Paulo, Faculdade de Medicina, Departamento de
Bioquímica e Imunologia, Ribeirão Preto, SP, Brazil
| | - Pâmela Paré
- Universidade Federal do Rio Grande do Sul, Departamento de Genética,
Laboratório de Evolução Humana e Molecular, Porto Alegre, RS, Brazil
| | - Luane Landau
- Universidade Federal do Rio Grande do Sul, Departamento de Genética,
Laboratório de Evolução Humana e Molecular, Porto Alegre, RS, Brazil
| | - Lucas H. Viscardi
- Universidade Federal do Rio Grande do Sul, Departamento de Genética,
Laboratório de Evolução Humana e Molecular, Porto Alegre, RS, Brazil
| | | | - Tiago Falótico
- Universidade de São Paulo, Escola de Artes, Ciências e Humanidades,
São Paulo, SP, Brazil
| | - Renan Maestri
- Universidade Federal do Rio Grande do Sul, Departamento de Ecologia,
Laboratório de Ecomorfologia e Macroevolução, Porto Alegre, RS, Brazil
| | - Maria Cátira Bortolini
- Universidade Federal do Rio Grande do Sul, Departamento de Genética,
Laboratório de Evolução Humana e Molecular, Porto Alegre, RS, Brazil
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Jakobson CM, Aguilar-Rodríguez J, Jarosz DF. Hsp90 shapes adaptation by controlling the fitness consequences of regulatory variation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564848. [PMID: 37961536 PMCID: PMC10634948 DOI: 10.1101/2023.10.30.564848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The essential stress-responsive chaperone Hsp90 impacts development and adaptation from microbes to humans. Yet despite evidence of its role in evolution, pathogenesis, and oncogenic transformation, the molecular mechanisms by which Hsp90 alters the consequences of mutations remain vigorously debated. Here we exploit the power of nucleotide-resolution genetic mapping in Saccharomyces cerevisiae to uncover more than 1,000 natural variant-to-phenotype associations governed by this molecular chaperone. Strikingly, Hsp90 more frequently modified the phenotypic effects of cis-regulatory variation than variants that altered protein sequence. Moreover, these interactions made the largest contribution to Hsp90-dependent heredity. Nearly all interacting variants-both regulatory and protein-coding-fell within clients of Hsp90 or targets of its direct binding partners. Hsp90 activity affected mutations in evolutionarily young genes, segmental deletions, and heterozygotes, highlighting its influence on variation central to evolutionary novelty. Reconciling the diverse epistatic effects of this chaperone, synthetic transcriptional regulation and reconstructions of natural alleles by genome editing revealed a central role for Hsp90 in regulating the fundamental relationship between activity and phenotype. Our findings establish that non-coding variation is a core driver of Hsp90's influence on heredity, offering a mechanistic explanation for the chaperone's strong effects on evolution and development across species.
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Affiliation(s)
- Christopher M. Jakobson
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally
| | - José Aguilar-Rodríguez
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biology, Stanford University, Stanford, CA, USA
- These authors contributed equally
| | - Daniel F. Jarosz
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
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25
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Ogbunugafor CB, Guerrero RF, Miller-Dickson MD, Shakhnovich EI, Shoulders MD. Epistasis and pleiotropy shape biophysical protein subspaces associated with drug resistance. Phys Rev E 2023; 108:054408. [PMID: 38115433 PMCID: PMC10935598 DOI: 10.1103/physreve.108.054408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 09/19/2023] [Indexed: 12/21/2023]
Abstract
Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few mentions of protein space consider how protein phenotypes can be described in terms of their biophysical components, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these components. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [k_{cat}, K_{M}, K_{i}, and T_{m} (melting temperature)]. We then examine how combinations of three mutations (eight alleles in total) display pleiotropy, or unique effects on individual subspace traits. We examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that future applications to bioengineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.
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Affiliation(s)
- C. Brandon Ogbunugafor
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Rafael F. Guerrero
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Eugene I. Shakhnovich
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Matthew D. Shoulders
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Li S, Kong L, Xiao X, Li P, Liu A, Li J, Gong J, Gong W, Ge Q, Shang H, Pan J, Chen H, Peng Y, Zhang Y, Lu Q, Shi Y, Yuan Y. Genome-wide artificial introgressions of Gossypium barbadense into G. hirsutum reveal superior loci for simultaneous improvement of cotton fiber quality and yield traits. J Adv Res 2023; 53:1-16. [PMID: 36460274 PMCID: PMC10658236 DOI: 10.1016/j.jare.2022.11.009] [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: 04/18/2022] [Revised: 10/31/2022] [Accepted: 11/24/2022] [Indexed: 12/02/2022] Open
Abstract
INTRODUCTION The simultaneous improvement of fiber quality and yield for cotton is strongly limited by the narrow genetic backgrounds of Gossypium hirsutum (Gh) and the negative genetic correlations among traits. An effective way to overcome the bottlenecks is to introgress the favorable alleles of Gossypium barbadense (Gb) for fiber quality into Gh with high yield. OBJECTIVES This study was to identify superior loci for the improvement of fiber quality and yield. METHODS Two sets of chromosome segment substitution lines (CSSLs) were generated by crossing Hai1 (Gb, donor-parent) with cultivar CCRI36 (Gh) and CCRI45 (Gh) as genetic backgrounds, and cultivated in 6 and 8 environments, respectively. The kmer genotyping strategy was improved and applied to the population genetic analysis of 743 genomic sequencing data. A progeny segregating population was constructed to validate genetic effects of the candidate loci. RESULTS A total of 68,912 and 83,352 genome-wide introgressed kmers were identified in the CCRI36 and CCRI45 populations, respectively. Over 90 % introgressions were homologous exchanges and about 21 % were reverse insertions. In total, 291 major introgressed segments were identified with stable genetic effects, of which 66(22.98 %), 64(21.99 %), 35(12.03 %), 31(10.65 %) and 18(6.19 %) were beneficial for the improvement of fiber length (FL), strength (FS), micronaire, lint-percentage (LP) and boll-weight, respectively. Thirty-nine introgression segments were detected with stable favorable additive effects for simultaneous improvement of 2 or more traits in Gh genetic background, including 6 could increase FL/FS and LP. The pyramiding effects of 3 pleiotropic segments (A07:C45Clu-081, D06:C45Clu-218, D02:C45Clu-193) were further validated in the segregating population. CONCLUSION The combining of genome-wide introgressions and kmer genotyping strategy showed significant advantages in exploring genetic resources. Through the genome-wide comprehensive mining, a total of 11 clusters (segments) were discovered for the stable simultaneous improvement of FL/FS and LP, which should be paid more attention in the future.
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Affiliation(s)
- Shaoqi Li
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Linglei Kong
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Xianghui Xiao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Pengtao Li
- School of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang 455000, China
| | - Aiying Liu
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Junwen Li
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Juwu Gong
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Wankui Gong
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Qun Ge
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Haihong Shang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Jingtao Pan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China
| | - Hong Chen
- Cotton Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China
| | - Yan Peng
- Third Division of the Xinjiang Production and Construction Corps Agricultural Research Institute, Tumushuke 843900, China
| | - Yuanming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Quanwei Lu
- School of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang 455000, China.
| | - Yuzhen Shi
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China.
| | - Youlu Yuan
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
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Wang Y, Sang M, Feng L, Gragnoli C, Griffin C, Wu R. A pleiotropic-epistatic entangelement model of drug response. Drug Discov Today 2023; 28:103790. [PMID: 37758020 DOI: 10.1016/j.drudis.2023.103790] [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: 08/06/2023] [Revised: 09/10/2023] [Accepted: 09/20/2023] [Indexed: 10/03/2023]
Abstract
Because drug response is multifactorial, graph models are uniquely powerful for comprehending its genetic architecture. We deconstruct drug response into many different and interdependent sub-traits, with each sub-trait controlled by multiple genes that act and interact in a complicated manner. The outcome of drug response is the consequence of multileveled intertwined interactions between pleiotropic effects and epistatic effects. Here, we propose a general statistical physics framework to chart the 3D geometric network that codes how epistasis pleiotropically influences a complete set of sub-traits to shape body-drug interactions. This model can dissect the topological architecture of epistatically induced pleiotropic networks (EiPN) and pleiotropically influenced epistatic networks (PiEN). We analyze and interpret the practical implications of the pleiotropic-epistatic entanglement model for pharmacogenomic studies.
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Affiliation(s)
- Yu Wang
- Center for Computational Biology, Beijing Forestry University, Beijing 100083, China
| | - Mengmeng Sang
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, Jiangsu 226019, China
| | - Li Feng
- Fisheries Engineering Institute, Chinese Academy of Fishery Sciences, Beijing 1000141, China
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Department of Medicine, Creighton University School of Medicine, Omaha, NE 68124, USA; Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome 00197, Italy
| | - Christopher Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Rongling Wu
- Center for Computational Biology, Beijing Forestry University, Beijing 100083, China; Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing 101408, China; Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China.
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Shih YT, Alipio JB, Sahay A. An inhibitory circuit-based enhancer of DYRK1A function reverses Dyrk1a-associated impairment in social recognition. Neuron 2023; 111:3084-3101.e5. [PMID: 37797581 PMCID: PMC10575685 DOI: 10.1016/j.neuron.2023.09.009] [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: 02/23/2023] [Revised: 06/29/2023] [Accepted: 09/07/2023] [Indexed: 10/07/2023]
Abstract
Heterozygous mutations in the dual-specificity tyrosine phosphorylation-regulated kinase 1a (Dyrk1a) gene define a syndromic form of autism spectrum disorder. The synaptic and circuit mechanisms mediating DYRK1A functions in social cognition are unclear. Here, we identify a social experience-sensitive mechanism in hippocampal mossy fiber-parvalbumin interneuron (PV IN) synapses by which DYRK1A recruits feedforward inhibition of CA3 and CA2 to promote social recognition. We employ genetic epistasis logic to identify a cytoskeletal protein, ABLIM3, as a synaptic substrate of DYRK1A. We demonstrate that Ablim3 downregulation in dentate granule cells of adult heterozygous Dyrk1a mice is sufficient to restore PV IN-mediated inhibition of CA3 and CA2 and social recognition. Acute chemogenetic activation of PV INs in CA3/CA2 of adult heterozygous Dyrk1a mice also rescued social recognition. Together, these findings illustrate how targeting DYRK1A synaptic and circuit substrates as "enhancers of DYRK1A function" harbors the potential to reverse Dyrk1a haploinsufficiency-associated circuit and cognition impairments.
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Affiliation(s)
- Yu-Tzu Shih
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA; Harvard Stem Cell Institute, Cambridge, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; BROAD Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jason Bondoc Alipio
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA; Harvard Stem Cell Institute, Cambridge, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; BROAD Institute of Harvard and MIT, Cambridge, MA, USA
| | - Amar Sahay
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA; Harvard Stem Cell Institute, Cambridge, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; BROAD Institute of Harvard and MIT, Cambridge, MA, USA.
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29
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Xie X, Sun X, Wang Y, Lehner B, Li X. Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles. Nat Commun 2023; 14:5551. [PMID: 37689712 PMCID: PMC10492795 DOI: 10.1038/s41467-023-41188-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 08/25/2023] [Indexed: 09/11/2023] Open
Abstract
An important challenge in genetics, evolution and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploids, mutations in a gene can combine on the same chromosome or on different chromosomes as a "heteroallelic combination". However, a direct comparison of the extent, sign, and stability of the genetic interactions between variants within and between alleles is lacking. Here we use thermodynamic models of protein folding and ligand-binding to show that interactions between mutations within and between alleles are expected in even very simple biophysical systems. Protein folding alone generates within-allele interactions and a single molecular interaction is sufficient to cause between-allele interactions and dominance. These interactions change differently, quantitatively and qualitatively as a system becomes more complex. Altering the concentration of a ligand can, for example, switch alleles from dominant to recessive. Our results show that intra-molecular epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of both intra-molecular epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning.
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Affiliation(s)
- Xuan Xie
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
| | - Xia Sun
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Yuheng Wang
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Ben Lehner
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, 08003, Spain.
- ICREA, Pg. Luis Companys 23, Barcelona, 08010, Spain.
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton, Cambridge, CB10 1SA, UK.
| | - Xianghua Li
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China.
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton, Cambridge, CB10 1SA, UK.
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK.
- Biomedical and Health Translational Centre of Zhejiang Province, Haizhou East Road 718, Haining, 314400, P. R. China.
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30
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Davyt M, Bharti N, Ignatova Z. Effect of mRNA/tRNA mutations on translation speed: Implications for human diseases. J Biol Chem 2023; 299:105089. [PMID: 37495112 PMCID: PMC10470029 DOI: 10.1016/j.jbc.2023.105089] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 07/18/2023] [Accepted: 07/20/2023] [Indexed: 07/28/2023] Open
Abstract
Recent discoveries establish tRNAs as central regulators of mRNA translation dynamics, and therefore cotranslational folding and function of the encoded protein. The tRNA pool, whose composition and abundance change in a cell- and tissue-dependent manner, is the main factor which determines mRNA translation velocity. In this review, we discuss a group of pathogenic mutations, in the coding sequences of either protein-coding genes or in tRNA genes, that alter mRNA translation dynamics. We also summarize advances in tRNA biology that have uncovered how variations in tRNA levels on account of genetic mutations affect protein folding and function, and thereby contribute to phenotypic diversity in clinical manifestations.
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Affiliation(s)
- Marcos Davyt
- Institute of Biochemistry and Molecular Biology, University of Hamburg, Hamburg, Germany
| | - Nikhil Bharti
- Institute of Biochemistry and Molecular Biology, University of Hamburg, Hamburg, Germany
| | - Zoya Ignatova
- Institute of Biochemistry and Molecular Biology, University of Hamburg, Hamburg, Germany.
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31
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Zhang R, Wu H, Li Y, Huang Z, Yin Z, Yang CX, Du ZQ. GWLD: an R package for genome-wide linkage disequilibrium analysis. G3 (BETHESDA, MD.) 2023; 13:jkad154. [PMID: 37431944 PMCID: PMC10468308 DOI: 10.1093/g3journal/jkad154] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/14/2023] [Accepted: 06/26/2023] [Indexed: 07/12/2023]
Abstract
Linkage disequilibrium (LD) analysis is fundamental to the investigation of the genetic architecture of complex traits (e.g. human disease, animal and plant breeding) and population structure and evolution dynamics. However, until now, studies primarily focus on LD status between genetic variants located on the same chromosome. Moreover, genome (re)sequencing produces unprecedented numbers of genetic variants, and fast LD computation becomes a challenge. Here, we have developed GWLD, a parallelized and generalized tool designed for the rapid genome-wide calculation of LD values, including conventional D/D', r2, and (reduced) mutual information (MI and RMI) measures. LD between genetic variants within and across chromosomes can be rapidly computed and visualized in either an R package or a standalone C++ software package. To evaluate the accuracy and speed of LD calculation, we conducted comparisons using 4 real datasets. Interchromosomal LD patterns observed potentially reflect levels of selection intensity across different species. Both versions of GWLD, the R package (https://github.com/Rong-Zh/GWLD/GWLD-R) and the standalone C++ software (https://github.com/Rong-Zh/GWLD/GWLD-C++), are freely available on GitHub.
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Affiliation(s)
- Rong Zhang
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Huaxuan Wu
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Yasai Li
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Zehang Huang
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Zongjun Yin
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Cai-Xia Yang
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Zhi-Qiang Du
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
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32
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Nguyen TN, Ingle C, Thompson S, Reynolds KA. The Genetic Landscape of a Metabolic Interaction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.28.542639. [PMID: 37645784 PMCID: PMC10461916 DOI: 10.1101/2023.05.28.542639] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Enzyme abundance, catalytic activity, and ultimately sequence are all shaped by the need of growing cells to maintain metabolic flux while minimizing accumulation of deleterious intermediates. While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focused on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We used deep mutational scanning to quantify the growth rate effect of 2,696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.
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Affiliation(s)
- Thuy N. Nguyen
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, USA, 75390
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, USA, 75390
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, USA, 75390
| | - Christine Ingle
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, USA, 75390
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, USA, 75390
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, USA, 75390
| | - Samuel Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Kimberly A. Reynolds
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, USA, 75390
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, USA, 75390
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, USA, 75390
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33
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Singhal P, Verma SS, Ritchie MD. Gene Interactions in Human Disease Studies-Evidence Is Mounting. Annu Rev Biomed Data Sci 2023; 6:377-395. [PMID: 37196359 DOI: 10.1146/annurev-biodatasci-102022-120818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.
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Affiliation(s)
- Pankhuri Singhal
- Genetics and Epigenetics Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Shefali Setia Verma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
- Penn Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Herken BW, Wong GT, Norman TM, Gilbert LA. Environmental challenge rewires functional connections among human genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.09.552346. [PMID: 37609173 PMCID: PMC10441384 DOI: 10.1101/2023.08.09.552346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
A fundamental question in biology is how a limited number of genes combinatorially govern cellular responses to environmental changes. While the prevailing hypothesis is that relationships between genes, processes, and ontologies could be plastic to achieve this adaptability, quantitatively comparing human gene functional connections between specific environmental conditions at scale is very challenging. Therefore, it remains unclear whether and how human genetic interaction networks are rewired in response to changing environmental conditions. Here, we developed a framework for mapping context-specific genetic interactions, enabling us to measure the plasticity of human genetic architecture upon environmental challenge for ~250,000 interactions, using cell cycle interruption, genotoxic perturbation, and nutrient deprivation as archetypes. We discover large-scale rewiring of human gene relationships across conditions, highlighted by dramatic shifts in the functional connections of epigenetic regulators (TIP60), cell cycle regulators (PP2A), and glycolysis metabolism. Our study demonstrates that upon environmental perturbation, intra-complex genetic rewiring is rare while inter-complex rewiring is common, suggesting a modular and flexible evolutionary genetic strategy that allows a limited number of human genes to enable adaptation to a large number of environmental conditions.
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Affiliation(s)
- Benjamin W. Herken
- Tetrad Graduate Program, University of California, San Francisco; San Francisco 94518, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco 94518, USA
| | - Garrett T. Wong
- Biological and Medical Informatics Graduate Program, University of California, San Francisco; San Francisco 94518, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco 94518, USA
| | | | - Luke A. Gilbert
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco 94518, USA
- Department of Urology, University of California, San Francisco, San Francisco 94518, USA
- Innovative Genomics Institute, University of California, San Francisco, San Francisco 94518, USA
- Arc Institute, Palo Alto 94305, USA
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35
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Vila JA. Protein structure prediction from the complementary science perspective. Biophys Rev 2023; 15:439-445. [PMID: 37681107 PMCID: PMC10480374 DOI: 10.1007/s12551-023-01107-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 07/25/2023] [Indexed: 09/09/2023] Open
Abstract
A comparative analysis between two problems-apparently unrelated-which are solved in a period of ~400 years, viz., the accurate prediction of both the planetary orbits and the protein structures, leads to inferred conjectures that go far beyond the existence of a common path in their resolution, i.e., observation → pattern recognition → modeling. The preliminary results from this analysis indicate that complementary science, together with a new perspective on protein folding, may help us discover common features that could contribute to a more in-depth understanding of still-unsolved problems such as protein folding.
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Affiliation(s)
- Jorge A. Vila
- IMASL-CONICET, Universidad Nacional de San Luis, Ejército de Los Andes 950, 5700 San Luis, Argentina
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36
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Vila JA. Protein folding rate evolution upon mutations. Biophys Rev 2023; 15:661-669. [PMID: 37681091 PMCID: PMC10480377 DOI: 10.1007/s12551-023-01088-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 06/24/2023] [Indexed: 09/09/2023] Open
Abstract
Despite the spectacular success of cutting-edge protein fold prediction methods, many critical questions remain unanswered, including why proteins can reach their native state in a biologically reasonable time. A satisfactory answer to this simple question could shed light on the slowest folding rate of proteins as well as how mutations-amino-acid substitutions and/or post-translational modifications-might affect it. Preliminary results indicate that (i) Anfinsen's dogma validity ensures that proteins reach their native state on a reasonable timescale regardless of their sequence or length, and (ii) it is feasible to determine the evolution of protein folding rates without accounting for epistasis effects or the mutational trajectories between the starting and target sequences. These results have direct implications for evolutionary biology because they lay the groundwork for a better understanding of why, and to what extent, mutations-a crucial element of evolution and a factor influencing it-affect protein evolvability. Furthermore, they may spur significant progress in our efforts to solve crucial structural biology problems, such as how a sequence encodes its folding.
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Affiliation(s)
- Jorge A. Vila
- IMASL-CONICET, Universidad Nacional de San Luis, Ejército de Los Andes 950, 5700 San Luis, Argentina
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37
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Jiang Y, Li Z, Sui D, Sharma G, Wang T, MacRenaris K, Takahashi H, Merz K, Hu J. Rational engineering of an elevator-type metal transporter ZIP8 reveals a conditional selectivity filter critically involved in determining substrate specificity. Commun Biol 2023; 6:778. [PMID: 37495662 PMCID: PMC10372143 DOI: 10.1038/s42003-023-05146-w] [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: 01/24/2023] [Accepted: 07/13/2023] [Indexed: 07/28/2023] Open
Abstract
Engineering of transporters to alter substrate specificity as desired holds great potential for applications, including metabolic engineering. However, the lack of knowledge on molecular mechanisms of substrate specificity hinders designing effective strategies for transporter engineering. Here, we applied an integrated approach to rationally alter the substrate preference of ZIP8, a Zrt-/Irt-like protein (ZIP) metal transporter with multiple natural substrates, and uncovered the determinants of substrate specificity. By systematically replacing the differentially conserved residues with the counterparts in the zinc transporter ZIP4, we created a zinc-preferring quadruple variant (Q180H/E343H/C310A/N357H), which exhibited largely reduced transport activities towards Cd2+, Fe2+, and Mn2+ whereas increased activity toward Zn2+. Combined mutagenesis, modeling, covariance analysis, and computational studies revealed a conditional selectivity filter which functions only when the transporter adopts the outward-facing conformation. The demonstrated approach for transporter engineering and the gained knowledge about substrate specificity will facilitate engineering and mechanistic studies of other transporters.
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Affiliation(s)
- Yuhan Jiang
- Department of Chemistry, Michigan State University, East Lansing, MI, 48824, USA
| | - Zhen Li
- Department of Chemistry, Michigan State University, East Lansing, MI, 48824, USA
| | - Dexin Sui
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Gaurav Sharma
- Department of Chemistry, Michigan State University, East Lansing, MI, 48824, USA
| | - Tianqi Wang
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Keith MacRenaris
- Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing, MI, 48824, USA
| | - Hideki Takahashi
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Kenneth Merz
- Department of Chemistry, Michigan State University, East Lansing, MI, 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Jian Hu
- Department of Chemistry, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA.
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Fevereiro-Martins M, Santos AC, Marques-Neves C, Guimarães H, Bicho M, On Behalf Of The GenE-Rop Study Group. Genetic Modulation of the Erythrocyte Phenotype Associated with Retinopathy of Prematurity-A Multicenter Portuguese Cohort Study. Int J Mol Sci 2023; 24:11817. [PMID: 37511576 PMCID: PMC10380881 DOI: 10.3390/ijms241411817] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/15/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
The development of retinopathy of prematurity (ROP) may be influenced by anemia or a low fetal/adult hemoglobin ratio. We aimed to analyze the association between DNA methyltransferase 3 β (DNMT3B) (rs2424913), methylenetetrahydrofolate reductase (MTHFR) (rs1801133), and lysine-specific histone demethylase 1A (KDM1A) (rs7548692) polymorphisms, erythrocyte parameters during the first week of life, and ROP. In total, 396 infants (gestational age < 32 weeks or birth weight < 1500 g) were evaluated clinically and hematologically. Genotyping was performed using a MicroChip DNA on a platform employing iPlex MassARRAY®. Multivariate regression was performed after determining risk factors for ROP using univariate regression. In the group of infants who developed ROP red blood cell distribution width (RDW), erythroblasts, and mean corpuscular volume (MCV) were higher, while mean hemoglobin and mean corpuscular hemoglobin concentration (MCHC) were lower; higher RDW was associated with KDM1A (AA), MTHFR (CC and CC + TT), KDM1A (AA) + MTHFR (CC), and KDM1A (AA) + DNMT3B (allele C); KDM1A (AA) + MTHFR (CC) were associated with higher RDW, erythroblasts, MCV, and mean corpuscular hemoglobin (MCH); higher MCV and MCH were also associated with KDM1A (AA) + MTHFR (CC) + DNMT3B (allele C). We concluded that the polymorphisms studied may influence susceptibility to ROP by modulating erythropoiesis and gene expression of the fetal/adult hemoglobin ratio.
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Affiliation(s)
- Mariza Fevereiro-Martins
- Ecogenetics and Human Health Unit, Environmental Health Institute-ISAMB, Associate Laboratory TERRA, Faculty of Medicine, University of Lisbon, Av. Professor Egas Moniz, 1649-028 Lisboa, Portugal
- Institute for Scientific Research Bento Rocha Cabral, Calçada Bento da Rocha Cabral 14, 1250-012 Lisboa, Portugal
- Department of Ophthalmology, Cuf Descobertas Hospital, Rua Mário Botas, 1998-018 Lisboa, Portugal
| | - Ana Carolina Santos
- Ecogenetics and Human Health Unit, Environmental Health Institute-ISAMB, Associate Laboratory TERRA, Faculty of Medicine, University of Lisbon, Av. Professor Egas Moniz, 1649-028 Lisboa, Portugal
| | - Carlos Marques-Neves
- Ecogenetics and Human Health Unit, Environmental Health Institute-ISAMB, Associate Laboratory TERRA, Faculty of Medicine, University of Lisbon, Av. Professor Egas Moniz, 1649-028 Lisboa, Portugal
- Center for the Study of Vision Sciences, Ophthalmology Clinic, Faculty of Medicine, University of Lisbon, Av. Professor Egas Moniz, Piso 1C, 1649-028 Lisboa, Portugal
| | - Hercília Guimarães
- Department of Gynecology-Obstetrics and Pediatrics, Faculty of Medicine, University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal
| | - Manuel Bicho
- Ecogenetics and Human Health Unit, Environmental Health Institute-ISAMB, Associate Laboratory TERRA, Faculty of Medicine, University of Lisbon, Av. Professor Egas Moniz, 1649-028 Lisboa, Portugal
- Institute for Scientific Research Bento Rocha Cabral, Calçada Bento da Rocha Cabral 14, 1250-012 Lisboa, Portugal
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39
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Cutter AD. Speciation and development. Evol Dev 2023; 25:289-327. [PMID: 37545126 DOI: 10.1111/ede.12454] [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: 03/07/2023] [Revised: 06/13/2023] [Accepted: 07/20/2023] [Indexed: 08/08/2023]
Abstract
Understanding general principles about the origin of species remains one of the foundational challenges in evolutionary biology. The genomic divergence between groups of individuals can spawn hybrid inviability and hybrid sterility, which presents a tantalizing developmental problem. Divergent developmental programs may yield either conserved or divergent phenotypes relative to ancestral traits, both of which can be responsible for reproductive isolation during the speciation process. The genetic mechanisms of developmental evolution involve cis- and trans-acting gene regulatory change, protein-protein interactions, genetic network structures, dosage, and epigenetic regulation, all of which also have roots in population genetic and molecular evolutionary processes. Toward the goal of demystifying Darwin's "mystery of mysteries," this review integrates microevolutionary concepts of genetic change with principles of organismal development, establishing explicit links between population genetic process and developmental mechanisms in the production of macroevolutionary pattern. This integration aims to establish a more unified view of speciation that binds process and mechanism.
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Affiliation(s)
- Asher D Cutter
- Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
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40
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Yang S, Kim SH, Kang M, Joo JY. Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges. Arch Pharm Res 2023:10.1007/s12272-023-01450-5. [PMID: 37261600 DOI: 10.1007/s12272-023-01450-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/26/2023] [Indexed: 06/02/2023]
Abstract
The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and is expected to predict the dependency or druggability of hidden mutations within the genome. Enormous mutational variants in coding and noncoding transcripts have been discovered along the genome by far, despite of the fine-tuned genetic proofreading machinery. These variants could be capable of inducing various pathological conditions, including neurological disorders, which require lifelong care. Several limitations and questions emerge, including the use of conventional processes via limited patient-driven sequence acquisitions and decoding-based inferences as well as how rare variants can be deduced as a population-specific etiology. These puzzles require harnessing of advanced systems for precise disease prediction, drug development and drug applications. In this review, we summarize the pathophysiological discoveries of pathogenic variants in both coding and noncoding transcripts in neurological disorders, and the current advantage of deep learning applications. In addition, we discuss the challenges encountered and how to outperform them with advancing interpretation.
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Affiliation(s)
- Sumin Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea
| | - Sung-Hyun Kim
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea
| | - Mingon Kang
- Department of Computer Science, University of Nevada, Las Vegas, NV, 89154, USA
| | - Jae-Yeol Joo
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea.
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41
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Kumar S, Gerstein M. Unified views on variant impact across many diseases. Trends Genet 2023; 39:442-450. [PMID: 36858880 PMCID: PMC10192142 DOI: 10.1016/j.tig.2023.02.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 03/03/2023]
Abstract
Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.
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Affiliation(s)
- Sushant Kumar
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06520, USA; Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA.
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42
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Baier F, Gauye F, Perez-Carrasco R, Payne JL, Schaerli Y. Environment-dependent epistasis increases phenotypic diversity in gene regulatory networks. SCIENCE ADVANCES 2023; 9:eadf1773. [PMID: 37224262 DOI: 10.1126/sciadv.adf1773] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 04/17/2023] [Indexed: 05/26/2023]
Abstract
Mutations to gene regulatory networks can be maladaptive or a source of evolutionary novelty. Epistasis confounds our understanding of how mutations affect the expression patterns of gene regulatory networks, a challenge exacerbated by the dependence of epistasis on the environment. We used the toolkit of synthetic biology to systematically assay the effects of pairwise and triplet combinations of mutant genotypes on the expression pattern of a gene regulatory network expressed in Escherichia coli that interprets an inducer gradient across a spatial domain. We uncovered a preponderance of epistasis that can switch in magnitude and sign across the inducer gradient to produce a greater diversity of expression pattern phenotypes than would be possible in the absence of such environment-dependent epistasis. We discuss our findings in the context of the evolution of hybrid incompatibilities and evolutionary novelties.
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Affiliation(s)
- Florian Baier
- Department of Fundamental Microbiology, University of Lausanne, Biophore Building, 1015 Lausanne, Switzerland
| | - Florence Gauye
- Department of Fundamental Microbiology, University of Lausanne, Biophore Building, 1015 Lausanne, Switzerland
| | | | - Joshua L Payne
- Institute of Integrative Biology, ETH Zurich, 8092 Zurich, Switzerland
| | - Yolanda Schaerli
- Department of Fundamental Microbiology, University of Lausanne, Biophore Building, 1015 Lausanne, Switzerland
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43
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Ghenu AH, Amado A, Gordo I, Bank C. Epistasis decreases with increasing antibiotic pressure but not temperature. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220058. [PMID: 37004727 PMCID: PMC10067269 DOI: 10.1098/rstb.2022.0058] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G × E), gene-by-gene (G × G or epistatic) or gene-by-gene-by-environment (G × G × E) interactions. We quantified G × G × E effects in Escherichia coli across environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single-nucleotide ABR mutations previously identified to vary in the extent of G × E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G × G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.
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Affiliation(s)
- Ana-Hermina Ghenu
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - André Amado
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Isabel Gordo
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
| | - Claudia Bank
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, Oeiras 2780-156, Portugal
- Division of Theoretical Ecology and Evolution, Institut für Ökologie und Evolution, Universität Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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44
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Weinstein JY, Martí-Gómez C, Lipsh-Sokolik R, Hoch SY, Liebermann D, Nevo R, Weissman H, Petrovich-Kopitman E, Margulies D, Ivankov D, McCandlish DM, Fleishman SJ. Designed active-site library reveals thousands of functional GFP variants. Nat Commun 2023; 14:2890. [PMID: 37210560 DOI: 10.1038/s41467-023-38099-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 04/13/2023] [Indexed: 05/22/2023] Open
Abstract
Mutations in a protein active site can lead to dramatic and useful changes in protein activity. The active site, however, is sensitive to mutations due to a high density of molecular interactions, substantially reducing the likelihood of obtaining functional multipoint mutants. We introduce an atomistic and machine-learning-based approach, called high-throughput Functional Libraries (htFuncLib), that designs a sequence space in which mutations form low-energy combinations that mitigate the risk of incompatible interactions. We apply htFuncLib to the GFP chromophore-binding pocket, and, using fluorescence readout, recover >16,000 unique designs encoding as many as eight active-site mutations. Many designs exhibit substantial and useful diversity in functional thermostability (up to 96 °C), fluorescence lifetime, and quantum yield. By eliminating incompatible active-site mutations, htFuncLib generates a large diversity of functional sequences. We envision that htFuncLib will be used in one-shot optimization of activity in enzymes, binders, and other proteins.
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Affiliation(s)
| | - Carlos Martí-Gómez
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Rosalie Lipsh-Sokolik
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Shlomo Yakir Hoch
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Demian Liebermann
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Reinat Nevo
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Haim Weissman
- Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | | | - David Margulies
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Dmitry Ivankov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - David M McCandlish
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 7610001, Israel.
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45
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Chen Y, Hu R, Li K, Zhang Y, Fu L, Zhang J, Si T. Deep Mutational Scanning of an Oxygen-Independent Fluorescent Protein CreiLOV for Comprehensive Profiling of Mutational and Epistatic Effects. ACS Synth Biol 2023; 12:1461-1473. [PMID: 37066862 PMCID: PMC10204710 DOI: 10.1021/acssynbio.2c00662] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Indexed: 04/18/2023]
Abstract
Oxygen-independent, flavin mononucleotide-based fluorescent proteins (FbFPs) are promising alternatives to green fluorescent protein in anaerobic contexts. Deep mutational scanning performs systematic profiling of protein sequence-function relationships but has not been applied to FbFPs. Focusing on CreiLOV from Chlamydomonas reinhardtii, we created and analyzed two comprehensive mutant collections: (1) single-residue, site-saturation mutagenesis libraries covering all 118 residues; and (2) a full combinatorial metagenesis library among 20 mutations at 15 residues, where mutation and residue selection was based on single-site mutagenesis results. Notably, the second type of library is indispensable to study higher-order epistasis but underrepresented in the literature. Using optimized FACS-seq assays, 2,185 (>92.5%) out of 2,360 possible single-site mutants and 165,428 (>89.7%) out of 184,320 possible combinatorial mutants were reliably assigned with fitness values. We constructed statistical and machine-learning models to analyze the CreiLOV data set, enabling accurate fitness prediction of higher-order mutants using lower-order mutagenesis data. In addition, we successfully isolated CreiLOV variants with improved fluorescence quantum yield and thermostability. This work provides new empirical data and design rules to engineer combinatorial protein variants.
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Affiliation(s)
- Yongcan Chen
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruyun Hu
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Keyi Li
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yating Zhang
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lihao Fu
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianzhi Zhang
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tong Si
- CAS
Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute
of Synthetic Biology, Shenzhen Institute
of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- BGI-Shenzhen, Shenzhen 518083, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
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46
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Vila JA. Rethinking the protein folding problem from a new perspective. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2023:10.1007/s00249-023-01657-w. [PMID: 37165178 DOI: 10.1007/s00249-023-01657-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 04/16/2023] [Accepted: 04/30/2023] [Indexed: 05/12/2023]
Abstract
One of the main concerns of Anfinsen was to reveal the connection between the amino-acid sequence and their biologically active conformation. This search gave rise to two crucial questions in structural biology, namely, why the proteins fold and how a sequence encodes its folding. As to the why, he proposes a plausible answer, namely, the thermodynamic hypothesis. As to the how, this remains an unsolved challenge. Consequently, the protein folding problem is examined here from a new perspective, namely, as an 'analytic whole'. Conceiving the protein folding in this way enabled us to (i) examine in detail why the force-field-based approaches have failed, among other purposes, in their ability to predict the three-dimensional structure of a protein accurately; (ii) propose how to redefine them to prevent these shortcomings, and (iii) conjecture on the origin of the state-of-the-art numerical-methods success to predict the tridimensional structure of proteins accurately.
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Affiliation(s)
- Jorge A Vila
- IMASL-CONICET, Universidad Nacional de San Luis, Ejército de Los Andes 950, 5700, San Luis, Argentina.
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47
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Abstract
Autoimmune diseases display a high degree of comorbidity within individuals and families, suggesting shared risk factors. Over the past 15 years, genome-wide association studies have established the polygenic basis of these common conditions and revealed widespread sharing of genetic effects, indicative of a shared immunopathology. Despite ongoing challenges in determining the precise genes and molecular consequences of these risk variants, functional experiments and integration with multimodal genomic data are providing valuable insights into key immune cells and pathways driving these diseases, with potential therapeutic implications. Moreover, genetic studies of ancient populations are shedding light on the contribution of pathogen-driven selection pressures to the increased prevalence of autoimmune disease. This Review summarizes the current understanding of autoimmune disease genetics, including shared effects, mechanisms, and evolutionary origins.
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Affiliation(s)
- Adil Harroud
- Department of Neurology and Neurosurgery, McGill University, Montréal, Quebec, Canada
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada
- The Neuro (Montreal Neurological Institute and Hospital), McGill University, Montréal, Quebec, Canada
| | - David A Hafler
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
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48
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Feng L, Yang W, Ding M, Hou L, Gragnoli C, Griffin C, Wu R. A personalized pharmaco-epistatic network model of precision medicine. Drug Discov Today 2023; 28:103608. [PMID: 37149282 DOI: 10.1016/j.drudis.2023.103608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/12/2023] [Accepted: 04/28/2023] [Indexed: 05/08/2023]
Abstract
Precision medicine, the utilization of targeted treatments to address an individual's disease, relies on knowledge about the genetic cause of that individual's drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine. Teaser: We present a functional graph (FunGraph) theory to draw a complete picture of pharmacogenetic architecture underlying interindividual variability in drug response. FunGraph can characterize how each gene acts and interacts with every other gene to mediate therapeutic response.
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Affiliation(s)
- Li Feng
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Wuyue Yang
- Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Mengdong Ding
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Luke Hou
- Ward Melville High School, East Setauket, NY 11733, USA
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Division of Endocrinology, Department of Medicine, Creighton University School of Medicine, Omaha, NE 68124, USA; Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome 00197, Italy
| | - Christipher Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing 101408, China; Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China.
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49
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Santos-Moreno J, Tasiudi E, Kusumawardhani H, Stelling J, Schaerli Y. Robustness and innovation in synthetic genotype networks. Nat Commun 2023; 14:2454. [PMID: 37117168 PMCID: PMC10147661 DOI: 10.1038/s41467-023-38033-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 04/13/2023] [Indexed: 04/30/2023] Open
Abstract
Genotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. They facilitate evolutionary innovation by enabling the exploration of different neighborhoods in genotype space. Genotype networks, first suggested by theoretical models, have been empirically confirmed for proteins and RNAs. Comparative studies also support their existence for gene regulatory networks (GRNs), but direct experimental evidence is lacking. Here, we report the construction of three interconnected genotype networks of synthetic GRNs producing three distinct phenotypes in Escherichia coli. Our synthetic GRNs contain three nodes regulating each other by CRISPR interference and governing the expression of fluorescent reporters. The genotype networks, composed of over twenty different synthetic GRNs, provide robustness in face of mutations while enabling transitions to innovative phenotypes. Through realistic mathematical modeling, we quantify robustness and evolvability for the complete genotype-phenotype map and link these features mechanistically to GRN motifs. Our work thereby exemplifies how GRN evolution along genotype networks might be driving evolutionary innovation.
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Affiliation(s)
- Javier Santos-Moreno
- Department of Fundamental Microbiology, University of Lausanne, Biophore Building, 1015, Lausanne, Switzerland
- Department of Medicine and Life Sciences, Pompeu Fabra University, 00803, Barcelona, Spain
| | - Eve Tasiudi
- Department of Biosystems Science and Engineering, ETH Zurich and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Hadiastri Kusumawardhani
- Department of Fundamental Microbiology, University of Lausanne, Biophore Building, 1015, Lausanne, Switzerland
| | - Joerg Stelling
- Department of Biosystems Science and Engineering, ETH Zurich and SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
| | - Yolanda Schaerli
- Department of Fundamental Microbiology, University of Lausanne, Biophore Building, 1015, Lausanne, Switzerland.
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50
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Heigwer F, Scheeder C, Bageritz J, Yousefian S, Rauscher B, Laufer C, Beneyto-Calabuig S, Funk MC, Peters V, Boulougouri M, Bilanovic J, Miersch T, Schmitt B, Blass C, Port F, Boutros M. A global genetic interaction network by single-cell imaging and machine learning. Cell Syst 2023; 14:346-362.e6. [PMID: 37116498 DOI: 10.1016/j.cels.2023.03.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/17/2022] [Accepted: 03/17/2023] [Indexed: 04/30/2023]
Abstract
Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level.
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Affiliation(s)
- Florian Heigwer
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany; Department of Life Sciences and Engineering, University of Applied Sciences Bingen, Bingen am Rhein, Germany
| | - Christian Scheeder
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Josephine Bageritz
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany; Center of Organismal Studies, Heidelberg University, Heidelberg, Germany
| | - Schayan Yousefian
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Benedikt Rauscher
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Christina Laufer
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Sergi Beneyto-Calabuig
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Maja Christina Funk
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Vera Peters
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Maria Boulougouri
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Jana Bilanovic
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Thilo Miersch
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Barbara Schmitt
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Claudia Blass
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Fillip Port
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Michael Boutros
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.
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