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Couce A, Limdi A, Magnan M, Owen SV, Herren CM, Lenski RE, Tenaillon O, Baym M. Changing fitness effects of mutations through long-term bacterial evolution. Science 2024; 383:eadd1417. [PMID: 38271521 DOI: 10.1126/science.add1417] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 12/12/2023] [Indexed: 01/27/2024]
Abstract
The distribution of fitness effects of new mutations shapes evolution, but it is challenging to observe how it changes as organisms adapt. Using Escherichia coli lineages spanning 50,000 generations of evolution, we quantify the fitness effects of insertion mutations in every gene. Macroscopically, the fraction of deleterious mutations changed little over time whereas the beneficial tail declined sharply, approaching an exponential distribution. Microscopically, changes in individual gene essentiality and deleterious effects often occurred in parallel; altered essentiality is only partly explained by structural variation. The identity and effect sizes of beneficial mutations changed rapidly over time, but many targets of selection remained predictable because of the importance of loss-of-function mutations. Taken together, these results reveal the dynamic-but statistically predictable-nature of mutational fitness effects.
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Affiliation(s)
- Alejandro Couce
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France
- Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain
| | - Anurag Limdi
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Melanie Magnan
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France
| | - Siân V Owen
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Cristina M Herren
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
- Department of Marine and Environmental Sciences, Northeastern University, Boston, MA 02115, USA
| | - Richard E Lenski
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824, USA
- Program in Ecology, Evolution, and Behavior, Michigan State University, East Lansing, MI 48824, USA
| | - Olivier Tenaillon
- Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France
- Université Paris Cité, Inserm, Institut Cochin, F-75014 Paris, France
| | - Michael Baym
- Department of Biomedical Informatics, and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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2
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Asar Y, Sauquet H, Ho SYW. Evaluating the Accuracy of Methods for Detecting Correlated Rates of Molecular and Morphological Evolution. Syst Biol 2023; 72:1337-1356. [PMID: 37695237 PMCID: PMC10924723 DOI: 10.1093/sysbio/syad055] [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: 08/02/2022] [Revised: 09/04/2023] [Accepted: 09/08/2023] [Indexed: 09/12/2023] Open
Abstract
Determining the link between genomic and phenotypic change is a fundamental goal in evolutionary biology. Insights into this link can be gained by using a phylogenetic approach to test for correlations between rates of molecular and morphological evolution. However, there has been persistent uncertainty about the relationship between these rates, partly because conflicting results have been obtained using various methods that have not been examined in detail. We carried out a simulation study to evaluate the performance of 5 statistical methods for detecting correlated rates of evolution. Our simulations explored the evolution of molecular sequences and morphological characters under a range of conditions. Of the methods tested, Bayesian relaxed-clock estimation of branch rates was able to detect correlated rates of evolution correctly in the largest number of cases. This was followed by correlations of root-to-tip distances, Bayesian model selection, independent sister-pairs contrasts, and likelihood-based model selection. As expected, the power to detect correlated rates increased with the amount of data, both in terms of tree size and number of morphological characters. Likewise, greater among-lineage rate variation in the data led to improved performance of all 5 methods, particularly for Bayesian relaxed-clock analysis when the rate model was mismatched. We then applied these methods to a data set from flowering plants and did not find evidence of a correlation in evolutionary rates between genomic data and morphological characters. The results of our study have practical implications for phylogenetic analyses of combined molecular and morphological data sets, and highlight the conditions under which the links between genomic and phenotypic rates of evolution can be evaluated quantitatively.
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Affiliation(s)
- Yasmin Asar
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia
| | - Hervé Sauquet
- National Herbarium of New South Wales (NSW), Royal Botanic Gardens and Domain Trust, Sydney, NSW 2000, Australia
- Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Simon Y W Ho
- School of Life and Environmental Sciences, University of Sydney, Sydney, NSW 2006, Australia
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3
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Flowers S, Holder KH, Rump GK, Gardner SM. Missed connections: Exploring features of undergraduate biology students' knowledge networks relating gene regulation, cell-cell communication, and phenotypic expression. CBE LIFE SCIENCES EDUCATION 2023; 22:ar44. [PMID: 37751503 PMCID: PMC10756040 DOI: 10.1187/cbe.22-03-0041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/09/2023] [Accepted: 08/16/2023] [Indexed: 09/28/2023]
Abstract
Explaining biological phenomena requires understanding how different processes function and describing interactions between components at various levels of organization over time and space in biological systems. This is a desired competency yet is a complicated and often challenging task for undergraduate biology students. Therefore, we need a better understanding of their integrated knowledge regarding important biological concepts. Informed by the theory of knowledge integration and mechanistic reasoning, in this qualitative case study, we elicited and characterized knowledge networks of nine undergraduate biology students. We investigated students' conceptions of and the various ways they connect three fundamental subsystems in biology: 1) gene regulation, 2) cell-cell communication, and 3) phenotypic expression. We found that only half of the conceptual questions regarding the three subsystems were answered correctly by the majority of students. Knowledge networks tended to be linear and unidirectional, with little variation in the types of relationships displayed. Students did not spontaneously express mechanistic connections, mainly described undefined, cellular, and macromolecular levels of organization, and mainly discussed unspecified and intracellular localizations. These results emphasize the need to support students' understanding of fundamental concepts, and promoting knowledge integration in the classroom could assist students' ability to understand biological systems.
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Affiliation(s)
- Sharleen Flowers
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907
| | - Kal H. Holder
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907
| | - Gabrielle K. Rump
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907
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4
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Gopalan-Nair R, Jardinaud MF, Legrand L, Lopez-Roques C, Bouchez O, Genin S, Guidot A. Transcriptomic profiling reveals host-specific evolutionary pathways promoting enhanced fitness in the plant pathogen Ralstonia pseudosolanacearum. Microb Genom 2023; 9:001142. [PMID: 38063495 PMCID: PMC10763508 DOI: 10.1099/mgen.0.001142] [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: 06/16/2023] [Accepted: 11/09/2023] [Indexed: 12/18/2023] Open
Abstract
The impact of host diversity on the genotypic and phenotypic evolution of broad-spectrum pathogens is an open issue. Here, we used populations of the plant pathogen Ralstonia pseudosolanacearum that were experimentally evolved on five types of host plants, either belonging to different botanical families or differing in their susceptibility or resistance to the pathogen. We investigated whether changes in transcriptomic profiles, associated with or independent of genetic changes, could occur during the process of host adaptation, and whether transcriptomic reprogramming was dependent on host type. Genomic and transcriptomic variations were established for 31 evolved clones that showed better fitness in their experimental host than the ancestral clone. Few genomic polymorphisms were detected in these clones, but significant transcriptomic variations were observed, with a large number of differentially expressed genes (DEGs). In a very clear way, a group of genes belonging to the network of regulation of the bacterial virulence such as efpR, efpH or hrpB, among others, were deregulated in several independent evolutionary lineages and appeared to play a key role in the transcriptomic rewiring observed in evolved clones. A double hierarchical clustering based on the 400 top DEGs for each clone revealed 2 major patterns of gene deregulation that depend on host genotype, but not on host susceptibility or resistance to the pathogen. This work therefore highlights the existence of two major evolutionary paths that result in a significant reorganization of gene expression during adaptive evolution and underscore clusters of co-regulated genes associated with bacterial adaptation on different host lines.
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Affiliation(s)
| | | | - Ludovic Legrand
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
| | | | | | - Stéphane Genin
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
| | - Alice Guidot
- LIPME, Université de Toulouse, INRAE, CNRS, Castanet-Tolosan, France
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5
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de Vienne D, Coton C, Dillmann C. The genotype-phenotype relationship and evolutionary genetics in the light of the Metabolic Control Analysis. Biosystems 2023; 232:105000. [PMID: 37586656 DOI: 10.1016/j.biosystems.2023.105000] [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: 05/10/2023] [Revised: 08/05/2023] [Accepted: 08/11/2023] [Indexed: 08/18/2023]
Abstract
Metabolic control analysis has long been used as a systemic model of the genotype-phenotype (GP) relationship. By considering kinetic parameters and enzyme concentrations as reflecting the genotype level and metabolic fluxes or pools as phenotypes related to fitness, MCA has given a biological basis to the relationship between these two levels. The non-linear and concave relationship between enzymes and fluxes can account for common genetic effects that reductionist approaches have been powerless to explain, such as the dominance of active alleles over less active alleles, the various types of epistasis and heterosis, and reveals the structural links between these genetic effects. The summation property of the flux control coefficients accounts for the L-shaped distribution of Quantitative Trait Locus (QTL) effects, irrespective of other possible causes. Metabolic models of response to selection results in evolutionary scenarios that are markedly different from those derived from the classical infinitesimal model of quantitative genetics. In particular, evolution towards selective neutrality appears to be a consequence of the diminishing return of the flux-enzyme relationship. In this paper, we survey the historical and recent achievements of MCA in genetics, quantitative genetics and evolution, focusing on epistasis and the evolution of flux in relation to enzyme concentrations.
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Affiliation(s)
- D de Vienne
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech. GQE-Le Moulon, IDEEV, 12, route 128, Gif-sur-Yvette, 91190, France.
| | - C Coton
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech. GQE-Le Moulon, IDEEV, 12, route 128, Gif-sur-Yvette, 91190, France.
| | - C Dillmann
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech. GQE-Le Moulon, IDEEV, 12, route 128, Gif-sur-Yvette, 91190, France.
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6
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Abstract
AbstractEvolutionary biologists have thought about the role of genetic variation during adaptation for a very long time-before we understood the organization of the genetic code, the provenance of genetic variation, and how such variation influenced the phenotypes on which natural selection acts. Half a century after the discovery of the structure of DNA and the unraveling of the genetic code, we have a rich understanding of these problems and the means to both delve deeper and widen our perspective across organisms and natural populations. The 2022 Vice Presidential Symposium of the American Society of Naturalists highlighted examples of recent insights into the role of genetic variation in adaptive processes, which are compiled in this special section. The work was conducted in different parts of the world, included theoretical and empirical studies with diverse organisms, and addressed distinct aspects of how genetic variation influences adaptation. In our introductory article to the special section, we discuss some important recent insights about the generation and maintenance of genetic variation, its impacts on phenotype and fitness, its fate in natural populations, and its role in driving adaptation. By placing the special section articles in the broader context of recent developments, we hope that this overview will also serve as a useful introduction to the field.
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7
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Johnson MS, Reddy G, Desai MM. Epistasis and evolution: recent advances and an outlook for prediction. BMC Biol 2023; 21:120. [PMID: 37226182 PMCID: PMC10206586 DOI: 10.1186/s12915-023-01585-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/30/2023] [Indexed: 05/26/2023] Open
Abstract
As organisms evolve, the effects of mutations change as a result of epistatic interactions with other mutations accumulated along the line of descent. This can lead to shifts in adaptability or robustness that ultimately shape subsequent evolution. Here, we review recent advances in measuring, modeling, and predicting epistasis along evolutionary trajectories, both in microbial cells and single proteins. We focus on simple patterns of global epistasis that emerge in this data, in which the effects of mutations can be predicted by a small number of variables. The emergence of these patterns offers promise for efforts to model epistasis and predict evolution.
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Affiliation(s)
- Milo S Johnson
- Department of Integrative Biology, University of California, Berkeley, CA, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Gautam Reddy
- Physics & Informatics Laboratories, NTT Research, Inc., Sunnyvale, CA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Michael M Desai
- Department of Organismic and Evolutionary Biology and Department of Physics, Harvard University, Cambridge, MA, USA.
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8
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Wei H, Li X. Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes. Front Genet 2023; 14:1087267. [PMID: 36713072 PMCID: PMC9878224 DOI: 10.3389/fgene.2023.1087267] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/02/2023] [Indexed: 01/13/2023] Open
Abstract
Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.
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Affiliation(s)
- Huijin Wei
- Zhejiang University—University of Edinburgh Institute, Zhejiang University, Haining, Zhejiang, China
| | - Xianghua Li
- Zhejiang University—University of Edinburgh Institute, Zhejiang University, Haining, Zhejiang, China
- Deanery of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom
- The Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China
- Biomedical and Health Translational Centre of Zhejiang Province, Haining, Zhejiang, China
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9
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Biesiedina AA, Demenko MM, Oleshko TM, Starchenko AY, Levchenko ZM. RELATIONSHIP OF DERMATOGLYPHICS WITH SPEED OF REACTION AND TYPE OF TEMPERAMENT OF ATHLETICS. WIADOMOSCI LEKARSKIE (WARSAW, POLAND : 1960) 2023; 76:2564-2571. [PMID: 38290018 DOI: 10.36740/wlek202312103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
OBJECTIVE The aim: To establish and practically substantiate the relationship between dermatoglyphic markers and the speed of reaction and type of temperament of track and field athletes. PATIENTS AND METHODS Materials and methods: Analysis of scientific and methodological literature, survey, dermatoglyphics, methods of mathematical and statistical processing of the received data. RESULTS Results: Track and field athletes among the population of the Sumy region of Ukraine have a tendency to decrease the number of whorls (p<0.05). Athletes of the sanguine type of temperament are most common (67,6%), and in the control group (among students of a medical university) the phlegmatic type of temperament is most common (68,4%). Also, among the subjects of the main group there is no such temperament as melancholic. We also determined the ATD angle for the palm. It was 37±4.88 in the main group, and 47±3.11 in the control group. This indicates a hereditary predisposition of this trait. The delta index in the control group has lower values (DI=9.5) than in the main group (DI=13.3). CONCLUSION Conclusions: We established and practically substantiated the relationship between dermatoglyphic markers and reaction speed and temperament type of track and field athletes. Determined the relationship between the anatomical features of the fingers and the speed of mastering movements. For track and field athletes of the population of Ukraine, there were characteristic features of the dermatoglyphic structure: higher values of the deltoid index and genetic markers of the distance between the triradii a and d of the fingers.
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10
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Azbukina N, Zharikova A, Ramensky V. Intragenic compensation through the lens of deep mutational scanning. Biophys Rev 2022; 14:1161-1182. [PMID: 36345285 PMCID: PMC9636336 DOI: 10.1007/s12551-022-01005-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/26/2022] [Indexed: 12/20/2022] Open
Abstract
A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them. Supplementary Information The online version contains supplementary material available at 10.1007/s12551-022-01005-w.
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Affiliation(s)
- Nadezhda Azbukina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
| | - Anastasia Zharikova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
| | - Vasily Ramensky
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 1-73, Leninskie Gory, 119991 Moscow, Russia
- National Medical Research Center for Therapy and Preventive Medicine, Petroverigsky per., 10, Bld.3, 101000 Moscow, Russia
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11
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Environmental selection and epistasis in an empirical phenotype-environment-fitness landscape. Nat Ecol Evol 2022; 6:427-438. [PMID: 35210579 DOI: 10.1038/s41559-022-01675-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 12/14/2021] [Indexed: 11/08/2022]
Abstract
Fitness landscapes, mappings of genotype/phenotype to their effects on fitness, are invaluable concepts in evolutionary biochemistry. Although widely discussed, measurements of phenotype-fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype (EC50) of VIM-2 β-lactamase across a 64-fold range of ampicillin concentrations. We then construct a phenotype-fitness landscape that takes variations in environmental selection pressure into account. We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, suggesting that the evolution of VIM-2 can be predicted on the basis of the selection environment. Our landscape provides new quantitative knowledge on the evolution of the β-lactamases and proteins in general, particularly their evolutionary dynamics under subinhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of non-specific epistasis.
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12
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Ahmed S, Manjunath K, Chattopadhyay G, Varadarajan R. Identification of stabilizing point mutations through mutagenesis of destabilized protein libraries. J Biol Chem 2022; 298:101785. [PMID: 35247389 PMCID: PMC8971944 DOI: 10.1016/j.jbc.2022.101785] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 02/18/2022] [Accepted: 02/26/2022] [Indexed: 01/22/2023] Open
Abstract
Although there have been recent transformative advances in the area of protein structure prediction, prediction of point mutations that improve protein stability remains challenging. It is possible to construct and screen large mutant libraries for improved activity or ligand binding. However, reliable screens for mutants that improve protein stability do not yet exist, especially for proteins that are well folded and relatively stable. Here, we demonstrate that incorporation of a single, specific, destabilizing mutation termed parent inactivating mutation into each member of a single-site saturation mutagenesis library, followed by screening for suppressors, allows for robust and accurate identification of stabilizing mutations. We carried out fluorescence-activated cell sorting of such a yeast surface display, saturation suppressor library of the bacterial toxin CcdB, followed by deep sequencing of sorted populations. We found that multiple stabilizing mutations could be identified after a single round of sorting. In addition, multiple libraries with different parent inactivating mutations could be pooled and simultaneously screened to further enhance the accuracy of identification of stabilizing mutations. Finally, we show that individual stabilizing mutations could be combined to result in a multi-mutant that demonstrated an increase in thermal melting temperature of about 20 °C, and that displayed enhanced tolerance to high temperature exposure. We conclude that as this method is robust and employs small library sizes, it can be readily extended to other display and screening formats to rapidly isolate stabilized protein mutants.
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Affiliation(s)
- Shahbaz Ahmed
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Kavyashree Manjunath
- Centre for Chemical Biology and Therapeutics, Institute of Stem Cell Science and Regenerative Medicine, Bangalore, India
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13
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Lagator M, Sarikas S, Steinrueck M, Toledo-Aparicio D, Bollback JP, Guet CC, Tkačik G. Predicting bacterial promoter function and evolution from random sequences. eLife 2022; 11:64543. [PMID: 35080492 PMCID: PMC8791639 DOI: 10.7554/elife.64543] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 01/09/2022] [Indexed: 12/12/2022] Open
Abstract
Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought.
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Affiliation(s)
- Mato Lagator
- School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Srdjan Sarikas
- Institute of Science and Technology Austria, Klosterneuburg, Austria.,Center for Physiology and Pharmacology, Medical University of Vienna, Klosterneuburg, Austria
| | | | | | - Jonathan P Bollback
- Institute of Integrative Biology, Functional and Comparative Genomics, University of Liverpool, Liverpool, United Kingdom
| | - Calin C Guet
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Gašper Tkačik
- Institute of Science and Technology Austria, Klosterneuburg, Austria
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14
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Gonzalez Somermeyer L, Fleiss A, Mishin AS, Bozhanova NG, Igolkina AA, Meiler J, Alaball Pujol ME, Putintseva EV, Sarkisyan KS, Kondrashov FA. Heterogeneity of the GFP fitness landscape and data-driven protein design. eLife 2022; 11:75842. [PMID: 35510622 PMCID: PMC9119679 DOI: 10.7554/elife.75842] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/25/2022] [Indexed: 11/24/2022] Open
Abstract
Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design - instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.
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Affiliation(s)
| | - Aubin Fleiss
- Synthetic Biology Group, MRC London Institute of Medical SciencesLondonUnited Kingdom,Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College LondonLondonUnited Kingdom
| | - Alexander S Mishin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of SciencesMoscowRussian Federation
| | - Nina G Bozhanova
- Department of Chemistry, Center for Structural Biology, Vanderbilt UniversityNashvilleUnited States
| | - Anna A Igolkina
- Gregor Mendel Institute, Austrian Academy of Sciences, Vienna BioCenterViennaAustria
| | - Jens Meiler
- Department of Chemistry, Center for Structural Biology, Vanderbilt UniversityNashvilleUnited States,Institute for Drug Discovery, Medical School, Leipzig UniversityLeipzigGermany
| | - Maria-Elisenda Alaball Pujol
- Synthetic Biology Group, MRC London Institute of Medical SciencesLondonUnited Kingdom,Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College LondonLondonUnited Kingdom
| | | | - Karen S Sarkisyan
- Synthetic Biology Group, MRC London Institute of Medical SciencesLondonUnited Kingdom,Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College LondonLondonUnited Kingdom,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of SciencesMoscowRussian Federation
| | - Fyodor A Kondrashov
- Institute of Science and Technology AustriaKlosterneuburgAustria,Evolutionary and Synthetic Biology Unit, Okinawa Institute of Science and Technology Graduate UniversityOkinawaJapan
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15
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Newman DL, Coakley A, Link A, Mills K, Wright LK. Punnett Squares or Protein Production? The Expert-Novice Divide for Conceptions of Genes and Gene Expression. CBE LIFE SCIENCES EDUCATION 2021; 20:ar53. [PMID: 34546102 PMCID: PMC8715778 DOI: 10.1187/cbe.21-01-0004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 07/26/2021] [Accepted: 08/13/2021] [Indexed: 05/09/2023]
Abstract
Concepts of molecular biology and genetics are difficult for many biology undergraduate students to master yet are crucial for deep understanding of how life works. By asking students to draw their ideas, we attempted to uncover the mental models about genes and gene expression held by biology students (n = 23) and experts (n = 18) using semistructured interviews. A large divide was identified between novice and expert conceptions. While experts typically drew box-and-line representations and thought about genes as regions of DNA that were used to encode products, students typically drew whole chromosomes rather than focusing on gene structure and conflated gene expression with simple phenotypic outcomes. Experts universally described gene expression as a set of molecular processes involving transcription and translation, whereas students often associated gene expression with Punnett squares and phenotypic outcomes. Follow-up survey data containing a ranking question confirmed students' alignment of their mental models with the images uncovered during interviews (n = 156 undergraduate biology students) and indicated that Advanced students demonstrate a shift toward expert-like thinking. An analysis of 14 commonly used biology textbooks did not show any relationship between Punnett squares and discussions of gene expression, so it is doubtful students' ideas originate directly from textbook reading assignments. Our findings add to the literature about mechanistic reasoning abilities of learners and provide new insights into how biology students think about genes and gene expression.
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Affiliation(s)
- Dina L. Newman
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623
| | - Aeowynn Coakley
- Department of Biological Sciences, San José State University, San José, CA 95192
- Department of Curriculum and Instruction and Department of Chemistry, University of Arkansas, Fayetteville, AR 72701
| | - Aidan Link
- Department of Curriculum and Instruction and Department of Chemistry, University of Arkansas, Fayetteville, AR 72701
| | - Korinne Mills
- Department of Curriculum and Instruction and Department of Chemistry, University of Arkansas, Fayetteville, AR 72701
- School of Arts and Sciences, Florida Southern College, Lakeland, FL 33801
| | - L. Kate Wright
- Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623
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16
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Martins FB, Moraes ACL, Aono AH, Ferreira RCU, Chiari L, Simeão RM, Barrios SCL, Santos MF, Jank L, do Valle CB, Vigna BBZ, de Souza AP. A Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses. FRONTIERS IN PLANT SCIENCE 2021; 12:737919. [PMID: 34745171 PMCID: PMC8569613 DOI: 10.3389/fpls.2021.737919] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 09/20/2021] [Indexed: 06/13/2023]
Abstract
Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.
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Affiliation(s)
- Felipe Bitencourt Martins
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), São Paulo, Brazil
| | - Aline Costa Lima Moraes
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), São Paulo, Brazil
| | - Alexandre Hild Aono
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), São Paulo, Brazil
| | | | - Lucimara Chiari
- Embrapa Gado de Corte, Brazilian Agricultural Research Corporation, Campo Grande, Brazil
| | - Rosangela Maria Simeão
- Embrapa Gado de Corte, Brazilian Agricultural Research Corporation, Campo Grande, Brazil
| | | | | | - Liana Jank
- Embrapa Gado de Corte, Brazilian Agricultural Research Corporation, Campo Grande, Brazil
| | | | | | - Anete Pereira de Souza
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), São Paulo, Brazil
- Department of Plant Biology, Biology Institute, University of Campinas (UNICAMP), São Paulo, Brazil
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17
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Sesta L, Uguzzoni G, Fernandez-de-Cossio-Diaz J, Pagnani A. AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational Approximated Landscape. Int J Mol Sci 2021; 22:10908. [PMID: 34681569 PMCID: PMC8535593 DOI: 10.3390/ijms222010908] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 09/24/2021] [Accepted: 09/27/2021] [Indexed: 01/12/2023] Open
Abstract
We present Annealed Mutational approximated Landscape (AMaLa), a new method to infer fitness landscapes from Directed Evolution experiments sequencing data. Such experiments typically start from a single wild-type sequence, which undergoes Darwinian in vitro evolution via multiple rounds of mutation and selection for a target phenotype. In the last years, Directed Evolution is emerging as a powerful instrument to probe fitness landscapes under controlled experimental conditions and as a relevant testing ground to develop accurate statistical models and inference algorithms (thanks to high-throughput screening and sequencing). Fitness landscape modeling either uses the enrichment of variants abundances as input, thus requiring the observation of the same variants at different rounds or assuming the last sequenced round as being sampled from an equilibrium distribution. AMaLa aims at effectively leveraging the information encoded in the whole time evolution. To do so, while assuming statistical sampling independence between sequenced rounds, the possible trajectories in sequence space are gauged with a time-dependent statistical weight consisting of two contributions: (i) an energy term accounting for the selection process and (ii) a generalized Jukes-Cantor model for the purely mutational step. This simple scheme enables accurately describing the Directed Evolution dynamics and inferring a fitness landscape that correctly reproduces the measures of the phenotype under selection (e.g., antibiotic drug resistance), notably outperforming widely used inference strategies. In addition, we assess the reliability of AMaLa by showing how the inferred statistical model could be used to predict relevant structural properties of the wild-type sequence.
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Affiliation(s)
- Luca Sesta
- Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy; (L.S.); (G.U.); (A.P.)
| | - Guido Uguzzoni
- Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy; (L.S.); (G.U.); (A.P.)
| | - Jorge Fernandez-de-Cossio-Diaz
- Laboratory of Physics of the Ecole Normale Supérieure, CNRS UMR 8023 & PSL Research, Sorbonne Université, 24 rue Lhomond, 75005 Paris, France
- Center of Molecular Immunology, Systems Biology Department, Playa, Havana CP 11600, Cuba
| | - Andrea Pagnani
- Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy; (L.S.); (G.U.); (A.P.)
- Italian Institute for Genomic Medicine, IRCCS Candiolo, SP-142, I-10060 Candiolo, Italy
- INFN, Sezione di Torino, I-10125 Torino, Italy
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18
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Patel V, Matange N. Adaptation and compensation in a bacterial gene regulatory network evolving under antibiotic selection. eLife 2021; 10:70931. [PMID: 34591012 PMCID: PMC8483737 DOI: 10.7554/elife.70931] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/22/2021] [Indexed: 12/28/2022] Open
Abstract
Gene regulatory networks allow organisms to generate coordinated responses to environmental challenges. In bacteria, regulatory networks are re-wired and re-purposed during evolution, though the relationship between selection pressures and evolutionary change is poorly understood. In this study, we discover that the early evolutionary response of Escherichia coli to the antibiotic trimethoprim involves derepression of PhoPQ signaling, an Mg2+-sensitive two-component system, by inactivation of the MgrB feedback-regulatory protein. We report that derepression of PhoPQ confers trimethoprim-tolerance to E. coli by hitherto unrecognized transcriptional upregulation of dihydrofolate reductase (DHFR), target of trimethoprim. As a result, mutations in mgrB precede and facilitate the evolution of drug resistance. Using laboratory evolution, genome sequencing, and mutation re-construction, we show that populations of E. coli challenged with trimethoprim are faced with the evolutionary ‘choice’ of transitioning from tolerant to resistant by mutations in DHFR, or compensating for the fitness costs of PhoPQ derepression by inactivating the RpoS sigma factor, itself a PhoPQ-target. Outcomes at this evolutionary branch-point are determined by the strength of antibiotic selection, such that high pressures favor resistance, while low pressures favor cost compensation. Our results relate evolutionary changes in bacterial gene regulatory networks to strength of selection and provide mechanistic evidence to substantiate this link.
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Affiliation(s)
- Vishwa Patel
- Dr. Vikram Sarabhai Institute of Cell and Molecular Biology, The Maharaja Sayajirao University of Baroda, Vadodara, India.,Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
| | - Nishad Matange
- Indian Institute of Science Education and Research (IISER), Pune, India
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19
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Billiard S, Castric V, Llaurens V. The integrative biology of genetic dominance. Biol Rev Camb Philos Soc 2021; 96:2925-2942. [PMID: 34382317 PMCID: PMC9292577 DOI: 10.1111/brv.12786] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/29/2021] [Accepted: 07/30/2021] [Indexed: 11/29/2022]
Abstract
Dominance is a basic property of inheritance systems describing the link between a diploid genotype at a single locus and the resulting phenotype. Models for the evolution of dominance have long been framed as an opposition between the irreconcilable views of Fisher in 1928 supporting the role of largely elusive dominance modifiers and Wright in 1929, who viewed dominance as an emerging property of the structure of enzymatic pathways. Recent theoretical and empirical advances however suggest that these opposing views can be reconciled, notably using models investigating the regulation of gene expression and developmental processes. In this more comprehensive framework, phenotypic dominance emerges from departures from linearity between any levels of integration in the genotype‐to‐phenotype map. Here, we review how these different models illuminate the emergence and evolution of dominance. We then detail recent empirical studies shedding new light on the diversity of molecular and physiological mechanisms underlying dominance and its evolution. By reconciling population genetics and functional biology, we hope our review will facilitate cross‐talk among research fields in the integrative study of dominance evolution.
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Affiliation(s)
- Sylvain Billiard
- Univ. Lille, CNRS, UMR 8198 - Evo-Eco-Paleo, F-59000, Lille, France
| | - Vincent Castric
- Univ. Lille, CNRS, UMR 8198 - Evo-Eco-Paleo, F-59000, Lille, France
| | - Violaine Llaurens
- Institut de Systématique, Evolution et Biodiversité, CNRS/MNHN/Sorbonne Université/EPHE, Museum National d'Histoire Naturelle, CP50, 57 rue Cuvier, 75005, Paris, France
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20
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Wang L, Sun F, Wan ZY, Ye B, Wen Y, Liu H, Yang Z, Pang H, Meng Z, Fan B, Alfiko Y, Shen Y, Bai B, Lee MSQ, Piferrer F, Schartl M, Meyer A, Yue GH. Genomic Basis of Striking Fin Shapes and Colors in the Fighting Fish. Mol Biol Evol 2021; 38:3383-3396. [PMID: 33871625 PMCID: PMC8321530 DOI: 10.1093/molbev/msab110] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Resolving the genomic basis underlying phenotypic variations is a question of great importance in evolutionary biology. However, understanding how genotypes determine the phenotypes is still challenging. Centuries of artificial selective breeding for beauty and aggression resulted in a plethora of colors, long-fin varieties, and hyper-aggressive behavior in the air-breathing Siamese fighting fish (Betta splendens), supplying an excellent system for studying the genomic basis of phenotypic variations. Combining whole-genome sequencing, quantitative trait loci mapping, genome-wide association studies, and genome editing, we investigated the genomic basis of huge morphological variation in fins and striking differences in coloration in the fighting fish. Results revealed that the double tail, elephant ear, albino, and fin spot mutants each were determined by single major-effect loci. The elephant ear phenotype was likely related to differential expression of a potassium ion channel gene, kcnh8. The albinotic phenotype was likely linked to a cis-regulatory element acting on the mitfa gene and the double-tail mutant was suggested to be caused by a deletion in a zic1/zic4 coenhancer. Our data highlight that major loci and cis-regulatory elements play important roles in bringing about phenotypic innovations and establish Bettas as new powerful model to study the genomic basis of evolved changes.
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Affiliation(s)
- Le Wang
- Molecular Population Genetics & Breeding Group, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Fei Sun
- Molecular Population Genetics & Breeding Group, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Zi Yi Wan
- Molecular Population Genetics & Breeding Group, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Baoqing Ye
- Molecular Population Genetics & Breeding Group, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Yanfei Wen
- Molecular Population Genetics & Breeding Group, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Huiming Liu
- Molecular Population Genetics & Breeding Group, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Zituo Yang
- Molecular Population Genetics & Breeding Group, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Hongyan Pang
- Molecular Population Genetics & Breeding Group, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Zining Meng
- School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Bin Fan
- Department of Food and Environmental Engineering, Yangjiang Polytechnic, Yangjiang, China
| | - Yuzer Alfiko
- Biotech Lab, Wilmar International, Jakarta, Indonesia
| | - Yubang Shen
- Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Shanghai Ocean University, Shanghai, China
| | - Bin Bai
- Molecular Population Genetics & Breeding Group, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - May Shu Qing Lee
- Molecular Population Genetics & Breeding Group, Temasek Life Sciences Laboratory, Singapore, Singapore
| | - Francesc Piferrer
- Institute of Marine Sciences (ICM), Spanish National Research Council (CSIC), Barcelona, Spain
| | - Manfred Schartl
- Developmental Biochemistry, Biocenter, University of Wuerzburg, Wuerzburg, Germany
- The Xiphophorus Genetic Stock Center, Department of Chemistry and Biochemistry, Texas State University, San Marcos, TX, USA
| | - Axel Meyer
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Gen Hua Yue
- Molecular Population Genetics & Breeding Group, Temasek Life Sciences Laboratory, Singapore, Singapore
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
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21
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Jakutis G, Stainier DYR. Genotype-Phenotype Relationships in the Context of Transcriptional Adaptation and Genetic Robustness. Annu Rev Genet 2021; 55:71-91. [PMID: 34314597 DOI: 10.1146/annurev-genet-071719-020342] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Genetic manipulations with a robust and predictable outcome are critical to investigate gene function, as well as for therapeutic genome engineering. For many years, knockdown approaches and reagents including RNA interference and antisense oligonucleotides dominated functional studies; however, with the advent of precise genome editing technologies, CRISPR-based knockout systems have become the state-of-the-art tools for such studies. These technologies have helped decipher the role of thousands of genes in development and disease. Their use has also revealed how limited our understanding of genotype-phenotype relationships is. The recent discovery that certain mutations can trigger the transcriptional modulation of other genes, a phenomenon called transcriptional adaptation, has provided an additional explanation for the contradicting phenotypes observed in knockdown versus knockout models and increased awareness about the use of each of these approaches. In this review, we first cover the strengths and limitations of different gene perturbation strategies. Then we highlight the diverse ways in which the genotype-phenotype relationship can be discordant between these different strategies. Finally, we review the genetic robustness mechanisms that can lead to such discrepancies, paying special attention to the recently discovered phenomenon of transcriptional adaptation. Expected final online publication date for the Annual Review of Genetics, Volume 55 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Gabrielius Jakutis
- Department of Developmental Genetics, Max Planck Institute for Heart and Lung Research, 61231 Bad Nauheim, Germany;
| | - Didier Y R Stainier
- Department of Developmental Genetics, Max Planck Institute for Heart and Lung Research, 61231 Bad Nauheim, Germany; .,German Centre for Cardiovascular Research (DZHK), Partner site Rhine-Main, 60590 Frankfurt am Main, Germany.,Excellence Cluster Cardio-Pulmonary Institute (CPI), 35392 Giessen, Germany
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22
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Fernandez-de-Cossio-Diaz J, Uguzzoni G, Pagnani A. Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan. Mol Biol Evol 2021; 38:318-328. [PMID: 32770229 PMCID: PMC7783173 DOI: 10.1093/molbev/msaa204] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable computational methods for data analysis, the prediction of mutational effects, and the generation of optimized sequences. We describe a computational method that, trained on sequencing samples from multiple rounds of a screening experiment, provides a model of the genotype-fitness relationship. We tested the method on five large-scale mutational scans, yielding accurate predictions of the mutational effects on fitness. The inferred fitness landscape is robust to experimental and sampling noise and exhibits high generalization power in terms of broader sequence space exploration and higher fitness variant predictions. We investigate the role of epistasis and show that the inferred model provides structural information about the 3D contacts in the molecular fold.
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Affiliation(s)
- Jorge Fernandez-de-Cossio-Diaz
- Systems Biology Department, Center of Molecular Immunology, Havana, Cuba.,Laboratory of Physics of the Ecole Normale Superieure, CNRS UMR 8023 & PSL Research, Paris, France
| | | | - Andrea Pagnani
- Politecnico di Torino, Torino, Italy.,Italian Institute for Genomic Medicine, IRCCS Candiolo, Candiolo, TO, Italy.,INFN, Sezione di Torino, Torino, Italy
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23
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Abstract
RNA viruses, such as hepatitis C virus (HCV), influenza virus, and SARS-CoV-2, are notorious for their ability to evolve rapidly under selection in novel environments. It is known that the high mutation rate of RNA viruses can generate huge genetic diversity to facilitate viral adaptation. However, less attention has been paid to the underlying fitness landscape that represents the selection forces on viral genomes, especially under different selection conditions. Here, we systematically quantified the distribution of fitness effects of about 1,600 single amino acid substitutions in the drug-targeted region of NS5A protein of HCV. We found that the majority of nonsynonymous substitutions incur large fitness costs, suggesting that NS5A protein is highly optimized. The replication fitness of viruses is correlated with the pattern of sequence conservation in nature, and viral evolution is constrained by the need to maintain protein stability. We characterized the adaptive potential of HCV by subjecting the mutant viruses to selection by the antiviral drug daclatasvir at multiple concentrations. Both the relative fitness values and the number of beneficial mutations were found to increase with the increasing concentrations of daclatasvir. The changes in the spectrum of beneficial mutations in NS5A protein can be explained by a pharmacodynamics model describing viral fitness as a function of drug concentration. Overall, our results show that the distribution of fitness effects of mutations is modulated by both the constraints on the biophysical properties of proteins (i.e., selection pressure for protein stability) and the level of environmental stress (i.e., selection pressure for drug resistance). IMPORTANCE Many viruses adapt rapidly to novel selection pressures, such as antiviral drugs. Understanding how pathogens evolve under drug selection is critical for the success of antiviral therapy against human pathogens. By combining deep sequencing with selection experiments in cell culture, we have quantified the distribution of fitness effects of mutations in hepatitis C virus (HCV) NS5A protein. Our results indicate that the majority of single amino acid substitutions in NS5A protein incur large fitness costs. Simulation of protein stability suggests viral evolution is constrained by the need to maintain protein stability. By subjecting the mutant viruses to selection under an antiviral drug, we find that the adaptive potential of viral proteins in a novel environment is modulated by the level of environmental stress, which can be explained by a pharmacodynamics model. Our comprehensive characterization of the fitness landscapes of NS5A can potentially guide the design of effective strategies to limit viral evolution.
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24
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Soo VWC, Swadling JB, Faure AJ, Warnecke T. Fitness landscape of a dynamic RNA structure. PLoS Genet 2021; 17:e1009353. [PMID: 33524037 PMCID: PMC7877785 DOI: 10.1371/journal.pgen.1009353] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 02/11/2021] [Accepted: 01/12/2021] [Indexed: 11/24/2022] Open
Abstract
RNA structures are dynamic. As a consequence, mutational effects can be hard to rationalize with reference to a single static native structure. We reasoned that deep mutational scanning experiments, which couple molecular function to fitness, should capture mutational effects across multiple conformational states simultaneously. Here, we provide a proof-of-principle that this is indeed the case, using the self-splicing group I intron from Tetrahymena thermophila as a model system. We comprehensively mutagenized two 4-bp segments of the intron. These segments first come together to form the P1 extension (P1ex) helix at the 5' splice site. Following cleavage at the 5' splice site, the two halves of the helix dissociate to allow formation of an alternative helix (P10) at the 3' splice site. Using an in vivo reporter system that couples splicing activity to fitness in E. coli, we demonstrate that fitness is driven jointly by constraints on P1ex and P10 formation. We further show that patterns of epistasis can be used to infer the presence of intramolecular pleiotropy. Using a machine learning approach that allows quantification of mutational effects in a genotype-specific manner, we demonstrate that the fitness landscape can be deconvoluted to implicate P1ex or P10 as the effective genetic background in which molecular fitness is compromised or enhanced. Our results highlight deep mutational scanning as a tool to study alternative conformational states, with the capacity to provide critical insights into the structure, evolution and evolvability of RNAs as dynamic ensembles. Our findings also suggest that, in the future, deep mutational scanning approaches might help reverse-engineer multiple alternative or successive conformations from a single fitness landscape.
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Affiliation(s)
- Valerie W. C. Soo
- Medical Research Council London Institute of Medical Sciences, London, United Kingdom
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Jacob B. Swadling
- Medical Research Council London Institute of Medical Sciences, London, United Kingdom
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Andre J. Faure
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Tobias Warnecke
- Medical Research Council London Institute of Medical Sciences, London, United Kingdom
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
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25
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McLeish MJ, Fraile A, García-Arenal F. Population Genomics of Plant Viruses: The Ecology and Evolution of Virus Emergence. PHYTOPATHOLOGY 2021; 111:32-39. [PMID: 33210987 DOI: 10.1094/phyto-08-20-0355-fi] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
The genomics era has revolutionized studies of adaptive evolution by monitoring large numbers of loci throughout the genomes of many individuals. Ideally, the investigation of emergence in plant viruses requires examining the population dynamics of both virus and host, their interactions with each other, with other organisms and the abiotic environment. Genetic mechanisms that affect demographic processes are now being studied with high-throughput technologies, traditional genetics methods, and new computational tools for big-data. In this review, we discuss the utility of these approaches to monitor and detect changes in virus populations within cells and individuals, and over wider areas across species and communities of ecosystems. The advent of genomics in virology has fostered a multidisciplinary approach to tackling disease risk. The ability to make sense of the information now generated in this integrated setting is by far the most substantial obstacle to the ultimate goal of plant virology to minimize the threats to food security posed by disease. To achieve this goal, it is imperative to understand and forecast how populations respond to future changes in complex natural systems.
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Affiliation(s)
- Michael J McLeish
- Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) and Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Aurora Fraile
- Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) and Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Fernando García-Arenal
- Centro de Biotecnología y Genómica de Plantas (CBGP), Universidad Politécnica de Madrid (UPM) and Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
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26
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Lyons DM, Zou Z, Xu H, Zhang J. Idiosyncratic epistasis creates universals in mutational effects and evolutionary trajectories. Nat Ecol Evol 2020; 4:1685-1693. [PMID: 32895516 PMCID: PMC7710555 DOI: 10.1038/s41559-020-01286-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 07/23/2020] [Indexed: 01/06/2023]
Abstract
Patterns of epistasis and shapes of fitness landscapes are of wide interest because of their bearings on a number of evolutionary theories. The common phenomena of slowing fitness increases during adaptations and diminishing returns from beneficial mutations are believed to reflect a concave fitness landscape and a preponderance of negative epistasis. Paradoxically, fitness decreases tend to decelerate and harm from deleterious mutations shrinks during the accumulation of random mutations-patterns thought to indicate a convex fitness landscape and a predominance of positive epistasis. Current theories cannot resolve this apparent contradiction. Here, we show that the phenotypic effect of a mutation varies substantially depending on the specific genetic background and that this idiosyncrasy in epistasis creates all of the above trends without requiring a biased distribution of epistasis. The idiosyncratic epistasis theory explains the universalities in mutational effects and evolutionary trajectories as emerging from randomness due to biological complexity.
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Affiliation(s)
| | | | | | - Jianzhi Zhang
- Correspondence to Jianzhi Zhang, Department of Ecology and Evolutionary Biology, University of Michigan, 4018 Biological Sciences Building, 1105 North University Avenue, Ann Arbor, MI 48109, USA, Phone: 734-763-0527,
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27
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de Wet TJ, Winkler KR, Mhlanga M, Mizrahi V, Warner DF. Arrayed CRISPRi and quantitative imaging describe the morphotypic landscape of essential mycobacterial genes. eLife 2020; 9:e60083. [PMID: 33155979 PMCID: PMC7647400 DOI: 10.7554/elife.60083] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 10/03/2020] [Indexed: 12/11/2022] Open
Abstract
Mycobacterium tuberculosis possesses a large number of genes of unknown or predicted function, undermining fundamental understanding of pathogenicity and drug susceptibility. To address this challenge, we developed a high-throughput functional genomics approach combining inducible CRISPR-interference and image-based analyses of morphological features and sub-cellular chromosomal localizations in the related non-pathogen, M. smegmatis. Applying automated imaging and analysis to 263 essential gene knockdown mutants in an arrayed library, we derive robust, quantitative descriptions of bacillary morphologies consequent on gene silencing. Leveraging statistical-learning, we demonstrate that functionally related genes cluster by morphotypic similarity and that this information can be used to inform investigations of gene function. Exploiting this observation, we infer the existence of a mycobacterial restriction-modification system, and identify filamentation as a defining mycobacterial response to histidine starvation. Our results support the application of large-scale image-based analyses for mycobacterial functional genomics, simultaneously establishing the utility of this approach for drug mechanism-of-action studies.
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Affiliation(s)
- Timothy J de Wet
- SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, Department of Pathology, University of Cape TownCape TownSouth Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape TownCape TownSouth Africa
| | - Kristy R Winkler
- SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, Department of Pathology, University of Cape TownCape TownSouth Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape TownCape TownSouth Africa
| | - Musa Mhlanga
- Institute of Infectious Disease and Molecular Medicine, University of Cape TownCape TownSouth Africa
- Department of Integrative Biomedical Sciences, University of Cape TownCape TownSouth Africa
| | - Valerie Mizrahi
- SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, Department of Pathology, University of Cape TownCape TownSouth Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape TownCape TownSouth Africa
- Wellcome Centre for Infectious Diseases Research in Africa, University of Cape TownCape TownSouth Africa
| | - Digby F Warner
- SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, Department of Pathology, University of Cape TownCape TownSouth Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape TownCape TownSouth Africa
- Wellcome Centre for Infectious Diseases Research in Africa, University of Cape TownCape TownSouth Africa
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28
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Wytock TP, Zhang M, Jinich A, Fiebig A, Crosson S, Motter AE. Extreme Antagonism Arising from Gene-Environment Interactions. Biophys J 2020; 119:2074-2086. [PMID: 33068537 DOI: 10.1016/j.bpj.2020.09.038] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/27/2020] [Accepted: 09/21/2020] [Indexed: 01/06/2023] Open
Abstract
Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost because of a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, whereas nonantagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.
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Affiliation(s)
- Thomas P Wytock
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois
| | - Manjing Zhang
- The Committee on Microbiology, University of Chicago, Chicago, Illinois
| | - Adrian Jinich
- Division of Infectious Diseases, Weill Department of Medicine, Weill-Cornell Medical College, New York, New York
| | - Aretha Fiebig
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan
| | - Sean Crosson
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan
| | - Adilson E Motter
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois; Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois; Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois.
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29
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Cao M, Tran VG, Zhao H. Unlocking nature's biosynthetic potential by directed genome evolution. Curr Opin Biotechnol 2020; 66:95-104. [PMID: 32721868 DOI: 10.1016/j.copbio.2020.06.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 06/22/2020] [Accepted: 06/22/2020] [Indexed: 01/22/2023]
Abstract
Microorganisms have been increasingly explored as microbial cell factories for production of fuels, chemicals, drugs, and materials. Among the various metabolic engineering strategies, directed genome evolution has emerged as one of the most powerful tools to unlock the full biosynthetic potential of microorganisms. Here we summarize the directed genome evolution strategies that have been developed in recent years, including adaptive laboratory evolution and various targeted genome-scale engineering strategies, and discuss their applications in basic and applied biological research.
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Affiliation(s)
- Mingfeng Cao
- Department of Chemical and Biomolecular Engineering, U.S. Department of Energy Center for Bioenergy and Bioproducts Innovation (CABBI), Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, U.S. Department of Energy Center for Bioenergy and Bioproducts Innovation (CABBI), Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, U.S. Department of Energy Center for Bioenergy and Bioproducts Innovation (CABBI), Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Departments of Chemistry, Biochemistry, and Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States.
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30
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Kemble H, Eisenhauer C, Couce A, Chapron A, Magnan M, Gautier G, Le Nagard H, Nghe P, Tenaillon O. Flux, toxicity, and expression costs generate complex genetic interactions in a metabolic pathway. SCIENCE ADVANCES 2020; 6:eabb2236. [PMID: 32537514 PMCID: PMC7269641 DOI: 10.1126/sciadv.abb2236] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 03/31/2020] [Indexed: 05/31/2023]
Abstract
Our ability to predict the impact of mutations on traits relevant for disease and evolution remains severely limited by the dependence of their effects on the genetic background and environment. Even when molecular interactions between genes are known, it is unclear how these translate to organism-level interactions between alleles. We therefore characterized the interplay of genetic and environmental dependencies in determining fitness by quantifying ~4000 fitness interactions between expression variants of two metabolic genes, starting from various environmentally modulated expression levels. We detect a remarkable variety of interactions dependent on initial expression levels and demonstrate that they can be quantitatively explained by a mechanistic model accounting for catabolic flux, metabolite toxicity, and expression costs. Complex fitness interactions between mutations can therefore be predicted simply from their simultaneous impact on a few connected molecular phenotypes.
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Affiliation(s)
- Harry Kemble
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
- Laboratory of Biochemistry (LBC), Chimie Biologie et Innovation, ESPCI Paris, PSL University, CNRS, 75005 Paris, France
| | | | - Alejandro Couce
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
- Department of Life Sciences, Imperial College, London SW7 2AZ, UK
| | - Audrey Chapron
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
| | - Mélanie Magnan
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
| | - Gregory Gautier
- Centre de Recherche sur l'Inflammation, INSERM, UMRS 1149, 75018 Paris, France
- Laboratoire d’Excellence INFLAMEX, Université de Paris, Sorbonne Paris Cité, 75018 Paris, France
| | - Hervé Le Nagard
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
| | - Philippe Nghe
- Laboratory of Biochemistry (LBC), Chimie Biologie et Innovation, ESPCI Paris, PSL University, CNRS, 75005 Paris, France
| | - Olivier Tenaillon
- IAME, INSERM, Université de Paris, Université Paris Nord, 75018 Paris, France
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31
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Flynn JM, Rossouw A, Cote-Hammarlof P, Fragata I, Mavor D, Hollins C, Bank C, Bolon DN. Comprehensive fitness maps of Hsp90 show widespread environmental dependence. eLife 2020; 9:53810. [PMID: 32129763 PMCID: PMC7069724 DOI: 10.7554/elife.53810] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 03/03/2020] [Indexed: 12/29/2022] Open
Abstract
Gene-environment interactions have long been theorized to influence molecular evolution. However, the environmental dependence of most mutations remains unknown. Using deep mutational scanning, we engineered yeast with all 44,604 single codon changes encoding 14,160 amino acid variants in Hsp90 and quantified growth effects under standard conditions and under five stress conditions. To our knowledge, these are the largest determined comprehensive fitness maps of point mutants. The growth of many variants differed between conditions, indicating that environment can have a large impact on Hsp90 evolution. Multiple variants provided growth advantages under individual conditions; however, these variants tended to exhibit growth defects in other environments. The diversity of Hsp90 sequences observed in extant eukaryotes preferentially contains variants that supported robust growth under all tested conditions. Rather than favoring substitutions in individual conditions, the long-term selective pressure on Hsp90 may have been that of fluctuating environments, leading to robustness under a variety of conditions.
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Affiliation(s)
- Julia M Flynn
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, United States
| | - Ammeret Rossouw
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, United States
| | - Pamela Cote-Hammarlof
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, United States
| | - Inês Fragata
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
| | - David Mavor
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, United States
| | - Carl Hollins
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, United States
| | - Claudia Bank
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
| | - Daniel Na Bolon
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, United States
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32
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Choudhury A, Fenster JA, Fankhauser RG, Kaar JL, Tenaillon O, Gill RT. CRISPR/Cas9 recombineering-mediated deep mutational scanning of essential genes in Escherichia coli. Mol Syst Biol 2020; 16:e9265. [PMID: 32175691 PMCID: PMC7073797 DOI: 10.15252/msb.20199265] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 02/12/2020] [Accepted: 02/13/2020] [Indexed: 01/14/2023] Open
Abstract
Deep mutational scanning can provide significant insights into the function of essential genes in bacteria. Here, we developed a high-throughput method for mutating essential genes of Escherichia coli in their native genetic context. We used Cas9-mediated recombineering to introduce a library of mutations, created by error-prone PCR, within a gene fragment on the genome using a single gRNA pre-validated for high efficiency. Tracking mutation frequency through deep sequencing revealed biases in the position and the number of the introduced mutations. We overcame these biases by increasing the homology arm length and blocking mismatch repair to achieve a mutation efficiency of 85% for non-essential genes and 55% for essential genes. These experiments also improved our understanding of poorly characterized recombineering process using dsDNA donors with single nucleotide changes. Finally, we applied our technology to target rpoB, the beta subunit of RNA polymerase, to study resistance against rifampicin. In a single experiment, we validate multiple biochemical and clinical observations made in the previous decades and provide insights into resistance compensation with the study of double mutants.
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Affiliation(s)
- Alaksh Choudhury
- Department of Chemical and Biological EngineeringUniversity of ColoradoBoulderCOUSA
- IAMEINSERMUniversité de ParisParisFrance
| | - Jacob A Fenster
- Department of Chemical and Biological EngineeringUniversity of ColoradoBoulderCOUSA
| | | | - Joel L Kaar
- Department of Chemical and Biological EngineeringUniversity of ColoradoBoulderCOUSA
| | | | - Ryan T Gill
- Department of Chemical and Biological EngineeringUniversity of ColoradoBoulderCOUSA
- Renewable & Sustainable Energy InstituteUniversity of ColoradoBoulderCOUSA
- Novo Nordisk Foundation Center for BiosustainabilityDanish Technical UniversityCopenhagenDenmark
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