scholarly journals QTL analysis of natural Saccharomyces cerevisiae isolates reveals unique alleles involved in lignocellulosic inhibitor tolerance

2019 ◽  
Vol 19 (5) ◽  
Author(s):  
R N de Witt ◽  
H Kroukamp ◽  
W H Van Zyl ◽  
I T Paulsen ◽  
H Volschenk
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Complex Traits ◽  
Genetic Basis ◽  
Strain Improvement ◽  
Complex Trait ◽  
Limited Range ◽  
Specific Response ◽  
Inhibitor Tolerance ◽  
Rational Engineering ◽  
Industrial Strains

ABSTRACTDecoding the genetic basis of lignocellulosic inhibitor tolerance in Saccharomyces cerevisiae is crucial for rational engineering of bioethanol strains with enhanced robustness. The genetic diversity of natural strains present an invaluable resource for the exploration of complex traits of industrial importance from a pan-genomic perspective to complement the limited range of specialised, tolerant industrial strains. Natural S. cerevisiae isolates have lately garnered interest as a promising toolbox for engineering novel, genetically encoded tolerance phenotypes into commercial strains. To this end, we investigated the genetic basis for lignocellulosic inhibitor tolerance of natural S. cerevisiae isolates. A total of 12 quantitative trait loci underpinning tolerance were identified by next-generation sequencing linked bulk-segregant analysis of superior interbred pools. Our findings corroborate the current perspective of lignocellulosic inhibitor tolerance as a multigenic, complex trait. Apart from a core set of genetic variants required for inhibitor tolerance, an additional genetic background-specific response was observed. Functional analyses of the identified genetic loci revealed the uncharacterised ORF, YGL176C and the bud-site selection XRN1/BUD13 as potentially beneficial alleles contributing to tolerance to a complex lignocellulosic inhibitor mixture. We present evidence for the consideration of both regulatory and coding sequence variants for strain improvement.

scholarly journals Robustness and Ethanol Production of Industrial Strains of Saccharomyces cerevisiae Using Different Sugarcane Bagasse Hydrolysates

2019 ◽  
Vol 7 (1) ◽  
pp. 23 ◽  
Author(s):  
Vanessa S. Teixeira ◽  
Suéllen P. H. Azambuja ◽  
Priscila H. Carvalho ◽  
Fátima A. A. Costa ◽  
Patricia R. Kitaka ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Ethanol Production ◽  
Sugarcane Bagasse ◽  
Ethanol Yield ◽  
Raw Materials ◽  
Fermentation Inhibitors ◽  
Severe Condition ◽  
Inhibitor Tolerance ◽  
Industrial Strains ◽  
Materials Used

Sugarcane bagasse is one of the main lignocellulosic raw materials used for the production of second-generation ethanol. Technological studies on fermentation processes have focused on the search for and development of more robust microorganisms that are able to produce bioethanol efficiently and are resistant to the main fermentation inhibitors. The purpose of this study was to evaluate the robustness and ethanol production of industrial strains of Saccharomyces cerevisiae using acid, alkaline, and enzymatic sugarcane bagasse hydrolysates. Hydrolysis was carried out to release fermentable sugars from sugarcane bagasse. Fermentations were performed in shake flasks containing sugarcane hydrolysates supplemented with 150 g L−1 glucose to evaluate the kinetic parameters of the reaction. Inhibitor tolerance was evaluated by incubating cells with different concentrations of inhibitors in 96-well plates. The biomass yield on substrate, ethanol yield on substrate, and ethanol productivity of the six strains were higher in 0.5% acid, 0.5% alkaline, and enzymatic hydrolysates (i.e., under milder conditions). The SA-1 (Santa Adélia-1) strain had a better performance in comparison with the other strains for its ability to produce ethanol in a very severe condition (7% acid hydrolysis) and for its robustness in growing at several inhibitor concentrations.

scholarly journals Thinking About the Evolution of Complex Traits in the Era of Genome-Wide Association Studies

2019 ◽  
Vol 20 (1) ◽  
pp. 461-493 ◽  
Author(s):  
Guy Sella ◽  
Nicholas H. Barton
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Practical Importance ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Heritable Variation ◽  
Genome Wide ◽  
Polygenic Adaptation

Many traits of interest are highly heritable and genetically complex, meaning that much of the variation they exhibit arises from differences at numerous loci in the genome. Complex traits and their evolution have been studied for more than a century, but only in the last decade have genome-wide association studies (GWASs) in humans begun to reveal their genetic basis. Here, we bring these threads of research together to ask how findings from GWASs can further our understanding of the processes that give rise to heritable variation in complex traits and of the genetic basis of complex trait evolution in response to changing selection pressures (i.e., of polygenic adaptation). Conversely, we ask how evolutionary thinking helps us to interpret findings from GWASs and informs related efforts of practical importance.

scholarly journals Evolution of quantitative trait locus hotspots in yeast species

2021 ◽  
Author(s):  
Emilien Peltier ◽  
Sabrina Bibi-Triki ◽  
Fabien Dutreux ◽  
Claudia Caradec ◽  
Anne Friedrich ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Complex Traits ◽  
Genetic Basis ◽  
Yeast Species ◽  
Model Organism ◽  
Complex Trait ◽  
Wide Range ◽  
Trait Locus ◽  
Real Challenge ◽  
Drug Free

Dissecting the genetic basis of complex trait remains a real challenge. The budding yeast Saccharomyces cerevisiae has become a model organism for studying quantitative traits, successfully increasing our knowledge in many aspects. However, the exploration of the genotype-phenotype relationship in non-model yeast species could provide a deeper insight into the genetic basis of complex traits. Here, we have studied this relationship in the Lachancea waltii species which diverged from the S. cerevisiae lineage prior to the whole-genome duplication. By performing linkage mapping analyses in this species, we identified 86 quantitative trait loci (QTL) affecting growth fitness in a large number of conditions. The distribution of these loci across the genome has revealed two major QTL hotspots. A first hotspot corresponds to a general fitness QTL, impacting a wide range of conditions. By contrast, the second hotspot highlighted a fitness trade-off with a disadvantageous allele for drug-free conditions which proved to be advantageous in the presence of several drugs. Finally, the comparison of the detected QTL in L. waltii with those which had been previously identified for the same traits in a closely related species, Lachancea kluyveri, clearly revealed the absence of interspecific conservation of these loci. Altogether, our results expand our knowledge on the variation of the QTL landscape across different yeast species.

scholarly journals Dissection of quantitative trait loci in the Lachancea waltii yeast species highlights major hotspots

2021 ◽  
Author(s):  
Emilien Peltier ◽  
Sabrina Bibi-Triki ◽  
Fabien Dutreux ◽  
Claudia Caradec ◽  
Anne Friedrich ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Genetic Basis ◽  
Yeast Species ◽  
Model Organism ◽  
Complex Trait ◽  
Yeast Saccharomyces Cerevisiae ◽  
Wide Range ◽  
Trait Loci

Abstract Dissecting the genetic basis of complex trait remains a real challenge. The budding yeast Saccharomyces cerevisiae has become a model organism for studying quantitative traits, successfully increasing our knowledge in many aspects. However, the exploration of the genotype-phenotype relationship in non-model yeast species could provide a deeper insight into the genetic basis of complex traits. Here, we have studied this relationship in the Lachancea waltii species which diverged from the S. cerevisiae lineage prior to the whole-genome duplication. By performing linkage mapping analyses in this species, we identified 86 quantitative trait loci (QTL) impacting the growth in a large number of conditions. The distribution of these loci across the genome has revealed two major QTL hotspots. A first hotspot corresponds to a general growth QTL, impacting a wide range of conditions. By contrast, the second hotspot highlighted a trade-off with a disadvantageous allele for drug-free conditions which proved to be advantageous in the presence of several drugs. Finally, a comparison of the detected QTL in L. waltii with those which had been previously identified for the same trait in a closely related species, Lachancea kluyveri was performed. This analysis clearly showed the absence of shared QTL across these species. Altogether, our results represent a first step toward the exploration of the genetic architecture of quantitative trait across different yeast species.

scholarly journals Contextualizing genetic risk score for disease screening and rare variant discovery

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dan Zhou ◽  
Dongmei Yu ◽  
Jeremiah M. Scharf ◽  
Carol A. Mathews ◽  
Lauren McGrath ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Clinical Decision Making ◽  
Genetic Risk Score ◽  
Sufficient Conditions ◽  
Clinical Decision ◽  
Generative Models ◽  
Variant Discovery ◽  
Models Of Disease ◽  
The Uk

AbstractStudies of the genetic basis of complex traits have demonstrated a substantial role for common, small-effect variant polygenic burden (PB) as well as large-effect variants (LEV, primarily rare). We identify sufficient conditions in which GWAS-derived PB may be used for well-powered rare pathogenic variant discovery or as a sample prioritization tool for whole-genome or exome sequencing. Through extensive simulations of genetic architectures and generative models of disease liability with parameters informed by empirical data, we quantify the power to detect, among cases, a lower PB in LEV carriers than in non-carriers. Furthermore, we uncover clinically useful conditions wherein the risk derived from the PB is comparable to the LEV-derived risk. The resulting summary-statistics-based methodology (with publicly available software, PB-LEV-SCAN) makes predictions on PB-based LEV screening for 36 complex traits, which we confirm in several disease datasets with available LEV information in the UK Biobank, with important implications on clinical decision-making.

scholarly journals Meta-QTL analysis and identification of candidate genes for quality, abiotic and biotic stress in durum wheat

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jose Miguel Soriano ◽  
Pasqualina Colasuonno ◽  
Ilaria Marcotuli ◽  
Agata Gadaleta
Keyword(s):  
Molecular Markers ◽  
Abiotic Stress ◽  
Durum Wheat ◽  
Candidate Genes ◽  
Biotic Stress ◽  
Qtl Analysis ◽  
Complex Traits ◽  
Genetic Basis ◽  
Agronomic Traits ◽  
Plant Performance

AbstractThe genetic improvement of durum wheat and enhancement of plant performance often depend on the identification of stable quantitative trait loci (QTL) and closely linked molecular markers. This is essential for better understanding the genetic basis of important agronomic traits and identifying an effective method for improving selection efficiency in breeding programmes. Meta-QTL analysis is a useful approach for dissecting the genetic basis of complex traits, providing broader allelic coverage and higher mapping resolution for the identification of putative molecular markers to be used in marker-assisted selection. In the present study, extensive QTL meta-analysis was conducted on 45 traits of durum wheat, including quality and biotic and abiotic stress-related traits. A total of 368 QTL distributed on all 14 chromosomes of genomes A and B were projected: 171 corresponded to quality-related traits, 127 to abiotic stress and 71 to biotic stress, of which 318 were grouped in 85 meta-QTL (MQTL), 24 remained as single QTL and 26 were not assigned to any MQTL. The number of MQTL per chromosome ranged from 4 in chromosomes 1A and 6A to 9 in chromosome 7B; chromosomes 3A and 7A showed the highest number of individual QTL (4), and chromosome 7B the highest number of undefined QTL (4). The recently published genome sequence of durum wheat was used to search for candidate genes within the MQTL peaks. This work will facilitate cloning and pyramiding of QTL to develop new cultivars with specific quantitative traits and speed up breeding programs.

scholarly journals Epistasis for Three Grain Yield Components in Rice (Oryxa sativa L.)

Genetics ◽  
1997 ◽  
Vol 145 (2) ◽  
pp. 453-465 ◽  
Author(s):  
Zhikang Li ◽  
Shannon R M Pinson ◽  
William D Park ◽  
Andrew H Paterson ◽  
James W Stansel
Keyword(s):  
Grain Yield ◽  
Complex Traits ◽  
Yield Components ◽  
Genetic Basis ◽  
Kernel Weight ◽  
Progeny Testing ◽  
Grain Yield Components ◽  
Main Effects ◽  
Grain Number Per Panicle

The genetic basis for three grain yield components of rice, 1000 kernel weight (KW), grain number per panicle (GN), and grain weight per panicle (GWP), was investigated using restriction fragment length polymorphism markers and F4 progeny testing from a cross between rice subspecies japonica (cultivar Lemont from USA) and indica (cv. Teqing from China). Following identification of 19 QTL affecting these traits, we investigated the role of epistasis in genetic control of these phenotypes. Among 63 markers distributed throughout the genome that appeared to be involved in 79 highly significant (P < 0.001) interactions, most (46 or 73%) did not appear to have “main” effects on the relevant traits, but influenced the trait(s) predominantly through interactions. These results indicate that epistasis is an important genetic basis for complex traits such as yield components, especially traits of low heritability such as GN and GWP. The identification of epistatic loci is an important step toward resolution of discrepancies between quantitative trait loci mapping and classical genetic dogma, contributes to better understanding of the persistence of quantitative genetic variation in populations, and impels reconsideration of optimal mapping methodology and marker-assisted breeding strategies for improvement of complex traits.

scholarly journals Eukaryotic translation factor eIF5A contributes to acetic acid tolerance in Saccharomyces cerevisiae via transcriptional factor Ume6p

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yanfei Cheng ◽  
Hui Zhu ◽  
Zhengda Du ◽  
Xuena Guo ◽  
Chenyao Zhou ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Acetic Acid ◽  
Adaptive Response ◽  
Acid Tolerance ◽  
Peptide Bond ◽  
Transcriptional Factor ◽  
Yeast Cells ◽  
Acetic Acid Tolerance ◽  
Translation Factor ◽  
Industrial Strains

Abstract Background Saccharomyces cerevisiae is well-known as an ideal model system for basic research and important industrial microorganism for biotechnological applications. Acetic acid is an important growth inhibitor that has deleterious effects on both the growth and fermentation performance of yeast cells. Comprehensive understanding of the mechanisms underlying S. cerevisiae adaptive response to acetic acid is always a focus and indispensable for development of robust industrial strains. eIF5A is a specific translation factor that is especially required for the formation of peptide bond between certain residues including proline regarded as poor substrates for slow peptide bond formation. Decrease of eIF5A activity resulted in temperature-sensitive phenotype of yeast, while up-regulation of eIF5A protected transgenic Arabidopsis against high temperature, oxidative or osmotic stress. However, the exact roles and functional mechanisms of eIF5A in stress response are as yet largely unknown. Results In this research, we compared cell growth between the eIF5A overexpressing and the control S. cerevisiae strains under various stressed conditions. Improvement of acetic acid tolerance by enhanced eIF5A activity was observed all in spot assay, growth profiles and survival assay. eIF5A prompts the synthesis of Ume6p, a pleiotropic transcriptional factor containing polyproline motifs, mainly in a translational related way. As a consequence, BEM4, BUD21 and IME4, the direct targets of Ume6p, were up-regulated in eIF5A overexpressing strain, especially under acetic acid stress. Overexpression of UME6 results in similar profiles of cell growth and target genes transcription to eIF5A overexpression, confirming the role of Ume6p and its association between eIF5A and acetic acid tolerance. Conclusion Translation factor eIF5A protects yeast cells against acetic acid challenge by the eIF5A-Ume6p-Bud21p/Ime4p/Bem4p axles, which provides new insights into the molecular mechanisms underlying the adaptive response and tolerance to acetic acid in S. cerevisiae and novel targets for construction of robust industrial strains.

scholarly journals Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits

2020 ◽  
Vol 10 (12) ◽  
pp. 4599-4613
Author(s):  
Fabio Morgante ◽  
Wen Huang ◽  
Peter Sørensen ◽  
Christian Maltecca ◽  
Trudy F. C. Mackay
Keyword(s):  
Precision Agriculture ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Cellular Response ◽  
Complex Trait ◽  
Sources Of Information ◽  
Starvation Resistance ◽  
Expression Levels ◽  
Chill Coma ◽  
Additional Layer

The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.

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