scholarly journals Stresses affect inbreeding depression in complex ways: disentangling stress-specific genetic effects from effects of initial size in plants

Heredity ◽  
2021 ◽  
Author(s):  
Tobias M. Sandner ◽  
Diethart Matthies ◽  
Donald M. Waller
Keyword(s):  
Gene Expression ◽  
Inbreeding Depression ◽  
Phenotypic Variation ◽  
Phenotypic Variability ◽  
Nutrient Deficiency ◽  
Plant Size ◽  
Genetic Effects ◽  
Initial Size ◽  
Copper Addition ◽  
Deleterious Alleles

AbstractThe magnitude of inbreeding depression (ID) varies unpredictably among environments. ID often increases in stressful environments suggesting that these expose more deleterious alleles to selection or increase their effects. More simply, ID could increase under conditions that amplify phenotypic variation (CV²), e.g., by accentuating size hierarchies among plants. These mechanisms are difficult to distinguish when stress increases both ID and phenotypic variation. We grew in- and outbred progeny of Mimulus guttatus under six abiotic stress treatments (control, waterlogging, drought, nutrient deficiency, copper addition, and clipping) with and without competition by the grass Poa palustris. ID differed greatly among stress treatments with δ varying from 7% (control) to 61% (waterlogging) but did not consistently increase with stress intensity. Poa competition increased ID under nutrient deficiency but not other stresses. Analyzing effects of initial size on performance of outbred plants suggests that under some conditions (low N, clipping) competition increased ID by amplifying initial size differences. In other cases (e.g., high ID under waterlogging), particular environments amplified the deleterious genetic effects of inbreeding suggesting differential gene expression. Interestingly, conditions that increased the phenotypic variability of inbred progeny regularly increased ID whereas variability among outbred progeny showed no relationship to ID. Our study reconciles the stress- and phenotypic variability hypotheses by demonstrating how specific conditions (rather than stress per se) act to increase ID. Analyzing CV² separately in inbred and outbred progeny while including effects of initial plant size improve our ability to predict how ID and gene expression vary across environments.

scholarly journals VipariNama: RNA viral vectors to rapidly elucidate the relationship between gene expression and phenotype

2021 ◽  
Author(s):  
Arjun Khakhar ◽  
Cecily Wang ◽  
Ryan Swanson ◽  
Sydney Stokke ◽  
Furva Rizvi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Viral Vectors ◽  
Viral Vector ◽  
Tobacco Rattle Virus ◽  
Plant Size ◽  
Great Promise ◽  
Attractive Alternative ◽  
Gibberellin Biosynthesis ◽  
In Planta

Abstract Synthetic transcription factors have great promise as tools to help elucidate relationships between gene expression and phenotype by allowing tunable alterations of gene expression without genomic alterations of the loci being studied. However, the years-long timescales, high cost, and technical skill associated with plant transformation have limited their use. In this work we developed a technology called VipariNama (ViN) in which vectors based on the Tobacco Rattle Virus (TRV) are used to rapidly deploy Cas9-based synthetic transcription factors and reprogram gene expression in planta. We demonstrate that ViN vectors can implement activation or repression of multiple genes systemically and persistently over several weeks in Nicotiana benthamiana, Arabidopsis (Arabidopsis thaliana), and tomato (Solanum lycopersicum). By exploring strategies including RNA scaffolding, viral vector ensembles, and viral engineering, we describe how the flexibility and efficacy of regulation can be improved. We also show how this transcriptional reprogramming can create predictable changes to metabolic phenotypes, such as gibberellin biosynthesis in N. benthamiana and anthocyanin accumulation in Arabidopsis, as well as developmental phenotypes, such as plant size in N. benthamiana, Arabidopsis, and tomato. These results demonstrate how ViN vector-based reprogramming of different aspects of gibberellin signaling can be used to engineer plant size in a range of plant species in a matter of weeks. In summary, VipariNama accelerates the timeline for generating phenotypes from over a year to just a few weeks, providing an attractive alternative to transgenesis for synthetic transcription factor-enabled hypothesis testing and crop engineering.

scholarly journals Selection, Load and Inbreeding Depression in a Large Metapopulation

Genetics ◽  
2002 ◽  
Vol 160 (3) ◽  
pp. 1191-1202 ◽  
Author(s):  
Michael C Whitlock
Keyword(s):  
Population Structure ◽  
Inbreeding Depression ◽  
Population Subdivision ◽  
Response To Selection ◽  
Mutation Load ◽  
Deleterious Alleles ◽  
Subdivided Population ◽  
Soft Selection ◽  
Mean Fitness ◽  
The Mean

Abstract The subdivision of a species into local populations causes its response to selection to change, even if selection is uniform across space. Population structure increases the frequency of homozygotes and therefore makes selection on homozygous effects more effective. However, population subdivision can increase the probability of competition among relatives, which may reduce the efficacy of selection. As a result, the response to selection can be either increased or decreased in a subdivided population relative to an undivided one, depending on the dominance coefficient FST and whether selection is hard or soft. Realistic levels of population structure tend to reduce the mean frequency of deleterious alleles. The mutation load tends to be decreased in a subdivided population for recessive alleles, as does the expected inbreeding depression. The magnitude of the effects of population subdivision tends to be greatest in species with hard selection rather than soft selection. Population structure can play an important role in determining the mean fitness of populations at equilibrium between mutation and selection.

scholarly journals Cell-to-cell heterogeneity in Escherichia coli stress response originates from pulsatile expression and growth

2020 ◽  
Author(s):  
Nadia M. V. Sampaio ◽  
Caroline M. Blassick ◽  
Jean-Baptiste Lugagne ◽  
Mary J. Dunlop
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Stress Response ◽  
Single Cell ◽  
Phenotypic Variability ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Cell Heterogeneity ◽  
Temporal Expression ◽  
Functional Consequences

AbstractCell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is critical because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth. Using single-cell time-lapse microscopy, we measured the temporal expression of a suite of stress response reporters in Escherichia coli, while simultaneously monitoring growth rate. In conditions without stress, we found widespread examples of pulsatile expression. Single-cell growth rates were often anti-correlated with gene expression, with changes in growth preceding changes in expression. These pulsatile dynamics have functional consequences, which we demonstrate by measuring survival after challenging cells with the antibiotic ciprofloxacin. Our results suggest that pulsatile expression and growth dynamics are common in stress response networks and can have direct consequences for survival.

scholarly journals VipariNama: RNA vectors to rapidly reprogram plant morphology and metabolism

2020 ◽  
Author(s):  
Arjun Khakhar ◽  
Cecily Wang ◽  
Ryan Swanson ◽  
Sydney Stokke ◽  
Furva Rizvi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Plant Morphology ◽  
Plant Size ◽  
Great Promise ◽  
Attractive Alternative ◽  
Biological Processes ◽  
In Planta ◽  
Transcriptional Reprogramming ◽  
Long Timescales

AbstractSynthetic transcription factors have great promise as tools to explore biological processes. By allowing precise alterations in gene expression, they can help elucidate relationships between gene expression and plant morphology or metabolism. However, the years-long timescales, high cost, and technical skill associated with plant transformation have dramatically slowed their use. In this work, we developed a new platform technology called VipariNama (ViN) in which RNA vectors are used to rapidly deploy synthetic transcription factors and reprogram gene expression in planta. We demonstrate how ViN vectors can direct activation or repression of multiple genes, systemically and persistently over several weeks, and in multiple plant species. We also show how this transcriptional reprogramming can create predictable changes to metabolic and morphological phenotypes in the model plants Nicotiana benthamiana and Arabidopsis thaliana in a matter of weeks. Finally, we show how a model of gibberellin signaling can guide ViN vector-based reprogramming to rapidly engineer plant size in both model species as well as the crop Solanum lycopersicum (tomato). In summary, using VipariNama accelerates the timeline for generating phenotypes from over a year to just a few weeks, providing an attractive alternative to transgenesis for synthetic transcription factor-enabled hypothesis testing and crop engineering.

scholarly journals Evaluating indirect genetic effects of siblings using singletons

2021 ◽  
Author(s):  
Laurence Howe ◽  
David Evans ◽  
Gibran Hemani ◽  
George Davey Smith ◽  
Neil Martin Davies
Keyword(s):  
Genetic Variation ◽  
Educational Attainment ◽  
Family Environment ◽  
Phenotypic Variation ◽  
Molecular Genetic ◽  
Genetic Effects ◽  
Polygenic Risk Score ◽  
Indirect Genetic Effects ◽  
Molecular Genetic Evidence ◽  
Older Siblings

Estimating effects of parental and sibling genotypes (indirect genetic effects) can provide insight into how the family environment influences phenotypic variation. There is growing molecular genetic evidence for effects of parental phenotypes on their offspring (e.g. parental educational attainment), but the extent to which siblings affect each other is currently unclear.Here we used data from samples of unrelated individuals, without (singletons) and with biological full-siblings (non-singletons), to investigate and estimate sibling effects. Indirect genetic effects of siblings increase (or decrease) the covariance between genetic variation and a phenotype. It follows that differences in genetic association estimates between singletons and non-singletons could indicate indirect genetic effects of siblings.We used UK Biobank data to estimate polygenic risk score (PRS) associations for height, BMI and educational attainment in singletons (N = 50,143) and non-singletons (N = 328,549). The educational attainment PRS association estimate was 12% larger (95% C.I. 3%, 21%) in the non-singleton sample than in the singleton sample, but the height and BMI PRS associations were consistent. Birth order data suggested that the difference in educational attainment PRS associations was driven by individuals with older siblings rather than firstborns. The relationship between number of siblings and educational attainment PRS associations was non-linear; PRS associations were 24% smaller in individuals with 6 or more siblings compared to the rest of the sample (95% C.I. 11%, 38%). We estimate that a 1 SD increase in sibling educational attainment PRS corresponds to a 0.025 year increase in the index individual’s years in schooling (95% C.I. 0.013, 0.036).Our results suggest that older siblings influence the educational attainment of younger siblings, adding to the growing evidence that effects of the environment on phenotypic variation partially reflect social effects of germline genetic variation in relatives.

scholarly journals Can random mutation explain phenotypic variability?

2018 ◽  
Author(s):  
Víctor Alejandro Zapata Trejo
Keyword(s):  
Gene Expression ◽  
Somatic Cell ◽  
Germ Line ◽  
Phenotypic Variability ◽  
Random Mutation ◽  
Differentiated Cells ◽  
Undifferentiated Cells ◽  
Joint Evolution

The epigenome regulates the gene expression of all differentiated cells and indicates which specific genes must be transcribed. It is argued that the expression factors that act on specific genes of the somatic cell involved in a behavior also act on the transcription of the same genes in the most undifferentiated cells of the germ line. It is proposed how a probabilistic view of the random mutation can explain the evolution of the phenotypes and integrate all the evidence pointing to a joint evolution with the environment.

scholarly journals Can random mutation explain the phenotypic variability?

2018 ◽  
Author(s):  
Víctor Alejandro Zapata Trejo
Keyword(s):  
Gene Expression ◽  
Somatic Cell ◽  
Germ Line ◽  
Phenotypic Variability ◽  
Random Mutation ◽  
Differentiated Cells ◽  
Undifferentiated Cells

The epigenome regulates the gene expression of all differentiated cells and indicates which specific genes must be transcribed. It is argued that the expression factors that act on specific genes of the somatic cell involved in a behavior also act on the transcription of the same genes in the most undifferentiated cells of the germ line. It is proposed how a probabilistic view of the random mutation can explain the evolution of the phenotypes and integrate all the evidence pointing to a conjunct evolution with the environment.

scholarly journals Hypothesis of the conjunct expression gene: can random mutation explain the phenotypic variability?

2018 ◽  
Author(s):  
Víctor A Zapata Trejo
Keyword(s):  
Gene Expression ◽  
Germ Line ◽  
Phenotypic Variability ◽  
Somatic Cells ◽  
Random Mutation ◽  
Differentiated Cells ◽  
Undifferentiated Cells

The epigenome regulates the gene expression of all differentiated cells and indicates which specific genes must be transcribed. It is argued that the expression factors that act in specific genes of the somatic cells involved in a behavior also act in the partial transcription of the same genes in the most undifferentiated cells of the germ line. It is proposed how a probabilistic view of the random mutation can explain the evolution of the phenotypes and integrate all the evidence pointing to a conjunct evolution with the environment.

scholarly journals A multivariate analysis with direct additive and inbreeding depression load effects

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Luis Varona ◽  
Juan Altarriba ◽  
Carlos Moreno ◽  
María Martínez-Castillero ◽  
Joaquim Casellas
Keyword(s):  
Beef Cattle ◽  
Inbreeding Depression ◽  
Negative Correlation ◽  
Simulated Data ◽  
Genetic Effects ◽  
Strong Negative Correlation ◽  
Additive Variance ◽  
Inbreeding Coefficients ◽  
Load Effects ◽  
Additive Genetic Effects

Abstract Background Inbreeding is caused by mating between related individuals and its most common consequence is inbreeding depression. Several studies have detected heterogeneity in inbreeding depression among founder individuals, and recently a procedure for predicting hidden inbreeding depression loads associated with founders and the Mendelian sampling of non-founders has been developed. The objectives of our study were to expand this model to predict the inbreeding loads for all individuals in the pedigree and to estimate the covariance between the inbreeding loads and the additive genetic effects for the trait of interest. We tested the proposed approach with simulated data and with two datasets of records on weaning weight from the Spanish Pirenaica and Rubia Gallega beef cattle breeds. Results The posterior estimates of the variance components with the simulated datasets did not differ significantly from the simulation parameters. In addition, the correlation between the predicted and simulated inbreeding loads were always positive and ranged from 0.27 to 0.82. The beef cattle datasets comprised 35,126 and 75,194 records on weights between 170 and 250 days of age, and pedigrees of 308,836 and 384,434 individual-sire-dam entries for the Pirenaica and Rubia Gallega breeds, respectively. The posterior mean estimates of the variance of inbreeding depression loads were 29,967.8 and 28,222.4 for the Pirenaica and Rubia Gallega breeds, respectively. They were larger than those of the additive variance (695.0 and 439.8 for Pirenaica and Rubia Gallega, respectively), because they should be understood as the variance of the inbreeding depression achieved by a fully inbred (100%) descendant. Therefore, the inbreeding loads have to be rescaled for smaller inbreeding coefficients. In addition, a strong negative correlation (− 0.43 ± 0.10) between additive effects and inbreeding loads was detected in the Pirenaica, but not in the Rubia Gallega breed. Conclusions The results of the simulation study confirmed the ability of the proposed procedure to predict inbreeding depression loads for all individuals in the populations. Furthermore, the results obtained from the two real datasets confirmed the variability in the inbreeding depression loads in both breeds and suggested a negative correlation of the inbreeding loads with the additive genetic effects in the Pirenaica breed.

scholarly journals Tissue specific regulation of transcription in endometrium and association with disease

2020 ◽  
Vol 35 (2) ◽  
pp. 377-393 ◽  
Author(s):  
Sally Mortlock ◽  
Raden I Kendarsari ◽  
Jenny N Fung ◽  
Greg Gibson ◽  
Fei Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Study ◽  
Genetic Variants ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Enrichment Analysis ◽  
Reproductive Traits ◽  
Genetic Effects ◽  
Genotype Data ◽  
Tissue Specific

Abstract STUDY QUESTION Are genetic effects on endometrial gene expression tissue specific and/or associated with reproductive traits and diseases? SUMMARY ANSWER Analyses of RNA-sequence data and individual genotype data from the endometrium identified novel and disease associated, genetic mechanisms regulating gene expression in the endometrium and showed evidence that these mechanisms are shared across biologically similar tissues. WHAT IS KNOWN ALREADY The endometrium is a complex tissue vital for female reproduction and is a hypothesized source of cells initiating endometriosis. Understanding genetic regulation specific to, and shared between, tissue types can aid the identification of genes involved in complex genetic diseases. STUDY DESIGN, SIZE, DURATION RNA-sequence and genotype data from 206 individuals was analysed and results were compared with large publicly available datasets. PARTICIPANTS/MATERIALS, SETTING, METHODS RNA-sequencing and genotype data from 206 endometrial samples was used to identify the influence of genetic variants on gene expression, via expression quantitative trait loci (eQTL) analysis and to compare these endometrial eQTLs with those in other tissues. To investigate the association between endometrial gene expression regulation and reproductive traits and diseases, we conducted a tissue enrichment analysis, transcriptome-wide association study (TWAS) and summary data-based Mendelian randomisation (SMR) analyses. Transcriptomic data was used to test differential gene expression between women with and without endometriosis. MAIN RESULTS AND THE ROLE OF CHANCE A tissue enrichment analysis with endometriosis genome-wide association study summary statistics showed that genes surrounding endometriosis risk loci were significantly enriched in reproductive tissues. A total of 444 sentinel cis-eQTLs (P < 2.57 × 10−9) and 30 trans-eQTLs (P < 4.65 × 10−13) were detected, including 327 novel cis-eQTLs in endometrium. A large proportion (85%) of endometrial eQTLs are present in other tissues. Genetic effects on endometrial gene expression were highly correlated with the genetic effects on reproductive (e.g. uterus, ovary) and digestive tissues (e.g. salivary gland, stomach), supporting a shared genetic regulation of gene expression in biologically similar tissues. The TWAS analysis indicated that gene expression at 39 loci is associated with endometriosis, including five known endometriosis risk loci. SMR analyses identified potential target genes pleiotropically or causally associated with reproductive traits and diseases including endometriosis. However, without taking account of genetic variants, a direct comparison between women with and without endometriosis showed no significant difference in endometrial gene expression. LARGE SCALE DATA The eQTL dataset generated in this study is available at http://reproductivegenomics.com.au/shiny/endo_eqtl_rna/. Additional datasets supporting the conclusions of this article are included within the article and the supplementary information files, or are available on reasonable request. LIMITATIONS, REASONS FOR CAUTION Data are derived from fresh tissue samples and expression levels are an average of expression from different cell types within the endometrium. Subtle cell-specifc expression changes may not be detected and differences in cell composition between samples and across the menstrual cycle will contribute to sample variability. Power to detect tissue specific eQTLs and differences between women with and without endometriosis was limited by the sample size in this study. The statistical approaches used in this study identify the likely gene targets for specific genetic risk factors, but not the functional mechanism by which changes in gene expression may influence disease risk. WIDER IMPLICATIONS OF THE FINDINGS Our results identify novel genetic variants that regulate gene expression in endometrium and the majority of these are shared across tissues. This allows analysis with large publicly available datasets to identify targets for female reproductive traits and diseases. Much larger studies will be required to identify genetic regulation of gene expression that will be specific to endometrium. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the National Health and Medical Research Council (NHMRC) under project grants GNT1026033, GNT1049472, GNT1046880, GNT1050208, GNT1105321, GNT1083405 and GNT1107258. G.W.M is supported by a NHMRC Fellowship (GNT1078399). J.Y is supported by an ARC Fellowship (FT180100186). There are no competing interests.

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