Interactions Between Natural Selection, Recombination and Gene Density in the Genes of Drosophila

Genetics ◽  
2002 ◽  
Vol 160 (2) ◽  
pp. 595-608 ◽  
Author(s):  
Jody Hey ◽  
Richard M Kliman
Keyword(s):  
Gene Expression ◽  
Natural Selection ◽  
Recombination Rate ◽  
Codon Bias ◽  
Gene Density ◽  
Drosophila Genome ◽  
Functional Variation ◽  
Subtle Effect ◽  
Efficiency Of Selection ◽  
The Impact

AbstractIn Drosophila, as in many organisms, natural selection leads to high levels of codon bias in genes that are highly expressed. Thus codon bias is an indicator of the intensity of one kind of selection that is experienced by genes and can be used to assess the impact of other genomic factors on natural selection. Among 13,000 genes in the Drosophila genome, codon bias has a slight positive, and strongly significant, association with recombination—as expected if recombination allows natural selection to act more efficiently when multiple linked sites segregate functional variation. The same reasoning leads to the expectation that the efficiency of selection, and thus average codon bias, should decline with gene density. However, this prediction is not confirmed. Levels of codon bias and gene expression are highest for those genes in an intermediate range of gene density, a pattern that may be the result of a tradeoff between the advantages for gene expression of close gene spacing and disadvantages arising from regulatory conflicts among tightly packed genes. These factors appear to overlay the more subtle effect of linkage among selected sites that gives rise to the association between recombination rate and codon bias.

scholarly journals Intragenomic variation in mutation biases causes underestimation of selection on synonymous codon usage

2021 ◽  
Author(s):  
Alexander L Cope ◽  
Premal Shah
Keyword(s):  
Population Genetics ◽  
Natural Selection ◽  
Codon Usage ◽  
Codon Bias ◽  
Synonymous Codon ◽  
Synonymous Codon Usage ◽  
Mutation Bias ◽  
Biased Gene Conversion ◽  
Intragenomic Variation ◽  
The Impact

Patterns of non-uniform usage of synonymous codons (codon bias) varies across genes in an organism and across species from all domains of life. The bias in codon usage is due to a combination of both non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolutionary forces. Most population genetics models quantify the effects of mutation bias and selection on shaping codon usage patterns assuming a uniform mutation bias across the genome. However, mutation biases can vary both along and across chromosomes due to processes such as biased gene conversion, potentially obfuscating signals of translational selection. Moreover, estimates of variation in genomic mutation biases are often lacking for non-model organisms. Here, we combine an unsupervised learning method with a population genetics model of synonymous codon bias evolution to assess the impact of intragenomic variation in mutation bias on the strength and direction of natural selection on synonymous codon usage across 49 Saccharomycotina budding yeasts. We find that in the absence of a priori information, unsupervised learning approaches can be used to identify regions evolving under different mutation biases. We find that the impact of intragenomic variation in mutation bias varies widely, even among closely-related species. We show that the overall strength and direction of selection on codon usage can be underestimated by failing to account for intragenomic variation in mutation biases. Interestingly, genes falling into clusters identified by machine learning are also often physically clustered across chromosomes, consistent with processes such as biased gene conversion. Our results indicate the need for more nuanced models of sequence evolution that systematically incorporate the effects of variable mutation biases on codon frequencies.

scholarly journals Effects of Recombination Rate and Gene Density on Transposable Element Distributions in Arabidopsis thaliana

2003 ◽  
Vol 13 (8) ◽  
pp. 1897-1903 ◽  
Author(s):  
Stephen I. Wright ◽  
Newton Agrawal ◽  
Thomas E. Bureau
Keyword(s):  
Gene Expression ◽  
Arabidopsis Thaliana ◽  
Recombination Rate ◽  
Random Distribution ◽  
Gene Density ◽  
Ectopic Recombination ◽  
Self Fertilization ◽  
Competing Models ◽  
Eukaryotic Genomes ◽  
Made In

Transposable elements (TEs) comprise a major component of eukaryotic genomes, and exhibit striking deviations from random distribution across the genomes studied, including humans, flies, nematodes, and plants. Although considerable progress has been made in documenting these patterns, the causes are subject to debate. Here, we use the genome sequence of Arabidopsis thaliana to test for the importance of competing models of natural selection against TE insertions. We show that, despite TE accumulation near the centromeres, recombination does not generally correlate with TE abundance, suggesting that selection against ectopic recombination does not influence TE distribution in A. thaliana. In contrast, a consistent negative correlation between gene density and TE abundance, and a strong under-representation of TE insertions in introns suggest that selection against TE disruption of gene expression is playing a more important role in A. thaliana. High rates of self-fertilization may reduce the importance of recombination rate in genome structuring in inbreeding organisms such as A. thaliana and Caenorhabditis elegans.

scholarly journals Model-based analysis of polymorphisms in an enhancer reveals cis-regulatory mechanisms

2020 ◽  
Author(s):  
F Khajouei ◽  
N Samper ◽  
NJ Djabrayan ◽  
B Lunt ◽  
G Jiménez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mechanistic Model ◽  
Regulatory Mechanisms ◽  
Regulatory Function ◽  
Drosophila Genome ◽  
Single Nucleotide ◽  
Disease Etiology ◽  
Functional Impact ◽  
The Impact

ABSTRACTIt is challenging to predict the impact of small genetic changes such as single nucleotide polymorphisms on gene expression, since mechanisms involved in gene regulation and their cis-regulatory encoding are not well-understood. Recent studies have attempted to predict the functional impact of non-coding variants based on available knowledge of cis-regulatory encoding, e.g., transcription factor (TF) motifs. In this work, we explore the relationship between regulatory variants and cis-regulatory encoding from the opposite angle, using the former to inform the latter. We employ sequence-to-expression modeling to resolve ambiguities regarding gene regulatory mechanisms using information about effects of single nucleotide variations in an enhancer. We demonstrate our methodology using a well-studied enhancer of the developmental gene intermediate neuroblasts defective (ind) in D. melanogaster. We first trained the thermodynamics-based model GEMSTAT to relate the neuroectodermal expression pattern of ind to its enhancer’s sequence, and constructed an ensemble of models that represent different parameter settings consistent with available data for this gene. We then predicted the effects of every possible single nucleotide variation within this enhancer, and compared these to SNP data recorded in the Drosophila Genome Reference Panel. We chose specific SNPs for which different models in the ensemble made conflicting predictions, and tested their effect in vivo. These experiments narrowed in on one mechanistic model as capable of explaining the observed effects. We further confirmed the generalizability of this model to orthologous enhancers and other related developmental enhancers. In conclusion, mechanistic models of cis-regulatory function not only help make specific predictions of variant impact, they may also be learned more accurately using data on variants.STATEMENT OF SIGNIFICANCEA central issue in analyzing variations in the non-coding genome is to interpret their functional impact, and their connections to phenotype differences and disease etiology. Machine learning methods based on statistical modeling have been developed to associate genetic variants to expression changes. However, associations predicted by these models may not be functionally relevant, despite being statisticaly significant. We describe how mathematical modeling of gene expression can be employed to systematically study the non-coding sequence and its relationship to gene expression. We demonstrate our method in a well studied developmental enhancer of the fruitfly. We establish the efficacy of mathematical models in combination with the polymorphism data to reveal new mechanistic insights.

scholarly journals Recombination facilitates adaptive evolution in rhizobial soil bacteria

2021 ◽  
Author(s):  
Maria Izabel A. Cavassim ◽  
Stig U. Andersen ◽  
Thomas Bataillon ◽  
Mikkel Heide Schierup
Keyword(s):  
Natural Selection ◽  
Amino Acid ◽  
Homologous Recombination ◽  
Adaptive Evolution ◽  
Recombination Rate ◽  
Rhizobium Leguminosarum ◽  
Core Protein ◽  
Bacterial Genome ◽  
Bacterial Populations ◽  
The Impact

AbstractHomologous recombination is expected to increase natural selection efficacy by decoupling the fate of beneficial and deleterious mutations and by readily creating new combinations of beneficial alleles. Here, we investigate how the proportion of amino acid substitutions fixed by adaptive evolution (α) depends on the recombination rate in bacteria. We analyze 3086 core protein-coding sequences from 196 genomes belonging to five closely-related Rhizobium leguminosarum species. We find that α varies from 0.07 to 0.39 across species and is positively correlated with the level of recombination. We then evaluate the impact of recombination within each species by dividing genes into three equally sized recombination classes based on their average level of intragenic linkage disequilibrium. Generally, we found a significant increase in α with an increased recombination rate. This is both due to a higher estimated rate of adaptive evolution and a lower estimated rate of non-adaptive evolution, suggesting that recombination both increases the fixation probability of advantageous variants and decreases the probability of fixation of deleterious variants. Our results demonstrate that recombination facilitates adaptive evolution not only in eukaryotes, but also in prokaryotes. Adaptive evolution could thus be a selective force that universally promotes recombination.Significance statementWhether homologous recombination has a net beneficial or detrimental effect on adaptive evolution is largely unexplored in natural bacterial populations. We address this question by evaluating polymorphism and divergence data across 196 bacterial genome sequences of five closely-related Rhizobium leguminosarum species. We show that the proportion of amino acid changes fixed due to adaptive evolution (α) increases with an increased recombination rate. This correlation is observed both in the interspecies and intraspecific comparisons. These results suggest that homologous recombination directly impacts the efficacy of natural selection in prokaryotes, as it has been shown previously to be in eukaryotes.

scholarly journals Aberrant expression of USF2 in refractory rheumatoid arthritis and its regulation of proinflammatory cytokines in Th17 cells

2020 ◽  
Vol 117 (48) ◽  
pp. 30639-30648
Author(s):  
Dan Hu ◽  
Emily C. Tjon ◽  
Karin M. Andersson ◽  
Gabriela M. Molica ◽  
Minh C. Pham ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
T Cells ◽  
Transcription Factor ◽  
Proinflammatory Cytokines ◽  
Th17 Cells ◽  
Gene Set Enrichment Analysis ◽  
Aberrant Expression ◽  
Signature Genes ◽  
The Impact

IL-17–producing Th17 cells are implicated in the pathogenesis of rheumatoid arthritis (RA) and TNF-α, a proinflammatory cytokine in the rheumatoid joint, facilitates Th17 differentiation. Anti-TNF therapy ameliorates disease in many patients with rheumatoid arthritis (RA). However, a significant proportion of patients do not respond to this therapy. The impact of anti-TNF therapy on Th17 responses in RA is not well understood. We conducted high-throughput gene expression analysis of Th17-enriched CCR6+CXCR3−CD45RA−CD4+T (CCR6+T) cells isolated from anti-TNF–treated RA patients classified as responders or nonresponders to therapy. CCR6+T cells from responders and nonresponders had distinct gene expression profiles. Proinflammatory signaling was elevated in the CCR6+T cells of nonresponders, and pathogenic Th17 signature genes were up-regulated in these cells. Gene set enrichment analysis on these signature genes identified transcription factor USF2 as their upstream regulator, which was also increased in nonresponders. Importantly, short hairpin RNA targetingUSF2in pathogenic Th17 cells led to reduced expression of proinflammatory cytokines IL-17A, IFN-γ, IL-22, and granulocyte-macrophage colony-stimulating factor (GM-CSF) as well as transcription factor T-bet. Together, our results revealed inadequate suppression of Th17 responses by anti-TNF in nonresponders, and direct targeting of the USF2-signaling pathway may be a potential therapeutic approach in the anti-TNF refractory RA.

scholarly journals Pre-treatment clinical and gene expression patterns predict developmental change in early intervention in autism

2021 ◽  
Author(s):  
Michael V. Lombardo ◽  
Elena Maria Busuoli ◽  
Laura Schreibman ◽  
Aubyn C. Stahmer ◽  
Tiziano Pramparo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Treatment Outcome ◽  
Early Intervention ◽  
Developmental Change ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Time In Treatment ◽  
Pre Treatment ◽  
Blood Leukocyte ◽  
The Impact

AbstractEarly detection and intervention are believed to be key to facilitating better outcomes in children with autism, yet the impact of age at treatment start on the outcome is poorly understood. While clinical traits such as language ability have been shown to predict treatment outcome, whether or not and how information at the genomic level can predict treatment outcome is unknown. Leveraging a cohort of toddlers with autism who all received the same standardized intervention at a very young age and provided a blood sample, here we find that very early treatment engagement (i.e., <24 months) leads to greater gains while controlling for time in treatment. Pre-treatment clinical behavioral measures predict 21% of the variance in the rate of skill growth during early intervention. Pre-treatment blood leukocyte gene expression patterns also predict the rate of skill growth, accounting for 13% of the variance in treatment slopes. Results indicated that 295 genes can be prioritized as driving this effect. These treatment-relevant genes highly interact at the protein level, are enriched for differentially histone acetylated genes in autism postmortem cortical tissue, and are normatively highly expressed in a variety of subcortical and cortical areas important for social communication and language development. This work suggests that pre-treatment biological and clinical behavioral characteristics are important for predicting developmental change in the context of early intervention and that individualized pre-treatment biology related to histone acetylation may be key.

scholarly journals Ovarian cycle stage critically affects 21-gene recurrence scores in Mmtv-Pymt mouse mammary tumours

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sarah M. Bernhardt ◽  
Pallave Dasari ◽  
Danielle J. Glynn ◽  
Lucy Woolford ◽  
Lachlan M. Moldenhauer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Menstrual Cycle ◽  
Recurrence Score ◽  
Gene Signature ◽  
Ovarian Cycle ◽  
Oncotype Dx ◽  
Mammary Tumours ◽  
Er Positive ◽  
The Impact

Abstract Background The Oncotype DX 21-gene Recurrence Score is predictive of adjuvant chemotherapy benefit for women with early-stage, estrogen receptor (ER)-positive, HER2-negative breast cancer. In premenopausal women, fluctuations in estrogen and progesterone during the menstrual cycle impact gene expression in hormone-responsive cancers. However, the extent to which menstrual cycling affects the Oncotype DX 21-gene signature remains unclear. Here, we investigate the impact of ovarian cycle stage on the 21-gene signature using a naturally cycling mouse model of breast cancer. Methods ER-positive mammary tumours were dissected from naturally cycling Mmtv-Pymt mice at either the estrus or diestrus phase of the ovarian cycle. The Oncotype DX 21-gene signature was assessed through quantitative real time-PCR, and a 21-gene experimental recurrence score analogous to the Oncotype DX Recurrence Score was calculated. Results Tumours collected at diestrus exhibited significant differences in expression of 6 Oncotype DX signature genes (Ki67, Ccnb1, Esr1, Erbb2, Grb7, Bag1; p ≤ 0.05) and a significant increase in 21-gene recurrence score (21.8 ± 2.4; mean ± SEM) compared to tumours dissected at estrus (15.5 ± 1.9; p = 0.03). Clustering analysis revealed a subgroup of tumours collected at diestrus characterised by increased expression of proliferation- (p < 0.001) and invasion-group (p = 0.01) genes, and increased 21-gene recurrence score (p = 0.01). No correlation between ER, PR, HER2, and KI67 protein abundance measured by Western blot and abundance of mRNA for the corresponding gene was observed, suggesting that gene expression is more susceptible to hormone-induced fluctuation compared to protein expression. Conclusions Ovarian cycle stage at the time of tissue collection critically affects the 21-gene signature in Mmtv-Pymt murine mammary tumours. Further studies are required to determine whether Oncotype DX Recurrence Scores in women are similarly affected by menstrual cycle stage.

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