Local adaptation contributes to gene expression divergence in maize

2020 ◽  
Author(s):  
Jennifer Blanc ◽  
Karl A. G. Kremling ◽  
Edward Buckler ◽  
Emily B. Josephs
Keyword(s):  
Gene Expression ◽  
Local Adaptation ◽  
Cold Response ◽  
Expression Levels ◽  
Maize Populations ◽  
Maize Genotypes ◽  
Wide Range ◽  
History Of ◽  
Insight Into ◽  
Gene Expression Levels

AbstractGene expression links genotypes to phenotypes, so identifying genes whose expression is shaped by selection will be important for understanding the traits and processes underlying local adaptation. However, detecting local adaptation for gene expression will require distinguishing between divergence due to selection and divergence due to genetic drift. Here, we adapt a QST –FST framework to detect local adaptation for transcriptome-wide gene expression levels in a population of diverse maize genotypes. We compare the number and types of selected genes across a wide range of maize populations and tissues, as well as selection on cold-response genes, drought-response genes, and coexpression clusters. We identify a number of genes whose expression levels are consistent with local adaptation and show that genes involved in stress-response show enrichment for selection. Due to its history of intense selective breeding and domestication, maize evolution has long been of interest to researchers, and our study provides insight into the genes and processes important for in local adaptation of maize.

scholarly journals Local adaptation contributes to gene expression divergence in maize

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Jennifer Blanc ◽  
Karl A G Kremling ◽  
Edward Buckler ◽  
Emily B Josephs
Keyword(s):  
Gene Expression ◽  
Local Adaptation ◽  
Cold Response ◽  
Expression Levels ◽  
Maize Populations ◽  
Maize Genotypes ◽  
Wide Range ◽  
History Of ◽  
Insight Into ◽  
Gene Expression Levels

Abstract Gene expression links genotypes to phenotypes, so identifying genes whose expression is shaped by selection will be important for understanding the traits and processes underlying local adaptation. However, detecting local adaptation for gene expression will require distinguishing between divergence due to selection and divergence due to genetic drift. Here, we adapt a QST−FST framework to detect local adaptation for transcriptome-wide gene expression levels in a population of diverse maize genotypes. We compare the number and types of selected genes across a wide range of maize populations and tissues, as well as selection on cold-response genes, drought-response genes, and coexpression clusters. We identify a number of genes whose expression levels are consistent with local adaptation and show that genes involved in stress response show enrichment for selection. Due to its history of intense selective breeding and domestication, maize evolution has long been of interest to researchers, and our study provides insight into the genes and processes important for in local adaptation of maize.

scholarly journals Expression of Interleukin 6 signaling receptors in carotid atherosclerosis

2020 ◽  
pp. 1358863X2097766
Author(s):  
Louise Ziegler ◽  
Jasmin Lundqvist ◽  
Kristian Dreij ◽  
Håkan Wallén ◽  
Ulf de Faire ◽  
...  
Keyword(s):  
Gene Expression ◽  
Plasma Levels ◽  
Receptor Expression ◽  
Cerebrovascular Event ◽  
Expression Levels ◽  
Carotid Plaques ◽  
History Of ◽  
Cerebral Event ◽  
Signaling Receptors ◽  
Gene Expression Levels

Interleukin (IL) 6 contributes to atherosclerotic plaque development through IL6 membrane-bound (IL6R and gp130) and soluble (sIL6R and sgp130) receptors. We investigated IL6 receptor expression in carotid plaques and its correlation with circulating IL6 and soluble receptor levels. Plasma samples and carotid plaques were obtained from 78 patients in the Biobank of Karolinska Endarterectomies study. IL6, sIL6R, and sgp130 were measured in plasma and IL6, IL6R, sIL6R, GP130, and s GP130-RAPS (s GP130) gene expression assessed in carotid plaques. Correlations between plaque IL6 signaling gene expression and plasma levels were determined by Spearman’s correlation. Differences in plasma and gene expression levels between patients with ( n = 53) and without ( n = 25) a history of a cerebral event and statin-treated ( n = 65) and non-treated ( n = 11), were estimated by Kruskal–Wallis. IL6 and its receptors were all expressed in carotid plaques. There was a positive, borderline significant, moderate correlation between plasma IL6 and sIL6R and the respective gene expression levels (rho 0.23 and 0.22, both p = 0.05). IL6R expression was higher in patients with a history of a cerebrovascular event compared to those without ( p = 0.007). Statin-treated had higher IL6R, sIL6R, and s GP130 expression levels and plasma sIL6R compared to non-treated patients (all p < 0.05). In conclusion, all components of the IL6 signaling pathways are expressed in carotid artery plaques and IL6 and sIL6R plasma levels correlate moderately with IL6 and sIL6R. Our data suggest that IL6 signaling in the circulation might mirror the system activity in the plaque, thus adding novel perspectives to the role of IL6 signaling in atherosclerosis.

The development of the DCIS score: Scaling and normalization in the Marin General Hospital cohort.

2012 ◽  
Vol 30 (27_suppl) ◽  
pp. 190-190 ◽  
Author(s):  
Frederick L. Baehner ◽  
Steven M Butler ◽  
Carl N. Yoshizawa ◽  
Che Prasad ◽  
Diana B. Cherbavaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
General Hospital ◽  
Risk Group ◽  
Recurrence Score ◽  
Low Risk ◽  
Tumor Characteristics ◽  
Clinicopathologic Characteristics ◽  
Expression Levels ◽  
Wide Range ◽  
Gene Expression Levels

190 Background: In selected low-risk patients with DCIS treated with wide local excision without radiation, the DCIS score was validated as a predictor of 10 year risk of an ipsilateral breast event (IBE - recurrence of in situ or invasive carcinoma) (p = 0.02) (Solin; SABCS 2011). As part of the development of the DCIS score, scaling from 0 to 100 and determination of risk group cutoff values was done using 100 patient DCIS samples from Marin General Hospital (MGH) selected to have a wide range of tumor characteristics. Methods: 100 patient specimens diagnosed with DCIS were provided by MGH. The Oncotype DX assay was performed, normalized expression levels for the 16 cancer related genes were determined, the DCIS Score and Recurrence Score (RS) were calculated. Distributions of the DCIS score, RS, and individual gene expression levels were described overall and by tumor characteristics. Treatment and pt outcome data were not available. Results: Samples for 96 pts had sufficient tumor and were evaluable; 47% had high nuclear grade, 52% comedo necrosis, 32% tumor size >10 mm, and 9% were ER-negative by IHC. After scaling and risk group cutoff determination, the DCIS score was low risk (0-38) for 49%, intermediate risk (39-54) for 27%, and high risk (≥55) for 24% of pts (Table). The DCIS score was widely distributed within subgroups defined by each of the clinical and pathology characteristics. Proliferation gene expression levels were low in DCIS, on average, relative to prior studies in invasive breast cancer. 92% had a proliferation gene group score <6.5, the threshold used when the RS is calculated; no threshold is used in calculating the DCIS score. Conclusions: Optimal scaling and risk cutoff determination for a wide range of all clinicopathologic characteristics provides for a wide distribution for the DCIS Score. [Table: see text]

scholarly journals Optimized gene expression from bacterial chromosome by high-throughput integration and screening

2021 ◽  
Vol 7 (7) ◽  
pp. eabe1767
Author(s):  
Tatyana E. Saleski ◽  
Meng Ting Chung ◽  
David N. Carruthers ◽  
Azzaya Khasbaatar ◽  
Katsuo Kurabayashi ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
High Throughput Screening ◽  
Efficient Production ◽  
Expression Levels ◽  
Pathway Gene ◽  
Pathway Expression ◽  
Wide Range ◽  
Cell To Cell Variability ◽  
Gene Expression Levels

Chromosomal integration of recombinant genes is desirable compared with expression from plasmids due to increased stability, reduced cell-to-cell variability, and elimination of the need for antibiotics for plasmid maintenance. Here, we present a new approach for tuning pathway gene expression levels via random integration and high-throughput screening. We demonstrate multiplexed gene integration and expression-level optimization for isobutanol production in Escherichia coli. The integrated strains could, with far lower expression levels than plasmid-based expression, produce high titers (10.0 ± 0.9 g/liter isobutanol in 48 hours) and yields (69% of the theoretical maximum). Close examination of pathway expression in the top-performing, as well as other isolates, reveals the complexity of cellular metabolism and regulation, underscoring the need for precise optimization while integrating pathway genes into the chromosome. We expect this method for pathway integration and optimization can be readily extended to a wide range of pathways and chassis to create robust and efficient production strains.

scholarly journals Quantifying genetic effects on disease mediated by assayed gene expression levels

2019 ◽  
Author(s):  
Douglas W. Yao ◽  
Luke J. O’Connor ◽  
Alkes L. Price ◽  
Alexander Gusev
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Genetic Effects ◽  
Genetic Component ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Expression Levels ◽  
Gene Sets ◽  
Wide Range ◽  
Gene Expression Levels

AbstractDisease variants identified by genome-wide association studies (GWAS) tend to overlap with expression quantitative trait loci (eQTLs). However, it remains unclear whether this overlap is driven by mediation of genetic effects on disease by expression levels, or whether it primarily reflects pleiotropic relationships instead. Here we introduce a new method, mediated expression score regression (MESC), to estimate disease heritability mediated by the cis-genetic component of assayed steady-state gene expression levels, using summary association statistics from GWAS and eQTL studies. We show that MESC produces robust estimates of expression-mediated heritability across a wide range of simulations. We applied MESC to GWAS summary statistics for 42 diseases and complex traits (average N = 323K) and cis-eQTL data across 48 tissues from the GTEx consortium. We determined that a statistically significant but low proportion of disease heritability (mean estimate 11% with S.E. 2%) is mediated by the cis-genetic component of assayed gene expression levels, with substantial variation across diseases (point estimates from 0% to 38%). We further partitioned expression-mediated heritability across various gene sets. We observed an inverse relationship between cis-heritability of expression and disease heritability mediated by expression, suggesting that genes with weaker eQTLs have larger causal effects on disease. Moreover, we observed broad patterns of expression-mediated heritability enrichment across functional gene sets that implicate specific gene sets in disease, including loss-of-function intolerant genes and FDA-approved drug targets. Our results demonstrate that eQTLs estimated from steady-state expression levels in bulk tissues are informative of regulatory disease mechanisms, but that such eQTLs are insufficient to explain the majority of disease heritability. Instead, additional assays are necessary to more fully capture the regulatory effects of GWAS variants.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

scholarly journals Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity

2021 ◽  
Vol 11 (13) ◽  
pp. 5859
Author(s):  
Fernando N. Santos-Navarro ◽  
Yadira Boada ◽  
Alejandro Vignoni ◽  
Jesús Picó
Keyword(s):  
Gene Expression ◽  
Gene Transcription ◽  
Performance Metrics ◽  
Cell Mass ◽  
Gene Copy ◽  
Substrate Uptake ◽  
Promoter Strength ◽  
Expression Levels ◽  
Trade Offs ◽  
Wide Range

Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, productivity rate, and yield (TRY). Here we use a multiscale model incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and expression of the protein of interest. Our model predicts cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the following main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. We evaluated the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature.

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