Introductory Chapter: Gene Expression and Phenotypic Traits

Abstract Domestic chickens are less fearful, have a faster sexual development, grow bigger, and lay more eggs than their primary ancestor, the red junglefowl. Several candidate genetic variants selected during domestication have been identified, but only a few studies have directly linked them with distinct phenotypic traits. Notably, a variant of the thyroid stimulating hormone receptor (TSHR) gene has been under strong positive selection over the past millennium, but it’s function and mechanisms of action are still largely unresolved. We therefore assessed the abundance of the domestic TSHR variant and possible genomic selection signatures in an extensive data set comprising multiple commercial and village chicken populations as well as wild-living extant members of the genus Gallus. Furthermore, by mean of extensive backcrossing we introgressed the wild-type TSHR variant from red junglefowl into domestic White Leghorn chickens and investigated gene expression, hormone levels, cold adaptation, and behavior in chickens possessing either the wild-type or domestic TSHR variant. While the domestic TSHR was the most common variant in all studied domestic populations and in one of two red junglefowl population, it was not detected in the other Gallus species. Functionally, the individuals with the domestic TSHR variant had a lower expression of the TSHR in the hypothalamus and marginally higher in the thyroid gland than wild-type TSHR individuals. Expression of TSHB and DIO2, two regulators of sexual maturity and reproduction in birds, was higher in the pituitary gland of the domestic-variant chickens. Furthermore, the domestic variant was associated with higher activity in the open field test. Our findings confirm that the spread of the domestic TSHR variant is limited to domesticated chickens, and to a lesser extent, their wild counterpart, the red junglefowl. Furthermore, we showed that effects of genetic variability in TSHR mirror key differences in gene expression and behavior previously described between the red junglefowl and domestic chicken.

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Genome-wide variation in DNA methylation linked to developmental stage and chromosomal suppression of recombination in white-throated sparrows

10.1101/2020.07.24.220582 ◽

2020 ◽

Cited By ~ 1

Author(s):

Dan Sun ◽

Thomas S. Layman ◽

Hyeonsoo Jeong ◽

Paramita Chatterjee ◽

Kathleen Grogan ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Chromosome Pair ◽

Dna Methyltransferases ◽

Model Organisms ◽

Phenotypic Traits ◽

Zonotrichia Albicollis ◽

Single Nucleotide ◽

Genome Wide ◽

Nucleotide Resolution

ABSTRACTDNA methylation is known to play critical roles in key biological processes. Most of our knowledge on regulatory impacts of DNA methylation has come from laboratory-bred model organisms, which may not exhibit the full extent of variation found in wild populations. Here, we investigated naturally-occurring variation in DNA methylation in a wild avian species, the white-throated sparrow (Zonotrichia albicollis). This species offers exceptional opportunities for studying the link between genetic differentiation and phenotypic traits because of a non-recombining chromosome pair linked to both plumage and behavioral phenotypes. Using novel single-nucleotide resolution methylation maps and gene expression data, we show that DNA methylation and the expression of DNA methyltransferases are significantly higher in adults than in nestlings. Genes for which DNA methylation varied between nestlings and adults were implicated in development and cell differentiation and were located throughout the genome. In contrast, differential methylation between plumage morphs was localized to the non-recombining chromosome pair. One subset of CpGs on the non-recombining chromosome was extremely hypomethylated and localized to transposable elements. Changes in methylation predicted changes in gene expression for both chromosomes. In summary, we demonstrate changes in genome-wide DNA methylation that are associated with development and with specific functional categories of genes in white-throated sparrows. Moreover, we observe substantial DNA methylation reprogramming associated with the suppression of recombination, with implications for genome integrity and gene expression divergence. These results offer an unprecedented view of ongoing epigenetic reprogramming in a wild population.

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Landscape of X chromosome inactivation across human tissues

Nature ◽

10.1038/nature24265 ◽

2017 ◽

Vol 550 (7675) ◽

pp. 244-248 ◽

Cited By ~ 245

Author(s):

Taru Tukiainen ◽

◽

Alexandra-Chloé Villani ◽

Angela Yen ◽

Manuel A. Rivas ◽

...

Keyword(s):

Gene Expression ◽

X Chromosome ◽

Mammalian Cells ◽

Phenotypic Diversity ◽

X Chromosome Inactivation ◽

Chromosome Inactivation ◽

Phenotypic Traits ◽

Human Tissues ◽

X Chromosomes ◽

Chromosomal Genes

Abstract X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of ‘escape’ from inactivation varying between genes and individuals1,2. The extent to which XCI is shared between cells and tissues remains poorly characterized3,4, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression5 and phenotypic traits6. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity6,7. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.

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Analysis of Differences in Gene Expression Associated with Variation in Biomass Composition in Sugarcane

Proceedings ◽

10.3390/proceedings2019036164 ◽

2020 ◽

Vol 36 (1) ◽

pp. 164

Author(s):

Virginie Perlo ◽

Agnelo Furtado ◽

Frikkie Botha ◽

Robert Henry

Keyword(s):

Gene Expression ◽

Biomass Composition ◽

Phenotypic Traits ◽

Food Industries ◽

Measurement And Analysis ◽

Analysis Workflow ◽

Relevant Explanation ◽

Profiling Analysis ◽

Second Generation Ethanol

Sugarcane has a high potential to support second-generation ethanol production and environmentally friendly by-products for use in chemical, pharmaceutical, medical, cosmetic and food industries. A crucial challenge for a long-term economic viability is to optimise the crop for production of a biomass composition that will ensure maximum economic benefit. Transcriptome data analysis provides a relevant explanation of phenotypic variances and gives a more accurate prediction of phenotypes than genomic information. This multi-omic approach, with an integrated transcriptomics and metabolomics analysis may reveal details of biological mechanisms and pathways. A global view of transcriptional regulation and the identification differentially expressed genes (DEGs) and metabolites may help the feasibility of tailoring engineering targeted biosynthetic pathways to improve the production of these bio-products from sugarcane. We propose a profiling analysis workflow (pipeline) to generate empirical correlations between gene expression, metabolites, proteins and phenotypic traits and pathway analysis, with a highlight focus on data visualisation. This study of genetic variation in gene expression and correlations with metabolic and protein phenotype relies on high-throughput methodology, measurement and analysis of 360 samples, 24 commercial sugarcane cultivars with different phenotypic characteristics at 5 different development stages with 3 replicates.

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Differential gene expression in femoral bone from red junglefowl and domestic chicken, differing for bone phenotypic traits

BMC Genomics ◽

10.1186/1471-2164-8-208 ◽

2007 ◽

Vol 8 (1) ◽

pp. 208 ◽

Cited By ~ 13

Author(s):

Carl-Johan Rubin ◽

Johan Lindberg ◽

Carolyn Fitzsimmons ◽

Peter Savolainen ◽

Per Jensen ◽

...

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Domestic Chicken ◽

Phenotypic Traits ◽

Femoral Bone ◽

Red Junglefowl ◽

Differential Gene

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Spatio-Temporal Correlates of Gene Expression and Cortical Morphology across Life Course and Aging

10.1101/2020.07.16.207753 ◽

2020 ◽

Author(s):

Anqi Qiu ◽

Han Zhang ◽

Brian K. Kennedy ◽

Annie Lee

Keyword(s):

Gene Expression ◽

Life Course ◽

Signaling Pathway ◽

Brain Aging ◽

Phenotypic Traits ◽

Signal Pathways ◽

Biological Mechanisms ◽

Spatio Temporal ◽

Development And Aging ◽

The Life Course

AbstractEvidence from independent neuroimaging and genetic studies supports the concept that brain aging mirrors development. However, it is unclear whether mechanisms linking brain development and aging provide new insights to delay aging and potentially reverse it. This study determined biological mechanisms and phenotypic traits underpinning brain alterations across the life course and in aging by examining spatio-temporal correlations between gene expression and cortical volumes (n=3391) derived from the life course dataset (3-82 years) and the aging dataset (55-82 years). We revealed that a large proportion of genes whose expression was associated with cortical volume across the life course were in astrocytes. These genes, which showed up-regulation during development and down-regulation during aging, contributed to fundamental homeostatic functions of astrocytes crucial, in turn, for neuronal functions. Included among these genes were those encoding components of cAMP and Ras signal pathways, as well as retrograde endocannabinoid signaling. Genes associated with cortical volumes in the aging dataset were also enriched for the sphingolipid signaling pathway, renin-angiotensin system (RAS), proteasome, and TGF-beta signaling pathway, which is linked to the senescence-associated secretory phenotype. Neuroticism, drinking, and smoking were the common phenotypic traits in the life course and aging, while memory was the unique phenotype associated with aging. These findings provide biological mechanisms and phenotypic traits mirroring development and aging as well as unique to aging.

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Australian black field crickets show changes in neural gene expression associated with socially-induced morphological, life-history, and behavioral plasticity

10.7287/peerj.preprints.1941 ◽

2016 ◽

Author(s):

Michael Kasumovic ◽

Zhiliang Chen ◽

Marc R Wilkins

Keyword(s):

Gene Expression ◽

Life History ◽

Developmental Plasticity ◽

Developmental Trajectories ◽

Field Cricket ◽

Phenotypic Traits ◽

Functional Relationships ◽

Teleogryllus Commodus ◽

Black Field ◽

Insight Into

Background: Ecological and evolutionary model organisms have provided extensive insight into the ecological triggers, adaptive benefits, and evolution of life-history driven developmental plasticity. Despite this, we still have a poor understanding of the underlying genetic changes that occur during shifts towards different developmental trajectories. The goal of this study is to determine whether we can identify underlying gene expression patterns that can describe the different life-history trajectories individuals follow in response to social cues of competition. To do this, we use the Australian black field cricket (Teleogryllus commodus), a species with sex-specific developmental trajectories moderated by the density and quality of calls heard during immaturity. In this study, we manipulated the social information males and females could hear by rearing individuals in either calling or silent treatments. We next used RNA-Seq to develop a reference transcriptome to study changes in brain gene expression at two points prior to sexual maturation. Results: We show accelerated development in both sexes when exposed to calling; changes were also seen in growth, lifespan, and reproductive effort. Functional relationships between genes and phenotypes were apparent from ontological enrichment analysis. We demonstrate that increased phenotypic expression was often associated with the expression of a greater number of genes with similar effect, thus providing a suite of candidate genes for future research in this and other invertebrate organisms. Conclusions: Our results provide interesting insight into the genomic underpinnings of developmental plasticity. We highlight the relationship between genes of known effect and behavioral and phenotypic traits that are under strong sexual selection in Teleogryllus commodus. We also demonstrate the variation in suites of genes associated with different developmental trajectories. Our results provide the opportunity for a genomic exploration of other evolutionary theories such as condition dependence and sexual conflict.

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An embedded gene selection method using knockoffs optimizing neural network

BMC Bioinformatics ◽

10.1186/s12859-020-03717-w ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Juncheng Guo ◽

Min Jin ◽

Yuanyuan Chen ◽

Jianxiao Liu

Keyword(s):

Neural Network ◽

Gene Expression ◽

Gene Selection ◽

Expression Profiles ◽

Research Work ◽

Selection Method ◽

Small Subset ◽

Phenotypic Traits ◽

Breast Cancer Dataset ◽

Gene Selection Method

Abstract Background Gene selection refers to find a small subset of discriminant genes from the gene expression profiles. How to select genes that affect specific phenotypic traits effectively is an important research work in the field of biology. The neural network has better fitting ability when dealing with nonlinear data, and it can capture features automatically and flexibly. In this work, we propose an embedded gene selection method using neural network. The important genes can be obtained by calculating the weight coefficient after the training is completed. In order to solve the problem of black box of neural network and further make the training results interpretable in neural network, we use the idea of knockoffs to construct the knockoff feature genes of the original feature genes. This method not only make each feature gene to compete with each other, but also make each feature gene compete with its knockoff feature gene. This approach can help to select the key genes that affect the decision-making of neural networks. Results We use maize carotenoids, tocopherol methyltransferase, raffinose family oligosaccharides and human breast cancer dataset to do verification and analysis. Conclusions The experiment results demonstrate that the knockoffs optimizing neural network method has better detection effect than the other existing algorithms, and specially for processing the nonlinear gene expression and phenotype data.

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SUN-LB106 The Transcriptomic Evidences on Role of Abdominal Visceral vs. Subcutaneous Adipose Tissue in the Pathophysiology of Diabetes in Asian Indian Indicates the Involvement of Both

Journal of the Endocrine Society ◽

10.1210/jendso/bvaa046.2266 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

Author(s):

Sandeep Kumar Mathur ◽

Anshul Kumar ◽

Pradeep tiwari ◽

aditya Saxena

Keyword(s):

Gene Expression ◽

Adipose Tissue ◽

Subcutaneous Adipose Tissue ◽

Asian Indians ◽

Gene Set Enrichment Analysis ◽

Phenotypic Traits ◽

Statistical Comparison ◽

Gene Sets ◽

Subcutaneous Adipose

Abstract Introduction: Asian Indians show “thin fat phenotype” characterized by higher visceral adipose tissue(VAT) and lower subcutaneous adipose tissue(SAT) mass and their higher cardio-metabolic risk has been attributed to this fat distribution. However, the underlying molecular pathology and role of these adipose depots in the pathogenesis of T2D in them remains unknown.Hypothesis: The comparison of transcription profiles of abdominal VAT and SAT and their correlation with diabetes related intermediate phenotypic traits could shed some light on their role in the pathophysiology of diabetes.MethodologySubjects: 19 diabetics (M: F ratio, 8:11) and 16 age and BMI matched controls (M: F ratio 5:11) undergoing abdominal surgery (non-malignant and non-infective conditions).Clinical Parameters: Anthropometry, Serum glucose, insulin, HOMA-R, HbA1c, lipid profile, FFA, adipocytokines. Abdominal VAT, SAT and liver fat were estimated by MRI.Adipose tissue biopsy: SAT and VAT samples were taken during surgery. Genome-wide gene expression profiling of these biopsies was performed using Affymetrix GeneChipPrimeView® arrays. The data was submitted to NCBI-GEO (Accession # GSE78721). Selected genes were validated by qPCR. Gene set enrichment analysis (GSEA) for functional and Weighted Gene Correlation Analysis (WGCNA) for statistical comparison was done.Results:Diabetics had higher waist circumference (p=0.05), HOMA-R (p=0.0002), Visceral fat content (p=0.02) and adipocyte size (p=0.02)GSEA: diabetics vs. controls: In VAT 16 gene sets were upregulated (FDR < 25%) enriching various immune system and inflammation-related pathways. In SAT too, various inflammatory genes were upregulated however they were statistically non-significant (FDR > 25%). Moreover, 12 out of 16 significantly enriched pathways in VAT were among the top 20 pathways in SAT. GSEA in diabetics: VAT vs SAT: None of the gene sets were found significant at FDR < 25% which substantiated our hypothesis that overall pathophysioloigcal alteration in both depots are similar. WGCNA for statistical comparison of VAT and SAT depots The correlation between measures of average gene expression and overall connectivity between both depots was significantly positive. Several modules of co-expressed genes in both VAT and SAT showed positive as well as negative correlation with various intermediate phenotypic traits of diabetes. In both depots they enriched several pathways otherwise known to be associated with pathological adipose tissue like inflammation, adipogenesis etc. Conclusions In Asian Indians, diabetes pathology inflicts similar molecular alternations in VAT and SAT, which are more intense in the former. The role of both adipose depots in the pathophysiology of diabetes is along similar lines and they enrich several molecular pathways which are otherwise known to be implicated in pathological adipose tissue.

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Predicting growth rate from gene expression

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1808080116 ◽

2018 ◽

Vol 116 (2) ◽

pp. 367-372 ◽

Cited By ~ 7

Author(s):

Thomas P. Wytock ◽

Adilson E. Motter

Keyword(s):

Gene Expression ◽

Growth Rate ◽

Population Level ◽

Phenotypic Traits ◽

Phenotypic Characteristics ◽

K Nearest Neighbors ◽

E Coli ◽

Learning Technique ◽

Genetic Level ◽

Antibiotic Efficacy

Growth rate is one of the most important and most complex phenotypic characteristics of unicellular microorganisms, which determines the genetic mutations that dominate at the population level, and ultimately whether the population will survive. Translating changes at the genetic level to their growth-rate consequences remains a subject of intense interest, since such a mapping could rationally direct experiments to optimize antibiotic efficacy or bioreactor productivity. In this work, we directly map transcriptional profiles to growth rates by gathering published gene-expression data from Escherichia coli and Saccharomyces cerevisiae with corresponding growth-rate measurements. Using a machine-learning technique called k-nearest-neighbors regression, we build a model which predicts growth rate from gene expression. By exploiting the correlated nature of gene expression and sparsifying the model, we capture 81% of the variance in growth rate of the E. coli dataset, while reducing the number of features from >4,000 to 9. In S. cerevisiae, we account for 89% of the variance in growth rate, while reducing from >5,500 dimensions to 18. Such a model provides a basis for selecting successful strategies from among the combinatorial number of experimental possibilities when attempting to optimize complex phenotypic traits like growth rate.

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