scholarly journals Gene expression of Tasmanian devil facial tumors differs among host populations

2021 ◽  
Author(s):  
Christopher P Kozakiewicz ◽  
Alexandra K Fraik ◽  
Austin H Patton ◽  
Manuel Ruiz-Aravena ◽  
David G Hamilton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Phenotypic Variation ◽  
Population Level ◽  
Host Responses ◽  
Tasmanian Devil ◽  
Population Declines ◽  
Expression Variation ◽  
Normal Tissues ◽  
Transmissible Cancer ◽  
Cancer Phenotypes

Abstract Background Transmissible cancers lie at the intersection of oncology and infectious disease, two traditionally divergent fields for which gene expression studies are particularly useful for identifying the molecular basis of phenotypic variation. In oncology, transcriptomics studies, which characterize the expression of thousands of genes, have identified processes leading to heterogeneity in cancer phenotypes and individual prognoses. More generally, transcriptomics studies of infectious diseases characterize interactions between host, pathogen, and environment to better predict population-level outcomes. Tasmanian devils have been impacted dramatically by a transmissible cancer (devil facial tumor disease; DFTD) that has led to widespread population declines. Despite initial predictions of extinction, populations have persisted at low levels, due in part to heterogeneity in host responses, particularly between sexes. However, the processes underlying this variation remain unknown. Results We sequenced transcriptomes from healthy and DFTD-infected devils, as well as DFTD tumors, to characterize host responses to DFTD infection, identify differing host-tumor molecular interactions between sexes, and investigate the extent to which tumor gene expression varies among host populations. We found minimal variation in gene expression of devil lip tissues, either with respect to DFTD infection status or sex. However, 4,088 genes were differentially expressed in tumors among our sampling localities. Pathways that were up- or downregulated in DFTD tumors relative to normal tissues exhibited the same patterns of expression with greater intensity in tumors from localities that experienced DFTD for longer. No RNA sequence variants were associated with expression variation. Conclusions Expression variation among localities may underly phenotypic variation in the tumor, potentially manifesting in morphological differences that alter ratios of normal-to-tumor cells within biopsies. Phenotypic variation in tumors may arise from environmental variation or differences in host immune response that were undetectable in lip biopsies, potentially reflecting variation in host-tumor coevolutionary relationships among sites that differ in the time since DFTD arrival.

scholarly journals Spatial variation in gene expression of Tasmanian devil facial tumors despite minimal host transcriptomic response to infection

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Christopher P. Kozakiewicz ◽  
Alexandra K. Fraik ◽  
Austin H. Patton ◽  
Manuel Ruiz-Aravena ◽  
David G. Hamilton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Phenotypic Variation ◽  
Population Level ◽  
Host Responses ◽  
Tasmanian Devil ◽  
Population Declines ◽  
Transcriptomic Response ◽  
Expression Variation ◽  
Normal Tissues ◽  
Cancer Phenotypes

Abstract Background Transmissible cancers lie at the intersection of oncology and infectious disease, two traditionally divergent fields for which gene expression studies are particularly useful for identifying the molecular basis of phenotypic variation. In oncology, transcriptomics studies, which characterize the expression of thousands of genes, have identified processes leading to heterogeneity in cancer phenotypes and individual prognoses. More generally, transcriptomics studies of infectious diseases characterize interactions between host, pathogen, and environment to better predict population-level outcomes. Tasmanian devils have been impacted dramatically by a transmissible cancer (devil facial tumor disease; DFTD) that has led to widespread population declines. Despite initial predictions of extinction, populations have persisted at low levels, due in part to heterogeneity in host responses, particularly between sexes. However, the processes underlying this variation remain unknown. Results We sequenced transcriptomes from healthy and DFTD-infected devils, as well as DFTD tumors, to characterize host responses to DFTD infection, identify differing host-tumor molecular interactions between sexes, and investigate the extent to which tumor gene expression varies among host populations. We found minimal variation in gene expression of devil lip tissues, either with respect to DFTD infection status or sex. However, 4088 genes were differentially expressed in tumors among our sampling localities. Pathways that were up- or downregulated in DFTD tumors relative to normal tissues exhibited the same patterns of expression with greater intensity in tumors from localities that experienced DFTD for longer. No mRNA sequence variants were associated with expression variation. Conclusions Expression variation among localities may reflect morphological differences in tumors that alter ratios of normal-to-tumor cells within biopsies. Phenotypic variation in tumors may arise from environmental variation or differences in host immune response that were undetectable in lip biopsies, potentially reflecting variation in host-tumor coevolutionary relationships among sites that differ in the time since DFTD arrival.

scholarly journals Empirical measures of mutational effects define neutral models of regulatory evolution in Saccharomyces cerevisiae

2019 ◽  
Vol 116 (42) ◽  
pp. 21085-21093 ◽  
Author(s):  
Andrea Hodgins-Davis ◽  
Fabien Duveau ◽  
Elizabeth A. Walker ◽  
Patricia J. Wittkopp
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Phenotypic Variation ◽  
Natural Populations ◽  
Point Mutations ◽  
Regulatory Evolution ◽  
Empirical Measures ◽  
Expression Variation ◽  
Neutral Models ◽  
New Mutations

Understanding how phenotypes evolve requires disentangling the effects of mutation generating new variation from the effects of selection filtering it. Tests for selection frequently assume that mutation introduces phenotypic variation symmetrically around the population mean, yet few studies have tested this assumption by deeply sampling the distributions of mutational effects for particular traits. Here, we examine distributions of mutational effects for gene expression in the budding yeast Saccharomyces cerevisiae by measuring the effects of thousands of point mutations introduced randomly throughout the genome. We find that the distributions of mutational effects differ for the 10 genes surveyed and are inconsistent with normality. For example, all 10 distributions of mutational effects included more mutations with large effects than expected for normally distributed phenotypes. In addition, some genes also showed asymmetries in their distribution of mutational effects, with new mutations more likely to increase than decrease the gene’s expression or vice versa. Neutral models of regulatory evolution that take these empirically determined distributions into account suggest that neutral processes may explain more expression variation within natural populations than currently appreciated.

scholarly journals Massively parallel gene expression variation measurement of a synonymous codon library

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alexander Schmitz ◽  
Fuzhong Zhang
Keyword(s):  
Gene Expression ◽  
Codon Usage ◽  
Single Cells ◽  
Massively Parallel ◽  
Protein Abundance ◽  
Translation Efficiency ◽  
Gene Expression Variation ◽  
Expression Variation ◽  
Change In Mean ◽  
Adaptation Index

Abstract Background Cell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood. Results Here, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV2), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance. Conclusions Our results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications.

scholarly journals Gene expression variation increase in trisomy 21 tissues

2008 ◽  
Vol 19 (6) ◽  
pp. 398-405 ◽  
Author(s):  
Ching Yu Chou ◽  
Li Yu Liu ◽  
Chien Yu Chen ◽  
Cheng Hsien Tsai ◽  
Hsiao Lin Hwa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Trisomy 21 ◽  
Gene Expression Variation ◽  
Expression Variation

scholarly journals Stochastic Kinetic Analysis of Developmental Pathway Bifurcation in Phage λ-Infected Escherichia coli Cells

Genetics ◽  
1998 ◽  
Vol 149 (4) ◽  
pp. 1633-1648 ◽  
Author(s):  
Adam Arkin ◽  
John Ross ◽  
Harley H McAdams
Keyword(s):  
Gene Expression ◽  
Kinetic Model ◽  
Kinetic Analysis ◽  
Molecular Level ◽  
Host Responses ◽  
Statistical Thermodynamic ◽  
Regulatory Systems ◽  
Regulatory Circuit ◽  
Phenotype Selection ◽  
Decision Circuit

Abstract Fluctuations in rates of gene expression can produce highly erratic time patterns of protein production in individual cells and wide diversity in instantaneous protein concentrations across cell populations. When two independently produced regulatory proteins acting at low cellular concentrations competitively control a switch point in a pathway, stochastic variations in their concentrations can produce probabilistic pathway selection, so that an initially homogeneous cell population partitions into distinct phenotypic subpopulations. Many pathogenic organisms, for example, use this mechanism to randomly switch surface features to evade host responses. This coupling between molecular-level fluctuations and macroscopic phenotype selection is analyzed using the phage λ lysis-lysogeny decision circuit as a model system. The fraction of infected cells selecting the lysogenic pathway at different phage:cell ratios, predicted using a molecular-level stochastic kinetic model of the genetic regulatory circuit, is consistent with experimental observations. The kinetic model of the decision circuit uses the stochastic formulation of chemical kinetics, stochastic mechanisms of gene expression, and a statistical-thermodynamic model of promoter regulation. Conventional deterministic kinetics cannot be used to predict statistics of regulatory systems that produce probabilistic outcomes. Rather, a stochastic kinetic analysis must be used to predict statistics of regulatory outcomes for such stochastically regulated systems.

Screening of cervical cancer-related hub genes based on comprehensive bioinformatics analysis

2021 ◽  
pp. 1-13
Author(s):  
Simei Tu ◽  
Hao Zhang ◽  
Xiaocheng Yang ◽  
Wen Wen ◽  
Kangjing Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Molecular Mechanisms ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Mrna Levels ◽  
Hub Genes ◽  
Normal Tissues ◽  
Significant Difference ◽  
Core Genes

BACKGROUND: Since the molecular mechanisms of cervical cancer (CC) have not been completely discovered, it is of great significance to identify the hub genes and pathways of this disease to reveal the molecular mechanisms of cervical cancer. OBJECTIVE: The study aimed to identify the biological functions and prognostic value of hub genes in cervical cancer. METHODS: The gene expression data of CC patients were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The core genes were screened out by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). R software, the STRING online tool and Cytoscape software were used to screen out the hub genes. The GEPIA public database was used to further verify the expression levels of the hub genes in normal tissues and tumour tissues and determine the disease-free survival (DFS) rates of the hub genes. The protein expression of the survival-related hub genes was identified with the Human Protein Atlas (HPA) database. RESULTS: A total of 64 core genes were screened, and 10 genes, including RFC5, POLE3, RAD51, RMI1, PALB2, HDAC1, MCM4, ESR1, FOS and E2F1, were identified as hub genes. Compared with that in normal tissues, RFC5, POLE3, RAD51,RMI1, PALB2, MCM4 and E2F1 were all significantly upregulated in cervical cancer, ESR1 was significantly downregulated in cervical cancer, and high RFC5 expression in CC patients was significantly related to OS. In the DFS analysis, no significant difference was observed in the expression level of RFC5 in cervical cancer patients. Finally, RFC5 protein levels verified by the HPA database were consistently upregulated with mRNA levels in CC samples. CONCLUSIONS: RFC5 may play important roles in the occurrence and prognosis of CC. It could be further explored and validated as a potential predictor and therapeutic target for CC.

Gene expression variation in natural populations of hexaploid and allododecaploid Spartina species (Poaceae)

2017 ◽  
Vol 303 (8) ◽  
pp. 1061-1079 ◽  
Author(s):  
Julie Ferreira de Carvalho ◽  
Julien Boutte ◽  
Pierre Bourdaud ◽  
Houda Chelaifa ◽  
Kader Ainouche ◽  
...  
Keyword(s):  
Gene Expression ◽  
Natural Populations ◽  
Gene Expression Variation ◽  
Expression Variation ◽  
Spartina Species
Sign in / Sign up

Export Citation Format

Share Document