Evolution of gene expression levels in the male reproductive organs of Anopheles mosquitoes

Modifications in gene expression determine many of the phenotypic differentiations between closely related species. This is particularly evident in reproductive tissues, where evolution of genes is more rapid, facilitating the appearance of distinct reproductive characteristics which may lead to species isolation and phenotypic variation. Large-scale, comparative analyses of transcript expression levels have been limited until recently by lack of inter-species data mining solutions. Here, by combining expression normalisation across lineages, multivariate statistical analysis, evolutionary rate, and protein–protein interaction analysis, we investigate ortholog transcripts in the male accessory glands and testes across five closely related species in the Anopheles gambiae complex. We first demonstrate that the differentiation by transcript expression is consistent with the known Anopheles phylogeny. Then, through clustering, we discover groups of transcripts with tissue-dependent expression patterns conserved across lineages, or lineage-dependent patterns conserved across tissues. The strongest associations with reproductive function, transcriptional regulatory networks, protein–protein subnetworks, and evolutionary rate are found for the groups of transcripts featuring large expression differences in lineage or tissue-conserved patterns.

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There is no magic bullet: the importance of testing reference gene stability in RT-qPCR experiments across multiple closely related species

PeerJ ◽

10.7717/peerj.9618 ◽

2020 ◽

Vol 8 ◽

pp. e9618

Author(s):

Bert Foquet ◽

Hojun Song

Keyword(s):

Gene Expression ◽

Related Species ◽

Reference Genes ◽

Target Genes ◽

Quantitative Polymerase Chain Reaction ◽

Magic Bullet ◽

Experimental Conditions ◽

Closely Related Species ◽

Expression Levels ◽

Gene Stability

Reverse Transcriptase quantitative Polymerase Chain Reaction (RT-qPCR) is the current gold standard tool for the study of gene expression. This technique is highly dependent on the validation of reference genes, which exhibit stable expression levels among experimental conditions. Often, reference genes are assumed to be stable a priori without a rigorous test of gene stability. However, such an oversight can easily lead to misinterpreting expression levels of target genes if the references genes are in fact not stable across experimental conditions. Even though most gene expression studies focus on just one species, comparative studies of gene expression among closely related species can be very informative from an evolutionary perspective. In our study, we have attempted to find stable reference genes for four closely related species of grasshoppers (Orthoptera: Acrididae) that together exhibit a spectrum of density-dependent phenotypic plasticity. Gene stability was assessed for eight reference genes in two tissues, two experimental conditions and all four species. We observed clear differences in the stability ranking of these reference genes, both between tissues and between species. Additionally, the choice of reference genes clearly influenced the results of a gene expression experiment. We offer suggestions for the use of reference genes in further studies using these four species, which should be taken as a cautionary tale for future studies involving RT-qPCR in a comparative framework.

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Discovering Distinct Patterns in Gene Expression Profiles

Journal of Integrative Bioinformatics ◽

10.1515/jib-2008-105 ◽

2008 ◽

Vol 5 (2) ◽

Cited By ~ 1

Author(s):

Li Teng ◽

Laiwan Chan

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Clustering Methods ◽

Gene Expressions ◽

Real Gene ◽

Large Scale Dataset ◽

Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

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Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab021 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Hitoshi Iuchi ◽

Michiaki Hamada

Keyword(s):

Gene Expression ◽

Time Series ◽

Time Course ◽

Time Series Data ◽

Expression Patterns ◽

Detection Methods ◽

Series Data ◽

Expression Levels ◽

Over Time ◽

Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

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CFTR ΔF508 mutation has minimal effect on the gene expression profile of differentiated human airway epithelia

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.00065.2005 ◽

2005 ◽

Vol 289 (4) ◽

pp. L545-L553 ◽

Cited By ~ 29

Author(s):

Joseph Zabner ◽

Todd E. Scheetz ◽

Hakeem G. Almabrazi ◽

Thomas L. Casavant ◽

Jian Huang ◽

...

Keyword(s):

Gene Expression ◽

Cystic Fibrosis ◽

Large Scale ◽

Expression Profiles ◽

Expression Patterns ◽

Primary Cultures ◽

Gene Expression Profiles ◽

Filter Method ◽

Tissue Destruction ◽

Airway Epithelia

Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial chloride channel regulated by phosphorylation. Most of the disease-associated morbidity is the consequence of chronic lung infection with progressive tissue destruction. As an approach to investigate the cellular effects of CFTR mutations, we used large-scale microarray hybridization to contrast the gene expression profiles of well-differentiated primary cultures of human CF and non-CF airway epithelia grown under resting culture conditions. We surveyed the expression profiles for 10 non-CF and 10 ΔF508 homozygote samples. Of the 22,283 genes represented on the Affymetrix U133A GeneChip, we found evidence of significant changes in expression in 24 genes by two-sample t-test ( P < 0.00001). A second, three-filter method of comparative analysis found no significant differences between the groups. The levels of CFTR mRNA were comparable in both groups. There were no significant differences in the gene expression patterns between male and female CF specimens. There were 18 genes with significant increases and 6 genes with decreases in CF relative to non-CF samples. Although the function of many of the differentially expressed genes is unknown, one transcript that was elevated in CF, the KCl cotransporter (KCC4), is a candidate for further study. Overall, the results indicate that CFTR dysfunction has little direct impact on airway epithelial gene expression in samples grown under these conditions.

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Joint profiling of gene expression and chromatin accessibility of amphioxus development at single cell resolution

10.21203/rs.3.rs-504113/v1 ◽

2021 ◽

Author(s):

Pengcheng Ma ◽

Xingyan Liu ◽

Huimin Liu ◽

Zaoxu Xu ◽

Xiangning Ding ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Fate ◽

Regulatory Networks ◽

Functional Divergence ◽

Expression Patterns ◽

Genetic Regulatory Networks ◽

Public Access ◽

Origin And Evolution ◽

Key Species

Abstract Vertebrate evolution was accompanied with two rounds of whole genome duplication followed by functional divergence in terms of regulatory circuits and gene expression patterns. As a basal and slow-evolving chordate species, amphioxus is an ideal paradigm for exploring the origin and evolution of vertebrates. Single cell sequencing has been widely employed to construct the developmental cell atlas of several key species of vertebrates (human, mouse, zebrafish and frog) and tunicate (sea squirts). Here, we performed single-nucleus RNA sequencing (snRNA-seq) and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) for different stages of amphioxus (covering embryogenesis and adult tissues). With the datasets generated we constructed the developmental tree for amphioxus cell fate commitment and lineage specification, and revealed the underlying key regulators and genetic regulatory networks. The generated data were integrated into an online platform, AmphioxusAtlas, for public access at http://120.79.46.200:81/AmphioxusAtlas.

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Comparative transcriptomics identifies differences in the regulation of the floral transition between Arabidopsis and Brassica rapa cultivars

10.1101/2020.08.26.266494 ◽

2020 ◽

Author(s):

Alexander Calderwood ◽

Jo Hepworth ◽

Shannon Woodhouse ◽

Lorelei Bilham ◽

D. Marc Jones ◽

...

Keyword(s):

Gene Expression ◽

Brassica Rapa ◽

Regulatory Networks ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Detailed Comparison ◽

Developmental Time ◽

Floral Transition ◽

Gene Regulatory

AbstractThe timing of the floral transition affects reproduction and yield, however its regulation in crops remains poorly understood. Here, we use RNA-Seq to determine and compare gene expression dynamics through the floral transition in the model species Arabidopsis thaliana and the closely related crop Brassica rapa. A direct comparison of gene expression over time between species shows little similarity, which could lead to the inference that different gene regulatory networks are at play. However, these differences can be largely resolved by synchronisation, through curve registration, of gene expression profiles. We find that different registration functions are required for different genes, indicating that there is no common ‘developmental time’ to which Arabidopsis and B. rapa can be mapped through gene expression. Instead, the expression patterns of different genes progress at different rates. We find that co-regulated genes show similar changes in synchronisation between species, suggesting that similar gene regulatory sub-network structures may be active with different wiring between them. A detailed comparison of the regulation of the floral transition between Arabidopsis and B. rapa, and between two B. rapa accessions reveals different modes of regulation of the key floral integrator SOC1, and that the floral transition in the B. rapa accessions is triggered by different pathways, even when grown under the same environmental conditions. Our study adds to the mechanistic understanding of the regulatory network of flowering time in rapid cycling B. rapa under long days and highlights the importance of registration methods for the comparison of developmental gene expression data.

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SOMDE: A scalable method for identifying spatially variable genes with self-organizing map

10.1101/2020.12.10.419549 ◽

2020 ◽

Author(s):

Minsheng Hao ◽

Kui Hua ◽

Xuegong Zhang

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Patterns ◽

Self Organizing Map ◽

Expression Data ◽

Spatial Expression ◽

Variable Expression ◽

Sequencing Technologies ◽

Physical Context ◽

Variable Genes

AbstractRecent developments of spatial transcriptomic sequencing technologies provide powerful tools for understanding cells in the physical context of tissue micro-environments. A fundamental task in spatial gene expression analysis is to identify genes with spatially variable expression patterns, or spatially variable genes (SVgenes). Several computational methods have been developed for this task. Their high computational complexity limited their scalability to the latest and future large-scale spatial expression data.We present SOMDE, an efficient method for identifying SVgenes in large-scale spatial expression data. SOMDE uses selforganizing map (SOM) to cluster neighboring cells into nodes, and then uses a Gaussian Process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5-50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in ~5 minutes in large datasets of more than 20,000 sequencing sites. SOMDE is available as a python package on PyPI at https://pypi.org/project/somde.

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Alterations of Growth and Focal Adhesion Molecules in Human Breast Cancer Cells Exposed to the Random Positioning Machine

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.672098 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jayashree Sahana ◽

Thomas J. Corydon ◽

Markus Wehland ◽

Marcus Krüger ◽

Sascha Kopp ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Target Genes ◽

Interaction Analysis ◽

Expression Patterns ◽

Focal Adhesions ◽

Multicellular Spheroids ◽

Down Regulation ◽

Random Positioning Machine ◽

Mcf 7

In this study, we evaluated changes in focal adhesions (FAs) in two types of breast cancer cell (BCC) lines (differentiated MCF-7 and the triple-negative MDA-MB-231 cell line) exposed to simulated microgravity (s-μg) created by a random positioning machine (RPM) for 24 h. After exposure, the BCC changed their growth behavior and exhibited two phenotypes in RPM samples: one portion of the cells grew as a normal two-dimensional monolayer [adherent (AD) BCC], while the other portion formed three-dimensional (3D) multicellular spheroids (MCS). After 1 h and 30 min (MDA-MB-231) and 1 h 40 min (MCF-7), the MCS adhered completely to the slide flask bottom. After 2 h, MDA-MB-231 MCS cells started to migrate, and after 6 h, a large number of the cells had left the MCS and continued to grow in a scattered pattern, whereas MCF-7 cells were growing as a confluent monolayer after 6 h and 24 h. We investigated the genes associated with the cytoskeleton, the extracellular matrix and FAs. ACTB, TUBB, FN1, FAK1, and PXN gene expression patterns were not significantly changed in MDA-MB-231 cells, but we observed a down-regulation of LAMA3, ITGB1 mRNAs in AD cells and of ITGB1, TLN1 and VCL mRNAs in MDA-MB-231 MCS. RPM-exposed MCF-7 cells revealed a down-regulation in the gene expression of FAK1, PXN, TLN1, VCL and CDH1 in AD cells and PXN, TLN and CDH1 in MCS. An interaction analysis of the examined genes involved in 3D growth and adhesion indicated a central role of fibronectin, vinculin, and E-cadherin. Live cell imaging of eGFP-vinculin in MCF-7 cells confirmed these findings. β-catenin-transfected MCF-7 cells revealed a nuclear expression in 1g and RPM-AD cells. The target genes BCL9, MYC and JUN of the Wnt/β-catenin signaling pathway were differentially expressed in RPM-exposed MCF-7 cells. These findings suggest that vinculin and β-catenin are key mediators of BCC to form MCS during 24 h of RPM-exposure.

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Large-Scale Metadata Analysis of Ovary Based Multi-Omics Datasets for Understanding the Genes Regulating Litter Size

10.21203/rs.3.rs-22487/v1 ◽

2020 ◽

Author(s):

Ayyappa Kumar Sista Kameshwar ◽

Julang Li

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Litter Size ◽

Large Scale ◽

Expression Patterns ◽

Genetic Selection ◽

Follicular Development ◽

Oocyte Quality ◽

Differentially Expressed ◽

Metadata Analysis

Abstract Background : Litter size is a very important production index in the livestock industry, which is controlled by various complex physiological processes. To understand and reveal the common gene expression patterns involved in controlling prolificacy, we have performed a large-scale metadata analysis of five genome-wide transcriptome datasets of pig and sheep ovary samples obtained from high and low litter groups, respectively. We analyzed separately each transcriptome dataset using GeneSpring v14.8 software by implementing standard, generic analysis pipelines and further compared the list of most significant and differentially expressed genes obtained from each dataset to identify genes that are found to be common and significant across all the studies. Results : We have observed a total of 62 differentially expressed genes common among more than two gene expression datasets. The KEGG pathway analysis of most significant genes has shown that they are involved in metabolism, the biosynthesis of lipids, cholesterol and steroid hormones, immune system, cell growth and death, cancer-related pathways and signal transduction pathways. Of these 62 genes, we further narrowed the list to the 25 most significant genes by focusing on the ones with fold change >1.5 and p<0.05. These genes are CYP11A1, HSD17B2, STAR, SCARB1, IGSF8, MSMB, SERPINA1 , FAM46C, HEXA, PTTG1, TIMP1, FAM167B, CCNG1, FAXDC2, HMGCS1, L2HGDH, Lipin1, MME, MSMO1, PARM1, PTGFR, SLC22A4, SLC35F5, CCNA2, CENPU, CEP55, RASSF2, and SLC16A3 . Conclusions : Interestingly, comparing the list of genes with the list of genes obtained from our literature search analysis, we found only three genes in common. These genes are HEXA, PTTG1, and TIMP1. Our finding points to the potential of a few genes that may be important for ovarian follicular development and oocyte quality. Future studies revealing the function of these genes will further our understanding of how litter size is controlled in the ovary while also providing insight on genetic selection of high litter gilts.

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Synthetic gene regulatory networks in the opportunistic human pathogen Streptococcus pneumoniae

10.1101/834689 ◽

2019 ◽

Cited By ~ 1

Author(s):

Robin A. Sorg ◽

Clement Gallay ◽

Jan-Willem Veening

Keyword(s):

Gene Expression ◽

Streptococcus Pneumoniae ◽

Regulatory Networks ◽

Expression Patterns ◽

Combinatorial Libraries ◽

Regulatory Elements ◽

Expression Control ◽

Gene Expression Control

AbstractStreptococcus pneumoniae can cause disease in various human tissues and organs, including the ear, the brain, the blood and the lung, and thus in highly diverse and dynamic environments. It is challenging to study how pneumococci control virulence factor expression, because cues of natural environments and the presence of an immune system are difficult to simulate in vitro. Here, we apply synthetic biology methods to reverse-engineer gene expression control in S. pneumoniae. A selection platform is described that allows for straightforward identification of transcriptional regulatory elements out of combinatorial libraries. We present TetR- and LacI-regulated promoters that show expression ranges of four orders of magnitude. Based on these promoters, regulatory networks of higher complexity are assembled, such as logic AND and IMPLY gates. Finally, we demonstrate single-copy genome-integrated toggle switches that give rise to bimodal population distributions. The tools described here can be used to mimic complex expression patterns, such as the ones found for pneumococcal virulence factors, paving the way for in vivo investigations of the importance of gene expression control on the pathogenicity of S. pneumoniae.

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