Cell-to-cell expression dispersion of B-cell surface proteins displays genetic variation among humans

ABSTRACTVariability in gene expression across a population of homogeneous cells is known to influence various biological processes. In model organisms, natural genetic variants were found that modify expression dispersion (variability at a fixed mean) but whether such effects exist in humans has not been fully demonstrated. Here, we analyzed single-cell expression of four proteins (CD23, CD55, CD63 and CD86) across cell lines derived from individuals of the Yoruba population. Using data from over 30 million cells, we found substantial inter-individual variation of dispersion. We demonstrate, via de novo cell line generation and subcloning experiments, that this variation exceeds the variation associated with cellular immortalization. By association mapping, we linked the expression dispersion of CD63 to the rs971 SNP. Our results show that human DNA variants can have inherently-probabilistic effects on gene expression. Such subtle genetic effects may participate to phenotypic variation and disease predisposition.

Download Full-text

Cell-to-cell expression dispersion of B-cell surface proteins is linked to genetic variants in humans

Communications Biology ◽

10.1038/s42003-020-1075-1 ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Gérard Triqueneaux ◽

Claire Burny ◽

Orsolya Symmons ◽

Stéphane Janczarski ◽

Henri Gruffat ◽

...

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

De Novo ◽

Surface Proteins ◽

Model Organisms ◽

Cellular Immortalization ◽

Human Dna ◽

Using Data ◽

Cell Expression ◽

Cell Line Generation

AbstractVariability in gene expression across a population of homogeneous cells is known to influence various biological processes. In model organisms, natural genetic variants were found that modify expression dispersion (variability at a fixed mean) but very few studies have detected such effects in humans. Here, we analyzed single-cell expression of four proteins (CD23, CD55, CD63 and CD86) across cell lines derived from individuals of the Yoruba population. Using data from over 30 million cells, we found substantial inter-individual variation of dispersion. We demonstrate, via de novo cell line generation and subcloning experiments, that this variation exceeds the variation associated with cellular immortalization. We detected a genetic association between the expression dispersion of CD63 and the rs971 SNP. Our results show that human DNA variants can have inherently-probabilistic effects on gene expression. Such subtle genetic effects may participate to phenotypic variation and disease outcome.

Download Full-text

The brain transcriptome of the wolf spider, Schizocosa ocreata

BMC Research Notes ◽

10.1186/s13104-021-05648-y ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Daniel Stribling ◽

Peter L. Chang ◽

Justin E. Dalton ◽

Christopher A. Conow ◽

Malcolm Rosenthal ◽

...

Keyword(s):

Gene Expression ◽

De Novo ◽

Transcriptome Assembly ◽

Model Organisms ◽

De Novo Transcriptome Assembly ◽

De Novo Transcriptome ◽

Wolf Spiders ◽

Schizocosa Ocreata ◽

Genomic Studies ◽

The Brain

Abstract Objectives Arachnids have fascinating and unique biology, particularly for questions on sex differences and behavior, creating the potential for development of powerful emerging models in this group. Recent advances in genomic techniques have paved the way for a significant increase in the breadth of genomic studies in non-model organisms. One growing area of research is comparative transcriptomics. When phylogenetic relationships to model organisms are known, comparative genomic studies provide context for analysis of homologous genes and pathways. The goal of this study was to lay the groundwork for comparative transcriptomics of sex differences in the brain of wolf spiders, a non-model organism of the pyhlum Euarthropoda, by generating transcriptomes and analyzing gene expression. Data description To examine sex-differential gene expression, short read transcript sequencing and de novo transcriptome assembly were performed. Messenger RNA was isolated from brain tissue of male and female subadult and mature wolf spiders (Schizocosa ocreata). The raw data consist of sequences for the two different life stages in each sex. Computational analyses on these data include de novo transcriptome assembly and differential expression analyses. Sample-specific and combined transcriptomes, gene annotations, and differential expression results are described in this data note and are available from publicly-available databases.

Download Full-text

A consensus-based ensemble approach to improve de novo transcriptome assembly

10.1101/2020.06.08.139964 ◽

2020 ◽

Cited By ~ 1

Author(s):

Adam Voshall ◽

Sairam Behera ◽

Xiangjun Li ◽

Xiao-Hong Yu ◽

Kushagra Kapil ◽

...

Keyword(s):

Gene Expression ◽

Expression Analysis ◽

Reference Genome ◽

De Novo ◽

Transcriptome Assembly ◽

Model Organisms ◽

Pathway Reconstruction ◽

Rnaseq Data ◽

Metabolic Pathway Reconstruction ◽

Assembly Performance

AbstractSystems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative splicing events could exacerbate such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. In this study, we provide a pipeline to generate a set of the benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including genome-guided, de novo, and ensemble methods. The results showed that the assembly performance deteriorates significantly when the reference is not available from the same genome (for genome-guided methods) or when alternative transcripts (isoforms) exist. We demonstrated the value of consensus between de novo assemblers in transcriptome assembly. Leveraging the overlapping predictions between the four de novo assemblers, we further present ConSemble, a consensus-based de novo ensemble transcriptome assembly pipeline. Without using a reference genome, ConSemble achieved an accuracy up to twice as high as any de novo assemblers we compared. It matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the ConSemble pipeline are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/.Author summaryObtaining the accurate representation of the gene expression is critical in many analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction. The state of the art high-throughput RNA-sequencing (RNAseq) technologies can be used to sequence the set of all transcripts in a cell, the transcriptome. Although many computational tools are available for transcriptome assembly from RNAseq data, assembling high-quality transcriptomes is difficult especially for non-model organisms. Different methods often produce different transcriptome models and there is no easy way to determine which are more accurate. In this study, we present an approach to evaluate transcriptome assembly performance using simulated benchmarking read sets. The results showed that the assembly performance of genome-guided assembly methods deteriorates significantly when the adequate reference genome is not available. The assembly performance of all methods is affected when alternative transcripts (isoforms) exist. We further demonstrated the value of consensus among assemblers in improving transcriptome assembly. Leveraging the overlapping predictions between the four de novo assemblers, we present ConSemble. Without using a reference genome, ConSemble achieved a much higher accuracy than any de novo assemblers we compared. It matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms.

Download Full-text

Assembly-free rapid differential gene expression analysis in non-model organisms using DNA-protein alignment

10.1101/2021.04.23.441097 ◽

2021 ◽

Author(s):

Anish M.S. Shrestha ◽

Joyce Emlyn B. Guiao ◽

Kyle Christian R. Santiago

Keyword(s):

Gene Expression ◽

Differential Expression ◽

Expression Analysis ◽

De Novo ◽

Transcriptome Assembly ◽

Differential Expression Analysis ◽

Homology Search ◽

Model Organisms ◽

Rna Seq ◽

Protein Database

AbstractRNA-seq is being increasingly adopted for gene expression studies in a panoply of non-model organisms, with applications spanning the fields of agriculture, aquaculture, ecology, and environment. Conventional differential expression analysis for organisms without reference sequences requires performing computationally expensive and error-prone de-novo transcriptome assembly, followed by homology search against a high-confidence protein database for functional annotation. We propose a shortcut, where we obtain counts for differential expression analysis by directly aligning RNA-seq reads to the protein database. Through experiments on simulated and real data, we show drastic reductions in run-time and memory usage, with no loss in accuracy. A Snakemake implementation of our workflow is available at:https://bitbucket.org/project_samar/samar

Download Full-text

Gray whale transcriptome reveals longevity adaptations associated with DNA repair and ubiquitination

10.1101/754218 ◽

2019 ◽

Author(s):

Dmitri Toren ◽

Anton Kulaga ◽

Mineshbhai Jethva ◽

Eitan Rubin ◽

Anastasia V Snezhkina ◽

...

Keyword(s):

Gene Expression ◽

De Novo ◽

Transcriptome Assembly ◽

Expression Patterns ◽

Model Organisms ◽

Gray Whale ◽

Aging Research ◽

Arctic Water ◽

Gray Whales ◽

Longevity Genes

AbstractOne important question in aging research is how differences in genomics and transcriptomics determine the maximum lifespan in various species. Despite recent progress, much is still unclear on the topic, partly due to the lack of samples in non-model organisms and due to challenges in direct comparisons of transcriptomes from different species. The novel ranking-based method that we employ here is used to analyze gene expression in the gray whale and compare its de novo assembled transcriptome with that of other long- and short-lived mammals. Gray whales are among the top 1% longest-lived mammals. Despite the extreme environment, or maybe due to a remarkable adaptation to its habitat (intermittent hypoxia, Arctic water, and high pressure), gray whales reach at least the age of 77 years. In this work, we show that long-lived mammals share common gene expression patterns between themselves, including high expression of DNA maintenance and repair, ubiquitination, apoptosis, and immune responses. Additionally, the level of expression for gray whale orthologs of pro- and anti-longevity genes found in model organisms is in support of their alleged role and direction in lifespan determination. Remarkably, among highly expressed pro-longevity genes many are stress-related, reflecting an adaptation to extreme environmental conditions. The conducted analysis suggests that the gray whale potentially possesses high resistance to cancer and stress, at least in part ensuring its longevity. This new transcriptome assembly also provides important resources to support the efforts of maintaining the endangered population of gray whales.

Download Full-text

Gene Expression-Based Drug Repurposing to Target Ageing

10.1101/253344 ◽

2018 ◽

Cited By ~ 1

Author(s):

Handan Melike Dönertaş ◽

Matías Fuentealba Valenzuela ◽

Linda Partridge ◽

Janet M. Thornton

Keyword(s):

Gene Expression ◽

Human Brain ◽

Defense Mechanisms ◽

De Novo ◽

Expression Profiles ◽

Model Organism ◽

Drug Repurposing ◽

Complex Process ◽

Brain Ageing ◽

Using Data

SummaryAgeing is the largest risk factor for a variety of non-communicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both life- and health-span. Ageing is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target-centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat ageing in human brain. Using multiple gene expression datasets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterise ageing. Then, we compared these changes in gene expression with drug perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as anti-ageing drugs by reversing the detrimental changes that occur during ageing, others by mimicking the cellular defense mechanisms. The drugs that we identified included significant number of already identified pro-longevity drugs, indicating that the method can discover de novo drugs that meliorate ageing. The approach has the advantages that, by using data from human brain ageing data it focuses on processes relevant in human ageing and that it is unbiased, making it possible to discover new targets for ageing studies.

Download Full-text

Comparability of reference-based and reference-free transcriptome analysis approaches at the gene expression level

BMC Bioinformatics ◽

10.1186/s12859-021-04226-0 ◽

2021 ◽

Vol 22 (S11) ◽

Author(s):

Sung-Gwon Lee ◽

Dokyun Na ◽

Chungoo Park

Keyword(s):

Gene Expression ◽

De Novo ◽

Expression Profiles ◽

Gene Families ◽

Cell Types ◽

Model Organisms ◽

Expression Levels ◽

Large Gene ◽

Gene Expression Quantification ◽

Gene Expression Levels

Abstract Background Lately, high-throughput RNA sequencing has been extensively used to elucidate the transcriptome landscape and dynamics of cell types of different species. In particular, for most non-model organisms lacking complete reference genomes with high-quality annotation of genetic information, reference-free (RF) de novo transcriptome analyses, rather than reference-based (RB) approaches, are widely used, and RF analyses have substantially contributed toward understanding the mechanisms regulating key biological processes and functions. To date, numerous bioinformatics studies have been conducted for assessing the workflow, production rate, and completeness of transcriptome assemblies within and between RF and RB datasets. However, the degree of consistency and variability of results obtained by analyzing gene expression levels through these two different approaches have not been adequately documented. Results In the present study, we evaluated the differences in expression profiles obtained with RF and RB approaches and revealed that the former tends to be satisfactorily replaced by the latter with respect to transcriptome repertoires, as well as from a gene expression quantification perspective. In addition, we urge cautious interpretation of these findings. Several genes that are lowly expressed, have long coding sequences, or belong to large gene families must be validated carefully, whenever gene expression levels are calculated using the RF method. Conclusions Our empirical results indicate important contributions toward addressing transcriptome-related biological questions in non-model organisms.

Download Full-text

The neurotranscriptome of the Aedes aegypti mosquito

10.1101/026823 ◽

2015 ◽

Author(s):

Benjamin J Matthews ◽

Carolyn S McBride ◽

Matthew DeGennaro ◽

Orion Despo ◽

Leslie B Vosshall

Keyword(s):

Gene Expression ◽

Aedes Aegypti ◽

Vector Control ◽

De Novo ◽

Transcriptome Assembly ◽

Molecular Genetic ◽

Blood Feeding ◽

Model Organisms ◽

Protein Coding ◽

Male And Female

Background A complete genome sequence and the advent of genome editing open up non-traditional model organisms to mechanistic genetic studies. The mosquito Aedes aegypti is an important vector of infectious diseases such as dengue, chikungunya, and yellow fever, and has a large and complex genome, which has slowed annotation efforts. We used comprehensive transcriptomic analysis of adult gene expression to improve the genome annotation and to provide a detailed tissue-specific catalogue of neural gene expression at different adult behavioral states. Results We carried out deep RNA sequencing across all major peripheral male and female sensory tissues, the brain, and (female) ovary. Furthermore, we examined gene expression across three important phases of the female reproductive cycle, a remarkable example of behavioral switching in which a female mosquito alternates between obtaining blood-meals from humans and laying eggs. Using genome-guided alignments and de novo transcriptome assembly, our re-annotation includes 572 new putative protein-coding genes and updates to 13.5% and 50.3% of existing transcripts within coding sequences and untranslated regions, respectively. Using this updated annotation, we detail gene expression in each tissue, identifying large numbers of transcripts regulated by blood-feeding and sexually dimorphic transcripts that may provide clues to the biology of male- and female-specific behaviors, such as mating and blood-feeding, which are areas of intensive study for those interested in vector control. Conclusions This neurotranscriptome forms a strong foundation for the study of genes in the mosquito nervous system and investigation of sensory-driven behaviors and their regulation. Furthermore, understanding the molecular genetic basis of mosquito chemosensory behavior has important implications for vector control.

Download Full-text