scholarly journals RNA sequencing of Stentor cell fragments reveals key processes underlying cellular regeneration

2017 ◽  
Author(s):  
Henning Onsbring Gustafson ◽  
Mahwash Jamy ◽  
Thijs J. G. Ettema
Keyword(s):  
Differential Expression ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Anterior Part ◽  
Differential Expression Analysis ◽  
Biological Studies ◽  
Ability To Regenerate ◽  
Cellular Regeneration ◽  
A Cell ◽  
Cell Fragments

SummaryWhile ciliates of the genus Stentor are known for their ability to regenerate when their cells are damaged or even fragmented, the physical and molecular mechanisms underlying this process are poorly understood. To identify genes involved in the regenerative capability of Stentor cells, RNA sequencing of individual Stentor polymorphus cell fragments was performed. After splitting a cell over the anterior-posterior axis, the posterior fragment has to regenerate the oral apparatus, while the anterior part needs to regenerate the hold fast. Altogether, differential expression analysis of both posterior and anterior S. polymorphus cell fragments for four different post-split time points revealed over 10,000 up-regulated genes throughout the regeneration process. Among these, genes involved in cell signaling, microtubule-based movement and cell cycle regulation seemed to be particularly important during cellular regeneration. We identified roughly nine times as many up-regulated genes in regenerating S. polymorphus posterior fragments as compared to anterior fragments, indicating that regeneration of the anterior oral apparatus is a complex process that involves many genes. Our analyses identified several expanded groups of genes such as dual-specific tyrosine-(Y)-phosphorylation regulated kinases and MORN domain containing proteins that seemingly act as key-regulators of cellular regeneration. In agreement with earlier morphological and cell biological studies, our differential expression analyses indicate that cellular regeneration and vegetative division share many similarities.

Cross-species Transcriptomes Reveal Species-specific and Shared Molecular Adaptations for Plants Development on Iron-rich Rocky Outcrops Soils

2021 ◽  
Author(s):  
Mariana Costa Dias ◽  
Cecílio Caldeira ◽  
Markus Gastauer ◽  
Silvio Ramos ◽  
Guilherme Oliveira
Keyword(s):  
Circadian Rhythm ◽  
Differential Expression ◽  
Native Species ◽  
Molecular Mechanisms ◽  
Light Stimulus ◽  
Differential Expression Analysis ◽  
Common Mechanism ◽  
Dependent Manner ◽  
Rocky Outcrops ◽  
Species Specific

Abstract BackgroundCanga is the Brazilian term for the savanna-like vegetation harboring several endemic species on iron-rich rocky outcrops, usually considered for mining activities. Parkia platycephala Benth. and Stryphnodendron pulcherrimum (Willd.) Hochr. naturally occur in the cangas of Serra dos Carajás (eastern Amazonia, Brazil) and the surrounding forest, indicating high phenotypic plasticity. The morphological and physiological mechanisms of the plants’ establishment in the canga environment are well studied, but the molecular adaptative responses are still unknown. We aimed to identify molecular mechanisms that allow the establishment of these plants in the canga environment.ResultsPlants were grown in canga and forest substrates collected in the Carajás Mineral Province. RNA was extracted from pooled leaf tissue, and RNA-seq paired-end reads were assembled into representative transcriptomes for P. platycephala and S. pulcherrimum containing 31,728 and 31,311 primary transcripts, respectively. We identified both species-specific and core molecular responses in plants grown in the canga substrate using differential expression analyses. In the species-specific analysis, we identified 1,112 and 838 differentially expressed genes for P. platycephala and S. pulcherrimum, respectively. Enrichment analyses showed unique biological processes and metabolic pathways affected for each species. Comparative differential expression analysis was based on shared single-copy orthologs. The overall pattern of ortholog expression was species-specific. Even so, almost 300 altered genes were identified between plants in canga and forest substrates, responding the same way in both species. The genes were functionally associated with the response to light stimulus and the circadian rhythm pathway.ConclusionsPlants possess species-specific adaptative responses to cope with the substrates. Our results also suggest that plants adapted to both canga and forest environments can adjust the circadian rhythm in a substrate-dependent manner. The circadian clock gene modulation might be a central mechanism regulating the plants’ development in the canga substrate in the studied legume species. The mechanism may be shared as a common mechanism to abiotic stress compensation in other native species.

scholarly journals UMI-count modeling and differential expression analysis for single-cell RNA sequencing

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Wenan Chen ◽  
Yan Li ◽  
John Easton ◽  
David Finkelstein ◽  
Gang Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Cell Rna Sequencing

scholarly journals A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1947
Author(s):  
Samarendra Das ◽  
Anil Rai ◽  
Michael L. Merchant ◽  
Matthew C. Cave ◽  
Shesh N. Rai
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Performance Metrics ◽  
Differential Expression Analysis ◽  
Individual Performance ◽  
Rna Seq ◽  
Gene Expressions ◽  
Single Cell Rna Sequencing

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

scholarly journals Impact of sickle cell trait hemoglobin on the intraerythrocytic transcriptional program of Plasmodium falciparum

2021 ◽  
Author(s):  
Joseph W Saelens ◽  
Jens E.V. Petersen ◽  
Elizabeth Freedman ◽  
Robert C Moseley ◽  
Drissa Konate ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Differential Expression ◽  
Molecular Mechanisms ◽  
Sickle Cell Trait ◽  
Differential Expression Analysis ◽  
Protein Export ◽  
In Vitro Cultivation ◽  
Transcriptional Program ◽  
Transcriptional Responses

Sickle-trait hemoglobin (HbAS) confers near-complete protection from severe, life-threatening falciparum malaria in African children. Despite this clear protection, the molecular mechanisms by which HbAS confers these protective phenotypes remain incompletely understood. As a forward genetic screen for aberrant parasite transcriptional responses associated with parasite neutralization in HbAS red blood cells (RBCs), we performed comparative transcriptomic analyses of Plasmodium falciparum in normal (HbAA) and HbAS erythrocytes during both in vitro cultivation of reference parasite strains and naturally-occurring P. falciparum infections in Malian children with HbAA or HbAS. During in vitro cultivation, parasites matured normally in HbAS RBCs, and the temporal expression was largely unperturbed of the highly ordered transcriptional program that underlies the parasites maturation throughout the intraerythrocytic development cycle (IDC). However, differential expression analysis identified hundreds of transcripts aberrantly expressed in HbAS, largely occurring late in the IDC. Surprisingly, transcripts encoding members of the Maurers clefts were overexpressed in HbAS despite impaired parasite protein export in these RBCs, while parasites in HbAS RBCs underexpressed transcripts associated with the endoplasmic reticulum and those encoding serine repeat antigen proteases that promote parasite egress. Analyses of P. falciparum transcriptomes from 32 children with uncomplicated malaria identified stage-specific differential expression: among infections composed of ring-stage parasites, only cyclophilin 19B was underexpressed in children with HbAS, while trophozoite-stage infections identified a range of differentially-expressed transcripts, including downregulation in HbAS of several transcripts associated with severe malaria in collateral studies. Collectively, our comparative transcriptomic screen in vitro and in vivo indicates that P. falciparum adapts to HbAS by altering its protein chaperone and folding machinery, oxidative stress response, and protein export machinery. Because HbAS consistently protects from severe P. falciparum, modulation of these responses may offer avenues by which to neutralize P. falciparum parasites.

scholarly journals A relative comparison between Hidden Markov- and Log-Linear-based models for differential expression analysis in a real time course RNA sequencing data

2018 ◽  
Author(s):  
Fatemeh Gholizadeh ◽  
Zahra Salehi ◽  
Ali Mohammad banaei-Moghaddam ◽  
Abbas Rahimi Foroushani ◽  
Kaveh kavousi
Keyword(s):  
Real Time ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Time Course ◽  
Hidden Markov ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Normalization Methods ◽  
Log Linear

AbstractWith the advent of the Next Generation Sequencing technologies, RNA-seq has become known as an optimal approach for studying gene expression profiling. Particularly, time course RNA-seq differential expression analysis has been used in many studies to identify candidate genes. However, applying a statistical method to efficiently identify differentially expressed genes (DEGs) in time course studies is challenging due to inherent characteristics of such data including correlation and dependencies over time. Here we aim to relatively compare EBSeq-HMM, a Hidden Markov-based model, with multiDE, a Log-Linear-based model, in a real time course RNA sequencing data. In order to conduct the comparison, common DEGs detected by edgeR, DESeq2 and Voom (referred to as Benchmark DEGs) were utilized as a measure. Each of the two models were compared using different normalization methods. The findings revealed that multiDE identified more Benchmark DEGs and showed a higher agreement with them than EBSeq-HMM. Furthermore, multiDE and EBSeq-HMM displayed their best performance using TMM and Upper-Quartile normalization methods, respectively.

scholarly journals Sex differences in the human brain transcriptome of cases with schizophrenia

2020 ◽  
Author(s):  
Gabriel E. Hoffman ◽  
Yixuan Ma ◽  
Kelsey S. Montgomery ◽  
Jaroslav Bendl ◽  
Manoj Kumar Jaiswal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Differential Expression ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Age Of Onset ◽  
Differential Expression Analysis ◽  
Gene Expression Signature ◽  
Synaptic Organization ◽  
Expression Signature

AbstractWhile schizophrenia differs between males and females in age of onset, symptomatology and the course of the disease, the molecular mechanisms underlying these differences remain uncharacterized. In order to address questions about the sex-specific effects of schizophrenia, we performed a large-scale transcriptome analysis of RNA-seq data from 437 controls and 341 cases from two distinct cohorts from the CommonMind Consortium. Analysis across the cohorts identifies a reproducible gene expression signature of schizophrenia that is highly concordant with previous work. Differential expression across sex is reproducible across cohorts and identifies X- and Y-linked genes, as well as those involved in dosage compensation. Intriguingly, the sex expression signature is also enriched for genes involved in neurexin family protein binding and synaptic organization. Differential expression analysis testing a sex-by-diagnosis interaction effect did not identify any genome-wide signature after multiple testing corrections. Gene coexpression network analysis was performed to reduce dimensionality and elucidate interactions among genes. We found enrichment of co-expression modules for sex-by-diagnosis differential expression signatures, which were highly reproducible across the two cohorts and involve a number of diverse pathways, including neural nucleus development, neuron projection morphogenesis, and regulation of neural precursor cell proliferation. Overall, our results indicate that the effect size of sex differences in schizophrenia gene expression signatures is small and underscore the challenge of identifying robust sex-by-diagnosis signatures, which will require future analyses in larger cohorts.

scholarly journals Exosomal microRNAs are novel circulating biomarkers in cigarette, waterpipe smokers, E-cigarette users and dual smokers

2020 ◽  
Author(s):  
Kameshwar P. Singh ◽  
Krishna P. Maremanda ◽  
Dongmei Li ◽  
Irfan Rahman
Keyword(s):  
Differential Expression ◽  
Health Effects ◽  
Molecular Mechanisms ◽  
Electronic Cigarettes ◽  
Differential Expression Analysis ◽  
Pairwise Comparison ◽  
Gene Set Enrichment Analysis ◽  
Cigarette Smoke Exposure ◽  
Tobacco Exposure ◽  
Non Coding Rnas

Abstract Background Electronic cigarettes (e-cigs) produce aerosolized substances by heating a liquid, which contains large number of chemicals. The aerosol generated by E-cig may produce serious health effects. Cigarette smoke exposure may causes various diseases including COPD, atherosclerosis, and lung cancer. Waterpipe tobacco smoking also causes various acute and chronic health effects including cardiopulmonary diseases. MicroRNAs are present in higher concentration in exosomes that play a major role in various normal physiological functions and diseases. We hypothesized that the non-coding RNAs transcript may serve as susceptibility to disease biomarkers by smoking and vaping. Results Our data show the enrichment of various non-coding RNAs that include microRNAs, tRNAs, piRNAs, snoRNAs, snRNAs, Mt-tRNAs, and other biotypes in exosomes. The detailed differential expression analysis of microRNAs, tRNAs and piRNA showed significant changes between pairwise comparisons of different groups. The common changes in differential expression of 8 microRNAs that are hsa-let-7a-5p, hsa-miR-21-5p, hsa-miR-29b-3p, hsa-let-7f-5p, hsa-miR-143-3p, hsa-miR-30a-5p, hsa-let-7i-5p, and hsa-let-7g-5p were found when compared with all smoking and vaping groups with non-smoking group. The e-cig group has differentially expressed 7 microRNAs (hsa-miR-224-5p, hsa-let-7c-5p, hsa-miR-193b-3p, hsa-miR-30e-5p, hsa-miR-423-3p, hsa-miR-500b-3p, hsa-miR-365a-3p|hsa-miR-365b-3p) that is specific for this group, not expressed in other three groups. Gene set enrichment analysis of microRNA showed significant changes in the top six enriched functions that consisted of biological pathway, biological process, molecular function, cellular component, site of expression and transcription factor in all groups. Further, the pairwise comparison of tRNAs and piRNA in all groups also revealed significant changes in differential expression. Conclusions Plasma exosomes of cigarette smokers, waterpipe smokers, e-cig users and dual smokers have common differential expression of microRNAs (hsa-let-7a-5p, hsa-miR-21-5p, hsa-miR-29b-3p, hsa-let-7f-5p, hsa-miR-143-3p, hsa-miR-30a-5p, hsa-let-7i-5p, and hsa-let-7g-5p), may be biomarker for tobacco exposure. Additionally, the e-cig users have also differential expressed microRNAs (hsa-miR-224-5p, hsa-let-7c-5p, hsa-miR-193b-3p, hsa-miR-30e-5p, hsa-miR-423-3p, hsa-miR-500b-3p, and hsa-miR-365a-3p|hsa-miR-365b-3p) that is specific for this group. This study will help to better understand molecular mechanisms of plasma exosome non-coding RNAs and in developing biomarkers that may be useful in diagnosis and therapy of pulmonary injury and disease by smoking and vaping.

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