scholarly journals Positive selection and enhancer evolution shaped lifespan and body mass in great apes

2021 ◽  
Author(s):  
Daniela Tejada-Martinez ◽  
Roberto A Avelar ◽  
Joao Pedro de Magalhaes ◽  
Ines Lopes ◽  
Bruce Zhang ◽  
...  
Keyword(s):  
Body Size ◽  
Positive Selection ◽  
Body Mass ◽  
Great Apes ◽  
Integrated Approach ◽  
Telomere Maintenance ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Evolutionary Forces ◽  
Longevity Genes

Within primates, the great apes are outliers both in terms of body size and lifespan, since they include the largest and longest-lived species in the order. Yet, the molecular bases underlying such features are poorly understood. Here, we leveraged an integrated approach to investigate multiple sources of molecular variation across primates, focusing on ~1,550 genes previously described as tumor suppressors, oncogenes, ageing genes in addition to a novel Build of the CellAge database of cell-senescence genes (version 2), herein presented for the first time. Specifically, we analyzed dN/dS rates, positive selection, gene expression (RNA-seq) and gene regulation (ChIP-seq). By analyzing the correlation between dN/dS, maximum lifespan and body mass we identified 67 genes that in primates co-evolved with those traits. Further, we identified 6 genes, important for immunity, neurodevelopment and telomere maintenance (including TERF2), under positive selection in the great ape ancestor. RNA-seq data, generated from the liver of six species representing all the primate lineages, revealed that ~8% of the longevity genes are differentially expressed in apes relative to other primates. Importantly, by integrating RNA-seq with ChIP-seq for H3K27ac (which marks active enhancers), we show that the differentially expressed longevity genes are significantly more likely than expected to be located near a novel ape-specific enhancer. Moreover, these particular ape-specific enhancers are enriched for young transposable elements, and specifically SINE-Vntr-Alus (SVAs). In summary, we demonstrate that multiple evolutionary forces have contributed to the evolution of lifespan and body size in primates.

scholarly journals Identification of Novel lncRNAs Differentially Expressed in Placentas of Chinese Ningqiang Pony and Yili Horse Breeds

Animals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 119
Author(s):  
Yabin Pu ◽  
Yanli Zhang ◽  
Tian Zhang ◽  
Jianlin Han ◽  
Yuehui Ma ◽  
...  
Keyword(s):  
Body Size ◽  
Limb Development ◽  
Small Body ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Myoblast Differentiation ◽  
Rna Seq ◽  
Skeletal Myoblast ◽  
Control Procedures ◽  
Non Coding Rnas

As a nutrient sensor, the placenta plays a key role in regulating fetus growth and development. Long non-coding RNAs (lncRNAs) have been shown to regulate growth-related traits. However, the biological function of lncRNAs in horse placentas remains unclear. To compare the expression patterns of lncRNAs in the placentas of the Chinese Ningqiang (NQ) and Yili (YL) breeds, we performed a transcriptome analysis using RNA sequencing (RNA-seq) technology. NQ is a pony breed with an average adult height at the withers of less than 106 cm, whereas that of YL is around 148 cm. Based on 813 million high-quality reads and stringent quality control procedures, 3011 transcripts coding for 1464 placental lncRNAs were identified and mapped to the horse reference genome. We found 107 differentially expressed lncRNAs (DELs) between NQ and YL, including 68 up-regulated and 39 down-regulated DELs in YL. Six (TBX3, CACNA1F, EDN3, KAT5, ZNF281, TMED2, and TGFB1) out of the 233 genes targeted by DELs were identified as being involved in limb development, skeletal myoblast differentiation, and embryo development. Two DELs were predicted to target the TBX3 gene, which was found to be under strong selection and associated with small body size in the Chinese Debao pony breed. This finding suggests the potential functional significance of placental lncRNAs in regulating horse body size.

A two-step integrated approach to detect differentially expressed genes in RNA-Seq data

2016 ◽  
Vol 14 (06) ◽  
pp. 1650034 ◽  
Author(s):  
Naim Al Mahi ◽  
Munni Begum
Keyword(s):  
Simulation Study ◽  
Negative Binomial ◽  
Real Data ◽  
Integrated Approach ◽  
Differentially Expressed ◽  
Data Sets ◽  
Rna Seq ◽  
Software Packages ◽  
Treatment Conditions ◽  
Integrated Approaches

One of the primary objectives of ribonucleic acid (RNA) sequencing or RNA-Seq experiment is to identify differentially expressed (DE) genes in two or more treatment conditions. It is a common practice to assume that all read counts from RNA-Seq data follow overdispersed (OD) Poisson or negative binomial (NB) distribution, which is sometimes misleading because within each condition, some genes may have unvarying transcription levels with no overdispersion. In such a case, it is more appropriate and logical to consider two sets of genes: OD and non-overdispersed (NOD). We propose a new two-step integrated approach to distinguish DE genes in RNA-Seq data using standard Poisson and NB models for NOD and OD genes, respectively. This is an integrated approach because this method can be merged with any other NB-based methods for detecting DE genes. We design a simulation study and analyze two real RNA-Seq data to evaluate the proposed strategy. We compare the performance of this new method combined with the three [Formula: see text]-software packages namely edgeR, DESeq2, and DSS with their default settings. For both the simulated and real data sets, integrated approaches perform better or at least equally well compared to the regular methods embedded in these [Formula: see text]-packages.

Bayesian analysis for detecting differentially expressed genes from RNA-seq data

2014 ◽  
Author(s):  
◽  
Shiqi Cui
Keyword(s):  
Differentially Expressed Genes ◽  
Time Course ◽  
Markov Models ◽  
Single Gene ◽  
Principal Component ◽  
Integrated Approach ◽  
Differentially Expressed ◽  
Monte Carlo Algorithm ◽  
Rna Seq ◽  
Paired Data

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This dissertation introduces hmmSeq, a model-based hierarchical Bayesian technique for detecting differentially expressed genes from RNA-seq data. Our novel hmmSeq methodology uses hidden Markov models to account for potential co-expression of neighboring genes. In addition, hmmSeq employs an integrated approach to studies with technical or biological replicates, automatically adjusting for any extra-Poisson variability. Moreover, for cases when paired data are available, hmmSeq includes a paired structure between treatments that incorporates subject-specific effects. To perform parameter estimation for the hmmSeq model, we develop an efficient Markov chain Monte Carlo algorithm. Further, we develop a procedure for detection of differentially expressed genes that automatically controls false discovery rate. A simulation study shows that the hmmSeq methodology performs better than competitors in terms of receiver operating characteristic curves. Finally, the analyses of three publicly available RNA-Seq datasets demonstrate the power and flexibility of the hmmSeq methodology. This dissertation also introduces an empirical Bayesian approach to detect differentially expressed genes in time course RNA-seq experiments. The proposed Bayesian method identifies major variation in gene expression profile by Bayesian principal component regression. The expression data are normalized for each gene, and the high dimentionality of time course data is first reduced by principal component analysis. The proposed model assumes a mixture distribution of expression parameters for differentially and nondifferentially expressed genes, borrows strength by sharing same variance across multiple subjects for each single gene, as well as shares information across genes by assuming gene-wise probabilities of being differentially expressed from the common beta prior distribution.

scholarly journals OP0021 IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES IN EARLY RHEUMATOID ARTHRITIS PATIENTS RESPONDING TO TOCILIZUMAB

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 12.2-12
Author(s):  
I. Muller ◽  
M. Verhoeven ◽  
H. Gosselt ◽  
M. Lin ◽  
T. De Jong ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
T Cells ◽  
Gene Ontology ◽  
Regulatory T Cells ◽  
Early Rheumatoid Arthritis ◽  
Whole Blood ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Double Blind

Background:Tocilizumab (TCZ) is a monoclonal antibody that binds to the interleukin 6 receptor (IL-6R), inhibiting IL-6R signal transduction to downstream inflammatory mediators. TCZ has shown to be effective as monotherapy in early rheumatoid arthritis (RA) patients (1). However, approximately one third of patients inadequately respond to therapy and the biological mechanisms underlying lack of efficacy for TCZ remain elusive (1). Here we report gene expression differences, in both whole blood and peripheral blood mononuclear cells (PBMC) RNA samples between early RA patients, categorized by clinical TCZ response (reaching DAS28 < 3.2 at 6 months). These findings could lead to identification of predictive biomarkers for TCZ response and improve RA treatment strategies.Objectives:To identify potential baseline gene expression markers for TCZ response in early RA patients using an RNA-sequencing approach.Methods:Two cohorts of RA patients were included and blood was collected at baseline, before initiating TCZ treatment (8 mg/kg every 4 weeks, intravenously). DAS28-ESR scores were calculated at baseline and clinical response to TCZ was defined as DAS28 < 3.2 at 6 months of treatment. In the first cohort (n=21 patients, previously treated with DMARDs), RNA-sequencing (RNA-seq) was performed on baseline whole blood PAXgene RNA (Illumina TruSeq mRNA Stranded) and differential gene expression (DGE) profiles were measured between responders (n=14) and non-responders (n=7). For external replication, in a second cohort (n=95 therapy-naïve patients receiving TCZ monotherapy), RNA-seq was conducted on baseline PBMC RNA (SMARTer Stranded Total RNA-Seq Kit, Takara Bio) from the 2-year, multicenter, double-blind, placebo-controlled, randomized U-Act-Early trial (ClinicalTrials.gov identifier: NCT01034137) and DGE was analyzed between 84 responders and 11 non-responders.Results:Whole blood DGE analysis showed two significantly higher expressed genes in TCZ non-responders (False Discovery Rate, FDR < 0.05): urotensin 2 (UTS2) and caveolin-1 (CAV1). Subsequent analysis of U-Act-Early PBMC DGE showed nine differentially expressed genes (FDR < 0.05) of which expression in clinical TCZ non-responders was significantly higher for eight genes (MTCOP12, ZNF774, UTS2, SLC4A1, FECH, IFIT1B, AHSP, and SPTB) and significantly lower for one gene (TND2P28M). Both analyses were corrected for baseline DAS28-ESR, age and gender. Expression of UTS2, with a proposed function in regulatory T-cells (2), was significantly higher in TCZ non-responders in both cohorts. Furthermore, gene ontology enrichment analysis revealed no distinct gene ontology or IL-6 related pathway(s) that were significantly different between TCZ-responders and non-responders.Conclusion:Several genes are differentially expressed at baseline between responders and non-responders to TCZ therapy at 6 months. Most notably, UTS2 expression is significantly higher in TCZ non-responders in both whole blood as well as PBMC cohorts. UTS2 could be a promising target for further analyses as a potential predictive biomarker for TCZ response in RA patients in combination with clinical parameters (3).References:[1]Bijlsma JWJ, Welsing PMJ, Woodworth TG, et al. Early rheumatoid arthritis treated with tocilizumab, methotrexate, or their combination (U-Act-Early): a multicentre, randomised, double-blind, double-dummy, strategy trial. Lancet. 2016;388(10042):343-55.[2]Bhairavabhotla R, Kim YC, Glass DD, et al. Transcriptome profiling of human FoxP3+ regulatory T cells. Human Immunology. 2016;77(2):201-13.[3]Gosselt HR, Verhoeven MMA, Bulatovic-Calasan M, et al. Complex machine-learning algorithms and multivariable logistic regression on par in the prediction of insufficient clinical response to methotrexate in rheumatoid arthritis. Journal of Personalized Medicine. 2021;11(1).Disclosure of Interests:None declared

scholarly journals OP0286 PROTEOMICS AND RNA SEQUENCING APPROACHES HIGHLIGHT THE ROLE OF ENDOTHELIAL CELL DYSREGULATION IN IL-1 AND IFN MEDIATED AUTOINFLAMMATORY DISEASES, NOMID AND CANDLE

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 178-179
Author(s):  
S. Alehashemi ◽  
M. Garg ◽  
B. Sellers ◽  
A. De Jesus ◽  
A. Biancotto ◽  
...  
Keyword(s):  
Endothelial Cell ◽  
Whole Blood ◽  
Cell Activation ◽  
Immune Cell ◽  
Differentially Expressed ◽  
Neutrophilic Dermatosis ◽  
Healthy Controls ◽  
Differentially Expressed Proteins ◽  
Rna Seq ◽  
Before And After

Background:Systemic Autoinflammatory diseases present with sterile inflammation. NOMID (Neonatal-Onset Multisystem Inflammatory Disease) is caused by gain-of-function mutations inNLRP3and excess IL-1 production, presents with fever, neutrophilic dermatosis, aseptic meningitis, hearing loss and eye inflammation; CANDLE (Chronic Atypical Neutrophilic Dermatosis, Lipodystrophy and Elevated Temperature) is caused by loss-of-function mutations in proteasome genes that lead to type-1 interferon signaling, characterized by fever, panniculitis, lipodystrophy, cytopenia, systemic and pulmonary hypertension and basal ganglia calcification. IL-1 blockers are approved for NOMID and JAK-inhibitors show efficacy in CANDLE treatment.Objectives:We used proteomic analysis to compare differentially expressed proteins in active NOMID and CANDLE compared to healthy controls before and after treatment, and whole blood bulk RNA seq to identify the immune cell signatures.Methods:Serum samples from active NOMID (n=12) and CANDLE (n=7) before and after treatment (table 1) and age matched healthy controls (HC) (n=7) were profiled using the SomaLogic platform (n=1125 proteins). Differentially expressed proteins in NOMID and CANDLE were ranked after non-parametric tests for unpaired (NOMIDp<0.05, CANDLE,p<0.1) and paired (p<0.05) analysis and assessed by enriched Gene Ontology pathways and network visualization. Whole blood RNA seq was performed (NOMID=7, CANDLE=7, Controls =5) and RPKM values were used to assess immune cells signatures.Table 1.Patient’s characteristicsNOMIDN=12, Male =6CANDLEN=7, Male =6AgeMedian (range)12 (2, 28)16 (3, 20)Ethnicity%White (Hispanic)80 (20)100 (30)GeneticsNLRP3mutation(2 Somatic, 10 Germline)mutations in proteasome component genes(1 digenic, 6 Homozygous/compound Heterozygous)Before treatmentAfter treatmentBefore treatmentAfter treatmentCRPMedian (range) mg/L52 (16-110)5 (0-23)5 (0-101)1 (0-4)IFN scoremedian (range)0NA328 (211-1135)3 (0-548)Results:Compared to control, 205 proteins (127 upregulated, 78 downregulated) were significantly different at baseline in NOMID, compared to 163 proteins (101 upregulated, and 62 downregulated) in CANDLE. 134 dysregulated proteins (85 upregulated, 49 downregulated) overlapped in NOMID and CANDLE (Figure 1). Pathway analysis identified neutrophil and monocyte chemotaxis signature in both NOMID and CANDLE. NOMID patients had neutrophilia and active neutrophils. CANDLE patients exhibited active neutrophils in whole blood RNA. Endothelial cell activation was the most prominent non-hematopoietic signature and suggest distinct endothelial cell dysregulation in NOMID and CANDLE. In NOMID, the signature included neutrophil transmigration (SELE) endothelial cell motility in response to angiogenesis (HGF, VEGF), while in CANDLE the endothelial signatures included extracellular matrix protein deposition (COL8A) suggesting increased vascular stiffness. CANDLE patients had higher expression of Renin, 4 out of 7 had hypertension, NOMID patients did not have hypertension. Treatment with anakinra and baricitinib normalized 143 and 142 of dysregulated proteins in NOMID and CANDLE respectively.Conclusion:Differentially expressed proteins in NOMID and CANDLE are consistent with innate immune cell activation. Distinct endothelial cell signatures in NOMID and CANDLE may provide mechanistic insight into differences in vascular phenotypes. Treatment with anakinra and Baricitinib in NOMID and CANDLE leaves 30% and 13% of the dysregulated proteins unchanged.Acknowledgments:This work was supported by Intramural Research atNational Institute of Allergy Immunology and Infectious Diseases of National Institutes of Health, Bethesda, Maryland, the Center of Human Immunology and was approved by the IRB.Disclosure of Interests:None declared

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

scholarly journals Long Intergenic Non-Coding RNAs in the Mammary Parenchyma and Fat Pad of Pre-Weaning Heifer Calves: Identification and Functional Analysis

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1268
Author(s):  
Shengchao Zhang ◽  
Sibtain Ahmad ◽  
Yuxia Zhang ◽  
Guohua Hua ◽  
Jianming Yi
Keyword(s):  
Mammary Gland ◽  
Crude Protein ◽  
Regulatory Mechanism ◽  
Differentially Expressed ◽  
Milk Replacer ◽  
Rna Seq ◽  
Fat Pad ◽  
Mammary Fat Pad ◽  
Non Coding Rnas ◽  
Gland Development

Enhanced plane of nutrition at pre-weaning stage can promote the development of mammary gland especially heifer calves. Although several genes are involved in this process, long intergenic non-coding RNAs (lincRNAs) are regarded as key regulators in the regulated network and are still largely unknown. We identified and characterized 534 putative lincRNAs based on the published RNA-seq data, including heifer calves in two groups: fed enhanced milk replacer (EH, 1.13 kg/day, including 28% crude protein, 25% fat) group and fed restricted milk replacer (R, 0.45 kg/day, including 20% crude protein, 20% fat) group. Sub-samples from the mammary parenchyma (PAR) and mammary fat pad (MFP) were harvested from heifer calves. According to the information of these lincRNAs’ quantitative trait loci (QTLs), the neighboring and co-expression genes were used to predict their function. By comparing EH vs R, 79 lincRNAs (61 upregulated, 18 downregulated) and 86 lincRNAs (54 upregulated, 32 downregulated) were differentially expressed in MFP and PAR, respectively. In MFP, some differentially expressed lincRNAs (DELs) are involved in lipid metabolism pathways, while, in PAR, among of DELs are involved in cell proliferation pathways. Taken together, this study explored the potential regulatory mechanism of lincRNAs in the mammary gland development of calves under different planes of nutrition.

scholarly journals Dynamic alteration in miRNA and mRNA expression profiles at different stages of chronic arsenic exposure-induced carcinogenesis in a human cell culture model of skin cancer

2021 ◽  
Author(s):  
Mayukh Banerjee ◽  
Ana Ferragut Cardoso ◽  
Laila Al-Eryani ◽  
Jianmin Pan ◽  
Theodore S. Kalbfleisch ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Mrna Expression ◽  
Hacat Cell ◽  
Arsenic Exposure ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Mesenchymal Transition ◽  
Chronic Arsenic Exposure ◽  
Pathway Analyses ◽  
Time Point

AbstractChronic arsenic exposure causes skin cancer, although the underlying molecular mechanisms are not well defined. Altered microRNA and mRNA expression likely play a pivotal role in carcinogenesis. Changes in genome-wide differential expression of miRNA and mRNA at 3 strategic time points upon chronic sodium arsenite (As3+) exposure were investigated in a well-validated HaCaT cell line model of arsenic-induced cutaneous squamous cell carcinoma (cSCC). Quadruplicate independent HaCaT cell cultures were exposed to 0 or 100 nM As3+ for up to 28-weeks (wk). Cell growth was monitored throughout the course of exposure and epithelial-mesenchymal transition (EMT) was examined employing immunoblot. Differentially expressed miRNA and mRNA profiles were generated at 7, 19, and 28-wk by RNA-seq, followed by identification of differentially expressed mRNA targets of differentially expressed miRNAs through expression pairing at each time point. Pathway analyses were performed for total differentially expressed mRNAs and for the miRNA targeted mRNAs at each time point. RNA-seq predictions were validated by immunoblot of selected target proteins. While the As3+-exposed cells grew slower initially, growth was equal to that of unexposed cells by 19-wk (transformation initiation), and exposed cells subsequently grew faster than passage-matched unexposed cells. As3+-exposed cells had undergone EMT at 28-wk. Pathway analyses demonstrate dysregulation of carcinogenesis-related pathways and networks in a complex coordinated manner at each time point. Immunoblot data largely corroborate RNA-seq predictions in the endoplasmic reticulum stress (ER stress) pathway. This study provides a detailed molecular picture of changes occurring during the arsenic-induced transformation of human keratinocytes.

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