scholarly journals Changes in Expressions of Spermatogenic Marker Genes and Spermatogenic Cell Population Caused by Stress

2021 ◽  
Vol 12 ◽  
Author(s):  
Pengxiang Tian ◽  
Zhiming Zhao ◽  
Yanli Fan ◽  
Na Cui ◽  
Baojun Shi ◽  
...  
Keyword(s):  
Chronic Stress ◽  
Imaging System ◽  
Quantitative Imaging ◽  
Bioinformatic Analysis ◽  
Marker Genes ◽  
Spermatogenic Cells ◽  
Rna Seq ◽  
Pathological Changes ◽  
Reproductive Dysfunction ◽  
Stress Models

Many young adults are in a state of stress due to social and psychological pressures, which may result in male reproductive dysfunction. To provide new insight into this phenomenon, we investigated the effect of stress on the regulation of key genes and biological events in specific stages of spermatogenesis. After establishing rat stress models of different time durations, we observed pathological changes in testis through haematoxylin and eosin staining, and analysed gene expression in testis by RNA-seq, bioinformatic analysis, and reverse transcription qPCR (RT-qPCR). Immunohistochemistry (IHC) with the TissueFAXS quantitative imaging system was used to verify changes of different population of spermatogenic cells marked by differentially expressed marker genes. Our results showed that prolonged stress can lead to pathological changes in the testes, such as thinning of the spermatogenic epithelium, a decreased number of spermatogenic epithelial cells, the disordered arrangement of spermatogenic cells, and a decreased number of mature sperms. RNA-seq revealed that key marker spermatogenesis-related genes such as Stra8, Sycp3, Piwil1, and Tnp1 had significantly decreased expression levels in chronic stress groups, and this was confirmed by RT-qPCR and IHC. Collectively, these findings suggest that chronic stress causes damaging pathological changes in testis and dysregulates the marker genes of specific stages of spermatogenesis and change the population of spermatogenic cells, which may be a critical responsible for male reproductive dysfunction.

scholarly journals A Novel NFIX-STAT6 Gene Fusion in Solitary Fibrous Tumor: A Case Report

2021 ◽  
Vol 22 (14) ◽  
pp. 7514
Author(s):  
David S. Moura ◽  
Juan Díaz-Martín ◽  
Silvia Bagué ◽  
Ruth Orellana-Fernandez ◽  
Ana Sebio ◽  
...  
Keyword(s):  
Solitary Fibrous Tumor ◽  
Gene Fusion ◽  
Wide Spectrum ◽  
Bioinformatic Analysis ◽  
Fusion Partner ◽  
Rna Seq ◽  
Genomic Location ◽  
Nuclear Factor I ◽  
New Gene ◽  
Fibrous Tumor

Solitary fibrous tumor is a rare subtype of soft-tissue sarcoma with a wide spectrum of histopathological features and clinical behaviors, ranging from mildly to highly aggressive tumors. The defining genetic driver alteration is the gene fusion NAB2–STAT6, resulting from a paracentric inversion within chromosome 12q, and involving several different exons in each gene. STAT6 (signal transducer and activator of transcription 6) nuclear immunostaining and/or the identification of NAB2–STAT6 gene fusion is required for the diagnostic confirmation of solitary fibrous tumor. In the present study, a new gene fusion consisting of Nuclear Factor I X (NFIX), mapping to 19p13.2 and STAT6, mapping to 12q13.3 was identified by targeted RNA-Seq in a 74-year-old female patient diagnosed with a deep-seated solitary fibrous tumor in the pelvis. Histopathologically, the neoplasm did not display nuclear pleomorphism or tumor necrosis and had a low proliferative index. A total of 378 unique reads spanning the NFIXexon8–STAT6exon2 breakpoint with 55 different start sites were detected in the bioinformatic analysis, which represented 59.5% of the reads intersecting the genomic location on either side of the breakpoint. Targeted RNA-Seq results were validated by RT-PCR/ Sanger sequencing. The identification of a new gene fusion partner for STAT6 in solitary fibrous tumor opens intriguing new hypotheses to refine the role of STAT6 in the sarcomatogenesis of this entity.

A 1H-NMR plasma metabonomic study of acute and chronic stress models of depression in rats

2013 ◽  
Vol 241 ◽  
pp. 86-91 ◽  
Author(s):  
Biyun Shi ◽  
Junsheng Tian ◽  
Huan Xiang ◽  
Xiaoqing Guo ◽  
Lizeng Zhang ◽  
...  
Keyword(s):  
Chronic Stress ◽  
1H Nmr ◽  
Acute And Chronic Stress ◽  
Stress Models

scholarly journals Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 952 ◽  
Author(s):  
Michael I. Love ◽  
Charlotte Soneson ◽  
Rob Patro
Keyword(s):  
Software Package ◽  
Gene Expression Analysis ◽  
Real Data ◽  
Transcript Level ◽  
Bioinformatic Analysis ◽  
Rna Seq ◽  
Statistical Framework ◽  
Gene Level ◽  
Show Evidence ◽  
Differential Gene

Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

scholarly journals JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

2020 ◽  
Author(s):  
Mohit Goyal ◽  
Guillermo Serrano ◽  
Ilan Shomorony ◽  
Mikel Hernaez ◽  
Idoia Ochoa
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Latent Space ◽  
Cell Type Specific ◽  
Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

scholarly journals Chronic Stress Alters Astrocyte Morphology in Mouse Prefrontal Cortex

2021 ◽  
Author(s):  
Sierra A. Codeluppi ◽  
Dipashree Chatterjee ◽  
Thomas D. Prevot ◽  
Keith A. Misquitta ◽  
Etienne Sibille ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Chronic Stress ◽  
Fluorescent Protein ◽  
Glial Fibrillary Acid Protein ◽  
Behavioral Deficits ◽  
Sholl Analysis ◽  
Acid Protein ◽  
Potential Link ◽  
Green Fluorescent ◽  
Stress Models

AbstractBackgroundNeuromorphological changes are consistently reported in the prefrontal cortex (PFC) of patients with stress-related disorders and in rodent stress models, but the effects of stress on astrocyte morphology and potential link to behavioral deficits are relatively unknown.MethodsTo answer these questions, transgenic mice expressing green fluorescent protein (GFP) under the glial fibrillary acid protein (GFAP) promotor were subjected to 7, 21 or 35 days of chronic restraint stress (CRS). CRS behavioral effects on anhedonia- and anxiety-like behaviours were measured using the sucrose intake and the PhenoTyper tests, respectively. PFC GFP+ or GFAP+ cells morphology was assessed using Sholl analysis and associations with behavior were determined using correlation analysis.ResultsCRS-exposed mice displayed anxiety-like behavior at 7, 21 and 35 days and anhedonia-like behavior at 35 days. Analysis of GFAP+ cell morphology revealed significant atrophy of distal processes following 21 and 35 days of CRS. CRS induced similar decreases in intersections at distal radii for GFP+ cells, accompanied by increased proximal processes. Additionally, the number of intersections at the most distal radius step significantly correlated with time spent in the shelter zone in the PhenoTyper test (r=-0.581, p<0.01) for GFP+ cells and with behavioural emotionality calculated by z-scoring all behavioral measured deficits, for both GFAP+ and GFP+ cells (r=-0.400, p<0.05; r=-0.399, p<0.05).ConclusionChronic stress exposure induces a progressive atrophy of cortical astroglial cells, potentially contributing to maladaptive neuroplastic changes associated with stress-related disorders.

Shared Transcriptional Signatures in Major Depressive Disorder and Mouse Chronic Stress Models

2020 ◽  
Vol 87 (9) ◽  
pp. S222
Author(s):  
Joseph Scarpa ◽  
Mena Fatma ◽  
Yong-Hwee E. Loh ◽  
Said Romaric Traore ◽  
Théo Stefan ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Chronic Stress ◽  
Major Depressive ◽  
Stress Models

scholarly journals Reprogramming of synovial macrophage metabolism by synovial fibroblasts under inflammatory conditions

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Noritaka Saeki ◽  
Yuuki Imai
Keyword(s):  
Gene Expression ◽  
Synovial Fibroblasts ◽  
Metabolic Reprogramming ◽  
Metabolic Status ◽  
Immunofluorescent Staining ◽  
Marker Genes ◽  
Rna Seq ◽  
Expression Levels ◽  
Inflammatory Conditions ◽  
Metabolic Genes

Abstract Background Macrophages adapt to microenvironments, and change metabolic status and functions to regulate inflammation and/or maintain homeostasis. In joint cavities, synovial macrophages (SM) and synovial fibroblasts (SF) maintain homeostasis. However, under inflammatory conditions such as rheumatoid arthritis (RA), crosstalk between SM and SF remains largely unclear. Methods Immunofluorescent staining was performed to identify localization of SM and SF in synovium of collagen antibody induced arthritis (CAIA) model mice and normal mice. Murine arthritis tissue-derived SM (ADSM), arthritis tissue-derived SF (ADSF) and normal tissue-derived SF (NDSF) were isolated and the purity of isolated cells was examined by RT-qPCR and flow cytometry analysis. RNA-seq was conducted to reveal gene expression profile in ADSM, NDSF and ADSF. Cellular metabolic status and expression levels of metabolic genes and inflammatory genes were analyzed in ADSM treated with ADSM-conditioned medium (ADSM-CM), NDSF-CM and ADSF-CM. Results SM and SF were dispersed in murine hyperplastic synovium. Isolations of ADSM, NDSF and ADSF to analyze the crosstalk were successful with high purity. From gene expression profiles by RNA-seq, we focused on secretory factors in ADSF-CM, which can affect metabolism and inflammatory activity of ADSM. ADSM exposed to ADSF-CM showed significantly upregulated glycolysis and mitochondrial respiration as well as glucose and glutamine uptake relative to ADSM exposed to ADSM-CM and NDSF-CM. Furthermore, mRNA expression levels of metabolic genes, such as Slc2a1, Slc1a5, CD36, Pfkfb1, Pfkfb3 and Irg1, were significantly upregulated in ADSM treated with ADSF-CM. Inflammation marker genes, including Nos2, Tnf, Il-1b and CD86, and the anti-inflammatory marker gene, Il-10, were also substantially upregulated by ADSF-CM. On the other hand, NDSF-CM did not affect metabolism and gene expression in ADSM. Conclusions These findings suggest that crosstalk between SM and SF under inflammatory conditions can induce metabolic reprogramming and extend SM viability that together can contribute to chronic inflammation in RA.

scholarly journals Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Qingnan Liang ◽  
Rachayata Dharmat ◽  
Leah Owen ◽  
Akbar Shakoor ◽  
Yumei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Retinal Cell ◽  
Peripheral Retina ◽  
Marker Genes ◽  
Rna Seq ◽  
Cell Type ◽  
Retinal Tissue ◽  
The Individual

AbstractSingle-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.

scholarly journals Assessment of Embryo-Induced Transcriptomic Changes in Hamster Uterus Using RNA-Seq

2018 ◽  
Vol 46 (5) ◽  
pp. 1868-1878 ◽  
Author(s):  
Ming-Yu Huang ◽  
Wen-Qian Zhang ◽  
Miao Zhao ◽  
Can Zhu ◽  
Jia-Peng He ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Embryo Implantation ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Implantation Site ◽  
Rna Seq ◽  
Promoter Regions ◽  
Functional Clustering ◽  
Gene Network Analysis ◽  
Transcriptomic Changes

Background/Aims: The mouse is widely used as an animal model for studying human embryo implantation. However, the mouse is unique in that both ovarian progesterone and estrogen are critical to implantation, whereas in the majority of species (e.g. human and hamster) implantation can occur in the presence of progesterone alone. Methods: In this study, we analyzed embryo-induced transcriptomic changes in the hamster uterus during embryo implantation by using RNA-seq. Differentially expressed genes were characterized by bioinformatic analysis. Results: We identified a total of 781 differentially expressed genes, of which 367 genes were up-regulated and 414 genes were down-regulated at the implantation site compared to the inter-implantation site. Functional clustering and gene network analysis highlighted the cell cycle process in uterus upon embryo implantation. By examining of the promoter regions of differentially expressed genes, we identified 7 causal transcription factors. Additionally, through connectivity map (CMap) analysis, multiple compounds were identified to have potential anti-implantation effects due to their ability to reverse embryo-induced transcriptomic changes. Conclusion: Our study provides a valuable resource for in-depth understanding of the mechanism underlying embryo implantation.

scholarly journals Review—Current Concepts in Inflammatory Skin Diseases Evolved by Transcriptome Analysis: In-Depth Analysis of Atopic Dermatitis and Psoriasis

2020 ◽  
Vol 21 (3) ◽  
pp. 699 ◽  
Author(s):  
Julius Schwingen ◽  
Mustafa Kaplan ◽  
Florian C. Kurschus
Keyword(s):  
Skin Diseases ◽  
Basic Research ◽  
Bioinformatic Analysis ◽  
Classification Systems ◽  
Rna Seq ◽  
Inflammatory Skin Diseases ◽  
Biologically Relevant ◽  
Depth Analysis ◽  
Inflammatory Skin ◽  
Review Current

During the last decades, high-throughput assessment of gene expression in patient tissues using microarray technology or RNA-Seq took center stage in clinical research. Insights into the diversity and frequency of transcripts in healthy and diseased conditions provide valuable information on the cellular status in the respective tissues. Growing with the technique, the bioinformatic analysis toolkit reveals biologically relevant pathways which assist in understanding basic pathophysiological mechanisms. Conventional classification systems of inflammatory skin diseases rely on descriptive assessments by pathologists. In contrast to this, molecular profiling may uncover previously unknown disease classifying features. Thereby, treatments and prognostics of patients may be improved. Furthermore, disease models in basic research in comparison to the human disease can be directly validated. The aim of this article is not only to provide the reader with information on the opportunities of these techniques, but to outline potential pitfalls and technical limitations as well. Major published findings are briefly discussed to provide a broad overview on the current findings in transcriptomics in inflammatory skin diseases.

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