scholarly journals Gene expression profiling of gilthead sea bream during early development and detection of stress-related genes by the application of cDNA microarray technology

2005 ◽  
Vol 23 (2) ◽  
pp. 182-191 ◽  
Author(s):  
Elena Sarropoulou ◽  
Georgios Kotoulas ◽  
Deborah M. Power ◽  
Robert Geisler
Keyword(s):  
Gene Expression ◽  
Early Development ◽  
Cdna Microarray ◽  
Evolutionary Biology ◽  
Large Scale ◽  
Expression Profiles ◽  
Gilthead Sea Bream ◽  
Sea Bream ◽  
Regulatory Pathways ◽  
Study Gene Expression

Large-scale gene expression studies were performed for one of the main European aquaculture species, the gilthead sea bream Sparus auratus L. For this purpose, a cDNA microarray containing 10,176 clones from a cDNA library of mixed embryonic and larval stages was constructed. In addition to its importance for aquaculture, the taxonomic position and the relatively small genome size of sea bream makes it a prospective model for evolutionary biology and comparative genomics. However, so far, no large-scale analysis of gene expression exists for this species. In the present study, gene expression was analyzed in gilthead sea bream during early development, a significant period in the determination of quantitative traits and therefore of considerable interest for aquaculture. Synexpression groups expressed primarily early and late in development were determined and were composed of both known and novel genes. Furthermore, it was possible to identify stress response genes induced by cortisol injections using the cDNA microarray generated. The creation of gene expression profiles for sea bream by microarray hybridization will accelerate identification of candidate genes involved in multifactorial traits and certain regulatory pathways and will also contribute to a better understanding of the genetic background of fish physiology, which may help to improve aquaculture practices.

scholarly journals LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanan Ren ◽  
Ting-You Wang ◽  
Leah C. Anderton ◽  
Qi Cao ◽  
Rendong Yang
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Samples ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Regulatory Pathways ◽  
Cancer Transcriptome ◽  
Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

scholarly journals Unraveling patterns of disrupted gene expression across a complex tissue

2021 ◽  
Author(s):  
Kelsie E Hunnicutt ◽  
Jeffrey M Good ◽  
Erica L Larson
Keyword(s):  
Gene Expression ◽  
Evolutionary Biology ◽  
Large Scale ◽  
Expression Profiles ◽  
Cell Types ◽  
Tissue Expression ◽  
Network Evolution ◽  
F1 Hybrids ◽  
Cellular Composition ◽  
Mus Musculus Musculus

Whole tissue RNASeq is the standard approach for studying gene expression divergence in evolutionary biology and provides a snapshot of the comprehensive transcriptome for a given tissue. However, whole tissues consist of diverse cell types differing in expression profiles, and the cellular composition of these tissues can evolve across species. Here, we investigate the effects of different cellular composition on whole tissue expression profiles. We compared gene expression from whole testes and enriched spermatogenesis populations in two species of house mice, Mus musculus musculus and M. m. domesticus, and their sterile and fertile F1 hybrids, which differ in both cellular composition and regulatory dynamics. We found that cellular composition differences skewed expression profiles and differential gene expression in whole testes samples. Importantly, both approaches were able to detect large-scale patterns such as disrupted X chromosome expression although whole testes sampling resulted in decreased power to detect differentially expressed genes. We encourage researchers to account for histology in RNASeq and consider methods that reduce sample complexity whenever feasible. Ultimately, we show that differences in cellular composition between tissues can modify expression profiles, potentially altering inferred gene ontological processes, insights into gene network evolution, and processes governing gene expression evolution.

scholarly journals G2S3: A gene graph-based imputation method for single-cell RNA sequencing data

2021 ◽  
Vol 17 (5) ◽  
pp. e1009029
Author(s):  
Weimiao Wu ◽  
Yunqing Liu ◽  
Qile Dai ◽  
Xiting Yan ◽  
Zuoheng Wang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Data ◽  
High Data ◽  
Study Gene Expression ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets.

scholarly journals GeneExpression in HL60 Granulocytoids and Human PolymorphonuclearLeukocytes Exposed to Candidaalbicans†

2004 ◽  
Vol 72 (1) ◽  
pp. 414-429 ◽  
Author(s):  
Alaka Mullick ◽  
Miria Elias ◽  
Penelope Harakidas ◽  
Anne Marcil ◽  
Malcolm Whiteway ◽  
...  
Keyword(s):  
Gene Expression ◽  
Polymorphonuclear Leukocytes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Dimorphic Fungus ◽  
Granulocytic Differentiation ◽  
Effector Cells ◽  
Study Gene Expression ◽  
Opportunistic Human Pathogen ◽  
Human Polymorphonuclear Leukocytes

ABSTRACT Candida albicans is an opportunistic human pathogen causing both superficial and disseminated diseases. It is a dimorphic fungus, switching between yeast and hyphal forms, depending on cues from its microenvironment. Hyphae play an important role in the pathogenesis of candidiasis. The host's response to Candida infection is multifaceted and includes the participation of granulocytes as key effector cells. The aim of this investigation was to study host gene expression during granulocyte-Candida interaction. Effector cells were generated by the granulocytic differentiation of HL60 cells. The resulting cell population was shown to be morphologically and functionally equivalent to granulocytes and is therefore referred to as HL60 granulocytoids for the purposes of this study. Gene expression profiles were determined 1 h after hosts were infected with C. albicans. Three Candida-granulocytoid ratios were chosen to reflect different degrees of HL60 granulocytoid inhibition of C. albicans. The data demonstrate that at the high pathogen-host ratio, C. albicans modulated the HL60 granulocytoid's response by downregulating the expression of known antimicrobial genes. In addition, looking at the expression of a large number of genes, not all of which have necessarily been implicated in candidastatic or candidacidal mechanisms, it has been possible to describe the physiological response of the HL60 granulocytoid to an infectious challenge with C. albicans. Finally, some of the observed changes in HL60 granulocytoid gene expression were investigated in freshly isolated human polymorphonuclear leukocytes infected with C. albicans. Similar changes were seen in these primary human cells, lending support to the validity of this model.

scholarly journals Analysis of blood-based gene expression in idiopathic Parkinson disease

Neurology ◽  
2017 ◽  
Vol 89 (16) ◽  
pp. 1676-1683 ◽  
Author(s):  
Ron Shamir ◽  
Christine Klein ◽  
David Amar ◽  
Eva-Juliane Vollstedt ◽  
Michael Bonin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson Disease ◽  
Gene Networks ◽  
Large Scale ◽  
Expression Profiles ◽  
Area Under The Curve ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
Gene Profiles ◽  
Independent Test

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
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.

scholarly journals 1446. Dynamics of Enterococcus faecalis Cardiolipin Synthase Gene Expression Reveal Compensatory Roles in Daptomycin Resistance

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S726-S726
Author(s):  
April Nguyen ◽  
Vinathi Polamraju ◽  
Truc T Tran ◽  
Diana Panesso-Botero ◽  
Ayesha Khan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Research Study ◽  
Expression Profiles ◽  
Scientific Research ◽  
Bacterial Survival ◽  
Acridine Orange Staining ◽  
Qrt Pcr ◽  
Study Investigator ◽  
Study Gene Expression ◽  
Cardiolipin Synthase

Abstract Background Daptomycin (DAP) is a lipopeptide antibiotic targeting membrane anionic phospholipids (APLs) at the division septum, and resistance (DAP-R) has been linked to mutations in genes encoding i) the LiaFSR stress response system or its effector LiaX, and ii) cardiolipin synthase (Cls). Activation of the E. faecalis (Efs) LiaFSR response is associated with DAP-R and redistribution of APL microdomains away from the septum, and cardiolipin is predicted to be a major component of these APL microdomains. Efs harbors two putative cls genes, cls1 and cls2. While changes in Cls1 have been implicated in DAP-R, the exact roles of each enzyme in resistance are unknown. We aim to characterize the contributions of Cls1 and Cls2 in the development of DAP-R. Methods cls1 and cls2 were deleted individually and in tandem from DAP-S Efs OG117 and DAP-R Efs OG117∆liaX (a DAP-R derivative strain with an activated LiaFSR response). Mutants were characterized by DAP minimum inhibitory concentration (MIC) using E-test on Mueller-Hinton II agar and localization of APL microdomains with 10-N-nonyl-acridine orange staining. Quantitative PCR (qRT-PCR) was used to study gene expression profiles of cls1 and cls2 in Efs OG117∆liaX relative to Efs OG117 across the cell growth cycle. Results qRT-PCR revealed differential expression profiles of cls1 and cls2 associated with DAP-R. cls1 was highly upregulated in stationary phase concurrent with a decrease in cls2 expression. However, independent deletion of cls1 or cls2 in the DAP-R background resulted in no significant changes in DAP MICs or localization of APL microdomains (remaining non-septal). Further studies revealed that cls2 expression is upregulated upon deletion of cls1 in both the DAP-S and DAP-R background, suggesting a potential compensatory role for Cls2. Double deletion of both cls genes in the DAP-R strain decreased DAP MIC and restored the septal localization of APL microdomains. Conclusion Cls1 is the major and predominant enzyme involved in cell membrane adaptation associated with the development of DAP-R in E. faecalis. However, we describe a novel compensatory and overlapping role for cardiolipin synthases to ensure bacterial survival upon attack from antimicrobial peptides and related antibiotics. Disclosures Cesar A. Arias, MD, MSc, PhD, FIDSA, Entasis Therapeutics (Scientific Research Study Investigator)MeMed (Scientific Research Study Investigator)Merck (Grant/Research Support)

Neural system-enriched gene expression: relationship to biological pathways and neurological diseases

2004 ◽  
Vol 18 (2) ◽  
pp. 167-183 ◽  
Author(s):  
Jianhua Zhang ◽  
Amy Moseley ◽  
Anil G. Jegga ◽  
Ashima Gupta ◽  
David P. Witte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Nervous System ◽  
Intracellular Signaling ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene List ◽  
Structure And Function ◽  
System Structure ◽  
Psychiatric Disease ◽  
And Function

To understand the commitment of the genome to nervous system differentiation and function, we sought to compare nervous system gene expression to that of a wide variety of other tissues by gene expression database construction and mining. Gene expression profiles of 10 different adult nervous tissues were compared with that of 72 other tissues. Using ANOVA, we identified 1,361 genes whose expression was higher in the nervous system than other organs and, separately, 600 genes whose expression was at least threefold higher in one or more regions of the nervous system compared with their median expression across all organs. Of the 600 genes, 381 overlapped with the 1,361-gene list. Limited in situ gene expression analysis confirmed that identified genes did represent nervous system-enriched gene expression, and we therefore sought to evaluate the validity and significance of these top-ranked nervous system genes using known gene literature and gene ontology categorization criteria. Diverse functional categories were present in the 381 genes, including genes involved in intracellular signaling, cytoskeleton structure and function, enzymes, RNA metabolism and transcription, membrane proteins, as well as cell differentiation, death, proliferation, and division. We searched existing public sites and identified 110 known genes related to mental retardation, neurological disease, and neurodegeneration. Twenty-one of the 381 genes were within the 110-gene list, compared with a random expectation of 5. This suggests that the 381 genes provide a candidate set for further analyses in neurological and psychiatric disease studies and that as a field, we are as yet, far from a large-scale understanding of the genes that are critical for nervous system structure and function. Together, our data indicate the power of profiling an individual biologic system in a multisystem context to gain insight into the genomic basis of its structure and function.

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