Transcriptome-based selection and validation of optimal house-keeping genes for skin research in goats (Capra hircus).
ABSTRACT: BACKGROUND:In quantitative real-time polymerase chain reaction (qRT-PCR) experiments, accurate and reliable target gene expression results are dependent on optimal amplification of house-keeping genes (HKGs). RNA-seq technology offers a novel approach to detect new HKGs with improved stability. Goat (Capra hircus) is an economically important livestock species and plays an indispensable role in the world animal fiber and meat industry. Unfortunately, uniform and reliable HKGs for skin research have not been identified in goat. Therefore, this study seeks to identify a set of stable HKGs for the skin tissue of C. hircus using high-throughput sequencing technology. RESULTS:Based on the transcriptome dataset of 39 goat skin tissue samples, 8 genes (SRP68, NCBP3, RRAGA, EIF4H, CTBP2, PTPRA, CNBP, and EEF2) with relatively stable expression levels were identified and selected as new candidate HKGs. Commonly used HKGs including SDHA and YWHAZ from a previous study, and 2 conventional genes (ACTB and GAPDH) were also examined. Four different experimental variables: (1) different development stages, (2) hair follicle cycle stages, (3) breeds, and (4) sampling sites were used for determination and validation. Four algorithms (geNorm, NormFinder, BestKeeper, and ?Ct method) and a comprehensive algorithm (ComprFinder, developed in-house) were used to assess the stability of each HKG. It was shown that NCBP3?+?SDHA?+?PTPRA were more stably expressed than previously used genes in all conditions analysis, and that this combination was effective at normalizing target gene expression. Moreover, a new algorithm for comprehensive analysis, ComprFinder, was developed and released. CONCLUSION:This study presents the first list of candidate HKGs for C. hircus skin tissues based on an RNA-seq dataset. We propose that the NCBP3?+?SDHA?+?PTPRA combination could be regarded as a triplet set of HKGs in skin molecular biology experiments in C. hircus and other closely related species. In addition, we also encourage researchers who perform candidate HKG evaluations and who require comprehensive analysis to adopt our new algorithm, ComprFinder.
Project description:Quantitative RT-PCR is often used as a research tool directed at gene transcription. Selection of optimal housekeeping genes (HKGs) as reference genes is critical to establishing sensitive and reproducible qRT-PCR-based assays. The current study was designed to identify the appropriate reference genes in blood leukocytes of bottlenose dolphins (Tursiops truncatus) for gene transcription research. Seventy-five blood samples collected from 7 bottlenose dolphins were used to analyze 15 candidate HKGs (ACTB, B2M, GAPDH, HPRT1, LDHB, PGK1, RPL4, RPL8, RPL18, RPS9, RPS18, TFRC, YWHAZ, LDHA, SDHA). HKG stability in qRT-PCR was determined using geNorm, NormFinder, BestKeeper and comparative delta Ct algorithms. Utilization of RefFinder, which combined all 4 algorithms, suggested that PGK1, HPRT1 and RPL4 were the most stable HKGs in bottlenose dolphin blood. Gene transcription perturbations in blood can serve as an indication of health status in cetaceans as it occurs prior to alterations in hematology and chemistry. This study identified HKGs that could be used in gene transcript studies, which may contribute to further mRNA relative quantification research in the peripheral blood leukocytes in captive cetaceans.
Project description:Housekeeping genes (HKGs) generally have fundamental functions in basic biochemical processes in organisms, and usually have relatively steady expression levels across various tissues. They play an important role in the normalization of microarray technology. Using Fourier analysis we transformed gene expression time-series from a Hela cell cycle gene expression dataset into Fourier spectra, and designed an effective computational method for discriminating between HKGs and non-HKGs using the support vector machine (SVM) supervised learning algorithm which can extract significant features of the spectra, providing a basis for identifying specific gene expression patterns. Using our method we identified 510 human HKGs, and then validated them by comparison with two independent sets of tissue expression profiles. Results showed that our predicted HKG set is more reliable than three previously identified sets of HKGs.
Project description:BACKGROUND: Normalizing to housekeeping gene (HKG) can make results from quantitative real-time PCR (qRT-PCR) more reliable. Recent studies have shown that no single HKG is universal for all experiments. Thus, a suitable HKG should be selected before its use. Only a few studies on HKGs have been done in plants, and none in soybean, an economically important crop. Therefore, the present study was conducted to identify suitable HKG(s) for normalization of gene expression in soybean. RESULTS: All ten HKGs displayed a wide range of Ct values in 21 sample pools, confirming that they were variably expressed. GeNorm was used to determine the expression stability of the HGKs in seven series sets. For all the sample pools analyzed, the stability rank was ELF1B, CYP2 > ACT11 > TUA > ELF1A > UBC2 > ACT2/7 > TUB > G6PD > UBQ10. For different tissues under the same developmental stage, the rank was ELF1B, CYP2 > ACT2/7 > UBC2 > TUA > ELF1A > ACT11 > TUB > G6PD > UBQ10. For the developmental stage series, the stability rank was ACT2/7, TUA > ELF1A > UBC2 > ELF1B > TUB > CYP2 > ACT11 > G6PD > UBQ10. For photoperiodic treatments, the rank was ACT11, ELF1B > CYP2 > TUA > ELF1A > UBC2 > ACT2/7 > TUB > G6PD > UBQ10. For different times of the day, the rank was ELF1A, TUA > ELF1B > G6PD > CYP2 > ACT11 > ACT2/7 > TUB > UBC2 > UBQ10. For different cultivars and leaves on different nodes of the main stem, the ten HKGs' stability did not differ significantly. Delta Ct approach and 'Stability index' were also used to analyze the expression stability in all 21 sample pools. Results from Delta Ct approach and geNorm indicated that ELF1B and CYP2 were the most stable HKGs, and UBQ10 and G6PD the most variable ones. Results from 'Stability index' analysis were different, with ACT11 and CYP2 being the most stable HKGs, and ELF1A and TUA the most variable ones. CONCLUSION: Our data suggests that HKGs are expressed variably in soybean. Based on the results from geNorm and Delta Ct analysis, ELF1B and CYP2 could be used as internal controls to normalize gene expression in soybean, while UBQ10 and G6PD should be avoided. To achieve accurate results, some conditions may require more than one HKG to be used for normalization.
Project description:Housekeeping genes (HKG) are presumed to be constitutively expressed throughout tissue types but recent studies have shown they vary with pathophysiology. Often, validation of appropriate HKG is not made. There is no consensus on which HKGs are most stably expressed in endometrial tissue so this study aimed to identify the most stable HKG in the endometrium of women with recurrent implantation failure (RIF) and recurrent miscarriages (RM). Inclusion criteria were women between 25-45 years (n?=?45) suffering recurrent miscarriage (RM), recurrent implantation failure (RIF) or fertile controls. Endometrial biopsies were taken and total RNA extraction, cDNA synthesis and PCR was performed using 10 candidate HKG. The genes were arranged in terms of stability and normalisation was determined. Several HKGs not previously tested in endometrial samples were found to be more stable than those previously identified as the most stable. Of these, the 5 most stable HKG (in order of stability) were Prdm4 (PR domain 4)?>?Ube4a (Ubiquitin-Conjugating Enzyme 4a)?>?Enox2 (Ecto-NOX Disulfide-Thiol Exchanger 2)?>?Ube2d2 (Ubiquitin-conjugating enzyme E2D 2)?>?Actb (Actin beta). We therefore recommend using at least four of the aforementioned HKG for normalisation of endometrial tissues taken from patients with RM and RIF.
Project description:Real-time quantitative-PCR has been a priceless tool for gene expression analyses. The reaction, however, needs proper normalization with the use of housekeeping genes (HKGs), whose expression remains stable throughout the experimental conditions. Often, the combination of several genes is required for accurate normalization. Most importantly, there are no universal HKGs which can be used since their expression varies among different organisms, tissues or experimental conditions. In the present study, nine common HKGs (RPL19, tbp, ubx, GAPDH, ?-TUB, ?-TUB, 14-3-3zeta, RPE and actin3) are evaluated in thirteen different body parts, developmental stages and reproductive and olfactory tissues of two insects of agricultural importance, the medfly and the olive fly. Three software programs based on different algorithms were used (geNorm, NormFinder and BestKeeper) and gave different ranking of HKG stabilities. This confirms once again that the stability of common HKGs should not be taken for granted and demonstrates the caution that is needed in the choice of the appropriate HKGs. Finally, by estimating the average of a standard score of the stability values resulted by the three programs we were able to provide a useful consensus key for the choice of the best HKG combination in various tissues of the two insects.
Project description:Gingival stem cells (GSCs) are recently isolated multipotent cells. Their osteogenic capacity has been validated in vitro and may be transferred to human cell therapy for maxillary large bone defects, as they share a neural crest cell origin with jaw bone cells. RT-qPCR is a widely used technique to study gene expression and may help us to follow osteoblast differentiation of GSCs. For accurate results, the choice of reliable housekeeping genes (HKGs) is crucial. The aim of this study was to select the most reliable HKGs for GSCs study and their osteogenic differentiation (dGSCs). The analysis was performed with ten selected HKGs using four algorithms: ?Ct comparative method, GeNorm, BestKeeper, and NormFinder. This study demonstrated that three HKGs, SDHA, ACTB, and B2M, were the most stable to study GSC, whereas TBP, SDHA, and ALAS1 were the most reliable to study dGSCs. The comparison to stem cells of mesenchymal origin (ASCs) showed that SDHA/HPRT1 were the most appropriate for ASCs study. The choice of suitable HKGs for GSCs is important as it gave access to an accurate analysis of osteogenic differentiation. It will allow further study of this interesting stem cells source for future human therapy.
Project description:Quantitative reverse transcription PCR (RT-qPCR) is one of the most efficient, reliable and widely used techniques to quantify gene expression. In this study, we evaluated the performance of six southern corn rootworm, Diabrotica undecimpunctata howardi (Barber), housekeeping genes (HKG), ?-actin (Actin), ?-tubulin (Tubulin), elongation factor 1 alpha (EF1?), glyceraldehyde-3 phosphate dehydrogenase (GAPDH), 40?S ribosomal protein S9 (RpS9) and ubiquitin-conjugating protein (Ubi), under different experimental conditions such as developmental stage, exposure of neonate and adults to dsRNA, exposure of adults to different temperatures, different 3rd instar larva tissues, and neonate starvation. The HKGs were analyzed with four algorithms, including geNorm, NormFinder, BestKeeper, and delta-CT. Although the six HKGs showed a relatively stable expression pattern among different treatments, some variability was observed. Among the six genes, EF1? exhibited the lowest Ct values for all treatments while Ubi exhibited the highest. Among life stages and across treatments, Ubi exhibited the least stable expression pattern. GAPDH, Actin, and EF1? were among the most stable HKGs in the majority of the treatments. This research provides HKG for accurate normalization of RT-qPCR data in the southern corn rootworm. Furthermore, this information can contribute to future genomic and functional genomic research in Diabrotica species.
Project description:OBJECTIVES:Black Bengal goat (Capra hircus), a member of the Bovidae family with the unique traits of high prolificacy, skin quality and low demand for food is the most socioeconomically significant goat breed in Bangladesh. Furthermore, the aptitude of adaptation and disease resistance capacity of it is highly notable which makes its whole genome information an area of research interest. DATA DESCRIPTION:The genomic DNA of a local (Chattogram, Bangladesh) healthy male Black Bengal goat (Capra hircus) was extracted and then sequenced. Sequencing was completed using the Illumina HiSeq 2500 sequencing platform and the draft assembly was generated using the "ARS1" genome as the reference. MAKER gene annotation pipeline was utilized to annotate 26,458 gene models. Genome completeness was assessed using BUSCO (Benchmarking Universal Single-Copy Orthologs) which showed 82.5% completeness of the assembled genome.
Project description:Careful selection of housekeeping genes (HKG) is prerequisite to yield sound qPCR results. HKG expression varies in response to hypoxia but the effect of manipulations of serum availability, a common experimental procedure, remains unknown. Also, no data on HKG expression stability across colon adenocarcinoma lines that would aid selection of normalizers suitable for studies involving several lines are available. Thus, we evaluated the effect of serum availability on the expression of commonly used HKG (ACTB, B2M, GAPDH, GUSB, HPRT1, IPO8, MRPL19, PGK1, PPIA, RPLP0, RPS23, SDHA, TBP, UBC, and YWHAZ) in seven colon adenocarcinoma cell lines (Caco-2, DLD-1, HCT116, HT29, Lovo, SW480, and SW620). Sets of stably expressed line-specific and pan-line HKG were validated against absolutely quantified CDKN1A, TP53, and MDK transcripts. Both serum availability and line type affected HKG expression. UBC was fourfold down-regulated and HPRT1 1.75-fold up-regulated in re-fed HT29 cultures. Line-to-line variability in HKG expression was more pronounced than that caused by altering serum availability and could be found even between isogenic cell lines. PPIA, RPLP0, YWHAZ, and IPO8 were repeatedly highly ranked while ACTB, B2M, UBC, and PGK1 were ranked poorly. Normalization against PPIA/RPLP0/SDHA was found optimal for studies involving various colon adenocarcinoma cell lines subjected to manipulations of serum availability. We found HKG expression to vary, more pronouncedly by line type than growth conditions with significant differences also between isogenic cell lines. Although using line-specific normalizers remains optimal, a set of pan-line HKG that yields good estimation of relative expression of target genes was proposed.
Project description:Background:Using DNA microarrays, we previously identified 451 genes expressed in 19 different human tissues. Although ubiquitously expressed, the variable expression patterns of these "housekeeping genes" (HKGs) could separate one normal human tissue type from another. Current focus on identifying "specific disease markers" is problematic as single gene expression in a given sample represents the specific cellular states of the sample at the time of collection. In this study, we examine the diagnostic and prognostic potential of the variable expressions of HKGs in lung cancers. Methods:Microarray and RNA-seq data for normal lungs, lung adenocarcinomas (AD), squamous cell carcinomas of the lung (SQCLC), and small cell carcinomas of the lung (SCLC) were collected from online databases. Using 374 of 451 HKGs, differentially expressed genes between pairs of sample types were determined via two-sided, homoscedastic t-test. Principal component analysis and hierarchical clustering classified normal lung and lung cancers subtypes according to relative gene expression variations. We used uni- and multi-variate cox-regressions to identify significant predictors of overall survival in AD patients. Classifying genes were selected using a set of training samples and then validated using an independent test set. Gene Ontology was examined by PANTHER. Results:This study showed that the differential expression patterns of 242, 245, and 99 HKGs were able to distinguish normal lung from AD, SCLC, and SQCLC, respectively. From these, 70 HKGs were common across the three lung cancer subtypes. These HKGs have low expression variation compared to current lung cancer markers (e.g., EGFR, KRAS) and were involved in the most common biological processes (e.g., metabolism, stress response). In addition, the expression pattern of 106 HKGs alone was a significant classifier of AD versus SQCLC. We further highlighted that a panel of 13 HKGs was an independent predictor of overall survival and cumulative risk in AD patients. Discussion:Here we report HKG expression patterns may be an effective tool for evaluation of lung cancer states. For example, the differential expression pattern of 70 HKGs alone can separate normal lung tissue from various lung cancers while a panel of 106 HKGs was a capable class predictor of subtypes of non-small cell carcinomas. We also reported that HKGs have significantly lower variance compared to traditional cancer markers across samples, highlighting the robustness of a panel of genes over any one specific biomarker. Using RNA-seq data, we showed that the expression pattern of 13 HKGs is a significant, independent predictor of overall survival for AD patients. This reinforces the predictive power of a HKG panel across different gene expression measurement platforms. Thus, we propose the expression patterns of HKGs alone may be sufficient for the diagnosis and prognosis of individuals with lung cancer.