Putative Internal Control Genes in Bovine Milk Small Extracellular Vesicles Suitable for Normalization in Quantitative Real Time-Polymerase Chain Reaction

Bovine milk small extracellular vesicles (sEVs) contain many biologically important molecules, including mRNAs. Quantitative real-time polymerase chain reaction (qRT-PCR) is a widely used method for quantifying mRNA in tissues and cells. However, the use, selection, and stability of suitable putative internal control genes in bovine milk sEVs for normalization in qRT-PCR have not yet been identified. Thus, the aim of the present study was to determine suitable putative internal control genes in milk sEVs for the normalization of qRT-PCR data. Milk sEVs were isolated from six healthy Holstein-Friesian cattle, followed by RNA extraction and cDNA synthesis. In total, 17 mRNAs were selected for investigation and quantification using qRT-PCR; they were further evaluated using geNorm, NormFinder, BestKeeper, and ∆CT algorithms to identify those that were highly stable putative internal control genes in milk sEVs. The final ranking of suitable putative internal control genes was determined using RefFinder. The mRNAs from TUB, ACTB, DGKZ, ETFDH, YWHAZ, STATH, DCAF11, and EGFLAM were detected in milk sEVs from six cattle by qRT-PCR. RefFinder demonstrated that TUB, ETFDH, and ACTB were highly stable in milk sEVs, and thus suitable for normalization of qRT-PCR data. The present study suggests that the use of these genes as putative internal control genes may further enhance the robustness of qRT-PCR in bovine milk sEVs. Since these putative internal control genes apply to healthy bovines, it would be helpful to include that the genes were stable in sEVs under “normal or healthy conditions”.

Housekeeping Gene Expression in the Fetal and Neonatal Murine Thymus Following Coxsackievirus B4 Infection

The thymus fulfills the role of T-cell production and differentiation. Studying transcription factors and genes involved in T-cell differentiation and maturation during the fetal and neonatal periods is very important. Nevertheless, no studies to date have been interested in evaluating the expressions of housekeeping genes as internal controls to assess the varying expressions of different genes inside this tissue during that period or in the context of viral infection. Thus, we evaluated by real-time quantitative polymerase chain reaction (qPCR) the expression of the most common internal control genes in the thymus of Swiss albino mice during the fetal and neonatal period, and following in utero infection with Coxsackievirus B4. The stability of expression of these reference genes in different samples was investigated using the geNorm application. Results demonstrated that the expression stability varied greatly between genes. Oaz1 was found to have the highest stability in different stages of development, as well as following Coxsackievirus B4 infection. The current study clearly demonstrated that Oaz1, with very stable expression levels that outperformed other tested housekeeping genes, could be used as a reference gene in the thymus and thymic epithelial cells during development and following Coxsackievirus B4 infection.

Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis

Background RNA sequencing allows the measuring of gene expression at a resolution unmet by expression arrays or RT-qPCR. It is however necessary to normalize sequencing data by library size, transcript size and composition, among other factors, before comparing expression levels. The use of internal control genes or spike-ins is advocated in the literature for scaling read counts, but the methods for choosing reference genes are mostly targeted at RT-qPCR studies and require a set of pre-selected candidate controls or pre-selected target genes. Results Here, we report an R-based pipeline to select internal control genes based solely on read counts and gene sizes. This novel method first normalizes the read counts to Transcripts per Million (TPM) and then excludes weakly expressed genes using the DAFS script to calculate the cut-off. It then selects as references the genes with lowest TPM covariance. We used this method to pick custom reference genes for the differential expression analysis of three transcriptome sets from transgenic Arabidopsis plants expressing heterologous fungal effector proteins tagged with GFP (using GFP alone as the control). The custom reference genes showed lower covariance and fold change as well as a broader range of expression levels than commonly used reference genes. When analyzed with NormFinder, both typical and custom reference genes were considered suitable internal controls, but the custom selected genes were more stably expressed. geNorm produced a similar result in which most custom selected genes ranked higher (i.e. were more stably expressed) than commonly used reference genes. Conclusions The proposed method is innovative, rapid and simple. Since it does not depend on genome annotation, it can be used with any organism, and does not require pre-selected reference candidates or target genes that are not always available.

Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis

Background : RNA sequencing allows the measuring of gene expression at a resolution unmet by expression arrays or RT-qPCR. It is however necessary to normalize sequencing data by library size, transcript size and composition, among other factors, before comparing expression levels. The use of internal control genes or spike-ins is advocated in the literature for scaling read counts, but the methods for choosing reference genes are mostly targeted at RT-qPCR studies and require a set of pre-selected candidate controls or pre-selected target genes. Results : Here, we report an R-based pipeline to select internal control genes based solely on read counts and gene sizes. This novel method first normalizes the read counts to Transcripts per Million (TPM) and then excludes weakly expressed genes using the DAFS script to calculate the cut-off. It then selects as references the genes with lowest TPM covariance. We used this method to pick custom reference genes for the differential expression analysis of three transcriptome sets from transgenic Arabidopsis plants expressing heterologous fungal effector proteins tagged with GFP (using GFP alone as the control). The custom reference genes showed lower covariance and fold change as well as a broader range of expression levels than commonly used reference genes. When analyzed with NormFinder, both typical and custom reference genes were considered suitable internal controls, but the expression of custom selected genes was more stable. geNorm produced a similar result in which most custom selected genes ranked higher ( i.e. expression more stable) than commonly used reference genes. Conclusions : The proposed method is innovative, rapid and simple. Since it does not depend on genome annotation, it can be used with any organism, and does not require pre-selected reference candidates or target genes that are not always available.

Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis

Background: RNA sequencing allows the measuring of gene expression at a resolution unmet by expression arrays or RT-qPCR. It is however necessary to normalize sequencing data by library size, transcript size and composition, among other factors, before comparing expression levels. The use of internal control genes or spike-ins is advocated in the literature for scaling read counts, but the methods for choosing reference genes are mostly targeted at RT-qPCR studies and require a set of pre-selected candidate controls or pre-selected target genes. Results: Here, we report an R-based script to select internal control genes based solely on read counts and gene sizes. This novel method first normalizes the read counts to Transcripts per Million (TPM) and then excludes weakly expressed genes using the DAFS script to calculate the cut-off. It then selects as references the genes with lowest TPM covariance. We used this method to pick custom reference genes for the differential expression analysis of three transcriptome sets from transgenic Arabidopsis plants expressing heterologous fungal effector proteins tagged with GFP (using GFP alone as the control). The custom reference genes showed lower covariance and fold change as well as a broader range of expression levels than commonly used reference genes. When analyzed with NormFinder, both typical and custom reference genes were considered suitable internal controls, but the expression of custom selected genes was more stable. geNorm produced a similar result in which most custom selected genes ranked higher (i.e. expression more stable) than commonly used reference genes. Conclusions: The proposed method is innovative, rapid and simple. Since it does not depend on genome annotation, it can be used with any organism, and does not require pre-selected reference candidates or target genes that are not always available.

189 Comparative transcriptomic analysis between Total RNA and mRNA isolated from feces of neonatal dairy calves

The fecal RNA method can be used to evaluate biological adaptations of the gastrointestinal tract of dairy calves through gene expression analysis. The process of RNA isolation from fecal samples presents several challenges, including the potential enrichment of prokaryotic (bacterial), RNA which can dilute the targeted eukaryotic RNA and consequently affect the sensitivity of the fecal RNA method to low expressed genes. Therefore, our objective in this study was to determine the differential eukaryotic RNA enrichment in total RNA vs mRNA from feces of healthy neonatal dairy calves. To test this, a comparative transcriptomic profiling of genes specific for epithelial cells, including cytokeratin 8 (KRT8) and aquaporin (AQP3), as well as inflammatory-related genes (TLR4 and IL1B) was performed in fecal samples collected from 6 pre-weaned Holstein calves. The total RNA was isolated from 200 mg of feces, using a Trizol based method along with the RNeasy Plus Mini Kit (Qiagen). Then, 45 μg of fecal total RNA was used to isolate mRNA through magnetic selection using Dynabeads® Oligo (dT)25 (Invitrogen). The standard curve was composite from all samples including cDNA from total RNA and mRNA. The internal control genes used in this experiment were B2M, ACTB, GAPDH, RPS9, and PPIA. Normalized gene expression data were log-transformed prior to statistical analysis using the Proc Mixed of SAS (SAS 9.4). The expression of KRT8 was greater (P = 0.03) in fecal mRNA than in fecal total RNA. A trend (P = 0.09) was observed for greater expression of TLR4 in fecal total RNA than in fecal mRNA. The expression of AQP3 and IL1B was not different. These preliminary data further confirms that fecal RNA method has potential to be used as a tool to evaluate gastrointestinal tract health in dairy calves, but further adjustments are needed to improve accuracy and robustness.

Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis

Background: RNA sequencing allows the measuring of gene expression at a resolution unmet by expression arrays or RT-qPCR. It is however necessary to normalize sequencing data by library size, transcript size and composition, among other factors, before comparing expression levels. The use of internal control genes or spike-ins is advocated in the literature for scaling read counts, but the methods for choosing reference genes are mostly targeted at RT-qPCR studies and require a set of pre-selected candidate controls or pre-selected target genes. Results: Here, we report an R-based script to select internal control genes based solely on read counts and gene sizes. This novel method first normalizes the read counts to Transcripts per Million (TPM) and then excludes weakly expressed genes using the DAFT script to calculate the cut-off. It then selects as references the genes with lowest TPM covariance. We used this method to pick custom reference genes for the differential expression analysis of three transcriptome sets from transgenic Arabidopsis plants expressing heterologous fungal effector proteins tagged with GFP (using GFP alone as the control). The custom reference genes showed lower covariance and fold change as well as a broader range of expression levels than commonly used reference genes. When analyzed with NormFinder, both typical and custom reference genes were considered suitable internal controls, but the custom selected genes were more stable. geNorm produced a similar result in which most custom selected genes ranked higher (i.e. were more stable) than commonly used reference genes. Conclusions: The proposed method is innovative, rapid and simple. Since it does not depend on genome annotation, it can be used with any organism, and does not require pre-selected reference candidates or target genes that are not always available.