Correction: A RNA-Seq Analysis of the Rat Supraoptic Nucleus Transcriptome: Effects of Salt Loading on Gene Expression

Abstract

There are a number of errors in the captions for S1 to S9 Fig Step 1, "Sequence Adaptor Clipping". Read pairs, 101bp in length, were adaptor clipped via FASTQ/A Clipper (http://hannonlab.cshl.edu). Broken read pairs as a result of clipping were discarded.Step 2, "Sequence Quality Inspection". Intact read pairs remaining post-clipping were import into CLCbio and the "Create Sequencing QC Report" tool used to generate one report per sample. These reports, each containing per-sequence and per-base quality statistics, were individually inspected and cross-compared to define universal hard-trimming rules to be applied to all samples.Step 3, "Sequence Quality Trimming & Filtering". The CLCbio "Trim Sequences" tool was used to hard-remove the first 15nt from the 5' end and the last 1nt from the 3' end of each read pair. The tool was also used to dynamically-trim away nucleotides having a call accuracy rate less than 95%. Read pairs having at least one sequence containing more than two ambiguities were also discarded as part of this Step as were read pairs having at least one sequence with a post trimmed length less than 15 nucleotides.Step 4, "Sequence Alignment and Enumeration". The CLCbio "RNA-Seq Analysis" tool was used to align read pairs to the Rat Genome (RN5) by sample using default parameters. Output provided by the tool included a Reads per kilo base per million (RPKM) expression value for 26,313 genes.Step 5, "RPKM Expression Pedelstalling". Output from Step 4 was imported into R (http://www.r-project.org/) and a value of two added to each RPKM expression value per sample. Step 6, "RPKM Expression Transformation". Pedestalled values from Step 5 (RPKM+2) were Log2 transformed using standard commands in R then filtered to keep only those genes having a post-transformed expression value (Log2(RPKM+2)) >1 for at least one sample.Step 7, "RPKM Expression Normalization". Transformed values from Step 5 were quantile normalized using standard commands in R.Step 8, "Exploratory Analysis". Normalized values from Step 7 (Quantile(Log2 (RPKM+2))) were interrogated in R by Tukey box plot, covariance-based principal component analysis (PCA) scatter plot and Pearson correlation-based heat map to confirm absence of outliers.Step 9, "Noise Analysis". For each gene, the coefficient of variation (CV) and mean expression was calculated by sample class using standard commands in R then modeled by sample class using the lowess() command.Step 10, "Confidence Criterion Selection". Lowess fits from Step 9 were visually inspected to define the mean expression value across sample PLOS ONE |

Osmoregulation and the Hypothalamic Supraoptic Nucleus: From Genes to Functions

“…Would this be sufficient to ensure the maintenance of the stimulus-secretion coupling when the MNCs are exposed to a long period (hours to days) of hyperosmotic challenge, such as during prolonged water deprivation or salt-loading? As we discuss in the coming sections, under chronic dehydration, the MNCs experience dramatic transcriptomic remodeling, which includes changes in the expression of several genes encoding plasma membrane channels and transporters ( Hindmarch et al, 2006 ; Greenwood M. P. et al, 2015 ; Johnson et al, 2015 ; Pauža et al, 2021 ). Another important adaptation is the increased complexity of the cytoskeleton array observed during sustained hyperosmolality ( Barad et al, 2020 ; Hicks et al, 2020 ), whose crucial role for osmosensitivity was previously demonstrated by Prager-Khoutorsky et al (2014) , also discussed later in this review.…”

“…To summarize the data related to the expression of membrane transporters and channels in the SON in response to prolonged hyperosmolality in rats, we have integrated data from four publications revealing new possible players in the regulation of MNCs’ electrophysiological response. While Hindmarch et al (2006) and Greenwood M. P. et al (2015) used the microarray technology to analyze the SON transcriptomic response, Johnson et al (2015) and Pauža et al (2021) took advantage of the next generation RNAseq technique. Furthermore, Hindmarch et al (2006) and Pauža et al (2021) used 3 days of water deprivation to stimulate the SON, while Johnson et al (2015) used 5 days of salt loading (hypertonic saline as the only fluid available), and Greenwood et al ( Greenwood M. P. et al, 2015 ) compared the SON transcriptomic response to 3 days of water deprivation or 7 days of salt loading.…”

“…While Hindmarch et al (2006) and Greenwood M. P. et al (2015) used the microarray technology to analyze the SON transcriptomic response, Johnson et al (2015) and Pauža et al (2021) took advantage of the next generation RNAseq technique. Furthermore, Hindmarch et al (2006) and Pauža et al (2021) used 3 days of water deprivation to stimulate the SON, while Johnson et al (2015) used 5 days of salt loading (hypertonic saline as the only fluid available), and Greenwood et al ( Greenwood M. P. et al, 2015 ) compared the SON transcriptomic response to 3 days of water deprivation or 7 days of salt loading. Water deprivation and salt loading can both increase Avp and Oxt mRNA expression and peptide secretion.…”

“… Integrated data on the expression of mRNA coding ion channels and membrane transporters in the SON in response to long-term exposure to hyperosmolality in rats. Microarray data obtained from water-deprived (WD) rats are from Hindmarch et al (2006) , microarray information from WD and salt loading (SL) rats is from Greenwood M. P. et al (2015) , RNAseq data obtained from SL rats are from Johnson et al (2015) , and finally data on RNAseq obtained from WD rats are from Pauža et al (2021) . This integrated analysis revealed seven core genes coding channels and transporters consistently upregulated by sustained hyperosmotic stimulation: Trpv2, Kcnk1, Slc1a4, Slc7a3, Slc20a1, Slc25a25, and Slc41a2 .…”

“…Despite all these facts, it was demonstrated that TRPV1 knockout rats show normal AVP secretion and thirst behavior in response to hypernatremia ( Tucker and Stocker, 2016 ), suggesting that other channels are involved in the osmosensation process. Several sodium channel genes are expressed in the SON, some of which are significantly regulated during osmotic challenge ( Hindmarch et al, 2006 ; Konopacka et al, 2015a ; Johnson et al, 2015 ; Pauža et al, 2021 ). Therefore, since the electrical activity itself may modulate the expression of sodium channels, as previously suggested ( Offord and Catterall, 1989 ), the remodeling of the electrogenic machinery may also be a crucial phenomenon for MNCs to maintain efficient neuropeptide secretion during a chronic osmotic disturbance.…”