Recent studies have emphasized a role of adaptive immunity, and particularly T cells, in the genesis of hypertension. We sought to determine the T cell subtypes that contribute to hypertension and renal inflammation in angiotensin II-induced hypertension. Using T cell receptor (TCR) spectratyping to examine TCR usage we demonstrated that CD8+ cells, but not CD4+ cells, in the kidney exhibited altered TCR transcript lengths in Vβ3, 8.1 and 17 families in response to angiotensin II-induced hypertension. Clonality was not observed in other organs. The hypertension caused by angiotensin II in CD4−/− and MHCII−/− mice was similar to that observed in WT mice, while CD8−/− mice and OT1xRAG-1−/− mice, which have only one TCR, exhibited a blunted hypertensive response to angiotensin II. Adoptive transfer of pan-T cells and CD8+ T cells but not CD4+/CD25− cells conferred hypertension to RAG-1−/− mice. In contrast, transfer of CD4+/CD25+ cells to wild type mice receiving angiotensin II decreased blood pressure. Mice treated with angiotensin II exhibited increased numbers of kidney CD4+ and CD8+ T cells. In response to a sodium/volume challenge, wild type and CD4−/− mice infused with angiotensin II retained water and sodium whereas CD8−/− mice did not. CD8−/− mice were also protected against angiotensin-induced endothelial dysfunction and vascular remodeling in the kidney. These data suggest that in the development of hypertension, an oligoclonal population of CD8+ cells accumulate in the kidney and likely contribute to hypertension by contributing to sodium and volume retention and vascular rarefaction.

BackgroundExome sequencing using next-generation sequencing technologies is a cost efficient approach to selectively sequencing coding regions of human genome for detection of disease variants. A significant amount of DNA fragments from the capture process fall outside target regions, and sequence data for positions outside target regions have been mostly ignored after alignment.ResultWe performed whole exome sequencing on 22 subjects using Agilent SureSelect capture reagent and 6 subjects using Illumina TrueSeq capture reagent. We also downloaded sequencing data for 6 subjects from the 1000 Genomes Project Pilot 3 study. Using these data, we examined the quality of SNPs detected outside target regions by computing consistency rate with genotypes obtained from SNP chips or the Hapmap database, transition-transversion (Ti/Tv) ratio, and percentage of SNPs inside dbSNP. For all three platforms, we obtained high-quality SNPs outside target regions, and some far from target regions. In our Agilent SureSelect data, we obtained 84,049 high-quality SNPs outside target regions compared to 65,231 SNPs inside target regions (a 129% increase). For our Illumina TrueSeq data, we obtained 222,171 high-quality SNPs outside target regions compared to 95,818 SNPs inside target regions (a 232% increase). For the data from the 1000 Genomes Project, we obtained 7,139 high-quality SNPs outside target regions compared to 1,548 SNPs inside target regions (a 461% increase).ConclusionsThese results demonstrate that a significant amount of high quality genotypes outside target regions can be obtained from exome sequencing data. These data should not be ignored in genetic epidemiology studies.

BackgroundSample size calculation is an important issue in the experimental design of biomedical research. For RNA-seq experiments, the sample size calculation method based on the Poisson model has been proposed; however, when there are biological replicates, RNA-seq data could exhibit variation significantly greater than the mean (i.e. over-dispersion). The Poisson model cannot appropriately model the over-dispersion, and in such cases, the negative binomial model has been used as a natural extension of the Poisson model. Because the field currently lacks a sample size calculation method based on the negative binomial model for assessing differential expression analysis of RNA-seq data, we propose a method to calculate the sample size.ResultsWe propose a sample size calculation method based on the exact test for assessing differential expression analysis of RNA-seq data.ConclusionsThe proposed sample size calculation method is straightforward and not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size method are presented; the results indicate our method works well, with achievement of desired power.

BackgroundWhen using Illumina high throughput short read data, sometimes the genotype inferred from the positive strand and negative strand are significantly different, with one homozygous and the other heterozygous. This phenomenon is known as strand bias. In this study, we used Illumina short-read sequencing data to evaluate the effect of strand bias on genotyping quality, and to explore the possible causes of strand bias.ResultWe collected 22 breast cancer samples from 22 patients and sequenced their exome using the Illumina GAIIx machine. By comparing the consistency between the genotypes inferred from this sequencing data with the genotypes inferred from SNP chip data, we found that, when using sequencing data, SNPs with extreme strand bias did not have significantly lower consistency rates compared to SNPs with low or no strand bias. However, this result may be limited by the small subset of SNPs present in both the exome sequencing and the SNP chip data. We further compared the transition and transversion ratio and the number of novel non-synonymous SNPs between the SNPs with low or no strand bias and those with extreme strand bias, and found that SNPs with low or no strand bias have better overall quality. We also discovered that the strand bias occurs randomly at genomic positions across these samples, and observed no consistent pattern of strand bias location across samples. By comparing results from two different aligners, BWA and Bowtie, we found very consistent strand bias patterns. Thus strand bias is unlikely to be caused by alignment artifacts. We successfully replicated our results using two additional independent datasets with different capturing methods and Illumina sequencers.ConclusionExtreme strand bias indicates a potential high false-positive rate for SNPs.

Little is known about the inheritance of very low heteroplasmy mitochondria DNA variations. Even with the development of new next-generation sequencing methods, the practical lower limit of measured heteroplasmy is still about 1% due to the inherent noise level of the sequencing. In this study, we sequenced the mitochondrial genome of 44 individuals using Illumina high-throughput sequencing technology and obtained high-coverage mitochondria sequencing data. Our study population contains many mother-offspring pairs. This unique study design allows us to bypass the usual heteroplasmy limitation by analyzing the correlation of mutation levels at each position in the mtDNA sequence between maternally related pairs and non-related pairs. The study showed that very low heteroplasmy variants, down to almost 0.1%, are inherited maternally and that this inheritance begins to decrease at about 0.5%, corresponding to a bottleneck of about 200 mtDNA.

BackgroundRNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. To date, there has not been a consensus on the best approach for conducting robust RNAseq analysis.ResultsIn this study, we designed a thorough experiment to evaluate six read count-based RNAseq analysis methods (DESeq, DEGseq, edgeR, NBPSeq, TSPM and baySeq) using both real and simulated data. We found the six methods produce similar fold changes and reasonable overlapping of differentially expressed genes based on p-values. However, all six methods suffer from over-sensitivity.ConclusionsBased on the evaluation of runtime using real data and area under the receiver operating characteristic curve (AUC-ROC) using simulated data, we found that edgeR achieves a better balance between speed and accuracy than the other methods.

BackgroundOne of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes.ResultsTo solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference’s distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/.ConclusionsRnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2191-5) contains supplementary material, which is available to authorized users.

Sample size determination is an important issue in the experimental design of biomedical research. Because of the complexity of RNA-seq experiments, however, the field currently lacks a sample size method widely applicable to differential expression studies utilizing RNA-seq technology. In this report, we propose several methods for sample size calculation for single-gene differential expression analysis of RNA-seq data under Poisson distribution. These methods are then extended to multiple genes, with consideration for addressing the multiple testing problem by controlling false discovery rate. Moreover, most of the proposed methods allow for closedform sample size formulas with specification of the desired minimum fold change and minimum average read count, and thus are not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size formulas are presented; the results indicate that our methods work well, with achievement of desired power. Finally, our sample size calculation methods are applied to three real RNA-seq data sets.