An unbiased approach to defining bona fide cancer neoepitopes that elicit immune-mediated cancer rejection.

Identification of neoepitopes that are effective in cancer therapy is a major challenge in creating cancer vaccines. Here, using an entirely unbiased approach, we queried all possible neoepitopes in a mouse cancer model and asked which of those are effective in mediating tumor rejection and, independently, in eliciting a measurable CD8 response. This analysis uncovered a large trove of effective anticancer neoepitopes that have strikingly different properties from conventional epitopes and suggested an algorithm to predict them. It also revealed that our current methods of prediction discard the overwhelming majority of true anticancer neoepitopes. These results from a single mouse model were validated in another antigenically distinct mouse cancer model and are consistent with data reported in human studies. Structural modeling showed how the MHC I-presented neoepitopes had an altered conformation, higher stability, or increased exposure to T cell receptors as compared with the unmutated counterparts. T cells elicited by the active neoepitopes identified here demonstrated a stem-like early dysfunctional phenotype associated with effective responses against viruses and tumors of transgenic mice. These abundant anticancer neoepitopes, which have not been tested in human studies thus far, can be exploited for generation of personalized human cancer vaccines.

Dosage Compensation and Gene Expression of the X Chromosome in Sheep.

Ohno's hypothesis predicts that the expression of the single X chromosome in males needs compensatory upregulation to balance its dosage with that of the diploid autosomes. Additionally, X chromosome inactivation ensures that quadruple expression of the two X chromosomes is avoided in females. These mechanisms have been actively studied in mice and humans but lag behind in domestic species. Using RNA sequencing data, we analyzed the X chromosome upregulation in sheep fetal tissues from day 135 of gestation under control, over or restricted maternal diets (100%, 140% and 60% of National Research Council Total Digestible Nutrients), and in conceptuses, juvenile, and adult somatic tissues. By computing the mean expression ratio of all X-linked genes to all autosomal genes (X:A), we found that all samples displayed some levels of X chromosome upregulation. The degrees of X upregulation were not significant (P-value = 0.74) between ovine females and males in the same somatic tissues. Brain, however, displayed complete X upregulation. Interestingly, the male and female reproduction-related tissues exhibited divergent X dosage upregulation. Moreover, expression upregulation of the X chromosome in fetal tissues was not affected by maternal diets. Maternal nutrition, however, did change expression levels of several X-linked genes, such as sex determination genes SOX3 and NR0B1 In summary, our results showed that X chromosome upregulation occurred in nearly all sheep somatic tissues analyzed, thus support Ohno's hypothesis in a new species. However, the levels of upregulation differed by different subgroups of genes such as those that are house-keeping and "dosage-sensitive".

Effects of maternal nutrition on the expression of genomic imprinted genes in ovine fetuses.

Genomic imprinting is an epigenetic phenomenon of differential allelic expression based on parental origin. To date, 263 imprinted genes have been identified among all investigated mammalian species. However, only 21 have been described in sheep, of which 11 are annotated in the current ovine genome. Here, we aim to i) use DNA/RNA high throughput sequencing to identify new monoallelically expressed and imprinted genes in day 135 ovine fetuses and ii) determine whether maternal diet (100%, 60%, or 140% of National Research Council Total Digestible Nutrients) influences expression of imprinted genes. We also reported strategies to solve technical challenges in the data analysis pipeline. We identified 80 monoallelically expressed, 13 new putative imprinted genes, and five known imprinted genes in sheep using the 263 genes stated above as a guide. Sanger sequencing confirmed allelic expression of seven genes, CASD1, COPG2, DIRAS3, INPP5F, PLAGL1, PPP1R9A, and SLC22A18. Among the 13 putative imprinted genes, five were localized in the known sheep imprinting domains of MEST on chromosome 4, DLK1/GTL2 on chromosome 18 and KCNQ1 on chromosome 21, and three were in a novel sheep imprinted cluster on chromosome 4, known in other species as PEG10/SGCE. The expression of DIRAS3, IGF2, PHLDA2, and SLC22A18 was altered by maternal diet, albeit without allelic expression reversal. Together, our results expanded the list of sheep imprinted genes to 34 and demonstrated that while the expression levels of four imprinted genes were changed by maternal diet, the allelic expression patterns were un-changed for all imprinted genes studied.

Inferring metabolic pathway activity levels from RNA-Seq data.

BACKGROUND: Assessing pathway activity levels is a plausible way to quantify metabolic differences between various conditions. This is usually inferred from microarray expression data. Wide availability of NGS technology has triggered a demand for bioinformatics tools capable of analyzing pathway activity directly from RNA-Seq data. In this paper we introduce XPathway, a set of tools that compares pathway activity analyzing mapping of contigs assembled from RNA-Seq reads to KEGG pathways. The XPathway analysis of pathway activity is based on expectation maximization and topological properties of pathway graphs. RESULTS: XPathway tools have been applied to RNA-Seq data from the marine bryozoan Bugula neritina with and without its symbiotic bacterium "Candidatus Endobugula sertula". We successfully identified several metabolic pathways with differential activity levels. The expression of enzymes from the identified pathways has been further validated through quantitative PCR (qPCR). CONCLUSIONS: Our results show that XPathway is able to detect and quantify the metabolic difference in two samples. The software is implemented in C, Python and shell scripting and is capable of running on Linux/Unix platforms. The source code and installation instructions are available at http://alan.cs.gsu.edu/NGS/?q=content/xpathway .