De novo transcriptome assembly of mouse male germ cells reveals novel genes, stage-specific bidirectional promoter activity, and noncoding RNA expression.

In mammals, the adult testis is the tissue with the highest diversity in gene expression. Much of that diversity is attributed to germ cells, primarily meiotic spermatocytes and postmeiotic haploid spermatids. Exploiting a newly developed cell purification method, we profiled the transcriptomes of such postmitotic germ cells of mice. We used a de novo transcriptome assembly approach and identified thousands of novel expressed transcripts characterized by features distinct from those of known genes. Novel loci tend to be short in length, monoexonic, and lowly expressed. Most novel genes have arisen recently in evolutionary time and possess low coding potential. Nonetheless, we identify several novel protein-coding genes harboring open reading frames that encode proteins containing matches to conserved protein domains. Analysis of mass-spectrometry data from adult mouse testes confirms protein production from several of these novel genes. We also examine overlap between transcripts and repetitive elements. We find that although distinct families of repeats are expressed with differing temporal dynamics during spermatogenesis, we do not observe a general mode of regulation wherein repeats drive expression of nonrepetitive sequences in a cell type-specific manner. Finally, we observe many fairly long antisense transcripts originating from canonical gene promoters, pointing to pervasive bidirectional promoter activity during spermatogenesis that is distinct and more frequent compared with somatic cells.

Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators.

Complex organisms originate from and are maintained by the information encoded in the genome. A major challenge of systems biology is to develop algorithms that describe the dynamic regulation of genome functions from large omics datasets. Here, we describe TETRAMER, which reconstructs gene-regulatory networks from temporal transcriptome data during cell fate transitions to predict "master" regulators by simulating cascades of temporal transcription-regulatory events.

Genomic prediction of celiac disease targeting HLA-positive individuals.

BACKGROUND: Genomic prediction aims to leverage genome-wide genetic data towards better disease diagnostics and risk scores. We have previously published a genomic risk score (GRS) for celiac disease (CD), a common and highly heritable autoimmune disease, which differentiates between CD cases and population-based controls at a clinically-relevant predictive level, improving upon other gene-based approaches. HLA risk haplotypes, particularly HLA-DQ2.5, are necessary but not sufficient for CD, with at least one HLA risk haplotype present in up to half of most Caucasian populations. Here, we assess a genomic prediction strategy that specifically targets this common genetic susceptibility subtype, utilizing a supervised learning procedure for CD that leverages known HLA-DQ2.5 risk. METHODS: Using L1/L2-regularized support-vector machines trained on large European case-control datasets, we constructed novel CD GRSs specific to individuals with HLA-DQ2.5 risk haplotypes (GRS-DQ2.5) and compared them with the predictive power of the existing CD GRS (GRS14) as well as two haplotype-based approaches, externally validating the results in a North American case-control study. RESULTS: Consistent with previous observations, both the existing GRS14 and the GRS-DQ2.5 had better predictive performance than the HLA haplotype approaches. GRS-DQ2.5 models, based on directly genotyped or imputed markers, achieved similar levels of predictive performance (AUC = 0.718-0.73), which were substantially higher than those obtained from the DQ2.5 zygosity alone (AUC = 0.558), the HLA risk haplotype method (AUC = 0.634), or the generic GRS14 (AUC = 0.679). In a screening model of at-risk individuals, the GRS-DQ2.5 lowered the number of unnecessary follow-up tests for CD across most sensitivity levels. Relative to a baseline implicating all DQ2.5-positive individuals for follow-up, the GRS-DQ2.5 resulted in a net saving of 2.2 unnecessary follow-up tests for each justified test while still capturing 90 % of DQ2.5-positive CD cases. CONCLUSIONS: Genomic risk scores for CD that target genetically at-risk sub-groups improve predictive performance beyond traditional approaches and may represent a useful strategy for prioritizing individuals at increased risk of disease, thus potentially reducing unnecessary follow-up diagnostic tests.