Historical evaluation of the in vivo adventitious virus test and its potential for replacement with next generation sequencing (NGS).

The Consortium on Adventitious Agent Contamination in Biomanufacturing (CAACB) collected historical data from 20 biopharmaceutical industry members on their experience with the in vivo adventitious virus test, the in vitro virus test, and the use of next generation sequencing (NGS) for viral safety. Over the past 20 years, only three positive in vivo adventitious virus test results were reported, and all were also detected in another concurrent assay. In more than three cases, data collected as a part of this study also found that the in vivo adventitious virus test had given a negative result for a sample that was later found to contain virus. Additionally, the in vivo adventitious virus test had experienced at least 21 false positives and had to be repeated an additional 21 times all while using more than 84,000 animals. These data support the consideration and need for alternative broad spectrum viral detection tests that are faster, more sensitive, more accurate, more specific, and more humane. NGS is one technology that may meet this need. Eighty one percent of survey respondents are either already actively using or exploring the use of NGS for viral safety. The risks and challenges of replacing in vivo adventitious virus testing with NGS are discussed. It is proposed to update the overall virus safety program for new biopharmaceutical products by replacing in vivo adventitious virus testing approaches with modern methodologies, such as NGS, that maintain or even improve the final safety of the product.

Enhanced pro-apoptosis gene signature following the activation of TAp63α in oocytes upon γ irradiation.

Specialized surveillance mechanisms are essential to maintain the genetic integrity of germ cells, which are not only the source of all somatic cells but also of the germ cells of the next generation. DNA damage and chromosomal aberrations are, therefore, not only detrimental for the individual but affect the entire species. In oocytes, the surveillance of the structural integrity of the DNA is maintained by the p53 family member TAp63α. The TAp63α protein is highly expressed in a closed and inactive state and gets activated to the open conformation upon the detection of DNA damage, in particular DNA double-strand breaks. To understand the cellular response to DNA damage that leads to the TAp63α triggered oocyte death we have investigated the RNA transcriptome of oocytes following irradiation at different time points. The analysis shows enhanced expression of pro-apoptotic and typical p53 target genes such as CDKn1a or Mdm2, concomitant with the activation of TAp63α. While DNA repair genes are not upregulated, inflammation-related genes become transcribed when apoptosis is initiated by activation of STAT transcription factors. Furthermore, comparison with the transcriptional profile of the ΔNp63α isoform from other studies shows only a minimal overlap, suggesting distinct regulatory programs of different p63 isoforms.

Population sequencing data reveal a compendium of mutational processes in the human germ line.

Biological mechanisms underlying human germline mutations remain largely unknown. We statistically decompose variation in the rate and spectra of mutations along the genome using volume-regularized nonnegative matrix factorization. The analysis of a sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. We provide a biological interpretation for seven of these processes. We associate one process with bulky DNA lesions that are resolved asymmetrically with respect to transcription and replication. Two processes track direction of replication fork and replication timing, respectively. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions and a mutagenic effect of long interspersed nuclear elements. We localize a mutagenic process specific to oocytes from population sequencing data. This process appears transcriptionally asymmetric.

Differences in the number of de novo mutations between individuals are due to small family-specific effects and stochasticity.

The number of de novo mutations (DNMs) in the human germline is correlated with parental age at conception, but this explains only part of the observed variation. We investigated whether there is a family-specific contribution to the number of DNMs in offspring. The analysis of DNMs in 111 dizygotic twin pairs did not identify a substantial family-specific contribution. This result was corroborated by comparing DNMs of 1669 siblings to those of age-matched unrelated offspring following correction for parental age. In addition, by modeling DNM data from 1714 multi-offspring families, we estimated that the family-specific contribution explains ∼5.2% of the variation in DNM number. Furthermore, we found no substantial difference between the observed number of DNMs and those predicted by a stochastic Poisson process. We conclude that there is a small family-specific contribution to DNM number and that stochasticity explains a large proportion of variation in DNM counts.

Multisite de novo mutations in human offspring after paternal exposure to ionizing radiation.

A genome-wide evaluation of the effects of ionizing radiation on mutation induction in the mouse germline has identified multisite de novo mutations (MSDNs) as marker for previous exposure. Here we present the results of a small pilot study of whole genome sequencing in offspring of soldiers who served in radar units on weapon systems that were emitting high-frequency radiation. We found cases of exceptionally high MSDN rates as well as an increased mean in our cohort: While a MSDN mutation is detected in average in 1 out of 5 offspring of unexposed controls, we observed 12 MSDNs in altogether 18 offspring, including a family with 6 MSDNs in 3 offspring. Moreover, we found two translocations, also resulting from neighboring mutations. Our findings indicate that MSDNs might be suited in principle for the assessment of DNA damage from ionizing radiation also in humans. However, as exact person-related dose values in risk groups are usually not available, the interpretation of MSDNs in single families would benefit from larger molecular epidemiologic studies on this new biomarker.

Author Correction: Parent-of-origin-specific signatures of de novo mutations.

In the version of this article published, the P values for the enrichment of single mutation categories were inadvertently not corrected for multiple testing. After multiple-testing correction, only two of the six mutation categories mentioned are still statistically significant. To reflect this, the text "More specifically, paternally derived DNMs are enriched in transitions in A[.]G contexts, especially ACG>ATG and ATG>ACG (Bonferroni-corrected P = 1.3 × 10-2 and P = 1 × 10-3, respectively). Additionally, we observed overrepresentation of ATA>ACA mutations (Bonferroni-corrected P = 4.28 × 10-2) for DNMs of paternal origin. Among maternally derived DNMs, CCA>CTA, GCA>GTA and TCT>TGT mutations were significantly overrepresented (Bonferroni-corrected P = 4 × 10-4, P = 5 × 10-4, P = 1 × 10-3, respectively)" should read "More specifically, CCA>CTA and GCA>GTA mutations were significantly overenriched on the maternal allele (Bonferroni-corrected P = 0.0192 and P = 0.048, respectively)." Additionally, the last sentence to the legend for Fig. 3b should read "Green boxes highlight the mutation categories that differ significantly" instead of "Green boxes highlight the mutation categories that differ more than 1% of mutation load with a bootstrapping P value <0.05." Corrected versions of Fig. 3b and Supplementary Table 25 appear with the Author Correction.

Germline de novo mutation clusters arise during oocyte aging in genomic regions with high double-strand-break incidence.

Clustering of mutations has been observed in cancer genomes as well as for germline de novo mutations (DNMs). We identified 1,796 clustered DNMs (cDNMs) within whole-genome-sequencing data from 1,291 parent-offspring trios to investigate their patterns and infer a mutational mechanism. We found that the number of clusters on the maternal allele was positively correlated with maternal age and that these clusters consisted of more individual mutations with larger intermutational distances than those of paternal clusters. More than 50% of maternal clusters were located on chromosomes 8, 9 and 16, in previously identified regions with accelerated maternal mutation rates. Maternal clusters in these regions showed a distinct mutation signature characterized by C>G transversions. Finally, we found that maternal clusters were associated with processes involving double-strand-breaks (DSBs), such as meiotic gene conversions and de novo deletion events. This result suggested accumulation of DSB-induced mutations throughout oocyte aging as the mechanism underlying the formation of maternal mutation clusters.

Parent-of-origin-specific signatures of de novo mutations.

De novo mutations (DNMs) originating in gametogenesis are an important source of genetic variation. We use a data set of 7,216 autosomal DNMs with resolved parent of origin from whole-genome sequencing of 816 parent-offspring trios to investigate differences between maternally and paternally derived DNMs and study the underlying mutational mechanisms. Our results show that the number of DNMs in offspring increases not only with paternal age, but also with maternal age, and that some genome regions show enrichment for maternally derived DNMs. We identify parent-of-origin-specific mutation signatures that become more pronounced with increased parental age, pointing to different mutational mechanisms in spermatogenesis and oogenesis. Moreover, we find DNMs that are spatially clustered to have a unique mutational signature with no significant differences between parental alleles, suggesting a different mutational mechanism. Our findings provide insights into the molecular mechanisms that underlie mutagenesis and are relevant to disease and evolution in humans.

Human genomics. The human transcriptome across tissues and individuals.

Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes­which is most clearly seen in blood­though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.