Nine hemophilia A dogs were treated with adeno-associated viral (AAV) gene therapy and followed for up to 10 years. Administration of AAV8 or AAV9 vectors expressing canine factor VIII (AAV-cFVIII) corrected the FVIII deficiency to 1.9%−11.3% of normal FVIII levels. In two of nine dogs, FVIII activity increased gradually starting about four years after treatment. None of the dogs showed evidence of tumors or altered liver function. Analysis of integration sites in liver samples from six treated dogs identified 1,741 unique AAV integration events in genomic DNA and expanded cell clones in five dogs, with 44% of these integrations near genes involved in cell growth. All recovered integrated vectors were partially deleted and/or rearranged. Our data suggest that the increase in FVIII protein expression in two dogs may have been due to clonal expansion of cells harboring integrated vectors. These results support the clinical development of liver-directed AAV gene therapy for hemophilia A while emphasizing the importance of long-term monitoring for potential genotoxicity.

BackgroundHistorically, the human womb has been thought to be sterile in healthy pregnancies, but this idea has been challenged by recent studies using DNA sequence-based methods, which have suggested that the womb is colonized with bacteria. For example, analysis of DNA from placenta samples yielded small proportions of microbial sequences which were proposed to represent normal bacterial colonization. However, an analysis by our group showed no distinction between background negative controls and placenta samples. Also supporting the idea that the womb is sterile is the observation that germ-free mammals can be generated by sterile delivery of neonates into a sterile isolator, after which neonates remain germ-free, which would seem to provide strong data in support of sterility of the womb.ResultsTo probe this further and to investigate possible placental colonization associated with spontaneous preterm birth, we carried out another study comparing microbiota in placenta samples from 20 term and 20 spontaneous preterm deliveries. Both 16S rRNA marker gene sequencing and shotgun metagenomic sequencing were used to characterize placenta and control samples. We first quantified absolute amounts of bacterial 16S rRNA gene sequences using 16S rRNA gene quantitative PCR (qPCR). As in our previous study, levels were found to be low in the placenta samples and indistinguishable from negative controls. Analysis by DNA sequencing did not yield a placenta microbiome distinct from negative controls, either using marker gene sequencing as in our previous work, or with shotgun metagenomic sequencing. Several types of artifacts, including erroneous read classifications and barcode misattribution, needed to be identified and removed from the data to clarify this point.ConclusionsOur findings do not support the existence of a consistent placental microbiome, in either placenta from term deliveries or spontaneous preterm births.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0575-4) contains supplementary material, which is available to authorized users.

Highlights d A family of human DNA viruses was identified and named Redondoviridae d Redondoviruses were the second most common virus in human respiratory virome samples d Some subjects with periodontitis and critical illness had higher redondovirus levels

In Table S1 of this article as originally published, the wrong sequence was mistakenly included for the pan-HCRV-AA qPCR assay probe during assembly of the table. The table has now been updated with the correct sequence (AAATGGAAGGGAGAGA GGCCTTTGG). This error does not alter the conclusions of the original paper. The authors apologize for any confusion or inconvenience this error may have caused.

Clinical guidelines recommend several risk factors to identify women in early pregnancy at high risk of developing pregnancy-associated hypertension. However, these variables result in low predictive accuracy. Here, we developed a prediction model for pregnancy-associated hypertension using graph-based semi-supervised learning. This is a secondary analysis of a prospective study of healthy pregnant women. To develop the prediction model, we compared the prediction performances across five machine learning methods (semi-supervised learning with both labeled and unlabeled data, semi-supervised learning with labeled data only, logistic regression, support vector machine, and random forest) using three different variable sets: [a] variables from clinical guidelines, [b] selected important variables from the feature selection, and [c] all routine variables. Additionally, the proposed prediction model was compared with placental growth factor, a predictive biomarker for pregnancy-associated hypertension. The study population consisted of 1404 women, including 1347 women with complete follow-up (labeled data) and 57 women with incomplete follow-up (unlabeled data). Among the 1347 with complete follow-up, 2.4% (33/1347) developed pregnancy-associated HTN. Graph-based semi-supervised learning using top 11 variables achieved the best average prediction performance (mean area under the curve (AUC) of 0.89 in training set and 0.81 in test set), with higher sensitivity (72.7% vs 45.5% in test set) and similar specificity (80.0% vs 80.5% in test set) compared to risk factors from clinical guidelines. In addition, our proposed model with graph-based SSL had a higher performance than that of placental growth factor for total study population (AUC, 0.71 vs. 0.80, p < 0.001). In conclusion, we could accurately predict the development pregnancy-associated hypertension in early pregnancy through the use of routine clinical variables with the help of graph-based SSL.