Background: Human papillomavirus (HPV) infection is a major cause of cervical cancer and associated precursor lesions. Multiple HPV genotype infections have been reported. However, their clinicopathological characteristics still remain elusive.Methods: For this study, 814 consecutive patients who had undergone colposcopy and HPV genotyping test using BD Onclarity HPV assay were retrospectively selected. Clinicopathological parameters of multiple HPV infections were compared with those of single HPV infection.Results: Multiple HPV infections were found in 110 out of 814 cases (13.5%). Multiple HPV infections were associated with a significantly higher incidence of high-grade intraepithelial lesions (HSILs) compared with single HPV infection. Other high-risk HPV genotypes, in addition to HPV 16, were found more frequently in the multiple HPV infections group; these included HPV 51, 52, 33/58, 56/59/66, and 35/39/68. No specific coinfection pattern was not identified. Additionally, the number of HPV genotypes in multiple HPV infections was not associated with the progression to HSIL or squamous cell carcinoma.Conclusions: Multiple HPV infections have distinct clinicopathological characteristics (compared with single HPV infection). As their biological behavior is uncertain, close and frequent follow-up is warranted.

Influenza viruses exploit host factors to successfully replicate in infected cells. Using small interfering RNA (siRNA) technology, we identified six human genes required for influenza A virus (IAV) replication. Here we focused on the role of acid phosphatase 2 (ACP2), as its knockdown showed the greatest inhibition of IAV replication. In IAV-infected cells, depletion of ACP2 resulted in a significant reduction in the expression of viral proteins and mRNA, and led to the attenuation of virus multi-cycle growth. ACP2 knockdown also decreased replication of seasonal influenza A and B viruses and avian IAVs of the H7 subtype. Interestingly, ACP2 depletion had no effect on the replication of Ebola or hepatitis C virus. Because ACP2 is known to be a lysosomal acid phosphatase, we assessed the role of ACP2 in influenza virus entry. While neither binding of the viral particle to the cell surface nor endosomal acidification was affected in ACP2-depleted cells, fusion of the endosomal and viral membranes was impaired. As a result, downstream steps in viral entry were blocked, including nucleocapsid uncoating and nuclear import of viral ribonucleoproteins. Our results established ACP2 as a necessary host factor for regulating the fusion step of influenza virus entry.

Summary: Human gene expression profiles have emerged as an effective model system for the dissection of quantitative genetic traits. Peripheral blood and transformed lymphoblasts are particularly attractive for their ready availability and repeatability, respectively, and the advent of relatively inexpensive genotyping and microarray analysis technologies has facilitated genome-wide association for transcript abundance in numerous settings. Thousands of genes have been shown to harbour regulatory polymorphisms that have large local effects on transcription, explaining 20% or more of the variance in many cases, but the focus on such results obscures the reality that the vast majority of the genetic component of transcriptional variance remains to be ascertained. This mini-review surveys the inferences derived from genome-wide association studies (GWAS) for gene expression to date, and discusses some of the issues we face in finding the remainder of the heritability and understanding how environmental and genetic regulatory factors orchestrate the highly structured architecture of transcriptional variation.

Background and objectives: Telomerase reverse transcriptase (TERT) promoter mutation, found in a subset of patients with thyroid cancer, is strongly associated with aggressive biologic behavior. Predicting TERT promoter mutation is thus necessary for the prognostic stratification of thyroid cancer patients. Materials and Methods: In this study, we evaluate TERT promoter mutation status in thyroid cancer through the deep learning approach using histologic images. Our analysis included 13 consecutive surgically resected thyroid cancers with TERT promoter mutations (either C228T or C250T) and 12 randomly selected surgically resected thyroid cancers with a wild-type TERT promoter. Our deep learning model was created using a two-step cascade approach. First, tumor areas were identified using convolutional neural networks (CNNs), and then TERT promoter mutations within tumor areas were predicted using the CNN–recurrent neural network (CRNN) model. Results: Using the hue–saturation–value (HSV)-strong color transformation scheme, the overall experiment results show 99.9% sensitivity and 60% specificity (improvements of approximately 25% and 37%, respectively, compared to image normalization as a baseline model) in predicting TERT mutations. Conclusions: Highly sensitive screening for TERT promoter mutations is possible using histologic image analysis based on deep learning. This approach will help improve the classification of thyroid cancer patients according to the biologic behavior of tumors.