Hyperactive mTOR and MNK1 phosphorylation of eIF4E confer tamoxifen resistance and estrogen independence through selective mRNA translation reprogramming.

The majority of breast cancers expresses the estrogen receptor (ER+) and is treated with anti-estrogen therapies, particularly tamoxifen in premenopausal women. However, tamoxifen resistance is responsible for a large proportion of breast cancer deaths. Using small molecule inhibitors, phospho-mimetic proteins, tamoxifen-sensitive and tamoxifen-resistant breast cancer cells, a tamoxifen-resistant patient-derived xenograft model, patient tumor tissues, and genome-wide transcription and translation studies, we show that tamoxifen resistance involves selective mRNA translational reprogramming to an anti-estrogen state by Runx2 and other mRNAs. Tamoxifen-resistant translational reprogramming is shown to be mediated by increased expression of eIF4E and its increased availability by hyperactive mTOR and to require phosphorylation of eIF4E at Ser209 by increased MNK activity. Resensitization to tamoxifen is restored only by reducing eIF4E expression or mTOR activity and also blocking MNK1 phosphorylation of eIF4E. mRNAs specifically translationally up-regulated with tamoxifen resistance include Runx2, which inhibits ER signaling and estrogen responses and promotes breast cancer metastasis. Silencing Runx2 significantly restores tamoxifen sensitivity. Tamoxifen-resistant but not tamoxifen-sensitive patient ER+ breast cancer specimens also demonstrate strongly increased MNK phosphorylation of eIF4E. eIF4E levels, availability, and phosphorylation therefore promote tamoxifen resistance in ER+ breast cancer through selective mRNA translational reprogramming.

Examining the effect of wound cleansing on the microbiome of venous stasis ulcers.

Common treatment for venous leg wounds includes topical wound dressings with compression. At each dressing change, wounds are debrided and washed; however, the effect of the washing procedure on the wound microbiome has not been studied. We hypothesized that wound washing may alter the wound microbiome. To characterize microbiome changes with respect to wound washing, swabs from 11 patients with chronic wounds were sampled before and after washing, and patient microbiomes were characterized using 16S rRNA sequencing and culturing. Microbiomes across patient samples prior to washing were typically polymicrobial but varied in the number and type of bacterial genera present. Proteus and Pseudomonas were the dominant genera in the study. We found that washing does not consistently change microbiome diversity but does cause consistent changes in microbiome composition. Specifically, washing caused a decrease in the relative abundance of the most highly represented genera in each patient cluster. The finding that venous leg ulcer wound washing, a standard of care therapy, can induce changes in the wound microbiome is novel and could be potentially informative for future guided therapy strategies.

RIVET: comprehensive graphic user interface for analysis and exploration of genome-wide translatomics data.

BACKGROUND: Translatomics data, particularly genome-wide ribosome profiling and polysome profiling, provide multiple levels of gene regulatory information that can be used to assess general transcription and translation, as well translational efficiency. The increasing popularity of these techniques has resulted in multiple algorithms to detect translational regulation, typically distributed in the form of command line tools that require a basic level of programming ability. Additionally, due to the static nature of current software, dynamic transcriptional and translational comparative analysis cannot be adequately achieved. In order to streamline hypothesis generation, investigators must have the ability to manipulate and interact with their data in real-time. RESULTS: To address the lack of integration in current software, we introduce RIVET, Ribosomal Investigation and Visualization to Evaluate Translation, an R shiny based graphical user interface for translatomics data exploration and differential analysis. RIVET can analyze either microarray or RNA sequencing data from polysome profiling and ribosome profiling experiments. RIVET provides multiple choices for statistical analysis as well as integration of transcription, translation, and translational efficiency data analytics and the ability to visualize all results dynamically. CONCLUSIONS: RIVET is a user-friendly tool designed for bench scientists with little to no programming background. RIVET facilitates the data analysis of translatomics data allowing for dynamic generation of results based on user-defined inputs and publication ready visualization. We expect RIVET will allow for scientists to efficiently make more comprehensive data observations that will lead to more robust hypothesis regarding translational regulation.

Effect of human H3N2 influenza virus reassortment on influenza incidence and severity during the 2017-18 influenza season in the USA: a retrospective observational genomic analysis.

BACKGROUND: During the 2017-18 influenza season in the USA, there was a high incidence of influenza illness and mortality. However, no apparent antigenic change was identified in the dominant H3N2 viruses, and the severity of the season could not be solely attributed to a vaccine mismatch. We aimed to investigate whether the altered virus properties resulting from gene reassortment were underlying causes of the increased case number and disease severity associated with the 2017-18 influenza season. METHODS: Samples included were collected from patients with influenza who were prospectively recruited during the 2016-17 and 2017-18 influenza seasons at the Johns Hopkins Hospital Emergency Departments in Baltimore, MD, USA, as well as from archived samples from Johns Hopkins Health System sites. Among 647 recruited patients with influenza A virus infection, 411 patients with whole-genome sequences were available in the Johns Hopkins Center of Excellence for Influenza Research and Surveillance network during the 2016-17 and 2017-18 seasons. Phylogenetic trees were constructed based on viral whole-genome sequences. Representative viral isolates of the two seasons were characterised in immortalised cell lines and human nasal epithelial cell cultures, and patients' demographic data and clinical outcomes were analysed. FINDINGS: Unique H3N2 reassortment events were observed, resulting in two predominant strains in the 2017-18 season: HA clade 3C.2a2 and clade 3C.3a, which had novel gene segment constellations containing gene segments from HA clade 3C.2a1 viruses. The reassortant re3C.2a2 viruses replicated with faster kinetics and to a higher peak titre compared with the parental 3C.2a2 and 3C.2a1 viruses (48 h vs 72 h). Furthermore, patients infected with reassortant 3C.2a2 viruses had higher Influenza Severity Scores than patients infected with the parental 3C.2a2 viruses (median 3·00 [IQR 1·00-4·00] vs 1·50 [1·00-2·00]; p=0·018). INTERPRETATION: Our findings suggest that the increased severity of the 2017-18 influenza season was due in part to two intrasubtypes, cocirculating H3N2 reassortant viruses with fitness advantages over the parental viruses. This information could help inform future vaccine development and public health policies. FUNDING: The Center of Excellence for Influenza Research and Response in the US, National Science and Technology Council, and Chang Gung Memorial Hospital in Taiwan.

MutaGAN: A sequence-to-sequence GAN framework to predict mutations of evolving protein populations.

The ability to predict the evolution of a pathogen would significantly improve the ability to control, prevent, and treat disease. Machine learning, however, is yet to be used to predict the evolutionary progeny of a virus. To address this gap, we developed a novel machine learning framework, named MutaGAN, using generative adversarial networks with sequence-to-sequence, recurrent neural networks generator to accurately predict genetic mutations and evolution of future biological populations. MutaGAN was trained using a generalized time-reversible phylogenetic model of protein evolution with maximum likelihood tree estimation. MutaGAN was applied to influenza virus sequences because influenza evolves quickly and there is a large amount of publicly available data from the National Center for Biotechnology Information's Influenza Virus Resource. MutaGAN generated 'child' sequences from a given 'parent' protein sequence with a median Levenshtein distance of 4.00 amino acids. Additionally, the generator was able to generate sequences that contained at least one known mutation identified within the global influenza virus population for 72.8 per cent of parent sequences. These results demonstrate the power of the MutaGAN framework to aid in pathogen forecasting with implications for broad utility in evolutionary prediction for any protein population.