Dysregulation of Transposon Transcription Profiles in Cancer Cells Resembles That of Embryonic Stem Cells.

Transposable elements (TEs) comprise a substantial portion of the mammalian genome, with potential implications for both embryonic development and cancer. This study aimed to characterize the expression profiles of TEs in embryonic stem cells (ESCs), cancer cell lines, tumor tissues, and the tumor microenvironment (TME). We observed similarities in TE expression profiles between cancer cells and ESCs, suggesting potential parallels in regulatory mechanisms. Notably, four TE RNAs (HERVH, LTR7, HERV-Fc1, HERV-Fc2) exhibited significant downregulation across cancer cell lines and tumor tissues compared to ESCs, highlighting potential roles in pluripotency regulation. The strong up-regulation of the latter two TEs (HERV-Fc1, HERV-Fc2) in ESCs has not been previously demonstrated and may be a first indication of their role in the regulation of pluripotency. Conversely, tandemly repeated sequences (MSR1, CER, ALR) showed up-regulation in cancer contexts. Moreover, a difference in TE expression was observed between the TME and the tumor bulk transcriptome, with distinct dysregulated TE profiles. Some TME-specific TEs were absent in normal tissues, predominantly belonging to LTR and L1 retrotransposon families. These findings not only shed light on the regulatory roles of TEs in both embryonic development and cancer but also suggest novel targets for anti-cancer therapy. Understanding the interplay between cancer cells and the TME at the TE level may pave the way for further research into therapeutic interventions.

The female condom: What do Australian women say?

BACKGROUND: The female condom is a barrier method for the prevention of sexually transmissible infections and unintended pregnancy. Uptake of this method remains low in Australia, although little research has been undertaken to explore this. METHODS: An interventional cross-sectional study was undertaken in 2019 to explore the views and experiences of women in New South Wales. After trying the female condom, they were invited to complete an online survey and/or structured interview. Training in the use of the female condom was not provided. This paper reports on qualitative findings from open-ended survey responses and interviews. RESULTS: In total, 284 participants completed the survey and 20 participated in an interview. Most were aware of the female condom prior to participating in the study, but few had used it previously. Four broad themes were identified from the data: (i) accessibility of the female condom, including cost and availability, (ii) supporting choice in different circumstances, (iii) aspects of empowerment and control and (iv) use of gendered language. CONCLUSIONS: The female condom may be an acceptable option for many women in Australia. To support the choice of method and promote uptake, it will be important to increase the accessibility of the female condom by raising awareness and addressing the issues of cost and availability. Further exploration of issues regarding inclusive language and messaging in health promotion campaigns and marketing is warranted to ensure that this product is accessible for all people who may wish to use it, regardless of gender or sexuality. Similar research could be undertaken with men/partners and members of the LGBTQ+ community to explore their perspectives of the female condom. SO WHAT?: To support contraceptive choice and promote the uptake of the female condom for those who desire this method, it will be important to address the issues of cost and availability. Accessibility will also be enhanced through the consideration of inclusive language and messaging in health promotion campaigns and marketing of the female condom.

PERISCOPE-Opt: Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli.

Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the Escherichia coli periplasm. Two sets of XGBoost classifiers were employed in the first stage to classify the expression levels of the target protein as high (>50 mg/L), medium (between 0.5 and 50 mg/L), or low (<0.5 mg/L). The second-stage framework consisted of three regression models involving support vector machines and random forest to predict the expression yields corresponding to each expression-level-class. Independent tests showed that the predictor achieved an overall average accuracy of 75% and a Pearson coefficient correlation of 0.91 for the correctly classified instances. Therefore, our model offers a reliable substitution of numerous trial-and-error experiments to identify the optimal fermentation conditions and yield for RPP. It is also implemented as an open-access webserver, PERISCOPE-Opt (http://periscope-opt.erc.monash.edu).