Ceramide is a lipid molecule that regulates diverse physiological and pathological reactions in part through inverting the topology of certain transmembrane proteins. This topological inversion is achieved through regulated alternative translocation (RAT), which reverses the direction by which membrane proteins are translocated across the endoplasmic reticulum during translation. However, owing to technical challenges in studying protein–ceramide interaction, it remains unclear how ceramide levels are sensed in cells to trigger RAT. Here, we report the synthesis of pac-C 7 -Cer, a photoactivatable and clickable short-chain ceramide analog that can be used as a probe to study protein–ceramide interactions. We demonstrate that translocating chain-associated membrane protein 2 (TRAM2), a protein known to control RAT of transmembrane 4 L6 subfamily member 20, and TRAM1, a homolog of TRAM2, interacted with molecules derived from pac-C 7 -Cer. This interaction was competed by naturally existing long-chain ceramide molecules. We showed that binding of ceramide and its analogs to TRAM2 correlated with their ability to induce RAT of transmembrane 4 L6 subfamily member 20. In addition to probing ceramide–TRAM interactions, we provide evidence that pac-C 7 -cer could be used for proteome-wide identification of ceramide-binding proteins. Our study provides mechanistic insights into RAT by identifying TRAMs as potential ceramide-binding proteins and establishes pac-C 7 -Cer as a valuable tool for future study of ceramide–protein interactions.

Background: Despite a 2010 Kenyan constitutional amendment limiting members of elected public bodies to < two-thirds of the same gender, only 22 percent of the 12th Parliament members inaugurated in 2017 were women. Investigating gender bias in the media is a useful tool for understanding socio-cultural barriers to implementing legislation for gender equality. Natural language processing (NLP) methods, such as word embedding and sentiment analysis, can efficiently quantify media biases at a scope previously unavailable in the social sciences.Methods: We trained GloVe and word2vec word embeddings on text from 1998 to 2019 from Kenya’s Daily Nation newspaper. We measured gender bias in these embeddings and used sentiment analysis to predict quantitative sentiment scores for sentences surrounding female leader names compared to male leader names.Results: Bias in leadership words for men and women measured from Daily Nation word embeddings corresponded to temporal trends in men and women’s participation in political leadership (i.e., parliamentary seats) using GloVe (correlation 0.8936, p = 0.0067, r2 = 0.799) and word2vec (correlation 0.844, p = 0.0169, r2 = 0.712) algorithms. Women continue to be associated with domestic terms while men continue to be associated with influence terms, for both regular gender words and female and male political leaders’ names. Male words (e.g., he, him, man) were mentioned 1.84 million more times than female words from 1998 to 2019. Sentiment analysis showed an increase in relative negative sentiment associated with female leaders (p = 0.0152) and an increase in positive sentiment associated with male leaders over time (p = 0.0216).Conclusion: Natural language processing is a powerful method for gaining insights into and quantifying trends in gender biases and sentiment in news media. We found evidence of improvement in gender equality but also a backlash from increased female representation in high-level governmental leadership.

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