The Shapes of the Fourth Estate During the Pandemic: Profiling COVID-19 News Consumption in Eight Countries (preprint)

News media is often referred to as the Fourth Estate, a recognition of its political power. New understandings of how media shape political beliefs and influence collective behaviors are urgently needed in an era when public opinion polls do not necessarily reflect election results and users influence each other in real-time under algorithm-mediated content personalization. In this work, we measure not only the average but also the distribution of audience political leanings for different media across different countries. The methodological components of these new measures include a high-fidelity COVID-19 tweet dataset; high-precision user geolocation extraction; and user political leaning estimated from the within-country retweet networks involving local politicians. We focus on geolocated users from eight countries, profile user leaning distribution for each country, and analyze bridging users who have interactions across multiple countries. Except for France and Turkey, we observe consistent bi-modal user leaning distributions in the other six countries, and find that cross-country retweeting behaviors do not oscillate across the partisan divide. More importantly, this study contributes a new set of media bias estimates by averaging the leaning scores of users who share the URLs from media domains. Through two validations, we find that the new average audience leaning scores strongly correlate with existing media bias scores. Lastly, we profile the COVID-19 news consumption by examining the audience leaning distribution for top media in each country, and for selected media across all countries. Those analyses help answer questions such as: Does center media Reuters have a more balanced audience base than partisan media CNN in the US? Does far-right media Breitbart attract any left-leaning readers in any countries? Does CNN reach a more balanced audience base in the US than in the UK?

Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit (preprint)

While transcriptome- and proteome-wide technologies to assess processes in protein biogenesis are now widely available, we still lack global approaches to assay post-ribosomal biogenesis events, in particular those occurring in the eukaryotic secretory system. We here developed a method, SECRiFY, to simultaneously assess the secretability of >105 protein fragments by two yeast species, S. cerevisiae and P. pastoris, using custom fragment libraries, surface display and a sequencing-based readout. Screening human proteome fragments with a median size of 50 - 100 amino acids, we generated datasets that enable datamining into protein features underlying secretability, revealing a striking role for intrinsic disorder and chain flexibility. SECRiFY is the first methodology that generates sufficient amounts of annotated data for advanced machine learning methods to deduce secretability predictors. The finding that secretability is indeed a learnable feature of protein sequences is of significant impact in the broad area of recombinant protein expression and de novo protein design.

Effects of SARS-CoV-2 Mutations on Protein Structures and Intraviral Protein-Protein Interactions (preprint)

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) causing coronavirus disease 2019 (COVID-19) has infected ten millions of people across the globe, and massive mutations in virus genome have occurred during the rapid spread of this novel coronavirus. Variance in protein sequence might lead to change in protein structure and interaction, then further affect the viral physiological characteristics, which could bring tremendous influence on the pandemic. In this study, we investigated 18 non-synonymous mutations in SARS-CoV-2 genome which incidence rates were all [≥]1% as of July 15th, 2020, then modeled the mutated protein structures and compared them with the reference ones. The results showed that four types of mutations could cause dramatic changes in protein structures (RMSD [≥]5.0 [A]), which were Q57H and G251V in open reading frames 3a (ORF3a), S194L and R203K/G204R in nucleocapsid (N). Next, we found that these mutations could affect the binding affinity of intraviral protein interactions. In addition, the hot spots within these docking complexes were altered, among which the mutation Q57H was involved in both Orf3a-Orf8 and Orf3a-S protein interactions. Besides, these mutations were widely distributed all over the world, and their occurrences fluctuated as time went on. Notably, the incidences of R203K/G204R in N and Q57H in Orf3a were both over 50% in some countries. Overall, our findings suggest that SARS-CoV-2 mutations can change viral protein structure, binding affinity and hot spots of the interface, thereby may have impacts on SARS-CoV-2 transmission, diagnosis and treatment of COVID-19.