The rDNA is biomolecular condensate formed by polymer-polymer phase separation and is sequestered in the nucleolus by transcription and R-loops

The nucleolus is the site of ribosome biosynthesis encompassing the ribosomal DNA (rDNA) locus in a phase separated state within the nucleus. In budding yeast, we find the rDNA locus and Cdc14, a protein phosphatase that co-localizes with the rDNA, behave like a condensate formed by polymer-polymer phase separation, while ribonucleoproteins behave like a condensate formed by liquid-liquid phase separation. The compaction of the rDNA and Cdc14's nucleolar distribution are dependent on the concentration of DNA cross-linkers. In contrast, ribonucleoprotein nucleolar distribution is independent of the concentration of DNA cross-linkers and resembles droplets in vivo upon replacement of the endogenous rDNA locus with high-copy plasmids. When ribosomal RNA is transcribed from the plasmids by Pol II, the rDNA-binding proteins and ribonucleoprotein signals are weakly correlated, but upon repression of transcription, ribonucleoproteins form a single, stable droplet that excludes rDNA-binding proteins from its center. Degradation of RNA-DNA hybrid structures, known as R-loops, by overexpression of RNase H1 results in the physical exclusion of the rDNA locus from the nucleolar center. Thus, the rDNA locus is a polymer-polymer phase separated condensate that relies on transcription and physical contact with RNA transcripts to remain encapsulated within the nucleolus

Lives versus Livelihoods? Perceived economic risk has a stronger association with support for COVID-19 preventive measures than perceived health risk

This paper examines whether compliance with COVID-19 mitigation measures is motivated by wanting to save lives or save the economy (or both), and which implications this carries to fight the pandemic. National representative samples were collected from 24 countries (N = 25,435). The main predictors were (1) perceived risk to contract coronavirus, (2) perceived risk to suffer economic losses due to coronavirus, and (3) their interaction effect. Individual and country-level variables were added as covariates in multilevel regression models. We examined compliance with various preventive health behaviors and support for strict containment policies. Results show that perceived economic risk consistently predicted mitigation behavior and policy support—and its effects were positive. Perceived health risk had mixed effects. Only two significant interactions between health and economic risk were identified—both positive

Associations of autozygosity with a broad range of human phenotypes

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F-ROH) for >1.4 million individuals, we show that F-ROH is significantly associated (p <0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F-ROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F-ROH are confirmed within full-sibling pairs, where the variation in F-ROH is independent of all environmental confounding.Peer reviewe

Identifying important individual‐ and country‐level predictors of conspiracy theorizing: a machine learning analysis

Psychological research on the predictors of conspiracy theorizing—explaining important social and political events or circumstances as secret plots by malevolent groups—has flourished in recent years. However, research has typically examined only a small number of predictors in one, or a small number of, national contexts. Such approaches make it difficult to examine the relative importance of predictors, and risk overlooking some potentially relevant variables altogether. To overcome this limitation, the present study used machine learning to rank-order the importance of 115 individual- and country-level variables in predicting conspiracy theorizing. Data were collected from 56,072 respondents across 28 countries during the early weeks of the COVID-19 pandemic. Echoing previous findings, important predictors at the individual level included societal discontent, paranoia, and personal struggle. Contrary to prior research, important country-level predictors included indicators of political stability and effective government COVID response, which suggests that conspiracy theorizing may thrive in relatively well-functioning democracies