The COVID-19 (Coronavirus disease-2019) pandemic, caused by the SARS-CoV-2 coronavirus, is a significant threat to public health and the global economy. SARS-CoV-2 is closely related to the more lethal but less transmissible coronaviruses SARS-CoV-1 and MERS-CoV. Here, we have carried out comparative viral-human protein-protein interaction and viral protein localization analysis for all three viruses. Subsequent functional genetic screening identified host factors that functionally impinge on coronavirus proliferation, including Tom70, a mitochondrial chaperone protein that interacts with both SARS-CoV-1 and SARS-CoV-2 Orf9b, an interaction we structurally characterized using cryo-EM. Combining genetically-validated host factors with both COVID-19 patient genetic data and medical billing records identified important molecular mechanisms and potential drug treatments that merit further molecular and clinical study.

Pooled CRISPR-Cas9 screens are a powerful method for functionally characterizing regulatory elements in the non-coding genome, but off-target effects in these experiments have not been systematically evaluated. Here, we investigate Cas9, dCas9, and CRISPRi/a off-target activity in screens for essential regulatory elements. The sgRNAs with the largest effects in genome-scale screens for essential CTCF loop anchors in K562 cells were not single guide RNAs (sgRNAs) that disrupted gene expression near the on-target CTCF anchor. Rather, these sgRNAs had high off-target activity that, while only weakly correlated with absolute off-target site number, could be predicted by the recently developed GuideScan specificity score. Screens conducted in parallel with CRISPRi/a, which do not induce double-stranded DNA breaks, revealed that a distinct set of off-targets also cause strong confounding fitness effects with these epigenome-editing tools. Promisingly, filtering of CRISPRi libraries using GuideScan specificity scores removed these confounded sgRNAs and enabled identification of essential regulatory elements.

Individual cells from a genetically identical population exhibit substantial variation in gene expression. A significant part of this variation is due to noise in the process of transcription that is intrinsic to each gene, and is determined by factors such as the rate with which the promoter transitions between transcriptionally active and inactive states, and the number of transcripts produced during the active state. However, we have a limited understanding of how the DNA sequence affects such promoter dynamics. Here, we used single-cell time-lapse microscopy to compare the effect on transcriptional dynamics of two distinct types of sequence changes in the promoter that can each increase the mean expression of a cell population by similar amounts but through different mechanisms. We show that increasing expression by strengthening a transcription factor binding site results in slower promoter dynamics and higher noise as compared with increasing expression by adding nucleosome-disfavoring sequences. Our results suggest that when achieving the same mean expression, the strategy of using stronger binding sites results in a larger number of transcripts produced from the active state, whereas the strategy of adding nucleosome-disfavoring sequences results in a higher frequency of promoter transitions between active and inactive states. In the latter strategy, this increased sampling of the active state likely reduces the expression variability of the cell population. Our study thus demonstrates the effect of cis-regulatory elements on expression variability and points to concrete types of sequence changes that may allow partial decoupling of expression level and noise.

Evolutionary constraint and acceleration are powerful, cell-type agnostic measures of functional importance. Previous studies in mammals were limited by species number and reliance on human-referenced alignments. We explore the evolution of placental mammals, including humans, through reference-free whole-genome alignment of 240 species and protein-coding alignments for 428 species. We estimate 10.7% of the human genome is evolutionarily constrained. We resolve constraint to single nucleotides, pinpointing functional positions, and refine and expand by over seven-fold the catalog of ultraconserved elements. Overall, 48.5% of constrained bases are as yet unannotated, suggesting yet-to-be-discovered functional importance. Using species-level phenotypes and an updated phylogeny, we associate coding and regulatory variation with olfaction and hibernation. Focusing on biodiversity conservation, we identify genomic metrics that predict species at risk of extinction.

How to optimize deep-brain stimulation Deep-brain stimulation as presently used in clinical settings, for example, to treat Parkinson’s disease, does not differentiate between different neural circuitries. Considerable improvements could thus be achieved with selective stimulation that targets particular neuronal populations. Spix et al . used optogenetics to develop a clever electrical stimulation protocol that enhances cell-type specificity (see the Perspective by Haas). The authors managed to drive population-specific neuromodulation in a brain region called the external globus pallidus with brief bursts of electrical stimulation, which then yielded a long-lasting effect in a mouse model of Parkinson’s disease. —PRS