Predicting base editing outcomes using position-specific sequence determinants.

CRISPR/Cas base editors promise nucleotide-level control over DNA sequences, but the determinants of their activity remain incompletely understood. We measured base editing frequencies in two human cell lines for two cytosine and two adenine base editors at ∼14 000 target sequences and find that base editing activity is sequence-biased, with largest effects from nucleotides flanking the target base. Whether a base is edited depends strongly on the combination of its position in the target and the preceding base, acting to widen or narrow the effective editing window. The impact of features on editing rate depends on the position, with sequence bias efficacy mainly influencing bases away from the center of the window. We use these observations to train a machine learning model to predict editing activity per position, with accuracy ranging from 0.49 to 0.72 between editors, and with better generalization across datasets than existing tools. We demonstrate the usefulness of our model by predicting the efficacy of disease mutation correcting guides, and find that most of them suffer from more unwanted editing than pure outcomes. This work unravels the position-specificity of base editing biases and allows more efficient planning of editing campaigns in experimental and therapeutic contexts

The Third International Symposium on Fungal Stress – ISFUS

Stress is a normal part of life for fungi, which can survive in environments considered inhospitable or hostile for other organisms. Due to the ability of fungi to respond to, survive in, and transform the environment, even under severe stresses, many researchers are exploring the mechanisms that enable fungi to adapt to stress. The International Symposium on Fungal Stress (ISFUS) brings together leading scientists from around the world who research fungal stress. This article discusses presentations given at the third ISFUS, held in São José dos Campos, São Paulo, Brazil in 2019, thereby summarizing the state-of-the-art knowledge on fungal stress, a field that includes microbiology, agriculture, environmental science, ecology, biotechnology, medicine, and astrobiology

First sagittarius A* Event Horizon Telescope results. VI. Testing the black hole metric

Galaxie

Resolving the inner parsec of the blazar J1924-2914 with the event horizon telescope

Galaxie

A universal power-law prescription for variability from synthetic images of black hole accretion flows

Instrumentatio

Millimeter light curves of sagittarius A* observed during the 2017 Event Horizon Telescope campaign

Galaxie

First Sagittarius A* Event Horizon Telescope results. I. The shadow of the supermassive black hole in the center of the Milky Way

Galaxie

Global analysis of suppressor mutations that rescue human genetic defects

Genetic suppression occurs when the deleterious effects of a primary "query" mutation, such as a disease-causing mutation, are rescued by a suppressor mutation elsewhere in the genome. To capture existing knowledge on suppression relationships between human genes, we examined 2,400 published papers for potential interactions identified through either genetic modification of cultured human cells or through association studies in patients. The resulting network encompassed 476 unique suppression interactions covering a wide spectrum of diseases and biological functions. The interactions frequently linked genes that operate in the same biological process. Suppressors were strongly enriched for genes with a role in stress response or signaling, suggesting that deleterious mutations can often be buffered by modulating signaling cascades or immune responses. Suppressor mutations tended to be deleterious when they occurred in absence of the query mutation, in apparent contrast with their protective role in the presence of the query. We formulated and quantified mechanisms of genetic suppression that could explain 71% of interactions and provided mechanistic insight into disease pathology. Finally, we used these observations to predict suppressor genes in the human genome. The global suppression network allowed us to define principles of genetic suppression that were conserved across diseases, model systems, and species. The emerging frequency of suppression interactions among human genes and range of underlying mechanisms, together with the prevalence of suppression in model organisms, suggest that compensatory mutations may exist for most genetic diseases