Animal development is an extremely robust process resulting in stereotyped outcomes. Canalization is a design principle wherein developmental pathways are stabilized to increase phenotypic reproducibility. Recent revelations into microRNA (miRNA) function suggest that miRNAs act as key players in canalizing genetic programs. We suggest that miRNA interactions with the network of protein-coding genes evolved to buffer stochastic perturbations and thereby confer robustness to developmental genetic programs.

Alu repetitive elements can be inserted into mature messenger RNAs via a splicing-mediated process termed exonization. To understand the molecular basis and the regulation of the process of turning intronic Alus into new exons, we compiled and analyzed a data set of human exonized Alus. We revealed a mechanism that governs 3' splice-site selection in these exons during alternative splicing. On the basis of these findings, we identified mutations that activated the exonization of a silent intronic Alu.

Vertebrate mRNAs are frequently targeted for post-transcriptional repression by microRNAs (miRNAs) through mechanisms involving pairing of 39 UTR seed matches to bases at the 59 end of miRNAs. Through analysis of expression array data following miRNA or siRNA overexpression or inhibition, we found that mRNA fold change increases multiplicatively (i.e., log-additively) with seed match count and that a single 8 mer seed match mediates down-regulation comparable to two 7 mer seed matches. We identified several targeting determinants that enhance seed match-associated mRNA repression, including the presence of adenosine opposite miRNA base 1 and of adenosine or uridine opposite miRNA base 9, independent of complementarity to the siRNA/miRNA. Increased sequence conservation in the ;50 bases 59 and 39 of the seed match and increased AU content 39 of the seed match were each independently associated with increased mRNA down-regulation. All of these determinants are enriched in the vicinity of conserved miRNA seed matches, supporting their activity in endogenous miRNA targeting. Together, our results enable improved siRNA off-target prediction, allow integrated ranking of conserved and nonconserved miRNA targets, and show that targeting by endogenous and exogenous miRNAs/siRNAs involves similar or identical determinants.

The relative contribution of immunological dysregulation and impaired epithelial barrier function to allergic diseases is still a matter of debate. Here we describe a new syndrome featuring severe dermatitis, multiple allergies and metabolic wasting (SAM syndrome) caused by homozygous mutations in DSG1. DSG1 encodes desmoglein 1, a major constituent of desmosomes, which connect the cell surface to the keratin cytoskeleton and play a crucial role in maintaining epidermal integrity and barrier function. SAM syndrome-causing mutations resulted in lack of membrane expression of DSG1, leading to loss of cell-cell adhesion. In addition, DSG1 deficiency was associated with increased expression of a number of genes encoding allergy-related cytokines. The deciphering of the pathogenesis of SAM syndrome substantiates the notion that allergy may result from a primary structural epidermal defect.

Effective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triaging patients, especially in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were confirmed to have COVID-19). Our model predicted COVID-19 test results with high accuracy using only eight binary features: sex, age ≥60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms. Overall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.