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International audience; Plant genomes are characterized by large and complex gene families that often result in similar and partially overlapping functions 1. This genetic redundancy severely hampers current efforts to uncover novel phenotypes, delaying basic genetic research and breeding programs 2. Here, we describe the development and validation of Multi-Knock, a genome-scale CRISPR toolbox that overcomes functional redundancy in Arabidopsis by simultaneously targeting multiple gene-family members, thus identifying genetically hidden components. We computationally designed 59,129 optimal single guide RNAs (sgRNAs) that each target 2 to 10 genes within a family at once. Furthermore, partitioning the library into ten sub-libraries directed towards a different functional group allows flexible and targeted genetic screens. From the 5,635 sgRNAs targeting the plant transportome, we generated over 3,500 independent Arabidopsis lines that allowed us to identify and characterize the first known cytokinin tonoplast-localized transporters in plants. With the ability to overcome functional redundancy in plants at the genome-scale level, the developed strategy can be readily deployed by scientists and breeders for basic research and to expedite breeding efforts.

PURPOSE: The biological and functional heterogeneity in very old patients constitutes a major challenge to prognostication and patient management in intensive care units (ICUs). In addition to the characteristics of acute diseases, geriatric conditions such as frailty, multimorbidity, cognitive impairment and functional disabilities were shown to influence outcome in that population. The goal of this study was to identify new and robust phenotypes based on the combination of these features to facilitate early outcome prediction.METHODS: Patients aged 80 years old or older with and without limitations of life-sustaining treatment and with complete data were recruited from the VIP2 study for phenotyping and from the COVIP study for external validation. The sequential organ failure assessment (SOFA) score and its sub-scores taken on admission to ICU as well as demographic and geriatric patient characteristics were subjected to clustering analysis. Phenotypes were identified after repeated bootstrapping and clustering runs.RESULTS: In patients from the VIP2 study without limitations of life-sustaining treatment (n = 1977), ICU mortality was 12% and 30-day mortality 19%. Seven phenotypes with distinct profiles of acute and geriatric characteristics were identified in that cohort. Phenotype-specific mortality within 30 days ranged from 3 to 57%. Among the patients assigned to a phenotype with pronounced geriatric features and high SOFA scores, 50% died in ICU and 57% within 30 days. Mortality differences between phenotypes were confirmed in the COVIP study cohort (n = 280).CONCLUSIONS: Phenotyping of very old patients on admission to ICU revealed new phenotypes with different mortality and potential need for anticipatory intervention.

It is well known that for some tasks, labeled data sets may be hard to gather. Self-training, or pseudo-labeling, tackles the problem of having insufficient training data. In the self-training scheme, the classifier is first trained on a limited, labeled dataset, and after that it is trained on an additional, unlabeled dataset, using its own predictions as labels, provided those predictions are made with high enough confidence. Using credible interval based on MC-dropout as a confidence measure, the proposed method is able to gain substantially better results comparing to several other pseudo-labeling methods and out-performs the former state-of-the-art pseudo-labeling technique by 7\(\%\) on the MNIST dataset. In addition to learning from large and static unlabeled datasets, the suggested approach may be more suitable than others as an online learning method where the classifier keeps getting new unlabeled data. The approach may be also applicable in the recent method of pseudo-gradients for training long sequential neural networks.

The discovery and development of the CRISPR-Cas9 system in the past few years has made eukaryotic genome editing, and specifically gene knockout for reverse genetics, a simpler, efficient, and effective task. The system is directed to the genomic target site by a programmed single-guide RNA (sgRNA) that base-pairs with the DNA target, subsequently leading to site-specific double-strand breaks. However, many gene families in eukaryotic genomes exhibit partially overlapping functions and, thus, the knockout of one gene might be concealed by the function of the other. In such cases, the reduced specificity of the CRISPR-Cas9 system, which may lead to the cleavage of genomic sites that are not identical to the sgRNA, can be harnessed for the simultaneous knockout of multiple homologous genes. Here, we introduce CRISPys, an algorithm for the optimal design of sgRNAs that would potentially target multiple members of a given gene family. CRISPys first clusters all the potential targets in the input sequences into a hierarchical tree structure that specifies the similarity among them. Then, sgRNAs are proposed in the internal nodes of the tree by embedding mismatches where needed, such that the cleavage efficiencies of the induced targets are maximized. We suggest several approaches for designing the optimal individual sgRNA, and an approach that provides a set of sgRNAs that also accounts for the homologous relationships among gene-family members. We further show by in-silico examination over all gene families in theSolanum lycopersicumgenome that our suggested approach outperforms simpler alignment-based techniques.Graphical abstractHighlightsMany genes in eukaryotic genomes exhibit partially overlapping functions. This imposes difficulties on reverse-genetics, as the knockout of one gene might be concealed by the function of the other.We present CRISPys, a graph-based algorithm for the optimal design of CRISPR systems given a set of redundant genes.CRISPys harnesses the lack of specificity of the CRISPR-Cas9 genome editing technique, providing researchers the ability to simultaneously mutate multiple genes.We show that CRISPys outperforms existing approaches that are based on simple alignment of the input gene family.

The development of the CRISPR-Cas9 system in recent years has made eukaryotic genome editing, and specifically gene knockout for reverse genetics, a simple and effective task. The system is directed to a genomic target site by a programmed single-guide RNA (sgRNA) that base-pairs with it, subsequently leading to site-specific modifications. However, many gene families in eukaryotic genomes exhibit partially overlapping functions, and thus, the knockout of one gene might be concealed by the function of the other. In such cases, the reduced specificity of the CRISPR-Cas9 system, which may lead to the modification of genomic sites that are not identical to the sgRNA, can be harnessed for the simultaneous knockout of multiple homologous genes. We introduce CRISPys, an algorithm for the optimal design of sgRNAs that would potentially target multiple members of a given gene family. CRISPys first clusters all the potential targets in the input sequences into a hierarchical tree structure that specifies the similarity among them. Then, sgRNAs are proposed in the internal nodes of the tree by embedding mismatches where needed, such that the efficiency to edit the induced targets is maximized. We suggest several approaches for designing the optimal individual sgRNA and an approach to compute the optimal set of sgRNAs for cases when the experimental platform allows for more than one. The latter may optionally account for the homologous relationships among gene-family members. We further show that CRISPys outperforms simpler alignment-based techniques by in silico examination over all gene families in the Solanum lycopersicum genome.