Multilayered VBC score predicts sgRNAs that efficiently generate loss-of-function alleles.

CRISPR-Cas9 screens have emerged as a transformative approach to systematically probe gene functions. The quality and success of these screens depends on the frequencies of loss-of-function alleles, particularly in negative-selection screens widely applied for probing essential genes. Using optimized screening workflows, we performed essentialome screens in cancer cell lines and embryonic stem cells and achieved dropout efficiencies that could not be explained by common frameshift frequencies. We find that these superior effect sizes are mainly determined by the impact of in-frame mutations on protein function, which can be predicted based on amino acid composition and conservation. We integrate protein features into a 'Bioscore' and fuse it with improved predictors of single-guide RNA activity and indel formation to establish a score that captures all relevant processes in CRISPR-Cas9 mutagenesis. This Vienna Bioactivity CRISPR score (www.vbc-score.org) outperforms previous prediction tools and enables the selection of sgRNAs that effectively produce loss-of-function alleles.

Differential transcriptional modulation of biological processes in adipocyte triglyceride lipase and hormone-sensitive lipase-deficient mice.

Adipocyte triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL) are intracellular lipases that mobilize triglycerides, the main energy source in mammals. Deletion of genes encoding ATGL (Pnpla2) or HSL (Lipe) in mice results in striking phenotypic differences, suggesting distinct roles for these lipases. The goal of the present study was to identify the biological processes that are modulated in the metabolic tissues of ATGL- and HSL-deficient mice. DNA microarrays were employed to provide full genome coverage concerning the types of genes that are differentially expressed in wild-type and mutant mice. For both mouse models, transcript signatures were identified in white adipose tissue, brown adipose tissue (BAT), skeletal muscle (SM), cardiac muscle (CM), and liver. Genetic ablation of ATGL and HSL alters the transcript levels of a large number of genes in metabolic tissues. The genes affected in the two models are, however, largely different ones. Indeed, only one biological process was modulated in the same way in both mouse models, namely the down-regulation of fatty acid metabolism in BAT. The most pronounced modulation of biological processes was observed in ATGL-/- CM, in which a concerted down-regulation of transcripts associated with oxidative pathways was observed. In HSL-/- mice, in contrast, the most marked changes were seen in SM, namely, alterations in transcript levels reflecting a change of energy source from lipid to carbohydrate. The transcript signatures also provided novel insights into the metabolic derangements that are characteristic of ATGL-/- mice. Our findings suggest that ATGL and HSL differentially modulate biological processes in metabolic tissues. We hypothesize that the intermediary metabolites of the lipolytic pathways are signaling molecules and activators of a wide range of biochemical and cellular processes in mammals.

Molecular processes during fat cell development revealed by gene expression profiling and functional annotation.

BACKGROUND: Large-scale transcription profiling of cell models and model organisms can identify novel molecular components involved in fat cell development. Detailed characterization of the sequences of identified gene products has not been done and global mechanisms have not been investigated. We evaluated the extent to which molecular processes can be revealed by expression profiling and functional annotation of genes that are differentially expressed during fat cell development. RESULTS: Mouse microarrays with more than 27,000 elements were developed, and transcriptional profiles of 3T3-L1 cells (pre-adipocyte cells) were monitored during differentiation. In total, 780 differentially expressed expressed sequence tags (ESTs) were subjected to in-depth bioinformatics analyses. The analysis of 3'-untranslated region sequences from 395 ESTs showed that 71% of the differentially expressed genes could be regulated by microRNAs. A molecular atlas of fat cell development was then constructed by de novo functional annotation on a sequence segment/domain-wise basis of 659 protein sequences, and subsequent mapping onto known pathways, possible cellular roles, and subcellular localizations. Key enzymes in 27 out of 36 investigated metabolic pathways were regulated at the transcriptional level, typically at the rate-limiting steps in these pathways. Also, coexpressed genes rarely shared consensus transcription-factor binding sites, and were typically not clustered in adjacent chromosomal regions, but were instead widely dispersed throughout the genome. CONCLUSIONS: Large-scale transcription profiling in conjunction with sophisticated bioinformatics analyses can provide not only a list of novel players in a particular setting but also a global view on biological processes and molecular networks.