Systematic decomposition of sequence determinants governing CRISPR/Cas9 specificity

The specificity of CRISPR/Cas9 genome editing is largely determined by the sequences of guide RNA (gRNA) and the targeted DNA, yet the sequence-dependent rules underlying off-target effects are not fully understood. Here we systematically investigated the sequence determinants governing CRISPR/Cas9 specificity by measuring the off-on ratios of 1,902 gRNAs on 13,314 target sequences using an improved synthetic system with dual-target design. Our study revealed a comprehensive set of rules including 3 factors in CRISPR/Cas9 off-targeting: 1) the nucleotide context and position of a single mismatch; 2) an epistasis-like combinatorial effect of multiple mismatches; and 3) a guide-intrinsic mismatch tolerance (GMT) independent of the mismatch context. Notably, the combinatorial effect and GMT are associated with the free-energy landscape in R-loop formation and are explainable by a multi-state kinetic model. Based on these rules, we developed a model-based off-target prediction tool (MOFF), which showed superior performance compared to the existing methods.

Eukaryotic ribosome recognizes 3 context and stimulates stop codon readthrough

It is known that the nucleotide context surrounding stop codons significantly affects the efficiency of translation termination. In eukaryotes, various 3 contexts have been described that are unfavourable for translation termination; however, the exact molecular mechanism that mediates their effect remains unknown. In this study, we used a reconstituted mammalian translation system to examine the efficiency of stop codons in different contexts, including several previously described weak 3 stop codon contexts. Our results revealed that ribosomes can independently recognize certain contexts and ignore stop codons that are followed by these sequences. Moreover, the efficiency of translation termination at the weak 3 contexts was almost equal to the one at the standard context. We propose that weak 3 contexts interact with the 18S rRNA provoking a conformational change in the U-turn-like structure of the stop codon in the A site of ribosome. This change makes incorporation of the near-cognate tRNA more preferable than recognition of the stop codon by the release factors and increases readthrough.

Gene expression and epigenetics reveal species-specific mechanisms acting upon common molecular pathways in the evolution of task division in bees

A striking feature of advanced insect societies is the existence of workers that forgo reproduction. Two broad types of workers exist in eusocial bees: nurses who care for their young siblings and the queen, and foragers who guard the nest and forage for food. Comparisons between these two worker subcastes have been performed in honeybees, but data from other bees are scarce. To understand whether similar molecular mechanisms are involved in nurse-forager differences across distinct species, we compared gene expression and DNA methylation profiles between nurses and foragers of the buff-tailed bumblebee Bombus terrestris and the stingless bee Tetragonisca angustula. These datasets were then compared to previous findings from honeybees. Our analyses revealed that although the expression pattern of genes is often species-specific, many of the biological processes and molecular pathways involved are common. Moreover, the correlation between gene expression and DNA methylation was dependent on the nucleotide context, and non-CG methylation appeared to be a relevant factor in the behavioral changes of the workers. In summary, task specialization in worker bees is characterized by a plastic and mosaic molecular pattern, with species-specific mechanisms acting upon broad common pathways across species.

Population dynamics of GC-changing mutations in humans and great apes

BackgroundThe nucleotide composition of the genome is a balance between origin and fixation rates of different mutations. For example, it is well-known that transitions occur more frequently than transversions, particularly at CpG sites. Differences in fixation rates of mutation types are less explored. Specifically, recombination-associated GC-biased gene conversion (gBGC) may differentially impact GC-changing mutations, due to differences in their genomic distributions and efficiency of mismatch repair mechanisms. Given that recombination evolves rapidly across species, we explore gBGC of different mutation types across human populations and among great ape species.ResultsWe report a stronger correlation between GC frequency and recombination for transitions than for transversions. Notably, CpG transitions are most strongly affected by gBGC. We show that the strength of gBGC differs for transitions and transversions but that its overall strength is positively correlated with effective population sizes of human populations and great ape species, with some notable exceptions, such as a stronger effect of gBGC on non-CpG transitions in populations of European descent. We study the dependence of gBGC dynamics on flanking nucleotides and show that some mutation types evolve in opposition to the gBGC expectation, likely due to hypermutability of specific nucleotide contexts.ConclusionsDifferences in GC-biased gene conversion are evident between different mutation types, and dependent on sex-specific recombination, population size and flanking nucleotide context. Our results therefore highlight the importance of different gBGC dynamics experienced by GC-changing mutations and their impact on nucleotide composition evolution.

Multiple lineages, same molecular basis: task specialization is commonly regulated across all eusocial bee groups

A striking feature of advanced insect societies is the existence of workers that forgo reproduction. Two broad types of workers exist in eusocial bees: nurses which care for their young siblings and the queen, and foragers who guard the nest and forage for food. Comparisons between this two worker subcastes have been performed in honeybees, but data from other bees are scarce. To understand whether similar molecular mechanisms are involved in nurse-forager differences across distinct species, we compared gene expression and DNA methylation profiles between nurses and foragers of the buff-tailed bumblebee Bombus terrestris and of the stingless bee Tetragonisca angustula. These datasets were then discussed comparatively to previous findings on honeybees. Our analyses revealed that although the expression pattern of genes is often species-specific, many of the biological processes and molecular pathways involved are common. Moreover, DNA methylation and gene expression correlation were dependent on the nucleotide context.

CloneSig: Joint inference of intra-tumor heterogeneity and signature deconvolution in tumor bulk sequencing data

The possibility to sequence DNA in cancer samples has triggered much effort recently to identify the forces at the genomic level that shape tumorigenesis and cancer progression. It has resulted in novel understanding or clarification of two important aspects of cancer genomics: (i) intra-tumor heterogeneity (ITH), as captured by the variability in observed prevalences of somatic mutations within a tumor, and (ii) mutational processes, as revealed by the distribution of the types of somatic mutation and their immediate nucleotide context. These two aspects are not independent from each other, as different mutational processes can be involved in different subclones, but current computational approaches to study them largely ignore this dependency. In particular, sequential methods that first estimate subclones and then analyze the mutational processes active in each clone can easily miss changes in mutational processes if the clonal decomposition step fails, and conversely information regarding mutational signatures is overlooked during the subclonal reconstruction. To address current limitations, we present CloneSig, a new computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data, including whole-exome sequencing (WES) data, by leveraging their dependency. We show through an extensive benchmark on simulated samples that CloneSig is always as good as or better than state-of-the-art methods for ITH inference and detection of mutational processes. We then apply CloneSig to a large cohort of 8,954 tumors with WES data from the cancer genome atlas (TCGA), where we obtain results coherent with previous studies on whole-genome sequencing (WGS) data, as well as new promising findings. This validates the applicability of CloneSig to WES data, paving the way to its use in a clinical setting where WES is increasingly deployed nowadays.

OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers

Motivation Identification of the genomic alterations driving tumorigenesis is one of the main goals in oncogenomics research. Given the evolutionary principles of cancer development, computational methods that detect signals of positive selection in the pattern of tumor mutations have been effectively applied in the search for cancer genes. One of these signals is the abnormal clustering of mutations, which has been shown to be complementary to other signals in the detection of driver genes. Results We have developed OncodriveCLUSTL, a new sequence-based clustering algorithm to detect significant clustering signals across genomic regions. OncodriveCLUSTL is based on a local background model derived from the simulation of mutations accounting for the composition of tri- or penta-nucleotide context substitutions observed in the cohort under study. Our method can identify known clusters and bona-fide cancer drivers across cohorts of tumor whole-exomes, outperforming the existing OncodriveCLUST algorithm and complementing other methods based on different signals of positive selection. Our results indicate that OncodriveCLUSTL can be applied to the analysis of non-coding genomic elements and non-human mutations data. Availability and implementation OncodriveCLUSTL is available as an installable Python 3.5 package. The source code and running examples are freely available at https://bitbucket.org/bbglab/oncodriveclustl under GNU Affero General Public License. Supplementary information Supplementary data are available at Bioinformatics online.

OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers

SummaryThe identification of the genomic alterations driving tumorigenesis is one of the main goals in oncogenomics research. Given the evolutionary principles of cancer development, computational methods that detect signals of positive selection in the pattern of tumor mutations have been effectively applied in the search for cancer genes. One of these signals is the abnormal clustering of mutations, which has been shown to be complementary to other signals in the detection of driver genes. We have developed OncodriveCLUSTL, a new sequence-based clustering algorithm to detect significant clustering signals across genomic regions. OncodriveCLUSTL is based on a local background model derived from the simulation of mutations accounting for the composition of tri- or penta-nucleotide context substitutions observed in the cohort under study. Our method is able to identify known clusters and bona-fide cancer drivers across cohorts of tumor whole-exomes, outperforming the existing OncodriveCLUST algorithm and complementing other methods based on different signals of positive selection. We show that OncodriveCLUSTL may be applied to the analysis of non-coding genomic elements and non-human mutations data.Availability and implementationOncodriveCLUSTL is available as an installable Python 3.5 package. The source code and running examples are freely available at https://bitbucket.org/bbglab/oncodriveclustl under GNU Affero General Public [email protected]