Search

Cystic fibrosis (CF) is a genetic disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. The 2789+5G>A CFTR mutation is a quite frequent defect causing an aberrant splicing and a non-functional CFTR protein. Here we used a CRISPR adenine base editing (ABE) approach to correct the mutation in the absence of DNA double-strand breaks (DSB). To select the strategy, we developed a minigene cellular model reproducing the 2789+5G>A splicing defect. We obtained up to 70% editing in the minigene model by adapting the ABE to the PAM sequence optimal for targeting 2789+5G>A with a SpCas9-NG (NG-ABE). Nonetheless, the on-target base correction was accompanied by secondary (bystander) A-to-G conversions in nearby nucleotides, which affected the wild-type CFTR splicing. To decrease the bystander edits, we used a specific ABE (NG-ABEmax), which was delivered as mRNA. The NG-ABEmax RNA approach was validated in patient-derived rectal organoids and bronchial epithelial cells showing sufficient gene correction to recover the CFTR function. Finally, in-depth sequencing revealed high editing precision genome-wide and allele-specific correction. Here we report the development of a base editing strategy to precisely repair the 2789+5G>A mutation resulting in restoration of the CFTR function, while reducing bystander and off-target activities.

The identification of the protospacer adjacent motif (PAM) sequences of Cas9 nucleases is crucial for their exploitation as genome editing tools. Here we interrogated a massively expanded dataset of metagenome and virome assemblies for accurate and comprehensive PAM predictions, in order to identify novel Cas9s selected for their PAM requirements. As a test bed we selectively isolated a Cas9 which uses a PAM sequence corresponding to a disease-causing mutation, P23H in the RHO gene. Our PAM prediction pipeline will be instrumental to generate a Cas9 nuclease repertoire responding to any PAM requirement leading towards a natural PAM-free genome editing toolbox.

Background Akkermansia muciniphila is a human gut microbe with a key role in the physiology of the intestinal mucus layer and reported associations with decreased body mass and increased gut barrier function and health. Despite its biomedical relevance, the genomic diversity of A. muciniphila remains understudied and that of closely related species, except for A. glycaniphila, unexplored. Results We present a large-scale population genomics analysis of the Akkermansia genus using 188 isolate genomes and 2226 genomes assembled from 18,600 metagenomes from humans and other animals. While we do not detect A. glycaniphila, the Akkermansia strains in the human gut can be grouped into five distinct candidate species, including A. muciniphila, that show remarkable whole-genome divergence despite surprisingly similar 16S rRNA gene sequences. These candidate species are likely human-specific, as they are detected in mice and non-human primates almost exclusively when kept in captivity. In humans, Akkermansia candidate species display ecological co-exclusion, diversified functional capabilities, and distinct patterns of associations with host body mass. Analysis of CRISPR-Cas loci reveals new variants and spacers targeting newly discovered putative bacteriophages. Remarkably, we observe an increased relative abundance of Akkermansia when cognate predicted bacteriophages are present, suggesting ecological interactions. A. muciniphila further exhibits subspecies-level genetic stratification with associated functional differences such as a putative exo/lipopolysaccharide operon. Conclusions We uncover a large phylogenetic and functional diversity of the Akkermansia genus in humans. This variability should be considered in the ongoing experimental and metagenomic efforts to characterize the health-associated properties of A. muciniphila and related bacteria.

The automatic discovery of causal relationships among human genes can shed light on gene regulatory processes and guide drug repositioning. To this end, a computationally-heavy method for causal discovery is distributed on a volunteer computing grid and, taking advantage of variable subsetting and stratification, proves to be useful for expanding local gene regulatory networks. The input data are purely observational measures of transcripts expression in human tissues and cell lines collected within the FANTOM project. The system relies on the BOINC platform and on optimized client code. The functional relevance of results, measured by analyzing the annotations of the identified interactions, increases significantly over the simple Pearson correlation between the transcripts. Additionally, in 82% of cases networks significantly overlap with known protein-protein interactions annotated in biological databases. In the two case studies presented, this approach has been used to expand the networks of genes associated with two severe human pathologies: prostate cancer and coronary artery disease. The method identified respectively 22 and 36 genes to be evaluated as novel targets for already approved drugs, demonstrating the effective applicability of the approach in pipelines aimed to drug repositioning.

Summary The classification of biological samples by means of their respective molecular profiles is a topic of great interest for its potential diagnostic, prognostic and investigational applications. rScudo is an R package for the classification of molecular profiles based on a radically new approach consisting in the analysis of the similarity of rank-based sample-specific signatures. The validity of rScudo unconventional approach has been validated through direct comparison with current methods in the international SBV IMPROVER Diagnostic Signature Challenge. Due to its novelty, there is ample room for conceptual improvements and for exploring additional applications. The rScudo package has been specifically designed to facilitate experimenting with the rank-based signature approach, to test its application to different types of molecular profiles and to simplify direct comparison with existing methods. Availability and implementation The package is available as part of the Bioconductor suite at https://bioconductor.org/packages/rScudo.

Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs. It is characterized by a complex histopathological and molecular heterogeneity, and it is composed of five main histological subtypes (mucinous, endometrioid, clear cell and high, and low grade serous), which have peculiar genetic, molecular, and clinical characteristics. As it occurs less frequently than advanced-stage EOC, its molecular features have not been thoroughly investigated. In this study, using in silico approaches and gene expression data, on a multicentric cohort composed of 208 snap-frozen tumor biopsies, we explored the subtype-specific molecular alterations that regulate tumor aggressiveness in stage I EOC. We found that single genes rather than pathways are responsible for histotype specificities and that a cAMP-PKA-CREB1 signaling axis seems to play a central role in histotype differentiation. Moreover, our results indicate that immune response seems to be, at least in part, involved in histotype differences, as a higher immune-reactive behavior of serous and mucinous samples was observed with respect to other histotypes.