Biology subjects

Hoff, B.

Publications and source records attributed to Hoff, B..

2 recordsLinked to original sources

bioRxiv2019

Multiplex genomic edition in Ashbya gossypii using CRISPR-Cpf1

The CRISPR/Cas technologies constitute an essential tool for rapid genome engineering of many organisms, including fungi. The CRISPR/Cas9 system adapted for the industrial fungus Ashbya gossypii enables the efficient genome editing for the introduction of deletions, insertions and nucleotide substitutions. However, the Cas9 system is constrained to the existence of an specific 5-NGG-3 PAM sequence in the target site.\n\nHere we present a new CRISPR/Cas system for A. gossypii that expands the molecular toolbox available for microbial engineering of this fungus. The use of Cpf1 nuclease from Lachnospiraceae bacterium allows to employ a T-rich PAM sequence (5-TTTN-3) and facilitates the implementation of a multiplexing CRISPR/Cpf1 system adapted for A. gossypii. The system has been validated for the introduction of large deletions into five different auxotrophic marker genes (HIS3, ADE2, TRP1, LEU2 and URA3). The use of both crRNA and dDNA arrays in a multi-CRISPR/Cpf1 system was demonstrated to be an efficient strategy for multiplex gene deletion of up to four genes using a single multi-CRISPR/Cpf1 plasmid. Our results also suggest that the selection of the target sequence may significantly affect to the edition efficiency of the system.

genomics↗

bioRxiv2018

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in clinical presentation with an urgent need for better stratification tools for clinical development and care. In this study we used a crowdsourcing approach to address the problem of ALS patient stratification. The DREAM Prize4Life ALS Stratification Challenge was a crowdsourcing initiative using data from >10,000 patients from completed ALS clinical trials and 1479 patients from community-based patient registers. Challenge participants used machine learning and clustering techniques to predict ALS progression and survival. By developing new approaches, the best performing teams were able to predict disease outcomes better than currently available methods. At the same time, the integration of clustering components across methods led to the emergence of distinct consensus clusters, separating patients into four consistent groups, each with its unique predictors for classification. This analysis reveals for the first time the potential of a crowdsourcing approach to uncover covert patient sub-populations, and to accelerate disease understanding and therapeutic development.

bioinformatics↗