Publications by authors named "Marcus Hogan"

We have developed a behavioral paradigm to study volitional olfactory investigation in mice over several months. We placed odor ports in the wall of a standard cage that administer a neutral odorant stimulus when a mouse pokes its nose inside. Even though animals were fed and watered ad libitum, and sampling from the port elicited no outcome other than the delivery of an odor, mice readily sampled these stimuli hundreds of times per day.

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Traditional genome-editing reagents such as CRISPR-Cas9 achieve targeted DNA modification by introducing double-strand breaks (DSBs), thereby stimulating localized DNA repair by endogenous cellular repair factors. While highly effective at generating heterogenous knockout mutations, this approach suffers from undesirable byproducts and an inability to control product purity. Here we develop a system in human cells for programmable, DSB-free DNA integration using Type I CRISPR-associated transposons (CASTs).

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Conventional genome engineering with CRISPR-Cas9 creates double-strand breaks (DSBs) that lead to undesirable byproducts and reduce product purity. Here we report an approach for programmable integration of large DNA sequences in human cells that avoids the generation of DSBs by using Type I-F CRISPR-associated transposases (CASTs). We optimized DNA targeting by the QCascade complex through protein design and developed potent transcriptional activators by exploiting the multi-valent recruitment of the AAA+ ATPase TnsC to genomic sites targeted by QCascade.

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Article Synopsis
  • The human microbiome significantly impacts how effective and safe certain drugs are for individuals, suggesting that personalized medicine should consider microbial effects.
  • The study introduces AGORA2, an updated computational resource that includes detailed data on 7,302 gut microorganism strains and their interactions with 98 different drugs, enhancing previous models.
  • AGORA2 shows high accuracy in predicting how gut microbes alter drugs, and it can tailor drug conversion predictions based on microbiome data from patients with colorectal cancer, highlighting the importance of individual factors like age and sex.
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