Binet, R. | bioRxiv Science

bioRxiv2023

DNA damage-induced interaction between a lineage addiction oncogenic transcription factor and the MRN complex shapes a tissue-specific DNA Damage Response and cancer predisposition

Since genome instability can drive cancer initiation and progression, cells have evolved highly effective and ubiquitous DNA Damage Response (DDR) programs. However, some cells, in skin for example, are normally exposed to high levels of DNA damaging agents. Whether such high-risk cells possess lineage-specific mechanisms that tailor DNA repair to the tissue remains largely unknown. Here we show, using melanoma as a model, that the microphthalmia-associated transcription factor MITF, a lineage addition oncogene that coordinates many aspects of melanocyte and melanoma biology, plays a non-transcriptional role in shaping the DDR. On exposure to DNA damaging agents, MITF is phosphorylated by ATM/DNA-PKcs, and unexpectedly its interactome is dramatically remodelled; most transcription (co)factors dissociate, and instead MITF interacts with the MRE11-RAD50-NBS1 (MRN) complex. Consequently, cells with high MITF levels accumulate stalled replication forks, and display defects in homologous recombination-mediated repair associated with impaired MRN recruitment to DNA damage. In agreement, high MITF levels are associated with increased SNV burden in melanoma. Significantly, the SUMOylation-defective MITF-E318K melanoma predisposition mutation recapitulates the effects of ATM/DNA-PKcs-phosphorylated MITF. Our data suggest that a non-transcriptional function of a lineage-restricted transcription factor contributes to a tissue-specialised modulation of the DDR that can impact cancer initiation.

molecular biology↗

bioRxiv2023

bettercallsal: better calling of Salmonella serotypes from enrichment cultures using shotgun metagenomic profiling and its application in an outbreak setting.

Precise and rapid identification of Salmonella serotypes from suspect food matrices is critical for successful source attribution of illness outbreaks (Scallan et al., 2011). Currently, close to 3% of U.S. foodborne Salmonella outbreaks have been attributed to multiple Salmonella serotypes (2.85%, 2000 - 2020) (CDC, 2022). Recent foodborne outbreaks that have been attributed to multiple Salmonella serotypes force us to question whether these are rare events or if previous methods simply did not have the throughput to provide an accurate picture of the complex ecology that is connected to outbreak etiologies. (Hassan et al., 2019; FDA, 2021; Whitney et al., 2021). An in-silico benchmark dataset, comprising 29 unique Salmonella, 46 non-Salmonella bacterial and 10 viral genomes, was generated with varying read depths. For outbreak samples, analysis was performed on previously sequenced pre-enrichments and selective enrichments of papayas and peaches (fruits and leaves) that led to the identification of multiple serovars. Data analyses was performed using a custom-built k-mer tool, SeqSero2, Kallisto and bettercallsal. The in-silico dataset analyzed with bettercallsal had accuracy, recall and specificity of 95%, 96% and 98 % respectively. In the papaya outbreak samples, bettercallsal identified multiple serovar presence in concordance with Bioplex assay results and the genome hits assigned to the samples are Salmonella isolates from the papaya outbreak as evident by NCBI SNP cluster information. In peach outbreak samples, bettercallsal identified both the serovars (Alachua and Gaminara) in concordance with k-mer analysis and the Luminex xMap assay. bettercallsal outperformed k-mer, Kallisto and Seqsero2 in identifying multiple serovars from enrichment cultures using shotgun metagenomics sequencing. Most Salmonella subtyping work has relied upon WGS methods which focuses on the high-resolution analysis of single genomes, or multiple single genomes picked from colonies on agar. Here we introduce laboratory and bioinformatics innovations for a metagenomic outbreak response workflow that accurately identifies multiple Salmonella serovars at the same time in a much higher throughput approach. bettercallsal is one of the first analysis tools that can potentially identify multiple Salmonella spp. serotypes from a metagenomic or quasi-metagenomic datasets with accuracy and can provide early insights into the etiology of the sample.

genomics↗