Mohammed, W. | bioRxiv Science

bioRxiv2020

Novel algorithms for PFGE bacterial typing: Number of co-migrated DNA fragments, linking PFGE to WGS results and computer simulations for evaluation of PulseNet international typing protocols

BackgroundStandard protocols for Pulsed-field gel electrophoresis (PFGE) were adopted and being used in a global scale for surveillance of many bacterial food-borne diseases. Matched PFGE bands are considered regardless of co-migration of different DNA fragments. Molecular epidemiology is turning toward whole genome sequencing (WGS). Although, WGS results can be digested In-silico, PFGE and WGS data are being compared separately. We describe a new image analysis algorithm that enables identification of how many DNA fragments co-migrate during PFGE. We built a database that compare described PFGE results to in-silico obtained digestion models (from WGS). Reliability of the method was assessed in-silico using novel computer simulation approach. From WGS, 1,816 digestion model (DMs) were obtained as recommended by PulseNet international. Simulation codes were designed to predict PFGE profiles when DMs are separated at 5% PFGE resolution in addition to expected co-migration levels. ResultsPFGE simulation has shown that about 35% of DNA fragments co-migrate at 5% PFGE resolution. Similar result was obtained when wet-lab PFGE profiles were analyzed using image analysis algorithm mentioned earlier. When image analysis results were compared to DMs, results returned by geltowgs.uofk.edu database revealed reasonable relatedness to DMs. In terms of number of PFGE typable DNA fragments, 45,517 were typable (representing 46.54% out of 97,801). Previously mentioned typable fragments (in terms of typable sizes) comprised 91.24% of the sum of nucleotides of all chromosomes tested (7.24 billion bp). However, significant variations were shown within and between different digestion protocols. ConclusionsIdentification of co-migration levels will reveal the third dimension of PFGE profiles. This will provide a better way for evaluating isolate relationships. Linking old PFGE results to WGS by means of simulation demonstrated here will provide a chance to link millions of PFGE epidemiological data accumulated during the last 24 years to the new WGS era. Evaluation of population dynamics of pathogenic bacteria will be deeper through space and time. Selection of restriction enzymes for PFGE typing will have a powerful in-silico evaluation tool.

microbiology↗

bioRxiv2019

Miswired enhancer logic drives translocation positive rhabdomyosarcoma

Core regularity transcription factors (CR TFs) define cell identity and lineage through an exquisitely precise and logical order during embryogenesis and development. These CR TFs regulate one another in three-dimensional space via distal enhancers that serve as logic gates embedded in their TF recognition sequences. Aberrant chromatin organization resulting in miswired circuitry of enhancer logic is a newly recognized feature in many cancers. Here, we report that PAX3-FOXO1 expression is driven by a translocated FOXO1 distal super enhancer (SE). Using 4C-seq, a technique detecting all genomic regions that interact with the translocated FOXO1 SE, we demonstrate its physical interaction with the PAX3 promotor only in the presence of the oncogenic translocation. Furthermore, RNA-seq and ChIP-seq in tumors bearing rare PAX translocations implicate enhancer miswiring is a pervasive feature across all FP-RMS tumors. HiChIP of enhancer mark H3K27ac showed extended connectivity between the distal FOXO1 SE and additional intra-domain enhancers and the PAX3 promoter. We show by CRISPR-paired-ChIP-Rx that PAX3-FOXO1 transcription is diminished when this network of enhancers is selectively ablated. Therefore, our data reveal a mechanism of a translocated hijacked enhancer which disrupts the normal CR TF logic during skeletal muscle development (PAX3 to MYOD to MYOG), replacing it with an infinite loop logic that locks rhabdomyosarcoma cells in an undifferentiated proliferating stage.

cancer biology↗