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Machado, M. P.

Publications and source records attributed to Machado, M. P..

2 recordsLinked to original sources

Origin, evolution, and distribution of the molecular machinery for biosynthesis of sialylated lipooligosaccharide structures in Campylobacter coli

Campylobacter jejuni and Campylobacter coli are the most common cause of bacterial gastroenteritis worldwide. Additionally, C. jejuni is the most common bacterial etiological agent in the autoimmune Guillain-Barre syndrome (GBS). Ganglioside mimicry by C. jejuni lipooligosaccharide (LOS) is the triggering factor of the disease. LOS-associated genes involved in the synthesis (neuABC) and transfer of sialic acid (sialyltranferases) are essential in C. jejuni to synthesize ganglioside-like LOS. Therefore these genes have been identified as GBS markers. So far, scarce genetic evidence supports C. coli as a GBS causative agent despite being isolated from GBS patients. Here we show that genes putatively involved in sialic acid transfer are widely distributed in the C. coli population. Evidence found herein suggests that a small group of C. coli strains are very likely to express ganglioside mimics, implying that C. coli can potentially trigger GBS. C. coli also presents a larger repertoire of sialyltransferases than C. jejuni and loss of functions of some those LOS-associated genes has happened during adaptation to agriculture niche. Nevertheless, the activity of these sialyltransferases and their role in shaping C. coli population is yet to be explored.

microbiology

chewBBACA: A complete suite for gene-by-gene schema creation and strain identification

Gene-by-gene approaches are becoming increasingly popular in bacterial genomic epidemiology and outbreak detection. However, there is a lack of open-source scalable software for schema definition and allele calling for these methodologies. The chewBBACA suite was designed to assist users in the creation and evaluation of novel whole-genome or core-genome gene-by-gene typing schemas and subsequent allele calling in bacterial strains of interest. The software can run in a laptop or in high performance clusters making it useful for both small laboratories and large reference centers. ChewBBACA is available at https://github.com/B-UMMI/chewBBACA or as a docker image at https://hub.docker.com/r/ummidock/chewbbaca/.\n\nDATA SUMMARYO_LIAssembled genomes used for the tutorial were downloaded from NCBI in August 2016 by selecting those submitted as Streptococcus agalactiae taxon or sub-taxa. All the assemblies have been deposited as a zip file in FigShare (https://figshare.com/s/9cbe1d422805db54cd52), where a file with the original ftp link for each NCBI directory is also available.\nC_LIO_LICode for the chewBBACA suite is available at https://github.com/B-UMMI/chewBBACA while the tutorial example is found at https://github.com/B-UMMI/chewBBACA_tutorial.\nC_LI\n\nI/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {boxtimes}\n\nIMPACT STATEMENTThe chewBBACA software offers a computational solution for the creation, evaluation and use of whole genome (wg) and core genome (cg) multilocus sequence typing (MLST) schemas. It allows researchers to develop wg/cgMLST schemes for any bacterial species from a set of genomes of interest. The alleles identified by chewBBACA correspond to potential coding sequences, possibly offering insights into the correspondence between the genetic variability identified and phenotypic variability. The software performs allele calling in a matter of seconds to minutes per strain in a laptop but is easily scalable for the analysis of large datasets of hundreds of thousands of strains using multiprocessing options. The chewBBACA software thus provides an efficient and freely available open source solution for gene-by-gene methods. Moreover, the ability to perform these tasks locally is desirable when the submission of raw data to a central repository or web services is hindered by data protection policies or ethical or legal concerns.

bioinformatics