BackgroundMost predictive methods currently available for the identification of protein secretion mechanisms have focused on classically secreted proteins. In fact, only two methods have been reported for predicting non-classically secreted proteins of Gram-positive bacteria. This study describes the implementation of a sequence-based classifier, denoted as NClassG+, for identifying non-classically secreted Gram-positive bacterial proteins.ResultsSeveral feature-based classifiers were trained using different sequence transformation vectors (frequencies, dipeptides, physicochemical factors and PSSM) and Support Vector Machines (SVMs) with Linear, Polynomial and Gaussian kernel functions. Nested k-fold cross-validation (CV) was applied to select the best models, using the inner CV loop to tune the model parameters and the outer CV group to compute the error. The parameters and Kernel functions and the combinations between all possible feature vectors were optimized using grid search.ConclusionsThe final model was tested against an independent set not previously seen by the model, obtaining better predictive performance compared to SecretomeP V2.0 and SecretPV2.0 for the identification of non-classically secreted proteins. NClassG+ is freely available on the web at http://www.biolisi.unal.edu.co/web-servers/nclassgpositive/
We report here the whole-genome sequence of the multidrug-resistant Beijing-like strain Mycobacterium tuberculosis 323, isolated from a 15-year-old female patient who died shortly after the initiation of second-line drug treatment. This strain is representative of the Beijing-like isolates from Colombia, where this lineage is becoming a public health concern.
Introduction: Rickettsioses are zoonotic diseases caused by pathogenic bacteria of the genus Rickettsia and transmitted to man by means of arthropod vectors such as ticks, fleas, mites and lice. Historically, Caldas Department has reported a significant number of cases of murine typhus to the Colombian national health surveillance system, and consequent studies of flea-borne rickettsiosis identified the circulation of Rickettsia typhi and Rickettsia felis in multiple municipalities. Our aim was to genotype species of Rickettsia detected in fleas collected from domestic and wild mammals in Caldas.
Methodology: Flea samples were taken by convenience sampling from dogs, cats and wild mammals (rodents and marsupials) in 26 municipalities. Specimens were classified by current taxonomic keys and pooled for DNA extraction and molecular screening for Rickettsia spp. by PCR amplification of gltA, htrA and sca5 genes. Positive samples were genotyped by enzyme digestion (htrA) and sequencing.
Results: A total of 1388 flea samples were collected. Rickettsia DNA was amplified in 818 (gltA), 883 (htrA) and 424 (sca5) flea pools. Alignment analysis with available Rickettsia DNA sequences showed greater similarity with R. asembonensis (gltA) and with R. felis (sca5 and htrA). Restriction pattern was compatible with R. felis. R. typhi was not identified.
Conclusion: The present study confirms the presence and high prevalence of R. asembonensis and R. felis in fleas from domestic and wild animals in different municipalities from Caldas Department.
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