Whole Genome Sequencing of Pediatric Klebsiella pneumoniae Strains Reveals Important Insights Into Their Virulence-Associated Traits.

Klebsiella pneumoniae is recognized as a common cause of nosocomial infections and outbreaks causing pneumonia, septicemia, and urinary tract infections. This opportunistic bacterium shows an increasing acquisition of antibiotic-resistance genes, which complicates treatment of infections. Hence, fast reliable strain typing methods are paramount for the study of this opportunistic pathogen's multi-drug resistance genetic profiles. In this study, thirty-eight strains of K. pneumoniae isolated from the blood of pediatric patients were characterized by whole-genome sequencing and genomic clustering methods. Genes encoding ß-lactamase were found in all the bacterial isolates, among which the bla SHV variant was the most prevalent (53%). Moreover, genes encoding virulence factors such as fimbriae, capsule, outer membrane proteins, T4SS and siderophores were investigated. Additionally, a multi-locus sequence typing (MLST) analysis revealed 24 distinct sequence types identified within the isolates, among which the most frequently represented were ST76 (16%) and ST70 (11%). Based on LPS structure, serotypes O1 and O3 were the most prevalent, accounting for approximately 63% of all infections. The virulence capsular types K10, K136, and K2 were present in 16, 13, and 8% of the isolates, respectively. Phylogenomic analysis based on virtual genome fingerprints correlated with the MLST data. The phylogenomic reconstruction also denoted association between strains with a higher abundance of virulence genes and virulent serotypes compared to strains that do not possess these traits. This study highlights the value of whole-genomic sequencing in the surveillance of virulence attributes among clinical K. pneumoniae strains.

Phylogenomic study and classification of mitochondrial DNA through virtual genomic fingerprints.

In the present study, we evaluated the ability of the Virtual Analysis Method for Phylogenomic fingerprint Estimation (VAMPhyRE) toolkit to classify human mitochondrial DNA (mtDNA) haplogroups. In total, 357 random mtDNA sequences were obtained from different haplogroups, based on the classification of PhyloTree. Additionally, we included a control group of five sequences (Pan paniscus, Pan troglodytes, Homo sapiens neanderthalensis, Yoruba15, and the revised Cambridge reference sequence). VAMPhyRE employs a virtual hybridization technique, using probes that specifically bind to their complementary sequences in the genome. We used 65,536 probes of 8 nucleotides to identify potential sites where hybridization occurs between the mtDNA and the specific probe, forming different heteroduplexes and thus, creating a unique and specific genomic fingerprint for each sequence. Genomic fingerprints were compared, and a table of distances was calculated to obtain a mitochondrial phylogenomic tree with the macrohaplogroups, L, N, M, and R, and their corresponding haplogroups, according to universal nomenclature. The results obtained suggest an accuracy of 97.25% for the distribution of the 357 mtDNA sequences in the four macrohaplogroups and their corresponding haplogroups when compared with other mtDNA classification tools that require reference sequences and do not offer an analysis based on an evolutionary approach. These data are available online at http://biomedbiotec.encb.ipn.mx/VAMPhyRE/.

Identification and Quantification of Adulterants in Coffee (Coffea arabica L.) Using FT-MIR Spectroscopy Coupled with Chemometrics.

Food adulteration is an illegal practice performed to elicit economic benefits. In the context of roasted and ground coffee, legumes, cereals, nuts and other vegetables are often used to augment the production volume; however, these adulterants lack the most important coffee compound, caffeine, which has health benefits. In this study, the mid-infrared Fourier transform spectroscopy (FT-MIR) technique coupled with chemometrics was used to identify and quantify adulterants in coffee (Coffea arabica L.). Coffee samples were adulterated with corn, barley, soy, oat, rice and coffee husks, in proportions ranging from 1-30%. A discrimination model was developed using the soft independent modeling of class analogy (SIMCA) framework, and quantitative models were developed using such algorithms as the partial least squares algorithms with one variable (PLS1) and multiple variables (PLS2) and principal component regression (PCR). The SIMCA model exhibited an accuracy of 100% and could discriminate among all the classes. The quantitative model with the highest performance corresponded to the PLS1 algorithm. The model exhibited an R2c: ≥ 0.99, standard error of calibration (SEC) of 0.39-0.82, and standard error of prediction (SEP) of 0.45-0.94. The developed models could identify and quantify the coffee adulterants, and it was considered that the proposed methodology can be applied to identify and quantify the adulterants used in the coffee industry.