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Bacteria are highly diverse organisms that are able to adapt to a broad range of environments and hosts due to their high genomic plasticity. Horizontal gene transfer plays a pivotal role in this genome plasticity and in evolution by leaps through the incorporation of large blocks of genome sequences, ordinarily known as genomic islands (GEIs). GEIs may harbor genes encoding virulence, metabolism, antibiotic resistance and symbiosis-related functions, namely pathogenicity islands (PAIs), metabolic islands (MIs), resistance islands (RIs) and symbiotic islands (SIs). Although many software for the prediction of GEIs exist, they only focus on PAI prediction and present other limitations, such as complicated installation and inconvenient user interfaces. Here, we present GIPSy, the genomic island prediction software, a standalone and user-friendly software for the prediction of GEIs, built on our previously developed pathogenicity island prediction software (PIPS). We also present four application cases in which we crosslink data from literature to PAIs, MIs, RIs and SIs predicted by GIPSy. Briefly, GIPSy correctly predicted the following previously described GEIs: 13 PAIs larger than 30kb in Escherichia coli CFT073; 1 MI for Burkholderia pseudomallei K96243, which seems to be a miscellaneous island; 1 RI of Acinetobacter baumannii AYE, named AbaR1; and, 1 SI of Mesorhizobium loti MAFF303099 presenting a mosaic structure. GIPSy is the first life-style-specific genomic island prediction software to perform analyses of PAIs, MIs, RIs and SIs, opening a door for a better understanding of bacterial genome plasticity and the adaptation to new traits.

Significance and Impact of the Study: This study showed that critical genetic events in Streptococcus agalactiae isolates pathogenic for fish have been missed by serotyping and multilocus sequence typing (MLST). A proposed genotyping scheme based on the evaluation of concatenated data from serotyping, MLST, and the presence/absence of virulence genes was created, and this was able to detect old and recent evolutionary events. It provided a better understanding of the genetic diversity of Strep. agalactiae populations from fish and will contribute to future studies of the molecular epidemiology, pathogenesis and evolutionary aspects of this pathogen. AbstractThis study aimed to assess the genetic diversity of fish isolates of Streptococcus agalactiae by capsular serotyping, MLST and the pattern of selected virulence genes. Forty-six isolates from Nile tilapia and Amazon catfish were screened by PCR for the twelve virulence genes. The molecular capsular type and sequence type (ST) were determined. Two capsular types (Ia and Ib) and four STs (103, 260, 552 and 553) were identified. The ST-552 and ST-553 represent new allelic combinations. Variable results were found for the genes gbs2018-6, lmb, hylB and cylE. The combined evaluation of serotype, sequence type and pattern of the presence or absence of cylE and hylB allowed the classification of isolates into nine genetic profiles (I-IX). The proposed scheme showed higher discriminatory power and was able to detect evolutionary events missed by MLST analysis. This study provides new information about the genetic diversity of fish pathogenic Strep. agalactiae, and the proposed scheme was shown to be an improved approach to genotyping these strains.

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Several studies have demonstrated a diversity of bacterial species in human milk, even in aseptically collected samples. The present study evaluated potential probiotic bacteria isolated from human milk and associated maternal variables. Milk samples were collected from 47 healthy women and cultured on selective and universal agar media under aerobic and anaerobic conditions. Bacterial isolates were counted and identified by Biotyper Matrix-Assisted Laser Desorption Ionization-Time of Flight mass spectrometry and then tested for probiotic properties. Total bacteria in human milk ranged from 1.5 to 4.0 log CFU/mL. The higher bacterial counts were found in colostrum (mean = 3.9 log CFU/mL, 95% CI 3.14-4.22, p = 0.00001). The most abundant species was Staphylococcus epidermidis (n = 76). The potential probiotic candidates were Lactobacillus gasseri (n = 4), Bifidobacterium breve (n = 1), and Streptococcus salivarius (n = 4). Despite the small sample size, L. gasseri was isolated only in breast milk from mothers classified into a normal weight range and after a vaginally delivered partum. No potential probiotics showed antagonism against pathogens, but all of them agglutinated different pathogens. Nine bacterial isolates belonging to the species L. gasseri, B. breve, and S. salivarius were selected as potential probiotics. The present study confirms the presence in breast milk of a bacterial microbiota that could be the source of potential probiotic candidates to be used in the formula of simulated maternal milk.