The aim of the present study was to characterize human milk microbiota (HMM) with 16S rRNA gene amplicon next-generation sequencing and cultivation/matrix-assisted laser desorption/ionization (MALDI)-time of flight (TOF) mass spectrometry (MS) identification approaches. We analyzed 31 human milk samples from healthy Slovenian mothers. To check the accuracy of MALDI-TOF MS identification, several colonies representing most abundant genera and those, which could not be reliably identified by MALDI-TOF, were subjected to Sanger sequencing of their 16S rRNA gene. We showed that cultivation/MALDI-TOF MS was a suitable tool for culture-dependent determination of HMM. With both approaches, Staphylococcus and Streptococcus were found as predominant genera in HMM and the abundance of Staphylococcus was associated with decreased microbial diversity. In addition, we characterized factors that might influence HMM. The use of a breast pump was significantly associated with composition of HMM, lower microbial load, and higher abundance of cultivable staphylococci. Moreover, our study suggests that administration of probiotics to the suckling infant might influence HMM by increased abundance of lactobacilli and the presence of viable probiotic bacteria in human milk. However, since our study was observational with relatively small sample size, more targeted studies are needed to study possible transfer of probiotics to the mammary gland via an external route and the physiological relevance of these events.
Microbial communities inhabiting the breast milk microenvironment are essential in supporting mammary gland health in lactating women and in providing gut-colonizing bacterial 'inoculum' for their infants’ gastro-intestinal development. Bacterial DNA was extracted from colostrum samples of 45 healthy Slovenian mothers. Characteristics of the communities in the samples were assessed by polymerase chain reaction (PCR) coupled with denaturing gradient gel electrophoresis (DGGE) and by quantitative real-time PCR (qPCR). PCR screening for the prevalence of bacteriocin genes was performed on DNA of culturable and total colostrum bacteria. DGGE profiling revealed the presence of Staphylococcus and Gemella in most of the samples and exposed 4 clusters based on the abundance of 3 bands: Staphylococcus epidermidis/Gemella, Streptococcus oralis/pneumonia and Streptococcus salivarius. Bacilli represented the largest proportion of the communities. High prevalence in samples at relatively low quantities was confirmed by qPCR for enterobacteria (100%), Clostridia (95.6%), Bacteroides-Prevotella group (62.2%) and bifidobacteria (53.3%). Bacterial quantities (genome equivalents ml-1) varied greatly among the samples; Staphylococcus epidermidis and staphylococci varied in the range of 4 logs, streptococci and all bacteria varied in the range of 2 logs, and other researched groups varied in the range of 1 log. The quantity of most bacterial groups was correlated with the amount of all bacteria. The majority of the genus Staphylococcus was represented by the species Staphylococcus epidermidis (on average 61%), and their abundances were linearly correlated. Determinants of salivaricin A, salivaricin B, streptin and cytolysin were found in single samples. This work provides knowledge on the colostrum microbial community composition of healthy lactating Slovenian mothers and reports bacteriocin gene prevalence.
The milk and mammary gland (MG) microbiome can be influenced by several factors, such as mode of delivery, breastfeeding, maternal lifestyle, health status, and diet. An increasing number of studies show a variety of positive effects of consumption of probiotics during pregnancy and breastfeeding on the mother and the newborn. The aim of this study was to investigate the effect of oral administration of probiotics Lactobacillus gasseri K7 (LK7) and Lactobacillus rhamnosus GG (LGG) during pregnancy and lactation on microbiota of the mouse mesenteric lymph nodes (MLN), MG, and milk. Pregnant FVB/N mice were fed skim milk or probiotics LGG or LK7 resuspended in skim milk during gestation and lactation. On d 3 and 8 postpartum, blood, feces, MLN, MG, and milk were analyzed for the presence of LGG or LK7. The effects of probiotics on MLN, MG, and milk microbiota was evaluated by real-time PCR and by 16S ribosomal DNA 454-pyrosequencing. In 5 of 8 fecal samples from the LGG group and in 5 of 8 fecal samples from the LK7 group, more than 1 × 10(3) of live LGG or LK7 bacterial cells were detected, respectively, whereas no viable LGG or LK7 cells were detected in the control group. Live lactic acid bacteria but no LGG or LK7 were detected in blood, MLN, and MG. Both probiotics significantly increased the total bacterial load as assessed by copies of 16S ribosomal DNA in MLN, and a similar trend was observed in MG. Metagenomic sequencing revealed that both probiotics increased the abundance of Firmicutes in MG, especially the abundance of lactic acid bacteria. The Lactobacillus genus appeared exclusively in MG from probiotic groups. Both probiotics influenced MLN microbiota by decreasing diversity (Chao1) and increasing the distribution of species (Shannon index). The LGG probiotic also affected the MG microbiota as it increased diversity and distribution of species and proportions of the genera Lactobacillus and Bifidobacterium. These results provide evidence that probiotics can modulate the bacterial composition of MLN and MG microbiota in ways that could improve the health of the MG and, ultimately, the health of the newborn.
The underlying mechanisms of probiotics and postbiotics are not well understood, but it is known that both affect the adaptive and innate immune responses. In addition, there is a growing concept that some probiotic strains have common core mechanisms that provide certain health benefits. Here, we aimed to elucidate the signalization of the probiotic bacterial strains Lactobacillus paragasseri K7, Limosilactobacillus fermentum L930BB, Bifidobacterium animalis subsp. animalis IM386 and Lactiplantibacillus plantarum WCFS1. We showed in in vitro experiments that the tested probiotics exhibit common TLR2-and TLR10-dependent downstream signalling cascades involving inhibition of NF-κB signal transduction. Under inflammatory conditions, the probiotics activated phosphatidylinositol 3-kinase (PI3K)/Akt antiapoptotic pathways and protein kinase C (PKC)-dependent pathways, which led to regulation of the actin cytoskeleton and tight junctions. These pathways contribute to the regeneration of the intestinal epithelium and modulation of the mucosal immune system, which, together with the inhibition of canonical TLR signalling, promote general immune tolerance. With this study we identified shared probiotic mechanisms and were the first to pinpoint the role of anti-inflammatory probiotic signalling through TLR10.
Lactobacillus gasseri K7 is a probiotic strain that produces bacteriocins gassericin K7 A and K7 B. In order to develop a real-time quantitative PCR assay for the detection of L. gasseri K7, 18 reference strains of the Lactobacillus acidophilus group and 45 faecal samples of adults who have never consumed strain K7 were tested with PCR using 14 pairs of primers specific for gassericin K7 A and K7 B gene determinants. Incomplete gassericin K7 A or K7 B gene clusters were found to be dispersed in different lactobacilli strains as well as in faecal microbiota. One pair of primers was found to be specific for the total gene cluster of gassericin K7A and one for gassericin K7B. The real-time PCR analysis of faecal samples spiked with K7 strain revealed that primers specific for the gene cluster of the gassericin K7 A were more suitable for quantitative determination than those for gassericin K7 B, due to the lower detection level. Targeting of the gassericin K7 A or K7 B gene cluster with specific primers could be used for detection and quantification of L. gasseri K7 in human faecal samples without prior cultivation. The results of this study also present new insights into the prevalence of bacteriocin-encoding genes in gastrointestinal tract.
Since health benefits conferred by probiotics are strain-specific, identification to the strain level is mandatory to allow the monitoring of the presence and the abundance of specific probiotic in a product or in a gastrointestinal tract. Compared to standard plate counts, the reduced duration of the assays and higher specificity makes PCR-based methods (standard PCR and quantitative PCR) very appropriate for detection or quantification of probiotics. Development of strain-specific assay consists of 4 main stages: (1) strain-specific marker identification; (2) construction of potential strain-specific primers; (3) validation on DNA from pure cultures of target and related strains; and (4) validation on spiked samples. The most important and also the most challenging step is the identification of strain-specific sequences, which can be subsequently targeted by specific primers or probes. Such regions can be identified on sequences derived from 16S-23S internally transcribed spacers, randomly amplified polymorphic DNA, representational difference analysis and suppression subtractive hybridisation. Already known phenotypic or genotypic characteristics of the target strain can also be used to develop the strain-specific assay. However, the initial stage of strain-specific assay development can be replaced by comparative genomics analysis of target genome with related genomes in public databases. Advances in whole genome sequencing (WGS) have resulted in a cost reduction for bacterial genome sequencing and consequently have made this approach available to most laboratories. In the present paper I reviewed the available literature on PCR and qPCR assays developed for detection of a specific probiotic strain and discussed future WGS and comparative genomics-based approaches.
Lactobacillus gasseri K7 is an isolate from infant feces and has in vitro and in vivo established probiotic properties. Here, we report the improved version of the draft genome sequence, which comprises 8 scaffolds (13 contigs), a total length of 1.99 Mb, and 1,841 predicted protein-coding sequences.
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