Abstract:Several lactic acid bacteria (LAB) isolates from the Lactobacillus genera have been applied in food preservation, partly due to their antimicrobial properties. Their application in the control of human pathogens holds promise provided appropriate strains are scientifically chosen and a suitable mode of delivery is utilized. Urinary tract infection (UTI) is a global problem, affecting mainly diabetic patients and women. Many uropathogens are developing resistance to commonly used antibiotics. There is a need for more research on the ability of LAB to inhibit uropathogens, with a view to apply them in clinical settings, while adhering to strict selection guidelines in the choice of candidate LAB. While several studies have indicated the ability of LAB to elicit inhibitory activities against uropathogens in vitro, more in vivo and clinical trials are essential to validate the efficacy of LAB in the treatment and prevention of UTI. The emerging applications of LAB such as in adjuvant therapy, oral vaccine development, and as purveyors of bioprotective agents, are relevant in infection prevention and amelioration. Therefore, this review explores the potential of LAB isolates and their bacteriocins to control uropathogens, with a view to limit clinical use of antibiotics.Keywords: bacteriocins; lactic acid bacteria; uropathogens
Although Kraken-GB does have higher sensitivity than Kraken, it sometimes makes surprising errors, which we discovered were caused by contaminant and adapter sequences in the contigs of some draft genomes. These contaminant sequences come from other bacteria, viruses or even human genomes, and they result in incorrectly labelled k-mers in the database. We attempted to remove these from Kraken-GB (Materials and methods), but some contaminants may still slip through any filters. Thus for now, the default version of Kraken uses only complete RefSeq genomes.
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We used Kraken to classify reads from three saliva samples collected as part of the Human Microbiome Project. Because these samples were obtained from humans, we created a Kraken database containing bacterial, viral and human genomes to classify these reads. Combining the three samples together, we report the taxonomic distribution of the classified reads (Figure 4). An analysis of the classified reads from the combined samples reveals that a majority of those reads were classified into one of three genera: Streptococcus (30%), Haemophilus (17%) and Prevotella (13%). Streptococcus mitis[13], Haemophilus parainfluenzae[14] and Prevotella melaninogenica[15], the most abundant species (by read count) of each of these three genera, are all known to be associated with human saliva. We also performed the classification on each sample separately (Additional file 1: Figures S1,S2,S3).
The HiSeq and MiSeq metagenomes were built using 20 sets of bacterial whole-genome shotgun reads. These reads were found either as part of the GAGE-B project [21] or in the NCBI Sequence Read Archive. Each metagenome contains sequences from ten genomes (Additional file 1: Table S1). For both the 10,000 and 10 million read samples of each of these metagenomes, 10% of their sequences were selected from each of the ten component genome data sets (i.e., each genome had equal sequence abundance). All sequences were trimmed to remove low quality bases and adapter sequences.
The simBA-5 metagenome was created by simulating reads from the set of complete bacterial and archaeal genomes in RefSeq. Replicons from those genomes were used if they were associated with a taxon that had an entry associated with the genus rank, resulting in a set of replicons from 607 genera. We then used the Mason read simulator [22] with its Illumina model to produce 10 million 100-bp reads from these genomes. First we created simulated genomes for each species, using a SNP rate of 0.1% and an indel rate of 0.1% (both default parameters), from which we generated the reads. For the simulated reads, we multiplied the default mismatch and indel rates by five, resulting in an average mismatch rate of 2% (ranging from 1% at the beginning of reads to 6% at the ends) and an indel rate of 1% (0.5% insertion probability and 0.5% deletion probability). For the simBA-5 metagenome, the 10,000 read set was generated from a random sample of the 10 million read set.
We classified the Human Microbiome Project data using a Kraken database made from complete RefSeq bacterial, archaeal and viral genomes, along with the GRCh37 human genome. We retrieved the sequences of three accessions (SRS019120, SRS014468 and SRS015055) from the NCBI Sequence Read Archive, and each accession had two runs submitted. All reads were trimmed to remove low quality bases and adapter sequences. Krona [24] was used to generate all taxonomic distribution plots.
Antimicrobial agents can be divided into groups based on the mechanism of antimicrobial activity. The main groups are: agents that inhibit cell wall synthesis, depolarize the cell membrane, inhibit protein synthesis, inhibit nuclei acid synthesis, and inhibit metabolic pathways in bacteria. Table 1 gives examples of drugs from each of these groups. It would seem that with such a wide range of mechanisms we would have better control over the organisms. Unfortunately, improper stewardship of antimicrobial agents has helped lead to the tremendous resistance issue that we now face. Factors that have contributed to the growing resistance problem include: increased consumption of antimicrobial drugs, both by humans and animals; and improper prescribing of antimicrobial therapy. Overuse of many common antimicrobials agents by physicians may occur because the choice of drug is based on a combination of low cost and low toxicity [3]. There may also be improper prescribing of antimicrobials drugs, such as the initial prescription of a broad-spectrum drug that is unnecessary, or ultimately found to be ineffective for the organism(s) causing the infection [4]. The danger is that excessive use of antibiotics in humans leads to emergence of resistant organisms [5],[6]. In addition, prior use of antimicrobial drugs puts a patient at risk for infection with a drug resistant organism, and those patients with the highest exposure to antimicrobials are most often those who are infected with resistant bacteria [3],[7].
Bacteria that lack a cell wall, such as Mycoplasma and related species, are therefore intrinsically resistant to all drugs that target the cell wall including β-lactams and glycopeptides [31]. Gram positive bacteria do not possess an outer membrane, and restricting drug access is not as prevalent. In the enterococci, the fact that polar molecules have difficulty penetrating the cell wall gives intrinsic resistance to aminoglycosides. Another gram positive bacteria, Staphylococcus aureus, recently has developed resistance to vancomycin. Of the two mechanisms that S. aureus uses against vancomycin, a yet unexplained mechanism allows the bacteria to produce a thickened cell wall which makes it difficult for the drug to enter the cell, and provides an intermediate resistance to vancomycin. These strains are designated as VISA strains [30],[32].
In those bacteria with large outer membranes, substances often enter the cell through porin channels. The porin channels in gram negative bacteria generally allow access to hydrophilic molecules [28],[33]. There are two main ways in which porin changes can limit drug uptake: a decrease in the number of porins present, and mutations that change the selectivity of the porin channel [29]. Members of the Enterobacteriaceae are known to become resistant due to reducing the number of porins (and sometime stopping production entirely of certain porins). As a group, these bacteria reduce porin number as a mechanism for resistance to carbapenems [34],[35]. Mutations that cause changes within the porin channel have been seen in E. aerogenes which then become resistant to imipenem and certain cephalosporins, and in Neisseria gonorrhoeae which then become resistant to β-lactams and tetracycline [33],[36].
The β-lactamases (originally called penicillinases and cephalosporinases) inactivate β-lactam drugs by hydrolyzing a specific site in the β-lactam ring structure, causing the ring to open. The open-ring drugs are not able to bind to their target PBP proteins. The known β-lactamases are wide-spread, and the group contains enzymes that are able to inactivate any of the current β-lactam drugs. The production of β-lactamases is the most common resistance mechanism used by gram negative bacteria against β-lactam drugs, and the most important resistance mechanism against penicillin and cephalosporin drugs [45],[58].
These enzymes may be innately found on the bacterial chromosome or may be acquired via a plasmid. Many members of the Enterobacteriaceae family of gram negative bacteria possess chromosomal β-lactamase genes. Other gram negative bacteria that possess these include Aeromonas spp., Acinetobacter spp., and Pseudomonas spp. Plasmid-carried β-lactamase genes are most commonly found in the Enterobacteriaceae, but may also be found in some species of gram positive bacteria such as Staphylococcus aureus, Enterococcus faecalis, and Enterococcus faecium[26],[59].
Most bacteria possess many different types of efflux pumps. There are five main families of efflux pumps in bacteria classified based on structure and energy source: the ATP-binding cassette (ABC) family, the multidrug and toxic compound extrusion (MATE) family, the small multidrug resistance (SMR) family, the major facilitator superfamily (MFS), and the resistance-nodulation-cell division (RND) family. Most of these efflux pump families are single-component pumps which transport substrates across the cytoplasmic membrane. The RND family are multi-component pumps (found almost exclusively in gram negative bacteria) that function in association with a periplasmic membrane fusion protein (MFP) and an outer membrane protein (OMP-porin) to efflux substrate across the entire cell envelope [28],[29],[73],[74]. There are instances where other efflux family members act with other cellular components as multicomponent pumps in gram negative bacteria. One member of the ABC family, MacB, works as a tripartite pump (MacAB-TolC) to extrude macrolide drugs. A member of the MFS, EmrB, works as a tripartite pump (EmrAB-TolC) to extrude nalidixic acid in E. coli[75],[76]. Figure 3 shows the basic structure of the various efflux pump families. 2ff7e9595c
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