Genomic islands as a marker to differentiate between clinical and environmental Burkholderia pseudomallei

Burkholderia pseudomallei-absent soil bacterial community results in secondary metabolites that kill this pathogen

Burkholderia pseudomallei is a Gram-negative bacterium found in soil and the causative agent of a severe disease in humans and animals known as melioidosis. It is intrinsically resistant to many antibiotics and has been reported resistant to the drugs of choice; ceftazidime. Microbial communities in soil in the presence and absence of B. pseudomallei were investigated using metagenomics approach. The variation in bacterial species diversity was significantly higher in soil samples without B. pseudomallei. Abundances of phyla Actinobacteria and Firmicutes were found significantly higher in B. pseudomallei-negative soils. Bacillus amyloliquefaciens KKU1 in phylum Firmicutes was discovered from negative soil and its secondary metabolites could inhibit clinical, environmental and drug resistant isolates of B. pseudomallei, together with some pathogenic Gram-negative but not Gram-positive bacteria. The antimicrobial activity from KKU 1 against B. pseudomallei was abolished when treated with proteinase K, stable in a wide range of pH and remained active after heating at 100 °C for 15 min. Precipitated proteins from KKU1 were demonstrated to cause lysis and corrugated surfaces of B. pseudomallei. The minimum inhibitory concentrations and minimum bactericidal concentrations of the precipitated proteins from KKU1 against B. pseudomallei were 0.97 μg/ml and 3.9 μg/ml. Interestingly, Native SDS-PAGE showed small active compounds of less than 6 kDa, along with other information collectively suggesting the properties of antimicrobial peptides. For the first time, culture-independent information in melioidosis endemic area could lead to a suspected source of metabolites that may help defense against B. pseudomallei and other pathogenic Gram-negative bacteria.

Virulence factor activity relationships (VFARs): a bioinformatics perspective

Virulence factor activity relationships (VFARs) - a concept loosely based on quantitative structure-activity relationships (QSARs) for chemicals was proposed as a predictive tool for ranking risks due to microorganisms relevant to water safety. A rapid increase in sequencing capabilities and bioinformatics tools has significantly increased the potential for VFAR-based analyses. This review summarizes more than 20 bioinformatics databases and tools, developed over the last decade, along with their virulence and antimicrobial resistance prediction capabilities. With the number of bacterial whole genome sequences exceeding 241 000 and metagenomic analysis projects exceeding 13 000 and the ability to add additional genome sequences for few hundred dollars, it is evident that further development of VFARs is not limited by the availability of information at least at the genomic level. However, additional information related to co-occurrence, treatment response, modulation of virulence due to environmental and other factors, and economic impact must be gathered and incorporated in a manner that also addresses the associated uncertainties. Of the bioinformatics tools, a majority are either designed exclusively for virulence/resistance determination or equipped with a dedicated module. The remaining have the potential to be employed for evaluating virulence. This review focusing broadly on omics technologies and tools supports the notion that these tools are now sufficiently developed to allow the application of VFAR approaches combined with additional engineering and economic analyses to rank and prioritize organisms important to a given niche. Knowledge gaps do exist but can be filled with focused experimental and theoretical analyses that were unimaginable a decade ago. Further developments should consider the integration of the measurement of activity, risk, and uncertainty to improve the current capabilities.

A hypervariable genomic island identified in clinical and environmental Mycobacterium avium subsp. hominissuis isolates from Germany

Mycobacterium avium subsp. hominissuis (MAH) is an opportunistic human pathogen widespread in the environment. Genomic islands (GI)s represent a part of the accessory genome of bacteria and influence virulence, drug-resistance or fitness and trigger bacterial evolution. We previously identified a novel GI in four MAH genomes. Here, we further explored this GI in a larger collection of MAH isolates from Germany (n=41), including 20 clinical and 21 environmental isolates. Based on comparative whole genome analysis, we detected this GI in 39/41 (95.1%) isolates. Although all these GIs integrated in the same insertion hotspot, there is high variability in the genetic structure of this GI: eight different types of GI have been identified, designated A-H (sized 6.2-73.3kb). These GIs were arranged as single GI (23/41, 56.1%), combination of two different GIs (14/41, 34.1%) or combination of three different GIs (2/41, 4.9%) in the insertion hotspot. Moreover, two GI types shared more than 80% sequence identity with sequences of M. canettii, responsible for Tuberculosis. A total of 253 different genes were identified in all GIs, among which the previously documented virulence-related genes mmpL10 and mce. The diversity of the GI and the sequence similarity with other mycobacteria suggests cross-species transfer, involving also highly pathogenic species. Shuffling of potential virulence genes such as mmpL10 via this GI may create new pathogens that can cause future outbreaks.

Utilization of Whole-Cell MALDI-TOF Mass Spectrometry to Differentiate Burkholderia pseudomallei Wild-Type and Constructed Mutants

Whole-cell matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (whole-cell MALDI-TOF MS) has been widely adopted as a useful technology in the identification and typing of microorganisms. This study employed the whole-cell MALDI-TOF MS to identify and differentiate wild-type and mutants containing constructed single gene mutations of Burkholderia pseudomallei, a pathogenic bacterium causing melioidosis disease in both humans and animals. Candidate biomarkers for the B. pseudomallei mutants, including rpoS, ppk, and bpsI isolates, were determined. Taxon-specific and clinical isolate-specific biomarkers of B. pseudomallei were consistently found and conserved across all average mass spectra. Cluster analysis of MALDI spectra of all isolates exhibited separate distribution. A total of twelve potential mass peaks discriminating between wild-type and mutant isolates were identified using ClinProTools analysis. Two peaks (m/z 2721 and 2748 Da) were specific for the rpoS isolate, three (m/z 3150, 3378, and 7994 Da) for ppk, and seven (m/z 3420, 3520, 3587, 3688, 4623, 4708, and 5450 Da) for bpsI. Our findings demonstrated that the rapid, accurate, and reproducible mass profiling technology could have new implications in laboratory-based rapid differentiation of extensive libraries of genetically altered bacteria.

Characterization of BPSS1521 (bprD), a regulator of Burkholderia pseudomallei virulence gene expression in the mouse model

The Gram-negative saprophytic bacterium Burkholderia pseudomallei is the causative agent of melioidosis, a severe infectious disease of both humans and animals. Severity of the disease is thought to be dependent on both the health status of the host, including diabetes mellitus and kidney disease, and bacterial-derived factors. To identify the bacterial factors important during an acute infection, gene expression profiles in the spleen, lung, and liver of BALB/c (Th2 prototype) and C57BL/6 mice (Th1 prototype) were determined using DNA microarrays. This analysis identified BPSS1521 (bprD), a predicted transcriptional regulator located in the type III secretion system (T3SS-3) operon, to be up regulated, specifically in C57BL/6 mice. BALB/c mice infected with a bprD mutant showed a shorter time to death and increased inflammation, as determined by histopathological analysis and enumeration of bacteria in the spleen. Elevated numbers of multinucleated giant cells (MNGCs), which is the hallmark of melioidosis, were detected in both the wild-type and the bprD mutants; a similar elevation occurs in melioidosis patients. One striking observation was the increased expression of BPSS1520 (bprC), located downstream of bprD, in the bprD mutant. BprC is a regulator of T6SS-1 that is required for the virulence of B. pseudomallei in murine infection models. Deletion of bprD led to the overexpression of bprC and a decreased time to death. bprD expression was elevated in C57BL/6--as compared to BALB/c--mice, suggesting a role for BprD in the natural resistance of C57BL/6 mice to B. pseudomallei. Ultimately, this analysis using mice with different immune backgrounds may enhance our understanding of the outcomes of infection in a variety of models.

Source-identifying biomarker ions between environmental and clinical Burkholderia pseudomallei using whole-cell matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)

Burkholderia pseudomallei is the causative agent of melioidosis, which is an endemic disease in Northeast Thailand and Northern Australia. Environmental reservoirs, including wet soils and muddy water, serve as the major sources for contributing bacterial infection to both humans and animals. The whole-cell matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (whole-cell MALDI-TOF MS) has recently been applied as a rapid, accurate, and high-throughput tool for clinical diagnosis and microbiological research. In this present study, we employed a whole-cell MALDI-TOF MS approach for assessing its potency in clustering a total of 11 different B. pseudomallei isolates (consisting of 5 environmental and 6 clinical isolates) with respect to their origins and to further investigate the source-identifying biomarker ions belonging to each bacterial group. The cluster analysis demonstrated that six out of eleven isolates were grouped correctly to their sources. Our results revealed a total of ten source-identifying biomarker ions, which exhibited statistically significant differences in peak intensity between average environmental and clinical mass spectra using ClinProTools software. Six out of ten mass ions were assigned as environmental-identifying biomarker ions (EIBIs), including, m/z 4,056, 4,214, 5,814, 7,545, 7,895, and 8,112, whereas the remaining four mass ions were defined as clinical-identifying biomarker ions (CIBIs) consisting of m/z 3,658, 6,322, 7,035, and 7,984. Hence, our findings represented, for the first time, the source-specific biomarkers of environmental and clinical B. pseudomallei.

Mining locus tags in PubMed Central to improve microbial gene annotation

BACKGROUND

The scientific literature contains millions of microbial gene identifiers within the full text and tables, but these annotations rarely get incorporated into public sequence databases. We propose to utilize the Open Access (OA) subset of PubMed Central (PMC) as a gene annotation database and have developed an R package called pmcXML to automatically mine and extract locus tags from full text, tables and supplements.

RESULTS

We mined locus tags from 1835 OA publications in ten microbial genomes and extracted tags mentioned in 30,891 sentences in main text and 20,489 rows in tables. We identified locus tag pairs marking the start and end of a region such as an operon or genomic island and expanded these ranges to add another 13,043 tags. We also searched for locus tags in supplementary tables and publications outside the OA subset in Burkholderia pseudomallei K96243 for comparison. There were 168 publications containing 48,470 locus tags and 83% of mentions were from supplementary materials and 9% from publications outside the OA subset.

CONCLUSIONS

B. pseudomallei locus tags within the full text and tables of OA publications represent only a small fraction of the total mentions in the literature. For microbial genomes with very few functionally characterized proteins, the locus tags mentioned in supplementary tables and within ranges like genomic islands contain the majority of locus tags. Significantly, the functions in the R package provide access to additional resources in the OA subset that are not currently indexed or returned by searching PMC.