An extensively validated whole-cell biosensor for specific, sensitive and high-throughput detection of antibacterial inhibitors targeting cell-wall biosynthesis.

BACKGROUND: Whole-cell biosensor strains are powerful tools for antibacterial drug discovery, in principle allowing the identification of inhibitors acting on specific, high-value target pathways. Whilst a variety of biosensors have been described for detecting cell-wall biosynthesis inhibitors (CWBIs), these strains typically lack specificity and/or sensitivity, and have for the most part not been rigorously evaluated as primary screening tools. Here, we describe several Staphylococcus aureus CWBI biosensors and show that specific and sensitive biosensor-based discovery of CWBIs is achievable. METHODS: Biosensors comprised lacZ reporter fusions with S. aureus promoters (PgltB, PilvD, PmurZ, PoppB, PORF2768, PsgtB) that are subject to up-regulation following inhibition of cell-wall biosynthesis. Induction of biosensors was detected by measuring expression of ß-galactosidase using fluorogenic or luminogenic substrates. RESULTS: Three of the six biosensors tested (those based on PgltB, PmurZ, PsgtB) exhibited apparently specific induction of ß-galactosidase expression in the presence of CWBIs. Further validation of one of these (PmurZ) using an extensive array of positive and negative control compounds and conditional mutants established that it responded appropriately and uniquely to inhibition of cell-wall biosynthesis. Using this biosensor, we established, validated and deployed a high-throughput assay that identified a potentially novel CWBI from a screen of >9000 natural product extracts. CONCLUSIONS: Our extensively validated PmurZ biosensor strain offers specific and sensitive detection of CWBIs, and is well-suited for high-throughput screening; it therefore represents a valuable tool for antibacterial drug discovery.

A platform for detecting cross-resistance in antibacterial drug discovery.

BACKGROUND: To address the growing antibiotic resistance problem, new antibacterial drugs must exert activity against pathogens resistant to agents already in use. With a view to providing a rapid means for deselecting antibacterial drug candidates that fail to meet this requirement, we report here the generation and application of a platform for detecting cross-resistance between established and novel antibacterial agents. METHODS: This first iteration of the cross-resistance platform (CRP) consists of 28 strains of defined resistance genotype, established in a uniform genetic background (the SH1000 strain of the clinically significant pathogen Staphylococcus aureus). Most CRP members were engineered through introduction of constitutively expressed resistance determinants on a low copy-number plasmid, with a smaller number selected as spontaneous resistant mutants. RESULTS: Members of the CRP collectively exhibit resistance to many of the major classes of antibacterial agent in use. We employed the CRP to test two antibiotics that have been proposed in the literature as potential drug candidates: γ-actinorhodin and batumin. No cross-resistance was detected for γ-actinorhodin, whilst a CRP member resistant to triclosan exhibited a 32-fold reduction in susceptibility to batumin. Thus, a resistance phenotype that already exists in clinical strains mediates profound resistance to batumin, implying that this compound is not a promising antibacterial drug candidate. CONCLUSIONS: By detecting cross-resistance between established and novel antibacterial agents, the CRP offers the ability to deselect compounds whose activity is substantially impaired by existing resistance mechanisms. The CRP therefore represents a useful addition to the antibacterial drug discovery toolbox.

Library-independent source tracking of fecal contamination in selected stations and tributaries of Laguna Lake, Philippines.

Laguna Lake is the largest inland freshwater body in the Philippines. Although it is classified to be usable for agricultural and recreational purposes by the country's Department of Environment and Natural Resources (DENR), studies looking at lake ecology revealed severe fecal contamination which contributes to the deterioration of water quality. Determining the sources of fecal contamination is necessary for lake protection and management. This study utilized a library-independent method of microbial source tracking (LIM-MST) to identify sources of fecal contamination in selected Laguna Lake stations and tributaries. Genetic markers of the host-associated Escherichia coli, heat-labile toxin (LTIIA) and heat-stable II (STII), were used to identify cattle and swine fecal contaminations, respectively. Meanwhile, human mitochondrial DNA (mtDNA) was used to identify human fecal contamination. Results identified the presence of agricultural and human fecal contamination in Laguna Lake Stations 1 and 5, Mangangate River, and Alabang River. The selected sites are known to be surrounded by residential and industrial complexes, and most of their discharges find their way into the lake. The identification of the specific sources of fecal contamination will guide management practices that aim to regulate the discharges in order to improve the water quality of Laguna Lake.

Selecting rep-PCR markers to source track fecal contamination in Laguna Lake, Philippines.

Fecal contamination is one of the factors causing deterioration of Laguna Lake. Although total coliform levels are constantly monitored, no protocol is in place to identify their origin. This can be addressed using the library-dependent microbial source tracking (MST) method, repetitive element sequence-based polymerase chain reaction (rep-PCR) fingerprinting. Serving as a prerequisite in developing the host-origin library, we assessed the discriminatory power of three fingerprinting primers, namely BOX-A1R, (GTG)5, and REP1R-1/2-1. Fingerprint profiles were obtained from 290 thermotolerant Escherichia coli isolated from sewage waters and fecal samples of cows, chickens, and pigs from regions surrounding the lake. Band patterns were converted into binary profiles and were classified using the discriminant analysis of principal components. Results show that: (1) REP1R-1/2-1 has a low genotyping success rate and information content; (2) increasing the library size led to more precise estimates of library accuracy; and (3) combining fingerprint profiles from BOX-A1R and (GTG)5 revealed the best discrimination (average rate of correct classification (ARCC) = 0.82 ± 0.06) in a two-way categorical split; while (4) no significant difference was found between the combined profiles (0.74 ± 0.15) and using solely BOX-A1R (0.76 ± 0.09) in a four-way split. Testing the library by identifying known isolates from a separate dataset has shown that a two-way classification performed better (ARCC = 0.66) than a four-way split (ARCC = 0.29). The library can be developed further by adding more representative isolates per host source. Nevertheless, our results have shown that combining profiles from BOX-A1R and (GTG)5 is recommended in developing the MST library for Laguna Lake.