Novel Anti-Campylobacter Compounds Identified Using High Throughput Screening of a Pre-selected Enriched Small Molecules Library

Campylobacter is a leading cause of foodborne bacterial gastroenteritis worldwide and infections can be fatal. The emergence of antibiotic-resistant Campylobacter spp. necessitates the development of new antimicrobials. We identified novel anti-Campylobacter small molecule inhibitors using a high throughput growth inhibition assay. To expedite screening, we made use of a “bioactive” library of 4,182 compounds that we have previously shown to be active against diverse microbes. Screening for growth inhibition of Campylobacter jejuni, identified 781 compounds that were either bactericidal or bacteriostatic at a concentration of 200 µM. Seventy nine of the bactericidal compounds were prioritized for secondary screening based on their physico-chemical properties. Based on the minimum inhibitory concentration against a diverse range of C. jejuni and a lack of effect on gut microbes, we selected 12 compounds. No resistance was observed to any of these 12 lead compounds when C. jejuni was cultured with lethal or sub-lethal concentrations suggesting that C. jejuni is less likely to develop resistance to these compounds. Top 12 compounds also possessed low cytotoxicity to human intestinal epithelial cells (Caco-2 cells) and no hemolytic activity against sheep red blood cells. Next, these 12 compounds were evaluated for ability to clear C. jejuni in vitro. A total of 10 compounds had an anti-C. jejuni effect in Caco-2 cells with some effective even at 25 µM concentrations. These novel 12 compounds belong to five established antimicrobial chemical classes; piperazines, aryl amines, piperidines, sulfonamide and pyridazinone. Exploitation of analogues of these chemical classes may provide Campylobacter specific drugs that can be applied in both human and animal medicine.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials. © 2015, The Author(s).

Real-Time PCR Assays for the Detection of Puccinia psidii

Puccinia psidii (Myrtle rust) is an emerging pathogen that has a wide host range in the Myrtaceae family; it continues to show an increase in geographic range and is considered to be a significant threat to Myrtaceae plants worldwide. In this study, we describe the development and validation of three novel real-time polymerase reaction (qPCR) assays using ribosomal DNA and β-tubulin gene sequences to detect P. psidii. All qPCR assays were able to detect P. psidii DNA extracted from urediniospores and from infected plants, including asymptomatic leaf tissues. Depending on the gene target, qPCR was able to detect down to 0.011 pg of P. psidii DNA. The most optimum qPCR assay was shown to be highly specific, repeatable, and reproducible following testing using different qPCR reagents and real-time PCR platforms in different laboratories. In addition, a duplex qPCR assay was developed to allow coamplification of the cytochrome oxidase gene from host plants for use as an internal PCR control. The most optimum qPCR assay proved to be faster and more sensitive than the previously published nested PCR assay and will be particularly useful for high-throughput testing and to detect P. psidii at the early stages of infection, before the development of sporulating rust pustules.

Seasonal and Species Variation of the Hepatotoxin Indospicine in Australian Indigofera Legumes as Measured by UPLC−MS/MS

Livestock industries have maintained a keen interest in pasture legumes because of the high protein content and nutritive value. Leguminous Indigofera plant species have been considered as having high feeding values to be utilized as pasture, but the occurrence of the toxic constituent indospicine in some species has restricted this utility. Indospicine has caused both primary and secondary hepatotoxicosis and also reproductive losses, but has only previously been determined in a small number of Indigofera species. This paper validates a high throughput ultra-performance liquid chromatography−tandem mass spectrometry (UPLC−MS/MS) method to determine indospicine content of various Indigofera species found in Australian pasture. Twelve species of Indigofera together with Indigastrum parviflorum plants were collected and analysed. Out of the 84 samples analyzed, *I. spicata contained the highest indospicine level (1003 ± 328 mg/kg DM, n = 4) followed by I. linnaei (755 ± 490 mg/kg DM, n = 51). Indospicine was not detected in 9 of the remaining 11 species, and at only low levels (<10 mg/kg DM) in 2 out of 8 I. colutea specimens and in 1 out of 5 I. linifolia specimens. Indospicine concentrations were below quantitation levels for other Indigofera spp. (I. adesmiifolia, I. georgei, I. hirsuta, I. leucotricha,* I. oblongifolia, I. australis and I. trita) and Indigastrum parviflorum. One of the more significant findings to emerge from this study is that the indospicine content of I. linnaei is highly variable (159 to 2128 mg/kg DM, n = 51), and differs across both regions and seasons. Its first re-growth after spring rain has a higher (p < 0.01) indospicine content than growth following more substantial summer rain. The species collected include the predominant Indigofera in Australia pasture, and of these, only *I. spicata and I. linnaei contain high enough levels of indospicine to pose a potential toxic threat to grazing herbivores.

A model of macadamia with application to pruning in orchards

A self-organising model of macadamia, expressed using L-Systems, was used to explore aspects of canopy management. A small set of parameters control the basic architecture of the model, with a high degree of self-organisation occurring to determine the fate and growth of buds. Light was sensed at the leaf level and used to represent vigour and accumulated basipetally. Buds also sensed light so as to provide demand in the subsequent redistribution of the vigour. Empirical relationships were derived from a set of 24 completely digitised trees after conversion to multiscale tree graphs (MTG) and analysis with the OpenAlea software library. The ability to write MTG files was embedded within the model so that various tree statistics could be exported for each run of the model. To explore the parameter space a series of runs was completed using a high-throughput computing platform. When combined with MTG generation and analysis with OpenAlea it provided a convenient way in which thousands of simulations could be explored. We allowed the model trees to develop using self-organisation and simulated cultural practices such as hedging, topping, removal of the leader and limb removal within a small representation of an orchard. The model provides insight into the impact of these practices on potential for growth and the light distribution within the canopy and to the orchard floor by coupling the model with a path-tracing program to simulate the light environment. The lessons learnt from this will be applied to other evergreen, tropical fruit and nut trees.