Heuer, C. | bioRxiv Science

bioRxiv2019

Antifungal susceptibility testing of Aspergillus niger on silicon microwells by intensity-based reflectometric interference spectroscopy

The increasing number of invasive fungal infections among immunocompromised patients and the emergence of antifungal resistant pathogens has resulted in the need for rapid and reliable antifungal susceptibility testing (AFST). Accelerating antifungal susceptibility testing allows for advanced treatment decisions and the reduction in future instances of antifungal resistance. In this work, we demonstrate the application of a silicon phase grating as sensor for the detection of growth of Aspergillus niger (A. niger) by intensity-based reflectometric interference spectroscopy and its use as an antifungal susceptibility test. The silicon gratings provide a solid-liquid interface to capture micron-sized Aspergillus conidia within microwell arrays. Fungal growth is optically tracked and detected by the reduction in the intensity of reflected light from the silicon grating. The growth of A. niger in the presence of various concentrations of the antifungal agents voriconazole and amphotericin B is investigated by intensity-based reflectometric interference spectroscopy and used for the determination of the minimal inhibitory concentrations (MIC), which are compared to standard broth microdilution testing. This assay allows for expedited detection of fungal growth and provides a label-free alternative to standard antifungal susceptibility testing methods, such as broth microdilution and agar diffusion methods.

bioengineering↗

bioRxiv2017

Omics-Based Hybrid Prediction In Maize

Accurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction (WGP) has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream \"omics\" data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of \"omics\" data. Here, we evaluate several \"omics\" predictors -- genomic, transcriptomic and metabolic data -- measured on parent lines at early developmental stages, and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream \"omics\" data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.\n\nKey messageComplementing genomic data with other \"omics\" predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits.\n\nConflict of InterestThe authors declare that they have no conflict of interest.

genetics↗