EnzymeML: seamless data flow and modeling of enzymatic data.

The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .

Phenotypic Array for Identification and Screening of Antifungals against Aspergillus Isolates from Respiratory Infections in KwaZulu Natal, South Africa.

The rapid emergence of invasive fungal infections correlates with the increasing population of immunocompromised individuals, with many cases leading to death. The progressive increase in the incidence of Aspergillus isolates is even more severe due to the clinical challenges in treating invasive infections in immunocompromised patients with respiratory conditions. Rapid detection and diagnosis are needed to reduce mortality in individuals with invasive aspergillosis-related infections and thus efficient identification impacts clinical success. The phenotypic array method was compared to conventional morphology and molecular identification on thirty-six Aspergillus species isolated from patients with respiratory infections at the Inkosi Albert Luthuli Hospital in Kwa-Zulu Natal. In addition, an antimicrobial array was also carried out to screen for possible novel antimicrobial compounds for treatment. Although traditional morphological techniques are useful, genetic identification was the most reliable, assigning 26 to Aspergillus fumigatus species, 8 Aspergillus niger, and 2 Aspergillus flavus including cryptic species of A. niger, A. tubingensis and A. welwitschiae. The phenotypic array technique was only able to identify isolates up to the genus level due to a lack of adequate reference clinical species in the database. However, this technique proved crucial in assessing a wide range of possible antimicrobial options after these isolates exhibited some resistance to azoles. Antifungal profiles of the thirty-six isolates on the routine azole voriconazole showed a resistance of 6%, with 61% having moderate susceptibility. All isolates resistant to the salvage therapy drug, posaconazole pose a serious concern. Significantly, A. niger was the only species resistant (25%) to voriconazole and has recently been reported as the species isolated from patients with COVID-19-associated pulmonary aspergillosis (CAPA). Phenotypic microarray showed that 83% of the isolates were susceptible to the 24 new compounds and novel compounds were identified for potentially effective combination treatment of fungal infections. This study also reports the first TR34/98 mutation in Aspergillus clinical isolates which is located in the cyp51A gene.