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chaperones, copper, heavy metals, metal, metal resistance, operon, pneumococcus, repressor, Streptococcus pneumoniae, and zinc

Any metal in excess can be toxic; therefore, metal homeostasis is critical to bacterial survival. Bacteria have developed specialized metal import and export systems for this purpose. For broadly toxic metals such as copper, bacteria have evolved only export systems. The copper export system (cop operon) usually consists of the operon repressor, the copper chaperone, and the copper exporter. In Streptococcus pneumoniae, the causative agent of pneumonia, otitis media, sepsis, and meningitis, little is known about operon regulation. This is partly due to the S. pneumoniae repressor, CopY, and copper chaperone, CupA, sharing limited homology to proteins of putative related function and confirmed established systems. In this study, we examined CopY metal crosstalk, CopY interactions with CupA, and how CupA can control the oxidation state of copper. We found that CopY bound zinc and increased the DNA-binding affinity of CopY by roughly an order of magnitude over that of the apo form of CopY. Once copper displaced zinc in CopY, resulting in operon activation, CupA chelated copper from CopY. After copper was acquired from CopY or other sources, if needed, CupA facilitated the reduction of Cu2+ to Cu1+, which is the exported copper state. Taken together, these data show novel mechanisms for copper processing in S. pneumoniae. IMPORTANCE As mechanisms of copper toxicity are emerging, bacterial processing of intracellular copper, specifically inside Streptococcus pneumoniae, remains unclear. In this study, we investigated two proteins encoded by the copper export operon: the repressor, CopY, and the copper chaperone, CupA. Zinc suppressed transcription of the copper export operon by increasing the affinity of CopY for DNA. Furthermore, CupA was able to chelate copper from CopY not bound to DNA and reduce it from Cu2+ to Cu1+. This reduced copper state is essential for bacterial copper export via CopA. In view of the fact that innate immune cells use copper to kill pathogenic bacteria, understanding the mechanisms of copper export could expose new small-molecule therapeutic targets that could work synergistically with copper against pathogenic bacteria.

Computer applications to medicine. Medical informatics and R858-859.7

BackgroundAsthma is a major chronic disease that poses a heavy burden on health care. To facilitate the allocation of care management resources aimed at improving outcomes for high-risk patients with asthma, we recently built a machine learning model to predict asthma hospital visits in the subsequent year in patients with asthma. Our model is more accurate than previous models. However, like most machine learning models, it offers no explanation of its prediction results. This creates a barrier for use in care management, where interpretability is desired. ObjectiveThis study aims to develop a method to automatically explain the prediction results of the model and recommend tailored interventions without lowering the performance measures of the model. MethodsOur data were imbalanced, with only a small portion of data instances linking to future asthma hospital visits. To handle imbalanced data, we extended our previous method of automatically offering rule-formed explanations for the prediction results of any machine learning model on tabular data without lowering the model’s performance measures. In a secondary analysis of the 334,564 data instances from Intermountain Healthcare between 2005 and 2018 used to form our model, we employed the extended method to automatically explain the prediction results of our model and recommend tailored interventions. The patient cohort consisted of all patients with asthma who received care at Intermountain Healthcare between 2005 and 2018, and resided in Utah or Idaho as recorded at the visit. ResultsOur method explained the prediction results for 89.7% (391/436) of the patients with asthma who, per our model’s correct prediction, were likely to incur asthma hospital visits in the subsequent year. ConclusionsThis study is the first to demonstrate the feasibility of automatically offering rule-formed explanations for the prediction results of any machine learning model on imbalanced tabular data without lowering the performance measures of the model. After further improvement, our asthma outcome prediction model coupled with the automatic explanation function could be used by clinicians to guide the allocation of limited asthma care management resources and the identification of appropriate interventions.