Indoline‐6‐Sulfonamide Inhibitors of the Bacterial Enzyme DapE

Inhibitors of the bacterial enzyme dapE-encoded N-succinyl-l,l-diaminopimelic acid desuccinylase (DapE; EC 3.5.1.18) hold promise as antibiotics with a new mechanism of action. Herein we describe the discovery of a new series of indoline sulfonamide DapE inhibitors from a high-throughput screen and the synthesis of a series of analogs. Inhibitory potency was measured by a ninhydrin-based DapE assay recently developed by our group. Molecular docking experiments suggest active site binding with the sulfonamide acting as a zinc-binding group (ZBG)

MPTP-Treated Zebrafish Recapitulate ‘Late-Stage’ Parkinson’s-like Cognitive Decline

The zebrafish is a promising model species in biomedical research, including neurotoxicology and neuroactive drug screening. 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) evokes degeneration of dopaminergic neurons and is commonly used to model Parkinson’s disease (PD) in laboratory animals, including zebrafish. However, cognitive phenotypes in MPTP-evoked experimental PD models remain poorly understood. Here, we established an LD50 (292 mg/kg) for intraperitoneal MPTP administration in adult zebrafish, and report impaired spatial working memory (poorer spontaneous alternation in the Y-maze) in a PD model utilizing fish treated with 200 µg of this agent. In addition to conventional behavioral analyses, we also employed artificial intelligence (AI)-based approaches to independently and without bias characterize MPTP effects on zebrafish behavior during the Y-maze test. These analyses yielded a distinct cluster for 200-µg MPTP (vs. other) groups, suggesting that high-dose MPTP produced distinct, computationally detectable patterns of zebrafish swimming. Collectively, these findings support MPTP treatment in adult zebrafish as a late-stage experimental PD model with overt cognitive phenotypes. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: The experiments were implemented using the equipment and unique scientific installation “Biological collection–Genetic biomodels of neuropsychiatric disorders” (No. 493387) of the Federal State Budgetary Scientific Institution “Scientific Research Institute of Neurosciences and Medicine” theme no. AAAA-A21-121011990039-2 (2021–2025). The study partially used the facilities and equipment of the Resource Fund of Applied Genetics MIPT (support grant 075-15-2021-684)

Online tools for patient counseling in bladder and kidney cancer—ready for prime time?

Gauging prognosis is a key element when facing treatment decisions in cancer care. Several prognostic tools, such as risk tables and nomograms are at hand to aid this process. In the context of patient-centered care, prognostic tools are of great interest to caregivers and -providers alike, as they can convey sizeable amounts of information and provide tailored, accurate estimates of prognosis. Given the rising number of prognostic tools in cancer care over the last two decades, and similarly, ever increasing presence of the Internet, we aimed to assess how this would translate into the availability of online tools for patient counseling. We used a modified systematic review to evaluate the web-based availability, format, and content of prognostic tools for bladder and kidney cancer care. Our search identified a total of twenty-three tools, offered by eight providers, which assessed a total of six (bladder cancer) and five (kidney cancer) different outcomes. Despite the restricted availability of online tools, we observed that the majority showed limited user-friendliness (including, for example, a statement/explanation of intended use, visualization of data, availability as application software for handheld devices). Only one tool included modifiable risk factors such as smoking behavior and body weight. Lastly, none of the tools incorporated genomic or molecular markers or treatment associated quality of life. Taken together, online tools for patient counseling in bladder and kidney cancer care are only beginning to align with the growing need in clinical reality. Further and future avenues include incorporation of health-related quality of life as well as genomic and biomarkers into prediction tools