Cysteine-bound sulfane sulfur atoms in proteins have received much attention as key factors in cellular redox homeostasis. However, the role of sulfane sulfur in zinc regulation has been underinvestigated. In this study, we identified growth inhibitory factor (GIF)/metallothionein-3 (MT-3) as a sulfane sulfur-binding protein from mouse brain. We also report here that cysteine-bound sulfane sulfur atoms serve as ligands to hold and release zinc ions in GIF/MT-3 with an unexpected C-S-S-Zn structure. Oxidation of such a zinc/persulfide cluster in ZnGIF/MT-3 results in the release of zinc ions, and intramolecular tetrasulfide bridges in apo-GIF/MT-3 efficiently undergo S-S bond cleavage by thioredoxin to regenerate ZnGIF/MT-3. Three-dimensional molecular modeling confirmed the critical role of the persulfide group in the thermostability and Zn-binding affinity of GIF/MT-3. The present discovery raises the fascinating possibility that the function of other Zn-binding proteins is controlled by sulfane sulfur.

Antibiotic susceptibility tests (ASTs) often fail to predict treatment outcomes because they do not account for biofilm-specific tolerance mechanisms. In the present study, we explored alternative approaches to predict tobramycin susceptibility of Pseudomonas aeruginosa biofilms that were experimentally evolved in physiologically relevant conditions. To this end, we used four analytical methods - whole-genome sequencing (WGS), matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), isothermal microcalorimetry (IMC) and multi-excitation Raman spectroscopy (MX-Raman). Machine learning models were trained on data outputs from these methods to predict tobramycin susceptibility of our evolved strains and validated with a collection of clinical isolates. For minimal inhibitory concentration (MIC) predictions of the evolved strains, the highest accuracy was achieved with MALDI-TOF MS (97.83%), while for biofilm prevention concentration (BPC) predictions, Raman spectroscopy performed best with an accuracy of 80.43%. Overall, all analytical methods demonstrated comparable predictive performance, showing their potential for improving biofilm AST.

There is a critical unmet need for scalable, accessible and objective diagnostic tests for stratification in dementia. Biofluid Raman spectroscopy (RS) due to its simplicity, holistic and label-free nature, is a powerful approach that has the potential to offer differential diagnosis across dementia types including Alzheimer's disease (AD). RS is a laser-based optical method that can rapidly provide chemically rich information ('spectral biomarkers') from biofluids but its utility for AD diagnosis has not been established in a 'real-world' context, specifically from a clinically heterogenous cohort of patients. We carried out RS measurements on cerebrospinal fluid (CSF) samples of patients from a mixed clinical cohort (N = 143). All patients reported cognitive complaints and were clinically diagnosed over 2 years with conditions including AD and other neurodegenerative diseases, as well as developmental and long-term chronic conditions. Machine-learning algorithms were trained, optimised and evaluated on Raman spectra to classify AD from non-AD. AD was classified with 93% accuracy for patients in the testing set. Time from sample to classification was < 1 h. Spectral biomarkers explaining AD classification were identified and primarily assigned to protein-derived aromatic amino acids, representing a difference in proteome signature between AD and non-AD groups. Signals from a subset of spectral biomarkers directly correlated with pathological CSF biomarker concentrations including amyloid-β 42, phosphorylated-tau 181, and total tau. This pre-clinical study is a first step towards realising the real-world application of RS for dementia diagnosis. Compared to current and emerging methods, RS does not require sophisticated instrumentation or specialised labs. It is reagentless and simple, offering unprecedented rapidity, scalability, accessibility for dementia diagnosis.

Antimicrobial resistance (AMR) poses a global healthcare challenge, where overprescription of antibiotics contributes to its prevalence. We have developed a rapid multi-excitation Raman spectroscopy methodology (MX-Raman) that outperforms conventional Raman spectroscopy and enhances specificity. A support vector machine (SVM) model was used to identify 20 clinical isolates of Pseudomonas aeruginosa with an accuracy of 93% using MX-Raman. Antibiotic sensitivity profiles for tobramycin, ceftazidime, ciprofloxacin, and imipenem were generated for the bacterial strains and compared with their Raman spectral signatures using MX-Raman. The 20 clinical strains were distinguished according to AMR profiles. Nine models were assessed for AMR classification performance, and SVM performed best, classifying AMR profiles of each strain with 91-96% accuracy. These data provide the basis for a new rapid clinical diagnostic platform that could screen for bacterial infection and recommend effective antibiotic treatment ahead of confirmation by conventional techniques, improving clinical outcomes and reducing the spread of AMR.

The gut microbiota plays a key role in shaping individual responses to drugs, but current tools have limited potential to probe drug-microbe interactions within the complex, individualised gut environment. This study employed bioorthogonal labelling to track and identify gut microbial taxa and molecular mechanisms involved in the bioaccumulation of entacapone, a Parkinson's disease drug. We synthesised alkyne-tagged derivatives of entacapone and evaluated their suitability as molecular probes in incubations with faecal communities or different () strains. Following incubation, tagged drugs were conjugated to a fluorescently labelled azide via click chemistry. Labelled cells were visualised, quantified, sorted via fluorescence-activated cell sorting (FACS), and identified via 16S ribosomal RNA (rRNA) gene amplicon sequencing. Entacapone alkyne derivatives retained the biological activity and effects of the original drug on the microbiota, significantly reducing microbial loads and shifting community composition across the three donors tested. Conjugation of alkyne-entacapone with a labelled azide revealed that between 80% to 96% of all microbial cells in a donor's faecal sample accumulate entacapone. Nearly all taxa detected in incubations were recovered in labelled FACS fractions, confirming widespread uptake of the drug. Finally, we demonstrate that different strains exhibit varying levels of entacapone accumulation and identify a siderophore transporter that plays a role in this process. Our findings reveal that entacapone is widely bioaccumulated by the gut microbiota across three donors and identify a key molecular mediator of this accumulation. This study expands the toolkit for investigating drug-microbiome interactions and holds significant potential to advance our understanding of drug-microbiome dynamics and therapeutic outcomes.