Searle, Brian C

INTRODUCTION: Reliable biomarkers for the diagnosis of chronic pancreatitis (CP) and pancreatic fibrosis severity are lacking, hindering effective treatment and management. Histologic fibrosis is a hallmark of late-stage CP, but noninvasive methods to evaluate fibrosis progression are limited. We used urine proteomics to discover biomarkers that identify patients with CP and predict fibrosis severity. METHODS: We performed a cross-sectional study of 130 total subjects (CP n = 50) selected based on clinical criteria in a tertiary care setting. Urine proteomics samples were quantified using data-independent acquisition mass spectrometry. Differential biomarker candidates were identified with false discovery rate-corrected pairwise comparisons. These proteins were validated with an independent paired urine and plasma sample cohort (n = 36). Machine learning was used to develop a protein panel that predicted Ammann scores for patients with histologic fibrosis. RESULTS: We found 34 proteins consistently differentially expressed between CP and controls in pairwise false discovery rate-controlled tests. Of these, 25 urine proteins outperformed 19 previously suggested CP blood-based biomarkers in an independent validation cohort. Isocitrate dehydrogenase (IDH1), calcyphosin (CAPS), synuclein gamma (SNCG), and protein S100-P (S100P) all produced receiver operator curve area under the curve values >0.95, while the best plasma marker was interleukin 2 receptor subunit alpha (receiver operator curve area under the curve = 0.80). A 12-protein panel of identified markers predicted fibrosis severity with a linear correlation R 2 value of 0.61. DISCUSSION: We identified a panel of proteins that may diagnose CP in children and developed a model to predict pancreatic fibrosis severity, offering promising tools for improving diagnostics and patient care.

ABSTRACT Mass spectrometry-based metaproteomics, the identification and quantification of thousands of proteins expressed by complex microbial communities, has become pivotal for unraveling functional interactions within microbiomes. However, metaproteomics data analysis encounters many challenges, including the search of tandem mass spectra against a protein sequence database using proteomics database search algorithms. We used a ground-truth dataset to assess a spectral library searching method against established database searching approaches. Mass spectrometry data collected by data-dependent acquisition (DDA-MS) was analyzed using database searching approaches (MaxQuant and FragPipe), as well as using Scribe with Prosit predicted spectral libraries. We used FASTA databases that included protein sequences from microbial species present in the ground-truth dataset along with background protein sequences, to estimate error rates and assess the effects on detection, peptide-spectral match quality, and quantification. Using the Scribe search engine resulted in more proteins detected at a 1% false discovery rate (FDR) compared to MaxQuant or FragPipe, while FragPipe detected more peptides verified by PepQuery. Scribe was able to detect more low-abundance proteins in the microbiome dataset and was more accurate in quantifying the microbial community composition. This research provides insights and guidance for metaproteomics researchers aiming to optimize results in their analysis of DDA-MS data.

ABSTRACT Orbitrap (OT) -based mass spectrometer platforms are a gold standard in high-resolution mass spectrometry, where their primary disadvantage is slower-scanning speed in comparison to time-of-flight or linear ion trap mass analyzers. In this study, we utilize long OT transients to extend the precursor dynamic range by modifying the selected ion monitoring method to multiplex several precursor m/z ranges from 400 to 1000 m/z into a single scan called “ M ultiple A ccumulation P recursor M ass S pectrometry” (MAP-MS). Our approach requires no software or hardware modifications and hides the additional ion accumulation steps during the time it takes to make other Orbitrap measurements, producing precursor spectra with nearly 2× dynamic range and essentially no consequences. We collected data using both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methods to evaluate a range of approaches. With DDA, MAP-MS precursor quantification improves with higher quality measurements. At the same time, DIA detection is enhanced by up to 11% when combining precursor and tandem mass spectra for peptide detection.