PURPOSE:The growing prevalence of obesity, MetS, and T2DM highlights the need to better understand host-microbiota interactions. While gut microbiota has been widely investigated, interactions between the oral microbiota and human salivary proteins remain largely unexplored. EXPERIMENTAL DESIGN:This study investigated the integrated human salivary proteome and bacterial secreted metaproteome in Brazilian individuals spanning different metabolic states: normal weight/control, overweight, obesity, MetS, and T2DM. Saliva samples were analyzed using mass spectrometry-based proteomics to identify differential protein profiles. RESULTS:Key findings revealed significant downregulation of human proteins MYSM1 (eta2 = 0.710, 95% CI: [0.642, 0.825], qadj< 0.001) and GAD65 (η2 = 0.478, 95% CI: [0.368, 0.681], qadj < 0.001) in obese, MetS, and T2DM groups, correlating negatively with BMI, waist circumference, and HOMA-IR, suggesting impaired anti-inflammatory and endocrine functions that exacerbate metabolic dysregulation. Conversely, carbonic anhydrase VI (CA6) was markedly upregulated (η2 = 0.374, 95% CI: [0.274, 0.570], qadj < 0.001), showing positive correlations with systolic blood pressure and glucose levels, indicative of an acidic, inflammatory oral microenvironment linked to chronic low-grade inflammation. In the bacterial secreted metaproteome, TrxC-2 (η2 = 0.557, 95% CI: [0.501, 0.727], qadj < 0.001), UMPK (η2 = 0.629, 95% CI: [0.571, 0.781], qadj < 0.001), and RsmH (η2 = 0.772, 95% CI: [0.718, 0.862], qadj 0.001) were significantly elevated in obesity, MetS, and T2DM, positively associated with anthropometric and insulin resistance markers, reflecting microbial adaptations to oxidative stress and enhanced virulence through biofilm formation and RNA biosynthesis. Interactome analysis demonstrated strong negative correlations between bacterial proteins and human proteins (MYSM1, GAD65), alongside positive correlations with CA6, unveiling a vicious cycle, where oral dysbiosis amplifies host inflammation and metabolic dysfunction, while altered human proteins perpetuate microbial imbalance. CONCLUSIONS AND CLINICAL RELEVANCE:These findings underscore the pivotal role of host-microbiota interactions in the oral cavity during the pathogenesis of obesity, MetS, and T2DM, highlighting the importance of further elucidating the molecular and functional mechanisms underlying microbiota-host crosstalk in metabolic diseases.
PURPOSE:Liver fibrosis and cirrhosis represent critical stages in the progression of chronic liver disease, yet their key molecular features remain incompletely understood. EXPERIMENTAL DESIGN:We performed large-scale Olink-based proteomic profiling in over 40,000 participants from the UK Biobank with a median follow-up of 15.6 years to elucidate disease pathophysiology and identify pre-diagnostic biomarkers. Cross-sectional analysis included 66 prevalent cirrhosis cases, and prospective analysis identified 224 incident cirrhosis cases. Machine learning and Mendelian randomization (MR) were applied. An independent cohort was used for validation. RESULTS:Distinct dysregulated proteins were observed in compensated cirrhosis (CC) and decompensated cirrhosis (DC). In the prospective analysis, 696 proteins were associated with disease onset. A proteomic panel based on these markers achieved an AUC of 0.832 for predicting incident cirrhosis, outperforming established fibrosis scores including FIB-4, APRI, and NFS, and demonstrated robust performance across CC and DC populations. The protein panel showed predictive value (AUC = 0.743) for disease progression in an independent cohort. MR identified 66 proteins with putative causal roles, including 11 potential therapeutic targets. CONCLUSIONS AND CLINICAL RELEVANCE:These findings provide novel molecular insights into cirrhosis development and support integrated proteomic biomarkers as a discovery and prioritization framework for early risk stratification.
BACKGROUND:The nasal cavity includestwo distinct epithelial regions: olfactory and respiratory which fulfilldifferent roles. Despite their differences, their mucus composition, however, is yet not well elucidated. METHODS:To analyze themucosal secretome, samples from the human olfactory mucus (OM) and respiratorymucus (RM) were collected in a volunteer study with 25 normosmic individualsand analyzed using label-free quantitative proteomics (LFQ), supervised machinelearning (Partial Least Squares Discriminant Analysis, PLS-DA), functionalenrichment via Gene Ontology (GO) and pathway analyses at Reactome database. RESULTS:A total of 1,780high-confidence proteins were quantified across 50 samples. The optimizedPLS-DA model achieved robust discrimination between OM and RM (AUC = 0.93 ±0.04), identifying distinct molecular signatures. Cross-validation across GO,Reactome, and PLS-DA macro-category analyses confirmed the robustness andbiological coherence of these findings. CONCLUSIONS:Overall, this study defines twocomplementary mucosal ecosystems: a dynamic olfactory mucus optimized for highmitochondrial activity, (non-motile) ciliary renewal, and autophagy, and animmune-active respiratory mucus specialized in host defense, providing acomprehensive molecular framework of nasal regional specialization.
BACKGROUND AND AIMS:Non-alcoholic Fatty Liver Disease (NAFLD) affects about a quarter of the world's population. Liver biopsy remains the gold standard for diagnosing the progressive form of NAFLD called Non-alcoholic Steatohepatitis (NASH) but it is invasive, prone to sampling errors and observer variability, and impractical for widespread use. Fecal non-invasive biomarkers have emerged as a new approach to diagnose, stage, and monitor NAFLD over time. METHODS:Proteomic analysis was performed using Liquid Chromatography-Mass Spectrometry (LC-MS/MS) in fecal samples derived from mice fed with high fat and sugar diet representing NAFLD model and with the addition of 5 cycles of dextran-sulphate in drinking water to induce a NASH. RESULTS:Qualitative and quantitative different protein profiles resulted in a comparison between the three groups of mice. An enrichment analysis of differentially expressed proteins explores the molecular pathways involved. A selection of the most significant modulated proteins between groups was performed. The proteins Transthyretin, Kallikrein1 and Trefoil Factor 3 were validated by ELISA. CONCLUSION:Our exploratory discovery study identified several proteins that, alone or in combination, enable a good separation between NASH, NAFLD and healthy mice, highlighting their potential as key targets for future research.
PURPOSE:Over the years, several proteins and peptides with diverse therapeutic properties such as anticancer, antimicrobial, antihypertensive effects have been discovered. However, only a few hundred proteins are considered druggable with US FDA approval, while most of them failed during clinical trials. EXPERIMENTAL DESIGN:This study systematically investigates the compositional and physicochemical properties of FDA-approved proteins to develop predictive models for identifying druggable proteins. Our main dataset comprises of 356 FDA approved proteins as positive dataset and equal number of randomly selected proteins as negative dataset. We used 80% data as training set and 20% as independent validation data, with no protein in validation dataset having more than 40% similarity with any protein in training dataset. RESULT:Random forest-based model developed using SVC-L1 selected features obtained maximum performance AUC of 0.80 with MCC 0.61 on validation data. In addition to this, we performed MERCI-based motif analysis to find exclusive motifs/ patterns in druggable proteins. Finally, we proposed an ensemble-based method combining best performing machine learning model with exclusive motifs and achieved AUC 0.92 with MCC 0.83 on independent validation dataset. CONCLUSION AND CLINICAL RELEVANCE:In order to serve the scientific community, web server and standalone package of "ThPPred" facilitating prediction and designing of druggable proteins is proposed (https://webs.iiitd.edu.in/raghava/thppred/).
Purpose Endometriosis is a prevalent inflammatory condition characterised by the presence of endometrial-like tissue outside the uterus and is associated with significant challenges, including diagnostic delays and continued reliance on laparoscopy. Although extensive research has investigated potential biomarkers in various biofluids, none have been validated for clinical use.Experimental design This pilot study explored the proteome of cervicovaginal fluid to identify biomarkers of endometriosis. SWATH-MS was performed over two experiments on cervicovaginal fluid sampled via a low vaginal swab, from people with (n = 20) and without (n = 19) surgically confirmed endometriosis. STRING, OPLS-DA modelling and ingenuity pathway analysis were used to interrogate the data. ELISA was performed validate SWATH-MS findings.Results There were 29 proteins in experiment one and 47 proteins in experiment two identified as differentially abundant between cases and controls. No proteins were identified as differentially abundant in both experiments. Legumain (LGMN) measured via ELISA was significantly increased in the cervicovaginal fluid of people with endometriosis.Conclusions and clinical relevance Cervicovaginal fluid proteins sampled via vaginal swab may have limited biomarker potential for endometriosis. A larger and more diverse cohort would be required to confirm the promise of cervicovaginal fluid LGMN as a candidate biomarker of endometriosis.
BACKGROUND:Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors offer a novel approach for reducing low-density lipoprotein cholesterol (LDL-C) levels in patients with familial hypercholesterolemia (FH). In our study, we aimed to compare the plasma proteome profiles of heterozygous FH (HeFH) patients and non-FH patients treated with PCSK9 inhibitors (PCSK9i). METHODS:We analyzed 132 plasma samples from 38 patients. Samples were collected before the start of the PCSK9i treatment and then again at the third, sixth, and 12th months of treatment. Using LC‒MS technology, we compared two approaches: depleted and nondepleted samples. RESULTS:Across all time points of treatment, numerous differentially expressed proteins were detected regardless of the FH status compared to baseline. We identified only three proteins with downregulated expression-PCSK9, CRTAC1, and PON1-with padj<0.05 in both the depleted and nondepleted datasets after one year of treatment. NRP1 and IGHV3-49 were differently expressed in HeFH patients compared to non-FH patients across the entire year of treatment in depleted samples (p<0.05). CONCLUSIONS:Our study suggests that there might be differences in the proteomes between individuals with HeFH and those without FH in response to PCSK9i treatment. Furthermore, PCSK9i treatment is likely to affect the plasma proteome, regardless of FH status.
Purpose Coronavirus disease 2019 (COVID-19) has highlighted significant neurological complications in severe cases. Cerebrospinal fluid (CSF) proteomics could reveal biomarkers related to clinical outcome among critically ill patients.Experimental Design We performed high-resolution proteomic analyses of CSF samples from 29 intensive care unit (ICU) patients with severe COVID-19 and 19 controls. Differentially expressed proteins and associated pathways were identified through bioinformatic and statistical analyses.Results Proteomic analysis identified 488 significantly altered proteins between COVID-19 patients and controls. Proteins linked to coagulation, inflammation, and blood-brain barrier dysfunction (e.g., SERPINC1, KNG1, PLG) were elevated in patients who survived ICU admission. Conversely, proteins associated with metabolic disruption, cellular stress, and neuroinflammation (e.g., FABP3, PDIA4) were upregulated in non-survivors. Pathway enrichment analyses confirmed involvement of immune activation, inflammatory responses, and coagulation cascades.Conclusions and clinical relevance CSF proteomics in severe COVID-19 patients reveals potential biomarkers predictive of patient outcomes. These findings support the involvement of systemic inflammation and blood-brain barrier disruption in COVID-19 pathophysiology, suggesting novel targets for personalized intervention strategies.
BACKGROUND:Colorectal cancer (CRC) is a major cause of morbidity and mortality, with chronic inflammation from inflammatory bowel disease (IBD) representing a well-established risk factor. Clarifying shared molecular mechanisms may facilitate early detection and prevention strategies. METHODS:Proteomic data from the UK Biobank were analysed using the Olink proximity extension assay for seven CRC-associated proteins (TFF3, TFF1, AHCY, RETN, LCN2, SELE and CEACAM5) previously identified via machine learning. Expression levels in CRC and IBD cases were compared with controls. Multilayer interaction networks, incorporating protein-protein, protein-metabolite and transcription factor-protein interactions, were generated using OmicsNet. Findings were validated in the Colonomics transcriptomic dataset. RESULTS:All seven proteins were significantly upregulated in CRC; six (excluding CEACAM5) were also elevated in IBD. Network analysis identified AHCY and LCN2 as central hubs linking inflammatory and metabolic pathways. NF-κB and GATA2 emerged as recurrent transcriptional regulators. Colonomics validation confirmed upregulation of AHCY, LCN2 and SELE in CRC tissues. CONCLUSIONS:This multi-omics network analysis reveals a shared molecular framework between IBD and CRC, with inflammation as a key driver of colorectal carcinogenesis.
PURPOSE:Ischemic stroke (IS) is a severe neurological disease with limited treatment options. Subcutaneous adipose tissue-derived small extracellular vesicles (SAT-sEVs), which are readily accessible and abundant, have emerged as promising biomarkers and therapeutic agents in various diseases. The objective of this research is to uncover the roles and regulatory mechanisms of proteins and miRNAs contained within SAT-sEVs in middle cerebral artery occlusion (MCAO) rats, aiming to discover innovative approaches and insights for IS treatment. EXPERIMENTAL DESIGN:To evaluate the therapeutic efficacy of SAT-sEVs, we intravenously administered them to MCAO rats and assessed neurological function and cerebral infarction 24 h after SAT-sEVs administration using behavioral scoring and TTC staining. To elucidate the potential mechanisms and ischemia-induced alterations in SAT-sEVs, we conducted integrated transcriptomic and proteomic analyses on vesicles isolated from both MCAO rats (24-h post-ischemia) and normal controls. RESULTS:SAT-sEVs markedly alleviated neurological impairments and decreased the volume of cerebral infarcts in MCAO rats. Significant alterations were also observed in the miRNAs and proteins within SAT-sEVs following ischemic injury. CONCLUSIONS AND CLINICAL RELEVANCE:This comprehensive analysis enhances our understanding of SAT-sEVs-mediated protective mechanisms and functional alterations in IS. It establishes a solid experimental basis for the potential clinical use of SAT-sEVs in stroke rehabilitation and other related diseases.
PURPOSE:In heart failure, dyssynchrony is associated with accelerated cardiac remodeling and a worse prognosis. Both restored with resynchronization. We have previously developed a mouse model of dyssynchrony and resynchronization and here assess changes in protein expression within that model. EXPERIMENTAL DESIGN:Mice were subjected to ischemia/reperfusion followed by pacemaker implantation. Three groups were defined: (i) sinus rhythm for 4 weeks-synchronous heart failure (SynHF), (ii) right ventricular pacing (RVP) for 4 weeks-dyssynchronous heart failure (DysHF), and (iii) RVP for 2 weeks followed by 2 weeks of sinus rhythm-resynchronized heart failure (ResynHF). Heart tissue was evaluated for protein content with mass spectrometry. RESULTS:A total of 3324 proteins were detected. The abundance of 589 proteins differed between DysHF and SynHF and 253 between DysHF and ResynHF. The changed proteins in the comparisons to DysHF overlapped to a great extent. Several of these proteins were connected to calcium handling or made up part of the sarcomere. CONCLUSION:Adding dyssynchrony to ischemic heart failure resulted in protein dysregulation, which was partly reversed by resynchronization. The dysregulation was characterized by a change in proteins related to contractility, which may be part of the positive inotropic effect and reverse remodeling observed with resynchronization.
The following article for this Special Issue was published in an earlier Issue . Q. L. Ma, Y. H. Zhang, L. Chen, Y. S. Bao, W. Guo, K. Y. Feng, T. Huang, Y.-D. Cai. “Machine Learning-Driven Discovery of Essential Binding Preference in Anti-CRISPR Proteins,” Proteomics. Clinical Applications , 19 , (2025): e70013. https://doi.org/10.1002/prca.70013 . https://onlinelibrary.wiley.com/doi/10.1002/prca.70013
In label-free mass spectrometry experiments, the data output is typically a proteome table that requires further processing, quality testing, and visualization to fully interpret the captured proteomic signals. Currently, post-quantification analysis of these tables often relies on complex programmatic pipelines, which can become challenging to use. Here, we introduce the Proteomics Eye (ProtE), a single-function R package designed to streamline the analysis of proteome tables generated by commonly used software tools (DIA-NN, ProteomeDiscoverer, and MaxQuant). ProtE provides a broad range of options for data processing, preparation, and statistical testing. It also performs gene set enrichment analysis and offers a comprehensive suite of visualization plots to assess data quality and facilitate biological interpretation. Given a categorical variable with two or more groups, ProtE enables group-wide and pairwise statistical comparisons across all group combinations, using both traditional statistical tests and linear models for differential expression analysis. By integrating all these features into a single, user-friendly R function, ProtE simplifies the analysis of large-scale label-free DDA and DIA datasets, making advanced proteomic analysis accessible to both experienced researchers and beginners.
Severe acute pancreatitis (SAP) involves dynamic shifts from inflammation to immunosuppression, where peptidomic profile evolution may reveal prognostic biomarkers. Here, the plasma peptidome of rats with taurocholate-induced SAP at 1, 3, 6, 12, and 24 h compared with controls was characterized using nLC-MS/MS. Ten peptides derived from eight precursor proteins were differentially regulated across time points. The 12-h period showed eight differentially regulated peptides, while the 6- and 24-h periods had four differentially regulated peptides, and one peptide was regulated between 1 and 3 h. Peptides derived from alpha-1-microglobulin (A1M) increased from 3 h onward, while peptides from actin showed major alterations at 12-24 h, coinciding with peak mortality (46%). Bioinformatic enrichment analyses revealed transient activation of mTOR, JAK/STAT, and cell adhesion pathways at 6 h, followed by bacterial invasion and actin cytoskeleton regulation pathways at later stages. These temporal profiles suggest an early antioxidant response and subsequent structural and infection-related remodeling. These findings suggest that A1M-derived peptides could represent potential early indicators of disease severity, although further validation in human clinical settings is required. These findings highlight the plasma peptidome as a promising tool for clinical diagnostics, providing a better understanding of SAP progression and identification of phase-specific biomarkers in pancreatitis. SUMMARY: This study reveals dynamic changes in the plasma peptidome during the progression of severe acute pancreatitis in rats. Through the identification of differentially regulated peptides, bioinformatic analysis was performed to define the altered pathways and genes, demonstrating the relationship between peptide alterations and disease progression. The 6-h time point after pancreatitis induction showed the highest number of signaling terms/pathways that characterize the inflammatory phase of the disease. In subsequent moments, the enrichment of pathways related to infection and the regulation of the actin cytoskeleton at 12- and 24-h post-pancreatitis induction suggests that this period is associated with the process of bacterial translocation and pancreatic necrosis infection. Therefore, the peptide profile and pathways may have implications for defining prognosis and early diagnosis of infection.
PURPOSE:Rugby players experience high-impact collisions, potentially increasing their risk of neurodegenerative conditions. This study investigates whether the plasma proteome of extracellular vesicles (EV) provides biomarkers to indicate differential risk associated with a rugby career. EXPERIMENTAL DESIGN:Twenty-four males were recruited: eight academy rugby players (18 ± 1 years), eight professional rugby players (33 ± 5 years; >10-year career), and eight CrossFit athletes (32 ± 5 years; no history of collision-related injuries). EV were enriched from plasma using strong-anion exchange magnetic microparticles and digested proteins were analyzed by LC-MS/MS for label-free quantitation. RESULTS:A total of 449 proteins were identified (false discovery rate <1%). Statistical analysis on 403 proteins quantified in at least n = 3 participants in each group highlighted 52 significant (p < 0.05, q < 0.01) differences, including 44 proteins that had abundance profiles unique to professional rugby players. Eight proteins which were depleted and three proteins which were elevated have previously recognized roles in neurodegenerative processes. CONCLUSIONS AND CLINICAL RELEVANCE:Proteins associated with neuroprotection were specifically depleted in the plasma EV proteome of long-serving professional rugby players. The proteins highlighted in professional rugby players could be used to develop biomarker panels for predicting at-risk athletes or for guiding treatment interventions. SUMMARY:Repetitive high-impact collisions experienced by rugby players may predispose them to neurodegenerative conditions, yet the biological processes underpinning this risk remain poorly understood. This study investigates whether the proteome of plasma extracellular vesicles (EV) could serve as early, minimally invasive biomarkers of neurodegenerative risk in athletes exposed to repeated head impacts. By comparing the EV proteome of professional rugby players, younger academy athletes, and non-collision sport controls, we identified specific proteins with known neuroprotective roles that were depleted in long-serving rugby professionals. These alterations suggest systemic biological changes related to prolonged exposure to collisions. Our findings provide novel insight by highlighting the potential of EV-based proteomic profiling as a tool for early detection and monitoring of neurodegeneration-related processes in at-risk athletic populations. This approach could ultimately inform strategies for risk stratification, early intervention, and tailored clinical monitoring in collision sport athletes.
BACKGROUND:Polycystic ovary syndrome (PCOS) is a metabolic disorder affecting women of reproductive age, and its etiology remains unclear. Therefore, it is crucial to identify biomarkers of the metabolic disturbances in PCOS. METHODS:A total of eight clinical PCOS samples and control group samples were analyzed using data-independent acquisition (DIA) proteomics. Clinical data were used to identify protein biomarkers, and enzyme-linked immunosorbent assay (ELISA) validation was performed on 27 PCOS and 23 control samples. RESULTS:In the PCOS samples, a total of 114 differentially expressed proteins were identified, with 37 upregulated and 77 downregulated. Further biofunctional analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed two differentially expressed proteins, lactate dehydrogenase A (LDHA) and triosephosphate isomerase 1 (TPI1), both of which were significantly upregulated in clinical PCOS samples. LDHA and TPI1 are involved in the glycolysis/gluconeogenesis metabolic pathway. Finally, ELISA was used to validate the high expression of LDHA and TPI1 in PCOS patients. CONCLUSION:DIA proteomics effectively identifies PCOS biomarkers. LDHA and TPI1 may serve as diagnostic biomarkers and could exert effects through glycolytic pathways.
PURPOSE:The identification of putative biomarkers for AAA can be achieved through shotgun proteomics. However, tissue heterogeneity hampers its reproducible homogenization and protein extraction. Thus, we aimed to optimize a protocol to maximize protein yield and develop an SOP to foster reproducibility and accelerate translation of proteomics findings. EXPERIMENTAL DESIGN:Using a bead-beating homogenization method, we compared the effect of beads' size, extraction cycles, beads-to-tissue mass ratio, lysis buffer volume, and chemistry on protein yield and/or qualitative and quantitative parameters of proteomics analysis (identifications, sequence coverage, coefficient of variation, functional enrichment analysis). RESULTS:Optimal conditions for protein extraction were achieved using 1.4 mm beads in two homogenization cycles, with a bead-to-tissue mass ratio of 30:1 and 20 µL of lysis buffer per mg of tissue. As for the buffer chemistry, RIPA is recommended to attain greater sequence coverage, while HEPES and Urea/thiourea are preferred when quantification performance is a priority. The SOP was applied to characterize the AAA tissue proteome, and key AAA pathogenesis-related pathways were highlighted by bioinformatic analysis. CONCLUSIONS AND CLINICAL RELEVANCE:The SOP is well-suited for identifying and quantifying aneurysmatic tissue proteins and can be applied to accelerate the translation of putative biomarkers into clinical diagnostic/prognostic tools. SUMMARY:Abdominal aortic aneurysm (AAA) is a life-threatening, non-communicable disease that remains underdiagnosed and poorly understood within the medical community. Furthermore, there is a lack of an effective medical therapy, aside from surgical intervention, that compels clinicians to address general cardiovascular risk factors for disease management. Thus, fishing putative new biomarkers and therapeutic targets from aneurysmatic tissue using untargeted proteomic approaches has emerged as a relevant strategy for the development of tools for earlier diagnosis, effective disease management, and a deeper understanding of the pathophysiology of AAA. However, given the heterogeneity of AAA tissue, the reproducibility of the results may be partially affected by the absence of a standardized method for protein extraction while ensuring high efficiency in protein yield. Therefore, this study aimed to address a key bottleneck in proteomic analysis of aneurysmatic study-heterogeneity in sample processing. Herein, we report the optimization of a protocol for AAA tissue homogenization and maximize protein extraction. A standard operating procedure (SOP) to process AAA tissue toward downstream proteomics applications is shared to enable more reliable and comparable data across studies and ultimately bolster the translation of tissue proteomics into clinically relevant tools for vascular medicine.
OBJECTIVE:The "blend sign" is a critical CT imaging marker for predicting hematoma expansion in intracerebral hemorrhage (ICH). This study aimed to elucidate its underlying pathological mechanisms by comparing proteomic profiles between hyperdense and hypodense regions within the hematoma. METHODS:Hematoma samples from nine ICH patients exhibiting the blend sign were obtained via minimally invasive puncture. Isotope-labeled proteomics and bioinformatic analyses were performed to identify differentially expressed proteins (DEPs), which were further validated by Western blotting and ELISA. RESULTS:A total of 77 DEPs were identified, including 66 upregulated and 11 downregulated in hyperdense regions compared to hypodense areas. Functional enrichment analysis revealed significant involvement of inflammatory responses, apoptosis, oxidative stress, and metabolic dysregulation. Notably, cytochrome C, growth-associated protein 43 (GAP43), and tau were markedly upregulated in hyperdense regions. CONCLUSIONS:The blend sign is associated with region-specific molecular changes involving inflammation, apoptosis, and metabolic alterations. These findings provide mechanistic insights into hematoma heterogeneity and lay a foundation for future studies exploring their role in hematoma expansion and clinical outcomes.
PURPOSE:Peptide-centric machine learning enhanced (PCML) data-independent acquisition tandem mass spectrometry (LC-MS/MS-DIA) matches low-abundance MS fragmentation spectra to in silico predicted peptide spectra deduced from libraries of customized protein sequences. The study's goal was to determine proteomic depth of coverage in microbial pathogen-containing clinical samples using that method. EXPERIMENTAL DESIGN:We employed a published machine learning method based on neural networks (Dia-NN) to the LC-MS/MS analysis of sputum protein digests derived from patients with lung infections. RESULTS:Nearly 6800 proteins in total and 1530 proteins of microbial origin were identified from single experiments, with CVs of protein quantities among technical replicates as low as 0.12. Conventional spectral library searches of data from these experiments yielded less than 1600 and 60 protein identifications, respectively. Samples of two patients revealed colonization by pathogens difficult to clear from chronically infected lungs, Pseudomonas aeruginosa and Stenotrophomonas maltophilia. Abundant virulence factors in the datasets were the insulin-cleaving metalloproteinase IcmP (P. aeruginosa) and an inducer of human interleukin-10 expression (S. maltophilia). Each bacterium showed signs of adaptation to a hostile milieu, such as the expression of systems to generate energy anaerobically and the acquisition of host-sequestered metals. CONCLUSIONS AND CLINICAL RELEVANCE:This work constitutes a step forward for protein-centered translational medicine on infectious diseases. SUMMARY:We demonstrate excellent depth of proteome coverage and experimental repeatability for low-abundance pathogen proteomes in human airway secretions via data-independent acquisition liquid chromatography tandem mass spectrometry leveraging machine learning for spectral analysis. The host's sputum proteome was also profiled, allowing inferences of immune defense mechanisms against pathogens. This proof-of-principle study shows the opportunity to gain insights into respiratory disease burdens and bacterial virulence by directly analyzing clinical specimens and the potential for biomarker discovery and pharmacodynamic response monitoring in interventional studies related to respiratory tract infections.
