Journal of Proteome Research最新文献

Hepatocellular carcinoma (HCC), commonly associated with cirrhosis, is a major cause of cancer-related mortality due to its poor prognosis. Herein, we investigated fucosylated glycoforms of serum haptoglobin (Hp) as potential biomarkers for HCC of metabolic associated dysfunction associated liver disease (MASLD) and alcohol related liver disease (ALD) etiologies. We analyzed 119 patient samples, including 60 with cirrhosis and 59 with HCC. Isolated Hp protein was digested using trypsin and Glu-C, and site-specific N-glycans were quantified using PRM with LC-HCD-MS/MS. Differential analysis revealed significant variations in fucosylated tetra-antennary glycoforms at N241(VVLHPN²⁴¹YSQVD and VVLHPN²⁴¹YSQVDIGLIK), particularly in distinguishing cirrhosis and HCC (P < 0.05). A combined analysis of identified tetra-antennary fucosylated markers, along with AFP, gender, and age, demonstrated improved AUC. Tetra-antennary glycoforms exhibited an AUC of 0.871 (95% CI: 0.80–0.93) when incorporated into the AFP + age + gender + marker panel compared to AFP alone (0.756) with a sensitivity of 0.763 at a specificity of 0.80. 3-fold cross validation was further used to assess the performance of the optimal biomarker panel. Thus, a combination of fucosylated tetra-antennary glycoforms may serve as important markers for distinguishing HCC from cirrhosis.

Streptococcus pneumoniae (pneumococcus) is a prominent cause of bacterial pneumonia, meningitis, and septicemia, causing high morbidity and high mortality, particularly in children and the elderly. In this study, proteomics- and genomics-based approaches were used for the identification of pneumococcal protein and peptide biomarkers of S. pneumoniae for diagnostics and prospective targets for treatment. Through a pan-genome analysis, 11 S. pneumoniae strains, demonstrating genetic variation within the species, were selected for proteomic characterization. Mass spectrometry-based proteomics, in combination with bacterial surface-shaving, were used to study the cell-surface proteome of S. pneumoniae. The data obtained from three biological replicates per strain were analyzed to identify and rank the proteins and peptides according to their presence in the strains, as well as their presence in all available S. pneumoniae proteomes (8,892) archived in public databases. Several highly ranked proteins have been described as “species-specific” for S. pneumoniae and as surface-associated virulence factors or demonstrate highly antigenic properties. Proteins (34) previously not recognized as S. pneumoniae-specific were proposed to be novel biomarkers, demonstrating high degrees of prevalence in all analyzed proteomes, with little or no sequence similarities to closely related species but common among the genetically diverse strains included in this study.

Early diagnosis of lung cancer is critical for improving patient outcomes. Noninvasive detection techniques based on volatile organic compounds (VOCs) are gaining attention due to their convenience and low risk. This study innovatively explores the application of dichloroacetate (DCA), a multifunctional small molecule, in lung cancer diagnosis by analyzing DCA-induced alterations in VOCs released from normal lung cells (BEAS-2B) and lung cancer cells (PC-9) using solid-phase microextraction coupled with gas chromatography-mass spectrometry (SPME-GC-MS). A DCA concentration of 5 mmol/L was selected to minimize adverse effects on normal cells. Results revealed that DCA induced distinct VOC profiles in normal and cancer cells, suggesting differential metabolic regulation. Concentration-gradient experiments demonstrated that 2-methyl-2-propanol release increased with DCA concentration in both cell types, but cancer cells responded only at higher concentrations. Acetoin level in cancer cells increased with DCA concentration, which was absent in normal cells. Similar results were observed in other lung cancer cell lines (A549 and NCI-H460), confirming reproducible DCA-induced VOC patterns. This study proposes a novel strategy combining DCA intervention with SPME-GC-MS to amplify cancer-specific VOC signatures, providing a promising foundation for breath-based early screening of lung cancer. The findings highlight the potential of metabolic modulation in enhancing noninvasive diagnostic technologies.

Glycosylation is a major protein post-translational modification in snake venom proteins and contributes to the diversification of proteomes. In this study, we carried out an in-depth analysis of the glycosylation profile of seven Bothrops venoms, including neutral sugar quantification, glycoprotein profiling by lectin blot, and determination of N-glycosylation sites in proteins and their N-glycan compositions, by direct, intact glycopeptide analysis by mass spectrometry. Interestingly, all identified N-glycosylated peptides were from enzymatic venom components, mainly proteolytic enzymes that are key in envenomation. All venoms revealed a prominent occurrence of fucose and sialic acid in all N-glycosylated toxins identified. The results indicated that in Bothrops venoms, there is an important level of variation in protein primary structure that is not restricted to regions containing N-sequons. Overall, the signatures of N-glycosylated and nonglycosylated peptide backbones and of N-glycan site occupation by different N-glycans revealed conservation of venom phenotype framework and diversification of N-glycan usage. Hence, the molecular mechanisms of toxin structure and function evolution are at the same time dynamic in that they involve a fine-tuning for the presence of distinct glycans as an evolutionary novelty and are subjected to some conservation that results in the clustering of Bothrops venoms according to the species phylogenetic classification.

A goal of multiomics experiments is to understand how mechanistic molecular biology is altered between conditions, typically a control group and experimental groups. Oftentimes, this involves studying changes in biomolecule relationships (e.g., interactions and metabolic relationships) of several types of biomolecules (e.g., lipids, metabolites, gene products like proteins). Though several databases contain relationships between biomolecules, understudied species may have little to no relationship information in databases and thus must be mined from the literature. There are several challenges to literature mining, including automated full-text extraction, duplicate biomolecule term collapsing, and implementation of complex machine learning tools. To make relationship extraction more accessible to the community, a Python package called DancePartner was developed to allow for the extraction of relationships from literature and databases, with functions to map biomolecule synonyms to standardized identifiers and visualize and characterize the resulting multiomics network. Here, two example data sets are provided to demonstrate the capabilities of DancePartner: one of 14,986 papers and abstracts for Caernohabditis elegans, and another of 33,606 papers and abstracts for Saccharomyces cerevisiae. These relationships are combined with relationships from KEGG, WikiPathways, UniProt, and LipidMaps, and they are visualized. Networks are then compared for their differences in build times.

Molecular heterogeneity in hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) complicates patient stratification. Here, we perform integrative proteomic analysis on 272 early stage HBV–HCC tumors from four East Asian cohorts, uncovering two robust molecular subtypes. Group B (n = 53) is associated with hyperproliferation, oncogenic signaling, and significantly poorer overall (P <0.001) and relapse-free survival (P <0.05). In contrast, group A (n = 219) is enriched in differentiation and metabolic pathways. These findings are validated by matched transcriptomics (n = 108), aligning with established classifications. We identify subtype-specific protein signatures, with CD46, HNF1A, and ATP1B1 exclusively expressed in the aggressive group B. Finally, computational drug sensitivity prediction, validated by molecular docking, nominates Sunitinib as a potential therapy for group B patients. Our work provides a proteomic framework for improved prognostication and targeted therapy in high-risk HBV–HCC.

The study aimed to investigate the acute effects of a single leg press resistance training sessions that differed in training intensity on blood serum proteins using differential scanning calorimetry (DSC), circular dichroism (CD), and fluorescence spectra. Seven men with strength training experience participated in a randomized crossover trial, performing two experimental sessions. Each session included four sets of leg press-to-fall exercises, using loads equivalent to either 30% or 70% of their one-repetition maximum (1RM), with a 3 min rest interval between sets. Aqueous solutions of serum from blood samples taken at baseline (BA) and immediately postexercise (POST) were analyzed. The measurement techniques used allowed to observe postexercise changes in blood serum. Changes were observed in DSC profiles of blood serum, 3D fluorescence maps, and CD spectra. Statistically significant differences between stages “before” and “after” effort have been found for parameters of the endothermic transition associated with the denaturation of serum proteins. The results demonstrate the possibility of monitoring the effect of exercise on serum changes using DSC, CD, and fluorescence.

Despite advancements in screening and treatment, the incidence of breast cancer (BC) and associated mortality are projected to increase. Therefore, developing a companion diagnostic for BC remains important. Herein, we explore the urinary proteome for biomarkers of BC: 130 urine samples from (1) newly diagnosed breast cancer (BC), n = 46, (2) benign breast disease (BBD), n = 36, (3) symptom control (SC), n = 30, and (4) healthy control (HC), n = 18. The BC class included preinvasive: ductal carcinoma in situ (DCIS) (n = 3), invasive ductal carcinoma (IDC) (n = 23), and IDC accompanied by DCIS (n = 8) classes. Protein profiling was performed using ThermoScientific ProteomeDiscoverer and analyzed using MetaboAnalyst v6.0, DAVID, and STRING v12.0. Analyses identified 346 significantly (p < 0.05) differentially expressed proteins (DEP) across BC, BBD, SC, and HC. Multivariate Receiver Operating Characteristic curves (five proteins) suggested Area Under the Curve values of 0.985, 0.989, and 0.999 distinguishing BC from BBD, SC, and HC, respectively. DEP elevated in BC included beta-glucuronidase isoform 1, fibrinogen gamma chain, alpha-actinin-1, peptidase inhibitor 16, cysteine-rich C-terminal protein 1 isoform X1, guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1, vascular cell adhesion protein 1, ATP-dependent translocase ABCB1, and tumor protein p63-regulated gene 1 isoform X1. BC types were differentiated based on calpain-2 and cystatin-C expression (p < 0.05). Thus, BC has distinct urinary–protein profiles based on clinical diagnosis, which could be used in real-time noninvasive BC monitoring.

Xenarthrans─armadillos, anteaters, and sloths─are endemic to the Americas, primarily inhabiting the Neotropics, where they represent a key component of faunal diversity. They have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Despite their frequent occurrence in archeological and paleontological contexts, their identification is often hindered by the highly fragmented and morphologically indistinct nature of bone remains. This limits our ability to track their biogeographic histories, population dynamics, and interactions with past human populations. To address this, we present a novel set of Zooarcheology by Mass Spectrometry (ZooMS) peptide markers for ten extant and extinct Xenarthran species, enabling taxonomic identification of fragmented and morphologically indistinct bone assemblages. By enhancing the taxonomic resolution of fragmented faunal material, this work advances the reconstruction of past species distributions, long-term biodiversity trends, and human–animal interactions. Furthermore, it provides a foundation for an improved understanding of Xenarthran extinction and adaptation dynamics and can support conservation and ecosystem restoration efforts by informing models of historical biogeography and species abundance.

Pelvic lymph node metastasis (PLNM) significantly affects the prognosis of cervical cancer (CC). However, current imaging examinations and serum squamous cell carcinoma antigen (SCCA) testing are inadequate for assessing the pelvic lymph node status in CC. To identify accurate noninvasive biomarkers for diagnosing PLNM and minimizing unnecessary postoperative lymphadenectomy and its associated complications, we performed a comprehensive proteomic and metabolomic analysis of plasma from 124 patients with CC, along with a proteomic analysis of 60 paired tissue samples. Through machine learning methods, we identified potential plasma biomarkers (TTR, MASP2, APOD, and 7α-hydroxy-cholestene-3-one) and constructed a diagnostic model. In the validation cohort, the diagnostic model combined with SCCA exhibited a higher sensitivity (72.4%) than SCCA (64.3%) and imaging examination (14.3%). The plasma protein biomarkers were consistently validated in paired tissue samples. Additionally, immune infiltration analysis demonstrated that CD4 and CD8 T cells were highly infiltrated in the PLNM group, suggesting a potentially enhanced response to immunotherapy. Here, we established a biomarker panel for PLNM and highlighted the altered immune characteristics associated with PLNM, offering valuable insights for the development of immunotherapy strategies for patients with PLNM.