Journal of Proteome Research最新文献

The use of protein biomarkers in blood for clinical settings is limited by the cost and accessibility of traditional venipuncture sampling. The dried blood spot (DBS) technique offers a less invasive and more accessible alternative. However, protein stability in DBS has not been well evaluated. Herein, we deployed a quantitative LC-MS/MS system to construct proteomic atlases of whole blood, DBSs, plasma, and blood cells. Approximately 4% of detected proteins' abundance was significantly altered during blood drying into blood spots, with overwhelming disturbances in cytoplasmic fraction. We also reported a novel finding suggesting a decrease in the level of membrane/cytoskeletal proteins (SLC4A1, RHAG, DSC1, DSP, and JUP) and an increase in the level of proteins (ATG3, SEC14L4, and NRBP1) related to intracellular trafficking. Furthermore, we identified 19 temporally dynamic proteins in DBS samples stored at room temperature for up to 6 months. There were three declined cytoskeleton-related proteins (RDX, SH3BGRL3, and MYH9) and four elevated proteins (XPO7, RAN, SLC2A1, and SLC29A1) involved in cytoplasmic transport as representatives. The instability was governed predominantly by hydrophilic proteins and enhanced significantly with an increasing storage time. Our analyses provide comprehensive knowledge of both short- and long-term storage stability of DBS proteins, forming the foundation for the widespread use of DBS in clinical proteomics and other analytical applications.

Diabetic nephropathy (DN) has become the main cause of end-stage renal disease worldwide, causing significant health problems. Early diagnosis of the disease is quite inadequate. To screen urine biomarkers of DN and explore its potential mechanism, this study collected urine from 87 patients with type 2 diabetes mellitus (which will be classified into normal albuminuria, microalbuminuria, and macroalbuminuria groups) and 38 healthy subjects. Twelve individuals from each group were then randomly selected as the screening cohort for proteomics analysis and the rest as the validation cohort. The results showed that humoral immune response, complement activation, complement and coagulation cascades, renin-angiotensin system, and cell adhesion molecules were closely related to the progression of DN. Five overlapping proteins (KLK1, CSPG4, PLAU, SERPINA3, and ALB) were identified as potential biomarkers by machine learning methods. Among them, KLK1 and CSPG4 were positively correlated with the urinary albumin to creatinine ratio (UACR), and SERPINA3 was negatively correlated with the UACR, which were validated by enzyme-linked immunosorbent assay (ELISA). This study provides new insights into disease mechanisms and biomarkers for early diagnosis of DN.

Tumor necrosis factor (TNF) has well-established roles in neuroinflammatory disorders, but the effect of TNF on the biochemistry of brain cells remains poorly understood. Here, we microinjected TNF into the brain to study its impact on glial and neuronal metabolism (glycolysis, pentose phosphate pathway, citric acid cycle, pyruvate dehydrogenase, and pyruvate carboxylase pathways) using ¹³C NMR spectroscopy on brain extracts following intravenous [1,2-¹³C]-glucose (to probe glia and neuron metabolism), [2-¹³C]-acetate (probing astrocyte-specific metabolites), or [3-¹³C]-lactate. An increase in [4,5-¹³C]-glutamine and [2,3-¹³C]-lactate coupled with a decrease in [4,5-¹³C]-glutamate was observed in the [1,2-¹³C]-glucose-infused animals treated with TNF. As glutamine is produced from glutamate by astrocyte-specific glutamine synthetase the increase in [4,5-¹³C]-glutamine reflects increased production of glutamine by astrocytes. This was confirmed by infusion with astrocyte substrate [2-¹³C]-acetate. As lactate is metabolized in the brain to produce glutamate, the simultaneous increase in [2,3-¹³C]-lactate and decrease in [4,5-¹³C]-glutamate suggests decreased lactate utilization, which was confirmed using [3-¹³C]-lactate as a metabolic precursor. These results suggest that TNF rearranges the metabolic network, disrupting the energy supply chain perturbing the glutamine-glutamate shuttle between astrocytes and the neurons. These insights pave the way for developing astrocyte-targeted therapeutic strategies aimed at modulating effects of TNF to restore metabolic homeostasis in neuroinflammatory disorders.

Proteome coverage and accurate protein quantification are both important for evaluating biological systems; however, compromises between quantification, coverage, and mass spectrometry (MS) resources are often necessary. Consequently, experimental parameters that impact coverage and quantification must be adjusted, depending on experimental goals. Among these parameters is offline prefractionation, which is utilized in MS-based proteomics to decrease sample complexity resulting in higher overall proteome coverage upon MS analysis. Prefractionation leads to increases in required MS analysis time, although this is often mitigated by isobaric labeling using tandem-mass tags (TMT), which allow samples to be multiplexed. Here we evaluate common prefractionation schemes, TMT variants, and MS acquisition methods and their impact on protein quantification and coverage. Furthermore, we provide recommendations for experimental design depending on the experimental goals.

Plasma membrane proteins (PMPs) play critical roles in a myriad of physiological and disease conditions. A unique subset of PMPs functions through interacting with each other in trans at the interface between two contacting cells. These trans-interacting PMPs (tiPMPs) include adhesion molecules and ligands/receptors that facilitate cell-cell contact and direct communication between cells. Among the tiPMPs, a significant number have apparent extracellular binding domains but remain orphans with no known binding partners. Identification of their potential binding partners is therefore important for the understanding of processes such as organismal development and immune cell activation. While a number of methods have been developed for the identification of protein binding partners in general, very few are applicable to tiPMPs, which interact in a two-dimensional fashion with low intrinsic binding affinities. In this review, we present the significance of tiPMP interactions, the challenges of identifying binding partners for tiPMPs, and the landscape of method development. We describe current avidity-based screening approaches for identifying novel tiPMP binding partners and discuss their advantages and limitations. We conclude by highlighting the importance of developing novel methods of identifying new tiPMP interactions for deciphering the complex protein interactome and developing targeted therapeutics for diseases.

Antibody sequences can be determined at 99% accuracy directly from the polypeptide product by using bottom-up proteomics techniques. Sequencing accuracy at the peptide level is limited by the isobaric residues leucine and isoleucine, incomplete fragmentation spectra in which the order of two or more residues remains ambiguous due to lacking fragment ions for the intermediate positions, and isobaric combinations of amino acids, of potentially different lengths, for example, GG = N and GA = Q. Here, we present several updates to Stitch (v1.5), which performs template-based assembly of de novo peptides to reconstruct antibody sequences. This version introduces a mass-based alignment algorithm that explicitly accounts for mass coincidence errors. In addition, it incorporates a postprocessing procedure to assign I/L residues based on secondary fragments (satellite ions, i.e., w-ions). Moreover, evidence for sequence assignments can now be directly evaluated with the addition of an integrated spectrum viewer. Lastly, input data from a wider selection of de novo peptide sequencing algorithms are allowed, now including Casanovo, PEAKS, Novor.Cloud, pNovo, and MaxNovo, in addition to flat text and FASTA. Combined, these changes make Stitch compatible with a larger range of data processing pipelines and improve its tolerance to peptide-level sequencing errors.

Native top-down mass spectrometry (nTDMS) allows characterization of protein structure and noncovalent interactions with simultaneous sequence mapping and proteoform characterization. The majority of nTDMS studies utilize purified recombinant proteins, with significant challenges hindering application to endogenous systems. To perform native top-down proteomics (nTDP), where endogenous proteins from complex biological systems are analyzed by nTDMS, it is essential to separate proteins under nondenaturing conditions. However, it remains difficult to achieve high resolution with MS-compatible online chromatography while preserving protein tertiary structure and noncovalent interactions. Herein, we report the use of online mixed-bed ion exchange chromatography (IEC) to enable separation of endogenous proteins from complex mixtures under nondenaturing conditions, preserving noncovalent interactions for nTDP analysis. We have successfully detected large proteins (>146 kDa) and identified endogenous metal-binding and oligomeric protein complexes in human heart tissue lysate. The use of a mixed-bed stationary phase allowed retention and elution of proteins over a wide range of isoelectric points without altering the sample or mobile phase pH. Overall, our method provides a simple online IEC-MS platform that can effectively separate proteins from complex mixtures under nondenaturing conditions and preserve higher-order structure for nTDP applications.

Chromatography-mass spectrometry-based lipidomics represents an essential tool for elucidating lipid dysfunction mechanisms and is extensively employed in investigating disease mechanisms and identifying biomarkers. However, the detection of low-abundance lipids in biological matrices, along with cumbersome operational procedures, complicates comprehensive lipidomic analyses, necessitating the development of highly sensitive, environmentally friendly, and automated methods. In this study, an online phase transition trapping-supercritical fluid extraction-chromatography-mass spectrometry (PTT-SFEC-MS/MS) method was developed and successfully applied to plasma lipidomics analysis in Type 1 diabetes (T1D) rats. The PTT strategy captured entire extracts at the column head by converting CO₂ from a supercritical state to a gaseous state, thereby preventing peak spreading, enhancing peak shape for precise quantification, and boosting sensitivity without any sample loss. This method utilized only 5 μL of plasma and accomplished sample extraction, separation, and detection within 27 min. Ultimately, 77 differential lipids were identified, including glycerophospholipids, sphingolipids, and glycerolipids, in T1D rat plasma. The results indicated that the progression of the disease might be linked to alterations in glycerophospholipid and sphingolipid metabolism. Our findings demonstrated a green, highly efficient, and automated method for the lipidomics analysis of biological samples, providing a scientific foundation for understanding the pathogenesis and diagnosis of T1D.

Cell-cell interactions, which allow cells to communicate with each other through molecules in their microenvironment, are critical for the growth, health, and functions of cells. Previous studies show that drug-resistant cells can interact with drug-sensitive cells to elevate their drug resistance level, which is partially responsible for cancer recurrence. Studying protein targets and pathways involved in cell-cell communication provides essential information for fundamental cell biology studies and therapeutics of human diseases. In the current studies, we performed direct coculture and indirect coculture of drug-resistant and drug-sensitive cell lines, aiming to investigate intracellular proteins responsible for cell communication. Comparative studies were carried out using monoculture cells. Shotgun bottom-up proteomics results indicate that the P53 signaling pathway has a strong association with drug resistance mechanisms, and multiple TP53-related proteins were upregulated in both direct and indirect coculture systems. In addition, cell-cell communication pathways, including the phagosome and the HIF-signaling pathway, contribute to both direct and indirect coculture systems. Consequently, AK3 and H3-3A proteins were identified as potential targets for cell-cell interactions that are relevant to drug resistance mechanisms. We propose that the P53 signaling pathway, in which mitochondrial proteins play an important role, is responsible for inducing drug resistance through communication between drug-resistant and drug-sensitive cancer cells.

Protein succinylation modification is a common post-translational modification (PTM) that plays an important role in bacterial metabolic regulation. In this study, quantitative analysis was conducted on the succinylated proteome of wild-type and florfenicol-resistant Vibrio alginolyticus to investigate the mechanism of succinylation regulating antibiotic resistance. Bioinformatic analysis showed that the differentially succinylated proteins were mainly enriched in energy metabolism, and it was found that the succinylation level of phosphoenolpyruvate carboxyl kinase (PEPCK) was highly expressed in the florfenicol-resistant strain. Site-directed mutagenesis was used to mutate the lysine (K) at the succinylation site of PEPCK to glutamic acid (E) and arginine (R), respectively, to investigate the function of lysine succinylation of PEPCK in the florfenicol resistance of V. alginolyticus. The detection of site-directed mutagenesis strain viability under florfenicol revealed that the survival rate of the E mutant was significantly higher than that of the R mutant and wild type, indicating that succinylation modification of PEPCK protein may affect the resistance of V. alginolyticus to florfenicol. This study indicates the important role of PEPCK during V. alginolyticus antibiotic-resistance evolution and provides a theoretical basis for the prevention and control of vibriosis and the development of new antibiotics.