Data-independent acquisition (DIA): An emerging proteomics technology for analysis of drug-metabolizing enzymes and transporters

Proteomics-based host-specific biomarkers for tuberculosis: The future of TB diagnosis

Tuberculosis (TB) is an infectious disease that remains one of the major global public health concerns. Early detection of Active Pulmonary TB is therefore of utmost importance for controlling lethality and disease spreading. Currently available TB diagnostics can be broadly categorized into microscopy, culture-based, and molecular approaches, all of which come with compromised sensitivity, limited efficacy, and high expenses. Hence, rapid, sensitive, and affordable diagnostic methods for TB is the current prerequisite for disease management. This review summarizes the proteomics investigations for host-specific biomarkers from serum, sputum, saliva, and urine samples of TB patients, along with patients having comorbidity. Thorough data mining from available literature led us to conclude that the host-specific proteins involved in immunity and defense, metabolic regulation, cellular adhesion, and motility, inflammatory responses, and tissue remodelling have shown significant deregulation upon Mycobacterium tuberculosis (Mtb) infection. Notably, the immunoregulatory protein orosomucoid (ORM) was up-regulated in active TB compared to non-TB individuals, as observed in multiple studies from diverse sample types. Mannose receptor C type 2 (MRC2) was identified as an upregulated, treatment response biomarker in two independent serum proteomics investigations. Thorough mechanistic investigation on these candidate proteins would be fascinating to dig into potential drug targets and customized therapeutics for TB patients, along with their diagnostic potentials.

RNA Isoforms as Broad Targets for Cancer Immunotherapy

While immunotherapy is typically reserved for cancer patients with a high mutational burden, neoantigens produced from post-transcriptional regulation provide a possible untapped reservoir of common immunogenic targets for new targeted cancer therapies. In this review, we describe new and emerging technologies, unconventional molecular targets and challenges for the precision immune targeting of diverse malignancies. In particular, we focus on the unique potential of targeting alternative mRNA isoforms as a source for broadly presented neoantigens and cell surface proteins. Finally, we discuss emerging challenges for alternative isoform immune targeting, with an emphasis in silico prioritization and high-throughput target validation.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Protein Profiles and Novel Molecular Biomarkers of Schizophrenia Based on 4D-DIA Proteomics

Schizophrenia is a severe psychological disorder. The current diagnosis mainly relies on clinical symptoms and lacks laboratory evidence, which makes it very difficult to make an accurate diagnosis especially at an early stage. Plasma protein profiles of schizophrenia patients were obtained and compared with healthy controls using 4D-DIA proteomics technology. Furthermore, 79 DEPs were identified between schizophrenia and healthy controls. GO functional analysis indicated that DEPs were predominantly associated with responses to toxic substances and platelet aggregation, suggesting the presence of metabolic and immune dysregulation in patients with schizophrenia. KEGG pathway enrichment analysis revealed that DEPs were primarily enriched in the chemokine signaling pathway and cytokine receptor interactions. A diagnostic model was ultimately established, comprising three proteins, namely, PFN1, GAPDH and ACTBL2. This model demonstrated an AUC value of 0.972, indicating its effectiveness in accurately identifying schizophrenia. PFN1, GAPDH and ACTBL2 exhibit potential as biomarkers for the early detection of schizophrenia. The findings of our studies provide novel insights into the laboratory-based diagnosis of schizophrenia.

MSe Collision Energy Optimization for the Analysis of Membrane Proteins Using HDX-cIMS

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) has evolved as an essential technique in structural proteomics. The use of ion mobility separation (IMS) coupled to HDX-MS has increased the applicability of the technique to more complex systems and has been shown to improve data quality and robustness. The first step when running any HDX-MS workflow is to confirm the sequence and retention time of the peptides resulting from the proteolytic digestion of the nondeuterated protein. Here, we optimized the collision energy ramp of HDMSE experiments for membrane proteins using a Waters SELECT SERIES cIMS-QTOF system following an HDX workflow using Phosphorylase B, XylE transporter, and Smoothened receptor (SMO) as model systems. Although collision energy (CE) ramp 10-50 eV gave the highest amount of positive identified peptides when using Phosphorylase B, XylE, and SMO, results suggest optimal CE ramps are protein specific, and different ramps can produce a unique set of peptides. We recommend cIMS users use different CE ramps in their HDMSE experiments and pool the results to ensure maximum peptide identifications. The results show how selecting an appropriate CE ramp can change the sequence coverage of proteins ranging from 4 to 94%.

Resistin as a potential diagnostic biomarker for sepsis: insights from DIA and ELISA analyses

PURPOSE

The primary objective of this investigation is to systematically screen and identify differentially expressed proteins (DEPs) within the plasma of individuals afflicted with sepsis. This endeavor employs both Data-Independent Acquisition (DIA) and enzyme-linked immunosorbent assay (ELISA) methodologies. The overarching goal is to furnish accessible and precise serum biomarkers conducive to the diagnostic discernment of sepsis.

METHOD

The study encompasses 53 sepsis patients admitted to the Affiliated Hospital of Southwest Medical University between January 2019 and December 2020, alongside a control cohort consisting of 16 individuals devoid of sepsis pathology. Subsequently, a subset comprising 10 randomly selected subjects from the control group and 22 from the sepsis group undergoes quantitative proteomic analysis via DIA. The acquired data undergoes Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analyses, facilitating the construction of a Protein-Protein Interaction (PPI) network to discern potential markers. Validation of core proteins is then accomplished through ELISA. Comparative analysis between the normal and sepsis groups ensues, characterized by Receiver Operating Characteristic (ROC) curve construction to evaluate diagnostic efficacy.

RESULT

A total of 187 DEPs were identified through bioinformatic methodologies. Examination reveals their predominant involvement in biological processes such as wound healing, coagulation, and blood coagulation. Functional pathway analysis further elucidates their engagement in the complement pathway and malaria. Resistin emerges as a candidate plasma biomarker, subsequently validated through ELISA. Notably, the protein exhibits significantly elevated levels in the serum of sepsis patients compared to the normal control group. ROC curve analysis underscores the robust diagnostic capacity of these biomarkers for sepsis.

CONCLUSION

Data-Independent Acquisition (DIA) and Enzyme-Linked Immunosorbent Assay (ELISA) show increased Resistin levels in sepsis patients, suggesting diagnostic potential, warranting further research.

New insights into oocyte cytoplasmic lattice-associated proteins

Oocyte maturation and preimplantation embryo development are critical to successful pregnancy outcomes and the correct establishment and maintenance of genomic imprinting. Thanks to novel technologies and omics studies in human patients and mouse models, the importance of the proteins associated with the cytoplasmic lattices (CPLs), highly abundant structures found in the cytoplasm of mammalian oocytes and preimplantation embryos, in the maternal to zygotic transition is becoming increasingly evident. This review highlights the recent discoveries on the role of these proteins in protein storage and other oocyte cytoplasmic processes, epigenetic reprogramming, and zygotic genome activation (ZGA). A better comprehension of these events may significantly improve clinical diagnosis and pave the way for targeted interventions aiming to correct or mitigate female fertility issues and genomic imprinting disorders.

Proteomic profiling of prostate cancer reveals molecular signatures under antiandrogen treatment

BACKGROUND

Tumorigenesis and progression of prostate cancer (PCa) are indispensably dependent on androgen receptor (AR). Antiandrogen treatment is the principal preference for patients with advanced PCa. However, the molecular characteristics of PCa with antiandrogen intervention have not yet been fully uncovered.

METHODS

We first performed proteome analysis with 32 PCa tumor samples and 10 adjacent tissues using data-independent acquisition (DIA)- parallel accumulation serial fragmentation (PASEF) proteomics. Then label-free quantification (LFQ) mass spectrometry was employed to analyze protein profiles in LNCaP and PC3 cells.

RESULTS

M-type creatine kinase CKM and cartilage oligomeric matrix protein COMP were demonstrated to have the potential to be diagnostic biomarkers for PCa at both mRNA and protein levels. Several E3 ubiquitin ligases and deubiquitinating enzymes (DUBs) were significantly altered in PCa and PCa cells under enzalutamide treatment, and these proteins might reprogram proteostasis at protein levels in PCa. Finally, we discovered 127 significantly varied proteins in PCa samples with antiandrogen therapy and further uncovered 4 proteins in LNCaP cells upon enzalutamide treatment.

CONCLUSIONS

Our research reveals new potential diagnostic biomarkers for prostate cancer and might help resensitize resistance to antiandrogen therapy.

Comparative Proteomic Analysis of Ridge Gourd Seed (Luffa acutangula (L.) Roxb.) during Artificial Aging

Seed aging is a complicated process influenced by environmental conditions, impacting biochemical processes in seeds and causing deterioration that results in reduced viability and vigor. In this study, we investigated the seed aging process of ridge gourd, which is one of the most exported commercial seeds in Thailand using sequential window acquisition of all theoretical fragment ion spectra mass spectrometry. A total of 855 proteins were identified among the two groups (0 d/15 d and 0 d/30 d). The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of differentially expressed proteins revealed that in ridge gourd seeds, the aging process altered the abundance of proteins related to the oxidative stress response, nutrient reservoir, and metabolism pathway. The most identified DEPs were mitochondrial proteins, ubiquitin-proteasome system proteins, ribosomal proteins, carbohydrate metabolism-related proteins, and stress response-related proteins. This study also presented the involvement of aconitase and glutathione pathway-associated enzymes in seed aging, with aconitase and total glutathione being determined as possible suggestive biomarkers for aged ridge gourd seeds. This acquired knowledge has the potential to considerably improve growing methods and seed preservation techniques, enhancing seed storage and maintenance.

Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP

Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 m/z), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% n-dodecyl-β-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts.

Unveiling the power of proteomics in advancing tropical animal health and production

Proteomics, the large-scale study of proteins in biological systems has emerged as a pivotal tool in the field of animal and veterinary sciences, mainly for investigating local and rustic breeds. Proteomics provides valuable insights into biological processes underlying animal growth, reproduction, health, and disease. In this review, we highlight the key proteomics technologies, methodologies, and their applications in domestic animals, particularly in the tropical context. We also discuss advances in proteomics research, including integration of multi-omics data, single-cell proteomics, and proteogenomics, all of which are promising for improving animal health, adaptation, welfare, and productivity. However, proteomics research in domestic animals faces challenges, such as sample preparation variation, data quality control, privacy and ethical considerations relating to animal welfare. We also provide recommendations for overcoming these challenges, emphasizing the importance of following best practices in sample preparation, data quality control, and ethical compliance. We therefore aim for this review to harness the full potential of proteomics in advancing our understanding of animal biology and ultimately improve animal health and productivity in local breeds of diverse animal species in a tropical context.

Using Omics to Identify Novel Therapeutic Targets in Heart Failure

Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies.

Increasing protein identifications in bottom-up proteomics of T. castaneum - Exploiting synergies of protein biochemistry and bioinformatics

Depending on the respective research question, LC-MS/MS based bottom-up proteomics poses challenges from the initial biological sample all the way to data evaluation. The focus of this study was to investigate the influence of sample preparation techniques and data analysis parameters on protein identification in Tribolium castaneum by applying free software proteomics platform Max Quant. Multidimensional protein extraction strategies in combination with electrophoretic or chromatographic off-line protein pre-fractionation were applied to enhance the spectrum of isolated proteins from T. castaneum and reduce the effect of co-elution and ion suppression effects during nano-LC-MS/MS measurements of peptides. For comprehensive data analysis, MaxQuant was used for protein identification and R for data evaluation. A wide range of parameters were evaluated to gain reproducible, reliable, and significant protein identifications. A simple phosphate buffer, pH 8, containing protease and phosphatase inhibitor cocktail and application of gentle extraction conditions were used as a first extraction step for T.castaneum proteins. Furthermore, a two-dimensional extraction procedure in combination with electrophoretic pre-fractionation of extracted proteins and subsequent in-gel digest resulted in almost 100% increase of identified proteins when compared to chromatographic fractionation as well as one-pot-analysis. The additionally identified proteins could be assigned to new molecular functions or cell compartments, emphasizing the positive effect of extended sample preparation in bottom-up proteomics. Besides the number of peptides during post-processing, MaxQuant's Match between Runs exhibited a crucial effect on the number of identified proteins. A maximum relative standard deviation of 2% must be considered for the data analysis. Our work with Tribolium castaneum larvae demonstrates that sometimes - depending on matrix and research question - more complex and time-consuming sample preparation can be advantageous for isolation and identification of additional proteins in bottom-up proteomics.

Noncanonical microprotein regulation of immunity

The immune system is highly regulated but, when dysregulated, suboptimal protective or overly robust immune responses can lead to immune-mediated disorders. The genetic and molecular mechanisms of immune regulation are incompletely understood, impeding the development of more precise diagnostics and therapeutics for immune-mediated disorders. Recently, thousands of previously unrecognized noncanonical microprotein genes encoded by small open reading frames have been identified. Many of these microproteins perform critical functions, often in a cell- and context-specific manner. Several microproteins are now known to regulate immunity; however, the vast majority are uncharacterized. Therefore, illuminating what is often referred to as the "dark proteome," may present opportunities to tune immune responses more precisely. Here, we review noncanonical microprotein biology, highlight recently discovered examples regulating immunity, and discuss the potential and challenges of modulating dysregulated immune responses by targeting microproteins.

MassDash: A Web-Based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization

With the increased usage and diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools, including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity, providing additional insights into the data. The modular framework is easily extendable, enabling rapid algorithm development of novel peak-picker techniques, such as deep-learning-based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app.

Uncommon posttranslational modifications in proteomics: ADP-ribosylation, tyrosine nitration, and tyrosine sulfation

Posttranslational modifications (PTMs) are covalent modifications of proteins that modulate the structure and functions of proteins and regulate biological processes. The development of various mass spectrometry-based proteomics workflows has facilitated the identification of hundreds of PTMs and aided the understanding of biological significance in a high throughput manner. Improvements in sample preparation and PTM enrichment techniques, instrumentation for liquid chromatography-tandem mass spectrometry (LC-MS/MS), and advanced data analysis tools enhance the specificity and sensitivity of PTM identification. Highly prevalent PTMs like phosphorylation, glycosylation, acetylation, ubiquitinylation, and methylation are extensively studied. However, the functions and impact of less abundant PTMs are not as well understood and underscore the need for analytical methods that aim to characterize these PTMs. This review focuses on the advancement and analytical challenges associated with the characterization of three less common but biologically relevant PTMs, specifically, adenosine diphosphate-ribosylation, tyrosine sulfation, and tyrosine nitration. The advantages and disadvantages of various enrichment, separation, and MS/MS techniques utilized to identify and localize these PTMs are described.

Experimental and data analysis advances in thermal proteome profiling

Method development for mass spectrometry (MS)-based thermal shift proteomic assays have advanced to probe small molecules with known and unknown protein-ligand interaction mechanisms and specificity, which is predominantly used in characterization of drug-protein interactions. In the discovery of target and off-target protein-ligand interactions, a thorough investigation of method development and their impact on the sensitivity and accuracy of protein-small molecule and protein-protein interactions is warranted. In this review, we discuss areas of improvement at each stage of thermal proteome profiling data analysis that includes processing of MS-based data, method development, and their effect on the overall quality of thermal proteome profiles. We also overview the optimization of experimental strategies and prioritization of an increased number of independent biological replicates over the number of evaluated temperatures.

Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells

Cervical cancer (CC) is a major global health problem and leading cause of cancer deaths among women worldwide. Early detection through screening programs has reduced mortality; however, screening compliance remains low. Identifying non-invasive biomarkers through proteomics for diagnosis and monitoring response to treatment could improve patient outcomes. Here we review recent proteomics studies which have uncovered biomarkers and potential drug targets for CC. Additionally, we explore into the role of cervical cancer stem cells and their potential implications in driving CC progression and therapy resistance. Although challenges remain, proteomics has the potential to revolutionize the field of cervical cancer research and improve patient outcomes.

Intra-ovarian inflammatory states and their associations with embryo quality in normal-BMI PCOS patients undergoing IVF treatment

BACKGROUND

Polycystic ovary syndrome (PCOS) is the main cause of anovulatory infertility in women of reproductive age, and low-grade chronic inflammation plays a key role in the occurrence and development of PCOS. However, obesity, as a likely confounding factor, can affect the inflammatory state of PCOS patients.

OBJECTIVE

The aim of this study was to comprehensively investigate intra-ovarian inflammatory states and their impact on embryo quality in PCOS patients with a normal BMI undergoing IVF treatment.

METHODS

DIA-mass spectrometry-based proteomics and bioinformatic analysis were combined to comprehensively profile the protein expression of granulosa cells (GCs) from 5 normal-BMI PCOS patients and 5 controls. Thirty-four cytokines were further systematically detected in follicular fluid (FF) from 32 age- and BMI-matched normal-BMI patients using Luminex liquid chip suspension technology. Next, the differentially expressed cytokines were evaluated by enzyme-linked immunosorbent assay (ELISA) in 24 newly recruited subjects, and the relationship between these cytokines and embryo quality in PCOS patients was analysed. Finally, these cytokine levels were compared and evaluated in PCOS patients with different androgen levels.

RESULTS

Proteomic analysis showed that the suppression of substance metabolism and steroid biosynthesis, more interestingly, resulted in an enhanced immune and inflammatory response in the GCs of normal-BMI PCOS patients and prompted the involvement of cytokines in this process. Luminex analysis further showed that FF macrophage inflammatory protein-1 beta (MIP-1β) and stromal cell-derived factor-1 alpha (SDF-1α) levels were significantly increased in normal-BMI PCOS patients compared to controls (P = 0.005; P = 0.035, respectively), and the ELISA results were consistent with these findings. Besides, FF MIP-1β showed an inverse correlation with the number of D3 good-quality embryos and the good-quality blastocyst rate in patients with PCOS (P = 0.006; P = 0.003, respectively), which remained significant after correction for multiple comparisons. Moreover, SDF-1α levels had no relationship with embryo development in PCOS patients. Additionally, SDF-1α levels were significantly lower in PCOS patients with high androgen levels than in controls (P = 0.031).

CONCLUSIONS

Local ovarian inflammation was present in normal-BMI PCOS patients, affecting follicular development, and FF MIP-1β may be a potential biomarker associated with embryo quality in normal-BMI PCOS patients.

Data independent acquisition for gas chromatographic MS/MS analysis of volatile compounds

This study presents a novel tandem mass spectrometry (MS/MS) approach utilizing a data independent acquisition (DIA) concept specifically designed with gas chromatography-electron ionization-triple quadrupole mass spectrometry (GC-EI-QqQMS). This allows compound identification based on comparison between all the experimental MS/MS product ion spectra and the simulated library data of >1,000 MS/MS transitions of 71 compounds. The simulation data were generated by using the Competitive Fragmentation Modeling (CFM-ID) 3.0 program. The approach for calculation of the DIA MS/MS library match scores was then established and applied for identification of a range of terpenoids and oxygenated compounds in perfume. The identity of each peak was confirmed using 4-241 MS/MS transitions. The established data collection and analysis methods are expected to be useful for increased confidence in volatile compound analysis.

Candidate biomarkers in brown adipose tissue for post-mortem diagnosis of fatal hypothermia

Post-mortem diagnosis of fatal hypothermia (FHT) is challenging in forensic practice because traditional morphological and biochemical methods lack specificity. Recent studies have reported that brown adipose tissue (BAT) is activated during cold-induced non-shivering thermogenesis in mammals, but BAT has not been used to diagnose FHT. The aim of this study was to identify novel biomarkers in BAT for FHT based on morphological changes and differential protein expression. Two FHT animal models were created by exposing mice to 4 or -20 °C at 50% humidity. Morphologically, the unilocular lipid droplet content was significantly increased in BAT of FHT model mice compared with that of control mice. Proteomics analysis revealed a total of 283 and 266 differentially expressed proteins (DEPs) between the 4 or -20 °C FHT subgroups and control group, respectively. In addition, 140 proteins were shared between the FHT subgroups. GO and KEGG analyses revealed that the shared DEPs were mainly enriched in pathways associated with metabolism, oxidative phosphorylation, and thermogenesis. Further screening (|log2FC| > 1.6, q-value (FDR) < 0.05) identified GMFB, KDM1A, DDX6, RAB1B, SHMT-1, CLPTM1, and LMF1 as candidate biomarkers of FHT. Subsequent validation experiments were performed in FHT model mice using classic immunohistochemistry and western blotting. RAB1B and GMFB expression was further verified in BAT specimens from human cases of FHT. The results demonstrate that BAT can be used as a target organ for FHT diagnosis employing RAB1B and GMFB as biological markers, thus providing a new strategy for the post-mortem diagnosis of FHT in forensic practice.

Label-Free Quantitation of Endogenous Peptides

Liquid chromatography-mass spectrometry (LC-MS)-based peptidomics methods allow for the detection and identification of many peptides in a complex biological mixture in an untargeted manner. Quantitative peptidomics approaches allow for comparisons of peptide abundance between different samples, allowing one to draw conclusions about peptide differences as a function of experimental treatment or physiology. While stable isotope labeling is a powerful approach for quantitative proteomics and peptidomics, advances in mass spectrometry instrumentation and analysis tools have allowed label-free methods to gain popularity in recent years. In a general label-free quantitative peptidomics experiment, peak intensity information for each peptide is compared across multiple LC-MS runs. Here, we outline a general approach for label-free quantitative peptidomics experiments, including steps for sample preparation, LC-MS data acquisition, data processing, and statistical analysis. Special attention is paid to address run-to-run variability, which can lead to several major problems in label-free experiments. Overall, our method provides researchers with a framework for the development of their own quantitative peptidomics workflows applicable to quantitation of peptides from a wide variety of different biological sources.

Genomic and proteomic analysis of Salmonella Enteritidis isolated from a patient with foodborne diarrhea

Salmonella is a major cause of foodborne diseases and clinical infections worldwide. This study aimed to investigate the drug resistance, genomic characteristics, and protein expression of foodborne Salmonella in Shanxi Province. We isolated a strain of Salmonella Enteritidis from patient feces and designated it 31A. The drug resistance of 31A against 14 antibiotics was determined using an antimicrobial susceptibility test. Whole-genome sequencing and quantitative proteomic analysis were performed on the 31A strain. Functional annotation of drug resistance genes/proteins and virulence genes/proteins was conducted using various databases, such as VFDB, ARDB, CAZY, COG, KOG, CARD, GO, and KEGG. The focus of this study was understanding the mechanisms related to food poisoning, and the genetic evolution of 31A was analyzed through comparative genomics. The 31A strain belonged to ST11 Salmonella Enteritidis and showed resistance to β-lactam and quinolone antibiotics. The genome of 31A had 70 drug resistance genes, 321 virulence genes, 12 SPIs, and 3 plasmid replicons. Functional annotation of these drug resistance and virulence genes revealed that drug resistance genes were mainly involved in defense mechanisms to confer resistance to antibiotics, while virulence genes were mainly associated with cellular motility. There were extensive interactions among the virulence genes, which included SPI-1, SPI-2, flagella, fimbriae, capsules and so on. The 31A strain had a close relationship with ASM2413794v1 and ASM130523v1, which were also ST11 Salmonella Enteritidis strains from Asia and originated from clinical patients, animals, and food. These results suggested minimal genomic differences among strains from different sources and the potential for interhost transmission. Differential analysis of the virulence and drug resistance-related proteins revealed their involvement in pathways related to human diseases, indicating that these proteins mediated bacterial invasion and infection. The integration of genomic and proteomic information led to the discovery that Salmonella can survive in a strong acid environment through various acid resistance mechanisms after entering the intestine with food and then invade intestinal epithelial cells to exert its effects. In this study, we comprehensively analyzed the drug resistance and virulence characteristics of Salmonella Enteritidis 31A using a combination of genomic and proteomic approaches, focusing on the pathogenic mechanism of Salmonella Enteritidis in food poisoning. We found significant fluctuations in various virulence factors during the survival, invasion, and infection of Salmonella Enteritidis, which collectively contributed to its pathogenicity. These results provide important information for the source tracing, prevention, and treatment of clinical infections caused by Salmonella Enteritidis.

4D DIA-PRM proteomic study identifying modulated pathways and biomarkers associated with pelvic organ prolapse

Pelvic organ prolapse (POP) is a highly disabling condition that negatively affects the quality of life of millions of women worldwide. However, the underlying mechanisms associated with the development and progression of the disease remain poorly understood. Here, an untargeted four-dimensional data-independent acquisition (4D DIA)-based proteomics approach was applied to vaginal wall tissue samples from POP (n = 19) and control (n = 8) patients to identify potential diagnostic biomarker(s) for POP and examine the molecular mechanisms underlying the disease. Of the 5713 tissue proteins that were detected, 1957 proteins were significantly changed in POP patients. Further bioinformatics analysis revealed that multiple biological processes including protein digestion & absorption, retrograde endocannabinoid signaling, tyrosine metabolism, and nucleotide metabolism were significantly enriched and associated with the pathogenesis of POP. Interestingly, 16 of these differentially expressed proteins associated with four pathways were also identified by targeted parallel reaction monitoring (PRM) proteomics analysis on the same 27 tissue samples. Changes in 94 % (15/16) of these proteins were consistent with the 4D DIA data. Furthermore, most proteins displayed good diagnostic accuracy with high area under the curve (AUC) values (AUC＞0.8). Specifically, five proteins including ELN, COL6A2, ENTPD1, AOC3, and COX7A2 distinguished between POP and control patients with very high accuracy (AUC ≥ 0.95) in both 4D DIA and PRM analyses, and may therefore be potential diagnostic biomarkers for POP. In summary, the present study not only provided several potential biomarker(s) for effective POP diagnosis, but extended our knowledge of the key regulatory pathways associated with the disease.

Advances in minimal residual disease monitoring in multiple myeloma

Multiple myeloma (MM) is characterized by the clonal expansion of plasma cells and the excretion of a monoclonal immunoglobulin (M-protein), or fragments thereof. This biomarker plays a key role in the diagnosis and monitoring of MM. Although there is currently no cure for MM, novel treatment modalities such as bispecific antibodies and CAR T-cell therapies have led to substantial improvement in survival. With the introduction of several classes of effective drugs, an increasing percentage of patients achieve a complete response. This poses new challenges to traditional electrophoretic and immunochemical M-protein diagnostics because these methods lack sensitivity to monitor minimal residual disease (MRD). In 2016, the International Myeloma Working Group (IMWG) expanded their disease response criteria with bone marrow-based MRD assessment using flow cytometry or next-generation sequencing in combination with imaging-based disease monitoring of extramedullary disease. MRD status is an important independent prognostic marker and its potential as a surrogate endpoint for progression-free survival is currently being studied. In addition, numerous clinical trials are investigating the added clinical value of MRD-guided therapy decisions in individual patients. Because of these novel clinical applications, repeated MRD evaluation is becoming common practice in clinical trials as well as in the management of patients outside clinical trials. In response to this, novel mass spectrometric methods that have been developed for blood-based MRD monitoring represent attractive minimally invasive alternatives to bone marrow-based MRD evaluation. This paves the way for dynamic MRD monitoring to allow the detection of early disease relapse, which may prove to be a crucial factor in facilitating future clinical implementation of MRD-guided therapy. This review provides an overview of state-of-the-art of MRD monitoring, describes new developments and applications of blood-based MRD monitoring, and suggests future directions for its successful integration into the clinical management of MM patients.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

The Resistance and Virulence Characteristics of Salmonella Enteritidis Strain Isolated from Patients with Food Poisoning Based on the Whole-Genome Sequencing and Quantitative Proteomic Analysis

Objective

This paper explores the drug resistance, genome and proteome expression characteristics of Salmonella from a food poisoning event.

Methods

A multidrug-resistant Salmonella Enteritidis strain, labeled as 27A, was isolated and identified from a food poisoning patient. Antimicrobial susceptibility testing determined the resistance of 27A strain to 14 antibiotics. Then, WGS analysis and comparative genomics analysis were performed on 27A, and the functional annotation of resistance genes, virulence genes were performed based on VFDB, ARDB, COG, CARD, GO, KEGG, and CAZY databases. Meanwhile, based on iTRAQ technology, quantitative proteomic analysis was conducted on 27A to analyze the functions and interactions of differentially expressed proteins related to bacterial resistance and pathogenicity.

Results

Strain 27A belonged to ST11 S. Enteritidis and was resistant to levofloxacin, ciprofloxacin, ampicillin, piperacillin, and ampicillin/sulbactam. There were 33 drug resistance genes, 384 virulence genes and 2 plasmid replicon, IncFIB(S) and IncFII(S), annotated by WGS. Proteomic analysis revealed significant changes in virulence and drug proteins, which were mainly involved in bacterial pathogenicity and metabolic processes. PPI prediction showed the relationship between virulence proteins and T3SS proteins, and PagN cooperated with proteins related to T3SS to jointly mediate the invasion of 27A strain on the human body. Phylogenetic analysis indicated that S. Enteritidis has potential transmission in humans, food, and animals.

Conclusion

This study comprehensively analyzed the drug resistance and virulence phenotypes of S. Enteritidis 27A using genomic and proteomic approaches. These helps reveal the drug resistance and virulence mechanisms of S. Enteritidis, and provides important information for the source tracing and the prevention of related diseases, which lays a foundation for research on food safety, public health monitoring, and the drug resistance and pathogenicity of S. Enteritidis.

Combining proteomic markers to construct a logistic regression model for polycystic ovary syndrome

Introduction

Proteomics technology has been used in various fields in recent years for the Q6 exploration of novel markers and the study of disease pathogenesis, and has become one of the most important tools for researchers to explore unknown areas. However, there are fewer studies related to the construction of clinical models using proteomics markers.

Methods

In our previous study we used DIA proteomics to screen for proteins that were significant in 31 PCOS patients compared to women of normal reproductive age. In this study, we used logistic regression among these protein markers to screen out variables with diagnostic value and constructed logistic regression models.

Results

We constructed a logistic model using these protein markers, where HIST1H4A (OR=1.037) was an independent risk factor for polycystic ovary syndrome and TREML1 (OR=0.976) were protective factors for the disease. The logistic regression model equation is: Logit (PCOS) =0.036*[HIST1H4A]-0.024*[TREML1]-16.368. The ROC curve analyzing the diagnostic value of the model has an AUC value of 0.977 and a Youden index of0.903, which gives a cutoff value of 0.518 at this point. The model has a sensitivity of 93.5% and a specificity of 96.8%. Calibration curves show fair consistency of the model.

Discussion

Our study is the first to use proteomic results with clinical biochemical data to construct a logistic regression model, and the model is consistent. However, our study still needs a more complete sample to confirm our findings.

Clinical application of serum-based proteomics technology in human tumor research

The rapid development of proteomics technology in the past decades has led to further human understanding of tumor research, and in some ways, the technology plays a very important supporting role in the early detection of tumors. Human serum has been shown to contain a variety of proteins closely related to life activities, and the dynamic change in proteins can often reflect the physiological and pathological conditions of the body. Serum has the advantage of easy extraction, so the application of proteomics technology in serum has become a hot spot and frontier area for the study of malignant tumors. However, there are still many difficulties in the standardized use of proteomic technologies, which inevitably limit the clinical application of proteomic technologies due to the heterogeneity of human proteins leading to incomplete whole proteome populations, in addition to most serum protein markers being now not highly specific in aiding the early detection of tumors. Nevertheless, further development of proteomics technologies will greatly increase our understanding of tumor biology and help discover more new tumor biomarkers with specificity that will enable medical technology.

Gancao Nourishing-Yin decoction combined with methotrexate in treatment of aging CIA mice: a study based on DIA proteomic analysis

BACKGROUND

Elderly rheumatoid arthritis (ERA) population faces multiple treatment dilemma. Here we aim to investigate if Gancao Nourishing-Yin decoction (GCNY) added to methotrexate (MTX) exhibit better effects in an ERA mice model.

METHODS

ERA mice model was established by adding D-galactose (Dgal) to collagen-induced arthritis (CIA) mice. The model was then assigned into control group (CIA + Dgal), MTX treatment group (MTX), GCNY treatment group (GCNY), and integrative treatment group (MTX + GCNY). Pathological scoring was performed to evaluate the severity between the groups. Proteomic analysis was applied to investigate the secretory phenotype of the ERA mouse model and the underlying mechanism of GCNY, MTX and their combination. Representative cytokines related to proteomic results were further validated by ELISAs.

RESULTS

CIA + Dgal mice showed more aggressive joints damage than the CIA mice. Besides changes in the inflammatory pathway such as Pi3k-Akt signaling pathway in both model, differential expressed proteins (DEPs) indicated metabolism-related pathways were more obvious in CIA + Dgal mice. Low-dose MTX failed to show pathological improvement in CIA + Dgal mice, while GCNY improved joints damage significantly. Besides down-regulated inflammation-related targets, GCNY-regulated DEPs (such as Apoc1 ~ 3, Grk2 and Creb3l3) were broadly enriched in metabolism-related pathways. MTX + GCNY showed the best therapeutic effect, and the DEPs enriched in a variety of inflammatory,metabolism and osteoclast differentiation signaling pathway. Notably, MTX + GCNY treatment up-regulated Dhfr, Cbr1, Shmt1 involved in folic acid biosynthesis and anti-folate resistance pathways indicated a coincidence synergic action. ELISAs confirmed CPR and Akt that elevated in CIA + Dgal mice were significantly ameliorated by treatments, and adding on GCNY elevated folic acid levels and its regulator Dhfr.

CONCLUSION

Aging aggravated joints damage in CIA, which probably due to metabolic changes rather than more severe inflammation. GCNY showed significant effects in the ERA mice model especially when integrated with MTX to obtain a synergic action.

Insulin resistance, autophagy and apoptosis in patients with polycystic ovary syndrome: Association with PI3K signaling pathway

Polycystic ovary syndrome (PCOS) is a disease in which endocrine metabolic abnormalities coexist with reproductive system abnormalities, with the main clinical manifestations including abnormal menstruation, hirsutism, acne, infertility, and obesity, and it is also a high risk for the development of many pregnancy complications, gynecological malignancies and other diseases. Therefore, timely intervention to prevent the progression of PCOS is of great significance for improving the quality of life of most female patients. Insulin resistance (IR) is one of the most common endocrine disorders in PCOS patients, with approximately 75% of PCOS patients experiencing varying degrees of IR. It is now believed that it is mainly related to the PI3K signaling pathway. The role of autophagy and apoptosis of ovarian granulosa cells (GCs) in the pathogenesis of PCOS has also been gradually verified in recent years. Coincidentally, it also seems to be associated with the PI3K signaling pathway. Our aim is to review these relevant studies, to explore the association between the IR, cellular autophagy and apoptosis in PCOS patients and the PI3K pathway. We summarize some of the drug studies that have improved PCOS as well. We have also found that proteomics holds great promise in exploring the pathogenesis of PCOS, and we have published our views on this.

Identification of noninvasive diagnostic biomarkers for ectopic pregnancy using data-independent acquisition (DIA)proteomics: a pilot study

At present, the diagnosis of ectopic pregnancy mainly depends on transvaginal ultrasound and β-hCG. However, these methods may delay diagnosis and treatment time. Therefore, we aimed to screen for serological molecular markers for the early diagnosis of ectopic pregnancy (EP).Using data-independent acquisition (DIA)proteomics, the differential proteins in serum were selected between the intrauterine pregnancy (IP) and EP groups. Then, the expression levels of these differential proteins were measured by enzyme-linked immunosorbent assay. The diagnostic value of the serum biomarkers was evaluated by receiver operating characteristic curve analysis.GSTO1, ECM-1 and β-hCG showed significant differences between the EP and IP groups (P < 0.05). The combination of GSTO1/ECM-1/β-hCG had an area under the curve of 0.93 (95% CI 0.88-0.99), a sensitivity of 88.89% (95% CI 73.94-96.89) and a specificity of 86.11% (95% CI 70.50-95.33) with a likelihood ratio of 6.40.The combination of GSTO1/ECM-1/β-hCG may be developed into a possible approach for the early diagnosis of EP.

Proteomics Combined with RNA Sequencing to Screen Biomarkers of Sepsis

Purpose

To screen biomarkers in the serum of patients with sepsis by proteomics combined with RNA sequencing technology, and to find new diagnostic and therapeutic targets for sepsis.

Patients and Methods

Blood samples of 22 sepsis patients (sepsis group) and 10 healthy volunteers (normal group) were collected from January 2019 to December 2020. Data-independent acquisition (DIA) method was employed for protein profiling, RNA sequencing was employed for gene sequencing. Subsequently, quality control and differential analysis (FC≥2; FDR<0.05) of DIA data and RNA sequencing data were performed. Then we identified expression trend-consistent divergence factors by nine-quadrant analysis; subsequent protein-protein interaction (PPI) and gene ontology (GO) functional enrichment analysis of intersection factors was performed, and meta-analysis of targets at transcriptome level was implemented using public datasets. Finally, five Peripheral blood mononuclear cell (PBMC) samples (NC=2; SIRS=1; SEPSIS =2) were collected, and cell localization analysis of core genes was performed by 10× single-cell RNA sequencing (scRNA-seq).

Results

Compared with the normal group, there were 4681 differentially expressed genes and 202 differentially expressed proteins in the sepsis group. Among them, 25 factors were expressed in both proteome and transcriptome, and the analysis of PPI and GO found that they were mainly involved in biological processes such as white blood cell and neutrophil response, inflammatory and immune response. Four core genes GSTO1, C1QA, RETN, and GRN were screened by meta-analysis, all of which were highly expressed in the sepsis group compared with the normal group (P<0.05); scRNA-seq showed the core genes were mainly localized in macrophage cell lines.

Conclusion

The core genes GSTO1, C1QA, RETN and GRN are mainly expressed in macrophages, widely involved in inflammation and immune responses, and are highly expressed in plasma in the sepsis, suggesting that they may become potential research targets for sepsis.