Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

OmicScope unravels systems-level insights from quantitative proteomics data

Shotgun proteomics analysis presents multifaceted challenges, demanding diverse tool integration for insights. Addressing this complexity, OmicScope emerges as an innovative solution for quantitative proteomics data analysis. Engineered to handle various data formats, it performs data pre-processing - including joining replicates, normalization, data imputation - and conducts differential proteomics analysis for both static and longitudinal experimental designs. Empowered by Enrichr with over 224 databases, OmicScope performs Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). Additionally, its Nebula module facilitates meta-analysis from independent datasets, providing a systems biology approach for enriched insights. Complete with a data visualization toolkit and accessible as Python package and a web application, OmicScope democratizes proteomics analysis, offering an efficient and high-quality pipeline for researchers.

Unbiasedly decoding the tumor microenvironment with single-cell multiomics analysis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a poor prognosis and limited therapeutic options. Research on the tumor microenvironment (TME) of PDAC has propelled the development of immunotherapeutic and targeted therapeutic strategies with a promising future. The emergence of single-cell sequencing and mass spectrometry technologies, coupled with spatial omics, has collectively revealed the heterogeneity of the TME from a multiomics perspective, outlined the development trajectories of cell lineages, and revealed important functions of previously underrated myeloid cells and tumor stroma cells. Concurrently, these findings necessitated more refined annotations of biological functions at the cell cluster or single-cell level. Precise identification of all cell clusters is urgently needed to determine whether they have been investigated adequately and to identify target cell clusters with antitumor potential, design compatible treatment strategies, and determine treatment resistance. Here, we summarize recent research on the PDAC TME at the single-cell multiomics level, with an unbiased focus on the functions and potential classification bases of every cellular component within the TME, and look forward to the prospects of integrating single-cell multiomics data and retrospectively reusing bulk sequencing data, hoping to provide new insights into the PDAC TME.

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

Tsc22d3 promotes morphine tolerance in mice through the GPX4 ferroptosis pathway

BACKGROUND

Morphine tolerance refers to gradual reduction in response to drug with continuous or repeated use of morphine, requiring higher doses to achieve same effect.

METHODS

The morphine tolerance dataset GSE7762 profiles, obtained from gene expression omnibus (GEO) database, were used to identify differentially expressed genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) was applied to explore core modules of DEGs related to morphine tolerance. Core genes were input into Comparative Toxicogenomics Database (CTD). Animal experiments were performed to validate role of Tsc22d3 in morphine tolerance and its relationship with ferroptosis-related pathway.

RESULTS

500 DEGs were identified. DEGs were primarily enriched in negative regulation of brain development, neuronal apoptosis processes, and neurosystem development. Core gene was identified as Tsc22d3. Tsc22d3 gene-associated miRNAs were mmu-miR-196b-5p and mmu-miR-196a-5p. Compared to Non-morphine tolerant group, Tsc22d3 expression was significantly upregulated in Morphine tolerant group. Tsc22d3 expression was upregulated in Morphine tolerant+Tsc22d3_OE, expression of HIF-1alpha, GSH, GPX4 in GPX4 ferroptosis-related pathway showed a more pronounced decrease. As Tsc22d3 expression was downregulated in Morphine tolerant+Tsc22d3_KO, expression of HIF-1alpha, GSH, GPX4 in GPX4 ferroptosis-related pathway exhibited a more pronounced increase. Upregulation of Tsc22d3 in Morphine tolerant+Tsc22d3_OE led to a more pronounced increase in expression of apoptosis proteins (P53, Caspase-3, Bax, SMAC, FAS). The expression of inflammatory factors (IL6, TNF-alpha, CXCL1, CXCL2) showed a more pronounced increase with upregulated Tsc22d3 expression in Morphine tolerant+Tsc22d3_OE.

CONCLUSIONS

Tsc22d3 is highly expressed in brain tissue of morphine-tolerant mice, activating ferroptosis pathway, enhancing apoptosis, promoting inflammatory responses in brain cells.

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

Structural proteomics of a bacterial mega membrane protein complex: FtsH-HflK-HflC

Recent advances in mass spectrometry (MS) yielding sensitive and accurate measurements along with developments in software tools have enabled the characterization of complex systems routinely. Thus, structural proteomics and cross-linking mass spectrometry (XL-MS) have become a useful method for structural modeling of protein complexes. Here, we utilized commonly used XL-MS software tools to elucidate the protein interactions within a membrane protein complex containing FtsH, HflK, and HflC, over-expressed in E. coli. The MS data were processed using MaxLynx, MeroX, MS Annika, xiSEARCH, and XlinkX software tools. The number of identified inter- and intra-protein cross-links varied among software. Each interaction was manually checked using the raw MS and MS/MS data and distance restraints to verify inter- and intra-protein cross-links. A total of 37 inter-protein and 148 intra-protein cross-links were determined in the FtsH-HflK-HflC complex. The 59 of them were new interactions on the lacking region of recently published structures. These newly identified interactions, when combined with molecular docking and structural modeling, present opportunities for further investigation. The results provide valuable information regarding the complex structure and function to decipher the intricate molecular mechanisms underlying the FtsH-HflK-HflC complex.

PDCD4-induced oxidative stress through FGR/NF-κB axis in rectal cancer radiotherapy-induced AKI

This study aimed to investigate the molecular mechanism of the effect of PDCD4 on radiotherapy-induced acute kidney injury (AKI) in rectal cancer through the regulation of FGR/NF-κB signaling. Differentially expressed genes were identified using Gene Expression Omnibus (GEO) datasets (GSE90627 for rectal cancer and GSE145085 for AKI) and R software. The human renal tubular epithelial cell line, HK-2, was used to establish an in vitro model of radiotherapy-induced AKI. RT-qPCR and western blotting were used to detect gene and protein expression levels, respectively. Cell proliferation and apoptosis were assessed using the CCK-8 assay and flow cytometry, respectively. The malondialdehyde and superoxide dismutase levels in the cell culture supernatants were determined. Additionally, an in vivo AKI model was established using BALB/c mice, and kidney tissue morphology, expression of the renal injury molecule KIM-1, apoptosis of renal tubular cells, and TAS and TOS in serum were evaluated. Bioinformatics analysis revealed the upregulated expression of PDCD4 in AKI. In vitro experiments demonstrated that PDCD4 induced apoptosis in renal tubular cells by promoting FGR expression, which activated the NF-κB signaling pathway and triggered an oxidative stress response. In vivo animal experiments confirmed that PDCD4 promoted oxidative stress response and radiotherapy-induced AKI through the activation of the FGR/NF-κB signaling pathway. Silencing PDCD4 attenuated radiotherapy-induced AKI. Our findings suggest that PDCD4 may induce radiotherapy-induced AKI in rectal cancer by promoting FGR expression, activating the NF-κB signaling pathway, and triggering an oxidative stress response.

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Bioinformation Analysis of Differential Expression Proteins in Different Processes of COVID-19

COVID-19 is a highly infectious respiratory disease whose progression has been associated with multiple factors. From SARS-CoV-2 infection to death, biomarkers capable of predicting different disease processes are needed to help us further understand the molecular progression of COVID-19 disease. The aim is to find differentially expressed proteins that are associated with the progression of COVID-19 disease or can be potential biomarkers, and to provide a reference for further understanding of the molecular mechanisms of COVID-19 occurrence, progression, and treatment. Data-independent Acquisition (DIA) proteomics to obtain sample protein expression data, using R language screening differentially expressed proteins. Gene Ontology and Kyoto Encyclopedia for Genes and Genomes analysis was performed on differential proteins and protein-protein interaction (PPI) network was constructed to screen key proteins. A total of 47 differentially expressed proteins were obtained from COVID-19 incubation patients and healthy population (L/H), mainly enriched in platelet-related functions, and complement and coagulation cascade reaction pathways, such as platelet degranulation and platelet aggregation. A total of 42 differential proteins were obtained in clinical and latent phase patients (C/L), also mainly enriched in platelet-related functions and in complement and coagulation cascade reactions, platelet activation pathways. A total of 10 differential proteins were screened in recovery and clinical phase patients (R/C), mostly immune-related proteins. The differentially expressed proteins in different stages of COVID-19 are mostly closely associated with coagulation, and key differential proteins, such as FGA, FGB, FGG, ACTB, PFN1, VCL, SERPZNCL, APOC3, LTF, and DEFA1, have the potential to be used as early diagnostic markers.

AlphaPept: a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Aptamer-Functionalized Interface Nanopores Enable Amino Acid-Specific Peptide Detection

Single-molecule proteomics based on nanopore technology has made significant advances in recent years. However, to achieve nanopore sensing with single amino acid resolution, several bottlenecks must be tackled: controlling nanopore sizes with nanoscale precision and slowing molecular translocation events. Herein, we address these challenges by integrating amino acid-specific DNA aptamers into interface nanopores with dynamically tunable pore sizes. A phenylalanine aptamer was used as a proof-of-concept: aptamer recognition of phenylalanine moieties led to the retention of specific peptides, slowing translocation speeds. Importantly, while phenylalanine aptamers were isolated against the free amino acid, the aptamers were determined to recognize the combination of the benzyl or phenyl and the carbonyl group in the peptide backbone, enabling binding to specific phenylalanine-containing peptides. We decoupled specific binding between aptamers and phenylalanine-containing peptides from nonspecific interactions (e.g., electrostatics and hydrophobic interactions) using optical waveguide lightmode spectroscopy. Aptamer-modified interface nanopores differentiated peptides containing phenylalanine vs. control peptides with structurally similar amino acids (i.e., tyrosine and tryptophan). When the duration of aptamer-target interactions inside the nanopore were prolonged by lowering the applied voltage, discrete ionic current levels with repetitive motifs were observed. Such reoccurring signatures in the measured signal suggest that the proposed method has the possibility to resolve amino acid-specific aptamer recognition, a step toward single-molecule proteomics.

Mass Spectrometry Strategies for O-Glycoproteomics

Glycoproteomics has accelerated in recent decades owing to numerous innovations in the analytical workflow. In particular, new mass spectrometry strategies have contributed to inroads in O-glycoproteomics, a field that lags behind N-glycoproteomics due to several unique challenges associated with the complexity of O-glycosylation. This review will focus on progress in sample preparation, enrichment strategies, and MS/MS techniques for the identification and characterization of O-glycoproteins.

Quantitative mass spectrometry imaging: therapeutics & biomolecules

Mass spectrometry imaging (MSI) has become increasingly utilized in the analysis of biological molecules. MSI grants the ability to spatially map thousands of molecules within one experimental run in a label-free manner. While MSI is considered by most to be a qualitative method, recent advancements in instrumentation, sample preparation, and development of standards has made quantitative MSI (qMSI) more common. In this feature article, we present a tailored review of recent advancements in qMSI of therapeutics and biomolecules such as lipids and peptides/proteins. We also provide detailed experimental considerations for conducting qMSI studies on biological samples, aiming to advance the methodology.

Shotgun Characterization of the Circulating IgM Antigenome of an Infectious Pathogen by Immunocapture-LC-MS/MS from Dried Serum Spots

One of the main challenges in compiling the complete collection of protein antigens from pathogens for the selection of vaccine candidates or intervention targets is to acquire a broad enough representation of them to be recognized by the highly diversified immunoglobulin repertoire in human populations. Dried serum spot sampling (DSS) retains a large repertoire of circulating immunoglobulins from each individual that can be representative of a population, according to the sample size. In this work, shotgun proteomics of an infectious pathogen based on DSS sampling coupled with IgM immunoprecipitation, liquid chromatography-mass spectrometry (LC-MS/MS), and bioinformatic analyses was combined to characterize the circulating IgM antigenome. Serum samples from a malaria endemic region at different clinical statuses were studied to optimize IgM binding efficiency and antibody leaching by varying serum/immunomagnetic bead ratios and elution conditions. The method was validated using Plasmodium falciparum extracts identifying 110 of its IgM-reactive antigens while minimizing the presence of human proteins and antibodies. Furthermore, the IgM antigen recognition profile differentiated between malaria-infected and noninfected individuals at the time of sampling. We conclude that a shotgun proteomics approach offers advantages in providing a high-throughput, reliable, and clean way to identify IgM-recognized antigens from trace amounts of serum. The mass spectrometry raw data and metadata have been deposited with ProteomeXchange via MassIVE with the PXD identifier PXD043800.

Application of SWATH Mass Spectrometry and Machine Learning in the Diagnosis of Inflammatory Bowel Disease Based on the Stool Proteome

Inflammatory bowel disease (IBD) flare-ups exhibit symptoms that are similar to other diseases and conditions, making diagnosis and treatment complicated. Currently, the gold standard for diagnosing and monitoring IBD is colonoscopy and biopsy, which are invasive and uncomfortable procedures, and the fecal calprotectin test, which is not sufficiently accurate. Therefore, it is necessary to develop an alternative method. In this study, our aim was to provide proof of concept for the application of Sequential Window Acquisition of All Theoretical Mass Spectra-Mass spectrometry (SWATH-MS) and machine learning to develop a non-invasive and accurate predictive model using the stool proteome to distinguish between active IBD patients and symptomatic non-IBD patients. Proteome profiles of 123 samples were obtained and data processing procedures were optimized to select an appropriate pipeline. The differentially abundant analysis identified 48 proteins. Utilizing correlation-based feature selection (Cfs), 7 proteins were selected for proceeding steps. To identify the most appropriate predictive machine learning model, five of the most popular methods, including support vector machines (SVMs), random forests, logistic regression, naive Bayes, and k-nearest neighbors (KNN), were assessed. The generated model was validated by implementing the algorithm on 45 prospective unseen datasets; the results showed a sensitivity of 96% and a specificity of 76%, indicating its performance. In conclusion, this study illustrates the effectiveness of utilizing the stool proteome obtained through SWATH-MS in accurately diagnosing active IBD via a machine learning model.

Metabolomic Changes in Rat Serum after Chronic Exposure to Glyphosate-Based Herbicide

Glyphosate-based herbicides (GBHs) have gained extensive popularity in recent decades. For many years, glyphosate has been regarded as harmless or minimally toxic to mammals due to the absence of its primary target, the shikimic acid pathway in humans. Nonetheless, mounting evidence suggests that glyphosate may cause adverse health effects in humans via other mechanisms. In this study, we described the metabolomic changes in the serum of experimental rats exposed to chronic GBH using the highly sensitive LC-MS/MS technique. We investigated the possible relationship between chronic exposure to GBH and neurological disorders. Our findings suggest that chronic exposure to GBH can alter spatial learning memory and the expression of some important metabolites that are linked to neurophysiological disorders in young rats, with the female rats showing higher susceptibility compared to the males. This indicates that female rats are more likely to show early symptoms of the disorder on exposure to chronic GBH compared to male rats. We observed that four important metabolites (paraxanthine, epinephrine, L-(+)-arginine, and D-arginine) showed significant changes and involvement in neurological changes as suggested by ingenuity pathway analysis. In conclusion, our results indicate that chronic exposure to GBH can increase the risk of developing neurological disorders.

Novel cuproptosis-related prognostic gene profiles in preeclampsia

BACKGROUND

Preeclampsia (PE) is a pregnancy-specific disorder with complex pathogenesis. Cuproptosis is a novel identified form of programmed cell death, however, the link between cuproptosis and clinical outcomes in PE is still not fully understood. In this study, we searched for cuproptosis-related genes (CRGs) in the placental tissues of normal and PE patients to clarify the importance of cuproptosis in the development of PE and provide potential predictive indicators for the occurrence of PE.

METHODS

Using RNA sequencing data in the GEO database, we conducted functional enrichment analysis of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA), supported by linear regression model and operating characteristic curve (ROC) curve analysis, and summarized the role of CRGs in preeclampsia.

RESULTS

A total of 2831 differentially expressed genes related to PE were screened through multiple database analyses. After further intersection with 19 reported CRGs, 5 CRGs have been closely associated with the pathogenesis of PE, including NFE2L2, PDHA1, PDHB, DLD and GLS. NFE2L2 was identified as a key central gene. Pearson correlation analysis showed that CRGs could be related to several maternal and fetal outcome factors, including the highest pregnancy blood pressure, placenta weight, umbilical blood flow pulsatility index (PI), and neonatal weight. Linear regression equation revealed that the expression of NFE2L2 is negatively correlated with the highest pregnancy blood pressure and umbilical blood flow PI but positively correlated with placental weight and neonatal weight. QRT-PCR showed that the expression of these CRGs was significantly lower in placental tissues.

CONCLUSIONS

This cuproptosis pattern may be a potential prognostic factor in patients with PE and could provide new insights into disease progression.

Metaproteomics of Gut Fungi in Gnotobiotic Mice

Intestinal fungi are a fundamental component of the gut microbiome and play important roles in mammalian host biology. At the same time, the contribution of gut fungi to host health and disease remains understudied due to their low abundance. In that respect, gnotobiotic animals with defined microbial populations of reduced complexity represent a well-suited model system that highlights the effects of low abundant gut fungi on host physiology and other members of the microbial community. In this chapter, a label-free quantitative metaproteomic approach for the characterization of simplified microbial communities in gnotobiotic mice is presented. The model allows for exploring various research questions on the role of gut fungi in disease pathogenesis, microbial ecosystem maturation, or host-microbiome crosstalk.

The Potential Mechanism of Liujunzi Decoction in the Treatment of Breast Cancer based on Network Pharmacology and Molecular Docking Technology

BACKGROUND

Liujunzi Decoction (LJZD) is a potential clinical treatment for Breast Cancer (BC), but the active ingredients and mechanisms underlying its effectiveness remain unclear.

OBJECTIVE

The study aimed to investigate the target gene of LJZD compatibility and the possible mechanism of action in the treatment of breast cancer by using network pharmacology and molecular docking.

METHODS

Based on TCMSP, ETCM, and BATMAN database searching and screening to obtain the ingredients of LJZD, the related targets were obtained. Breast cancer-related targets were collected through GEO, Geencards, OMIM, and other databases, and drug-disease Venn diagrams were drawn by R. The PPI network map was constructed by using Cytoscape. The intersecting targets were imported into the STRING database, and the core targets were analyzed and screened. The intersected targets were analyzed by the DAVID database for GO and KEGG enrichment. AutoDock Vina and Gromacs were used for molecular docking and simulation of the core targets and active ingredients.

RESULTS

126 active ingredients of LJZD were obtained; 241 targets related to breast cancer were sought after screening, and 180 intersection targets were identified through Venn diagram analysis. The core targets were FOS and ESR1. KEGG enrichment analysis mainly involved PI3K/Akt, MAPK, and other signaling pathways.

CONCLUSION

This study has explored the possible targets and signaling pathways of LJZD in treating breast cancer through network pharmacology and bioinformatics analysis. Molecular docking and simulation have further validated the potential mechanism of action of LJZD in breast cancer treatment, providing essential experimental data for future studies.

Marine Bioprospecting: Enzymes and Stress Proteins from the Sea Anemones Anthopleura dowii and Lebrunia neglecta

The bioprospecting of sea anemone tissues and secretions has revealed that they are natural libraries of polypeptides with diverse biological activities that can be utilized to develop of biotechnological tools with potential medical and industrial applications. This study conducted a proteomic analysis of crude venom extracts from Anthopleura dowii Verrill, 1869, and Lebrunia neglecta Duchassaing & Michelotti, 1860. The obtained data allowed us to identify 201 polypeptides, of which 39% were present in both extracts. Among the obtained sequences, hydrolase-type enzymes, oxidoreductases, transferases, heat shock proteins, adhesion proteins, and protease inhibitors, among others, were identified. Interaction analysis and functional annotation indicated that these proteins are primarily involved in endoplasmic reticulum metabolic processes such as carbon metabolism and protein processing. In addition, several proteins related to oxidative stress were identified, including superoxide dismutase, peroxiredoxins, thioredoxin, and glutathione oxidase. Our results provide novel information on the polypeptide composition of the crude venom extract from sea anemones, which can be utilized to develop molecules for therapeutic tools and industrial applications.

Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis

Breast cancer (BC) has emerged as an extremely destructive malignancy, causing significant harm to female patients and society at large. Proteomic research holds great promise for early diagnosis and treatment of diseases, and the integration of proteomics with genomics can offer valuable assistance in the early diagnosis, treatment, and improved prognosis of BC patients. In this study, we downloaded breast cancer protein expression data from The Cancer Genome Atlas (TCGA) and combined proteomics with genomics to construct a proteomic-based prognostic model for BC. This model consists of nine proteins (HEREGULIN, IDO, PEA15, MERIT40_pS29, CIITA, AKT2, CD171 DVL3, and CABL9). The accuracy of the model in predicting the survival prognosis of BC patients was further validated through risk curve analysis, survival curve analysis, and independent prognostic analysis. We further confirmed the impact of differential expression of these nine key proteins on overall survival in BC patients, and the differential expression of the key proteins and their encoding genes was validated using immunohistochemical staining. Enrichment analysis revealed functional associations primarily related to PPAR signaling pathway, steroid hormone metabolism, chemokine signaling pathway, DNA conformation changes, immunoglobulin production, and immunoglobulin complex in the high- and low-risk groups. Immune infiltration analysis revealed differential expression of immune cells between the high- and low-risk groups, providing a theoretical basis for subsequent immunotherapy. The model constructed in this study can predict the survival of BC patients, and the identified key proteins may serve as biomarkers to aid in the early diagnosis of BC. Enrichment analysis and immune infiltration analysis provide a necessary theoretical basis for further exploration of the molecular mechanisms and subsequent immunotherapy.

Hub biomarkers in ultrasound-guided bladder cancer and osteosarcoma: Myosin light chain kinase and caldesmon

Bladder cancer and osteosarcoma are 2 types of cancers that originate from epithelial tissues inside the bladder and bone or muscle tissues. Ultrasound-guided biopsies provide crucial support for the diagnosis and treatment of bladder cancer and osteosarcoma. However, the relationship between myosin light chain kinase (MYLK) and caldesmon (CALD1) and bladder cancer and osteosarcoma remains unclear. The bladder cancer datasets GSE65635 and GSE100926, the osteosarcoma dataset GSE39058, were obtained from gene expression omnibus. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis was performed. The construction and analysis of protein-protein interaction network, functional enrichment analysis, gene set enrichment analysis. Gene expression heat map was drawn and immune infiltration analysis was performed. The comparative toxicogenomics database analysis were performed to find disease most related to core gene. Western blotting experiments were performed. TargetScan screened miRNAs that regulated central DEGs. We obtained 54 DEGs. Functional enrichment analysis revealed significant enrichment in terms of cellular differentiation, cartilage development, skeletal development, muscle actin cytoskeleton, actin filament, Rho GTPase binding, DNA binding, fibroblast binding, MAPK signaling pathway, apoptosis, and cancer pathways. Gene set enrichment analysis indicated that DEGs were primarily enriched in terms of skeletal development, cartilage development, muscle actin cytoskeleton, MAPK signaling pathway, and apoptosis. The immune infiltration analysis showed that when T cells regulatory were highly expressed, Eosinophils exhibited a similar high expression, suggesting a strong positive correlation between T cells regulatory and Eosinophils, which might influence the disease progression in osteosarcoma. We identified 6 core genes (SRF, CTSK, MYLK, VCAN, MEF2C, CALD1). MYLK and CALD1 were significantly correlated with survival rate and exhibited lower expression in bladder cancer and osteosarcoma samples compared to normal samples. Comparative toxicogenomics database analysis results indicated associations of core genes with osteosarcoma, bladder tumors, bladder diseases, tumors, inflammation, and necrosis. The results of Western blotting showed that the expression levels of MYLK and CALD1 in bladder cancer and osteosarcoma were lower than those in normal tissues. MYLK and CALD1 likely play a role in regulating muscle contraction and smooth muscle function in bladder cancer and osteosarcoma. The lower expression of MYLK and CALD1 is associated with poorer prognosis.

Proteomic advance of ischemic stroke: preclinical, clinical, and intervention

Ischemic stroke (IS) is the most common type of stroke and is characterized by high rates of mortality and long-term injury. The prediction and early diagnosis of IS are therefore crucial for optimal clinical intervention. Proteomics has provided important techniques for exploring protein markers associated with IS, but there has been no systematic evaluation and review of research that has used these techniques. Here, we review the differential proteins that have been found in cell- and animal- based studies and clinical trials of IS in the past 10 years; determine the key pathological proteins that have been identified in clinical trials; summarize the target proteins affected by interventions aimed at treating IS, with a focus on traditional Chinese medicine treatments. Overall, we clarify findings and problems that have been identified in recent proteomics research on IS and provide suggestions for improvements in this area. We also suggest areas that could be explored for determining the pathogenesis and developing interventions for IS.

Using proteomics for stratification and risk prediction in patients with solid tumors

Proteomics, the study of proteins and their functions, has greatly evolved due to advances in analytical chemistry and computational biology. Unlike genomics or transcriptomics, proteomics captures the dynamic and diverse nature of proteins, which play crucial roles in cellular processes. This is exemplified in cancer, where genomic and transcriptomic information often falls short in reflecting actual protein expression and interactions. Liquid chromatography-mass spectrometry (LC-MS) is pivotal in proteomic data generation, enabling high-throughput analysis of protein samples. The MS-based workflow involves protein digestion, chromatographic separation, ionization, and fragmentation, leading to peptide identification and quantification. Computational biostatistics, particularly using tools in R (R Foundation for Statistical Computing, Vienna, Austria; www.R-project.org ), aid in data analysis, revealing protein expression patterns and correlations with clinical variables. Proteomic studies can be explorative, aiming to characterize entire proteomes, or targeted, focusing on specific proteins of interest. The integration of proteomics with genomics addresses database limitations and enhances peptide identification. Case studies in intrahepatic cholangiocarcinoma, glioblastoma multiforme, and pancreatic ductal adenocarcinoma highlight proteomics' clinical applications, from subtyping cancers to identifying diagnostic markers. Moreover, proteomic data augment molecular tumor boards by providing deeper insights into pathway activities and genomic mutations, supporting personalized treatment decisions. Overall, proteomics contributes significantly to advancing our understanding of cellular biology and improving clinical care.

Normative Data on Serum and Plasma Tryptophan and Kynurenine Concentrations from 8089 Individuals Across 120 Studies: A Systematic Review and Meta-Analysis

In this systematic review and meta-analysis, a normative dataset is generated from the published literature on the kynurenine pathway in control participants extracted from case-control and methodological validation studies. Study characteristics were mapped, and studies were evaluated in terms of analytical rigour and methodological validation. Meta-analyses of variance between types of instruments, sample matrices and metabolites were conducted. Regression analyses were applied to determine the relationship between metabolite, sample matrix, biological sex, participant age and study age. The grand mean concentrations of tryptophan in the serum and plasma were 60.52 ± 15.38 μM and 51.45 ± 10.47 μM, respectively. The grand mean concentrations of kynurenine in the serum and plasma were 1.96 ± 0.51 μM and 1.82 ± 0.54 μM, respectively. Regional differences in metabolite concentrations were observed across America, Asia, Australia, Europe and the Middle East. Of the total variance within the data, mode of detection (MOD) accounted for up to 2.96%, sample matrix up to 3.23%, and their interaction explained up to 1.53%; the latter of which was determined to be negligible. This review was intended to inform future empirical research and method development studies and successfully synthesised pilot data. The pilot data reported in this study will inform future precision medicine initiatives aimed at targeting the kynurenine pathway by improving the availability and quality of normative data.

Unveiling the potential of proteomics in addressing food and feed safety challenges

The food and feed sector in Europe is rapidly evolving to address contemporary challenges, striving for fairer, safer, greener and more sustainable food systems. This includes the exploration of new protein sources for human consumption and animal feed such as protein derived from insects, algae or novel plant-derived proteins, and the re-evaluation of existing sources like processed animal protein (PAP). To generate reliable data on the diverse array of emerging protein sources for future food and feed safety assessments, a growing demand for the development and implementation of advanced analytical techniques exists. New approach methodologies (NAMs) including, mass spectrometry (MS)-based proteomics methods have been emerging as valuable techniques which potentially can be implemented in regulatory laboratory settings to complement conventional approaches in this realm. These MS-driven strategies have already proven their utility in diverse applications, including the detection of prohibited substances in feed, identification of allergens, differentiation of fish species in complex mixtures for fraud detection and the verification of novel foods and alternative protein sources. This EU-FORA programme was focused on three core objectives namely: (i) the training of the fellow in utilising MS-based proteomics for food and feed safety analyses, (ii) the involvement of the fellow in the development of standardised operating procedures (SOP) for targeted and non-targeted proteomic MS-based workflows for species and tissues specific PAP identification in a national reference laboratory (NRL) and (iii) the transfer and implementation of MS-based approaches and standardised protocols for PAP analysis at the fellow's home institution. Altogether, this programme facilitates the broadening and diversification of use of MS-based proteomic methodologies for reinforcing their significance within the domains of food and feed safety research and regulatory science applications.

Insights into enhancing Centella asiatica organ cell biofactories via hairy root protein profiling

Recent advancements in plant biotechnology have highlighted the potential of hairy roots as a biotechnological platform, primarily due to their rapid growth and ability to produce specialized metabolites. This study aimed to delve deeper into hairy root development in C. asiatica and explore the optimization of genetic transformation for enhanced bioactive compound production. Previously established hairy root lines of C. asiatica were categorized based on their centelloside production capacity into HIGH, MID, or LOW groups. These lines were then subjected to a meticulous label-free proteomic analysis to identify and quantify proteins. Subsequent multivariate and protein network analyses were conducted to discern proteome differences and commonalities. Additionally, the quantification of rol gene copy numbers was undertaken using qPCR, followed by gene expression measurements. From the proteomic analysis, 213 proteins were identified. Distinct proteome differences, especially between the LOW line and other lines, were observed. Key proteins related to essential processes like photosynthesis and specialized metabolism were identified. Notably, potential biomarkers, such as the Tr-type G domain-containing protein and alcohol dehydrogenase, were found in the HIGH group. The presence of ornithine cyclodeaminase in the hairy roots emerged as a significant biomarker linked with centelloside production capacity lines, indicating successful Rhizobium-mediated genetic transformation. However, qPCR results showed an inconsistency with rol gene expression levels, with the HIGH line displaying notably higher expression, particularly of the rolD gene. The study unveiled the importance of ornithine cyclodeaminase as a traceable biomarker for centelloside production capacity. The strong correlation between this biomarker and the rolD gene emphasizes its potential role in optimizing genetic transformation processes in C. asiatica.

Dehydroalanine and dehydrobutyrine in aging and cataractous lenses reveal site-specific consequences of spontaneous protein degradation

Introduction

Protein post-translational modifications (PTMs) have been associated with aging and age-related diseases. PTMs are particularly impactful in long-lived proteins, such as those found in the ocular lens, because they accumulate with age. Two PTMs that lead to protein-protein crosslinks in aged and cataractous lenses are dehydroalanine (DHA) and dehydrobutyrine (DHB); formed from cysteine/serine and threonine residues, respectively. The purpose of this study was to quantitate DHA and DHB in human lens proteins as a function of age and cataract status.

Methods

Human lenses of various ages were divided into five donor groups: transparent lenses (18-22-year-old, 48-64-year-old, and 70-93-year-old) and cataractous human lenses of two age groups (48-64-year-old lenses, and 70-93-year-old lenses) and were subjected to proteomic analysis. Relative DHA and DHB peptide levels were quantified and compared to their non-modified peptide counterparts.

Results

For most lens proteins containing DHA or DHB, higher amounts of DHA- and DHB-modified peptides were detected in aged and cataractous lenses. DHA-containing peptides were classified into three groups based on abundance changes with age and cataract: those that (1) increased only in age-related nuclear cataract (ARNC), (2) increased in aged and cataractous lenses, and (3) decreased in aged lenses and ARNC. There was no indication that DHA or DHB levels were dependent on lens region. In most donor groups, proteins with DHA and DHB were more likely to be found among urea-insoluble proteins rather than among water- or urea-soluble proteins.

Discussion

DHA and DHB formation may induce structural effects that make proteins less soluble in water that leads to age-related protein insolubility and possibly aggregation and light scattering.

High-performance peptide and disulfide mapping by direct injection of intact proteins using on-line coupled UV-liquid chromatography microdroplet mass spectrometry (UVLC-MMS)

Microdroplet mass spectrometry (MMS), achieving ultra-fast enzyme digestion in the ionization source, holds great promises for innovating protein analysis. Here, in-depth protein characterization is demonstrated by direct injection of intact protein mixtures via on-line coupling MMS with capillary C4 liquid chromatography (LC) containing UV windows (UVLC-MMS) through an enzyme introduction tee. We showed complete sets of peptides of individual proteins (hemoglobin, bovine serum albumin, and ribonuclease A) in a mixture could be obtained in one injection. Such full (100%) sequence coverage, however, could not be achieved by conventional nanoLC-MS method using bottom-up approach with single enzyme. Moreover, direct injection of a chaperone α-crystalline (α-Cry) complex yielded identification of post-translational modifications including novel sites and semi-quantitative characterization including 3:1 stoichiometry ratio of αA- and αB-Cry sub-units and ∼1.4 phosphorylation/subunit on S45 (novel site) and S122 (main site) of αA-Cry, ∼0.7 phosphorylation/subunit on S19 (main site) and S45 of αB-Cry, as well as 100% acetylation on both N-termini of each subunits by matching the mass and retention time of the intact and its digested peptides. Furthermore, trifluoroacetic acid was able to be used in the mobile phase with UVLC-MMS to improve the separation of differentially reduced intact species and detectability of the droplet-digested products. This allowed us to completely map four disulfide linkages of ribonuclease A based on collision-induced dissociation of disulfide clusters, some of which would otherwise not be detected, preventing scrambling or shuffling errors arising from lengthy bulk solution digestion by the bottom-up approach. Integration of UVLC and MMS greatly improves droplet digestion efficiency and MS detection, enabling highly efficient workflow for in-depth and accurate protein characterization.

Identification of A Novel Gene Signature Combining Ferroptosis- and Immunity-Related Genes for Prognostic Prediction, Immunotherapy and Potential Therapeutic Targets in Gastric Cancer

Gastric cancer (GC) is one of the most prevalent cancers worldwide. Ferroptosis and the immune status of tumor tissue play vital roles in the initiation and progression of GC. However, the role and functional mechanisms of ferroptosis- and immunity-related genes (FIRGs) in GC pathogenesis and their correlations with GC prognosis have not been elucidated. We aim to establish a prognostic prediction model based on the FIRGs signature for GC patients. Differentially expressed genes were screened from the Cancer Genome Atlas (TCGA) GC cohorts. The least absolute shrinkage and selection operator (LASSO) regression was performed to establish a FIRGs-based risk model. This gene signature with 7 FIRGs was identified as an independent prognostic factor. A nomogram incorporating clinical parameters and the FIRG signature was constructed to individualize outcome predictions. Finally, we provided in vivo and in vitro evidence to verify the reliability of FIRG signature for GC prognosis, and validate the expression and function of FIRGs contributing to the development and progression of GC. Herein, our work represents great therapeutic and prognostic potentials for GC.

Proteomic analysis to unravel the biochemical mechanisms triggered by Bacillus toyonensis SFC 500-1E under chromium(VI) and phenol stress

Bacillus toyonensis SFC 500-1E is a member of the consortium SFC 500-1 able to remove Cr(VI) and simultaneously tolerate high phenol concentrations. In order to elucidate mechanisms utilized by this strain during the bioremediation process, the differential expression pattern of proteins was analyzed when it grew with or without Cr(VI) (10 mg/L) and Cr(VI) + phenol (10 and 300 mg/L), through two complementary proteomic approaches: gel-based (Gel-LC) and gel-free (shotgun) nanoUHPLC-ESI-MS/MS. A total of 400 differentially expressed proteins were identified, out of which 152 proteins were down-regulated under Cr(VI) and 205 up-regulated in the presence of Cr(VI) + phenol, suggesting the extra effort made by the strain to adapt itself and keep growing when phenol was also added. The major metabolic pathways affected include carbohydrate and energetic metabolism, followed by lipid and amino acid metabolism. Particularly interesting were also ABC transporters and the iron-siderophore transporter as well as transcriptional regulators that can bind metals. Stress-associated global response involving the expression of thioredoxins, SOS response, and chaperones appears to be crucial for the survival of this strain under treatment with both contaminants. This research not only provided a deeper understanding of B. toyonensis SFC 500-1E metabolic role in Cr(VI) and phenol bioremediation process but also allowed us to complete an overview of the consortium SFC 500-1 behavior. This may contribute to an improvement in its use as a bioremediation strategy and also provides a baseline for further research.

ROSes-FINDER: a multi-task deep learning framework for accurate prediction of microorganism reactive oxygen species scavenging enzymes

Reactive oxygen species (ROS) are highly reactive molecules that play important roles in microbial biological processes. However, excessive accumulation of ROS can lead to oxidative stress and cellular damage. Microorganism have evolved a diverse suite of enzymes to mitigate the harmful effects of ROS. Accurate prediction of ROS scavenging enzymes classes (ROSes) is crucial for understanding the mechanisms of oxidative stress and developing strategies to combat related diseases. Nevertheless, the existing approaches for categorizing ROS-related proteins exhibit certain drawbacks with regards to their precision and inclusiveness. To address this, we propose a new multi-task deep learning framework called ROSes-FINDER. This framework integrates three component methods using a voting-based approach to predict multiple ROSes properties simultaneously. It can identify whether a given protein sequence is a ROSes and determine its type. The three component methods used in the framework are ROSes-CNN, which extracts raw sequence encoding features, ROSes-NN, which predicts protein functions based on sequence information, and ROSes-XGBoost, which performs functional classification using ensemble machine learning. Comprehensive experiments demonstrate the superior performance and robustness of our method. ROSes-FINDER is freely available at https://github.com/alienn233/ROSes-Finder for predicting ROSes classes.

Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.

IQMMA: Efficient MS1 Intensity Extraction Pipeline Using Multiple Feature Detection Algorithms for DDA Proteomics

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called "features", in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years, each relying on its own matching algorithm. Here, we provide an integrated solution, the intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of untargeted peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of Yeast and E. coli digests spiked at different concentrations into the Human K562 digest used as a background, and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2, and OpenMS FeatureFinder. None of them was found optimal when applied individually to all the data sets employed in this work; however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.

Metabolomic and proteomic analyses of primary Sjogren's syndrome

The pathogenesis of primary Sjogren's syndrome (pSS) has not been fully elucidated. We explored differentially expressed proteins and metabolic pathways in pSS using proteomics and metabolomics. 456 named proteins in total were identified, among which 50 were significantly changed in the pSS. Altered proteins were significantly associated with signaling pathways such as antigen processing and presentation, human immunodeficiency virus 1 infection, and FC gamma R-mediated phagocytosis. Meanwhile, 12 proteins, such as SH3BGRL3, TPM4, and CA1, can be used as potential clinical molecular markers. Moreover, 128 metabolites were significantly expressed in the pSS group. A total of 96 pathways were significantly enriched including central carbon metabolism in cancer, taurine and hypotaurine metabolism, and ABC transporters. Notably, both proteomics and metabolomics enriched glycolysis/gluconeogenesis metabolism, pentose phosphate pathway, and glutathione metabolism pathways. In this study, the progression mechanism of pSS was analyzed and novel biomarkers were identified by proteomics and metabolomics.

Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases

The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.

NDUFB11 and NDUFS3 play a role in atherosclerosis and chronic stress

OBJECTIVE

Atherosclerosis is characterized by the formation of fibrofatty plaques in the intima of arteries, resulting in thickening of the vessel wall and narrowing of the lumen. Chronic stress refers to individuals in a state of long-term chronic stress. However, the relationship between NDUFB11 and NDUFS3 and atherosclerosis and chronic stress is unclear.

METHOD

The atherosclerosis with chronic stress group data file was used. DEGs were screened and WGCNA was performed. Construction and analysis of PPI Network. Functional enrichment analysis, GSEA, gene expression heatmap, immune infiltration analysis and mRNA analysis were performed. CTD was used to find diseases most related to core genes. WB was performed. TargetScan was used to screen miRNAs of DEGs.

RESULTS

1708 DEGs were identified. According to GO analysis, they were mainly enriched in catabolic processes, organic acid metabolism processes, carboxylic acid metabolism processes. KEGG analysis showed that they were mainly enriched in metabolic pathways, fatty acid metabolism, pentose phosphate pathway, glycolysis / gluconeogenesis, fructose and mannose metabolism. Gene expression heatmap showed that the core genes (NDUFB11, NDUFS3) were lowly expressed in samples of those with atherosclerosis accompanied by chronic stress and highly expressed in the normal samples. NDUFB11 and NDUFS3 were associated with necrosis, hyperplasia, inflammation, renal disease, weight loss, memory impairment, and cognitive impairment. WB showed that the expression level of NDUFS3 in atherosclerosis and chronic stress was lower than that in control group.

CONCLUSIONS

NDUFB11 and NDUFS3 are underexpressed in atherosclerosis and chronic stress; the lower NDUFB11 and NDUFS3 levels, the worse the prognosis.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

CEACAM1 as a molecular target in oral cancer

OBJECTIVE

The majority of oral cancer is caused by malignant transformation of squamous cells in surface of the oral mucosa. However, the relationship between CEACAM1 and oral cancer is unclear.

METHODS

GSE23558 and GSE25099 profiles were downloaded from gene expression omnibus (GEO). Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis (WGCNA) was performed. Construction and analysis of protein-protein interaction (PPI) Network. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG), gene set enrichment analysis (GSEA), gene expression heatmap, immune infiltration analysis, comparative toxicogenomics database (CTD) were performed. TargetScan screened miRNAs that regulated central DEGs. Western blotting (WB) experiment was performed.

RESULTS

1269 DEGs were identified. According to GO analysis, they were mainly enriched in same protein binding, signal receptor binding, cell surface, epithelial cell development. KEGG analysis showed that they were mainly enriched in cancer pathways, PI3K Akt signaling pathway, TNF signaling pathway, NF kappa B signaling pathway, TGF beta signaling pathway. PPI network showed that 11 genes (CDCA8, CCNA2, MELK, KIF2C, CDC45, HMMR, TPX2, CENPF, CDK1, CEP55, CEACAM1) were obtained. Gene expression heatmap showed that CEP55 and MELK were highly expressed in oral cancer samples. CEACAM1 was lowly expressed in oral cancer samples. CEACAM1, CEP55 and MELK were involved in tumor, inflammation, necrosis, and proliferation. Western blotting (WB) showed that CEACAM1 in oral cancer samples was lower than that in normal samples, after CEACAM1 knockdown, it was lower than that in oral cancer samples.

CONCLUSION

CEACAM1 is lowly expressed in oral cancer, the lower CEACAM1, the worse prognosis.

Prognostic Biomarkers Based on Proteomic Technology in COPD: A Recent Review

Chronic obstructive pulmonary disease (COPD) is a common heterogeneous respiratory disease which is characterized by persistent and incompletely reversible airflow limitation. Due to the heterogeneity and phenotypic complexity of COPD, traditional diagnostic methods provide limited information and pose a great challenge to clinical management. In recent years, with the development of omics technologies, proteomics, metabolomics, transcriptomics, etc., have been widely used in the study of COPD, providing great help to discover new biomarkers and elucidate the complex mechanisms of COPD. In this review, we summarize the prognostic biomarkers of COPD based on proteomic studies in recent years and evaluate their association with COPD prognosis. Finally, we present the prospects and challenges of COPD prognostic-related studies. This review is expected to provide cutting-edge evidence in prognostic evaluation of clinical patients with COPD and to inform future proteomic studies on prognostic biomarkers of COPD.

Impact of Bioinformatics Search Parameters for Peptides' Identification and Their Post-Translational Modifications: A Case Study of Proteolysed Gelatines from Beef, Pork, and Fish

Bioinformatics software, allowing the identification of peptides by the comparison of peptide fragmentation spectra obtained by mass spectrometry versus targeted databases or directly by de novo sequencing, is now mandatory in peptidomics/proteomics approaches. Programming the identification software requires specifying, among other things, the mass measurement accuracy of the instrument and the digestion enzyme used with the number of missed cleavages allowed. Moreover, these software algorithms are able to identify a large number of post-translational modifications (PTMs). However, peptide and PTM identifications are challenging in the agrofood field due to non-specific cleavage sites of physiological- or food-grade enzymes and the number and location of PTMs. In this study, we show the importance of customized software programming to obtain a better peptide and PTM identification rate in the agrofood field. A gelatine product and one industrial gelatine hydrolysate from three different sources (beef, pork, and fish), each digested by simulated gastrointestinal digestion, MS-grade trypsin, or both, were used to perform the comparisons. Two main points are illustrated: (i) the impact of the set-up of specific enzyme versus no specific enzyme use and (ii) the impact of a maximum of six PTMs allowed per peptide versus the standard of three. Prior knowledge of the composition of the raw proteins is an important asset for better identification of peptide sequences.

Functional response of Acinetobacter guillouiae SFC 500-1A to tannery wastewater as revealed by a complementary proteomic approach

Acinetobacter guillouiae SFC 500-1 A is a promising candidate for the bioremediation of tannery wastewater. In this study, we applied shotgun proteomic technology in conjunction with a gel-based assay (Gel-LC) to explore the strain's intracellular protein profile when grown in tannery wastewater as opposed to normal culture conditions. A total of 1775 proteins were identified, 52 of which were unique to the tannery wastewater treatment. Many of them were connected to the degradation of aromatic compounds and siderophore biosynthesis. On the other hand, 1598 proteins overlapped both conditions but were differentially expressed in each. Those that were upregulated in wastewater (109) were involved in the processes mentioned above, as well as in oxidative stress mitigation and intracellular redox state regulation. Particularly interesting were the downregulated proteins under the same treatment (318), which were diverse but mainly linked to the regulation of basic cellular functions (replication, transcription, translation, cell cycle, and wall biogenesis); metabolism (amino acids, lipids, sulphate, energetic processes); and other more complex responses (cell motility, exopolysaccharide production, biofilm formation, and quorum sensing). The findings suggest that SFC 500-1 A engages in survival and stress management strategies to cope with the toxic effects of tannery wastewater, and that such strategies may be mostly oriented at keeping metabolic processes to a minimum. Altogether, the results might be useful in the near future to improve the strain's effectiveness if it will be applied for bioremediation.

Automated Retrieval of Heterogeneous Proteomic Data for Machine Learning

Proteomics instrumentation and the corresponding bioinformatics tools have evolved at a rapid pace in the last 20 years, whereas the exploitation of deep learning techniques in proteomics is on the horizon. The ability to revisit proteomics raw data, in particular, could be a valuable resource for machine learning applications seeking new insight into protein expression and functions of previously acquired data from different instruments under various lab conditions. We map publicly available proteomics repositories (such as ProteomeXchange) and relevant publications to extract MS/MS data to form one large database that contains the patient history and mass spectrometric data acquired for the patient sample. The extracted mapped dataset should enable the research to overcome the issues attached to the dispersions of proteomics data on the internet, which makes it difficult to apply emerging new bioinformatics tools and deep learning algorithms. The workflow proposed in this study enables a linked large dataset of heart-related proteomics data, which could be easily and efficiently applied to machine learning and deep learning algorithms for futuristic predictions of heart diseases and modeling. Data scraping and crawling offer a powerful tool to harvest and prepare the training and test datasets; however, the authors advocate caution because of ethical and legal issues, as well as the need to ensure the quality and accuracy of the data that are being collected.

SynBioTools: a one-stop facility for searching and selecting synthetic biology tools

BACKGROUND

The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology.

RESULTS

We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system.

CONCLUSIONS

SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.

Low expression of SEMA4D as a potential predictive molecular marker of poor survival in patients with melanoma combined with liver cancer

This study explored the correlation between semaphorin 4D (SEMA4D) and the prognosis and survival time of patients with melanoma combined with liver cancer. A total of 272 patients were recruited, and clinical and follow-up data were recorded. The expression levels of SEMA4D and SEMA3B were determined. Pearson's χ² test and Spearman's rank correlation coefficient were used to analyze the relationship between prognosis and the assessed parameters of melanoma patients. Univariate and multivariate Logistic regression and Cox proportional risk regression analyses were used for further analysis. Additionally, receiver operating characteristic curve and survival curves of subjects were plotted. The Pearson's χ² test showed that the prognosis of melanoma patients was significantly correlated with age, tumor grade, and decreased SEMA4D expression. Additionally, Spearman's correlation coefficient analysis showed that age, tumor grade, and SEMA4D expression were significantly correlated with prognosis. Univariate logistic regression analysis showed that age and tumor grade, and SEMA4D expression, were significantly correlated with prognosis. Older patients, a higher tumor grade, and lower SEMA4D expression were associated with a poorer prognosis. Multivariate logistic regression analysis showed that older patients had a poorer prognosis, and patients with lower SEMA4D expression levels had a significantly worse prognosis than patients with higher SEMA4D expression levels. Kaplan-Meier analysis showed that the survival time of older patients was lower than that of the younger patients. The survival times of patients with lower SEMA4D expression levels were significantly lower than that of patients with higher SEMA4D expression levels. Multivariate Cox regression analysis showed that the survival time of older patients was lower than that of younger patients. The survival time of melanoma patients with low SEMA4D expression was significantly lower than that of patients with higher SEMA4D expression. SEMA4D was significantly associated with melanoma, and lower SEMA4D expression was associated with a poorer survival prognosis in melanoma patients.

Proteomic Analysis of Skeletal Muscle and White Adipose Tissue after Aerobic Exercise Training in High Fat Diet Induced Obese Mice

Obesity is associated with excessive fat accumulation in adipose tissue and other organs, such as skeletal muscle, whereas aerobic exercise (AE) plays an important role in managing obesity through profound protein regulation. Our study aimed to investigate the impact of AE on proteomic changes in both the skeletal muscle and the epididymal fat pad (EFP) of high-fat-diet-induced obese mice. Bioinformatic analyses were performed on differentially regulated proteins using gene ontology enrichment analysis and ingenuity pathway analysis. Eight weeks of AE significantly reduced body weight, increased the serum FNDC5 level, and improved the homeostatic model assessment of insulin resistance. A high-fat diet caused alterations in a subset of proteins involved in the sirtuin signaling pathway and the production of reactive oxygen species in both skeletal muscle and EFP, leading to insulin resistance, mitochondrial dysfunction, and inflammation. On the other hand, AE upregulated skeletal muscle proteins (NDUFB5, NDUFS2, NDUFS7, ETFD, FRDA, and MKNK1) that enhance mitochondrial function and insulin sensitivity. Additionally, the upregulation of LDHC and PRKACA and the downregulation of CTBP1 in EFP can promote the browning of white adipose tissue with the involvement of FNDC5/irisin in the canonical pathway. Our study provides insights into AE-induced molecular responses and may help further develop exercise-mimicking therapeutic targets.

Bringing synapses into focus: Recent advances in synaptic imaging and mass-spectrometry for studying synaptopathy

Synapses are integral for healthy brain function and are becoming increasingly recognized as key structures in the early stages of brain disease. Understanding the pathological processes driving synaptic dysfunction will unlock new therapeutic opportunities for some of the most devastating diseases of our time. To achieve this we need a solid repertoire of imaging and molecular tools to interrogate synaptic biology at greater resolution. Synapses have historically been examined in small numbers, using highly technical imaging modalities, or in bulk, using crude molecular approaches. However, recent advances in imaging techniques are allowing us to analyze large numbers of synapses, at single-synapse resolution. Furthermore, multiplexing is now achievable with some of these approaches, meaning we can examine multiple proteins at individual synapses in intact tissue. New molecular techniques now allow accurate quantification of proteins from isolated synapses. The development of increasingly sensitive mass-spectrometry equipment means we can now scan the synaptic molecular landscape almost in totality and see how this changes in disease. As we embrace these new technical developments, synapses will be viewed with clearer focus, and the field of synaptopathy will become richer with insightful and high-quality data. Here, we will discuss some of the ways in which synaptic interrogation is being facilitated by methodological advances, focusing on imaging, and mass spectrometry.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Mass Spectrometry-Based Proteomics of Minor Species in the Bulk: Questions to Raise with Respect to the Untargeted Analysis of Viral Proteins in Human Tissue

(1) Background: Untargeted mass spectrometry (MS)-based proteomic analysis is highly amenable to automation. Software algorithms translate raw spectral data into protein information obtained by a comparison to sequence databases. However, the technology has limitations, especially for analytes measured at the limit of detection. In a protein expression study of human gastric biopsies, the question arose whether or not it is possible, as well as sensible, to search for viral proteins in addition to those from the human host. (2) Methods: Experimental data-independent MS data were analyzed using protein sequences for oncoviruses, and BLAST analyses were performed to elucidate the level of sequence homology to host proteins. (3) Results: About one hundred viral proteins were assigned, but there was also up to 43% sequence homology to human proteins. (4) Conclusions: There are at least two reasons why the matches to viral proteins should be used with care. First, it is not plausible that large amounts of viral proteins should be present in human gastric biopsies, so the spectral quality of the peptides derived from viral proteins is likely low. As a consequence, the number of false assignments is high. Second, homologous peptides found both in human and virus proteomes contribute to matching errors. Thus, though shotgun proteomics raw data can technically be analyzed using any database, meaningful results cannot be always expected and a sanity check must be performed. Both instrumentation and bioinformatic processing in MS-based proteomics are continuously improving at lowering the limit of detection even further. Nevertheless, data output should always be controlled in order to avoid the over-interpretation of results.