High-throughput proteomics: a methodological mini-review

Discrimination of three commercial tuna species through species-specific peptides: From high-resolution mass spectrometry discovery to MRM validation

The risk of tuna adulteration is high driven by economic benefits. The authenticity of tuna is required to protect both consumers and tuna stocks. Given this, the study is designed to identify species-specific peptides for distinguishing three commercial tropical tuna species. The peptides derived from trypsin digestion were separated and detected using ultrahigh-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF/MS) in data-dependent acquisition (DDA) mode. Venn analysis showed that there were differences in peptide composition among the three tested tuna species. The biological specificity screening through the National Center for Biotechnology Information's Basic Local Alignment Search Tool (NCBI BLAST) revealed that 93 peptides could serve as potential species-specific peptides. Finally, the detection specificity of species-specific peptides of raw meats and processed products was carried out by multiple reaction monitoring (MRM) mode based on a Q-Trap mass spectrometer. The results showed that three, one and two peptides of Katsuwonus pelamis, Thunnus obesus and Thunnus albacores, respectively could serve as species-specific peptides.

Reyanning mixture inhibits M1 macrophage polarization through the glycogen synthesis pathway to improve lipopolysaccharide-induced acute lung injury

ETHNOPHARMACOLOGICAL RELEVANCE

Reyanning (RYN) mixture is a traditional Chinese medicine composed of Taraxacum, Polygonum cuspidatum, Scutellariae Barbatae and Patrinia villosa and is used for the treatment of acute respiratory system diseases with significant clinical efficacy.

AIM OF THE STUDY

Acute lung injury (ALI) is a common clinical disease characterized by acute respiratory failure. This study was conducted to evaluate the therapeutic effects of RYN on ALI and to explore its mechanism of action.

MATERIALS AND METHODS

Ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to analyze the chemical components of RYN. 7.5 mg/kg LPS was administered to induce ALI in rats. RYN was administered by gavage at doses of 2 ml/kg, 4 ml/kg or 8 ml/kg every 8 h for a total of 6 doses. Observations included lung histomorphology, lung wet/dry (W/D) weight ratio, lung permeability index (LPI), HE staining, Wright-Giemsa staining. ELISA was performed to detect the levels of TNF-α, IL-6, IL-10, Arg-1,UDPG. Immunohistochemical staining detected IL-6, F4/80 expression. ROS, MDA, SOD, GSH/GSSG were detected in liver tissues. Multiple omics techniques were used to predict the potential mechanism of action of RYN, which was verified by in vivo closure experiments. Immunofluorescence staining detected the co-expression of CD86 and CD206, CD86 and P2Y14, CD86 and UGP2 in liver tissues. qRT-PCR detected the mRNA levels of UGP2, P2Y14 and STAT1, and immunoblotting detected the protein expression of UGP2, P2Y14, STAT1, p-STAT1.

RESULTS

RYN was detected to contain 1366 metabolites, some of the metabolites with high levels have anti-inflammatory, antibacterial, antiviral and antioxidant properties. RYN (2, 4, and 8 ml/kg) exerted dose-dependent therapeutic effects on the ALI rats, by reducing inflammatory cell infiltration and oxidative stress damage, inhibiting CD86 expression, decreasing TNF-α and IL-6 levels, and increasing IL-10 and Arg-1 levels. Transcriptomics and proteomics showed that glucose metabolism provided the pathway for the anti-ALI properties of RYN and that RYN inhibited lung glycogen production and distribution. Immunofluorescence co-staining showed that RYN inhibited CD86 and UGP2 expressions. In vivo blocking experiments revealed that blocking glycogen synthesis reduced UDPG content, inhibited P2Y14 and CD86 expressions, decreased P2Y14 and STAT1 mRNA and protein expressions, reduced STAT1 protein phosphorylation expression, and had the same therapeutic effect as RYN.

CONCLUSION

RYN inhibits M1 macrophage polarization to alleviate ALI. Blocking glycogen synthesis and inhibiting the UDPG/P2Y14/STAT1 signaling pathway may be its molecular mechanism.

Construction of protein-protein interaction network in sulfate-reducing bacteria: Unveiling of global response to Hg

Sulfate-reducing bacteria (SRB) play pivotal roles in the biotransformation of mercury (Hg). However, unrevealed global responses of SRB to Hg have restricted our understanding of details of Hg biotransformation processes. The absence of protein-protein interaction (PPI) network under Hg stimuli has been a bottleneck of proteomic analysis for molecular mechanisms of Hg transformation. This study constructed the first comprehensive PPI network of SRB in response to Hg, encompassing 67 connected nodes, 26 independent nodes, and 121 edges, covering 93% of differentially expressed proteins from both previous studies and this study. The network suggested that proteomic changes of SRB in response to Hg occurred globally, including microbial metabolism in diverse environments, carbon metabolism, nucleic acid metabolism and translation, nucleic acid repair, transport systems, nitrogen metabolism, and methyltransferase activity, partial of which could cover the known knowledge. Antibiotic resistance was the original response revealed by this network, providing insights into of Hg biotransformation mechanisms. This study firstly provided the foundational network for a comprehensive understanding of SRB's responses to Hg, convenient for exploration of potential targets for Hg biotransformation. Furthermore, the network indicated that Hg enhances the metabolic activities and modification pathways of SRB to maintain cellular activities, shedding light on the influences of Hg on the carbon, nitrogen, and sulfur cycles at the cellular level.

Proteomic Analysis of Milk Fat Globule Membrane Protein Modulation of Differently Expressed Proteins in Lactobacillus plantarum under Bile Salt Stress

Tolerance to bile stress is a crucial property for lactic acid bacteria (LAB) to survive in the gastrointestinal tract and exert their beneficial effects. Whey powder enriched with milk fat globule membrane proteins (M-WPI) as a functional component is protective for strains under stress conditions. The current study investigated the key mechanisms of action involved in Lactobacillus plantarum (L. plantarum) CGMCC 23701 survival in the presence of bile and the protective mechanism of M-WPI. According to proteomic analysis (proteomics), there could be several reasons for the greater protective effect of M-WPI. These include promoting the synthesis of fatty acids and peptidoglycans to repair the structure of the cell surface, regulating the metabolism of carbohydrates and amino acids to release energy and produce a range of precursors, enabling the expression of the repair system to repair damaged DNA, and promoting the expression of proteins associated with the multidrug efflux pump, which facilitates the exocytosis of intracellular bile salts. This study helps us to better understand the changes in proteome of L. plantarum CGMCC 23701 under bile salt stress and M-WPI protection, which will provide a new method for the protection and development of functional LAB.

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.

Identification of biomimetic nanoplatform-mediated delivery of si-ISG15 for treatment of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is recognized as the most malicious form of breast cancer and exhibits an alarming tendency for recurrence, a heightened propensity for metastasis, and an overwhelmingly grim prognosis. Therefore, effective therapy approaches for TNBC are urgently required. In this study, the interferon-stimulated gene 15 (ISG15) expression level was analyzed by bioinformatics and verified by Western blot analysis. The effects of ISG15 on the proliferation and metastasis of TNBC cells were assessed using MTT, Colony formation, EdU, Transwell, and Flow cytometry assays. We also developed a cancer cell-biomimetic nanoparticle delivery system and evaluated its therapeutic efficacy in vivo. In this study, we reported that ISG15 was upregulated in TNBC, and its high expression level correlated with an increased risk of tumorigenesis. Through in vitro and in vivo studies, we discovered that ISG15 knockdown drastically suppressed cell proliferation, invasion, and migration and induced apoptosis in TNBC cells. Our findings revealed that ISG15 was a candidate therapeutic target in TNBC because of its key role in malignant growth and invasion. Moreover, co-immunoprecipitation showed that ISG15 exerted oncogenic functions through its interaction with ATP binding cassette subfamily E member 1 and activated the Janus kinase/signal transducers and activators of the transcription signaling pathway. Furthermore, we created a nanoparticle-based siRNA camouflaged using a cancer cell membrane vesicle delivery system (the CM@NP complex) and confirmed its therapeutic effects in vivo. Our findings confirmed that ISG15 may play a pivotal oncogenic role in the development of TNBC and that CM@siRNA-NP complexes are an effective delivery system and a novel biological strategy for treating TNBC.

CsMIEL1 effectively inhibits the accumulation of anthocyanins under low temperatures in tea plants (Camelliasinensis)

Tea is one of the most prevalent non-alcoholic beverages. The leaves of tea plants hyperaccumulate anthocyanins under cold stress, resulting in enhanced bitterness. Previously, we determined that the RING-type E3 ubiquitin ligase CsMIEL1 from the tea plant (Camellia sinensis (L.) O. Kuntze) is involved in the response to stress conditions. This study aimed to determine the role of CsMIEL1 in anthocyanin accumulation at the post-translational modification level. The results showed that the heterologous expression of CsMIEL1 led to an 86% decrease in anthocyanin levels, resulting in a significant decrease in the mRNA levels of related genes in Arabidopsis at low temperatures but no significant differences in other phenotypes. Furthermore, multi-omics analysis and yeast two-hybrid library screening were performed to identify potential downstream targets of CsMIEL1. The results showed that the overexpression of CsMIEL1 resulted in 45% (448) of proteins being differentially expressed, of which 8% (36) were downregulated in A.thaliana, and most of these differentially expressed proteins (DEPs) were clustered in the plant growth and secondary metabolic pathways. Among the 71 potential targets that may interact with CsMIEL1, CsMYB90 and CsGSTa, which are related to anthocyanin accumulation, were selected. In subsequent analyses, these two proteins were verified to interact with CsMIEL1 via yeast two-hybrid (Y2H) and pull-down analyses in vitro. In summary, we explored the potential mechanism by which the E3 ligase relieves anthocyanin hyperaccumulation at low temperatures in tea plants. These results provide a new perspective on the mechanisms of anthocyanin regulation and the molecular breeding of tea plants.

Capillary blood self-collection for high-throughput proteomics

In this study, we sought to compare protein concentrations obtained from a high-throughput proteomics platform (Olink) on samples collected using capillary blood self-collection (with the Tasso+ device) versus standard venipuncture (control). Blood collection was performed on 20 volunteers, including one sample obtained via venipuncture and two via capillary blood using the Tasso+ device. Tasso+ samples were stored at 2°C-8°C for 24-hs (Tasso-24) or 48-h (Tasso-48) prior to processing to simulate shipping times from a study participant's home. Proteomics were analyzed using Olink (384 Inflammatory Panel). Tasso+ blood collection was successful in 37/40 attempts. Of 230 proteins included in our analysis, Pearson correlations (r) and mean coefficient of variation (CV) between Tasso-24 or Tasso-48 versus venipuncture were variable. In the Tasso-24 analysis, 34 proteins (14.8%) had both a correlation r > 0.5 and CV < 0.20. In the Tasso-48 analysis, 68 proteins (29.6%) had a correlation r > 0.5 and CV < 0.20. Combining the Tasso-24 and Tasso-48 analyses, 26 (11.3%) proteins met these thresholds. We concluded that protein concentrations from Tasso+ samples processed 24-48 h after collection demonstrated wide technical variability and variable correlation with a venipuncture gold-standard. Use of home capillary blood self-collection for large-scale proteomics should be limited to select proteins with good agreement with venipuncture.

Proteomic alterations in patients with atopic dermatitis

INTRODUCTION

Atopic Dermatitis (AD) is the most common inflammatory skin disease with a complex and multifactorial pathogenesis. The use of proteomics in understanding AD has yielded the discovery of novel biomarkers and may further expand therapeutic options.

AREAS COVERED

This review summarizes the most recent proteomic studies and the methodologies used in AD. It describes novel biomarkers that may monitor disease course and therapeutic response. The review also highlights skin and blood biomarkers characterizing different AD phenotypes and differentiates AD from other inflammatory skin disorders. A literature search was conducted by querying Scopus, Google Scholar, Pubmed/Medline, and Clinicaltrials.gov up to June 2023.

EXPERT OPINION

The integration of proteomics into research efforts in atopic dermatitis has broadened our understanding of the molecular profile of AD through the discovery of new biomarkers. In addition, proteomics may contribute to the development of targeted treatments ultimately improving personalized medicine. An increasing number of studies are utilizing proteomics to explore this heterogeneous disease.

Proteomic Blood Profiles Obtained by Totally Blind Biological Clustering in Stable and Exacerbated COPD Patients

Although Chronic Obstructive Pulmonary Disease (COPD) is highly prevalent, it is often underdiagnosed. One of the main characteristics of this heterogeneous disease is the presence of periods of acute clinical impairment (exacerbations). Obtaining blood biomarkers for either COPD as a chronic entity or its exacerbations (AECOPD) will be particularly useful for the clinical management of patients. However, most of the earlier studies have been characterized by potential biases derived from pre-existing hypotheses in one or more of their analysis steps: some studies have only targeted molecules already suggested by pre-existing knowledge, and others had initially carried out a blind search but later compared the detected biomarkers among well-predefined clinical groups. We hypothesized that a clinically blind cluster analysis on the results of a non-hypothesis-driven wide proteomic search would determine an unbiased grouping of patients, potentially reflecting their endotypes and/or clinical characteristics. To check this hypothesis, we included the plasma samples from 24 clinically stable COPD patients, 10 additional patients with AECOPD, and 10 healthy controls. The samples were analyzed through label-free liquid chromatography/tandem mass spectrometry. Subsequently, the Scikit-learn machine learning module and K-means were used for clustering the individuals based solely on their proteomic profiles. The obtained clusters were confronted with clinical groups only at the end of the entire procedure. Although our clusters were unable to differentiate stable COPD patients from healthy individuals, they segregated those patients with AECOPD from the patients in stable conditions (sensitivity 80%, specificity 79%, and global accuracy, 79.4%). Moreover, the proteins involved in the blind grouping process to identify AECOPD were associated with five biological processes: inflammation, humoral immune response, blood coagulation, modulation of lipid metabolism, and complement system pathways. Even though the present results merit an external validation, our results suggest that the present blinded approach may be useful to segregate AECOPD from stability in both the clinical setting and trials, favoring more personalized medicine and clinical research.

Proteomic change in the upper lobe of the left lung of Beagle dogs at the lung migration stage of Toxocara canis infection

BACKGROUND

Toxocara canis is considered one of the most neglected parasitic zoonoses and threatens the health of millions of people worldwide with a predilection for pediatric and adolescent populations in impoverished communities. Exploring the invasion and developmental mechanisms associated with T. canis infection in its definitive canine hosts will help to better control zoonotic toxocariasis.

METHODS

Proteomic changes in samples from the upper lobe of the left lung of Beagle puppies were systematically analyzed by quantitative proteomic technology of data-independent acquisition (DIA) at 96 h post-infection (hpi) with T. canis. Proteins with P-values < 0.05 and fold change > 1.5 or < 0.67 were considered proteins with differential abundance (PDAs).

RESULTS

A total of 28 downregulated PDAs and 407 upregulated PDAs were identified at 96 hpi, including RhoC, TM4SFs and LPCAT1, which could be associated with the maintenance and repair of lung homeostasis. GO annotation and KEGG pathway enrichment analyses of all identified proteins and PDAs revealed that many lung proteins have correlation to signal transduction, lipid metabolism and immune system.

CONCLUSIONS

The present study revealed lung proteomic alterations in Beagle dogs at the lung migration stage of T. canis infection and identified many PDAs of Beagle dog lung, which may play important roles in the pathogenesis of toxocariasis, warranting further experimental validation.

Metabolomic and proteomic applications to exercise biomedicine

Objectives

'OMICs encapsulates study of scaled data acquisition, at the levels of DNA, RNA, protein, and metabolite species. The broad objectives of OMICs in biomedical exercise research are multifarious, but commonly relate to biomarker development and understanding features of exercise adaptation in health, ageing and metabolic diseases.

Methods

This field is one of exponential technical (i.e., depth of feature coverage) and scientific (i.e., in health, metabolic conditions and ageing, multi-OMICs) progress adopting targeted and untargeted approaches.

Results

Key findings in exercise biomedicine have led to the identification of OMIC features linking to heritability or adaptive responses to exercise e.g., the forging of GWAS/proteome/metabolome links to cardiovascular fitness and metabolic health adaptations. The recent addition of stable isotope tracing to proteomics ('dynamic proteomics') and metabolomics ('fluxomics') represents the next phase of state-of-the-art in 'OMICS.

Conclusions

These methods overcome limitations associated with point-in-time 'OMICs and can be achieved using substrate-specific tracers or deuterium oxide (D2O), depending on the question; these methods could help identify how individual protein turnover and metabolite flux may explain exercise responses. We contend application of these methods will shed new light in translational exercise biomedicine.

Plasma proteomics for prediction of subclinical coronary artery calcifications in primary prevention

BACKGROUND AND AIMS

Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery calcifications (CAC) in asymptomatic individuals and if they add predictive value beyond traditional risk factors.

METHODS

Using proximity extension assays, 1,342 plasma proteins were measured in 1,827 individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 50-64 years without history of ischaemic heart disease and with CAC assessed by computed tomography. After data-driven feature selection, extreme gradient boosting machine learning models were trained on the IGTM cohort to predict the presence of CAC using combinations of proteins and traditional risk factors. The trained models were validated in SCAPIS.

RESULTS

The best plasma protein subset (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the validation cohort. However, this was not better than prediction by traditional risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 proteins were highly correlated with traditional risk factors.

CONCLUSIONS

A plasma protein subset that could predict the presence of subclinical CAC was identified but it did not outperform nor improve a model based on traditional risk factors. Thus, support for this targeted proteomics platform to predict subclinical CAC beyond traditional risk factors was not found.

Harnessing the potential of reverse-phase protein array technology: Advancing precision oncology strategies

The last few decades have seen remarkable strides in the field of cancer therapy. Precision oncology coupled with comprehensive genomic profiling has become routine clinical practice for solid tumors, the advent of immune checkpoint inhibitors has transformed the landscape of oncology treatment, and the number of cancer drug approvals has continued to increase. Nevertheless, the application of genomics-driven precision oncology has thus far benefited only 10%-20% of cancer patients, leaving the majority without matched treatment options. This limitation underscores the need to explore alternative avenues with regard to selecting patients for targeted therapies. In contrast with genomics-based approaches, proteomics-based strategies offer a more precise understanding of the intricate biological processes driving cancer pathogenesis. This perspective underscores the importance of integrating complementary proteomic analyses into the next phase of precision oncology to establish robust biomarker-drug associations and surmount challenges related to drug resistance. One promising technology in this regard is the reverse-phase protein array (RPPA), which excels in quantitatively detecting protein modifications, even with limited amounts of sample. Its cost-effectiveness and rapid turnaround time further bolster its appeal for application in clinical settings. Here, we review the current status of genomics-driven precision oncology, as well as its limitations, with an emphasis on drug resistance. Subsequently, we explore the application of RPPA technology as a catalyst for advancing precision oncology. Through illustrative examples drawn from clinical trials, we demonstrate its utility for unraveling the molecular mechanisms underlying drug responses and resistance.

Identification of MAD2L1 and BUB1B as Potential Biomarkers Associated with Progression and Prognosis of Ovarian Cancer

Ovarian cancer develops insidiously and is frequently diagnosed at advanced stages. Screening for ovarian cancer is an effective strategy for reducing mortality. This study aimed to investigate the molecular mechanisms underlying the development of ovarian cancer and identify novel tumor biomarkers for the diagnosis and prognosis of ovarian cancer. Three databases containing gene expression profiles specific to serous ovarian cancer (GSE18520, GSE12470, and GSE26712) were acquired. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were analyzed for the differentially expressed gene (DEGs). The protein-protein interaction (PPI) network was constructed using the STRING database. The pivotal genes in the PPI network were screened using the Cytoscape software. Survival curve analysis was performed using a Kaplan-Meier Plotter. The cancer genome atlas and Gene Expression Omnibus databases were used to find the relationship between Hub gene and serous ovarian cancer. PCR and immunohistochemistry were used to detect the expression of Hub gene in serous ovarian cancer tissues and cells. Downstream pathways of the candidate tumor marker genes were predicted using Gene Set Enrichment Analysis. In this study, 252 DEGs were screened for pathway enrichment. 20 Hub genes were identified. Survival analysis suggested that Aurka, Bub1b, Cenpf, Cks1b, Kif20a, Mad2l1, Racgap1, and Ube2c were associated with the survival of patients with serous ovarian cancer. MAD2L1 and BUB1B levels were significantly different in serous ovarian cancer at different stages. Finally, Mad2l1 was found to play a role in the cell cycle, oocyte meiosis, and ubiquitin-mediated proteolysis. Meanwhile, Bub1b may play a role in the cell cycle, ubiquitin-mediated proteolysis, and spliceosome processes. Mad2l1 and Bub1b could be used as markers to predict ovarian carcinogenesis and prognosis, providing candidate targets for the diagnosis and treatment of serous ovarian cancer.

Characterization of Human B Cell Hematological Malignancies Using Protein-Based Approaches

The maturation of B cells is a complex, multi-step process. During B cell differentiation, errors can occur, leading to the emergence of aberrant versions of B cells that, finally, constitute a malignant tumor. These B cell malignancies are classified into three main groups: leukemias, myelomas, and lymphomas, the latter being the most heterogeneous type. Since their discovery, multiple biological studies have been performed to characterize these diseases, aiming to define their specific features and determine potential biomarkers for diagnosis, stratification, and prognosis. The rise of advanced -omics approaches has significantly contributed to this end. Notably, proteomics strategies appear as promising tools to comprehensively profile the final molecular effector of these cells. In this narrative review, we first introduce the main B cell malignancies together with the most relevant proteomics approaches. Then, we describe the core studies conducted in the field and their main findings and, finally, we evaluate the advantages and drawbacks of flow cytometry, mass cytometry, and mass spectrometry for the profiling of human B cell disorders.

Proteome, Lysine Acetylome, and Succinylome Identify Posttranslational Modification of STAT1 as a Novel Drug Target in Silicosis

Inhalation of crystalline silica dust induces incurable lung damage, silicosis, and pulmonary fibrosis. However, the mechanisms of the lung injury remain poorly understood, with limited therapeutic options aside from lung transplantation. Posttranslational modifications can regulate the function of proteins and play an important role in studying disease mechanisms. To investigate changes in posttranslational modifications of proteins in silicosis, combined quantitative proteome, acetylome, and succinylome analyses were performed with lung tissues from silica-injured and healthy mice using liquid chromatography-mass spectrometry. Combined analysis was applied to the three omics datasets to construct a protein landscape. The acetylation and succinylation of the key transcription factor STAT1 were found to play important roles in the silica-induced pathophysiological changes. Modulating the acetylation level of STAT1 with geranylgeranylacetone effectively inhibited the progression of silicosis. This report revealed a comprehensive landscape of posttranslational modifications in silica-injured mouse and presented a novel therapeutic strategy targeting the posttranslational level for silica-induced lung diseases.

[Research progress on bioinformatics in pulmonary arterial hypertension]

Pulmonary arterial hypertension (PAH) is a severe disease characterized by abnormal pulmonary vascular remodeling and increased right ventricular pressure load, posing a significant threat to patient health. While some pathological mechanisms of PAH have been revealed, the deeper mechanisms of pathogenesis remain to be elucidated. In recent years, bioinformatics has provided a powerful tool for a deeper understanding of the complex mechanisms of PAH through the integration of techniques such as multi-omics analysis, artificial intelligence, and Mendelian randomization. This review focuses on the bioinformatics methods and technologies used in PAH research, summarizing their current applications in the study of disease mechanisms, diagnosis, and prognosis assessment. Additionally, it analyzes the existing challenges faced by bioinformatics and its potential applications in the clinical and basic research fields of PAH in the future.

Cross-platform proteomics signatures of extreme old age

In previous work we used a Somalogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals.

Key Proteomics Tools for Fundamental and Applied Microalgal Research

Microscopic, photosynthetic prokaryotes and eukaryotes, collectively referred to as microalgae, are widely studied to improve our understanding of key metabolic pathways (e.g., photosynthesis) and for the development of biotechnological applications. Omics technologies, which are now common tools in biological research, have been shown to be critical in microalgal research. In the past decade, significant technological advancements have allowed omics technologies to become more affordable and efficient, with huge datasets being generated. In particular, where studies focused on a single or few proteins decades ago, it is now possible to study the whole proteome of a microalgae. The development of mass spectrometry-based methods has provided this leap forward with the high-throughput identification and quantification of proteins. This review specifically provides an overview of the use of proteomics in fundamental (e.g., photosynthesis) and applied (e.g., lipid production for biofuel) microalgal research, and presents future research directions in this field.

CoGSPro-net:A graph neural network based on protein-protein interaction for classifying lung cancer-relatrd proteins

This paper proposes a deep learning algorithm named CoGSPro for classifying lung cancer-related proteins. CoGSPro combines graph neural networks and attention mechanisms to extract key features from protein data and accurately classify proteins. It utilizes large-scale protein expression datasets to train and validate the model, enabling it to identify subtle patterns related to lung cancer. CoGSPro integrates protein-protein interaction network information to improve its predictive accuracy. The experimental results indicate that CoGSPro achieves cutting-edge performance, attaining an accuracy of 96.60% in the classification of lung cancer proteins, surpassing other baseline methods. Additionally, CoGSPro has uncovered new biomarkers for lung cancer, offering potential targets for early detection and treatment.

Thioredoxin (Trx): A redox target and modulator of cellular senescence and aging-related diseases

Thioredoxin (Trx) is a compact redox-regulatory protein that modulates cellular redox state by reducing oxidized proteins. Trx exhibits dual functionality as an antioxidant and a cofactor for diverse enzymes and transcription factors, thereby exerting influence over their activity and function. Trx has emerged as a pivotal biomarker for various diseases, particularly those associated with oxidative stress, inflammation, and aging. Recent clinical investigations have underscored the significance of Trx in disease diagnosis, treatment, and mechanistic elucidation. Despite its paramount importance, the intricate interplay between Trx and cellular senescence-a condition characterized by irreversible growth arrest induced by multiple aging stimuli-remains inadequately understood. In this review, our objective is to present a comprehensive and up-to-date overview of the structure and function of Trx, its involvement in redox signaling pathways and cellular senescence, its association with aging and age-related diseases, as well as its potential as a therapeutic target. Our review aims to elucidate the novel and extensive role of Trx in senescence while highlighting its implications for aging and age-related diseases.

No evidence of association between either Modic change or disc degeneration and five circulating inflammatory proteins

Introduction

Intervertebral disc degeneration and Modic change are the main spinal structural changes associated with chronic low back pain (LBP). Both conditions are thought to manifest local inflammation and if inflammatory proteins translocate to the blood circulation could be detected systemically. The work here assesses whether the presence of disc degeneration is associated with detectable blood level changes of five inflammatory markers and whether chronic LBP is associated with these changes.

Materials and Methods

Two hundred and forty TwinsUK cohort participants with both MRI disc degeneration grade and Modic change extent, and IL-6, IL-8, IL-8 TNF, and CX3CL1 protein blood concentration measurements were included in this work. Linear mixed effects models were used to test the association of blood cytokine concentration with disc degeneration score and Modic change volumetric score. Association of chronic LBP status from questionnaires with disc degeneration, Modic change, and cytokine blood concentration was also tested.

Results

No statistically significant association between disc degeneration or Modic change with cytokine blood concentration was found. Instead, regression analysis pointed strong association between cytokine blood concentration with body mass index for IL-6 and with age for IL-6 and TNF. Mild association was found between IL-8 blood concentration and body mass index. Additionally, LBP status was associated with Modic change volumetric score but not associated with any cytokine concentration.

Conclusions

We found no evidence that Modic change and disc degeneration are able to produce changes in tested blood cytokine concentration. However, age and body mass index have strong influence on cytokine concentration and both are associated with the conditions studied which may confound associations found in the literature. It is then unlikely that cytokines produced in the disc or vertebral bone marrow induce chronic LBP.

Serum proteome analysis identifies a potential biomarker for axial psoriatic arthritis

BACKGROUND

To identify potential serum biomarkers for differentiating between axial psoriatic arthritis (axPsA) and peripheral psoriatic arthritis (pPsA).

METHODS

Serum samples were collected from patients with PsA to create a biomarker discovery cohort and a verification cohort. Patients with PsA were classified into axial or peripheral subtypes based on imaging criteria. Untargeted proteomics technology was used in the discovery phase to screen for biomarkers, and candidate biomarkers were evaluated using enzyme-linked immunosorbent assay (ELISA) in the verification phase.

RESULTS

We identified 45 significantly differentially expressed proteins (DEPs) between axPsA (n = 20) and pPsA (n = 20) with liquid chromatography-mass spectrometry. Among these DEPs, serum pigment epithelium-derived factor (PEDF) was identified as a candidate biomarker using the Boruta algorithm and lasso regression. Results of ELISA further confirmed that the level of serum PEDF expression was significantly higher in axPsA (n = 37) than in pPsA (n = 51) at the verification cohort (37.9 ± 10.1 vs. 30.5 ± 8.9 μg/mL, p < 0.001). Receiver operating characteristics analysis showed that PEDF had an area under the curve (AUC) of 0.72. Serum PEDF was positively correlated with body mass index and C-reactive protein. Additionally, there was a tendency towards a positive correlation between PEDF and the Bath Ankylosing Spondylitis Disease Activity Index.

CONCLUSIONS

This study provided a comprehensive characterization of the proteome in axPsA and pPsA and identified a candidate biomarker, PEDF, that may contribute to early diagnosis for axPsA.

Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke

The immune response is considered essential for pathology of ischemic stroke (IS), but it remains unclear which immune response-related proteins exhibit altered expression in IS patients. Here, we used Olink proteomics to examine the expression levels of 92 immune response-related proteins in the sera of IS patients (n = 88) and controls (n = 88), and we found that 59 of these proteins were differentially expressed. Feature variables were screened from the differentially expressed proteins by the least absolute shrinkage and selection operator (LASSO) and the random forest and by determining whether their proteins had an area under the curve (AUC) greater than 0.8. Ultimately, we identified six potential protein biomarkers of IS, namely, MASP1, STC1, HCLS1, CLEC4D, PTH1R, and PIK3AP1, and established a logistic regression model that used these proteins to diagnose IS. The AUCs of the models in the internal validation and the test set were 0.962 (95% confidence interval (CI): 0.895-1.000) and 0.954 (95% CI: 0.884-1.000), respectively, and the same protein detection method was performed in an external independent validation set (AUC: 0.857 (95% CI: 0.801-0.913)). These proteins may play a role in immune regulation via the C-type lectin receptor signaling pathway, the PI3K-AKT signaling pathway, and the B-cell receptor signaling pathway.

Opticool: Cutting-edge transgenic optical tools

Only a few short decades have passed since the sequencing of GFP, yet the modern repertoire of transgenically encoded optical tools implies an exponential proliferation of ever improving constructions to interrogate the subcellular environment. A myriad of tags for labeling proteins, RNA, or DNA have arisen in the last few decades, facilitating unprecedented visualization of subcellular components and processes. Development of a broad array of modern genetically encoded sensors allows real-time, in vivo detection of molecule levels, pH, forces, enzyme activity, and other subcellular and extracellular phenomena in ever expanding contexts. Optogenetic, genetically encoded optically controlled manipulation systems have gained traction in the biological research community and facilitate single-cell, real-time modulation of protein function in vivo in ever broadening, novel applications. While this field continues to explosively expand, references are needed to assist scientists seeking to use and improve these transgenic devices in new and exciting ways to interrogate development and disease. In this review, we endeavor to highlight the state and trajectory of the field of in vivo transgenic optical tools.

Predicting Radiation-Induced Lung Injury in Patients With Lung Cancer: Challenges and Opportunities

Radiation-induced lung injury (RILI) is one of the main dose-limiting toxicities in radiation therapy (RT) for lung cancer. Approximately 10% to 20% of patients show signs of RILI of variable severity. The reason for the wide range of RILI severity and the mechanisms underlying its development are only partially understood. A number of clinical risk factors have been identified that can aid in clinical decision making. Technological advancements in RT and the use of strict organ-at-risk dose constraints have helped to reduce RILI. Predicting patients at risk for RILI may be further improved with a combination of cytokine assessments, γH2AX-assays in leukocytes, or epigenetic markers. A complicating factor is the lack of an objective definition of RILI. Tools such as computed tomography densitometry, fluorodeoxyglucose-positron emission tomography uptake, changes in lung function measurements, and exhaled breath analysis can be implemented to better define and quantify RILI. This can aid in the search for new biomarkers, which can be accelerated by omics techniques, single-cell RNA sequencing, mass cytometry, and advances in patient-specific in vitro cell culture models. An objective quantification of RILI combined with these novel techniques can aid in the development of biomarkers to better predict patients at risk and allow personalized treatment decisions.

Unlocking protein-based biomarker potential for graft-versus-host disease following allogenic hematopoietic stem cell transplants

Despite the numerous advantages of allogeneic hematopoietic stem cell transplants (allo-HSCT), there exists a notable association with risks, particularly during the preconditioning period and predominantly post-intervention, exemplified by the occurrence of graft-versus-host disease (GVHD). Risk stratification prior to symptom manifestation, along with precise diagnosis and prognosis, relies heavily on clinical features. A critical imperative is the development of tools capable of early identification and effective management of patients undergoing allo-HSCT. A promising avenue in this pursuit is the utilization of proteomics-based biomarkers obtained from non-invasive biospecimens. This review comprehensively outlines the application of proteomics and proteomics-based biomarkers in GVHD patients. It delves into both single protein markers and protein panels, offering insights into their relevance in acute and chronic GVHD. Furthermore, the review provides a detailed examination of the site-specific involvement of GVHD. In summary, this article explores the potential of proteomics as a tool for timely and accurate intervention in the context of GVHD following allo-HSCT.

Exploring the apoptotic effects of sericin on HCT116 cells through comprehensive nanostring transcriptomics and proteomics analysis

Sericin, a silk protein from Bombyx mori (silkworms), has many applications, including cosmetics, anti-inflammation, and anti-cancer. Sericin complexes with nanoparticles have shown promise for breast cancer cell lines. Apoptosis, a programmed cell death mechanism, stops cancer cell growth. This study found that Sericin urea extract significantly affected HCT116 cell viability (IC50 = 42.00 ± 0.002 µg/mL) and caused apoptosis in over 80% of treated cells. S-FTIR analysis showed significant changes in Sericin-treated cells' macromolecule composition, particularly in the lipid and nucleic acid areas, indicating major cellular modifications. A transcriptomics study found upregulation of the apoptotic signaling genes FASLG, TNFSF10, CASP3, CASP7, CASP8, and CASP10. Early apoptotic proteins also showed that BAD, AKT, CASP9, p53, and CASP8 were significantly upregulated. A proteomics study illuminated Sericin-treated cells' altered protein patterns. Our results show that Sericin activated the extrinsic apoptosis pathway via the caspase cascade (CASP8/10 and CASP3/7) and the death receptor pathway, involving TNFSF10 or FASLG, in HCT116 cells. Upregulation of p53 increases CASP8, which activates CASP3 and causes HCT116 cell death. This multi-omics study illuminates the molecular mechanisms of Sericin-induced apoptosis, sheds light on its potential cancer treatment applications, and helps us understand the complex relationship between silk-derived proteins and cellular processes.

Hist-i-fy-a multiple histidine post-translational-modification (PTM) prediction server based on protein sequences using convolution neural network: a case study on mass spectroscopy data

Computational characterization of multiple Histidine (His) post-translational-modifications (PTM) at enzyme active sites complements tedious experimental characterization in proteins-of-unknown-functions (PUFs) and domain-of-unknown-functions (DUFs). There are only a handful of Histidine-PTM-prediction-tools and those also annotate only a single function. Here, we addressed the problem using artificial neural networks on functional histidine dataset curated from enzyme (protein) sequences available in UniProt database (sample size n = 1584). The convolution-neural-network (CNN) model ('Hist-i-fy') performed the best with 75% overall accuracy/F1-score. A case study was performed on histidine-phosphorylation (n = 34) obtained from mass spectroscopy data. For the first time, we report multiple His-PTM-prediction-tool (https://histify.streamlit.app/& https://github.com/dibyansu24-maker/Histify), with optimal performance. The inputs to the tool are (i) protein sequence containing histidine, and (ii) the histidine residue number. Prediction output is one out of the eight histidine functions-acetylation, ribosylation, glycosylation, hydroxylation, methylation, oxidation, phosphorylation, and protein splicing.Communicated by Ramaswamy H. Sarma.

High throughput methods to study protein-protein interactions during host-pathogen interactions

The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.

How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.

An omics review and perspective of researches on intrahepatic cholestasis of pregnancy

Intrahepatic cholestasis of pregnancy (ICP) is one of the common pregnancy complications that may threaten the health of both pregnant women and their fetuses. Hence, it is of vital importance to identify key moleculars and the associated functional pathways of ICP, which will help us to better understand the pathological mechanisms as well as to develop precise clinical biomarkers. The emerging and developing of multiple omics approaches enable comprehensive studies of the genome, transcriptome, proteome and metabolome of clinical samples. The present review collected and summarized the omics based studies of ICP, aiming to provide an overview of the current progress, limitations and future directions. Briefly, these studies covered a broad range of research contents by the comparing of different experimental groups including ICP patients, ICP subtypes, ICP fetuses, ICP models and other complications. Correspondingly, the studied samples contain various types of clinical samples, in vitro cultured tissues, cell lines and the samples from animal models. According to the main research objectives, we further categorized these studies into two groups: pathogenesis and diagnosis analyses. The pathogenesis studies identified tens of functional pathways that may represent the key regulatory events for the occurrence, progression, treatment and fetal effects of ICP. On the other hand, the diagnosis studies tested more than 40 potential models for the early-prediction, diagnosis, grading, prognosis or differential diagnosis of ICP. Apart from these achievements, we also evaluated the limitations of current studies, and emphasized that many aspects of clinical characteristics, sample processing, and analytical method can greatly affect the reliability and repeatability of omics results. Finally, we also pointed out several new directions for the omics based analyses of ICP and other perinatal associated conditions in the future.

Integrative proteome and metabolome analyses reveal molecular basis underlying growth and nutrient composition in the Pacific oyster, Crassostrea gigas

In order to comprehend the molecular basis of growth, nutrient composition, and color pigmentation in oysters, comparative proteome and metabolome analyses of two selectively bred oyster strains with contrasting growth rate and shell color were used in this study. A total of 289 proteins and 224 metabolites were identified differentially expressed between the two strains. We identified a series of specifically enriched functional clusters implicated in protein biosynthesis (RPL4, MRPS7, and CARS), fatty acid metabolism (ACSL5, PEX3, ACOXI, CPTIA, FABP6, and HSD17B12), energy metabolism (FH, PPP1R7, CLAM2, and RGN), cell proliferation (MYB, NFYC, DOHH, TOP2a, SMARCA5, and SMARCC2), material transport (ABCB1, ABCB8, VPS16, and VPS33a), and pigmentation (RDH7, RDH13, Retsat, COX15, and Cyp3a9). Integrated proteome and metabolome analyses indicate that fast-growing strain utilize energy-efficient mechanisms of ATP generation while promoting protein and polyunsaturated fatty acid synthesis, activating the cell cycle to increase cell proliferation and thus promoting their biomass increase. These results uncovered molecular mechanisms underlying growth regulation, nutrition quality, and pigmentation and provided candidate biomarkers for molecular breeding in oysters. SIGNIFICANCE: Rapid growth has always been the primary breeding objective to increase the production profits of Pacific oyster (Crassostrea gigas), while favorable nutritional quality and beautiful color add commercial value. In recent years, proteomic and metabolomic techniques have been widely used in marine organisms, although these techniques are seldom utilized to study oyster growth and development. In this study, two C. gigas strains with contrasted phenotypes in growth and shell color provided an ideal model for unraveling the molecular basis of growth and nutrient composition through a comparison of the proteome and metabolome. Since proteins and metabolites are the critical undertakers and the end products of cellular regulatory processes, identifying the differentially expressed proteins and metabolites would allow for discovering biomarkers and pathways that were implicated in cell growth, proliferation, and other critical functions. This work provides valuable resources in assistance with molecular breeding of oyster strains with superior production traits of fast-growth and high-quality nutrient value.

Proteomics: Unraveling the Cross Talk Between Innate Immunity and Disease Pathophysiology, Diagnostics, and Treatment Options

Inflammation is crucial in diseases, and proteins play a key role in the interplay between innate immunity and pathology. This review explores how proteomics helps understanding this relationship, focusing on diagnosis and treatment. We explore the dynamic innate response and the significance of proteomic techniques in deciphering the complex network of proteins involved in prevalent diseases, including infections, cancer, autoimmune and neurodegenerative disorders. Proteomics identifies key proteins in host-pathogen interactions, shedding light on infection mechanisms and inflammation. These discoveries hold promise for diagnostic tools, therapies, and vaccines. In cancer research, proteomics reveals innate signatures associated with tumor development, immune evasion, and therapeutic response. Additionally, proteomic analysis has unveiled autoantigens and dysregulation of the innate immune system in autoimmunity, offering opportunities for early diagnosis, disease monitoring, and new therapeutic targets. Moreover, proteomic analysis has identified altered protein expression patterns in neurodegenerative diseases like Alzheimer's and Parkinson's, providing insights into potential therapeutic strategies. Proteomics of the innate immune system provides a comprehensive understanding of disease mechanisms, identifies biomarkers, and enables effective interventions in various diseases. Despite still in its early stages, this approach holds great promise to revolutionize innate immunity research and significantly improve patient outcomes across a wide range of diseases.

Functional genomics and small molecules in mitochondrial neurodevelopmental disorders

Mitochondria are critical for brain development and homeostasis. Therefore, pathogenic variation in the mitochondrial or nuclear genome which disrupts mitochondrial function frequently results in developmental disorders and neurodegeneration at the organismal level. Large-scale application of genome-wide technologies to individuals with mitochondrial diseases has dramatically accelerated identification of mitochondrial disease-gene associations in humans. Multi-omic and high-throughput studies involving transcriptomics, proteomics, metabolomics, and saturation genome editing are providing deeper insights into the functional consequence of mitochondrial genomic variation. Integration of deep phenotypic and genomic data through allelic series continues to uncover novel mitochondrial functions and permit mitochondrial gene function dissection on an unprecedented scale. Finally, mitochondrial disease-gene associations illuminate disease mechanisms and thereby direct therapeutic strategies involving small molecules and RNA-DNA therapeutics. This review summarizes progress in functional genomics and small molecule therapeutics in mitochondrial neurodevelopmental disorders.

Protein profile pattern analysis: A multifarious, in vitro diagnosis technique for universal screening

Universal health care is attracting increased attention nowadays, because of the large increase in population all over the world, and a similar increase in life expectancy, leading to an increase in the incidence of non-communicable (various cancers, coronary diseases, neurological and old-age-related diseases) and communicable diseases/pandemics like SARS-COVID 19. This has led to an immediate need for a healthcare technology that should be cost-effective and accessible to all. A technology being considered as a possible one at present is liquid biopsy, which looks for markers in readily available samples like body fluids which can be accessed non- or minimally- invasive manner. Two approaches are being tried now towards this objective. The first involves the identification of suitable, specific markers for each condition, using established methods like various Mass Spectroscopy techniques (Surface-Enhanced Laser Desorption/Ionization Mass Spectroscopy (SELDI-MS), Matrix-Assisted Laser Desorption/Ionization (MALDI-MS), etc., immunoassays (Enzyme-Linked Immunoassay (ELISA), Proximity Extension Assays, etc.) and separation methods like 2-Dimensional Polyacrylamide Gel Electrophoresis (2-D PAGE), Sodium Dodecyl-Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE), Capillary Electrophoresis (CE), etc. In the second approach, no attempt is made the identification of specific markers; rather an efficient separation method like High-Performance Liquid Chromatography/ Ultra-High-Performance Liquid Chromatography (HPLC/UPLC) is used to separate the protein markers, and a profile of the protein pattern is recorded, which is analysed by Artificial Intelligence (AI)/Machine Learning (MI) methods to derive characteristic patterns and use them for identifying the disease condition. The present report gives a summary of the current status of these two approaches and compares the two in the use of their suitability for universal healthcare.

Navigating the Frontier: Emerging Techniques for Detecting Microvascular Complications in Type 2 Diabetes Mellitus: A Comprehensive Review

This review comprehensively explores emerging techniques for detecting microvascular complications in Type 2 Diabetes Mellitus (T2DM), addressing the critical need for advancements in early detection and management. As T2DM continues to rise globally, microvascular complications, including retinopathy, nephropathy, and neuropathy, contribute significantly to the morbidity and mortality associated with the condition. The review synthesizes key findings, revealing various emerging technologies, from advanced imaging modalities to genomic and proteomic approaches. It underscores the potential for personalized medicine, emphasizing the importance of tailoring diagnostic strategies to individual patient profiles. Challenges, including the lack of standardized criteria and issues related to patient adherence, highlight the necessity for collaborative efforts. The conclusion issues a call to action, advocating for enhanced collaboration, increased research investment, patient empowerment through education, and seamless integration of emerging diagnostic techniques into routine clinical care. The review envisions a transformative shift in detecting and managing microvascular complications in T2DM, ultimately improving patient outcomes and contributing to a healthier future for individuals affected by this prevalent metabolic disorder.

Structural and biological insights into outer membrane protein lipotoxin F of Pseudomonas aeruginosa: Implications for vaccine application

Due to the increasing antibiotic resistance of Pseudomonas aeruginosa (PA), an effective vaccine is urgently needed. However, no PA vaccine has been approved to date, and new protective antigens are needed to improve their efficacy. In this study, Luminex beads were used to identify new candidate antigens, after which their crystal structure was determined, and their potential contribution to bacterial pathogenesis was assessed in vitro and in vivo. Notably, a significant antibody response against the outer membrane protein LptF (lipotoxin F) was detected in sera from 409 volunteers. Moreover, vaccination with recombinant LptF conferred effective protection in an acute PA pneumonia model. The crystal structure showed that LptF comprises a 3-stranded β-sheet (β1-β3) and three α-helices (α1-α3) that are organized in an α/β/α/β/α/β pattern, which is structurally homologous to OmpA and related outer membrane proteins. In addition, LptF binds to peptidoglycan in an atypical manner, contributing to the pathogenesis and survival of PA under stress. Our data indicate that LptF is an important virulence factor and thus a promising candidate antigen for PA vaccines.

High-aspect-ratio nanostructured hydroxyapatite: towards new functionalities for a classical material

Hydroxyapatite-based materials have been widely used in countless applications, such as bone regeneration, catalysis, air and water purification or protein separation. Recently, much interest has been given to controlling the aspect ratio of hydroxyapatite crystals from bulk samples. The ability to exert control over the aspect ratio may revolutionize the applications of these materials towards new functional materials. Controlling the shape, size and orientation of HA crystals allows obtaining high aspect ratio structures, improving several key properties of HA materials such as molecule adsorption, ion exchange, catalytic reactions, and even overcoming the well-known brittleness of ceramic materials. Regulating the morphogenesis of HA crystals to form elongated oriented fibres has led to flexible inorganic synthetic sponges, aerogels, membranes, papers, among others, with applications in sustainability, energy and catalysis, and especially in the biomedical field.

Using the power of innate immunoprofiling to understand vaccine design, infection, and immunity

In the field of immunology, a systems biology approach is crucial to understanding the immune response to infection and vaccination considering the complex interplay between genetic, epigenetic, and environmental factors. Significant progress has been made in understanding the innate immune response, including cell players and critical signaling pathways, but many questions remain unanswered, including how the innate immune response dictates host/pathogen responses and responses to vaccines. To complicate things further, it is becoming increasingly clear that the innate immune response is not a linear pathway but is formed from complex networks and interactions. To further our understanding of the crosstalk and complexities, systems-level analyses and expanded experimental technologies are now needed. In this review, we discuss the most recent immunoprofiling techniques and discuss systems approaches to studying the global innate immune landscape which will inform on the development of personalized medicine and innovative vaccine strategies.

From simple to complex: Protein-based biomarker discovery in tuberculosis

Tuberculosis (TB) is a deadly infectious disease that affects millions of people globally. TB proteomics signature discovery has been a rapidly growing area of research that aims to identify protein biomarkers for the early detection, diagnosis, and treatment monitoring of TB. In this review, we have highlighted recent advances in this field and how it is moving from the study of single proteins to high-throughput profiling and from only using proteomics to include additional types of data in multi-omics studies. We have further covered the different sample types and experimental technologies used in TB proteomics signature discovery, focusing on studies of HIV-negative adults. The published signatures were defined as either coming from hypothesis-based protein targeting or from unbiased discovery approaches. The methodological approaches influenced the type of proteins identified and were associated with the circulating protein abundance. However, both approaches largely identified proteins involved in similar biological pathways, including acute-phase responses and T-helper type 1 and type 17 responses. By analysing the frequency of proteins in the different signatures, we could also highlight potential robust biomarker candidates. Finally, we discuss the potential value of integration of multi-omics data and the importance of control cohorts and signature validation.

Use of 4-D proteomics to differentiate between bovine and camel lactoferrin

Lactoferrin is a multifunctional protein that has various biological activities and applications. However, different sources of lactoferrin may have different properties and characteristics. In this study, we hypothesized that ultra-performance liquid chromatography quadrupole time-of-flight mass spectroscopy (UPLC-QTOF-IMS) coupled with UNIFI software can differentiate bovine lactoferrin from camel lactoferrin based on the unique peptides produced by trypsin digestion. We enzymatically digested the proteins using trypsin and analyzed the resulting peptides using Uniport software and in silico digestion. We identified 14 marker peptides that were unique to bovine lactoferrin and could be used to distinguish it from camel lactoferrin. We also demonstrated the advantages of 4D proteomics over 3D proteomics in separating and identifying peptides based on their mass, retention time, intensity, and ion mobility. This method can be applied to other lactoferrin sources and improve the quality control and authentication of lactoferrin products.

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Gastric cancer (GC) is a highly malignant disease affecting humans worldwide and has a poor prognosis. Most GC cases are detected at advanced stages due to the cancer lacking early detectable symptoms. Therefore, there is great interest in improving early diagnosis by implementing targeted prevention strategies. Markers are necessary for early detection and to guide clinicians to the best personalized treatment. The current semi-invasive endoscopic methods to detect GC are invasive, costly, and time-consuming. Recent advances in proteomics technologies have enabled the screening of many samples and the detection of novel biomarkers and disease-related signature signaling networks. These biomarkers include circulating proteins from different fluids (e.g., plasma, serum, urine, and saliva) and extracellular vesicles. We review relevant published studies on circulating protein biomarkers in GC and detail their application as potential biomarkers for GC diagnosis. Identifying highly sensitive and highly specific diagnostic markers for GC may improve patient survival rates and contribute to advancing precision/personalized medicine.

A Simple Strategy for Identifying Conserved Features across Non-independent Omics Studies

False discovery is an ever-present concern in omics research, especially for burgeoning technologies with unvetted specificity of their biomolecular measurements, as such unknowns obscure the ability to characterize biologically informative features from studies performed with any single platform. Accordingly, performing replication studies of the same samples using different omics platforms is a viable strategy for identifying high-confidence molecular associations that are conserved across studies. However, an important caveat of replication studies that include the same samples is that they are inherently non-independent, leading to overestimation of conservation if studies are treated otherwise. Strategies for accounting for such inter-study dependencies have been proposed for meta-analysis methods that are devised to increase statistical power to detect molecular associations present in one-or-more studies but are not immediately suited for identifying conserved molecular associations across multiple studies. Here we present a unifying strategy for performing inter-study conservation analysis as an alternative to meta-analysis strategies for aggregating summary statistical results of shared features across complementary studies, while accounting for inter-study dependency. This method, which we refer to as "adjusted maximum p-value" (AdjMaxP), is easy to implement with both inter-study dependency and conservation estimated directly from the p-values from molecular feature-level association testing results from each study. Through simulation-based assessment we demonstrate that AdjMaxP's improved performance for accurately identifying conserved features over a related meta-analysis strategy for non-independent studies.

Proteomic analysis investigating kidney transplantation outcomes- a scoping review

BACKGROUND

Kidney transplantation is the optimal treatment option for most patients with end-stage kidney disease given the significantly lower morbidity and mortality rates compared to remaining on dialysis. Rejection and graft failure remain common in transplant recipients with limited improvement in long-term transplant outcomes despite therapeutic advances. There is an unmet need in the development of non-invasive biomarkers that specifically monitor graft function and predict transplant pathologies that affect outcomes. Despite the potential of proteomic investigatory approaches, up to now, no candidate biomarkers of sufficient sensitivity or specificity have translated into clinical use. The aim of this review was to collate and summarise protein findings and protein pathways implicated in the literature to date, and potentially flag putative biomarkers worth validating in independent patient cohorts.

METHODS

This review followed the Joanna Briggs' Institute Methodology for a scoping review. MedlineALL, Embase, Web of Science Core Collection, Scopus and Google Scholar databases were searched from inception until December 2022. Abstract and full text review were undertaken independently by two reviewers. Data was collated using a pre-designed data extraction tool.

RESULTS

One hundred one articles met the inclusion criteria. The majority were single-centre retrospective studies of small sample size. Mass spectrometry was the most used technique to evaluate differentially expressed proteins between diagnostic groups and studies identified various candidate biomarkers such as immune or structural proteins.

DISCUSSION

Putative immune or structural protein candidate biomarkers have been identified using proteomic techniques in multiple sample types including urine, serum and fluid used to perfuse donor kidneys. The most consistent findings implicated proteins associated with tubular dysfunction and immunological regulatory pathways such as leukocyte trafficking. However, clinical translation and adoption of candidate biomarkers is limited, and these will require comprehensive evaluation in larger prospective, multicentre trials.

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.

CSF proteomics in autosomal dominant Alzheimer's disease highlights parallels with sporadic disease

Autosomal dominant Alzheimer's disease (ADAD) offers a unique opportunity to study pathophysiological changes in a relatively young population with few comorbidities. A comprehensive investigation of proteome changes occurring in ADAD could provide valuable insights into AD-related biological mechanisms and uncover novel biomarkers and therapeutic targets. Furthermore, ADAD might serve as a model for sporadic AD, but in-depth proteome comparisons are lacking. We aimed to identify dysregulated CSF proteins in ADAD and determine the degree of overlap with sporadic AD. We measured 1472 proteins in CSF of PSEN1 or APP mutation carriers (n = 22) and age- and sex-matched controls (n = 20) from the Amsterdam Dementia Cohort using proximity extension-based immunoassays (PEA). We compared protein abundance between groups with two-sided t-tests and identified enriched biological pathways. Using the same protein panels in paired plasma samples, we investigated correlations between CSF proteins and their plasma counterparts. Finally, we compared our results with recently published PEA data from an international cohort of sporadic AD (n = 230) and non-AD dementias (n = 301). All statistical analyses were false discovery rate-corrected. We detected 66 differentially abundant CSF proteins (65 increased, 1 decreased) in ADAD compared to controls (q < 0.05). The most strongly upregulated proteins (fold change >1.8) were related to immunity (CHIT1, ITGB2, SMOC2), cytoskeletal structure (MAPT, NEFL) and tissue remodelling (TMSB10, MMP-10). Significant CSF-plasma correlations were found for the upregulated proteins SMOC2 and LILR1B. Of the 66 differentially expressed proteins, 36 had been measured previously in the sporadic dementias cohort, 34 of which (94%) were also significantly upregulated in sporadic AD, with a strong correlation between the fold changes of these proteins in both cohorts (rs = 0.730, P < 0.001). Twenty-nine of the 36 proteins (81%) were also upregulated among non-AD patients with suspected AD co-pathology. This CSF proteomics study demonstrates substantial biochemical similarities between ADAD and sporadic AD, suggesting involvement of the same biological processes. Besides known AD-related proteins, we identified several relatively novel proteins, such as TMSB10, MMP-10 and SMOC2, which have potential as novel biomarkers. With shared pathophysiological CSF changes, ADAD study findings might be translatable to sporadic AD, which could greatly expedite therapy development.

Carotenoids in Health as Studied by Omics-Related Endpoints

Carotenoids have been associated with risk reduction for several chronic diseases, including the association of their dietary intake/circulating levels with reduced incidence of obesity, type 2 diabetes, certain types of cancer, and even lower total mortality. In addition to some carotenoids constituting vitamin A precursors, they are implicated in potential antioxidant effects and pathways related to inflammation and oxidative stress, including transcription factors such as nuclear factor κB and nuclear factor erythroid 2-related factor 2. Carotenoids and metabolites may also interact with nuclear receptors, mainly retinoic acid receptor/retinoid X receptor and peroxisome proliferator-activated receptors, which play a role in the immune system and cellular differentiation. Therefore, a large number of downstream targets are likely influenced by carotenoids, including but not limited to genes and proteins implicated in oxidative stress and inflammation, antioxidation, and cellular differentiation processes. Furthermore, recent studies also propose an association between carotenoid intake and gut microbiota. While all these endpoints could be individually assessed, a more complete/integrative way to determine a multitude of health-related aspects of carotenoids includes (multi)omics-related techniques, especially transcriptomics, proteomics, lipidomics, and metabolomics, as well as metagenomics, measured in a variety of biospecimens including plasma, urine, stool, white blood cells, or other tissue cellular extracts. In this review, we highlight the use of omics technologies to assess health-related effects of carotenoids in mammalian organisms and models.