Targeted and Untargeted Proteomics Approaches in Biomarker Development

Seminal plasma proteomics as putative biomarkers for male infertility diagnosis

Male infertility represents a significant global public health issue that is currently emerging as a prominent research focus. Presently, laboratories adhere to the guidelines outlined by the World Health Organization (WHO) manuals for conducting routine semen analysis to diagnose male infertility. However, the accuracy of results in predicting sperm quality and fertility is limited because some individuals with a normal semen analysis report, an unremarkable medical history, and a physical examination may still experience infertility. As a result, the importance of employing more advanced techniques to investigate sperm function and male fertility in the treatment of male infertility and/or subfertility becomes apparent. The standard test for evaluating human semen has been improved by more complex tests that look at things like reactive oxygen species (ROS) levels, total antioxidant capacity (TAC), sperm DNA fragmentation levels, DNA compaction, apoptosis, genetic testing, and the presence and location of anti-sperm antibodies. Recent discoveries of novel biomarkers have significantly enriched our understanding of male fertility. Moreover, the notable biological diversity among samples obtained from the same individual complicates the efficacy of routine semen analysis. Therefore, unraveling the molecular mechanisms involved in fertilization is pivotal in expanding our understanding of factors contributing to male infertility. By understanding how these proteins work and what role they play in sperm activity, we can look at the expression profile in men who can't have children to find diagnostic biomarkers. This review examines the various sperm and seminal plasma proteins associated with infertility, as well as proteins that are either deficient or exhibit aberrant expression, potentially contributing to male infertility causes.

Predicting Preterm Birth Using Proteomics

The complexity of preterm birth (PTB), both spontaneous and medically indicated, and its various etiologies and associated risk factors pose a significant challenge for developing tools to accurately predict risk. This review focuses on the discovery of proteomics signatures that might be useful for predicting spontaneous PTB or preeclampsia, which often results in PTB. We describe methods for proteomics analyses, proteomics biomarker candidates that have so far been identified, obstacles for discovering biomarkers that are sufficiently accurate for clinical use, and the derivation of composite signatures including clinical parameters to increase predictive power.

Proteomic reference map for sarcopenia research: mass spectrometric identification of key muscle proteins of organelles, cellular signaling, bioenergetic metabolism and molecular chaperoning

During the natural aging process, frailty is often associated with abnormal muscular performance. Although inter-individual differences exit, in most elderly the tissue mass and physiological functionality of voluntary muscles drastically decreases. In order to study age-related contractile decline, animal model research is of central importance in the field of biogerontology. Here we have analyzed wild type mouse muscle to establish a proteomic map of crude tissue extracts. Proteomics is an advanced and large-scale biochemical method that attempts to identify all accessible proteins in a given biological sample. It is a technology-driven approach that uses mass spectrometry for the characterization of individual protein species. Total protein extracts were used in this study in order to minimize the potential introduction of artefacts due to excess subcellular fractionation procedures. In this report, the proteomic survey of aged muscles has focused on organellar marker proteins, as well as proteins that are involved in cellular signaling, the regulation of ion homeostasis, bioenergetic metabolism and molecular chaperoning. Hence, this study has establish a proteomic reference map of a highly suitable model system for future aging research.

Towards Personalized Medicine in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic, incurable, multisystem, inflammatory disease characterized by synovitis and extra-articular features. Although several advanced therapies targeting inflammatory mechanisms underlying the disease are available, no advanced therapy is universally effective. Therefore, a ceiling of treatment response is currently accepted where no advanced therapy is superior to another. The current challenge for medical research is the discovery and integration of predictive markers of drug response that can be used to personalize medicine so that the patient is started on "the right drug at the right time". This review article summarizes our current understanding of predicting response to anti-rheumatic drugs in RA, obstacles impeding the development of personalized medicine approaches and future research priorities to overcome these barriers.

Establishing Personalized Blood Protein Reference Ranges Using Noninvasive Microsampling and Targeted Proteomics: Implications for Antidoping Strategies

To prevent doping practices in sports, the World Anti-Doping Agency implemented the Athlete Biological Passport (ABP) program, monitoring biological variables over time to indirectly reveal the effects of doping rather than detect the doping substance or the method itself. In the context of this program, a highly multiplexed mass spectrometry-based proteomics assay for 319 peptides corresponding to 250 proteins was developed, including proteins associated with blood-doping practices. "Baseline" expression profiles of these potential biomarkers in capillary blood (dried blood spots (DBS)) were established using multiple reaction monitoring (MRM). Combining DBS microsampling with highly multiplexed MRM assays is the best-suited technology to enhance the effectiveness of the ABP program, as it represents a cost-effective and robust alternative analytical method with high specificity and selectivity of targets in the attomole range. DBS data were collected from 10 healthy athlete volunteers over a period of 140 days (28 time points per participant). These comprehensive findings provide a personalized targeted blood proteome "fingerprint" showcasing that the targeted proteome is unique to an individual and likely comparable to a DNA fingerprint. The results can serve as a baseline for future studies investigating doping-related perturbations.

Assessing the precision of a detergent-assisted cartridge precipitation workflow for non-targeted quantitative proteomics

Detergent-based workflows incorporating sodium dodecyl sulfate (SDS) necessitate additional steps for detergent removal ahead of mass spectrometry (MS). These steps may lead to variable protein recovery, inconsistent enzyme digestion efficiency, and unreliable MS signals. To validate a detergent-based workflow for quantitative proteomics, we herein evaluate the precision of a bottom-up sample preparation strategy incorporating cartridge-based protein precipitation with organic solvent to deplete SDS. The variance of data-independent acquisition (SWATH-MS) data was isolated from sample preparation error by modelling the variance as a function of peptide signal intensity. Our SDS-assisted cartridge workflow yield a coefficient of variance (CV) of 13%-14%. By comparison, conventional (detergent-free) in-solution digestion increased the CV to 50%; in-gel digestion provided lower CVs between 14% and 20%. By filtering peptides predicting to display lower precision, we further enhance the validity of data in global comparative proteomics. These results demonstrate the detergent-based precipitation workflow is a reliable approach for in depth, label-free quantitative proteome analysis.

Urinary Biomarkers for Lupus Nephritis: A Systems Biology Approach

Systemic lupus erythematosus (SLE) is the prototypical systemic autoimmune disorder. Kidney involvement, termed lupus nephritis (LN), is seen in 40-60% of patients with systemic lupus erythematosus (SLE). After the diagnosis, serial measurement of proteinuria is the most common method of monitoring treatment response and progression. However, present treatments for LN-corticosteroids and immunosuppressants-target inflammation, not proteinuria. Furthermore, subclinical renal inflammation can persist despite improving proteinuria. Serial kidney biopsies-the gold standard for disease monitoring-are also not feasible due to their inherent risk of complications. Biomarkers that reflect the underlying renal inflammatory process and better predict LN progression and treatment response are urgently needed. Urinary biomarkers are particularly relevant as they can be measured non-invasively and may better reflect the compartmentalized renal response in LN, unlike serum studies that are non-specific to the kidney. The past decade has overseen a boom in applying cutting-edge technologies to dissect the pathogenesis of diseases at the molecular and cellular levels. Using these technologies in LN is beginning to reveal novel disease biomarkers and therapeutic targets for LN, potentially improving patient outcomes if successfully translated to clinical practice.

Quantitative Pattern of hPTMs by Mass Spectrometry-Based Proteomics with Implications for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is known for its aggressive nature, and TNBC management is currently challenging due to the lack of effective targets. Despite the importance of histone post-translational modifications (hPTMs) in breast cancer, their associations with molecular subtypes of breast cancer, especially TNBC, are poorly understood. In this study, a combination of untargeted and targeted proteomics approaches, supplemented by a derivatization method, was applied to breast cancer cells and tissue samples. Untargeted proteomics of eight breast cancer cell lines belonging to different molecular subtypes revealed 36 modified peptides with 12 lysine modification sites in histone H3, and the most frequently reported top 5 histone H3 methylation and acetylation sites were covered. Then, targeted proteomics was carried out to quantify the total 20 target hPTMs at the covered modification sites (i.e., mono-, di-, trimethylation, and acetylation for each site), indicating the difficulty in distinguishing TNBC cells from normal cells. Subsequently, the analysis in TNBC patients revealed significant expression differences in 4 specific hPTMs (H3K14ac, H3K27me1, H3K36me2, and H3K36me3) between TNBC and adjacent normal tissue samples. These unique hPTM patterns allowed for the differentiation of TNBC from normal cases. This finding provides promising implications for advancing targeted treatment strategies for TNBC in the future.

A Simplified Proteomics LC-MRM-MS Assay for Determination of apoE Genotypes in Plasma Samples

Apolipoprotein E (apoE), a polymorphic plasma protein, plays a pivotal role in lipid transportation. The human apoE gene possesses three major alleles (ε2, ε3, and ε4), which differ by single amino acid (cysteine to arginine) substitutions. The ε4 allele represents the primary genetic risk factor for Alzheimer's disease (AD), whereas the ε2 allele protects against the disease. Knowledge of a patient's apoE genotype has high diagnostic value. A recent study has introduced an LC-MRM-MS-based proteomic approach for apoE isoform genotyping using stable isotope-labeled peptide internal standards (SIS). Here, our goal was to develop a simplified LC-MRM-MS assay for identifying apoE genotypes in plasma samples, eliminating the need for the use of SIS peptides. To determine the apoE genotypes, we monitored the chromatographic peak area ratios of isoform-specific peptides relative to a peptide that is common to all apoE isoforms. The assay results correlated well with the standard TaqMan allelic discrimination assay, and we observed a concordance between the two methods for all but three out of 172 samples. DNA sequencing of these three samples has confirmed that the results of the LC-MRM-MS method were correct. Thus, our simplified UPLC-MRM-MS assay is a feasible and reliable method for identifying apoE genotypes without using SIS internal standard peptides. The approach can be seamlessly incorporated into existing quantitative proteomics assays and kits, providing additional valuable apoE genotype information. The principle of using signal ratios of the protein isoform-specific peptides to the peptide common for all of the protein isoforms has the potential to be used for protein isoform determination in general.

The Importance, Challenges, and Possible Solutions for Sharing Proteomics Data While Safeguarding Individuals' Privacy

Proteomics data sharing has profound benefits at the individual level as well as at the community level. While data sharing has increased over the years, mostly due to journal and funding agency requirements, the reluctance of researchers with regard to data sharing is evident as many shares only the bare minimum dataset required to publish an article. In many cases, proper metadata is missing, essentially making the dataset useless. This behavior can be explained by a lack of incentives, insufficient awareness, or a lack of clarity surrounding ethical issues. Through adequate training at research institutes, researchers can realize the benefits associated with data sharing and can accelerate the norm of data sharing for the field of proteomics, as has been the standard in genomics for decades. In this article, we have put together various repository options available for proteomics data. We have also added pros and cons of those repositories to facilitate researchers in selecting the repository most suitable for their data submission. It is also important to note that a few types of proteomics data have the potential to re-identify an individual in certain scenarios. In such cases, extra caution should be taken to remove any personal identifiers before sharing on public repositories. Data sets that will be useless without personal identifiers need to be shared in a controlled access repository so that only authorized researchers can access the data and personal identifiers are kept safe.

Biomarker discovery and validation for gastrointestinal tumors: A comprehensive review of colorectal, gastric, and liver cancers

Gastrointestinal (GI) malignancies, encompassing gastric, hepatic, colonic, and rectal cancers, are prevalent forms of cancer globally and contribute substantially to cancer-related mortality. Although there have been improvements in methods for diagnosing and treating GI cancers, the chances of survival for these types of cancers are still extremely low. According to the World Cancer Research International Fund's most recent figures, stomach cancer was responsible for roughly one million deaths worldwide in 2020. This emphasizes the importance of developing more effective tools for detecting, diagnosing, and predicting the outcome of these cancers at an early stage. Biomarkers, quantitative indications of biological processes or disease states, have emerged as promising techniques for enhancing the diagnosis and prognosis of GI malignancies. Recently, there has been a considerable endeavor to discover and authenticate biomarkers for various GI cancers by the utilization of diverse methodologies, including genomics, proteomics, and metabolomics. This review provides a thorough examination of the current state of biomarker research in the field of gastrointestinal malignancies, with a specific emphasis on colorectal, stomach, and liver cancers. A thorough literature search was performed on prominent databases such as PubMed, Scopus, and Web of Science to find pertinent papers published until November, 2023 for the purpose of compiling this review. The diverse categories of biomarkers, encompassing genetic, epigenetic, and protein-based biomarkers, and their potential utility in the fields of diagnosis, prognosis, and treatment selection, are explored. Recent progress in identifying and confirming biomarkers, as well as the obstacles that persist in employing biomarkers in clinical settings are emphasized. The utilization of biomarkers in GI cancers has significant potential in enhancing patient outcomes. Ongoing research is expected to uncover more efficient biomarkers for the diagnosis and prognosis of these cancers.

Multiple reaction monitoring assays for large-scale quantitation of proteins from 20 mouse organs and tissues

Mouse is the mammalian model of choice to study human health and disease due to its size, ease of breeding and the natural occurrence of conditions mimicking human pathology. Here we design and validate multiple reaction monitoring mass spectrometry (MRM-MS) assays for quantitation of 2118 unique proteins in 20 murine tissues and organs. We provide open access to technical aspects of these assays to enable their implementation in other laboratories, and demonstrate their suitability for proteomic profiling in mice by measuring normal protein abundances in tissues from three mouse strains: C57BL/6NCrl, NOD/SCID, and BALB/cAnNCrl. Sex- and strain-specific differences in protein abundances are identified and described, and the measured values are freely accessible via our MouseQuaPro database: http://mousequapro.proteincentre.com . Together, this large library of quantitative MRM-MS assays established in mice and the measured baseline protein abundances represent an important resource for research involving mouse models.

Proteomic analysis discovers potential biomarkers of early traumatic axonal injury in the brainstem

OBJECTIVE

Application of Tandem Mass Tags (TMT)-based LC-MS/MS analysis to screen for differentially expressed proteins (DEPs) in traumatic axonal injury (TAI) of the brainstem and to predict potential biomarkers and key molecular mechanisms of brainstem TAI.

METHODS

A modified impact acceleration injury model was used to establish a brainstem TAI model in Sprague-Dawley rats, and the model was evaluated in terms of both functional changes (vital sign measurements) andstructural changes (HE staining, silver-plating staining and β-APP immunohistochemical staining). TMT combined with LC-MS/MS was used to analyse the DEPs in brainstem tissues from TAI and Sham groups. The biological functions of DEPs and potential molecular mechanisms in the hyperacute phase of TAI were analysed by bioinformatics techniques, and candidate biomarkers were validated using western blotting and immunohistochemistry on brainstem tissues from animal models and humans.

RESULTS

Based on the successful establishment of the brainstem TAI model in rats, TMT-based proteomics identified 65 DEPs, and bioinformatics analysis indicated that the hyperacute phase of TAI involves multiple stages of biological processes including inflammation, oxidative stress, energy metabolism, neuronal excitotoxicity and apoptosis. Three DEPs, CBR1, EPHX2 and CYP2U1, were selected as candidate biomarkers and all three proteins were found to be significantly expressed in brainstem tissue 30 min-7 days after TAI in both animal models and humans.

CONCLUSION

Using TMT combined with LC-MS/MS analysis for proteomic study of early TAI in rat brainstem, we report for the first time that CBR1, EPHX2 and CYP2U1 can be used as biomarkers of early TAI in brainstem by means of western blotting and immunohistochemical staining, compensating for the limitations of silver-plating staining and β-APP immunohistochemical staining, especially in the case of very short survival time after TAI (shorter than 30 min). A number of other proteins that also have a potential marker role are also presented, providing new insights into the molecular mechanisms, therapeutic targets and forensic identification of early TAI in brainstem.

Identifying secreted biomarkers of dopaminergic ventral midbrain progenitor cells

BACKGROUND

Ventral midbrain (VM) dopaminergic progenitor cells derived from human pluripotent stem cells have the potential to replace endogenously lost dopamine neurons and are currently in preclinical and clinical development for treatment of Parkinson's Disease (PD). However, one main challenge in the quality control of the cells is that rostral and caudal VM progenitors are extremely similar transcriptionally though only the caudal VM cells give rise to dopaminergic (DA) neurons with functionality relevant for cell replacement in PD. Therefore, it is critical to develop assays which can rapidly and reliably discriminate rostral from caudal VM cells during clinical manufacturing.

METHODS

We performed shotgun proteomics on cell culture supernatants from rostral and caudal VM progenitor cells to search for novel secreted biomarkers specific to DA progenitors from the caudal VM. Key hits were validated by qRT-PCR and ELISA.

RESULTS

We identified and validated novel secreted markers enriched in caudal VM progenitor cultures (CPE, LGI1 and PDGFC), and found these markers to correlate strongly with the expression of EN1, which is a predictive marker for successful graft outcome in DA cell transplantation products. Other markers (CNTN2 and CORIN) were found to conversely be enriched in the non-dopaminergic rostral VM cultures. Key novel ELISA markers were further validated on supernatant samples from GMP-manufactured caudal VM batches.

CONCLUSION

As a non-invasive in-process quality control test for predicting correctly patterned batches of caudal VM DA cells during clinical manufacturing, we propose a dual ELISA panel measuring LGI1/CORIN ratios around day 16 of differentiation.

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.

Proteomics provides insights into the theranostic potential of extracellular vesicles

Extracellular vesicles (EVs) encompass a diverse range of membranous structures derived from cells, including exosomes and microvesicles. These vesicles are present in biological fluids and play vital roles in various physiological and pathological processes. They facilitate intercellular communication by enabling the exchange of proteins, lipids, and genetic material between cells. Understanding the cellular processes that govern EV biology is essential for unraveling their physiological and pathological functions and their potential clinical applications. Despite significant advancements in EV research in recent years, there is still much to learn about these vesicles. The advent of improved mass spectrometry (MS)-based techniques has allowed for a deeper characterization of EV protein composition, providing valuable insights into their roles in different physiological and pathological conditions. In this chapter, we provide an overview of proteomics studies conducted to identify the protein contents of EVs, which contribute to their therapeutic and pathological features. We also provided evidence on the potential of EV proteome contents as biomarkers for early disease diagnosis, progression, and treatment response, as well as factors that influence their composition. Additionally, we discuss the available databases containing information on EV proteome contents, and finally, we highlight the need for further research to pave the way toward their utilization in clinical settings.

Multiomics tools for improved atherosclerotic cardiovascular disease management

Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple 'omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care.

CSF 14-3-3β is associated with progressive cognitive decline in Alzheimer's disease

Alzheimer's disease is a neurodegenerative disorder characterized pathologically by amyloid-beta plaques, tau tangles and neuronal loss. In clinical practice, the 14-3-3 isoform beta (β) is a biomarker that aids in the diagnosis of sporadic Creutzfeldt-Jakob disease. Recently, a proteomics study found increased CSF 14-3-3β levels in Alzheimer's disease patients, suggesting a potential link between CSF 14-3-3β and Alzheimer's disease. Our present study aimed to further investigate the role of CSF 14-3-3β in Alzheimer's disease by analysing the data of 719 participants with available CSF 14-3-3β measurements from the Alzheimer's Disease Neuroimaging Initiative. Higher CSF 14-3-3β levels were observed in the mild cognitive impairment group compared to the cognitively normal group, with the highest CSF 14-3-3β levels in the Alzheimer's disease dementia group. This study also found significant associations between CSF 14-3-3β levels and CSF biomarkers of p-tau, t-tau, pTau/Aβ42 ratios and GAP-43, as well as other Alzheimer's disease biomarkers such as Aβ-PET. An early increase in CSF 14-3-3β levels was observed prior to Aβ-PET-positive status, and CSF 14-3-3β levels continued to rise after crossing the Aβ-PET positivity threshold before reaching a plateau. The diagnostic accuracy of CSF 14-3-3β (area under the receiver operating characteristic curve = 0.819) was moderate compared to other established Alzheimer's disease biomarkers in distinguishing cognitively normal Aβ pathology-negative individuals from Alzheimer's disease Aβ pathology-positive individuals. Higher baseline CSF 14-3-3β levels were associated with accelerated cognitive decline, reduced hippocampus volumes and declining fluorodeoxyglucose-PET values over a 4-year follow-up period. Patients with mild cognitive impairment and high CSF 14-3-3β levels at baseline had a significantly increased risk [hazard ratio = 2.894 (1.599-5.238), P < 0.001] of progression to Alzheimer's disease dementia during follow-up. These findings indicate that CSF 14-3-3β may be a potential biomarker for Alzheimer's disease and could provide a more comprehensive understanding of the underlying pathological changes of Alzheimer's disease, as well as aid in the diagnosis and monitoring of disease progression.

Tuning Hydrophobicity of Paper Substrates for Effective Colorimetric detection of Glucose and Nucleic acids

This study investigated the colorimetric response of standard glucose, serum glucose, and nucleic acid assays on various paper surfaces with different wettability, including hydrophilic, hydrophobic, and nearly superhydrophobic surfaces. Water contact angles (WCA) formed by water droplets on each surface were measured using ImageJ software. The hydrophilic surface showed no contact angle, while the hydrophobic and nearly superhydrophobic surfaces exhibited contact angles of 115.667° and 133.933°, respectively. The colorimetric sensitivity of the standard glucose assay was analyzed on these surfaces, revealing enhanced sensitivity on the nearly superhydrophobic surface due to the high molecular crowding effect owing to its non-wetting behavior and eventually confined reaction product at the sample loading zone. The hydrophobic nature of the surface restricts the spreading and diffusion of the reaction product, leading to a controlled and localized concentration of the assay product leading to moderate colorimetric intensity. On the other hand, the hydrophilic surface showed the least enhancement in colorimetric sensitivity; this is attributed to the high wettability of the hydrophilic surface causing the reaction product to spread extensively, resulting in a larger area of dispersion and consequently a lower colorimetric intensity. The measured limit of detection (LOD) for nucleic acid on nearly superhydrophobic surfaces was found to be 16.15 ng/µL, which was almost four-fold lower than on hydrophilic surfaces (60.08 ng/µL). Additionally, the LODs of standard glucose and clinical serum samples were two-fold lower on nearly superhydrophobic surfaces compared to hydrophilic surfaces. Our findings clearly highlight the promising potential of utilizing superhydrophobic surfaces to significantly enhance colorimetric sensitivity in paper-based diagnostic applications. This innovative approach holds promise for advancing point-of-care diagnostics and improving disease detection in resource-limited settings.

Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment

Breast cancer, a multi-networking heterogeneous disease, has emerged as a serious impediment to progress in clinical oncology. Although technological advancements and emerging cancer research studies have mitigated breast cancer lethality, a precision cancer-oriented solution has not been achieved. Thus, this review will persuade the acquiescence of proteomics-based diagnostic and therapeutic options in breast cancer management. Recently, the evidence of breast cancer health surveillance through imaging proteomics, single-cell proteomics, interactomics, and post-translational modification (PTM) tracking, to construct proteome maps and proteotyping for stage-specific and sample-specific cancer subtyping have outperformed conventional ways of dealing with breast cancer by increasing diagnostic efficiency, prognostic value, and predictive response. Additionally, the paradigm shift in applied proteomics for designing a chemotherapy regimen to identify novel drug targets with minor adverse effects has been elaborated. Finally, the potential of proteomics in alleviating the occurrence of chemoresistance and enhancing reprofiled drugs' effectiveness to combat therapeutic obstacles has been discussed. Owing to the enormous potential of proteomics techniques, the clinical recognition of proteomics in breast cancer management can be achievable and therapeutic intricacies can be surmountable.

Healthy lifestyle linked to innate immunity and lipoprotein metabolism: a cross-sectional comparison using untargeted proteomics

This study used untargeted proteomics to compare blood proteomic profiles in two groups of adults that differed widely in lifestyle habits. A total of 52 subjects in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females) participated in this cross-sectional study. Age, education level, marital status, and height did not differ significantly between LIFE and CON groups. The LIFE and CON groups differed markedly in body composition, physical activity patterns, dietary intake patterns, disease risk factor prevalence, blood measures of inflammation, triglycerides, HDL-cholesterol, glucose, and insulin, weight-adjusted leg/back and handgrip strength, and mood states. The proteomics analysis showed strong group differences for 39 of 725 proteins identified in dried blood spot samples. Of these, 18 were downregulated in the LIFE group and collectively indicated a lower innate immune activation signature. A total of 21 proteins were upregulated in the LIFE group and supported greater lipoprotein metabolism and HDL remodeling. Lifestyle-related habits and biomarkers were probed and the variance (> 50%) in proteomic profiles was best explained by group contrasts in indicators of adiposity. This cross-sectional study established that a relatively small number of proteins are associated with good lifestyle habits.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Non-targeted proteomic analysis of Asian elephant (Elephas maximus) seminal plasma using an in-solution digestion technique and liquid chromatography tandem-mass spectrometry

Seminal plasma proteins have recently been reported to play a significant role as valuable materials for understanding male reproductive biology, identifying causes of fertility problems, and developing reproductive biomarkers. Proteomic analysis of seminal plasma holds promise in advancing the understanding of male Asian elephant reproductive biology. This study aims to explore seminal plasma proteins of Asian elephants and their probable functions to provide fundamental information about male reproduction in this species. The protein solution from pooled seminal plasma from 10 bulls (a total of 33 ejaculates) was digested into peptides and identified using LC-MS/MS. Out of 986 proteins, 597 were mapped and matched with 58 species in UniProt databases, including Elephas maximus. These mapped proteins were mostly involved in binding function, catalytic activity, cellular process, and metabolic process. Only 29 mapped proteins were recognized to be related in reproductive process, mainly associated in spermatogenesis and sperm capacitation. Additionally, several seminal plasma proteins related to fertility or semen quality in other mammals were also found in Asian elephant semen, such as keratin type I, aldose reductase, thrombospondon-1, fibronectin 1, platelet-activating factor acetyl hydrolase, mannosidase, and semenogelin-2. This discovery clearly reveals the beneficial protein profile in seminal plasma of the Asian elephant and serves as a crucial step in investigating infertility and poor semen quality in this valuable species.

Omics approaches for the assessment of biological responses to nanoparticles

Nanotechnology has enabled the development of innovative therapeutics, diagnostics, and drug delivery systems. Nanoparticles (NPs) can influence gene expression, protein synthesis, cell cycle, metabolism, and other subcellular processes. While conventional methods have limitations in characterizing responses to NPs, omics approaches can analyze complete sets of molecular entities that change upon exposure to NPs. This review discusses key omics approaches, namely transcriptomics, proteomics, metabolomics, lipidomics and multi-omics, applied to the assessment of biological responses to NPs. Fundamental concepts and analytical methods used for each approach are presented, as well as good practices for omics experiments. Bioinformatics tools are essential to analyze, interpret and visualize large omics data, and to correlate observations in different molecular layers. The authors envision that conducting interdisciplinary multi-omics analyses in future nanomedicine studies will reveal integrated cell responses to NPs at different omics levels, and the incorporation of omics into the evaluation of targeted delivery, efficacy, and safety will improve the development of nanomedicine therapies.

Extracellular Matrix Proteomics: The mdx-4cv Mouse Diaphragm as a Surrogate for Studying Myofibrosis in Dystrophinopathy

The progressive degeneration of the skeletal musculature in Duchenne muscular dystrophy is accompanied by reactive myofibrosis, fat substitution, and chronic inflammation. Fibrotic changes and reduced tissue elasticity correlate with the loss in motor function in this X-chromosomal disorder. Thus, although dystrophinopathies are due to primary abnormalities in the DMD gene causing the almost-complete absence of the cytoskeletal Dp427-M isoform of dystrophin in voluntary muscles, the excessive accumulation of extracellular matrix proteins presents a key histopathological hallmark of muscular dystrophy. Animal model research has been instrumental in the characterization of dystrophic muscles and has contributed to a better understanding of the complex pathogenesis of dystrophinopathies, the discovery of new disease biomarkers, and the testing of novel therapeutic strategies. In this article, we review how mass-spectrometry-based proteomics can be used to study changes in key components of the endomysium, perimysium, and epimysium, such as collagens, proteoglycans, matricellular proteins, and adhesion receptors. The mdx-4cv mouse diaphragm displays severe myofibrosis, making it an ideal model system for large-scale surveys of systematic alterations in the matrisome of dystrophic fibers. Novel biomarkers of myofibrosis can now be tested for their appropriateness in the preclinical and clinical setting as diagnostic, pharmacodynamic, prognostic, and/or therapeutic monitoring indicators.

An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

INTRODUCTION

Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics.

AREAS COVERED

In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value.

EXPERT OPINION

A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.

DiffN Selection of Tandem Mass Spectrometry Precursors

Current data-dependent acquisition (DDA) approaches select precursor ions for tandem mass spectrometry (MS/MS) characterization based on their absolute intensity, known as a TopN approach. Low-abundance species may not be identified as biomarkers in a TopN approach. Herein, a new DDA approach is proposed, DiffN, which uses the relative differential intensity of ions between two samples to selectively target species undergoing the largest fold changes for MS/MS. Using a dual nano-electrospray (nESI) ionization source which allows samples contained in separate capillaries to be analyzed in parallel, the DiffN approach was developed and validated with well-defined lipid extracts. A dual nESI source and DiffN DDA approach was applied to quantify the differences in lipid abundance between two colorectal cancer cell lines. The SW480 and SW620 lines represent a matched pair from the same patient: the SW480 cells from a primary tumor and the SW620 cells from a metastatic lesion. A comparison of TopN and DiffN DDA approaches on these cancer cell samples highlights the ability of DiffN to increase the likelihood of biomarker discovery and the decreased probability of TopN to efficiently select lipid species that undergo large fold changes. The ability of the DiffN approach to efficiently select precursor ions of interest makes it a strong candidate for lipidomic analyses. This DiffN DDA approach may also apply to other molecule classes (e.g., other metabolites or proteins) that are amenable to shotgun analyses.

Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization

BACKGROUND

The analysis of mass spectrometry-based quantitative proteomics data can be challenging given the variety of established analysis platforms, the differences in reporting formats, and a general lack of approachable standardized post-processing analyses such as sample group statistics, quantitative variation and even data filtering. We developed tidyproteomics to facilitate basic analysis, improve data interoperability and potentially ease the integration of new processing algorithms, mainly through the use of a simplified data-object.

RESULTS

The R package tidyproteomics was developed as both a framework for standardizing quantitative proteomics data and a platform for analysis workflows, containing discrete functions that can be connected end-to-end, thus making it easier to define complex analyses by breaking them into small stepwise units. Additionally, as with any analysis workflow, choices made during analysis can have large impacts on the results and as such, tidyproteomics allows researchers to string each function together in any order, select from a variety of options and in some cases develop and incorporate custom algorithms.

CONCLUSIONS

Tidyproteomics aims to simplify data exploration from multiple platforms, provide control over individual functions and analysis order, and serve as a tool to assemble complex repeatable processing workflows in a logical flow. Datasets in tidyproteomics are easy to work with, have a structure that allows for biological annotations to be added, and come with a framework for developing additional analysis tools. The consistent data structure and accessible analysis and plotting tools also offers a way for researchers to save time on mundane data manipulation tasks.

Spatial Proteomics for the Molecular Characterization of Breast Cancer

Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.

Nano-DESI Mass Spectrometry Imaging of Proteoforms in Biological Tissues with High Spatial Resolution

Mass spectrometry imaging (MSI) is a powerful tool for label-free mapping of the spatial distribution of proteins in biological tissues. We have previously demonstrated imaging of individual proteoforms in biological tissues using nanospray desorption electrospray ionization (nano-DESI), an ambient liquid extraction-based MSI technique. Nano-DESI MSI generates multiply charged protein ions, which is advantageous for their identification using top-down proteomics analysis. In this study, we demonstrate proteoform mapping in biological tissues with a spatial resolution down to 7 μm using nano-DESI MSI. A substantial decrease in protein signals observed in high-spatial-resolution MSI makes these experiments challenging. We have enhanced the sensitivity of nano-DESI MSI experiments by optimizing the design of the capillary-based probe and the thickness of the tissue section. In addition, we demonstrate that oversampling may be used to further improve spatial resolution at little or no expense to sensitivity. These developments represent a new step in MSI-based spatial proteomics, which complements targeted imaging modalities widely used for studying biological systems.

Precise diagnosis and typing of early-stage renal immunoglobulin-derived amyloidosis by label-free quantification of parallel reaction monitoring-based targeted proteomics

BACKGROUND

Early diagnosis and typing are crucial for improving the prognosis of patients with renal amyloidosis. Currently, Untargeted proteomics based precise diagnosis and typing of amyloid deposits are crucial for guiding patient management. Although untargeted proteomics achieve ultra-high-throughput by selecting the most abundant eluting cationic peptide precursors in series for tandem MS events, it lacks in sensitivity and reproducibility, which may not be suitable for early-stage renal amyloidosis with minor damages. Here, we aimed to develop parallel reaction monitoring (PRM)-based targeted proteomics to achieve high sensitivity and specificity by determining absolute abundances and codetecting all transitions of highly repeatable peptides of preselected amyloid signature and typing proteins in identifying early-stage renal immunoglobulin-derived amyloidosis.

METHODS AND RESULTS

In 10 discovery cohort cases, Congo red-stained FFPE slices were micro-dissected and analyzed by data-dependent acquisition-based untargeted proteomics for preselection of typing specific proteins and peptides. Further, a list of proteolytic peptides from amyloidogenic proteins and internal standard proteins were quantified by PRM-based targeted proteomics to validate performance for diagnosis and typing in 26 validation cohort cases. The diagnosis and typing effectiveness of PRM-based targeted proteomics in 10 early-stage renal amyloid cases was assessed via a comparison with untargeted proteomics. A peptide panel of amyloid signature proteins, immunoglobulin light chain and heave chain in PRM-based targeted proteomics showed significantly distinguishing ability and amyloid typing performance in patients. The diagnostic algorithm of targeted proteomics with a low amount of amyloid deposits in early-stage renal immunoglobulin-derived amyloidosis showed better performance than untargeted proteomics in amyloidosis typing.

CONCLUSIONS

This study demonstrates that the utility of these prioritized peptides in PRM-based targeted proteomics ensure high sensitivity and reliability for identifying early-stage renal amyloidosis. Owing to the development and clinical application of this method, rapid acceleration of the early diagnosis, and typing of renal amyloidosis is expected.

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.

Disease biomarker identification based on sample network optimization

A large amount of evidence shows that biomarkers are discriminant features related to disease development. Thus, the identification of disease biomarkers has become a basic problem in the analysis of complex diseases in the medical fields, such as disease stage judgment, disease diagnosis and treatment. Research based on networks have become one of the most popular methods. Several algorithms based on networks have been proposed to identify biomarkers, however the networks of genes or molecules ignored the similarities and associations among the samples. It is essential to further understand how to construct and optimize the networks to make the identified biomarkers more accurate. On this basis, more effective strategies can be developed to improve the performance of biomarkers identification. In this study, a multi-objective evolution algorithm based on sample similarity networks has been proposed for disease biomarker identification. Specifically, we design the sample similarity networks to extract the structural characteristic information among samples, which used to calculate the influence of the sample to each class. Besides, based on the networks and the group of biomarkers we choose in every iteration, we can divide samples into different classes by the importance for each class. Then, in the process of evolution algorithm population iteration, we develop the elite guidance strategy and fusion selection strategy to select the biomarkers which make the sample classification more accurate. The experiment results on the five gene expression datasets suggests that the algorithm we proposed is superior over some state-of-the-art disease biomarker identification methods.

Fiber-Type Shifting in Sarcopenia of Old Age: Proteomic Profiling of the Contractile Apparatus of Skeletal Muscles

The progressive loss of skeletal muscle mass and concomitant reduction in contractile strength plays a central role in frailty syndrome. Age-related neuronal impairments are closely associated with sarcopenia in the elderly, which is characterized by severe muscular atrophy that can considerably lessen the overall quality of life at old age. Mass-spectrometry-based proteomic surveys of senescent human skeletal muscles, as well as animal models of sarcopenia, have decisively improved our understanding of the molecular and cellular consequences of muscular atrophy and associated fiber-type shifting during aging. This review outlines the mass spectrometric identification of proteome-wide changes in atrophying skeletal muscles, with a focus on contractile proteins as potential markers of changes in fiber-type distribution patterns. The observed trend of fast-to-slow transitions in individual human skeletal muscles during the aging process is most likely linked to a preferential susceptibility of fast-twitching muscle fibers to muscular atrophy. Studies with senescent animal models, including mostly aged rodent skeletal muscles, have confirmed fiber-type shifting. The proteomic analysis of fast versus slow isoforms of key contractile proteins, such as myosin heavy chains, myosin light chains, actins, troponins and tropomyosins, suggests them as suitable bioanalytical tools of fiber-type transitions during aging.

Defining atherosclerotic plaque biology by mass spectrometry-based omics approaches

Atherosclerosis is the principal cause of vascular diseases and one of the leading causes of worldwide death. Even though several insights into its natural course, risk factors and interventions have been identified, it is still an ongoing global pandemic. Since the structure and biochemical composition of the plaques show high heterogeneity, a comprehensive understanding of the intraplaque composition, its microenvironment, and the mechanisms of the progression and instability across different vascular beds at their progression stages is crucial for better risk stratification and treatment modalities. Even though several cell-based studies, animal studies, and extensive multicentric population studies have been conducted concerning cardiovascular diseases for assessing the risk factors and plaque biology, the studies on human clinical samples are very limited. New novel approaches utilize samples from percutaneous coronary interventions, which could possibly gain more access to clinical samples at different stages of the diseases without complex invasive resections. As an emerging technological platform in disease discovery research, mass spectrometry-based omics technologies offer capabilities for a comprehensive understanding of the mechanisms linked to several vascular diseases. Here, we discuss the cellular and molecular processes of atherosclerosis, different mass spectrometry-based omics approaches, and the studies mostly done on clinical samples of atheroma plaque using mass spectrometry-based proteomics, metabolomics and lipidomics approaches.

Multi-omics for biomarker approaches in the diagnostic evaluation and management of abdominal pain and irritable bowel syndrome: what lies ahead

Reliable biomarkers for common disorders of gut-brain interaction characterized by abdominal pain, including irritable bowel syndrome (IBS), are critically needed to enhance care and develop individualized therapies. The dynamic and heterogeneous nature of the pathophysiological mechanisms that underlie visceral hypersensitivity have challenged successful biomarker development. Consequently, effective therapies for pain in IBS are lacking. However, recent advances in modern omics technologies offer new opportunities to acquire deep biological insights into mechanisms of pain and nociception. Newer methods for large-scale data integration of complementary omics approaches have further expanded our ability to build a holistic understanding of complex biological networks and their co-contributions to abdominal pain. Here, we review the mechanisms of visceral hypersensitivity, focusing on IBS. We discuss candidate biomarkers for pain in IBS identified through single omics studies and summarize emerging multi-omics approaches for developing novel biomarkers that may transform clinical care for patients with IBS and abdominal pain.

A highly efficient protein corona-based proteomic analysis strategy for the discovery of pharmacodynamic biomarkers

The composition of serum is extremely complex, which complicates the discovery of new pharmacodynamic biomarkers via serum proteome for disease prediction and diagnosis. Recently, nanoparticles have been reported to efficiently reduce the proportion of high-abundance proteins and enrich low-abundance proteins in serum. Here, we synthesized a silica-coated iron oxide nanoparticle and developed a highly efficient and reproducible protein corona (PC)-based proteomic analysis strategy to improve the range of serum proteomic analysis. We identified 1,070 proteins with a median coefficient of variation of 12.56% using PC-based proteomic analysis, which was twice the number of proteins identified by direct digestion. There were also more biological processes enriched with these proteins. We applied this strategy to identify more pharmacodynamic biomarkers on collagen-induced arthritis (CIA) rat model treated with methotrexate (MTX). The bioinformatic results indicated that 485 differentially expressed proteins (DEPs) were found in CIA rats, of which 323 DEPs recovered to near normal levels after treatment with MTX. This strategy can not only help enhance our understanding of the mechanisms of disease and drug action through serum proteomics studies, but also provide more pharmacodynamic biomarkers for disease prediction, diagnosis, and treatment.

Useful genes for predicting the efficacy of transarterial chemoembolization in hepatocellular carcinoma

Transarterial chemoembolization (TACE) is generally used to treat patients with hepatocellular carcinoma (HCC), a common and deadly cancer; however, its efficacy varies according to factors such as tumor volume, stage, serum alpha-fetoprotein level, and chosen feeding artery. In addition, gene-related factors have been recently suggested to be involved in the regulation and prediction of TACE outcomes. Accordingly, genes could serve as effective biomarkers to select patients who can benefit from TACE. These gene-related factors can activate signaling pathways affecting cancer cell survival while regulating the epithelial-mesenchymal transition, angiogenesis, and the tumor microenvironment, all directly associated with tumor progression, thereby affecting TACE efficacy. Moreover, this disordered gene expression is associated with poor prognosis in patients with HCC, including TACE resistance, postoperative recurrence, and metastasis. To identify the exact relationship between various genes and TACE efficacy, this review summarizes the involvement of protein-coding and non-coding genes and single nucleotide polymorphisms in TACE efficacy for predicting the efficacy of TACE; the present findings may help improve the efficacy of TACE in clinical settings.

Proteomics in Inherited Metabolic Disorders

Inherited metabolic disorders (IMD) are rare medical conditions caused by genetic defects that interfere with the body's metabolism. The clinical phenotype is highly variable and can present at any age, although it more often manifests in childhood. The number of treatable IMDs has increased in recent years, making early diagnosis and a better understanding of the natural history of the disease more important than ever. In this review, we discuss the main challenges faced in applying proteomics to the study of IMDs, and the key advances achieved in this field using tandem mass spectrometry (MS/MS). This technology enables the analysis of large numbers of proteins in different body fluids (serum, plasma, urine, saliva, tears) with a single analysis of each sample, and can even be applied to dried samples. MS/MS has thus emerged as the tool of choice for proteome characterization and has provided new insights into many diseases and biological systems. In the last 10 years, sequential window acquisition of all theoretical fragmentation spectra mass spectrometry (SWATH-MS) has emerged as an accurate, high-resolution technique for the identification and quantification of proteins differentially expressed between healthy controls and IMD patients. Proteomics is a particularly promising approach to help obtain more information on rare genetic diseases, including identification of biomarkers to aid early diagnosis and better understanding of the underlying pathophysiology to guide the development of new therapies. Here, we summarize new and emerging proteomic technologies and discuss current uses and limitations of this approach to identify and quantify proteins. Moreover, we describe the use of proteomics to identify the mechanisms regulating complex IMD phenotypes; an area of research essential to better understand these rare disorders and many other human diseases.

Regulation of piglet T-cell immune responses by thioredoxin peroxidase from Cysticercus cellulosae excretory-secretory antigens

Taenia solium (T. solium) cysticercosis is a serious threat to human health and animal husbandry. During parasitization, Cysticercus cellulosae (C. cellulosae) can excrete and secrete antigens that modulate the host's T-cell immune responses. However, the composition of C. cellulosae excretory-secretory antigens (ESAs) is complex. This study sought to identify the key molecules in C. cellulosae ESAs involved in regulating T-cell immune responses. Thus, we screened for thioredoxin peroxidase (TPx), with the highest differential expression, as the key target by label-free quantification proteomics of C. cellulosae and its ESAs. In addition, we verified whether TPx protein mainly exists in C. cellulosae ESAs. The TPx recombinant protein was prepared by eukaryotic expression, and ESAs were used as the experimental group to further investigate the effect of TPx protein on the immune response of piglet T cells in vitro. TPx protein induced an increase in CD4+ T cells in piglet peripheral blood mononuclear cells (PBMCs), while CD8+ T cells did not change significantly. This resulted in an imbalance in the CD4+/CD8+ T-cell ratio and an increase in CD4+CD25+Foxp3+ Treg cells in the PBMCs. In addition, TPx protein initiated T helper 2 (Th2)-type immune responses by secreting IL-4 and IL-10 and suppressed Th1/Th17-type immune responses. The results showed that ESAs were involved in regulating piglet T-cell immune responses cells. This suggests that TPx protein found in ESAs plays an essential role to help the parasite evade host immune attack. Moreover, this lays a foundation for the subsequent exploration of the mechanism through which TPx protein regulates signaling molecules to influence T-cell differentiation.

Differential proteomic of plasma provides a new perspective on scientific diagnosis and drug screening for dampness heat diarrhea in calves

Dampness heat diarrhea (DHD) is one of the most common syndromes of calf diarrhea. Its complex etiology and lack of objective diagnostic criteria bring great challenges to the diagnosis and treatment of this disease. This study aims to screen some prospective diagnostic biomarkers or therapeutic targets for calves with DHD by investigating the differential protein profiles of plasma between DHD calves and clinically healthy calves by mass spectrometry-based proteomic. A total of 120 DHD calves and 90 clinically healthy calves were divided into two groups randomly, 30 DHD calves and 30 clinically healthy calves in the test group, and 90 DHD calves and 60 clinically healthy calves in the validation group. In the test group, a total of 52 proteins were differentially expressed between calves with DHD and clinically healthy calves, 13 proteins were significantly increased and 39 proteins were significantly decreased. The differentially expressed proteins were associated with the intestinal immune network of IgA production, caffeine metabolism, purine metabolism, and PI3K signaling pathway. In the validation group, 13 proteins were selected from 52 differential expression proteins for parallel reaction monitoring validation to verify their associations with DHD calves. The targeted proteomic results showed that fibronectin precursor (FN1) and apolipoprotein C-IV precursor (APOC4) were significantly associated with DHD in calves, and they were downregulated in sick calves. In conclusion, the differential expression of plasma proteins was associated with DHD pathogenesis in calves, and the FN1 and APOC4 might be the potential clinical biomarkers for diagnosis of DHD in calves, and the intestinal immune network of IgA production, caffeine metabolism, purine metabolism, and PI3K signaling pathway are the candidate targets to treat DHD in calves. Our finding provides a reference for further investigating the pathogenesis, developing techniques of diagnosis, and screening treatment drugs for DHD in calves.

Photopolymerization with EDTA and Riboflavin for Proteins Analysis in Polyacrylamide Gel Electrophoresis

A new method for photosensitized polymerization of polyacrylamide gels was proposed. Photopolymerization of acrylamide/N,N'-methylenebisacrylamide (AM/Bis) was assisted with combination of catalyst ethylenediaminetetraacetic acid disodium salt dihydrate (EDTA) and photoinitiator riboflavin (RF). The prepared cross-linked AM/Bis + EDTA/RF gels were tested in electrophoretic SDS-PAGE system at high concentration of AM (20 wt%). The efficiency of these systems for electrophoretic separation of histones of human blood lymphocytes was demonstrated. In principle, such gels with small pores in the separation zone can offer advantages for resolution of proteins. The advantages of proposed method also include simple technique and possibility of gel preparation in a timely manner (for 10-15 min). However, in microporous gel systems some limitations in electroblotting technique could occur, which is particularly crucial for hydrophobic proteins.

Accelerating the Validation of Endogenous On-Target Engagement and In Cellulo Kinetic Assessment for Covalent Inhibitors of KRASG12C in Early Drug Discovery

Covalent inhibition is a valuable modality in drug discovery because of its potential ability in decoupling pharmacokinetics from pharmacodynamics by prolonging the residence time of the drug on the target of interest. This increase in target occupancy is limited only by the rate of target turnover. However, a limitation in such studies is to translate the in vitro inhibition assessment to the appropriate in cellulo target engagement parameter by covalent probes. Estimation of such parameters is often impeded by the low-throughput nature of current probe-free approaches. In this study, an ultra-performance liquid chromatography-multiple reaction monitoring mass spectrometry platform was utilized to develop a targeted proteomics workflow that can evaluate cellular on-target engagement of covalent molecules in an increased throughput manner. This workflow enabled a throughput increase of 5-10 fold when compared to traditional nanoLC-based proteomics studies. To demonstrate the applicability of the method, KRAS^G12C was used as a model system to investigate the interaction of an irreversible covalent small molecule, compound 25, both in vitro and in cellulo. Initial biochemical studies confirmed that the small molecule forms an adduct with the targeted cysteine on the protein, as assessed at the level of both intact protein and on the target peptide. In cellulo studies were carried out to quantify target engagement and allele selectivity assessment for the small molecule in the heterozygous NCI-H358 cell line for KRAS^G12C with respect to the WT type protein. The workflow enabled evaluation of in vitro and in cellulo target engagement kinetics, providing mechanistic insights into the irreversible mode of inhibition. In summary, the method has the potential for target agnostic application in the assessment of on-target engagement of covalent probes compatible with the high-throughput requirements of early drug discovery.

Circulating proteins as predictive and prognostic biomarkers in breast cancer

Breast cancer (BC) is the most common cancer and among the leading causes of cancer death in women. It is a heterogeneous group of tumours with numerous morphological and molecular subtypes, making predictions of disease evolution and patient outcomes difficult. Therefore, biomarkers are needed to help clinicians choose the best treatment for each patient. For the last years, studies have increasingly focused on biomarkers obtainable by liquid biopsy. Circulating proteins (from serum or plasma) can be used for inexpensive and minimally invasive determination of disease risk, early diagnosis, treatment adjusting, prognostication and disease progression monitoring. We provide here a review of the main published studies on serum proteins in breast cancer and elaborate on the potential of circulating proteins to be predictive and/or prognostic biomarkers in breast cancer.

Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks

Signal transduction governs cellular behavior, and its dysregulation often leads to human disease. To understand this process, we can use network models based on prior knowledge, where nodes represent biomolecules, usually proteins, and edges indicate interactions between them. Several computational methods combine untargeted omics data with prior knowledge to estimate the state of signaling networks in specific biological scenarios. Here, we review, compare, and classify recent network approaches according to their characteristics in terms of input omics data, prior knowledge and underlying methodologies. We highlight existing challenges in the field, such as the general lack of ground truth and the limitations of prior knowledge. We also point out new omics developments that may have a profound impact, such as single‐cell proteomics or large‐scale profiling of protein conformational changes. We provide both an introduction for interested users seeking strategies to study cell signaling on a large scale and an update for seasoned modelers.

A Strategy for Rapid Discovery of Marker Peptides Associated with Fibrinolytic Efficacy of Pheretima aspergillum Based on Bioinformatics Combined with Parallel Reaction Monitoring

Quality control of animal-derived traditional Chinese medicines has improved dramatically as proteomics research advanced in the past few decades. However, it remains challenging to identify quality attributes with routine proteomics approaches since protein with fibrinolytic activity is rarely reported in pheretima, a typical animal-derived traditional medicine. A novel strategy based on bioinformatics combined with parallel reaction monitoring (PRM) was developed here to rapidly discover the marker peptides associated with a fibrinolytic effect. Potential marker peptides were found by lumbrokinase sequences’ alignment and in silico digestion. The fibrinogen zymography was used to visually identify fibrinolytic proteins in pheretima. As a result, it was found that the fibrinolytic activity varied among different portions of pheretima. Fibrinolytic proteins were distributed regionally in the anterior and anterior-mid portion and there was no significant fibrinogenolytic activity observed in the mid-posterior and posterior portion. Finally, PRM experiments were deployed to validate and quantify selected marker peptides and a total of 11 peptides were identified as marker peptides, which could be potentially used in quality control of pheretima. This strategy provides a robust workflow to benefit the quality control of other animal-derived traditional medicines.

A Non-Hazardous Deparaffinization Protocol Enables Quantitative Proteomics of Core Needle Biopsy-Sized Formalin-Fixed and Paraffin-Embedded (FFPE) Tissue Specimens

Most human tumor tissues that are obtained for pathology and diagnostic purposes are formalin-fixed and paraffin-embedded (FFPE). To perform quantitative proteomics of FFPE samples, paraffin has to be removed and formalin-induced crosslinks have to be reversed prior to proteolytic digestion. A central component of almost all deparaffinization protocols is xylene, a toxic and highly flammable solvent that has been reported to negatively affect protein extraction and quantitative proteome analysis. Here, we present a 'green' xylene-free protocol for accelerated sample preparation of FFPE tissues based on paraffin-removal with hot water. Combined with tissue homogenization using disposable micropestles and a modified protein aggregation capture (PAC) digestion protocol, our workflow enables streamlined and reproducible quantitative proteomic profiling of FFPE tissue. Label-free quantitation of FFPE cores from human ductal breast carcinoma in situ (DCIS) xenografts with a volume of only 0.79 mm³ showed a high correlation between replicates (r² = 0.992) with a median %CV of 16.9%. Importantly, this small volume is already compatible with tissue micro array (TMA) cores and core needle biopsies, while our results and its ease-of-use indicate that further downsizing is feasible. Finally, our FFPE workflow does not require costly equipment and can be established in every standard clinical laboratory.

Breast cancer in the era of integrating “Omics” approaches

Worldwide, breast cancer is the leading cause of cancer-related deaths in women. Breast cancer is a heterogeneous disease characterized by different clinical outcomes in terms of pathological features, response to therapies, and long-term patient survival. Thus, the heterogeneity found in this cancer led to the concept that breast cancer is not a single disease, being very heterogeneous both at the molecular and clinical level, and rather represents a group of distinct neoplastic diseases of the breast and its cells. Indubitably, in the past decades we witnessed a significant development of innovative therapeutic approaches, including targeted and immunotherapies, leading to impressive results in terms of increased survival for breast cancer patients. However, these multimodal treatments fail to prevent recurrence and metastasis. Therefore, it is urgent to improve our understanding of breast tumor and metastasis biology. Over the past few years, high-throughput “omics” technologies through the identification of novel biomarkers and molecular profiling have shown their great potential in generating new insights in the study of breast cancer, also improving diagnosis, prognosis and prediction of response to treatment. In this review, we discuss how the implementation of “omics” strategies and their integration may lead to a better comprehension of the mechanisms underlying breast cancer. In particular, with the aim to investigate the correlation between different “omics” datasets and to define the new important key pathway and upstream regulators in breast cancer, we applied a new integrative meta-analysis method to combine the results obtained from genomics, proteomics and metabolomics approaches in different revised studies.

Wound fluid sampling methods for proteomic studies: A scoping review

Understanding why some wounds are hard to heal is important for improving care and developing more effective treatments. The method of sample collection used is an integral step in the research process and thus may affect the results obtained. The primary objective of this study was to summarise and map the methods currently used to sample wound fluid for protein profiling and analysis. Eligible studies were those that used a sampling method to collect wound fluid from any human wound for analysis of proteins. A search for eligible studies was performed using MEDLINE, Embase and CINAHL Plus in May 2020. All references were screened for eligibility by one reviewer, followed by discussion and consensus with a second reviewer. Quantitative data were mapped and visualised using appropriate software and summarised via a narrative summary. After screening, 280 studies were included in this review. The most commonly used group of wound fluid collection methods were vacuum, drainage or use of other external devices, with surgical wounds being the most common sample source. Other frequently used collection methods were extraction from absorbent materials, collection beneath an occlusive dressing and direct collection of wound fluid. This scoping review highlights the variety of methods used for wound fluid collection. Many studies had small sample sizes and short sample collection periods; these weaknesses have hampered the discovery and validation of novel biomarkers. Future research should aim to assess the reproducibility and feasibility of sampling and analytical methods for use in larger longitudinal studies.

Testing the effects of processing on donor human Milk: Analytical methods

Holder pasteurization is the current recommended method for donor human milk treatment. This method effectively eliminates most life-threatening contaminants in donor milk, but it also greatly reduces some of its biological properties. Consequently, there is a growing interest for developing novel processing methods that can ensure both microbial inactivation and a higher retention of the functional components of donor milk. Our aim was to offer a comprehensive overview of the analytical techniques available for the evaluation of such methods. To suggest an efficient workflow for the analysis of processed donor milk, a safety analytical panel as well as a nutritional value and functionality analytical panel are discussed, together with the principles, benefits, and drawbacks of the available techniques. Concluding on the suitability of a novel method requires a multifactorial approach which can be achieved by a combination of analytical targets and by using complementary assays to cross-validate the obtained results.