What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry

Quantification of snake venom proteomes by mass spectrometry-considerations and perspectives

The advent of soft ionization mass spectrometry-based proteomics in the 1990s led to the development of a new dimension in biology that conceptually allows for the integral analysis of whole proteomes. This transition from a reductionist to a global-integrative approach is conditioned to the capability of proteomic platforms to generate and analyze complete qualitative and quantitative proteomics data. Paradoxically, the underlying analytical technique, molecular mass spectrometry, is inherently nonquantitative. The turn of the century witnessed the development of analytical strategies to endow proteomics with the ability to quantify proteomes of model organisms in the sense of "an organism for which comprehensive molecular (genomic and/or transcriptomic) resources are available." This essay presents an overview of the strategies and the lights and shadows of the most popular quantification methods highlighting the common misuse of label-free approaches developed for model species' when applied to quantify the individual components of proteomes of nonmodel species (In this essay we use the term "non-model" organisms for species lacking comprehensive molecular (genomic and/or transcriptomic) resources, a circumstance that, as we detail in this review-essay, conditions the quantification of their proteomes.). We also point out the opportunity of combining elemental and molecular mass spectrometry systems into a hybrid instrumental configuration for the parallel identification and absolute quantification of venom proteomes. The successful application of this novel mass spectrometry configuration in snake venomics represents a proof-of-concept for a broader and more routine application of hybrid elemental/molecular mass spectrometry setups in other areas of the proteomics field, such as phosphoproteomics, metallomics, and in general in any biological process where a heteroatom (i.e., any atom other than C, H, O, N) forms integral part of its mechanism.

Cyanobacterial Proteomics: Diversity and Dynamics

Cyanobacteria (oxygenic photoautrophs) comprise a diverse group holding significance both environmentally and for biotechnological applications. The utilization of proteomic techniques has significantly influenced investigations concerning cyanobacteria. Application of proteomics allows for large-scale analysis of protein expression and function within cyanobacterial systems. The cyanobacterial proteome exhibits tremendous functional, spatial, and temporal diversity regulated by multiple factors that continuously modify protein abundance, post-translational modifications, interactions, localization, and activity to meet the dynamic needs of these tiny blue greens. Modern mass spectrometry-based proteomics techniques enable system-wide examination of proteome complexity through global identification and high-throughput quantification of proteins. These powerful approaches have revolutionized our understanding of proteome dynamics and promise to provide novel insights into integrated cellular behavior at an unprecedented scale. In this Review, we present modern methods and cutting-edge technologies employed for unraveling the spatiotemporal diversity and dynamics of cyanobacterial proteomics with a specific focus on the methods used to analyze post-translational modifications (PTMs) and examples of dynamic changes in the cyanobacterial proteome investigated by proteomic approaches.

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

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

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

Integrating Targeted Metabolomics and Targeted Proteomics to Study the Responses of Wheat Plants to Engineered Nanomaterials

This manuscript presents a multiomics investigation into the metabolic and proteomic responses of wheat to molybdenum (Mo)- and copper (Cu)-based engineered nanomaterials (ENMs) exposure via root and leaf application methods. Wheat plants underwent a four-week growth period with a 16 h photoperiod (light intensity set at 150 μmol·m-2·s-1), at 22 °C and 60% humidity. Six distinct treatments were applied, including control conditions alongside exposure to Mo- and Cu-based ENMs through both root and leaf routes. The exposure dosage amounted to 6.25 mg of the respective element per plant. An additional treatment with a lower dose (0.6 mg Mo/plant) of Mo ENM exclusively through the root system was introduced upon the detection of phytotoxicity. Utilizing LC-MS/MS analysis, 82 metabolites across various classes and 24 proteins were assessed in different plant tissues (roots, stems, leaves) under diverse treatments. The investigation identified 58 responsive metabolites and 19 responsive proteins for Cu treatments, 71 responsive metabolites, and 24 responsive proteins for Mo treatments, mostly through leaf exposure for Cu and root exposure for Mo. Distinct tissue-specific preferences for metabolite accumulation were revealed, highlighting the prevalence of organic acids and fatty acids in stem or root tissues, while sugars and amino acids were abundant in leaves, mirroring their roles in energy storage and photosynthesis. Joint-pathway analysis was conducted and unveiled 23 perturbed pathways across treatments. Among these, Mo exposure via roots impacted all identified pathways, whereas exposure via leaf affected 15 pathways, underscoring the reliance on exposure route of metabolic and proteomic responses. The coordinated response observed in protein and metabolite concentrations, particularly in amino acids, highlighted a dynamic and interconnected proteomic-to-metabolic-to-proteomic relationship. Furthermore, the contrasting expression patterns observed in glutamate dehydrogenase (upregulation at 1.38 ≤ FC ≤ 1.63 with high Mo dose, and downregulation at 0.13 ≤ FC ≤ 0.54 with low Mo dose) and its consequential impact on glutamine expression (7.67 ≤ FC ≤ 39.60 with high Mo dose and 1.50 ≤ FC ≤ 1.95 with low Mo dose) following Mo root exposure highlighted dose-dependent regulatory trends influencing proteins and metabolites. These findings offer a multidimensional understanding of plant responses to ENMs exposure, guiding agricultural practices and environmental safety protocols while advancing knowledge on nanomaterial impacts on plant biology.

Assessing the Impacts of Cu and Mo Engineered Nanomaterials on Crop Plant Growth Using a Targeted Proteomics Approach

In this study, we investigated the effects of molybdenum (Mo)-based nanofertilizer and copper (Cu)-based nanopesticide exposure on wheat through a multifaceted approach, including physiological measurements, metal uptake and translocation analysis, and targeted proteomics analysis. Wheat plants were grown under a 16 h photoperiod (light intensity 150 μmol·m-2·s-1) for 4 weeks at 22 °C and 60% humidity with 6 different treatments, including control, Mo, and Cu exposure through root and leaf. The exposure dose was 6.25 mg of element per plant through either root or leaf. An additional low-dose (0.6 mg Mo/plant) treatment of Mo through root was added after phytotoxicity was observed. Using targeted proteomics approach, 24 proteins involved in 12 metabolomic pathways were quantitated to understand the regulation at the protein level. Mo exposure, particularly through root uptake, induced significant upregulation of 16 proteins associated with 11 metabolic pathways, with the fold change (FC) ranging from 1.28 to 2.81. Notably, a dose-dependent response of Mo exposure through the roots highlighted the delicate balance between nutrient stimulation and toxicity as a high Mo dose led to robust protein upregulation but also resulted in depressed physiological measurements, while a low Mo dose resulted in no depression of physiological measurements but downregulations of proteins, especially in the first leaf (0.23 < FC < 0.68) and stem (0.13 < FC < 0.68) tissues. Conversely, Cu exposure exhibited tissue-specific effects, with pronounced downregulation (18 proteins involved in 11 metabolic pathways) particularly in the first leaf tissues (root exposure: 0.35 < FC < 0.74; leaf exposure: 0.49 < FC < 0.72), which indicated the quick response of plants to Cu-induced stress in the early stage of exposure. By revealing the complexities of plants' response to engineered nanomaterials at both physiological and molecular levels, this study provides insights for optimizing nutrient management practices in crop production and advancing toward sustainable agriculture.

On the excessive use of coefficient of variation as a metric of quantitation quality in proteomics

The coefficient of variation (CV) is often used in proteomics as a proxy to characterize the performance of a quantitation method and/or the related software. In this note, we question the excessive reliance on this metric in quantitative proteomics that may result in erroneous conclusions. We support this note using a ground-truth Human-Yeast-E. coli dataset demonstrating in a number of cases that erroneous data processing methods may lead to a low CV which has nothing to do with these methods' performances in quantitation.

Targeted proteomics in a population-based study identifies serum PECAM-1 and TRIM21 as inflammation markers for periodontitis

OBJECTIVES

Periodontitis (PD) can cause systematic inflammation and is associated with various metabolic processes in the body. However, robust serum markers for these relationships are still lacking. This study aims to identify novel circulating inflammation-related proteins associated with PD using targeted proteomics.

MATERIALS AND METHODS

We used population-based, cross-sectional data from 619 participants of the Polish Longitudinal University Study (Bialystok PLUS). Mean pocket probing depth (mPPD) and proportion of bleeding on probing (pBOP) served as exposure variables. Fifty-two inflammation-related proteins were measured using the Olink Target 96 Cardiovascular III and the Olink Target 96 Immune Response panels. Associations between periodontal measures and proteins were tested using covariate-adjusted linear regression models.

RESULTS

At a false discovery rate of < 0.05, we identified associations of mPPD and pBOP with platelet-endothelial cell adhesion molecule-1 (PECAM-1) and tripartite motif-containing protein 21 (TRIM21).

CONCLUSION

This study revealed novel associations between PD and serum levels of PECAM-1 and TRIM21. Our results suggest that these proteins might be affected by molecular processes that take place in the inflamed periodontium.

CLINICAL RELEVANCE

Novel associations of PECAM-1 and TRIM21 with PD indicate promising serum markers for understanding the disease's pathophysiological processes and call for further biomedical investigations.

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.

Advances in minimal residual disease monitoring in multiple myeloma

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

Mass spectrometry quantifies target engagement for a KRASG12C inhibitor in FFPE tumor tissue

BACKGROUND

Quantification of drug-target binding is critical for confirming that drugs reach their intended protein targets, understanding the mechanism of action, and interpreting dose-response relationships. For covalent inhibitors, target engagement can be inferred by free target levels before and after treatment. Targeted mass spectrometry assays offer precise protein quantification in complex biological samples and have been routinely applied in pre-clinical studies to quantify target engagement in frozen tumor tissues for oncology drug development. However, frozen tissues are often not available from clinical trials so it is critical that assays are applicable to formalin-fixed, paraffin-embedded (FFPE) tissues in order to extend mass spectrometry-based target engagement studies into clinical settings.

METHODS

Wild-type RAS and RASG12C was quantified in FFPE tissues by a highly optimized targeted mass spectrometry assay that couples high-field asymmetric waveform ion mobility spectrometry (FAIMS) and parallel reaction monitoring (PRM) with internal standards. In a subset of samples, technical reproducibility was evaluated by analyzing consecutive tissue sections from the same tumor block and biological variation was accessed among adjacent tumor regions in the same tissue section.

RESULTS

Wild-type RAS protein was measured in 32 clinical non-small cell lung cancer tumors (622-2525 amol/µg) as measured by FAIMS-PRM mass spectrometry. Tumors with a known KRASG12C mutation (n = 17) expressed a wide range of RASG12C mutant protein (127-2012 amol/µg). The variation in wild-type RAS and RASG12C measurements ranged 0-18% CV across consecutive tissue sections and 5-20% CV among adjacent tissue regions. Quantitative target engagement was then demonstrated in FFPE tissues from 2 xenograft models (MIA PaCa-2 and NCI-H2122) treated with a RASG12C inhibitor (AZD4625).

CONCLUSIONS

This work illustrates the potential to expand mass spectrometry-based proteomics in preclinical and clinical oncology drug development through analysis of FFPE tumor biopsies.

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach

Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.

Optimization of Targeted Plant Proteomics Using Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS)

This study was conducted to optimize a targeted plant proteomics approach from signature peptide selection and liquid chromatography with tandem mass spectrometry (LC-MS/MS) analytical method development and optimization to sample preparation method optimization. Three typical protein extraction and precipitation methods, including trichloroacetic acid (TCA)/acetone method, phenol method, and TCA/acetone/phenol method, and two digestion methods, including trypsin digestion and LysC/trypsin digestion, were evaluated for selected proteins related to the impact of engineered nanomaterials (ENMs) on wheat (Triticum aestivum) plant growth. In addition, we compared two plant tissue homogenization methods: grinding freeze-dried tissue and fresh tissue into a fine powder using a mortar and pestle aided with liquid nitrogen. Wheat plants were grown under a 16 h photoperiod (light intensity 150 μmol·m^-2·s^-1) for 4 weeks at 22 °C with a relative humidity of 60% and were watered daily to maintain a 70-90% water content in the soil. Processed samples were analyzed with an optimized LC-MS/MS method. The concentration of selected signature peptides for the wheat proteins of interest indicated that the phenol extraction method using fresh plant tissue, coupled with trypsin digestion, was the best sample preparation method for the targeted proteomics study. Overall, the optimized approach yielded the highest total peptide concentration (68,831 ng/g, 2.4 times the lowest concentration) as well as higher signature peptide concentrations for most peptides (19 out of 28). In addition, three of the signature peptides could only be detected using the optimized approach. This study provides a workflow for optimizing targeted proteomics studies.

Biological and proteomic analysis of a new isolate of the nematophagous fungus lecanicillium sp

BACKGROUND

In our continuing search for biologically active natural enemies from North of Africa with special reference to Tunisian fungi, our teamwork screened fungi from different ecological habitats in Tunisia. Our previous study on the comparative effectiveness of filamentous fungi in the biocontrol of Meloidogyne javanica, a taxon (Lecanicillium) showed high potentiality against M. javanica. We undertook the present study to evaluate the ability and understand the mechanism of this fungal parasite as a biological control candidate against the root-knot nematode M. javanica. This study used in vitro bioassays with fungal filtrate cultures, scanning electron microscopy (SEM) observation, and isobaric tag for relative and absolute quantitation (iTRAQ) methodology to characterize the biological and molecular features of this fungus.

RESULTS

The microscopic and SEM observation revealed that Lecanicillium sp. exhibited exceptional hyperparasitism against M. javanica eggs. The hyphae of this fungi penetrated the eggs, causing destructive damage to the outer eggshell. The exposure to five concentrations of Lecanicillium sp. filtrate cultures showed high inhibition of egg hatching, which increases depending on the exposure time; the best results are recorded at 50%, 75%, and 100% dilutions after seven days of exposure. The SEM observation of nematode-parasitized eggs and juveniles suggests that the production of lytic enzymes degrades the egg cuticle and fungal hyphae penetrate unhatched M.javanica juveniles. Forty-seven unique proteins were identified from the Lecanicillium sp. isolate. These proteins have signalling and stress response functions, bioenergy, metabolism, and protein synthesis and degradation.

CONCLUSION

Collectively, Lecanicillium sp. had ovicidal potentiality proved by SEM and proteomic analysis against root-knot nematode' eggs. This study recommended applying this biological control candidate as a bio-agent on vegetable crops grown in situ.

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.

The potential of proteomics in the study of processed meat products

Proteomics is a field that has grown rapidly since its emergence in the mid-1990s, reaching many disciplines such as food technology. The application of proteomic techniques in the study of complex biological samples such as foods, specifically meat products, allows scientists to decipher the underlying cellular mechanisms behind different quality traits. Lately, much emphasis has been placed on the discovery of biomarkers that facilitate the prediction of biochemical transformations of the product and provide key information on parameters associated with traceability and food safety. This review study focuses on the contribution of proteomics in the improvement of processed meat products. Different techniques and strategies have recently been successfully carried out in the study of the proteome of these products that can help the development of foods with a higher sensory quality, while ensuring consumer safety through early detection of microbiological contamination and fraud. SIGNIFICANCE: The food industry and the academic world work together with the aim of responding to market demands, always seeking excellence. In particular, the meat industry has to face a series of challenges such as, achieving sensory attributes in accordance with the standards required by the consumer and maintaining a high level of safety and transparency, avoiding deliver adulterated and/or contaminated products. This review work exposes how the aforementioned challenges are attempted to be solved through proteomic technology, discussing the latest and most outstanding research in this regard, which undoubtedly contribute to improving the quality, in all the extension of the word, of meat products, providing relevant knowledge in the field of proteomic research.

Translational proteomics and phosphoproteomics: Tissue to extracellular vesicles

We are currently experiencing a rapidly developing era in terms of translational and clinical medical sciences. The relatively mature state of nucleic acid examination has significantly improved our understanding of disease mechanism and therapeutic potential of personalized treatment, but misses a large portion of phenotypic disease information. Proteins, in particular phosphorylation events that regulates many cellular functions, could provide real-time information for disease onset, progression and treatment efficacy. The technical advances in liquid chromatography and mass spectrometry have realized large-scale and unbiased proteome and phosphoproteome analyses with disease relevant samples such as tissues. However, tissue biopsy still has multiple shortcomings, such as invasiveness of sample collection, potential health risk for patients, difficulty in protein preservation and extreme heterogeneity. Recently, extracellular vesicles (EVs) have offered a great promise as a unique source of protein biomarkers for non-invasive liquid biopsy. Membranous EVs provide stable preservation of internal proteins and especially labile phosphoproteins, which is essential for effective routine biomarker detection. To aid efficient EV proteomic and phosphoproteomic analyses, recent developments showcase clinically-friendly EV techniques, facilitating diagnostic and therapeutic applications. Ultimately, we envision that with streamlined sample preparation from tissues and EVs proteomics and phosphoproteomics analysis will become routine in clinical settings.

A companion to the preclinical common data elements for proteomics, lipidomics, and metabolomics data in rodent epilepsy models. A report of the TASK3-WG4 omics working group of the ILAE/AES joint translational TASK force

The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force established the TASK3 working groups to create common data elements (CDEs) for various preclinical epilepsy research disciplines. This is the second in a two-part series of omics papers, with the other including genomics, transcriptomics, and epigenomics. The aim of the CDEs was to improve the standardization of experimental designs across a range of epilepsy research-related methods. We have generated CDE tables with key parameters and case report forms (CRFs) containing the essential contents of the study protocols for proteomics, lipidomics, and metabolomics of samples from rodent models and people with epilepsy. We discuss the important elements that need to be considered for the proteomics, lipidomics, and metabolomics methodologies, providing a rationale for the parameters that should be documented.

Recent advances on characterization of protein oxidation in aquatic products: A comprehensive review

In addition to microbial spoilage and lipid peroxidation, protein oxidation is increasingly recognized as a major cause for quality deterioration of muscle-based foods. Although protein oxidation in muscle-based foods has attracted tremendous interest in the past decade, specific oxidative pathways and underlying mechanisms of protein oxidation in aquatic products remain largely unexplored. The present review covers the aspects of the origin and site-specific nature of protein oxidation, progress on the characterization of protein oxidation, oxidized proteins in aquatic products, and impact of protein oxidation on protein functionalities. Compared to meat protein oxidation, aquatic proteins demonstrate a less extent of oxidation on aromatic amino acids and are more susceptible to be indirectly oxidized by lipid peroxidation products. Different from traditional measurement of protein carbonyls and thiols, proteomics-based strategy better characterizes the targeted oxidation sites within proteins. The future trends using more robust and accurate targeted proteomics, such as parallel reaction monitoring strategy, to characterize protein oxidation in aquatic products are also given.

Multi-Omics Approaches to Improve Clubroot Resistance in Brassica with a Special Focus on Brassica oleracea L

Brassica oleracea is an agronomically important species of the Brassicaceae family, including several nutrient-rich vegetables grown and consumed across the continents. But its sustainability is heavily constrained by a range of destructive pathogens, among which, clubroot disease, caused by a biotrophic protist Plasmodiophora brassicae, has caused significant yield and economic losses worldwide, thereby threatening global food security. To counter the pathogen attack, it demands a better understanding of the complex phenomenon of Brassica-P. brassicae pathosystem at the physiological, biochemical, molecular, and cellular levels. In recent years, multiple omics technologies with high-throughput techniques have emerged as successful in elucidating the responses to biotic and abiotic stresses. In Brassica spp., omics technologies such as genomics, transcriptomics, ncRNAomics, proteomics, and metabolomics are well documented, allowing us to gain insights into the dynamic changes that transpired during host-pathogen interactions at a deeper level. So, it is critical that we must review the recent advances in omics approaches and discuss how the current knowledge in multi-omics technologies has been able to breed high-quality clubroot-resistant B. oleracea. This review highlights the recent advances made in utilizing various omics approaches to understand the host resistance mechanisms adopted by Brassica crops in response to the P. brassicae attack. Finally, we have discussed the bottlenecks and the way forward to overcome the persisting knowledge gaps in delivering solutions to breed clubroot-resistant Brassica crops in a holistic, targeted, and precise way.

Mass Spectrometry-Based Quantification of the Antigens in Aluminum Hydroxide-Adjuvanted Diphtheria-Tetanus-Acellular-Pertussis Combination Vaccines

Vaccines undergo stringent batch-release testing, most often including in-vivo assays for potency. For combination vaccines, such as diphtheria-tetanus-pertussis (DTaP), chemical modification induced by formaldehyde inactivation, as well as adsorption to aluminum-based adjuvants, complicates antigen-specific in-vitro analysis. Here, a mass spectrometric method was developed that allows the identification and quantitation of DTaP antigens in a combination vaccine. Isotopically labeled, antigen-specific internal standard peptides were employed that permitted absolute quantitation of their antigen-derived peptide counterparts and, consequently, the individual antigens. We evaluated the applicability of the method on monovalent non-adjuvanted antigens, on final vaccine lots and on experimental vaccine batches, where certain antigens were omitted from the drug product. Apart from the applicability for final batch release, we demonstrated the suitability of the approach for in-process control monitoring. The peptide quantification method facilitates antigen-specific identification and quantification of combination vaccines in a single assay. This may contribute, as part of the consistency approach, to a reduction in the number of animal tests required for vaccine-batch release.

The Use of Bacteriophages in Biotechnology and Recent Insights into Proteomics

Phages have certain features, such as their ability to form protein-protein interactions, that make them good candidates for use in a variety of beneficial applications, such as in human or animal health, industry, food science, food safety, and agriculture. It is essential to identify and characterize the proteins produced by particular phages in order to use these viruses in a variety of functional processes, such as bacterial detection, as vehicles for drug delivery, in vaccine development, and to combat multidrug resistant bacterial infections. Furthermore, phages can also play a major role in the design of a variety of cheap and stable sensors as well as in diagnostic assays that can either specifically identify specific compounds or detect bacteria. This article reviews recently developed phage-based techniques, such as the use of recombinant tempered phages, phage display and phage amplification-based detection. It also encompasses the application of phages as capture elements, biosensors and bioreceptors, with a special emphasis on novel bacteriophage-based mass spectrometry (MS) applications.

PASIV: A Pooled Approach-Based Workflow to Overcome Toxicity-Induced Design of Experiments Failures and Inefficiencies

We present here a newly developed workflow—which we have called PASIV—designed to provide a solution to a practical problem with design of experiments (DoE) methodology: i.e., what can be done if the scoping phase of the DoE cycle is severely hampered by burden and toxicity issues (caused by either the metabolite or an intermediary), making it unreliable or impossible to proceed to the screening phase? PASIV—standing for pooled approach, screening, identification, and visualization—was designed so the (viable) region of interest can be made to appear through an interplay between biology and software. This was achieved by combining multiplex construction in a pooled approach (one-pot reaction) with a viability assay and with a range of bioinformatics tools (including a novel construct matching tool). PASIV was tested on the exemplar of the lycopene pathway—under stressful constitutive expression—yielding a region of interest with comparatively stronger producers.

Shotgun Proteomics as a Powerful Tool for the Study of the Proteomes of Plants, Their Pathogens, and Plant-Pathogen Interactions

The interaction between plants and pathogenic microorganisms is a multifaceted process mediated by both plant- and pathogen-derived molecules, including proteins, metabolites, and lipids. Large-scale proteome analysis can quantify the dynamics of proteins, biological pathways, and posttranslational modifications (PTMs) involved in the plant-pathogen interaction. Mass spectrometry (MS)-based proteomics has become the preferred method for characterizing proteins at the proteome and sub-proteome (e.g., the phosphoproteome) levels. MS-based proteomics can reveal changes in the quantitative state of a proteome and provide a foundation for understanding the mechanisms involved in plant-pathogen interactions. This review is intended as a primer for biologists that may be unfamiliar with the diverse range of methodology for MS-based shotgun proteomics, with a focus on techniques that have been used to investigate plant-pathogen interactions. We provide a summary of the essential steps required for shotgun proteomic studies of plants, pathogens and plant-pathogen interactions, including methods for protein digestion, identification, separation, and quantification. Finally, we discuss how protein PTMs may directly participate in the interaction between a pathogen and its host plant.

Targeted Mass Spectrometry Analysis of Protein Phosphorylation by Selected Ion Monitoring Coupled to Parallel Reaction Monitoring (tSIM/PRM)

Recent developments in targeted mass spectrometry-based proteomics have provided new methodological solutions for accurate and quantitative analysis of proteins and their posttranslational control, which has significantly advanced our understanding of stress responses in different plant species. Instrumentation allowing high-resolution, accurate-mass (HR/AM) analysis has provided new acquisition strategies for targeted quantitative proteomic analysis by targeted selected ion monitoring (tSIM) and parallel reaction monitoring (PRM). Here we report a sensitive and accurate method for targeted analysis of protein phosphorylation by tSIM coupled to PRM (tSIM/PRM). The tSIM/PRM method takes advantage of HR/AM mass spectrometers and benefits from the combination of highly sensitive precursor ion quantification by tSIM and highly confident peptide identification by spectral library matching in PRM. The detailed protocol describes tSIM/PRM analysis of Arabidopsis thaliana foliar proteins, from the building of a spectral library to sample preparation, mass spectrometry, and data analysis, and provides a methodological approach for specifying the molecular mechanisms of interest.

An Introduction to Advanced Targeted Acquisition Methods

Targeted proteomics via selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) enables fast and sensitive detection of a preselected set of target peptides. However, the number of peptides that can be monitored in conventional targeting methods is usually rather small. Recently, a series of methods has been described that employ intelligent acquisition strategies to increase the efficiency of mass spectrometers to detect target peptides. These methods are based on one of two strategies. First, retention time adjustment-based methods enable intelligent scheduling of target peptide retention times. These include Picky, iRT, as well as spike-in free real-time adjustment methods such as MaxQuant.Live. Second, in spike-in triggered acquisition methods such as SureQuant, Pseudo-PRM, TOMAHAQ, and Scout-MRM, targeted scans are initiated by abundant labeled synthetic peptides added to samples before the run. Both strategies enable the mass spectrometer to better focus data acquisition time on target peptides. This either enables more sensitive detection or a higher number of targets per run. Here, we provide an overview of available advanced targeting methods and highlight their intrinsic strengths and weaknesses and compatibility with specific experimental setups. Our goal is to provide a basic introduction to advanced targeting methods for people starting to work in this field.

•

Advanced acquisition methods improve focus of mass spectrometers on target peptides.

•

This review discusses existing methods based on two strategies.

•

Retention time adjustment-based methods enable intelligent scheduling of peptide RTs.

•

In spike-in triggered acquisition methods targeted scans are initiated by spike-ins.

Proteomes Are of Proteoforms: Embracing the Complexity

Proteomes are complex-much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current 'state-of-the-field'. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?

Use of Linear Ion Traps in Data-Independent Acquisition Methods Benefits Low-Input Proteomics

The need for a better understanding of cellular heterogeneity has pushed mass spectrometry technologies to the analysis of single-cell and single-cell-type proteomes, although several challenges still limit their widespread implementation. Among the efforts toward single-cell and low-input analyses, there is the adoption of data-independent acquisition methods to increase analytical sensitivity. Here, we revisited the use of linear ion traps mass analyzers in data-independent acquisition methods and demonstrate their benefits to boost peptide and protein identifications in low-input proteomes.

Proteomic Advances in Cereal and Vegetable Crops

The importance of vegetables in human nutrition, such as cereals, which in many cases represent the main source of daily energy for humans, added to the impact that the incessant increase in demographic pressure has on the demand for these plant foods, entails the search for new technologies that can alleviate this pressure on markets while reducing the carbon footprint of related activities. Plant proteomics arises as a response to these problems, and through research and the application of new technologies, it attempts to enhance areas of food science that are fundamental for the optimization of processes. This review aims to present the different approaches and tools of proteomics in the investigation of new methods for the development of vegetable crops. In the last two decades, different studies in the control of the quality of crops have reported very interesting results that can help us to verify parameters as important as food safety, the authenticity of the products, or the increase in the yield by early detection of diseases. A strategic plan that encourages the incorporation of these new methods into the industry will be essential to promote the use of proteomics and all the advantages it offers in the optimization of processes and the solution of problems.

Recent advances in proteomics and its implications in pituitary endocrine disorders

Pituitary adenoma is considered as one of the most frequent intracranial tumors having salient impact on human health such as mass effects, hypopituitarism and visual defects etc. During the past few decades, there has been enormous advancement in mass spectrometry (MS)-based proteomics. However, very little is known about the molecular pathogenesis of pituitary adenomas in the context of proteomics. In this review article, we have focused on the provenance of pituitary tumors and their pathogenesis with the help of MS-based proteomics approaches. Recent advancements in quantitative proteomic approaches are outlined here that would be useful in the near pituitary adenoma proteomics research. This review discusses the enormous potential of pituitary adenomas research through proteomics with a common aim of deciphering disease pathobiology and identifying the work done in studying pituitary tumors during past decade.

Emerging Approaches to Understanding Microvascular Endothelial Heterogeneity: A Roadmap for Developing Anti-Inflammatory Therapeutics

The endothelium is the inner layer of all blood vessels and it regulates hemostasis. It also plays an active role in the regulation of the systemic inflammatory response. Systemic inflammatory disease often results in alterations in vascular endothelium barrier function, increased permeability, excessive leukocyte trafficking, and reactive oxygen species production, leading to organ damage. Therapeutics targeting endothelium inflammation are urgently needed, but strong concerns regarding the level of phenotypic heterogeneity of microvascular endothelial cells between different organs and species have been expressed. Microvascular endothelial cell heterogeneity in different organs and organ-specific variations in endothelial cell structure and function are regulated by intrinsic signals that are differentially expressed across organs and species; a result of this is that neutrophil recruitment to discrete organs may be regulated differently. In this review, we will discuss the morphological and functional variations in differently originated microvascular endothelia and discuss how these variances affect systemic function in response to inflammation. We will review emerging in vivo and in vitro models and techniques, including microphysiological devices, proteomics, and RNA sequencing used to study the cellular and molecular heterogeneity of endothelia from different organs. A better understanding of microvascular endothelial cell heterogeneity will provide a roadmap for developing novel therapeutics to target the endothelium.

One-step fabrication of strongly hydrophilic mesoporous silica for comprehensive analysis of serum glycopeptidome

Glycopeptidome represents reliable predictors of physiological and pathological status. Obstructions mainly including low abundance of endogenous glycopeptides and varied interference necessitate glycopeptide enrichment prior to MS analysis. Inspired by the prevalence of hydrophilic interaction chromatography for glycopeptide enrichment, a novel magnetic mesoporous silica nanomaterial (Fe₃O₄@mSiO₂-TSG) with strongly hydrophilic property was developed through a one-pot method. In this work, the gluconamide-containing organosilane is innovatively proposed to directly serve as the strongly hydrophilic silica source for fabrication of hydrophilic mesoporous silica nanomaterial for glycopeptidomics research. Apart from excellent hydrophilicity, Fe₃O₄@mSiO₂-TSG also was equipped with large specific surface area, ordered mesopore channels and great magnetic responsiveness. With all the advantages, Fe₃O₄@mSiO₂-TSG displayed remarkable size-exclusion effect and considerable reusability. Moreover, combined with nano-LC-MS/MS, the glycopeptidome of serum from breast cancer patients was analyzed comprehensively, which showed noteworthy difference from healthy serum through gene ontology analysis, indicating great potential of the approach for glycopeptidomics research.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.

Proteomics in systems toxicology

Proteins are the ultimate product of gene expression. As they hinge between gene transcription and phenotype, they offer a more realistic perspective of toxicopathic effects, responses and even susceptibility to insult than targeting genes and mRNAs while dodging some inter-individual variability that hinders measuring downstream endpoints like metabolites or enzyme activity. Toxicologists have long focused on proteins as biomarkers but the advent of proteomics shifted risk assessment from narrow single-endpoint analyses to whole-proteome screening, enabling deriving protein-centric adverse outcome pathways (AOPs), which are pivotal for the derivation of Systems Biology informally named Systems Toxicology. Especially if coupled pathology, the identification of molecular initiating events (MIEs) and AOPs allow predictive modeling of toxicological pathways, which now stands as the frontier for the next generation of toxicologists. Advances in mass spectrometry, bioinformatics, protein databases and top-down proteomics create new opportunities for mechanistic and effects-oriented research in all fields, from ecotoxicology to pharmacotoxicology.

Application of Advanced Mass Spectrometry-Based Proteomics to Study Hypoxia Driven Cancer Progression

Cancer is one of the largest contributors to the burden of chronic disease in the world and is the second leading cause of death globally. It is associated with episodes of low-oxygen stress (hypoxia or ischemia/reperfusion) that promotes cancer progression and therapeutic resistance. Efforts have been made in the past using traditional proteomic approaches to decipher oxygen deprivation stress-related mechanisms of the disease initiation and progression and to identify key proteins as a therapeutic target for the treatment and prevention. Despite the potential benefits of proteomic in translational research for the discovery of new drugs, the therapeutic outcome with this approach has not met expectations in clinical trials. This is mainly due to the disease complexity which possess a multifaceted molecular pathology. Therefore, novel strategies to identify and characterize clinically important sets of modulators and molecular events for multi-target drug discovery are needed. Here, we review important past and current studies on proteomics in cancer with an emphasis on recent pioneered labeling approaches in mass spectrometry (MS)-based systematic quantitative analysis to improve clinical success. We also discuss the results of the selected innovative publications that integrate advanced proteomic technologies (e.g. MALDI-MSI, pSILAC/SILAC/iTRAQ/TMT-LC-MS/MS, MRM-MS) for comprehensive analysis of proteome dynamics in different biosystems, including cell type, cell species, and subcellular proteome (i.e. secretome and chromatome). Finally, we discuss the future direction and challenges in the application of these technological advancements in mass spectrometry within the context of cancer and hypoxia.

Proteomics for the study of new biomarkers in Fabry disease: State of the art

Nephropathy represents a major complication of Fabry Disease and its accurate characterization is of paramount importance in predicting the disease progression and assessing the therapeutic responses. The diagnostic process still relies on performing renal biopsy, nevertheless many efforts have been made to discover early reliable biomarkers allowing us to avoid invasive procedures. In this field, proteomics offers a sensitive and fast method leading to an accurate detection of specific pathological proteins and the discovery of diagnostic and prognostic biomarkers that reflect disease progression and facilitate the evaluation of therapeutic responses. Here, we report a review of selected literature focusing on the investigation of several proteomic techniques highlighting their advantages, limitations and future perspectives in their application in the routine study of Fabry Nephropathy.

Probing the methotrexate-protein interactions by proteomics and thermostability assay for drug resistance study

Screening of drug targets is critical to understand the mechanism of action of the drug. The aim of this study is to screen the drug-resistant target proteins of the anticancer drug methotrexate (MTX) by using chemical proteomics and to further study the interaction between MTX and its target protein in vitro and in vivo according to the principle of the increasing thermal stability of the target protein after binding with the drug molecule. The results showed that 21 drug resistance related proteins of MTX including phosphoglycerate kinase 1 (PGK1) were detected by quantitative proteomics. The expression of PGK1 increased with the prolongation of incubation time of MTX, indicating PGK1 protein is affected by MTX time dependently in cells. Further the results of the study on the interaction between MTX and PGK1 in vitro and in vivo using cellular thermal shift assay (CETSA) showed that the level of PGK1 in MTX-treated groups was higher than that in the control group under the stimulation of higher temperature conditions, indicating that PGK1 has direct interactions with MTX. The present study provided the data and theoretical support for the study of the resistant target proteins of MTX and a novel point for the extension application of MTX.

Biological Applications for LC-MS-Based Proteomics

Since its inception, liquid chromatography-mass spectrometry (LC-MS) has been continuously improved upon in many aspects, including instrument capabilities, sensitivity, and resolution. Moreover, the costs to purchase and operate mass spectrometers and liquid chromatography systems have decreased, thus increasing affordability and availability in sectors outside of academic and industrial research. Processing power has also grown immensely, cutting the time required to analyze samples, allowing more data to be feasibly processed, and allowing for standardized processing pipelines. As a result, proteomics via LC-MS has become popular in many areas of biological sciences, forging an important seat for itself in targeted and untargeted assays, pure and applied science, the laboratory, and the clinic. In this chapter, many of these applications of LC-MS-based proteomics and an outline of how they can be executed will be covered. Since the field of personalized medicine has matured alongside proteomics, it has also come to rely on various mass spectrometry methods and will be elaborated upon as well. As time goes on and mass spectrometry evolves, there is no doubt that its presence in these areas, and others, will only continue to grow.

Urinary vitronectin identifies patients with high levels of fibrosis in kidney grafts

BACKGROUND

In kidney transplantation, fibrosis represents the final and irreversible consequence of the pathogenic mechanisms that lead to graft failure, and in the late stages it irremediably precedes the loss of renal function. The invasiveness of kidney biopsy prevents this condition from being frequently monitored, while clinical data are rather unspecific. The objective of this study was to find noninvasive biomarkers of kidney rejection.

METHODS

We carried out proteomic analysis of the urinary Extracellular Vesicles (uEVs) from a cohort of kidney transplant recipients (n = 23) classified according to their biopsy-based diagnosis and clinical parameters as interstitial fibrosis and tubular atrophy (IFTA), acute cellular rejection (ACR), calcineurin inhibitors toxicity (CNIT) and normal kidney function (NKF).

RESULTS

Shotgun mass spectrometry of uEV-proteins identified differential expression of several proteins among these different groups. Up to 23 of these proteins were re-evaluated using targeted proteomics in a new independent cohort of patients (n = 41) classified in the same diagnostic groups. Among other results, we found a differential expression of vitronectin (VTN) in patients displaying chronic interstitial and tubular lesions (ci and ct mean > 2 according to Banff criteria). These results were further confirmed by a pilot study using enzyme-linked immunosorbent assay (ELISA).

CONCLUSION

Urinary vitronectin levels are a potential stand-alone biomarker to monitor fibrotic changes in kidney transplant recipients in a non-invasive fashion.

Differentially modulated proteins associated with Leishmaniasis-a systematic review of in-vivo and in-vitro studies

High-throughput proteomic technologies are widely used for understanding the disease mechanism, drug-resistant mechanism, and to identify drug targets and markers for diagnostics. Studies with proteomics applications, relating to Leishmaniasis, are being constantly reported in the literature. However, from such studies, a readily accessible knowledge of differentially modulated proteins associated with Leishmaniasis is lacking. Hence, we performed a systematic review concerning differentially modulated proteins (DMP) in Leishmania as well as host infected with Leishmania from the published articles between the years 2000 and 2019. This review is classified into five different sections, namely, DMP in the host after Leishmania infection, DMP between different strains of Leishmania, DMP in drug-resistant Leishmania, DMP in Leishmania under stress, and DMP in different life stages of Leishmania. A lot of consensuses could be observed among the DMP in drug-resistant and stressed Leishmania. In addition to the review, a database was constructed with the data collected in this study (protein accession ID, protein name, gene name, host organism, experimental conditions, fold change, and regulatory data). A total of 2635 records are available in the database. We believe this review and the database will help the researcher in understanding the disease better and provide information for the targeted proteomics study related to Leishmaniasis. Database availability: http://ldepdb.biomedinformri.com/.

Optimized Sample Preparation and Data Processing of Data-Independent Acquisition Methods for the Robust Quantification of Trace-Level Host Cell Protein Impurities in Antibody Drug Products

Host cell proteins (HCPs) are a major class of bioprocess-related impurities generated by the host organism and are generally present at low levels in purified biopharmaceutical products. The monitoring of these impurities is identified as an important critical quality attribute of monoclonal antibody (mAb) formulations not only due to the potential risk for the product stability and efficacy but also concerns linked to the immunogenicity of some of them. While overall HCP levels are usually monitored by enzyme-linked immunosorbent assay (ELISA), mass spectrometry (MS)-based approaches have been emerging as powerful and promising alternatives providing qualitative and quantitative information. However, a major challenge for liquid chromatography (LC)-MS-based methods is to deal with the wide dynamic range of drug products and the extreme sensitivity required to detect trace-level HCPs. In this study, we developed powerful and reproducible MS-based analytical workflows coupling optimized and efficient sample preparations, the library-free data-independent acquisition (DIA) method, and stringent validation criteria. The performances of several preparation protocols and DIA versus classical data-dependent acquisition (DDA) were evaluated using a series of four commercially available drug products. Depending on the selected protocols, the user has access to different information: on the one hand, a deep profiling of tens of identified HCPs and on the other hand, accurate and reproducible (coefficients of variation (CVs) < 12%) quantification of major HCPs. Overall, a final global HCP amount of a few tens of ng/mg mAb in these mAb samples was measured, while reaching a sensitivity down to the sub-ng/mg mAb level. Thus, this straightforward and robust approach can be intended as a routine quality control for any drug product analysis.

Proteomic Insights into the Biology of the Most Important Foodborne Parasites in Europe

Foodborne parasitoses compared with bacterial and viral-caused diseases seem to be neglected, and their unrecognition is a serious issue. Parasitic diseases transmitted by food are currently becoming more common. Constantly changing eating habits, new culinary trends, and easier access to food make foodborne parasites' transmission effortless, and the increase in the diagnosis of foodborne parasitic diseases in noted worldwide. This work presents the applications of numerous proteomic methods into the studies on foodborne parasites and their possible use in targeted diagnostics. Potential directions for the future are also provided.

Accelerating protein biomarker discovery and translation from proteomics research for clinical utility

Discovery proteomics research has made significant progress in the past several years; however, the number of protein biomarkers deployed in clinical practice remains rather limited. There are several scientific and procedural gaps between discovery proteomics research and clinical implementation, which have contributed to poor biomarker validity and few clinical applications. The complexity and low throughput of proteomics approaches have added additional barriers for biomarker assay translation to clinical applications. Recently, targeted proteomics have become a powerful tool to bridge the biomarker discovery to clinical validation. In this perspective, we discuss the challenges and strategies in proteomics research from a clinical perspective, and propose several recommendations for discovery proteomics research to accelerate protein biomarker discovery and translation for future clinical applications.

Quantitative and targeted proteomics-based identification and validation of drug efficacy biomarkers

Proteomics refers to the large-scale study of proteins, providing comprehensive and quantitative information on proteins in tissue, blood, and cell samples. In many studies, proteomics utilizes liquid chromatography-mass spectrometry. Proteomics has developed from a qualitative methodology of protein identification to a quantitative methodology for comparing protein expression, and it is currently classified into two distinct methodologies: quantitative and targeted proteomics. Quantitative proteomics comprehensively identifies proteins in samples, providing quantitative information on large-scale comparative profiles of protein expression. Targeted proteomics simultaneously quantifies only target proteins with high sensitivity and specificity. Therefore, in biomarker research, quantitative proteomics is used for the identification of biomarker candidates, and targeted proteomics is used for the validation of biomarkers. Understanding the specific characteristics of each method is important for conducting appropriate proteomics studies. In this review, we introduced the different characteristics and applications of quantitative and targeted proteomics, and then discussed the results of our recent proteomics studies that focused on the identification and validation of biomarkers of drug efficacy. These findings may enable us to predict the outcomes of cancer therapy and drug-drug interactions with antibiotics through changes in the intestinal microbiome.

Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models

Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.