Mass Spectrometry-based Proteomics and Peptidomics for Systems Biology and Biomarker Discovery

Preliminary investigation on the impact of salty and sugary former foods on pig liver and plasma profiles using OMICS approaches

Replacing cereals with food leftovers could reduce feed-food competition and keep nutrients and energy in the food chain. Former food products (FFPs) are industrial food leftovers no more intended for human but still suitable as alternative and sustainable feedstuffs for monogastric. In this study, omics approaches were applied to evaluate the impact of dietary FFPs on pig liver proteome and plasma peptidome. Thirty-six Swiss Large White male castrated pigs were randomly assigned to three dietary treatments [control (CTR), 30% CTR replaced with salty FFP (SA), 30% CTR replaced with sugary FFP (SU)] from the start of the growing phase (22.4 ± 1.7 kg) until slaughtering (110 ± 3 kg). The low number of differentially regulated proteins in each comparison matrix (SA/SU vs. CTR) and the lack of metabolic interaction indicated a marginal impact on hepatic lipid metabolism. The plasma peptidomics investigation showed low variability between the peptidome of the three dietary groups and identified three possible bioactive peptides in the SA group associated with anti-hypertension and vascular homeostasis regulation. To conclude, the limited modulation of liver proteome and plasma peptidome by the SA and SU diets strenghtened the idea of reusing FFPs as feed ingredients to make pig production more sustainable.

Functionalizing tandem mass tags for streamlining click-based quantitative chemoproteomics

Mapping the ligandability or potential druggability of all proteins in the human proteome is a central goal of mass spectrometry-based covalent chemoproteomics. Achieving this ambitious objective requires high throughput and high coverage sample preparation and liquid chromatography-tandem mass spectrometry analysis for hundreds to thousands of reactive compounds and chemical probes. Conducting chemoproteomic screens at this scale benefits from technical innovations that achieve increased sample throughput. Here we realize this vision by establishing the silane-based cleavable linkers for isotopically-labeled proteomics-tandem mass tag (sCIP-TMT) proteomic platform, which is distinguished by early sample pooling that increases sample preparation throughput. sCIP-TMT pairs a custom click-compatible sCIP capture reagent that is readily functionalized in high yield with commercially available TMT reagents. Synthesis and benchmarking of a 10-plex set of sCIP-TMT reveal a substantial decrease in sample preparation time together with high coverage and high accuracy quantification. By screening a focused set of four cysteine-reactive electrophiles, we demonstrate the utility of sCIP-TMT for chemoproteomic target hunting, identifying 789 total liganded cysteines. Distinguished by its compatibility with established enrichment and quantification protocols, we expect sCIP-TMT will readily translate to a wide range of covalent chemoproteomic applications.

Exploring novel protein biomarkers for early-stage diagnosis and prognosis of T-acute lymphoblastic leukemia (T-ALL)

BACKGROUND

Efficient classification of T-acute lymphoblastic leukemia (T-ALL) involves considering various factors, such as age, white blood cell count, and chromosomal alterations. However, studying protein markers are crucial to improving T-ALL patients' diagnosis and treatment. A study analyzing the expression of proteomes was conducted to identify promising early-stage biomarkers for T-ALL patients METHODS: Label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to analyze the blood proteins of both patients and healthy individuals to identify new biomarkers for T-ALL. The findings were validated by RT-PCR, ELISA and computational analysis RESULTS: The study identified 1467 proteins in the blood, of which nine were upregulated and 35 were downregulated by more than 2-fold. T-ALL patients showed a significant increase in specific disease-related proteins, such as eleven-nineteen lysine-rich leukemia protein, triggering receptor expressed on myeloid cells 1, cisplatin resistance-associated-overexpressed protein, X-ray radiation resistance-associated protein 1, tumor necrosis factor receptor superfamily member 10D, protein S100-A8, and copine-4, by more than 3-fold CONCLUSION: The findings of this study provide a valuable protein map of leukemic cells and identify potential biomarkers for leukemic aggressiveness. However, further studies using larger T-ALL patient samples must confirm these preliminary results.

Comprehensive workflow encompassing discovery, verification, and quantification of indicator peptide in snail mucin using LC-quadrupole Orbitrap high-resolution tandem mass spectrometry

Peptidomics analysis was conducted using high-resolution tandem mass spectrometry (MS2) to determine the peptide profile of snail-derived mucin extract (SM). The study was also aimed to identify an indicator peptide and validate a quantification method for this peptide. The peptide profiling and identification were conducted using discovery-based peptidomics analysis employing data-dependent acquisition, whereas the selected peptides were verified and quantified using parallel reaction monitoring acquisition. Among the 16 identified peptides, the selected octapeptide (TEAPLNPK) was quantified via precursor ion ionization (m/z 435.2400), followed by quantification of the corresponding quantifier ion fragment (m/z 639.3824) using MS2. The quantification method was optimized and validated in terms of specificity, linearity, accuracy, precision, and limit of detection/quantification. The validated method accurately quantified the TEAPLNPK content in the SM as 7.5 ± 0.2 μg/g. Our study not only identifies an indicator peptide from SM but also introduces a novel validation method, involving precursor ion ionization and quantification of specific fragments. Our findings may serve as a comprehensive workflow for the monitoring, selection, and quantification of indicator peptides from diverse food resources.

Systematic review and meta-analysis of mass spectrometry proteomics applied to ocular fluids to assess potential biomarkers of age-related macular degeneration

BACKGROUND

Age-related macular degeneration (AMD) is a significant cause of severe vision loss. The main purpose of this study was to identify mass spectrometry proteomics-based potential biomarkers of AMD that contribute to understanding the mechanisms of disease and aiding in early diagnosis.

METHODS

This study retrieved studies that aim to detect differences relate to proteomics in AMD patients and healthy control groups by mass spectrometry (MS) proteomics approaches. The search process was accord with PRISMA guidelines (PROSPERO database: CRD42023388093). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes Pathway Analysis (KEGG) were performed on differentially expressed proteins (DEPs) in the included articles using the DAVID database. DEPs were included in a meta-analysis when their effect size could be computed in at least two research studies. The effect size of measured proteins was transformed to the log2-fold change. Protein‒protein interaction (PPI) analysis was conducted on proteins that were statistically significant in the meta-analysis using the String online database.

RESULTS

Eleven studies fulfilled the inclusion criteria, and 161 DEPs were identified. The GO analysis showed that AMD is significantly related to proteolysis, extracellular exosome and protein binding. In KEGG, the most significant pathway was the complement and coagulation cascades. Meta-analysis results suggested that eight proteins were statistically significant, and according to PPI results, the most significant four proteins were serotransferrin (TF), apolipoprotein A1 (APOA1), complement C3 (C3) and lipocalin-1 (LCN1).

CONCLUSIONS

Four possible biomarkers, TF, APOA1, C3 and LCN1, were found to be significant in the pathogenesis of AMD and need to be further validated. Further studies should be performed to evaluate diagnostic and therapeutic value of these proteins.

Empowering peptidomics: utilizing computational tools and approaches

Bioinformatics plays a critical role in the advancement of peptidomics by providing powerful tools for data analysis, interpretation and integration. Peptidomics is concerned with the study of peptides, short chains of amino acids with diverse biological functions. This area includes peptide identification and characterization, database construction, de novo sequencing, functional annotation, omics data integration and systems biology. Artificial intelligence techniques, such as machine learning and natural language processing, aid in the interpretation of peptide sequence data and the generation of biological insights. By using bioinformatics approaches, peptidomics researchers can accelerate peptide discovery, understand their functions and gain insights into complex molecular interactions.

Proteomics in Childhood Acute Lymphoblastic Leukemia: Challenges and Opportunities

Acute lymphoblastic leukemia (ALL) is the most common cancer in children and one of the success stories in cancer therapeutics. Risk-directed therapy based on clinical, biologic and genetic features has played a significant role in this accomplishment. Despite the observed improvement in survival rates, leukemia remains one of the leading causes of cancer-related deaths. Implementation of next-generation genomic and transcriptomic sequencing tools has illustrated the genomic landscape of ALL. However, the underlying dynamic changes at protein level still remain a challenge. Proteomics is a cutting-edge technology aimed at deciphering the mechanisms, pathways, and the degree to which the proteome impacts leukemia subtypes. Advances in mass spectrometry enable high-throughput collection of global proteomic profiles, representing an opportunity to unveil new biological markers and druggable targets. The purpose of this narrative review article is to provide a comprehensive overview of studies that have utilized applications of proteomics in an attempt to gain insight into the pathogenesis and identification of biomarkers in childhood ALL.

Clinical Peptidomics: Advances in Instrumentation, Analyses, and Applications

Extensive effort has been devoted to the discovery, development, and validation of biomarkers for early disease diagnosis and prognosis as well as rapid evaluation of the response to therapeutic interventions. Genomic and transcriptomic profiling are well-established means to identify disease-associated biomarkers. However, analysis of disease-associated peptidomes can also identify novel peptide biomarkers or signatures that provide sensitive and specific diagnostic and prognostic information for specific malignant, chronic, and infectious diseases. Growing evidence also suggests that peptidomic changes in liquid biopsies may more effectively detect changes in disease pathophysiology than other molecular methods. Knowledge gained from peptide-based diagnostic, therapeutic, and imaging approaches has led to promising new theranostic applications that can increase their bioavailability in target tissues at reduced doses to decrease side effects and improve treatment responses. However, despite major advances, multiple factors can still affect the utility of peptidomic data. This review summarizes several remaining challenges that affect peptide biomarker discovery and their use as diagnostics, with a focus on technological advances that can improve the detection, identification, and monitoring of peptide biomarkers for personalized medicine.

Neurobiochemical, Peptidomic, and Bioinformatic Approaches to Characterize Tauopathy Peptidome Biomarker Candidates in Experimental Mouse Model of Traumatic Brain Injury

Traumatic brain injury (TBI) is a multidimensional damage, and currently, no FDA-approved medicine is available. Multiple pathways in the cell are triggered through a head injury (e.g., calpain and caspase activation), which truncate tau and generate variable fragment sizes (MW 400-45,000 K). In this study, we used an open-head TBI mouse model generated by controlled cortical impact (CCI) and collected ipsilateral (IC) and contralateral (CC) mice htau brain cortices at one (D1) three (D3), and seven (D7) days post-injury. We implemented immunological (antibody-based detection) and peptidomic approaches (nano-reversed-phase liquid chromatography/tandem mass spectrometry) to investigate proteolytic tau peptidome (low molecular weight (LMW) < 10 K)) and pathological phosphorylation sites (high-molecular-weight (HMW); > 10 K) derived from CCI-TBI animal models. Our immunoblotting analysis verified tau hyperphosphorylation, HMW, and HMW breakdown products (HMW-BDP) formation of tau (e.g., pSer₂₀₂, pThr₁₈₁, pThr₂₃₁, pSer₃₉₆, and pSer₄₀₄), following CCI-TBI. Peptidomic data revealed unique sequences of injury-dependent proteolytic peptides generated from human tau protein. Among the N-terminal tau peptides, EIPEGTTAEEAGIGDTPSLEDEAAGHVTQA (a.a. 96-125) and AQPHTEIPEGTTAEEAGIGDTPSLEDEAAGHVTQARM (a.a. 91-127). Examples of tau C-terminal peptides identified include NVSSTGSIDMVDSPQLATLADEVSASLAKQGL (a.a. 410-441) and QLATLADEVSASLAKQGL (a.a. 424-441). Our peptidomic bioinformatic tools showed the association of proteases, such as CAPN1, CAPN2, and CTSL; CASP1, MMP7, and MMP9; and ELANE, GZMA, and MEP1A, in CCI-TBI tau peptidome. In clinical trials for novel TBI treatments, it might be useful to monitor a subset of tau peptidome as targets for biomarker utility and use them for a "theranostic" approach.

Potential Urine Proteomic Biomarkers for Focal Segmental Glomerulosclerosis and Minimal Change Disease

Primary focal segmental glomerulosclerosis (FSGS), along with minimal change disease (MCD), are diseases with primary podocyte damage that are clinically manifested by the nephrotic syndrome. The pathogenesis of these podocytopathies is still unknown, and therefore, the search for biomarkers of these diseases is ongoing. Our aim was to determine of the proteomic profile of urine from patients with FSGS and MCD. Patients with a confirmed diagnosis of FSGS (n = 30) and MCD (n = 9) were recruited for the study. For a comprehensive assessment of the severity of FSGS a special index was introduced, which was calculated as follows: the first score was assigned depending on the level of eGFR, the second score—depending on the proteinuria level, the third score—resistance to steroid therapy. Patients with the sum of these scores of less than 3 were included in group 1, with 3 or more—in group 2. The urinary proteome was analyzed using liquid chromatography/mass spectrometry. The proteome profiles of patients with severe progressive FSGS from group 2, mild FSGS from group 1 and MCD were compared. Results of the label free analysis were validated using targeted LC-MS based on multiple reaction monitoring (MRM) with stable isotope labelled peptide standards (SIS) available for 47 of the 76 proteins identified as differentiating between at least one pair of groups. Quantitative MRM SIS validation measurements for these 47 proteins revealed 22 proteins with significant differences between at least one of the two group pairs and 14 proteins were validated for both comparisons. In addition, all of the 22 proteins validated by MRM SIS analysis showed the same direction of change as at the discovery stage with label-free LC-MS analysis, i.e., up or down regulation in MCD and FSGS1 against FSGS2. Patients from the FSGS group 2 showed a significantly different profile from both FSGS group 1 and MCD. Among the 47 significantly differentiating proteins, the most significant were apolipoprotein A-IV, hemopexin, vitronectin, gelsolin, components of the complement system (C4b, factors B and I), retinol- and vitamin D-binding proteins. Patients with mild form of FSGS and MCD showed lower levels of Cystatin C, gelsolin and complement factor I.

Recent advances in isobaric labeling and applications in quantitative proteomics

Mass spectrometry (MS) has emerged at the forefront of quantitative proteomic techniques. Liquid chromatography-mass spectrometry (LC-MS) can be used to determine abundances of proteins and peptides in complex biological samples. Several methods have been developed and adapted for accurate quantification based on chemical isotopic labeling. Among various chemical isotopic labeling techniques, isobaric tagging approaches rely on the analysis of peptides from MS2-based quantification rather than MS1-based quantification. In this review, we will provide an overview of several isobaric tags along with some recent developments including complementary ion tags, improvements in sensitive quantitation of analytes with lower abundance, strategies to increase multiplexing capabilities, and targeted analysis strategies. We will also discuss limitations of isobaric tags and approaches to alleviate these restrictions through bioinformatic tools and data acquisition methods. This review will highlight several applications of isobaric tags, including biomarker discovery and validation, thermal proteome profiling, cross-linking for structural investigations, single-cell analysis, top-down proteomics, along with applications to different molecules including neuropeptides, glycans, metabolites, and lipids, while providing considerations and evaluations to each application.

Systematic Review and Meta-Analysis of Mass Spectrometry Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Schizophrenia

Mass spectrometry (MS)-based techniques can be a powerful tool to identify neuropsychiatric disorder biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids of schizophrenia (SCZ) patients to identify disease biomarkers and relevant networks of biological pathways. Following PRISMA guidelines, a search was performed for studies that used MS proteomics approaches to identify proteomic differences between SCZ patients and healthy control groups (PROSPERO database: CRD42021274183). Nineteen articles fulfilled the inclusion criteria, allowing the identification of 217 differentially expressed proteins. Gene ontology analysis identified lipid metabolism, complement and coagulation cascades, and immune response as the main enriched biological pathways. Meta-analysis results suggest the upregulation of FCN3 and downregulation of APO1, APOA2, APOC1, and APOC3 in SCZ patients. Despite the proven ability of MS proteomics to characterize SCZ, several confounding factors contribute to the heterogeneity of the findings. In the future, we encourage the scientific community to perform studies with more extensive sampling and validation cohorts, integrating omics with bioinformatics tools to provide additional comprehension of differentially expressed proteins. The produced information could harbor potential proteomic biomarkers of SCZ, contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

Peptide-based therapeutics are here to stay and will prosper in the future. A key step in identifying novel peptide-drugs is the determination of their bioactivities. Recent advances in peptidomics screening approaches hold promise as a strategy for identifying novel drug targets. However, these screenings typically generate an immense number of peptides and tools for ranking these peptides prior to planning functional studies are warranted. Whereas a couple of tools in the literature predict multiple classes, these are constructed using multiple binary classifiers. We here aimed to use an innovative deep learning approach to generate an improved peptide bioactivity classifier with capacity of distinguishing between multiple classes. We present MultiPep: a deep learning multi-label classifier that assigns peptides to zero or more of 20 bioactivity classes. We train and test MultiPep on data from several publically available databases. The same data are used for a hierarchical clustering, whose dendrogram shapes the architecture of MultiPep. We test a new loss function that combines a customized version of Matthews correlation coefficient with binary cross entropy (BCE), and show that this is better than using class-weighted BCE as loss function. Further, we show that MultiPep surpasses state-of-the-art peptide bioactivity classifiers and that it predicts known and novel bioactivities of FDA-approved therapeutic peptides. In conclusion, we present innovative machine learning techniques used to produce a peptide prediction tool to aid peptide-based therapy development and hypothesis generation.

Urinary Protein and Peptide Markers in Chronic Kidney Disease

Chronic kidney disease (CKD) is a non-specific type of kidney disease that causes a gradual decline in kidney function (from months to years). CKD is a significant risk factor for death, cardiovascular disease, and end-stage renal disease. CKDs of different origins may have the same clinical and laboratory manifestations but different progression rates, which requires early diagnosis to determine. This review focuses on protein/peptide biomarkers of the leading causes of CKD: diabetic nephropathy, IgA nephropathy, lupus nephritis, focal segmental glomerulosclerosis, and membranous nephropathy. Mass spectrometry (MS) approaches provided the most information about urinary peptide and protein contents in different nephropathies. New analytical approaches allow urinary proteomic-peptide profiles to be used as early non-invasive diagnostic tools for specific morphological forms of kidney disease and may become a safe alternative to renal biopsy. MS studies of the key pathogenetic mechanisms of renal disease progression may also contribute to developing new approaches for targeted therapy.

A comparative proteomic study of plasma in Colombian childhood acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Owing to the incorporation of risk-adapted therapy and the arrival of new directed agents, the cure rate and survival of patients with ALL have improved dramatically, get near to 90%. In Latin American countries, the mortality rates of ALL are high, for example in Colombia, during the last decade, ALL has been the most prevalent cancer among children between 0–14 years of age. In the face of this public health problem and coupled with the fact that the knowledge of the proteome of the child population is little, our investigation proposes the study of the plasma proteome of Colombian children diagnosed with B-cell ALL (B-ALL) to determine potential disease markers that could reflect processes altered by the presence of the disease or in response to it. A proteomic study by LC-MS/MS and quantification by label-free methods were performed in search of proteins differentially expressed between healthy children and those diagnosed with B-ALL. We quantified a total of 472 proteins in depleted blood plasma, and 25 of these proteins were differentially expressed (fold change >2, Bonferroni-adjusted P-values <0.05). Plasma Aggrecan core protein, alpha-2-HS-glycoprotein, coagulation factor XIII A chain and gelsolin protein were examined by ELISA assay and compared to shotgun proteomics results. Our data provide new information on the plasma proteome of Colombian children. Additionally, these proteins may also have certain potential as illness markers or as therapeutic targets in subsequent investigations.

A Complete Mass Spectrometry (MS)-Based Peptidomic Description of Gluten Peptides Generated During In Vitro Gastrointestinal Digestion of Durum Wheat: Implication for Celiac Disease

Resistance of gluten to gastrointestinal digestion is involved in immune-mediated adverse reactions to wheat, since several peptides produced by the incomplete digestion are able to trigger, in predisposed individuals, the immune response responsible, for instance, of celiac disease (CD) and other adverse reactions. Even if several peptides have been identified, an exhaustive description of the peptidome generated by wheat digestion is lacking. To this end, in the present work, durum wheat proteins were fractionated, digested, and then subjected to various proteomic techniques, including single stage and multiple stage mass spectrometry (MS) (SDS-PAGE, UPLC/ESI-MS, UPLC/ESI-MS/MS, and LTQ-Orbitrap). Based on SDS-PAGE, although proteins were severely degraded after in vitro gastrointestinal digestion, some differences were observed among protein profile of the different digests. Through untargeted UPLC techniques, 227 peptide sequences were identified, with only few sequences shared by the different digests. In particular, 9 gluten peptides involved in CD were identified. Based on target proteomic, the quantification of these peptides revealed significant (p ≤ 0.05) differences among the different extracts. Taken together, all the proteomic tools confirmed that gluten digestion is closely related to the matrix regardless of wheat genotype.

The challenges of peptidomics in complementing proteomics in a clinical context

Naturally occurring peptides, including growth factors, hormones, and neurotransmitters, represent an important class of biomolecules and have crucial roles in human physiology. The study of these peptides in clinical samples is therefore as relevant as ever. Compared to more routine proteomics applications in clinical research, peptidomics research questions are more challenging and have special requirements with regard to sample handling, experimental design, and bioinformatics. In this review, we describe the issues that confront peptidomics in a clinical context. After these hurdles are (partially) overcome, peptidomics will be ready for a successful translation into medical practice.

Probing the endogenous peptidomes of cancer for biomarkers: A new endeavor

The life span of cancer patients can be prolonged with appropriate therapies if detected early. Mass screening for early detection of cancer, however, requires sensitive and specific biomarkers obtainable from body fluids such as blood or urine. To date, most biomarker discovery programs focus on the proteome rather than the endogenous peptidome. It has been long-established that tumor cells and stromal cells produce tumor resident proteases (TRPs) to remodel the surrounding tumor microenvironment in support of tumor progression. In fact, proteolytic products of TRPs have been shown to correlate with malignant behavior. Being of low molecular weight, these unique peptides can pass through the endothelial barrier of the vasculature into the bloodstream. As such, the cancer peptidome has increasingly become a focus for biomarker discovery. In this review, we discuss on the various aspects of the peptidome in cancer biomarker research.

Study of the plasma proteome of Atlantic cod (Gadus morhua): Effect of exposure to two PAHs and their corresponding diols

Occurrence of polycyclic aromatic hydrocarbon (PAH) contamination in the marine environment represents a risk to marine life and humans. In this study, plasma samples from Atlantic cod (Gadus morhua) were analysed by shotgun mass spectrometry to investigate the plasma proteome in response to exposure to single PAHs (naphthalene or chrysene) and their corresponding metabolites (dihydrodiols). In total, 369 proteins were identified and ranked according to their relative abundance. The levels of 12 proteins were found significantly altered in PAH exposed fish and are proposed as new biomarker candidates. Eleven proteins were upregulated, primarily immunoglobulin components, and one protein was downregulated (antifreeze protein type IV.) The uniformity of the upregulated proteins suggests a triggered immune response in the exposed fish. Overall, the results provide valuable knowledge for future studies of the Atlantic cod plasma proteome and generate grounds for establishing new plasma protein biomarkers for environmental monitoring of PAH related exposure.

Technological advances and proteomic applications in drug discovery and target deconvolution: identification of the pleiotropic effects of statins

Proteomic-based techniques provide a powerful tool for identifying the full spectrum of protein targets of a drug, elucidating its mechanism(s) of action, and identifying biomarkers of its efficacy and safety. Herein, we outline the technological advancements in the field, and illustrate the contribution of proteomics to the definition of the pharmacological profile of statins, which represent the cornerstone of the prevention and treatment of cardiovascular diseases (CVDs). Statins act by inhibiting 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase, thus reducing cholesterol biosynthesis and consequently enhancing the clearance of low-density lipoproteins from the blood; however, HMG-CoA reductase inhibition can result in a multitude of additional effects beyond lipid lowering, known as 'pleiotropic effects'. The case of statins highlights the unique contribution of proteomics to the target profiling of a drug molecule.

Proteomic profiling of mitochondria: what does it tell us about the ageing brain?

Mitochondrial dysfunction is evident in numerous neurodegenerative and age-related disorders. It has also been linked to cellular ageing, however our current understanding of the mitochondrial changes that occur are unclear. Functional studies have made some progress reporting reduced respiration, dynamic structural modifications and loss of membrane potential, though there are conflicts within these findings. Proteomic analyses, together with functional studies, are required in order to profile the mitochondrial changes that occur with age and can contribute to unravelling the complexity of the ageing phenotype. The emergence of improved protein separation techniques, combined with mass spectrometry analyses has allowed the identification of age and cell-type specific mitochondrial changes in energy metabolism, antioxidants, fusion and fission machinery, chaperones, membrane proteins and biosynthesis pathways. Here, we identify and review recent data from the analyses of mitochondria from rodent brains. It is expected that knowledge gained from understanding age-related mitochondrial changes of the brain should lead to improved biomarkers of normal ageing and also age-related disease progression.

Sensing parasites: Proteomic and advanced bio-detection alternatives

Parasitic diseases have a great impact in human and animal health. The gold standard for the diagnosis of the majority of parasitic infections is still conventional microscopy, which presents important limitations in terms of sensitivity and specificity and commonly requires highly trained technicians. More accurate molecular-based diagnostic tools are needed for the implementation of early detection, effective treatments and massive screenings with high-throughput capacities. In this respect, sensitive and affordable devices could greatly impact on sustainable control programmes which exist against parasitic diseases, especially in low income settings. Proteomics and nanotechnology approaches are valuable tools for sensing pathogens and host alteration signatures within microfluidic detection platforms. These new devices might provide novel solutions to fight parasitic diseases. Newly described specific parasite derived products with immune-modulatory properties have been postulated as the best candidates for the early and accurate detection of parasitic infections as well as for the blockage of parasite development. This review provides the most recent methodological and technological advances with great potential for bio-sensing parasites in their hosts, showing the newest opportunities offered by modern "-omics" and platforms for parasite detection and control.

Stable isotope labelling methods in mass spectrometry-based quantitative proteomics

Mass-spectrometry based proteomics has evolved as a promising technology over the last decade and is undergoing a dramatic development in a number of different areas, such as; mass spectrometric instrumentation, peptide identification algorithms and bioinformatic computational data analysis. The improved methodology allows quantitative measurement of relative or absolute protein amounts, which is essential for gaining insights into their functions and dynamics in biological systems. Several different strategies involving stable isotopes label (ICAT, ICPL, IDBEST, iTRAQ, TMT, IPTL, SILAC), label-free statistical assessment approaches (MRM, SWATH) and absolute quantification methods (AQUA) are possible, each having specific strengths and weaknesses. Inductively coupled plasma mass spectrometry (ICP-MS), which is still widely recognised as elemental detector, has recently emerged as a complementary technique to the previous methods. The new application area for ICP-MS is targeting the fast growing field of proteomics related research, allowing absolute protein quantification using suitable elemental based tags. This document describes the different stable isotope labelling methods which incorporate metabolic labelling in live cells, ICP-MS based detection and post-harvest chemical label tagging for protein quantification, in addition to summarising their pros and cons.

Recent advances and challenges in plant phosphoproteomics

Plants are sessile organisms that need to respond to environmental changes quickly and efficiently. They can accomplish this by triggering specialized signaling pathways often mediated by protein phosphorylation and dephosphorylation. Phosphorylation is a fast response that can switch on or off a myriad of biological pathways and processes. Proteomics and MS are the main tools employed in the study of protein phosphorylation. Advances in the technologies allow simultaneous identification and quantification of thousands of phosphopeptides and proteins that are essential to understanding the sophisticated biological systems and regulations. In this review, we summarize the advances in phosphopeptide enrichment and quantitation, MS for phosphorylation site mapping and new data acquisition methods, databases and informatics, interpretation of biological insights and crosstalk with other PTMs, as well as future directions and challenges in the field of phosphoproteomics.

Mechanistic peptidomics: factors that dictate specificity in the formation of endogenous peptides in human milk

An extensive mass spectrometry analysis of the human milk peptidome has revealed almost 700 endogenous peptides from 30 different proteins. Two in-house computational tools were created and used to visualize and interpret the data through both alignment of the peptide quasi-molecular ion intensities and estimation of the differential enzyme participation. These results reveal that the endogenous proteolytic activity in the mammary gland is remarkably specific and well conserved. Certain proteins-not necessarily the most abundant ones-are digested by the proteases present in milk, yielding endogenous peptides from selected regions. Our results strongly suggest that factors such as the presence of specific proteases, the position and concentration of cleavage sites, and, more important, the intrinsic disorder of segments of the protein drive this proteolytic specificity in the mammary gland. As a consequence of this selective hydrolysis, proteins that typically need to be cleaved at specific positions in order to exert their activity are properly digested, and bioactive peptides encoded in certain protein sequences are released. Proteins that must remain intact in order to maintain their activity in the mammary gland or in the neonatal gastrointestinal tract are unaffected by the hydrolytic environment present in milk. These results provide insight into the intrinsic structural mechanisms that facilitate the selectivity of the endogenous milk protease activity and might be useful to those studying the peptidomes of other biofluids.