Amount Of Peptide Research Articles

Influence of growth medium and yeast species on the formation of γ-glutamyl peptides

γ-Glutamyl peptides are known to impart a kokumi sensation and increase the palatability of food. To compare the γ-glutamyl peptides produced by different yeasts, Saccharomyces cerevisiae and Debaryomyces hansenii were cultured in a conventional medium (YPG) and a medium composed of porcine hemoglobin hydrolysates (YHG) for six days. Quantification of γ-glutamyl peptides as well as analysis of yeast growth and γ-glutamyltransferase (GGT) activity were carried out. Results showed that both strains could form a range of γ-glutamyl dipeptides and glutathione in either medium. The total concentration of dipeptides and the concentration of glutathione in each medium showed an upward trend over fermentation time. Compared with yeast species, medium composition had a greater impact on the formation of γ-glutamyl dipeptides. During most of the fermentation period, D. hansenii produced more γ-glutamyl dipeptides (reaching a maximum of 2.395 μM on day 6 in YHG), while S. cerevisiae generated more glutathione (reaching a maximum of 13.465 μM on day 5 in YHG). In addition, D. hansenii showed higher GGT activity than S. cerevisiae in the latter half of fermentation. This study illustrates the influence of growth medium and the crucial choice of yeast species for obtaining target γ-glutamyl peptides for kokumi sensation.

Extraction and quantification of antimicrobial peptides from medicinal plants through TrisNaCl and PBS buffer

Nature has provided plants with their own specific defence system that protect the plant from several traumatic conditions. These include environmental conditions like drought, harsh climate changes, wounding, pathogen attack and other biological as well as a biological stresses. In order to deal with all these harmful occurrences, plants synthesize a wide range of defence factors that include both primary as well as secondary metabolites. Out of these the most popular are the defence proteins which are known as antimicrobial peptides (AMP). These AMPs are actually the pathogenesis-related (PR) defence proteins.  These proteins are activated under the control of defence system of plant whenever triggered by the alarming situation. In the current study Crude protein extraction of four medicinally important plants named as Cassia fistula, Albizia lebbeck, Saccharum officinarum & Cymbopogon citratus was performen. Extraction was done in TrisNaCl and PBS buffer. Quantification of the protein content in the extract was done by Bradford assay. Concentration of protein from TrisNaCl buffer extracts was high as compared to the extracts from PBS buffer. As these proteins play their protective role in defence of the plants against pathogen attack so these extracts can better be used to check the antimicrobial activity of these plants in future to treat several infectious diseases in humans.

An analytical quality by design approach towards a simple and novel HPLC-UV method for quantification of the antifibrotic peptide N-acetyl-seryl-aspartyl-lysyl-proline

N-acetyl-seryl-aspartyl-lysyl proline (Ac-SDKP) is a tetrapeptide possessing anti-fibrotic, angiogenic, anti-inflammatory, anti-apoptotic, and immunomodulatory properties. Currently, the main method to quantify the peptide is liquid chromatography-tandem mass spectrometry (LC-MS/MS) and enzyme-linked immunosorbent assay (ELISA), both of which are labour intensive and require expensive equipment and consumables. Furthermore, these techniques are generally utilised to detect very low or trace concentrations, such as in biological samples. The use of high concentrations of analyte might overload the extraction column or the separation column in LC-MS/MS or the ELISA plates, so the response could be a non-linear relationship at high analyte concentrations. Thus, they are not ideal for formulation development where detection of dose-equivalent concentrations is typically required. Therefore, a cost-effective, simple, and accurate quantification method for the peptide at a higher concentration needs to be developed. In this study, a simple and novel HPLC-UV method is proposed and validated using an Analytical Quality by Design (AQbD) approach. The method is first screened and optimised using chromatographic responses including capacity factor, resolution, tailing factor, and theoretical plate counts, fulfilling the International Council for Harmonisation (ICH) Q2 (R1) guidelines. The resultant optimised chromatography conditions utilised 10 mM phosphate buffer at pH 2.5 and acetonitrile as mobile phases, starting at 3% (v/v) acetonitrile and 97% (v/v) buffer and increasing to 9.7% (v/v) acetonitrile and 90.3% (v/v) buffer over 15 min at a flow rate of 1 mL/min at the column temperature of 25 °C. The injection volume is set at 10 μL and the VWD detector wavelength is 220 nm. The method established is suitable for detecting the peptide at a relatively high concentration, with a quantifiable range from 7.8 μg/mL to 2.0 mg/mL. In addition, the use of a relatively simple HPLC-UV approach could significantly reduce costs and allow easier access to quantify the peptide concentration. A limitation of this method is lower sensitivity compared with using LC-MS/MS and ELISA methods but running costs are lower and the methodology is simpler. The method is capable to quantify the peptide in various tested matrix solutions, with successful quantitation of the peptide in samples obtained from in vitro drug release study in PBS and from a chitosan-TPP nanogels formulation. Therefore, the method developed here offers a complementary approach to the existing quantification methods, quantifying this peptide at increased concentrations in simple to intermediately complex matrix solutions, such as HBSS, DMEM and FluoroBrite cell culture media.

Abstract 5645: BAL0891: A novel dual TTK/PLK1 mitotic checkpoint inhibitor (MCI) that drives aberrant tumor cell division resulting in potent anti-cancer activity

Abstract Background: BAL0891 is a dual inhibitor of threonine tyrosine kinase (TTK) and polo-like kinase 1 (PLK1). These kinases collaborate in activating the mitotic spindle assembly checkpoint (SAC) at the kinetochore (KT) to regulate chromosome alignment and segregation prior to mitotic exit. In vitro, BAL0891 has a combined prolonged effect on TTK and a transient effect on PLK1, leading to rapid disruption of the SAC that potentiates aberrant mitotic progression of tumor cells. In this work, efficacy of BAL0891 was investigated in mouse models of human triple negative breast cancer (TNBC) including evaluation of dose-dependency, drug exposure, target occupancy and a screen of activity across a panel of PDX models. Methods: The MDA-MB-231 cell line was grown sc in nude mice and treated with BAL0891, administered IV weekly (QW) or twice-weekly (2QW). Thirteen sc TNBC PDX models were screened for BAL0891 response using 2QW administration. Efficacy was quantified as deltaT/C (treated/control tumors). Plasma and tumor were analyzed for drug levels or TTK target occupancy by LC-MS/MS. The latter used a biotinylated TTK-specific probe and streptavidin-mediated isolation of unoccupied TTK, trypsin digestion and quantification of TTK-representative peptides. Results: BAL0891 efficacy was tested in the TNBC xenograft model MDA-MB-231 with QW or 2QW IV dosing schedules. All treatments were well tolerated, with no drug-related animal deaths. With MTD dosing, tumor regressions were observed, while different MTD fractions for both QW and 2QW schedules showed dose-dependent anti-tumor activity. The weekly MTD group was followed for an additional 20 days after treatment cessation on day 100. Strikingly, 3 of 8 tumors continued to shrink resulting in 2 (25%) pathologically confirmed cures. Consistent with the potent efficacy of intermittent MTD dosing, and prolonged tumor drug exposure, tumor TTK was fully drug-occupied for ≥ 6 days after the last administration; target occupancy was also dose-and drug exposure-dependent. To further evaluate BAL0891 anti-cancer activity in TNBC, a screen in 13 TNBC PDX models was conducted. Seven models exhibited deltaT/C < 50%, with regressions observed in 3. Of these, 2 models showed persistent regressions ≥ 70% vs. baseline. Interestingly, evaluation of TTK target occupancy in selected models showed high target occupancy independent of tumor response, indicating target dependency rather than drug availability is important for anti-cancer activity. Conclusion: BAL0891 is a novel dual TTK/PLK1 mitotic checkpoint inhibitor with potent anti-cancer activity in TNBC models. Intermittent IV administration is well tolerated and associated with prolonged tumor drug exposure, prolonged TTK inhibition and notable anti-tumor efficacy. These data support further investigation of BAL0891 for the treatment of cancer patients (incl. TNBC). Citation Format: Heidi A. Lane, Felix Bachmann, Elisa Zanini, Paul McSheehy, Karine Litherland, Nicole Forster-Gross, Luc Bury, Diep Vu-Pham, Jos de Man, Wilhelmina E. van Riel, Guido JR Zaman, Rogier C. Buijsman, Laurenz Kellenberger. BAL0891: A novel dual TTK/PLK1 mitotic checkpoint inhibitor (MCI) that drives aberrant tumor cell division resulting in potent anti-cancer activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5645.

Radiopharmaceutical Quality Control Considerations for Accelerator-Produced Actinium Therapies.

Background: Alpha-particle-emitting radiotherapies are of great interest for the treatment of disseminated cancer. Actinium-225 (225Ac) produces four α-particles through its decay and is among the most attractive radionuclides for use in targeted radiotherapy applications. However, supply issues for this isotope have limited availability and increased cost for research and translation. Efforts have focused on accelerator-based methods that produce 225Ac in addition to long-lived 227Ac. Objective: The authors investigated the impact of 225Ac/227Ac material in the radiolabeling and radiopharmaceutical quality control evaluation of a DOTA chelate-conjugated peptide under good manufacturing practices. The authors use an automated module under identical conditions with either generator or accelerator-produced actinium radiolabeling. Methods: The authors have performed characterization of the radiolabeled products, including thin-layer chromatography, high-pressure liquid chromatography, gamma counting, and high-energy resolution gamma spectroscopy. Results: Peptide was radiolabeled and assessed at >95% radiochemical purity with high yields for generator produced 225Ac. The radiolabeling results produced material with subtle but detectable differences when using 225Ac/227Ac. Gamma spectroscopy was able to identify peptide initially labeled with 227Th, and at 100 d for quantification of 225Ac-bearing peptide. Conclusion: Peptides produced using 225Ac/227Ac material may be suitable for translation, but raise new issues that include processing times, logistics, and contaminant detection.

Follicular Fluid: A Powerful Tool for the Understanding and Diagnosis of Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) represents one of the leading causes of anovulatory infertility and affects 5% to 20% of women worldwide. Until today, both the subsequent etiology and pathophysiology of PCOS remain unclear, and patients with PCOS that undergo assisted reproductive techniques (ART) might present a poor to exaggerated response, low oocyte quality, ovarian hyperstimulation syndrome, as well as changes in the follicular fluid metabolites pattern. These abnormalities originate a decrease of Metaphase II (MII) oocytes and decreased rates for fertilization, cleavage, implantation, blastocyst conversion, poor egg to follicle ratio, and increased miscarriages. Focus on obtaining high-quality embryos has been taken into more consideration over the years. Nowadays, the use of metabolomic analysis in the quantification of proteins and peptides in biological matrices might predict, with more accuracy, the success in assisted reproductive technology. In this article, we review the use of human follicular fluid as the matrix in metabolomic analysis for diagnostic and ART predictor of success for PCOS patients.

Predicting fragment intensities and retention time of iTRAQ- and TMTPro-labeled peptides with Prosit-TMT.

Isobaric labeling increases the throughput of proteomics by enabling the parallel identification and quantification of peptides and proteins. Over the past decades, a variety of isobaric tags have been developed allowing the multiplexed analysis of up to 18 samples. However, experiments utilizing such tags often exhibit reduced identification rates and thus show decreased analytical depth. Re-scoring has been shown to rescue otherwise missed identifications but was not yet systematically applied on isobarically labeled data. Because iTRAQ 4/8-plex and the recently released TMTpro 16/18-plex share similar characteristics with TMT 6/10/11-plex, we hypothesized that Prosit-TMT, trained exclusively on 6/10/11-plex labeled peptides, may be applicable to these isobaric labeling strategies as well. To investigate this, we re-analyzed nine publicly available datasets covering iTRAQ and TMTpro labeling for samples with human and mouse origin. We highlight that Prosit-TMT shows remarkably good performance when comparing experimentally acquired and predicted fragmentation spectra (R of 0.84 - 0.9) and retention times (ΔRT95% of 3% - 10% gradient time) of peptides. Furthermore, re-scoring substantially increases the number of confidently identified spectra, peptides, and proteins.

Response boosting-based approach for absolute quantification of gelatin peptides using LC-MS/MS

Response-boosting of MS signal was observed in gelatin samples due to abundant Glycine residues produced by collagen enzymolysis. In this work, a new strategy utilizing response-boosting to enhance detection sensitivity was developed for absolute quantification of Asini Corii Colla, a kind of gelatin commonly used as food therapy products in Asia, by high performance liquid chromatography coupled to tandem mass spectrometry. Peptidomics analysis was used to evaluate the similarity between eight different protein matrices, and deer-hide gelatin was selected as the appropriate simulated matrix. Isotope-labelled internal standard was used to compensate the matrix effect and construct matrix-matched calibration curves. The established method showed reliability in absolute quantification of three species-specific gelatin peptides with good linearity (r2 > 0.997), precision (RSD < 8.5%), repeatability (RSD < 8.9%), accuracy (recovery 89.4%∼106.5%) and sensitivity (LOD 0.02 ∼ 0.98 ng/mL). Thus, the present response-boosting based protocol provides a promising application in quality control of food rich in gelatins.

SIMSI-Transfer: Software-Assisted Reduction of Missing Values in Phosphoproteomic and Proteomic Isobaric Labeling Data Using Tandem Mass Spectrum Clustering

Isobaric stable isotope labeling techniques such as tandem mass tags (TMTs) have become popular in proteomics because they enable the relative quantification of proteins with high precision from up to 18 samples in a single experiment. While missing values in peptide quantification are rare in a single TMT experiment, they rapidly increase when combining multiple TMT experiments. As the field moves toward analyzing ever higher numbers of samples, tools that reduce missing values also become more important for analyzing TMT datasets. To this end, we developed SIMSI-Transfer (Similarity-based Isobaric Mass Spectra 2 [MS2] Identification Transfer), a software tool that extends our previously developed software MaRaCluster (© Matthew The) by clustering similar tandem MS2 from multiple TMT experiments. SIMSI-Transfer is based on the assumption that similarity-clustered MS2 spectra represent the same peptide. Therefore, peptide identifications made by database searching in one TMT batch can be transferred to another TMT batch in which the same peptide was fragmented but not identified. To assess the validity of this approach, we tested SIMSI-Transfer on masked search engine identification results and recovered >80% of the masked identifications while controlling errors in the transfer procedure to below 1% false discovery rate. Applying SIMSI-Transfer to six published full proteome and phosphoproteome datasets from the Clinical Proteomic Tumor Analysis Consortium led to an increase of 26 to 45% of identified MS2 spectra with TMT quantifications. This significantly decreased the number of missing values across batches and, in turn, increased the number of peptides and proteins identified in all TMT batches by 43 to 56% and 13 to 16%, respectively.

Global Quantification of Glutathione S-Transferases in Human Serum Using LC-MS/MS Coupled with Affinity Enrichment.

The members of the glutathione S-transferase (GST) superfamily often exhibit functional overlap and can compensate for each other. Their concentrations in serum are considered as disease biomarkers. A global and quantitative evaluation of serum GSTs is therefore urgent, but there is a lack of efficient approaches due to technological limitations. GSH magnetic beads were examined for their affinity to enrich GSTs in serum, and the enriched GSTs were quantitatively targeted using a Q Exactive HF-X mass spectrometer in parallel reaction monitoring (PRM) mode. To optimize the quantification of GST peptides, sample types, trypsin digestion, and serum loading were carefully assessed; a biosynthetic method was employed to generate isotope-labeled GST peptides, and instrumental parameters were systematically optimized. A total of 134 clinical sera were collected for GST quantification from healthy donors and patients with four liver diseases. Using the new approach, GSTs in healthy sera were profiled: 14 GST peptides were quantified, and the abundance of five GST families was ranked GSTM > GSTP > GSTA > MGST1 > GSTT1, ranging from 0.1 to 4 pmol/L. Furthermore, combining the abundance of multiple GST peptides could effectively distinguish different types of liver diseases. Quantification of serum GSTs through targeted proteomics, therefore, has apparent clinical potential for disease diagnosis.

Assessing normalization methods in mass spectrometry-based proteome profiling of clinical samples

BackgroundLarge-scale proteomic studies have to deal with unwanted variability, especially when samples originate from different centers and multiple analytical batches are needed. Such variability is typically added throughout all the steps of a clinical research study, from human biological sample collection and storage, sample preparation, spectral data acquisition, to peptide and protein quantification. In order to remove such diverse and unwanted variability, normalization of the protein data is performed. There have been already several published reviews comparing normalization methods in the -omics field, but reports focusing on proteomic data generated with mass spectrometry (MS) are much fewer. Additionally, most of these reports have only dealt with small datasets. ResultsAs a case study, here we focused on the normalization of a large MS-based proteomic dataset obtained from an overweight and obese pan-European cohort, where different normalization methods were evaluated, namely: center standardize, quantile protein, quantile sample, global standardization, ComBat, median centering, mean centering, single standard and removal of unwanted variation (RUV); some of these are generic normalization methods while others have been specifically created to deal with genomic or metabolomic data. We checked how relationships between proteins and clinical variables (e.g., gender, levels of triglycerides or cholesterol) were improved after normalizing the data with the different methods. ConclusionsSome normalization methods were better adapted for this particular large-scale shotgun proteomic dataset of human plasma samples labeled with isobaric tags and analyzed with liquid chromatography-tandem MS. In particular, quantile sample normalization, RUV, mean and median centering showed very good performances, while quantile protein normalization provided worse results than those obtained with unnormalized data.

MSSort-DIAXMBD: A deep learning classification tool of the peptide precursors quantified by OpenSWATH

OpenSWATH is an analysis toolkit commonly used for data independent acquisition (DIA). Although the output of OpenSWATH is controlled at 1% false discovery rate (FDR), the output report still contains many peptide precursors with low similarity fragments. At the last step of OpenSWATH for peptide quantification, researchers usually need to manually check the similarity of the extracted ion chromatograms (XICs) of fragments to distinguish the high confidence and the low confidence peptide precursors. Here we developed an algorithm with a Graphic User Interface named MSSort-DIAXMBD, which combines the deep convolutional neural network (CNN) and the double-threshold segmentation process, to automatically recognize the high confidence precursors and low confidence precursors. To train the model of MSSort-DIAXMBD, we built a database contained about 50,000 manually classified peptide precursors acquired from different instrument platforms and different species. With the double-threshold segmentation strategy, MSSort-DIAXMBD can reduce the number of the low confidence peptides required for manual inspections to less than 10% and be used as the last step of OpenSWATH to visualize and classify the MS/MS data of peptide precursors. SignificanceAlthough the output of OpenSWATH is controlled at 1% false discovery rate (FDR), the output report still contains many peptide precursors with low similarity fragments. At the last step of OpenSWATH for peptide quantification, researchers usually need to manually check the similarity of fragment XICs to distinguish the high confidence and the low confidence peptide precursors. However, manual inspection is inefficient. For instance, it takes about 50 h to sort even a small dataset of 1000 MS/MS spectra manually. In this paper we developed a software named MSSort-DIAXMBD to automatically recognize the high confidence precursors. We manually classify 50,000 peptide precursors as training set to train a convolutional neural network. After training finished, MSSort-DIAXMBD takes only a few minutes to automatically classify 20,000 peptide precursors, leaving a small portion of fuzzy ones for manual inspection. On the benchmarked dataset, MSSort-DIAXMBD can significantly improve the efficiency and accuracy of recognition of high confidence peptide precursors.

Electrochemical Sensing of the Peptide Drug Exendin-4 Using a Versatile Nucleic Acid Nanostructure.

Although endogenous peptides and peptide-based therapeutics are both highly relevant to human health, there are few approaches for sensitive biosensing of this class of molecules with minimized workflow. In this work, we have further expanded on the generalizability of our recently developed DNA nanostructure architecture by applying it to electrochemical (EC) peptide quantification. While DNA-small molecule conjugates were used in a prior work to make sensors for small molecule and protein analytes, here DNA-peptide conjugates were incorporated into the nanostructure at the electrode surfaces, and antibody displacement permitted rapid peptide sensing. Interestingly, multivalent DNA-peptide conjugates were found to be detrimental to the assay readout, yet these effects could be minimized by solution-phase bioconjugation. The final biosensor was validated for quantifying exendin-4 (4.2 kDa)─a human glucagon-like peptide-1 receptor agonist important in diabetes therapy─for the first time using EC methods with minimal workflow. The sensor was functional in 98% human serum, and the low nanomolar assay range lies between the injected dose concentration and the therapeutic range, boding well for future applications in therapeutic drug monitoring.

A method to identify and quantify the complete peptide composition in protein hydrolysates

Automated approaches from proteomics are used to characterise peptides for food applications and in protein digests. Peptide annotations and confidence in these annotations are then based on the fragment spectra. Low reproducibility in repeat analyses has been reported even for annotations with high confidence. When analysing protein hydrolysates (in food) it is important to determine criteria that yield highly reproducible annotations. This study provides a structured approach to determine these criteria. Tryptic hydrolysates of α-lactalbumin, β-lactoglobulin and β-casein were analysed manually and automatically, using an UPLC-PDA-MS method for untargeted identification and absolute label-free quantification of peptides. A lock mass with two components was introduced resulting in an average mass error of 1 ppm. Processing filters were set to ensure reliable annotations based on MS/MS fragmentation, while maintaining maximum amount of information. Peptides in the individual hydrolysates with an MS intensity above the limit of annotation represented 99% of total MS intensity and were 100% consistently annotated between four replicates. Amino acid and peptide sequence coverages for the individual protein hydrolysates were 99–100% and 89–95%, respectively. Mixing the hydrolysates resulted in a loss of 11% of the peptide annotations above the LOA and lower reproducibility (97%) for the remaining annotations, as well as more co-eluting peptides. Calculated concentrations of co-eluting peptides in mixed hydrolysates varied 37 ± 21% from the value for single hydrolysates. The proposed approach allows complete description of peptide composition with highly repeatable annotations and quantification of peptides even in mixed hydrolysates.

Abstract P4-11-01: Development of a clinico-bio-behavioral model for cancer-related fatigue (CRF) incorporating inflammatory biomarkers and proteomic data

Abstract Background: We previously developed a clinico-behavioral model of CRF and reported an increased risk of severe CRF among survivors of breast cancer (BC) receiving adjuvant hormonal therapy (HT) (Di Meglio A, ASCO 2021). We now aim to comprehensively explore the contribution of relevant serum proteins in explaining CRF. We adopted a multimodal approach, both (1) hypothesis-driven, based on the rationale that deregulation of systemic inflammatory processes and mediators of immunologic or neuroendocrine activation are associated with vulnerability to CRF, and (2) discovery-driven, based on proteomic analyses. Methods: Women with stage I-III HR+/HER2- tumors receiving HT (N=1153) were included from the multicenter, prospective CANTO cohort (NCT01993498). The primary outcome of interest was severe post-treatment global CRF at year-2 (Y2) after diagnosis (score ≥ 40/100, EORTC QLQ-C30). Secondary outcomes included CRF dimensions (physical, emotional, cognitive; EORTC QLQ-FA12). For the hypothesis-driven analyses, pre-treatment blood samples were profiled (Randox Laboratories Limited, UK) at diagnosis of BC, using a multi-biomarker panel assessing IL6, TNFα, IL1RA, CRP, IL2, IL1β, IFNγ, IL10, IL1A, IL4, and IL8. Pre-specified pre-treatment clinico-behavioral covariates (age, BMI, smoking status, psychological, and pre-treatment symptom burden, based on previously developed models) were forced into a multivariable logistic regression. Biomarkers were retained by Augmented Backwards Elimination (p&amp;lt;0.05) only if significantly associated with CRF. For the discovery approach, we used hyper-reaction monitoring mass spectrometry for the unbiased quantification of all detectable peptides and proteins in human plasma samples at diagnosis (Biognosys, CH), among a discovery subset (N=462). We then aimed to identify a proteomic signature associated with severe CRF at Y2. Log-transformed protein intensities were analyzed in terms of differential expression. The proteins that were identified to be significantly different among the patients reporting and not reporting severe CRF were then used to train a logistic regression model. Results: Prevalence of severe global CRF increased from 21.6% at diagnosis to 34.8% at Y2. In the final model, higher pre-treatment levels of IL6 and lower levels of IFNγ and IL10 were significant predictors of severe global CRF at Y2 (Table). The AUC of this clinico-bio-behavioral model was 0.78 (95%CI 0.75 - 0.82) and was suggestive of an improved performance as compared to clinico-behavioral models. Among CRF dimensions, a significant association emerged only between CRP and severe cognitive CRF (outcome prevalence at Y2 14.2%; adjusted OR per CRP log-unit increase 1.40 [95%CI 1.01-1.93]). In the discovery subset, several proteins were identified as differentially regulated (p&amp;lt;0.05) among patients reporting and not reporting severe CRF at Y2. Most of these were related to coagulation pathways (including C4BPA, C4BPB, HABP2, PLF4, PROS). However, models incorporating proteomic data did not seem to augment the predictive ability compared to clinico-behavioral models. Conclusions: Using clinical and biological pre-treatment measurements, it may be possible to identify a subset of BC patients at high risk for increased post-treatment CRF while on HT. This provides the possibility of testing dedicated preventive interventions. Table. Clinico-bio-behavioral model of pre-treatment predictors of severe global CRF at Y2, incorporating circulating inflammatory biomarkers.Adjusted OR§ (95% CI)Age, per additional 1 year0.98 (0.96-0.99)BMI, per additional unit1.02 (0.99-1.06)Current smoker, vs never2.27 (1.47-3.51)Former smoker, vs never0.97 (0.64-1.46)Anxiety case*, vs normal1.13 (0.75-1.70)Doubtful anxiety*, vs normal1.11 (0.73-1.68)Pre-treatment Insomnia**, per additional 10 points1.09 (1.04-1.15)Pre-treatment Pain**, per additional 10 points1.10 (1.01-1.18)Severe pre-treatment CRF**, vs no4.70 (3.13-7.05)IL6***1.72 (1.25-2.36)IL1RA***1.24 (0.85-1.81)IL2***1.43 (0.99-2.08)IFNγ***0.54 (0.30-0.95)IL10***0.40 (0.18-0.87)IL4***1.47 (0.67-3.20)IL8***1.15 (0.83-1.60)OR= Odds Ratio; CI= Confidence Interval; §by all factors in Table; *HADS; **QLQ-C30; ***per log-unit increase Citation Format: Antonio Di Meglio, Stergios Christodoulidis, Davide Soldato, Antonin Della Noce, Daniele Presti, Julie Havas, Florine Dubuisson, Barbara Pistilli, Valerie Camara-Clayette, Cecile Charles, Patricia A Ganz, Julienne Bower, Ann H Partridge, Alexandra Jacquet, Sibille Everhard, Sandrine Boyault, Fabrice André, Paul-Henry Cournede, Stefan Michiels, Caroline Pradon, Ines Vaz-Luis. Development of a clinico-bio-behavioral model for cancer-related fatigue (CRF) incorporating inflammatory biomarkers and proteomic data [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-11-01.

Blood amyloid and tau biomarkers as predictors of cerebrospinal fluid profiles

IntroductionBlood biomarkers represent a major advance for improving the management, diagnosis, and monitoring of Alzheimer's disease (AD). However, their context of use in relation to routine cerebrospinal fluid (CSF) analysis for the quantification of amyloid peptides and tau proteins remains to be determined.MethodsWe studied in two independent cohorts, the performance of blood biomarkers in detecting “nonpathological” (A−/T−/N−), amyloid (A+) or neurodegenerative (T+ /N+) CSF profiles.ResultsPlasma Aβ1–42/Aβ1–40 ratio and phosphorylated tau (p-tau(181)) were independent and complementary predictors of the different CSF profile and in particular of the nonpathological (A−/T−/N−) profile with a sensitivity and specificity close to 85%. These performances and the corresponding biomarker thresholds were significantly different from those related to AD detection.ConclusionThe use of blood biomarkers to identify patients who may benefit from secondary CSF testing represents an attractive stratification strategy in the clinical management of patients visiting memory clinics. This could reduce the need for lumbar puncture and foreshadow the use of blood testing on larger populations.

Development of multiplexed proteomic quantification of GAA and IDUA signature peptides in dried blood spots and buccal swabs by immuno-SRM-MS/MS for second-tier screening of Pompe disease and Hurler syndrome

Development of multiplexed proteomic quantification of GAA and IDUA signature peptides in dried blood spots and buccal swabs by immuno-SRM-MS/MS for second-tier screening of Pompe disease and Hurler syndrome

Quantification of Lipid and Peptide Content in Antigenic Peptide-loaded Liposome Formulations by Reversed-phase UPLC using UV Absorbance and Evaporative Light Scattering Detection

Antigenic peptide-loaded cationic liposomes have shown promise as cancer vaccines. Quantification of both peptides and lipids is critical for quality control of such vaccines for clinical translation. In this work we describe a reversed phase ultra-performance liquid chromatography (RP-UPLC) method that separates lipids (DOTAP, DOPC and their degradation products) and two physicochemically different peptides within 12 min. Samples were prepared by dilution in a 1:1 (v/v) mixture of methanol and water. Peptide quantification was done via UV detection and lipids were quantified by an evaporative light scattering detector (ELSD), both coupled to the RP-UPLC system, with high precision (RSD < 3.5%). We showed that the presence of lipids and peptides did not mutually influence their quantification. Limit of detection (LOD) and limit of quantification (LOQ), as determined in the ICH guidelines, were 6 and 20 ng for DOTAP, 12 ng and 40 ng for DOPC, 3.0 ng and 8.0 ng for peptide A and 2.4 ng and 7.2 ng for the more hydrophobic peptide B. Finally, lipid degradation of DOTAP and DOPC was monitored in peptide loaded DOTAP:DOPC liposomes upon storage at 4 °C and 40 °C.

A comprehensive and comparative proteomic analysis of horse serum proteins in colitis

Equine colitis is a diarrhoeal disease caused by inflammation of the large bowel and can potentially be life-threatening due to its rapid progression. Pathogenesis is multifactorial and pathophysiology is highly complicated, therefore, reliable diagnostic biomarkers are needed in the veterinary field. Serum is one of the most commonly used diagnostic tools in equine clinical investigation. To discover diagnostic or prognostic protein markers for colitis in horse serum, comprehensive and comparative proteomic analysis was conducted using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Case-control study. Serum samples were collected from 36 healthy Thoroughbreds and 12 Thoroughbreds with colitis. Serum from each horse suffering from colitis was collected daily until death or recovery. Collected sera were digested with trypsin. Peptides obtained from serum proteins were measured by Q-Exactive HF Orbitrap mass spectrometer. The identification and quantification of peptides were performed using Proteome Discoverer version 2.2. On day 1 of treatment, eight proteins in the colitis group were upregulated (P<.05, more than a twofold change) compared with the healthy group. Among the eight proteins, biliverdin reductase B was significantly upregulated (P<.05) in the non-survivor group (n=5) compared with the survivor group (n=7). On the last day of the treatment, haemoglobin subunit alpha, clusterin, glyceraldehyde-3-phosphate dehydrogenase, lipopolysaccharide-binding protein, and biliverdin reductase B showed significant increases (P<.05) in the non-survivor group. The number of the identified proteins is limited due to the existence of abundant proteins. Measuring the changes of these proteins together may enable a potential prognosis or early diagnosis of horses suffering from colitis.

Peptidomics as a useful tool in the follow-up of food bioactive peptides.

There is an intense research activity on bioactive peptides derived from food proteins in view of their health benefits for consumers. However, their identification is quite challenging as a consequence of their small size and low abundance in complex matrices such as foods or hydrolyzates. Recent advances in peptidomics and bioinformatics are getting improved sensitivity and accuracy and therefore such tools are contributing to the development of sophisticated methodologies for the identification and quantification of peptides. These developments are very useful for the follow-up of peptides released through proteolysis either in the food itself through the action of endogenous peptidases during processing stages like fermentation, drying or ripening, or from food proteins hydrolyzed by commercial peptidases or microorganisms with proteolytic activity. This chapter is presenting the latest advances in peptidomics and its use for the identification and quantification of peptides, and as a useful tool for controlling the proteolysis phenomena in foods and protein hydrolyzates.