Fractionation of proteoforms is currently the most challenging topic in the field of protein purification. The need for considering the existence of proteoforms into experimental approaches is not only important in Life Science research in general but especially in the manufacturing of therapeutic proteins (TPs) like recombinant therapeutic antibodies (mAbs). Some of the proteoforms of TPs have significantly decreased actions or even cause side effects. The identification and removal of proteoforms differing from the main species, having the desired action, is challenging because the difference in the composition of atoms often is very small and their concentration in comparison to the main proteoform can be small. In this study we demonstrate that sample displacement batch chromatography (SDBC) is an easy to handle, economic and efficient method for fractionating proteoforms. As a model sample a commercial ovalbumin fraction was used, containing many ovalbumin proteoforms. The most promising parameters for the SDBC were determined by a screening approach and applied for a 10-segment fractionation of the ovalbumin with cation exchange chromatography resin. Mass spectrometry of intact proteoforms was used for characterizing the SDBC fractionation process. By SDBC a significant separation of different proteoforms was obtained.

Previous studies have established the association of sex with gene and protein expression. This study investigated the association of sex with the abundance of endogenous urinary peptides, using capillary electrophoresis-coupled to mass spectrometry datasets from 2008 healthy individuals and patients with type II diabetes, divided in one discovery and two validation cohorts. Statistical analysis using the Mann-Whitney test, adjusted for multiple testing, revealed 143 sex-associated peptides in the discovery cohort. Of these, 90 peptides were associated with sex in at least one of the validation cohorts and showed agreement in their regulation trends across all cohorts. The 90 sex-associated peptides were fragments of 29 parental proteins. Comparison with previously published transcriptomics data demonstrated that the genes encoding 16 of these parental proteins had sex-biased expression. The 143 sex-associated peptides were combined into a support vector machine-based classifier that could discriminate males from females in two independent sets of healthy individuals and patients with type II diabetes, with an AUC of 89% and 81%, respectively. Collectively, the urinary peptidome contains multiple sex-associated differences, which may enable a better understanding of sex-biased molecular mechanisms and the development of more accurate diagnostic, prognostic or predictive classifiers for each individual sex.

Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise its adoption. In this context, there are numerous improvements that are possible in the area of open science including the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include e.g. increased data sharing practices and availability of tailored open data standards. Additionally, the field would benefit from the development of open analysis workflows that can enable e.g. data reuse of public datasets, something that is increasingly common in other proteomics fields. We present an open and modular platform for the analysis and visualisation of TD proteomics data called TopDownApp. It can be used as a flexible analysis platform, through the use of a common workflow engine, common data formats for input/output, and software containerisation. It can also serve as a tool for visual inspection through its simple setup. As a key point, it can also be used as a development platform for new tools through the use of Python, a modular design, software containerisation and common data formats. TopDownApp is open source and freely available at: https://github.com/mwalzer/TopDownApp.

In proteomics, fast, efficient and highly reproducible sample preparation is of utmost importance, particularly in view of fast scanning mass spectrometers enabling analyses of large sample series. To address this need, we have developed the web application MassSpecPreppy that operates on the open science OT-2 liquid handling robot from Opentrons. This platform can prepare up to 96 samples at once, performing tasks like BCA protein concentration determination, sample digestion with normalization, reduction/alkylation and peptide elution into vials or loading specified peptide amounts onto Evotips in an automated and flexible manner. The performance of the developed workflows using MassSpecPreppy was compared with standard manual sample preparation workflows. The BCA assay experiments revealed an average recovery of 101.3% (SD: ±7.82%) for the MassSpecPreppy workflow, while the manual workflow had a recovery of 96.3% (SD: ±9.73%). The species mix used in the evaluation experiments showed that 94.5% of protein groups for OT-2 digestion and 95% for manual digestion passed the significance thresholds with comparable peptide level coefficient of variations. These results demonstrate that MassSpecPreppy is a versatile and scalable platform for automated sample preparation, producing injection-ready samples for proteomics research.

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of prior knowledge networks is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Cancer-associated cachexia is a wasting syndrome that results in dramatic loss of whole-body weight, predominantly due to loss of skeletal muscle mass. It has been established that cachexia inducing cancer cells secrete proteins and extracellular vesicles (EVs) that can induce muscle atrophy. Though several studies examined these cancer-cell derived factors, targeting some of these components have shown little or no clinical benefit. To develop new therapies, understanding of the dysregulated proteins and signalling pathways that regulate catabolic gene expression during muscle wasting is essential. Here, we sought to examine the effect of conditioned media (CM) that contain secreted factors and EVs from cachexia inducing C26 colon cancer cells on C2C12 myotubes using mass spectrometry-based label-free quantitative proteomics. We identified significant changes in the protein profile of C2C12 cells upon exposure to C26-derived CM. Functional enrichment analysis revealed enrichment of proteins associated with inflammation, mitochondrial dysfunction, muscle catabolism, ROS production, and ER stress in CM treated myotubes. Furthermore, strong downregulation in muscle structural integrity and development and/or regenerative pathways were observed. Together, these enriched proteins in atrophied muscle could be utilized as potential muscle wasting markers and the dysregulated biological processes could be employed for therapeutic benefit in cancer-induced muscle wasting.

This pilot experiment examines if a loss in muscle proteostasis occurs in people with obesity and whether endurance exercise positively influences either the abundance profile or turnover rate of proteins in this population. Men with (n = 3) or without (n = 4) obesity were recruited and underwent a 14-d measurement protocol of daily deuterium oxide (D2O) consumption and serial biopsies of vastus lateralis muscle. Men with obesity then completed 10-weeks of high-intensity interval training (HIIT), encompassing 3 sessions per week of cycle ergometer exercise with 1 min intervals at 100 % maximum aerobic power interspersed by 1 min recovery periods. The number of intervals per session progressed from 4 to 8, and during weeks 8-10 the 14-d measurement protocol was repeated. Proteomic analysis detected 352 differences (p < 0.05, false discovery rate < 5%) in protein abundance and 19 (p < 0.05) differences in protein turnover, including components of the ubiquitin-proteasome system. HIIT altered the abundance of 53 proteins and increased the turnover rate of 22 proteins (p < 0.05) and tended to benefit proteostasis by increasing muscle protein turnover rates. Obesity and insulin resistance are associated with compromised muscle proteostasis, which may be partially restored by endurance exercise.

We propose an updated approach for approximating the isotope distribution of average peptides given their monoisotopic mass. Our methodology involves in-silico cleavage of the entire UNIPROT database of Human reviewed proteins using Trypsin, generating a theoretical peptide dataset. The isotope distribution is computed using BRAIN. We apply a compositional data modelling strategy that utilizes an additive log-ratio transformation for the isotope probabilities followed by a penalized spline regression. Furthermore, due to the impact of the number of Sulphur atoms on the course of the isotope distribution, we develop separate models for peptides containing zero up to five Sulphur atoms. Additionally, we propose three methods to estimate the number of Sulphur atoms based on an observed isotope distribution. The performance of the spline models and the Sulphur prediction approaches is evaluated using a mean squared error and a modified Pearson’s χ² goodness-of-fit measure on an experimental UPS2 data set. Our analysis reveals that the variability in spectral accuracy contributes more to the errors than the approximation of the theoretical isotope distribution by our proposed average peptide model. Moreover, we find that the accuracy of predicting the number of Sulphur atoms based on the observed isotope distribution is limited by measurement accuracy.

CCOC s a relatively rare subtype of ovarian cancer with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. We analyzed the proteome of CCOC tissue samples from two independent cohorts using DIA-MS. A total of 8697 proteins were characterized in the first cohort (H1 cohort, 32 patients, 35 FFPE samples) and 9409 proteins in the second cohort (H2 cohort, 24 patients, 28 FF samples). After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with RFS in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix, and mitochondrial metabolism. We further developed a 13-protein model to predict the prognosis of patients with CCOC in H2 cohort, and validated the model in the H1 cohort in both DIA and PRM data. Finally, we verified the modulated pathways from our CCOC proteomic dataset in several published CCOC transcriptome and proteome datasets. Taken together, this study presents a CCOC proteomic data resource and a promising 13-protein panel which could potentially predict the recurrence and survival of CCOC.

Immunotherapy harnesses neoantigens encoded within the human genome, but their therapeutic potential is hampered by low expression, which may be controlled by the Nonsense-Mediated Decay (NMD) pathway. This study investigates the impact of UPF1-knockdown on the expression of non-canonical/mutant proteins, employing proteogenomic to explore UPF1 role within the NMD pathway. Additionally, we conducted a comprehensive pan-cancer analysis of UPF1 expression and evaluated UPF1 expression in Triple-Negative Breast Cancer (TNBC) tissue in-vivo. Our findings reveal that UPF1-knockdown leads to increased transcription of non-canonical/mutant proteins, particularly those originating from retained-introns, pseudogenes, long non-coding RNAs, and unannotated transcript biotypes. Moreover, our analysis demonstrates elevated UPF1 expression in various cancer types, with notably heightened protein levels in patient-derived TNBC tumours compared to adjacent tissues. This study elucidates UPF1 role in mitigating transcriptional noise by degrading transcripts encoding non-canonical/mutant proteins. Intriguingly, we observe an upregulation of the NMD pathway in cancer, potentially acting as a “neoantigen-masking” mechanism that suppresses non-canonical/mutant protein expression. Targeting this mechanism may reveal a new spectrum of neoantigens accessible to the antigen presentation pathway. Our novel findings provide a strong foundation for the development of therapeutic strategies aimed at targeting UPF1 or modulating the NMD pathway.

The mzIdentML file format, originally developed by the Proteomics Standards Initiative in 2011, is the open XML data standard for peptide and protein identification results coming from mass spectrometry. We present mzIdentML version 1.3.0, which introduces new functionality and support for additional use cases. First of all, a new mechanism for encoding identifications based on multiple spectra. Furthermore, the main mzIdentML specification document can now be supplemented by extension documents which provide further guidance for encoding specific use cases for different proteomics subfields. One extension document has been added, covering additional use cases for the encoding of crosslinked peptide identifications. The ability to add extension documents facilitates keeping the mzIdentML standard up to date with advances in the proteomics field, without having to change the main specification document. The crosslinking extension document provides further explanation of the crosslinking use cases already supported in mzIdentML version 1.2.0, and provides support for encoding additional scenarios that are critical to reflect developments in the crosslinking field and facilitate its integration in structural biology. These are: (i) support for cleavable crosslinkers, (ii) support for internally linked peptides, (iii) support for noncovalently associated peptides, and (iv) improved support for encoding scores and the corresponding thresholds.

Botryllus schlosseri, is a model marine invertebrate for studying immunity, regeneration, and stress-induced evolution. Conditions for validating its predicted proteome were optimized using nanoElute® 2 deep-coverage LCMS, revealing up to 4,930 protein groups and 20,984 unique peptides per sample. Spectral libraries were generated and filtered to remove interferences, low-quality transitions, and only retain proteins with >3 unique peptides. The resulting DIA assay library enabled label-free quantitation of 3,426 protein groups represented by 22,593 unique peptides. Quantitative comparisons of a laboratory-raised with two field-collected populations revealed (1) a more unique proteome in the laboratory-raised population, and (2) proteins with high/low individual variabilities in each population. DNA repair/replication, ion transport, and intracellular signaling processes were unique in laboratory-cultured colonies. Spliceosome and Wnt signaling proteins were the least variable (highly functionally constrained) in all populations. In conclusion, we present the first colonial tunicate’s deep quantitative proteome analysis, identifying functional protein clusters associated with laboratory conditions, different habitats, and strong versus relaxed abundance constraints. These results empower research on B. schlosseri with proteomics resources and enable quantitative molecular phenotyping of changes associated with transfer from in situ to ex situ and from in vivo to in vitro culture conditions.

The tooth is one of the ideal models for developmental study, involving in epithelial-mesenchymal transition and cell differentiation. The essential factors and pathways identified in tooth development will help understand the natural development process and the malformations of mineralized tissues such as skeleton. The time-dependent proteomic changes were investigated by healthy human molars proteomics of embryonic stages from the cap-to-early bell stage. A total of 713 differentially expressed proteins (DEPs) with five temporal expression patterns were filtered. 24 potential driver proteins of tooth development were screened by weighted gene co-expression network analysis (WGCNA) including CHID1, RAP1GDS1, HAPLN3, AKAP12, WLS, GSS, DDAH1, CLSTN1, AFM, RBP1, AGO1, SET, HMGB2, HMGB1, ANP32A, SPON1, FREM1, C8B, PRPS2, FCHO2, PPP1R12A, GPALPP1, U2AF2 and RCC2. The hub proteins in different temporal expression patterns were extracted. And the potential cell resources and the temporal expression patterns at transcriptomic level were explored using single cell RNA-sequencing (scRNA-seq). This study provides invaluable resources for the mechanistic studies of human embryonic epithelial and mesenchymal cell differentiation and tooth development.

The group 2 σ factor for RNA polymerase SigE plays important role in regulating central carbon metabolism in cyanobacteria. However, the regulation of SigE for these pathways at a proteome level remains unknown. Using a sigE-deficient strain (ΔsigE) of Synechocystis sp. PCC 6803 and quantitative proteomics, we found that SigE depletion induces differential protein expression for sugar catabolic pathways including glycolysis, oxidative pentose phosphate (OPP) pathway, and glycogen catabolism. Two glycogen debranching enzyme homologues Slr1857 and Slr0237 are found differentially expressed in ΔsigE. Glycogen determination indicated that Δslr0237 accumulated glycogen under photomixotrophic conditions but was unable to utilize these reserves in the dark, whereas Δslr1857 accumulates and utilize glycogen in a similar way as the WT strain does in the same conditions. These results suggest that Slr0237 plays the major role as the glycogen debranching enzyme in Synechocystis. To our knowledge, this is the first study to report the functional difference of two glycogen debranching enzyme in Synechocystis and the research highlights the intricate regulation of glycogen breakdown.

We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database, the most popular proteomics data repository. Our system utilizes two advanced Large Language Models (LLM), llama2-13b and chatglm2-6b, and includes a web service API (Application Programming Interface), web interface, and sophisticated algorithms. We have developed a novel approach to construct vector-based representations for enabling the LLM responses, featuring a curated version and a comprehensive database of relevant links and paragraphs for each generated response. An important part of the framework is a benchmark component based on an Elo-ranking system, providing a scalable method for evaluating not only the performance of llama2-13b and chatglm2-6b but also, of any other available and future open-source LLMs. Throughout the benchmarking process, the PRIDE documentation for external users was refined to enhance the clarity and efficacy in addressing user queries. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector-based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure.

Endometrial cancer is the most prevalent gynaecological cancer globally. Its association with obesity and metabolic diseases is a key aetiology, increasingly among younger females. Early diagnosis and improved treatment decisions are crucial for these women whose outcomes could be improved by discovering new biomarkers. We took a new approach to extracellular vesicle (EV) biomarker discovery - profiling the proteome of enriched EVs isolated directly from frozen biobanked endometrial cancers. Nine tissue pools, each generating collagenase-digested tissue and matched small EVs, were analysed using label-free proteomics. Three clinical subgroups: Endometrioid low BMI (body mass index), Endometrioid high BMI, and Serous, irrespective of BMI, were compared to identify shared secreted proteins, proteins associated with histological subtype, and proteins related to BMI. EVs were enriched for common EV markers and large secreted proteins. Cell lysates were enriched in mitochondrial and blood proteins. EV protein profiles were most different between the high BMI subgroup and the others, highlighting a significant influence of comorbidities on the intra-tumoural EV secretome. Proteins differentially abundant between subgroups in tissues were strikingly not also differential in the matched EVs. This work has identified secreted proteins implicated in the complex pathophysiology of endometrial cancer and pinpointed candidate biomarkers for diagnosis.

High-throughput proteomics is an effective methodology for identifying a variety of virulence factors of pathogens. Proteomic data are commonly evaluated against annotated sequences present in publicly available database repositories. A proteogenomic approach can be used if annotated sequences are not available or to identify novel proteins/peptides. However, a single genome is commonly utilized in proteomic and proteogenomic analyses. We pose the question of whether utilizing a number of different genome assemblies of a bacterial pathogen would be beneficial. Here, we used previously obtained shot-gun label-free nano-LC‒MS/MS data of the exoprotein fraction of four reference ERIC I–IV genotypes of Paenibacillus larvae and evaluated them against publicly available annotated sequences (from NCBI-protein, RefSeq, UniProt) together with an array of protein sequences generated using a six-frame direct translation of 15 genomic assemblies available in GenBank. The wide search through 18 database components reliably identified 453 protein hits. UpSet analysis categorized the hits into 50 groups based on the success protein identification by databases. The relatively high variability in successful identification among the genome assemblies facilitated the mining of markers based on uniqueness and contrasting results prior to considering proteome differences. Data evaluation provided novel and interesting markers that can be studied further.