TANDEM: matching proteins with tandem mass spectra

Evolutionary Quantitative Proteomics of Reproductive Protein Divergence in Drosophila

Reproductive traits often evolve rapidly between species. Understanding the causes and consequences of this rapid divergence requires characterization of female and male reproductive proteins and their effect on fertilization success. Species in the Drosophila virilis clade exhibit rampant interspecific reproductive incompatibilities, making them ideal for studies on diversification of reproductive proteins and their role in speciation. Importantly, the role of intraejaculate protein abundance and allocation in interspecific divergence is poorly understood. Here, we identify and quantify the transferred male ejaculate proteome using multiplexed isobaric labeling of the lower female reproductive tract before and immediately after mating using three species of the virilis group. We identified over 200 putative male ejaculate proteins, many of which show differential abundance between species, suggesting that males transfer a species-specific allocation of seminal fluid proteins during copulation. We also identified over 2000 female reproductive proteins, which contain female-specific serine-type endopeptidases that showed differential abundance between species and elevated rates of molecular evolution, similar to that of some male seminal fluid proteins. Our findings suggest that reproductive protein divergence can also manifest in terms of species-specific protein abundance patterns.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Pyruvate Kinase M (PKM) binds ribosomes in a poly-ADP ribosylation dependent manner to induce translational stalling

In light of the numerous studies identifying post-transcriptional regulators on the surface of the endoplasmic reticulum (ER), we asked whether there are factors that regulate compartment specific mRNA translation in human cells. Using a proteomic survey of spatially regulated polysome interacting proteins, we identified the glycolytic enzyme Pyruvate Kinase M (PKM) as a cytosolic (i.e. ER-excluded) polysome interactor and investigated how it influences mRNA translation. We discovered that the PKM-polysome interaction is directly regulated by ADP levels-providing a link between carbohydrate metabolism and mRNA translation. By performing enhanced crosslinking immunoprecipitation-sequencing (eCLIP-seq), we found that PKM crosslinks to mRNA sequences that are immediately downstream of regions that encode lysine- and glutamate-enriched tracts. Using ribosome footprint protection sequencing, we found that PKM binding to ribosomes causes translational stalling near lysine and glutamate encoding sequences. Lastly, we observed that PKM recruitment to polysomes is dependent on poly-ADP ribosylation activity (PARylation)-and may depend on co-translational PARylation of lysine and glutamate residues of nascent polypeptide chains. Overall, our study uncovers a novel role for PKM in post-transcriptional gene regulation, linking cellular metabolism and mRNA translation.

AIomics: Exploring More of the Proteome Using Mass Spectral Libraries Extended by Artificial Intelligence

The unbounded permutations of biological molecules, including proteins and their constituent peptides, present a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides.

The effect of obesity on uterine receptivity is mediated by endometrial extracellular vesicles that control human endometrial stromal cell decidualization and trophoblast invasion

The objectives of the present study were to determine whether obesity impacts human decidualization and the endometrial control of trophoblast invasion (both of which are required for embryo implantation) and evaluate the potential involvement of endometrial extracellular vesicles (EVs) in the regulation of these physiological processes. Using primary human cell cultures, we first demonstrated that obesity is associated with significantly lower in vitro decidualization of endometrial stromal cells (ESCs). We then showed that a trophoblastic cell line's invasive ability was greater in the presence of conditioned media from cultures of ESCs from obese women. The results of functional assays indicated that supplementation of the culture medium with EVs from nonobese women can rescue (at least in part) the defect in in vitro decidualization described in ESCs from obese women. Furthermore, exposure to endometrial EVs from obese women (vs. nonobese women) was associated with significantly greater invasive activity by HTR-8/SVneo cells. Using mass-spectrometry-based quantitative proteomics, we found that EVs isolated from uterine supernatants of biopsies from obese women (vs. nonobese women) presented a molecular signature focused on cell remodelling and angiogenesis. The proteomics analysis revealed two differentially expressed proteins (fibronectin and angiotensin-converting enzyme) that might be involved specifically in the rescue of the decidualization capacity in ESCs from obese women; both of these proteins are abundantly present in endometrial EVs from nonobese women, and both are involved in the decidualization process. In conclusion, our results provided new insights into the endometrial EVs' pivotal role in the poor uterine receptivity observed in obese women.

Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIA^XMBD, for direct analysis of DIA data. Dear-DIA^XMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIA^XMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIA^XMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

Mistle: bringing spectral library predictions to metaproteomics with an efficient search index

MOTIVATION

Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics.

RESULTS

In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes.

AVAILABILITY AND IMPLEMENTATION

Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle.

Small extracellular vesicles but not microvesicles from Opisthorchis viverrini promote cell proliferation in human cholangiocytes

Chronic infection with O. viverrini has been linked to the development of cholangiocarcinoma (CCA), which is a major public health burden in the Lower Mekong River Basin countries, including Thailand, Lao PDR, Vietnam and Cambodia. Despite its importance, the exact mechanisms by which O. viverrini promotes CCA are largely unknown. In this study, we characterized different extracellular vesicle populations released by O. viverrini (OvEVs) using proteomic and transcriptomic analyses and investigated their potential role in host-parasite interactions. While 120k OvEVs promoted cell proliferation in H69 cells at different concentrations, 15k OvEVs did not produce any effect compared to controls. The proteomic analysis of both populations showed differences in their composition that could contribute to this differential effect. Furthermore, the miRNAs present in 120k EVs were analysed and their potential interactions with human host genes was explored by computational target prediction. Different pathways involved in inflammation, immune response and apoptosis were identified as potentially targeted by the miRNAs present in this population of EVs. This is the first study showing specific roles for different EV populations in the pathogenesis of a parasitic helminth, and more importantly, an important advance towards deciphering the mechanisms used in establishment of opisthorchiasis and liver fluke infection-associated malignancy.

PepQuery2 democratizes public MS proteomics data for rapid peptide searching

We present PepQuery2, which leverages a new tandem mass spectrometry (MS/MS) data indexing approach to enable ultrafast, targeted identification of novel and known peptides in any local or publicly available MS proteomics datasets. The stand-alone version of PepQuery2 allows directly searching more than one billion indexed MS/MS spectra in the PepQueryDB or any public datasets from PRIDE, MassIVE, iProX, or jPOSTrepo, whereas the web version enables users to search datasets in PepQueryDB with a user-friendly interface. We demonstrate the utilities of PepQuery2 in a wide range of applications including detecting proteomic evidence for genomically predicted novel peptides, validating novel and known peptides identified using spectrum-centric database searching, prioritizing tumor-specific antigens, identifying missing proteins, and selecting proteotypic peptides for targeted proteomics experiments. By putting public MS proteomics data directly into the hands of scientists, PepQuery2 opens many new ways to transform these data into useful information for the broad research community.

Mitochondria and cytochrome components released into the plasma of severe COVID-19 and ICU acute respiratory distress syndrome patients

INTRODUCTION

Proteomic analysis of human plasma by LC-ESI-MS/MS has discovered a limited number of new cellular protein biomarkers that may be confirmed by independent biochemical methods. Analysis of COVID-19 plasma has indicated the re-purposing of known biomarkers that might be used as prognostic markers of COVID-19 infection. However, multiple molecular approaches have previously indicated that the SARS-COV2 infection cycle is linked to the biology of mitochondria and that the response to infections may involve the action of heme containing oxidative enzymes.

METHODS

Human plasma from COVID-19 and ICU-ARDS was analyzed by classical analytical biochemistry techniques and classical frequency-based statistical approaches to look for prognostic markers of severe COVID-19 lung damage. Plasma proteins from COVID-19 and ICU-ARDS were identified and enumerated versus the controls of normal human plasma (NHP) by LC-ESI-MS/MS. The observation frequency of proteins detected in COVID-19 and ICU-ARDS patients were compared to normal human plasma, alongside random and noise MS/MS spectra controls, using the Chi Square (χ²) distribution.

RESULTS

PCR showed the presence of MT-ND1 DNA in the plasma of COVID-19, ICU-ARDS, as well as normal human plasma. Mitochondrial proteins such as MRPL, L2HGDH, ATP, CYB, CYTB, CYP, NDUF and others, were increased in COVID-19 and ICU-ARDS plasma. The apparent activity of the cytochrome components were tested alongside NHP by dot blotting on PVDF against a purified cytochrome c standard preparation for H₂O₂ dependent reaction with luminol as measured by enhanced chemiluminescence (ECL) that showed increased activity in COVID-19 and ICU-ARDS patients.

DISCUSSION

The results from PCR, LC-ESI-MS/MS of tryptic peptides, and cytochrome ECL assays confirmed that mitochondrial components were present in the plasma, in agreement with the established central role of the mitochondria in SARS-COV-2 biology. The cytochrome activity assay showed that there was the equivalent of at least nanogram amounts of cytochrome(s) in the plasma sample that should be clearly detectable by LC-ESI-MS/MS. The release of the luminol oxidase activity from cells into plasma forms the basis of a simple and rapid test for the severity of cell damage and lung injury in COVID-19 infection and ICU-ARDS.

Challenges and Opportunities for Single-cell Computational Proteomics

Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it is necessary to realize that current algorithms being employed on single-cell data were designed for bulk data and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

Comparison of SPEED, S-Trap, and In-Solution-Based Sample Preparation Methods for Mass Spectrometry in Kidney Tissue and Plasma

Mass spectrometry is a powerful technique for investigating renal pathologies and identifying biomarkers, and efficient protein extraction from kidney tissue is essential for bottom-up proteomic analyses. Detergent-based strategies aid cell lysis and protein solubilization but are poorly compatible with downstream protein digestion and liquid chromatography-coupled mass spectrometry, requiring additional purification and buffer-exchange steps. This study compares two well-established detergent-based methods for protein extraction (in-solution sodium deoxycholate (SDC); suspension trapping (S-Trap)) with the recently developed sample preparation by easy extraction and digestion (SPEED) method, which uses strong acid for denaturation. We compared the quantitative performance of each method using label-free mass spectrometry in both sheep kidney cortical tissue and plasma. In kidney tissue, SPEED quantified the most unique proteins (SPEED 1250; S-Trap 1202; SDC 1197). In plasma, S-Trap produced the most unique protein quantifications (S-Trap 150; SDC 148; SPEED 137). Protein quantifications were reproducible across biological replicates in both tissue (R2 = 0.85–0.90) and plasma (SPEED R2 = 0.84; SDC R2 = 0.76, S-Trap R2 = 0.65). Our data suggest SPEED as the optimal method for proteomic preparation in kidney tissue and S-Trap or SPEED as the optimal method for plasma, depending on whether a higher number of protein quantifications or greater reproducibility is desired.

Bottom-Up Proteomics: Advancements in Sample Preparation

Liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomics is a powerful technique for profiling proteomes of cells, tissues, and body fluids. Typical bottom-up proteomic workflows consist of the following three major steps: sample preparation, LC–MS/MS analysis, and data analysis. LC–MS/MS and data analysis techniques have been intensively developed, whereas sample preparation, a laborious process, remains a difficult task and the main challenge in different applications. Sample preparation is a crucial stage that affects the overall efficiency of a proteomic study; however, it is prone to errors and has low reproducibility and throughput. In-solution digestion and filter-aided sample preparation are the typical and widely used methods. In the past decade, novel methods to improve and facilitate the entire sample preparation process or integrate sample preparation and fractionation have been reported to reduce time, increase throughput, and improve reproducibility. In this review, we have outlined the current methods used for sample preparation in proteomics, including on-membrane digestion, bead-based digestion, immobilized enzymatic digestion, and suspension trapping. Additionally, we have summarized and discussed current devices and methods for integrating different steps of sample preparation and peptide fractionation.

Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data

Single-cell proteomics is emerging as an important subfield in the proteomics and mass spectrometry communities, with potential to reshape our understanding of cell development, cell differentiation, disease diagnosis, and the development of new therapies. Compared with significant advancements in the "hardware" that is used in single-cell proteomics, there has been little work comparing the effects of using different "software" packages to analyze single-cell proteomics datasets. To this end, seven popular proteomics programs were compared here, applying them to search three single-cell proteomics datasets generated by three different platforms. The results suggest that MSGF+, MSFragger, and Proteome Discoverer are generally more efficient in maximizing protein identifications, that MaxQuant is better suited for the identification of low-abundance proteins, that MSFragger is superior in elucidating peptide modifications, and that Mascot and X!Tandem are better for analyzing long peptides. Furthermore, an experiment with different loading amounts was carried out to investigate changes in identification results and to explore areas in which single-cell proteomics data analysis may be improved in the future. We propose that this comparative study may provide insight for experts and beginners alike operating in the emerging subfield of single-cell proteomics.

Proteomics-based scoring of cellular response to stimuli for improved characterization of signaling pathway activity

Omics technologies focus on uncovering the complex nature of molecular mechanisms in cells and organisms, including biomarkers and drug targets discovery. Aiming at these tasks, we see that information extracted from omics data is still underused. In particular, characteristics of differentially regulated molecules can be combined in a single score to quantify the signaling pathway activity. Such a metric can be useful for comprehensive data interpretation to follow: (1) developing molecular responses in time; (2) potency of a drug on a certain cell culture; (3) ranking the signaling pathway activity in stimulated cells; and (4) integration of the omics data and assay-based measurements of cell viability, cytotoxicity, and proliferation. With recent advances in ultrafast mass spectrometry for quantitative proteomics allowing data collection in a few minutes, proteomics score for cellular response to stimuli can become a fast, accurate, and informative complement to bioassays. Here, we utilized an interquartile-based selection of differentially regulated features and a variety of schemes for quantifying cellular responses to come up with the quantitative metric for total cellular response and pathway activity. Validation was performed using antiproliferative and virus assays and label-free proteomics data collected for cancer cells subjected to drug stimulation.

Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite

The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

Scribe: Next Generation Library Searching for DDA Experiments

Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.

The Cancer Testes Antigen, HORMAD1, is a Tumor-Specific Replication Fork Protection Factor

Tumors frequently activate the expression of genes that are only otherwise required for meiosis. HORMAD1, which is essential for meiotic recombination in multiple species, is expressed in over 50% of human lung adenocarcinoma cells (LUAD). We previously found that HORMAD1 promotes DNA double strand break (DSB) repair in LUAD. Here, we report that HORMAD1 takes on an additional role in protecting genomic integrity. Specifically, we find HORMAD1 is critical for protecting stalled DNA replication forks in LUAD. Loss of HORMAD1 leads to nascent DNA degradation, an event which is mediated by the MRE11-DNA2-BLM pathway. Moreover, following exogenous induction of DNA replication stress, HORMAD1 deleted cells accumulate single stranded DNA (ssDNA). We find that these phenotypes are the result of a lack of RAD51 and BRCA2 loading onto stalled replication forks. Ultimately, loss of HORMAD1 leads to increased DSBs and chromosomal aberrations in response to replication stress. Collectively, our data support a model where HORMAD1 expression is selected to mitigate DNA replication stress, which would otherwise induce deleterious genomic instability.

Improving the genome and proteome annotations of the marine model diatom Thalassiosira pseudonana using a proteogenomics strategy

Diatoms are unicellular eukaryotic phytoplankton that account for approximately 20% of global carbon fixation and 40% of marine primary productivity; thus, they are essential for global carbon biogeochemical cycling and climate. The availability of ten diatom genome sequences has facilitated evolutionary, biological and ecological research over the past decade; however, a complimentary map of the diatom proteome with direct measurements of proteins and peptides is still lacking. Here, we present a proteome map of the model marine diatom Thalassiosira pseudonana using high-resolution mass spectrometry combined with a proteogenomic strategy. In-depth proteomic profiling of three different growth phases and three nutrient-deficient samples identified 9526 proteins, accounting for ~ 81% of the predicted protein-coding genes. Proteogenomic analysis identified 1235 novel genes, 975 revised genes, 104 splice variants and 234 single amino acid variants. Furthermore, our quantitative proteomic analysis experimentally demonstrated that a considerable number of novel genes were differentially translated under different nutrient conditions. These findings substantially improve the genome annotation of T. pseudonana and provide insights into new biological functions of diatoms. This relatively comprehensive diatom proteome catalog will complement available diatom genome and transcriptome data to advance biological and ecological research of marine diatoms.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-022-00161-y.

Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly

Monoclonal antibodies are biotechnologically produced proteins with various applications in research, therapeutics and diagnostics. Their ability to recognize and bind to specific molecule structures makes them essential research tools and therapeutic agents. Sequence information of antibodies is helpful for understanding antibody-antigen interactions and ensuring their affinity and specificity. De novo protein sequencing based on mass spectrometry is a valuable method to obtain the amino acid sequence of peptides and proteins without a priori knowledge. In this study, we evaluated six recently developed de novo peptide sequencing algorithms (Novor, pNovo 3, DeepNovo, SMSNet, PointNovo and Casanovo), which were not specifically designed for antibody data. We validated their ability to identify and assemble antibody sequences on three multi-enzymatic data sets. The deep learning-based tools Casanovo and PointNovo showed an increased peptide recall across different enzymes and data sets compared with spectrum-graph-based approaches. We evaluated different error types of de novo peptide sequencing tools and their performance for different numbers of missing cleavage sites, noisy spectra and peptides of various lengths. We achieved a sequence coverage of 97.69-99.53% on the light chains of three different antibody data sets using the de Bruijn assembler ALPS and the predictions from Casanovo. However, low sequence coverage and accuracy on the heavy chains demonstrate that complete de novo protein sequencing remains a challenging issue in proteomics that requires improved de novo error correction, alternative digestion strategies and hybrid approaches such as homology search to achieve high accuracy on long protein sequences.

Hyperphosphorylation of hepatic proteome characterizes nonalcoholic fatty liver disease in S-adenosylmethionine deficiency

Methionine adenosyltransferase 1a (MAT1A) is responsible for hepatic S-adenosyl-L-methionine (SAMe) biosynthesis. Mat1a ^-/- mice have hepatic SAMe depletion, develop nonalcoholic steatohepatitis (NASH) which is reversed with SAMe administration. We examined temporal alterations in the proteome/phosphoproteome in pre-disease and NASH Mat1a ^-/- mice, effects of SAMe administration, and compared to human nonalcoholic fatty liver disease (NAFLD). Mitochondrial and peroxisomal lipid metabolism proteins were altered in pre-disease mice and persisted in NASH Mat1a ^-/- mice, which exhibited more progressive alterations in cytoplasmic ribosomes, ER, and nuclear proteins. A common mechanism found in both pre-disease and NASH livers was a hyperphosphorylation signature consistent with casein kinase 2α (CK2α) and AKT1 activation, which was normalized by SAMe administration. This was mimicked in human NAFLD with a metabolomic signature (M-subtype) resembling Mat1a ^-/- mice. In conclusion, we have identified a common proteome/phosphoproteome signature between Mat1a ^-/- mice and human NAFLD M-subtype that may have pathophysiological and therapeutic implications.

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19%-46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency

The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or "signatures" to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public.

Identification of Arginylated Proteins by Mass Spectrometry

Here, we describe the method for the identification of arginylated proteins by mass spectrometry. This method has been originally applied to the identification of N-terminally added Arg on proteins and peptides and then expanded to the side chain modification which has been recently described by our groups. The key steps in this method include the use of the mass spectrometry instruments that can identify peptides with very high pass accuracy (Orbitrap) and apply stringent mass cutoffs during automated data analysis, followed by manual validation of the identified spectra. These methods can be used with both complex and purified protein samples and, to date, constitute the only reliable way to confirm arginylation at a particular site on a protein or peptide.

Metabolic and proteomic indications of diabetes progression in human aqueous humor

Diabetes mellitus is a multiorgan systemic disease impacting numerous ocular structures that results in significant ocular morbidity and often results in more frequent corneal and glaucoma surgeries for affected individuals. We hypothesize that the systemic metabolic and proteomic derangement observed in the progression of diabetes influences the composition of the aqueous humor (AH), which ultimately impacts the anterior segment health of the eye. To identify changes associated with diabetes progression, we mapped the metabolite profile and proteome of AH samples from patients with varying severities of type II diabetes (T2DM). Patients were classified as nondiabetic (ND or control), non-insulin-dependent diabetic without advanced features of disease (NAD-ni), insulin-dependent diabetic without advanced features (NAD-i), or diabetic with advanced features (AD). AH samples collected from the anterior chamber during elective ophthalmic surgery were evaluated for metabolite and protein expression changes associated with diabetic severity via gas chromatography/mass spectrometry and ultra-high performance liquid chromatography tandem mass spectrometry, respectively. Metabolic and proteomic pathway analyses were conducted utilizing MetaboAnalyst 4.0 and Ingenuity Pathway Analysis. A total of 14 control, 12 NAD-ni, 4 NAD-I, and 14 AD samples were included for analysis. Elevated levels of several branched amino acids (e.g., valine, leucine, isoleucine), and lipid metabolites (e.g., palmitate) were found only with increasing diabetic severity (i.e., the AD group). Similar proteomic trends were noted in amino acid and fatty acid metabolism and the unfolded protein/stress response. These results represent the first report of both metabolomic and proteomic evaluation of aqueous humor. Diabetes results in metabolic and proteomic perturbations detectable in the AH, and unique changes become manifest as T2DM severity worsens. Changes in AH composition may serve as an indicator of disease severity, risk assessment of anterior segment cells and structures, and potential future therapies.

Peptidomics Methods Applied to the Study of Flower Development

Understanding the global and dynamic nature of plant developmental processes requires not only the study of the transcriptome, but also of the proteome, including its largely uncharacterized peptidome fraction. Recent advances in proteomics and high-throughput analyses of translating RNAs (ribosome profiling) have begun to address this issue, evidencing the existence of novel, uncharacterized, and possibly functional peptides. To validate the accumulation in tissues of sORF-encoded polypeptides (SEPs), the basic setup of proteomic analyses (i.e., LC-MS/MS) can be followed. However, the detection of peptides that are small (up to ~100 aa, 6-7 kDa) and novel (i.e., not annotated in reference databases) presents specific challenges that need to be addressed both experimentally and with computational biology resources. Several methods have been developed in recent years to isolate and identify peptides from plant tissues. In this chapter, we outline two different peptide extraction protocols and the subsequent peptide identification by mass spectrometry using the database search or the de novo identification methods.

Multi-Omics Methods Applied to Flower Development

Developmental processes in multicellular organisms depend on the proficiency of cells to orchestrate different gene expression programs. Over the past years, several studies of reproductive organ development have considered genomic analyses of transcription factors and global gene expression changes, modeling complex gene regulatory networks. Nevertheless, the dynamic view of developmental processes requires, as well, the study of the proteome in its expression, complexity, and relationship with the transcriptome. In this chapter, we describe a dual extraction method-for protein and RNA-for the characterization of genome expression at proteome level and its correlation to transcript expression data. We also present a shotgun proteomic procedure (LC-MS/MS) followed by a pipeline for the imputation of missing values in mass spectrometry results.

Biochemical and structural insights into SARS-CoV-2 polyprotein processing by Mpro

SARS-CoV-2, a human coronavirus, is the causative agent of the COVID-19 pandemic. Its genome is translated into two large polyproteins subsequently cleaved by viral papain-like protease and main protease (Mpro). Polyprotein processing is essential yet incompletely understood. We studied Mpro-mediated processing of the nsp7-11 polyprotein, whose mature products include cofactors of the viral replicase, and identified the order of cleavages. Integrative modeling based on mass spectrometry (including hydrogen-deuterium exchange and cross-linking) and x-ray scattering yielded a nsp7-11 structural ensemble, demonstrating shared secondary structural elements with individual nsps. The pattern of cross-links and HDX footprint of the C145A Mpro and nsp7-11 complex demonstrate preferential binding of the enzyme active site to the polyprotein junction sites and additional transient contacts to help orient the enzyme on its substrate for cleavage. Last, proteolysis assays were used to characterize the effect of inhibitors/binders on Mpro processing/inhibition using the nsp7-11 polyprotein as substrate.

Proteomics dataset from 26th Dynasty Egyptian mummified remains sampled using minimally invasive skin sampling tape strips

Paleoproteomics typically involves the destructive sampling of precious bioarchaeological materials. This analysis aims to investigate the proteins identifiable via nanoLC-MS/MS from highly degraded 26th Dynasty Egyptian mummified human remains (NMR.29.1-8) after non-destructive sampling with commercially available dermatology-grade skin sampling tape strips. A collection of cranial and other bone fragments were sampled with the tape strips then subsequently analysed using a shotgun proteomics approach. The number of proteins identified using this method ranged from 18 to 437 at a peptide FDR of <1%. Deamidation ratios were assessed using an in-house R script, with asparagine deamidation averaging ∼20-30% and glutamine deamidation averaging ∼15-25%.

Adaptation of a microbial community to demand-oriented biological methanation

BACKGROUND

Biological conversion of the surplus of renewable electricity and carbon dioxide (CO2) from biogas plants to biomethane (CH4) could support energy storage and strengthen the power grid. Biological methanation (BM) is linked closely to the activity of biogas-producing Bacteria and methanogenic Archaea. During reactor operations, the microbiome is often subject to various changes, e.g., substrate limitation or pH-shifts, whereby the microorganisms are challenged to adapt to the new conditions. In this study, various process parameters including pH value, CH4 production rate, conversion yields and final gas composition were monitored for a hydrogenotrophic-adapted microbial community cultivated in a laboratory-scale BM reactor. To investigate the robustness of the BM process regarding power oscillations, the biogas microbiome was exposed to five hydrogen (H2)-feeding regimes lasting several days.

RESULTS

Applying various "on-off" H2-feeding regimes, the CH4 production rate recovered quickly, demonstrating a significant resilience of the microbial community. Analyses of the taxonomic composition of the microbiome revealed a high abundance of the bacterial phyla Firmicutes, Bacteroidota and Thermotogota followed by hydrogenotrophic Archaea of the phylum Methanobacteriota. Homo-acetogenic and heterotrophic fermenting Bacteria formed a complex food web with methanogens. The abundance of the methanogenic Archaea roughly doubled during discontinuous H2-feeding, which was related mainly to an increase in acetoclastic Methanothrix species. Results also suggested that Bacteria feeding on methanogens could reduce overall CH4 production. On the other hand, using inactive biomass as a substrate could support the growth of methanogenic Archaea. During the BM process, the additional production of H2 by fermenting Bacteria seemed to support the maintenance of hydrogenotrophic methanogens at non-H2-feeding phases. Besides the elusive role of Methanothrix during the H2-feeding phases, acetate consumption and pH maintenance at the non-feeding phase can be assigned to this species.

CONCLUSIONS

Taken together, the high adaptive potential of microbial communities contributes to the robustness of BM processes during discontinuous H2-feeding and supports the commercial use of BM processes for energy storage. Discontinuous feeding strategies could be used to enrich methanogenic Archaea during the establishment of a microbial community for BM. Both findings could contribute to design and improve BM processes from lab to pilot scale.

Rapid Proteomic Characterization of Bacteriocin-Producing Enterococcus faecium Strains from Foodstuffs

Enterococcus belongs to a group of microorganisms known as lactic acid bacteria (LAB), which constitute a broad heterogeneous group of generally food-grade microorganisms historically used in food preservation. Enterococci live as commensals of the gastrointestinal tract of warm-blooded animals, although they also are present in food of animal origin (milk, cheese, fermented sausages), vegetables, and plant materials because of their ability to survive heat treatments and adverse environmental conditions. The biotechnological traits of enterococci can be applied in the food industry; however, the emergence of enterococci as a cause of nosocomial infections makes their food status uncertain. Recent advances in high-throughput sequencing allow the subtyping of bacterial pathogens, but it cannot reflect the temporal dynamics and functional activities of microbiomes or bacterial isolates. Moreover, genetic analysis is based on sequence homologies, inferring functions from databases. Here, we used an end-to-end proteomic workflow to rapidly characterize two bacteriocin-producing Enterococcus faecium (Efm) strains. The proteome analysis was performed with liquid chromatography coupled to a trapped ion mobility spectrometry-time-of-flight mass spectrometry instrument (TimsTOF) for high-throughput and high-resolution characterization of bacterial proteins. Thus, we identified almost half of the proteins predicted in the bacterial genomes (>1100 unique proteins per isolate), including quantifying proteins conferring resistance to antibiotics, heavy metals, virulence factors, and bacteriocins. The obtained proteomes were annotated according to function, resulting in 22 complete KEGG metabolic pathway modules for both strains. The workflow used here successfully characterized these bacterial isolates and showed great promise for determining and optimizing the bioengineering and biotechnology properties of other LAB strains in the food industry.

The low-density lipoprotein receptor-mTORC1 axis coordinates CD8+ T cell activation

Activation of T cells relies on the availability of intracellular cholesterol for an effective response after stimulation. We investigated the contribution of cholesterol derived from extracellular uptake by the low-density lipoprotein (LDL) receptor in the immunometabolic response of T cells. By combining proteomics, gene expression profiling, and immunophenotyping, we described a unique role for cholesterol provided by the LDLR pathway in CD8+ T cell activation. mRNA and protein expression of LDLR was significantly increased in activated CD8+ compared to CD4+ WT T cells, and this resulted in a significant reduction of proliferation and cytokine production (IFNγ, Granzyme B, and Perforin) of CD8+ but not CD4+ T cells from Ldlr -/- mice after in vitro and in vivo stimulation. This effect was the consequence of altered cholesterol routing to the lysosome resulting in a lower mTORC1 activation. Similarly, CD8+ T cells from humans affected by familial hypercholesterolemia (FH) carrying a mutation on the LDLR gene showed reduced activation after an immune challenge.

A Hybrid Spectral Library and Protein Sequence Database Search Strategy for Bottom-Up and Top-Down Proteomic Data Analysis

Tandem mass spectrometry (MS/MS) is widely employed for the analysis of complex proteomic samples. While protein sequence database searching and spectral library searching are both well-established peptide identification methods, each has shortcomings. Protein sequence databases lack fragment peak intensity information, which can result in poor discrimination between correct and incorrect spectrum assignments. Spectral libraries usually contain fewer peptides than protein sequence databases, which limits the number of peptides that can be identified. Notably, few post-translationally modified peptides are represented in spectral libraries. This is because few search engines can both identify a broad spectrum of PTMs and create corresponding spectral libraries. Also, programs that generate spectral libraries using deep learning approaches are not yet able to accurately predict spectra for the vast majority of PTMs. Here, we address these limitations through use of a hybrid search strategy that combines protein sequence database and spectral library searches to improve identification success rates and sensitivity. This software uses Global PTM Discovery (G-PTM-D) to produce spectral libraries for a wide variety of different PTMs. These features, along with a new spectrum annotation and visualization tool, have been integrated into the freely available and open-source search engine MetaMorpheus.

RAD51AP1 regulates ALT-HDR through chromatin-directed homeostasis of TERRA

Alternative lengthening of telomeres (ALT) is a homology-directed repair (HDR) mechanism of telomere elongation that controls proliferation in subsets of aggressive cancer. Recent studies have revealed that telomere repeat-containing RNA (TERRA) promotes ALT-associated HDR (ALT-HDR). Here, we report that RAD51AP1, a crucial ALT factor, interacts with TERRA and utilizes it to generate D- and R-loop HR intermediates. We also show that RAD51AP1 binds to and might stabilize TERRA-containing R-loops as RAD51AP1 depletion reduces R-loop formation at telomere DNA breaks. Proteomic analyses uncover a role for RAD51AP1-mediated TERRA R-loop homeostasis in a mechanism of chromatin-directed suppression of TERRA and prevention of transcription-replication collisions (TRCs) during ALT-HDR. Intriguingly, we find that both TERRA binding and this non-canonical function of RAD51AP1 require its intrinsic SUMO-SIM regulatory axis. These findings provide insights into the multi-contextual functions of RAD51AP1 within the ALT mechanism and regulation of TERRA.

Toolbox Accelerating Glycomics (TAG): Improving Large-Scale Serum Glycomics and Refinement to Identify SALSA-Modified and Rare Glycans

Glycans are involved in many fundamental cellular processes such as growth, differentiation, and morphogenesis. However, their broad structural diversity makes analysis difficult. Glycomics via mass spectrometry has focused on the composition of glycans, but informatics analysis has not kept pace with the development of instrumentation and measurement techniques. We developed Toolbox Accelerating Glycomics (TAG), in which glycans can be added manually to the glycan list that can be freely designed with labels and sialic acid modifications, and fast processing is possible. In the present work, we improved TAG for large-scale analysis such as cohort analysis of serum samples. The sialic acid linkage-specific alkylamidation (SALSA) method converts differences in linkages such as α2,3- and α2,6-linkages of sialic acids into differences in mass. Glycans modified by SALSA and several structures discovered in recent years were added to the glycan list. A routine to generate calibration curves has been implemented to explore quantitation. These improvements are based on redefinitions of residues and glycans in the TAG List to incorporate information on glycans that could not be attributed because it was not assumed in the previous version of TAG. These functions were verified through analysis of purchased sera and 74 spectra with linearity at the level of R2 > 0.8 with 81 estimated glycan structures obtained including some candidate of rare glycans such as those with the N,N’-diacetyllactosediamine structure, suggesting they can be applied to large-scale analyses.

Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs

In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize and structure the different types of graphs that occur and to compare them between data sets. We observed a large influence of the accepted minimum peptide length during in silico digestion. When changing from theoretical peptides to measured ones, the graph structures are subject to two opposite effects. On the one hand, the graphs based on measured peptides are on average smaller and less complex compared to graphs using theoretical peptides. On the other hand, the proportion of protein nodes without unique peptides, which are a complicated case for protein inference and quantification, is considerably larger for measured data. Additionally, the proportion of graphs containing at least one protein node without unique peptides rises when going from database to quantitative level. The fraction of shared peptides and proteins without unique peptides as well as the complexity and size of the graphs highly depends on the data set and organism. Large differences between the structures of bipartite peptide-protein graphs have been observed between database and quantitative level as well as between analyzed species. In the analyzed measured data sets, the proportion of protein nodes without unique peptides ranged from 6.4% to 55.0%. This highlights the need for novel methods that can quantify proteins without unique peptides. The knowledge about the structure of the bipartite peptide-protein graphs gained in this study will be useful for the development of such algorithms.

The rate and role of pseudogenes of the Mycobacterium tuberculosis complex

Whole-genome sequence analyses have significantly contributed to the understanding of virulence and evolution of the Mycobacterium tuberculosis complex (MTBC), the causative pathogens of tuberculosis. Most MTBC evolutionary studies are focused on single nucleotide polymorphisms and deletions, but rare studies have evaluated gene content, whereas none has comprehensively evaluated pseudogenes. Accordingly, we describe an extensive study focused on quantifying and predicting possible functions of MTBC and Mycobacterium canettii pseudogenes. Using NCBI's PGAP-detected pseudogenes, we analysed 25 837 pseudogenes from 158 MTBC and M. canetii strains and combined transcriptomics and proteomics of M. tuberculosis H37Rv to gain insights about pseudogenes' expression. Our results indicate significant variability concerning rate and conservancy of in silico predicted pseudogenes among different ecotypes and lineages of tuberculous mycobacteria and pseudogenization of important virulence factors and genes of the metabolism and antimicrobial resistance/tolerance. We show that in silico predicted pseudogenes contribute considerably to MTBC genetic diversity at the population level. Moreover, the transcription machinery of M. tuberculosis can fully transcribe most pseudogenes, indicating intact promoters and recent pseudogene evolutionary emergence. Proteomics of M. tuberculosis and close evaluation of mutational lesions driving pseudogenization suggest that few in silico predicted pseudogenes are likely capable of neofunctionalization, nonsense mutation reversal, or phase variation, contradicting the classical definition of pseudogenes. Such findings indicate that genome annotation should be accompanied by proteomics and protein function assays to improve its accuracy. While indels and insertion sequences are the main drivers of the observed mutational lesions in these species, population bottlenecks and genetic drift are likely the evolutionary processes acting on pseudogenes' emergence over time. Our findings unveil a new perspective on MTBC's evolution and genetic diversity.

DCAF7 regulates cell proliferation through IRS1-FOXO1 signaling

Cell proliferation is dependent on growth factors insulin and IGF1. We sought to identify interactors of IRS1, the most proximal mediator of insulin/IGF1 signaling, that regulate cell proliferation. Using proximity-dependent biotin identification (BioID), we detected 40 proteins displaying proximal interactions with IRS1, including DCAF7 and its interacting partners DYRK1A and DYRK1B. In HepG2 cells, DCAF7 knockdown attenuated cell proliferation by inducing cell cycle arrest at G2. DCAF7 expression was required for insulin-stimulated AKT phosphorylation, and its absence promoted nuclear localization of the transcription factor FOXO1. DCAF7 knockdown induced expression of FOXO1-target genes implicated in G2 cell cycle inhibition, correlating with G2 cell cycle arrest. In Drosophila melanogaster, wing-specific knockdown of DCAF7/wap caused smaller wing size and lower wing cell number; the latter recovered upon double knockdown of wap and dfoxo. We propose that DCAF7 regulates cell proliferation and cell cycle via IRS1-FOXO1 signaling, of relevance to whole organism growth.

Pangenomic analysis of Chinese gastric cancer

Pangenomic study might improve the completeness of human reference genome (GRCh38) and promote precision medicine. Here, we use an automated pipeline of human pangenomic analysis to build gastric cancer pan-genome for 185 paired deep sequencing data (370 samples), and characterize the gene presence-absence variations (PAVs) at whole genome level. Genes ACOT1, GSTM1, SIGLEC14 and UGT2B17 are identified as highly absent genes in gastric cancer population. A set of genes from unaligned sequences with GRCh38 are predicted. We successfully locate one of predicted genes GC0643 on chromosome 9q34.2. Overexpression of GC0643 significantly inhibits cell growth, cell migration and invasion, cell cycle progression, and induces cell apoptosis in cancer cells. The tumor suppressor functions can be reversed by shGC0643 knockdown. The GC0643 is approved by NCBI database (GenBank: MW194843.1). Collectively, the robust pan-genome strategy provides a deeper understanding of the gene PAVs in the human cancer genome.

Human pan-genomics are increasing our knowledge of genomic diversity and genetic factors in disease. Here, the authors built a gastric cancer pan-genome that included the sequences of Chinese Han patients, and predicted putative and previously unaligned genes associated with gastric cancer.

The in vivo Interaction Landscape of Histones H3.1 and H3.3

Chromatin structure, transcription, DNA replication, and repair are regulated via locus-specific incorporation of histone variants and posttranslational modifications that guide effector chromatin-binding proteins. Here we report unbiased, quantitative interactomes for the replication-coupled (H3.1) and replication-independent (H3.3) histone H3 variants based on BioID proximity labeling, which allows interactions in intact, living cells to be detected. Along with a significant proportion of previously reported interactions detected by affinity purification followed by mass spectrometry, three quarters of the 608 histone-associated proteins that we identified are new, uncharacterized histone associations. The data reveal important biological nuances not captured by traditional biochemical means. For example, we found that the chromatin assembly factor-1 histone chaperone not only deposits the replication-coupled H3.1 histone variant during S-phase but also associates with H3.3 throughout the cell cycle in vivo. We also identified other variant-specific associations, such as with transcription factors, chromatin regulators, and with the mitotic machinery. Our proximity-based analysis is thus a rich resource that extends the H3 interactome and reveals new sets of variant-specific associations.

A filarial parasite-encoded human IL-10 receptor antagonist reveals a novel strategy to modulate host responses

Interleukin (IL)-10 is the primary cytokine driving the modulation of the host response in filarial infections. We performed binding assays with Brugia malayi antigen extracts and human IL-10R1. Bm5539 was the top-binding hit. We identified a short sequence, termed truncated Bm5339, that has structural similarities to the human IL-10 functional dimer. Sequence comparisons revealed that other filarial parasites possess Bm5539 orthologues. Using recombinant Bm5539 in a modified Luciferase Immunoprecipitation System assay, we confirmed that both the truncated and full-length forms of the protein can bind to human IL-10R1. Truncated Bm5539 could inhibit human IL-10-driven phosphorylation of STAT3, thereby demonstrating that Bm5539 acts as an IL-10 antagonist, most likely through competitive binding to the receptor. We provide a structural basis for these observations using computational modeling and simulations. This parasite-encoded cytokine receptor antagonist provides an additional lens through which parasite-induced modulation of the host immune response can be examined.

Data-independent acquisition proteomics methods for analyzing post-translational modifications

Protein post-translational modifications (PTMs) increase the functional diversity of the cellular proteome. Accurate and high throughput identification and quantification of protein PTMs is a key task in proteomics research. Recent advancements in data-independent acquisition (DIA) mass spectrometry (MS) technology have achieved deep coverage and accurate quantification of proteins and PTMs. This review provides an overview of DIA data processing methods that cover three aspects of PTMs analysis, i.e., detection of PTMs, site localization, and characterization of complex modification moieties, such as glycosylation. In addition, a survey of deep learning methods that boost DIA-based PTMs analysis is presented, including in silico spectral library generation, as well as feature scoring and error rate control. The limitations and future directions of DIA methods for PTMs analysis are also discussed. Novel data analysis methods will take advantage of advanced MS instrumentation techniques to empower DIA MS for in-depth and accurate PTMs measurements. This article is protected by copyright. All rights reserved.

Identification of a novel deFADding activity in human, yeast and bacterial 5' to 3' exoribonucleases

Identification of metabolite caps including FAD on the 5' end of RNA has uncovered a previously unforeseen intersection between cellular metabolism and gene expression. To understand the function of FAD caps in cellular physiology, we characterised the proteins interacting with FAD caps in budding yeast. Here we demonstrate that highly conserved 5'-3' exoribonucleases, Xrn1 and Rat1, physically interact with the RNA 5' FAD cap and both possess FAD cap decapping (deFADding) activity and subsequently degrade the resulting RNA. Xrn1 deFADding activity was also evident in human cells indicating its evolutionary conservation. Furthermore, we report that the recently identified bacterial 5'-3' exoribonuclease RNase AM also possesses deFADding activity that can degrade FAD-capped RNAs in vitro and in Escherichia coli cells. To gain a molecular understanding of the deFADding reaction, an RNase AM crystal structure with three manganese ions coordinated by a sulfate molecule and the active site amino acids was generated that provided details underlying hydrolysis of the FAD cap. Our findings reveal a general propensity for 5'-3' exoribonucleases to hydrolyse and degrade RNAs with 5' end noncanonical caps in addition to their well characterized 5' monophosphate RNA substrates indicating an intrinsic property of 5'-3' exoribonucleases.

Label-Free Quantitative Proteomics Reveal the Involvement of PRT6 in Arabidopsis thaliana Seed Responsiveness to Ethylene

In Arabidopsis thaliana, the breaking of seed dormancy in wild type (Col-0) by ethylene at 100 μL L^-1 required at least 30 h application. A mutant of the proteolytic N-degron pathway, lacking the E3 ligase PROTEOLYSIS 6 (PRT6), was investigated for its role in ethylene-triggered changes in proteomes during seed germination. Label-free quantitative proteomics was carried out on dormant wild type Col-0 and prt6 seeds treated with (+) or without (-) ethylene. After 16 h, 1737 proteins were identified, but none was significantly different in protein levels in response to ethylene. After longer ethylene treatment (30 h), 2552 proteins were identified, and 619 Differentially Expressed Proteins (DEPs) had significant differences in protein abundances between ethylene treatments and genotypes. In Col, 587 DEPs were enriched for those involved in signal perception and transduction, reserve mobilization and new material generation, which potentially contributed to seed germination. DEPs up-regulated by ethylene in Col included S-adenosylmethionine synthase 1, methionine adenosyltransferase 3 and ACC oxidase involved in ethylene synthesis and of Pyrabactin Resistance1 acting as an ABA receptor, while DEPs down-regulated by ethylene in Col included aldehyde oxidase 4 involved in ABA synthesis. In contrast, in prt6 seeds, ethylene did not result in strong proteomic changes with only 30 DEPs. Taken together, the present work demonstrates that the proteolytic N-degron pathway is essential for ethylene-mediated reprogramming of seed proteomes during germination.

LEDGF is a new growth factor in fetal serum

Fetal serum supports the immortal growth of mammalian cell lines in culture while adult serum leads to the terminal differentiation and death of cells in culture. Many of the proteins in fetal serum that support the indefinite division and growth of cancerous cell lines remain obscure. The peptides and proteins of fetal versus adult serum were analyzed by liquid chromatography, nano electrospray ionization and tandem mass spectrometry (LC-ESI-MS/MS). Three batches of fetal serum contained the Alpha Fetoprotein marker while adult serum batches did not. Insulin (INS), and insulin-like growth factor (ILGF), fibroblast growth factor (FGF), epidermal growth factor (EGF) and platelet derived growth factor (PDGF) were increased in fetal serum. New fetal growth factors including MEGF, HDGFRP and PSIP1 and soluble growth receptors such as TNFR, EGFR, NTRK2 and THRA were discovered. Addition of insulin or the homeotic transcription factor PSIP1, also referred to as Lens Epithelium Derived Growth Factor (LEDGF), partially restored the rounded phenotype of rapidly dividing cells but was not as effective as fetal serum. Thus, a new growth factor in fetal serum, LEDGF/PSIP1, was directly observed by tandem mass spectrometry and confirmed by add back experiments to cell culture media alongside insulin.

Identification of proteome markers for drug-induced liver injury in zebrafish embryos

The zebrafish embryo (ZFE) is a promising alternative non-rodent model in toxicology, and initial studies suggested its applicability in detecting hepatic responses related to drug-induced liver injury (DILI). Here, we hypothesize that detailed analysis of underlying mechanisms of hepatotoxicity in ZFE contributes to the improved identification of hepatotoxic properties of compounds and to the reduction of rodents used for hepatotoxicity assessment. ZFEs were exposed to nine reference hepatotoxicants, targeted at induction of steatosis, cholestasis, and necrosis, and effects compared with negative controls. Protein profiles of the individual compounds were generated using LC-MS/MS. We identified differentially expressed proteins and pathways, but as these showed considerable overlap, phenotype-specific responses could not be distinguished. This led us to identify a set of common hepatotoxicity marker proteins. At the pathway level, these were mainly associated with cellular adaptive stress-responses, whereas single proteins could be linked to common hepatotoxicity-associated processes. Applying several stringency criteria to our proteomics data as well as information from other data sources resulted in a set of potential robust protein markers, notably Igf2bp1, Cox5ba, Ahnak, Itih3b.2, Psma6b, Srsf3a, Ces2b, Ces2a, Tdo2b, and Anxa1c, for the detection of adverse responses.

Englerin A Rewires Phosphosignaling via Hsp27 Hyperphosphorylation to Induce Cytotoxicity in Renal Cancer Cells

Englerin A (EA) is a small-molecule natural product with selective cytotoxicity against renal cancer cells. EA has been shown to induce apoptosis and cell death through cell-cycle arrest and/or insulin signaling pathways. However, its biological mode of action or targets in renal cancer remains enigmatic. In this study, we employed advanced mass spectrometry-based phosphoproteomics approaches to identify EA's functional roles in renal cancer. We identified 10,940 phosphorylation sites, of which 706 sites exhibited EA-dependent phosphorylation changes. Integrated analysis of motifs and interaction networks suggested activation of stress-activated kinases including p38 upon EA treatment. Of note, a downstream target of p38, Hsp27, was found to be hyperphosphorylated on multiple sites upon EA treatment. Among these, a novel site Ser65 on Hsp27, which was further validated by targeted proteomics, was shown to be crucial for EA-induced cytotoxicity in renal cancer cells. Taken together, these data reveal the complex signaling cascade that is induced upon EA treatment and importantly provide insights into its effects on downstream molecular signaling.

Ddp1 Cooperates with Ppx1 to Counter a Stress Response Initiated by Nonvacuolar Polyphosphate

In diverse cells from bacterial to mammalian species, inorganic phosphate is stored in long chains called polyphosphate (polyP). These nearly universal polymers, ranging from three to thousands of phosphate moieties in length, are associated with molecular functions, including energy homeostasis, protein folding, and cell signaling. In many cell types, polyphosphate is concentrated in subcellular compartments or organelles. In the budding yeast Saccharomyces cerevisiae, polyP synthesis by the membrane-bound vacuolar transporter chaperone (VTC) complex is coupled to its translocation into the lumen of the vacuole, a lysosome-like organelle, where it is stored at high concentrations. In contrast, the ectopic expression of the bacterial polyphosphate kinase (PPK) results in the toxic accumulation of polyP outside the vacuole. In this study, we used label-free mass spectrometry to investigate the mechanisms underlying this toxicity. We find that PPK expression results in the activation of a stress response mediated in part by the Hog1 and Yak1 kinases and the Msn2/Msn4 transcription factors as well as by changes in protein kinase A (PKA) activity. This response is countered by the combined action of the Ddp1 and Ppx1 polyphosphatases that function together to counter polyP accumulation and downstream toxicity. In contrast, the ectopic expression of previously proposed mammalian polyphosphatases did not impact PPK-mediated toxicity in this model, suggesting either that these enzymes do not function directly as polyphosphatases in vivo or that they require cofactors unique to higher eukaryotes. Our work provides insight into why polyP accumulation outside lysosome-like organelles is toxic. Furthermore, it serves as a resource for exploring how polyP may impact conserved biological processes at a molecular level.

MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates

Immunopeptidomics aims to identify major histocompatibility complex (MHC)-presented peptides on almost all cells that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the nontryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS²PIP and retention time predictions by DeepLC have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS²PIP was tailored toward tryptic peptides, we have here retrained MS²PIP to include nontryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides but also yield further improvements for tryptic peptides. We show that the integration of new MS²PIP models, DeepLC, and Percolator in one software package, MS²Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS²Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Altogether, MS²Rescore thus allows substantially improved identification of novel epitopes from existing immunopeptidomics workflows.

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MS²Rescore significantly boosts immunopeptide identification rates

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Data-driven post-processing allows for a ten-fold increase in specificity

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MS²PIP and DeepLC predictors are integrated with Percolator post-processing

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MS²Rescore accepts identification results from MaxQuant, PEAKS, MS-GF+ and X!Tandem

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MS²Rescore shows great promise to extend current neo- and xeno-epitope landscapes