TANDEM: matching proteins with tandem mass spectra

APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control

Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.

Soil Metaproteomics for Microbial Community Profiling: Methodologies and Challenges

Soil represents a complex and dynamic ecosystem, hosting a myriad of microorganisms that coexist and play vital roles in nutrient cycling and organic matter transformation. Among these microorganisms, bacteria and fungi are key members of the microbial community, profoundly influencing the fate of nitrogen, sulfur, and carbon in terrestrial environments. Understanding the intricacies of soil ecosystems and the biological processes orchestrated by microbial communities necessitates a deep dive into their composition and metabolic activities. The advent of next-generation sequencing and 'omics' techniques, such as metagenomics and metaproteomics, has revolutionized our understanding of microbial ecology and the functional dynamics of soil microbial communities. Metagenomics enables the identification of microbial community composition in soil, while metaproteomics sheds light on the current biological functions performed by these communities. However, metaproteomics presents several challenges, both technical and computational. Factors such as the presence of humic acids and variations in extraction methods can influence protein yield, while the absence of high-resolution mass spectrometry and comprehensive protein databases limits the depth of protein identification. Notwithstanding these limitations, metaproteomics remains a potent tool for unraveling the intricate biological processes and functions of soil microbial communities. In this review, we delve into the methodologies and challenges of metaproteomics in soil research, covering aspects such as protein extraction, identification, and bioinformatics analysis. Furthermore, we explore the applications of metaproteomics in soil bioremediation, highlighting its potential in addressing environmental challenges.

Multi-scale analysis of heat stress acclimation in Arabidopsis seedlings highlights the primordial contribution of energy-transducing organelles

Much progress has been made in understanding the molecular mechanisms of plant adaptation to heat stress. However, the great diversity of models and stress conditions, and the fact that analyses are often limited to a small number of approaches, complicate the picture. We took advantage of a liquid culture system in which Arabidopsis seedlings are arrested in their development, thus avoiding interference with development and drought stress responses, to investigate through an integrative approach seedlings' global response to heat stress and acclimation. Seedlings perfectly tolerate a noxious heat shock (43°C) when subjected to a heat priming treatment at a lower temperature (38°C) the day before, displaying a thermotolerance comparable to that previously observed for Arabidopsis. A major effect of the pre-treatment was to partially protect energy metabolism under heat shock and favor its subsequent rapid recovery, which was correlated with the survival of seedlings. Rapid recovery of actin cytoskeleton and mitochondrial dynamics were another landmark of heat shock tolerance. The omics confirmed the role of the ubiquitous heat shock response actors but also revealed specific or overlapping responses to priming, heat shock, and their combination. Since only a few components or functions of chloroplast and mitochondria were highlighted in these analyses, the preservation and rapid recovery of their bioenergetic roles upon acute heat stress do not require extensive remodeling of the organelles. Protection of these organelles is rather integrated into the overall heat shock response, thus allowing them to provide the energy required to elaborate other cellular responses toward acclimation.

Data-independent acquisition in metaproteomics

INTRODUCTION

Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data.

AREAS COVERED

This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed.

EXPERT OPINION

Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

The quantification of zebrafish ocular-associated proteins provides hints for sex-biased visual impairments and perception

Biochemical differences between sexes can also be seen in non-sexual organs and may affect organ functions and susceptibility to diseases. It has been shown that there are sex-biased visual perceptions and impairments. Abundance differences of eye proteins could provide explanations for some of these. Exploration of the ocular proteome was performed to find sex-based protein abundance differences in zebrafish Danio rerio. A label-free protein quantification workflow using high-resolution mass spectrometry was employed to find proteins with significant differences between the sexes. In total, 3740 unique master proteins were identified and quantified, and 49 proteins showed significant abundance differences between the eyes of male and female zebrafish. Those proteins belong to lipoproteins, immune system, blood coagulation, antioxidants, iron and heme-binding proteins, ion channels, pumps and exchangers, neuronal and photoreceptor proteins, and the cytoskeleton. An extensive literature review provided clues for the possible links between the sex-biased level of proteins and visual perception and impairments. In conclusion, sexual dimorphism at the protein level was discovered for the first time in the eye of zebrafish and should be accounted for in ophthalmological studies. Data are available via ProteomeXchange with identifier PXD033338.

Proteomes of plasmodium knowlesi early and late ring-stage parasites and infected host erythrocytes

The emerging malaria parasite Plasmodium knowlesi threatens the goal of worldwide malaria elimination due to its zoonotic spread in Southeast Asia. After brief ex-vivo culture we used 2D LC/MS/MS to examine the early and late ring stages of infected Macaca mulatta red blood cells harboring P. knowlesi. The M. mulatta clathrin heavy chain and T-cell and macrophage inhibitor ERMAP were overexpressed in the early ring stage; glutaredoxin 3 was overexpressed in the late ring stage; GO term differential enrichments included response to oxidative stress and the cortical cytoskeleton in the early ring stage. P. knowlesi clathrin heavy chain and 60S acidic ribosomal protein P2 were overexpressed in the late ring stage; GO term differential enrichments included vacuoles in the early ring stage, ribosomes and translation in the late ring stage, and Golgi- and COPI-coated vesicles, proteasomes, nucleosomes, vacuoles, ion-, peptide-, protein-, nucleocytoplasmic- and RNA-transport, antioxidant activity and glycolysis in both stages. SIGNIFICANCE: Due to its zoonotic spread, cases of the emerging human pathogen Plasmodium knowlesi in southeast Asia, and particularly in Malaysia, threaten regional and worldwide goals for malaria elimination. Infection by this parasite can be fatal to humans, and can be associated with significant morbidity. Due to zoonotic transmission from large macaque reservoirs that are untreatable by drugs, and outdoor biting mosquito vectors that negate use of preventive measures such as bed nets, its containment remains a challenge. Its biology remains incompletely understood. Thus we examine the expressed proteome of the early and late ex-vivo cultured ring stages, the first intraerythrocyte developmental stages after infection of host rhesus macaque erythrocytes. We used GO term enrichment strategies and differential protein expression to compare early and late ring stages. The early ring stage is characterized by the enrichment of P. knowlesi vacuoles, and overexpression of the M. mulatta clathrin heavy chain, important for clathrin-coated pits and vesicles, and clathrin-mediated endocytosis. The M. mulatta protein ERMAP was also overexpressed in the early ring stage, suggesting a potential role in early ring stage inhibition of T-cells and macrophages responding to P. knowlesi infection of reticulocytes. This could allow expansion of the host P. knowlesi cellular niche, allowing parasite adaptation to invasion of a wider age range of RBCs than the preferred young RBCs or reticulocytes, resulting in proliferation and increased pathogenesis in infected humans. Other GO terms differentially enriched in the early ring stage include the M. mulatta cortical cytoskeleton and response to oxidative stress. The late ring stage is characterized by overexpression of the P. knowlesi clathrin heavy chain. Combined with late ring stage GO term enrichment of Golgi-associated and coated vesicles, and enrichment of COPI-coated vesicles in both stages, this suggests the importance to P. knowlesi biology of clathrin-mediated endocytosis. P. knowlesi ribosomes and translation were also differentially enriched in the late ring stage. With expression of a variety of heat shock proteins, these results suggest production of folded parasite proteins is increasing by the late ring stage. M. mulatta endocytosis was differentially enriched in the late ring stage, as were clathrin-coated vesicles and endocytic vesicles. This suggests that M. mulatta clathrin-based endocytosis, perhaps in infected reticulocytes rather than mature RBC, may be an important process in the late ring stage. Additional ring stage biology from enriched GO terms includes M. mulatta proteasomes, protein folding and the chaperonin-containing T complex, actin and cortical actin cytoskeletons. P knowlesi biology also includes proteasomes, as well as nucleosomes, antioxidant activity, a variety of transport processes, glycolysis, vacuoles and protein folding. Mature RBCs have lost internal organelles, suggesting infection here may involve immature reticulocytes still retaining organelles. P. knowlesi parasite proteasomes and translational machinery may be ring stage drug targets for known selective inhibitors of these processes in other Plasmodium species. To our knowledge this is the first examination of more than one timepoint within the ring stage. Our results expand knowledge of both host and parasite proteins, pathways and organelles underlying P. knowlesi ring stage biology.

A learned score function improves the power of mass spectrometry database search

MOTIVATION

One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide sequence that was responsible for generating the spectrum. Two primary classes of methods are used to solve this problem: database search and de novo peptide sequencing. State-of-the-art methods for de novo sequencing use machine learning methods, whereas most database search engines use hand-designed score functions to evaluate the quality of a match between an observed spectrum and a candidate peptide from the database. We hypothesized that machine learning models for de novo sequencing implicitly learn a score function that captures the relationship between peptides and spectra, and thus may be re-purposed as a score function for database search. Because this score function is trained from massive amounts of mass spectrometry data, it could potentially outperform existing, hand-designed database search tools.

RESULTS

To test this hypothesis, we re-engineered Casanovo, which has been shown to provide state-of-the-art de novo sequencing capabilities, to assign scores to given peptide-spectrum pairs. We then evaluated the statistical power of this Casanovo score function, Casanovo-DB, to detect peptides on a benchmark of three mass spectrometry runs from three different species. In addition, we show that re-scoring with the Percolator post-processor benefits Casanovo-DB more than other score functions, further increasing the number of detected peptides.

Extraction of naturally occurring peptides versus the tryptic digestion of proteins from fetal versus adult bovine serum for LC-ESI-MS/MS

The naturally occurring peptides and digested proteins of fetal versus adult bovine serum were compared by LC-ESI-MS/MS after correction against noise from blank injections and random MS/MS spectra as statistical controls. Serum peptides were extracted by differential precipitation with mixtures of acetonitrile and water. Serum proteins were separated by partition chromatography over quaternary amine resin followed by tryptic digestion. The rigorous X!TANDEM goodness of fit algorithm that has a low error rate as demonstrated by low FDR q-values (q ≤ 0.01) showed qualitative and quantitative agreement with the SEQUEST cross correlation algorithm on 12,052 protein gene symbols. Tryptic digestion provided a quantitative identification of the serum proteins where observation frequency reflected known high abundance. In contrast, the naturally occurring peptides reflected the cleavage of common serum proteins such as C4A, C3, FGB, HPX, A2M but also proteins in lower concentration such as F13A1, IK, collagens and protocadherins. Proteins associated with cellular growth and development such as actins (ACT), ribosomal proteins like Ribosomal protein S6 (RPS6), synthetic enzymes and extracellular matrix factors were enriched in fetal calf serum. In contrast to the large literature from cord blood, IgG light chains were absent from fetal serum as observed by LC-ESI-MS/MS and confirmed by ELISA.

Maackia amurensis seed lectin (MASL) and soluble human podoplanin (shPDPN) sequence analysis and effects on human oral squamous cell carcinoma (OSCC) cell migration and viability

Maackia amurensis lectins serve as research and botanical agents that bind to sialic residues on proteins. For example, M. amurensis seed lectin (MASL) targets the sialic acid modified podoplanin (PDPN) receptor to suppress arthritic chondrocyte inflammation, and inhibit tumor cell growth and motility. However, M. amurensis lectin nomenclature and composition are not clearly defined. Here, we sought to definitively characterize MASL and its effects on tumor cell behavior. We utilized SDS-PAGE and LC-MS/MS to find that M. amurensis lectins can be divided into two groups. MASL is a member of one group which is composed of subunits that form dimers, evidently mediated by a cysteine residue in the carboxy region of the protein. In contrast to MASL, members of the other group do not dimerize under nonreducing conditions. These data also indicate that MASL is composed of 4 isoforms with an identical amino acid sequence, but unique glycosylation sites. We also produced a novel recombinant soluble human PDPN receptor (shPDPN) with 17 threonine residues glycosylated with sialic acid moieties with potential to act as a ligand trap that inhibits OSCC cell growth and motility. In addition, we report here that MASL targets PDPN with very strong binding kinetics in the nanomolar range. Moreover, we confirm that MASL can inhibit the growth and motility of human oral squamous cell carcinoma (OSCC) cells that express the PDPN receptor. Taken together, these data characterize M. amurensis lectins into two major groups based on their intrinsic properties, clarify the composition of MASL and its subunit isoform sequence and glycosylation sites, define sialic acid modifications on the PDPN receptor and its ability to act as a ligand trap, quantitate MASL binding to PDPN with KD in the nanomolar range, and verify the ability of MASL to serve as a potential anticancer agent.

A combined lipidomic and proteomic profiling of Arabidopsis thaliana plasma membrane

The plant plasma membrane (PM) plays a key role in perception of environmental signals, and set-up of adaptive responses. An exhaustive and quantitative description of the whole set of lipids and proteins constituting the PM is necessary to understand how these components allow to fulfill such essential physiological functions. Here we provide by state-of-the-art approaches the first combined reference of the plant PM lipidome and proteome from Arabidopsis thaliana suspension cell culture. We identified and quantified a reproducible core set of 2165 proteins, which is by far the largest set of available data concerning this plant PM proteome. Using the same samples, combined lipidomic approaches, allowing the identification and quantification of an unprecedented repertoire of 414 molecular species of lipids showed that sterols, phospholipids, and sphingolipids are present in similar proportions in the plant PM. Within each lipid class, the precise amount of each lipid family and the relative proportion of each molecular species were further determined, allowing to establish the complete lipidome of Arabidopsis PM, and highlighting specific characteristics of the different molecular species of lipids. Results obtained point to a finely tuned adjustment of the molecular characteristics of lipids and proteins. More than a hundred proteins related to lipid metabolism, transport, or signaling have been identified and put in perspective of the lipids with which they are associated. This set of data represents an innovative resource to guide further research relative to the organization and functions of the plant PM.

Alpha-glucans from bacterial necromass indicate an intra-population loop within the marine carbon cycle

Phytoplankton blooms provoke bacterioplankton blooms, from which bacterial biomass (necromass) is released via increased zooplankton grazing and viral lysis. While bacterial consumption of algal biomass during blooms is well-studied, little is known about the concurrent recycling of these substantial amounts of bacterial necromass. We demonstrate that bacterial biomass, such as bacterial alpha-glucan storage polysaccharides, generated from the consumption of algal organic matter, is reused and thus itself a major bacterial carbon source in vitro and during a diatom-dominated bloom. We highlight conserved enzymes and binding proteins of dominant bloom-responder clades that are presumably involved in the recycling of bacterial alpha-glucan by members of the bacterial community. We furthermore demonstrate that the corresponding protein machineries can be specifically induced by extracted alpha-glucan-rich bacterial polysaccharide extracts. This recycling of bacterial necromass likely constitutes a large-scale intra-population energy conservation mechanism that keeps substantial amounts of carbon in a dedicated part of the microbial loop.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.

Mapping the global interactome of the ARF family reveals spatial organization in cellular signaling pathways

The ADP-ribosylation factors (ARFs) and ARF-like (ARL) GTPases serve as essential molecular switches governing a wide array of cellular processes. In this study, we used proximity-dependent biotin identification (BioID) to comprehensively map the interactome of 28 out of 29 ARF and ARL proteins in two cellular models. Through this approach, we identified ∼3000 high-confidence proximal interactors, enabling us to assign subcellular localizations to the family members. Notably, we uncovered previously undefined localizations for ARL4D and ARL10. Clustering analyses further exposed the distinctiveness of the interactors identified with these two GTPases. We also reveal that the expression of the understudied member ARL14 is confined to the stomach and intestines. We identified phospholipase D1 (PLD1) and the ESCPE-1 complex, more precisely, SNX1, as proximity interactors. Functional assays demonstrated that ARL14 can activate PLD1 in cellulo and is involved in cargo trafficking via the ESCPE-1 complex. Overall, the BioID data generated in this study provide a valuable resource for dissecting the complexities of ARF and ARL spatial organization and signaling.

Salmonella exploits membrane reservoirs for invasion of host cells

Salmonella utilizes a type 3 secretion system to translocate virulence proteins (effectors) into host cells during infection1. The effectors modulate host cell machinery to drive uptake of the bacteria into vacuoles, where they can establish an intracellular replicative niche. A remarkable feature of Salmonella invasion is the formation of actin-rich protuberances (ruffles) on the host cell surface that contribute to bacterial uptake. However, the membrane source for ruffle formation and how these bacteria regulate membrane mobilization within host cells remains unclear. Here, we show that Salmonella exploits membrane reservoirs for the generation of invasion ruffles. The reservoirs are pre-existing tubular compartments associated with the plasma membrane (PM) and are formed through the activity of RAB10 GTPase. Under normal growth conditions, membrane reservoirs contribute to PM homeostasis and are preloaded with the exocyst subunit EXOC2. During Salmonella invasion, the bacterial effectors SipC, SopE2, and SopB recruit exocyst subunits from membrane reservoirs and other cellular compartments, thereby allowing exocyst complex assembly and membrane delivery required for bacterial uptake. Our findings reveal an important role for RAB10 in the establishment of membrane reservoirs and the mechanisms by which Salmonella can exploit these compartments during host cell invasion.

Computational approaches to identify sites of phosphorylation

Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.

Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome

Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.

RNF4 sustains Myc-driven tumorigenesis by facilitating DNA replication

The mammalian SUMO-targeted E3 ubiquitin ligase Rnf4 has been reported to act as a regulator of DNA repair, but the importance of RNF4 as a tumor suppressor has not been tested. Using a conditional-knockout mouse model, we deleted Rnf4 in the B cell lineage to test the importance of RNF4 for growth of somatic cells. Although Rnf4-conditional-knockout B cells exhibited substantial genomic instability, Rnf4 deletion caused no increase in tumor susceptibility. In contrast, Rnf4 deletion extended the healthy lifespan of mice expressing an oncogenic c-myc transgene. Rnf4 activity is essential for normal DNA replication, and in its absence, there was a failure in ATR-CHK1 signaling of replication stress. Factors that normally mediate replication fork stability, including members of the Fanconi anemia gene family and the helicases PIF1 and RECQL5, showed reduced accumulation at replication forks in the absence of RNF4. RNF4 deficiency also resulted in an accumulation of hyper-SUMOylated proteins in chromatin, including members of the SMC5/6 complex, which contributes to replication failure by a mechanism dependent on RAD51. These findings indicate that RNF4, which shows increased expression in multiple human tumor types, is a potential target for anticancer therapy, especially in tumors expressing c-myc.

Mapping the global interactome of the ARF family reveals spatial organization in cellular signaling pathways

The ADP-ribosylation factors (ARFs) and ARF-like (ARLs) GTPases serve as essential molecular switches governing a wide array of cellular processes. In this study, we utilized proximity-dependent biotin identification (BioID) to comprehensively map the interactome of 28 out of 29 ARF and ARL proteins in two cellular models. Through this approach, we identified ~3000 high-confidence proximal interactors, enabling us to assign subcellular localizations to the family members. Notably, we uncovered previously undefined localizations for ARL4D and ARL10. Clustering analyses further exposed the distinctiveness of the interactors identified with these two GTPases. We also reveal that the expression of the understudied member ARL14 is confined to the stomach and intestines. We identified phospholipase D1 (PLD1) and the ESCPE-1 complex, more precisely SNX1, as proximity interactors. Functional assays demonstrated that ARL14 can activate PLD1 in cellulo and is involved in cargo trafficking via the ESCPE-1 complex. Overall, the BioID data generated in this study provide a valuable resource for dissecting the complexities of ARF and ARL spatial organization and signaling.

Aberrant DNA repair reveals a vulnerability in histone H3.3-mutant brain tumors

Pediatric high-grade gliomas (pHGG) are devastating and incurable brain tumors with recurrent mutations in histone H3.3. These mutations promote oncogenesis by dysregulating gene expression through alterations of histone modifications. We identify aberrant DNA repair as an independent mechanism, which fosters genome instability in H3.3 mutant pHGG, and opens new therapeutic options. The two most frequent H3.3 mutations in pHGG, K27M and G34R, drive aberrant repair of replication-associated damage by non-homologous end joining (NHEJ). Aberrant NHEJ is mediated by the DNA repair enzyme polynucleotide kinase 3'-phosphatase (PNKP), which shows increased association with mutant H3.3 at damaged replication forks. PNKP sustains the proliferation of cells bearing H3.3 mutations, thus conferring a molecular vulnerability, specific to mutant cells, with potential for therapeutic targeting.

A diterpene synthase from the sandfly Lutzomyia longipalpis produces the pheromone sobralene

The phlebotomine sandfly, Lutzomyia longipalpis, a major vector of the Leishmania parasite, uses terpene pheromones to attract conspecifics for mating. Examination of the L. longipalpis genome revealed a putative terpene synthase (TPS), which-upon heterologous expression in, and purification from, Escherichia coli-yielded a functional enzyme. The TPS, termed LlTPS, converted geranyl diphosphate (GPP) into a mixture of monoterpenes with low efficiency, of which β-ocimene was the major product. (E,E)-farnesyl diphosphate (FPP) principally produced small amounts of (E)-β-farnesene, while (Z,E)- and (Z,Z)-FPP yielded a mixture of bisabolene isomers. None of these mono- and sesquiterpenes are known volatiles of L. longipalpis. Notably, however, when provided with (E,E,E)-geranylgeranyl diphosphate (GGPP), LlTPS gave sobralene as its major product. This diterpene pheromone is released by certain chemotypes of L. longipalpis, in particular those found in the Ceará state of Brazil. Minor diterpene components were also seen as products of the enzyme that matched those seen in a sandfly pheromone extract.

A Comprehensive Understanding of Post-Translational Modification of Sox2 via Acetylation and O-GlcNAcylation in Colorectal Cancer

Aberrant expression of the pluripotency-associated transcription factor Sox2 is associated with poor prognosis in colorectal cancer (CRC). We investigated the regulatory roles of major post-translational modifications in Sox2 using two CRC cell lines, SW480 and SW620, derived from the same patient but with low and high Sox2 expression, respectively. Acetylation of K75 in the Sox2 nuclear export signal was relatively increased in SW480 cells and promotes Sox2 nucleocytoplasmic shuttling and proteasomal degradation of Sox2. LC-MS-based proteomics analysis identified HDAC4 and p300 as binding partners involved in the acetylation-mediated control of Sox2 expression in the nucleus. Sox2 K75 acetylation is mediated by the acetyltransferase activity of CBP/p300 and ACSS3. In SW620 cells, HDAC4 deacetylates K75 and is regulated by miR29a. O-GlcNAcylation on S246, in addition to K75 acetylation, also regulates Sox2 stability. These findings provide insights into the regulation of Sox2 through multiple post-translational modifications and pathways in CRC.

Rat Model of Type 2 Diabetes Mellitus Recapitulates Human Disease in the Anterior Segment of the Eye

Changes in the anterior segment of the eye due to type 2 diabetes mellitus (T2DM) are not well-characterized, in part due to the lack of a reliable animal model. This study evaluates changes in the anterior segment, including crystalline lens health, corneal endothelial cell density, aqueous humor metabolites, and ciliary body vasculature, in a rat model of T2DM compared with human eyes. Male Sprague-Dawley rats were fed a high-fat diet (45% fat) or normal diet, and rats fed the high-fat diet were injected with streptozotocin i.p. to generate a model of T2DM. Cataract formation and corneal endothelial cell density were assessed using microscopic analysis. Diabetes-related rat aqueous humor alterations were assessed using metabolomics screening. Transmission electron microscopy was used to assess qualitative ultrastructural changes ciliary process microvessels at the site of aqueous formation in the eyes of diabetic rats and humans. Eyes from the diabetic rats demonstrated cataracts, lower corneal endothelial cell densities, altered aqueous metabolites, and ciliary body ultrastructural changes, including vascular endothelial cell activation, pericyte degeneration, perivascular edema, and basement membrane reduplication. These findings recapitulated diabetic changes in human eyes. These results support the use of this model for studying ocular manifestations of T2DM and support a hypothesis postulating blood-aqueous barrier breakdown and vascular leakage at the ciliary body as a mechanism for diabetic anterior segment pathology.

Identification of structural and regulatory cell-shape determinants in Haloferax volcanii

Archaea play indispensable roles in global biogeochemical cycles, yet many crucial cellular processes, including cell-shape determination, are poorly understood. Haloferax volcanii, a model haloarchaeon, forms rods and disks, depending on growth conditions. Here, we used a combination of iterative proteomics, genetics, and live-cell imaging to identify mutants that only form rods or disks. We compared the proteomes of the mutants with wild-type cells across growth phases, thereby distinguishing between protein abundance changes specific to cell shape and those related to growth phases. The results identified a diverse set of proteins, including predicted transporters, transducers, signaling components, and transcriptional regulators, as important for cell-shape determination. Through phenotypic characterization of deletion strains, we established that rod-determining factor A (RdfA) and disk-determining factor A (DdfA) are required for the formation of rods and disks, respectively. We also identified structural proteins, including an actin homolog that plays a role in disk-shape morphogenesis, which we named volactin. Using live-cell imaging, we determined volactin's cellular localization and showed its dynamic polymerization and depolymerization. Our results provide insights into archaeal cell-shape determination, with possible implications for understanding the evolution of cell morphology regulation across domains.

Intestinal permeability and inflammatory features of juvenile age correlate with the eventual systemic autoimmunity in lupus-prone female SWR × NZB F1 (SNF1) mice

The incidence of systemic lupus erythematosus (SLE) is about nine times higher in women than in men, and the underlying mechanisms that contribute to this gender bias are not fully understood. Previously, using lupus-prone (SWR × NZB)F1 (SNF1) mice, we have shown that the intestinal immune system could play a role in the initiation and progression of disease in SLE, and depletion of gut microbiota produces more pronounced disease protection in females than in males. Here, we show that the gut permeability features of lupus-prone female SNF1 mice at juvenile ages directly correlate with the expression levels of pro-inflammatory factors, faecal IgA abundance and nAg reactivity and the eventual systemic autoantibody levels and proteinuria onset. Furthermore, we observed that the disease protection achieved in female SNF1 mice upon depletion of gut microbiota correlates with the diminished gut inflammatory protein levels, intestinal permeability and circulating microbial DNA levels. However, faecal microbiota transplant from juvenile male and females did not result in modulation of gut inflammatory features or permeability. Overall, these observations suggest that the early onset of intestinal inflammation, systemic autoantibody production and clinical stage disease in lupus-prone females is linked to higher gut permeability in them starting at as early as juvenile age. While the higher gut permeability in juvenile lupus-prone females is dependent on the presence of gut microbes, it appears to be independent of the composition of gut microbiota.

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. 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% to 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.

Interaction of Whitefly Effector G4 with Tomato Proteins Impacts Whitefly Performance

The phloem-feeding insect Bemisia tabaci is an important pest, responsible for the transmission of several crop-threatening virus species. While feeding, the insect secretes a cocktail of effectors to modulate plant defense responses. Here, we present a set of proteins identified in an artificial diet on which B. tabaci was salivating. We subsequently studied whether these candidate effectors can play a role in plant immune suppression. Effector G4 was the most robust suppressor of an induced- reactive oxygen species (ROS) response in Nicotiana benthamiana. In addition, G4 was able to suppress ROS production in Solanum lycopersicum (tomato) and Capsicum annuum (pepper). G4 localized predominantly in the endoplasmic reticulum in N. benthamiana leaves and colocalized with two identified target proteins in tomato: REF-like stress related protein 1 (RSP1) and meloidogyne-induced giant cell protein DB141 (MIPDB141). Silencing of MIPDB141 in tomato reduced whitefly fecundity up to 40%, demonstrating that the protein is involved in susceptibility to B. tabaci. Together, our data demonstrate that effector G4 impairs tomato immunity to whiteflies by interfering with ROS production and via an interaction with tomato susceptibility protein MIPDB141. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Proximity Mapping of Ciliary Proteins by BioID

The primary cilium is a highly conserved microtubule-based organelle present in most vertebrate cell types. Mutations in ciliary protein genes can lead to dysfunctional or absent cilia and are the cause of a large group of heterogeneous diseases known as ciliopathies. ARL13B is a member of the ARF family of regulatory GTPases and is highly enriched on the ciliary membrane. The absence of ARL13B disrupts cilia architecture and mutations have been linked to several diseases; yet there remain major gaps in our understanding of the role that ARL13B plays in primary cilia function. Here, we demonstrate how in cellulo proximity-dependent biotinylation (BioID) can be used to generate a comprehensive protein proximity map of ciliary proteins by performing BioID on N- and C-terminally BirA*-tagged ARL13B. This method can theoretically provide insight into any cilia protein, identifying key interactors that play a critical role in ciliary biology.

Bioinformatic Workflows for Metaproteomics

The strong influence of microbiomes on areas such as ecology and human health has become widely recognized in the past years. Accordingly, various techniques for the investigation of the composition and function of microbial community samples have been developed. Metaproteomics, the comprehensive analysis of the proteins from microbial communities, allows for the investigation of not only the taxonomy but also the functional and quantitative composition of microbiome samples. Due to the complexity of the investigated communities, methods developed for single organism proteomics cannot be readily applied to metaproteomic samples. For this purpose, methods specifically tailored to metaproteomics are required. In this work, a detailed overview of current bioinformatic solutions and protocols in metaproteomics is given. After an introduction to the proteomic database search, the metaproteomic post-processing steps are explained in detail. Ten specific bioinformatic software solutions are focused on, covering various steps including database-driven identification and quantification as well as taxonomic and functional assignment.

Proteomic Analyses of the Mouse Brain

Proteomics is a scientific field that aims to identify and characterize all proteins within a biological system, including their posttranslational modifications (PTMs), quantitative changes, and protein-protein interactions. Over the last two decades, proteomic approaches have been widely used in neuroscience research, providing multidimensional insights into the biology and pathology of the brain.Here, we present a basic protocol for profiling protein expression in the mouse brain, which involves total protein extraction, fractionation, digestion, and identification through liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). This method is compatible with many prevalent techniques used for protein quantitation, PTM analysis, and protein-protein interaction mapping.

Making MS Omics Data ML-Ready: SpeCollate Protocols

The increasing complexity and volume of mass spectrometry (MS) data have presented new challenges and opportunities for proteomics data analysis and interpretation. In this chapter, we provide a comprehensive guide to transforming MS data for machine learning (ML) training, inference, and applications. The chapter is organized into three parts. The first part describes the data analysis needed for MS-based experiments and a general introduction to our deep learning model SpeCollate-which we will use throughout the chapter for illustration. The second part of the chapter explores the transformation of MS data for inference, providing a step-by-step guide for users to deduce peptides from their MS data. This section aims to bridge the gap between data acquisition and practical applications by detailing the necessary steps for data preparation and interpretation. In the final part, we present a demonstrative example of SpeCollate, a deep learning-based peptide database search engine that overcomes the problems of simplistic simulation of theoretical spectra and heuristic scoring functions for peptide-spectrum matches by generating joint embeddings for spectra and peptides. SpeCollate is a user-friendly tool with an intuitive command-line interface to perform the search, showcasing the effectiveness of the techniques and methodologies discussed in the earlier sections and highlighting the potential of machine learning in the context of mass spectrometry data analysis. By offering a comprehensive overview of data transformation, inference, and ML model applications for mass spectrometry, this chapter aims to empower researchers and practitioners in leveraging the power of machine learning to unlock novel insights and drive innovation in the field of mass spectrometry-based omics.

Metaproteomic Analysis of Biogas Plants: A Complete Workflow from Lab to Bioinformatics

Metaproteomics represents a promising and fast method to analyze the taxonomic and functional composition of biogas plant microbiomes. However, metaproteomics sample preparation and bioinformatics analysis is still challenging due to the sample complexity and contaminants. In this chapter, a tailored workflow including sampling, phenol extraction in a ball mill, amido black protein quantification, FASP digestion, LC-MS/MS measurement as well as bioinformatics and biostatistical data evaluation are here described for the metaproteomics advancements applied to biogas plant samples.

Peptide-Based Mass Spectrometry for the Investigation of Protein Complexes

In the last two decades, biological mass spectrometry has become the gold standard for the identification of proteins in biological samples. The technological advancement of mass spectrometers and the development of methods for ionization, gas phase transfer, peptide fragmentation as well as for acquisition of high-resolution mass spectrometric data marked the success of the technique. This chapter introduces peptide-based mass spectrometry as a tool for the investigation of protein complexes. It provides an overview of the main steps for sample preparation starting from protein fractionation, reduction, alkylation and focus on the final step of protein digestion. The basic concepts of biological mass spectrometry as well as details about instrumental analysis and data acquisition are described. Finally, the most common methods for data analysis and sequence determination are summarized with an emphasis on its application to protein-protein complexes.

Insight into the mechanism of gallstone disease by proteomic and metaproteomic characterization of human bile

Introduction

Cholesterol gallstone disease is a prevalent condition that has a significant economic impact. However, the role of the bile microbiome in its development and the host's responses to it remain poorly understood.

Methods

In this study, we conducted a comprehensive analysis of microbial and human bile proteins in 40 individuals with either gallstone disease or gallbladder polyps. We employed a combined proteomic and metaproteomic approach, as well as meta-taxonomic analysis, functional pathway enrichment, and Western blot analyses.

Results

Our metaproteomic analysis, utilizing the lowest common ancestor algorithm, identified 158 microbial taxa in the bile samples. We discovered microbial taxa that may contribute to gallstone formation, including β-glucuronidase-producing bacteria such as Streptococcus, Staphylococcus, and Clostridium, as well as those involved in biofilm formation like Helicobacter, Cyanobacteria, Pseudomonas, Escherichia coli, and Clostridium. Furthermore, we identified 2,749 human proteins and 87 microbial proteins with a protein false discovery rate (FDR) of 1% and at least 2 distinct peptides. Among these proteins, we found microbial proteins crucial to biofilm formation, such as QDR3, ompA, ndk, pstS, nanA, pfIB, and dnaK. Notably, QDR3 showed a gradual upregulation from chronic to acute cholesterol gallstone disease when compared to polyp samples. Additionally, we discovered other microbial proteins that enhance bacterial virulence and gallstone formation by counteracting host oxidative stress, including sodB, katG, rbr, htrA, and ahpC. We also identified microbial proteins like lepA, rtxA, pckA, tuf, and tpiA that are linked to bacterial virulence and potential gallstone formation, with lepA being upregulated in gallstone bile compared to polyp bile. Furthermore, our analysis of the host proteome in gallstone bile revealed enhanced inflammatory molecular profiles, including innate immune molecules against microbial infections. Gallstone bile exhibited overrepresented pathways related to blood coagulation, folate metabolism, and the IL-17 pathway. However, we observed suppressed metabolic activities, particularly catabolic metabolism and transport activities, in gallstone bile compared to polyp bile. Notably, acute cholelithiasis bile demonstrated significantly impaired metabolic activities compared to chronic cholelithiasis bile.

Conclusion

Our study provides a comprehensive metaproteomic analysis of bile samples related to gallstone disease, offering new insights into the microbiome-host interaction and gallstone formation mechanism.

Accurate de novo peptide sequencing using fully convolutional neural networks

De novo peptide sequencing, which does not rely on a comprehensive target sequence database, provides us with a way to identify novel peptides from tandem mass spectra. However, current de novo sequencing algorithms suffer from low accuracy and coverage, which hinders their application in proteomics. In this paper, we present PepNet, a fully convolutional neural network for high accuracy de novo peptide sequencing. PepNet takes an MS/MS spectrum (represented as a high-dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. The PepNet model is trained using a total of 3 million high-energy collisional dissociation MS/MS spectra from multiple human peptide spectral libraries. Evaluation results show that PepNet significantly outperforms current best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) in both peptide-level accuracy and positional-level accuracy. PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool to database search engines for peptide identification in proteomics. In addition, PepNet runs around 3x and 7x faster than PointNovo and DeepNovo on GPUs, respectively, thus being more suitable for the analysis of large-scale proteomics data.

Recruitment of FBXO22 for Targeted Degradation of NSD2

Targeted protein degradation (TPD) is an emerging therapeutic strategy that would benefit from new chemical entities with which to recruit a wider variety of ubiquitin E3 ligases to target proteins for proteasomal degradation. Here, we describe a TPD strategy involving the recruitment of FBXO22 to induce degradation of the histone methyltransferase and oncogene NSD2. UNC8732 facilitates FBXO22-mediated degradation of NSD2 in acute lymphoblastic leukemia cells harboring the NSD2 gain of function mutation p.E1099K, resulting in growth suppression, apoptosis, and reversal of drug resistance. The primary amine of UNC8732 is metabolized to an aldehyde species, which engages C326 of FBXO22 in a covalent and reversible manner to recruit the SCF FBXO22 Cullin complex. We further demonstrate that a previously reported alkyl amine-containing degrader targeting XIAP is similarly dependent on SCF FBXO22 . Overall, we present a highly potent NSD2 degrader for the exploration of NSD2 disease phenotypes and a novel FBXO22-dependent TPD strategy.

Transcriptome and Secretome Analyses of Endophyte Methylobacterium mesophilicum and Pathogen Xylella fastidiosa Interacting Show Nutrient Competition

Xylella fastidiosa is the causal agent of several plant diseases affecting fruit and nut crops. Methylobacterium mesophilicum strain SR1.6/6 was isolated from Citrus sinensis and shown to promote plant growth by producing phytohormones, providing nutrients, inhibiting X. fastidiosa, and preventing Citrus Variegated Chlorosis. However, the molecular mechanisms involved in the interaction among these microbes are still unclear. The present work aimed to analyze physiological and molecular aspects of M. mesophilicum SR1.6/6 and X. fastidiosa 9a5c in co-culture. The transcriptome and secretome analyses indicated that X. fastidiosa down-regulates cell division and transport genes and up-regulates stress via induction of chaperones and pathogenicity-related genes including, the lipase-esterase LesA, a protease, as well as an oligopeptidase in response to M. mesophilicum competition. On the other hand, M. mesophilicum also down-regulated transport genes, except for iron uptake, which was up-regulated. Secretome analysis identified four proteins in M. mesophilicum exclusively produced in co-culture with X. fastidiosa, among these, three are related to phosphorous uptake. These results suggest that M. mesophilicum inhibits X. fastidiosa growth mainly due to nutrient competition for iron and phosphorous, thus promoting X. fastidiosa starvation, besides producing enzymes that degrade X. fastidiosa cell wall, mainly hydrolases. The understanding of these interactions provides a direction for control and management of the phytopathogen X. fastidiosa, and consequently, helps to improve citrus growth and productivity.

Proteogenomics Reveal the Overexpression of HLA-I in Cancer

An accurate quantification of HLA class I gene expression is important in understanding the interplay with the tumor microenvironment of antitumor cytotoxic T cell activities. Because HLA-I sequences are highly variable, standard RNAseq and mass spectrometry-based quantification workflows using common genome and protein sequence references do not provide HLA-I allele specific quantifications. Here, we used personalized HLA-I nucleotide and protein reference sequences based on the subjects' HLA-I genotypes and surveyed tumor and adjacent normal samples from patients across nine cancer types. Mass spectrometry using data dependent acquisition data was validated to be sufficient to estimate HLA-A protein expression at the allele level. We found that HLA-I proteins were present in significantly higher levels in tumors compared to adjacent normal tissues from 41 to 63% of head and neck squamous cell carcinoma, uterine corpus endometrial carcinoma, and clear cell renal cell carcinoma patients, and this was driven by increased levels of HLA-I gene transcripts. Most immune cell types are universally enriched in HLA-I high tumors, while endothelial and neuronal cells showed divergent relationships with HLA-I. Pathway analysis revealed that tumor senescence and autophagy activity influence the level of HLA-I proteins in glioblastoma. Genes correlated to HLA-I protein expression are mostly the ones directly involved in HLA-I function in immune response and cell death, while glycosylation genes are exclusively co-expressed with HLA-I at the protein level.

Empowering peptidomics: utilizing computational tools and approaches

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

GPU-acceleration of the distributed-memory database peptide search of mass spectrometry data

Database peptide search is the primary computational technique for identifying peptides from the mass spectrometry (MS) data. Graphical Processing Units (GPU) computing is now ubiquitous in the current-generation of high-performance computing (HPC) systems, yet its application in the database peptide search domain remains limited. Part of the reason is the use of sub-optimal algorithms in the existing GPU-accelerated methods resulting in significantly inefficient hardware utilization. In this paper, we design and implement a new-age CPU-GPU HPC framework, called GiCOPS, for efficient and complete GPU-acceleration of the modern database peptide search algorithms on supercomputers. Our experimentation shows that the GiCOPS exhibits between 1.2 to 5[Formula: see text] speed improvement over its CPU-only predecessor, HiCOPS, and over 10[Formula: see text] improvement over several existing GPU-based database search algorithms for sufficiently large experiment sizes. We further assess and optimize the performance of our framework using the Roofline Model and report near-optimal results for several metrics including computations per second, occupancy rate, memory workload, branch efficiency and shared memory performance. Finally, the CPU-GPU methods and optimizations proposed in our work for complex integer- and memory-bounded algorithmic pipelines can also be extended to accelerate the existing and future peptide identification algorithms. GiCOPS is now integrated with our umbrella HPC framework HiCOPS and is available at: https://github.com/pcdslab/gicops .

Differential Expression of Proteins Associated with Bipolar Disorder as Identified Using the PeptideShaker Software

The prevalence of bipolar disorder (BD) in modern society is growing rapidly, but due to the lack of paraclinical criteria, its differential diagnosis with other mental disorders is somewhat challenging. In this regard, the relevance of proteomic studies is increasing due to the development of methods for processing large data arrays; this contributes to the discovery of protein patterns of pathological processes and the creation of new methods of diagnosis and treatment. It seems promising to search for proteins involved in the pathogenesis of BD in an easily accessible material-blood serum. Sera from BD patients and healthy individuals were purified via affinity chromatography to isolate 14 major proteins and separated using 1D SDS-PAGE. After trypsinolysis, the proteins in the samples were identified via HPLC/mass spectrometry. Mass spectrometric data were processed using the OMSSA and X!Tandem search algorithms using the UniProtKB database, and the results were analyzed using PeptideShaker. Differences in proteomes were assessed via an unlabeled NSAF-based analysis using a two-tailed Bonferroni-adjusted t-test. When comparing the blood serum proteomes of BD patients and healthy individuals, 10 proteins showed significant differences in NSAF values. Of these, four proteins were predominantly present in BD patients with the maximum NSAF value: 14-3-3 protein zeta/delta; ectonucleoside triphosphate diphosphohydrolase 7; transforming growth factor-beta-induced protein ig-h3; and B-cell CLL/lymphoma 9 protein. Further exploration of the role of these proteins in BD is warranted; conducting such studies will help develop new paraclinical criteria and discover new targets for BD drug therapy.

A comprehensive spectral assay library to quantify the Halobacterium salinarum NRC-1 proteome by DIA/SWATH-MS

Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E. coli and few other organisms. However, a spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, a model organism that contributed to several notable discoveries, is not publicly available yet. Here, we report a comprehensive spectral assay library to measure 2,563 of 2,646 annotated H. salinarum NRC-1 proteins. We demonstrate the utility of this library by measuring global protein abundances over time under standard growth conditions. The H. salinarum NRC-1 library includes 21,074 distinct peptides representing 97% of the predicted proteome and provides a new, valuable resource to confidently measure and quantify any protein of this archaeon. Data and spectral assay libraries are available via ProteomeXchange (PXD042770, PXD042774) and SWATHAtlas (SAL00312-SAL00319).

Assessment and Comparison of Database Search Engines for Peptidomic Applications

Protein database search engines are an integral component of mass spectrometry-based peptidomic analyses. Given the unique computational challenges of peptidomics, many factors must be taken into consideration when optimizing search engine selection, as each platform has different algorithms by which tandem mass spectra are scored for subsequent peptide identifications. In this study, four different database search engines, PEAKS, MS-GF+, OMSSA, and X! Tandem, were compared with Aplysia californica and Rattus norvegicus peptidomics data sets, and various metrics were assessed such as the number of unique peptide and neuropeptide identifications, and peptide length distributions. Given the tested conditions, PEAKS was found to have the highest number of peptide and neuropeptide identifications out of the four search engines in both data sets. Furthermore, principal component analysis and multivariate logistic regression were employed to determine whether specific spectral features contribute to false C-terminal amidation assignments by each search engine. From this analysis, it was found that the primary features influencing incorrect peptide assignments were the precursor and fragment ion m/z errors. Finally, an assessment employing a mixed species protein database was performed to evaluate search engine precision and sensitivity when searched against an enlarged search space containing human proteins.

The Breast Cancer Proteome and Precision Oncology

The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.

Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery

Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.

The NCOR-HDAC3 co-repressive complex modulates the leukemogenic potential of the transcription factor ERG

The ERG (ETS-related gene) transcription factor is linked to various types of cancer, including leukemia. However, the specific ERG domains and co-factors contributing to leukemogenesis are poorly understood. Drug targeting a transcription factor such as ERG is challenging. Our study reveals the critical role of a conserved amino acid, proline, at position 199, located at the 3' end of the PNT (pointed) domain, in ERG's ability to induce leukemia. P199 is necessary for ERG to promote self-renewal, prevent myeloid differentiation in hematopoietic progenitor cells, and initiate leukemia in mouse models. Here we show that P199 facilitates ERG's interaction with the NCoR-HDAC3 co-repressor complex. Inhibiting HDAC3 reduces the growth of ERG-dependent leukemic and prostate cancer cells, indicating that the interaction between ERG and the NCoR-HDAC3 co-repressor complex is crucial for its oncogenic activity. Thus, targeting this interaction may offer a potential therapeutic intervention.

The Research Progress of Bioactive Peptides Derived from Traditional Natural Products in China

Traditional natural products in China have a long history and a vast pharmacological repertoire that has garnered significant attention due to their safety and efficacy in disease prevention and treatment. Among the bioactive components of traditional natural products in China, bioactive peptides (BPs) are specific protein fragments that have beneficial effects on human health. Despite many of the traditional natural products in China ingredients being rich in protein, BPs have not received sufficient attention as a critical factor influencing overall therapeutic efficacy. Therefore, the purpose of this review is to provide a comprehensive summary of the current methodologies for the preparation, isolation, and identification of BPs from traditional natural products in China and to classify the functions of discovered BPs. Insights from this review are expected to facilitate the development of targeted drugs and functional foods derived from traditional natural products in China in the future.

Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms; its executable files, user manual, and sample data sets are freely available at https://ms.iis.sinica.edu.tw/comics/Software_FPF.html . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline.

Metaproteomic Analysis of Nasopharyngeal Swab Samples to Identify Microbial Peptides in COVID-19 Patients

During the COVID-19 pandemic, impaired immunity and medical interventions resulted in cases of secondary infections. The clinical difficulties and dangers associated with secondary infections in patients necessitate the exploration of their microbiome. Metaproteomics is a powerful approach to study the taxonomic composition and functional status of the microbiome under study. In this study, the mass spectrometry (MS)-based data of nasopharyngeal swab samples from COVID-19 patients was used to investigate the metaproteome. We have established a robust bioinformatics workflow within the Galaxy platform, which includes (a) generation of a tailored database of the common respiratory tract pathogens, (b) database search using multiple search algorithms, and (c) verification of the detected microbial peptides. The microbial peptides detected in this study, belong to several opportunistic pathogens such as Streptococcus pneumoniae, Klebsiella pneumoniae, Rhizopus microsporus, and Syncephalastrum racemosum. Microbial proteins with a role in stress response, gene expression, and DNA repair were found to be upregulated in severe patients compared to negative patients. Using parallel reaction monitoring (PRM), we confirmed some of the microbial peptides in fresh clinical samples. MS-based clinical metaproteomics can serve as a powerful tool for detection and characterization of potential pathogens, which can significantly impact the diagnosis and treatment of patients.

Plant phase extraction: A method for enhanced discovery of the RNA-binding proteome and its dynamics in plants

RNA-binding proteins (RBPs) play critical roles in posttranscriptional gene regulation. Current methods of systematically profiling RBPs in plants have been predominantly limited to proteins interacting with polyadenylated (poly(A)) RNAs. We developed a method called plant phase extraction (PPE), which yielded a highly comprehensive RNA-binding proteome (RBPome), uncovering 2,517 RBPs from Arabidopsis (Arabidopsis thaliana) leaf and root samples with a highly diverse array of RNA-binding domains. We identified traditional RBPs that participate in various aspects of RNA metabolism and a plethora of nonclassical proteins moonlighting as RBPs. We uncovered constitutive and tissue-specific RBPs essential for normal development and, more importantly, revealed RBPs crucial for salinity stress responses from a RBP-RNA dynamics perspective. Remarkably, 40% of the RBPs are non-poly(A) RBPs that were not previously annotated as RBPs, signifying the advantage of PPE in unbiasedly retrieving RBPs. We propose that intrinsically disordered regions contribute to their nonclassical binding and provide evidence that enzymatic domains from metabolic enzymes have additional roles in RNA binding. Taken together, our findings demonstrate that PPE is an impactful approach for identifying RBPs from complex plant tissues and pave the way for investigating RBP functions under different physiological and stress conditions at the posttranscriptional level.