The neXtProt knowledgebase in 2020: data, tools and usability improvements

Nanotechnology in Parkinson's Disease: overcoming drug delivery challenges and enhancing therapeutic outcomes

The global prevalence of Parkinson's Disease (PD) is on the rise, driven by an ageing population and ongoing environmental conditions. To gain a better understanding of PD pathogenesis, it is essential to consider its relationship with the ageing process, as ageing stands out as the most significant risk factor for this neurodegenerative condition. PD risk factors encompass genetic predisposition, exposure to environmental toxins, and lifestyle influences, collectively increasing the chance of PD development. Moreover, early and precise PD diagnosis remains elusive, relying on clinical assessments, neuroimaging techniques, and emerging biomarkers. Conventional management of PD involves dopaminergic medications and surgical interventions, but these treatments often become less effective over time and do not address disease treatment. Challenges persist due to the blood-brain barrier's (BBB) impermeability, hindering drug delivery. Recent advancements in nanotechnology offer promising novel approaches for PD management. Various drug delivery systems (DDS), including nanosized polymers, lipid-based carriers, and nanoparticles (such as metal/metal oxide, protein, and carbonaceous particles), aim to enhance drug and gene delivery. These modifications seek to improve BBB permeability, ultimately benefiting PD patients. This review underscores the critical role of ageing in PD development and explores how age-related neuronal decline contributes to substantia nigra loss and PD manifestation in susceptible individuals. The review also highlights the advancements and ongoing challenges in nanotechnology-based therapies for PD.

Proteomic patterns associated with ketamine response in major depressive disorders

BACKGROUND

Major depressive disorder (MDD) is characterized by persistent feelings of sadness and loss of interest. Ketamine has been widely used to treat MDD owing to its rapid effect in relieving depressive symptoms. Importantly, not all patients respond to ketamine treatment. Identifying sub-populations who will benefit from ketamine, as well as those who may not, prior to treatment initiation, would significantly advance precision medicine in patients with MDD.

METHODS

Here, we used mass spectrometry-based plasma proteomics to analyze matched pre- and post-ketamine treatment samples from a cohort of 30 MDD patients whose treatment outcomes and demographic and clinical characteristics were considered.

RESULTS

Ketamine responders and non-responders were identified according to their individual outcomes after two weeks of treatment. We analyzed proteomic alterations in post-treatment samples from responders and non-responders and identified a collection of six proteins pivotal to the antidepressive effect of ketamine. Subsequent co-regulation analysis revealed that pathways related to immune response were involved in ketamine response. By comparing the proteomic profiles of samples from the same individuals at the pre- and post-treatment time points, dynamic proteomic rearrangements induced by ketamine revealed that immune-related processes were activated in association with its antidepressive effect. Furthermore, receiver operating characteristic curve analysis of pre-treatment samples revealed three proteins with strong predictive performance in determining the response of patients to ketamine before receiving treatment.

CONCLUSIONS

These findings provide valuable knowledge about ketamine response, which will ultimately lead to more personalized and effective treatments for patients.

TRIAL REGISTRATION

The study was registered in the Chinese Clinical Trials Registry (ChiCTR-OOC-17012239) on May 26, 2017.

LCRAnnotationsDB: a database of low complexity regions functional and structural annotations

Low Complexity Regions (LCRs) are segments of proteins with a low diversity of amino acid composition. These regions play important roles in proteins. However, annotations describing these functions are dispersed across databases and scientific literature. LCRAnnotationsDB aims to consolidate knowledge about LCRs and store relevant annotations in a single place. To unify redundant annotations, we assigned them categories based on similarity in function, protein structure, and biological process. Categories are organized hierarchically by linking them to Gene Ontology terms. The LCRAnnotationsDB database can be accessed at https://lcrannotdb.lcr-lab.org/ .

Integrated proteomic and transcriptomic landscape of human placenta in small for gestational age infants

Small for gestational age (SGA) infants affected by placental insufficiency are exposed to the risk of stillbirth and long-term complications. Based on RNA-seq and mass spectrometry, we identified dysregulated RNAs and proteins from the comparisons of SGA placental tissues and controls. We revealed two SGA-relevant co-expression modules (SRMs) that also significantly distinguished SGA from controls. Then we performed an integrated analysis of transcriptomic and proteomic profiles to trace their links to SGA as well as their significant correlations. For the core functional molecules we screened, we revealed their potential upstream regulators and validated them experimentally in an independent cohort. Overall, we pointed out insights into different molecular pathways for the pathological mechanisms of SGA and indicated potential target molecules that may be drivers of placental aberrations in the SGA infants.

AKR1B10 and digestive tumors development: a review

Aldo-keto reductase family 1 member B10 (AKR1B10) is a member of the AKR1B subfamily. It is mainly found in cytoplasm, and it is typically expressed in the stomach and intestines. Given that its expression is low or absent in other tissues, AKR1B10 is a potential diagnostic and therapeutic biomarker for various digestive system diseases. Here, we review recent research progress on AKR1B10 in digestive system tumors such as hepatocellular carcinoma, gastric carcinoma, colorectal carcinoma, pancreatic carcinoma, oral squamous cell carcinoma, laryngeal squamous cell carcinoma, cholangiocarcinoma, and nasopharyngeal carcinoma, over the last 5 years. We also discuss the current trends and future research directions for AKR1B10 in both oncological and non-oncological diseases to provide a scientific reference for further exploration of this gene.

The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

PgxSAVy: A tool for comprehensive evaluation of variant peptide quality in proteogenomics - catching the (un)usual suspects

Variant peptides resulting from single nucleotide polymorphisms (SNPs) can lead to aberrant protein functions and have translational potential for disease diagnosis and personalized therapy. Variant peptides detected by proteogenomics are fraught with high number of false positives, but there is no uniform and comprehensive approach to assess variant quality across analysis pipelines. Despite class-specific FDR along with ad-hoc filters, the problem is far from solved. These protocols are typically manual and tedious, and thus not uniform across labs. We demonstrate that variant peptide rescoring, integrated with intensity, variant event information and search result features, allows better discrimination of correct variant peptides. Implemented into PgxSAVy - a tool for quality control of variant peptides, this method can tackle the high rate of false positives. PgxSAVy provides a rigorous framework for quality control and annotations of variant peptides on the basis of (i) variant quality, (ii) isobaric masses, and (iii) disease annotation. PgxSAVy demonstrated high accuracy by identifying true variants with 98.43% accuracy on simulated data. Large-scale proteogenomic reanalysis of ∼2.8 million spectra (PXD004010 and PXD001468) resulted in 12,705 variant peptide spectrum matches (PSMs), of which PgxSAVy evaluated 3028 (23.8%), 1409 (11.1%) and 8268 (65.1%) as confident, semi-confident and doubtful respectively. PgxSAVy also annotates the variants based on their pathogenicity and provides support for assisted manual validation. The analysis of proteins carrying variants can provide fine granularity in discovering important pathways. PgxSAVy will advance personalized medicine by providing a comprehensive framework for quality control and prioritization of proteogenomics variants. PgxSAVy is freely available at https://pgxsavy.igib.res.in/ as a webserver and https://github.com/anuragraj/PgxSAVy as a stand-alone tool.

A deep audit of the PeptideAtlas database uncovers evidence for unannotated coding genes and aberrant translation

The human genome has been the subject of intense scrutiny by experimental and manual curation projects for more than two decades. Novel coding genes have been proposed from large-scale RNASeq, ribosome profiling and proteomics experiments. Here we carry out an in-depth analysis of an entire proteomics database. We analysed the proteins, peptides and spectra housed in the human build of the PeptideAtlas proteomics database to identify coding regions that are not yet annotated in the GENCODE reference gene set. We find support for hundreds of missing alternative protein isoforms and unannotated upstream translations, and evidence of cross-contamination from other species. There was reliable peptide evidence for 34 novel unannotated open reading frames (ORFs) in PeptideAtlas. We find that almost half belong to coding genes that are missing from GENCODE and other reference sets. Most of the remaining ORFs were not conserved beyond human, however, and their peptide confirmation was restricted to cancer cell lines. We show that this is strong evidence for aberrant translation, raising important questions about the extent of aberrant translation and how these ORFs should be annotated in reference genomes.

Same Day Access to Folded Synthetic Proteins

Understanding protein function is a cornerstone of modern biology. Research centers worldwide dedicate significant efforts to prepare individual proteins for study, the isolation and purification of which can take days to months. We developed a workflow that enables same-day access to functional synthetic proteins. Chemical synthesis provides access to crude protein chains in hours, but the removal of the synthetic side products is typically a days-long process. We find that chemical modifications on side products lead to significant and unpredictable changes in the folding behavior. Consistent with these findings, we discovered that approaches based on biophysical properties characteristic of the folded protein target can discriminate against chemically similar species. Confirming our protocol with nine protein targets, we show that appropriate desalting followed by different folding strategies enables isolation of functional single-domain proteins in hours instead of days. Each target was isolated in under 10 h, including some proteins with post-translational modifications, non-natural amino acids, and disulfide bonds. Rapid biological discovery requires on-demand access to proteins, and the folding pipeline described here is uniquely suited to enabling these efforts. The folding process presented here was not assessed on complex proteins, and therefore, it may require further optimization in those cases.

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

Identifying PE2 and PE5 Proteins from Existing Mass Spectrometry Data Using pFind

The Chromosome-Centric Human Proteome Project (C-HPP) aims to identify all proteins encoded by the human genome. Currently, the human proteome still contains approximately 2000 PE2-PE5 proteins, referring to annotated coding genes that lack sufficient protein-level evidence. During the past 10 years, it has been increasingly difficult to identify PE2-PE5 proteins in C-HPP approaches due to the limited occurrence. Therefore, we proposed that reanalyzing massive MS data sets in repository with newly developed algorithms may increase the occurrence of the peptides of these proteins. In this study, we downloaded 1000 MS data sets via the ProteomeXchange database. Using pFind software, we identified peptides referring to 1788 PE2-PE5 proteins. Among them, 11 PE2 and 16 PE5 proteins were identified with at least 2 peptides, and 12 of them were identified using 2 peptides in a single data set, following the criteria of the HPP guidelines. We found translation evidence for 16 of the 11 PE2 and 16 PE5 proteins in our RNC-seq data, supporting their existence. The properties of the PE2 and PE5 proteins were similar to those of the PE1 proteins. Our approach demonstrated that mining PE2 and PE5 proteins in massive data repository is still worthy, and multidata set peptide identifications may support the presence of PE2 and PE5 proteins or at least prompt additional studies for validation. Extremely high throughput could be a solution to finding more PE2 and PE5 proteins.

Identification and Quantification of Human Relaxin Proteins by Immunoaffinity-Mass Spectrometry

The human relaxins belong to the Insulin/IGF/Relaxin superfamily of peptide hormones, and their physiological function is primarily associated with reproduction. In this study, we focused on a prostate tissue-specific relaxin RLN1 (REL1_HUMAN protein) and a broader tissue specificity RLN2 (REL2_HUMAN protein). Due to their structural similarity, REL1 and REL2 proteins were collectively named a 'human relaxin protein' in previous studies and were exclusively measured by immunoassays. We hypothesized that the highly selective and sensitive immunoaffinity-selected reaction monitoring (IA-SRM) assays would reveal the identity and abundance of the endogenous REL1 and REL2 in biological samples and facilitate the evaluation of these proteins for diagnostic applications. High levels of RLN1 and RLN2 transcripts were found in prostate and breast cancer cell lines by RT-PCR. However, no endogenous prorelaxin-1 or mature REL1 were detected by IA-SRM in cell lines, seminal plasma, or blood serum. The IA-SRM assay of REL2 demonstrated its undetectable levels (<9.4 pg/mL) in healthy control female and male sera and relatively high levels of REL2 in maternal sera across different gestational weeks (median 331 pg/mL; N = 120). IA-SRM assays uncovered potential cross-reactivity and nonspecific binding for relaxin immunoassays. The developed IA-SRM assays will facilitate the investigation of the physiological and pathological roles of REL1 and REL2 proteins.

Mexican patient with Ellis-van Creveld syndrome and cleft palate: Importance of functional hemizygosity and phenotype expansion

BACKGROUND

Ellis-van Creveld syndrome (EvCS) is a chondroectodermal dysplasia caused by germline pathogenic variants in ciliary complex subunit 1 and 2 genes (EVC, EVC2) on chromosome 4p16.2. This disease has a broad phenotype, and there are few described phenotype-genotype correlations.

METHODS

Ethical Compliance: Written informed consent was obtained from the parents. Here, we report a genetically confirmed Mexican patient with EvCS having two inherited pathogenic variants in trans in EVC2: c.[1195C>T];[2161delC].

RESULTS

This patient allowed a genotypic-phenotypic comparison with another Mexican subject who presented a more attenuated phenotype; furthermore, our patient also presented cleft palate, a rarely reported feature.

CONCLUSION

Our case shows the importance of comparing functional hemizygosity between patient's phenotypes when they share a variant, and our case also supports the association of alterations in the palate as part of the EvCS phenotype.

From papers to RDF-based integration of physicochemical data and adverse outcome pathways for nanomaterials

Adverse Outcome Pathways (AOPs) have been proposed to facilitate mechanistic understanding of interactions of chemicals/materials with biological systems. Each AOP starts with a molecular initiating event (MIE) and possibly ends with adverse outcome(s) (AOs) via a series of key events (KEs). So far, the interaction of engineered nanomaterials (ENMs) with biomolecules, biomembranes, cells, and biological structures, in general, is not yet fully elucidated. There is also a huge lack of information on which AOPs are ENMs-relevant or -specific, despite numerous published data on toxicological endpoints they trigger, such as oxidative stress and inflammation. We propose to integrate related data and knowledge recently collected. Our approach combines the annotation of nanomaterials and their MIEs with ontology annotation to demonstrate how we can then query AOPs and biological pathway information for these materials. We conclude that a FAIR (Findable, Accessible, Interoperable, Reusable) representation of the ENM-MIE knowledge simplifies integration with other knowledge. SCIENTIFIC CONTRIBUTION: This study introduces a new database linking nanomaterial stressors to the first known MIE or KE. Second, it presents a reproducible workflow to analyze and summarize this knowledge. Third, this work extends the use of semantic web technologies to the field of nanoinformatics and nanosafety.

OVCH1 Antisense RNA 1 is differentially expressed between non-frail and frail old adults

While some old adults stay healthy and non-frail up to late in life, others experience multimorbidity and frailty often accompanied by a pro-inflammatory state. The underlying molecular mechanisms for those differences are still obscure. Here, we used gene expression analysis to understand the molecular underpinning between non-frail and frail individuals in old age. Twenty-four adults (50% non-frail and 50% frail) from InCHIANTI study were included. Total RNA extracted from whole blood was analyzed by Cap Analysis of Gene Expression (CAGE). CAGE identified transcription start site (TSS) and active enhancer regions. We identified a set of differentially expressed (DE) TSS and enhancer between non-frail and frail and male and female participants. Several DE TSSs were annotated as lncRNA (XIST and TTTY14) and antisense RNAs (ZFX-AS1 and OVCH1 Antisense RNA 1). The promoter region chr6:366,786,54-366,787,97;+ was DE and overlapping the longevity CDKN1A gene. GWAS-LD enrichment analysis identifies overlapping LD-blocks with the DE regions with reported traits in GWAS catalog (isovolumetric relaxation time and urinary tract infection frequency). Furthermore, we used weighted gene co-expression network analysis (WGCNA) to identify changes of gene expression associated with clinical traits and identify key gene modules. We performed functional enrichment analysis of the gene modules with significant trait/module correlation. One gene module is showing a very distinct pattern in hub genes. Glycogen Phosphorylase L (PYGL) was the top ranked hub gene between non-frail and frail. We predicted transcription factor binding sites (TFBS) and motif activity. TF involved in age-related pathways (e.g., FOXO3 and MYC) shows different expression patterns between non-frail and frail participants. Expanding the study of OVCH1 Antisense RNA 1 and PYGL may help understand the mechanisms leading to loss of homeostasis that ultimately causes frailty.

New Function Annotation of PROSER2 in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis due to the absence of diagnostic markers and molecular targets. Here, we took an unconventional approach to identify new molecular targets for pancreatic cancer. We chose uncharacterized protein evidence level 1 without function annotation from extensive proteomic research on pancreatic cancer and focused on proline and serine-rich 2 (PROSER2), which ranked high in the cell membrane and cytoplasm. In our study using cell lines and patient-derived orthotopic xenograft cells, PROSER2 exhibited a higher expression in cells derived from primary tumors than in those from metastatic tissues. PROSER2 was localized in the cell membrane and cytosol by immunocytochemistry. PROSER2 overexpression significantly reduced the metastatic ability of cancer cells, whereas its suppression had the opposite effect. Proteomic analysis revealed that PROSER2 interacts with STK25 and PDCD10, and their binding was confirmed by immunoprecipitation and immunocytochemistry. STK25 knockdown enhanced metastasis by decreasing p-AMPK levels, whereas PROSER2-overexpressing cells increased the level of p-AMPK, indicating that PROSER2 suppresses invasion via the AMPK pathway by interacting with STK25. This is the first demonstration of the novel role of PROSER2 in antagonizing tumor progression via the STK25-AMPK pathway in PDAC. LC-MS/MS data are available at MassIVE (MSV000092953) and ProteomeXchange (PXD045646).

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

Identification of protein components of the transformation system in the cell line of immortalized human keratinocytes HaCaT exposed to surfactants

Using the method of shotgun mass spectrometry, we have evaluated changes in the proteomic profile of HaCat cells in response to the treatment with sodium dodecyl sulfate (anionic surfactant) and Triton-X100 (non-ionic surfactant) in two concentrations (12.5 µg/ml and 25.0 µg/ml). The study revealed induction of orphan CYP2S1 (biotransformation phase I) in response to Triton-X100. We have identified proteins of II (glutathione-S-transferases, GSTs) and III (solute carrier proteins, SLCs) biotransformation phases, as well as antioxidant proteins (peroxiredoxins, PRDXs; catalase, CAT; thioredoxin, TXN). Thus, proteins of all three xenobiotic detoxification phases were detected. The presented results suggest a new prospect of using HaCaT keratinocytes as a model of human epidermis for studying the metabolism of drugs/toxicants in human skin in vitro.

Transcriptomics and Spatial Proteomics for Discovery and Validation of Missing Proteins in the Human Ovary

Efforts to understand the complexities of human biology encompass multidimensional aspects, with proteins emerging as crucial components. However, studying the human ovary introduces unique challenges due to its complex dynamics and changes over a lifetime, varied cellular composition, and limited sample access. Here, four new RNA-seq samples of ovarian cortex spanning ages of 7 to 32 were sequenced and added to the existing data in the Human Protein Atlas (HPA) database www.proteinatlas.org, opening the doors to unique possibilities for exploration of oocyte-specific proteins. Based on transcriptomics analysis of the four new tissue samples representing both prepubertal girls and women of fertile age, we selected 20 protein candidates that lacked previous evidence at the protein level, so-called "missing proteins" (MPs). The proteins were validated using high-resolution antibody-based profiling and single-cell transcriptomics. Fourteen proteins exhibited consistent single-cell expression patterns in oocytes and granulosa cells, confirming their presence in the ovary and suggesting that these proteins play important roles in ovarian function, thus proposing that these 14 proteins should no longer be classified as MPs. This research significantly advances the understanding of MPs, unearthing fresh avenues for prospective exploration. By integrating innovative methodologies and leveraging the wealth of data in the HPA database, these insights contribute to refining our understanding of protein roles within the human ovary and opening the doors for further investigations into missing proteins and human reproduction.

The Digital World of Cytogenetic and Cytogenomic Web Resources

The dynamic growth of technological capabilities at the cellular and molecular level has led to a rapid increase in the amount of data on the genes and genomes of organisms. In order to store, access, compare, validate, classify, and understand the massive data generated by different researchers, and to promote effective communication among research communities, various genome and cytogenetic online databases have been established. These data platforms/resources are essential not only for computational analyses and theoretical syntheses but also for helping researchers select future research topics and prioritize molecular targets. Furthermore, they are valuable for identifying shared recurrent genomic patterns related to human diseases and for avoiding unnecessary duplications among different researchers. The website interface, menu, graphics, animations, text layout, and data from databases are displayed by a front end on the screen of a monitor or smartphone. A database front-end refers to the user interface or application that enables accessing tabular, structured, or raw data stored in the database. The Internet makes it possible to reach a greater number of users around the world and gives them quick access to information stored in databases. The number of ways of presenting this data by front-ends increases as well. This requires unifying the ways of operating and presenting information by front-ends and ensuring contextual switching between front-ends of different databases. This chapter aims to present selected cytogenetic and cytogenomic Internet resources in terms of obtaining the needed information and to indicate how to increase the efficiency of access to stored information. Through a brief introduction of these databases and by providing examples of their usage in cytogenetic analyses, we aim to bridge the gap between cytogenetics and molecular genomics by encouraging their utilization.

Molecular and translational biology of the blood-based VeriStrat® proteomic test used in cancer immunotherapy treatment guidance

INTRODUCTION

The VeriStrat® test (VS) is a blood-based assay that predicts a patient's response to therapy by analyzing eight features in a spectrum obtained from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) analysis of human serum and plasma. In a recent analysis of the INSIGHT clinical trial (NCT03289780), it was found that the VS labels, VS Good and VS Poor, can effectively predict the responsiveness of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitor (ICI) therapy. However, while VS measures the intensities of spectral features using MALDI-TOF analysis, the specific proteoforms underlying these features have not been comprehensively identified.

OBJECTIVES

The objective of this study was to identify the proteoforms that are measured by VS.

METHODS

To resolve the features obtained from the low-resolution MALDI-TOF procedure used to acquire mass spectra for VS DeepMALDI® analysis of serum was employed. This technique allowed for the identification of finer peaks within these features. Additionally, a combination of reversed-phase fractionation and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then used to identify the proteoforms associated with these peaks.

RESULTS

The analysis revealed that the primary constituents of the spectrum measured by VS are serum amyloid A1, serum amyloid A2, serum amyloid A4, C-reactive protein, and beta-2 microglobulin.

CONCLUSION

Proteoforms involved in host immunity were identified as significant components of these features. This newly acquired information improves our understanding of how VS can accurately predict patient response to therapy. It opens up additional studies that can expand our understanding even further.

Europe PMC annotated full-text corpus for gene/proteins, diseases and organisms

Named entity recognition (NER) is a widely used text-mining and natural language processing (NLP) subtask. In recent years, deep learning methods have superseded traditional dictionary- and rule-based NER approaches. A high-quality dataset is essential to fully leverage recent deep learning advancements. While several gold-standard corpora for biomedical entities in abstracts exist, only a few are based on full-text research articles. The Europe PMC literature database routinely annotates Gene/Proteins, Diseases, and Organisms entities. To transition this pipeline from a dictionary-based to a machine learning-based approach, we have developed a human-annotated full-text corpus for these entities, comprising 300 full-text open-access research articles. Over 72,000 mentions of biomedical concepts have been identified within approximately 114,000 sentences. This article describes the corpus and details how to access and reuse this open community resource.

Proteogenomics in Cancer: Then and Now

For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.

Methylomic, proteomic, and metabolomic correlates of traffic-related air pollution: A systematic review, pathway analysis, and network analysis relating traffic-related air pollution to subclinical and clinical cardiorespiratory outcomes

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease, and highlight contemporary challenges and opportunities associated with such efforts.

Inherited ARPC5 mutations cause an actinopathy impairing cell motility and disrupting cytokine signaling

We describe the first cases of germline biallelic null mutations in ARPC5, part of the Arp2/3 actin nucleator complex, in two unrelated patients presenting with recurrent and severe infections, early-onset autoimmunity, inflammation, and dysmorphisms. This defect compromises multiple cell lineages and functions, and when protein expression is reestablished in-vitro, the Arp2/3 complex conformation and functions are rescued. As part of the pathophysiological evaluation, we also show that interleukin (IL)-6 signaling is distinctively impacted in this syndrome. Disruption of IL-6 classical but not trans-signaling highlights their differential roles in the disease and offers perspectives for therapeutic molecular targets.

A comparison of approaches to accessing existing biological and chemical relational databases via SPARQL

Current biological and chemical research is increasingly dependent on the reusability of previously acquired data, which typically come from various sources. Consequently, there is a growing need for database systems and databases stored in them to be interoperable with each other. One of the possible solutions to address this issue is to use systems based on Semantic Web technologies, namely on the Resource Description Framework (RDF) to express data and on the SPARQL query language to retrieve the data. Many existing biological and chemical databases are stored in the form of a relational database (RDB). Converting a relational database into the RDF form and storing it in a native RDF database system may not be desirable in many cases. It may be necessary to preserve the original database form, and having two versions of the same data may not be convenient. A solution may be to use a system mapping the relational database to the RDF form. Such a system keeps data in their original relational form and translates incoming SPARQL queries to equivalent SQL queries, which are evaluated by a relational-database system. This review compares different RDB-to-RDF mapping systems with a primary focus on those that can be used free of charge. In addition, it compares different approaches to expressing RDB-to-RDF mappings. The review shows that these systems represent a viable method providing sufficient performance. Their real-life performance is demonstrated on data and queries coming from the neXtProt project.

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

New trends in synthetic drugs and natural products targeting 20S proteasomes in cancers

Cancer is a global health challenge that remains to be a field of extensive research aiming to find new anticancer therapeutics. The 20S proteasome complex is one of the targets of anticancerdrugs, as it is correlated with several cancer types. Herein, we aim to discuss the 20S proteasome subunits and investigatethe currently studied proteasome inhibitors targeting the catalytically active proteasome subunits. In this review, we summarize the proteindegradation mechanism of the 20S proteasome complex and compareit with the 26S proteasome complex. Afterwards, the localization of the 20S proteasome is summarized as well as its use as a diagnosticandprognostic marker. The FDA-approved proteasome inhibitors (PIs) under clinical trials are summarized and their current limited use in solid tumors is also reviewed in addition to the expression of theβ5 subunit in differentcell lines. The review discusses in-silico analysis of the active subunit of the 20S proteasome complex. For development of new proteasome inhibitor drugs, the natural products inhibiting the 20S proteasome are summarized, as well as novel methodologies and challenges for the natural product discovery and current information about the biosynthetic gene clusters encoding them. We herein briefly summarize some resistancemechanismsto the proteasomeinhibitors. Additionally, we focus on the three main classes of proteasome inhibitors: 1] boronic acid, 2] beta-lactone and 3] epoxide inhibitor classes, as well as other PI classes, and their IC₅₀ values and their structure-activity relationship (SAR). Lastly,we summarize several future prospects of developing new proteasome inhibitors towards the treatment of tumors, especially solid tumors.

Integrated View of Baseline Protein Expression in Human Tissues

The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

AlacatDesigner─Computational Design of Peptide Concatamers for Protein Quantitation

Protein quantitation via mass spectrometry relies on peptide proxies for the parent protein from which abundances are estimated. Owing to the variability in signal from individual peptides, accurate absolute quantitation usually relies on the addition of an external standard. Typically, this involves stable isotope-labeled peptides, delivered singly or as a concatenated recombinant protein. Consequently, the selection of the most appropriate surrogate peptides and the attendant design in recombinant proteins termed QconCATs are challenges for proteome science. QconCATs can now be built in a "a-la-carte" assembly method using synthetic biology: ALACATs. To assist their design, we present "AlacatDesigner", a tool that supports the peptide selection for recombinant protein standards based on the user's target protein. The user-customizable tool considers existing databases, occurrence in the literature, potential post-translational modifications, predicted miscleavage, predicted divergence of the peptide and protein quantifications, and ionization potential within the mass spectrometer. We show that peptide selections are enriched for good proteotypic and quantotypic candidates compared to empirical data. The software is freely available to use either via a web interface AlacatDesigner, downloaded as a Desktop application or imported as a Python package for the command line interface or in scripts.

Unifying the identification of biomedical entities with the Bioregistry

The standardized identification of biomedical entities is a cornerstone of interoperability, reuse, and data integration in the life sciences. Several registries have been developed to catalog resources maintaining identifiers for biomedical entities such as small molecules, proteins, cell lines, and clinical trials. However, existing registries have struggled to provide sufficient coverage and metadata standards that meet the evolving needs of modern life sciences researchers. Here, we introduce the Bioregistry, an integrative, open, community-driven metaregistry that synthesizes and substantially expands upon 23 existing registries. The Bioregistry addresses the need for a sustainable registry by leveraging public infrastructure and automation, and employing a progressive governance model centered around open code and open data to foster community contribution. The Bioregistry can be used to support the standardized annotation of data, models, ontologies, and scientific literature, thereby promoting their interoperability and reuse. The Bioregistry can be accessed through https://bioregistry.io and its source code and data are available under the MIT and CC0 Licenses at https://github.com/biopragmatics/bioregistry .

Tissue Proteogenomic Landscape Reveals the Role of Uncharacterized SEL1L3 in Progression and Immunotherapy Response in Lung Adenocarcinoma

The fundamental pursuit to complete the human proteome atlas and the unmet clinical needs in lung adenocarcinoma have prompted us to study the functional role of uncharacterized proteins and explore their implications in cancer biology. In this study, we characterized SEL1L3, a previously uncharacterized protein encoded from chromosome 4 as a dysregulated protein in lung adenocarcinoma from the large-scale tissue proteogenomics data set established using the cohort of Taiwan Cancer Moonshot. SEL1L3 was expressed in abundance in the tumor parts compared with paired adjacent normal tissues in 90% of the lung adenocarcinoma patients in our cohorts. Moreover, survival analysis revealed the association of SEL1L3 with better clinical outcomes. Intriguingly, silencing of SEL1L3 imposed a reduction in cell viability and activation of ER stress response pathways, indicating a role of SEL1L3 in the regulation of cell stress. Furthermore, the immune profiles of patients with higher SEL1L3 expression were corroborated with its active role in immunophenotype and favorable clinical outcomes in lung adenocarcinoma. Taken together, our study revealed that SEL1L3 might play a vital role in the regulation of cell stress, interaction with cancer cells and the immune microenvironment. Our research findings provide promising insights for further investigation of its molecular signaling network and also suggest SEL1L3 as a potential emerging adjuvant for immunotherapy in lung adenocarcinoma.

Everolimus-Loaded Reconstituted High-Density Lipoprotein Prepared by a Novel Dual Centrifugation Approach for Anti-Atherosclerotic Therapy

PURPOSE

The conventional techniques for the preparation of reconstituted high-density lipoprotein (rHDL) are hampered by long process times, the need for large amounts of starting material, and harsh preparation conditions. Here, we present a novel rHDL preparation method to overcome these challenges. Furthermore, we propose a dual mode of action for rHDL loaded with the immunosuppressant drug everolimus (Eve-rHDL) in the context of atherosclerosis and cardiovascular disease.

METHODS

We use dual centrifugation for rHDL nanoparticle preparation and characterize the physicochemical properties by NS-TEM, N-PAGE, DLS, AF4, and HPLC. In addition, we determine the biological efficacy in human and murine cell culture with regard to cellular uptake, cholesterol efflux, and proliferation.

RESULTS

We confirm the characteristic particle size of 10 nm, discoidal morphology, and chemical composition of the rHDL preparations and identify dual centrifugation as an ideal method for cost-effective aseptic rHDL manufacturing. rHDL can be prepared in approx. 1.5 h with batch sizes as little as 89 µL. Moreover, we demonstrate the cholesterol efflux capacity and anti-proliferative activity of Eve-rHDL in vitro. The anti-proliferative effects were comparable to free Eve, thus confirming the suitability of rHDL as a capable drug delivery vehicle.

CONCLUSION

Eve-rHDL shows great efficacy in vitro and may further be employed to target atherosclerotic plaques in vivo. Highly effective anti-atherosclerotic therapy might be feasible by reducing both inflammatory- and lipid burden of the plaques. Dual centrifugation is an ideal technique for the efficient application of the rHDL platform in cardiovascular disease and beyond.

Chromosome 17 translocation affects sperm morphology: Two case studies and literature review

We present two cases of infertile males with teratozoospermia stemming from chromosome 17 translocation. The patients present karyotypes that have not been previously reported. Genes located on breakpoints (17p11.2, 9q31, and 11p15) were analysed to find the probable mechanism affecting sperm morphology. Our results suggest that ALKBH5, TOP3A, and LLGL1 interactions may be an underlying cause of abnormal sperm head morphology. Translocation of chromosome 17 occurred in conjunction with chromosome 9 and chromosome 11 translocation in the two cases, resulting in oligozoospermia and asthenozoospermia, respectively. These abnormal phenotypes may involve meiosis- and motility-related genes such as LDHC, DNHD1, UBQLN3, and NUP98. Translocation is thus a risk factor for sperm morphological abnormalities and motility deficiency. The interaction network of 22 genes on breakpoints suggests that they contribute to spermatogenesis as a group. In conclusion, this study highlighted the importance of investigating genes linked to sperm morphology, together with chromosome 17 translocation and reproductive risks. For patients interested in screening before a future pregnancy, we recommend preimplantation genetic diagnosis to reduce the risk of karyotypically unbalanced foetuses and birth defects.

Spatial Proteomics for Further Exploration of Missing Proteins: A Case Study of the Ovary

In the quest for "missing proteins" (MPs), the proteins encoded by the human genome still lacking evidence of existence at the protein level, novel approaches are needed to detect this challenging group of proteins. The current count stands at 1,343 MPs, and it is likely that many of these proteins are expressed at low levels, in rare cell or tissue types, or the cells in which they are expressed may only represent a small minority of the tissue. Here, we used an integrated omics approach to identify and explore MPs in human ovaries. By taking advantage of publicly available transcriptomics and antibody-based proteomics data in the Human Protein Atlas (HPA), we selected 18 candidates for further immunohistochemical analysis using an exclusive collection of ovarian tissues from women and patients of reproductive age. The results were compared with data from single-cell mRNA sequencing, and seven proteins (CTXN1, MRO, RERGL, TTLL3, TRIM61, TRIM73, and ZNF793) could be validated at the single-cell type level with both methods. We present for the first time the cell type-specific spatial localization of 18 MPs in human ovarian follicles, thereby showcasing the utility of the HPA database as an important resource for identification of MPs suitable for exploration in specialized tissue samples. The results constitute a starting point for further quantitative and qualitative analysis of the human ovaries, and the novel data for the seven proteins that were validated with both methods should be considered as evidence of existence of these proteins in human ovary.

Global Profiling of Genes Expressed in the Silk Glands of Philippine-Reared Mulberry Silkworms (Bombyx mori)

RNA sequencing was used to assemble transcriptome data for Philippine-reared silkworm and compare gene expression profiles of strains reared in high- and low-temperature environments. RNA was isolated from the silk glands of fifth instar larvae and mRNA-enriched libraries were sequenced using Illumina NextSeq 500. Transcriptome reads were assembled using reference-based and de novo assemblers, and assemblies were evaluated using different metrics for transcriptome quality, including the read mapping rate, E90N50, RSEM-eval, and the presence of single-copy orthologs. All transcriptome assemblies were able to reconstruct >40,000 transcripts. Differential expression analysis found 476 differentially expressed genes (DEGs; 222 upregulated, 254 downregulated) in strains reared in different temperatures. Among the top DEGs were myrosinase, heat shock proteins, serine protease inhibitors, dehydrogenases, and regulators of the juvenile hormone. Validation of some of the top DEGs using qPCR supported the findings of the in silico analysis. GO term enrichment analysis reveals an overrepresentation of GO terms related to nucleotide metabolism and biosynthesis, lipid and carbohydrate metabolic processes, regulation of transcription, nucleotide binding, protein binding, metal binding, catalytic activity, oxidoreductase activity, and hydrolase activity. The data provided here will serve as a resource for improving local strains and increasing silk production of Philippine-reared B. mori strains.

Platelet proteome reveals features of cell death, antiviral response and viral replication in covid-19

Coronavirus disease 2019 (COVID-19) has affected over 400 million people worldwide, leading to 6 million deaths. Among the complex symptomatology of COVID-19, hypercoagulation and thrombosis have been described to directly contribute to lethality, pointing out platelets as an important SARS-CoV-2 target. In this work, we explored the platelet proteome of COVID-19 patients through a label-free shotgun proteomics approach to identify platelet responses to infection, as well as validation experiments in a larger patient cohort. Exclusively detected proteins (EPs) and differentially expressed proteins (DEPs) were identified in the proteomic dataset and thus classified into biological processes to map pathways correlated with pathogenesis. Significant changes in the expression of proteins related to platelet activation, cell death, and antiviral response through interferon type-I were found in all patients. Since the outcome of COVID-19 varies highly among individuals, we also performed a cross-comparison of proteins found in survivors and nonsurvivors. Proteins belonging to the translation pathway were strongly highlighted in the nonsurvivor group. Moreover, the SARS-CoV-2 genome was fully sequenced in platelets from five patients, indicating viral internalization and preprocessing, with CD147 as a potential entry route. In summary, platelets play a significant role in COVID-19 pathogenesis via platelet activation, antiviral response, and disease severity.

Case Report: Balanced Reciprocal Translocation t (17; 22) (p11.2; q11.2) and 10q23.31 Microduplication in an Infertile Male Patient Suffering From Teratozoospermia

Two chromosomal abnormalities are described in an infertile man suffering from teratozoospermia: balanced reciprocal translocation t (17; 22) (p11.2; q11.2) and a microduplication in the region 10q23.31. Twenty genes located on the breakpoints of translocation (e.g., ALKBH5, TOP3A, SPECC1L, and CDC45) are selected due to their high expression in testicular tissues and might be influenced by chromosome translocation. Four genes located on the breakpoints of microduplication including FLJ37201, KIF20B, LINC00865, and PANK1 result in an increased dosage of genes, representing an imbalance in the genome. These genes have been reported to be associated with developmental disorders/retardation and might be risk factors affecting spermatogenesis. Bioinformatics analysis is carried out on these key genes, intending to find the pathogenic process of reproduction in the context of the translocation and microduplication encountered in the male patient. The combination of the two chromosomal abnormalities carries additional risks for gametogenesis and genomic instability and is apparently harmful to male fertility. Overall, our findings could contribute to the knowledge of male infertility caused by genetic factors.

Revealing the human mucinome

Mucin domains are densely O-glycosylated modular protein domains found in various extracellular and transmembrane proteins. Mucin-domain glycoproteins play important roles in many human diseases, such as cancer and cystic fibrosis, but the scope of the mucinome remains poorly defined. Recently, we characterized a bacterial O-glycoprotease, StcE, and demonstrated that an inactive point mutant retains binding selectivity for mucin-domain glycoproteins. In this work, we leverage inactive StcE to selectively enrich and identify mucin-domain glycoproteins from complex samples like cell lysate and crude ovarian cancer patient ascites fluid. Our enrichment strategy is further aided by an algorithm to assign confidence to mucin-domain glycoprotein identifications. This mucinomics platform facilitates detection of hundreds of glycopeptides from mucin domains and highly overlapping populations of mucin-domain glycoproteins from ovarian cancer patients. Ultimately, we demonstrate our mucinomics approach can reveal key molecular signatures of cancer from in vitro and ex vivo sources.

Mucin-domain glycoproteins are densely O-glycosylated proteins with unique secondary structure that imparts a large influence on cellular environments. Here, the authors develop a technique to selectively enrich and characterize mucin-domain glycoproteins from cell lysate and patient biofluids.

Venus: Elucidating the Impact of Amino Acid Variants on Protein Function Beyond Structure Destabilisation

Exploring the functional effect of a non-synonymous coding variant at the protein level requires multiple pieces of information to be interpreted appropriately. This is particularly important when embarking on the study of a potentially pathogenic variant linked to a rare or monogenic disease. Whereas accurate protein stability predictions alone are generally informative, other effects, such as disruption of post-translational modifications or weakened ligand binding, may also contribute to the disease phenotype. Furthermore, consideration of nearby variants that are found in the healthy population may strengthen or refute a given mechanistic hypothesis. Whilst there are several bioinformatics tools available that score a genetic variant in terms of deleteriousness, there is no single tool that assembles multiple effects of a variant on the encoded protein, beyond structural stability, and presents them on the structure for inspection. Venus is a web application which, given a protein substitution, rapidly estimates the predicted effect on protein stability of the variant, flags if the variant affects a post-translational modification site, a predicted linear motif or known annotation, and determines the effect on protein stability of variants which affect nearby residues and have been identified in healthy populations. Venus is built upon Michelanglo and the results can be exported to it, allowing them to be annotated and shared with other researchers. Venus is freely accessible at https://venus.cmd.ox.ac.uk and its source code is openly available at https://github.com/CMD-Oxford/Michelanglo-and-Venus.

Method for Independent Estimation of the False Localization Rate for Phosphoproteomics

Phosphoproteomic methods are commonly employed to identify and quantify phosphorylation sites on proteins. In recent years, various tools have been developed, incorporating scores or statistics related to whether a given phosphosite has been correctly identified or to estimate the global false localization rate (FLR) within a given data set for all sites reported. These scores have generally been calibrated using synthetic datasets, and their statistical reliability on real datasets is largely unknown, potentially leading to studies reporting incorrectly localized phosphosites, due to inadequate statistical control. In this work, we develop the concept of scoring modifications on a decoy amino acid, that is, one that cannot be modified, to allow for independent estimation of global FLR. We test a variety of amino acids, on both synthetic and real data sets, demonstrating that the selection can make a substantial difference to the estimated global FLR. We conclude that while several different amino acids might be appropriate, the most reliable FLR results were achieved using alanine and leucine as decoys. We propose the use of a decoy amino acid to control false reporting in the literature and in public databases that re-distribute the data. Data are available via ProteomeXchange with identifier PXD028840.

Multiaspect Examinations of Possible Alternative Mappings of Identified Variant Peptides: A Case Study on the HEK293 Cell Line

Adopting proteogenomics approach to validate single nucleotide variation events by identifying corresponding single amino acid variant peptides from mass spectrometry (MS)-based proteomics data facilitates translational and clinical research. Although variant peptides are usually identified from MS data with a stringent false discovery rate (FDR), FDR control could fail to eliminate dubious results caused by several issues; thus, postexamination to eliminate dubious results is required. However, comprehensive postexaminations of identification results are still lacking. Therefore, we propose a framework of three bottom-up levels, peptide-spectrum match, peptide, and variant event levels, that consists of rigorous 11-aspect examinations from the MS perspective to further confirm the reliability of variant events. As a proof of concept and showing feasibility, we demonstrate 11 examinations on the identified variant peptides from an HEK293 cell line data set, where various database search strategies were applied to maximize the number of identified variant PSMs with an FDR <1% for postexaminations. The results showed that only FDR criterion is insufficient to validate identified variant peptides and the 11 postexaminations can reveal low-confidence variant events detected by shotgun proteomics experiments. Therefore, we suggest that postexaminations of identified variant events based on the proposed framework are necessary for proteogenomics studies.

Transcriptome profiling and proteomic validation reveals targets of the androgen receptor signaling in the BT-474 breast cancer cell line

BACKGROUND

Accumulating evidence suggests that the androgen receptor (AR) and its endogenous ligands influence disease progression in breast cancer (BCa). However, AR-mediated changes in BCa differ among the various BCa subtypes according to their hormone receptor profile [i.e., presence/absence of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2, (HER2)]. Thus, we explored the androgen-regulated transcriptomic changes in the ER⁺PR⁺HER2⁺ BCa cell line, BT-474, and compared them with PR-mediated changes.

METHODS

We performed RNA sequencing analysis in treated BT-474 cells with dihydrotestosterone (DHT) and progesterone. Validation of the top ten differentially androgen-regulated genes and a number of other genes found in enriched signaling pathways was performed by qRT-PCR in BT-474 and other BCa cell lines. In addition, a parallel reaction monitoring targeted proteomic approach was developed to verify selected transcripts at the protein level.

RESULTS

In total 19,450 transcripts were detected, of which 224 were differentially regulated after DHT treatment. The increased expression of two well-known androgen-regulated genes, KLK2 (p < 0.05) and KLK3 (p < 0.001), confirmed the successful androgen stimulation in BT-474 cells. The transcription factor, ZBTB16, was the most highly upregulated gene, with ~ 1000-fold change (p < 0.001). Pathway enrichment analysis revealed downregulation of the DNA replication processes (p < 0.05) and upregulation of the androgen signaling and fatty acid metabolism pathways (p < 0.05). Changes related to progesterone treatment showed opposite effects in gene expression than DHT treatment. Similar expression profiles were observed among other BCa cell lines expressing high levels of AR (ZR75.1 and MBA-MB-453). The parallel reaction monitoring targeted proteomic analysis further confirmed that altered protein expression (KLK3, ALOX15B) in the supernatant and cell lysate of DHT-treated BT-474 cells, compared to control cells.

DISCUSSION

Our findings suggest that AR modulates the metabolism of BT-474 cells by affecting the expression of a large number of genes and proteins. Based on further pathway analysis, we suggest that androgen receptor acts as a tumor suppressor in the BT-474 cells.

A Community-Driven, Openly Accessible Molecular Pathway Integrating Knowledge on Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a highly aggressive malignancy mainly triggered by exposure to asbestos and characterized by complex biology. A significant body of knowledge has been generated over the decades by the research community which has improved our understanding of the disease toward prevention, diagnostic opportunities and new treatments. Omics technologies are opening for additional levels of information and hypotheses. Given the growing complexity and technological spread of biological knowledge in MPM, there is an increasing need for an integrating tool that may allow scientists to access the information and analyze data in a simple and interactive way. We envisioned that a platform to capture this widespread and fast-growing body of knowledge in a machine-readable and simple visual format together with tools for automated large-scale data analysis could be an important support for the work of the general scientist in MPM and for the community to share, critically discuss, distribute and eventually advance scientific results. Toward this goal, with the support of experts in the field and informed by existing literature, we have developed the first version of a molecular pathway model of MPM in the biological pathway database WikiPathways. This provides a visual and interactive overview of interactions and connections between the most central genes, proteins and molecular pathways known to be involved or altered in MPM. Currently, 455 unique genes and 247 interactions are included, derived after stringent manual curation of an initial 39 literature references. The pathway model provides a directly employable research tool with links to common databases and repositories for the exploration and the analysis of omics data. The resource is publicly available in the WikiPathways database (Wikipathways : WP5087) and continues to be under development and curation by the community, enabling the scientists in MPM to actively participate in the prioritization of shared biological knowledge.

Validating Amino Acid Variants in Proteogenomics Using Sequence Coverage by Multiple Reads

Mass spectrometry-based proteome analysis implies matching the mass spectra of proteolytic peptides to amino acid sequences predicted from genomic sequences. Reliability of peptide variant identification in proteogenomic studies is often lacking. We propose a way to interpret shotgun proteomics results, specifically in the data-dependent acquisition mode, as protein sequence coverage by multiple reads as it is done in nucleic acid sequencing for calling of single nucleotide variants. Multiple reads for each sequence position could be provided by overlapping distinct peptides, thus confirming the presence of certain amino acid residues in the overlapping stretch with a lower false discovery rate. Overlapping distinct peptides originate from miscleaved tryptic peptides in combination with their properly cleaved counterparts and from peptides generated by multiple proteases after the same specimen is subject to parallel digestion and analyzed separately. We illustrate this approach using publicly available multiprotease data sets and our own data generated for the HEK-293 cell line digests obtained using trypsin, LysC, and GluC proteases. Totally, up to 30% of the whole proteome was covered by tryptic peptides with up to 7% covered twofold and more. The proteogenomic analysis of the HEK-293 cell line revealed 36 single amino acid variants, seven of which were supported by multiple reads.

Human endogenous retrovirus K in the respiratory tract is associated with COVID-19 physiopathology

BACKGROUND

Critically ill 2019 coronavirus disease (COVID-19) patients under invasive mechanical ventilation (IMV) are 10 to 40 times more likely to die than the general population. Although progression from mild to severe COVID-19 has been associated with hypoxia, uncontrolled inflammation, and coagulopathy, the mechanisms involved in the progression to severity are poorly understood.

METHODS

The virome of tracheal aspirates (TA) from 25 COVID-19 patients under IMV was assessed through unbiased RNA sequencing (RNA-seq), and correlation analyses were conducted using available clinical data. Unbiased sequences from nasopharyngeal swabs (NS) from mild cases and TA from non-COVID patients were included in our study for further comparisons.

RESULTS

We found higher levels and differential expression of human endogenous retrovirus K (HERV-K) genes in TA from critically ill and deceased patients when comparing nasopharyngeal swabs from mild cases to TA from non-COVID patients. In critically ill patients, higher HERV-K levels were associated with early mortality (within 14 days of diagnosis) in the intensive care unit. Increased HERV-K expression in deceased patients was associated with IL-17-related inflammation, monocyte activation, and an increased consumption of clotting/fibrinolysis factors. Moreover, increased HERV-K expression was detected in human primary monocytes from healthy donors after experimental SARS-CoV-2 infection in vitro.

CONCLUSION

Our data implicate the levels of HERV-K transcripts in the physiopathology of COVID-19 in the respiratory tract of patients under invasive mechanical ventilation. Video abstract.

Proteomics and Metabolomics Profiling of Platelets and Plasma Mediators of Thrombo-Inflammation in Gestational Hypertension and Preeclampsia

Platelets may be pivotal mediators of the thrombotic and coagulopathic complications of preeclampsia (PE), linking inflammation and thrombosis with endothelial and vascular dysfunction. Both PE and gestational hypertension (GH) fall within the spectrum of hypertensive complications of pregnancy, with GH being a risk factor for preeclampsia. However, it is unclear what biomarkers distinguish PE from GH. Using a discovery size cohort, we aimed to characterize specific plasma and platelet thrombo-inflammatory drivers indicative of PE and differentiate PE from GH. We performed multiplex immunoassays, platelet and plasma quantitative proteomics and metabolomics of PE patients, comparing with non-pregnant (NP), healthy pregnant controls (PC) and GH participants. The expression pattern of plasma proteins and metabolites in PE/GH platelets was distinct from that of NP and PC. Whilst procoagulation in PC may be fibrinogen driven, inter-alpha-trypsin inhibitors ITIH2 and ITIH3 are likely mediators of thrombo-inflammation in GH and PE, and fibronectin and S100A8/9 may be major procoagulant agonists in PE only. Also enriched in PE were CCL1 and CCL27 plasma cytokines, and the platelet leucine-rich repeat-containing protein 27 and 42 (LRRC27/42), whose effects on platelets were explored using STRING analysis. Through protein-protein interactions analysis, we generated a new hypothesis for platelets’ contribution to the thrombo-inflammatory states of preeclampsia.

Number of Detected Proteins as the Function of the Sensitivity of Proteomic Technology in Human Liver Cells

Aims:

The main goal of the Russian part of C-HPP is to detect and functionally annotate missing proteins (PE2-PE4) encoded by human chromosome 18. To achieve this goal, it is necessary to use the most sensitive methods of analysis.

Background:

However, identifying such proteins in a complex biological mixture using mass spectrometry (MS)-based methods is difficult due to the insufficient sensitivity of proteomic analysis methods. A possible solution to the problem is the pre-fractionation of a complex biological sample at the sample preparation stage.

Objective:

This study aims to measure the detection limit of SRM SIS analysis using a standard set of UPS1 proteins and find a way to enhance the sensitivity of the analysis and to, detect proteins encoded by the human chromosome 18 in liver tissue samples, and compare the data with transcriptomic analysis of the same samples.

Methods:

Mass spectrometry, data-dependent acquisition, selected reaction monitoring, highperformance liquid chromatography, data-dependent acquisition in combination with pre-fractionation by alkaline reversed-phase chromatography, selected reaction monitoring in combination with prefractionation by alkaline reversed-phase chromatography methods were used in this study.

Results:

The results revealed that 100% of UPS1 proteins in a mixture could only be identified at a concentration of at least 10-9 М. The decrease in concentration leads to protein losses associated with technology sensitivity, and no UPS1 protein is detected at a concentration of 10-13 М. Therefore, the two-dimensional fractionation of samples was applied to improve sensitivity. The human liver tissue was examined by selected reaction monitoring and shotgun methods of MS analysis using onedimensional and two-dimensional fractionation to identify the proteins encoded by human chromosome 18. A total of 134 proteins were identified. The overlap between proteomic and transcriptomic data in human liver tissue was ~50%.

Conclusion:

The sample concentration technique is well suited for a standard UPS1 system that is not contaminated with a complex biological sample. However, it is not suitable for use with a complex biological protein mixture. Thus, it is necessary to develop more sophisticated fractionation systems for the detection of all low-copy proteins. This weak convergence is due to the low sensitivity of proteomic technology compared to transcriptomic approaches. Also, total mRNA was used to perform RNA-seq analysis, but not all detected mRNA molecules could be translated into proteins. This introduces additional uncertainty in the data; in the future, we plan to study only translated mRNA molecules-the translatome. Data is available via ProteomeXchange with identifier PXD026997.

Evolution of Protein Functional Annotation: Text Mining Study

Within the Human Proteome Project initiative framework for creating functional annotations of uPE1 proteins, the neXt-CP50 Challenge was launched in 2018. In analogy with the missing-protein challenge, each command deciphers the functional features of the proteins in the chromosome-centric mode. However, the neXt-CP50 Challenge is more complicated than the missing-protein challenge: the approaches and methods for solving the problem are clear, but neither the concept of protein function nor specific experimental and/or bioinformatics protocols have been standardized to address it. We proposed using a retrospective analysis of the key HPP repository, the neXtProt database, to identify the most frequently used experimental and bioinformatic methods for analyzing protein functions, and the dynamics of accumulation of functional annotations. It has been shown that the dynamics of the increase in the number of proteins with known functions are greater than the progress made in the experimental confirmation of the existence of questionable proteins in the framework of the missing-protein challenge. At the same time, the functional annotation is based on the guilty-by-association postulate, according to which, based on large-scale experiments on API-MS and Y2H, proteins with unknown functions are most likely mapped through “handshakes” to biochemical processes.