Large-scale Analysis Of Proteins Research Articles

Unveiling Novel Insights in Helminth Proteomics: Advancements, Applications, and Implications for Parasitology and Beyond

Helminths have developed intricate mechanisms to survive and evade the host’s immune responses. Hence, understanding the excretory-secretory products (ESPs) by helminths is crucial for developing control tools, including drug targets, vaccines, and potential therapies for inflammatory and metabolic disorders caused by them. Proteomics, the large-scale analysis of proteins, offers a powerful approach to unravel the complex proteomes of helminths and gain insights into their biology. Proteomics, as a science that delves into the functions of proteins, has the potential to revolutionize clinical therapies against parasitic infections that have developed anthelminthic resistance. Proteomic technologies lay a framework for accompanying genomic, reverse genetics, and pharmacokinetic approaches to provide more profound or broader coverage of the cellular mechanisms that underlie the response to anthelmintics. With the development of vaccines against helminth infections, proteomics has brought a major change to parasitology. The proteome of helminths can be analyzed comprehensively, revealing the complex network of proteins that enable parasite survival and pathogenicity. Furthermore, it reveals how parasites interact with hosts’ immune systems. The current article reviews the latest advancements in helminth proteomics and highlights their valuable contributions to the search for anthelminthic vaccines.

Accurate and efficient protein embedding using multi-teacher distillation learning.

Protein embedding, which represents proteins as numerical vectors, is a crucial step in various learning-based protein annotation/classification problems, including gene ontology prediction, protein-protein interaction prediction, and protein structure prediction. However, existing protein embedding methods are often computationally expensive due to their large number of parameters, which can reach millions or even billions. The growing availability of large-scale protein datasets and the need for efficient analysis tools have created a pressing demand for efficient protein embedding methods. We propose a novel protein embedding approach based on multi-teacher distillation learning, which leverages the knowledge of multiple pre-trained protein embedding models to learn a compact and informative representation of proteins. Our method achieves comparable performance to state-of-the-art methods while significantly reducing computational costs and resource requirements. Specifically, our approach reduces computational time by ∼70% and maintains ±1.5% accuracy as the original large models. This makes our method well-suited for large-scale protein analysis and enables the bioinformatics community to perform protein embedding tasks more efficiently. The source code of MTDP is available via https://github.com/KennthShang/MTDP.

Proteomic Methods to Study Autophagy in Skin Exposed to Pollutants.

Autophagy refers to the natural cellular process by which cells degrade and recycle their own damaged or dysfunctional cellular components. It is an essential mechanism for maintaining cellular homeostasis removing toxic substances and providing energy during times of stress or nutrient deprivation. When autophagy is dysregulated or impaired, it can have detrimental effects on cell function and overall health. Studying autophagy in skin exposed to pollutants can provide valuable insights into the cellular mechanisms underlying pollutant-induced skin damage. Proteomic methods, which involve the large-scale analysis of proteins, can be employed to investigate the changes in protein expression associated with biological processes including autophagy. Here, we thus describe a method where LC-MS/MS was applied to identify the deregulated proteins in pollutant exposed-skin. Using bioinformatics and statistical analysis, we extracted the qualitative and quantitative information for proteins involved in autophagy. These deregulated proteins were then validated by immunohistochemistry (IHC). These methods help to understand how the pollutants affect the autophagy process.

Origin, evolution and diversity of SINA E3 ubiquitin ligases in plants

SINA (Seven in absentia) E3 ligases are critical components of the ubiquitin-proteasome system (UPS). They are responsible for ubiquitination and are involved in numerous cellular processes. The functional mechanisms of SINAs have been extensively studied in a few angiosperms. However, our understanding of the origin and evolution of plant SINA genes remain limited. Here we performed a large-scale comprehensive analysis of SINA proteins from various plant lineages. The plant SINA family genes likely originated from a common ancestral gene prior to the divergence of bryophyte and gave rise to two clades. Within clade I, subsequent parallel innovations in lycophytes and ferns resulted in the formation of two branches. All SINA proteins contain an N-terminal cysteine-rich really interesting new gene (RING) domain for ubiquitination, two zinc-finger motifs, and a C-terminal domain required for substrate-binding and dimerization. The SINA genes gains and losses occurred in angiosperms, resulting in an increase in the number of gene copies in eudicots and monocots. The Gossypium genus SINAs showed a tendency for expansion via whole-genome duplication and polyploidy. Finally, global expression patterns revealed the functional diversification of SINA genes in developmental stage and response to hormones and abiotic stresses in Arabidopsis and rice. These findings provide an insight into the evolution and diversification of SINA E3 ligases in plants and enhance our understanding of the role they play in determining substrate specificity and environment stress adaptations in angiosperms.

Current Advances in Single-Cell RNA Sequencing in Diabetic Retinopathy

With the development of high-throughput sequencing technology, humans have been able to conduct large-scale analysis of DNA sequence, chromatin structure, RNA transcripts, proteins, metabolites and other genomes and their products. Traditional high-throughput transcriptome sequencing techniques based on tissue samples (RNA Seq) are used to centrally sequence thousands of cells, each of which varies in size, protein levels, and mRNA expression transcription. Measuring the average of multiple cells grouped together can mask significant differences in gene expression between cells. Single-cell RNA sequencing is a technique for high-throughput sequencing of the genome, transcriptome, and epigenome at the single-cell level. Based on the single cell RNA transcription map, the intraocular cells can be distinguished from other subtypes, and the different subtypes are found to have significant differences in morphology, physiology and specific expression genes. In recent years, the application of single-cell RNA sequencing technology in the field of ophthalmology has increased, mainly including cell type and cell subtype identification, retinal development process, and eye disease research. This paper systematically summarized the latest application of single-cell sequencing technology in the field of diabetic retinopathy, and summarized marker genes and potential therapeutic targets. It has guiding significance for the clinical treatment of diabetic retinopathy.

Tear film proteome changes following Tobradex® therapy in anterior blepharitis.

The management of blepharitis continues to challenge clinicians due to the poorly understood aetiology of the condition. We recently identified the family of intracellular plakin proteins as essential driving forces underlying anterior blepharitis. A large-scale protein analysis was used to study if a topical dexamethasone/tobramycin solution could be used to reverse the expression of plakin proteins. Tear film samples from treatment naïve patients with anterior blepharitis (n = 15) were collected with Schirmer filtration paper. A subgroup of the patients (n = 10) received treatment with a dexamethasone/tobramycin 1 + 3 mg/mL ophthalmic suspension (Tobradex®) for 3 weeks and collection of tear film samples was repeated. The samples were analysed with label-free quantification nano liquid chromatography-tandem mass spectrometry requiring quantification in at least 70% of the samples in each group. Proteins were considered differentially expressed if p < 0.05. Following Tobradex® intervention, 27 proteins were upregulated while 61 proteins were downregulated. Regulated proteins after Tobradex® treatment were involved in intermediate filament cytoskeleton organization including downregulation of the plakin proteins envoplakin, epiplakin and periplakin. Plectin, a protein of the plakin family, remained unchanged after Tobradex® therapy. Tobradex® treatment resulted in the regulation of proteins involved in translation including a cluster of downregulated ribosomal proteins. Tobradex® intervention was associated with the regulation of proteins involved in fructose metabolism and glycolytic processes including fructose-1.6-bisphosphatase 1, fructose-bisphosphate aldolases A and B, pyruvate kinase PKM and transketolase. Ig lambda chain V-I region, prominin-1, and protein Niban were upregulated after Tobradex® treatment. Tobradex treatment reversed the expression of plakin proteins in anterior blepharitis. Topical solutions which inhibit the expression of plakin proteins may have the potential to restore the ocular surface integrity in anterior blepharitis and should be explored further.

Expanding the repertoire of human tandem repeat RNA-binding proteins.

Protein regions consisting of arrays of tandem repeats are known to bind other molecular partners, including nucleic acid molecules. Although the interactions between repeat proteins and DNA are already widely explored, studies characterising tandem repeat RNA-binding proteins are lacking. We performed a large-scale analysis of human proteins devoted to expanding the knowledge about tandem repeat proteins experimentally reported as RNA-binding molecules. This work is timely because of the release of a full set of accurate structural models for the human proteome amenable to repeat detection using structural methods. The main goal of our analysis was to build a comprehensive set of human RNA-binding proteins that contain repeats at the sequence or structure level. Our results showed that the combination of sequence and structural methods finds significantly more tandem repeat proteins than either method alone. We identified 219 tandem repeat proteins that bind RNA molecules and characterised the overlap between repeat regions and RNA-binding regions as a first step towards assessing their functional relationship. We observed differences in the characteristics of repeat regions predicted by sequence-based or structure-based methods in terms of their sequence composition, their functions and their protein domains.

Large-Scale Quantitative Proteomic Analysis during Different Stages of Somatic Embryogenesis in Larix olgensis.

Larix olgensis is an economically important tree species native to northeastern China. The use of somatic embryogenesis (SE) is efficient and enables the rapid production of varieties with desirable qualities. Here, isobaric labeling via tandem mass tags was used to conduct a large-scale quantitative proteomic analysis of proteins in three critically important stages of SE in L. olgensis: the primary embryogenic callus, the single embryo, and the cotyledon embryo. We identified 6269 proteins, including 176 shared differentially expressed proteins across the three groups. Many of these proteins are involved in glycolipid metabolism, hormone response/signal transduction, cell synthesis and differentiation, and water transport; proteins involved in stress resistance and secondary metabolism, as well as transcription factors, play key regulatory roles in SE. The results of this study provide new insights into the key pathways and proteins involved in SE in Larix. Our findings have implications for the expression of totipotency, the preparation of synthetic seeds, and genetic transformation.

Use of Nuclear and Chromatin Enrichment Procedures for Quantitation of Yeast DNA Replication Proteins Using SILAC.

DNA replication is a highly complex process that achieves the faithful transmission of genetic information from parent to progeny. Recruitment of DNA replication proteins to DNA is dynamically regulated during the cell cycle and in response to replication stresses. For a large-scale analysis of DNA replication proteins, I established a method for analysis of chromatin-bound proteins by SILAC (stable isotope labeling by amino acids in cell culture)-based quantitative proteomics. Here I describe a detailed methodology for SILAC labeling of budding yeast Saccharomyces cerevisiae, then nuclear isolation and chromatin preparation from synchronized yeast cells, prior to quantitative proteomic analysis of DNA replication proteins.

Gel-Based and Gel-Free Phosphoproteomics to Measure and Characterize Mitochondrial Phosphoproteins.

The mitochondrion is a key intracellular organelle regulating metabolic processes, oxidative stress, energy production, calcium homeostasis, and cell survival. Protein phosphorylation plays an important role in regulating mitochondrial functions and cellular signaling pathways. Dysregulation of protein phosphorylation status can cause protein malfunction and abnormal signal transduction, leading to organ dysfunction and disease. Investigating the mitochondrial phosphoproteins is therefore crucial to better understand the molecular and pathogenic mechanisms of many metabolic disorders. Conventional analyses of phosphoproteins, for instance, via western blotting, can be done only for proteins for which specific antibodies to their phosphorylated forms are available. Moreover, such an approach is not suitable for large-scale study of phosphoproteins. Currently, proteomics represents an important tool for large-scale analysis of proteins and their post-translational modifications, including phosphorylation. Here, we provide step-by-step protocols for the proteomics analysis of mitochondrial phosphoproteins (the phosphoproteome), using renal tubular cells as an example. These protocols include methods to effectively isolate mitochondria and to validate the efficacy of mitochondrial enrichment as well as its purity. We also provide detailed protocols for performing both gel-based and gel-free phosphoproteome analyses. The gel-based analysis involves two-dimensional gel electrophoresis and phosphoprotein-specific staining, followed by protein identification via mass spectrometry, whereas the gel-free approach is based on in-solution mass spectrometric identification of specific phosphorylation sites and residues. In all, these approaches allow large-scale analyses of mitochondrial phosphoproteins that can be applied to other cells and tissues of interest. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Mitochondrial isolation/purification from renal tubular cells Support Protocol: Validation of enrichment efficacy and purity of mitochondrial isolation Basic Protocol 2: Gel-based phosphoproteome analysis Basic Protocol 3: Gel-free phosphoproteome analysis.

Machine Learning for Mass Spectrometry Data Analysis in Proteomics

With the rapid development of high-throughput techniques, mass spectrometry has been widely used for large-scale protein analysis. To search for the existing proteins, discover biomarkers, and diagnose and prognose diseases, machine learning methods are applied in mass spectrometry data analysis. This paper reviews the applications of five kinds of machine learning methods to mass spectrometry data analysis from an algorithmic point of view, including support vector machine, decision tree, random forest, naive Bayesian classifier and deep learning.

Proteomics and Food Analysis: Principles, Techniques, and Applications

Proteomics can be considered the discipline of the large-scale analysis of proteins in a particular biological system [...]

Aqueous Fibronectin Correlates With Severity of Macular Edema and Visual Acuity in Patients With Branch Retinal Vein Occlusion: A Proteome Study.

PurposeLarge-scale protein analysis may bring important insights into molecular changes following branch retinal vein occlusion (BRVO). Using proteomic techniques this study compared aqueous humor samples from patients with BRVO to age-matched controls.MethodsAqueous humor samples from treatment naive patients with BRVO complicated by macular edema (n = 19) and age-matched controls (n = 18) were analyzed with label-free quantification nano liquid chromatography – tandem mass spectrometry (LFQ nLC-MS/MS). The severity of macular edema was measured as central retinal thickness (CRT) with optical coherence tomography. Control samples were obtained prior to cataract surgery. Proteins were filtered by requiring quantification in at least 50% of the samples in each group without imputation of missing values. Significantly changed proteins were identified with a permutation-based calculation with a false discovery rate at 0.05.ResultsIn BRVO, 52 proteins were differentially expressed. Regulated proteins were involved in cell adhesion, coagulation, and acute-phase response. Apolipoprotein C-III, complement C3, complement C5, complement factor H, fibronectin, and fibrinogen chains were increased in BRVO and correlated with CRT. Fibronectin also correlated with best corrected visual acuity (BCVA) and vascular endothelial growth factor (VEGF). Monocyte differentiation antigen CD14 (CD14) and lipopolysaccharide-binding protein (LBP) were upregulated in BRVO. Contactin-1 and alpha-enolase were downregulated in BRVO and correlated negatively with CRT.ConclusionsMultiple proteins, including complement factors, fibrinogen chains, and apolipoprotein C-III, correlated with CRT, indicating a multifactorial response. Fibronectin correlated with BCVA, CRT, and VEGF. Fibronectin may reflect the severity of BRVO. The proinflammatory proteins CD14 and LBP were upregulated in BRVO.

MHOLline 2.0: Workflow for automatic large-scale modeling and analysis of proteins

Genomes and their proteins can be analyzed by different perspectives, with different goals and applicability of results in several areas of research. In bioinformatics and computational biology, it is common the use of multiple combinations of programs and databases to extract information from the raw data. Choosing the proper program, setting its parameters, filtering the input files, extracting information from output files, and creating scripts for automating tasks can be challenging. This work describes the MHOLline, an online scientific workflow that provides a set of modules that enables a broad large-scale analysis of proteins (e.g. whole genomes) in a few hours. The version 1.0 (released in 2010), was wholly reformulated, and the new version 2.0 is available at www.mholline2.lncc.br. This version presents new features, modules, interface, code optimization, runtime reduction, more security, a new results visualization interface, and an automatic and user-friendly refinement page

Molecular and functional diversity of organelle RNA editing mediated by RNA recognition motif-containing protein ORRM4 in tomato.

Plant organellar RNA editing is a distinct type of post-transcriptional RNA modification that is critical for plant development. We showed previously that the RNA editing factor SlORRM4 is required for mitochondrial function and fruit ripening in tomato (Solanum lycopersicum). However, a comprehensive atlas of the RNA editing mediated by SlORRM4 is lacking. We observed that SlORRM4 is targeted to both chloroplasts and mitochondria, and its knockout results in pale-green leaves and delayed fruit ripening. Using high-throughput sequencing, we identified 12 chloroplast editing sites and 336 mitochondrial editing sites controlled by SlORRM4, accounting for 23% of chloroplast sites in leaves and 61% of mitochondrial sites in fruits, respectively. Analysis of native RNA immunoprecipitation sequencing revealed that SlORRM4 binds to 31 RNA targets; 19 of these targets contain SlORRM4-dependent editing sites. Large-scale analysis of putative SlORRM4-interacting proteins identified SlRIP1b, a RIP/MORF protein. Moreover, functional characterization demonstrated that SlRIP1b is involved in tomato fruit ripening. Our results indicate that SlORRM4 binds to RNA targets and interacts with SlRIP1b to broadly affect RNA editing in tomato organelles. These results provide insights into the molecular and functional diversity of RNA editing factors in higher plants.

ProdMX: Rapid query and analysis of protein functional domain based on compressed sparse matrices

Large-scale protein analysis has been used to characterize large numbers of proteins across numerous species. One of the applications is to use as a high-throughput screening method for pathogenicity of genomes. Unlike sequence homology methods, protein comparison at a functional level provides us with a unique opportunity to classify proteins, based on their functional structures without dealing with sequence complexity of distantly related species. Protein functions can be abstractly described by a set of protein functional domains, such as PfamA domains; a set of genomes can then be mapped to a matrix, with each row representing a genome, and the columns representing the presence or absence of a given functional domain. However, a powerful tool is needed to analyze the large sparse matrices generated by millions of genomes that will become available in the near future. The ProdMX is a tool with user-friendly utilities developed to facilitate high-throughput analysis of proteins with an ability to be included as an effective module in the high-throughput pipeline. The ProdMX employs a compressed sparse matrix algorithm to reduce computational resources and time used to perform the matrix manipulation during functional domain analysis. The ProdMX is a free and publicly available Python package which can be installed with popular package mangers such as PyPI and Conda, or with a standard installer from source code available on the ProdMX GitHub repository at https://github.com/visanuwan/prodmx.

Large-Scale Molecular Evolutionary Analysis Uncovers a Variety of Polynucleotide Kinase Clp1 Family Proteins in the Three Domains of Life

Clp1, a polyribonucleotide 5′-hydroxyl kinase in eukaryotes, is involved in pretRNA splicing and mRNA 3′-end formation. Enzymes similar in amino acid sequence to Clp1, Nol9, and Grc3, are present in some eukaryotes and are involved in prerRNA processing. However, our knowledge of how these Clp1 family proteins evolved and diversified is limited. We conducted a large-scale molecular evolutionary analysis of the Clp1 family proteins in all living organisms for which protein sequences are available in public databases. The phylogenetic distribution and frequencies of the Clp1 family proteins were investigated in complete genomes of Bacteria, Archaea and Eukarya. In total, 3,557 Clp1 family proteins were detected in the three domains of life, Bacteria, Archaea, and Eukarya. Many were from Archaea and Eukarya, but a few were found in restricted, phylogenetically diverse bacterial species. The domain structures of the Clp1 family proteins also differed among the three domains of life. Although the proteins were, on average, 555 amino acids long (range, 196–2,728), 122 large proteins with >1,000 amino acids were detected in eukaryotes. These novel proteins contain the conserved Clp1 polynucleotide kinase domain and various other functional domains. Of these proteins, >80% were from Fungi or Protostomia. The polyribonucleotide kinase activity of Thermus scotoductus Clp1 (Ts-Clp1) was characterized experimentally. Ts-Clp1 preferentially phosphorylates single-stranded RNA oligonucleotides (Km value for ATP, 2.5 µM), or single-stranded DNA at higher enzyme concentrations. We propose a comprehensive assessment of the diversification of the Clp1 family proteins and the molecular evolution of their functional domains.

Proteomic Landscape of the Mature Roots in a Rubber-Producing Grass Taraxacum Kok-saghyz.

The rubber grass Taraxacum kok-saghyz (TKS) contains large amounts of natural rubber (cis-1,4-polyisoprene) in its enlarged roots and it is an alternative crop source of natural rubber. Natural rubber biosynthesis (NRB) and storage in the mature roots of TKS is a cascade process involving many genes, proteins and their cofactors. The TKS genome has just been annotated and many NRB-related genes have been determined. However, there is limited knowledge about the protein regulation mechanism for NRB in TKS roots. We identified 371 protein species from the mature roots of TKS by combining two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS). Meanwhile, a large-scale shotgun analysis of proteins in TKS roots at the enlargement stage was performed, and 3545 individual proteins were determined. Subsequently, all identified proteins from 2-DE gel and shotgun MS in TKS roots were subject to gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and most proteins were involved in carbon metabolic process with catalytic activity in membrane-bounded organelles, followed by proteins with binding ability, transportation and phenylpropanoid biosynthesis activities. Fifty-eight NRB-related proteins, including eight small rubber particle protein (SRPP) and two rubber elongation factor(REF) members, were identified from the TKS roots, and these proteins were involved in both mevalonate acid (MVA) and methylerythritol phosphate (MEP) pathways. To our best knowledge, it is the first high-resolution draft proteome map of the mature TKS roots. Our proteomics of TKS roots revealed both MVA and MEP pathways are important for NRB, and SRPP might be more important than REF for NRB in TKS roots. These findings would not only deepen our understanding of the TKS root proteome, but also provide new evidence on the roles of these NRB-related proteins in the mature TKS roots.

Quantitative assay of targeted proteome in tomato trichome glandular cells using a large-scale selected reaction monitoring strategy

BackgroundGlandular trichomes found in vascular plants are called natural cell factories because they synthesize and store secondary metabolites in glandular cells. To systematically understand the metabolic processes in glandular cells, it is indispensable to analyze cellular proteome dynamics. The conventional proteomics methods based on mass spectrometry have enabled large-scale protein analysis, but require a large number of trichome samples for in-depth analysis and are not suitable for rapid and sensitive quantification of targeted proteins.ResultsHere, we present a high-throughput strategy for quantifying targeted proteins in specific trichome glandular cells, using selected reaction monitoring (SRM) assays. The SRM assay platform, targeting proteins in type VI trichome gland cells of tomato as a model system, demonstrated its effectiveness in quantifying multiple proteins from a limited amount of sample. The large-scale SRM assay uses a triple quadrupole mass spectrometer connected online to a nanoflow liquid chromatograph, which accurately measured the expression levels of 221 targeted proteins contained in the glandular cell sample recovered from 100 glandular trichomes within 120 min. Comparative quantitative proteomics using SRM assays of type VI trichome gland cells between different organs (leaves, green fruits, and calyx) revealed specific organ-enriched proteins.ConclusionsWe present a targeted proteomics approach using the established SRM assays which enables quantification of proteins of interest with minimum sampling effort. The remarkable success of the SRM assay and its simple experimental workflow will increase proteomics research in glandular trichomes.

Abstract #103: Non-Invasive Brain Stimulation Affects Cytosolic and Synaptic Proteomics in Rat Hippocampus

Introduction: Converging evidence suggests transcranial direct current stimulation (tDCS) enhances learning and memory, however the regulatory pathways remain unclear. Proteomics is a method for large-scale analysis of proteins that can predict protein interactions in functional pathways. In this study, proteomics was employed to obtain more information about previously identified tDCS-affected neural pathways involved in learning and memory and identify less known pathways.