Functional annotation and biological interpretation of proteomics data

Cathepsin B Regulates Ovarian Reserve Quality and Quantity via Mitophagy by Modulating IGF1R Turnover

The quality and quantity of the ovarian reserve are meticulously regulated through various cell death pathways to guarantee the availability of high-quality oocytes for fertilization. While apoptosis is recognized for contributing to maintaining ovarian reserve, the involvement of other cell death pathways remains unclear. Employing chemical genetics and proteomics, this study reveals the crucial involvement of Cathepsin B in maintaining the ovarian reserve. Results indicate that apoptosis and autophagy play pivotal roles, and inhibiting these pathways significantly increases follicle numbers. Proteomics reveals a dynamic shift from apoptosis to autophagy during follicular development, with Cathepsin B emerging as a key player in this transition. Inhibiting Cathepsin B not only mimics the augmented oocyte reserve observed with autophagy inhibition but also upregulated IGF1R and AKT-mTOR pathways without compromising fertility in pre- and postpubertal mice. Further, IGF1R inhibition partially compromised the protective effects of Cathepsin B inhibition on oocyte reserves, suggesting their interdependence. This association is further supported by the finding that Cathepsin B can degrade IGF1R in vitro. Moreover, the increased IGF1R levels enhance the oocyte mitochondrial membrane potential via transcriptional regulation of mitochondrial biogenesis and mitophagy genes. Remarkably, this Cathepsin B-dependent ovarian reserve maintenance mechanism is conserved in higher-order vertebrates. Cumulatively, our study sheds valuable light on the intricate interplay of autophagy, Cathepsin B, and growth factors in ovarian reserve maintenance, offering potential therapeutic strategies to delay ovarian aging and preserve fertility.

Regulatory element map of sheep reproductive tissues: functional annotation of tissue-specific strong active enhancers

Introduction

Comprehensive functional annotation of the genome is crucial for elucidating the molecular mechanisms underlying complex traits and diseases. Although functional annotation has been partially completed in sheep, a systematic annotation focused on reproductive tissues remains absent.

Methods

In this study, we integrated 60 transcriptomic and epigenomic datasets from five reproductive tissues. Using a multi-omics approach, we predicted 15 distinct chromatin states and conducted thorough functional annotation.

Results

We established the first regulatory element atlas for sheep reproductive tissues and examined the roles of these elements in reproductive traits and disease. In total, we annotated 1,680,172 regulatory elements, including 83,980 tissue-specific strong active enhancers (EnhAs).

Discussion

Enhancers were identified as critical drivers of tissue-specific functions, operating through sequence-specific transcription factor binding and direct regulation of target genes. Key transcription factors associated with reproductive function included INHBA (ovary), KITLG (oviduct), Snai2 (cervix), WNT7A (uterine horn), FOLR1 (uterine body), and SALL1 (shared uterine regions). Additionally, our findings support the potential of sheep as a promising model for investigating embryonic development and miscarriage. This work lays a theoretical foundation for future research into the molecular mechanisms of complex traits and diseases in sheep.

Liver click dECM hydrogels for engineering hepatic microenvironments

Decellularized extracellular matrix (dECM) hydrogels provide tissue-specific microenvironments which accommodate physiological cellular phenotypes in 3D in vitro cell cultures. However, their formation hinges on collagen fibrillogenesis, a complex process which limits regulation of physicochemical properties. Hence, achieving reproducible results with dECM hydrogels poses as a challenge. Here, we demonstrate that thiolation of solubilized liver dECM enables rapid formation of covalently crosslinked hydrogels via Michael-type addition, allowing for precise control over mechanical properties and superior organotypic biological activity. Investigation of various decellularization methodologies revealed that treatment of liver tissue with Triton X-100 and ammonium hydroxide resulted in near complete DNA removal with significant retention of the native liver proteome. Chemical functionalization of pepsin-solubilized liver dECMs via 1-ethyl-3(3-dimethylamino)propyl carbodiimide (EDC)/N-hydroxysuccinimide (NHS) coupling of l-Cysteine created thiolated liver dECM (dECM-SH), which rapidly reacted with 4-arm polyethylene glycol (PEG)-maleimide to form optically clear hydrogels under controlled conditions. Importantly, Young's moduli could be precisely tuned between 1 - 7 kPa by varying polymer concentrations, enabling close replication of healthy and fibrotic liver conditions in in vitro cell cultures. Click dECM-SH hydrogels were cytocompatible, supported growth of HepG2 and HepaRG liver cells, and promoted liver-specific functional phenotypes as evidenced by increased metabolic activity, as well CYP1A2 and CYP3A4 activity and excretory function when compared to monolayer culture and collagen-based hydrogels. Our findings demonstrate that click-functionalized dECM hydrogels offer a highly controlled, reproducible alternative to conventional tissue-derived hydrogels for in vitro cell culture applications. STATEMENT OF SIGNIFICANCE: Traditional dECM hydrogels face challenges in reproducibility and mechanical property control due to variable crosslinking processes. We introduce a click hydrogel based on porcine liver decellularized extracellular matrix (dECM) that circumnavigates these challenges. After optimizing liver decellularization for ECM retention, we integrated thiol-functionalized liver dECM with polyethylene-glycol derivatives through Michael-type addition click chemistry, enabling rapid, room-temperature gelation. This offers enhanced control over the hydrogel's mechanical and biochemical properties. The resultant click dECM hydrogels mimic the liver's natural ECM and exhibit greater mechanical tunability and handling ease, facilitating their application in high-throughput and industrial settings. Moreover, these hydrogels significantly improve the function of HepaRG-derived hepatocytes in 3D culture, presenting an advancement for liver tissue cell culture models for drug testing applications.

Regulation of Proline Accumulation and Protein Secretion in Sorghum under Combined Osmotic and Heat Stress

Plants reprogramme their proteome to alter cellular metabolism for effective stress adaptation. Intracellular proteomic responses have been extensively studied, and the extracellular matrix stands as a key hub where peptide signals are generated/processed to trigger critical adaptive signal transduction cascades inaugurated at the cell surface. Therefore, it is important to study the plant extracellular proteome to understand its role in plant development and stress response. This study examined changes in the soluble extracellular sub-proteome of sorghum cell cultures exposed to a combination of sorbitol-induced osmotic stress and heat at 40 °C. The combined stress significantly reduced metabolic activity and altered protein secretion. While cells treated with osmotic stress alone had elevated proline content, the osmoprotectant in the combined treatment remained unchanged, confirming that sorghum cells exposed to combined stress utilise adaptive processes distinct from those invoked by the single stresses applied separately. Reactive oxygen species (ROS)-metabolising proteins and proteases dominated differentially expressed proteins identified in cells subjected to combined stress. ROS-generating peroxidases were suppressed, while ROS-degrading proteins were upregulated for protection from oxidative damage. Overall, our study provides protein candidates that could be used to develop crops better suited for an increasingly hot and dry climate.

Proteomic-Based Discovery of Predictive Biomarkers for Drug Therapy Response and Personalized Medicine in Chronic Immune Thrombocytopenia

Purpose

ITP is the most prevalent autoimmune blood disorder. The lack of predictive biomarkers for therapeutic response is a major challenge for physicians caring of chronic ITP patients. This study is aimed at identifying predictive biomarkers for drug therapy responses.

Methods

2D gel electrophoresis (2-DE) was performed to find differentially expressed proteins. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometer (MALDI-TOF MS) analysis was performed to identify protein spots. The Cytoscape software was employed to visualize and analyze the protein-protein interaction (PPI) network. Then, enzyme-linked immunosorbent assays (ELISA) were used to confirm the results of the proteins detected in the blood. The DAVID online software was used to explore the Gene Ontology and pathways involved in the disease.

Results

Three proteins, including APOA1, GC, and TF, were identified as hub-bottlenecks and confirmed by ELISA. Enrichment analysis results showed the importance of several biological processes and pathway, such as the PPAR signaling pathway, complement and coagulation cascades, platelet activation, vitamin digestion and absorption, fat digestion and absorption, cell adhesion molecule binding, and receptor binding. Conclusion and Clinical Relevance. Our results indicate that plasma proteins (APOA1, GC, and TF) can be suitable biomarkers for the prognosis of the response to drug therapy in ITP patients.

An atlas of regulatory elements in chicken: A resource for chicken genetics and genomics

A comprehensive characterization of regulatory elements in the chicken genome across tissues will have substantial impacts on both fundamental and applied research. Here, we systematically identified and characterized regulatory elements in the chicken genome by integrating 377 genome-wide sequencing datasets from 23 adult tissues. In total, we annotated 1.57 million regulatory elements, representing 15 distinct chromatin states, and predicted about 1.2 million enhancer-gene pairs and 7662 super-enhancers. This functional annotation of the chicken genome should have wide utility on identifying regulatory elements accounting for gene regulation underlying domestication, selection, and complex trait regulation, which we explored. In short, this comprehensive atlas of regulatory elements provides the scientific community with a valuable resource for chicken genetics and genomics.

An informatic workflow for the enhanced annotation of excretory/secretory proteins of Haemonchus contortus

Major advances in genomic and associated technologies have demanded reliable bioinformatic tools and workflows for the annotation of genes and their products via comparative analyses using well-curated reference data sets, accessible in public repositories. However, the accurate in silico annotation of molecules (proteins) encoded in organisms (e.g., multicellular parasites) which are evolutionarily distant from those for which these extensive reference data sets are available, including invertebrate model organisms (e.g., Caenorhabditis elegans - free-living nematode, and Drosophila melanogaster - the vinegar fly) and vertebrate species (e.g., Homo sapiens and Mus musculus), remains a major challenge. Here, we constructed an informatic workflow for the enhanced annotation of biologically-important, excretory/secretory (ES) proteins ("secretome") encoded in the genome of a parasitic roundworm, called Haemonchus contortus (commonly known as the barber's pole worm). We critically evaluated the performance of five distinct methods, refined some of them, and then combined the use of all five methods to comprehensively annotate ES proteins, according to gene ontology, biological pathways and/or metabolic (enzymatic) processes. Then, using optimised parameter settings, we applied this workflow to comprehensively annotate 2591 of all 3353 proteins (77.3%) in the secretome of H. contortus. This result is a substantial improvement (10-25%) over previous annotations using individual, "off-the-shelf" algorithms and default settings, indicating the ready applicability of the present, refined workflow to gene/protein sequence data sets from a wide range of organisms in the Tree-of-Life.

Application of WGCNA and PloGO2 in the Analysis of Complex Proteomic Data

In this protocol we describe our workflow for analyzing complex, multi-condition quantitative proteomic experiments, with the aim to extract biological insights. The tool we use is an R package, PloGO2, contributed to Bioconductor, which we can optionally precede by running correlation network analysis with WGCNA. We describe the data required and the steps we take, including detailed code examples and outputs explanation. The package was designed to generate gene ontology or pathway summaries for many data subsets at the same time, visualize protein abundance summaries for each biological category examined, help determine enriched protein subsets by comparing them all to a reference set, and suggest key highly correlated hub proteins, if the optional network analysis is employed.

Chronic Fluoxetine Treatment of Socially Isolated Rats Modulates Prefrontal Cortex Proteome

Fluoxetine (Flx) is the most commonly used antidepressant to treat major depressive disorder. However, its molecular mechanisms of action are not defined as yet. A comparative proteomic approach was used to identify proteome changes in the prefrontal cortex (PFC) cytosolic and non-synaptic mitochondria (NSM)-enriched fractions of adult male Wistar rats following chronic social isolation (CSIS), a rat model of depression, and Flx treatment in CSIS and control rats, using liquid chromatography online tandem mass spectrometry. Flx reversed CSIS-induced depressive - like behavior according to preference for sucrose and immobility in the forced swim test, indicating its antidepressant effect. Flx treatment in controls led to an increase of the expression of cytosolic proteins involved in the microtubule cytoskeleton and intracellular calcium homeostasis and of enzymes involved in bioenergetic and transmembrane transport in NSM. CSIS downregulated the cytosolic proteins involved in proteasome pathway, and glutathione antioxidative system, and upregulated the expression of enzymes participating in mitochondrial-energy metabolism and transport. The presence of cytochrome c in the cytosol may suggest compromised mitochondrial membrane integrity. Flx treatment in CSIS rats downregulated protein involved in oxidative phosphorylation, such as complex III and manganese superoxide dismutase, and upregulated vesicle-mediated transport and synaptic signaling proteins in the cytosol, and neuronal calcium-binding protein 1 in NSM. Our study identified PFC modulated proteins and affected biochemical pathways that may represent potential markers/targets underlying CSIS-induced depression and effective Flx treatment, and highlights the role of protein systems involved in NSM and various metabolic pathways potentially involved in neuronal plasticity.

Wheat grain proteomic and protein-metabolite interactions analyses provide insights into plant growth promoting bacteria-arbuscular mycorrhizal fungi-wheat interactions

Proteomic, protein-protein and protein-metabolite interaction analyses in wheat inoculated with PGPB and AMF identified key proteins and metabolites that may have a role in enhancing yield and biofortification. Plant growth-promoting bacteria (PGPB) and arbuscular mycorrhizal fungi (AMF) have an impact on grain yield and nutrition. This dynamic yet complex interaction implies a broad reprogramming of the plant's metabolic and proteomic activities. However, little information is available regarding the role of native PGPB and AMF and how they affect the plant proteome, especially under field conditions. Here, proteomic, protein-protein and protein-metabolite interaction studies in wheat triggered by PGPB, Bacillus subtilis CP4 either alone or together with AMF under field conditions was carried out. The dual inoculation with native PGPB (CP4) and AMF promoted the differential abundance of many proteins, such as histones, glutenin, avenin and ATP synthase compared to the control and single inoculation. Interaction study of these differentially expressed proteins using STRING revealed that they interact with other proteins involved in seed development and abiotic stress tolerance. Furthermore, these interacting proteins are involved in carbon fixation, sugar metabolism and biosynthesis of amino acids. Molecular docking predicted that wheat seed storage proteins, avenin and glutenin interact with secondary metabolites, such as trehalose, and sugars, such as xylitol. Mapping of differentially expressed proteins to KEGG pathways showed their involvement in sugar metabolism, biosynthesis of secondary metabolites and modulation of histones. These proteins and metabolites can serve as markers for improving wheat-PGPB-AMF interactions leading to higher yield and biofortification.

MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses

Interpreting proteomics data remains challenging due to the large number of proteins that are quantified by modern mass spectrometry methods. Weighted gene correlation network analysis (WGCNA) can identify groups of biologically related proteins using only protein intensity values by constructing protein correlation networks. However, WGCNA is not widespread in proteomic analyses due to challenges in implementing workflows. To facilitate the adoption of WGCNA by the proteomics field, we created MetaNetwork, an open-source, R-based application to perform sophisticated WGCNA workflows with no coding skill requirements for the end user. We demonstrate MetaNetwork's utility by employing it to identify groups of proteins associated with prostate cancer from a proteomic analysis of tumor and adjacent normal tissue samples. We found a decrease in cytoskeleton-related protein expression, a known hallmark of prostate tumors. We further identified changes in module eigenproteins indicative of dysregulation in protein translation and trafficking pathways. These results demonstrate the value of using MetaNetwork to improve the biological interpretation of quantitative proteomics experiments with 15 or more samples.

Genetic Improvement in Sunflower Breeding—Integrated Omics Approach

Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement.

High-throughput proteomics and metabolomic studies guide re-engineering of metabolic pathways in eukaryotic microalgae: A review

Eukaryotic microalgae are a rich source of commercially important metabolites including lipids, pigments, sugars, amino acids and enzymes. However, their inherent genetic potential is usually not enough to support high level production of metabolites of interest. In order to move on from the traditional approach of improving product yields by modification of the cultivation conditions, understanding the metabolic pathways leading to the synthesis of the bioproducts of interest is crucial. Identification of new targets for strain engineering has been greatly facilitated by the rapid development of high-throughput sequencing and spectroscopic techniques discussed in this review. Despite the availability of high throughput analytical tools, examples of gathering and application of proteomic and metabolomic data for metabolic engineering of microalgae are few and mainly limited to lipid production. The present review highlights the application of contemporary proteomic and metabolomic techniques in eukaryotic microalgae for redesigning pathways for enhanced production of algal metabolites.

Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study

Gene set analysis (GSA) has become the common methodology for analyzing transcriptomics data. However, self-contained GSA techniques are rarely, if ever, used for proteomics data analysis. Here we present a self-contained proteome level GSA of four consensus molecular subtypes (CMSs) previously established by transcriptome dissection of colon carcinoma specimens. Despite notable difference in structure of proteomics and transcriptomics data, many pathway-wide characteristic features of CMSs found at the mRNA level were reproduced at the protein level. In particular, CMS1 features show heavy involvement of immune system as well as the pathways related to mismatch repair, DNA replication and functioning of proteasome, while CMS4 tumors upregulate complement pathway and proteins participating in epithelial-to-mesenchymal transition (EMT). In addition, protein level GSA yielded a set of novel observations visible at the proteome, but not at the transcriptome level, including possible involvement of major histocompatibility complex II (MHC-II) antigens in the known immunogenicity of CMS1 and a connection between cholesterol trafficking and the regulation of Integrin-linked kinase (ILK) in CMS3. Overall, this study proves utility of self-contained GSA approaches as a critical tool for analyzing proteomics data in general and dissecting protein-level molecular portraits of human tumors in particular.

Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology

Supplemental Digital Content is available in the text.

Background:

A systems biology approach to cardiac physiology requires a comprehensive representation of how coordinated processes operate in the heart, as well as the ability to interpret relevant transcriptomic and proteomic experiments. The Gene Ontology (GO) Consortium provides structured, controlled vocabularies of biological terms that can be used to summarize and analyze functional knowledge for gene products.

Methods and Results:

In this study, we created a computational resource to facilitate genetic studies of cardiac physiology by integrating literature curation with attention to an improved and expanded ontological representation of heart processes in the Gene Ontology. As a result, the Gene Ontology now contains terms that comprehensively describe the roles of proteins in cardiac muscle cell action potential, electrical coupling, and the transmission of the electrical impulse from the sinoatrial node to the ventricles. Evaluating the effectiveness of this approach to inform data analysis demonstrated that Gene Ontology annotations, analyzed within an expanded ontological context of heart processes, can help to identify candidate genes associated with arrhythmic disease risk loci.

Conclusions:

We determined that a combination of curation and ontology development for heart-specific genes and processes supports the identification and downstream analysis of genes responsible for the spread of the cardiac action potential through the heart. Annotating these genes and processes in a structured format facilitates data analysis and supports effective retrieval of gene-centric information about cardiac defects.

Enhancing the rate of genetic gain in public-sector plant breeding programs: lessons from the breeder's equation

The integration of new technologies into public plant breeding programs can make a powerful step change in agricultural productivity when aligned with principles of quantitative and Mendelian genetics. The breeder's equation is the foundational application of quantitative genetics to crop improvement. Guided by the variables that describe response to selection, emerging breeding technologies can make a powerful step change in the effectiveness of public breeding programs. The most promising innovations for increasing the rate of genetic gain without greatly increasing program size appear to be related to reducing breeding cycle time, which is likely to require the implementation of parent selection on non-inbred progeny, rapid generation advance, and genomic selection. These are complex processes and will require breeding organizations to adopt a culture of continuous optimization and improvement. To enable this, research managers will need to consider and proactively manage the, accountability, strategy, and resource allocations of breeding teams. This must be combined with thoughtful management of elite genetic variation and a clear separation between the parental selection process and product development and advancement process. With an abundance of new technologies available, breeding teams need to evaluate carefully the impact of any new technology on selection intensity, selection accuracy, and breeding cycle length relative to its cost of deployment. Finally breeding data management systems need to be well designed to support selection decisions and novel approaches to accelerate breeding cycles need to be routinely evaluated and deployed.

Protein Profiling of Arabidopsis Roots Treated With Humic Substances: Insights Into the Metabolic and Interactome Networks

Background and Aim: Humic substances (HSs) influence the chemical and physical properties of the soil, and are also known to affect plant physiology and nutrient uptake. This study aimed to elucidate plant metabolic pathways and physiological processes influenced by HS activity. Methods: Arabidopsis roots were treated with HS for 8 h. Quantitative mass spectrometry-based proteomics analysis of root proteins was performed using the iTRAQ (Isobaric Tag for Relative and Absolute Quantification) technique. Out of 902 protein families identified and quantified for HS treated vs. untreated roots, 92 proteins had different relative content. Bioinformatic tools such as STRING, KEGG, IIS and Cytoscape were used to interpret the biological function, pathway analysis and visualization of network amongst the identified proteins. Results: From this analysis it was possible to evaluate that all of the identified proteins were functionally classified into several categories, mainly redox homeostasis, response to inorganic substances, energy metabolism, protein synthesis, cell trafficking, and division. Conclusion: In the present study an overview of the metabolic pathways most modified by HS biological activity is provided. Activation of enzymes of the glycolytic pathway and up regulation of ribosomal protein indicated a stimulation in energy metabolism and protein synthesis. Regulation of the enzymes involved in redox homeostasis suggest a pivotal role of reactive oxygen species in the signaling and modulation of HS-induced responses.

Altered Proteins in the Hippocampus of Patients with Mesial Temporal Lobe Epilepsy

Mesial temporal lobe epilepsy (MTLE) is usually associated with drug-resistant seizures and cognitive deficits. Efforts have been made to improve the understanding of the pathophysiology of MTLE for new therapies. In this study, we used proteomics to determine the differential expression of proteins in the hippocampus of patients with MTLE compared to control samples. By using the two-dimensional electrophoresis method (2-DE), the proteins were separated into spots and analyzed by LC-MS/MS. Spots that had different densitometric values for patients and controls were selected for the study. The following proteins were found to be up-regulated in patients: isoform 1 of serum albumin (ALB), proton ATPase catalytic subunit A (ATP6V1A), heat shock protein 70 (HSP70), dihydropyrimidinase-related protein 2 (DPYSL2), isoform 1 of myelin basic protein (MBP), and dihydrolipoamide S-acethyltransferase (DLAT). The protein isoform 3 of the spectrin alpha chain (SPTAN1) was down-regulated while glutathione S-transferase P (GSTP1) and protein DJ-1 (PARK7) were found only in the hippocampus of patients with MTLE. Interactome analysis of the nine proteins of interest revealed interactions with 20 other proteins, most of them involved with metabolic processes (37%), presenting catalytic activity (37%) and working as hydrolyses (25%), among others. Our results provide evidence supporting a direct link between synaptic plasticity, metabolic disturbance, oxidative stress with mitochondrial damage, the disruption of the blood–brain barrier and changes in CNS structural proteins with cell death and epileptogenesis in MTLE. Besides this, the presence of markers of cell survival indicated a compensatory mechanism. The over-expression of GSTP1 in MTLE could be related to drug-resistance.

Approaches to the discovery of non-invasive urinary biomarkers of prostate cancer

Prostate cancer (PCa) continues to be one of the most common cancers in men worldwide. Prostate specific antigen (PSA) measured in blood has been used for decades as an aid for physicians to detect the presence of prostate cancer. However, the PSA test has limited sensitivity and specificity, leading to unnecessary biopsies, overdiagnosis and overtreatment of patients. For these reasons, there is an urgent need for more accurate PCa biomarkers that can detect PCa with high sensitivity and specificity. Urine is a unique source of potential protein biomarkers that can be measured in a non-invasive way. This review comprehensively summarizes state of the art approaches used in the discovery and validation of urinary biomarkers for PCa. Numerous strategies are currently being used in the discovery of urinary biomarkers for prostate cancer including gel-based separation techniques, mass spectrometry, activity-based proteomic assays and software approaches. Antibody-based approaches remain preferred method for validation of candidate biomarkers with rapidly advancing multiplex immunoassays and MS-based targeted approaches. In the last decade, there has been a dramatic acceleration in the development of new techniques and approaches in the discovery of protein biomarkers for prostate cancer including computational, statistical and data mining methods. Many urinary-based protein biomarkers have been identified and have shown significant promise in initial studies. Examples of these potential biomarkers and the methods utilized in their discovery are also discussed in this review.

Characterization of Protein Complexes Using Chemical Cross-Linking Coupled Electrospray Mass Spectrometry

Identification and characterization of large protein complexes is a mainstay of biochemical toolboxes. Utilization of cross-linking chemicals can facilitate the capture and identification of transient or weak interactions of a transient nature (Huang and Kim, PloS One 8:e61430, 2013; Gao et al., J Vis Exp doi: 10.3791/51387, 2014). Here we describe a detailed methodology for a cell culture-based proteomic approach. We describe the generation of cells stably expressing green fluorescent protein (GFP)-tagged proteins under the tetracycline-inducible promoter and subsequent proteomic analysis of GFP-interacting proteins. We include a list of proteins that were identified as interactors of GFP.

Bioinformatics Resources for Interpreting Proteomics Mass Spectrometry Data

Developments in mass spectrometry (MS) instrumentation have supported the advance of a variety of proteomic technologies that have enabled scientists to assess differences between healthy and diseased states. In particular, the ability to identify altered biological processes in a cell has led to the identification of novel drug targets, the development of more effective therapeutic drugs, and the growth of new diagnostic approaches and tools for personalized medicine applications. Nevertheless, large-scale proteomic data generated by modern mass spectrometers are extremely complex and necessitate equally complex bioinformatics tools and computational algorithms for their interpretation. A vast number of commercial and public resources have been developed for this purpose, often leaving the researcher perplexed at the overwhelming list of choices that exist. To address this challenge, the aim of this chapter is to provide a roadmap to the basic steps that are involved in mass spectrometry data acquisition and processing, and to describe the most common tools that are available for placing the results in biological context.

Flux control through protein phosphorylation in yeast

Protein phosphorylation is one of the most important mechanisms regulating metabolism as it can directly modify metabolic enzymes by the addition of phosphate groups. Attributed to such a rapid and reversible mechanism, cells can adjust metabolism rapidly in response to temporal changes. The yeast Saccharomyces cerevisiae, a widely used cell factory and model organism, is reported to show frequent phosphorylation events in metabolism. Studying protein phosphorylation in S. cerevisiae allows for gaining new insight into the function of regulatory networks, which may enable improved metabolic engineering as well as identify mechanisms underlying human metabolic diseases. Here we collect functional phosphorylation events of 41 enzymes involved in yeast metabolism and demonstrate functional mechanisms and the application of this information in metabolic engineering. From a systems biology perspective, we describe the development of phosphoproteomics in yeast as well as approaches to analysing the phosphoproteomics data. Finally, we focus on integrated analyses with other omics data sets and genome-scale metabolic models. Despite the advances, future studies improving both experimental technologies and computational approaches are imperative to expand the current knowledge of protein phosphorylation in S. cerevisiae.

Ontological interpretation of biomedical database content

Background

Biological databases store data about laboratory experiments, together with semantic annotations, in order to support data aggregation and retrieval. The exact meaning of such annotations in the context of a database record is often ambiguous. We address this problem by grounding implicit and explicit database content in a formal-ontological framework.

Methods

By using a typical extract from the databases UniProt and Ensembl, annotated with content from GO, PR, ChEBI and NCBI Taxonomy, we created four ontological models (in OWL), which generate explicit, distinct interpretations under the BioTopLite2 (BTL2) upper-level ontology. The first three models interpret database entries as individuals (IND), defined classes (SUBC), and classes with dispositions (DISP), respectively; the fourth model (HYBR) is a combination of SUBC and DISP. For the evaluation of these four models, we consider (i) database content retrieval, using ontologies as query vocabulary; (ii) information completeness; and, (iii) DL complexity and decidability. The models were tested under these criteria against four competency questions (CQs).

Results

IND does not raise any ontological claim, besides asserting the existence of sample individuals and relations among them. Modelling patterns have to be created for each type of annotation referent. SUBC is interpreted regarding maximally fine-grained defined subclasses under the classes referred to by the data. DISP attempts to extract truly ontological statements from the database records, claiming the existence of dispositions. HYBR is a hybrid of SUBC and DISP and is more parsimonious regarding expressiveness and query answering complexity. For each of the four models, the four CQs were submitted as DL queries. This shows the ability to retrieve individuals with IND, and classes in SUBC and HYBR. DISP does not retrieve anything because the axioms with disposition are embedded in General Class Inclusion (GCI) statements.

Conclusion

Ambiguity of biological database content is addressed by a method that identifies implicit knowledge behind semantic annotations in biological databases and grounds it in an expressive upper-level ontology. The result is a seamless representation of database structure, content and annotations as OWL models.

Electronic supplementary material

The online version of this article (doi:10.1186/s13326-017-0127-z) contains supplementary material, which is available to authorized users.

Novel proteins from proteomic analysis of the trunk disease fungus Lasiodiplodia theobromae (Botryosphaeriaceae)

Many basic science questions remain regarding protein functions in the pathogen: host interaction, especially in the trunk disease fungi family, the Botryosphaeriaceae, which are a global problem for economically important plants, especially fruiting trees. Proteomics is a highly useful technology for studying protein expression and for discovering novel proteins in unsequenced and poorly annotated organisms. Current fungal proteomics approaches involve 2D SDS-PAGE and extensive, complex, protein extraction methodologies. In this work, a modified Folch extraction was applied to protein extraction to perform both de novo peptide sequencing and peptide fragmentation analysis/protein identification of the plant and human fungal pathogen Lasiodiplodia theobromae. Both bioinformatics approaches yielded novel peptide sequences from proteins produced by L. theobromae in the presence of exogenous triglycerides and glucose. These proteins and the functions they may possess could be targeted for further functional characterization and validation efforts, due to their potential uses in biotechnology and as new paradigms for understanding fungal biochemistry, such as the finding of allergenic enolases, as well as various novel proteases, including zinc metalloproteinases homologous to those found in snake venom. This work contributes to genomic annotation efforts, which, hand in hand with genomic sequencing, will help improve fungal bioinformatics databases for future studies of Botryosphaeriaceae. All data, including raw data, are available via the ProteomeXchange data repository with identifier PXD005283. This is the first study of its kind in Botryosphaeriaceae.

Proteomic insight into the mitigation of wheat root drought stress by arbuscular mycorrhizae

Arbuscular mycorrhizal fungi (AMF) are plant growth promoters that ameliorate plant-water relations and the nutrient uptake of wheat. In this work, two cultivars of Triticum spp., a bread and a durum wheat, grown under drought stress and inoculated or not by AMF, are evaluated through a shotgun proteomic approach. The AMF association had beneficial effects as compared to non-mycorrhizal roots, in both bread and durum wheat. The beneficial symbiosis was confirmed by measuring morphological and physiological traits. In our work, we identified 50 statistically differential proteins in the bread wheat cultivar and 66 differential proteins in the durum wheat cultivar. The findings highlighted a modulation of proteins related to sugar metabolism, cell wall rearrangement, cytoskeletal organization and sulphur-containing proteins, as well as proteins related to plant stress responses. Among differentially expressed proteins both cultivars evidenced a decrease in sucrose:fructan 6-fructosyltransferas. In durum wheat oxylipin signalling pathway was involved with two proteins: increased 12-oxo-phytodienoic acid reductase and decreased jasmonate-induced protein, both related to the biosynthesis of jasmonic acid. Interactome analysis highlighted the possible involvement of ubiquitin although not evidenced among differentially expressed proteins. The AMF association helps wheat roots reducing the osmotic stress and maintaining cellular integrity.

BIOLOGICAL SIGNIFICANCE

Drought is one of the major constraints that plants must face in some areas of the world, associated to climate change, negatively affecting the worldwide plant productivity. The adoption of innovative agronomic protocols may represent a winning strategy in facing this challenge. The arbuscular mycorrhizal fungi (AMF) inoculation may represent a natural and sustainable way to mitigate the negative effects due to drought in several crop, ameliorating plant growth and development. Studies on the proteomic responses specific to AMF in drought-stressed plants will help clarify how mycorrhization elicits plant growth, nutrient uptake, and stress-tolerance responses. Such studies also offer the potential to find biological markers and genetic targets to be used during breeding for new drought-resistant varieties.

The Use of Omic Technologies Applied to Traditional Chinese Medicine Research

Natural products represent one of the most important reservoirs of structural and chemical diversity for the generation of leads in the drug development process. A growing number of researchers have shown interest in the development of drugs based on Chinese herbs. In this review, the use and potential of omic technologies as powerful tools in the modernization of traditional Chinese medicine are discussed. The analytical combination from each omic approach is crucial for understanding the working mechanisms of cells, tissues, organs, and organisms as well as the mechanisms of disease. Gradually, omic approaches have been introduced in every stage of the drug development process to generate high-quality Chinese medicine-based drugs. Finally, the future picture of the use of omic technologies is a promising tool and arena for further improvement in the modernization of traditional Chinese medicine.

A Systematic Bioinformatics Approach to Identify High Quality Mass Spectrometry Data and Functionally Annotate Proteins and Proteomes

In the past decade, proteomics and mass spectrometry have taken tremendous strides forward, particularly in the life sciences, spurred on by rapid advances in technology resulting in generation and conglomeration of vast amounts of data. Though this has led to tremendous advancements in biology, the interpretation of the data poses serious challenges for many practitioners due to the immense size and complexity of the data. Furthermore, the lack of annotation means that a potential gold mine of relevant biological information may be hiding within this data. We present here a simple and intuitive workflow for the research community to investigate and mine this data, not only to extract relevant data but also to segregate usable, quality data to develop hypotheses for investigation and validation. We apply an MS evidence workflow for verifying peptides of proteins from one's own data as well as publicly available databases. We then integrate a suite of freely available bioinformatics analysis and annotation software tools to identify homologues and map putative functional signatures, gene ontology and biochemical pathways. We also provide an example of the functional annotation of missing proteins in human chromosome 7 data from the NeXtProt database, where no evidence is available at the proteomic, antibody, or structural levels. We give examples of protocols, tools and detailed flowcharts that can be extended or tailored to interpret and annotate the proteome of any novel organism.

Toxicological proteomic responses of halophyte Suaeda salsa to lead and zinc

The long term (30 days) toxicological effects of environmentally relevant concentrations of Pb²⁺ (20μg/L) and Zn²⁺ (100μg/L) were characterized in Suaeda salsa using proteomics techniques. The responsive proteins were related to metabolism (Krebs cycle and Calvin cycle), protein biosynthesis, stress and defense, energy, signaling pathway and photosynthesis in Pb²⁺, Zn²⁺ and Pb²⁺+ Zn²⁺ exposed groups in S. salsa after exposures for 30 days. The proteomic profiles also showed differential responses in S. salsa to metal exposures. In Pb²⁺-treated group, the proteins were categorized into cystein metabolism and pentose phosphate pathway. The responsive proteins were basically involved in glutathione metabolism, glycolysis, cystein and methane metabolism, and voltage-dependent anion channel in Zn²⁺-treated group. In Pb²⁺+ Zn²⁺-treated group, the proecular mechanism at protein level remtein responses were devided into tyrosine metabolism and glycolysis. Our results showed that the two typical heavy metals, lead and zinc, could induce toxicological effects in halophyte S. salsa at protein level.

Silkomics: Insight into the Silk Spinning Process of Spiders

The proteins from the silk-producing glands were identified using both a bottom-up gel-based proteomic approach as well as from a shotgun proteomic approach. Additionally, the relationship between the functions of identified proteins and the spinning process was studied. A total of 125 proteins were identified in the major ampullate, 101 in the flagelliform, 77 in the aggregate, 75 in the tubuliform, 68 in the minor ampullate, and 23 in aciniform glands. On the basis of the functional classification using Gene Ontology, these proteins were organized into seven different groups according to their general function: (i) web silk proteins-spidroins, (ii) proteins related to the folding/conformation of spidroins, (iii) proteins that protect silk proteins from oxidative stress, (iv) proteins involved in fibrillar preservation of silks in the web, (v) proteins related to ion transport into and out of the glands during silk fiber spinning, (vi) proteins involved in prey capture and pre-digestion, and (vii) housekeeping proteins from all of the glands. Thus, a general mechanism of action for the identified proteins in the silk-producing glands from the Nephila clavipes spider was proposed; the current results also indicate that the webs play an active role in prey capture.

Biomarker discovery in mass spectrometry-based urinary proteomics

Urinary proteomics has become one of the most attractive topics in disease biomarker discovery. MS-based proteomic analysis has advanced continuously and emerged as a prominent tool in the field of clinical bioanalysis. However, only few protein biomarkers have made their way to validation and clinical practice. Biomarker discovery is challenged by many clinical and analytical factors including, but not limited to, the complexity of urine and the wide dynamic range of endogenous proteins in the sample. This article highlights promising technologies and strategies in the MS-based biomarker discovery process, including study design, sample preparation, protein quantification, instrumental platforms, and bioinformatics. Different proteomics approaches are discussed, and progresses in maximizing urinary proteome coverage and standardization are emphasized in this review. MS-based urinary proteomics has great potential in the development of noninvasive diagnostic assays in the future, which will require collaborative efforts between analytical scientists, systems biologists, and clinicians.

KPP: KEGG Pathway Painter

Background

High-throughput technologies became common tools to decipher genome-wide changes of gene expression (GE) patterns. Functional analysis of GE patterns is a daunting task as it requires often recourse to the public repositories of biological knowledge. On the other hand, in many cases researcher's inquiry can be served by a comprehensive glimpse. The KEGG PATHWAY database is a compilation of manually verified maps of biological interactions represented by the complete set of pathways related to signal transduction and other cellular processes. Rapid mapping of the differentially expressed genes to the KEGG pathways may provide an idea about the functional relevance of the gene lists corresponding to the high-throughput expression data.

Results

Here we present a web based graphic tool KEGG Pathway Painter (KPP). KPP paints pathways from the KEGG database using large sets of the candidate genes accompanied by "overexpressed" or "underexpressed" marks, for example, those generated by microarrays or miRNA profilings.

Conclusion

KPP provides fast and comprehensive visualization of the global GE changes by consolidating a list of the color-coded candidate genes into the KEGG pathways. KPP is freely available and can be accessed at http://web.cos.gmu.edu/~gmanyam/kegg/