System-Wide Quantitative Proteomics of the Metabolic Syndrome in Mice: Genotypic and Dietary Effects

Comparative proteome profiling of fusarium chlamydosporum and elucidation of pigment biosynthetic pathway under nitrogen stress

The main aim of this study was to understand the protein expression of F. chlamydosporum in two different medium composition in varying concentrations of nitrogen. The interesting phenomenon of producing diverse pigments by a single strain in different concentrations of Nitrogen made us further to explore the difference in the protein expression of the fungus when grown in these two media. For this, we had adopted non-gel-based method of protein separation by LC-MS/MS analysis followed by label free identification of proteins by SWATH analysis. The molecular and biological functions of each protein and their Gene Ontology annotations were analyzed by UniProt KB and KEGG pathway; the secondary metabolite pathways and the carbohydrate metabolic pathways were analyzed by DAVID bioinformatics tool. The positively regulated proteins biologically functioned for the secondary metabolite production in optimized medium were Diphosphomevalonate decarboxylase (terpenoid backbone biosynthesis), Phytoene synthase (carotenoid biosynthesis), 6,7-dimethyl-8-ribityllumazine synthase (riboflavin biosynthesis). The main characteristic change observed was that proteins related to carotenoid biosynthesis and terpenoid synthesis were not regulated in nitrogen limited medium. Except for the protein 6,7-dimethyl-8-ribityllumazine synthase, all the enzymes related to fatty acid biosynthesis and polyketide chain elongation were up regulated. Apart from the proteins related to secondary metabolite production, two novel proteins were found to be up regulated in Nitrogen Limited Medium; C-fem protein responsible for fungal pathogenesis and DAO domain containing protein which functions as a neuromodulator and catalyzes the synthesis of dopamine. SIGNIFICANCE: This particular strain of F. chlamydosporum of immense genetic and biochemical diversity represents an interesting example of a microorganism which can produce a variety of bioactive compounds and this can be exploited in various industries. The production of carotenoids and polyketides by this fungus when grown in the same media with different concentrations of Nitrogen has been published by us following which we analyzed the proteome sequence of the fungus in varying Nutrient conditions. Following the proteome analysis and expression, we could derive the pathway leading to the biosynthesis of varying secondary metabolites by the fungus which has not been published or studied so far.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

The tissue proteome in the multi-omic landscape of kidney disease

Kidney research is entering an era of 'big data' and molecular omics data can provide comprehensive insights into the molecular footprints of cells. In contrast to transcriptomics, proteomics and metabolomics generate data that relate more directly to the pathological symptoms and clinical parameters observed in patients. Owing to its complexity, the proteome still holds many secrets, but has great potential for the identification of drug targets. Proteomics can provide information about protein synthesis, modification and degradation, as well as insight into the physical interactions between proteins, and between proteins and other biomolecules. Thus far, proteomics in nephrology has largely focused on the discovery and validation of biomarkers, but the systematic analysis of the nephroproteome can offer substantial additional insights, including the discovery of mechanisms that trigger and propagate kidney disease. Moreover, proteome acquisition might provide a diagnostic tool that complements the assessment of a kidney biopsy sample by a pathologist. Such applications are becoming increasingly feasible with the development of high-throughput and high-coverage technologies, such as versatile mass spectrometry-based techniques and protein arrays, and encourage further proteomics research in nephrology.

The Influence of Metabolic Syndrome and Sex on the DNA Methylome in Schizophrenia

Introduction

The mechanism by which metabolic syndrome occurs in schizophrenia is not completely known; however, previous work suggests that changes in DNA methylation may be involved which is further influenced by sex. Within this study, the DNA methylome was profiled to identify altered methylation associated with metabolic syndrome in a schizophrenia population on atypical antipsychotics.

Methods

Peripheral blood from schizophrenia subjects was utilized for DNA methylation analyses. Discovery analyses (n = 96) were performed using an epigenome-wide analysis on the Illumina HumanMethylation450K BeadChip based on metabolic syndrome diagnosis. A secondary discovery analysis was conducted based on sex. The top hits from the discovery analyses were assessed in an additional validation set (n = 166) using site-specific methylation pyrosequencing.

Results

A significant increase in CDH22 gene methylation in subjects with metabolic syndrome was identified in the overall sample. Additionally, differential methylation was found within the MAP3K13 gene in females and the CCDC8 gene within males. Significant differences in methylation were again observed for the CDH22 and MAP3K13 genes, but not CCDC8, in the validation sample set.

Conclusions

This study provides preliminary evidence that DNA methylation may be associated with metabolic syndrome and sex in schizophrenia.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

Current trends in quantitative proteomics - an update

Proteins can provide insights into biological processes at the functional level, so they are very promising biomarker candidates. The quantification of proteins in biological samples has been routinely used for the diagnosis of diseases and monitoring the treatment. Although large-scale protein quantification in complex samples is still a challenging task, a great amount of effort has been made to advance the technologies that enable quantitative proteomics. Seven years ago, in 2009, we wrote an article about the current trends in quantitative proteomics. In writing this current paper, we realized that, today, we have an even wider selection of potential tools for quantitative proteomics. These tools include new derivatization reagents, novel sampling formats, new types of analyzers and scanning techniques, and recently developed software to assist in assay development and data analysis. In this review article, we will discuss these innovative methods, and their current and potential applications in proteomics. Copyright © 2017 John Wiley & Sons, Ltd.