Project description:To understand the impact and mechanism of removing fat and skin tissue on the nutritional metabolism of Chinese dry cured ham, the differential metabolites (DMs) profile between lean ham (LH) and fatty ham (FH) was explored though untargeted metabolomics based on UPLC-MS/MS. The results showed significant differences of the metabolite profiles between FH and LH. A total of 450 defined metabolites were detected, and 266 metabolites among them had significantly different abundances between the two hams, mainly including organic acids and derivatives, and lipids and lipid-like molecules, as well as organoheterocyclic compounds. Furthermore, 131 metabolites were identified as DMs, among which 101 and 30 DMs showed remarkably higher contents in FH and LH, respectively. The further Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that DMs can be mostly enriched in the pathways of ABC transporters, amino acid biosynthesis, protein digestion and absorption, aminoacyl-tRNA biosynthesis, and 2-oxocarboxylic acid metabolism. Moreover, the metabolic network of DMs revealed that the prominent DMs in FH, such as 9(S)-HODE, 9,10-EpOME, 13-Oxo-ODE, L-palmitoyl carnitine, and D-fructose, were primarily involved in the endogenous oxidation and degradation of fat and glycogen. Nevertheless, the dominant DMs in LH, such as 2-isopropylmalic acid, indolelactic acid, and hydroxyisocaproic acid, were mainly the microbial metabolites of amino acids and derivates. These findings could help us understand how fat-deficiency affects the nutritional metabolism of Chinese dry-cured hams from a metabolic perspective.
Project description:The physicochemical property, volatile flavor compounds, and microbial community structure of Jinhua fatty ham (FH) and lean ham (LH) were investigated and compared by high-throughput sequencing and HS-GC-IMS. Results showed that FH had higher pH and slightly lighter and yellower color than LH. Meanwhile, 33 volatile flavor compounds were identified from FH and LH, among which LH showed higher abundance of total alcohols and acids, but FH had generally richer aldehydes, ketones, esters, heterocyclic, and sulfur-containing compounds. Moreover, FH and LH did not have significant difference in α-diversity of bacterial community, but LH presented a much lower α-diversity of fungal community than FH. Besides, the dominant microorganisms (relative abundance >2%) in FH were Ruminococcaceae UCG-005, Staphylococcus, Ruminococcaceae UCG-014, Meyerozyma, and Aspergillus at the genus level, while in LH were Staphylococcus, Psychrobacter, Halomonas, Propionicicella, Ruminococcaceae UCG-005, Meyerozyma, Yamadazyma, and Aspergillus. Furthermore, the analysis of Pearson's correlation and metabolic network confirmed that the discriminative flavor compounds of FH were mainly β-oxidation and degradation products of fatty acids, while those of LH were mostly derived from the Strecker reaction or microbial metabolism of amino acids. The present study could help understand the potential pathway of characteristic microorganisms affecting flavor formation of fat-deficient dry-cured hams and provide theoretical supports for developing healthier fermented meat products.
Project description:Microbes in different aged workshops play important roles in the flavor formation of Jinhua ham. However, microbial diversity, community structure and age related changes in workshops are poorly understood. The microbial community structure and diversity in Jinhua ham produced in factories that have 5, 15, and 30 years of history in processing hams were compared using the pyrosequencing technique. Results showed that 571,703 high-quality sequences were obtained and located in 242 genera belonging to 18 phyla. Bacterial diversity and microbial community structure were significantly different with the years of workshops. Three-phase model to characterize the changes of ham microbial communities was proposed. Gas chromatography-mass spectrometry assays indicated that the hams produced in different aged workshops have great differences in number and relative contents of volatiles compounds. These results suggest that different aged factories could form special and well-balanced microbial diversity, which may contribute to the unique flavor characteristics in Jinhua ham.
Project description:Heat stress is an important adverse environmental stress that influences the growth and development of Hypsizygus marmoreus (white var.). However, the molecular basis of heat stress response in H. marmoreus remains poorly understood. In this study, label-free comparative proteomic technique was applied to investigate global protein expression profile of H. marmoreus mycelia under heat stress. Confocal laser scanning microscope observation revealed that mycelia underwent autolysis and apoptosis under heat stress. Autolysis was mediated by upregulating the expression of cell wall degradation enzymes and inhibiting cell wall synthesis enzymes, and apoptosis might be induced by ROS and activation of caspases. TBARS analysis indicated that ROS was accumulated in H. marmoreus mycelia under heat stress. H. marmoreus induced antioxidant defense system by upregulating the expression of catalases, superoxide dismutases and peroxidases to prevent oxidative damage. MAPK cascade was found to be involved in heat stress signal transduction. The stress signal induced a ubiquitous defense response: inducible expression of different kinds of heat shock proteins. Trehalose synthesis enzymes were also upregulated, suggesting the accumulation of stress protector trehalose under heat stress. Besides, upregulated proteasome was identified, which could prevented the accumulation of non-functional misfolding proteins. To satisfy ATP depletion in heat response cellular processes, such as ROS scavenging, and protein folding and synthesis, enzymes involved in energy production (carbon metabolism and ATP synthesis) system were upregulated under heat stress. Taken together, these findings improve our understanding of the molecular mechanisms underlying the response of heat stress in H. marmoreus.
Project description:Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses reveal a complex interaction between these parameters that greatly impacts the quantitative fidelity of protein- and peptide-level quantification. At a high replicate number (n = 8), both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies yield accurate protein quantification across statistical approaches. However, at a low replicate number (n = 4), only DIA in combination with linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS) produced a high level of quantitative fidelity. Quantitative accuracy at low replicates is also greatly impacted by FDR corrections, with Benjamini-Hochberg and Storey corrections yielding variable true positive rates for DDA workflows. For peptide quantification, replicate number and acquisition methodology are even more critical. A higher number of replicates in combination with DIA and LIMMA produce high quantitative fidelity, while DDA performs poorly regardless of replicate number or statistical approach. These results underscore the importance of pairing instrument acquisition methodology with the appropriate replicate number and statistical approach for optimal quantification performance.
Project description:Black pepper (Piper nigrum L.), a tropical spice crop of global acclaim, is susceptible to Phytophthora capsici, an oomycete pathogen which causes the highly destructive foot rot disease. A systematic understanding of this phytopathosystem has not been possible owing to lack of genome or proteome information. In this study, we explain an integrated transcriptome-assisted label-free quantitative proteomics pipeline to study the basal immune components of black pepper when challenged with P. capsici. We report a global identification of 532 novel leaf proteins from black pepper, of which 518 proteins were functionally annotated using BLAST2GO tool. A label-free quantitation of the protein datasets revealed 194 proteins common to diseased and control protein datasets of which 22 proteins showed significant up-regulation and 134 showed significant down-regulation. Ninety-three proteins were identified exclusively on P. capsici infected leaf tissues and 245 were expressed only in mock (control) infected samples. In-depth analysis of our data gives novel insights into the regulatory pathways of black pepper which are compromised during the infection. Differential down-regulation was observed in a number of critical pathways like carbon fixation in photosynthetic organism, cyano-amino acid metabolism, fructose, and mannose metabolism, glutathione metabolism, and phenylpropanoid biosynthesis. The proteomics results were validated with real-time qRT-PCR analysis. We were also able to identify the complete coding sequences for all the proteins of which few selected genes were cloned and sequence characterized for further confirmation. Our study is the first report of a quantitative proteomics dataset in black pepper which provides convincing evidence on the effectiveness of a transcriptome-based label-free proteomics approach for elucidating the host response to biotic stress in a non-model spice crop like P. nigrum, for which genome information is unavailable. Our dataset will serve as a useful resource for future studies in this plant. Data are available via ProteomeXchange with identifier PXD003887.
Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.
Project description:With the prospect of resolving whole protein molecules into their myriad proteoforms on a proteomic scale, the question of their quantitative analysis in discovery mode comes to the fore. Here, we demonstrate a robust pipeline for the identification and stringent scoring of abundance changes of whole protein forms <30 kDa in a complex system. The input is ~100-400 μg of total protein for each biological replicate, and the outputs are graphical displays depicting statistical confidence metrics for each proteoform (i.e., a volcano plot and representations of the technical and biological variation). A key part of the pipeline is the hierarchical linear model that is tailored to the original design of the study. Here, we apply this new pipeline to measure the proteoform-level effects of deleting a histone deacetylase (rpd3) in S. cerevisiae. Over 100 proteoform changes were detected above a 5% false positive threshold in WT vs the Δrpd3 mutant, including the validating observation of hyperacetylation of histone H4 and both H2B isoforms. Ultimately, this approach to label-free top down proteomics in discovery mode is a critical technical advance for testing the hypothesis that whole proteoforms can link more tightly to complex phenotypes in cell and disease biology than do peptides created in shotgun proteomics.
Project description:Male breast cancer (MBC) is a rare malignancy that accounts for about 1.8% of all breast cancer cases. In contrast to the high number of the "omics" studies in breast cancer in women, only recently molecular approaches have been performed in MBC research. High-throughput proteomics based methodologies are promisor strategies to characterize the MBC proteomic signatures and their association with clinico-pathological parameters. In this study, the label-free quantification-mass spectrometry and bioinformatics approaches were applied to analyze the proteomic profiling of a MBC case using the primary breast tumor and the corresponding axillary metastatic lymph nodes and adjacent non-tumor breast tissues. The differentially expressed proteins were identified in the signaling pathways of granzyme B, sirtuins, eIF2, actin cytoskeleton, eNOS, acute phase response and calcium and were connected to the upstream regulators MYC, PI3K SMARCA4 and cancer-related chemical drugs. An additional proteomic comparative analysis was performed with a primary breast tumor of a female patient and revealed an interesting set of proteins, which were mainly involved in cancer biology. Together, our data provide a relevant data source for the MBC research that can help the therapeutic strategies for its management.
Project description:IntroductionSoil salinization has become one of the most serious environmental issues globally. Excessive accumulation of soluble salts will adversely affect the survival, growth, and reproduction of plants. Elaeagnus angustifolia L., commonly known as oleaster or Russian olive, has the characteristics of tolerance to drought and salt. Arbuscular mycorrhizal (AM) fungi are considered to be bio-ameliorator of saline soils that can enhance the salt tolerance of the host plants. However, there is little information on the root proteomics of AM plants under salt stress.MethodsIn this study, a label-free quantitative proteomics method was employed to identify the differentially abundant proteins in AM E. angustifolia seedlings under salt stress.ResultsThe results showed that a total of 170 proteins were significantly differentially regulated in E.angustifolia seedlings after AMF inoculation under salt stress. Mycorrhizal symbiosis helps the host plant E. angustifolia to respond positively to salt stress and enhances its salt tolerance by regulating the activities of some key proteins related to amino acid metabolism, lipid metabolism, and glutathione metabolism in root tissues.ConclusionAspartate aminotransferase, dehydratase-enolase-phosphatase 1 (DEP1), phospholipases D, diacylglycerol kinase, glycerol-3-phosphate O-acyltransferases, and gamma-glutamyl transpeptidases may play important roles in mitigating the detrimental effect of salt stress on mycorrhizal E. angustifolia . In conclusion, these findings provide new insights into the salt-stress tolerance mechanisms of AM E. angustifolia seedlings and also clarify the role of AM fungi in the molecular regulation network of E. angustifolia under salt stress.