Calculation of partial isotope incorporation into peptides measured by mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Stable isotope probing (SIP) technique was developed to link function, structure and activity of microbial cultures metabolizing carbon and nitrogen containing substrates to synthesize their biomass. Currently, available methods are restricted solely to the estimation of fully saturated heavy stable isotope incorporation and convenient methods with sufficient accuracy are still missing. However in order to track carbon fluxes in microbial communities new methods are required that allow the calculation of partial incorporation into biomolecules.</p> <p>Results</p> <p>In this study, we use the characteristics of the so-called 'half decimal place rule' (HDPR) in order to accurately calculate the partial¹³C incorporation in peptides from enzymatic digested proteins. Due to the clade-crossing universality of proteins within bacteria, any available high-resolution mass spectrometry generated dataset consisting of tryptically-digested peptides can be used as reference.</p> <p>We used a freely available peptide mass dataset from <it>Mycobacterium tuberculosis </it>consisting of 315,579 entries. From this the error of estimated versus known heavy stable isotope incorporation from an increasing number of randomly drawn peptide sub-samples (100 times each; no repetition) was calculated. To acquire an estimated incorporation error of less than 5 atom %, about 100 peptide masses were needed. Finally, for testing the general applicability of our method, peptide masses of tryptically digested proteins from <it>Pseudomonas putida </it>ML2 grown on labeled substrate of various known concentrations were used and¹³C isotopic incorporation was successfully predicted. An easy-to-use script <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> was further developed to guide users through the calculation procedure for their own data series.</p> <p>Conclusion</p> <p>Our method is valuable for estimating¹³C incorporation into peptides/proteins accurately and with high sensitivity. Generally, our method holds promise for wider applications in qualitative and especially quantitative proteomics.</p

Microbial community functioning during plant litter decomposition

International audienceAbstract Microbial life in soil is fueled by dissolved organic matter (DOM) that leaches from the litter layer. It is well known that decomposer communities adapt to the available litter source, but it remains unclear if they functionally compete or synergistically address different litter types. Therefore, we decomposed beech, oak, pine and grass litter from two geologically distinct sites in a lab-scale decomposition experiment. We performed a correlative network analysis on the results of direct infusion HR-MS DOM analysis and cross-validated functional predictions from 16S rRNA gene amplicon sequencing and with DOM and metaproteomic analyses. Here we show that many functions are redundantly distributed within decomposer communities and that their relative expression is rapidly optimized to address litter-specific properties. However, community changes are likely forced by antagonistic mechanisms as we identified several natural antibiotics in DOM. As a consequence, the decomposer community is specializing towards the litter source and the state of decomposition (community divergence) but showing similar litter metabolomes (metabolome convergence). Our multi-omics-based results highlight that DOM not only fuels microbial life, but it additionally holds meta-metabolomic information on the functioning of ecosystems

Quantification of glyphosate and aminomethylphosphonic acid from microbiome reactor fluids

Rationale: Glyphosate is one of the most widely used herbicides and it is suspected to affect the intestinal microbiota through inhibition of aromatic amino acid synthesis via the shikimate pathway.In vitromicrobiome bioreactors are increasingly used as model systems to investigate effects on intestinal microbiota and consequently methods for the quantitation of glyphosate and its degradation product aminomethylphosphonic acid (AMPA) in microbiome model systems are required. Methods: An optimized protocol enables the analysis of both glyphosate and AMPA by simple extraction with methanol:acetonitrile:water (2:3:1) without further enrichment steps. Glyphosate and AMPA are separated by liquid chromatography on an amide column and identified and quantified with a targeted tandem mass spectrometry method using a QTRAP 5500 system (AB Sciex). Results: Our method has a limit of detection (LOD) in extracted water samples of <2 ng/mL for both glyphosate and AMPA. In complex intestinal medium, the LOD is 2 and 5 ng/mL for glyphosate and AMPA, respectively. These LODs allow for measurement at exposure-relevant concentrations. Glyphosate levels in a bioreactor model of porcine colon were determined and consequently it was verified whether AMPA was produced by porcine gut microbiota. Conclusions: The method presented here allows quantitation of glyphosate and AMPA in complex bioreactor fluids and thus enables studies of the impact of glyphosate and its metabolism on intestinal microbiota. In addition, the extraction protocol is compatible with an untargeted metabolomics analysis, thus allowing one to look for other perturbations caused by glyphosate in the same sample

The Activation of Mucosal-Associated Invariant T (MAIT) Cells Is Affected by Microbial Diversity and Riboflavin Utilization in vitro

Recent research has demonstrated that MAIT cells are activated by individual bacterial or yeasts species that possess the riboflavin biosynthesis pathway. However, little is known about the MAIT cell activating potential of microbial communities and the contribution of individual community members. Here, we analyze the MAIT cell activating potential of a human intestinal model community (SIHUMIx) as well as intestinal microbiota after bioreactor cultivation. We determined the contribution of individual SIHUMIx community members to the MAIT cell activating potential and investigated whether microbial stress can influence their MAIT cell activating potential. The MAIT cell activating potential of SIHUMIx was directly related to the relative species abundances in the community. We therefore suggest an additive relationship between the species abundances and their MAIT cell activating potential. In diverse microbial communities, we found that a low MAIT cell activating potential was associated with high microbial diversity and a high level of riboflavin demand and vice versa. We suggest that microbial diversity might affect MAIT cell activation via riboflavin utilization within the community. Microbial acid stress significantly reduced the MAIT cell activating potential of SIHUMIx by impairing riboflavin availability through increasing the riboflavin demand.We show that MAIT cells can perceive microbial stress due to changes in riboflavin utilization and that riboflavin availability might also play a central role for the MAIT cell activating potential of diverse microbiota

Global ecological predictors of the soil priming effect

Identifying the global drivers of soil priming is essential to understanding C cycling in terrestrial ecosystems. We conducted a survey of soils across 86 globally-distributed locations, spanning a wide range of climates, biotic communities, and soil conditions, and evaluated the apparent soil priming effect using 13C-glucose labeling. Here we show that the magnitude of the positive apparent priming effect (increase in CO2 release through accelerated microbial biomass turnover) was negatively associated with SOC content and microbial respiration rates. Our statistical modeling suggests that apparent priming effects tend to be negative in more mesic sites associated with higher SOC contents. In contrast, a single-input of labile C causes positive apparent priming effects in more arid locations with low SOC contents. Our results provide solid evidence that SOC content plays a critical role in regulating apparent priming effects, with important implications for the improvement of C cycling models under global change scenarios

DIGE Proteome Analysis Reveals Suitability of Ischemic Cardiac In Vitro Model for Studying Cellular Response to Acute Ischemia and Regeneration

Proteomic analysis of myocardial tissue from patient population is suited to yield insights into cellular and molecular mechanisms taking place in cardiovascular diseases. However, it has been limited by small sized biopsies and complicated by high variances between patients. Therefore, there is a high demand for suitable model systems with the capability to simulate ischemic and cardiotoxic effects in vitro, under defined conditions. In this context, we established an in vitro ischemia/reperfusion cardiac disease model based on the contractile HL-1 cell line. To identify pathways involved in the cellular alterations induced by ischemia and thereby defining disease-specific biomarkers and potential target structures for new drug candidates we used fluorescence 2D-difference gel electrophoresis. By comparing spot density changes in ischemic and reperfusion samples we detected several protein spots that were differentially abundant. Using MALDI-TOF/TOF-MS and ESI-MS the proteins were identified and subsequently grouped by functionality. Most prominent were changes in apoptosis signalling, cell structure and energy-metabolism. Alterations were confirmed by analysis of human biopsies from patients with ischemic cardiomyopathy

MicroRNA-21 targets tumor suppressor genes ANP32A and SMARCA4

MicroRNA-21 (miR-21) is a key regulator of oncogenic processes. It is significantly elevated in the majority of human tumors and functionally linked to cellular proliferation, survival and migration. In this study, we used two experimental-based strategies to search for novel miR-21 targets. On the one hand, we performed a proteomic approach using two-dimensional differential gel electrophoresis (2D-DIGE) to identify proteins suppressed upon enhanced miR-21 expression in LNCaP human prostate carcinoma cells. The tumor suppressor acidic nuclear phosphoprotein 32 family, member A (ANP32A) (alias pp32 or LANP) emerged as the most strongly downregulated protein. On the other hand, we applied a mathematical approach to select correlated gene sets that are negatively correlated with primary-miR-21 (pri-miR-21) expression in published transcriptome data from 114 B-cell lymphoma cases. Among these candidates, we found tumor suppressor SMARCA4 (alias BRG1) together with the already validated miR-21 target, PDCD4. ANP32A and SMARCA4, which are both involved in chromatin remodeling processes, were confirmed as direct miR-21 targets by immunoblot analysis and reporter gene assays. Furthermore, knock down of ANP32A mimicked the effect of enforced miR-21 expression by enhancing LNCaP cell viability, whereas overexpression of ANP32A in the presence of high miR-21 levels abrogated the miR-21-mediated effect. In A172 glioblastoma cells, enhanced ANP32A expression compensated for the effects of anti-miR-21 treatment on cell viability and apoptosis. In addition, miR-21 expression clearly increased the invasiveness of LNCaP cells, an effect also seen in part upon downregulation of ANP32A. In conclusion, these results suggest that downregulation of ANP32A contributes to the oncogenic function of miR-21

Genomewide meta-analysis identifies loci associated with IGF-I and IGFBP-3 levels with impact on age-related traits

The growth hormone/insulin-like growth factor (IGF) axis can be manipulated in animal models to promote longevity, and IGF-related proteins including IGF-I and IGF-binding protein-3 (IGFBP-3) have also been implicated in risk of human diseases including cardiovascular diseases, diabetes, and cancer. Throug

Proteomic study uncovers molecular principles of single-cell-level phenotypic heterogeneity in lipid storage of Nannochloropsis oceanica

Abstract Background Nannochloropsis oceanica belongs to a large group of photoautotrophic eukaryotic organisms that play important roles in fixation and cycling of atmospheric CO2. Its capability of storing solar energy and carbon dioxide in the form of triacylglycerol (TAG) of up to 60% of total weight under nitrogen deprivation stress sparked interest in its use for biofuel production. Phenotypes varying in lipid accumulation among an N. oceanica population can be disclosed by single-cell analysis/sorting using fluorescence-activated cell sorting (FACS); yet the phenomenon of single cell heterogeneity in an algae population remains to be fully understood at the molecular level. In this study, combination of FACS and proteomics was used for identification, quantification and differentiation of these heterogeneities on the molecular level. Results For N. oceanica cultivated under nitrogen deplete (−N) and replete (+N) conditions, two groups differing in lipid content were distinguished. These differentiations could be recognized on the population as well as the single-cell levels; proteomics uncovered alterations in carbon fixation and flux, photosynthetic machinery, lipid storage and turnover in the populations. Although heterogeneity patterns have been affected by nitrogen supply and cultivation conditions of the N. oceanica populations, differentiation itself seems to be very robust against these factors: cultivation under +N, −N, in shaker bottles, and in a photo-bioreactor all split into two subpopulations. Intriguingly, population heterogeneity resumed after subpopulations were separately recultivated for a second round, refuting the possible development of genetic heterogeneity in the course of sorting and cultivation. Conclusions This work illustrates for the first time the feasibility of combining FACS and (prote)-omics for mechanistic understanding of phenotypic heterogeneity in lipid-producing microalgae. Such combinatorial method can facilitate molecular breeding and design of bioprocesses