Discovering mercury protein modifications in whole proteomes using natural isotope distributions observed in liquid chromatography-tandem mass spectrometry

Machine learning approach for the prediction of the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution

RATIONALE

The observed isotope distribution is an important attribute for the identification of peptides and proteins in mass spectrometry-based proteomics. Sulphur atoms have a very distinctive elemental isotope definition, and therefore, the presence of sulphur atoms has a substantial effect on the isotope distribution of biomolecules. Hence, knowledge of the number of sulphur atoms can improve the identification of peptides and proteins.

METHODS

In this paper, we conducted a theoretical investigation on the isotope properties of sulphur-containing peptides. We proposed a gradient boosting approach to predict the number of sulphur atoms based on the aggregated isotope distribution. We compared prediction accuracy and assessed the predictive power of the features using the mass and isotope abundance information from the first three, five and eight aggregated isotope peaks.

RESULTS

Mass features alone are not sufficient to accurately predict the number of sulphur atoms. However, we reach near-perfect prediction when we include isotope abundance features. The abundance ratios of the eighth and the seventh, the fifth and the fourth, and the third and the second aggregated isotope peaks are the most important abundance features. The mass difference between the eighth, the fifth or the third aggregated isotope peaks and the monoisotopic peak are the most predictive mass features.

CONCLUSIONS

Based on the validation analysis it can be concluded that the prediction of the number of sulphur atoms based on the isotope profile fails, because the isotope ratios are not measured accurately. These results indicate that it is valuable for future instrument developments to focus more on improving spectral accuracy to measure peak intensities of higher-order isotope peaks more accurately.

Analysis of the Mercury Distribution in Blood as a Potential Tool for Exposure Assessment - Results from Two Artisanal and Small-Scale Gold Mining Areas in Zimbabwe

People in artisanal and small-scale gold mining (ASGM) areas are frequently exposed to high levels of mercury (Hg). Hg analyses in urine and whole blood are the gold standard of biomonitoring, although this may not provide sufficient information about the source of exposure, e.g., due to the use of Hg for gold extraction or due to nutrition. To evaluate, whether the pharmacokinetic properties of individual Hg species may be useful for exposure assessment, we determined the Hg levels in different blood components from 199 participants. Therefore, whole blood was centrifuged on-site to yield erythrocytes and plasma. Globin was isolated from the erythrocytes by precipitation with ethyl acetate. Albumin was isolated from plasma by gradual precipitation with saturated ammonium sulfate solution. Hg levels in all samples were determined by using a direct Hg analyzer. Median Hg levels for whole blood, erythrocytes, and plasma were 2.7, 3.7, and 1.3 μg/l, respectively. In globin and albumin, median Hg levels were 10.3 and 7.9 μg/kg, respectively. The distribution of Hg was strongly correlated with whole blood Hg levels (p < 0.01) and the time between the last use of Hg and the date of the participation (p < 0.01). The results suggest that the distribution of Hg in blood is substantially affected by the extent and the frequency of the exposure to elemental Hg. Therefore, the analysis of Hg in erythrocytes and plasma may be a valuable tool for Hg exposure assessment in ASGM areas.

In-depth quantitative profiling of post-translational modifications of Timothy grass pollen allergome in relation to environmental oxidative stress

An association between pollution (e.g., from traffic emissions) and the increased prevalence of respiratory allergies has been observed. Field-realistic exposure studies provide the most relevant assessment of the effects of the intensity and diversity of urban and industrial contamination on pollen structure and allergenicity. The significance of in-depth post-translational modification (PTM) studies of pollen proteomes, when compared with studies on other aspects of pollution and altered pollen allergenicity, has not yet been determined; hence, little progress has been made within this field. We undertook a comprehensive comparative analysis of multiple polluted and environmentally preserved Phleum pratense (Timothy grass) pollen samples using scanning electron microscopy, in-depth PTM profiling, determination of organic and inorganic pollutants, analysis of the release of sub-pollen particles and phenols/proteins, and analysis of proteome expression using high resolution tandem mass spectrometry. In addition, we used quantitative enzyme-linked immunosorbent assays (ELISA) and immunoglobulin E (IgE) immunoblotting. An increased phenolic content and release of sub-pollen particles was found in pollen samples from the polluted area, including a significantly higher content of mercury, cadmium, and manganese, with irregular long spines on pollen grain surface structures. Antioxidative defense-related enzymes were significantly upregulated and seven oxidative PTMs were significantly increased (methionine, histidine, lysine, and proline oxidation; tyrosine glycosylation, lysine 4-hydroxy-2-nonenal adduct, and lysine carbamylation) in pollen exposed to the chemical plant and road traffic pollution sources. Oxidative modifications affected several Timothy pollen allergens; Phl p 6, in particular, exhibited several different oxidative modifications. The expression of Phl p 6, 12, and 13 allergens were downregulated in polluted pollen, and IgE binding to pollen extract was substantially lower in the 18 patients studied, as measured by quantitative ELISA. Quantitative, unrestricted, and detailed PTM searches using an enrichment-free approach pointed to modification of Timothy pollen allergens and suggested that heavy metals are primarily responsible for oxidative stress effects observed in pollen proteins.

Transcriptional responses of Escherichia coli during recovery from inorganic or organic mercury exposure

Background

The protean chemical properties of mercury have long made it attractive for diverse applications, but its toxicity requires great care in its use, disposal, and recycling. Mercury occurs in multiple chemical forms, and the molecular basis for the distinct toxicity of its various forms is only partly understood. Global transcriptomics applied over time can reveal how a cell recognizes a toxicant and what cellular subsystems it marshals to repair and recover from the damage. The longitudinal effects on the transcriptome of exponential phase E. coli were compared during sub-acute exposure to mercuric chloride (HgCl₂) or to phenylmercuric acetate (PMA) using RNA-Seq.

Results

Differential gene expression revealed common and distinct responses to the mercurials throughout recovery. Cultures exhibited growth stasis immediately after each mercurial exposure but returned to normal growth more quickly after PMA exposure than after HgCl₂ exposure. Correspondingly, PMA rapidly elicited up-regulation of a large number of genes which continued for 30 min, whereas fewer genes were up-regulated early after HgCl₂ exposure only some of which overlapped with PMA up-regulated genes. By 60 min gene expression in PMA-exposed cells was almost indistinguishable from unexposed cells, but HgCl₂ exposed cells still had many differentially expressed genes. Relative expression of energy production and most metabolite uptake pathways declined with both compounds, but nearly all stress response systems were up-regulated by one or the other mercurial during recovery.

Conclusions

Sub-acute exposure influenced expression of ~45% of all genes with many distinct responses for each compound, reflecting differential biochemical damage by each mercurial and the corresponding resources available for repair. This study is the first global, high-resolution view of the transcriptional responses to any common toxicant in a prokaryotic model system from exposure to recovery of active growth. The responses provoked by these two mercurials in this model bacterium also provide insights about how higher organisms may respond to these ubiquitous metal toxicants.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4413-z) contains supplementary material, which is available to authorized users.

Organic and inorganic mercurials have distinct effects on cellular thiols, metal homeostasis, and Fe-binding proteins in Escherichia coli

The protean chemical properties of the toxic metal mercury (Hg) have made it attractive in diverse applications since antiquity. However, growing public concern has led to an international agreement to decrease its impact on health and the environment. During a recent proteomics study of acute Hg exposure in E. coli, we also examined the effects of inorganic and organic Hg compounds on thiol and metal homeostases. On brief exposure, lower concentrations of divalent inorganic mercury Hg(II) blocked bulk cellular thiols and protein-associated thiols more completely than higher concentrations of monovalent organomercurials, phenylmercuric acetate (PMA) and merthiolate (MT). Cells bound Hg(II) and PMA in excess of their available thiol ligands; X-ray absorption spectroscopy indicated nitrogens as likely additional ligands. The mercurials released protein-bound iron (Fe) more effectively than common organic oxidants and all disturbed the Na(+)/K(+) electrolyte balance, but none provoked efflux of six essential transition metals including Fe. PMA and MT made stable cysteine monothiol adducts in many Fe-binding proteins, but stable Hg(II) adducts were only seen in CysXxx(n)Cys peptides. We conclude that on acute exposure: (a) the distinct effects of mercurials on thiol and Fe homeostases reflected their different uptake and valences; (b) their similar effects on essential metal and electrolyte homeostases reflected the energy dependence of these processes; and (c) peptide phenylmercury-adducts were more stable or detectable in mass spectrometry than Hg(II)-adducts. These first in vivo observations in a well-defined model organism reveal differences upon acute exposure to inorganic and organic mercurials that may underlie their distinct toxicology.

A Fast Numerical Method for Max-Convolution and the Application to Efficient Max-Product Inference in Bayesian Networks

Observations depending on sums of random variables are common throughout many fields; however, no efficient solution is currently known for performing max-product inference on these sums of general discrete distributions (max-product inference can be used to obtain maximum a posteriori estimates). The limiting step to max-product inference is the max-convolution problem (sometimes presented in log-transformed form and denoted as "infimal convolution," "min-convolution," or "convolution on the tropical semiring"), for which no O(k log(k)) method is currently known. Presented here is an O(k log(k)) numerical method for estimating the max-convolution of two nonnegative vectors (e.g., two probability mass functions), where k is the length of the larger vector. This numerical max-convolution method is then demonstrated by performing fast max-product inference on a convolution tree, a data structure for performing fast inference given information on the sum of n discrete random variables in O(nk log(nk)log(n)) steps (where each random variable has an arbitrary prior distribution on k contiguous possible states). The numerical max-convolution method can be applied to specialized classes of hidden Markov models to reduce the runtime of computing the Viterbi path from nk(2) to nk log(k), and has potential application to the all-pairs shortest paths problem.

Tracing environmental markers of autoimmunity: introducing the infectome

We recently introduced the concept of the infectome as a means of studying all infectious factors which contribute to the development of autoimmune disease. It forms the infectious part of the exposome, which collates all environmental factors contributing to the development of disease and studies the sum total of burden which leads to the loss of adaptive mechanisms in the body. These studies complement genome-wide association studies, which establish the genetic predisposition to disease. The infectome is a component which spans the whole life and may begin at the earliest stages right up to the time when the first symptoms manifest, and may thus contribute to the understanding of the pathogenesis of autoimmunity at the prodromal/asymptomatic stages. We provide practical examples and research tools as to how we can investigate disease-specific infectomes, using laboratory approaches employed from projects studying the “immunome” and “microbiome”. It is envisioned that an understanding of the infectome and the environmental factors that affect it will allow for earlier patient-specific intervention by clinicians, through the possible treatment of infectious agents as well as other compounding factors, and hence slowing or preventing disease development.

Molecular Isotopic Distribution Analysis (MIDAs) with adjustable mass accuracy

In this paper, we present Molecular Isotopic Distribution Analysis (MIDAs), a new software tool designed to compute molecular isotopic distributions with adjustable accuracies. MIDAs offers two algorithms, one polynomial-based and one Fourier-transform-based, both of which compute molecular isotopic distributions accurately and efficiently. The polynomial-based algorithm contains few novel aspects, whereas the Fourier-transform-based algorithm consists mainly of improvements to other existing Fourier-transform-based algorithms. We have benchmarked the performance of the two algorithms implemented in MIDAs with that of eight software packages (BRAIN, Emass, Mercury, Mercury5, NeutronCluster, Qmass, JFC, IC) using a consensus set of benchmark molecules. Under the proposed evaluation criteria, MIDAs's algorithms, JFC, and Emass compute with comparable accuracy the coarse-grained (low-resolution) isotopic distributions and are more accurate than the other software packages. For fine-grained isotopic distributions, we compared IC, MIDAs's polynomial algorithm, and MIDAs's Fourier transform algorithm. Among the three, IC and MIDAs's polynomial algorithm compute isotopic distributions that better resemble their corresponding exact fine-grained (high-resolution) isotopic distributions. MIDAs can be accessed freely through a user-friendly web-interface at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/midas/index.html.

Properties of isotope patterns and their utility for peptide identification in large-scale proteomic experiments

RATIONALE

In proteomic experiments, isotope patterns are routinely generated for all detected peptides. While this pattern is determined by peptide composition, it has not been evaluated as a parameter that can help in the process of peptide identification.

METHODS

First, we investigated how the relative isotope abundance (RIA) accuracy in proteomic data sets depends on the spectral intensity, resolution, and the number of mass spectrometry (MS) 1 scans, using an Orbitrap Velos mass spectrometer. Next, we explored the discriminatory power of isotope patterns in the context of proteome analyses of various complexities, either alone or in combination with a Mascot database search. Finally, we provide a theoretical framework for the required accuracies of both peptide mass and RIA for peptide identification.

RESULTS

We demonstrate that the RIA error obtained in routine proteome analyses is 4-5%, and that this is only modestly influenced by spectral intensity, resolution, and the number of MS1 scans. While RIA alone has no discriminatory power, in a Mascot search isotope patterns can distinguish top scoring hits from runner-up hits in 70-95% of cases. Our theoretical approach shows that RIA accuracy needs to be ~0.2% in order to uniquely identify peptides in full proteomes.

CONCLUSIONS

Our results demonstrate that isotope patterns can have discriminatory power when used in combination with a classical database search. Inclusion of this parameter in proteomic workflows may help to increase confidence in peptide identification, but in practical terms this will be limited to small proteomes.

Mechanisms regulating mercury bioavailability for methylating microorganisms in the aquatic environment: a critical review

Mercury is a potent neurotoxin for humans, particularly if the metal is in the form of methylmercury. Mercury is widely distributed in aquatic ecosystems as a result of anthropogenic activities and natural earth processes. A first step toward bioaccumulation of methylmercury in aquatic food webs is the methylation of inorganic forms of the metal, a process that is primarily mediated by anaerobic bacteria. In this Review, we evaluate the current state of knowledge regarding the mechanisms regulating microbial mercury methylation, including the speciation of mercury in environments where methylation occurs and the processes that control mercury bioavailability to these organisms. Methylmercury production rates are generally related to the presence and productivity of methylating bacteria and also the uptake of inorganic mercury to these microorganisms. Our understanding of the mechanisms behind methylation is limited due to fundamental questions related to the geochemical forms of mercury that persist in anoxic settings, the mode of uptake by methylating bacteria, and the biochemical pathway by which these microorganisms produce and degrade methylmercury. In anoxic sediments and water, the geochemical forms of mercury (and subsequent bioavailability) are largely governed by reactions between Hg(II), inorganic sulfides, and natural organic matter. These interactions result in a mixture of dissolved, nanoparticulate, and larger crystalline particles that cannot be adequately represented by conventional chemical equilibrium models for Hg bioavailability. We discuss recent advances in nanogeochemistry and environmental microbiology that can provide new tools and unique perspectives to help us solve the question of how microorganisms methylate mercury. An understanding of the factors that cause the production and degradation of methylmercury in the environment is ultimately needed to inform policy makers and develop long-term strategies for controlling mercury contamination.

Infectome: a platform to trace infectious triggers of autoimmunity

The "exposome" is a term recently used to describe all environmental factors, both exogenous and endogenous, which we are exposed to in a lifetime. It represents an important tool in the study of autoimmunity, complementing classical immunological research tools and cutting-edge genome wide association studies (GWAS). Recently, environmental wide association studies (EWAS) investigated the effect of environment in the development of diseases. Environmental triggers are largely subdivided into infectious and non-infectious agents. In this review, we introduce the concept of the "infectome", which is the part of the exposome referring to the collection of an individual's exposures to infectious agents. The infectome directly relates to geoepidemiological, serological and molecular evidence of the co-occurrence of several infectious agents associated with autoimmune diseases that may provide hints for the triggering factors responsible for the pathogenesis of autoimmunity. We discuss the implications that the investigation of the infectome may have for the understanding of microbial/host interactions in autoimmune diseases with long, pre-clinical phases. It may also contribute to the concept of the human body as a superorganism where the microbiome is part of the whole organism, as can be seen with mitochondria which existed as microbes prior to becoming organelles in eukaryotic cells of multicellular organisms over time. A similar argument can now be made in regard to normal intestinal flora, living in symbiosis within the host. We also provide practical examples as to how we can characterise and measure the totality of a disease-specific infectome, based on the experimental approaches employed from the "immunome" and "microbiome" projects.

Detection and identification of heme c-modified peptides by histidine affinity chromatography, high-performance liquid chromatography-mass spectrometry, and database searching

Multiheme c-type cytochromes (proteins with covalently attached heme c moieties) play important roles in extracellular metal respiration in dissimilatory metal-reducing bacteria. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) characterization of c-type cytochromes is hindered by the presence of multiple heme groups, since the heme c modified peptides are typically not observed or, if observed, not identified. Using a recently reported histidine affinity chromatography (HAC) procedure, we enriched heme c tryptic peptides from purified bovine heart cytochrome c, two bacterial decaheme cytochromes, and subjected these samples to LC-MS/MS analysis. Enriched bovine cytochrome c samples yielded 3- to 6-fold more confident peptide-spectrum matches to heme c containing peptides than unenriched digests. In unenriched digests of the decaheme cytochrome MtoA from Sideroxydans lithotrophicus ES-1, heme c peptides for 4 of the 10 expected sites were observed by LC-MS/MS; following HAC fractionation, peptides covering 9 out of 10 sites were obtained. Heme c peptide spiked into E. coli lysates at mass ratios as low as 1×10(-4) was detected with good signal-to-noise after HAC and LC-MS/MS analysis. In addition to HAC, we have developed a proteomics database search strategy that takes into account the unique physicochemical properties of heme c peptides. The results suggest that accounting for the double thioether link between heme c and peptide, and the use of the labile heme fragment as a reporter ion, can improve database searching results. The combination of affinity chromatography and heme-specific informatics yielded increases in the number of peptide-spectrum matches of 20-100-fold for bovine cytochrome c.