An evolving computational platform for biological mass spectrometry: workflows, statistics and data mining with MASSyPup64

Technical Note: mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming Language Julia

Julia combines the virtues of high-level and low-level programming languages: The code is human-readable, and the performance of the created binaries competes with machine-orientated compilers. Thus, Julia is popular in "Big Data" sciences. Reading mass spectrometry (MS) data with Julia was impossible until now due to missing libraries. Here, we present a Julia library for importing mass spectrometry (MS) data in HUPO standard mzML and imzML formats and demonstrate its function with direct and ambient ionization MS, liquid chromatography-MS, and MS imaging data on standard platforms (Windows, Linux, and Mac OS). The processing speed of Julia for reading imzML MS imaging files was up to 214 times faster than the comparable code in R. Julia can remove bottlenecks for computationally demanding tasks in large-scale MS-Omics and MS imaging data processing workflows and supports their agile development. In addition, time-critical and complex data evaluation tasks become possible, such as following the real-time monitoring of biological processes and pattern recognition in large MS imaging projects. Our mzML/imzML libraries and code examples are available under the terms of the MIT license from https://github.com/CINVESTAV-LABI/julia_mzML_imzML.

Guided analysis of ambient ionization mass spectrometry data with the MQ_Assistant

RATIONALE

Ambient ionization mass spectrometry (AIMS) delivers realistic data from samples in their native state. In addition, AIMS methods reduce time and costs for sample preparation and have less environmental impact. However, AIMS data are often complex and require substantial processing before interpretation.

METHODS

We developed an interactive R script for guided mass spectrometry (MS) data processing. The "MQ_Assistant" is based on MALDIquant, a popular R package for MS data processing. In each step, the user can try and preview the effect of chosen parameters before deciding on the values with the best result and proceeding to the next stage. The outcome of the MQ_Assistant is a feature matrix that can be further analyzed in R and statistics tools such as MetaboAnalyst.

RESULTS

Using 360 AIMS example spectra, we demonstrate the step-by-step processing for creating a feature matrix. In addition, we show how to visualize the results of three biological replicates of a plant-microbe interaction between Arabidopsis and Trichoderma as a heatmap using R and upload them to MetaboAnalyst. The final parameter set can be saved for reuse in MALDIquant workflows of similar data.

CONCLUSIONS

The MQ_Assistant helps novices and experienced users to develop workflows for (AI)MS data processing. The interactive procedure supports the quick finding of appropriate settings. These parameters can be exported and reused in future projects. The stepwise operation with visual feedback also suggests the use of the MQ_Assistant in education.

Contrast optimization of mass spectrometry imaging (MSI) data visualization by threshold intensity quantization (TrIQ)

Mass spectrometry imaging (MSI) enables the unbiased characterization of surfaces with respect to their chemical composition. In biological MSI, zones with differential mass profiles hint towards localized physiological processes, such as the tissue-specific accumulation of secondary metabolites, or diseases, such as cancer. Thus, the efficient discovery of 'regions of interest' (ROI) is of utmost importance in MSI. However, often the discovery of ROIs is hampered by high background noise and artifact signals. Especially in ambient ionization MSI, unmasking biologically relevant information from crude data sets is challenging. Therefore, we implemented a Threshold Intensity Quantization (TrIQ) algorithm for augmenting the contrast in MSI data visualizations. The simple algorithm reduces the impact of extreme values ('outliers') and rescales the dynamic range of mass signals. We provide an R script for post-processing MSI data in the imzML community format (https://bitbucket.org/lababi/msi.r) and implemented the TrIQ in our open-source imaging software RmsiGUI (https://bitbucket.org/lababi/rmsigui/). Applying these programs to different biological MSI data sets demonstrated the universal applicability of TrIQ for improving the contrast in the MSI data visualization. We show that TrIQ improves a subsequent detection of ROIs by sectioning. In addition, the adjustment of the dynamic signal intensity range makes MSI data sets comparable.

Target-Decoy MineR for determining the biological relevance of variables in noisy data sets

MOTIVATION

Machine learning algorithms excavate important variables from big data. However, deciding on the relevance of identified variables is challenging. The addition of artificial noise, 'decoy' variables, to raw data, 'target' variables, enables calculating a false-positive rate (FPR) and a biological relevance probability (BRp) for each variable rank. These scores allow the setting of a cut-off for informative variables, depending on the required sensitivity/specificity of a scientific question.

RESULTS

We tested the function of the Target-Decoy MineR (TDM) using synthetic data with different degrees of perturbation. Following, we applied the TDM to experimental Omics (metabolomics, transcriptomics, and proteomics) results. The TDM graphs indicate the degree of difference between sample groups. Further, the TDM reports the contribution of each variable to correct classification, i.e., its biological relevance.

AVAILABILITY

An implementation of the algorithm in R is freely available from https://bitbucket.org/cesaremov/targetdecoy_mining/. The Target-Decoy MineR is applicable to different types of quantitative data in tabular format.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Open-Source Software Tools, Databases, and Resources for Single-Cell and Single-Cell-Type Metabolomics

In this age of -omics data-guided big data revolution, metabolomics has received significant attention as compared to genomics, transcriptomics, and proteomics for its proximity to the phenotype, the promises it makes and the challenges it throws. Although metabolomes of entire organisms, organs, biofluids, and tissues are of immense interest, a cell-specific resolution is deemed critical for biomedical applications where a granular understanding of cellular metabolism at cell-type and subcellular resolution is desirable. Mass spectrometry (MS) is a versatile technique that is used to analyze a broad range of compounds from different species and cell-types, with high accuracy, resolution, sensitivity, selectivity, and fast data acquisition speeds. With recent advances in MS and spectroscopy-based platforms, the research community is able to generate high-throughput data sets from single cells. However, it is challenging to handle, store, process, analyze, and interpret data in a routine manner. In this treatise, I present a workflow of metabolomics data generation from single cells and single-cell types to their analysis, visualization, and interpretation for obtaining biological insights.

Plasma protein adsorption on Fe3O4-PEG nanoparticles activates the complement system and induces an inflammatory response

BACKGROUND

Understanding of iron oxide nanoparticles (IONP) interaction with the body milieu is crucial to guarantee their efficiency and biocompatibility in nanomedicine. Polymer coating to IONP, with polyethyleneglycol (PEG) and polyvinylpyrrolidone (PVP), is an accepted strategy to prevent toxicity and excessive protein binding.

AIM

The aim of this study was to investigate the feature of IONP adsorption of complement proteins, their activation and consequent inflammatory response as a strategy to further elucidate their biocompatibility.

METHODS

Three types of IONP with different surface characteristics were used: bare (IONP-bare), coated with PVP (IONP-PVP) and PEG-coated (IONP-PEG). IONPs were incubated with human plasma and adsorbed proteins were identified. BALB/c mice were intravenously exposed to IONP to evaluate complement activation and proinflammatory response.

RESULTS

Protein corona fingerprinting showed that PEG surface around IONP promoted a selective adsorption of complement recognition molecules which would be responsible for the complement system activation. Furthermore, IONP-PEG activated in vitro, the complement system and induced a substantial increment of C3a and C4a anaphylatoxins while IONP-bare and IONP-PVP did not. In vivo IONP-PEG induced an increment in complement activation markers (C5a and C5b-9), and proinflammatory cytokines (IL-1β, IL-6, TNF-α).

CONCLUSION

The engineering of nanoparticles must incorporate the association between complement proteins and nanomedicines, which will regulate the immunostimulatory effects through a selective adsorption of plasma proteins and will enable a safer application of IONP in human therapy.

Modulation of steroidal glycoalkaloid biosynthesis in tomato (Solanum lycopersicum) by jasmonic acid

Jasmonic acid (JA) is a phytohormone involved in plant development and defense. A major role of JA is the enhancement of secondary metabolite production, such as response to herbivory. Systemin is a bioactive plant peptide of 18 amino acids that contributes to the induction of local and systemic defense responses in tomato (Solanum lycopersicum) through JA biosynthesis. The overexpression of systemin (PS-OE) results in constitutive JA accumulation and enhances pest resistance in plants. Conversely, mutant plants affected in linolenic acid synthesis (spr2) are negatively compromised in the production of JA which favors damage and oviposition by insect herbivores. With undirected mass fingerprinting analyses, we found global metabolic differences between genotypes with modified jasmonic acid production. The spr2 mutants were enriched in di-unsaturated fatty acids and generally showed more changes. The PS-OE genotype produced an unidentified compound with a mass-to-charge ratio of 695 (MZ695). Most strikingly, the steroidal glycoalkaloid biosynthesis was negatively affected in the spr2 genotype. Complementation with jasmonic acid could restore the tomatine pathway, which strongly suggests the control of steroidal glycoalkaloid biosynthesis by jasmonic acid. spr2 plants were more susceptible to fungal infection with Fusarium oxysporum f.sp. ciceris, but not to bacterial infection with Clavibacter michiganensis subsp. michiganensis which supports the involvement of steroidal glycoalkaloids in the plant response against fungi.

The organophosphate pesticide methamidophos opens the blood-testis barrier and covalently binds to ZO-2 in mice

Methamidophos (MET) is an organophosphate (OP) pesticide widely used in agriculture in developing countries. MET causes adverse effects in male reproductive function in humans and experimental animals, but the underlying mechanisms remain largely unknown. We explored the effect of MET on mice testes (5 mg/kg/day/4 days), finding that this pesticide opens the blood-testis barrier and perturbs spermatogenesis, generating the appearance of immature germ cells in the epididymis. In the seminiferous tubules, MET treatment changed the level of expression or modified the stage-specific localization of tight junction (TJ) proteins ZO-1, ZO-2, occludin, and claudin-3. In contrast, claudin-11 was barely altered. MET also modified the shape of claudin-11, and ZO-2 at the cell border, from a zigzag to a more linear pattern. In addition, MET diminished the expression of ZO-2 in spermatids present in seminiferous tubules, induced the phosphorylation of ZO-2 and occludin in testes and reduced the interaction between these proteins assessed by co-immunoprecipitation. MET formed covalent bonds with ZO-2 in serine, tyrosine and lysine residues. The covalent modifications formed on ZO-2 at putative phosphorylation sites might interfere with ZO-2 interaction with regulatory molecules and other TJ proteins. MET bonds formed at ZO-2 ubiquitination sites likely interfere with ZO-2 degradation and TJ sealing, based on results obtained in cultured epithelial cells transfected with ZO-2 mutated at a MET target lysine residue. Our results shed light on MET male reproductive toxicity and are important to improve regulations regarding the use of OP pesticides and to protect the health of agricultural workers.

Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery

This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.

Genomic history of the origin and domestication of common bean unveils its closest sister species

Background

Modern civilization depends on only a few plant species for its nourishment. These crops were derived via several thousands of years of human selection that transformed wild ancestors into high-yielding domesticated descendants. Among cultivated plants, common bean (Phaseolus vulgaris L.) is the most important grain legume. Yet, our understanding of the origins and concurrent shaping of the genome of this crop plant is limited.

Results

We sequenced the genomes of 29 accessions representing 12 Phaseolus species. Single nucleotide polymorphism-based phylogenomic analyses, using both the nuclear and chloroplast genomes, allowed us to detect a speciation event, a finding further supported by metabolite profiling. In addition, we identified ~1200 protein coding genes (PCGs) and ~100 long non-coding RNAs with domestication-associated haplotypes. Finally, we describe asymmetric introgression events occurring among common bean subpopulations in Mesoamerica and across hemispheres.

Conclusions

We uncover an unpredicted speciation event in the tropical Andes that gave rise to a sibling species, formerly considered the “wild ancestor” of P. vulgaris, which diverged before the split of the Mesoamerican and Andean P. vulgaris gene pools. Further, we identify haplotypes strongly associated with genes underlying the emergence of domestication traits. Our findings also reveal the capacity of a predominantly autogamous plant to outcross and fix loci from different populations, even from distant species, which led to the acquisition by domesticated beans of adaptive traits from wild relatives. The occurrence of such adaptive introgressions should be exploited to accelerate breeding programs in the near future.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1190-6) contains supplementary material, which is available to authorized users.

Metabolomics, Nutrition, and Potential Biomarkers of Food Quality, Intake, and Health Status

Diet, dietary patterns, and other environmental factors such as exposure to toxins are playing an important role in the prevention/development of many diseases, like obesity, type 2 diabetes, and consequently on the health status of individuals. A major challenge nowadays is to identify novel biomarkers to detect as early as possible metabolic dysfunction and to predict evolution of health status in order to refine nutritional advices to specific population groups. Omics technologies such as genomics, transcriptomics, proteomics, and metabolomics coupled with statistical and bioinformatics tools have already shown great potential in this research field even if so far only few biomarkers have been validated. For the past two decades, important analytical techniques have been developed to detect as many metabolites as possible in human biofluids such as urine, blood, and saliva. In the field of food science and nutrition, many studies have been carried out for food authenticity, quality, and safety, as well as for food processing. Furthermore, metabolomic investigations have been carried out to discover new early biomarkers of metabolic dysfunction and predictive biomarkers of developing pathologies (obesity, metabolic syndrome, type-2 diabetes, etc.). Great emphasis is also placed in the development of methodologies to identify and validate biomarkers of nutrients exposure.

Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era

Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era.

Structural Basis for Redox Regulation of Cytoplasmic and Chloroplastic Triosephosphate Isomerases from Arabidopsis thaliana

In plants triosephosphate isomerase (TPI) interconverts glyceraldehyde 3-phosphate (G3P) and dihydroxyacetone phosphate (DHAP) during glycolysis, gluconeogenesis, and the Calvin-Benson cycle. The nuclear genome of land plants encodes two tpi genes, one gene product is located in the cytoplasm and the other is imported into the chloroplast. Herein we report the crystal structures of the TPIs from the vascular plant Arabidopsis thaliana (AtTPIs) and address their enzymatic modulation by redox agents. Cytoplasmic TPI (cTPI) and chloroplast TPI (pdTPI) share more than 60% amino acid identity and assemble as (β-α)₈ dimers with high structural homology. cTPI and pdTPI harbor two and one accessible thiol groups per monomer respectively. cTPI and pdTPI present a cysteine at an equivalent structural position (C13 and C15 respectively) and cTPI also contains a specific solvent accessible cysteine at residue 218 (cTPI-C218). Site directed mutagenesis of residues pdTPI-C15, cTPI-C13, and cTPI-C218 to serine substantially decreases enzymatic activity, indicating that the structural integrity of these cysteines is necessary for catalysis. AtTPIs exhibit differential responses to oxidative agents, cTPI is susceptible to oxidative agents such as diamide and H₂O₂, whereas pdTPI is resistant to inhibition. Incubation of AtTPIs with the sulfhydryl conjugating reagents methylmethane thiosulfonate (MMTS) and glutathione inhibits enzymatic activity. However, the concentration necessary to inhibit pdTPI is at least two orders of magnitude higher than the concentration needed to inhibit cTPI. Western-blot analysis indicates that residues cTPI-C13, cTPI-C218, and pdTPI-C15 conjugate with glutathione. In summary, our data indicate that AtTPIs could be redox regulated by the derivatization of specific AtTPI cysteines (cTPI-C13 and pdTPI-C15 and cTPI-C218). Since AtTPIs have evolved by gene duplication, the higher resistance of pdTPI to redox agents may be an adaptive consequence to the redox environment in the chloroplast.