Degradation of gaseous hydrocarbons in aerated stirred bioreactors inoculated with Rhodococcus erythropolis: Effect of the carbon source and SIFT-MS method development.

The study of microbial hydrocarbons removal is of great importance for the development of future bioremediation strategies. In this study, we evaluated the removal of a gaseous mixture containing toluene, m-xylene, ethylbenzene, cyclohexane, butane, pentane, hexane and heptane in aerated stirred bioreactors inoculated with Rhodococcus erythropolis and operated under non-sterile conditions. For the real-time measurement of hydrocarbons, a novel systematic approach was implemented using Selected-Ion Flow Tube Mass Spectrometry (SIFT-MS). The effect of the carbon source (∼9.5 ppmv) on (i) the bioreactors' performance (BR1: dosed with only cyclohexane as a single hydrocarbon versus BR2: dosed with a mixture of the 8 hydrocarbons) and (ii) the evolution of microbial communities over time were investigated. The results showed that cyclohexane reached a maximum removal efficiency (RE) of 53% ± 4% in BR1. In BR2, almost complete removal of toluene, m-xylene and ethylbenzene, being the most water-soluble and easy-to-degrade carbon sources, was observed. REs below 32% were obtained for the remaining compounds. By exposing the microbial consortium to only the five most recalcitrant hydrocarbons, REs between 45% ± 5% and 98% ± 1% were reached. In addition, we observed that airborne microorganisms populated the bioreactors and that the type of carbon source influenced the microbial communities developed. The abundance of species belonging to the genus Rhodococcus was below 10% in all bioreactors at the end of the experiments. This work provides fundamental insights to understand the complex behavior of gaseous hydrocarbon mixtures in bioreactors, along with a systematic approach for the development of SIFT-MS methods.

Exploring the diversity of dissolved organic matter (DOM) properties and sources in different functional areas of a typical macrophyte - derived lake combined with optical spectroscopy and FT-ICR MS analysis.

Lake Baiyangdian is one of China's largest macrophyte - derived lakes, facing severe challenges related to water quality maintenance and eutrophication prevention. Dissolved organic matter (DOM) was a huge carbon pool and its abundance, property, and transformation played important roles in the biogeochemical cycle and energy flow in lake ecosystems. In this study, Lake Baiyangdian was divided into four distinct areas: Unartificial Area (UA), Village Area (VA), Tourism Area (TA), and Breeding Area (BA). We examined the diversity of DOM properties and sources across these functional areas. Our findings reveal that DOM in this lake is predominantly composed of protein - like substances, as determined by excitation - emission matrix and parallel factor analysis (EEM - PARAFAC). Notably, the exogenous tyrosine-like component C1 showed a stronger presence in VA and BA compared to UA and TA. Ultrahigh - resolution mass spectrometry (FT - ICR MS) unveiled a similar DOM molecular composition pattern across different functional areas due to the high relative abundances of lignan compounds, suggesting that macrophytes significantly influence the material structure of DOM. DOM properties exhibited specific associations with water quality indicators in various functional areas, as indicated by the Mantel test. The connections between DOM properties and NO3N and NH3N were more pronounced in VA and BA than in UA and TA. Our results underscore the viability of using DOM as an indicator for more precise and scientific water quality management.

From whence it came: Mitochondrial mRNA leaves, a protein returns.

Small peptides have previously been reported to be encoded in mitochondrial rRNA and translated by cytosolic ribosomes. In this issue of Cell Metabolism, Hu et al. use mass spectrometry to identify a cytosolically translated protein, encoded instead in mitochondrial mRNA, that is surprisingly targeted back into the mitochondrial matrix.

Metabolic signatures and risk of sarcopenia in suburb-dwelling older individuals by LC-MS-based untargeted metabonomics.

Background: Untargeted metabonomics has provided new insight into the pathogenesis of sarcopenia. In this study, we explored plasma metabolic signatures linked to a heightened risk of sarcopenia in a cohort study by LC-MS-based untargeted metabonomics. Methods: In this nested case-control study from the Adult Physical Fitness and Health Cohort Study (APFHCS), we collected blood plasma samples from 30 new-onset sarcopenia subjects (mean age 73.2 ± 5.6 years) and 30 healthy controls (mean age 74.2 ± 4.6 years) matched by age, sex, BMI, lifestyle, and comorbidities. An untargeted metabolomics methodology was employed to discern the metabolomic profile alterations present in individuals exhibiting newly diagnosed sarcopenia. Results: In comparing individuals with new-onset sarcopenia to normal controls, a comprehensive analysis using liquid chromatography-mass spectrometry (LC-MS) identified a total of 62 metabolites, predominantly comprising lipids, lipid-like molecules, organic acids, and derivatives. Receiver operating characteristic (ROC) curve analysis indicated that the three metabolites hypoxanthine (AUC=0.819, 95% CI=0.711-0.927), L-2-amino-3-oxobutanoic acid (AUC=0.733, 95% CI=0.598-0.868) and PC(14:0/20:2(11Z,14Z)) (AUC= 0.717, 95% CI=0.587-0.846) had the highest areas under the curve. Then, these significant metabolites were observed to be notably enriched in four distinct metabolic pathways, namely, "purine metabolism"; "parathyroid hormone synthesis, secretion and action"; "choline metabolism in cancer"; and "tuberculosis". Conclusion: The current investigation elucidates the metabolic perturbations observed in individuals diagnosed with sarcopenia. The identified metabolites hold promise as potential biomarkers, offering avenues for exploring the underlying pathological mechanisms associated with sarcopenia.

Chemical Constitutes from Mycelia of Laetiporus versisporus (Agaricomycetes).

Edible mushrooms, both wild and cultivated, can be seen as healthy functional food. More and more valuable compounds are obtained from mycelia of macromycetes. However, there was limited report about the medicinal fungus Laetiporus versisporus (Lloyd) Imazeki. Herein, L. versisporus was fermented on rice media and the secondary metabolites of mycelia were investigated. In this study, two-step method was used to obtain fermented products, silica gel column chromatography, recrystallization, medium pressure column chromatography, preparative thin-layer chromatography were applied to separate the chemical constituents. Nine chemical compounds (1-9) including one new triterpenoid acid versisponic acid F were identified by NMR (nuclear magnetic resonance) spectroscopy and MS (mass spectrometry). Seven compounds including monolinoleoyl glycerol, linoleic acid, ergosta-5, 7, 22-triene-3ß-ol, ß-sitosterol, daucosterol, versisponic acid F were isolated for the first time from L. versisporus.

Obscurity of chlorophyll tails - Is chlorophyll with farnesyl tail incorporated into PSII complexes?

Chlorophyll is essential in photosynthesis, converting sunlight into chemical energy in plants, algae, and certain bacteria. Its structure, featuring a porphyrin ring enclosing a central magnesium ion, varies in forms like chlorophyll a, b, c, d, and f, allowing light absorption at a broader spectrum. With a 20-carbon phytyl tail (except for chlorophyll c), chlorophyll is anchored to proteins. Previous findings suggested the presence of chlorophyll with a modified farnesyl tail in thermophilic cyanobacteria Thermosynechoccocus vestitus. In our Arabidopsis thaliana PSII cryo-EM map, specific chlorophylls showed incomplete phytyl tails, suggesting potential farnesyl modifications. However, further high-resolution mass spectrometry (HRMS) analysis in A. thaliana and T. vestitus did not confirm the presence of any farnesyl tails. Instead, we propose the truncated tails in PSII models may result from binding pocket flexibility rather than actual modifications.

Exploring the Alternative Proteome with OpenProt and Mass Spectrometry.

Proteogenomics has revealed the translation of unannotated open reading frames (ORFs) present in mRNAs and in noncoding RNAs (ncRNAs). OpenProt annotates all ORFs with a minimum of 30 codons in the transcriptome of several species and displays many functional features associated with the corresponding proteins. Two types of proteins are annotated: reference or canonical proteins which are proteins already annotated in UniProt, RefSeq, or Ensembl and noncanonical proteins. Noncanonical proteins form two groups: predicted novel isoforms that display a significant level of homology with a reference protein and alternative proteins that are new proteins with no significant homology to known proteins. This chapter describes how to check whether a gene and/or transcript contains multiple open reading frames and how to use OpenProt databases for the detection of alternative proteins and novel isoforms by mass spectrometry-based proteomics.

Identification of Novel Bacterial Microproteins Encoded by Small Open Reading Frames Using a Computational Proteogenomics Workflow.

Genome annotation has historically ignored small open reading frames (smORFs), which encode a class of proteins shorter than 100 amino acids, collectively referred to as microproteins. This cutoff was established to avoid thousands of false positives due to limitations of pure genomics pipelines. Proteogenomics, a computational approach that combines genomics, transcriptomics, and proteomics, makes it possible to accurately identify these short sequences by overlaying different levels of omics evidence. In this chapter, we showcase the use of µProteInS, a bioinformatics pipeline developed for the identification of unannotated microproteins encoded by smORFs in bacteria. The workflow covers all the steps from quality control and transcriptome assembly to the scoring and post-processing of mass spectrometry data. Additionally, we provide an example on how to apply the pipeline's machine learning method to identify high-confidence spectra and pinpoint the most reliable identifications from large datasets.

Understanding PTM Cross Talk Through a Visualization Tool, PTMViz.

The advancement of sequencing technologies has expanded our understanding of biological complexity through mechanisms such as allelic variations, alternative splicing of RNA, degradation of RNA by microRNAs, and posttranslational modifications (PTMs). In this chapter, we describe a method, PTMViz, for analyzing proteoforms identified by mass spectrometry. This interactive platform provides differential abundance analysis and visualization of protein and posttranslational modifications. We describe the detailed steps to prepare mass spectrometry database search results into the necessary format for PTMViz, how to set up the experimental conditions for differential abundance analysis, and the visualization of the results. The application is freely available at https://github.com/ByrumLab/PTMViz .

Making MS Omics Data ML-Ready: SpeCollate Protocols.

The increasing complexity and volume of mass spectrometry (MS) data have presented new challenges and opportunities for proteomics data analysis and interpretation. In this chapter, we provide a comprehensive guide to transforming MS data for machine learning (ML) training, inference, and applications. The chapter is organized into three parts. The first part describes the data analysis needed for MS-based experiments and a general introduction to our deep learning model SpeCollate-which we will use throughout the chapter for illustration. The second part of the chapter explores the transformation of MS data for inference, providing a step-by-step guide for users to deduce peptides from their MS data. This section aims to bridge the gap between data acquisition and practical applications by detailing the necessary steps for data preparation and interpretation. In the final part, we present a demonstrative example of SpeCollate, a deep learning-based peptide database search engine that overcomes the problems of simplistic simulation of theoretical spectra and heuristic scoring functions for peptide-spectrum matches by generating joint embeddings for spectra and peptides. SpeCollate is a user-friendly tool with an intuitive command-line interface to perform the search, showcasing the effectiveness of the techniques and methodologies discussed in the earlier sections and highlighting the potential of machine learning in the context of mass spectrometry data analysis. By offering a comprehensive overview of data transformation, inference, and ML model applications for mass spectrometry, this chapter aims to empower researchers and practitioners in leveraging the power of machine learning to unlock novel insights and drive innovation in the field of mass spectrometry-based omics.

PGFinder, an Open-Source Software for Peptidoglycomics: The Structural Analysis of Bacterial Peptidoglycan by LC-MS.

Peptidoglycan is a major and essential component of the bacterial cell envelope that confers cell shape and provides protection against internal osmotic pressure. This complex macromolecule is made of glycan strands cross-linked by short peptides, and its structure is continually modified throughout growth via a process referred to as "remodeling." Peptidoglycan remodeling allows cells to grow, adapt to their environment, and release fragments that can act as signaling molecules during host-pathogen interactions. Preparing peptidoglycan samples for structural analysis first requires purification of the peptidoglycan sacculus, followed by its enzymatic digestion into disaccharide peptides (muropeptides). These muropeptides can then be characterized by liquid chromatography coupled mass spectrometry (LC-MS) and used to infer the structure of intact peptidoglycan sacculi. Due to the presence of unusual crosslinks, noncanonical amino acids, and amino sugars, the analysis of peptidoglycan LC-MS datasets cannot be handled by traditional proteomics software. In this chapter, we describe a protocol to perform the analysis of peptidoglycan LC-MS datasets using the open-source software PGFinder. We provide a step-by-step strategy to deconvolute data from various mass spectrometry instruments, generate muropeptide databases, perform a PGFinder search, and process the data output.

Analysis of Sphingosine and Sphinganine from the Aqueous Humor for Signaling Studies Using Ultrahigh-Performance Liquid Chromatography-Mass Spectrometry.

Sphingolipids, including sphingosine and sphinganine, are one of the major classes of lipids. They serve as constituents of cell membranes and lipid rafts and aid in the performance of cell-cell communication and adhesion. Abnormal levels of sphingolipids in the aqueous humor can indicate impaired sphingolipid metabolism and associated ocular pathologies. Sphingolipids can be extracted from the aqueous humor by the methyl-tert-butyl ether (MTBE) lipid extraction method and subsequently analyzed by liquid chromatography-mass spectrometry (LC-MS). This chapter describes a modified protocol for an MTBE lipid extraction from the aqueous humor, followed by analysis with ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS).

Diacylglycerol Signaling in Retinal Ganglion Cells.

With impaired retinal ganglion cell (RGC) function and eventual RGC death, there is a heightened risk of experiencing glaucoma-induced blindness or other optic neuropathies. Poor RGC efficiency leads to limited transmission of visual signals between the retina and the brain by RGC axons. Increased focus on studying lipid messengers found in neurons such as endocannabinoids (eCBs) has importance due to their potential axonal pathway regenerative properties. 2-Arachidonoylglycerol (2-AG), a common eCB, is synthesized from an sn-1 hydrolysis reaction between diacylglycerol (DAG) and diacylglycerol lipase (DAGL). Examination of DAG production allows for future downstream analysis in relation to DAGL functionality. Here, we describe protocol guidelines for extracting RGCs from mouse retinas and subsequent mass spectrometry analysis of the DAG content present within the RGCs.

Estimation for Raw Material Plants of a Henna Product Using LC-High Resolution MS and Multivariate Analysis.

Henna is a plant-based dye obtained from the powdered leaf of the pigmented plant Lawsonia inermis, and has often been used for grey hair dyeing, treatment, and body painting. As a henna product, the leaves of Indigofera tinctoria and Cassia auriculata can be blended to produce different colour variations. Although allergy from henna products attributed to p-phenylenediamine, which is added to enhance the dye, is reported occasionally, raw material plants of henna products could also contribute to the allergy. In this study, we reported that raw material plants of commercial henna products distributed in Japan can be estimated by LC-high resolution MS (LC-HRMS) and multivariate analysis. Principal Component Analysis (PCA) score plot clearly separated 17 samples into three groups [I; henna, II; blended henna primarily comprising Indigofera tinctoria, III; Cassia auriculata]. This grouping was consistent with the ingredient lists of products except that one sample listed as henna was classified as Group III, indicating that its ingredient label may differ from the actual formulation. The ingredients characteristic to Groups I, II, and III by PCA were lawsone (1), indirubin (2), and rutin (3), respectively, which were reported to be contained in each plant as ingredients. Therefore, henna products can be considered to have been manufactured from these plants. This study is the first to estimate raw material plants used in commercial plant-based dye by LC-HRMS and multivariate analysis.

Developing δ15N and δ13C isoscapes using whole blood from Magellanic penguins, Spheniscus magellanicus.

RATIONALE: Understanding the migration of marine animals is hindered by the limitations of traditional tracking methods. It is therefore crucial to develop alternative methods. Stable isotope-based tracking has proven useful for this task, although it requires detailed isoscapes in the focal area. Here, we present predator-based isoscapes of the coastal zone of the Patagonian Shelf Large Marine Ecosystem (PSLME), which offers a novel tool for geolocation. METHODS: Whole-blood samples from breeding Magellanic penguins nesting at 11 colonies were used to create δ15N and δ13C isoscapes. Isotopic values were assigned to random positions inside their corresponding foraging area. Spatial analysis and data interpolation resulted in δ15N and δ13C isoscapes for the coastal zone of the PSLME, which were validated through cross-validation. RESULTS: The isoscapes mean standard error ranged from 0.05 to 0.41 for δ15N and from 0.07 to 0.3 for δ13C, similar to the error range of the mass spectrometer used for measuring isotope ratios. Predictive surfaces reflected the latitudinal trends, with δ13C and δ15N values increasing northwards. δ13C values showed a strong latitudinal gradient, while δ15N values had two distinct domains, with higher values in the north. The error surface indicated the highest certainty within 130 km from the shore and within the reported Magellanic penguin foraging areas. CONCLUSIONS: Both isoscapes revealed strong spatial variation. The δ13C isoscape showed a latitudinal gradient, consistent with patterns in other oceans. The δ15N isoscape clearly separated northern and southern colonies, likely influenced by nitrogen sources. The error obtained fell within the measurement error ranges, adding credibility to the models.

Rapid identification of various chemical components in Cinnamomi ramulus by UPLC-Q-Orbitrap-MS.

Cinnamomi ramulus (CR) is a common Chinese herbal medicine with a long history. It is often used to treat exogenous wind-cold diseases in clinic, but its chemical compositions remain to be studied. In this study, CR was extracted with 75% ethanol, and UPLC-Q-Orbitrap-MS combined with data post-processing method was used to identify the chemical components in the extract. Through this technology, the components in CR can be separated and accurately identified. A total of 61 compounds were identified, including 14 simple phenylpropanoids, 3 coumarins, 5 lignans, 14 flavonoids, 10 benzoic acids, 8 organic acids, and 7 others. This study confirmed the existence of these compounds in CR and speculated the cleavage pathways of each compound, which enriched the mass spectrometry data and cleavage rules. This study can provide a reference for CR and other research.

Analysis of Polypeptides by Amino Acid Analysis.

Amino acid analysis is an accurate method for the composition and quantitation of polypeptides and among these synthetic peptides. Combined with mass spectrometry, it yields a reliable control of peptide quality and quantity prior to conjugation and immunization.Initially peptides are hydrolyzed, preferably in the gas phase, with 6-M HCl at 110 °C for 20-24 h and the resulting amino acids analyzed by chromatography, where the most reliable form is ion exchange chromatography with post-column ninhydrin derivatization. Depending on the hydrolysis conditions, tryptophan is destroyed, and likewise cysteine, unless derivatized, and the amides, glutamine, and asparagine are deamidated to glutamic acid and aspartic acid, respectively. Three different ways of calculating results are suggested, and taking the above limitations into account, a quantitation better than 5% can usually be obtained.

Characterization of Synthetic Peptides by Mass Spectrometry.

In the quality control of synthetic peptides, mass spectroscopy (MS) serves as an optimal method for evaluating authenticity and integrity. Typically, the sequence of a synthetic peptide is already established, thereby directing the focus of analysis towards validating its identity and purity. This chapter outlines straightforward methodologies for conducting MS analyses specifically tailored for synthetic peptides.

Small and Large Extracellular Vesicles of Porcine Seminal Plasma Differ in Lipid Profile.

Seminal plasma contains a heterogeneous population of extracellular vesicles (sEVs) that remains poorly characterized. This study aimed to characterize the lipidomic profile of two subsets of differently sized sEVs, small (S-) and large (L-), isolated from porcine seminal plasma by size-exclusion chromatography and characterized by an orthogonal approach. High-performance liquid chromatography-high-resolution mass spectrometry was used for lipidomic analysis. A total of 157 lipid species from 14 lipid classes of 4 major categories (sphingolipids, glycerophospholipids, glycerolipids, and sterols) were identified. Qualitative differences were limited to two cholesteryl ester species present only in S-sEVs. L-sEVs had higher levels of all quantified lipid classes due to their larger membrane surface area. The distribution pattern was different, especially for sphingomyelins (more in S-sEVs) and ceramides (more in L-sEVs). In conclusion, this study reveals differences in the lipidomic profile of two subsets of porcine sEVs, suggesting that they differ in biogenesis and functionality.