Normalizing Gas-Chromatography-Mass Spectrometry Data: Method Choice can Alter Biological Inference

Electronic tongue measurements as a predictor for sensory properties of vacuum-packed minced beef - A preliminary study

Changes in meat sensory quality during storage are difficult to assess accurately and objectively. Advanced analytical technologies for sensory assessment could help predict sensory changes. In this study, vacuum-packed (VP) minced beef was incubated at four different temperatures until the end of its quality shelf-life, before conducting instrumental taste analysis with an Insent electronic tongue and analysis of volatile compounds with headspace SPME GC-MS. It was found that storage time and storage temperature variables explained variation in odour, sourness and umami (adjusted R-squared values of 61 %, 58 % and 63 % respectively), with a relationship identified between sourness and umami tastes and the abundance of volatile compounds (2-butanone, methyl butyrate, methyl valerate and ethyl acetate), and trained panel scores of the odour of beef. The results highlighted that instrumental taste measurements could be used as a potential predictor of sensory shelf-life in vacuum packed beef.

Sawfly Sex Pheromones: Analysis of Their Impact on Pine Odor Attractive to Egg Parasitoids

Pinus sylvestris trees are known to efficiently defend themselves against eggs of the herbivorous sawfly Diprion pini. Their direct defense against eggs is primable by prior exposure to the sex pheromones of this species and their indirect defense involves attraction of egg parasitoids by egg-induced pine needle odor. But it is unknown whether exposure of pine to D. pini sex pheromones also affects pine indirect defense against sawfly eggs. In this study, we investigated the influence of exposure of P. sylvestris trees to the sex pheromones of D. pini on indirect defense mediated by egg parasitoids. Behavioral assays with Closterocerus ruforum, a key parasitoid of sawfly eggs, revealed no significant attraction to odor from egg-free pines pre-exposed to pheromones. Chemical analyses of odor from egg-free pines showed no pheromone-induced change in the emission rates of the known key terpenoids promoting parasitoid attraction. Further comparative analyses of odor from egg-laden pines pre-exposed to the sex pheromones and of odor from egg-laden pines unexposed to pheromones neither revealed significant differences in the emission rates of terpenoids relevant for parasitoid attraction. The results suggest that a pheromone-induced or pheromone-primed, egg-induced pine indirect defense seems to be redundant in addition to the known pheromone-primable pine direct defense against the eggs and the known egg-inducible indirect defense.

Development and Optimization Method for Determination of the Strawberries' Aroma Profile

The strawberry (genus Fragaria) is a plant from the rose family (Rosaceae). As the fruits are likely to be picked mechanically, they are grown close to consumption centers. The aim of this work was to develop a suitable method for detecting as many molecules as possible in order to be able to distinguish between different strawberry cultivars and geographical origins in the future. Whole strawberries of the "Albion" cultivar, grown in the Jagodica Purgerica region of Zagreb, were used. Gas chromatography-mass spectrometry (GC-MS) in SCAN mode was used to analyze the aroma profile and to determine the proportion of individual components. The samples were prepared and analyzed using the solid-phase microextraction method (SPME). The impact of SPME fiber selection and GC column type was investigated, as well as sample weight, ionic strength, agitation temperature, and sampling time. A higher ionic strength was achieved by adding a 20% NaCl solution to the sample. The aroma profile of the studied strawberry cultivar consisted of furanone, esters, aldehydes, and carboxylic acids. Optimal results were achieved by adjusting the ionic strength during 15 min of extraction and incubation. The individual compounds were identified using NIST, Wiley libraries, and the "area normalization" method.

Associational Effects of Desmodium Intercropping on Maize Resistance and Secondary Metabolism

Intercropping is drawing increasing attention as a strategy to increase crop yields and manage pest pressure, however the mechanisms of associational resistance in diversified cropping systems remain controversial. We conducted a controlled experiment to assess the impact of co-planting with silverleaf Desmodium (Desmodium uncinatum) on maize secondary metabolism and resistance to herbivory by the spotted stemborer (Chilo partellus). Maize plants were grown either in the same pot with a Desmodium plant or adjacent to it in a separate pot. Our findings indicate that co-planting with Desmodium influences maize secondary metabolism and herbivore resistance through both above and below-ground mechanisms. Maize growing in the same pot with a Desmodium neighbor was less attractive for oviposition by spotted stemborer adults. However, maize exposed only to above-ground Desmodium cues generally showed increased susceptibility to spotted stemborer herbivory (through both increased oviposition and larval consumption). VOC emissions and tissue secondary metabolite titers were also altered in maize plants exposed to Desmodium cues, with stronger effects being observed when maize and Desmodium shared the same pot. Specifically, benzoxazinoids were strongly suppressed in maize roots by direct contact with a Desmodium neighbor while headspace emissions of short-chain aldehydes and alkylbenzenes were increased. These results imply that direct root contact or soil-borne cues play an important role in mediating associational effects on plant resistance in this system.

Predicting Clinically Significant Prostate Cancer Using Urine Metabolomics via Liquid Chromatography Mass Spectrometry

PURPOSE

Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles.

MATERIALS AND METHODS

Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion.

RESULTS

The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88-0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column.

CONCLUSIONS

Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Evaluating the Effect of Data Merging and Postacquisition Normalization on Statistical Analysis of Untargeted High-Resolution Mass Spectrometry Based Urinary Metabolomics Data

Urine is one of the most widely used biofluids in metabolomic studies because it can be collected noninvasively and is available in large quantities. However, it shows large heterogeneity in sample concentration and consequently requires normalization to reduce unwanted variation and extract meaningful biological information. Biological samples like urine are commonly measured with electrospray ionization (ESI) coupled to a mass spectrometer, producing data sets for positive and negative modes. Combining these gives a more complete picture of the total metabolites present in a sample. However, the effect of this data merging on subsequent data analysis, especially in combination with normalization, has not yet been analyzed. To address this issue, we conducted a neutral comparison study to evaluate the performance of eight postacquisition normalization methods under different data merging procedures using 1029 urine samples from the Food Chain plus (FoCus) cohort. Samples were measured with a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS). Normalization methods were evaluated by five criteria capturing the ability to remove sample concentration variation and preserve relevant biological information. Merging data after normalization was generally favorable for quality control (QC) sample similarity, sample classification, and feature selection for most of the tested normalization methods. Merging data after normalization and the usage of probabilistic quotient normalization (PQN) in a similar setting are generally recommended. Relying on a single analyte to capture sample concentration differences, like with postacquisition creatinine normalization, seems to be a less preferable approach, especially when data merging is applied.

The effects of pollutant mixture released from grafted adipose tissues on fatty acid and lipid metabolism in the skeletal muscles, kidney, heart, and lungs of male mice

Persistent organic pollutants (POPs) accumulated in the adipose tissue can affect the fatty acid and lipid metabolism in the body. Gas chromatography-mass spectrometry (GC-MS) metabolomics analysis was carried out to study the metabolic changes induced by internal exposure to the POPs in mouse skeletal muscle (soleus, plantaris, and gastrocnemius), kidney, heart, and lungs. Male donor mice were injected with a mixture of 10 POPs at concentrations of 0 × and 5 × lowest-observed-adverse-effect level (LOAEL). Their adipose tissue (AT) containing the POP was then grafted onto the host mice and the metabolic change of the host mice was monitored for 3 or 21 days. The metabolites related to fatty acid and lipid metabolism were studied. For the host mice engrafted with POP-containing fat pad, there was dysregulation of the fatty acids and glycerides observed in all the organs studied 3 days after the graft. However, there was no longer a significant change in the metabolites 21 days after the graft. The difference in significant values and metabolite regulation in each of the skeletal muscles showed that the POP mixture affects different types of skeletal muscle in a heterogeneous manner. Fold change analysis showed that certain metabolites in the kidney of host mice exposed to POPs for 3 days were greatly affected. Using multivariate analysis, apart from the plantaris, most treated groups exposed to POPs for 3 days are well distinguished from the control groups. However, for host mice exposed to POPs for 21 days, apart from the kidney and heart, groups are not well-distinguished from the control group. This study helps bring new insight into the effects of the pollutants mixture released from AT on fatty acid and lipid metabolism at different periods and how the dysregulation of metabolites might result in diseases associated with the organs.

Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study

PURPOSE

Currently, there are no accurate markers for predicting potentially lethal prostate cancer (PC) before biopsy. This study aimed to develop urine tests to predict clinically significant PC (sPC) in men at risk.

METHODS

Urine samples from 928 men, namely, 660 PC patients and 268 benign subjects, were analyzed by gas chromatography/quadrupole time-of-flight mass spectrophotometry (GC/Q-TOF MS) metabolomic profiling to construct four predictive models. Model I discriminated between PC and benign cases. Models II, III, and GS, respectively, predicted sPC in those classified as having favorable intermediate risk or higher, unfavorable intermediate risk or higher (according to the National Comprehensive Cancer Network risk groupings), and a Gleason sum (GS) of ≥ 7. Multivariable logistic regression was used to evaluate the area under the receiver operating characteristic curves (AUC).

RESULTS

In Models I, II, III, and GS, the best AUCs (0.94, 0.85, 0.82, and 0.80, respectively; training cohort, N = 603) involved 26, 24, 26, and 22 metabolites, respectively. The addition of five clinical risk factors (serum prostate-specific antigen, patient age, previous negative biopsy, digital rectal examination, and family history) significantly improved the AUCs of the models (0.95, 0.92, 0.92, and 0.87, respectively). At 90% sensitivity, 48%, 47%, 50%, and 36% of unnecessary biopsies could be avoided. These models were successfully validated against an independent validation cohort (N = 325). Decision curve analysis showed a significant clinical net benefit with each combined model at low threshold probabilities. Models II and III were more robust and clinically relevant than Model GS.

CONCLUSION

This urine test, which combines urine metabolic markers and clinical factors, may be used to predict sPC and thereby inform the necessity of biopsy in men with an elevated PC risk.

Comparison of decision tree and naïve Bayes algorithms in detecting trace residue of gasoline based on gas chromatography-mass spectrometry data

Fire debris analysis aims to detect and identify any ignitable liquid residues in burnt residues collected at a fire scene. Typically, the burnt residues are analysed using gas chromatography-mass spectrometry (GC-MS) and are manually interpreted. The interpretation process can be laborious due to the complexity and high dimensionality of the GC-MS data. Therefore, this study aims to compare the potential of classification and regression tree (CART) and naïve Bayes (NB) algorithms in analysing the pixel-level GC-MS data of fire debris. The data comprise 14 positive (i.e. fire debris with traces of gasoline) and 24 negative (i.e. fire debris without traces of gasoline) samples. The differences between the positive and negative samples were first inspected based on the mean chromatograms and scores plots of the principal component analysis technique. Then, CART and NB algorithms were independently applied to the GC-MS data. Stratified random resampling was applied to prepare three sets of 200 pairs of training and testing samples (i.e. split ratio of 7:3, 8:2, and 9:1) for estimating the prediction accuracies. Although both the positive and negative samples were hardly differentiated based on the mean chromatograms and scores plots of principal component analysis, the respective NB and CART predictive models produced satisfactory performances with the normalized GC-MS data, i.e. majority achieved prediction accuracy >70%. NB consistently outperformed CART based on the prediction accuracies of testing samples and the corresponding risk of overfitting except when evaluated using only 10% of samples. The accuracy of CART was found to be inversely proportional to the number of testing samples; meanwhile, NB demonstrated rather consistent performances across the three split ratios. In conclusion, NB seems to be much better than CART based on the robustness against the number of testing samples and the consistent lower risk of overfitting.

Food Fingerprinting: LC-ESI-IM-QTOF-Based Identification of Blumeatin as a New Marker Metabolite for the Detection of Origanum majorana Admixtures to O. onites/vulgare

Oregano (Origanum vulgare and O. onites) is one of the most frequently counterfeited herbs in the world and is diluted with the leaves of a wide variety of plants. In addition to olive leaves, marjoram (O. majorana) is often used for this purpose in order to achieve a higher profit. However, apart from arbutin, no marker metabolites are known to reliably detect marjoram admixtures in oregano batches at low concentrations. In addition, arbutin is relatively widespread in the plant kingdom, which is why it is of great relevance to look for further marker metabolites in order to secure the analysis accordingly. Therefore, the aim of the present study was to use a metabolomics-based approach to identify additional marker metabolites with the aid of an ion mobility mass spectrometry instrument. The focus of the analysis was on the detection of non-polar metabolites, as this study was preceded by nuclear magnetic resonance spectroscopic investigations of the same samples based mainly on the detection of polar analytes. Using the MS-based approach, numerous marjoram specific features could be detected in admixtures of marjoram >10% in oregano. However, only one feature was detectable in admixtures of >5% marjoram. This feature was identified as blumeatin, which belongs to the class of flavonoid compounds. Initially, blumeatin was identified based on MS/MS spectra and collision cross section values using a database search. In addition, the identification of blumeatin was confirmed by a reference standard. Moreover, dried leaves of olive, myrtle, thyme, sage and peppermint, which are also known to be used to adulterate oregano, were measured. Blumeatin could not be detected in these plants, so this substance can be considered as an excellent marker compound for the detection of marjoram admixtures.

Chemical characterization, anti-nociceptive and anti-inflammatory activities of Plukenetia conophora seed oil in experimental rodent models

ETHNOPHARMACOLOGICAL RELEVANCE

The seed of the African walnut, Plukenetia conophora Mull.-Arg is well-known for its nutritional and medicinal values. The seed oil is widely used in massages to relieve pain, as nerve tonic and to enhance sexual performance.

OBJECTIVE

The study aimed at investigating the chemical profile, antinociceptive and anti-inflammatory activities of P. conophora oil (PCO).

METHODS

Seed oil of P. conophora was characterized using Gas-Liquid Chromatographic method (GC-MS) and oral acute toxicity evaluated at 2000 mg/kg. Antinociceptive effects were evaluated in hot plate, acetic acid and formalin-induced paw licking tests. The anti-inflammatory effects were investigated in egg albumin and carrageenan-, formalin and complete Freund adjuvant (CFA)-induced paw oedema models. The levels of pro-inflammatory cytokines in the fluid exudates were also evaluated in carrageenan air pouch model.

RESULTS

PCO exhibited high content of alpha linolenic acid (ALA). No toxicity was observed at 2000 mg/kg of PCO. PCO (50-200 mg/kg) demonstrated significant anti-nociceptive activity in pain models. PCO exhibited anti-inflammatory activity against oedema formation by phlogistic agents. The increased inflammatory oedema and oxidative stress in CFA-treated rats were also attenuated by PCO. The PCO (100 and 200 mg/kg) significantly reduced the levels of TNF-α (59.3% and 85.2%) and IL-6 (27.5% and 72.5%) in carrageenan-induced air pouch model.

CONCLUSION

The results of this study suggest that ALA-rich seed oil of Plukenetia conophora demonstrated anti-nociceptive and anti-inflammatory activities via inhibition of pro-inflammatory cytokines and oxidative stress, lending supportive evidences for its use in painful inflammatory conditions.

The untargeted urine volatilome for biomedical applications: methodology and volatilome database

Chemically diverse in compounds, urine can give us an insight into metabolic breakdown products from foods, drinks, drugs, environmental contaminants, endogenous waste metabolites, and bacterial by-products. Hundreds of them are volatile compounds; however, their composition has never been provided in detail, nor has the methodology used for urine volatilome untargeted analysis. Here, we summarize key elements for the untargeted analysis of urine volatilome from a comprehensive compilation of literature, including the latest reports published. Current achievements and limitations on each process step are discussed and compared. 34 studies were found retrieving all information from the urine treatment to the final results obtained. In this report, we provide the first specific urine volatilome database, consisting of 841 compounds from 80 different chemical classes.

Breath volatile metabolome reveals the impact of dietary fibres on the gut microbiota: Proof of concept in healthy volunteers

Background

Current data suggest that dietary fibre (DF) interaction with the gut microbiota largely contributes to their physiological effects. The bacterial fermentation of DF leads to the production of metabolites, most of them are volatile. This study analyzed the breath volatile metabolites (BVM) profile in healthy individuals (n=15) prior and after a 3-week intervention with chitin-glucan (CG, 4.5 g/day), an insoluble fermentable DF.

Methods

The present exploratory study presents the original data related to the secondary outcomes, notably the analysis of BVM. BVM were analyzed throughout the test days -in fasting state and after standardized meals - using selected ion flow tube mass spectrometry (SIFT-MS). BVM production was correlated to the gut microbiota composition (Illumina sequencing, primary outcome), analyzed before and after the intervention.

Findings

The data reveal that the post-prandial state versus fasting state is a key determinant of BVM fingerprint. Correlation analyses with fecal microbiota spotlighted butyrate-producing bacteria, notably Faecalibacterium, as dominant bacteria involved in butyrate and other BVM expiration. CG intervention promotes interindividual variations of fasting BVM, and decreases or delays the expiration of most exhaled BVM in favor of H₂ expiration, without any consequence on gastrointestinal tolerance.

Interpretation

Assessing BVM is a non-invasive methodology allowing to analyze the influence of DF intervention on the gut microbiota.

Funding

FiberTAG project was initiated from a European Joint Programming Initiative “A Healthy Diet for a Healthy Life” (JPI HDHL) and was supported by the Service Public de Wallonie (SPW-EER, convention 1610365, Belgium).

Aqueous Extracts of Lemon Basil Straw as Chemical Stimulator for Gray Oyster Mushroom Cultivation

To reduce the burning of lemon basil straw (LBS)—the byproduct of basil seed production—we propose utilizing LBS as a replacement substrate for mushroom cultivation. LBS can stimulate both mycelial growth and percentage biological efficiency; however, the rigidity of this material limits particle size reduction. In this work, aqueous extractions were facilely performed without using either hazardous chemicals or complex procedures to valorize LBS as a stimulator for gray oyster mushroom cultivation. An aqueous extraction at solid-to-liquid of 50 g/L was employed. The macerated-LBS and decocted-LBS extracts were tested for mycelial growth in potato dextrose agar and sorghum grains. Following this, both aqueous extracts were applied as a wetting agent in cylindrical baglog cultivation to estimate mycelial growth, biological efficiency, and productivity. It was found that LBS extracts insignificantly enhanced the mycelia growth rate on all media, while the diluted LBS (1:1 v/v) extracts improved 1.5-fold of percentage biological efficiency. Gas chromatograph-mass spectrometer results indicated 9-octadecaenamide is a major component in LBS aqueous extract. Results demonstrated that the LBS extract is a good stimulator for the production of Pleurotus mushroom.

Mining RNAseq data reveals dynamic metaboloepigenetic profiles in human, mouse and bovine pre-implantation embryos

Metaboloepigenetic regulation has been reported in stem cells, germ cells, and tumor cells. Embryonic metaboloepigenetics, however, have just begun to be described. Here we analyzed RNAseq data to characterize the metaboloepigenetic profiles of human, mouse, and bovine pre-implantation embryos. In embryos, metaboloepigenetic reprogramming was species-specific, varied with the developmental stage and was disrupted with in vitro culture. Metabolic pathways and gene expressions were strongly correlated with early embryo DNA methylation and were changed with in vitro culture. Although the idea that the in vitro environment may influence development is not new, there has been little progress on improving pregnancy rates after decades using in vitro fertilization. Hence, the present data will contribute to understanding how the in vitro manipulation affects the metaboloepigenetic status of early embryos, which can be used to establish culture strategies aimed at improving the in vitro environment and, consequently, pregnancy rates and offspring health.

•

Embryonic metaboloepigenetic reprogramming is stage- and species-specific

•

In vitro culture disrupts the in vivo embryonic metaboloepigenetic reprogramming

•

Metabolic genes and pathways are highly correlated with embryo methylome

Breath biomarkers of insulin resistance in pre-diabetic Hispanic adolescents with obesity

Insulin resistance (IR) affects a quarter of the world's adult population and is a major factor in the pathogenesis of cardio-metabolic disease. In this pilot study, we implemented a non-invasive breathomics approach, combined with random forest machine learning, to investigate metabolic markers from obese pre-diabetic Hispanic adolescents as indicators of abnormal metabolic regulation. Using the ReCIVA breathalyzer device for breath collection, we have identified a signature of 10 breath metabolites (breath-IR model), which correlates with Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) (R = 0.95, p < 0.001). A strong correlation was also observed between the breath-IR model and the blood glycemic profile (fasting insulin R = 0.91, p < 0.001 and fasting glucose R = 0.80, p < 0.001). Among tentatively identified metabolites, limonene, undecane, and 2,7-dimethyl-undecane, significantly cluster individuals based on HOMA-IR (p = 0.003, p = 0.002, and p < 0.001, respectively). Our breath-IR model differentiates between adolescents with and without IR with an AUC-ROC curve of 0.87, after cross-validation. Identification of a breath signature indicative of IR shows utility of exhaled breath metabolomics for assessing systemic metabolic dysregulation. A simple and non-invasive breath-based test has potential as a diagnostic tool for monitoring IR progression, allowing for earlier detection of IR and implementation of early interventions to prevent onset of type 2 diabetes mellitus.

Optimization of metabolomic data processing using NOREVA

A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.

Phloem exudate metabolic content reflects the response to water-deficit stress in pea plants (Pisum sativum L.)

Drought stress impacts the quality and yield of Pisum sativum. Here, we show how short periods of limited water availability during the vegetative stage of pea alters phloem sap content and how these changes are connected to strategies used by plants to cope with water deficit. We have investigated the metabolic content of phloem sap exudates and explored how this reflects P. sativum physiological and developmental responses to drought. Our data show that drought is accompanied by phloem-mediated redirection of the components that are necessary for cellular respiration and the proper maintenance of carbon/nitrogen balance during stress. The metabolic content of phloem sap reveals a shift from anabolic to catabolic processes as well as the developmental plasticity of P. sativum plants subjected to drought. Our study underlines the importance of phloem-mediated transport for plant adaptation to unfavourable environmental conditions. We also show that phloem exudate analysis can be used as a useful proxy to study stress responses in plants. We propose that the decrease in oleic acid content within phloem sap could be considered as a potential marker of early signalling events mediating drought response.

Characteristics of Dietary Fatty Acids Isolated from Historic Dental Calculus of the 17th- and 18th-Century Inhabitants of the Subcarpathian Region (Poland)

Dental calculus analysis can be a valuable source of archaeological knowledge, since it preserves not only microbial and host biomolecules but also dietary and environmental debris, as well as metabolic products likely originating from dietary and craft activities. Here we described GC-MS analysis of a set of historic dental calculus samples from the front teeth of the mandibles of seven individuals found in 17th- and 18th-century graves in the city of Rzeszow, located in South-eastern Poland. We have found that only saturated fatty acids, which are characteristic for fats of animal origin, were present in the tested samples. Our preliminary results indicate that the diet of modern-period inhabitants of Rzeszow was rich in animal products, such as meat and dairy products.

A Statistical Workflow to Evaluate the Modulation of Wine Metabolome and Its Contribution to the Sensory Attributes

A data-processing and statistical analysis workflow was proposed to evaluate the metabolic changes and its contribution to the sensory characteristics of different wines. This workflow was applied to rosé wines from different fermentation strategies. The metabolome was acquired by means of two high-throughput techniques: gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS) for volatile and non-volatile metabolites, respectively, in an untargeted approach, while the sensory evaluation of the wines was performed by a trained panel. Wine volatile and non-volatile metabolites modulation was independently evaluated by means of partial least squares discriminant analysis (PLS-DA), obtaining potential markers of the fermentation strategies. Then, the complete metabolome was integrated by means of sparse generalised canonical correlation analysis discriminant analysis (sGCC-DA). This integrative approach revealed a high link between the volatile and non-volatile data, and additional potential metabolite markers of the fermentation strategies were found. Subsequently, the evaluation of the contribution of metabolome to the sensory characteristics of wines was carried out. First, the all-relevant metabolites affected by the different fermentation processes were selected using PLS-DA and random forest (RF). Each set of volatile and non-volatile metabolites selected was then related to the sensory attributes of the wines by means of partial least squares regression (PLSR). Finally, the relationships among the three datasets were complementary evaluated using regularised generalised canonical correlation analysis (RGCCA), revealing new correlations among metabolites and sensory data.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Exploring the external exposome using wearable passive samplers - The China BAPE study

Environmental exposures are one of the greatest threats to human health, yet we lack tools to answer simple questions about our exposures: what are our personal exposure profiles and how do they change overtime (external exposome), how toxic are these chemicals, and what are the sources of these exposures? To capture variation in personal exposures to airborne chemicals in the gas and particulate phases and identify exposures which pose the greatest health risk, wearable exposure monitors can be deployed. In this study, we deployed passive air sampler wristbands with 84 healthy participants (aged 60-69 years) as part of the Biomarkers for Air Pollutants Exposure (China BAPE) study. Participants wore the wristband samplers for 3 days each month for five consecutive months. Passive samplers were analyzed using a novel gas chromatography high resolution mass spectrometry data-processing workflow to overcome the bottleneck of processing large datasets and improve confidence in the resulting identified features. The toxicity of chemicals observed frequently in personal exposures were predicted to identify exposures of potential concern via inhalation route or other routes of airborne contaminant exposure. Three exposures were highlighted based on elevated toxicity: dichlorvos from insecticides (mosquito/malaria control), naphthalene partly from mothballs, and 183 polyaromatic hydrocarbons from multiple sources. Other exposures explored in this study are linked to diet and personal care products, cigarette smoke, sunscreen, and antimicrobial soaps. We highlight the potential for this workflow employing wearable passive samplers for prioritizing chemicals of concern at both the community and individual level, and characterizing sources of exposures for follow up interventions.

Oviductal Extracellular Vesicles Improve Post-Thaw Sperm Function in Red Wolves and Cheetahs

Artificial insemination (AI) is a valuable tool for ex situ wildlife conservation, allowing the re-infusion and dissemination of genetic material, even after death of the donor. However, the application of AI to species conservation is still limited, due mainly to the poor survival of cryopreserved sperm. Recent work demonstrated that oviductal extracellular vesicles (oEVs) improved cat sperm motility and reduced premature acrosomal exocytosis. Here, we build on these findings by describing the protein content of dog and cat oEVs and investigating whether the incubation of cryopreserved red wolf and cheetah sperm with oEVs during thawing improves sperm function. Both red wolf and cheetah sperm thawed with dog and cat oEVs, respectively, had more intact acrosomes than the non-EV controls. Moreover, red wolf sperm thawed in the presence of dog oEVs better maintained sperm motility over time (>15%) though such an improvement was not observed in cheetah sperm. Our work demonstrates that dog and cat oEVs carry proteins important for sperm function and improve post-thaw motility and/or acrosome integrity of red wolf and cheetah sperm in vitro. The findings show how oEVs can be a valuable tool for improving the success of AI with cryopreserved sperm in threatened species.

Knowing Me, Knowing You: Anal Gland Secretion of European Badgers (Meles meles) Codes for Individuality, Sex and Social Group Membership

European badgers, Meles meles, are group-living in the UK, and demarcate their ranges with shared latrines. As carnivores, badgers possess paired anal glands, but olfactory information on the content of badger anal gland secretion (AGS) is largely uninvestigated. Here, we examined the volatile organic compounds (VOCs) of AGS samples from 57 free-living badgers using solid-phase microextraction (SPME) and gas chromatography—mass spectrometry. AGS was rich in alkanes (C7–C15, 14.3% of identified compounds), aldehydes (C5–C14, 9.7%), phenols (C6–C15, 9.5%), alcohols (C5–C10, 7.3%), aromatic hydrocarbons (C6–C13, 6.8%), ketones (C6–C13, 6.3%) and carboxylic acids (C3–C12, 5.6%) and contained a variety of esters, sulfurous and nitrogenous compounds, and ethers. The number of VOCs per profile ranged from 20 to 111 (mean = 65.4; ± 22.7 SD), but no compound was unique for any of the biological categories. After normalization of the raw data using Probabilistic Quotient Normalization, we produced a resemblance matrix by calculating the Euclidian distances between all sample pairs. PERMANOVA revealed that AGS composition differs between social groups, and concentration and complexity in terms of number of measurable VOCs varies between seasons and years. AGS VOC profiles encode individual identity, sex and vary with female reproductive state, indicating an important function in intraspecific communication. Because AGS is excreted together with fecal deposits, we conclude that chemical complexity of AGS enables particularly latrine-using species, such as badgers, to advertise more complex individual-specific information than in feces alone.