Machine learning-based health environmental-clinical risk scores in European children.

BACKGROUND: Early life environmental stressors play an important role in the development of multiple chronic disorders. Previous studies that used environmental risk scores (ERS) to assess the cumulative impact of environmental exposures on health are limited by the diversity of exposures included, especially for early life determinants. We used machine learning methods to build early life exposome risk scores for three health outcomes using environmental, molecular, and clinical data. METHODS: In this study, we analyzed data from 1622 mother-child pairs from the HELIX European birth cohorts, using over 300 environmental, 100 child peripheral, and 18 mother-child clinical markers to compute environmental-clinical risk scores (ECRS) for child behavioral difficulties, metabolic syndrome, and lung function. ECRS were computed using LASSO, Random Forest and XGBoost. XGBoost ECRS were selected to extract local feature contributions using Shapley values and derive feature importance and interactions. RESULTS: ECRS captured 13%, 50% and 4% of the variance in mental, cardiometabolic, and respiratory health, respectively. We observed no significant differences in predictive performances between the above-mentioned methods.The most important predictive features were maternal stress, noise, and lifestyle exposures for mental health; proteome (mainly IL1B) and metabolome features for cardiometabolic health; child BMI and urine metabolites for respiratory health. CONCLUSIONS: Besides their usefulness for epidemiological research, our risk scores show great potential to capture holistic individual level non-hereditary risk associations that can inform practitioners about actionable factors of high-risk children. As in the post-genetic era personalized prevention medicine will focus more and more on modifiable factors, we believe that such integrative approaches will be instrumental in shaping future healthcare paradigms.

Growing up in different environments can greatly affect children's health later in life. This research looked at how living in cities, being exposed to chemicals, and other experiences before birth and during childhood, work together to influence children's mental, cardiovascular and respiratory health. We used advanced computer programs to help us understand these effects and estimate health risk scores. These scores are simple numerical measures that help us quantify the likelihood of children developing health issues based on their environmental exposures. Using those scores, the study identified key factors impacting children's health, in particular psycho-social, perceived environmental and prenatal pollutant exposures for mental health. It also revealed complex patterns and interactions between environmental factors. The results highlighted the potential of such risk scores to support the identification of actionable factors in high-risk children, informing tailored prevention measures in healthcare.

A Metabolomics Assay to Diagnose Citrus Huanglongbing Disease and to Aid in Assessment of Treatments to Prevent or Cure Infection.

Citrus greening disease, or Huanglongbing (HLB), has devastated citrus crops globally in recent years. The causal bacterium, 'Candidatus Liberibacter asiaticus', presents a sampling issue for qPCR diagnostics and results in a high false negative rate. In this work, we compared six metabolomics assays to identify HLB-infected citrus trees from leaf tissue extracted from 30 control and 30 HLB-infected trees. A liquid chromatography-mass spectrometry-based assay was most accurate. A final partial least squares-discriminant analysis (PLS-DA) model was trained and validated on 690 leaf samples with corresponding qPCR measures from three citrus varieties (Rio Red grapefruit, Hamlin sweet orange, and Valencia sweet orange) from orchards in Florida and Texas. Trees were naturally infected with HLB transmitted by the insect vector Diaphorina citri. In a randomized validation set, the assay was 99.9% accurate to classify diseased from nondiseased samples. This model was applied to samples from trees receiving plant defense-inducer compounds or biological treatments to prevent or cure HLB infection. From two trials, HLB-related metabolite abundances and PLS-DA scores were tracked longitudinally and compared with those of control trees. We demonstrate how our assay can assess tree health and the efficacy of HLB treatments and conclude that no trialed treatment was efficacious.

Data-Driven Approach to Modeling Microfabricated Chemical Sensor Manufacturing.

We have developed a statistical model-based approach to the quality analysis (QA) and quality control (QC) of a gas micro pre-concentrator chip (µPC) performance when manufactured at scale for chemical and biochemical analysis of volatile organic compounds (VOCs). To test the proposed model, a medium-sized university-led production batch of 30 wafers of chips were subjected to rigorous chemical performance testing. We quantitatively report the outcomes of each manufacturing process step leading to the final functional chemical sensor chip. We implemented a principal component analysis (PCA) model to score individual chip chemical performance, and we observed that the first two principal components represent 74.28% of chemical testing variance with 111 of 118 viable chips falling into the 95% confidence interval. Chemical performance scores and chip manufacturing data were analyzed using a multivariate regression model to determine the most influential manufacturing parameters and steps. In our analysis, we find the amount of sorbent mass present in the chip (variable importance score = 2.6) and heater and the RTD resistance values (variable importance score = 1.1) to be the manufacturing parameters with the greatest impact on chemical performance. Other non-obvious latent manufacturing parameters also had quantified influence. Statistical distributions for each manufacturing step will allow future large-scale production runs to be statistically sampled during production to perform QA/QC in a real-time environment. We report this study as the first data-driven, model-based production of a microfabricated chemical sensor.

Characterization of the microbiome and volatile compounds in anal gland secretions from domestic cats (Felis catus) using metagenomics and metabolomics.

Many mammals rely on volatile organic chemical compounds (VOCs) produced by bacteria for their communication and behavior, though little is known about the exact molecular mechanisms or bacterial species that are responsible. We used metagenomic sequencing, mass-spectrometry based metabolomics, and culturing to profile the microbial and volatile chemical constituents of anal gland secretions in twenty-three domestic cats (Felis catus), in attempts to identify organisms potentially involved in host odor production. We found that the anal gland microbiome was dominated by bacteria in the genera Corynebacterium, Bacteroides, Proteus, Lactobacillus, and Streptococcus, and showed striking variation among individual cats. Microbiome profiles also varied with host age and obesity. Metabolites such as fatty-acids, ketones, aldehydes and alcohols were detected in glandular secretions. Overall, microbiome and metabolome profiles were modestly correlated (r = 0.17), indicating that a relationship exists between the bacteria in the gland and the metabolites produced in the gland. Functional analyses revealed the presence of genes predicted to code for enzymes involved in VOC metabolism such as dehydrogenases, reductases, and decarboxylases. From metagenomic data, we generated 85 high-quality metagenome assembled genomes (MAGs). Of importance were four MAGs classified as Corynebacterium frankenforstense, Proteus mirabilis, Lactobacillus johnsonii, and Bacteroides fragilis. They represent strong candidates for further investigation of the mechanisms of volatile synthesis and scent production in the mammalian anal gland.

IκB kinase-α coordinates BRD4 and JAK/STAT signaling to subvert DNA damage-based anticancer therapy.

Activation of the IκB kinase (IKK) complex has recurrently been linked to colorectal cancer (CRC) initiation and progression. However, identification of downstream effectors other than NF-κB has remained elusive. Here, analysis of IKK-dependent substrates in CRC cells after UV treatment revealed that phosphorylation of BRD4 by IKK-α is required for its chromatin-binding at target genes upon DNA damage. Moreover, IKK-α induces the NF-κB-dependent transcription of the cytokine LIF, leading to STAT3 activation, association with BRD4 and recruitment to specific target genes. IKK-α abrogation results in defective BRD4 and STAT3 functions and consequently irreparable DNA damage and apoptotic cell death upon different stimuli. Simultaneous inhibition of BRAF-dependent IKK-α activity, BRD4, and the JAK/STAT pathway enhanced the therapeutic potential of 5-fluorouracil combined with irinotecan in CRC cells and is curative in a chemotherapy-resistant xenograft model. Finally, coordinated expression of LIF and IKK-α is a poor prognosis marker for CRC patients. Our data uncover a functional link between IKK-α, BRD4, and JAK/STAT signaling with clinical relevance.

Molecular signature associated with cladribine treatment in patients with multiple sclerosis.

Introduction: Little is known about the molecular profiling associated with the effect of cladribine in patients with multiple sclerosis (MS). Here, we aimed first to characterize the transcriptomic and proteomic profiles induced by cladribine in blood cells, and second to identify potential treatment response biomarkers to cladribine in patients with MS. Methods: Gene, protein and microRNA (miRNA) expression profiles were determined by microarrays (genes, miRNAs) and mass spectrometry (proteins) in peripheral blood mononuclear cells (PBMCs) from MS patients after in vitro treatment with cladribine in its active and inactive forms. Two bioinformatics approaches to integrate the three obtained datasets were applied: (i) a multiomics discriminant analysis (DIABLO - Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies); and (ii) a multi-stage integration of features selected in differential expression analysis on each dataset and then merged. Selected molecules from the in vitro study were quantified by qPCR ex vivo in PBMCs from MS patients receiving cladribine. Results: PBMCs treated in vitro with cladribine were characterized by a major downregulation of gene, protein, and miRNA expression compared with the untreated cells. An intermediate pattern between the cladribine-treated and untreated conditions was observed in PBMCs treated with cladribine in its inactive form. The differential expression analysis of each dataset led to the identification of four genes and their encoded proteins, and twenty-two miRNAs regulating their expression, that were associated with cladribine treatment. Two of these genes (PPIF and NHLRC2), and three miRNAs (miR-21-5p, miR-30b-5p, and miR-30e-5p) were validated ex vivo in MS patients treated with cladribine. Discussion: By using a combination of omics data and bioinformatics approaches we were able to identify a multiomics molecular profile induced by cladribine in vitro in PBMCs. We also identified a number of biomarkers that were validated ex vivo in PBMCs from patients with MS treated with cladribine that have the potential to become treatment response biomarkers to this drug.

Peptidomics Methods Applied to the Study of Flower Development.

Understanding the global and dynamic nature of plant developmental processes requires not only the study of the transcriptome, but also of the proteome, including its largely uncharacterized peptidome fraction. Recent advances in proteomics and high-throughput analyses of translating RNAs (ribosome profiling) have begun to address this issue, evidencing the existence of novel, uncharacterized, and possibly functional peptides. To validate the accumulation in tissues of sORF-encoded polypeptides (SEPs), the basic setup of proteomic analyses (i.e., LC-MS/MS) can be followed. However, the detection of peptides that are small (up to ~100 aa, 6-7 kDa) and novel (i.e., not annotated in reference databases) presents specific challenges that need to be addressed both experimentally and with computational biology resources. Several methods have been developed in recent years to isolate and identify peptides from plant tissues. In this chapter, we outline two different peptide extraction protocols and the subsequent peptide identification by mass spectrometry using the database search or the de novo identification methods.

Thymine DNA glycosylase regulates cell-cycle-driven p53 transcriptional control in pluripotent cells.

Cell cycle progression is linked to transcriptome dynamics and variations in the response of pluripotent cells to differentiation cues, mostly through unknown determinants. Here, we characterized the cell-cycle-associated transcriptome and proteome of mouse embryonic stem cells (mESCs) in naive ground state. We found that the thymine DNA glycosylase (TDG) is a cell-cycle-regulated co-factor of the tumor suppressor p53. Furthermore, TDG and p53 co-bind ESC-specific cis-regulatory elements and thereby control transcription of p53-dependent genes during self-renewal. We determined that the dynamic expression of TDG is required to promote the cell-cycle-associated transcriptional heterogeneity. Moreover, we demonstrated that transient depletion of TDG influences cell fate decisions during the early differentiation of mESCs. Our findings reveal an unanticipated role of TDG in promoting molecular heterogeneity during the cell cycle and highlight the central role of protein dynamics for the temporal control of cell fate during development.

Oxylipin concentration shift in exhaled breath condensate (EBC) of SARS-CoV-2 infected patients.

Infection of airway epithelial cells with severe acute respiratory coronavirus 2 (SARS-CoV-2) can lead to severe respiratory tract damage and lung injury with hypoxia. It is challenging to sample the lower airways non-invasively and the capability to identify a highly representative specimen that can be collected in a non-invasive way would provide opportunities to investigate metabolomic consequences of COVID-19 disease. In the present study, we performed a targeted metabolomic approach using liquid chromatography coupled with high resolution chromatography (LC-MS) on exhaled breath condensate (EBC) collected from hospitalized COVID-19 patients (COVID+) and negative controls, both non-hospitalized and hospitalized for other reasons (COVID-). We were able to noninvasively identify and quantify inflammatory oxylipin shifts and dysregulation that may ultimately be used to monitor COVID-19 disease progression or severity and response to therapy. We also expected EBC-based biochemical oxylipin changes associated with COVID-19 host response to infection. The results indicated ten targeted oxylipins showing significative differences between SAR-CoV-2 infected EBC samples and negative control subjects. These compounds were prostaglandins A2 and D2, LXA4, 5-HETE, 12-HETE, 15-HETE, 5-HEPE, 9-HODE, 13-oxoODE and 19(20)-EpDPA, which are associated with specific pathways (i.e. P450, COX, 15-LOX) related to inflammatory and oxidative stress processes. Moreover, all these compounds were up-regulated by COVID+, meaning their concentrations were higher in subjects with SAR-CoV-2 infection. Given that many COVID-19 symptoms are inflammatory in nature, this is interesting insight into the pathophysiology of the disease. Breath monitoring of these and other EBC metabolites presents an interesting opportunity to monitor key indicators of disease progression and severity.

Associations of four biological age markers with child development: A multi-omic analysis in the European HELIX cohort.

Background: While biological age in adults is often understood as representing general health and resilience, the conceptual interpretation of accelerated biological age in children and its relationship to development remains unclear. We aimed to clarify the relationship of accelerated biological age, assessed through two established biological age indicators, telomere length and DNA methylation age, and two novel candidate biological age indicators, to child developmental outcomes, including growth and adiposity, cognition, behavior, lung function and the onset of puberty, among European school-age children participating in the HELIX exposome cohort. Methods: The study population included up to 1173 children, aged between 5 and 12 years, from study centres in the UK, France, Spain, Norway, Lithuania, and Greece. Telomere length was measured through qPCR, blood DNA methylation, and gene expression was measured using microarray, and proteins and metabolites were measured by a range of targeted assays. DNA methylation age was assessed using Horvath's skin and blood clock, while novel blood transcriptome and 'immunometabolic' (based on plasma proteins and urinary and serum metabolites) clocks were derived and tested in a subset of children assessed six months after the main follow-up visit. Associations between biological age indicators with child developmental measures as well as health risk factors were estimated using linear regression, adjusted for chronological age, sex, ethnicity, and study centre. The clock derived markers were expressed as Δ age (i.e. predicted minus chronological age). Results: Transcriptome and immunometabolic clocks predicted chronological age well in the test set (r=0.93 and r=0.84 respectively). Generally, weak correlations were observed, after adjustment for chronological age, between the biological age indicators.Among associations with health risk factors, higher birthweight was associated with greater immunometabolic Δ age, smoke exposure with greater DNA methylation Δ age, and high family affluence with longer telomere length.Among associations with child developmental measures, all biological age markers were associated with greater BMI and fat mass, and all markers except telomere length were associated with greater height, at least at nominal significance (p<0.05). Immunometabolic Δ age was associated with better working memory (p=4 e-3) and reduced inattentiveness (p=4 e-4), while DNA methylation Δ age was associated with greater inattentiveness (p=0.03) and poorer externalizing behaviors (p=0.01). Shorter telomere length was also associated with poorer externalizing behaviors (p=0.03). Conclusions: In children, as in adults, biological aging appears to be a multi-faceted process and adiposity is an important correlate of accelerated biological aging. Patterns of associations suggested that accelerated immunometabolic age may be beneficial for some aspects of child development while accelerated DNA methylation age and telomere attrition may reflect early detrimental aspects of biological aging, apparent even in children. Funding: UK Research and Innovation (MR/S03532X/1); European Commission (grant agreement numbers: 308333; 874583).

Although age is generally measured by the number of years since birth, many factors contribute to the rate at which a person physically ages. In adults, linking these measurements to age gives a measure of overall health and resilience. This 'biological age' offers a better prediction of remaining life and disease risk than the number of years lived. Multiple factors can be used to calculate biological age, such as measuring the length of telomeres ­ protective caps on the end of chromosomes ­ which shorten as people age. The rate at which they shorten can give an indication of how quickly someone is ageing. Researchers can also study epigenetic factors: these mechanisms lead to certain genes being switched on or off, and they can be combined into a 'epigenetic clock' to assess biological age. However, compared with adults, the relationship between biological age and child health and developmental maturity is less well understood. Robinson et al. studied 1,173 school-aged children from six European countries, measuring telomere length, epigenetic factors and other biological indicators related to metabolism and the immune system. The relationships between these factors and an array of child developmental measures such as height, weight, behaviour and the age of onset of puberty were established. The findings showed that biological age indicators are only weakly linked to each other in children. Despite this, biological age was related to greater amount of body fat across all tested indicators ­ which is also associated with biological age in adults and is an important determinant of lifespan. Among several observed effects on development, analysis found that shorter telomere length and older epigenetic age were associated with greater behavioural problems, suggesting they may be detrimental to child development. On the other hand, a greater age due to metabolic and immune related changes was associated with greater cognitive and behavioural maturity. Environmental factors were also linked to biological ageing, with children exposed to smoking in their homes or while their mother was pregnant displaying an older epigenetic age. Robinson et al. showed that biological ageing in children is multifaceted and can have both beneficial and harmful impacts on development. This knowledge is important for identifying early life risk factors that might influence healthy ageing in later life. Future work will help researchers to understand these complex interactions and the long-term consequences for health and well-being.

Active sampling of volatile chemicals for non-invasive classification of chicken eggs by sex early in incubation.

According to industry estimates, approximately 7 billion day-old male chicks are disposed of annually worldwide because they are not of use to the layer industry. A practical process to identify the sex of the egg early in incubation without penetrating the egg would improve animal welfare, reduce food waste and mitigate environmental impact. We implemented a moderate vacuum pressure system through commercial egg-handling suction cups to collect volatile organic compounds (VOCs). Three separate experiments were set up to determine optimal conditions to collect eggs VOCs to discriminate male from female embryos. Optimal extraction time (2 min), storage conditions (short period of incubation during egg storage (SPIDES) at days 8-10 of incubation), and sampling temperature (37.5°C) were determined. Our VOC-based method could correctly differentiate male from female embryos with more than 80% accuracy. These specifications are compatible with the design of specialized automation equipment capable of high-throughput, in-ovo sexing based on chemical sensor microchips.

Characterization of the microbiome and volatile compounds in anal gland secretions from domestic cats (Felis catus) using metagenomics and metabolomics.

Animals rely on volatile chemical compounds for their communication and behavior. Many of these compounds are sequestered in endocrine and exocrine glands and are synthesized by anaerobic microbes. While the volatile organic compound (VOC) or microbiome composition of glandular secretions has been investigated in several mammalian species, few have linked specific bacterial taxa to the production of volatiles or to specific microbial gene pathways. Here, we use metagenomic sequencing, mass-spectrometry based metabolomics, and culturing to profile the microbial and volatile chemical constituents of anal gland secretions in twenty-three domestic cats (Felis catus), in attempts to identify organisms potentially involved in host odor production. We found that the anal gland microbiome was dominated by bacteria in the genera Corynebacterium, Bacteroides, Proteus, Lactobacillus, and Streptococcus, and showed striking variation among individual cats. Microbiome profiles also varied with host age and obesity. Metabolites such as fatty-acids, ketones, aldehydes and alcohols were detected in glandular secretions. Overall, microbiome and metabolome profiles were modestly correlated (r=0.17), indicating that a relationship exists between the bacteria in the gland and the metabolites produced in the gland. Functional analyses revealed the presence of genes predicted to code for enzymes involved in VOC metabolism such as dehydrogenases, reductases, and decarboxylases. From metagenomic data, we generated 85 high-quality metagenome assembled genomes (MAGs). Of these, four were inferred to have high relative abundance in metagenome profiles and had close relatives that were recovered as cultured isolates. These four MAGs were classified as Corynebacterium frankenforstense, Proteus mirabilis, Lactobacillus johnsonii, and Bacteroides fragilis. They represent strong candidates for further investigation of the mechanisms of volatile synthesis and scent production in the mammalian anal gland.

Exhaled breath condensate profiles of U.S. Navy divers following prolonged hyperbaric oxygen (HBO) and nitrogen-oxygen (Nitrox) chamber exposures.

Prolonged exposure to hyperbaric hyperoxia can lead to pulmonary oxygen toxicity (PO2tox). PO2tox is a mission limiting factor for special operations forces divers using closed-circuit rebreathing apparatus and a potential side effect for patients undergoing hyperbaric oxygen (HBO) treatment. In this study, we aim to determine if there is a specific breath profile of compounds in exhaled breath condensate (EBC) that is indicative of the early stages of pulmonary hyperoxic stress/PO2tox. Using a double-blind, randomized 'sham' controlled, cross-over design 14 U.S. Navy trained diver volunteers breathed two different gas mixtures at an ambient pressure of 2 ATA (33 fsw, 10 msw) for 6.5 h. One test gas consisted of 100% O2(HBO) and the other was a gas mixture containing 30.6% O2with the balance N2(Nitrox). The high O2stress dive (HBO) and low O2stress dive (Nitrox) were separated by at least seven days and were conducted dry and at rest inside a hyperbaric chamber. EBC samples were taken immediately before and after each dive and subsequently underwent a targeted and untargeted metabolomics analysis using liquid chromatography coupled to mass spectrometry (LC-MS). Following the HBO dive, 10 out of 14 subjects reported symptoms of the early stages of PO2tox and one subject terminated the dive early due to severe symptoms of PO2tox. No symptoms of PO2tox were reported following the nitrox dive. A partial least-squares discriminant analysis of the normalized (relative to pre-dive) untargeted data gave good classification abilities between the HBO and nitrox EBC with an AUC of 0.99 (±2%) and sensitivity and specificity of 0.93 (±10%) and 0.94 (±10%), respectively. The resulting classifications identified specific biomarkers that included human metabolites and lipids and their derivatives from different metabolic pathways that may explain metabolomic changes resulting from prolonged HBO exposure.

Effectiveness of a Meningococcal Group B Vaccine (4CMenB) in Children.

BACKGROUND: In September 2015, the four-component, protein-based meningococcal serogroup B vaccine (4CMenB; Bexsero) became available for private purchase in Spain. METHODS: We conducted a nationwide matched case-control study to assess the effectiveness of 4CMenB in preventing invasive meningococcal disease in children. The study included all laboratory-confirmed cases of invasive meningococcal disease in children younger than 60 months of age between October 5, 2015, and October 6, 2019, in Spain. Each case patient was matched with four controls according to date of birth and province. 4CMenB vaccination status of the case patients and controls was compared with the use of multivariate conditional logistic regression. RESULTS: We compared 306 case patients (243 [79.4%] with serogroup B disease) with 1224 controls. A total of 35 case patients (11.4%) and 298 controls (24.3%) had received at least one dose of 4CMenB. The effectiveness of complete vaccination with 4CMenB (defined as receipt of at least 2 doses, administered in accordance with the manufacturer's recommendations) was 76% (95% confidence interval [CI], 57 to 87) against invasive meningococcal disease caused by any serogroup, and partial vaccination was 54% (95% CI, 18 to 74) effective. Complete vaccination resulted in an effectiveness of 71% (95% CI, 45 to 85) against meningococcal serogroup B disease. Vaccine effectiveness with at least one dose of 4CMenB was 64% (95% CI, 41 to 78) against serogroup B disease and 82% (95% CI, 21 to 96) against non-serogroup B disease. With the use of the genetic Meningococcal Antigen Typing System, serogroup B strains that were expected to be covered by 4CMenB were detected in 44 case patients, none of whom had been vaccinated. CONCLUSIONS: Complete vaccination with 4CMenB was found to be effective in preventing invasive disease by serogroup B and non-serogroup B meningococci in children younger than 5 years of age.

Controlled air exchange rate method to evaluate reduction of volatile organic compounds by indoor air cleaners.

Air cleaning technologies are needed to reduce indoor concentrations and exposure to volatile organic compounds (VOCs). Currently, air cleaning technologies lack an accepted test standard to evaluate their VOC removal performance. A protocol to evaluate the VOC removal performance of air cleaning devices was developed and piloted with two devices. This method injects a VOC mixture and carbon dioxide into a test chamber, supplies outdoor air at a standard building ventilation rate, periodically measures the VOC concentrations in the chamber using solid phase microextraction-gas chromatography-mass spectrometry over a 3-h decay period, and compares the decay rate of VOCs to carbon dioxide to measure the VOC removal air cleaning performance. The method was demonstrated with both a hydroxyl radical generator and an activated carbon air cleaner. It was shown that the activated carbon air cleaner device tested had a clean air delivery rate an order of magnitude greater than the hydroxyl radical generator device (72.10 vs 6.32 m3/h).

Non-destructive method to classify walnut kernel freshness from volatile organic compound (VOC) emissions using gas chromatography-differential mobility spectrometry (GC-DMS) and machine learning analysis.

Analysis of volatile organic compounds (VOCs) can be an effective strategy to inspect the quality of horticultural commodities and following their degradation. In this work, we report that VOCs emitted by walnuts can be studied using gas chromatography-differential mobility spectrometry (GC-DMS), and those GC-DMS data can be analyzed to predict the rancidity of walnuts, i.e., classify walnuts into grades of freshness. Walnut kernels were assigned a class n depending on their level of freshness as determined by a peroxide assay. VOC samples were analyzed using GC-DMS. From these VOC data, a partial least square regression (PLSR) model provided a freshness prediction value m, which corresponded to the rancid class n when m=n±0.5. The PLSR model had an accuracy of 80% to predict walnut grade and demonstrated a minimal root mean squared error of 0.42 for the m response variables (representative of walnut grade) with the GC-DMS data. We also conducted gas chromatography-mass spectrometry (GC-MS) experiments to identify volatiles that emerged or were enhanced with more rancid walnuts. The findings of the GC-MS study of walnut VOCs align excellently with the GC-DMS study. Based on our results, we conclude that a GC-DMS device deployed with a pre-trained machine learning model can be a very effective device for classifying walnut grades in the industry.

A combination of molecular and clinical parameters provides a new strategy for high-grade serous ovarian cancer patient management.

BACKGROUND: High-grade serous carcinoma (HGSC) is the most common and deadly subtype of ovarian cancer. Although most patients will initially respond to first-line treatment with a combination of surgery and platinum-based chemotherapy, up to a quarter will be resistant to treatment. We aimed to identify a new strategy to improve HGSC patient management at the time of cancer diagnosis (HGSC-1LTR). METHODS: A total of 109 ready-available formalin-fixed paraffin-embedded HGSC tissues obtained at the time of HGSC diagnosis were selected for proteomic analysis. Clinical data, treatment approach and outcomes were collected for all patients. An initial discovery cohort (n = 21) were divided into chemoresistant and chemosensitive groups and evaluated using discovery mass-spectrometry (MS)-based proteomics. Proteins showing differential abundance between groups were verified in a verification cohort (n = 88) using targeted MS-based proteomics. A logistic regression model was used to select those proteins able to correctly classify patients into chemoresistant and chemosensitive. The classification performance of the protein and clinical data combinations were assessed through the generation of receiver operating characteristic (ROC) curves. RESULTS: Using the HGSC-1LTR strategy we have identified a molecular signature (TKT, LAMC1 and FUCO) that combined with ready available clinical data (patients' age, menopausal status, serum CA125 levels, and treatment approach) is able to predict patient response to first-line treatment with an AUC: 0.82 (95% CI 0.72-0.92). CONCLUSIONS: We have established a new strategy that combines molecular and clinical parameters to predict the response to first-line treatment in HGSC patients (HGSC-1LTR). This strategy can allow the identification of chemoresistance at the time of diagnosis providing the optimization of therapeutic decision making and the evaluation of alternative treatment strategies. Thus, advancing towards the improvement of patient outcome and the individualization of HGSC patients' care.

Predominant SARS-CoV-2 variant impacts accuracy when screening for infection using exhaled breath vapor.

BACKGROUND: New technologies with novel and ambitious approaches are being developed to diagnose or screen for SARS-CoV-2, including breath tests. The US FDA approved the first breath test for COVID-19 under emergency use authorization in April 2022. Most breath-based assays measure volatile metabolites exhaled by persons to identify a host response to infection. We hypothesized that the breathprint of COVID-19 fluctuated after Omicron became the primary variant of transmission over the Delta variant. METHODS: We collected breath samples from 142 persons with and without a confirmed COVID-19 infection during the Delta and Omicron waves. Breath samples were analyzed by gas chromatography-mass spectrometry. RESULTS: Here we show that based on 63 exhaled compounds, a general COVID-19 model had an accuracy of 0.73 ± 0.06, which improved to 0.82 ± 0.12 when modeling only the Delta wave, and 0.84 ± 0.06 for the Omicron wave. The specificity improved for the Delta and Omicron models (0.79 ± 0.21 and 0.74 ± 0.12, respectively) relative to the general model (0.61 ± 0.13). CONCLUSIONS: We report that the volatile signature of COVID-19 in breath differs between the Delta-predominant and Omicron-predominant variant waves, and accuracies improve when samples from these waves are modeled separately rather than as one universal approach. Our findings have important implications for groups developing breath-based assays for COVID-19 and other respiratory pathogens, as the host response to infection may significantly differ depending on variants or subtypes.

In recent decades, scientists have found we exhale thousands of compounds that reveal much about our health, including whether we are sick with COVID-19. Our team asked whether the breath profile of someone infected with the Delta variant of COVID-19 would match the breath profile caused by the Omicron variant­a version of the virus that is more transmissible. We analyzed breath samples from 142 people, some sick with either the Delta or Omicron variant of COVID-19, and others who were negative for COVID-19. Our results indicate that the Delta variant altered the contents of our breath in a different way than the Omicron variant, and breath-based tests improved when optimized to detect only one of the variants. These findings might impact the design of future breath-based tests for COVID-19.

Multi-omics signatures of the human early life exposome.

Environmental exposures during early life play a critical role in life-course health, yet the molecular phenotypes underlying environmental effects on health are poorly understood. In the Human Early Life Exposome (HELIX) project, a multi-centre cohort of 1301 mother-child pairs, we associate individual exposomes consisting of >100 chemical, outdoor, social and lifestyle exposures assessed in pregnancy and childhood, with multi-omics profiles (methylome, transcriptome, proteins and metabolites) in childhood. We identify 1170 associations, 249 in pregnancy and 921 in childhood, which reveal potential biological responses and sources of exposure. Pregnancy exposures, including maternal smoking, cadmium and molybdenum, are predominantly associated with child DNA methylation changes. In contrast, childhood exposures are associated with features across all omics layers, most frequently the serum metabolome, revealing signatures for diet, toxic chemical compounds, essential trace elements, and weather conditions, among others. Our comprehensive and unique resource of all associations ( https://helixomics.isglobal.org/ ) will serve to guide future investigation into the biological imprints of the early life exposome.

Mass Spectrometry Identification of Biomarkers in Extracellular Vesicles From Plasmodium vivax Liver Hypnozoite Infections.

Latent liver stages termed hypnozoites cause relapsing Plasmodium vivax malaria infection and represent a major obstacle in the goal of malaria elimination. Hypnozoites are clinically undetectable, and presently, there are no biomarkers of this persistent parasite reservoir in the human liver. Here, we have identified parasite and human proteins associated with extracellular vesicles (EVs) secreted from in vivo infections exclusively containing hypnozoites. We used P. vivax-infected human liver-chimeric (huHEP) FRG KO mice treated with the schizonticidal experimental drug MMV048 as hypnozoite infection model. Immunofluorescence-based quantification of P. vivax liver forms showed that MMV048 removed schizonts from chimeric mice livers. Proteomic analysis of EVs derived from FRG huHEP mice showed that human EV cargo from infected FRG huHEP mice contain inflammation markers associated with active schizont replication and identified 66 P. vivax proteins. To identify hypnozoite-specific proteins associated with EVs, we mined the proteome data from MMV048-treated mice and performed an analysis involving intragroup and intergroup comparisons across all experimental conditions followed by a peptide compatibility analysis with predicted spectra to warrant robust identification. Only one protein fulfilled this stringent top-down selection, a putative filamin domain-containing protein. This study sets the stage to unveil biological features of human liver infections and identify biomarkers of hypnozoite infection associated with EVs.