Partial Least Squares Discriminant Analysis and Bayesian Networks for Metabolomic Prediction of Childhood Asthma

Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study

Introduction

The fields of allergy and immunology are increasingly recognizing the transformative potential of artificial intelligence (AI). Its adoption is reshaping research directions, clinical practices, and healthcare systems. However, a systematic overview identifying current statuses, emerging trends, and future research hotspots is lacking.

Methods

This study applied bibliometric analysis methods to systematically evaluate the global research landscape of AI applications in allergy and immunology. Data from 3,883 articles published by 21,552 authors across 1,247 journals were collected and analyzed to identify leading contributors, prevalent research themes, and collaboration patterns.

Results

Analysis revealed that the USA and China are currently leading in research output and scientific impact in this domain. AI methodologies, especially machine learning (ML) and deep learning (DL), are predominantly applied in drug discovery and development, disease classification and prediction, immune response modeling, clinical decision support, diagnostics, healthcare system digitalization, and medical education. Emerging trends indicate significant movement toward personalized medical systems integration.

Discussion

The findings demonstrate the dynamic evolution of AI in allergy and immunology, highlighting the broadening scope from basic diagnostics to comprehensive personalized healthcare systems. Despite advancements, critical challenges persist, including technological limitations, ethical concerns, and regulatory frameworks that could potentially hinder further implementation and integration.

Conclusion

AI holds considerable promise for advancing allergy and immunology globally by enhancing healthcare precision, efficiency, and accessibility. Addressing existing technological, ethical, and regulatory challenges will be crucial to fully realizing its potential, ultimately improving global health outcomes and patient well-being.

Artificial intelligence in pediatric allergy research

Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous diseases, can co-exist in their development, and manifest complex associations with other disorders and environmental and hereditary factors. Elucidating these intricacies by identifying clinically distinguishable groups and actionable risk factors will allow for better understanding of the diseases, which will enhance clinical management and benefit society and affected individuals and families. Artificial intelligence (AI) is a promising tool in this context, enabling discovery of meaningful patterns in complex data. Numerous studies within pediatric allergy have and continue to use AI, primarily to characterize disease endotypes/phenotypes and to develop models to predict future disease outcomes. However, most implementations have used relatively simplistic data from one source, such as questionnaires. In addition, methodological approaches and reporting are lacking. This review provides a practical hands-on guide for conducting AI-based studies in pediatric allergy, including (1) an introduction to essential AI concepts and techniques, (2) a blueprint for structuring analysis pipelines (from selection of variables to interpretation of results), and (3) an overview of common pitfalls and remedies. Furthermore, the state-of-the art in the implementation of AI in pediatric allergy research, as well as implications and future perspectives are discussed.

CONCLUSION

AI-based solutions will undoubtedly transform pediatric allergy research, as showcased by promising findings and innovative technical solutions, but to fully harness the potential, methodologically robust implementation of more advanced techniques on richer data will be needed.

WHAT IS KNOWN

• Pediatric allergies are heterogeneous and common, inflicting substantial morbidity and societal costs. • The field of artificial intelligence is undergoing rapid development, with increasing implementation in various fields of medicine and research.

WHAT IS NEW

• Promising applications of AI in pediatric allergy have been reported, but implementation largely lags behind other fields, particularly in regard to use of advanced algorithms and non-tabular data. Furthermore, lacking reporting on computational approaches hampers evidence synthesis and critical appraisal. • Multi-center collaborations with multi-omics and rich unstructured data as well as utilization of deep learning algorithms are lacking and will likely provide the most impactful discoveries.

SFMBT2 regulates plumage color via serum metabolites in Chinese Anyi tile-like gray chickens

Plumage color is an important characteristic of chicken breeds, and molecular genetic research is significant for resource conservation and product quality control. Anyi tile-like gray chicken is a high-quality local chicken breed resource generated through long-term natural selection and artificial breeding in China. However, the molecular mechanisms underlying plumage color formation in Anyi tile-like gray chickens remain unclear. In this study, nontargeted liquid chromatography and tandem mass spectrometry (LC-MS/MS) was performed to identify serum metabolites associated with plumage color in 93 Anyi tile-like gray chickens, including 60 tile-like gray and 33 black chickens. Notably, 12 serum metabolites were significantly enriched in Anyi tile-like gray chickens, including deoxyuridine and inosine, which were the key biomarkers distinguishing tile-like gray chickens from black chickens. Additionally, nine serum metabolites were significantly enriched in black chickens. Moreover, we identified 225 significant SNPs (P < 9.71 × 10-8) on chromosomes 1, 2, 3, 4, 11, 15, and 21 that were associated with deoxyuridine, inosine, 3-hydroxybenzoic acid, and L-methionine S-oxide through metabolome genome-wide association studies (mGWAS). Importantly, chromosome 1 harbored a region, 172.79-kb, which was the most likely quantitative trait locus (QTL) interval. RNA sequencing (RNA-seq) and quantitative reverse transcription polymerase chain reaction (qRT-PCR) showed that SFMBT2 was the only differentially expressed gene in the QTL interval, and its expression was correlated with the abundance of specific serum metabolites. Conclusively, SFMBT2-mediated changes in serum metabolites contribute to plumage color development in Anyi tile-like gray chicken. This study provides important insights into the interaction between serum metabolites and host genes, and offers a theoretical basis for the breeding of Anyi tile-like gray chickens.

Inflammatory factors and risk of lung adenocarcinoma: a Mendelian randomization study mediated by blood metabolites

Background

Lung adenocarcinoma (LUAD) is the most common type of lung cancer, and its pathogenesis remains not fully elucidated. Inflammation and metabolic dysregulation are considered to play crucial roles in LUAD development, but their causal relationships and specific mechanisms remain unclear.

Methods

This study employed a two-sample Mendelian randomization (MR) approach to systematically evaluate the causal associations between 91 circulating inflammatory factors, 1,400 serum metabolites, and LUAD. We utilized LUAD genome-wide association studies (GWAS) data from the FinnGen biobank and GWAS data of metabolites and inflammatory factors from the GWAS catalog to conduct two-sample MR analyses. For the identified key metabolites, we further used mediator MR to investigate their mediating effects in the influence of IL-17A on LUAD and explored potential mechanisms through protein-protein interaction and functional enrichment analyses.

Results

The MR analyses revealed that IL-17A (OR 0.78, 95%CI 0.62-0.99) was negatively associated with LUAD, while 71 metabolites were significantly associated with LUAD. Among them, ferulic acid 4-sulfate may play a crucial mediating role in the suppression of LUAD by IL-17A (OR 0.87, 95%CI 0.78-0.97). IL-17A may exert its anti-LUAD effects through extensive interactions with genes related to ferulic acid 4-sulfate metabolism (such as SULT1A1, CYP1A1, etc.), inhibiting oxidative stress and inflammatory responses, as well as downstream tumor-related pathways of ferulic acid 4-sulfate (such as MAPK, NF-κB, etc.).

Conclusion

This study discovered causal associations between IL-17A, multiple serum metabolites, and LUAD occurrence, revealing the key role of inflammatory and metabolic dysregulation in LUAD pathogenesis. Our findings provide new evidence-based medical support for specific inflammatory factors and metabolites as early predictive and risk assessment biomarkers for LUAD, offering important clues for subsequent mechanistic studies and precision medicine applications.

Unique urine and serum metabolomic signature in patients with excessive alcohol use: An exploratory study

BACKGROUND

Excessive alcohol consumption has a multifaceted impact on the body's metabolic pathways and organ systems. The objectives of this study were to characterize global metabolomic changes and identify specific pathways that are altered in individuals with excessive alcohol use.

METHODS

This exploratory study included 22 healthy controls with no known history of excessive alcohol use and 38 patients identified as using alcohol excessively. A Fibrosis-4 score was used to determine the risk of underlying alcohol-associated liver disease among the excessive drinkers.

RESULTS

We found significantly altered urinary and serum metabolites among excessive drinkers, affecting various metabolic pathways including the metabolism of lipids, amino acids and peptides, cofactors and vitamins, carbohydrates, and nucleotides. Levels of two steroid hormones-5alpha-androstan-3beta,17beta-diol disulfate and androstenediol (3beta,17beta) disulfate-were significantly higher in both the serum and urine samples of excessive drinkers. These elevated levels may be associated with a higher risk of liver fibrosis in individuals with excessive alcohol use.

CONCLUSION

Alcohol consumption leads to marked alterations in multiple metabolic pathways, highlighting the systemic impact of alcohol on various tissues and organ systems. These findings provide a foundation for future mechanistic studies aimed at elucidating alcohol-induced changes in these metabolic pathways and their implications.

Bacteroidales reduces growth rate through serum metabolites and cytokines in Chinese Ningdu yellow chickens

Increasing evidence has indicated that the gut microbiome plays an important role in chicken growth traits. However, the cecal microbial taxa associated with the growth rates of the Chinese Ningdu yellow chickens are unknown. In this study, shotgun metagenomic sequencing was used to identify cecal bacterial species associated with the growth rate of the Chinese Ningdu yellow chickens. We found that nine cecal bacterial species differed significantly between high and low growth rate chickens, including three species (Succinatimonas hippei, Phocaeicola massiliensis, and Parabacteroides sp. ZJ-118) that were significantly enriched in high growth rate chickens. We identified six Bacteroidales that were significantly enriched in low growth rate chickens, including Barnesiella sp. An22, Barnesiella sp. ET7, and Bacteroidales bacterium which were key biomarkers in differentiating high and low growth rate chickens and were associated with alterations in the functional taxa of the cecal microbiome. Untargeted serum metabolome analysis revealed that 8 metabolites showing distinct enrichment patterns between high and low growth rate chickens, including triacetate lactone and N-acetyl-a-neuraminic acid, which were at higher concentrations in low growth rate chickens and were positively and significantly correlated with Barnesiella sp. An22, Barnesiella sp. ET7, and Bacteroidales bacterium. Furthermore, the results suggest that serum cytokines, such as IL-5, may reduce growth rate and are related to changes in serum metabolites and gut microbes (e.g., Barnesiella sp. An22 and Barnesiella sp. ET7). These results provide important insights into the effects of the cecal microbiome, serum metabolism and cytokines in Ningdu yellow chickens.

Consistent Multi-Omic Relationships Uncover Molecular Basis of Pediatric Asthma IgE Regulation

Serum total immunoglobulin E levels (total IgE) capture the state of the immune system in relation to allergic sensitization. High levels are associated with airway obstruction and poor clinical outcomes in pediatric asthma. Inconsistent patient response to anti-IgE therapies motivates discovery of molecular mechanisms underlying serum IgE level differences in children with asthma. To uncover these mechanisms using complementary metabolomic and transcriptomic data, abundance levels of 529 named metabolites and expression levels of 22,772 genes were measured among children with asthma in the Childhood Asthma Management Program (CAMP, N=564) and the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS, N=309) via the TOPMed initiative. Gene-metabolite associations dependent on IgE were identified within each cohort using multivariate linear models and were interpreted in a biochemical context using network topology, pathway and chemical enrichment, and representation within reactions. A total of 1,617 total IgE-dependent gene-metabolite associations from GACRS and 29,885 from CAMP met significance cutoffs. Of these, glycine and guanidinoacetic acid (GAA) were associated with the most genes in both cohorts, and the associations represented reactions central to glycine, serine, and threonine metabolism and arginine and proline metabolism. Pathway and chemical enrichment analysis further highlighted additional related pathways of interest. The results of this study suggest that GAA may modulate total IgE levels in two independent pediatric asthma cohorts with different characteristics, supporting the use of L-Arginine as a potential therapeutic for asthma exacerbation. Other potentially new targetable pathways are also uncovered.

Metabolite signatures associated with microRNA miR-143-3p serve as drivers of poor lung function trajectories in childhood asthma

BACKGROUND

Lung function trajectories (LFTs) have been shown to be an important measure of long-term health in asthma. While there is a growing body of metabolomic studies on asthma status and other phenotypes, there are no prospective studies of the relationship between metabolomics and LFTs or their genomic determinants.

METHODS

We utilized ordinal logistic regression to identify plasma metabolite principal components associated with four previously-published LFTs in children from the Childhood Asthma Management Program (CAMP) (n = 660). The top significant metabolite principal component (PCLF) was evaluated in an independent cross-sectional child cohort, the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (n = 1151) and evaluated for association with spirometric measures. Using meta-analysis of CAMP and GACRS, we identified associations between PCLF and microRNA, and SNPs in their target genes. Statistical significance was determined using an false discovery rate-adjusted Q-value.

FINDINGS

The top metabolite principal component, PCLF, was significantly associated with better LFTs after multiple-testing correction (Q-value = 0.03). PCLF is composed of the urea cycle, caffeine, corticosteroid, carnitine, and potential microbial (secondary bile acid, tryptophan, linoleate, histidine metabolism) metabolites. Higher levels of PCLF were also associated with increases in lung function measures and decreased circulating neutrophil percentage in both CAMP and GACRS. PCLF was also significantly associated with microRNA miR-143-3p, and SNPs in three miR-143-3p target genes; CCZ1 (P-value = 2.6 × 10-5), SLC8A1 (P-value = 3.9 × 10-5); and TENM4 (P-value = 4.9 × 10-5).

INTERPRETATION

This study reveals associations between metabolites, miR-143-3p and LFTs in children with asthma, offering insights into asthma physiology and possible interventions to enhance lung function and long-term health.

FUNDING

Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).

Characteristics of intestinal bacteriophages and their relationship with Bacteria and serum metabolites during quail sexual maturity transition

BACKGROUND

Bacteriophages are prokaryotic viruses that rank among the most abundant microbes in the gut but remain among the least understood, especially in quails. In this study, we surveyed the gut bacteriophage communities in 22 quails at different ages (days 20 and 70) using shotgun metagenomic sequencing. We then systematically evaluated the relationships with gut bacteria and host serum metabolites.

RESULTS

We discovered that Myoviridae and Siphoviridae were the dominant bacteriophage families in quails. Through a random forest and LEfSe analysis, we identified 23 differential bacteriophages with overlapping presence. Of these, 21 bacteriophages (e.g., Enterococcus phage IME-EFm5 and Enterococcus phage IME-EFm1) showed higher abundances in the day 20 group, while two bacteriophages (Bacillus phage Silence and Bacillus virus WPh) were enriched in the day 70 group. These key bacteriophages can serve as biomarkers for quail sexual maturity. Additionally, the differential bacteriophages significantly correlated with specific bacterial species and shifts in the functional capacities of the gut microbiome. For example, Enterococcus phages (e.g., Enterococcus phage EFP01, Enterococcus phage IME-EFm5, and Enterococcus phage IME-EFm1) were significantly (P < 0.001, FDR) and positively correlated with Enterococcus faecalis. However, the relationships between the host serum metabolites and either bacteriophages or bacterial species varied. None of the bacteriophages significantly (P > 0.05, FDR) correlated with nicotinamide riboside and triacetate lactone. In contrast, some differential bacterial species (e.g., Christensenella massiliensis and Bacteroides neonati) significantly (P < 0.05, FDR) correlated with nicotinamide riboside and triacetate lactone. Furthermore, characteristic successional alterations in gut bacteriophages, bacteria, and host serum metabolites across different ages highlighted a sexual maturity transition coexpression network.

CONCLUSION

This study improves our understanding of the gut bacteriophage characteristics in quails and offers profound insights into the interactions among gut bacteriophages, bacteria, and host serum metabolites during the quail's sexual maturity transition.

A pilot study of alternative substrates in the critically Ill subject using a ketogenic feed

Bioenergetic failure caused by impaired utilisation of glucose and fatty acids contributes to organ dysfunction across multiple tissues in critical illness. Ketone bodies may form an alternative substrate source, but the feasibility and safety of inducing a ketogenic state in physiologically unstable patients is not known. Twenty-nine mechanically ventilated adults with multi-organ failure managed on intensive care units were randomised (Ketogenic n = 14, Control n = 15) into a two-centre pilot open-label trial of ketogenic versus standard enteral feeding. The primary endpoints were assessment of feasibility and safety, recruitment and retention rates and achievement of ketosis and glucose control. Ketogenic feeding was feasible, safe, well tolerated and resulted in ketosis in all patients in the intervention group, with a refusal rate of 4.1% and 82.8% retention. Patients who received ketogenic feeding had fewer hypoglycaemic events (0.0% vs. 1.6%), required less exogenous international units of insulin (0 (Interquartile range 0-16) vs.78 (Interquartile range 0-412) but had slightly more daily episodes of diarrhoea (53.5% vs. 42.9%) over the trial period. Ketogenic feeding was feasible and may be an intervention for addressing bioenergetic failure in critically ill patients. Clinical Trials.gov registration: NCT04101071.

Characteristics and potential biomarkers of flavor compounds in four Chinese indigenous chicken breeds

Chinese indigenous chickens have a long history of natural and artificial selection and are popular for their excellent meat quality and unique flavor. This study investigated six meat quality-related traits in Ningdu yellow, Baier yellow, Kangle, and Shengze 901 chickens. Two-dimensional gas chromatography-time-of-flight mass spectrometry was used to detect unique flavors in 24 breast muscle samples from the same phenotyped chickens. Overall, 685, 618, 502, and 487 volatile organic compounds were identified in Ningdu yellow, Baier yellow, Kangle, and Shengze 901 chickens, respectively. The flavor components were separated into eight categories, including hydrocarbons and aldehydes. Multivariate analyses of the identified flavor components revealed some outstanding features of these breeds. For example, the hydrocarbons (22.09%) and aldehydes (14.76%) were higher in Ningdu yellow chickens and the highest content of N, N-dimethyl-methylamine was in Ningdu yellow, Baier yellow, and Shengze 901 chickens, indicating the maximum attribution to the overall flavor (ROAV = 439.57, 289.21, and 422.80). Furthermore, we found that 27 flavor compounds differed significantly among the four Chinese breeds, including 20 (e.g., 1-octen-3-ol), two (e.g., 2-methyl-naphthalene), four (e.g., 2,6-lutidine), and one (benzophenone) flavor components were showed significant enrichment in Ningdu yellow, Baier yellow, Kangle, and Shengze 901 chickens, respectively. The flavor components enriched in each breed were key biomarkers distinguishing breeds and most were significantly correlated with meat quality trait phenotypes. These results provide novel insights into indigenous Chinese chicken meat flavors.

A catalog of microbial genes and metagenome-assembled genomes from the quail gut microbiome

The gut microbiome plays an important role in quail feed efficiency, immunity, production, and even behavior. Gut microbial gene catalogs and reference genomes are important for understanding the quail gut microbiome. However, quail gut microbes are lacked sequenced genomes and functional information to date. In this study, we report the first catalog of the microbial genes and metagenome-assembled genomes (MAGs) in fecal and cecum luminal content samples from 3 quail breeds using deep metagenomic sequencing. We identified a total of 2,419,425 nonredundant genes in the quail genome catalog, and a total of 473 MAGs were reconstructed through binning analysis. At 95% average nucleotide identity, the 473 MAGs were clustered into 283 species-level genome bins (SGBs), of which 225 SGBs belonged to species without any available genomes in the current database. Based on the quail gene catalog and MAGs, we identified 142 discriminative bacterial species and 244 discriminative MAGs between Chinese yellow quails and Japanese quails. The discriminative MAGs suggested a strain-level difference in the gut microbial composition. Additionally, a total of 25 Kyoto Encyclopedia of Genes and Genomes functional terms and 88 carbohydrate-active enzymes were distinctly enriched between Chinese yellow quails and Japanese quails. Most of the different species and MAGs were significantly interrelated with the shifts in the functional capacities of the quail gut microbiome. Taken together, we constructed a quail gut microbial gene catalog and enlarged the reference of quail gut microbial genomes. The results of this study provide a powerful and invaluable resource for quail gut microbiome-related research.

Amino Acid Profiling Identifies Disease-Specific Signatures in IgE-Mediated and Non-IgE-Mediated Food Allergy in Pediatric Patients with Atopic Dermatitis

An IgE-mediated food allergy (FA) in atopic dermatitis (AD) children should be easily differentiated from other immune-mediated adverse effects related to food. Specific IgEs for particular protein components has provided additional diagnostic value. However, component-resolved diagnostics (CRD) has not solved all diagnostic problems either. We analysed the serum profile of 42 amino acids (AAs) in 76 AD children aged 2-60 months with an IgE-mediated FA (n = 36), with a non-IgE-mediated FA (n = 15) and without an FA (n = 25) using high-performance liquid chromatography coupled with mass spectrometry (LC-MS/MS) and an aTRAQ kit. We identified homocitrulline (Hcit), sarcosine (Sar) and L-tyrosine (Tyr) as features that differentiated the studied groups (one-way ANOVA with least significant difference post hoc test). The Hcit concentrations in the non-IgE-mediated FA group were significantly decreased compared with the IgE-mediated FA group (p = 0.018) and the control group (p = 0.008). In AD children with a non-IgE-mediated FA, the Tyr levels were also significantly reduced compared with the controls (p = 0.009). The mean concentration of Sar was the highest in the non-IgE-mediated FA group and the lowest in the IgE-mediated FA group (p = 0.047). Future studies should elucidate the involvement of these AAs in the molecular pathway of IgE- and non-IgE-mediated allergic responses.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Gut microbiome and serum metabolome analyses identify biomarkers associated with sexual maturity in quails

Increasing evidence indicates that the gut microbiome plays an important role in host aging and sexual maturity. However, the gut microbial taxa associated with sexual maturity in quails are unknown. This study used shotgun metagenomic sequencing to identify bacterial taxa associated with sexual maturity in d 20 and d 70 quails. We found that 17 bacterial species and 67 metagenome-assembled genomes (e.g., Bacteroides spp. and Enterococcus spp.) significantly differed between the d 20 and d 70 groups, including 5 bacterial species (e.g., Enterococcus faecalis) enriched in the d 20 group and 12 bacterial species (e.g., Christensenella massiliensis, Clostridium sp. CAG:217, and Bacteroides neonati) which had high abundances in the d 70 group. The bacterial species enriched in d 20 or d 70 were key biomarkers distinguishing sexual maturity and significantly correlated with the shifts in the functional capacities of the gut microbiome. Untargeted serum metabolome analysis revealed that 5 metabolites (e.g., nicotinamide riboside) were enriched in the d 20 group, and 6 metabolites (e.g., D-ribose, stevioside, and barbituric acid) were enriched in the d 70 group. Furthermore, metabolites with high abundances in the d 20 group were significantly enriched for the KEGG pathways of arginine biosynthesis, nicotinate and nicotinamide metabolism, and lysine degradation. However, glutathione metabolism and valine, leucine and isoleucine biosynthesis were enriched in high-abundance metabolites from the d 70 group. These results provide important insights into the effects of gut microbiome and host metabolism on quail sexual maturity.

Identifying biomarkers of the gut bacteria, bacteriophages and serum metabolites associated with three weaning periods in piglets

Background

The establishment of the piglet gut microbiome has a prolonged influence on host health, as it sets the stage for establishment of the adult swine microbiome. Substantial changes in host metabolism and immunity around the time of weaning may be accompanied by alterations in the gut microbiome. In this study, we systematically evaluated differences in the gut microbiome and host metabolites among three weaning periods using shotgun metagenomic sequencing and untargeted metabolomic profiling in piglets.

Results

We identified that P. copri was the most significantly different species among three weaning periods, and was the key bacterial species for mitigating piglet adaptation during the weaning transition, while Bacillus_phage_BCD7, the only differential bacteriophages, was significantly and positively correlated with P. copri enriched in day 28 group. Additionally, P. copri and Bacillus_phage_BCD7 was significantly correlated with the shifts of functional capacities of the gut microbiome and several CAZymes in day 28 group. Furthermore, the altered metabolites we observed were enriched in pathways matched to the functional capacity of the gut microbiome e.g., aminoacyl-tRNA biosynthesis.

Conclusion

The results from this study indicate that the bacteria-phage interactions and host-microbial interactions during the weaning transition impact host metabolism, leading to beneficial host changes among three weaning periods.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-022-03203-w.

Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle

Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

The Effects of Indoor Pollutants Exposure on Allergy and Lung Inflammation: An Activation State of Neutrophils and Eosinophils in Sputum

BACKGROUND

To explore the inflammation phenotypes following indoor pollutants exposure based on marker expression on eosinophils and neutrophils with the application of chemometric analysis approaches.

METHODS

A cross-sectional study was undertaken among secondary school students in eight suburban and urban schools in the district of Hulu Langat, Selangor, Malaysia. The survey was completed by 96 students at the age of 14 by using the International Study of Asthma and Allergies in Children (ISAAC) and European Community Respiratory Health Survey (ECRHS) questionnaires. The fractional exhaled nitric oxide (FeNO) was measured, and an allergic skin prick test and sputum induction were performed for all students. Induced sputum samples were analysed for the expression of CD11b, CD35, CD63, and CD66b on eosinophils and neutrophils by flow cytometry. The particulate matter (PM2.5 and PM10), NO2, CO2, and formaldehyde were measured inside the classrooms.

RESULTS

Chemometric and regression results have clustered the expression of CD63 with PM2.5, CD11b with NO2, CD66b with FeNO levels, and CO2 with eosinophils, with the prediction accuracy of the models being 71.88%, 76.04%, and 76.04%, respectively. Meanwhile, for neutrophils, the CD63 and CD66b clustering with PM2.5 and CD11b with FeNO levels showed a model prediction accuracy of 72.92% and 71.88%, respectively.

CONCLUSION

The findings indicated that the exposure to PM2.5 and NO2 was likely associated with the degranulation of eosinophils and neutrophils, following the activation mechanisms that led to the inflammatory reactions.

Metabolic Dysfunction and Asthma: Current Perspectives

The increasing knowledge of the mechanisms involved in metabolism is shifting the paradigms by which the pathophysiology of many pulmonary diseases is understood. Metabolic dysfunction is recognized in obesity-associated asthma, but other metabolic conditions have been shown to be independently related to asthma. Novel insights have also recently been brought by metabolomics in this filed. The purpose of this review is to discuss current perspectives regarding metabolic dysfunction in asthma, from obesity-related asthma to other metabolic conditions and the role of current pharmacological therapeutic strategies and lifestyle interventions. Obesity is a well-recognized risk factor for asthma across the lifespan, which is generally associated with poorer response to current available treatments, rendering a more severe, refractory disease status. Besides the epidemiological and clinical link, untargeted metabolomics studies have recently supported the obesity-associated asthma phenotype at the molecular level. Not only obesity-related, but also other aspects of metabolic dysregulation can be independently linked to asthma. These include hyperinsulinemia, dyslipidemia and hypertension, which need to be taken into account, even in the non-obese patient. Untargeted metabolomics studies have further highlighted several other metabolic pathways that can be altered in asthma, namely regarding oxidative stress and systemic inflammation, and also suggesting the importance of microbiota in asthma pathogenesis. Considering the reduced response to corticosteroids, other pharmacologic treatments have been shown to be effective regardless of body mass index. Non-pharmacologic treatments (namely weight reduction and dietary changes) may bring substantial benefit to the asthmatic patient. Taken together, this evidence points towards the need to improve our knowledge in this filed and, in particular, to address the influence of environmental factors in metabolic dysfunction and asthma development. Personalized medicine is definitely needed to optimize treatment, including a holistic view of the asthmatic patient in order to set accurate pharmacologic therapy together with dietary, physical exercise and lifestyle interventions.

Alterations in Serum-Free Amino Acid Profiles in Childhood Asthma

Asthma often begins in childhood, although making an early diagnosis is difficult. Clinical manifestations, the exclusion of other causes of bronchial obstruction, and responsiveness to anti-inflammatory therapy are the main tool of diagnosis. However, novel, precise, and functional biochemical markers are needed in the differentiation of asthma phenotypes, endotypes, and creating personalized therapy. The aim of the study was to search for metabolomic-based asthma biomarkers among free amino acids (AAs). A wide panel of serum-free AAs in asthmatic children, covering both proteinogenic and non-proteinogenic AAs, were analyzed. The examination included two groups of individuals between 3 and 18 years old: asthmatic children and the control group consisted of children with neither asthma nor allergies. High-performance liquid chromatography combined with tandem mass spectrometry (LC-MS/MS technique) was used for AA measurements. The data were analyzed by applying uni- and multivariate statistical tests. The obtained results indicate the decreased serum concentration of taurine, L-valine, DL-β-aminoisobutyric acid, and increased levels of ƴ-amino-n-butyric acid and L-arginine in asthmatic children when compared to controls. The altered concentration of these AAs can testify to their role in the pathogenesis of childhood asthma. The authors' results should contribute to the future introduction of new diagnostic markers into clinical practice.

Plasma 25-Hydroxyvitamin D Concentrations are Associated with Polyunsaturated Fatty Acid Metabolites in Young Children: Results from the Vitamin D Antenatal Asthma Reduction Trial

Vitamin D deficiency contributes to a multitude of health conditions, but its biological mechanisms are not adequately understood. Untargeted metabolomics offers the opportunity to comprehensively examine the metabolic profile associated with variations in vitamin D concentrations. The objective of the current analysis was to identify metabolites and metabolic pathways associated with plasma 25-hydroxyvitamin D [25(OH)D] concentrations. The current study included children of pregnant women in the Vitamin D Antenatal Asthma Reduction Trial, who had 25(OH)D and global metabolomics data at age 1 and 3 years. We assessed the cross-sectional associations between individual metabolites and 25(OH)D using linear regression adjusting for confounding factors. Twelve metabolites were significantly associated with plasma 25(OH)D concentrations at both age 1 and 3 after correction for multiple comparisons, including three members of the n-6 polyunsaturated fatty acid (PUFA) metabolism pathway (linoleate, arachidonate, and docosapentaenoate) inversely associated with 25(OH)D. These PUFAs along with four other significant metabolites were replicated in the independent Childhood Asthma Management Program (CAMP) cohort. Both vitamin D and n-6 PUFAs are involved in inflammatory processes, and evidence from cell and animal studies demonstrate a plausible biological mechanism where the active form of 25(OH)D may influence n-6 PUFA metabolism. These relationships warrant further investigation in other populations.

Pharmacometabolomics of Bronchodilator Response in Asthma and the Role of Age-Metabolite Interactions

The role of metabolism in modifying age-related differential responses to asthma medications is insufficiently understood. The objective of this study was to determine the role of the metabolome in modifying the effect of age on bronchodilator response (BDR) in individuals with asthma. We used longitudinal measures of BDR and plasma metabolomic profiling in 565 children with asthma from the Childhood Asthma Management Program (CAMP) to identify age by metabolite interactions on BDR. The mean ages at the three studied time-points across 16 years of follow-up in CAMP were 8.8, 12.8, and 16.8 years; the mean BDRs were 11%, 9% and 8%, respectively. Of 501 identified metabolites, 39 (7.8%) demonstrated a significant interaction with age on BDR (p-value < 0.05). We were able to validate two significant interactions in 320 children with asthma from the Genetics of Asthma in Costa Rica Study; 2-hydroxyglutarate, a compound involved in butanoate metabolism (interaction; CAMP: β = -0.004, p = 1.8 × 10^-4; GACRS: β = -0.015, p = 0.018), and a cholesterol ester; CE C18:1 (CAMP: β = 0.005, p = 0.006; GACRS: β = 0.023, p = 0.041) Five additional metabolites had a p-value < 0.1 in GACRS, including Gammaminobutyric acid (GABA), C16:0 CE, C20:4 CE, C18.0 CE and ribothymidine. These findings suggest Cholesterol esters and GABA may modify the estimated effect of age on bronchodilator response.