Pretreating and normalizing metabolomics data for statistical analysis

Multi-omics driven biomarker discovery and pathological insights into Pseudomonas aeruginosa pneumonia

BACKGROUND

Pseudomonas aeruginosa (P. aeruginosa) is a leading cause of hospital-acquired pneumonia, contributing significantly to morbidity and mortality, especially in immunocompromised patients. Understanding the molecular mechanisms underlying this infection is crucial for developing targeted therapeutic strategies. This study aims to elucidate the local and systemic pathways and biomarkers involved in the pathogenesis of P. aeruginosa pneumonia through an integrated multi-omics approach.

METHODS

We performed a comprehensive proteomic and metabolomic analysis on clinical samples from patients diagnosed with P. aeruginosa pneumonia, including both bronchoalveolar lavage fluid (BALF) and serum to capture local and systemic host responses. Data were analyzed using advanced statistical techniques to identify differentially expressed proteins and metabolites. Pathway enrichment analysis was performed to highlight significant biological processes associated with the infection.

RESULTS

Our findings revealed a significant upregulation of biomarkers associated with neutrophil extracellular traps (NETs) and oxidative stress, underscoring their pivotal roles in immune response and inflammatory pathology. Key proteins such as LCN2, CALR, and TPI1 were identified as central players in NET formation and oxidative stress pathways. Our integrated approach uniquely highlights the simultaneous local and systemic impact of NETs and oxidative stress. Additionally, by analyzing both BALF and serum, we observed distinct disruptions in metabolic pathways, particularly those related to amino acid metabolism and energy production, suggesting a bioenergetic crisis in response to infection. The combined analysis revealed key interactions between local and systemic immune responses, indicating a reprogramming of host energy pathways to meet the heightened immune demands, contributing to disease progression.

CONCLUSION

This study provides a comprehensive understanding of the molecular mechanisms driving P. aeruginosa pneumonia by uniquely integrating BALF and serum analyses to explore both local and systemic host responses. Our findings highlight the dual role of NETs in both pathogen containment and tissue damage, as well as the metabolic reprogramming required to sustain immune activity. The identification of key biomarkers and disrupted pathways presents promising targets for therapeutic intervention, with the potential to refine diagnostic precision and improve patient outcomes.

CLINICAL TRIAL NUMBER

Not applicable.

The fermented cabbage metabolome and its protection against cytokine-induced intestinal barrier disruption of Caco-2 monolayers

Fermented vegetables, such as fermented cabbage (sauerkraut), have garnered growing interest for their associations with a myriad of health benefits. However, the mechanistic details underlying the outcomes of consuming these foods require further investigation. This study examined the capacity of soluble metabolites in laboratory-scale and commercial-fermented cabbage to protect against disruption of polarized Caco-2 monolayers by interferon gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). Laboratory-scale ferments (LSF) were prepared with and without the addition of Lactiplantibacillus plantarum NCIMB8826R (LP8826R) and sampled after 7 and 14 days of incubation. Trans-epithelial electrical resistance (TER) and paracellular permeability to fluorescein isothiocyanate (FITC)-dextran revealed that fermented cabbage, but not raw cabbage or brine, protected against cytokine-induced damage to the Caco-2 monolayers. Barrier-protective effects occurred despite increased IL-8 production following cytokine exposure. Metabolomic analyses performed using gas and liquid chromatography resulted in the identification of 149 and 333 metabolites, respectively. Significant differences were found between raw and fermented cabbage. LSF metabolomes changed over time, and the profiles of LSF with LP8826R best resembled the commercial product. Overall, fermentation resulted in lower carbohydrate and increased lactic acid, lipid, amino acid derivative (including D-phenyl-lactate [D-PLA], indole-3-lactate [ILA], and γ-aminobutyric acid [GABA]), and phenolic compound concentrations. Lactate, D-PLA, and ILA tested individually and combined only partially protected against cytokine-induced TER reductions and increases in paracellular permeability of Caco-2 monolayers. The findings show that intestinal barrier-protective compounds are consistently enriched during cabbage fermentations, irrespective of the scale or microbial additions, which may contribute to the health-promoting potential of these foods.IMPORTANCEFermented vegetables are increasingly associated with health benefits. However, the importance of microbial transformations to foods during the fermentation process remains to be determined. We found that the metabolites in spontaneously fermented cabbage protected polarized intestinal epithelial cells against damage induced by proinflammatory cytokines. Cabbage fermentations resulted in consistent metabolome profiles enriched in bioactive compounds known to be made by beneficial members of the human gut microbiome, including D-phenyl-lactate (D-PLA) and indole-3-lactate (ILA). The metabolomes were distinct from raw cabbage and were further differentiated between commercial and lab ferments, sampling time, and the presence of an exogenous Lactiplantibacillus plantarum strain. Because only partial protection against intestinal barrier disruption was found when individual metabolites (D-PLA, ILA, and lactate) were applied, the findings indicate that the complex mixture of metabolites in a cabbage fermentation offers advantages over single metabolites to benefit intestinal health.

Application of Liquid Chromatography-Mass Spectrometry-Based Untargeted Metabolomics to Reveal Metabolites Related to Antioxidant Activity in Buckwheat Honey

Buckwheat honey is known for its high antioxidant activity, yet the compounds responsible for this effect have not been fully identified. This study used LC-MS-based untargeted metabolomics to investigate the metabolite profile of buckwheat honey and its relationship with antioxidant activity and total phenolic content, assessed by DPPH, FRAP, and Folin-Ciocalteu assays. A key objective was also to optimize data preprocessing parameters to improve the accuracy and robustness of metabolomic analyses. Multivariate analyses (PCA, OPLS-DA) effectively differentiated honey samples with high and low antioxidant potential. A total of 43 features were associated with increased antioxidant activity and about 30 compounds, including organic acids, free amino acids, and Amadori compounds-early Maillard reaction products-were identified. The amounts of most of these compounds exhibited strong positive correlation (r > 0.8) with measured antioxidant potential. These findings suggest that, in addition to polyphenols, other compound classes such as melanoidin precursors known as transition metal chelators significantly contribute to the antioxidant properties of buckwheat honey. This approach provides valuable insight into the bioactive composition of honey and supports the identification of potential antioxidant markers.

The Role of Aromatic Amino Acids in Polycystic Ovary Syndrome through Patients' Blood Metabolic Profiling: A Systematic Review of the Past Five Years

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder in women of reproductive age that encompasses a multitude of signs and symptoms, including hyperandrogenism, polycystic ovarian morphology, ovulatory dysfunction, and insulin resistance. The study aims to explore the role of aromatic amino acid (AAA) disorders in the syndrome. A systematic search on the databases Scopus, PubMed, and Google Scholar until 20 July 2024 over the past 5 years regarding metabolomic studies on PCOS patients' blood and the status of AAAs resulted in 12 related papers. Our review showed that AAA metabolic pathways are dysregulated, and their levels in the blood serum and plasma of PCOS patients in most studies are elevated due to inflammation and oxidative stress which, assisted by gut dysbiosis, give rise to insulin resistance that develops into PCOS. AAA abnormalities can also directly induce the defining symptoms of the syndrome through diminished neurotransmitter availability and impaired signaling. According to our review, AAA perturbations are detected in every stage of PCOS pathophysiology, making them valuable biomarkers for early diagnosis and management of the syndrome. Further investigation of the biological function, role, and impact of AAAs, probably alongside other metabolites, including BCAAs, could lead to the discovery of new tools for preventing and managing PCOS symptoms.

Metabolic dysregulation in amyotrophic lateral sclerosis: insights from 1H NMR-based metabolomics in a tertiary care center in India

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder characterized by motor neuron loss, leading to severe physical impairment and mortality. Despite available treatments like Riluzole and Edaravone, their limited efficacy highlights the need for improved understanding of ALS pathology. This study has explored metabolic alterations in North Indian ALS patients using 1H Nuclear Magnetic Resonance (NMR)-based metabolomics. A case-control study, involving 45 ALS patients and 30 healthy controls (HCs) was performed. Serum samples were analyzed using 600-MHz NMR spectrometer, revealing significant metabolic differences between ALS and HC groups. Multivariate analyses identified nine dysregulated metabolites-pyruvate, glutamine, histidine, isoleucine, leucine, imidazole, arginine, creatinine, and choline-with ROC analysis showing isoleucine as a promising biomarker (AUC 83%). Pathway enrichment analysis highlighted disruptions in key metabolic pathways, including the Glucose-Alanine Cycle, Urea Cycle, Ammonia Recycling, and the Warburg Effect, suggesting potential links to neuroinflammatory and mitochondrial dysfunction in ALS pathogenesis. This pilot study provides insight into ALS-specific metabolic alterations in Indian cohort and demonstrates the potential of these metabolites as diagnostic biomarkers. Our findings identify potential biomarkers that require validation in larger, multi-centric cohorts to support diagnosis, prognosis, and improved management of ALS.

Selective Influence of Hemp Fiber Ingestion on Post-Exercise Gut Permeability: A Metabolomics-Based Analysis

Objectives: This study investigated the effects of 2-week ingestion of hemp fiber (high and low doses) versus placebo bars on gut permeability and plasma metabolite shifts during recovery from 2.25 h intensive cycling. Hemp hull powder is a rich source of two bioactive compounds, N-trans-caffeoyl tyramine (NCT) and N-trans-feruloyl tyramine (NFT), with potential gut health benefits. Methods: The study participants included 23 male and female cyclists. A three-arm randomized, placebo-controlled, double-blind, crossover design was used with two 2-week supplementation periods and 2-week washout periods. Supplement bars provided 20, 5, or 0 g/d of hemp hull powder. Participants engaged in an intensive 2.25 h cycling bout at the end of each of the three supplementation periods. Five blood samples were collected before and after supplementation (overnight fasted state), and at 0 h-, 1.5 h-, and 3 h-post-exercise. Five-hour urine samples were collected pre-supplementation and post-2.25 h cycling after ingesting a sugar solution containing 5 g of lactulose, 100 mg of 13C mannitol, and 1.9 g of mannitol in 450 mL of water. An increase in the post-exercise lactulose/13C mannitol ratio (L:13CM) was used as the primary indicator of altered gut permeability. Other outcome measures included muscle damage biomarkers (serum creatine kinase, myoglobin), serum cortisol, complete blood cell counts, and shifts in plasma metabolites using untargeted metabolomics. Results: No trial differences were found for L:13CM, cortisol, blood cell counts, and muscle damage biomarkers. Orthogonal partial least-squares discriminant analysis (OPLSDA) showed distinct trial differences when comparing high- and low-dose hemp fiber compared to placebo supplementation (R2Y = 0.987 and 0.995, respectively). Variable Importance in Projection (VIP) scores identified several relevant metabolites, including 3-hydroxy-4-methoxybenzoic acid (VIP = 1.9), serotonin (VIP = 1.5), 5-hydroxytryptophan (VIP = 1.4), and 4-methoxycinnamic acid (VIP = 1.4). Mummichog analysis showed significant effects of hemp fiber intake on multiple metabolic pathways, including alpha-linolenic acid, porphyrin, sphingolipid, arginine and proline, tryptophan, and primary bile acid metabolism. Conclusions: Hemp fiber intake during a 2-week supplementation period did not have a significant effect on post-exercise gut permeability in cyclists (2.25 h cycling bout) using urine sugar data. On the contrary, untargeted metabolomics showed that the combination of consuming nutrient-rich hemp fiber bars and exercising for 135 min increased levels of beneficial metabolites, including those derived from the gut in healthy cyclists.

Multivariate Strategy for Understanding Soil Features from Rare-Earth Element Profiles: A Focus on Data Normalization

The interest in assessing the behavior of rare-earth elements (REE) in the natural environment is constantly increasing due to their numerous applications in both environmental and technological fields. However, current methodologies for analyzing REE distributions are based on normalization of REE concentration profiles to lithological values, potentially resulting in different outcomes depending on which lithological values are used for normalization, affecting the interpretability of the data. The present work proposes an alternative approach for analyzing REE concentration profiles by applying principal component analysis (PCA) to create REE chemometric maps. The data compression allows the visualization of the REE distribution using a red-green-blue (RGB) color scale (PC1 = red channel; PC2 = green channel; PC3 = blue channel) directly on a geographical map, reflecting the chemical properties of rare-earth elements. This highlights similarities and differences in the compositional REE distribution of natural soils, facilitating the interpretability of REE data and potentially leading to new insights related to seemingly unrelated samples. Additionally, PCA applied to soil variables correlates with REE patterns and provides deeper insights into soil properties in an unsupervised manner, enhancing the interpretation of soil characteristics and implementing the ability to monitor environmental changes and study soil evolution processes. Of particular significance is the fact that applying the proposed methodology to non-normalized data yields results that are consistent with those derived from normalized data sets. Therefore, this approach not only overcomes normalization challenges but also supports the classical approach from a new methodological perspective, paving the way for broader applications.

Targeted and Non-Targeted Metabolomic Evaluation of Cerebrospinal Fluid in Early Phase Schizophrenia: A Pilot Study from the Hopkins First Episode Psychosis Project

(1) Background: The lack of reliable biomarkers remains a significant barrier to improving outcomes for patients with schizophrenia. While metabolomic analyses of blood, urine, and feces have been explored, results have been inconsistent. Compared to peripheral compartments, cerebrospinal fluid (CSF) more closely reflects the chemical composition of brain extracellular fluid. Given that brain dysregulation may be more pronounced during the first episode of psychosis (FEP), we hypothesized that metabolomic analysis of CSF from FEP patients could reveal disease-associated biomarkers. (2) Methods: We recruited 15 patients within 24 months of psychosis onset (DSM-4 criteria) and 14 control participants through the Johns Hopkins Schizophrenia Center. CSF samples were analyzed using both non-targeted and targeted liquid chromatography-mass spectrometry. (3) Results: The non-targeted analysis identified lower levels of N-acetylneuraminic acid and N-acetyl-L-aspartic acid in the FEP group, while levels of uric acid were elevated. The targeted analysis focused on indolic and phenolic molecules previously linked to neuropsychiatric disorders. Notably, L-phenylalanine and 4-hydroxycinnamic acid levels were lower in the FEP group, and this difference remained significant after adjusting for age and sex. However, none of the significant differences in analyte levels between the groups survived an adjustment for multiple comparisons. (4) Conclusions: Our intriguing but preliminary associations align with results from other investigational approaches and highlight potential CSF analytes that warrant further study in larger samples.

Plant species richness promotes the decoupling of leaf and root defence traits while species-specific responses in physical and chemical defences are rare

The increased positive impact of plant diversity on ecosystem functioning is often attributed to the accumulation of mutualists and dilution of antagonists in diverse plant communities. While increased plant diversity alters traits related to resource acquisition, it remains unclear whether it reduces defence allocation, whether this reduction differs between roots and leaves, or varies among species. To answer these questions, we assessed the effect of plant species richness, plant species identity and their interaction on the expression of 23 physical and chemical leaf and fine root defence traits of 16 plant species in a 19-yr-old biodiversity experiment. Only leaf mass per area, leaf and root dry matter content and root nitrogen, traits associated with both, resource acquisition and defence, responded consistently to species richness. However, species richness promoted a decoupling of these defences in leaves and fine roots, possibly in response to resource limitations in diverse communities. Species-specific responses were rare and related to chemical defence and mutualist collaboration, likely responding to species-specific antagonists' dilution and mutualists' accumulation. Overall, our study suggests that resource limitation in diverse communities might mediate the relationship between plant defence traits and antagonist dilution.

Warming Disrupts Plant-Fungal Endophyte Symbiosis More Severely in Leaves Than Roots

Disruptions to functionally important symbionts with global change will negatively impact plant fitness, with broader consequences for species' abundances, distribution, and community composition. Fungal endophytes that live inside plant leaves and roots could potentially mitigate plant heat stress from global warming. Conversely, disruptions of these symbioses could exacerbate the negative impacts of warming. To better understand the consistency and strength of warming-induced changes to fungal endophytes, we examined fungal leaf and root endophytes in three grassland warming experiments in the US ranging from 2 to 25 years and spanning 2000 km, 12°C of mean annual temperature, and 600 mm of precipitation. We found that experimental warming disrupted symbiosis between plants and fungal endophytes. Colonization of plant tissues by septate fungi decreased in response to warming by 90% in plant leaves and 35% in roots. Warming also reduced fungal diversity and changed community composition in plant leaves, but not roots. The strength, but not direction, of warming effects on fungal endophytes varied by up to 75% among warming experiments. Finally, warming decoupled fungal endophytes from host metabolism by decreasing the correlation between endophyte community and host metabolome dissimilarity. These effects were strongest in the shorter-term experiment, suggesting endophyte-host metabolome function may acclimate to warming over decades. Overall, warming-driven disruption of fungal endophyte community structure and function suggests that this symbiosis may not be a reliable mechanism to promote plant resilience and ameliorate stress responses under global change.

Differences in plasma metabolome between non-Hispanic White and non-Hispanic Black women

BACKGROUND

To understand potential racial differences in disease susceptibility and develop targeted prevention strategies, it is essential to establish biological differences between racial groups in healthy individuals. However, knowledge about how race impacts metabolites is limited. We therefore performed a cross-sectional study using comprehensive metabolomics analysis to investigate racial differences in metabolites among 506 non-Hispanic White (NHW) women and 163 non-Hispanic Black (NHB) women.

METHODS

We performed untargeted plasma metabolomic profiling using Metabolon's platform (Durham, NC®) and identified 1074 metabolites in 9 super-pathways. We used multivariable linear regression models, adjusted for confounders, to identify associations between race and metabolites. We applied a Bonferroni correction (p-value < 10-5) to account for multiple testing.

RESULTS

We identified 26 metabolites that differed significantly between NHW and NHB women. Seven, 10, 17, and 23 metabolites showed absolute percentage differences ≥ 50, ≥ 40%, ≥ 30%, and ≥ 20%, respectively. Xenobiotics (n = 5) and amino acids (n = 2) exhibited the largest absolute percentage differences (≥ 50%) between NHB and NHW women. In the xenobiotics super-pathway, NHB women had higher thymol sulfate, 2-naphthol sulfate, and 2-hydroxyfluorene sulfate, derived from the exposure to polycyclic aromatic hydrocarbons, while NHW women had higher xanthine metabolites. In the amino acid super-pathway, lysine and tryptophan metabolites were lower in NHB women.

CONCLUSIONS

We report differences in several metabolites between NHW and NHB women. These findings require validation in a different study and could provide insight into investigating how racial differences in metabolites may impact disease burden across diverse populations.

Mitochondrial dysfunction drives a neuronal exhaustion phenotype in methylmalonic aciduria

Methylmalonic aciduria (MMA) is an inborn error of metabolism resulting in loss of function of the enzyme methylmalonyl-CoA mutase (MMUT). Despite acute and persistent neurological symptoms, the pathogenesis of MMA in the central nervous system is poorly understood, which has contributed to a dearth of effective brain specific treatments. Here we utilised patient-derived induced pluripotent stem cells and in vitro differentiation to generate a human neuronal model of MMA. We reveal strong evidence of mitochondrial dysfunction caused by deficiency of MMUT in patient neurons. By employing patch-clamp electrophysiology, targeted metabolomics, and bulk transcriptomics, we expose an altered state of excitability, which is exacerbated by application of dimethyl-2-oxoglutarate, and we suggest may be connected to metabolic rewiring. Our work provides first evidence of mitochondrial driven neuronal dysfunction in MMA, which through our comprehensive characterisation of this paradigmatic model, enables first steps to identifying effective therapies.

Residential Proximity Land Use Characteristics and Exhaled Volatile Organic Compounds' Impact on Pulmonary Function in Asthmatic Children

BACKGROUND

Urban air pollution adversely affects children's respiratory systems, but the impact of volatile organic compounds (VOCs) on children's pulmonary function remains unclear. This study aims to identify exhaled VOCs linked to land use characteristics and reduced pulmonary function in asthmatic children, as well as to explore environmental thresholds influencing VOC exposure levels.

METHODS

We enrolled 97 asthmatic children, aged 7 to 20, from Changhua County, Taiwan, and collected personal and residential data, collected exhaled VOC samples, and conducted pulmonary function tests. Land use characteristics were derived from the children's residential addresses. This study used two models to explore the relationships between land use, VOC levels, and pulmonary function.

RESULTS

Our results show that m/p-xylene, 1,3,5-trimethylbenzene, and 1,2,4-trimethylbenzene were key contributors to FEV1/FVC and significantly predicted FEV1/FVC < 90% (AUC = 0.66; 95% CI: 0.53 to 0.79). These VOCs were also linked to major road areas within a 300 m buffer around children's homes.

CONCLUSIONS

This study fills a research gap on low-level outdoor VOC exposure and pediatric respiratory health, examining 1,3,5-trimethylbenzene, 1,2,4-trimethylbenzene, and m/p-xylene as potential biomarkers for impaired pulmonary function in children.

Modular comparison of untargeted metabolomics processing steps

BACKGROUND

Untargeted metabolomics requires robust and reliable strategies for data processing to extract relevant information form the underlying raw data. Multiple platforms for data processing are available, but the choice of software tool can have an impact on the analysis. This study provides a comprehensive evaluation of four workflows based on commonly used metabolomics software tools: XCMS, Compound Discoverer, MS-DIAL, and MZmine. These tools were applied to a dataset derived from bovine saliva samples spiked with small polar molecules analyzed by anion exchange chromatography coupled to high resolution mass spectrometry.

RESULTS

The analysis revealed significant differences in the number and overlap of detected features, with only approximately 8 % of the features included in all four peak tables. Among the overlapping features, MS-DIAL demonstrated the greatest similarity to manual integration, while XCMS and MZmine also performed well. In contrast, Compound Discoverer had issues to reliably integrate high baseline peaks. This study also explores various post-processing strategies, including missing value imputation, transformation, scaling, and filtering. The assessment of missing values indicated that they primarily originated from low abundance, making imputation with small values the most effective approach. No clear evidence suggested that transformation is necessary for downstream statistical analyses. Auto scaling emerged as the most suitable strategy for data scaling. Low thresholds for blank filtering were found to be the most effective in enhancing data quality. The optimization of filtering thresholds required a careful balance to remove unnecessary information while retaining vital data.

SIGNIFICANCE AND NOVELTY

This work provides an overview of commonly applied strategies in untargeted metabolomics analysis, emphasizing the importance of careful workflow selection and optimization. It serves as a resource for refining data processing strategies to achieve accurate and reliable results, while also offering fresh insights into the challenges encountered throughout the untargeted metabolomics processing pipeline.

Response of human metabolism to ultra-low and high nicotine cigarettes based on urine metabolomics and bioinformatic analysis

INTRODUCTION

This study aimed to evaluate the metabolomic profiles of urine samples obtained from smokers who smoked cigarettes with low and high nicotine content.

METHODS

Three smokers participated in this study. They were given low-nicotine (LN) cigarettes, and urine was collected at the end of the third day for the LN group. After 1 week of not smoking, they were given high-nicotine (HN) cigarettes, and urine was collected for the HN group. Untargeted metabolomics and bioinformatic analysis methods were used for urine analysis.

RESULTS

PCA showed a high degree of similarity between samples within the group and a large distance between samples between groups, indicating a significant difference between the two groups. A total of 1150 significantly differential metabolites were selected between the HN and LN groups, such as cotinine and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol-N-glucuronide. Two-way hierarchical clustering analysis also suggested noticeable differences between the two comparison groups Enrichment analysis indicates that the differential metabolites between the two groups were mainly enriched in 19 pathways, such as the protein kinase G (cGMP)-protein kinase G (PKG) signaling pathway, adenosine monophosphate (AMP)-activated protein kinase signaling pathway, mammalian target of rapamycin signaling pathway, and Parkinson's disease.

CONCLUSIONS

Cigarettes with different nicotine content may alter the metabolism of smokers. A total of 1150 significantly different metabolites were identified between the HN and LN groups, which were mainly enriched in ABC transporters, protein kinase G (cGMP)-protein kinase G (PKG) signaling pathway, caffeine metabolism, and arginine biosynthesis pathways.

Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data

Background: Dry eye disease is a common disorder of the ocular surface, leading patients to seek eye care. Clinical signs and symptoms are currently used to diagnose dry eye disease. Metabolomics, a method for analyzing biological systems, has been found helpful in identifying distinct metabolites in patients and in detecting metabolic profiles that may indicate dry eye disease at early stages. In this study, we explored the use of machine learning and metabolomics data to identify cataract patients who suffer from dry eye disease, a topic that, to our knowledge, has not been previously explored. As there is no one-size-fits-all machine learning model for metabolomics data, choosing the most suitable model can significantly affect the quality of predictions and subsequent metabolomics analyses. Methods: To address this challenge, we conducted a comparative analysis of eight machine learning models on two metabolomics data sets from cataract patients with and without dry eye disease. The models were evaluated and optimized using nested k-fold cross-validation. To assess the performance of these models, we selected a set of suitable evaluation metrics tailored to the data set's challenges. Results: The logistic regression model overall performed the best, achieving the highest area under the curve score of 0.8378, balanced accuracy of 0.735, Matthew's correlation coefficient of 0.5147, an F1-score of 0.8513, and a specificity of 0.5667. Additionally, following the logistic regression, the XGBoost and Random Forest models also demonstrated good performance. Conclusions: The results show that the logistic regression model with L2 regularization can outperform more complex models on an imbalanced data set with a small sample size and a high number of features, while also avoiding overfitting and delivering consistent performance across cross-validation folds. Additionally, the results demonstrate that it is possible to identify dry eye in cataract patients from tear film metabolomics data using machine learning models.

Spatial distribution patterns and hotspots of extreme agro-climatic resources in the Horro Guduru Wollega Zone, Northwestern Ethiopia

Extreme temperatures and rainfall influence crop yields, soil health, and natural ecosystems. This study examined the extent of extreme agro-climatic factors in Northwestern Ethiopia, with a focus on identifying vulnerability hotspots. Rainfall and temperature data from 1982 to 2022 were collected from eight meteorological stations of the Ethiopian Meteorological Institute, and missing values and outliers were corrected using imputation and Z-scores. ClimPact2 software extracted agro-climatic indicators, and trend analyses were performed using the Mann-Kendall test and Sen's slope. Consecutive dry days (CDD) ranged from 27 in Fincha'a to 57 in Obora, with Obora showing an annual increase of 2.033 days. Consecutive wet days (CWD) varied from 12 in Obora to 138 in Fincha'a. A positive trend in the warmest maximum temperatures (TXx) and a negative trend in the cold night index (TN10P) were observed. The Amuru District recorded the highest vulnerability index at 61, with most districts ranging from 42 to 60. These variations may significantly affect agriculture and water management in the region, necessitating the adoption of heat-tolerant crops and improved irrigation practices to enhance climate resilience.

Genomic and Untargeted Metabolomic Analysis of Secondary Metabolites in the Streptomyces griseoaurantiacus Strain MH191 Shows Media-Based Dependency for the Production of Bioactive Compounds with Potential Antifungal Activity

Streptomyces species can form beneficial relationships with hosts as endophytes, including the phytopathogen-inhibiting strain, Streptomyces griseoaurantiacusMH191, isolated from wheat plants. Using genomic characterization and untargeted metabolomics, we explored the capacity of strain MH191 to inhibit a range of fungal phytopathogens through the production of secondary metabolites. Complete genome assembly of strain MH191 predicted 24 biosynthetic gene clusters. Secondary metabolite production was assessed following culture on six different media, with the detection of 205 putative compounds. Members of the manumycin family, undecylprodigiosin, and desferrioxamine were identified as the predominant metabolites. Antifungal activity was validated for undecylprodigiosin and manumycin. These compounds were produced from different BGCs, which showed similarity to asukamycin, undecylprodigiosin, and FW0622 gene clusters, respectively. The growth of strain MH191 on different media illustrated the metabolic regulation of these gene clusters and the strain's extended chemical potential, with the asukamycin gene cluster alone, producing a variety of antifungal metabolites. The study highlights the extended chemical capability of strain MH191, which could be exploited as a biological control agent for designing future crop protection solutions.

A Multiomics Evaluation of the Countermeasure Influence of 4-Week Cranberry Beverage Supplementation on Exercise-Induced Changes in Innate Immunity

OBJECTIVES

This study examined the effect of a 4-week unsweetened cranberry beverage (CRAN) (317 mg polyphenols) versus placebo beverage (PLAC) ingestion (240 mL/day) on moderating exercise-induced changes in innate immunity.

METHODS

Participants included 25 male and female non-elite cyclists. A randomized, placebo-controlled, double-blind crossover design was used with two 4-week supplementation periods and a 2-week washout period. Supplementation periods were followed by an intensive 2.25 h cycling bout. Six blood samples were collected before and after supplementation (in an overnight fasted state) and at 0 h, 1.5 h, 3 h, and 24 h post-exercise. Stool and urine samples were collected pre- and post-supplementation. Outcome measures included serum creatine kinase, myoglobin, and cortisol, complete blood counts, plasma untargeted proteomics, plasma-targeted oxylipins, untargeted urine metabolomics, and stool microbiome composition via whole genome shotgun (WGS) sequencing.

RESULTS

Urine CRAN-linked metabolites increased significantly after supplementation, but no trial differences in alpha or beta microbiota diversity were found in the stool samples. The 2.25 h cycling bout caused significant increases in plasma arachidonic acid (ARA) and 53 oxylipins (FDR q-value < 0.05). The patterns of increase for ARA, four oxylipins generated from ARA-cytochrome P-450 (CYP) (5,6-, 8,9-, 11,12-, and 14,15-diHETrEs), two oxylipins from linoleic acid (LA) and CYP (9,10-DiHOME, 12,13-DiHOME), and two oxylipins generated from LA and lipoxygenase (LOX) (9-HODE, 13-HODE) were slightly but significantly higher for the CRAN versus PLAC trial (all interaction effects, p < 0.05). The untargeted proteomics analysis showed that two protein clusters differed significantly between the CRAN and PLAC trials, with CRAN-related elevations in proteins related to innate immune activation and reduced levels of proteins related to the regulation of the complement cascade, platelet activation, and binding and uptake of ligands by scavenger receptors. No trial differences were found for cortisol and muscle damage biomarkers.

CONCLUSIONS

CRAN versus PLAC juice resulted in a significant increase in CRAN-related metabolites but no differences in the gut microbiome. CRAN supplementation was associated with a transient and modest but significant post-exercise elevation in selected oxylipins and proteins associated with the innate immune system.

Propionic Acidemia, Methylmalonic Acidemia, and Cobalamin C Deficiency: Comparison of Untargeted Metabolomic Profiles

Methylmalonic acidemia (MMA), propionic acidemia (PA), and cobalamin C deficiency (cblC) share a defect in propionic acid metabolism. In addition, cblC is also involved in the process of homocysteine remethylation. These three diseases produce various phenotypes and complex downstream metabolic effects. In this study, we used an untargeted metabolomics approach to investigate the biochemical differences and the possible connections among the pathophysiology of each disease. The significantly changed metabolites in the untargeted urine metabolomic profiles of 21 patients (seven MMA, seven PA, seven cblC) were identified through statistical analysis (p < 0.05; log2FC > |1|) and then used for annotation. Annotated features were associated with different metabolic pathways potentially involved in the disease's development. Comparative statistics showed markedly different metabolomic profiles between MMA, PA, and cblC, highlighting the characteristic species for each disease. The most affected pathways were related to the metabolism of organic acids (all diseases), amino acids (all diseases), and glycine and its conjugates (in PA); the transsulfuration pathway; oxidative processes; and neurosteroid hormones (in cblC). The untargeted metabolomics study highlighted the presence of significant differences between the three diseases, pointing to the most relevant contrast in the cblC profile compared to MMA and PA. Some new biomarkers were proposed for PA, while novel data regarding the alterations of steroid hormone profiles and biomarkers of oxidative stress were obtained for cblC disease. The elevation of neurosteroids in cblC may indicate a potential connection with the development of ocular and neuronal deterioration.

Untargeted metabolomics reveal signatures of a healthy lifestyle

This cross-sectional study investigated differences in the plasma metabolome in two groups of adults that were of similar age but varied markedly in body composition and dietary and physical activity patterns. Study participants included 52 adults in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). The results using an extensive untargeted ultra high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) metabolomics analysis with 10,535 metabolite peaks identified 486 important metabolites (variable influence on projections scores of VIP ≥ 1) and 16 significantly enriched metabolic pathways that differentiated LIFE and CON groups. A novel metabolite signature of positive lifestyle habits emerged from this analysis highlighted by lower plasma levels of numerous bile acids, an amino acid profile characterized by higher histidine and lower glutamic acid, glutamine, β-alanine, phenylalanine, tyrosine, and proline, an elevated vitamin D status, higher levels of beneficial fatty acids and gut microbiome catabolism metabolites from plant substrates, and reduced levels of N-glycan degradation metabolites and environmental contaminants. This study established that the plasma metabolome is strongly associated with body composition and lifestyle habits. The robust lifestyle metabolite signature identified in this study is consistent with an improved life expectancy and a reduced risk for chronic disease.

Estimating Gestational Age and Prediction of Preterm Birth Using Metabolomics Biomarkers

Preterm birth (PTB) is a leading cause of morbidity and mortality in children aged under 5 years globally, especially in low-resource settings. It remains a challenge in many low-income and middle-income countries to accurately measure the true burden of PTB due to limited availability of accurate measures of gestational age (GA), first trimester ultrasound dating being the gold standard. Metabolomics biomarkers are a promising area of research that could provide tools for both early identification of high-risk pregnancies and for the estimation of GA and preterm status of newborns postnatally.

From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.

Tutorial on survival modeling with applications to omics data

MOTIVATION

Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes.

RESULTS

We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are especially useful for high-dimensional data, but the framework is applicable more generally.

AVAILABILITY AND IMPLEMENTATION

A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics.

Statistical normalization methods in microbiome data with application to microbiome cancer research

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

Recent Analytical Advances for Decoding Metabolic Reprogramming in Lung Cancer

Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.