Variable selection and validation in multivariate modelling

Metabolomic profiling of cannabis use and cannabis intoxication in humans

Acute intoxication from Δ9-tetrahydrocannabinol (THC, the primary active ingredient of cannabis) can lead to neurocognitive impairment and interference with day-to-day operations, such as driving. Present evaluations of THC-induced impairment in legal settings rely on biological drug tests that solely establish cannabis use, rather than cannabis impairment. The current study evaluated the metabolome in blood collected from occasional and chronic cannabis users (N = 35) at baseline and following treatments with cannabis (300 μg/kg THC) and placebo, with the aim to identify unique metabolic alterations that are associated with acute cannabis intoxication and cannabis use frequency. Blood samples were collected at baseline and repeatedly during 70 min after treatment. Sustained attention performance and ratings of subjective high were taken twice within 40 min after treatment. Metabolomic fingerprints of occasional and chronic cannabis users were distinctly different at baseline, when both groups were not intoxicated. A total of 14 metabolites, mainly related to endocannabinoid and amino acid metabolism, were identified that distinguished chronic from occasional cannabis users and that yielded a discriminant analysis model with an 80% classification rate (95% CI: 61-91%). Distinct metabolomic fingerprints were found for occasional cannabis users who, in contrast to chronic cannabis users, showed attentional impairment and elevated ratings of subjective high during cannabis intoxication. These included increments in organic acids, β-hydroxybutyrate and second messenger ceramides. The current study demonstrates the feasibility of the metabolomics approach to identify metabolic changes that are specific to the neurocognitive state of cannabis intoxication and to the history of cannabis use.

Omics Approach for Personalised Prevention of Type 2 Diabetes Mellitus for African and European Populations (OPTIMA): a protocol paper

INTRODUCTION

The prevalence of type 2 diabetes (T2D) within sub-Saharan Africa (SSA) is increasing. Despite the pathophysiology of T2D differing by ethnicity and sex, risk stratification and guidelines for the prevention of T2D are generic, relying on evidence from studies including predominantly Europeans. Accordingly, this study aims to develop ethnic-specific and sex-specific risk prediction models for the early detection of dysglycaemia (impaired glucose tolerance and T2D) to inform clinically feasible, culturally acceptable and cost-effective risk management and prevention strategies using dietary modification in SSA and European populations.

METHODS AND ANALYSIS

This multinational collaboration will include the prospective cohort data from two African cohorts, the Middle-Aged Soweto Cohort from South Africa and the Research on Obesity and Diabetes among African Migrants Prospective cohort from Ghana and migrants living in Europe, and a Swedish cohort, the Pre-Swedish CArdioPulmonary bioImage Study. Targeted proteomics, as well as targeted and untargeted metabolomics, will be performed at baseline to discover known and novel ethnic-specific and sex-specific biomarkers that predict incident dysglycaemia in the different longitudinal cohorts. Dietary patterns that explain maximum variation in the biomarker profiles and that associate with dysglycaemia will be identified in the SSA and European cohorts and used to build the prototypes for dietary interventions to prevent T2D. A comparative cost-effectiveness analysis of the dietary interventions will be estimated in the different populations. Finally, the perceptions of at-risk participants and healthcare providers regarding ethnic-specific and sex-specific dietary recommendations for the prevention of T2D will be assessed using focus group discussions and in-depth interviews in South Africa, Ghana, Germany (Ghanaian migrants) and Sweden.

ETHICS AND DISSEMINATION

Ethical clearance has been obtained from all participating sites. The study results will be disseminated at scientific conferences and in journal publications, and through community engagement events and diabetes organisations in the respective countries.

Biosensors, Artificial Intelligence Biosensors, False Results and Novel Future Perspectives

Medical biosensors have set the basis of medical diagnostics, and Artificial Intelligence (AI) has boosted diagnostics to a great extent. However, false results are evident in every method, so it is crucial to identify the reasons behind a possible false result in order to control its occurrence. This is the first critical state-of-the-art review article to discuss all the commonly used biosensor types and the reasons that can give rise to potential false results. Furthermore, AI is discussed in parallel with biosensors and their misdiagnoses, and again some reasons for possible false results are discussed. Finally, an expert opinion with further future perspectives is presented based on general expert insights, in order for some false diagnostic results of biosensors and AI biosensors to be surpassed.

Integrated renin angiotensin system dysregulation and immune profiles predict COVID-19 disease severity in a South African cohort

Renin-angiotensin system (RAS) dysregulation is an important component of the complex pathophysiology of SARS-CoV-2 and other coronavirus infections. Thus, angiotensin-converting enzyme 2 (ACE2), the entry receptor and key to the alternative RAS, was proposed as a severity/prognostic biomarker for risk-stratification. However, experimental RAS data from diverse cohorts are limited, particularly analyses integrating RAS with immune biomarkers. Participants (n = 172) in Cape Town were sampled longitudinally (including a recovery timepoint [> 3-month]), across WHO asymptomatic to critical severity. Using fluorometric assays and LC-MS/MS RAS Fingerprinting®, results show serum ACE1 activity significantly decreases with increasing COVID-19 severity (P < 0.01) and mortality (P < 0.05), while increased ACE2 activity is associated with worse severity (P < 0.01). Neither enzyme activity correlates with viral load proxy or nasal ACE mRNA levels. ACE1 and ACE2 activities were the most effective severity biomarkers compared to 96 established immune markers obtained via proximity extension assay, as demonstrated by principal component analysis. A multivariate variable selection model using random forest classification identified biomarkers discriminating COVID-19 severity (AUC = 0.82), the strongest being HGF, EN-RAGE, cathepsin L. Adding ACE1 activity and anti-SARS-CoV-2 antibody titres improved differentiation between ambulatory and hospitalised participants. Notably, RAS dysregulation has unique severity associations in coronavirus infections with implications for treatment and pathophysiological mechanisms.

Demystifying food flavor: Flavor data interpretation through machine learning

Flavor data obtained from analytical techniques are vast and complex, which increases the difficulty of multi-factorial analysis. This study aims to provide a machine learning (ML)-based framework to interpret flavor data, exploiting four widely used techniques, i.e., Principal Component Analysis (PCA), Redundancy Analysis (RDA), Partial Least Squares (PLS), and Random Forest (RF). To demonstrate the potential of these ML techniques, two case studies, one with semi-quantitative data and the other with quantitative data, were discussed. Results indicate that PCA is useful for data exploration; RDA can quantify the statistical significance of factors; combining feature importance analysis results from PLS and RF offer a comprehensive understanding of marker compounds. Regarding classification performance, PLS excels in handling collinear data, whereas RF captures complex patterns if sufficient data are available. However, overfitting is a risk for datasets with small sample sizes. Overall, carefully selecting and integrating those ML techniques could demystify food flavor.

Malignant Transformed and Non-Transformed Oral Leukoplakias Are Metabolically Different

Understanding the early molecular events driving oral carcinogenesis is vital for diagnosing oral squamous cell carcinoma (OSCC) promptly. While metabolic differences between oral leukoplakia (OLK), OSCC, and healthy oral mucosa have been reported, the metabolic changes distinguishing malignant transformed OLKs (MT-OLK) from non-transformed OLKs (NT-OLK) remain unexplored. Here, we examine the metabolomic profiles of 5 cases of MT-OLK and 15 of NT-OLK to identify key predictive molecules using untargeted high-performance liquid chromatography-mass spectrometry. The potentially discriminant compounds were highlighted through a robust statistical analysis workflow, and the dysregulated metabolic pathways were illustrated by enrichment analysis. Seventeen molecular features, primarily lipids-including phospholipids, oxidised lipids, cholesteryl esters, and fatty acids-were identified as discriminants between MT-OLK and NT-OLK across statistical and bioinformatic approaches. Pathway enrichment analysis revealed alterations in lipid metabolism, particularly fatty acid synthesis and degradation, steroid hormone biosynthesis, and glycerophospholipid metabolism. Predictive models showed high accuracy (AUC = 0.88) in distinguishing the two groups. This study suggests that metabolomics has the potential to differentiate between MT-OLK and NT-OLK by identifying candidate biomarkers that may contribute to the understanding of malignant transformation. Validation in larger cohorts is warranted to translate these findings into clinical practice.

Source attribution of Listeria monocytogenes in the Netherlands

The aim of this study was to determine the relative contributions of various potential food sources of human listeriosis and to identify source-specific risk factors, at exposure level, for human Listeria monocytogenes (Lm) infection. To achieve this, available Lm isolates from human cases (n = 756) and food/animal sources (n = 950) from national surveillance systems in the Netherlands (2010-2020) were whole genome sequenced. Additionally, questionnaire-based exposure data for human cases was collected. Source attribution analysis was performed using a Random Forest model based on core-genome multilocus sequence typing (cgMLST). Risk factors for human Lm infection of cattle, chicken and seafood origin were determined using beta regression analysis on the cgMLST-based attribution estimates. Results indicated that the 756 human Lm isolates were mainly attributed to cattle (62.3 %), chicken (19.4 %), and seafood (16.9 %). Specifically, fresh meat (86.2 %), including fresh bovine meat (43.7 %) and fresh chicken meat (39.3 %), accounted for most cases. These attributions stemmed from Lm contamination of either the food products or their production environments. Consumption of steak tartare and smoked salmon was associated with an increased risk of human Lm infections attributed to cattle and seafood, respectively, while no specific risk factors for chicken-borne listeriosis were identified. This study indicated that Lm isolates of cattle origin, particularly those from fresh bovine meat and associated production environments, are estimated to be the primary cause of human listeriosis in the Netherlands. This aligns with several other European source attribution studies on Lm. Moreover, the identified risk factors for human Lm infection from cattle (i.e. steak tartare) and seafood (i.e. smoked salmon) clearly indicated their attributable sources. This joint analysis of core genome and epidemiological data provided novel insights into the origins and transmission pathways of human listeriosis.

The type of environment has a greater impact on the larval microbiota of Anopheles arabiensis than on the microbiota of their breeding water

Mosquito larvae of the genus Anopheles develop entirely in water, frequently visiting the surface for air. The aquatic environment plays a key role in shaping their microbiota, but the connection between environmental characteristics of breeding sites and larval microbiota remains underexplored. This study focuses on Anopheles arabiensis, which inhabits the surface microlayer (SML) of breeding sites, a zone with high particle density. We hypothesized that the SML could allow us to capture the diversity of the surrounding environment, and in turn its influence on the larval microbial communities. To test this, we collected A. arabiensis larvae and SML samples from various breeding sites categorized by environmental features. Our results confirm that breeding site characteristics are significant drivers of the bacterial species present in mosquito larvae. Additionally, we found that the larval micro-environment selectively shapes its microbiota, highlighting a dynamic interplay between environmental and internal factors. Interestingly, specific bacterial families were associated with the presence or absence of larvae in breeding sites, suggesting potential ecological roles. These findings expand our understanding of vector-mosquito microbiota, emphasizing the importance of breeding site features in shaping larval microbial communities and providing a foundation for future research on mosquito ecology and control strategies.

Medicago truncatula genotype drives the plant nutritional strategy and its associated rhizosphere bacterial communities

Harnessing the plant microbiome through plant genetics is of increasing interest to those seeking to improve plant nutrition and health. While genome-wide association studies (GWAS) have been conducted to identify plant genes driving the plant microbiome, more multidisciplinary studies are required to assess the relationships among plant genetics, plant microbiome and plant fitness. Using a metabarcoding approach, we characterized the rhizosphere bacterial communities of a core collection of 155 Medicago truncatula genotypes along with the plant phenotype and investigated the plant genetic effects through GWAS. The different genotypes within the M. truncatula core collection showed contrasting growth and nutritional strategies but few loci were associated with these ecophysiological traits. To go further, we described its associated rhizosphere bacterial communities, dominated by Proteobacteria, Actinobacteria and Bacteroidetes, and defined a core rhizosphere bacterial community. Next, the occurrences of bacterial candidates predicting plant ecophysiological traits of interest were identified using random forest analyses. Some of them were heritable and plant loci were identified, pinpointing genes related to response to hormone stimulus, systemic acquired resistance, response to stress, nutrient starvation or transport, and root development. Together, these results suggest that plant genetics can affect plant growth and nutritional strategies by harnessing keystone bacteria in a well-connected interaction network.

Distinct immune profiles in children living with HIV based on timing and duration of suppressive antiretroviral treatment

Timely initiation of antiretroviral therapy (ART) remains a major challenge in the effort to treat children living with HIV ("CLH") and little is known regarding the dynamics of immune normalization following ART in CLH with varying times to and durations of ART. Here, we leveraged two cohorts of virally-suppressed CLH from Nairobi, Kenya to examine differences in the peripheral immune systems between two cohorts of age-matched children (to control for immune changes with age): one group which initiated ART during early HIV infection and had been on ART for 5-6 years at evaluation (early, long-term treated; "ELT" cohort), and one group which initiated ART later and had been on ART for approximately 9 months at evaluation (delayed, short-term treated; "DST" cohort). We profiled PBMC and purified NK cells from these two cohorts by mass cytometry time-of-flight (CyTOF). Although both groups of CLH had undetectable viral RNA load at evaluation, there were marked differences in both immune composition and immune phenotype between the ELT cohort and the DST cohort. DST donors had reduced CD4 T cell percentages, decreased naive to effector memory T cell ratios, and markedly higher expression of stress-induced markers. Conversely, ELT donors had higher naive to effector memory T cell ratios, low expression of stress-induced markers, and increased expression of markers associated with an effective antiviral response and resolution of inflammation. Collectively, our results demonstrate key differences in the immune systems of virally-suppressed CLH with different ages at ART initiation and durations of treatment and provide further rationale for emphasizing early onset of ART.

Identification of a 5-Plex Cytokine Signature that Differentiates Patients with Multiple Systemic Inflammatory Diseases

Patients with non-infectious systemic inflammation may suffer from one of many diseases, including hyperinflammation (HI), autoinflammatory disorders (AID), and systemic autoimmune disease (AI). Despite their clinical overlap, the pathophysiology and patient management differ between these disorders. We aimed to investigate blood biomarkers able to discriminate between patient groups. We included 44 patients with active clinical and/or genetic systemic inflammatory disease (9 HI, 27 AID, 8 systemic AI) and 16 healthy controls. We quantified 55 serum proteins and combined multiple machine learning algorithms to identify five proteins (CCL26, CXCL10, ICAM-1, IL-27, and SAA) that maximally separated patient groups. High ICAM-1 was associated with HI. AID was characterized by an increase in SAA and decrease in CXCL10 levels. A trend for higher CXCL10 and statistically lower SAA was observed in patients with systemic AI. Principal component analysis and unsupervised hierarchical clustering confirmed separation of disease groups. Logistic regression modelling revealed a high statistical significance for HI (P = 0.001), AID, and systemic AI (P < 0.0001). Predictive accuracy was excellent for systemic AI (AUC 0.94) and AID (0.91) and good for HI (0.81). Further research is needed to validate findings in a larger prospective cohort. Results will contribute to a better understanding of the pathophysiology of systemic inflammatory disorders and can improve diagnosis and patient management.

Using machine learning to identify environmental factors that collectively determine microbial community structure of activated sludge

Activated sludge (AS) microbial communities are influenced by various environmental variables. However, a comprehensive analysis of how these variables jointly and nonlinearly shape the AS microbial community remains challenging. In this study, we employed advanced machine learning techniques to elucidate the collective effects of environmental variables on the structure and function of AS microbial communities. Applying Dirichlet multinomial mixtures analysis to 311 global AS samples, we identified four distinct microbial community types (AS-types), each characterized by unique microbial compositions and metabolic profiles. We used 14 classical linear and nonlinear machine learning methods to select a baseline model. The extremely randomized trees demonstrated optimal performance in learning the relationship between environmental factors and AS types (with an accuracy of 71.43%). Feature selection identified critical environmental factors and their importance rankings, including latitude (Lat), longitude (Long), precipitation during sampling (Precip), solids retention time (SRT), effluent total nitrogen (Effluent TN), average temperature during sampling month (Avg Temp), mixed liquor temperature (Mixed Temp), influent biochemical oxygen demand (Influent BOD), and annual precipitation (Annual Precip). Significantly, Lat, Long, Precip, Avg Temp, and Annual Precip, influenced metabolic variations among AS types. These findings emphasize the pivotal role of environmental variables in shaping microbial community structures and enhancing metabolic pathways within activated sludge. Our study encourages the application of machine learning techniques to design artificial activated sludge microbial communities for specific environmental purposes.

Identification and Replication of Urine Metabolites Associated With Short-Term and Habitual Intake of Sweet and Fatty Snacks in European Children and Adolescents

BACKGROUND

Intake of sweet and fatty snacks may partly contribute to the occurrence of obesity and other health conditions in childhood. Traditional dietary assessment methods may be limited in accurately assessing the intake of sweet and fatty snacks in children. Metabolite biomarkers may aid the objective assessment of children's food intake and support establishing diet-disease relationships.

OBJECTIVES

The present study aimed to identify biomarkers of sweet and fatty snack intake in 2 independent cohorts of European children.

METHODS

We used data from the IDEFICS/I.Family cohort from baseline (2007/2008) and 2 follow-up examination waves (2009/2010 and 2013/2014). In total, 1788 urine samples from 599 children were analyzed for untargeted metabolomics using high-resolution liquid chromatography-mass spectrometry. Short-term dietary intake was assessed by 24-h dietary recalls, and habitual dietary intake was calculated with the National Cancer Institute method. Data from the Dortmund Nutritional and Anthropometric Longitudinal Designed (DONALD) cohort of 24-h urine samples (n = 567) and 3-d weighted dietary records were used for external replication of results. Multivariate modeling with unbiased variable selection in R algorithms and linear mixed models were used to identify novel biomarkers. Metabolite features significantly associated with dietary intake were then annotated.

RESULTS

In total, 66 metabolites were discovered and found to be statistically significant for chocolate candy; cakes, puddings, and cookies; candy and sweets; ice cream; and crisps. Most of the features (n = 62) could not be annotated. Short-term and habitual chocolate intake were positively associated with theobromine, xanthosine, and cyclo(L-prolyl-L-valyl). These results were replicated in the DONALD cohort. Short-term candy and sweet intake was negatively associated with octenoylcarnitine.

CONCLUSIONS

Of the potential metabolite biomarkers of sweet and fatty snacks in children, 3 biomarkers of chocolate intake, namely theobromine, xanthosine, and cyclo(L-prolyl-L-valyl), are externally replicated. However, these potential biomarkers require further validation in children.

Quantitative modelling for dengue and Aedes mosquitoes in Africa: A systematic review of current approaches and future directions for Early Warning System development

The rapid spread and growing number of dengue cases worldwide, alongside the absence of comprehensive vaccines and medications, highlights the critical need for robust tools to monitor, prevent, and control the disease. This review aims to provide an updated overview of important covariates and quantitative modelling techniques used to predict or forecast dengue and/or its vector Aedes mosquitoes in Africa. A systematic search was conducted across multiple databases, including PubMed, EMBASE, EBSCOhost, and Scopus, restricted to studies conducted in Africa and published in English. Data management and extraction process followed the 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses' (PRISMA) framework. The review identified 30 studies, with the majority (two-thirds) focused on models for predicting Aedes mosquito populations dynamics as a proxy for dengue risk. The remainder of the studies utilized human dengue cases, incidence or prevalence data as an outcome. Input data for mosquito and dengue risk models were mainly obtained from entomological studies and cross-sectional surveys, respectively. More than half of the studies (56.7%) incorporated climatic factors, such as rainfall, humidity, and temperature, alongside environmental, demographic, socio-economic, and larval/pupal abundance factors as covariates in their models. Regarding quantitative modelling techniques, traditional statistical regression methods like logistic and linear regression were preferred (60.0%), followed by machine learning models (16.7%) and mixed effects models (13.3%). Notably, only 36.7% of the models disclosed variable selection techniques, and a mere 20.0% conducted model validation, highlighting a significant gap in reporting methodology and assessing model performance. Overall, this review provides a comprehensive overview of potential covariates and methodological approaches currently applied in the African context for modelling dengue and/or its vector, Aedes mosquito. It also underscores the gaps and challenges posed by limited surveillance data availability, which hinder the development of predictive models to be used as early warning systems in Africa.

Metabolite profiles of distinct obesity phenotypes integrating impacts of altitude and their association with diet and metabolic disorders in Tibetans

OBJECTIVE

Improved understanding of metabolic obesity phenotypes holds great promise for personalized strategies to combat obesity and its co-morbidities. Such investigation is however lacking in Tibetans with unique living environments and lifestyle in the highlands. Effects of altitude on heterogeneous metabolic obesity phenotypes remain unexplored.

METHODS

We defined metabolic obesity phenotypes i.e., metabolically healthy/unhealthy and obesity/normal weight in Tibetans (n = 1204) living at 2800 m in the suburb or over 4000 m in pastoral areas. 129 lipoprotein parameters and 25 low-molecular-weight metabolites were quantified and their associations with each phenotype were assessed using logistic regression models adjusting for potential confounders. The metabolic BMI (mBMI) was generated using a machine learning strategy and its relationship with prevalence of obesity co-morbidities and dietary exposures were investigated.

RESULTS

Ultrahigh altitude positively associated with the metabolically healthy and non-obese phenotype and had a tendency towards a negative association with metabolically unhealthy phenotype. Phenotype-specific associations were found for 107 metabolites (e.g., lipoprotein subclasses, N-acetyl-glycoproteins, amino acids, fatty acids and lactate, p < 0.05), among which 55 were manipulated by altitude. The mBMI showed consistent yet more pronounced associations with cardiometabolic outcomes than BMI. The ORs for diabetes, prediabetes and hypertriglyceridemia were reduced in individuals residing at ultrahigh altitude compared to those residing at high altitude. The mBMI mediated the negative association between pastoral diet and prevalence of prediabetes, hypertension and hypertriglyceridemia, respectively.

CONCLUSIONS

We found metabolite markers representing distinct obesity phenotypes associated with obesity co-morbidities and the modification effect of altitude, deciphering mechanisms underlying protective effect of ultrahigh altitude and the pastoral diet on metabolic health.

Plasma Lipidomic Profiles Improve upon Traditional Risk Factors for the Prediction of Arterial Stiffness Among Patients with Type 2 Diabetes Mellitum: A Randomized, Placebo-Controlled Trial

BACKGROUND

Exercise or vitamin D intervention can reduce the risk of arterial stiffness; however, the underlying mechanisms of lipid metabolism remain unexplored. To examine the effects of a 12-week moderate and vigorous exercise program (65-80% maximal heart rate, 60 min/time, 2~3 times/week) with or without vitamin D supplementation (1000 IU/day) on the reduction in arterial stiffness and further explore whether the effects of interventions could be associated with the basal lipidome among patients with Type 2 diabetes mellitum (T2DM).

METHOD

61 patients with T2DM were randomly assigned to the following groups: control (CON, n = 15), exercise (EX, n = 14), vitamin D (VD, n = 16), and exercise + vitamin D (EX + VD, n = 16). Arterial stiffness risk factors (ankle-brachial index (ABI); brachial-ankle pulse wave velocity (baPWV), systolic blood pressure (SBP), and diastolic blood pressure (DBP)) were evaluated before and after the intervention. The plasma lipidome was determined using ultra-performance liquid chromatography coupled with tandem mass spectrometry. Machine learning was applied to establish prediction models for the responsiveness to arterial stiffness.

RESULT

Vitamin D supplementation could inhibit the decrease in the ankle-brachial index (mean ± SD: EX + VD and VD, -0.001 ± 0.058; EX + CON, -0.047 ± -0.089; p = 0.03). We observed high inter-individual variability in the arterial stiffness risk factors in response to the interventions. We also found that optimally selecting the lipid predictors at baseline, such as SM d44:6, LPE 18:2, and Hex2Cer 29:0, could enhance the predictive power by 100% for arm SBP changes in the exercise group. Basal levels of Cer (33:1) and GM3 (44:4) could enhance the predictive power by 100% for changes in baPWV in the vitamin D group.

CONCLUSIONS

A 12-week vitamin D supplementation was beneficial in preventing arterial stiffness. Compared with traditional clinical risk factors, specific lipids at baseline could significantly improve the ability to predict intervention-induced changes in the reduction of arterial stiffness.

Amino acid metabolomics and machine learning-driven assessment of future liver remnant growth after hepatectomy in livers of various backgrounds

Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with different liver backgrounds is a pressing clinical issue. Amino acid (AA) metabolism plays a crucial role in liver regeneration. In this study, we combined metabolomics and machine learning (ML) to develop a generalized future liver remnant assessment model for multiple liver backgrounds. The liver index was calculated at 0, 6, 24, 48, 72 and 168 h after 70 % PH in healthy mice and mice with nonalcoholic steatohepatitis or liver fibrosis. The serum levels of 39 amino acids (AAs) were measured using UPLC-MS/MS. The dataset was randomly divided into training and testing sets at a 2:1 ratio, and orthogonal partial least squares regression (OPLS) and minimally biased variable selection in R (MUVR) were used to select a metabolite signature of AAs. To assess liver remnant growth, nine ML models were built, and evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The post-Pareto technique for order preference by similarity to the ideal solution (TOPSIS) was employed for ranking the ML algorithms, and a stacking technique was utilized to establish consensus among the superior algorithms. Compared with those of OPLS, the signature AAs set identified by MUVR (Thr, Arg, EtN, Phe, Asa, 3MHis, Abu, Asp, Tyr, Leu, Ser, and bAib) are more concise. Post-Pareto TOPSIS ranking demonstrated that the majority of ML algorithm in combinations with MUVR outperformed those with OPLS. The established SVM-KNN consensus model performed best, with an R2 of 0.79, an MAE of 0.0029, and an RMSE of 0.0035 for the testing set. This study identified a metabolite signature of 12 AAs and constructed an SVM-KNN consensus model to assess future liver remnant growth after PH in mice with different liver backgrounds. Our preclinical study is anticipated to establish an alternative and generalized assessment method for liver regeneration.

Acarbose enhances the efficacy of immunotherapy against solid tumours by modulating the gut microbiota

The crucial role of gut microbiota in shaping immunotherapy outcomes has prompted investigations into potential modulators. Here we show that oral administration of acarbose significantly increases the anti-tumour response to anti-PD-1 therapy in female tumour-bearing mice. Acarbose modulates the gut microbiota composition and tryptophan metabolism, thereby contributing to changes in chemokine expression and increased T cell infiltration within tumours. We identify CD8+ T cells as pivotal components determining the efficacy of the combined therapy. Further experiments reveal that acarbose promotes CD8+ T cell recruitment through the CXCL10-CXCR3 pathway. Faecal microbiota transplantation and gut microbiota depletion assays indicate that the effects of acarbose are dependent on the gut microbiota. Specifically, acarbose enhances the efficacy of anti-PD-1 therapy via the tryptophan catabolite indoleacetate, which promotes CXCL10 expression and thus facilitates CD8+ T cell recruitment, sensitizing tumours to anti-PD-1 therapy. The bacterial species Bifidobacterium infantis, which is enriched by acarbose, also improves response to anti-PD-1 therapy. Together, our study endorses the potential combination of acarbose and anti-PD-1 for cancer immunotherapy.

Metabolomics signatures of sweetened beverages and added sugar are related to anthropometric measures of adiposity in young individuals: results from a cohort study

BACKGROUND

The associations of sweetened beverages (SBs) and added sugar (AS) intake with adiposity are still debated. Metabolomics could provide insights into the mechanisms linking their intake to adiposity.

OBJECTIVES

We aimed to identify metabolomics biomarkers of intake of low- and no-calorie sweetened beverages (LNCSBs), sugar-sweetened beverages (SSBs), and ASs and to investigate their associations with body mass index, body fat percentage, and waist circumference.

METHODS

We analyzed 3 data sets from the Dortmund Nutritional and Anthropometric Longitudinally Designed (DONALD) cohort study, of children who provided 2 urine samples (n = 297), adolescents who provided a single urine sample (n = 339), and young adults who provided a single plasma sample (n = 195). Urine and plasma were analyzed using untargeted metabolomics. Dietary intakes were assessed using 3-d weighed dietary records. The random forest, partial least squares, and least absolute shrinkage and selection operator were jointly used for metabolite selection. We examined associations of intakes with metabolites and anthropometric measures using linear and mixed-effects regression.

RESULTS

In adolescents, LNCSB were positively associated with acesulfame (β: 0.0012; 95% confidence interval [CI]: 0.0006, 0.0019) and saccharin (β: 0.0009; 95% CI: 0.0002, 0.0015). In children, the association was observed with saccharin (β: 0.0016; 95% CI: 0.0005, 0.0027). In urine and plasma, SSBs were positively associated with 1-methylxanthine (β: 0.0005; 95% CI: 0.0003, 0.0008; and β: 0.0010, 95% CI 0.0004, 0.0015, respectively) and 5-acetylamino-6-amino-3-methyluracil (β: 0.0005; 95% CI: 0.0002, 0.0008; and β: 0.0009; 95% CI: 0.0003, 0.0014, respectively). AS was associated with urinary sucrose (β: 0.0095; 95% CI: 0.0069, 0.0121) in adolescents. Some of the food-related metabolomics profiles were also associated with adiposity measures.

CONCLUSIONS

We identified SBs- and AS-related metabolites, which may be important for understanding the interplay between these intakes and adiposity in young individuals.

Identification of CCL20 as a Prognostic Predictor for Severe Fever With Thrombocytopenia Syndrome Based on Plasma Proteomics

BACKGROUND

Severe fever with thrombocytopenia syndrome (SFTS), a lethal tick-borne hemorrhagic fever, prompted our investigation into prognostic predictors and potential drug targets using plasma Olink Proteomics.

METHODS

Employing the Olink assay, we analyzed 184 plasma proteins in 30 survivors and 8 nonsurvivors of SFTS. Validation was performed in a cohort of 154 patients with SFTS via enzyme-linked immunosorbent assay. We utilized the Drug-Gene Interaction Database to identify protein-drug interactions.

RESULTS

Nonsurvivors exhibited 110 differentially expressed proteins as compared with survivors, with functional enrichment in the cell chemotaxis-related pathway. Thirteen differentially expressed proteins-including C-C motif chemokine 20 (CCL20), calcitonin gene-related peptide alpha, and pleiotrophin-were associated with multiple-organ dysfunction syndrome. CCL20 emerged as the top predictor of death, demonstrating an area under the curve of 1 (P = .0004) and 0.9033 (P < .0001) in the discovery and validation cohorts, respectively. Patients with CCL20 levels exceeding 45.74 pg/mL exhibited a fatality rate of 45.65%, while no deaths occurred in those with lower CCL20 levels. Furthermore, we identified 202 Food and Drug Administration-approved drugs targeting 37 death-related plasma proteins.

CONCLUSIONS

Distinct plasma proteomic profiles characterize SFTS cases with different outcomes, with CCL20 emerging as a novel, sensitive, accurate, and specific biomarker for predicting SFTS prognosis.

Effects of Protein on Green Tea Quality in a Milk-Tea Model during Heat Treatment: Antioxidant Activity, Foaming Properties, and Unbound Small-molecule Metabolome

Tea drinks/beverage has a long history and milk is often added to enhance its taste and nutritional value, whereas the interaction between the tea bioactive compounds with proteins has not been systematically investigated. In this study, a milk-tea model was prepared by mixing green tea solution with milk and then heated at 100°C for 15 min. The milk tea was then measured using biochemical assay, antioxidant detection kit, microscopy as well as HPLC-QTOF-MS/MS after ultrafiltration. The study found that as the concentration of milk protein increased in the milk-tea system, the total phenol-protein binding rate raised from 19.63% to 51.08%, which led to a decrease in free polyphenol content. This decrease of polyphenol was also revealed in the antioxidant capacity, including 2,2-diphenyl-1-picrylhydrazyl radical scavenging ability and ferric ion reducing antioxidant power, in a dose-dependent manner. Untargeted metabolomics results revealed that the majority of small-molecule compounds/polyphenols in tea, such as epigallocatechin gallate, (-)-epicatechin gallate, and Catechin 5,7,-di-O-gallate, bound to milk proteins and were removed by ultrafiltration after addition of milk and heat treatment. The SDS-PAGE and Native-PAGE results further indicated that small molecule compounds in tea formed covalent and non-covalent complexes by binding to milk proteins. All above results partially explained that milk proteins form conjugates with tea small-molecule compounds. Consistently, the particle size of the tea-milk system increased as the tea concentration increased, but the polymer dispersity index decreased, indicating a more uniform molecular weight distribution of the particles in the system. Addition of milk protein enhanced foam ability in the milk-tea system but reduced foam stability. In summary, our findings suggest that the proportion of milk added to tea infusion needs to be considered to maintain the quality of milk-tea from multiple perspectives, including stability, nutritional quality and antioxidant activity.

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

BACKGROUND

Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance.

RESULTS

We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model.

CONCLUSIONS

We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

Volatile organic compounds in exhaled breath: a promising approach for accurate differentiation of lung adenocarcinoma and squamous cell carcinoma

Lung cancer subtyping, particularly differentiating adenocarcinoma (ADC) from squamous cell carcinoma (SCC), is paramount for clinicians to develop effective treatment strategies. In this study, we aimed: (i) to discover volatile organic compound (VOC) biomarkers for precise diagnosis of ADC and SCC, (ii) to investigated the impact of risk factors on ADC and SCC prediction, and (iii) to explore the metabolic pathways of VOC biomarkers. Exhaled breath samples from patients with ADC (n= 149) and SCC (n= 94) were analyzed by gas chromatography-mass spectrometry. Both multivariate and univariate statistical analysis method were employed to identify VOC biomarkers. Support vector machine (SVM) prediction models were developed and validated based on these VOC biomarkers. The impact of risk factors on ADC and SCC prediction was investigated. A panel of 13 VOCs was found to differ significantly between ADC and SCC. Utilizing the SVM algorithm, the VOC biomarkers achieved a specificity of 90.48%, a sensitivity of 83.50%, and an area under the curve (AUC) value of 0.958 on the training set. On the validation set, these VOC biomarkers attained a predictive power of 85.71% for sensitivity and 73.08% for specificity, along with an AUC value of 0.875. Clinical risk factors exhibit certain predictive power on ADC and SCC prediction. Integrating these risk factors into the prediction model based on VOC biomarkers can enhance its predictive accuracy. This work indicates that exhaled breath holds the potential to precisely detect ADCs and SCCs. Considering clinical risk factors is essential when differentiating between these two subtypes.

Premature skewing of T cell receptor clonality and delayed memory expansion in HIV-exposed infants

While preventing vertical HIV transmission has been very successful, HIV-exposed uninfected infants (iHEU) experience an elevated risk to infections compared to HIV-unexposed and uninfected infants (iHUU). Here we present a longitudinal multimodal analysis of infant immune ontogeny that highlights the impact of HIV/ARV exposure. Using mass cytometry, we show alterations in T cell memory differentiation between iHEU and iHUU being significant from week 15 of life. The altered memory T cell differentiation in iHEU was preceded by lower TCR Vβ clonotypic diversity and linked to TCR clonal depletion within the naïve T cell compartment. Compared to iHUU, iHEU had elevated CD56loCD16loPerforin+CD38+CD45RA+FcεRIγ+ NK cells at 1 month postpartum and whose abundance pre-vaccination were predictive of vaccine-induced pertussis and rotavirus antibody responses post 3 months of life. Collectively, HIV/ARV exposure disrupted the trajectory of innate and adaptive immunity from birth which may underlie relative vulnerability to infections in iHEU.

Fasting plasma metabolites reflecting meat consumption and their associations with incident type 2 diabetes in two Swedish cohorts

BACKGROUND

Consumption of processed red meat has been associated with increased risk of developing type 2 diabetes (T2D), but challenges in dietary assessment call for objective intake biomarkers.

OBJECTIVES

This study aimed to investigate metabolite biomarkers of meat intake and their associations with T2D risk.

METHODS

Fasting plasma samples were collected from a case-control study nested within Västerbotten Intervention Program (VIP) (214 females and 189 males) who developed T2D after a median follow-up of 7 years. Panels of biomarker candidates reflecting the consumption of total, processed, and unprocessed red meat and poultry were selected from the untargeted metabolomics data collected on the controls. Observed associations were then replicated in Swedish Mammography clinical subcohort in Uppsala (SMCC) (n = 4457 females). Replicated metabolites were assessed for potential association with T2D risk using multivariable conditional logistic regression in the discovery and Cox regression in the replication cohorts.

RESULTS

In total, 15 metabolites were associated with ≥1 meat group in both cohorts. Acylcarnitines 8:1, 8:2, 10:3, reflecting higher processed meat intake [r > 0.22, false discovery rate (FDR) < 0.001 for VIP and r > 0.05; FDR < 0.001 for SMCC) were consistently associated with higher T2D risk in both data sets. Conversely, lysophosphatidylcholine 17:1 and phosphatidylcholine (PC) 15:0/18:2 were associated with lower processed meat intake (r < -0.12; FDR < 0.023, for VIP and r < -0.05; FDR < 0.001, for SMCC) and with lower T2D risk in both data sets, except for PC 15:0/18:2, which was significant only in the VIP cohort. All associations were attenuated after adjustment for BMI (kg/m2).

CONCLUSIONS

Consistent associations of biomarker candidates involved in lipid metabolism between higher processed red meat intake with higher T2D risk and between those reflecting lower intake with the lower risk may suggest a relationship between processed meat intake and higher T2D risk. However, attenuated associations after adjusting for BMI indicates that such a relationship may at least partly be mediated or confounded by BMI.

Adjusting for covariates and assessing modeling fitness in machine learning using MUVR2

Motivation

Machine learning (ML) methods are frequently used in Omics research to examine associations between molecular data and for example exposures and health conditions. ML is also used for feature selection to facilitate biological interpretation. Our previous MUVR algorithm was shown to generate predictions and variable selections at state-of-the-art performance. However, a general framework for assessing modeling fitness is still lacking. In addition, enabling to adjust for covariates is a highly desired, but largely lacking trait in ML. We aimed to address these issues in the new MUVR2 framework.

Results

The MUVR2 algorithm was developed to include the regularized regression framework elastic net in addition to partial least squares and random forest modeling. Compared with other cross-validation strategies, MUVR2 consistently showed state-of-the-art performance, including variable selection, while minimizing overfitting. Testing on simulated and real-world data, we also showed that MUVR2 allows for the adjustment for covariates using elastic net modeling, but not using partial least squares or random forest.

Availability and implementation

Algorithms, data, scripts, and a tutorial are open source under GPL-3 license and available in the MUVR2 R package at https://github.com/MetaboComp/MUVR2.

Predictive phage therapy for Escherichia coli urinary tract infections: Cocktail selection for therapy based on machine learning models

This study supports the development of predictive bacteriophage (phage) therapy: the concept of phage cocktail selection to treat a bacterial infection based on machine learning (ML) models. For this purpose, ML models were trained on thousands of measured interactions between a panel of phage and sequenced bacterial isolates. The concept was applied to Escherichia coli associated with urinary tract infections. This is an important common infection in humans and companion animals from which multidrug-resistant (MDR) bloodstream infections can originate. The global threat of MDR infection has reinvigorated international efforts into alternatives to antibiotics including phage therapy. E. coli exhibit extensive genome-level variation due to horizontal gene transfer via phage and plasmids. Associated with this, phage selection for E. coli is difficult as individual isolates can exhibit considerable variation in phage susceptibility due to differences in factors important to phage infection including phage receptor profiles and resistance mechanisms. The activity of 31 phage was measured on 314 isolates with growth curves in artificial urine. Random Forest models were built for each phage from bacterial genome features, and the more generalist phage, acting on over 20% of the bacterial population, exhibited F1 scores of >0.6 and could be used to predict phage cocktails effective against previously untested strains. The study demonstrates the potential of predictive ML models which integrate bacterial genomics with phage activity datasets allowing their use on data derived from direct sequencing of clinical samples to inform rapid and effective phage therapy.

Associations of the placental metabolome with immune maturation up to one year of age in the Swedish NICE-cohort

INTRODUCTION

Allergies and other immune-mediated diseases are thought to result from incomplete maturation of the immune system early in life. We previously showed that infants' metabolites at birth were associated with immune cell subtypes during infancy. The placenta supplies the fetus with nutrients, but may also provide immune maturation signals.

OBJECTIVES

To examine the relationship between metabolites in placental villous tissue and immune maturation during the first year of life and infant and maternal characteristics (gestational length, birth weight, sex, parity, maternal age, and BMI).

METHODS

Untargeted metabolomics was measured using Liquid Chromatography-Mass Spectrometry. Subpopulations of T and B cells were measured using flow cytometry at birth, 48 h, one, four, and 12 months. Random forest analysis was used to link the metabolomics data with the T and B cell sub populations as well as infant and maternal characteristics.

RESULTS

Modest associations (Q2 = 0.2-0.3) were found between the placental metabolome and kappa-deleting recombination excision circles (KREC) at birth and naïve B cells and memory T cells at 12 months. Weak associations were observed between the placental metabolome and sex and parity. Still, most metabolite features of interest were of low intensity compared to associations previously found in cord blood, suggesting that underlying metabolites were not of placental origin.

CONCLUSION

Our results indicate that metabolomic measurements of the placenta may not effectively recognize metabolites important for immune maturation.

Exploration of differential responses to FODMAPs and gluten in people with irritable bowel syndrome- a double-blind randomized cross-over challenge study

INTRODUCTION

There is large variation in response to diet in irritable bowel syndrome (IBS) and determinants for differential response are poorly understood.

OBJECTIVES

Our aim was to investigate differential clinical and molecular responses to provocation with fermentable oligo-, di-, monosaccharides, and polyols (FODMAPs) and gluten in individuals with IBS.

METHODS

Data were used from a crossover study with week-long interventions with either FODMAPs, gluten or placebo. The study also included a rapid provocation test. Molecular data consisted of fecal microbiota, short chain fatty acids, and untargeted plasma metabolomics. IBS symptoms were evaluated with the IBS severity scoring system. IBS symptoms were modelled against molecular and baseline questionnaire data, using Random Forest (RF; regression and clustering), Parallel Factor Analysis (PARAFAC), and univariate methods.

RESULTS

Regression and classification RF models were in general of low predictive power (Q2 ≤ 0.22, classification rate < 0.73). Out of 864 clustering models, only 2 had significant associations to clusters (0.69 < CR < 0.73, p < 0.05), but with no associations to baseline clinical measures. Similarly, PARAFAC revealed no clear association between metabolome data and IBS symptoms.

CONCLUSION

Differential IBS responses to FODMAPs or gluten exposures could not be explained from clinical and molecular data despite extensive exploration with different data analytical approaches. The trial is registered at www.

CLINICALTRIALS

gov as NCT03653689 31/08/2018.

Metabolome biomarkers linking dietary fibre intake with cardiometabolic effects: results from the Danish Diet, Cancer and Health-Next Generations MAX study

Biomarkers associated with dietary fibre intake, as complements to traditional dietary assessment tools, may improve the understanding of its role in human health. Our aim was to discover metabolite biomarkers related to dietary fibre intake and investigate their association with cardiometabolic risk factors. We used data and samples from the Danish Diet Cancer and Health Next Generation (DCH-NG) MAX-study, a one-year observational study with evaluations at baseline, six and 12 months (n = 624, 55% female, mean age: 43 years, 1353 observations). Direct associations between fibre intake and plasma concentrations of 2,6-dihydroxybenzoic acid (2,6-DHBA) and indolepropionic acid were observed at the three time-points. Both metabolites showed an intraclass-correlation coefficient (ICC) > 0.50 and were associated with the self-reported intake of wholegrain cereals, and of fruits and vegetables, respectively. Other metabolites associated with dietary fibre intake were linolenoyl carnitine, 2-aminophenol, 3,4-DHBA, and proline betaine. Based on the metabolites associated with dietary fibre intake we calculated predicted values of fibre intake using a multivariate, machine-learning algorithm. Metabolomics-based predicted fibre, but not self-reported fibre values, showed negative associations with cardiometabolic risk factors (i.e. high sensitivity C-reactive protein, systolic and diastolic blood pressure, all FDR-adjusted p-values <0.05). Furthermore, different correlations with gut microbiota composition were observed. In conclusion, 2,6-DHBA and indolepropionic acid in plasma may better link dietary fibre intake with its metabolic effects than self-reported values. These metabolites may represent a novel class of biomarkers reflecting both dietary exposure and host and/or gut microbiota characteristics providing a read-out that is differentially related to cardiometabolic risk.

Dietary patterns, untargeted metabolite profiles and their association with colorectal cancer risk

We investigated data-driven and hypothesis-driven dietary patterns and their association to plasma metabolite profiles and subsequent colorectal cancer (CRC) risk in 680 CRC cases and individually matched controls. Dietary patterns were identified from combined exploratory/confirmatory factor analysis. We assessed association to LC-MS metabolic profiles by random forest regression and to CRC risk by multivariable conditional logistic regression. Principal component analysis was used on metabolite features selected to reflect dietary exposures. Component scores were associated to CRC risk and dietary exposures using partial Spearman correlation. We identified 12 data-driven dietary patterns, of which a breakfast food pattern showed an inverse association with CRC risk (OR per standard deviation increase 0.89, 95% CI 0.80-1.00, p = 0.04). This pattern was also inversely associated with risk of distal colon cancer (0.75, 0.61-0.96, p = 0.01) and was more pronounced in women (0.69, 0.49-0.96, p = 0.03). Associations between meat, fast-food, fruit soup/rice patterns and CRC risk were modified by tumor location in women. Alcohol as well as fruit and vegetables associated with metabolite profiles (Q2 0.22 and 0.26, respectively). One metabolite reflecting alcohol intake associated with increased CRC risk, whereas three metabolites reflecting fiber, wholegrain, and fruit and vegetables associated with decreased CRC risk.

OMICs Signatures Linking Persistent Organic Pollutants to Cardiovascular Disease in the Swedish Mammography Cohort

Cardiovascular disease (CVD) development may be linked to persistent organic pollutants (POPs), including organochlorine compounds (OCs) and perfluoroalkyl and polyfluoroalkyl substances (PFAS). To explore underlying mechanisms, we investigated metabolites, proteins, and genes linking POPs with CVD risk. We used data from a nested case-control study on myocardial infarction (MI) and stroke from the Swedish Mammography Cohort - Clinical (n = 657 subjects). OCs, PFAS, and multiomics (9511 liquid chromatography-mass spectrometry (LC-MS) metabolite features; 248 proteins; 8110 gene variants) were measured in baseline plasma. POP-related omics features were selected using random forest followed by Spearman correlation adjusted for confounders. From these, CVD-related omics features were selected using conditional logistic regression. Finally, 29 (for OCs) and 12 (for PFAS) unique features associated with POPs and CVD. One omics subpattern, driven by lipids and inflammatory proteins, associated with MI (OR = 2.03; 95% CI = 1.47; 2.79), OCs, age, and BMI, and correlated negatively with PFAS. Another subpattern, driven by carnitines, associated with stroke (OR = 1.55; 95% CI = 1.16; 2.09), OCs, and age, but not with PFAS. This may imply that OCs and PFAS associate with different omics patterns with opposite effects on CVD risk, but more research is needed to disentangle potential modifications by other factors.

Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

The effects of substituting red and processed meat for mycoprotein on biomarkers of cardiovascular risk in healthy volunteers: an analysis of secondary endpoints from Mycomeat

PURPOSE

Mycoprotein is a relatively novel food source produced from the biomass of Fusarium venenatum. It has previously been shown to improve CVD risk markers in intervention trials when it is compared against total meat. It has not hitherto been assessed specifically for benefits relative to red and processed meat.

METHODS

We leveraged samples from Mycomeat, an investigator-blind randomised crossover controlled trial in metabolically healthy male adults (n = 20), randomised to consume 240 g/day of red and processed meat for 14 days followed by mycoprotein, or vice versa. Blood biochemical indices were a priori defined secondary endpoints.

RESULTS

Mycoprotein consumption led to a 6.74% reduction in total cholesterol (P = 0.02) and 12.3% reduction in LDL cholesterol (P = 0.02) from baseline values. Change in fasted triglycerides was not significantly different between diets (+ 0.19 ± 0.11 mmol/l with mycoprotein, P = 0.09). There was a small but significant reduction in waist circumference for mycoprotein relative to meat (- 0.95 ± 0.42 cm, P = 0.04). Following the mycoprotein diet, mean systolic (- 2.41 ± 1.89 mmHg, P = 0.23) and diastolic blood pressure (- 0.80 ± 1.23 mmHg, P = 0.43) were reduced from baseline. There were no statistically significant effects of the intervention on urinary sodium, nitrite or TMAO; while urinary potassium (+ 126.12 ± 50.30 mmol/l, P = 0.02) and nitrate (+ 2.12 ± 0.90 mmol/l, P = 0.04) were both significantly higher with mycoprotein relative to meat. The study population comprised metabolically healthy adults, therefore, changes in plasma lipids had little effect on cardiovascular risk scores (- 0.34% FRS for mycoprotein P = 0.24).

CONCLUSIONS

These results confirm potential cardiovascular benefits when displacing red and processed meat with mycoprotein in the diet. Longer trials in higher risk study populations are needed to fully elucidate suggested benefits for blood pressure and body composition.

CLINICALTRIALS

gov Identifier: NCT03944421.

Broccoli-Derived Exosome-like Nanoparticles Alleviate Loperamide-Induced Constipation, in Correlation with Regulation on Gut Microbiota and Tryptophan Metabolism

Constipation, a common gastrointestinal dysfunction, damages patients' life quality and predisposes them to other serious diseases. Current strategies against constipation often cause drug dependency and side effects. Here, we demonstrated that broccoli-derived exosome-like nanoparticles (BENs), a natural product with high gastrointestinal stability, ameliorated LOP-induced constipation in mice. Specifically, orally administered BENs (17.5 mg/kg/d) effectively shortened defecation time, sped up intestinal propulsion rate, and increased feces amount in constipated mice. BENs also raised excitatory neurotransmitters SP and MTL and reduced inhibitory neurotransmitters VIP and ET-1. Mechanistically, BENs were taken up by gut microbes, restored LOP-disordered gut microbiota, and altered microbial metabolism of SCFAs and tryptophan, as evidenced by the results of fluorescence microscopy, 16S rRNA gene sequencing, and nontargeted metabolomics. Thereinto, BEN-enriched SCFA-producing microorganisms are closely associated with the feces amount and SP and VIP levels and BEN-elevated indole-3-pyruvic acid and 3-indoleacetic acid are highly linked to ET-1, SP, and MTL levels. Conclusively, BENs, mitigating constipation by regulating gut microbiota and microbial tryptophan metabolism, showed high potential to be developed as alternative regimens for constipation.

The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models

How to effectively obtain species-related low-dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high-dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes.

Comprehensive analyses of circulating cardiometabolic proteins and objective measures of fat mass

BACKGROUND

The underlying molecular pathways for the effect of excess fat mass on cardiometabolic diseases is not well understood. Since body mass index is a suboptimal measure of body fat content, we investigated the relationship of fat mass measured by dual-energy X-ray absorptiometry with circulating cardiometabolic proteins.

METHODS

We used data from a population-based cohort of 4950 Swedish women (55-85 years), divided into discovery and replication samples; 276 proteins were assessed with three Olink Proseek Multiplex panels. We used random forest to identify the most relevant biomarker candidates related to fat mass index (FMI), multivariable linear regression to further investigate the associations between FMI characteristics and circulating proteins adjusted for potential confounders, and principal component analysis (PCA) for the detection of common covariance patterns among the proteins.

RESULTS

Total FMI was associated with 66 proteins following adjustment for multiple testing in discovery and replication multivariable analyses. Five proteins not previously associated with body size were associated with either lower FMI (calsyntenin-2 (CLSTN2), kallikrein-10 (KLK10)), or higher FMI (scavenger receptor cysteine-rich domain-containing group B protein (SSC4D), trem-like transcript 2 protein (TLT-2), and interleukin-6 receptor subunit alpha (IL-6RA)). PCA provided an efficient summary of the main variation in FMI-related circulating proteins involved in glucose and lipid metabolism, appetite regulation, adipocyte differentiation, immune response and inflammation. Similar patterns were observed for regional fat mass measures.

CONCLUSIONS

This is the first large study showing associations between fat mass and circulating cardiometabolic proteins. Proteins not previously linked to body size are implicated in modulation of postsynaptic signals, inflammation, and carcinogenesis.

Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progression

OBJECTIVE

Amyotrophic lateral sclerosis (ALS) is a heterogeneous disease with a complex etiology that lacks biomarkers predicting disease progression. The objective of this study was to use longitudinal cerebrospinal fluid (CSF) samples to identify biomarkers that distinguish fast progression (FP) from slow progression (SP) and assess their temporal response.

METHODS

We utilized mass spectrometry (MS)-based proteomics to identify candidate biomarkers using longitudinal CSF from a discovery cohort of SP and FP ALS patients. Immunoassays were used to quantify and validate levels of the top biomarkers. A state-transition mathematical model was created using the longitudinal MS data that also predicted FP versus SP.

RESULTS

We identified a total of 1148 proteins in the CSF of all ALS patients. Pathway analysis determined enrichment of pathways related to complement and coagulation cascades in FPs and synaptogenesis and glucose metabolism in SPs. Longitudinal analysis revealed a panel of 59 candidate markers that could segregate FP and SP ALS. Based on multivariate analysis, we identified three biomarkers (F12, RBP4, and SERPINA4) as top candidates that segregate ALS based on rate of disease progression. These proteins were validated in the discovery and a separate validation cohort. Our state-transition model determined that the overall variance of the proteome over time was predictive of the disease progression rate.

INTERPRETATION

We identified pathways and protein biomarkers that distinguish rate of ALS disease progression. A mathematical model of the CSF proteome determined that the change in entropy of the proteome over time was predictive of FP versus SP.

Analysis of Zinc and Stromal Immunity in Disuse Osteoporosis: Mendelian Randomization and Transcriptomic Analysis

OBJECTIVE

Disuse osteoporosis is known to be primarily caused by a lack of exercise. However, the causal relationships between zinc and immunity and disuse osteoporosis remain unknown. This study investigated these relationships and their potential mechanisms.

METHODS

This study was an integrative study combining genome-wide association studies and transcriptomics. Two-sample Mendelian randomization analysis (MR) was used to analyze the causal relationships between exposures (zinc, immunity, physical activity) and the outcome (osteoporosis) with the aid of single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs). Four models, MR-Egger, inverse variance weighted, weighted median and MR-Pleiotrophy RESidual Sum and Outlier (MRPRESSO), were used to calculate odds ratio values. Sensitivity and heterogeneity analyses were also performed using MRPRESSO and MR-Egger methods. The mRNA transcriptomic analysis was subsequently conducted. Zinc metabolism scores were acquired through single-sample Gene Set Enrichment Analysis algorithms. Stromal scores were obtained using the R Package "estimate" algorithms. Important Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathways were also derived through gene set variation analysis. Cytoscape software helped construct the transcription factor (TF)-mRNA-microRNA (miRNA) network. Virtual screening and molecular docking were performed. Polymerase chain reaction validation was also carried out in vivo.

RESULTS

Causal relationships were demonstrated between zinc and exercise (95% confidence interval [CI] = 1.30-2.95, p = 0.001), exercise and immunity (95% CI = 0.36-0.80, p = 0.002), exercise and osteoporosis (95% CI = 0.97-0.99, p = 0.0007), and immunity disorder and osteoporosis (95% CI = 1.30-2.03, p = 0.00002). One hundred and seventy-nine mRNAs in important modules were screened. Combining the differential expressional genes (DEGs) and the Boruta selection, six DEGs were screened (AHNAK, CSF2, ADAMTS12, SRA1, RUNX2, and SLC39A14). TF HOXC10 and miRNA hsa-miR-204 were predicted. Then, the TF-mRNA-miRNA network was successfully constructed. RUNX2 and SLC39A14 were identified as hub mRNAs in the TF-mRNA-miRNA network. Eventually, the novel small drug C6O4NH5 was designed according to the pharmacophore structure of SLC39A14. The docking energy for the novel drug was -5.83 kcal/mol. SLC39A14 and RUNX2 were downregulated (of statistical significance p-value < 0.05) in our animal experiment.

CONCLUSION

This study revealed that zinc had a protective causal relationship with disuse osteoporosis by promoting exercise and immunity. SLC39A14 and RUNX2 mRNA participated in this zinc-related mechanism.

Untargeted plasma metabolomics and risk of colorectal cancer-an analysis nested within a large-scale prospective cohort

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, but if discovered at an early stage, the survival rate is high. The aim of this study was to identify novel markers predictive of future CRC risk using untargeted metabolomics.

METHODS

This study included prospectively collected plasma samples from 902 CRC cases and 902 matched cancer-free control participants from the population-based Northern Sweden Health and Disease Study (NSHDS), which were obtained up to 26 years prior to CRC diagnosis. Using reverse-phase liquid chromatography-mass spectrometry (LC-MS), data comprising 5015 metabolic features were obtained. Conditional logistic regression was applied to identify potentially important metabolic features associated with CRC risk. In addition, we investigated if previously reported metabolite biomarkers of CRC risk could be validated in this study population.

RESULTS

In the univariable analysis, seven metabolic features were associated with CRC risk (using a false discovery rate cutoff of 0.25). Two of these could be annotated, one as pyroglutamic acid (odds ratio per one standard deviation increase = 0.79, 95% confidence interval, 0.70-0.89) and another as hydroxytigecycline (odds ratio per one standard deviation increase = 0.77, 95% confidence interval, 0.67-0.89). Associations with CRC risk were also found for six previously reported metabolic biomarkers of prevalent and/or incident CRC: sebacic acid (inverse association) and L-tryptophan, 3-hydroxybutyric acid, 9,12,13-TriHOME, valine, and 13-OxoODE (positive associations).

CONCLUSIONS

These findings suggest that although the circulating metabolome may provide new etiological insights into the underlying causes of CRC development, its potential application for the identification of individuals at higher risk of developing CRC is limited.

Differential Responders to a Mixed Meal Tolerance Test Associated with Type 2 Diabetes Risk Factors and Gut Microbiota-Data from the MEDGI-Carb Randomized Controlled Trial

The global prevalence of type 2 diabetes mellitus (T2DM) has surged in recent decades, and the identification of differential glycemic responders can aid tailored treatment for the prevention of prediabetes and T2DM. A mixed meal tolerance test (MMTT) based on regular foods offers the potential to uncover differential responders in dynamical postprandial events. We aimed to fit a simple mathematical model on dynamic postprandial glucose data from repeated MMTTs among participants with elevated T2DM risk to identify response clusters and investigate their association with T2DM risk factors and gut microbiota. Data were used from a 12-week multi-center dietary intervention trial involving high-risk T2DM adults, comparing high- versus low-glycemic index foods within a Mediterranean diet context (MEDGICarb). Model-based analysis of MMTTs from 155 participants (81 females and 74 males) revealed two distinct plasma glucose response clusters that were associated with baseline gut microbiota. Cluster A, inversely associated with HbA1c and waist circumference and directly with insulin sensitivity, exhibited a contrasting profile to cluster B. Findings imply that a standardized breakfast MMTT using regular foods could effectively distinguish non-diabetic individuals at varying risk levels for T2DM using a simple mechanistic model.

Genetic Parameter and Hyper-Parameter Estimation Underlie Nitrogen Use Efficiency in Bread Wheat

Estimation and prediction play a key role in breeding programs. Currently, phenotyping of complex traits such as nitrogen use efficiency (NUE) in wheat is still expensive, requires high-throughput technologies and is very time consuming compared to genotyping. Therefore, researchers are trying to predict phenotypes based on marker information. Genetic parameters such as population structure, genomic relationship matrix, marker density and sample size are major factors that increase the performance and accuracy of a model. However, they play an important role in adjusting the statistically significant false discovery rate (FDR) threshold in estimation. In parallel, there are many genetic hyper-parameters that are hidden and not represented in the given genomic selection (GS) model but have significant effects on the results, such as panel size, number of markers, minor allele frequency, number of call rates for each marker, number of cross validations and batch size in the training set of the genomic file. The main challenge is to ensure the reliability and accuracy of predicted breeding values (BVs) as results. Our study has confirmed the results of bias-variance tradeoff and adaptive prediction error for the ensemble-learning-based model STACK, which has the highest performance when estimating genetic parameters and hyper-parameters in a given GS model compared to other models.

Toward Machine Learning-Driven Mass Spectrometric Identification of Trichothecenes in the Absence of Standard Reference Materials

While a significant body of work exists on the detection of commonly known trichothecene toxins, biological, environmental, and other transformational processes can generate many under-characterized and unknown modified trichothecenes. Lacking both analytical reference standards and associated mass spectral databases, identification of these modified compounds reflects both a challenge and a critical gap from forensic and public health perspectives. We report here the application of machine learning (ML) techniques toward identification of discriminative fragment ions from mass spectrometric data that can be exploited to detect evidence of type A and B trichothecenes. The goal of this work is to establish a new method for the identification of unknown, though structurally similar trichothecenes, by leveraging objective ML techniques. Discriminative fragments derived from a series of gradient-boosted machine learners are then used to develop ML-driven precursor ion scan (PIS) methods on a triple quadrupole mass spectrometer (QQQ) for screening of "unknown unknown" trichothecenes. Specifically, we apply the PIS method to a laboratory-synthesized trichothecene, a first step in demonstrating the power of alternative, machine learning-driven mass spectrometric methods.

IBS randomized study: FODMAPs alter bile acids, phenolic- and tryptophan metabolites, while gluten modifies lipids

Diet is considered a culprit for symptoms in irritable bowel syndrome (IBS), although the mechanistic understanding of underlying causes is lacking. Metabolomics, i.e., the analysis of metabolites in biological samples may offer a diet-responsive fingerprint for IBS. Our aim was to explore alterations in the plasma metabolome after interventions with fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) or gluten versus control in IBS, and to relate such alterations to symptoms. People with IBS (n = 110) were included in a double-blind, randomized, crossover study with 1-wk provocations of FODMAPs, gluten, or placebo. Symptoms were evaluated with the IBS severity scoring system (IBS-SSS). Untargeted metabolomics was performed on plasma samples using LC-qTOF-MS. Discovery of metabolite alterations by treatment was performed using random forest followed by linear mixed modeling. Associations were studied using Spearman correlation. The metabolome was affected by FODMAP [classification rate (CR) 0.88, P < 0.0001], but less by gluten intake CR 0.72, P = 0.01). FODMAP lowered bile acids, whereas phenolic-derived metabolites and 3-indolepropionic acid (IPA) were higher compared with placebo. IPA and some unidentified metabolites correlated weakly to abdominal pain and quality of life. Gluten affected lipid metabolism weakly, but with no interpretable relationship to IBS. FODMAP affected gut microbial-derived metabolites relating to positive health outcomes. IPA and unknown metabolites correlated weakly to IBS severity. Minor symptom worsening by FODMAP intake must be weighed against general positive health aspects of FODMAP. The gluten intervention affected lipid metabolism weakly with no interpretable association to IBS severity. Registration: www.clinicaltrials.gov as NCT03653689.NEW & NOTEWORTHY In irritable bowel syndrome (IBS), fermentable oligo-, di-, monosaccharides, and polyols (FODMAPs) affected microbial-derived metabolites relating to positive health outcomes such as reduced risk of colon cancer, inflammation, and type 2 diabetes, as shown in previous studies. The minor IBS symptom induction by FODMAP intake must be weighed against the positive health aspects of FODMAP consumption. Gluten affected lipids weakly with no association to IBS severity.

Prediction of Feed Efficiency and Performance-Based Traits in Fish via Integration of Multiple Omics and Clinical Covariates

Fish aquaculture is a rapidly expanding global industry, set to support growing demands for sources of marine protein. Enhancing feed efficiency (FE) in farmed fish is required to reduce production costs and improve sector sustainability. Recognising that organisms are complex systems whose emerging phenotypes are the product of multiple interacting molecular processes, systems-based approaches are expected to deliver new biological insights into FE and growth performance. Here, we establish 14 diverse layers of multi-omics and clinical covariates to assess their capacities to predict FE and associated performance traits in a fish model (Oncorhynchus tshawytscha) and uncover the influential variables. Inter-omic relatedness between the different layers revealed several significant concordances, particularly between datasets originating from similar material/tissue and between blood indicators and some of the proteomic (liver), metabolomic (liver), and microbiomic layers. Single- and multi-layer random forest (RF) regression models showed that integration of all data layers provide greater FE prediction power than any single-layer model alone. Although FE was among the most challenging of the traits we attempted to predict, the mean accuracy of 40 different FE models in terms of root-mean square errors normalized to percentage was 30.4%, supporting RF as a feature selection tool and approach for complex trait prediction. Major contributions to the integrated FE models were derived from layers of proteomic and metabolomic data, with substantial influence also provided by the lipid composition layer. A correlation matrix of the top 27 variables in the models highlighted FE trait-associations with faecal bacteria (Serratia spp.), palmitic and nervonic acid moieties in whole body lipids, levels of free glycerol in muscle, and N-acetylglutamic acid content in liver. In summary, we identified subsets of molecular characteristics for the assessment of commercially relevant performance-based metrics in farmed Chinook salmon.