The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseases

BACKGROUND

As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles.

METHODS

We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence.

FINDINGS

We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (ORICUadmission = 6.7, p-value = 1.2 × 10-08, ORmortality = 4.7, p-value = 1.6 × 10-04) and influenza (ORincidence = 2.9; p-values = 2.2 × 10-4, βrecurrence = 1.03; p-value = 5.1 × 10-3). We observed similar severity associations when recapitulating this susceptibility endotype using metabolomics from individuals during and after acute COVID infection. We demonstrate the value of using metabolomic endotyping to identify a metabolically susceptible group for two-and potentially more-IDs that are driven by increases in specific amino acids, including microbial-related metabolites such as tryptophan, bile acids, histidine, polyamine, phenylalanine, and tyrosine metabolism, as well as carbohydrates involved in glycolysis.

INTERPRETATIONS

These metabolites may be identified prior to infection to enable protective measures for these individuals.

FUNDING

The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health.

Recent advances in mass spectrometry-based computational metabolomics

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".

Precision information extraction for rare disease epidemiology at scale

BACKGROUND

The United Nations recently made a call to address the challenges of an estimated 300 million persons worldwide living with a rare disease through the collection, analysis, and dissemination of disaggregated data. Epidemiologic Information (EI) regarding prevalence and incidence data of rare diseases is sparse and current paradigms of identifying, extracting, and curating EI rely upon time-intensive, error-prone manual processes. With these limitations, a clear understanding of the variation in epidemiology and outcomes for rare disease patients is hampered. This challenges the public health of rare diseases patients through a lack of information necessary to prioritize research, policy decisions, therapeutic development, and health system allocations.

METHODS

In this study, we developed a newly curated epidemiology corpus for Named Entity Recognition (NER), a deep learning framework, and a novel rare disease epidemiologic information pipeline named EpiPipeline4RD consisting of a web interface and Restful API. For the corpus creation, we programmatically gathered a representative sample of rare disease epidemiologic abstracts, utilized weakly-supervised machine learning techniques to label the dataset, and manually validated the labeled dataset. For the deep learning framework development, we fine-tuned our dataset and adapted the BioBERT model for NER. We measured the performance of our BioBERT model for epidemiology entity recognition quantitatively with precision, recall, and F1 and qualitatively through a comparison with Orphanet. We demonstrated the ability for our pipeline to gather, identify, and extract epidemiology information from rare disease abstracts through three case studies.

RESULTS

We developed a deep learning model to extract EI with overall F1 scores of 0.817 and 0.878, evaluated at the entity-level and token-level respectively, and which achieved comparable qualitative results to Orphanet's collection paradigm. Additionally, case studies of the rare diseases Classic homocystinuria, GRACILE syndrome, Phenylketonuria demonstrated the adequate recall of abstracts with epidemiology information, high precision of epidemiology information extraction through our deep learning model, and the increased efficiency of EpiPipeline4RD compared to a manual curation paradigm.

CONCLUSIONS

EpiPipeline4RD demonstrated high performance of EI extraction from rare disease literature to augment manual curation processes. This automated information curation paradigm will not only effectively empower development of the NIH Genetic and Rare Diseases Information Center (GARD), but also support the public health of the rare disease community.

LC-MS and 1H-NMR Untargeted Metabolomic Profiling of Probiotic Yogurt Consumption in C56BL6/N Mice

Objectives

Probiotic yogurt consumption is associated with gut health, including nutrient production and immune regulation, largely via modulation of the gut microbiome. However, metabolites that mediate host-microbiome interactions are largely unknown. We hypothesize consumption of probiotic yogurt results in distinct metabolic signatures in colon, including decreased levels of branched-chain amino acids, triglycerides, and bile acids relative to probiotic-free yogurt, milk, and water controls. We further hypothesize these differences are driven by increased abundances of Lactobacilli and Bifidobacteria following probiotic yogurt consumption.

Methods

A juvenile mouse model (3 weeks old, n = 32 male, 32 female) was fed AIN-93G diet for 3 weeks, followed by 3 weeks of daily gavage of 150 µL probiotic yogurt (PY), heat-inactivated yogurt (HY), milk (M), or water (W). Animals were cohoused in the initial phase and singly housed in the gavage phase. Polar and nonpolar colon extracts were analyzed with ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) and 1H NMR metabolomics. Features detected by1H-NMR and C18 LC-MS in positive mode were compared, pairwise, between diet groups.

Results

Metabolite classes detected via UHPLC-HRMS included bile acids, amino acids and phospholipids. NMR yielded the following number of features with an absolute log2-fold change > 2 and unadjusted P-value < 0.05 between dietary groups: 74 (PY v HY), 17 (PY v M), 57 (PY v C), 79 (HY v M), 14 (HY v C), and 47 (M v C). C18 LC-MS in positive mode yielded the following numbers of metabolites with the same cutoffs used in NMR: 220 (PY v HY), 39 (PY v M), 196 (PY v C), 80 (HY v M), 58 (HY v C), and 61 (M v C).

Conclusions

As hypothesized, probiotic yogurt consumption for 3 weeks significantly altered the colon metabolome relative to controls. Further metabolite identification, pathway enrichment analysis, and next generation microbiome sequencing analyses will aid in identifying specific pathways altered upon PY consumption and correlated with alterations in colonic microbial taxa abundances. Ultimately, this work will help us to better understand how PY consumption influences gastrointestinal health in children.

Funding Sources

Dannon Gut Microbiome, Yogurt and Probiotics Fellowship Grant awarded to HC. Sample analyses partially supported by an NIH Award.

Vitamin A and D Absorption in Adults with Metabolic Syndrome versus Healthy Controls: A Pilot Study Utilizing Targeted and Untargeted LC-MS Lipidomics

SCOPE

Persons with metabolic syndrome (MetS) absorb less vitamin E than healthy controls. It is hypothesized that absorption of fat-soluble vitamins (FSV) A and D2 would also decrease with MetS status and that trends would be reflected in lipidomic responses between groups.

METHODS AND RESULTS

Following soymilk consumption (501 IU vitamin A, 119 IU vitamin D2 ), the triglyceride-rich lipoprotein fractions (TRL) from MetS and healthy subjects (n = 10 age- and gender-matched subjects/group) are assessed using LC-MS/MS. Absorption is calculated using area under the time-concentration curves (AUC) from samples collected at 0, 3, and 6 h post-ingestion. MetS subjects have ≈6.4-fold higher median vitamin A AUC (retinyl palmitate) versus healthy controls (P = 0.07). Vitamin D2 AUC is unaffected by MetS status (P = 0.48). Untargeted LC-MS lipidomics reveals six phospholipids and one cholesterol ester with concentrations correlating (r = 0.53-0.68; P < 0.001) with vitamin A concentration.

CONCLUSIONS

The vitamin A-phospholipid association suggests increased hydrolysis by PLB, PLRP2, and/or PLA2 IB may be involved in the trend in higher vitamin A bioavailability in MetS subjects. Previously observed differences in circulating levels of these vitamins are likely not due to absorption. Alternate strategies should be investigated to improve FSV status in MetS.

Untargeted Metabolomic Profiling of Early Diet Energy Intake in C57BL/6 N Mice Reveals Differences Associated with Diet and Aberrant Crypt Foci

Objectives

A high-fat (H) diet leads to obesity, a known risk factor for colorectal cancer (CRC). In contrast, calorie restriction (E) is associated with reduced CRC risk. However, the metabolome associated with H vs. E-associated CRC risk has never been directly compared. The different influences of these diets on the proximal (PC), medial (MC), and distal (DC) colon metabolome has also not been studied. Thus, the objective is to elucidate metabolites associated with abberant crypt foci (ACF) number, a marker of CRC risk, in each colon region after consumption of H, E, or a normocaloric control diet (C).

Methods

3-week-old C57BL/6 N mice were fed a C, E, or H initiation diet for 13 weeks. In weeks 16–21, animals were injected with azoxymethane to initiate ACF formation, and switched to a C, E, or H progression diet (for a total of 9 diet groups: CC, CH, CE, HH, HC, HE, EE, EC, EH). Polar extracts of the colon regions (i.e., PC, MC, and DC) were analyzed using ultra-high performance liquid chromatography-high resolution mass spectrometry method (HRMS) and 1H NMR metabolomics methods. Linear models assessed the main effects of ACF, initiation diet, progression diet, as well as the diet * ACF interaction, on relative metabolite concentration in each colon region.

Results

Following HILIC-HRMS analysis of extracts in positive and negative ionization mode, 492 and 415 metabolites were detected, respectively. Linear models revealed 21 metabolites were significantly associated with initiation E diet * ACF (8 unique to MC, 13 unique to PC), 14 with initiation H diet * ACF (only in DC), 27 with progression H diet * ACF (14 unique to DC, 2 to MC, 11 to PC) and 20 with progression E diet * ACF (17 unique to DC, 1 to PC, and 1 common to both). Pathway integration and authentication of tentative metabolite identities with chemical standards is underway.

Conclusions

Diet * ACF interaction significantly influences multiple metabolite concentrations. Little to no overlap is observed between metabolites associated with ACF in a given colon region and the other regions tested, revealing that the diet * ACF interaction is region-specific. Future studies in humans will determine if these metabolites may serve as early biomarkers for CRC diagnosis.

Funding Sources

Sample analyses were supported by NIH Award Number Grant P30 CA016058, OSU, and OSUCCC.