Simultaneous Polar Metabolite and N-Glycan Extraction Workflow for Joint-Omics Analysis: A Synergistic Approach for Novel Insights into Diseases.

Bioinformatics and machine learning tools have made it possible to integrate data across different -omics platforms for novel multiomic insights into diseases. To synergistically process -omics data in an integrative manner, analyte extractions for each -omics type need to be done on the same set of clinical samples. Therefore, we introduce a simultaneous dual extraction method for generating both metabolomic (polar metabolites only) and glycomic (protein-derived N-glycans only) profiles from one sample with good extraction efficiency and reproducibility. As proof of the usefulness of the extraction and joint-omics workflow, we applied it on platelet samples obtained from a cohort study comprising 66 coronary heart disease (CHD) patients and 34 matched healthy community-dwelling controls. The metabolomics and N-glycomics data sets were subjected to block partial least-squares-discriminant analysis (block-PLS-DA) based on sparse generalized canonical correlation analysis (CCA) for identifying relevant mechanistic interactions between metabolites and glycans. This joint-omics investigation revealed intermodulative roles that protein-bound carbohydrates or glycoproteins and amino acids have in metabolic pathways and through intermediate protein dysregulations. It also suggested a protective role of the glyco-redox network in CHD, demonstrating proof-of-principle for a joint-omics analysis in providing new insights into disease mechanisms, as enabled by a simultaneous polar metabolite and protein-derived N-glycan extraction workflow.

N-glycan profiles of acute myocardial infarction patients reveal potential biomarkers for diagnosis, severity assessment and treatment monitoring.

Acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Diagnostic challenges remain in this highly time-sensitive condition. Using capillary electrophoresis-laser-induced fluorescence, we analyzed the blood plasma N-glycan profile in a cohort study comprising 103 patients with AMI and 69 controls. Subsequently, the data generated was subjected to classification modeling to identify potential AMI biomarkers. An area under the Receiving Operating Characteristic curve (AUCROC) of 0.81 was obtained when discriminating AMI vs. non-MI patients. We postulate that the glycan profile involves a switch from a pro- to an anti-inflammatory state in the AMI pathophysiology. This was supported by significantly decreased levels in galactosylation, alongside increased levels in sialylation, afucosylation and GlcNAc bisection levels in the blood plasma of AMI patients. By substantiating the glycomics analysis with immunoglobulin G (IgG) protein measurements, robustness of the glycan-based classifiers was demonstrated. Changes in AMI-related IgG activities were also confirmed to be associated with alterations at the glycosylation level. Additionally, a glycan-biomarker panel derived from glycan features and current clinical biomarkers performed remarkably (AUCROC = 0.90, sensitivity = 0.579 at 5% false positive rate) when discriminating between patients with ST-segment elevation MI (n = 84) and non-ST-segment elevation MI (n = 19). Moreover, by applying the model trained using glycomics information, AMI and controls can still be discriminated at 1 and 6 months after baseline. Thus, glycomics biomarkers could potentially serve as a valuable complementary test to current diagnostic biomarkers. Additional research on their utility and associated biomechanisms via a large-scale study is recommended.

Antibiotic resistance patterns and therapeutic outcomes of pediatric Helicobacter pylori infection in a high-migrant Singaporean cohort.

BACKGROUND: Variation in Helicobacter pylori (H. pylori) disease in terms of prevalence and antibiotic resistance prevails globally requiring a need to develop region-specific surveillance. We aimed to assess the influence of immigration factors upon the interpretation of local Singaporean epidemiological trends in antimicrobial susceptibility patterns and therapeutic outcomes in children with culture-positive H. pylori. MATERIALS AND METHODS: We retrospectively analyzed eradication outcomes of children with culture-proven H. pylori infections between 2011 and 2020 at our center, and we also analyzed the antimicrobial susceptibility profiles of the corresponding H. pylori isolates. The cohort was classified into two groups: (1) Native Singaporeans and (2) Non-native Singaporeans (First-/Second-generation immigrants and Non-residents) to correlate with resistance patterns and eradication outcomes. H. pylori culture was done via Kirby-Bauer disk diffusion for the era 2011-2016 and bioMérieux E test for 2016-2020. RESULTS: A total of 70 children (median age 14 [2-17] years) were included in the analysis. 42.9% (30/70) of the cohort displayed some form of antibiotic resistance; clarithromycin resistance was the most prevalent (30.0%), followed by metronidazole (27.5%) and amoxicillin (7.1%). Comparing to natives, non-native Singaporeans were significantly younger at presentation (mean 11.7 vs. 13.7 years, p = 0.043), and a significantly higher proportion of non-natives carried clarithromycin-resistant (51.4% vs. 8.6%, p < 0.001), metronidazole-resistant (47.1% vs. 8.6%, p < 0.001), or multidrug-resistant (resistant to ≥2 drugs) (40.0% vs. 2.9%, p < 0.001] strains. Non-natives were significantly more likely to fail first-line eradication therapy (48.5% failure vs. 23.3%, p = 0.038). The proportion of pan-sensitive H. pylori was significantly lower in first-generation (25.0%, p = 0.001) and second-generation (42.9%, p = 0.018) immigrants compared to natives (82.86%). These conclusions did not vary when the analysis was repeated for each culture method. CONCLUSIONS: An antibiotic susceptibility-based approach should be advocated for all patients but especially so for non-natives, who are at higher risk for antimicrobial resistant strains and poorer eradication outcomes.

Evaluation of Anti-Inflammatory Effects of Celery Leaf and Stem Extracts in LPS-Induced RAW 264.7 Cells Using Nitric Oxide Assay and LC-MS Based Metabolomics.

The present work demonstrated and compared the anti-inflammatory effects of celery leaf (CLE) and stem (CSE) extracts. LC-MS-based metabolomics were an effective approach to achieve the biomarker identification and pathway elucidation associated with the reduction in inflammatory responses. The celery extracts suppressed LPS-induced NO production in RAW 264.7 cells, and CLE was five times more effective than CSE. Distinct differences were revealed between the control and celery-treated samples among the 24 characteristic metabolites that were identified. In celery-treated LPS cells, reversals of intracellular (citrulline, proline, creatine) and extracellular (citrulline, lysine) metabolites revealed that the therapeutic outcomes were closely linked to arginine metabolism. Reversals of metabolites when treated with CLE (aspartate, proline) indicated targeted effects on the TCA and urea cycles, while, in the case of CSE (histidine, glucose), the glycolysis and the pentose phosphate pathways were implicated. Subsequently, apigenin and bergapten in CLE were identified as potential biomarkers mediating the anti-inflammatory response.

Targeted Plasma Metabolomics Reveals Association of Acute Myocardial Infarction Risk with the Dynamic Balance between Trimethylamine-N-oxide, Betaine, and Choline.

The relationship between trimethylamine-N-oxide (TMAO), betaine, and choline with acute myocardial infarction (AMI) end point remains unclear. We analyzed plasma TMAO, betaine, and choline concentrations in AMI cases and non-AMI community-dwelling controls by LC-MS/MS to understand how the balance between these metabolites helps to reduce AMI risk. Results showed that the odds ratio (OR) for the highest versus lowest quartiles of betaine was 0.30 (95% CI, 0.10-0.82) after adjustment for AMI risk factors, and the unadjusted OR for quartile 3 versus quartile 1 of TMAO was 2.47 (95% CI, 1.02-6.17) (p < 0.05). The study populations with "high betaine + low TMAO" had a significant protective effect concerning AMI with a multivariable-adjusted OR of 0.20 (95% CI, 0.07-0.55) (p < 0.01). Multivariate linear regression showed that the chronological age was correlated with TMAO concentrations among AMI patients (95% CI, 0.05-3.24, p < 0.01) but not among the controls. This implies a further potential interplay between age and metabolite combination─AMI risk association.

Plasma metallomics reveals potential biomarkers and insights into the ambivalent associations of elements with acute myocardial infarction.

Acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Using a validated and efficient ICP-MS/MS-based workflow, a total of 30 metallomic features were profiled in a study comprising 101 AMI patients and 66 age-matched healthy controls. The metallomic features include 12 essential elements (Ca, Co, Cu, Fe, K, Mg, Mn, Na, P, S, Se, Zn), 8 non-essential/toxic elements (Al, As, Ba, Cd, Cr, Ni, Rb, Sr, U, V), and 10 clinically relevant element-pair product/ratios (Ca/Mg, Ca×P, Cu/Se, Cu/Zn, Fe/Cu, P/Mg, Na/K, Zn/Se). Preliminary linear regression with feature selection confirmed smoking status as a predominant determinant for the non-essential/toxic elements, and revealed potential routes of action. Univariate assessments with adjustments for covariates revealed insights into the ambivalent relationships of Cu, Fe, and P with AMI, while also confirming cardioprotective associations of Se. Also, beyond their roles as risk factors, Cu and Se may be involved in the response mechanism in AMI onset/intervention, as demonstrated via longitudinal data analysis with 2 additional time-points (1-/6-month follow-up). Finally, based on both univariate tests and multivariate classification modelling, potentially more sensitive markers measured as element-pair ratios were identified (e.g., Cu/Se, Fe/Cu). Overall, metallomics-based biomarkers may have utility for AMI prediction.

Single-cell and bulk ICP-MS investigation of accumulation patterns of Pt-based metallodrugs in cisplatin-sensitive and -resistant cell models.

In research enabling preclinical development and attaining a deeper understanding of the behavior of metallodrugs in cancer cells with acquired resistance, intracellular Pt accumulation could be considered an important biomarker and analytical focus. In this work, Pt accumulation patterns in terms of the number of cells and Pt mass in single cells were precisely defined by using inductively coupled plasma-mass spectrometry (ICP-MS) operating in a fast time-resolved analysis mode. This technique is otherwise known as single-cell (SC)-ICP-MS. By applying the nascent and validated SC-ICP-MS technique, comparisons across three Pt drugs (cisplatin, carboplatin, and oxaliplatin) in the A2780 and A2780cis ovarian cancer cell models could be made. Additional roles of transporters on top of passive diffusion and the drugs' bioactivity could be postulated. The SC-ICP-MS-based observations also served as a cross-validation point to augment preexisting research findings on Pt-resistance mechanisms. Conjectures regarding S and Fe metabolism were also derived based on an additional and direct ICP-MS analysis of endogenous elements. Overall, our work not only confirms the utility of SC-ICP-MS in chemotherapeutic research, but also provided insights into further ICP-MS-based analytical capacities to be developed.

Cross-identification of N-Glycans by CE-LIF using two capillary coatings and three labeling dyes.

Recombinant protein biopharmaceuticals comprise a significant portion of the current drug development landscape. The glycosylation profile of these proteins is a key quality parameter as it can affect their safety, efficacy, and stability. However, glycan analysis is challenging because of the complexity of their structures. To overcome this challenge in achieving accurate glycan identification, cross-identification of N-Glycans by CE-LIF method using two capillary coatings and three labeling dyes was developed in this work. This work explored whether complementary separation capabilities can be achieved using homemade polyvinyl alcohol (PVA) coating and commercial Guarant™ (Guarant) coating in the analysis of N-glycans. Similar separation profiles were observed using the two capillary coatings, and hence the N-glycan GU databases generated by these coatings were comparable and complementary. The performance of cross-validation by labeling with three fluorescent dyes indicated that low covariance of APTS and Turquoise™ labeling can be obtained, and hence these two labeling mechanisms provided better accuracy for the identification of glycans. Superior reproducibility with RSDs less than 1% for all target glycan standards was achieved by the internal standards (IS) method using maltodextrin ladders as additives in the separation buffer. The developed CE-LIF analysis method was applied to the identification of N-glycans in IgG samples.

Multi-Omics Investigation into Acute Myocardial Infarction: An Integrative Method Revealing Interconnections amongst the Metabolome, Lipidome, Glycome, and Metallome.

Acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. This work aims to investigate the translational potential of a multi-omics study (comprising metabolomics, lipidomics, glycomics, and metallomics) in revealing biomechanistic insights into AMI. Following the N-glycomics and metallomics studies performed by our group previously, untargeted metabolomic and lipidomic profiles were generated and analysed in this work via the use of a simultaneous metabolite/lipid extraction and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis workflow. The workflow was applied to blood plasma samples from AMI cases (n = 101) and age-matched healthy controls (n = 66). The annotated metabolomic (number of features, n = 27) and lipidomic (n = 48) profiles, along with the glycomic (n = 37) and metallomic (n = 30) profiles of the same set of AMI and healthy samples were integrated and analysed. The integration method used here works by identifying a linear combination of maximally correlated features across the four omics datasets, via utilising both block-partial least squares-discriminant analysis (block-PLS-DA) based on sparse generalised canonical correlation analysis. Based on the multi-omics mapping of biomolecular interconnections, several postulations were derived. These include the potential roles of glycerophospholipids in N-glycan-modulated immunoregulatory effects, as well as the augmentation of the importance of Ca-ATPases in cardiovascular conditions, while also suggesting contributions of phosphatidylethanolamine in their functions. Moreover, it was shown that combining the four omics datasets synergistically enhanced the classifier performance in discriminating between AMI and healthy subjects. Fresh and intriguing insights into AMI, otherwise undetected via single-omics analysis, were revealed in this multi-omics study. Taken together, we provide evidence that a multi-omics strategy may synergistically reinforce and enhance our understanding of diseases.

Application of omics beyond the central dogma in coronary heart disease research: A bibliometric study and literature review.

Despite remarkable progress in disease diagnosis and treatment, coronary heart disease (CHD) remains the number one leading cause of death worldwide. Many practical challenges still faced in clinical settings necessitates the pursuit of omics studies to identify alternative/orthogonal biomarkers, as well as to discover novel insights into disease mechanisms. Albeit relatively nascent as compared to the omics frontrunners (genomics, transcriptomics, and proteomics), omics beyond the central dogma (OBCD; e.g., metabolomics, lipidomics, glycomics, and metallomics) have undeniable contributions and prospects in CHD research. In this bibliometric study, we characterised the global trends in publication/citation outputs, collaborations, and research hotspots concerning OBCD-CHD, with a focus on the more prolific fields of metabolomics and lipidomics. As for glycomics and metallomics, there were insufficient publication records on their applications in CHD research for quantitative bibliometrics analysis. Thus, we reviewed their applications in health/disease research in general, discussed and justified their potential in CHD research, and suggested important/promising research avenues. By summarising evidence obtained both quantitatively and qualitatively, this study offers a first and comprehensive picture of OBCD applications in CHD, facilitating the establishment of future research directions.

Quality of Life in Presbyopes with Low and High Myopia Using Single-Vision and Progressive-Lens Correction.

This study evaluates the impact of the severity of myopia and the type of visual correction in presbyopia on vision-related quality of life (QOL), using the refractive status and vision profile (RSVP) questionnaire. A total of 149 subjects aged 41-75 years with myopic presbyopia were recruited: 108 had low myopia and 41 had high myopia. The RSVP questionnaire was administered. Rasch analysis was performed on five subscales: perception, expectation, functionality, symptoms, and problems with glasses. Highly myopic subjects had a significantly lower mean QOL score (51.65), compared to low myopes (65.24) (p < 0.001). They also had a significantly lower functionality score with glasses (49.38), compared to low myopes (57.00) (p = 0.018), and they had a worse functionality score without glasses (29.12), compared to low myopes (36.24) (p = 0.045). Those who wore progressive addition lenses (PAL) in the high-myope group (n = 25) scored significantly better, compared to those who wore single-vision distance (SVD) lenses (n = 14), with perception scores of 61.19 and 46.94, respectively (p = 0.029). Highly myopic presbyopes had worse overall QOL and functionality, both with and without glasses, compared to presbyopes with low myopia. High-myopic PAL users had a better perception outcome than SVD lens wearers. Low-myopic PAL wearers had a better QOL than SVD wearers.

Rapid and Sensitive Direct Detection of Endotoxins by Pyrolysis-Gas Chromatography-Mass Spectrometry.

The capability of pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) for the direct analysis of endotoxins is demonstrated in this research article using the lipopolysaccharides of Pseudomonas aeruginosa 10. Analytical methods based on evolved gas analysis-MS, single-shot (SS) Py-GC-MS, and multishot heart cut Py-GC-MS were investigated. Among the various methods developed, the SS Py-GC-MS method shows superior potential for identifying bacterial endotoxins effectively through their biomarkers. The results obtained were validated with conventional mass spectral analysis after hydrolysis. The method was also evaluated for its robustness based on quality control criteria indicated by the U.S. EPA Method 8270D. When applied onto endotoxins of different Gram-negative bacteria, this method produced vastly distinct pyrograms. The results show that rapid and sensitive direct detection of endotoxins is possible with the Py-GC-MS method developed.

Untargeted LC-QTOF-MS/MS based metabolomics approach for revealing bioactive components in probiotic fermented coffee brews.

Amidst trends in non-dairy probiotic foods and functional coffees, we recently developed a fermented coffee brew containing high live counts of the probiotics Lacticaseibacillus rhamnosus GG and Saccharomyces boulardii CNCM-I745. However, probiotic fermentation did not alter levels of principal coffee bioactive components based on targeted analyses. Here, to provide therapeutic justification compared to other non-fermented coffee brews, we aimed to discover postbiotics in coffee brews fermented with L. rhamnosus GG and/or S. boulardii CNCM-I745. By using an untargeted LC-QTOF-MS/MS based metabolomics approach coupled with validated multivariate analyses, 37 differential metabolites between fermentation treatments were putatively annotated. These include the production of postbiotics such as 2-isopropylmalate by S. boulardii CNCM-I745, and aromatic amino acid catabolites (indole-3-lactate, p-hydroxyphenyllactate, 3-phenyllactate), and hydroxydodecanoic acid by L. rhamnosus GG. Overall, LC-QTOF based untargeted metabolomics can be an effective approach to uncover postbiotics, which may substantiate additional potential functionalities of probiotic fermented foods compared to their non-fermented counterparts.