Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies

Genomic and Transcriptomic Evidence for Carbohydrate Consumption among Microorganisms in a Cold Seep Brine Pool

The detailed lifestyle of microorganisms in deep-sea brine environments remains largely unexplored. Using a carefully calibrated genome binning approach, we reconstructed partial to nearly-complete genomes of 51 microorganisms in biofilms from the Thuwal cold seep brine pool of the Red Sea. The recovered metagenome-assembled genomes (MAGs) belong to six different phyla: Actinobacteria, Proteobacteria, Candidatus Cloacimonetes, Candidatus Marinimicrobia, Bathyarchaeota and Thaumarchaeota. By comparison with close relatives of these microorganisms, we identified a number of unique genes associated with organic carbon metabolism and energy generation. These genes included various glycoside hydrolases, nitrate and sulfate reductases, putative bacterial microcompartment biosynthetic clusters (BMC), and F420H2 dehydrogenases. Phylogenetic analysis suggested that the acquisition of these genes probably occurred through horizontal gene transfer (HGT). Metatranscriptomics illustrated that glycoside hydrolases are among the most highly expressed genes. Our results suggest that the microbial inhabitants are well adapted to this brine environment, and anaerobic carbohydrate consumption mediated by glycoside hydrolases and electron transport systems (ETSs) is a dominant process performed by microorganisms from various phyla within this ecosystem

ROCK Inhibitor Y27632 Induced Morphological Shift and Enhanced Neurite Outgrowth-Promoting Property of Olfactory Ensheathing Cells via YAP-Dependent Up-Regulation of L1-CAM

Olfactory ensheathing cells (OECs) are heterogeneous in morphology, antigenic profiles and functions, and these OEC subpopulations have shown different outcomes following OEC transplantation for central nervous system (CNS) injuries. Morphologically, OECs are divided into two subpopulations, process-bearing (Schwann cells-like) and flattened (astrocytes-like) OECs, which could switch between each other and are affected by extracellular and intracellular factors. However, neither the relationship between the morphology and function of OECs nor their molecular mechanisms have been clarified. In the present study, we first investigated morphological and functional differences of OECs under different cytokine exposure conditions. It demonstrated that OECs mainly displayed a process-bearing shape under pro-inflammatory conditions (lipopolysaccharide, LPS), while they displayed a flattened shape under anti-inflammatory conditions [interleukin-4 (IL-4) and transforming growth factor-β1 (TGF-β1)]. The morphological changes were partially reversible and the Rho-associated coiled-coil-containing protein kinase (ROCK)/F-actin pathway was involved. Functionally, process-bearing OECs under pro-inflammatory conditions showed increased cellular metabolic activity and a higher migratory rate when compared with flattened OECs under anti-inflammatory conditions and significantly promoted neurite outgrowth and extension. Remarkably, the morphological shift towards process-bearing OECs induced by ROCK inhibitor Y27632 enhanced the neurite outgrowth-promoting property of OECs. Furthermore, as the downstream of the ROCK pathway, transcriptional co-activator Yes-associated protein (YAP) mediated morphological shift and enhanced the neurite outgrowth-promoting property of OECs through upregulating the expression of the neural adhesion molecule L1-CAM. Our data provided evidence that OECs with specific shapes correspond to specific functional phenotypes and opened new insights into the potential combination of OECs and small-molecule ROCK inhibitors for the regeneration of CNS injuries

Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis

Introduction: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. Objectives: To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. Methods: A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively. Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease

Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and a major cause of human morbidity and mortality. In addition to early detection, close monitoring of disease progression in CRC can be critical for patient prognosis and treatment decisions. Efforts have been made to develop new methods for improved early detection and patient monitoring; however, research focused on CRC surveillance for treatment response and disease recurrence using metabolomics has yet to be reported. In this proof of concept study, we applied a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolic profiling approach focused on sequential metabolite ratio analysis of serial serum samples to monitor disease progression from 20 CRC patients. The use of serial samples reduces patient to patient metabolic variability. A partial least squares-discriminant analysis (PLS-DA) model using a panel of five metabolites (succinate, N2, N2-dimethylguanosine, adenine, citraconic acid, and 1-methylguanosine) was established, and excellent model performance (sensitivity = 0.83, specificity = 0.94, area under the receiver operator characteristic curve (AUROC) = 0.91 was obtained, which is superior to the traditional CRC monitoring marker carcinoembryonic antigen (sensitivity = 0.75, specificity = 0.76, AUROC = 0.80). Monte Carlo cross validation was applied, and the robustness of our model was clearly observed by the separation of true classification models from the random permutation models. Our results suggest the potential utility of metabolic profiling for CRC disease monitoring

The metabolome regulates the epigenetic landscape during naive-to-primed human embryonic stem cell transition.

For nearly a century developmental biologists have recognized that cells from embryos can differ in their potential to differentiate into distinct cell types. Recently, it has been recognized that embryonic stem cells derived from both mice and humans exhibit two stable yet epigenetically distinct states of pluripotency: naive and primed. We now show that nicotinamide N-methyltransferase (NNMT) and the metabolic state regulate pluripotency in human embryonic stem cells (hESCs).  Specifically, in naive hESCs, NNMT and its enzymatic product 1-methylnicotinamide are highly upregulated, and NNMT is required for low S-adenosyl methionine (SAM) levels and the H3K27me3 repressive state. NNMT consumes SAM in naive cells, making it unavailable for histone methylation that represses Wnt and activates the HIF pathway in primed hESCs. These data support the hypothesis that the metabolome regulates the epigenetic landscape of the earliest steps in human development

Metabolomics method to comprehensively analyze amino acids in different domains

Amino acids play essential roles in both metabolism and the proteome. Many studies have profiled free amino acids (FAAs) or proteins; however, few have connected the measurement of FAA with individual amino acids in the proteome. In this study, we developed a metabolomics method to comprehensively analyze amino acids in different domains, using two examples of different sample types and disease models. We first examined the responses of FAAs and insoluble-proteome amino acids (IPAAs) to the Myc oncogene in Tet21N human neuroblastoma cells. The metabolic and proteomic amino acid profiles were quite different, even under the same Myc condition, and their combination provided a better understanding of the biological status. In addition, amino acids were measured in 3 domains (FAAs, free and soluble-proteome amino acids (FSPAAs), and IPAAs) to study changes in serum amino acid profiles related to colon cancer. A penalized logistic regression model based on the amino acids from the three domains had better sensitivity and specificity than that from each individual domain. To the best of our knowledge, this is the first study to perform a combined analysis of amino acids in different domains, and indicates the useful biological information available from a metabolomics analysis of the protein pellet. This study lays the foundation for further quantitative tracking of the distribution of amino acids in different domains, with opportunities for better diagnosis and mechanistic studies of various diseases

Global, Regional, and National Change Patterns in the Incidence of Low Back Pain From 1990 to 2019 and Its Predicted Level in the Next Decade

Objectives: To analyze and describe the spatiotemporal trends of Low back pain (LBP) burdens from 1990 to 2019 and anticipate the following decade’s incidence.Methods: Using data from the Global Burden of Disease (GBD) 2019 Study, we described net drifts, local drifts, age effects, and period cohort effects in incidence and forecasted incidence rates and cases by sex from 2020 to 2029 using the Nordpred R package.Results: LBP remained the leading cause of the musculoskeletal disease burden globally and across all socio-demographic index (SDI) regions. China is the top country. For recent periods, high-SDI countries faced unfavorable or worsening risks. The relative risk of incidence showed improving trends over time and in successively younger birth cohorts amongst low-middle-, middle- and high-middle-SDI countries. Additionally, the age-standardized incidence rates (ASIR) of LBP in both sexes globally showed a decreasing trend, but the incident cases would increase from 223 to 253 million overall in the next decade.Conclusion: As the population ages, incident cases will rise but ASIR will fall. To minimise LBP, public awareness and disease prevention and control are needed

Associations of Amylin with Inflammatory Markers and Metabolic Syndrome in Apparently Healthy Chinese

BACKGROUND: Cellular and animal studies implicate multiple roles of amylin in regulating insulin action, glucose and lipid metabolisms. However, the role of amylin in obesity related metabolic disorders has not been thoroughly investigated in humans. Therefore, we aimed to evaluate the distribution of circulating amylin and its association with metabolic syndrome (MetS) and explore if this association is influenced by obesity, inflammatory markers or insulin resistance in apparently healthy Chinese. METHODS: A population-based sample of 1,011 Chinese men and women aged 35-54 years was employed to measure plasma amylin, inflammatory markers (C-reactive protein [CRP] and interleukin-6 [IL-6]), insulin, glucose and lipid profiles. MetS was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian-Americans. RESULTS: Plasma amylin concentrations were higher in overweight/obese participants than normal-weight counterparts (P<0.001) without sex difference. Circulating amylin was positively associated with CRP, IL-6, BMI, waist circumference, blood pressure, fasting glucose, insulin, amylin/insulin ratio, HOMA-IR, LDL cholesterol and triglycerides, while negatively associated with HDL cholesterol (all P<0.001). After multiple adjustments, the risk of MetS was significantly higher (odds ratio 3.71; 95% confidence interval: 2.53 to 5.46) comparing the highest with the lowest amylin quartile. The association remained significant even further controlling for BMI, inflammatory markers, insulin or HOMA-IR. CONCLUSIONS: Our study suggests that amylin is strongly associated with inflammatory markers and MetS. The amylin-MetS association is independent of established risk factors of MetS, including obesity, inflammatory markers and insulin resistance. The causal role of hyperamylinemia in the development of MetS needs to be confirmed prospectively