Tyrosine and Glutamine-Leucine Are Metabolic Markers of Early-Stage Colorectal Cancers

Fano enhancement of SERS for rapid early diagnosis of colorectal cancer

Patients benefit greatly from early detection of colorectal cancer, but present diagnostic procedures have high costs, low sensitivity, and low specificity. However, it is still difficult to develop a strategy that can effectively detect cancer early using high-throughput blood analysis. Fano resonance-boosted SERS platform label-free serum creates an effective diagnostic system at the point of care. We obtained 220 high-quality SERS serum spectral datasets from 88 healthy volunteers and 132 patients with colorectal cancer. The biomarker detected in serum was further evaluated using 100 colorectal cancer tissues and adjacent normal intestinal tissues collected from West China Biobanks, West China Hospital, Sichuan University. The results showed that in 97 out of 100 paired samples, the biomarkers were successfully detected using the SERS platform. This demonstrates that Fano resonance-based SERS is highly effective for diagnosing colorectal cancer.

LC-MS analysis of chiral amino acids in human urine reveals D-amino acids as potential biomarkers for colorectal cancer

Colorectal cancer (CRC) is a common malignant tumor in the gastrointestinal tract. Changes in amino acid metabolites have been implicated in tumorigenesis and disease progression. Biomarkers on the basis of chiral amino acids, especially D-amino acids, have not been established for early diagnosis of CRC. Quantification of chiral amino acids, especially very low concentrations of endogenous D-amino acids, is technically challenging. We report here the quantification of L- and D-amino acids in urine samples collected from 115 CRC patients and 155 healthy volunteers, using an improved method. The method of chiral labeling, liquid chromatography, and tandem mass spectrometry enabled separation and detection of 28 amino acids (14 L-amino acids, 13 D-amino acids and Gly). Orthogonal partial least squares discriminant analysis identified 14 targeted variables among these chiral amino acids that distinguished the CRC from the healthy controls. Binary logistic regression analysis revealed that D-α-aminobutyric acid (D-AABA), L-alanine (L-Ala), D-alanine (D-Ala), D-glutamine (D-Gln) and D-serine (D-Ser) could be potential biomarkers for CRC. A receiver operating characteristic curve analysis of combined multi-variables contributed to an area under the curve (AUC) of 0.995 with 98.3 % sensitivity and 96.8 % specificity. A model constructed with D-AABA, D-Ala, D-Gln, and D-Ser achieved an AUC of 0.988, indicating important contributions of D-amino acids to the association with CRC. Further analysis also demonstrated that the metabolic aberration was associated with age and the development of CRC, D-methionine (D-Met) was decreased in CRC patients with age over 50, and D/L-Gln in patients at stage IV was higher than patients at stage I. This study provides the signature of D-amino acids in urine samples and offers a promising strategy for developing non-invasive diagnosis of CRC.

Causality of blood metabolites and metabolic pathways on peripheral arteriosclerosis: a Mendelian randomization study

Background

Peripheral arteriosclerosis is caused by any atherosclerosis outside the heart and brain. However, the underlying biological mechanisms are not fully understood. This study aims to explore the causal relationship between blood metabolites and peripheral arteriosclerosis.

Methods

A Mendelian randomization (MR) analysis was implemented to estimate the causality of blood metabolites on peripheral arteriosclerosis. A genome-wide association study (GWAS) of 1,400 metabolites was used as the exposure, whereas two different GWAS datasets of peripheral arteriosclerosis were the outcomes. Inverse-variance weighted (IVW) was the main analysis of causal analysis. MR-Egger, the simple mode, weighted median and weighted mode were used to increase the stability and robustness of the results. Cochran Q test, MR-Egger intercept test, the funnel plot, and MR-Pleiotropy RESidual Sum and Outlier were used for sensitivity analyses. Furthermore, metabolic pathway enrichment analysis was performed using MetaboAnalyst5.0.

Results

In this MR study, eight blood metabolites have a strong causal relationship with peripheral arteriosclerosis, including 1-myristoyl-2-arachidonoyl-GPC (14:0/20:4), 1-palmitoyl-2-arachidonoyl-gpc (16:0/20:4n6), 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE, 1-palmitoyl-2-dihomo-linolenoyl-GPC, Gamma-glutamylleucine, Deoxycholic acid glucuronide and two named X- (X-24546, X-26111). In addition, five important metabolic pathways in peripheral arteriosclerosis were identified through metabolic pathway analysis.

Conclusion

This study provides evidence for the causal relationship between blood metabolites and peripheral arteriosclerosis, and these eight blood metabolites provide new perspectives for screening and prevention of peripheral arteriosclerosis in the future.

Bioactive Peptide Profiling in Collagen Hydrolysates: Comparative Analysis Using Targeted and Untargeted Liquid Chromatography-Tandem Mass Spectrometry Quantification

The investigation of collagen hydrolysates (CHs) is essential due to their widespread use in health, cosmetic, and therapeutic industries, attributing to the presence of bioactive dipeptides (DPs) and tripeptides (TPs). This study developed a novel targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method with propyl chloroformate (PCF) derivatization to measure three bioactive peptides-Hydroxyprolyl-glycine (Hyp-Gly), Glycyl-prolyl-hydroxyproline (Gly-Pro-Hyp), and Prolyl-hydroxyproline (Pro-Hyp)-in CHs, with strong correlation coefficients (0.992, 1.000, and 0.995, respectively) and low limits of detection (LODs) of 1.40, 0.14, and 1.16 µM, respectively. Untargeted data-dependent acquisition (DDA) analyses measured peptide size distribution, while amino acid analysis assessed nutritional content. The analysis of ten commercial CHs revealed similar amino acid profiles but varied peptide lengths, indicating diverse hydrolysis conditions. Products with higher proportions of smaller peptides showed elevated levels of the targeted bioactive peptides, suggesting that a smaller peptide size may increase bioactivity. These findings can inform the optimization of CH supplements, providing consumers with detailed peptide content for more informed choices. Data are available via ProteomeXchange with the identifier PXD051699.

UHPLC-HRMS-based Multiomics to Explore the Potential Mechanisms and Biomarkers for Colorectal Cancer

BACKGROUND

Understanding the metabolic changes in colorectal cancer (CRC) and exploring potential diagnostic biomarkers is crucial for elucidating its pathogenesis and reducing mortality. Cancer cells are typically derived from cancer tissues and can be easily obtained and cultured. Systematic studies on CRC cells at different stages are still lacking. Additionally, there is a need to validate our previous findings from human serum.

METHODS

Ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics and lipidomics were employed to comprehensively measure metabolites and lipids in CRC cells at four different stages and serum samples from normal control (NR) and CRC subjects. Univariate and multivariate statistical analyses were applied to select the differential metabolites and lipids between groups. Biomarkers with good diagnostic efficacy for CRC that existed in both cells and serum were screened by the receiver operating characteristic curve (ROC) analysis. Furthermore, potential biomarkers were validated using metabolite standards.

RESULTS

Metabolite and lipid profiles differed significantly among CRC cells at stages A, B, C, and D. Dysregulation of glycerophospholipid (GPL), fatty acid (FA), and amino acid (AA) metabolism played a crucial role in the CRC progression, particularly GPL metabolism dominated by phosphatidylcholine (PC). A total of 46 differential metabolites and 29 differential lipids common to the four stages of CRC cells were discovered. Eight metabolites showed the same trends in CRC cells and serum from CRC patients compared to the control groups. Among them, palmitoylcarnitine and sphingosine could serve as potential biomarkers with the values of area under the curve (AUC) more than 0.80 in the serum and cells. Their panel exhibited excellent performance in discriminating CRC cells at different stages from normal cells (AUC = 1.00).

CONCLUSIONS

To our knowledge, this is the first research to attempt to validate the results of metabolism studies of serum from CRC patients using cell models. The metabolic disorders of PC, FA, and AA were closely related to the tumorigenesis of CRC, with PC being the more critical factor. The panel composed of palmitoylcarnitine and sphingosine may act as a potential biomarker for the diagnosis of CRC, aiding in its prevention.

Engineering the Signal Resolution of a Paper-Based Cell-Free Glutamine Biosensor with Genetic Engineering, Metabolic Engineering, and Process Optimization

Specialized cancer treatments have the potential to exploit glutamine dependence to increase patient survival rates. Glutamine diagnostics capable of tracking a patient's response to treatment would enable a personalized treatment dosage to optimize the tradeoff between treatment success and dangerous side effects. Current clinical glutamine testing requires sophisticated and expensive lab-based tests, which are not broadly available on a frequent, individualized basis. To address the need for a low-cost, portable glutamine diagnostic, this work engineers a cell-free glutamine biosensor to overcome assay background and signal-to-noise limitations evident in previously reported studies. The findings from this work culminate in the development of a shelf-stable, paper-based, colorimetric glutamine test with a high signal strength and a high signal-to-background ratio for dramatically improved signal resolution. While the engineered glutamine test is important progress towards improving the management of cancer and other health conditions, this work also expands the assay development field of the promising cell-free biosensing platform, which can facilitate the low-cost detection of a broad variety of target molecules with high clinical value.

HCMMD: systematic evaluation of metabolites in body fluids as liquid biopsy biomarker for human cancers

Metabolomics is a rapidly expanding field in systems biology used to measure alterations of metabolites and identify metabolic biomarkers in response to disease processes. The discovery of metabolic biomarkers can improve early diagnosis, prognostic prediction, and therapeutic intervention for cancers. However, there are currently no databases that provide a comprehensive evaluation of the relationship between metabolites and cancer processes. In this review, we summarize reported metabolites in body fluids across pan-cancers and characterize their clinical applications in liquid biopsy. We conducted a search for metabolic biomarkers using the keywords ("metabolomics" OR "metabolite") AND "cancer" in PubMed. Of the 22,254 articles retrieved, 792 were deemed potentially relevant for further review. Ultimately, we included data from 573,300 samples and 17,083 metabolic biomarkers. We collected information on cancer types, sample size, the human metabolome database (HMDB) ID, metabolic pathway, area under the curve (AUC), sensitivity and specificity of metabolites, sample source, detection method, and clinical features were collected. Finally, we developed a user-friendly online database, the Human Cancer Metabolic Markers Database (HCMMD), which allows users to query, browse, and download metabolite information. In conclusion, HCMMD provides an important resource to assist researchers in reviewing metabolic biomarkers for diagnosis and progression of cancers.

Distinct plasma molecular profiles between early-onset and late-onset colorectal cancer patients revealed by metabolic and lipidomic analyses

Colorectal cancer (CRC) incidence in younger adults has been steadily rising, warranting an in-depth investigation into the distinctions between early-onset CRC (EOCRC, < 50 years) and late-onset CRC (LOCRC, ≥ 50 years). Despite extensive study of clinical, pathological, and molecular traits, differentiating EOCRC from LOCRC and identifying potential biomarkers remain elusive. We analyzed plasma samples from healthy individuals, EOCRC, and LOCRC patients using liquid-chromatography mass spectrometry (LC/MS)-based metabolomics and lipidomics. Distinct polar metabolite and lipid profiles with significant metabolites altered in CRC group (e.g., choline and DG 40:4) were identified. Notably, EOCRC exhibited distinct polar metabolomic and differential lipidomic profiles compared to LOCRC, with polar metabolites like aminoadipate and uridine contributing significantly to the difference, and originating from pathways such as lysine biosynthesis and nucleotide metabolism. Furthermore, gene set enrichment analysis (GSEA) using independent TCGA gene expression data identified pathways significantly enriched in either EOCRC or LOCRC. Integrating gene expression and metabolomics data revealed numerous associations differentiating EOCRC and LOCRC. Our multi-omics integration underscores critical molecular distinctions, offers insights into the EOCRC development mechanisms and potential plasma biomarkers for diagnosis.

The role of Akkermansia muciniphila in colorectal cancer: A double-edged sword of treatment or disease progression?

Colorectal cancer (CRC) is the second most cancer-related death worldwide. In recent years, probiotics have been used to reduce the potential risks of CRC and tumors with various mechanisms. Different bacteria have been suggested to play different roles in the progression, prevention, or treatment of CRC. Akkermansia muciniphila is considered a next-generation probiotic for preventing and treating some diseases. Therefore, in this review article, we aimed to describe and discuss different mechanisms of A. muciniphila as an intestinal microbiota or probiotic in CRC. Some studies suggested that the abundance of A. muciniphila was higher or increased in CRC patients compared to healthy individuals. However, the decreased abundance of A. muciniphila was associated with severe symptoms of CRC, indicating that A. muciniphila did not play a role in the development of CRC. In addition, A. muciniphila administration elevates gene expression of proliferation-associated molecules such as S100A9, Dbf4, and Snrpd1, or markers for cell proliferation. Some other studies suggested that inflammation and tumorigenesis in the intestine might promoted by A. muciniphila. Overall, the role of A. muciniphila in CRC development or inhibition is still unclear and controversial. Various methods of bacterial supplementation, such as viability, bacterial number, and abundance, could all influence the colonization effect of A. muciniphila administration and CRC progression. Overall, A. mucinipila has been revealed to modulate the therapeutic potential of immune checkpoint inhibitors. Preliminary human data propose that oral consumption of A. muciniphila is safe, but its efficacy needs to be confirmed in more human clinical studies.

Profiling the metabolic disorder and detection of colorectal cancer based on targeted amino acids metabolomics

BACKGROUND

The morbidity of cancer keeps growing worldwide, and among that, the colorectal cancer (CRC) has jumped to third. Existing early screening tests for CRC are limited. The aim of this study was to develop a diagnostic strategy for CRC by plasma metabolomics.

METHODS

A targeted amino acids metabolomics method was developed to quantify 32 plasma amino acids in 130 CRC patients and 216 healthy volunteers, to identify potential biomarkers for CRC, and an independent sample cohort comprising 116 CRC subjects, 33 precancerosiss patients and 195 healthy volunteers was further used to validate the diagnostic model. Amino acids-related genes were retrieved from Gene Expression Omnibus and Molecular Signatures Database and analyzed.

RESULTS

Three were chosen out of the 32 plasma amino acids examined. The tryptophan / sarcosine / glutamic acid -based receiver operating characteristic (ROC) curve showed the area under the curve (AUC) of 0.955 (specificity 83.3% and sensitivity 96.8%) for all participants, and the logistic regression model were used to distinguish between early stage (I and II) of CRC and precancerosiss patients, which showed superiority to the commonly used carcinoembryonic antigen. The GO and KEGG enrichment analysis proved many alterations in amino acids metabolic pathways in tumorigenesis.

CONCLUSION

This altered plasma amino acid profile could effectively distinguish CRC patients from precancerosiss patients and healthy volunteers with high accuracy. Prognostic tests based on the tryptophan/sarcosine/glutamic acid biomarkers in the large population could assess the clinical significance of CRC early detection and intervention.

Serum Metabolomic Profiles for Distinguishing Lung Cancer From Pulmonary Tuberculosis: Identification of Rapid and Noninvasive Biomarker

BACKGROUND

Pulmonary tuberculosis (PTB) and lung cancer (LC) have similar clinical symptoms and atypical imaging findings, which are easily misdiagnosed. There is an urgent need for a noninvasive and accurate biomarker to distinguish LC from PTB.

METHODS

A total of 694 subjects were enrolled and divided into discovery set (n = 122), identification set (n = 214), and validation set (n = 358). Metabolites were identified by multivariate and univariate analyses. Receiver operating characteristic curve were used to evaluate the diagnostic efficacy of biomarkers.

RESULTS

Seven metabolites were identified and validated. Phenylalanylphenylalanine for distinguishing LC from PTB yielded an area under the curve of 0.89, sensitivity of 71%, and specificity of 92%. It also showed good diagnostic abilities in discovery set and identification set. Compared with that in healthy volunteers (median [interquartile range], 1.57 [1.01, 2.34] μg/mL), it was elevated in LC (4.76 [2.74, 7.08] μg/mL; ratio of median, [ROM] = 3.03, P < .01) and reduced in PTB (1.06 [0.51, 2.09] μg/mL; ROM = 0.68, P < .05).

CONCLUSIONS

The metabolomic profile of LC and PTB was described and a key biomarker identified. We produced a rapid and noninvasive method to supplement existing clinical diagnostic examinations for distinguishing LC from PTB.

LC-MS/MS based metabolomic analysis of serum from patients with cerebrovascular stenosis

Cerebrovascular stenosis (CVS) is the main cause of ischemic stroke, which greatly threatens human life. Hence, it's important to perform early screenings for CVS. Metabolomics is an emerging omics approach that has great advantages in disease screening and diagnosis. Therefore, we aim to elucidate the correlation between CVS and metabolomics, which can aid in conducting CVS screening at an early stage. Patients with CVS in Beijing Hospital were included in the study. A total of 36 participants, including 18 patients diagnosed with CVS and 18 healthy individuals, were recruited at Beijing Hospital between May 2022 and October 2021. The serum samples were analyzed for liquid chromatography-tandem mass spectrometry (LC-MS/MS). Then, multivariate statistical methods, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed. Differential metabolites were obtained and demonstrated by volcano plot and heatmap. The study recruited 36 participants, including 18 patients with CVS and 18 healthy participants. A total of 150 metabolites were identified. Multivariate statistical analysis revealed significant differences between patients and healthy participants. Furthermore, 30 serum metabolites levels differed significantly between two groups. Differential metabolites were enriched in phenylalanine, tyrosine, and tryptophan biosynthesis; primary bile acid biosynthesis, and other pathways. This study identified differential metabolites in patients with CVS and elucidated the relevant metabolic pathways. Thus, these findings aid in the study of the pathogenesis of CVS and its early diagnosis. DATA AVAILABILITY STATEMENT: The datasets generated for this study are available on request to the corresponding author.

A chemical derivatization-based pseudotargeted LC-MS/MS method for high coverage determination of dipeptides

Dipeptides (DPs) have attracted more and more attention in many research fields due to their important biological functions and promising roles as disease biomarkers. However, the determination of DPs in biological samples is very challenging owing to the limited availability of commercial standards, high structure diversity, distinct physical and chemical characteristics, wide concentration range, and the extensive existence of isomers. In this study, a pseudotargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method coupled with chemical derivatization for the simultaneous analysis of 400 DPs and their constructing amino acids (AAs) in biospecimens is established. Dansyl chloride (Dns-Cl) chemical derivatization was introduced to provide characteristic MS fragments for annotation and improve the chromatographic separation of DP isomers. A retention time (RT) prediction model was constructed using 83 standards (63 DPs and 20 AAs) based on their quantitative structural retention relationship (QSRR) after the Dns-Cl labeling, which largely facilitated the annotation of the DPs without standards. Finally, we applied this method to investigate the profile change of DPs in a cisplatin-induced acute kidney injury (AKI) rat model. The established workflow provides a platform to profile DPs and expand our understanding of these little-studied metabolites.

Metabolomics analysis of amino acid and fatty acids in colorectal cancer patients based on tandem mass spectrometry

Metabolic differences between colorectal cancer (CRC) and NI (NI) play an important role in early diagnoses and in-time treatments. We investigated the metabolic alterations between CRC patients and NI, and identified some potential biomarkers, and these biomarkers might be used as indicators for diagnosis of CRC. In this study, there were 79 NI, 50 CRC I patients, 52 CRC II patients, 56 CRC III patients, and 52 CRC IV patients. MS-MS was used to measure the metabolic alterations. Univariate and multivariate data analysis and metabolic pathway analysis were applied to analyze metabolic data and determine differential metabolites. These indicators revealed that amino acid and fatty acids could separate these groups. Several metabolites indicated an excellent variables capability in the separation of CRC patients and NI. Ornithine, arginine, octadecanoyl carnitine, palmitoyl carnitine, adipoyl carnitine, and butyryl carnitine/propanoyl carnitine were selected to distinguish the CRC patients and NI. And methionine and propanoyl carnitine, were directly linked to different stages of CRC. Receiver operating characteristics curves and variables importance in projection both represented an excellent performance of these metabolites. In conclusion, we assessed the difference between CRC patients and NI, which supports guidelines for an early diagnosis and effective treatment.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

ENPP2 inhibitor improves proliferation in AOM/DSS-induced colorectal cancer mice via remodeling the gut barrier function and gut microbiota composition

In our previous multicenter study, we delineated the inherent metabolic features of colorectal cancer (CRC). Therein, we identified a member of the ectonucleotide pyrophosphatase/ phosphodiesterase family (ENPP2) as a significant differential metabolite of CRC. In this study, the role of ENPP2 in CRC has been demonstrated using established in vitro and in vivo models including ENPP2 gene knockdown, and use of the ENPP2 inhibitor, GLPG1690. We found that CRC proliferation was decreased after either ENPP2 gene knockdown or use of ENPP2 inhibitors. We further evaluated the role of GLPG1690 in AOM/DSS-induced CRC mice via intestinal barrier function, macrophage polarization, inflammatory response and microbial homeostasis. Results of immunofluorescence staining and Western blotting showed that GLPG1690 can restore gut-barrier function by increasing the expression of tight junction proteins, claudin-1, occludin and ZO-1. M2 tumor-associated macrophage polarization and colonic inflammation were attenuated after treatment with GLPG1690 using the Azoxymethane/Dextran Sodium Sulfate (AOM/DSS) model. Moreover, 16 S rDNA pyrosequencing and metagenomic analysis showed that GLPG1690 could alleviate gut dysbiosis in mice. Furthermore, administration of GLPG1690 with antibiotics as well as fecal microbiota transplantation assays demonstrated a close link between the efficacy of GLPG1690 and the gut microbiota composition. Finally, results of metabolomic analysis implicated mainly the gut microbiota-derived metabolites of aromatic amino acids in CRC progression. These findings may provide novel insights into the development of small-molecule ENPP2 inhibitors for the treatment of CRC.

L-Arginine, as an essential amino acid, is a potential substitute for treating COPD via regulation of ROS/NLRP3/NF-κB signaling pathway

BACKGROUNDS

Chronic obstructive pulmonary disease (COPD) is a frequent and common disease in clinical respiratory medicine and its mechanism is unclear. The purpose of this study was to find the new biomarkers of COPD and elucidate its role in the pathogenesis of COPD. Analysis of metabolites in plasma of COPD patients were performed by ultra-high performance liquid chromatography (UPLC) and quadrupole time-of-flight mass spectrometry (TOF-MS). The differential metabolites were analyzed and identified by multivariate analysis between COPD patients and healthy people. The role and mechanisms of the differential biomarkers in COPD were verified with COPD rats, arginosuccinate synthetase 1 (ASS-l) KO mice and bronchial epithelial cells (BECs). Meanwhile, whether the differential biomarkers can be the potential treatment targets for COPD was also investigated. 85 differentials metabolites were identified between COPD patients and healthy people by metabonomic.

RESULTS

L-Arginine (LA) was the most obvious differential metabolite among the 85 metabolites. Compare with healthy people, the level of LA was markedly decreased in serum of COPD patients. It was found that LA had protective effects on COPD with in vivo and in vitro experiments. Silencing Ass-1, which regulates LA metabolism, and α-methy-DL-aspartic (NHLA), an Ass-1 inhibitor, canceled the protective effect of LA on COPD. The mechanism of LA in COPD was related to the inhibition of ROS/NLRP3/NF-κB signaling pathway. It was also found that exogenous LA significantly improved COPD via regulation of ROS/NLRP3/NF-κB signaling pathway. L-Arginine (LA) as a key metabolic marker is identified in COPD patients and has a protective effect on COPD via regulation of ROS/NLRP3/NF-κB signaling pathway.

CONCLUSION

LA may be a novel target for the treatment of COPD and also a potential substitute for treating COPD.

Elevated serum β-hydroxybutyrate, a circulating ketone metabolite, accelerates colorectal cancer proliferation and metastasis via ACAT1

Colorectal cancer (CRC) ranks third in incidence and second in mortality worldwide. Metabolic disorders are known to be closely associated with CRC. Functional metabolomics aims to translate metabolomics-derived biomarkers to disease mechanisms. Previous work based on untargeted liquid chromatography identified 30 differential metabolites of CRC. Among them, only β-hydroxybutyrate (BHB) was elevated in CRC. Here, we first confirm the increased level of β-hydroxybutyrate by targeted metabolomic analysis using an independent cohort of 400 serum samples by UPLC-QQQ-MS/MS analysis. Using appropriate cell and animal models, we find that treatment with pathological levels of β-hydroxybutyrate expedites CRC proliferation and metastasis. Out of four major rate-limiting enzymes of ketolysis, only acetyl-coenzyme A acetyltransferase1 (ACAT1) expression is increased in paired human CRC tissues. These findings suggest probable clinical relevance for the functional implications of β-hydroxybutyrate in CRC. We demonstrate that β-hydroxybutyrate may exert its tumorigenic effects via regulation of ACAT1, due to induction of downstream isocitrate dehydrogenase1 (IDH1) acetylation. Genetic silencing of ACAT1 significantly suppresses the progression of CRC and abrogates the effects of β-hydroxybutyrate both in vitro and in vivo. Overall, this study suggests that targeting β-hydroxybutyrate and its major rate-limiting enzyme ACAT1 may provide a new avenue for therapeutic intervention in CRC.

Serum and urine metabolomics analyses reveal metabolic pathways and biomarkers in relation to nasopharyngeal carcinoma

RATIONALE

Nasopharyngeal carcinoma (NPC) is a malignant tumor that is endemic in Southeast Asia, North Africa, and southern China. There is an urgent need for effective early diagnosis and treatment of this disease since NPC is currently often detected at advanced stages.

METHODS

To reveal the underlying metabolic mechanisms and discover potential diagnostic biomarkers of NPC, we employed ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) and UHPLC-Q-Exactive Orbitrap MS, respectively, to analyze 54 serum samples and 54 urine samples from 27 patients with NPC and 27 healthy control individuals.

RESULTS

A total of 1230 metabolites were determined in serum samples, and 181 of the 1230 metabolites were significantly changed in NPC patients. The 181 metabolites were enriched in 16 pathways, including biosynthesis of unsaturated fatty acids, cholesterol metabolism, and ferroptosis. A total of 2509 metabolites were detected in the urine samples. Among them, 179 metabolites were significantly altered in NPC patients, and these metabolites were enriched in eight pathways, including the tricarboxylic acid (TCA) cycle and caffeine metabolism. Seven metabolites, including creatinine and paraxanthine, were found to be significantly changed in both NPC serum and urine samples. Based on them, further biomarker analysis revealed that the panel of three serum metabolites, octanoylcarnitine, creatinine, and decanoyl-l-carnitine, displayed a perfect diagnostic performance (area under the curve [AUC] = 0.973) to distinguish NPC patients from controls, while the other three-metabolite biomarker panel, consisting of stachydrine, decanoyl-l-carnitine, and paraxanthine, had an AUC = 0.809 to distinguish NPC and control in urine samples.

CONCLUSION

This work highlights the key metabolites and metabolic pathways disturbed in NPC and presents potential biomarkers for effective diagnosis of this disease.

Circulating amino acid levels and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition and UK Biobank cohorts

BACKGROUND

Amino acid metabolism is dysregulated in colorectal cancer patients; however, it is not clear whether pre-diagnostic levels of amino acids are associated with subsequent risk of colorectal cancer. We investigated circulating levels of amino acids in relation to colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank cohorts.

METHODS

Concentrations of 13-21 amino acids were determined in baseline fasting plasma or serum samples in 654 incident colorectal cancer cases and 654 matched controls in EPIC. Amino acids associated with colorectal cancer risk following adjustment for the false discovery rate (FDR) were then tested for associations in the UK Biobank, for which measurements of 9 amino acids were available in 111,323 participants, of which 1221 were incident colorectal cancer cases.

RESULTS

Histidine levels were inversely associated with colorectal cancer risk in EPIC (odds ratio [OR] 0.80 per standard deviation [SD], 95% confidence interval [CI] 0.69-0.92, FDR P-value=0.03) and in UK Biobank (HR 0.93 per SD, 95% CI 0.87-0.99, P-value=0.03). Glutamine levels were borderline inversely associated with colorectal cancer risk in EPIC (OR 0.85 per SD, 95% CI 0.75-0.97, FDR P-value=0.08) and similarly in UK Biobank (HR 0.95, 95% CI 0.89-1.01, P=0.09) In both cohorts, associations changed only minimally when cases diagnosed within 2 or 5 years of follow-up were excluded.

CONCLUSIONS

Higher circulating levels of histidine were associated with a lower risk of colorectal cancer in two large prospective cohorts. Further research to ascertain the role of histidine metabolism and potentially that of glutamine in colorectal cancer development is warranted.

Glutamine deficiency promotes recurrence and metastasis in colorectal cancer through enhancing epithelial–mesenchymal transition

Background

Glutamine is the most abundant amino acid in the body and plays a vital role in colorectal cancer (CRC) cell metabolism. However, limited studies have investigated the clinical and prognostic significance of preoperative serum glutamine levels in patients with colorectal cancer, and the underlying mechanism has not been explored.

Methods

A total of 121 newly diagnosed CRC patients between 2012 and 2016 were enrolled in this study. Serum glutamine levels were detected, and their associations with clinicopathological characteristics, systemic inflammation markers, carcinoembryonic antigen (CEA) and prognosis were analysed. In addition, the effect of glutamine depletion on recurrence and metastasis was examined in SW480 and DLD1 human CRC cell lines, and epithelial–mesenchymal transition (EMT)-related markers were detected to reveal the possible mechanism.

Results

A decreased preoperative serum level of glutamine was associated with a higher T-class and lymph node metastasis (P < 0.05). A higher serum level of glutamine correlated with a lower CEA level (r = − 0.25, P = 0.02). Low glutamine levels were correlated with shorter overall survival (OS) and disease-free survival (DFS). Multivariate Cox regression analysis showed that serum glutamine was an independent prognostic factor for DFS (P = 0.018), and a nomogram predicting the probability of 1-, 3- and 5-year DFS after radical surgery was built. In addition, glutamine deficiency promoted the migration and invasion of CRC cells. E-cadherin, a vital marker of EMT, was decreased, and EMT transcription factors, including zeb1and zeb2, were upregulated in this process.

Conclusions

This study elucidated that preoperative serum glutamine is an independent prognostic biomarker to predict CRC progression and suggested that glutamine deprivation might promote migration and invasion in CRC cells by inducing the EMT process.

Gamma-glutamyl-leucine levels are causally associated with elevated cardio-metabolic risks

Objective

Gamma-glutamyl dipeptides are bioactive peptides involved in inflammation, oxidative stress, and glucose regulation. Gamma-glutamyl-leucine (Gamma-Glu-Leu) has been extensively reported to be associated with the risk of cardio-metabolic diseases, such as obesity, metabolic syndrome, and type 2 diabetes. However, the causality remains to be uncovered. The aim of this study was to explore the causal-effect relationships between Gamma-Glu-Leu and metabolic risk.

Materials and methods

In this study, 1,289 subjects were included from a cross-sectional survey on metabolic syndrome (MetS) in eastern China. Serum Gamma-Glu-Leu levels were measured by untargeted metabolomics. Using linear regressions, a two-stage genome-wide association study (GWAS) for Gamma-Glu-Leu was conducted to seek its instrumental single nucleotide polymorphisms (SNPs). One-sample Mendelian randomization (MR) analyses were performed to evaluate the causality between Gamma-Glu-Leu and the metabolic risk.

Results

Four SNPs are associated with serum Gamma-Glu-Leu levels, including rs12476238, rs56146133, rs2479714, and rs12229654. Out of them, rs12476238 exhibits the strongest association (Beta = -0.38, S.E. = 0.07 in discovery stage, Beta = -0.29, S.E. = 0.14 in validation stage, combined P-value = 1.04 × 10^-8). Each of the four SNPs has a nominal association with at least one metabolic risk factor. Both rs12229654 and rs56146133 are associated with body mass index, waist circumference (WC), the ratio of WC to hip circumference, blood pressure, and triglyceride (5 × 10^-5 < P < 0.05). rs56146133 also has nominal associations with fasting insulin, glucose, and insulin resistance index (5 × 10^-5 < P < 0.05). Using the four SNPs serving as the instrumental SNPs of Gamma-Glu-Leu, the MR analyses revealed that higher Gamma-Glu-Leu levels are causally associated with elevated risks of multiple cardio-metabolic factors except for high-density lipoprotein cholesterol and low-density lipoprotein cholesterol (P > 0.05).

Conclusion

Four SNPs (rs12476238, rs56146133, rs2479714, and rs12229654) may regulate the levels of serum Gamma-Glu-Leu. Higher Gamma-Glu-Leu levels are causally linked to cardio-metabolic risks. Future prospective studies on Gamma-Glu-Leu are required to explain its role in metabolic disorders.

The gut microbe Bacteroides fragilis ameliorates renal fibrosis in mice

Renal fibrosis is an inevitable outcome of various manifestations of progressive chronic kidney diseases (CKD). The need for efficacious treatment regimen against renal fibrosis can therefore not be overemphasized. Here we show a novel protective role of Bacteroides fragilis (B. fragilis) in renal fibrosis in mice. We demonstrate decreased abundance of B. fragilis in the feces of CKD patients and unilateral ureteral obstruction (UUO) mice. Oral administration of live B. fragilis attenuates renal fibrosis in UUO and adenine mice models. Increased lipopolysaccharide (LPS) levels are decreased after B. fragilis administration. Results of metabolomics and proteomics studies show decreased level of 1,5-anhydroglucitol (1,5-AG), a substrate of SGLT2, which increases after B. fragilis administration via enhancement of renal SGLT2 expression. 1,5-AG is an agonist of TGR5 that attenuates renal fibrosis by inhibiting oxidative stress and inflammation. Madecassoside, a natural product found via in vitro screening promotes B. fragilis growth and remarkably ameliorates renal fibrosis. Our findings reveal the ameliorative role of B. fragilis in renal fibrosis via decreasing LPS and increasing 1,5-AG levels.

Metabolomic profiling identifies biomarkers and metabolic impacts of surgery for colorectal cancer

Background

Colorectal cancer (CRC) is one of the most common malignant tumors with recurrence and metastasis after surgical resection. This study aimed to identify the physiological changes after surgery and explore metabolites and metabolic pathways with potential prognostic value for CRC.

Methods

An ultra-high-performance liquid chromatography Q-exactive mass spectrometry was used to profile serum metabolites from 67 CRC patients and 50 healthy volunteers. Principal component analysis (PCA) and orthogonal projections to latent structures-discriminant analysis were used to distinguish the internal characteristics of data in different groups. Multivariate statistics were compiled to screen the significant metabolites and metabolic pathways.

Result

A total of 180 metabolites were detected. Under the conditions of variable importance in projection >1 and p-value <0.05, 46 differentially expressed metabolites were screened for further pathway enrichment analysis. Based on the Kyoto Encyclopedia of Genes and Genomes database and Small Molecule Pathway Database, three metabolic pathways—arginine and proline metabolism, ascorbate and aldarate metabolism, and phenylalanine metabolism—were significantly altered after surgical resection and identified as associated with the removal of CRC. Notably, gamma-linolenic acid was upregulated in the CRC preoperative patients compared with those in healthy volunteers but returned to healthy levels after surgery.

Conclusion

Through serum-based metabolomics, our study demonstrated the differential metabolic characteristics in CRC patients after surgery compared with those before surgery. Our results suggested that metabonomic analysis may be a powerful method for exploring physiological alterations in CRC patients after surgery as well as a useful tool for identifying candidate biomarkers and monitoring disease recurrence.

Metabolic-Dysregulation-Based iEESI-MS Reveals Potential Biomarkers Associated with Early-Stage and Progressive Colorectal Cancer

The application of rapid and accurate diagnostic methods can improve colorectal cancer (CRC) survival rates dramatically. Here, we used a non-targeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry (iEESI-MS) to detect metabolite ions associated with the progression of CRC from 172 tissues (45 stage I/II CRC, 41 stage III/IV CRC, and 86 well-matched normal tissues). A support vector machine (SVM) model based on 10 differential metabolite ions for differentiating early-stage CRC from normal tissues was built with a good prediction accuracy of 92.6%. The biomarker panel consisting of lysophosphatidylcholine (LPC) (18:0) has good diagnostic potential in differentiating early-stage CRC from advanced-stage CRC. We showed that the down-regulation of LPC (18:0) in tumor tissues is associated with CRC progression and related to the regulation of the epidermal growth factor receptor. Pathway analysis showed that metabolic pathways in CRC are related to glycerophospholipid metabolism and purine metabolism. In conclusion, we built an SVM model with good performance to distinguish between early-stage CRC and normal groups based on iEESI-MS and found that LPC (18:0) is associated with the progression of CRC.

Vaginal microbiota and personal risk factors associated with HPV status conversion—A new approach to reduce the risk of cervical cancer?

Vaginal microbiota (VMB) is associated with changes in Human papilloma virus (HPV) status, which consequently influences the risk of cervical cancer. This association was often confounded by personal risk factors. This pilot research aimed to explore the relationship between vaginal microbiota, personal risk factors and their interactions with HPV status conversion to identify the vaginal microbiota that was associated with HPV clearance under heterogeneous personal risk factors. A total of 38 women participated by self-collecting a cervicovaginal mucus (CVM) sample that was sent for metagenomics sequencing. Most of the participants also filled in personal risk factors questionnaire through an eHealth platform and authorized the use of their previous HPV genotyping results stored in this eHealth platform. Based on the two HPV results, the participants were grouped into three cohorts, namely HPV negative, HPV persistent infection, and HPV status conversion. The relative abundance of VMB and personal factors were compared among these three cohorts. A correlation investigation was performed between VMB and the significant personal factors to characterize a robustness of the panel for HPV status change using R programming. At baseline, 12 participants were HPV-negative, and 22 were HPV-positive. Within one year, 18 women remained HPV-positive, 12 were HPV-negative and 4 participants showed HPV clearance. The factors in the eHealth questionnaire were systematically evaluated which identified several factors significantly associated with persistent HPV infection, including age, salary, history of reproductive tract infection, and the total number of sexual partners. Concurrent vaginal microbiome samples suggest that a candidate biomarker panel consisting of Lactobacillus gasseri, Streptococcus agalactiae, and Timona prevotella bacteria, which may be associated with HPV clearance. This pilot study indicates a stable HPV status-related vaginal microbe environment. To establish a robust biomarker panel for clinical use, larger cohorts will be recruited into follow-up studies.

Integrative metabolomic characterization identifies plasma metabolomic signature in the diagnosis of papillary thyroid cancer

Discrimination of malignancy from thyroid nodules poses challenges in clinical practice. We aimed to identify the plasma metabolomic biomarkers in discriminating papillary thyroid cancer (PTC) from benign thyroid nodule (BTN). Metabolomics profiling of plasma was performed in two independent cohorts of 651 subjects of PTC (n = 215), BTN (n = 230), and healthy controls (n = 206). In addition, 132 patients with thyroid micronodules (<1 cm) and 44 patients with BTN suspected malignancy by ultrasound were used for biomarker validation. Recursive feature elimination algorithm was used for metabolic biomarkers selecting. Significant differential metabolites were demonstrated in patients with thyroid nodules (PTC and BTN) from healthy controls (P = 0.0001). A metabolic biomarker panel (17 differential metabolites) was identified to discriminate PTC from BTN with an AUC of 97.03% (95% CI: 95.28-98.79%), 91.89% sensitivity, and 92.63% specificity in discovery cohort. The panel had an AUC of 92.72% (95% CI: 87.46-97.99%), 86.57% sensitivity, and 92.50% specificity in validation cohort. The metabolic biomarker signature could correctly identify 84.09% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. Moreover, high accuracy of 87.88% for diagnosis of papillary thyroid microcarcinoma was displayed by this panel and showed significant improvement in accuracy, AUC and specificity when compared with ultrasound. We identified a novel metabolic biomarker signature to discriminate PTC from BTN. The clinical use of this biomarker panel would have improved diagnosis stratification of thyroid microcarcinoma in comparison to ultrasound.

Fusobacterium nucleatum Affects Cell Apoptosis by Regulating Intestinal Flora and Metabolites to Promote the Development of Colorectal Cancer

Background/Aims

Intestinal flora, especially Fusobacterium nucleatum (Fn), can affect the development of colorectal cancer (CRC). In this study, we examined the composition of intestinal flora and their metabolites in the tissues, serum and feces of CRC patients.

Materials and Methods

CRC tissues, adjacent normal colonic tissues, fecal and serum samples were collected from CRC patients who received surgical treatment between January 2018 and January 2020. Fecal and serum samples were collected from healthy individuals for comparison. In addition, fecal samples were collected from BALB/c female mice. SW480, a human CRC cell line, was utilized for in vitro studies. The experiments involved ¹H-NMR-based metabolomics analysis, targeted and untargeted mass spectrometry analysis, and intestinal flora 16S rDNA V4 region sequencing.

Results

The abundance of Bacteroides and propionic acid concentration were decreased and that of Lactobacillus and lactic acid concentration were increased in CRC tissues. In addition, the abundances of Ruminococcus, Prevotella, and Sutterell were decreased in CRC patients. The levels of leucine and isoleucine were decreased in the serum and tumor tissues of CRC patients. Aspartate, glutamate and glutathione levels were elevated in the tissues of CRC patients only. The serum glutamine, tyrosine, valine, alanine, and histidine levels were decreased significantly. Lactic acid inhibited and propionic acid promoted apoptosis among SW480 CRC cells.

Conclusion

Fn affected the apoptosis of CRC cells and promoted the progression of CRC by affecting the distribution of intestinal flora, which altered the concentrations of metabolites such as lactic acid, propionic acid. Intestinal flora could regulate amino acid metabolism.

Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants

Background

Metabolic syndrome (MetS) is a cluster of multiple cardiometabolic risk factors that increase the risk of type 2 diabetes and cardiovascular diseases. Identifying novel biomarkers of MetS and their genetic associations could provide insights into the mechanisms of cardiometabolic diseases.

Methods

Potential MetS-associated metabolites were screened and internally validated by untargeted metabolomics analyses among 693 patients with MetS and 705 controls. External validation was conducted using two well-established targeted metabolomic methods among 149 patients with MetS and 253 controls. The genetic associations of metabolites were determined by linear regression using our previous genome-wide SNP data. Causal relationships were assessed using a one-sample Mendelian Randomization (MR) approach.

Findings

Nine metabolites were ultimately found to be associated with MetS or its components. Five metabolites, including LysoPC(14:0), LysoPC(15:0), propionyl carnitine, phenylalanine, and docosapentaenoic acid (DPA) were selected to construct a metabolite risk score (MRS), which was found to have a dose-response relationship with MetS and metabolic abnormalities. Moreover, MRS displayed a good ability to differentiate MetS and metabolic abnormalities. Three SNPs (rs11635491, rs7067822, and rs1952458) were associated with LysoPC(15:0). Two SNPs, rs1952458 and rs11635491 were found to be marginally correlated with several MetS components. MR analyses showed that a higher LysoPC(15:0) level was causally associated with the risk of overweight/obesity, dyslipidaemia, high uric acid, high insulin and high HOMA-IR.

Interpretation

We identified five metabolite biomarkers of MetS and three SNPs associated with LysoPC(15:0). MR analyses revealed that abnormal LysoPC metabolism may be causally linked the metabolic risk.

Funding

This work was supported by grants from the National Key Research and Development Program of China (2017YFC0907004).

Akkermansia Muciniphila Potentiates the Antitumor Efficacy of FOLFOX in Colon Cancer

FOLFOX (oxaliplatin, fluorouracil and calcium folinate) is the first-line chemotherapy regimen for colon cancer therapy in the clinic. It provides superior efficacy than oxaliplatin alone, but the underlying mechanism remains unclear. In the present study, pharmacomicrobiomics integrated with metabolomics was conducted to uncover the role of the gut microbiome behind this. First, in vivo study demonstrated that FOLFOX exhibited better efficacy than oxaliplatin alone in colon cancer animal models. Second, 16S rDNA gene sequencing analysis showed that the abundance of Akkermansia muciniphila (A. muciniphila) remarkably increased in the FOLFOX treated individuals and positively correlated with the therapeutic effect. Third, further exploration confirmed A. muciniphila colonization significantly enhanced the anti-cancer efficacy of FOLFOX. Last, metabolomics analysis suggested dipeptides containing branched-chain amino acid (BCAA) might be responsible for gut bacteria mediated FOLFOX efficacy. In conclusion, our study revealed the key role of A. muciniphila in mediating FOLFOX efficacy, and manipulating A. muciniphila might serve as a novel strategy for colon cancer therapy.

SARS-CoV-2 Host Receptor ACE2 Protein Expression Atlas in Human Gastrointestinal Tract

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects host cells through interactions with its receptor, Angiotensin-converting enzyme 2 (ACE2), causing severe acute respiratory syndrome and death in a considerable proportion of people. Patients infected with SARS-CoV-2 experience digestive symptoms. However, the precise protein expression atlas of ACE2 in the gastrointestinal tract remains unclear. In this study, we aimed to explore the ACE2 protein expression pattern and the underlying function of ACE2 in the gastrointestinal tract, including the colon, stomach, liver, and pancreas.

Methods

We measured the protein expression of ACE2 in the gastrointestinal tract using immunohistochemical (IHC) staining with an ACE2-specific antibody of paraffin-embedded colon, stomach, liver, and pancreatic tissues. The correlation between the protein expression of ACE2 and the prognosis of patients with gastrointestinal cancers was analyzed by the log-rank (Mantel–Cox) test. The influence of ACE2 on colon, stomach, liver, and pancreatic tumor cell line proliferation was tested using a Cell Counting Kit 8 (CCK-8) assay.

Results

ACE2 presented heterogeneous expression patterns in the gastrointestinal tract, and it showed a punctate distribution in hepatic cells. Compared to that in parallel adjacent non-tumor tissues, the protein expression of ACE2 was significantly increased in colon cancer, stomach cancer, and pancreatic cancer tissues but dramatically decreased in liver cancer tissues. However, the expression level of the ACE2 protein was not correlated with the survival of patients with gastrointestinal cancers. Consistently, ACE2 did not affect the proliferation of gastrointestinal cancer cells in vitro.

Conclusion

The ACE2 protein is widely expressed in the gastrointestinal tract, and its expression is significantly altered in gastrointestinal tumor tissues. ACE2 is not an independent prognostic marker of gastrointestinal cancers.

Metabolomics and molecular docking to compare of the efficacies of wild-simulated and transplanted Astragali Radix based on its compatibility with Angelicae Sinensis Radix against blood deficiency

Astragali Radix (AR) has been widely used as bulk medicines in China. Currently, there are mainly two types in the market, wild-simulated AR (WAR) and transplanted AR (TAR). The present work was designed to compare their effects in treatment of blood deficiency with its compatibility with Angelicae Sinensis Radix, a classical formula Danggui Buxue decoction (DBD). UHPLC-Q-Exactive-MS-based serum metabolomics was firstly applied to compare their metabolic regulations in blood deficiency mice. Metabolic results showed that 17 metabolites were closely related to blood deficiency. WAR-DBD and TAR-DBD significantly improved 13 and 8 metabolites among them, respectively. Further, fourteen genes were selected as promising targets for treating blood deficiency by searching GeneCards and CTD database. The results of molecular docking also showed that eight components of AR and three serum metabolites had strongest correlations with blood deficiency targets. The results further revealed that WAR-DBD was better than TAR-DBD against blood deficiency. The method provided a new way to compare the hematopoietic differences of AR of different cultivation methods. In addition, molecular docking plays an irreplaceable role in explaining the molecular mechanism of biological processes.

Identification of Metabolic-Associated Genes for the Prediction of Colon and Rectal Adenocarcinoma

Background and Aim

Uncontrolled proliferation is the most prominent biological feature of tumors. In order to rapidly proliferate, tumor cells regulate their metabolic behavior by controlling the expression of metabolism-related genes (MRGs) to maximize the utilization of available nutrients. In this study, we aimed to construct prognosis models for colorectal adenocarcinoma (COAD) and rectum adenocarcinoma (READ) using MRGs to predict the prognoses of patients.

Methods

We first acquired the gene expression profiles of COAD and READ from the TCGA database, and then utilized univariate Cox analysis, Lasso regression, and multivariable Cox analysis to identify the MRGs for risk models.

Results

Eight genes (CPT1C, PLCB2, PLA2G2D, GAMT, ENPP2, PIP4K2B, GPX3, and GSR) in the colon cancer risk model and six genes (TDO2, PKLR, GAMT, EARS2, ACO1, and WAS) in the rectal cancer risk model were identified successfully. Multivariate Cox analysis indicated that these two models could accurately and independently predict overall survival (OS) for patients with COAD or READ. Furthermore, functional enrichment analysis was used to identify the metabolism pathway of MRGs in the risk models and analyzed these genes comprehensively. Then, we verified the prognosis model in independent COAD cohorts (GSE17538) and detected the correlations of the protein expression levels of GSR and ENPP2 with prognosis for COAD or READ.

Conclusion

In this study, 14 MRGs were identified as potential prognostic biomarkers and therapeutic targets for colorectal cancer.

Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease

The diagnosis of Vogt-Koyanagi-Harada (VKH) disease is mainly based on a complex clinical manifestation while it lacks objective laboratory biomarkers. To explore the potential molecular biomarkers for diagnosis and disease activity in VKH, we performed an untargeted urine metabolomics analysis by ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS). Through univariate and multivariate statistical analysis, we found 9 differential metabolites when comparing VKH patients with healthy controls, and 26 differential metabolites were identified when comparing active VKH patients with inactive VKH patients. Pathway enrichment analysis showed that glycine, serine and threonine metabolism, and arginine and proline metabolism were significantly altered in VKH versus healthy controls. Lysine degradation and biotin metabolism pathways were significantly altered in active VKH versus inactive VKH. Furthermore, the receiver operating characteristic (ROC) curve analysis revealed that the combination of acetylglycine and gamma-glutamylalanine could differentiate VKH from healthy controls with an area under the curve (AUC) of 0.808. A combination of ureidopropionic acid and 5′-phosphoribosyl-5-amino-4-imidazolecarboxamide (AICAR) had an excellent AUC of 0.958 for distinguishing active VKH from inactive VKH. In summary, this study identified abnormal metabolites in urine of patients with VKH disease. Further studies are needed to confirm whether these metabolites are specific for this disease.

Body roundness index is a superior indicator to associate with the cardio-metabolic risk: evidence from a cross-sectional study with 17,000 Eastern-China adults

Background

To investigate the ability of body shape index (ABSI), body roundness index (BRI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and body adiposity index (BAI) for predicting non-adipose cardio-metabolic risk.

Methods

A total of 17,360 Chinese subjects aged 18–95 years old who escaped cardiovascular disease (CVD) or diabetes were recruited in the cross-sectional study. Anthropometric and biochemical parameters were assessed. Receiver operating characteristic curve (ROC) and multinomial logistic regression were conducted to examine the association of anthropometric indicators with cardio-metabolic risk factors.

Results

The mean age of subjects were 53.7(13.1) years, 41.6 % were males. The areas under the curve (AUC) demonstrated that WC, BMI, WHR, WHtR and BRI were able to predict high cardio-metabolic risk (AUC > 0.70). Meanwhile, multinomial logistic regression showed BRI was significantly associated with high cardio-metabolic risk (OR 3.27, 95% CI 3.01–3.55). The optimal cut-off values of BRI for high cardio-metabolic risk were (< 60 y: 3.49 vs. ≥60 y: 3.46) in males and (< 60 y: 3.47 vs. ≥60 y: 3.60) in females.

Conclusions

WC, BMI WHR, and WHtR were potential obesity indicators in discriminating high cardio-metabolic risk, while BAI or ABSI was not. Moreover, BRI revealed superior predictive capacity and significant association with accumulated cardio-metabolic risk factors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-01905-x.

Clinical serum metabolomics study on fluoxetine hydrochloride for depression

BACKGROUND

Fluoxetine hydrochloride is one of the familiar antidepressants of the second generation and has the effect of inhibiting the reuptake of 5-hydroxytryptamine by central nervous system. Both clinical trials and animal experiments show that it has good antidepressant effect, but there are few reports on its clinical efficacy in treating depression patients from the perspective of metabolomics. This study aimed at evaluating the antidepressant effect of fluoxetine hydrochloride by metabolomics, so that to find out its specific biomarkers and related metabolic characteristics of depression in the treatment of depression and analyze the intervention mechanism of fluoxetine hydrochloride in depression.

METHOD

Twenty depression patients and twenty healthy volunteers were recruited in clinical. Using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) to analyze serum metabolites of depression patients pretherapy and post-treatment and compared with healthy people.

RESULT

Finally, we have detected 16 specific biomarkers of depression. Compared with the healthy group, the level of 10 biomarkers in the depression group was significantly increased (P < 0.05) and 6 biomarkers were significantly decreased (P < 0.01). After 8 weeks of fluoxetine hydrochloride treatment, all the biomarkers have showed a tendency of callback. The metabolic pathways involved amino acid metabolism, energy metabolism and lipid metabolism.

CONCLUSION

In our study, the antidepressant effect of fluoxetine hydrochloride in clinic was proved by metabolomics and provided basis for clinical use of fluoxetine hydrochloride. At the same time, the biomarkers that may be related to the occurrence of depression are determined to provide objective basis for the diagnosis of depression.

Cancer Cell Metabolites: Updates on Current Tracing Methods

Cancer is the second leading cause of death-1 in 6 deaths globally is due to cancer. Cancer metabolism is a complex and one of the most actively researched area in cancer biology. Metabolic reprogramming in cancer cells entails activities that involve several enzymes and metabolites to convert nutrient into building blocks that alter energy metabolism to fuel rapid cell division. Metabolic dependencies in cancer generate signature metabolites that have key regulatory roles in tumorigenesis. In this minireview, we highlight recent advances in the popular methods ingrained in biochemistry research such as stable and flux isotope analysis, as well as radioisotope labeling, which are valuable in elucidating cancer metabolites. These methods together with analytical tools such as chromatography, nuclear magnetic resonance spectroscopy and mass spectrometry have helped to bring about exploratory work in understanding the role of important as well as obscure metabolites in cancer cells. Information obtained from these analyses significantly contribute in the diagnosis and prognosis of tumors leading to potential therapeutic targets for cancer therapy.

Integrated proteomics and metabolomics reveals the comprehensive characterization of antitumor mechanism underlying Shikonin on colon cancer patient-derived xenograft model

Colorectal cancer (CRC) is a common malignancy occurring in the digestive system. Despite progress in surgery and therapy options, CRC is still a considerable cause of cancer mortality worldwide. In this study, a colon cancer patient-derived xenograft model was established to evaluate the antitumor activity of Shikonin. The protective effect underlying Shikonin was determined through assessing serum levels of liver enzymes (ALT, AST) and kidney functions (BuN, Scr) in PDX mice. Proteomics and metabolomics profiles were integrated to provide a systematic perspective in dynamic changes of proteins and global endogenous metabolites as well as their perturbed pathways. A total of 456 differently expressed proteins (DEPs), 32 differently expressed metabolites (DEMs) in tumor tissue, and 20 DEMs in mice serum were identified. The perturbation of arginine biosynthesis, purine metabolism, and biosynthesis of amino acids may mainly account for therapeutic mechanism of Shikonin. Furthermore, the expression of mRNAs participating in arginine biosynthesis (CPS1, OTC, Arg1) and do novo purine synthesis (GART, PAICS, ATIC) were validated through RT-qPCR. Our study provides new insights into the drug therapeutic strategies and a better understanding of antitumor mechanisms that might be valuable for further studies on Shikonin in the clinical treatment of colorectal cancer.

Metabolomics Study Revealing the Potential Risk and Predictive Value of Fragmented QRS for Acute Myocardial Infarction

Patients with nonobstructive coronary artery disease (NOCAD) have high risk associated with acute myocardial infarction (AMI), and fragmented QRS (fQRS) has a predictive value of AMI after percutaneous coronary intervention (PCI). A cohort of 254 participants were recruited including 136 NOCAD and 118 AMI patients from Xi'an No. 1 Hospital. Comprehensive metabolomics was performed by UPLC-Q/TOF-MS with multivariate statistical analyses. Hazard ratios were measured to discriminate the prognostic in AMI after PCI between differential metabolites and fQRS. OPLS-DA separated metabolites from NOCAD and AMI in serum. A total of 23 differential metabolites were identified between NOCAD and AMI. In addition, four differential metabolites, namely, acetylglycine, threoninyl-glycine, glutarylglycine, and nonanoylcarnitine, were identified between fQRS and non-fQRS in AMI. The hazard ratios demonstrate that the metabolites were associated with the risk of cardiac death, recurrent angina, readmissions, and major adverse cardiovascular events, which may clarify the mechanism of fQRS as a predictor in the prognostic of AMI after PCI. This study identified novel differential metabolites to distinguish the difference from NOCAD to AMI and clarify the mechanism of fQRS in prognostic of AMI after PCI, which may provide novel insights into potential risks and prognostic of AMI.

Undetectable Free Aromatic Amino Acids in Nails of Breast Carcinoma: Biomarker Discovery by a Novel Metabolite Purification VTGE System

Background: Metabolic reprogramming in breast cancer is depicted as a crucial change in the tumor microenvironment. Besides the molecular understanding of metabolic heterogeneity, appreciable attention is drawn to characterizing metabolite profiles in tumor tissue and derived biological fluids and tissue materials. Several findings reported on the metabolic alterations of free aromatic amino acids (FAAAs) and other metabolites in biological fluids. Furthermore, there is a significant gap in the development of a suitable method for the purification and analysis of metabolite biomarkers in nails of cancer patients. Methods: To address the metabolite alterations, specifically FAAA levels in nails, fingernail clippings of breast cancer patients (N = 10) and healthy subjects (N-12) were used for extraction and purification of metabolites. Here, we reported a novel and specifically designed vertical tube gel electrophoresis (VTGE) system that helped in the purification of metabolites in the range of 100-1,000 Da from nail materials. Here, the VTGE system uses 15% polyacrylamide under non-denaturing and non-reducing conditions, which makes eluted metabolites directly compatible with LC-HRMS and other analytical techniques. Qualitative and quantitative determination of FAAAs in nail lysates was done in positive ESI mode of the Agilent LC-HRMS platform. Results: The analysis on collected data of nail metabolites clearly suggested that FAAAs including tryptophan, tyrosine, phenylalanine, and histidine were undetectable in nail lysates of breast cancer over healthy subjects. This is a first report that showed highly reduced levels of FAAAs in nails of breast cancer patients. Furthermore, the present observation is in consonance with previous findings that showed cancer cachexia and high amino acid catabolism in breast cancer patients that drive metabolite-led cancer growth and proliferation. Conclusion: This paper provides a proof of concept for a novel and specifically developed VTGE process that showed first evidence on the undetectable level of FAAAs in nails of breast cancer patients as metabolite biomarkers. Here, the authors propose the potential use of a VTGE-assisted process to achieve metabolomic discovery in nails of breast cancer and other tumor types.

Label-free diagnosis for colorectal cancer through coffee ring-assisted surface-enhanced Raman spectroscopy on blood serum

Surface-enhanced Raman spectroscopy (SERS) is garnering considerable attention for the swift diagnosis of pathogens and abnormal biological status, that is, cancers. In this work, a simple, fast and inexpensive optical sensing platform is developed by the design of SERS sampling and data analysis. The pretreatment of spectral measurement employed gold nanoparticle colloid mixing with the serum from patients with colorectal cancer (CRC). The droplet of particle-serum mixture formed coffee-ring-like region at the rim, providing strong and stable SERS profiles. The obtained spectra from cancer patients and healthy volunteers were analyzed by unsupervised principal component analysis (PCA) and supervised machine learning model, such as support-vector machine (SVM), respectively. The results demonstrate that the SVM model provides the superior performance in the classification of CRC diagnosis compared with PCA. In addition, the values of carcinoembryonic antigen from the blood samples were compiled with the corresponding SERS spectra for SVM calculation, yielding improved prediction results.