AFDN deficiency promotes liver tropism of metastatic colorectal cancer.

Liver metastasis is a major cause of morbidity and mortality in patients with colorectal cancer. A better understanding of the biological mechanisms underlying liver tropism and metastasis in colorectal cancer could help to identify improved prevention and treatment strategies. In this study, we performed genome-side CRISPR loss-of-function screening in a mouse colorectal cancer model and identified deficiency of AFDN, a protein involved in establishing and maintaining cell-cell contacts, as a driver of liver metastasis. Elevated AFDN expression was correlated with prolonged survival in patients with colorectal cancer. AFDN-deficient colorectal cancer cells preferentially metastasized to the liver but not in the lungs. AFDN loss in colorectal cancer cells at the primary site promoted cancer cell migration and invasion by disrupting tight intercellular junctions. Additionally, CXCR4 expression was increased in AFDN-deficient colorectal cancer cells via the JAK-STAT signaling pathway, which reduced the motility of AFDN-deficient colorectal cancer cells and facilitated their colonization of the liver. Collectively, these data shed light on the mechanism by which AFDN deficiency promotes liver tropism in metastatic colorectal cancer.

Adrenal pheochromocytoma impacts three main pathways: cysteine-methionine, pyrimidine, and tyrosine metabolism. / è‚¾ä¸Šè ºå—œé“¬ç»†èƒžç˜¤å½±å“ä¸‰æ¡ä¸»è¦ä»£è°¢é€šè·¯ï¼šåŠèƒ±æ°¨é ¸-è›‹æ°¨é ¸ä»£è°¢ã€å˜§å•¶ä»£è°¢å’Œé ªæ°¨é ¸ä»£è°¢é€šè·¯.

Pheochromocytomas and paragangliomas (PPGLs) cause symptoms by altering the circulation levels of catecholamines and peptide hormones. Currently, the diagnosis of PPGLs relies on diagnostic imaging and the detection of catecholamines. In this study, we used ultra-performance liquid chromatography (UPLC)/quadrupole time-of-flight mass spectrometry (Q-TOF MS) analysis to identify and measure the perioperative differential metabolites in the plasma of adrenal pheochromocytoma patients. We identified differentially expressed genes by comparing the transcriptomic data of pheochromocytoma with the normal adrenal medulla. Through conducting two steps of metabolomics analysis, we identified 111 differential metabolites between the healthy group and the patient group, among which 53 metabolites were validated. By integrating the information of differential metabolites and differentially expressed genes, we inferred that the cysteine-methionine, pyrimidine, and tyrosine metabolism pathways were the three main metabolic pathways altered by the neoplasm. The analysis of transcription levels revealed that the tyrosine and cysteine-methionine metabolism pathways were downregulated in pheochromocytoma, whereas the pyrimidine pathway showed no significant difference. Finally, we developed an optimized diagnostic model of two metabolites, L-dihydroorotic acid and vanylglycol. Our results for these metabolites suggest that they may serve as potential clinical biomarkers and can be used to supplement and improve the diagnosis of pheochromocytoma.

Targeting the devil: Strategies against cancer-associated fibroblasts in colorectal cancer.

Cancer-associated fibroblasts (CAFs), as significant constituents of the tumor microenvironment (TME), play a pivotal role in the progression of cancers, including colorectal cancer (CRC). In this comprehensive review, we presented the origins and activation mechanisms of CAFs in CRC, elaborating on how CAFs drive tumor progression through their interactions with CRC cells, immune cells, vascular endothelial cells, and the extracellular matrix within the TME. We systematically outline the intricate web of interactions among CAFs, tumor cells, and other TME components, and based on this complex interplay, we summarize various therapeutic strategies designed to target CAFs in CRC. It is also essential to recognize that CAFs represent a highly heterogeneous group, encompassing various subtypes such as myofibroblastic CAF (myCAF), inflammatory CAF (iCAF), antigen-presenting CAF (apCAF), vessel-associated CAF (vCAF). Herein, we provide a summary of studies investigating the heterogeneity of CAFs in CRC and the characteristic expression patterns of each subtype. While the majority of CAFs contribute to the exacerbation of CRC malignancy, recent findings have revealed specific subtypes that exert inhibitory effects on CRC progression. Nevertheless, the comprehensive landscape of CAF heterogeneity still awaits exploration. We also highlight pivotal unanswered questions that need to be addressed before CAFs can be recognized as feasible targets for cancer treatment. In conclusion, the aim of our review is to elucidate the significance and challenges of advancing in-depth research on CAFs, while outlining the pathway to uncover the complex roles of CAFs in CRC and underscore their significant potential as therapeutic targets.

MSNSegNet: attention-based multi-shape nuclei instance segmentation in histopathology images.

In clinical research, the segmentation of irregularly shaped nuclei, particularly in mesenchymal areas like fibroblasts, is crucial yet often neglected. These irregular nuclei are significant for assessing tissue repair in immunotherapy, a process involving neovascularization and fibroblast proliferation. Proper segmentation of these nuclei is vital for evaluating immunotherapy's efficacy, as it provides insights into pathological features. However, the challenge lies in the pronounced curvature variations of these non-convex nuclei, making their segmentation more difficult than that of regular nuclei. In this work, we introduce an undefined task to segment nuclei with both regular and irregular morphology, namely multi-shape nuclei segmentation. We propose a proposal-based method to perform multi-shape nuclei segmentation. By leveraging the two-stage structure of the proposal-based method, a powerful refinement module with high computational costs can be selectively deployed only in local regions, improving segmentation accuracy without compromising computational efficiency. We introduce a novel self-attention module to refine features in proposals for the sake of effectiveness and efficiency in the second stage. The self-attention module improves segmentation performance by capturing long-range dependencies to assist in distinguishing the foreground from the background. In this process, similar features get high attention weights while dissimilar ones get low attention weights. In the first stage, we introduce a residual attention module and a semantic-aware module to accurately predict candidate proposals. The two modules capture more interpretable features and introduce additional supervision through semantic-aware loss. In addition, we construct a dataset with a proportion of non-convex nuclei compared with existing nuclei datasets, namely the multi-shape nuclei (MsN) dataset. Our MSNSegNet method demonstrates notable improvements across various metrics compared to the second-highest-scoring methods. For all nuclei, the D i c e score improved by approximately 1.66 % , A J I by about 2.15 % , and D i c e obj by roughly 0.65 % . For non-convex nuclei, which are crucial in clinical applications, our method's A J I improved significantly by approximately 3.86 % and D i c e obj by around 2.54 % . These enhancements underscore the effectiveness of our approach on multi-shape nuclei segmentation, particularly in challenging scenarios involving irregularly shaped nuclei.

Alternative polyadenylation quantitative trait methylation mapping in human cancers provides clues into the molecular mechanisms of APA.

Genetic variants are involved in the orchestration of alternative polyadenylation (APA) events, while the role of DNA methylation in regulating APA remains unclear. We generated a comprehensive atlas of APA quantitative trait methylation sites (apaQTMs) across 21 different types of cancer (1,612 to 60,219 acting in cis and 4,448 to 142,349 in trans). Potential causal apaQTMs in non-cancer samples were also identified. Mechanistically, we observed a strong enrichment of cis-apaQTMs near polyadenylation sites (PASs) and both cis- and trans-apaQTMs in proximity to transcription factor (TF) binding regions. Through the integration of ChIP-signals and RNA-seq data from cell lines, we have identified several regulators of APA events, acting either directly or indirectly, implicating novel functions of some important genes, such as TCF7L2, which is known for its involvement in type 2 diabetes and cancers. Furthermore, we have identified a vast number of QTMs that share the same putative causal CpG sites with five different cancer types, underscoring the roles of QTMs, including apaQTMs, in the process of tumorigenesis. DNA methylation is extensively involved in the regulation of APA events in human cancers. In an attempt to elucidate the potential underlying molecular mechanisms of APA by DNA methylation, our study paves the way for subsequent experimental validations into the intricate biological functions of DNA methylation in APA regulation and the pathogenesis of human cancers. To present a comprehensive catalog of apaQTM patterns, we introduce the Pancan-apaQTM database, available at https://pancan-apaqtm-zju.shinyapps.io/pancanaQTM/.

Nucleus-Aware Self-Supervised Pretraining Using Unpaired Image-to-Image Translation for Histopathology Images.

Self-supervised pretraining attempts to enhance model performance by obtaining effective features from unlabeled data, and has demonstrated its effectiveness in the field of histopathology images. Despite its success, few works concentrate on the extraction of nucleus-level information, which is essential for pathologic analysis. In this work, we propose a novel nucleus-aware self-supervised pretraining framework for histopathology images. The framework aims to capture the nuclear morphology and distribution information through unpaired image-to-image translation between histopathology images and pseudo mask images. The generation process is modulated by both conditional and stochastic style representations, ensuring the reality and diversity of the generated histopathology images for pretraining. Further, an instance segmentation guided strategy is employed to capture instance-level information. The experiments on 7 datasets show that the proposed pretraining method outperforms supervised ones on Kather classification, multiple instance learning, and 5 dense-prediction tasks with the transfer learning protocol, and yields superior results than other self-supervised approaches on 8 semi-supervised tasks. Our project is publicly available at https://github.com/zhiyuns/UNITPathSSL.

OpenNAU: An open-source platform for normalizing, analyzing, and visualizing cancer untargeted metabolomics data.

Objective: As an important part of metabolomics analysis, untargeted metabolomics has become a powerful tool in the study of tumor mechanisms and the discovery of metabolic markers with high-throughput spectrometric data which also poses great challenges to data analysis, from the extraction of raw data to the identification of differential metabolites. To date, a large number of analytical tools and processes have been developed and constructed to serve untargeted metabolomics research. The different selection of analytical tools and parameter settings lead to varied results of untargeted metabolomics data. Our goal is to establish an easily operated platform and obtain a repeatable analysis result. Methods: We used the R language basic environment to construct the preprocessing system of the original data and the LAMP (Linux+Apache+MySQL+PHP) architecture to build a cloud mass spectrum data analysis system. Results: An open-source analysis software for untargeted metabolomics data (openNAU) was constructed. It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks. A reference metabolomics database based on public databases was also constructed. Conclusions: A complete analysis system platform for untargeted metabolomics was established. This platform provides a complete template interface for the addition and updating of the analysis process, so we can finish complex analyses of untargeted metabolomics with simple human-computer interactions. The source code can be downloaded from https://github.com/zjuRong/openNAU.

Specific intracellular retention of circSKA3 promotes colorectal cancer metastasis by attenuating ubiquitination and degradation of SLUG.

Our previous study demonstrated that tumor-suppressor circular RNAs (circRNAs) can be specifically secreted outside of colorectal cancer (CRC) cells within exosomes to maintain tumor cell fitness. However, whether tumor-driving circRNAs can be specifically retained in cells to facilitate tumor progression remains unknown. In this study, circRNA-seq showed that circSKA3 was significantly upregulated in CRC tissues but downregulated in serum samples from CRC patients. In addition, circSKA3 promoted CRC progression in vitro and in vivo and was retained in CRC cells via a specific cellmotif element. Interestingly, the cellmotif element was also the site of interaction of circSKA3 with SLUG, which inhibited SLUG ubiquitination degradation and promoted CRC epithelial-mesenchymal transition (EMT). Moreover, FUS was identified as a key circularization regulator of circSKA3 that bound to the key element. Finally, we designed and synthesized specific antisense oligonucleotides (ASOs) targeting circularization and cellmotif elements, which repressed circSKA3 expression, abolished the SLUG-circSKA3 interaction, and further inhibited CRC EMT and metastasis in vitro and in vivo.

SCGN and STAT3 expressions are associated with the prognosis of ccRCC.

Clear cell renal cell carcinoma (ccRCC) is highly heterogeneous and accounts for about 70% of RCC. Its prognosis is worse than that of most histological types of RCC. In order to find potential biomarkers that may influence the prognosis and survival in ccRCC patients, we explored the expressions of STAT3, PDL1 and SCGN (secretagogin) in ccRCC based on the data of TCGA (n = 529), EMATAB-1980 (n = 99) and our own cohort (n = 99). Our study demonstrated that ccRCC patients with low STAT3 expression and high SCGN expression might have a better prognosis. No significant difference in the positive rate of SCGN expression was found when comparing the primary lesion with the matched metastatic liver lesions. The percentage of high SCGN expression in the primary lesion of metastatic ccRCC patients was significantly lower than that of patients with only the renal lesion. In view of the conclusion that STAT3 high expression cases are resistant to sunitinib, STAT3 immunohistochemistry results are essential for designing non-operative treatments. SCGN has the potential to become an indicator for subtype classification of ccRCC.

AMER1 deficiency promotes the distant metastasis of colorectal cancer by inhibiting SLC7A11- and FTL-mediated ferroptosis.

The crosstalk between ferroptosis and cancer metastasis remains unclear. Here, we identify AMER1 as a key regulator of ferroptosis. AMER1 loss causes resistance to ferroptosis in colorectal cancer (CRC) cells. Interestingly, AMER1-deficient CRC cells preferentially form distant metastases, while AMER1-naive CRC cells mainly invade lymph nodes. Moreover, the ferroptosis inhibitor liproxstatin-1 effectively promotes hematogenous transfer of AMER1-naive cells. Mechanistically, AMER1 binds to SLC7A11 and ferritin light chain (FTL) and recruits ß-TrCP1/2, which degrade SLC7A11 and FTL by ubiquitination. Therefore, AMER1 deficiency increases cellular cystine levels but decreases the pool of labile free iron, thereby enhancing resistance to ferroptosis in CRC cells. Thus, AMER1 deficiency increases the survival of CRC cells in the blood under conditions of high oxidative stress and then promotes hematogenous metastasis of CRC. In conclusion, AMER1 mediates the crosstalk between ferroptosis and cancer metastasis, which provides a window of opportunity for treating metastatic colorectal cancer patients with AMER1 mutations.

Towards understandings of serine/arginine-rich splicing factors.

Serine/arginine-rich splicing factors (SRSFs) refer to twelve RNA-binding proteins which regulate splice site recognition and spliceosome assembly during precursor messenger RNA splicing. SRSFs also participate in other RNA metabolic events, such as transcription, translation and nonsense-mediated decay, during their shuttling between nucleus and cytoplasm, making them indispensable for genome diversity and cellular activity. Of note, aberrant SRSF expression and/or mutations elicit fallacies in gene splicing, leading to the generation of pathogenic gene and protein isoforms, which highlights the therapeutic potential of targeting SRSF to treat diseases. In this review, we updated current understanding of SRSF structures and functions in RNA metabolism. Next, we analyzed SRSF-induced aberrant gene expression and their pathogenic outcomes in cancers and non-tumor diseases. The development of some well-characterized SRSF inhibitors was discussed in detail. We hope this review will contribute to future studies of SRSF functions and drug development targeting SRSFs.

In Vivo Staging the Progression of Colitis and Associated Cancer by Concurrent Microimaging of Key Biomarkers.

Currently colorectal cancer (CRC) staging (colitis, adenoma, and carcinoma) mainly relies on ex vivo pathologic analysis requiring an invasive surgical process with limited sample collection and increased metastatic risk. Thus, in vivo noninvasive pathological diagnosis is extremely demanded. By verifying the samples of clinical patients and CRC mouse models, it was found that vascular endothelial growth factor receptor 2 (VEGFR2) was barely expressed in the colitis stage and only appeared in adenoma and carcinoma stages with obvious elevation, while prostaglandin E receptor 4 (PTGER4) could be observed from colitis to adenoma and carcinoma stages with a gradient increase of expression. VEGFR2 and PTGER4 were further chosen as key biomarkers for molecular pathological diagnosis in vivo and corresponding molecular probes were constructed. The feasibility of in vivo noninvasive CRC staging by concurrent microimaging of dual biomarkers using confocal laser endoscopy (CLE) was verified in CRC mouse models and further confirmed by ex vivo pathological analysis. In vivo CLE imaging exhibited the correlation of severe colonic crypt structural alteration with a higher biomarker expression in adenoma and carcinoma stages. This strategy shows promise in benefiting patients undergoing CRC progression with in-time, noninvasive, and precise pathological staging, thus providing valuable guidance for selecting therapeutic strategies.

Targeting serine- and arginine-rich splicing factors to rectify aberrant alternative splicing.

Serine- and arginine-rich splicing factors are pivotal modulators of constitutive splicing and alternative splicing that bind to the cis-acting elements in precursor mRNAs and facilitate the recruitment and assembly of the spliceosome. Meanwhile, SR proteins shuttle between the nucleus and cytoplasm with a broad implication in multiple RNA-metabolizing events. Recent studies have demonstrated the positive correlation of overexpression and/or hyperactivation of SR proteins and development of the tumorous phenotype, indicating the therapeutic potentials of targeting SR proteins. In this review, we highlight key findings concerning the physiological and pathological roles of SR proteins. We have also investigated small molecules and oligonucleotides that effectively modulate the functions of SR proteins, which could benefit future studies of SR proteins.

Cyclic Learning: Bridging Image-Level Labels and Nuclei Instance Segmentation.

Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manual annotations, which is especially time-consuming and laborious for the high nuclei density. To alleviate the annotation burden, we seek to solve the problem through image-level weakly supervised learning, which is underexplored for nuclei instance segmentation. Compared with most existing methods using other weak annotations (scribble, point, etc.) for nuclei instance segmentation, our method is more labor-saving. The obstacle to using image-level annotations in nuclei instance segmentation is the lack of adequate location information, leading to severe nuclei omission or overlaps. In this paper, we propose a novel image-level weakly supervised method, called cyclic learning, to solve this problem. Cyclic learning comprises a front-end classification task and a back-end semi-supervised instance segmentation task to benefit from multi-task learning (MTL). We utilize a deep learning classifier with interpretability as the front-end to convert image-level labels to sets of high-confidence pseudo masks and establish a semi-supervised architecture as the back-end to conduct nuclei instance segmentation under the supervision of these pseudo masks. Most importantly, cyclic learning is designed to circularly share knowledge between the front-end classifier and the back-end semi-supervised part, which allows the whole system to fully extract the underlying information from image-level labels and converge to a better optimum. Experiments on three datasets demonstrate the good generality of our method, which outperforms other image-level weakly supervised methods for nuclei instance segmentation, and achieves comparable performance to fully-supervised methods.

Elevated serum ß-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 ß-hydroxybutyrate (BHB) was elevated in CRC. Here, we first confirm the increased level of ß-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 ß-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 ß-hydroxybutyrate in CRC. We demonstrate that ß-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 ß-hydroxybutyrate both in vitro and in vivo. Overall, this study suggests that targeting ß-hydroxybutyrate and its major rate-limiting enzyme ACAT1 may provide a new avenue for therapeutic intervention in CRC.

Local-to-global spatial learning for whole-slide image representation and classification.

Whole-slide image (WSI) provides an important reference for clinical diagnosis. Classification with only WSI-level labels can be recognized for multi-instance learning (MIL) tasks. However, most existing MIL-based WSI classification methods have moderate performance on correlation mining between instances limited by their instance- level classification strategy. Herein, we propose a novel local-to-global spatial learning method to mine global position and local morphological information by redefining the MIL-based WSI classification strategy, better at learning WSI-level representation, called Global-Local Attentional Multi-Instance Learning (GLAMIL). GLAMIL can focus on regional relationships rather than single instances. It first learns relationships between patches in the local pool to aggregate region correlation (tissue types of a WSI). These correlations then can be further mined to fulfill WSI-level representation, where position correlation between different regions can be modeled. Furthermore, Transformer layers are employed to model global and local spatial information rather than being simply used as feature extractors, and the corresponding structure improvements are present. In addition, we evaluate GIAMIL on three benchmarks considering various challenging factors and achieve satisfactory results. GLAMIL outperforms state-of-the-art methods and baselines by about 1 % and 10 %, respectively.

2 Hydroxybutyric Acid-Producing Bacteria in Gut Microbiome and Fusobacterium nucleatum Regulates 2 Hydroxybutyric Acid Level In Vivo.

2-hydroxybutyric acid (2HB) serves as an important regulatory factor in a variety of diseases. The circulating level of 2HB in serum is significantly higher in multiple diseases, such as cancer and type 2 diabetes (T2D). However, there is currently no systematic study on 2HB-producing bacteria that demonstrates whether gut bacteria contribute to the circulating 2HB pool. To address this question, we used BLASTP to reveal the taxonomic profiling of 2HB-producing bacteria in the human microbiome, which are mainly distributed in the phylum Proteobacteria and Firmicutes. In vitro experiments showed that most gut bacteria (21/32) have at least one path to produce 2HB, which includes Aspartic acid, methionine, threonine, and 2-aminobutyric acid. Particularly, Fusobacterium nucleatum has the strongest ability to synthesize 2HB, which is sufficient to alter colon 2HB concentration in mice. Nevertheless, neither antibiotic (ABX) nor Fusobacterium nucleatum gavage significantly affected mouse serum 2HB levels during the time course of this study. Taken together, our study presents the profiles of 2HB-producing bacteria and demonstrates that gut microbiota was a major contributor to 2HB concentration in the intestinal lumen but a relatively minor contributor to serum 2HB concentration.

Weakly supervised histopathology image segmentation with self-attention.

Accurate segmentation in histopathology images at pixel-level plays a critical role in the digital pathology workflow. The development of weakly supervised methods for histopathology image segmentation liberates pathologists from time-consuming and labor-intensive works, opening up possibilities of further automated quantitative analysis of whole-slide histopathology images. As an effective subgroup of weakly supervised methods, multiple instance learning (MIL) has achieved great success in histopathology images. In this paper, we specially treat pixels as instances so that the histopathology image segmentation task is transformed into an instance prediction task in MIL. However, the lack of relations between instances in MIL limits the further improvement of segmentation performance. Therefore, we propose a novel weakly supervised method called SA-MIL for pixel-level segmentation in histopathology images. SA-MIL introduces a self-attention mechanism into the MIL framework, which captures global correlation among all instances. In addition, we use deep supervision to make the best use of information from limited annotations in the weakly supervised method. Our approach makes up for the shortcoming that instances are independent of each other in MIL by aggregating global contextual information. We demonstrate state-of-the-art results compared to other weakly supervised methods on two histopathology image datasets. It is evident that our approach has generalization ability for the high performance on both tissue and cell histopathology datasets. There is potential in our approach for various applications in medical images.

Loss of GLTSCR1 causes congenital heart defects by regulating NPPA transcription.

Precise and specific spatiotemporal domains of gene expression regulation are critical for embryonic development. Recent studies have identified GLTSCR1 as a gene transcriptional elongation regulator in cancer research. However, the function of GLTSCR1, especially in embryonic development, remains poorly understood. Here, we found that GLTSCR1 was essential for cardiac development because Gltscr1 knockout (Gltscr1-/-) led to embryonic lethality in mice with severe congenital heart defects (CHDs). Ventricular septal defect and double outflow right ventricular were also observed in neural crest cells with conditional deletion of Gltscr1, which were associated with neonatal lethality in mice. Mechanistically, GLTSCR1 deletion promoted NPPA expression by coordinating the CHD risk G allele of rs56153133 in the NPPA enhancer and releasing the transcription factor ZNF740-binding site on the NPPA promoter. These findings demonstrated that GLTSCR1 acts as a candidate CHD-related gene.

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.