Metabolomics technology and bioinformatics for precision medicine

Exploiting omic-based approaches to decipher Traditional Chinese Medicine

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese Medicine (TCM), an ancient health system, faces significant research challenges due to the complexity of its active components and targets, as well as a historical lack of detailed annotation. However, recent advances in omics technologies have begun to unravel these complexities, providing a more informed and nuanced understanding of TCM's therapeutic potential in contemporary healthcare.

AIM OF THE REVIEW

This review summarizes the application of omics technologies in TCM modernization, emphasizing components analysis, quality control, biomarker discovery, target identification, and treatment optimization. In addition, future perspectives on using omics for precision TCM treatment are also discussed.

MATERIALS AND METHODS

We have explored several databases (including PubMed, ClinicalTrials, Google Scholar, and Web of Science) to review related articles, focusing on Traditional Chinese Medicine, Omics Strategy, Precision Medicine, Biomarkers, Quality Control, and Molecular Mechanisms. Paper selection criteria involved English grammar, publication date, high citations, and broad applicability, exclusion criteria included low credibility, non-English publications, and those full-text inaccessible ones.

RESULTS

TCM and the popularity of Chinese herbal medicines (CHMs) are gaining increasing attention worldwide. This is driven, in part, by a large number of technologies, especially omics strategy, which are aiding the modernization of TCM. They contribute to the quality control of CHMs, the identification of cellular targets, discovery of new drugs and, most importantly, the understanding of their mechanisms of action.

CONCLUSION

To fully integrate TCM into modern medicine, further development of robust omics strategies is essential. This vision includes personalized medicine, backed by advanced computational power and secure data infrastructure, to facilitate global acceptance and seamless integration of TCM practices.

Identification and validation of diagnostic markers related to immunogenic cell death and infiltration of immune cells in diabetic nephropathy

INTRODUCTION

Immunogenic cell death (ICD) is a unique cell death triggered by chemotherapy. However, studies elucidating the potential therapeutic role of ICD and the underlying mechanism in diabetic nephropathy (DN) are limited.

METHODS

WGCNA was conducted on the human kidney biopsy data linked to DN, analyzing gene sets associated with ICD. Gene Set Enrichment Analysis and Gene Set Variation Analysis were utilized to examine the discrepancy in biological function. We used Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, and the GeneMANIA database to investigate the function of the signature genes. An analysis using the receiver operating characteristic (ROC) was conducted to validate the diagnostic value of hub genes. Additionally, immune infiltration-related analyses were also performed. In conclusion, we examined the association between the glomerular filtration rate, serum creatinine, and hub genes. Hub genes were validated by immunohistochemistry using db/db mice kidneys.

RESULTS

WGCNA revealed that the targets in the turquoise unit (1674 genes) exhibited the highest positive correlation with ICD. Furthermore, 4222 statistically significant DEGs were identified when comparing the DN and healthy control groups. Significantly, the KEGG pathway enrichment analysis indicated a connection between ICD and the nuclear factor-kappa B signaling pathway and the synthesis of cytokines (tumor necrosis factor superfamily). ROC analysis revealed that 16 hub genes exhibited strong discriminatory potential as biomarkers for DN. Therefore, immunohistochemical validation, with the potential involvement of chemokines (CCL11, CCR2, CCR7, CX3CR1, CXCL10, CXCL12, and CXCR5) and immune cells (CD3G, CD5, and CD247) may be crucial for the diagnosis and therapy of DN.

CONCLUSIONS

DKK3, NR4A1, NR4A2, VEGFA, and DUSP1 may be associated with the development of DN. The pathogenesis of DN may specifically involve chemokines (CCL11, CCR2, CCR7, CX3CR1, CXCL10, CXCL12, and CXCR5) and immune cells (CD3G, CD5, and CD247), with LCP2 playing a significant role.

LC-MS metabolomics analysis of serum metabolites during neoadjuvant chemoradiotherapy in locally advanced rectal cancer

BACKGROUND

This study aimed to investigate the serum metabolite profiles during neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC) using liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis.

METHODS

60 serum samples were collected from 20 patients with LARC before, during, and after radiotherapy. LC-MS metabolomics analysis was performed to identify the metabolite variations. Functional annotation was applied to discover altered metabolic pathways. The key metabolites were screened and their ability to predict sensitivity to radiotherapy was calculated using random forests and ROC curves.

RESULTS

The results showed that NCRT led to significant changes in the serum metabolite profiles. The serum metabolic profiles showed an apparent separation between different time points and different sensitivity groups. Moreover, the functional annotation showed that the differential metabolites were associated with a series of important metabolic pathways. Pre-radiotherapy (3Z,6Z)-3,6-Nonadiena and pro-radiotherapy 1-Hydroxyibuprofen showed good predictive performance in discriminating the sensitive and non-sensitive group to NCRT, with an AUC of 0.812 and 0.75, respectively. Importantly, the combination of different metabolites significantly increased the predictive ability.

CONCLUSION

This study demonstrated the potential of LC-MS metabolomics for revealing the serum metabolite profiles during NCRT in LARC. The identified metabolites may serve as potential biomarkers and therapeutic targets for the management of this disease. Furthermore, the understanding of the affected metabolic pathways may help design more personalized therapeutic strategies for LARC patients.

A Metabolomic Approach to Unexplained Syncope

Background: This study aims to identify a metabolomic signature that facilitates the classification of syncope and the categorization of the unexplained syncope (US) to aid in its management. Methods: We compared a control group (CTRL, n = 10) with a transient loss of consciousness (TLC) group divided into the OH group (n = 23) for orthostatic syncope, the NMS group (n = 26) for neuromediated syncope, the CS group (n = 9) for cardiological syncope, and the US group (n = 27) for US defined as syncope without a precise categorization after first- and second-level diagnostic approaches. Results: The CTRL and the TLC groups significantly differed in metabolic profile. A new logistic regression model has been developed to predict how the US will be clustered. Using differences in lysophosphatidylcholine with 22 carbon atom (C22:0-LPC) levels, 96% of the US belongs to the NMS and 4% to the CS subgroup. Differences in glutamine and lysine (GLN/LYS) levels clustered 95% of the US in the NMS and 5% in the CS subgroup. Conclusions: We hypothesize a possible role of C22:0 LPC and GLN/LYS in re-classifying US and differentiating it from cardiological syncope.

Role of SIRPG gene in type 1 diabetes and lichen planus

Type 1 diabetes (T1D) is a form of diabetes caused by pancreatic β-cell destruction and absolute insulin deficiency. Lichen planus (LP) is an idiopathic inflammatory skin disease of unclear etiology. The role of SIRPG gene dysregulation in T1D and LP remains unclear. Mendelian randomization (MR) using matched samples was employed to study causal relationship between T1D and increased risk of LP. T1D-related single nucleotide polymorphism identification was conducted. Datasets GSE156035 for T1D and GSE52130 for LP were obtained from gene expression omnibus. Differentially expressed genes were identified, analyses included weighted gene co-expression network analysis, functional enrichment, gene set enrichment analysis, and protein-protein interaction network construction and analysis. Heatmaps of gene expression levels were generated. Comparative toxicogenomics database was used to identify diseases most relevant to core genes. Inverse variance weighted, MR-Egger, weighted median methods estimated genetic predisposition between T1D and LP, showing consistent positive correlations using both weighted median and inverse variance weighted methods. Horizontal pleiotropy analysis with MR-Egger intercept indicated no evidence of significant directional pleiotropy (P = .70645) for LP. There was no evidence of directional pleiotropy effects between T1D and LP. One hundred eighteen differentially expressed genes were identified. In biological processes, they were mainly enriched in apoptosis, inflammatory response, insulin receptor signaling pathway, glucose metabolism. In cellular components, enrichment was observed in mediator complex and replication fork. In molecular function, they were concentrated in leukotriene receptor activity and helicase activity. Kyoto Encyclopedia of Genes and Genomes analysis revealed enrichment in metabolic pathways, PI3K-Akt signaling pathway, cell cycle, p53 signaling pathway, AGE-RAGE signaling pathway in diabetic complications. Weighted gene co-expression network analysis with a soft threshold power of 4. SIRPG showing high expression in both T1D and LP samples. There is a positive causal relationship between T1D and LP. Comparative toxicogenomics database analysis revealed associations of core genes with metabolic syndrome, lipid metabolism disorders, cardiovascular diseases, immune system diseases, peripheral neuropathic pain, and inflammation. SIRPG is highly expressed in both T1D and LP, providing a new insight into the pathogenesis of T1D and LP.

H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort

BACKGROUND

Obesity is a complex, diverse and multifactorial disease that has become a major public health concern in the last decades. The current classification systems relies on anthropometric measurements, such as BMI, that are unable to capture the physiopathological diversity of this disease. The aim of this study was to redefine the classification of obesity based on the different H-NMR metabolomics profiles found in individuals with obesity to better assess the risk of future development of cardiometabolic disease.

MATERIALS AND METHODS

Serum samples of a subset of the Di@bet.es cohort consisting of 1387 individuals with obesity were analyzed by H-NMR. A K-means algorithm was deployed to define different H-NMR metabolomics-based clusters. Then, the association of these clusters with future development of cardiometabolic disease was evaluated using different univariate and multivariate statistical approaches. Moreover, machine learning-based models were built to predict the development of future cardiometabolic disease using BMI and waist-to-hip circumference ratio measures in combination with H-NMR metabolomics.

RESULTS

Three clusters with no differences in BMI nor in waist-to-hip circumference ratio but with very different metabolomics profiles were obtained. The first cluster showed a metabolically healthy profile, whereas atherogenic dyslipidemia and hypercholesterolemia were predominant in the second and third clusters, respectively. Individuals within the cluster of atherogenic dyslipidemia were found to be at a higher risk of developing type 2 DM in a 8 years follow-up. On the other hand, individuals within the cluster of hypercholesterolemia showed a higher risk of suffering a cardiovascular event in the follow-up. The individuals with a metabolically healthy profile displayed a lower association with future cardiometabolic disease, even though some association with future development of type 2 DM was still observed. In addition, H-NMR metabolomics improved the prediction of future cardiometabolic disease in comparison with models relying on just anthropometric measures.

CONCLUSIONS

This study demonstrated the benefits of using precision techniques like H-NMR to better assess the risk of obesity-derived cardiometabolic disease.

The peritumoral edema index and related mechanisms influence the prognosis of GBM patients

Background

Peritumoral brain edema (PTBE) represents a characteristic phenotype of intracranial gliomas. However, there is a lack of consensus regarding the prognosis and mechanism of PTBE. In this study, clinical imaging data, along with publicly available imaging data, were utilized to assess the prognosis of PTBE in glioblastoma (GBM) patients, and the associated mechanisms were preliminarily analyzed.

Methods

We investigated relevant imaging features, including edema, in GBM patients using ITK-SNAP imaging segmentation software. Risk factors affecting progression-free survival (PFS) and overall survival (OS) were assessed using a Cox proportional hazard regression model. In addition, the impact of PTBE on PFS and OS was analyzed in clinical GBM patients using the Kaplan-Meier survival analysis method, and the results further validated by combining data from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Finally, functional enrichment analysis based on TCIA and TCGA datasets identified several pathways potentially involved in the mechanism of edema formation.

Results

This study included a total of 32 clinical GBM patients and 132 GBM patients from public databases. Univariate and multivariate analyses indicated that age and edema index (EI) are independent risk factors for PFS, but not for OS. Kaplan-Meier curves revealed consistent survival analysis results between IE groups among both clinical patients and TCIA and TCGA patients, suggesting a significant effect of PTBE on PFS but not on OS. Furthermore, functional enrichment analysis predicted the involvement of several pathways related mainly to cellular bioenergetics and vasculogenic processes in the mechanism of PTBE formation. While these novel results warrant confirmation in a larger patient cohort, they support good prognostic value for PTBE assessment in GBM.

Conclusions

Our results indicate that a low EI positively impacts disease control in GBM patients, but this does not entirely translate into an improvement in OS. Multiple genes, signaling pathways, and biological processes may contribute to the formation of peritumoral edema in GBM through cytotoxic and vascular mechanisms.

Big data analytics in the healthcare sector: Opportunities and challenges in developing countries. A literature review

Background: Despite the ongoing efforts to digitalize the healthcare sector in developing countries, the full adoption of big data analytics in healthcare settings is yet to be attained Exploring opportunities and challenges encountered is essential for designing and implementing effective interventional strategies. Objective: Exploring opportunities and challenges towards integrating big data analytics technologies in the healthcare industry in developing countries. Methodology: This was a narrative review study design. A literature search on different databases was conducted including PubMed, ScienceDirect, MEDLINE, Scopus, and Google Scholar. Articles with predetermined keywords and written in English were included. Results: Big data analytics finds its application in population health management and clinical decision-support systems even in developing countries. The major challenges towards the integration of big data analytics in the healthcare sector in developing countries include fragmentation of healthcare data and lack of interoperability, data security, privacy and confidentiality concerns, limited resources and inadequate regulatory and policy frameworks for governing big data analytics technologies and limited reliable power and internet infrastructures. Conclusion: Digitalization of healthcare delivery in developing countries faces several significant challenges. However, the integration of big data analytics can potentially open new avenues for enhancing healthcare delivery with cost-effective benefits.

Integrating metabolomics into reproduction: Sperm metabolism and fertility enhancement in pigs

The last decades of research have revealed that many other factors besides gamete genomes are able to determine the reproductive outcomes. Indeed, paternal factors have been observed to be capable of modulating multiple crucial features of the reproductive process, such as sperm physiology, the maternal environment and, even, the offspring health. These recent advances have been encompassed with the emergence of OMICS technologies, as they comprehensively characterise the molecular composition of biological systems. The present narrative review aimed to take a closer look at the potential of these technologies in the field of reproductive biology. This literature revision shows that most studies up to date have followed a non-targeted approach to screen mammalian seminal plasma (SP) and sperm metabolite composition through different metabolome platforms. These studies have proposed metabolites of multiple natures as potential in vivo fertility biomarkers. Yet, targeted approaches can be used to answer specific biological question, and their power is exemplified herein. For instance, metabolomic studies have uncovered not only that glycolysis is the main ATP energy source of pig sperm, but also that sperm metabolism can trigger DNA damage, hence compromise embryo development. In conclusion, this review shows the potential of both non-targeted and targeted metabolomics for the discovery of cell pathways that govern the reproductive process. Understanding these systems could help make progress in different areas, including livestock efficient breeding, the improvement of artificial reproductive technologies, and the development of biomarkers for infertility detection.

Plasma metabolomic signature of healthy lifestyle, structural brain reserve and risk of dementia

Although the association between healthy lifestyle and dementia risk has been documented, the relationship between a metabolic signature indicative of healthy lifestyle and dementia risk and the mediating role of structural brain impairment remain unknown. We retrieved 136 628 dementia-free participants from UK Biobank. Elastic net regression was used to obtain a metabolic signature that represented lifestyle behaviours. Cox proportional hazard models were fitted to explore the associations of lifestyle-associated metabolic signature with incident dementia. Causal associations between identified metabolites and dementia were investigated using Mendelian randomization. Mediation analysis was also conducted to uncover the potential mechanisms involving 19 imaging-derived phenotypes (brain volume, grey matter volume, white matter volume and regional grey matter volumes). During a follow-up of 12.55 years, 1783 incident cases of all-cause dementia were identified, including 725 cases of Alzheimer's dementia and 418 cases of vascular dementia. We identified 83 metabolites that could represent healthy lifestyle behaviours using elastic net regression. The metabolic signature was associated with a lower dementia risk, and for each standard deviation increment in metabolic signature, the hazard ratio was 0.89 [95% confidence interval (CI): 0.85, 0.93] for all-cause dementia, 0.95 (95% CI: 0.88, 1.03) for Alzheimer's dementia and 0.84 (95% CI: 0.77, 0.91) for vascular dementia. Mendelian randomization revealed potential causal associations between the identified metabolites and risk of dementia. In addition, the specific structural brain reserve, including the hippocampus, grey matter in the hippocampus, parahippocampal gyrus and middle temporal gyrus, were detected to mediate the effects of metabolic signature on dementia risk (mediated proportion ranging from 6.21% to 11.98%). The metabolic signature associated with a healthy lifestyle is inversely associated with dementia risk, and greater structural brain reserve plays an important role in mediating this relationship. These findings have significant implications for understanding the intricate connections between lifestyle, metabolism and brain health.

Profiling of metabolic dysregulation in ovarian cancer tissues and biofluids

Ovarian cancer (OC) is the most lethal gynecologic cancer, mainly due to late diagnosis with widespread peritoneal spread at first presentation. We performed metabolomic analyses of ovarian and paired control tissues using capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry to understand its metabolomic dysregulation. Of the 130 quantified metabolites, 96 metabolites of glycometabolism, including glycolysis, tricarboxylic acid cycles, urea cycles, and one-carbon metabolites, showed significant differences between the samples. To evaluate the local and systemic metabolomic differences in OC, we also analyzed low or non-invasively available biofluids, including plasma, urine, and saliva collected from patients with OC and benign gynecological diseases. All biofluids and tissue samples showed consistently elevated concentrations of N1,N12-diacetylspermine compared to controls. Four metabolites, polyamines, and betaine, were significantly and consistently elevated in both plasma and tissue samples. These data indicate that plasma metabolic dysregulation, which the most reflected by those of OC tissues. Our metabolomic profiles contribute to our understanding of metabolomic abnormalities in OC and their effects on biofluids.

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

Mining and exploration of appendicitis nursing targets: An observational study

Appendicitis is an inflammation caused by obstruction of the appendiceal lumen or termination of blood supply leading to appendiceal necrosis followed by secondary bacterial infection. The relationship between TYROBP gene and the nursing of appendicitis remains unclear. The appendicitis dataset GSE9579 profile was downloaded from the gene expression omnibus database generated from GPL571. Differentially expressed genes were screened, followed by weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, construction and analysis of protein-protein interaction network, Comparative Toxicogenomics Database analysis, and immune infiltration analysis. Heatmaps of gene expression levels were plotted. A total of 1570 differentially expressed genes were identified. According to gene ontology analysis, they were mainly enriched in organic acid metabolic process, condensed chromosome kinetochore, oxidoreductase activity. In Kyoto Encyclopedia of Gene and Genome analysis, they mainly concentrated in metabolic pathways, P53 signaling pathway, PPAR signaling pathway. The soft threshold power in weighted gene co-expression network analysis was set to 12. Through the construction and analysis of protein-protein interaction network, 5 core genes (FCGR2A, IL1B, ITGAM, TLR2, TYROBP) were obtained. Heatmap of core gene expression levels revealed high expression of TYROBP in appendicitis samples. Comparative Toxicogenomics Database analysis found that core genes (FCGR2A, IL1B, ITGAM, TLR2, TYROBP) were closely related to abdominal pain, gastrointestinal dysfunction, fever, and inflammation occurrence. TYROBP gene is highly expressed in appendicitis, and higher expression of TYROBP gene indicates worse prognosis. TYROBP may serve as a molecular target for appendicitis and its nursing.

Metabolomic discoveries for early diagnosis and traditional Chinese medicine efficacy in ischemic stroke

Ischemic stroke (IS), a devastating cerebrovascular accident, presents with high mortality and morbidity. Following IS onset, a cascade of pathological changes, including excitotoxicity, inflammatory damage, and blood-brain barrier disruption, significantly impacts prognosis. However, current clinical practices struggle with early diagnosis and identifying these alterations. Metabolomics, a powerful tool in systems biology, offers a promising avenue for uncovering early diagnostic biomarkers for IS. By analyzing dynamic metabolic profiles, metabolomics can not only aid in identifying early IS biomarkers but also evaluate Traditional Chinese Medicine (TCM) efficacy and explore its mechanisms of action in IS treatment. Animal studies demonstrate that TCM interventions modulate specific metabolite levels, potentially reflecting their therapeutic effects. Identifying relevant metabolites in cerebral ischemia patients holds immense potential for early diagnosis and improved outcomes. This review focuses on recent metabolomic discoveries of potential early diagnostic biomarkers for IS. We explore variations in metabolites observed across different ages, genders, disease severity, and stages. Additionally, the review examines how specific TCM extracts influence IS development through metabolic changes, potentially revealing their mechanisms of action. Finally, we emphasize the importance of integrating metabolomics with other omics approaches for a comprehensive understanding of IS pathophysiology and TCM efficacy, paving the way for precision medicine in IS management.

SPAG5 and ASPM play important roles in gastric cancer: An observational study

Gastric cancer typically originates from the abnormal proliferation of normal cells within the gastric mucosa, eventually forming tumors. The roles of sperm-associated antigen 5 (SPAG5) and abnormal spindle-like microcephaly (ASPM) associated genes in gastric cancer are not yet clear. Gastric cancer datasets GSE51575 and GSE36076 profiles were downloaded from the GPL13607 and GPL570-generated gene expression omnibus database. The analysis included filtering for differentially expressed genes, weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, immune infiltration analysis, construction and analysis of the protein-protein interaction network, survival analysis, and Comparative Toxicogenomics Database analysis. Heatmaps of gene expression were also created. A total of 1457 differentially expressed genes were identified. According to gene ontology analysis, they are primarily enriched in the metabolic processes of organic acids, condensed chromosome centromere regions, and oxidoreductase activity. Kyoto Encyclopedia of Gene and Genome analysis showed they are mainly involved in metabolic pathways, P53 signaling pathway, and PPAR signaling pathway. The soft threshold power for weighted gene co-expression network analysis was set to 8. Three core genes (CENPE, SPAG5, and ASPM) were identified. Heatmaps of core gene expression revealed that SPAG5 and ASPM are highly expressed in gastric cancer samples and low in normal samples. Comparative Toxicogenomics Database analysis indicated that the core genes (CENPE, SPAG5, and ASPM) are associated with gastric tumors, gastric diseases, gastritis, gastric ulcers, tumors, inflammation, and necrosis. The SPAG5 and ASPM genes are overexpressed in gastric cancer tissues, and higher expression levels are associated with worse prognosis, may serve as potential prognostic markers.

Joint effects of CD8A and ICOS in Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS)

BACKGROUND

Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS) are complex disorders with unclear origins, underscoring the need for in-depth molecular investigations into their mechanisms. The main aim of this study is to identify the shared key genes between LQTS and BWS, shedding light on potential common molecular pathways underlying these syndromes.

METHODS

The LQTS and BWS datasets are available for download from the GEO database. Differential expression genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) was used to detect significant modules and central genes. Gene enrichment analysis was performed. CIBERSORT was used for immune cell infiltration analysis. The predictive protein interaction (PPI) network of core genes was constructed using STRING, and miRNAs regulating central genes were screened using TargetScan.

RESULTS

Five hundred DEGs associated with Long QT Syndrome and Beckwith-Wiedemann Syndrome were identified. GSEA analysis revealed enrichment in pathways such as T cell receptor signaling, MAPK signaling, and adrenergic signaling in cardiac myocytes. Immune cell infiltration indicated higher levels of memory B cells and naive CD4 T cells. Four core genes (CD8A, ICOS, CTLA4, LCK) were identified, with CD8A and ICOS showing low expression in the syndromes and high expression in normal samples, suggesting potential inverse regulatory roles.

CONCLUSION

The expression of CD8A and ICOS is low in long QT syndrome and Beckwith-Wiedemann syndrome, indicating their potential as key genes in the pathogenesis of these syndromes. The identification of shared key genes between LQTS and BWS provides insights into common molecular mechanisms underlying these disorders, potentially facilitating the development of targeted therapeutic strategies.

Tsc22d3 promotes morphine tolerance in mice through the GPX4 ferroptosis pathway

BACKGROUND

Morphine tolerance refers to gradual reduction in response to drug with continuous or repeated use of morphine, requiring higher doses to achieve same effect.

METHODS

The morphine tolerance dataset GSE7762 profiles, obtained from gene expression omnibus (GEO) database, were used to identify differentially expressed genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) was applied to explore core modules of DEGs related to morphine tolerance. Core genes were input into Comparative Toxicogenomics Database (CTD). Animal experiments were performed to validate role of Tsc22d3 in morphine tolerance and its relationship with ferroptosis-related pathway.

RESULTS

500 DEGs were identified. DEGs were primarily enriched in negative regulation of brain development, neuronal apoptosis processes, and neurosystem development. Core gene was identified as Tsc22d3. Tsc22d3 gene-associated miRNAs were mmu-miR-196b-5p and mmu-miR-196a-5p. Compared to Non-morphine tolerant group, Tsc22d3 expression was significantly upregulated in Morphine tolerant group. Tsc22d3 expression was upregulated in Morphine tolerant+Tsc22d3_OE, expression of HIF-1alpha, GSH, GPX4 in GPX4 ferroptosis-related pathway showed a more pronounced decrease. As Tsc22d3 expression was downregulated in Morphine tolerant+Tsc22d3_KO, expression of HIF-1alpha, GSH, GPX4 in GPX4 ferroptosis-related pathway exhibited a more pronounced increase. Upregulation of Tsc22d3 in Morphine tolerant+Tsc22d3_OE led to a more pronounced increase in expression of apoptosis proteins (P53, Caspase-3, Bax, SMAC, FAS). The expression of inflammatory factors (IL6, TNF-alpha, CXCL1, CXCL2) showed a more pronounced increase with upregulated Tsc22d3 expression in Morphine tolerant+Tsc22d3_OE.

CONCLUSIONS

Tsc22d3 is highly expressed in brain tissue of morphine-tolerant mice, activating ferroptosis pathway, enhancing apoptosis, promoting inflammatory responses in brain cells.

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

Multiple-matrix metabolomics analysis for the distinct detection of colorectal cancer and adenoma

OBJECTIVES

Although colorectal cancer (CRC) is the leading cause of cancer-related morbidity and mortality, current diagnostic tests for early-stage CRC and colorectal adenoma (CRA) are suboptimal. Therefore, there is an urgent need to explore less invasive screening procedures for CRC and CRA diagnosis.

METHODS

Untargeted gas chromatography-mass spectrometry (GC-MS) metabolic profiling approach was applied to identify candidate metabolites. We performed metabolomics profiling on plasma samples from 412 subjects including 200 CRC patients, 160 CRA patients and 52 normal controls (NC). Among these patients, 45 CRC patients, 152 CRA patients and 50 normal controls had their fecal samples tested simultaneously.

RESULTS

Differential metabolites were screened in the adenoma-carcinoma sequence. Three diagnostic models were further developed to identify cancer group, cancer stage, and cancer microsatellite status using those significant metabolites. The three-metabolite-only classifiers used to distinguish the cancer group always keeps the area under the receiver operating characteristic curve (AUC) greater than 0.7. The AUC performance of the classifiers applied to discriminate CRC stage is generally greater than 0.8, and the classifiers used to distinguish microsatellite status of CRC is greater than 0.9.

CONCLUSION

This finding highlights potential early-driver metabolites in CRA and early-stage CRC. We also find potential metabolic markers for discriminating the microsatellite state of CRC. Our study and diagnostic model have potential applications for non-invasive CRC and CRA detection.

CDK1 and CCNA2 play important roles in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is a malignant tumor that occurs in oral cavity and is dominated by squamous cells. The relationship between CDK1, CCNA2, and OSCC is still unclear. The OSCC datasets GSE74530 and GSE85195 configuration files were downloaded from the Gene Expression Omnibus (GEO) database and were derived from platforms GPL570 and GPL6480. Differentially expressed genes (DEGs) were screened. The weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, Comparative Toxicogenomics Database analysis were performed. Gene expression heatmap was drawn. TargetScan was used to screen miRNAs that regulate central DEGs. A total of 1756 DEGs were identified. According to Gene Ontology (GO) analysis, they were predominantly enriched in processes related to organic acid catabolic metabolism, centromeric, and chromosomal region condensation, and oxidoreductase activity. In Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the DEGs were mainly concentrated in metabolic pathways, P53 signaling pathway, and PPAR signaling pathway. Weighted gene co-expression network analysis was performed with a soft-thresholding power set at 9, leading to the identification of 6 core genes (BUB1B, CCNB1, KIF20A, CCNA2, CDCA8, CDK1). The gene expression heatmap revealed that core genes (CDK1, CCNA2) were highly expressed in OSCC samples. Comparative Toxicogenomics Database analysis demonstrated associations between the 6 genes (BUB1B, CCNB1, KIF20A, CCNA2, CDCA8, CDK1) and oral tumors, precancerous lesions, inflammation, immune system disorders, and tongue tumors. The associated miRNAs for CDK1 gene were hsa-miR-203a-3p.2, while for CCNA2 gene, they were hsa-miR-6766-3p, hsa-miR-4782-3p, and hsa-miR-219a-5p. CDK1 and CCNA2 are highly expressed in OSCC. The higher the expression of CDK1 and CCNA2, the worse the prognosis.

Glucagon kinetics assessed by mathematical modelling during oral glucose administration in people spanning from normal glucose tolerance to type 2 diabetes

Background/Objectives

Glucagon is important in the maintenance of glucose homeostasis, with also effects on lipids. In this study, we aimed to apply a recently developed model of glucagon kinetics to determine the sensitivity of glucagon variations (especially, glucagon inhibition) to insulin levels ("alpha-cell insulin sensitivity"), during oral glucose administration.

Subjects/Methods

We studied 50 participants (spanning from normal glucose tolerance to type 2 diabetes) undergoing frequently sampled 5-hr oral glucose tolerance test (OGTT). The alpha-cell insulin sensitivity and the glucagon kinetics were assessed by a mathematical model that we developed previously.

Results

The alpha-cell insulin sensitivity parameter (named SGLUCA; "GLUCA": "glucagon") was remarkably variable among participants (CV=221%). SGLUCA was found inversely correlated with the mean glycemic values, as well as with 2-hr glycemia of the OGTT. When stratifying participants into two groups (normal glucose tolerance, NGT, N=28, and impaired glucose regulation/type 2 diabetes, IGR_T2D, N=22), we found that SGLUCA was lower in the latter (1.50 ± 0.50·10-2 vs. 0.26 ± 0.14·10-2 ng·L-1 GLUCA/pmol·L-1 INS, in NGT and IGR_T2D, respectively, p=0.009; "INS": "insulin").

Conclusions

The alpha-cell insulin sensitivity is highly variable among subjects, and it is different in groups at different glucose tolerance. This may be relevant for defining personalized treatment schemes, in terms of dietary prescriptions but also for treatments with glucagon-related agents.

PROS1 is a crucial gene in the macrophage efferocytosis of diabetic foot ulcers: a concerted analytical approach through the prisms of computer analysis

BACKGROUND

Diabetic foot ulcers (DFUs) pose a serious long-term threat because of elevated mortality and disability risks. Research on its biomarkers is still, however, very limited. In this paper, we have effectively identified biomarkers linked with macrophage excretion in diabetic foot ulcers through the application of bioinformatics and machine learning methodologies. These findings were subsequently validated using external datasets and animal experiments. Such discoveries are anticipated to offer novel insights and approaches for the early diagnosis and treatment of DFU.

METHODS

In this work, we used the Gene Expression Omnibus (GEO) database's datasets GSE68183 and GSE80178 as the training dataset to build a gene model using machine learning methods. After that, we used the training and validation sets to validate the model (GSE134431). On the model genes, we performed enrichment analysis using both gene set variant analysis (GSVA) and gene set enrichment analysis (GSEA). Additionally, the model genes were subjected to immunological association and immune function analyses.

RESULTS

In this study, PROS1 was identified as a potential key target associated with macrophage efflux in DFU by machine learning and bioinformatics approaches. Subsequently, the key biomarker status of PROS1 in DFU was also confirmed by external datasets. In addition, PROS1 also plays a key role in macrophage exudation in DFU. This gene may be associated with macrophage M1, CD4 memory T cells, naïve B cells, and macrophage M2, and affects IL-17, Rap1, hedgehog, and JAK-STAT signaling pathways.

CONCLUSIONS

PROS1 was identified and validated as a biomarker for DFU. This finding has the potential to provide a target for macrophage clearance of DFU.

Bioinformatics analysis identifies TGF-β signaling pathway-associated molecular subtypes and gene signature in diabetic foot

The role of transforming growth factor β (TGF-β) in inflammation and immune response is established, but the mechanism of TGF-β signaling pathway-related genes (TRGs) in diabetic foot ulcer (DFU) is not fully understood. We aimed to investigate the contribution of TRGs in the identification, molecular categorization, and immune infiltration of DFU through bioinformatics analysis. TGF-β signaling pathway is activated in DFU. 33 TRGs were upregulated. Regression analysis revealed TGFBR1 and TGFB1 as significant differential expression core genes, validated by quantitative real-time PCR. The diagnostic model with core genes had high clinical validity (AUC = 0.909). Core gene expression was associated with immune cell infiltration. A total of 5672 genes showed differential expression in TGF-related patterns, with differences in biological functions and immune infiltration. TGF-β signaling pathway may be critical in DFU development.

LCN2 and ELANE overexpression induces sepsis

Sepsis is a syndrome characterized by a systemic inflammatory response due to the invasion of pathogenic microorganisms. The relationship between Lipocalin-2 (LCN2), elastase, neutrophil expressed (ELANE) and sepsis remains unclear. The sepsis datasets GSE137340 and GSE154918 profiles were downloaded from gene expression omnibus generated from GPL10558. Batch normalization, differentially expressed Genes (DEGs) screening, weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, construction and analysis of protein-protein interaction (PPI) networks, Comparative Toxicogenomics Database (CTD) analysis were performed. Gene expression heatmaps were generated. TargetScan was used to screen miRNAs of DEGs. 328 DEGs were identified. According to Gene Ontology (GO), in the Biological Process analysis, they were mainly enriched in immune response, apoptosis, inflammatory response, and immune response regulation signaling pathways. In cellular component analysis, they were mainly enriched in vesicles, cytoplasmic vesicles, and secretory granules. In Molecular Function analysis, they were mainly concentrated in hemoglobin binding, Toll-like receptor binding, immunoglobulin binding, and RAGE receptor binding. In Kyoto Encyclopedia of Genes and Genomes (KEGG), they were mainly enriched in NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, TNF signaling pathway, P53 signaling pathway, and legionellosis. Seventeen modules were generated. The PPI network identified 4 core genes (MPO, ELANE, CTSG, LCN2). Gene expression heatmaps revealed that core genes (MPO, ELANE, CTSG, LCN2) were highly expressed in sepsis samples. CTD analysis found that MPO, ELANE, CTSG and LCN2 were associated with sepsis, peritonitis, meningitis, pneumonia, infection, and inflammation. LCN2 and ELANE are highly expressed in sepsis and may serve as molecular targets.

New Perspectives on the Role of Liquid Biopsy in Bladder Cancer: Applicability to Precision Medicine

Bladder cancer (BC) is one of the most common tumors in the world. Cystoscopy and tissue biopsy are the standard methods in screening and early diagnosis of suspicious bladder lesions. However, they are invasive procedures that may cause pain and infectious complications. Considering the limitations of both procedures, and the recurrence and resistance to BC treatment, it is necessary to develop a new non-invasive methodology for early diagnosis and multiple evaluations in patients under follow-up for bladder cancer. In recent years, liquid biopsy has proven to be a very useful diagnostic tool for the detection of tumor biomarkers. This non-invasive technique makes it possible to analyze single tumor components released into the peripheral circulation and to monitor tumor progression. Numerous biomarkers are being studied and interesting clinical applications for these in BC are being presented, with promising results in early diagnosis, detection of microscopic disease, and prediction of recurrence and response to treatment.

Dogs can detect an odor profile associated with Staphylococcus aureus biofilms in cultures and biological samples

Introduction

The study investigated the utilization of odor detection dogs to identify the odor profile of Staphylococcus aureus (S. aureus) biofilms in pure in vitro samples and in in vivo biosamples from animals and humans with S. aureus periprosthetic joint infection (PJI). Biofilms form when bacterial communities aggregate on orthopedic implants leading to recalcitrant infections that are difficult to treat. Identifying PJI biofilm infections is challenging, and traditional microbiological cultures may yield negative results even in the presence of clinical signs.

Methods

Dogs were trained on pure in vitro S. aureus biofilms and tested on lacrimal fluid samples from an in vivo animal model (rabbits) and human patients with confirmed S. aureus PJI.

Results

The results demonstrated that dogs achieved a high degree of sensitivity and specificity in detecting the odor profile associated with S. aureus biofilms in rabbit samples. Preliminary results suggest that dogs can recognize S. aureus volatile organic compounds (VOCs) in human lacrimal fluid samples.

Discussion

Training odor detection dogs on in vitro S. aureus, may provide an alternative to obtaining clinical samples for training and mitigates biosecurity hazards. The findings hold promise for culture-independent diagnostics, enabling early disease detection, and improved antimicrobial stewardship. In conclusion, this research demonstrates that dogs trained on in vitro S. aureus samples can identify the consistent VOC profile of PJI S. aureus biofilm infections. The study opens avenues for further investigations into a retained VOC profile of S. aureus biofilm infection. These advancements could revolutionize infectious disease diagnosis and treatment, leading to better patient outcomes and addressing the global challenge of antimicrobial resistance.

Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics

Adjuvant chemotherapy improves the survival outlook for patients undergoing operations for lung metastases caused by colorectal cancer (CRC). However, a multidisciplinary approach that evaluates several factors related to patient and tumor characteristics is necessary for managing chemotherapy treatment in metastatic CRC patients with lung disease, as such factors dictate the timing and drug regimen, which may affect treatment response and prognosis. In this study, we explore the potential of spatial metabolomics for evaluating metabolic phenotypes and therapy outcomes during the local delivery of the anticancer drug, oxaliplatin, to the lung. 12 male Yorkshire pigs underwent a 3 h left lung in vivo lung perfusion (IVLP) with various doses of oxaliplatin (7.5, 10, 20, 40, and 80 mg/L), which were administered to the perfusion circuit reservoir as a bolus. Biocompatible solid-phase microextraction (SPME) microprobes were combined with global metabolite profiling to obtain spatiotemporal information about the activity of the drug, determine toxic doses that exceed therapeutic efficacy, and conduct a mechanistic exploration of associated lung injury. Mild and subclinical lung injury was observed at 40 mg/L of oxaliplatin, and significant compromise of the hemodynamic lung function was found at 80 mg/L. This result was associated with massive alterations in metabolic patterns of lung tissue and perfusate, resulting in a total of 139 discriminant compounds. Uncontrolled inflammatory response, abnormalities in energy metabolism, and mitochondrial dysfunction next to accelerated kynurenine and aldosterone production were recognized as distinct features of dysregulated metabolipidome. Spatial pharmacometabolomics may be a promising tool for identifying pathological responses to chemotherapy.

Duck hepatitis A virus type 1 infection induces hepatic metabolite and gut microbiota changes in ducklings

Duck hepatitis A virus type 1 (DHAV-1) can cause severe liver damage in infected ducklings and is a fatal and contagious pathogen that endangers the Chinese duck industry. The objective of this study was to explore the correlation mechanism of liver metabolism-gut microbiota in DHAV-1 infection. Briefly, liquid chromatography-mass spectrometry and 16S rDNA sequencing combined with multivariate statistical analysis were used to evaluate the effects of DHAV-1 infection on liver metabolism, gut microbiota regulation, and other potential mechanisms in ducklings. In DHAV-1-infected ducklings at 72 h postinfection, changes were found in metabolites associated with key metabolic pathways such as lipid metabolism, sugar metabolism, and nucleotide metabolism, which participated in signaling networks and ultimately affecting the function of the liver. The abundance and composition of gut microbiota were also changed, and gut microbiota is significantly involved in lipid metabolism in the liver. The evident correlation between gut microbiota and liver metabolites indicates that DHAV-host gut microbiome interactions play important roles in the development of duck viral hepatitis (DVH).

Complementary biomarkers of computed tomography for diagnostic grading of gastric cancer: DSCC1 and GINS1

OBJECTIVE

Computed tomography (CT) is an important tool for grading gastric cancer. Gastric cancer typically originates from epithelial cells of gastric mucosa. However, complementary markers for gastric cancer, relationship between DSCC1, GINS1 and gastric cancer remain unclear.

METHODS

Gastric cancer data were obtained from gene expression omnibus (GEO). Differentially expressed genes (DEGs) were identified, weighted gene co-expression network analysis (WGCNA) was conducted. Protein-protein interaction (PPI) network was constructed and analyzed. Functional enrichment analysis, gene set enrichment analysis (GSEA), gene expression heatmaps, immune infiltration analysis were performed. The most relevant diseases related to core genes were identified using Comparative Toxicogenomics Database (CTD). TargetScan was used to screen miRNAs. Validation was carried out using Western blotting (WB) and reverse transcription-polymerase chain reaction (RT-PCR).

RESULTS

1243 DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses revealed significant enrichment in cell cycle regulation, macrophage migration control, basement membrane, extracellular regions, growth factor binding, protein complex binding, P53 signaling pathway, protein digestion and absorption, metabolic pathways. Immune infiltration analysis indicated that high expression of activated Mast cells and Neutrophils, with a strong positive correlation between them, may influence progression of gastric cancer. CTD analysis revealed associations between DSCC1, GINS1 and gastric tumors, gastrointestinal diseases, tumors, gastritis, inflammation, necrosis. WB and RT-PCR results demonstrated high expression of DSCC1 and GINS1 in gastric cancer.

CONCLUSION

The expressions of DSCC1 and GINS1 are up-regulated in gastric cancer, which can be used as supplementary markers for CT diagnostic grading of gastric cancer.

Il1r2 and Tnfrsf12a in transcranial magnetic stimulation effect of ischemic stroke via bioinformatics analysis

Ischemic stroke refers to ischemic necrosis or softening of localized brain tissue. Transcranial magnetic stimulation (TMS) is a painless, noninvasive and green treatment method, which acts on the central nervous system through a pulsed magnetic field to assist in the treatment of central nervous system injury diseases. However, the role of Il1r2 and Tnfrsf12a in this is unknown. The ischemic stroke datasets GSE81302 and TMS datasets GSE230148 were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis (WGCNA) was performed. The construction and analysis of protein-protein interaction (PPI) network and functional enrichment analysis were performed. Draw heat map gene expression. Through the Comparative Toxicogenomics Database (CTD) to find the most relevant and core gene diseases. TargetScan was used to screen miRNAs regulating DEGs. A total of 39 DEGs were identified. According to gene ontology (GO) analysis results, in biological process (BP) analysis, they were mainly enriched in the positive regulation of apoptosis process, inflammatory response, positive regulation of p38MAPK cascade, and regulation of cell cycle. In cellular component (CC) analysis, they were mainly enriched in the cell surface, cytoplasm, and extracellular space. In Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, they were mainly enriched in nf-κB signaling pathway, fluid shear stress and atherosclerosis, P53 signaling pathway, TNF signaling pathway, and apoptosis. Among the enrichment items of metascape, negative regulation of T cell activation, hematopoietic cell lineage, positive regulation of apoptotic process, fluid shear stress and atherosclerosis were observed in GO enrichment items. Five core genes (Socs3, Irf1, Il1r2, Ccr1, and Tnfrsf12a) were obtained, which were highly expressed in ischemic stroke samples. Il1r2 and Tnfrsf12a were lowly expressed in TMS samples. CTD analysis found that the core gene (Socs3, Irf1 and Il1r2, Ccr1, Tnfrsf12a) and ischemic stroke, atherosclerosis, hypertension, hyperlipidemia, thrombosis, stroke, myocardial ischemia, myocardial infarction, and inflammation. Il1r2 and Tnfrsf12a are highly expressed in ischemic stroke, but lowly expressed in TMS samples.

3D superstructure based metabolite profiling for glaucoma diagnosis

Metabolome analysis has gained widespread application in disease diagnosis owing to its ability to provide comprehensive information, including disease phenotypes. In this study, we utilized 3D superstructures fabricated through evaporation-induced microprinting to analyze the metabolome for glaucoma diagnosis. 3D superstructures offer the following advantages: high hotspot density per unit volume of the structure extending from two to three dimensions, excellent signal repeatability due to the reproducibility and defect tolerance of 3D printing, and high thermal stability due to the PVP-enclosed capsule form. Leveraging the superior optical properties of the 3D superstructure, we aimed to classify patients with glaucoma. The signal obtained from the 3D superstructure was employed in a Deep Neural Network (DNN) classification model to accurately classify glaucoma patients. The sensitivity and specificity of the model were determined as 92.05% and 93.51%, respectively. Additionally, the fabrication of 3D superstructures can be performed at any stage, significantly reducing measurement time. Furthermore, their thermal stability allows for the analysis of smaller samples. One notable advantage of 3D superstructures is their versatility in accommodating different target materials. Consequently, they can be utilized for a wide range of metabolic analyses and disease diagnoses.

Integrated proteomics and metabolomics reveals metabolism disorders in the α-syn mice and potential therapeutic effect of Acanthopanax senticosus extracts

ETHNOPHARMACOLOGICAL RELEVANCE

Acanthopanax senticosus (Rupr.et.Maxim.)Harms(AS) is an extract of Eleutherococcus senticocus Maxim(Rupr.et.Maxim.). In modern medical interpretation, Acanthopanax senticosus can be used to treat Parkinson's disease, and a large number of modern pharmacological and clinical studies also support this application. Our study demonstrated that AS extracts can increase the activity of various antioxidant enzymes and improve the symptoms of Parkinson's disease in mice.

AIM OF THE STUDY

The current study looked at the protective effect of Acanthopanax senticosus extracts(ASE) in preventing PD.

METHODS AND MATERIALS

First, the α-syn-overexpressing mice were chosen as suitable models for Parkinson's disease in vivo. HE staining was used to observe the pathological changes in the substantia nigra. Meanwhile, TH expression in substantia nigra was analyzed by immunohistochemistry. Behavioral and biochemical tests evaluated neuroprotective effects of ASE on PD mice. Subsequently, combined with proteomics and metabolomics analysis, the changes in brain proteins and metabolites in mice treated with ASE for PD were studied. Finally, Western blot was used to detect metabolome-related and proteomic proteins in the brain tissue of α-syn mice.

RESULTS

Forty-nine common differentially expressed proteins were screened by proteomics analysis, among which 28 were significantly up-regulated,and 21 were significantly down-regulated. Metabolomics analysis showed that twenty-five potentially important metabolites were involved in the therapeutic effect of ASE on PD. Most of the different proteins and metabolites were considered to be enriched in a variety of species in metabolic pathways, including glutathione metabolism and alanine aspartate and glutamate metabolism and other pathways, which means that ASE may have molecular mechanisms to ameliorate PD dysfunction. In addition, we found that decreases in glutathione and glutathione disulfide levels may play a critical role in these systemic changes and warrant further investigation. In the glutathione metabolic pathway, ASE also acts on GPX4, GCLC and GCLM.

CONCLUSIONS

ASE can effectively relieve behavioral symptoms of α-syn mice and relieve oxidative stress in brain tissue. These findings suggest that ASE offers a potential solution to target these pathways for the treatment of PD.

EPYC functions as a novel prognostic biomarker for pancreatic cancer

Pancreatic cancer (PC) has become a worldwide challenge attributed to its difficult early diagnosis and rapid progression. Treatments continue to be limited besides surgical resection. Hence, we aimed to discover novel biological signatures as clinically effective therapeutic targets for PC via the mining of public tumor databases. We found that epiphycan (EPYC) could function as an independent risk factor to predict the poor prognosis in PC based on integrated bioinformatics analysis. We downloaded associated PC data profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) online websites, then applied the software Rstudio to filter out genes under the strict criteria. After the batch survival analysis using Log-rank test and univariate cox regression, we obtained 39 candidate genes. Subsequently, we narrowed the scope to 8 genes by establishing a Lasso regression model. Eventually, we focused on 2 genes (EPYC and MET) by further building a multivariate cox regression model. Given that the role of EPYC in PC remains obscure, we then performed a series of molecular functional experiments, including RT-qPCR, CCK8, EdU, colony formation, Transwell, western blot, cell live-dead staining, subcutaneous tumor formation, to enhance our insight into its underlying molecular mechanisms. The above results demonstrated that EPYC was highly expressed in PC cell lines and could promote the proliferation of PCs via PI3K-AKT signaling pathway in vivo and in vitro. We arrived at a conclusion that EPYC was expected to be a biological neo-biomarker for PC followed by being a potential therapeutic target.

The association between atherosclerosis and nonalcoholic fatty liver disease

Atherosclerosis (AS) is closely related to nonalcoholic fatty liver disease (NAFLD), which promotes and exacerbates the development of AS. However, it is uncertain how the precise underlying mechanism occurs. Here, we attempted to further explore the association underlying atherosclerosis and nonalcoholic fatty liver disease through integrated bioinformatics analysis. Microarray data for atherosclerosis and nonalcoholic fatty liver disease were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to identify the genes related to atherosclerosis and nonalcoholic fatty liver disease showing co-expression. Additionally, the common gene targets associated with atherosclerosis and nonalcoholic fatty liver disease were also analyzed and screened using data from 3 public databases [comparative toxicogenomics database (CTD), DISEASES, and GeneCards]. The Gene Ontology (GO) enrichment analysis and the Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis were performed using Metascape R, respectively. The protein-protein interaction networks (PPI) network was constructed using Cytoscape. According to the results of an analysis of common genes, matrix metalloproteinase 9 (MMP9) is co-expressed up-regulated in AS and NAFLD and is enriched in inflammatory and immune-related collaterals. Consequently, MMP9 may work together through immunity and inflammation to treat AS and NAFLD and may be a potential therapeutic target in the future. The findings of this study provide new insights into the shared association between AS and NAFLD. MMP9 is co-expressed up-regulated in AS and NAFLD, which be able to reveal the presence of co-expressed genes in atherosclerosis and NAFLD.

Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients

Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients' health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system's coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79-0.95 (sensitivity 80-92%, specificity 62-94%) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-023-00218-x.

Overexpression of SIRT6 regulates NRF2/HO-1 and NF-κB signaling pathways to alleviate UVA-induced photoaging in skin fibroblasts

Skin photoaging, resulting from prolonged exposure to sunlight, especially UVA rays, has been identified as a key contributor to age-related skin degeneration. However, the mechanism by which UVA radiation induces skin cell senescence has not been fully elucidated. In this investigation, bioinformatics technology was employed to identify SIRT6 as the core hub gene involved in the progression of skin photoaging. The study evinced that prolonged exposure of cutaneous fibroblasts to UVA radiation results in a marked reduction in the expression of SIRT6, both in vivo and in vitro. Knockdown of SIRT6 in skin fibroblasts resulted in the upregulation of genes associated with cellular aging, thereby exacerbating the effects of UVA radiation-induced photoaging. Conversely, overexpression of SIRT6 decreased the expression of cell aging-related genes, indicating that SIRT6 plays a role in the regulation of senescence in skin fibroblasts induced by UVA radiation. We proffer substantiation that overexpression of SIRT6 protects skin fibroblasts from UVA-induced oxidative stress by activating the NRF2/HO-1 signaling cascade. Moreover, SIRT6 overexpression also reduced UVA-induced type I collagen degradation by inhibiting NF-κB signaling cascade. In summary, our findings showed that overexpression of SIRT6 inhibits UVA-induced senescence phenotype and type I collagen degradation in skin fibroblasts by modulating the NRF2/HO-1 and NF-κB signaling pathways. And the regulation of these signaling pathways by SIRT6 may be achieved through its deacetylase activity. Therefore, SIRT6 is a novel and promising therapeutic target for skin aging related to age and UV.

High expression of NADH Ubiquinone Oxidoreductase Subunit B11 induces catheter-associated venous thrombosis on continuous blood purification

Venous thromboembolism (VTE) is a common vascular disease of venous return disorders, including deep venous thrombosis and pulmonary embolism (PE), with high morbidity and high mortality. However, the relationship between oxidative phosphorylation and NDUFB11 and venous thromboembolism is still unclear. The venous thromboembolism datasets GSE48000 and GSE19151 were downloaded, and the differentially expressed Genes (DEGs) were screened. The protein-protein interaction (PPI) network was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for functional enrichment analysis. The comparative toxicogenomics database (CTD) was used to identify the diseases most associated with the core genes. TargetScan was used to screen miRNA regulating central DEGs. Western blotting (WB) experiment and real-time quantitative PCR (RT-qPCR) experiment were performed. A total of 500 DEGs were identified. GO analysis showed that the DEGs were mainly enriched in ATP synthesis coupled electron transport, respiratory electron transport chain, cytoplasm, enzyme binding, nonalcoholic fatty liver disease, oxidative phosphorylation, and Alzheimer disease. Enrichment items were similar to GO and KEGG enrichment items of DEGs. The result of CTD showed that 12 genes (RPS24, FAU, RPLP0, RPS15A, RPS29, RPL9, RPL31, RPL27, NDUFB11, RPL34, COX7B, RPS27L) were associated with chemical and drug-induced liver injury, inflammation, kidney disease, and congenital pure red cell aplasia. WB and RT-qPCR results showed that the expression levels of 12 genes in venous thromboembolism were higher than normal whole blood tissue samples. NDUFB11 is highly expressed in catheter-related venous thromboembolism during continuous blood purification, which may lead to the formation of venous thrombosis through oxidative phosphorylation pathway.

Fgf2 and Ptpn11 play a role in cerebral injury caused by sevoflurane anesthesia

Sevoflurane is a new inhaled anesthetic, which has better physical properties than the existing inhalational anesthetics, rapid induction, less tissue uptake, and faster recovery. Sevoflurane can directly dilators cerebral blood vessels and increase cerebral blood flow, but it also reduces cerebral oxygen metabolism rate, thereby reducing cerebral blood flow. However, the role of Fgf2 and Ptpn11 in cerebral injury caused by sevoflurane anesthesia remains unclear. The sevoflurane anesthesia brain tissue datasets GSE139220 and GSE141242 were downloaded from gene expression omnibus (GEO). Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis (WGCNA) was performed. Construction and analysis of protein-protein interaction (PPI) Network. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG), comparative toxicogenomics database (CTD) were performed. A heat map of gene expression was drawn. TargetScan was used to screen miRNAs regulating DEGs. 500 DEGs were identified. According to GO, in Biological Process analysis, they were mainly enriched in response to hypoxia, blood vessel development, inner ear development, neural tube closure, and aging. In Cellular Component (CC), they were mainly enriched in plasma membrane, integral component of membrane, and basal lamina. In Molecular Function (MF), they were mainly associated with protein binding, Wnt-activated receptor activity, and organic anion transmembrane transporter activity. In the KEGG analysis, they were mainly enriched in proteoglycans in cancer, pathways in cancer, transcriptional misregulation in cancer, basal cell carcinoma, thyroid hormone signaling pathway. In the Metascape enrichment analysis, the GO enrichment items revealed upregulated regulation of vascular endothelial cell proliferation, platelet-derived growth factor receptor signaling pathway, inner ear development, and response to hypoxia. A total of 20 modules were generated. Gene Expression Heatmap showed that the core genes (Fgf2, Pdgfra, Ptpn11, Slc2a1) were highly expressed in sevoflurane anesthesia brain tissue samples. CTD Analysis showed that the 4 core genes (Fgf2, Pdgfra, Ptpn11, Slc2a1) were associated with neurodegenerative diseases, brain injuries, memory disorders, cognitive disorders, neurotoxicity, drug-induced abnormalities, neurological disorders, developmental disorders, and intellectual disabilities. Fgf2 and Ptpn11 are highly expressed in brain tissue after sevoflurane anesthesia, higher the expression level of Fgf2 and Ptpn11, worse the prognosis.

Revealing the impact of TOX3 on osteoarthritis: insights from bioinformatics

Osteoarthritis, a prevalent long-term condition of the joints, primarily impacts older individuals, resulting in discomfort, restrictions in mobility, and a decrease in overall well-being. Although Osteoarthritis is widely spread, there is a lack of successful interventions to stop the advancement of the condition. Numerous signaling pathways have been emphasized in recent research on Osteoarthritis, yet the diagnostic significance of numerous genes has not been investigated. To identify genes that were expressed differently in osteoarthritis, we utilized the Gene Expression Omnibus database. To identify marker genes, we built machine learning models including Least Absolute Shrinkage and Selection Operator and Random Forest. We categorized Osteoarthritis samples and performed immune cell infiltration analysis based on the expression patterns of these characteristic genes. Both the Least Absolute Shrinkage and Selection Operator and Random Forest models selected six marker genes (TOX3, ARG1, CST7, RERGL, COL11A1, NCRNA00185) out of a total of 17 differentially expressed genes. The osteoarthritis samples were categorized into two groups, namely a high expression group and a low expression group, based on the median levels of TOX3 expression. Comparative analysis of these groups identified 85 differentially expressed genes, showing notable enrichment in pathways related to lipid metabolism in the group with high expression. Analysis of immune cell infiltration revealed noticeable differences in immune profiles among the two groups. The group with high expression of TOX3 showed a notable increase in Mast cells and Type II IFN Response, whereas B cells, Cytolytic activity, Inflammation-promoting cells, NK cells, pDCs, T cell co-inhibition, Th1 cells, and Th2 cells were significantly decreased. We constructed a ceRNA network for TOX3, revealing 57 lncRNAs and 18 miRNAs involved in 57 lncRNA-miRNA interactions, and 18 miRNA-mRNA interactions with TOX3. Validation of TOX3 expression was confirmed using an external dataset (GSE29746), revealing a notable increase in Osteoarthritis samples. In conclusion, our study presents a comprehensive analysis identifying TOX3 as a potential feature gene in Osteoarthritis. The distinct immune profiles and involvement in fat metabolism pathways associated with TOX3 expression suggest its significance in Osteoarthritis pathogenesis. The study establishes a basis for comprehending the intricate correlation between characteristic genes and Osteoarthritis, as well as for the formulation of individualized therapeutic approaches.

Roles of DSCC1 and GINS1 in gastric cancer

Gastric carcinoma is a common malignant tumor originating from gastric mucosal epithelium. However, role of DS-cell cycle-dependent protein 1 (DSCC1) and GINS1 in gastric carcinoma remains unclear. The gastric carcinoma datasets GSE79973 and GSE118916 were downloaded from gene expression omnibus. Multiple datasets were merged and batched. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis was performed. Functional enrichment analysis, gene set enrichment analysis and immune infiltration analysis were performed. Construction and analysis of protein-protein interaction Network. Survival analysis and comparative toxicogenomics database were performed. A heat map of gene expression was drawn. Target Scan screen miRNAs regulating DEGs. Two thousand forty-four DEGs were identified. According to gene ontology analysis, in biological process, they were mainly enriched in cell migration, transforming growth factor β receptor signaling pathway, angiogenesis, and steroid metabolism process. In cellular component, they were mainly enriched in extracellular vesicles, basement membrane, endoplasmic reticulum lumen, and extracellular space. In molecular function, they focused on extracellular matrix structural components, protein binding, platelet-derived growth factor binding, and catalytic activity. In Kyoto encyclopedia of genes and genomes, they were mainly enriched in protein digestion and absorption, metabolic pathways, fatty acid degradation, Glycerophospholipid metabolism, ether lipid metabolism. Gene set enrichment analysis showed that DEGs were mainly enriched in transforming growth factor β receptor signaling pathway, steroid metabolism process, basement membrane, endoplasmic reticulum lumen, structural components of extracellular matrix, platelet-derived growth factor binding, Glycerophospholipid metabolism, ether lipid metabolism. The results of immune infiltration analysis showed that expression of T cell CD4 memory resting was lower in the samples of gastric cancer. The core genes (TRIP13, CHEK1, DSCC1, GINS1) are protective factors, their expression shows a downward trend with increase of risk score. Comparative toxicogenomics database analysis showed that TRIP13, CHEK1, DSCC1, GINS1 were related to gastric tumors, gastric diseases, tumors, inflammation, and necrosis. DSCC1 and GINS1 are highly expressed in gastric cancer. Higher expression levels of DSCC1 and GINS1, worse the prognosis.

NDUFB11 and NDUFS3 play a role in atherosclerosis and chronic stress

OBJECTIVE

Atherosclerosis is characterized by the formation of fibrofatty plaques in the intima of arteries, resulting in thickening of the vessel wall and narrowing of the lumen. Chronic stress refers to individuals in a state of long-term chronic stress. However, the relationship between NDUFB11 and NDUFS3 and atherosclerosis and chronic stress is unclear.

METHOD

The atherosclerosis with chronic stress group data file was used. DEGs were screened and WGCNA was performed. Construction and analysis of PPI Network. Functional enrichment analysis, GSEA, gene expression heatmap, immune infiltration analysis and mRNA analysis were performed. CTD was used to find diseases most related to core genes. WB was performed. TargetScan was used to screen miRNAs of DEGs.

RESULTS

1708 DEGs were identified. According to GO analysis, they were mainly enriched in catabolic processes, organic acid metabolism processes, carboxylic acid metabolism processes. KEGG analysis showed that they were mainly enriched in metabolic pathways, fatty acid metabolism, pentose phosphate pathway, glycolysis / gluconeogenesis, fructose and mannose metabolism. Gene expression heatmap showed that the core genes (NDUFB11, NDUFS3) were lowly expressed in samples of those with atherosclerosis accompanied by chronic stress and highly expressed in the normal samples. NDUFB11 and NDUFS3 were associated with necrosis, hyperplasia, inflammation, renal disease, weight loss, memory impairment, and cognitive impairment. WB showed that the expression level of NDUFS3 in atherosclerosis and chronic stress was lower than that in control group.

CONCLUSIONS

NDUFB11 and NDUFS3 are underexpressed in atherosclerosis and chronic stress; the lower NDUFB11 and NDUFS3 levels, the worse the prognosis.

CEACAM1 as a molecular target in oral cancer

OBJECTIVE

The majority of oral cancer is caused by malignant transformation of squamous cells in surface of the oral mucosa. However, the relationship between CEACAM1 and oral cancer is unclear.

METHODS

GSE23558 and GSE25099 profiles were downloaded from gene expression omnibus (GEO). Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis (WGCNA) was performed. Construction and analysis of protein-protein interaction (PPI) Network. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG), gene set enrichment analysis (GSEA), gene expression heatmap, immune infiltration analysis, comparative toxicogenomics database (CTD) were performed. TargetScan screened miRNAs that regulated central DEGs. Western blotting (WB) experiment was performed.

RESULTS

1269 DEGs were identified. According to GO analysis, they were mainly enriched in same protein binding, signal receptor binding, cell surface, epithelial cell development. KEGG analysis showed that they were mainly enriched in cancer pathways, PI3K Akt signaling pathway, TNF signaling pathway, NF kappa B signaling pathway, TGF beta signaling pathway. PPI network showed that 11 genes (CDCA8, CCNA2, MELK, KIF2C, CDC45, HMMR, TPX2, CENPF, CDK1, CEP55, CEACAM1) were obtained. Gene expression heatmap showed that CEP55 and MELK were highly expressed in oral cancer samples. CEACAM1 was lowly expressed in oral cancer samples. CEACAM1, CEP55 and MELK were involved in tumor, inflammation, necrosis, and proliferation. Western blotting (WB) showed that CEACAM1 in oral cancer samples was lower than that in normal samples, after CEACAM1 knockdown, it was lower than that in oral cancer samples.

CONCLUSION

CEACAM1 is lowly expressed in oral cancer, the lower CEACAM1, the worse prognosis.

Optimization of Mass Spectrometric Parameters in Data Dependent Acquisition for Untargeted Metabolomics on the Basis of Putative Assignments

Optimization of mass spectrometric parameters for a data dependent acquisition (DDA) experiment is essential to increase the MS/MS coverage and hence increase metabolite identifications in untargeted metabolomics. We explored the influence of mass spectrometric parameters including mass resolution, radio frequency (RF) level, signal intensity threshold, number of MS/MS events, cycle time, collision energy, maximum ion injection time (MIT), dynamic exclusion, and automatic gain control (AGC) target value on metabolite annotations on an Exploris 480-Orbitrap mass spectrometer. Optimal annotation results were obtained by performing ten data dependent MS/MS scans with a mass isolation window of 2.0 m/z and a minimum signal intensity threshold of 1 × 104 at a mass resolution of 180,000 for MS and 30,000 for MS/MS, while maintaining the RF level at 70%. Furthermore, combining an AGC target value of 5 × 106 and MIT of 100 ms for MS and an AGC target value of 1 × 105 and an MIT of 50 ms for MS/MS scans provided an improved number of annotated metabolites. A 10 s exclusion duration and a two stepped collision energy were optimal for higher spectral quality. These findings confirm that MS parameters do influence metabolomics results, and propose strategies for increasing metabolite coverage in untargeted metabolomics. A limitation of this work is that our parameters were only optimized for one RPLC method on single matrix and may be different for other protocols. Additionally, no metabolites were identified at level 1 confidence. The results presented here are based on metabolite annotations and need to be validated with authentic standards.

NMR-based metabolomic profiling identifies inflammation and muscle-related metabolites as predictors of incident type 2 diabetes mellitus beyond glucose: the Di@bet.es study

AIMS

The aim of this study was to combine nuclear magnetic resonance-based metabolomics and machine learning to find a glucose-independent molecular signature associated with future type 2 diabetes mellitus development in a subgroup of individuals from the Di@bet.es study.

METHODS

The study group included 145 individuals developing type 2 diabetes mellitus during the 8-year follow-up, 145 individuals matched by age, sex and BMI who did not develop diabetes during the follow-up but had equal glucose concentrations to those who did and 145 controls matched by age and sex. A metabolomic analysis of serum was performed to obtain the lipoprotein and glycoprotein profiles and 15 low molecular weight metabolites. Several machine learning-based models were trained.

RESULTS

Logistic regression performed the best classification between individuals developing type 2 diabetes during the follow-up and glucose-matched individuals. The area under the curve was 0.628, and its 95% confidence interval was 0.510-0.746. Glycoprotein-related variables, creatinine, creatine, small HDL particles and the Johnson-Neyman intervals of the interaction of Glyc A and Glyc B were statistically significant.

CONCLUSIONS

The model highlighted a relevant contribution of inflammation (glycosylation pattern and HDL) and muscle (creatinine and creatine) in the development of type 2 diabetes as independent factors of hyperglycemia.

Multi-omics in Crohn's disease: New insights from inside

Crohn's disease (CD) is an inflammatory bowel disease (IBD) with complex clinical manifestations such as chronic diarrhea, weight loss and hematochezia. Despite the increasing incidence worldwide, cure of CD remains extremely difficult. The rapid development of high-throughput sequencing technology with integrated-omics analyses in recent years has provided a new means for exploring the pathogenesis, mining the biomarkers and designing targeted personalized therapeutics of CD. Host genomics and epigenomics unveil heredity-related mechanisms of susceptible individuals, while microbiome and metabolomics map host-microbe interactions in CD patients. Proteomics shows great potential in searching for promising biomarkers. Nonetheless, single omics technology cannot holistically connect the mechanisms with heterogeneity of pathological behavior in CD. The rise of multi-omics analysis integrates genetic/epigenetic profiles with protein/microbial metabolite functionality, providing new hope for comprehensive and in-depth exploration of CD. Herein, we emphasized the different omics features and applications of CD and discussed the current research and limitations of multi-omics in CD. This review will update and deepen our understanding of CD from integration of broad omics spectra and will provide new evidence for targeted individualized therapeutics.

Metabolic signature of healthy lifestyle and risk of rheumatoid arthritis: observational and Mendelian randomization study

BACKGROUND

While substantial evidence reveals that healthy lifestyle behaviors are associated with a lower risk of rheumatoid arthritis (RA), the underlying metabolic mechanisms remain unclear.

OBJECTIVES

This study aimed to identify the metabolic signature reflecting a healthy lifestyle and investigate its observational and genetic linkage with RA risk.

METHODS

This study included 87,258 UK Biobank participants (557 cases of incident RA) aged 37 to 73 years with complete lifestyle, genotyping and nuclear magnetic resonance (NMR) metabolomics data. A healthy lifestyle was assessed based on five factors: healthy diet, regular exercise, not smoking, moderate alcohol consumption, and normal body mass index. The metabolic signature was developed by summing selected metabolites' concentrations weighted by the coefficients using elastic net regression. We used multivariate Cox model to assess the associations between metabolic signatures and RA risk, and examined the mediating role of the metabolic signature in the impact of a healthy lifestyle on RA. We performed genome-wide association analysis (GWAS) to obtain genetic variants associated with the metabolic signature, then conducted Mendelian randomization (MR) analyses to detect causality.

RESULTS

The metabolic signature comprised of 81 metabolites, robustly correlated with healthy lifestyle ( r = 0.45, P = 4.2 × 10^-15). The metabolic signature was inversely associated with RA risk (HR per SD increment: 0.76, 95% CI: 0.70-0.83), and largely explained protective effects of healthy lifestyle on RA with 64% (95%CI: 50.4-83.3) mediation proportion. One and two-sample MR analyses also consistently showed the associations of genetically inferred per SD increment in metabolic signature with a reduction in RA risk (HR: 0.84, 95% CI: 0.75-0.94, P = 0.002 and OR: 0.84, 95% CI: 0.73-0.97, P = 0.02 respectively).

CONCLUSION

Our findings implicate the metabolic signature reflecting healthy lifestyle as a potential causal mediator in the development of RA, highlighting the importance of early lifestyle intervention and metabolic tracking for precise prevention of RA.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Analyses of widely targeted metabolic profiling reveals mechanisms of metabolomic variations during Tibetan sheep (Ovis aries) testis development

In mammals, the testis is the organ with the highest transcriptional activity. After gene transcription, translation, and post-translational protein modification, the transcriptional results are finally presented at the metabolic level. Metabolites not only essential for cell signaling and energy transfer, but also directly influenced by the physiological and pathological changes in tissues and accurately reflect the physiological changes. The fact that the testes are oxygen-deprived organs can explain why Sertoli cells and germ cells may use distinctive metabolic pathways to obtain energy in their different stages of development. Therefore, studying metabolic changes during testis development can better elucidate metabolic profile of the testis, which is essential to revealing characteristic metabolic pathways. The present study applied a widely targeted UPLC-MS/MS-based metabolomics approach with large-scale detection, identification and quantification to investigate the widespread metabolic changes during Tibetan sheep testis development. Firstly, a total of 847 metabolites were detected in the sheep testis, and their changes along with the three testis-development stages were further investigated. The results indicated that those metabolites were clustered into amino acids and their derivatives, carbohydrates and their derivatives, organic acids and their derivatives, benzene and substituted derivatives, alcohols and amines, lipids, nucleotides and their derivatives, bile acids, coenzymes and vitamins, hormones and hormone-related compounds, etc. Among them, the most abundant metabolites in the testis were amino acids and lipid metabolites. The results showed that most of the lipids, carbohydrates with their derivatives, as well as alcohol and amines metabolites were high in sexually immature sheep while organic acids, amino acids and nucleotides showed a continuously increasing trend along with testis development stages. Among them, the content of metabolites with antioxidant effects increased along with testis development, while those related with energy synthesis was downregulated with age. Further correlation analyses of each metabolite-metabolite pair emphasized the cross talk between differential metabolisms across testis development, suggesting a significant correlation between lipids and other metabolites. Finally, based on KEGG pathway analysis, we found that the metabolic pathways in Tibetan sheep testis development were mainly clustered into energy metabolism, gonadal development, and anti-oxidative stress. Reactive oxygen species (ROS) are by-products of normal cellular metabolism and are inevitable during testicular energy metabolism. Thus, the anti-oxidative stress function is a key process in maintaining the normal physiological function of testis. These results contributed to a broader view of the testis metabolome and a comprehensive analysis on metabolomic variation among different testis-development stages, providing a theoretical basis for us to understand the sheep testis metabolic mechanism.

Expression analysis of human glioma susceptibility gene and P53 in human glioma and its clinical significance based on bioinformatics

Background

The exact mechanism of glioblastoma multiforme (GBM) remains unclear. This study was to clarify the expression of P53 in glioma and its molecular mechanism, and to explore the possibility of P53 as a potential therapeutic target of glioma and its clinical application value, so as to provide a new theoretical basis for the treatment of glioma.

Methods

Firstly, a dataset was established to analyze the expression of P53 in different stages of glioma and its relationship with prognosis by using The Cancer Genome Atlas (TCGA) database, RNA-seq data, and survival data of glioma and normal control samples in gene expression profiling and interactive analysis (GEPIA). The genes co-expressed with P53 were screened out, their differential expression between glioma and normal control group was analyzed, and their functions were analyzed by enrichment analysis. The TGGA database was used for data verification and analysis. The correlation between P53 expression and clinicopathological parameters was analyzed. Kaplan-Meier survival analysis was used to analyze the relationship between P53 expression and overall survival (OS) and progression-free survival (PFS) of glioma patients, and Cox regression analysis was used to analyze the independent factors affecting OS and PFS of glioma patients.

Results

The results of TCGA data analysis were as follows: The expression level of P53 was different from that of different stages of glioma, namely, the expression level of P53 between grade II and grade III, grade III and grade IV, and grade II and grade IV were significantly different (P<0.05). The results of P53 gene-related survival analysis showed that KNL1 high expression and low expression were significantly different in OS, and the high expression group was associated with poor prognosis (P<0.05).

Conclusions

The P53 expression can be an effective biological indicator of poor prognosis of glioma.