Molecular exploration of the diurnal alteration of glycogen structural fragility and stability in time-restricted-feeding mouse liver.

The structure of glycogen α particles in healthy mouse liver has two states: stability and fragility. In contrast, glycogen α particles in diabetic liver present consistent fragility, which may exacerbate hyperglycemia. Currently, the molecular mechanism behind glycogen structural alteration is still unclear. In this study, we characterized the fine molecular structure of liver glycogen α particles in healthy mice under time-restricted feeding (TRF) mode during a 24-h cycle. Then, differentially expressed genes (DEGs) in the liver during daytime and nighttime were revealed via transcriptomics, which identified that the key downregulated DEGs were mainly related to insulin secretion in daytime. Furthermore, GO annotation and KEGG pathway enrichment found that negative regulation of the glycogen catabolic process and insulin secretion process were significantly downregulated in the daytime. Therefore, transcriptomic analyses indicated that the structural stability of glycogen α particles might be correlated with the glycogen degradation process via insulin secretion downregulation. Further molecular experiments confirmed the significant upregulation of glycogen phosphorylase (PYGL), phosphorylated PYGL (p-PYGL), and glycogen debranching enzyme (AGL) at the protein level during the daytime. Overall, we concluded that the downregulation of insulin secretion in the daytime under TRF mode facilitated glycogenolysis, contributing to the structural stability of glycogen α-particles.

Pseudo-Siamese network combined with label-free Raman spectroscopy for the quantification of mixed trace amounts of antibiotics in human milk: A feasibility study.

The utilization of antibiotics is prevalent among lactating mothers. Hence, the rapid determination of trace amounts of antibiotics in human milk is crucial for ensuring the healthy development of infants. In this study, we constructed a human milk system containing residual doxycycline (DXC) and/or tetracycline (TC). Machine learning models and clustering algorithms were applied to classify and predict deficient concentrations of single and mixed antibiotics via label-free SERS spectra. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.85% across optimal hyperparameter combinations. Furthermore, we employed Independent Component Analysis (ICA) and the pseudo-Siamese Convolutional Neural Network (pSCNN) to quantify the ratios of individual antibiotics in mixed human milk samples. Integrating the SERS technique with machine learning algorithms shows significant potential for rapid discrimination and precise quantification of single and mixed antibiotics at deficient concentrations in human milk.

Rapid discrimination and ratio quantification of mixed antibiotics in aqueous solution through integrative analysis of SERS spectra via CNN combined with NN-EN model.

INTRODUCTION: Abusing antibiotic residues in the natural environment has become a severe public health and ecological environmental problem. The side effects of its biochemical and physiological consequences are severe. To avoid antibiotic contamination in water, implementing universal and rapid antibiotic residue detection technology is critical to maintaining antibiotic safety in aquatic environments. Surface-enhanced Raman spectroscopy (SERS) provides a powerful tool for identifying small molecular components with high sensitivity and selectivity. However, it remains a challenge to identify pure antibiotics from SERS spectra due to coexisting components in the mixture. OBJECTIVES: In this study, an intelligent analysis model for the SERS spectrum based on a deep learning algorithm was proposed for rapid identification of the antibiotic components in the mixture and quantitative determination of the ratios of these components. METHODS: We established a water environment system containing three antibiotic residues of ciprofloxacin, doxycycline, and levofloxacin. To facilitate qualitative and quantitative analysis of the SERS spectra antibiotic mixture datasets, we developed a computational framework integrating a convolutional neural network (CNN) and a non-negative elastic network (NN-EN) method. RESULTS: The experimental results demonstrate that the CNN model has a recognition accuracy of 98.68%, and the interpretation analysis of Shapley Additive exPlanations (SHAP) shows that our model can specifically focus on the characteristic peak distribution. In contrast, the NN-EN model can accurately quantify each component's ratio in the mixture. CONCLUSION: Integrating the SERS technique assisted by the CNN combined with the NN-EN model exhibits great potential for rapid identification and high-precision quantification of antibiotic residues in aquatic environments.

The global landscape of immune-derived lncRNA signature in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a highly heterogeneous cancer. This heterogeneity has an impact on the efficacy of immunotherapy. Long noncoding RNAs (lncRNAs) have been found to play regulatory functions in cancer immunity. However, the global landscape of immune-derived lncRNA signatures has not yet been explored in colorectal cancer. METHODS: In this study, we applied DESeq2 to identify differentially expressed lncRNAs in colon cancer. Next, we performed an integrative analysis to globally identify immune-driven lncRNA markers in CRC, including immune-associated pathways, tumor immunogenomic features, tumor-infiltrating immune cells, immune checkpoints, microsatellite instability (MSI) and tumor mutation burden (TMB). RESULTS: We also identified dysregulated lncRNAs, such as LINC01354 and LINC02257, and their clinical relevance in CRC. Our findings revealed that the differentially expressed lncRNAs were closely associated with immune pathways. In addition, we found that RP11-354P11.3 and RP11-545G3.1 had the highest association with the immunogenomic signature. As a result, these signatures could serve as markers to assess immunogenomic activity in CRC. Among the immune cells, resting mast cells and M0 macrophages had the highest association with lncRNAs in CRC. The AC006129.2 gene was significantly associated with several immune checkpoints, for example, programmed cell death protein 1 (PD-1) and B and T lymphocyte attenuator (BTLA). Therefore, the AC006129.2 gene could be targeted to regulate the condition of immune cells or immune checkpoints to enhance the efficacy of immunotherapy in CRC patients. Finally, we identified 15 immune-related lncRNA-generated open reading frames (ORFs) corresponding to 15 cancer immune epitopes. CONCLUSION: In conclusion, we provided a genome-wide immune-driven lncRNA signature for CRC that might provide new insights into clinical applications and immunotherapy.

Structural abnormality of hepatic glycogen in rat liver with diethylnitrosamine-induced carcinogenic injury.

Growing evidence confirms associations between glycogen metabolic re-wiring and the development of liver cancer. Previous studies showed that glycogen structure changes abnormally in liver diseases such as cystic fibrosis, diabetes, etc. However, few studies focus on glycogen molecular structural characteristics during liver cancer development, which is worthy of further exploration. In this study, a rat model with carcinogenic liver injury induced by diethylnitrosamine (DEN) was successfully constructed, and hepatic glycogen structure was characterized. Compared with glycogen structure in the healthy rat liver, glycogen chain length distribution (CLD) shifts towards a short region. In contrast, glycogen particles were mainly present in small-sized ß particles in DEN-damaged carcinogenic rat liver. Comparative transcriptomic analysis revealed significant expression changes of genes and pathways involved in carcinogenic liver injury. A combination of transcriptomic analysis, RT-qPCR, and western blot showed that the two genes, Gsy1 encoding glycogen synthase and Gbe1 encoding glycogen branching enzyme, were significantly altered and might be responsible for the structural abnormality of hepatic glycogen in carcinogenic liver injury. Taken together, this study confirmed that carcinogenic liver injury led to structural abnormality of hepatic glycogen, which provided clues to the future development of novel drug targets for potential therapeutics of carcinogenic liver injury.

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms.

Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings. Graphical abstract.

Identification of geographic origins of Morus alba Linn. through surfaced enhanced Raman spectrometry and machine learning algorithms.

The leaves of Morus alba Linn., which is also known as white mulberry, have been commonly used in many of traditional systems of medicine for centuries. In traditional Chinese medicine (TCM), mulberry leaf is mainly used for anti-diabetic purpose due to its enrichment in bioactive compounds such as alkaloids, flavonoids and polysaccharides. However, these components are variable due to the different habitats of the mulberry plant. Therefore, geographic origin is an important feature because it is closely associated with bioactive ingredient composition that further influences medicinal qualities and effects. As a low-cost and non-invasive method, surface enhanced Raman spectrometry (SERS) is able to generate the overall fingerprints of chemical compounds in medicinal plants, which holds the potential for the rapid identification of their geographic origins. In this study, we collected mulberry leaves from five representative provinces in China, namely, Anhui, Guangdong, Hebei, Henan and Jiangsu. SERS spectrometry was applied to characterize the fingerprints of both ethanol and water extracts of mulberry leaves, respectively. Through the combination of SERS spectra and machine learning algorithms, mulberry leaves were well discriminated with high accuracies in terms of their geographic origins, among which the deep learning algorithm convolutional neural network (CNN) showed the best performance. Taken together, our study established a novel method for predicting the geographic origins of mulberry leaves through the combination of SERS spectra with machine learning algorithms, which strengthened the application potential of the method in the quality evaluation, control and assurance of mulberry leaves.

Comparative transcriptome analysis of diurnal alterations of liver glycogen structure: A pilot study.

Molecular mechanisms behind structural alterations between fragile and stable glycogen α particles in liver are not clear yet. In this pilot study, we re-examined the diurnal alterations of glycogen structure from the perspective of liver tissue transcriptome. By comparing the structures of liver glycogen from mice at 12 am, 8 am, 12 pm, and 8 pm (light-on: 6 am; light-off: 6 pm), we re-confirmed that the liver glycogen was fragile at 12 am and 8 am and stable at 12 pm and 8 pm as previously reported. The structural differences of glycogen particles at 12 am and 12 pm were thoroughly compared via transcriptomics. Differentially expressed genes (DEGs) with statistical significance were identified, while expression level of the gene ppp1r3g (log2Fold_Change = -6.368, P-value = 2.89E-04) that encoded PPP1R3G with glycogen binding domain was most significantly changed, which provided preliminary clues to the structural alterations of glycogen α particles during the diurnal cycle.

Bioinformatics and Experimental Analysis of the Prognostic and Predictive Value of the CHPF Gene on Breast Cancer.

Background: Recent studies in the United States have shown that breast cancer accounts for 30% of all new cancer diagnoses in women and has become the leading cause of cancer deaths in women worldwide. Chondroitin Polymerizing Factor (CHPF), is an enzyme involved in chondroitin sulfate (CS) elongation and a novel key molecule in the poor prognosis of many cancers. However, its role in the development and progression of breast cancer remains unclear. Methods: The transcript expression of CHPF in the Cancer Genome Atlas-Breast Cancer (TCGA-BRCA), Gene Expression Omnibus (GEO) database was analyzed separately using the limma package of R software, and the relationship between CHPF transcriptional expression and CHPF DNA methylation was investigated in TCGA-BRCA. Kaplan-Meier curves were plotted using the Survival package to further assess the prognostic impact of CHPF DNA methylation/expression. The association between CHPF transcript expression/DNA methylation and cancer immune infiltration and immune markers was investigated using the TIMER and TISIDB databases. We also performed gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis with the clusterProfiler package. Western blotting and RT-PCR were used to verify the protein level and mRNA level of CHPF in breast tissue and cell lines, respectively. Small interfering plasmids and lentiviral plasmids were constructed for transient and stable transfection of breast cancer cell lines MCF-7 and SUM1315, respectively, followed by proliferation-related functional assays, such as CCK8, EDU, clone formation assays; migration and invasion-related functional assays, such as wound healing assay and transwell assays. We also conducted a preliminary study of the mechanism. Results: We observed that CHPF was significantly upregulated in breast cancer tissues and correlated with poor prognosis. CHPF gene transcriptional expression and methylation are associated with immune infiltration immune markers. CHPF promotes proliferation, migration, invasion of the breast cancer cell lines MCF-7 and SUM1315, and is significantly enriched in pathways associated with the ECM-receptor interaction and PI3K-AKT pathway. Conclusion: CHPF transcriptional expression and DNA methylation correlate with immune infiltration and immune markers. Upregulation of CHPF in breast cancer promotes malignant behavior of cancer cells and is associated with poorer survival in breast cancer, possibly through ECM-receptor interactions and the PI3K-AKT pathway.

Pretreatment of Huaiqihuang extractum protects against cisplatin-induced nephrotoxicity.

Cisplatin is a commonly used chemotherapeutic agent in the treatment of different types of malignant tumors, but nephrotoxicity limits its usage. Therefore, in this study, we aimed to determine the possible protective effect of Huaiqihuang (HQH) extractum, a kind of Chinese herbal complex that consists of Trametes robiniophila Murr., Lycium barbarum and Polygonatum sibiricum, against nephrotoxicity induced by cisplatin in mice. We found that pretreatment with HQH significantly attenuated the cisplatin-induced increase in blood urea nitrogen (BUN), interstitial congestion, acute renal tubular injury and tubular cell apoptosis and necroptosis. It was further shown that HQH administration reduced cisplatin-induced release and nuclear-cytoplasmic translocation of HMGB1 and inactivated its downstream signaling molecules, TLR4 and NFκB, in renal tubular cells; as a result, HQH repressed cisplatin-induced TNF-α production. As dexamethasone (Dex) exerts renoprotective effects in severe Acute kidney injury (AKI), we compared it with HQH and found that HQH showed similar renoprotective effects to dexamethasone via similar mechanisms. Considering the potential side effects of corticosteroids, reducing the effectiveness of treatment and shortening survival in solid tumor patients, we suggest administration of HQH as a potential adjuvant for cisplatin therapy in solid tumor patients to preserve renal function.

Adipose-derived mesenchymal stem cells employed exosomes to attenuate AKI-CKD transition through tubular epithelial cell dependent Sox9 activation.

Acute kidney injury (AKI) predisposes patients to an increased risk into progressive chronic kidney disease (CKD), however effective treatments are still elusive. This study aimed to investigate the therapeutic efficacy of human adipose-derived MSCs (hAD-MSCs) in the prevention of AKI-CKD transition, and illuminate the role of Sox9, a vital transcription factor in the development of kidney, in this process. C57BL/6 mice were subjected to unilateral renal ischemia/reperfusion (I/R) with or without hAD-MSC treatment. We found that hAD-MSC treatment upregulated the expression of tubular Sox9, promoted tubular regeneration, attenuated AKI, and mitigated subsequent renal fibrosis. However, these beneficial effects were abolished by a drug inhibiting the release of exosomes from hAD-MSCs. Similarly, Sox9 inhibitors reversed these protective effects. Further, we verified that hAD-MSCs activated tubular Sox9 and prevented TGF-ß1-induced transformation of TECs into pro-fibrotic phenotype through exosome shuttling in vitro, but the cells did not inhibit TGF-ß1-induced transition of fibroblasts into myofibroblasts. Inhibiting the release of exosomes from hAD-MSCs or the expression of Sox9 in TECs reversed these antifibrotic effects. In conclusion, hAD-MSCs employed exosomes to mitigate AKI-CKD transition through tubular epithelial cell dependent activation of Sox9.

Melatonin promoted renal regeneration in folic acid-induced acute kidney injury via inhibiting nucleocytoplasmic translocation of HMGB1 in tubular epithelial cells.

Melatonin (N-acetyl-5-methoxytryptamine), a circadian-regulating hormone, has been reported to exert a protective role during acute kidney injury (AKI) induced by renal ischemia-reperfusion injury (I/R). High-mobility group box 1 (HMGB1) is a novel member of the damage-associated molecular pattern (DAMP) family, and has been verified to be an inflammatory cytokine mediating AKI induced by I/R and cisplatin. However, the effect of melatonin on HMGB1, as well as the relationship of these two with folic acid induced AKI are elusive. In this study, we sought to identify the role of melatonin on folic acid induced AKI and its association with HMGB1. Pretreatment with melatonin significantly attenuated folic acid-induced increase in serum creatinine and BUN levels, renal tubular epithelial cell (TEC) apoptosis, and the infiltration of inflammatory cells and secretion of cytokines. Moreover, melatonin pretreatment promoted renal tubular proliferation and improved cell cycle arrest of TECs after folic acid-induced renal damage. This protective role of melatonin was closely related to the inhibition of nucleocytoplasmic translocation of HMGB1 in TECs. These data provide a strong proof that administering melatonin prior to folic acid insult may shed light on a potential treatment for AKI.

Continuous AMD3100 Treatment Worsens Renal Fibrosis through Regulation of Bone Marrow Derived Pro-Angiogenic Cells Homing and T-Cell-Related Inflammation.

AMD3100 is a small molecule inhibitor of chemokine receptor type 4 (CXCR4), which is located in the cell membranes of CD34+ cells and a variety of inflammatory cells and has been reported to reduce organ fibrosis in the lung, liver and myocardium. However, the effect of AMD3100 on renal fibrosis is unknown. This study investigated the impact of AMD3100 on renal fibrosis. C57bl/6 mice were subjected to unilateral ureteral obstruction (UUO) surgery with or without AMD3100 administration. Tubular injury, collagen deposition and fibrosis were detected and analyzed by histological staining, immunocytochemistry and Western Blot. Bone marrow derived pro-angiogenic cells (CD45+, CD34+ and CD309+ cells) and capillary density (CD31+) were measured by flow cytometry (FACS) and immunofluorescence (IF). Inflammatory cells, chemotactic factors and T cell proliferation were characterized. We found that AMD3100 treatment did not alleviate renal fibrosis but, rather, increased tissue damage and renal fibrosis. Continuous AMD3100 administration did not improve bone marrow derived pro-angiogenic cells mobilization but, rather, inhibited the migration of bone marrow derived pro-angiogenic cells into the fibrotic kidney. Additionally, T cell infiltration was significantly increased in AMD3100-treated kidneys compared to un-treated kidneys. Thus, treatment of UUO mice with AMD3100 led to an increase in T cell infiltration, suggesting that AMD3100 aggravated renal fibrosis.