TXNIP deficiency attenuates renal fibrosis by modulating mTORC1/TFEB-mediated autophagy in diabetic kidney disease

Thioredoxin-interacting protein (TXNIP) is an important regulatory protein for thioredoxin (TRX) that elicits the generation of reactive oxygen species (ROS) by inhibiting the redox function of TRX. Abundant evidence suggests that TXNIP is involved in the fibrotic process of diabetic kidney disease (DKD). However, the potential mechanism of TXNIP in DKD is not yet well understood. In this study, we found that TXNIP knockout suppressed renal fibrosis and activation of mammalian target of rapamycin complex 1 (mTORC1) and restored transcription factor EB (TFEB) and autophagy activation in diabetic kidneys. Simultaneously, TXNIP interference inhibited epithelial-to-mesenchymal transformation (EMT), collagen I and fibronectin expression, and mTORC1 activation, increased TFEB nuclear translocation, and promoted autophagy restoration in HK-2 cells exposed to high glucose (HG). Rapamycin, an inhibitor of mTORC1, increased TFEB nuclear translocation and autophagy in HK-2 cells under HG conditions. Moreover, the TFEB activators, curcumin analog C1 and trehalose, effectively restored HG-induced autophagy, and abrogated HG-induced EMT and collagen I and fibronectin expression in HK-2 cells. Taken together, these findings suggest that TXNIP deficiency ameliorates renal fibrosis by regulating mTORC1/TFEB-mediated autophagy in diabetic kidney diseases.</p

Image_1_Brain region-specific genome-wide deoxyribonucleic acid methylation analysis in patients with Alzheimer’s disease.JPEG

ObjectiveAlzheimer’s disease (AD) is a neurodegenerative disease characterized by neuropathology and cognitive decline and associated with age. The comprehensive deoxyribonucleic acid methylation (DNAm)-transcriptome profile association analysis conducted in this study aimed to establish whole-genome DNAm profiles and explore DNAm-related genes and their potential functions. More appropriate biomarkers were expected to be identified in terms of AD.Materials and methodsIllumina 450KGSE59685 dataset AD (n = 54) and HC (n = 21) and ribonucleic-acid-sequencing data GSE118553 dataset AD patients (n = 21) and HCs (n = 13) were obtained from the gene expression omnibus database before a comprehensive DNAm-transcriptome profile association analysis, and we performed functional enrichment analysis by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses (KEGG). Three transgenic mice and three wild-type mice were used to validate the hub genes.ResultsA total of 18,104 DNAm sites in healthy controls (n = 21) and AD patients (n = 54) were surveyed across three brain regions (superior temporal gyrus, entorhinal cortex, and dorsolateral prefrontal cortex). With the addition of the transcriptome analysis, eight hypomethylated-related highly expressed genes and 61 hypermethylated-related lowly expressed genes were identified. Based on 69 shared differentially methylated genes (DMGs), the function enrichment analysis indicated Guanosine triphosphate enzymes (GTPase) regulator activity, a synaptic vesicle cycle, and tight junction functioning. Following this, mice-based models of AD were constructed, and five hub DMGs were verified, which represented a powerful, disease-specific DNAm signature for AD.ConclusionThe results revealed that the cross-brain region DNAm was altered in those with AD. The alterations in DNAm affected the target gene expression and participated in the key biological processes of AD. The study provides a valuable epigenetic resource for identifying DNAm-based diagnostic biomarkers, developing effective drugs, and studying AD pathogenesis.</p

Image_3_Brain region-specific genome-wide deoxyribonucleic acid methylation analysis in patients with Alzheimer’s disease.JPEG

ObjectiveAlzheimer’s disease (AD) is a neurodegenerative disease characterized by neuropathology and cognitive decline and associated with age. The comprehensive deoxyribonucleic acid methylation (DNAm)-transcriptome profile association analysis conducted in this study aimed to establish whole-genome DNAm profiles and explore DNAm-related genes and their potential functions. More appropriate biomarkers were expected to be identified in terms of AD.Materials and methodsIllumina 450KGSE59685 dataset AD (n = 54) and HC (n = 21) and ribonucleic-acid-sequencing data GSE118553 dataset AD patients (n = 21) and HCs (n = 13) were obtained from the gene expression omnibus database before a comprehensive DNAm-transcriptome profile association analysis, and we performed functional enrichment analysis by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses (KEGG). Three transgenic mice and three wild-type mice were used to validate the hub genes.ResultsA total of 18,104 DNAm sites in healthy controls (n = 21) and AD patients (n = 54) were surveyed across three brain regions (superior temporal gyrus, entorhinal cortex, and dorsolateral prefrontal cortex). With the addition of the transcriptome analysis, eight hypomethylated-related highly expressed genes and 61 hypermethylated-related lowly expressed genes were identified. Based on 69 shared differentially methylated genes (DMGs), the function enrichment analysis indicated Guanosine triphosphate enzymes (GTPase) regulator activity, a synaptic vesicle cycle, and tight junction functioning. Following this, mice-based models of AD were constructed, and five hub DMGs were verified, which represented a powerful, disease-specific DNAm signature for AD.ConclusionThe results revealed that the cross-brain region DNAm was altered in those with AD. The alterations in DNAm affected the target gene expression and participated in the key biological processes of AD. The study provides a valuable epigenetic resource for identifying DNAm-based diagnostic biomarkers, developing effective drugs, and studying AD pathogenesis.</p

Image_2_Brain region-specific genome-wide deoxyribonucleic acid methylation analysis in patients with Alzheimer’s disease.JPEG

ObjectiveAlzheimer’s disease (AD) is a neurodegenerative disease characterized by neuropathology and cognitive decline and associated with age. The comprehensive deoxyribonucleic acid methylation (DNAm)-transcriptome profile association analysis conducted in this study aimed to establish whole-genome DNAm profiles and explore DNAm-related genes and their potential functions. More appropriate biomarkers were expected to be identified in terms of AD.Materials and methodsIllumina 450KGSE59685 dataset AD (n = 54) and HC (n = 21) and ribonucleic-acid-sequencing data GSE118553 dataset AD patients (n = 21) and HCs (n = 13) were obtained from the gene expression omnibus database before a comprehensive DNAm-transcriptome profile association analysis, and we performed functional enrichment analysis by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses (KEGG). Three transgenic mice and three wild-type mice were used to validate the hub genes.ResultsA total of 18,104 DNAm sites in healthy controls (n = 21) and AD patients (n = 54) were surveyed across three brain regions (superior temporal gyrus, entorhinal cortex, and dorsolateral prefrontal cortex). With the addition of the transcriptome analysis, eight hypomethylated-related highly expressed genes and 61 hypermethylated-related lowly expressed genes were identified. Based on 69 shared differentially methylated genes (DMGs), the function enrichment analysis indicated Guanosine triphosphate enzymes (GTPase) regulator activity, a synaptic vesicle cycle, and tight junction functioning. Following this, mice-based models of AD were constructed, and five hub DMGs were verified, which represented a powerful, disease-specific DNAm signature for AD.ConclusionThe results revealed that the cross-brain region DNAm was altered in those with AD. The alterations in DNAm affected the target gene expression and participated in the key biological processes of AD. The study provides a valuable epigenetic resource for identifying DNAm-based diagnostic biomarkers, developing effective drugs, and studying AD pathogenesis.</p

Data_Sheet_1_Brain region-specific genome-wide deoxyribonucleic acid methylation analysis in patients with Alzheimer’s disease.docx

ObjectiveAlzheimer’s disease (AD) is a neurodegenerative disease characterized by neuropathology and cognitive decline and associated with age. The comprehensive deoxyribonucleic acid methylation (DNAm)-transcriptome profile association analysis conducted in this study aimed to establish whole-genome DNAm profiles and explore DNAm-related genes and their potential functions. More appropriate biomarkers were expected to be identified in terms of AD.Materials and methodsIllumina 450KGSE59685 dataset AD (n = 54) and HC (n = 21) and ribonucleic-acid-sequencing data GSE118553 dataset AD patients (n = 21) and HCs (n = 13) were obtained from the gene expression omnibus database before a comprehensive DNAm-transcriptome profile association analysis, and we performed functional enrichment analysis by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses (KEGG). Three transgenic mice and three wild-type mice were used to validate the hub genes.ResultsA total of 18,104 DNAm sites in healthy controls (n = 21) and AD patients (n = 54) were surveyed across three brain regions (superior temporal gyrus, entorhinal cortex, and dorsolateral prefrontal cortex). With the addition of the transcriptome analysis, eight hypomethylated-related highly expressed genes and 61 hypermethylated-related lowly expressed genes were identified. Based on 69 shared differentially methylated genes (DMGs), the function enrichment analysis indicated Guanosine triphosphate enzymes (GTPase) regulator activity, a synaptic vesicle cycle, and tight junction functioning. Following this, mice-based models of AD were constructed, and five hub DMGs were verified, which represented a powerful, disease-specific DNAm signature for AD.ConclusionThe results revealed that the cross-brain region DNAm was altered in those with AD. The alterations in DNAm affected the target gene expression and participated in the key biological processes of AD. The study provides a valuable epigenetic resource for identifying DNAm-based diagnostic biomarkers, developing effective drugs, and studying AD pathogenesis.</p

Image_1_Deep analysis of skin molecular heterogeneities and their significance on the precise treatment of patients with psoriasis.jpeg

BackgroundPsoriasis is a highly heterogeneous autoinflammatory disease. At present, heterogeneity in disease has not been adequately translated into concrete treatment options. Our aim was to develop and verify a new stratification scheme that identifies the heterogeneity of psoriasis by the integration of large-scale transcriptomic profiles, thereby identifying patient subtypes and providing personalized treatment options whenever possible.MethodsWe performed functional enrichment and network analysis of upregulated differentially expressed genes using microarray datasets of lesional and non-lesional skin samples from 250 psoriatic patients. Unsupervised clustering methods were used to identify the skin subtypes. Finally, an Xgboost classifier was utilized to predict the effects of methotrexate and commonly prescribed biologics on skin subtypes.ResultsBased on the 163 upregulated differentially expressed genes, psoriasis patients were categorized into three subtypes (subtypes A–C). Immune cells and proinflammatory-related pathways were markedly activated in subtype A, named immune activation. Contrastingly, subtype C, named stroma proliferation, was enriched in integrated stroma cells and tissue proliferation-related signaling pathways. Subtype B was modestly activated in all the signaling pathways. Notably, subtypes A and B presented good responses to methotrexate and interleukin-12/23 inhibitors (ustekinumab) but inadequate responses to tumor necrosis factor-α inhibitors and interleukin-17A receptor inhibitors. Contrastly, subtype C exhibited excellent responses to tumor necrosis factor-α inhibitors (etanercept) and interleukin-17A receptor inhibitors (brodalumab) but not methotrexate and interleukin-12/23 inhibitors.ConclusionsPsoriasis patients can be assorted into three subtypes with different molecular and cellular characteristics based on the heterogeneity of the skin's immune cells and the stroma, determining the clinical responses of conventional therapies.</p

Additional file 1 of Genetically predicted obstructive sleep apnea is causally associated with an increased risk for periodontitis

Supplementary Material

Table_1_Deep analysis of skin molecular heterogeneities and their significance on the precise treatment of patients with psoriasis.docx

BackgroundPsoriasis is a highly heterogeneous autoinflammatory disease. At present, heterogeneity in disease has not been adequately translated into concrete treatment options. Our aim was to develop and verify a new stratification scheme that identifies the heterogeneity of psoriasis by the integration of large-scale transcriptomic profiles, thereby identifying patient subtypes and providing personalized treatment options whenever possible.MethodsWe performed functional enrichment and network analysis of upregulated differentially expressed genes using microarray datasets of lesional and non-lesional skin samples from 250 psoriatic patients. Unsupervised clustering methods were used to identify the skin subtypes. Finally, an Xgboost classifier was utilized to predict the effects of methotrexate and commonly prescribed biologics on skin subtypes.ResultsBased on the 163 upregulated differentially expressed genes, psoriasis patients were categorized into three subtypes (subtypes A–C). Immune cells and proinflammatory-related pathways were markedly activated in subtype A, named immune activation. Contrastingly, subtype C, named stroma proliferation, was enriched in integrated stroma cells and tissue proliferation-related signaling pathways. Subtype B was modestly activated in all the signaling pathways. Notably, subtypes A and B presented good responses to methotrexate and interleukin-12/23 inhibitors (ustekinumab) but inadequate responses to tumor necrosis factor-α inhibitors and interleukin-17A receptor inhibitors. Contrastly, subtype C exhibited excellent responses to tumor necrosis factor-α inhibitors (etanercept) and interleukin-17A receptor inhibitors (brodalumab) but not methotrexate and interleukin-12/23 inhibitors.ConclusionsPsoriasis patients can be assorted into three subtypes with different molecular and cellular characteristics based on the heterogeneity of the skin's immune cells and the stroma, determining the clinical responses of conventional therapies.</p