The regulatory effects of PTPN6 on inflammatory process: Reports from mice to men

Identification and experimental validation of genes associated with programmed cell death in dendritic cells of the thyroid tissue in Hashimoto's thyroiditis

INTRODUCTION

Hashimoto's thyroiditis (HT) is a chronic autoimmune disorder. As antigen-presenting cells, dendritic cells(DCs) play a pivotal role in inducing programmed cell death (PCD) types, contributing to immune disorders. This study aimed to identify genes associated with multiple PCD pathways in dendritic cells within the thyroid tissue of patients with HT.

METHODS

ce single-cell RNA-sequencing dataset HRA001684 was obtained from the National Genomics Data Center (NGDC) to calculate the area under the curve (AUC) scores for PCD-related genes. Additionally, mRNA sequencing datasets GSE138198 and HRA001684 were sourced from the Gene Expression Omnibus(GEO) and NGDC, respectively. Differentially expressed genes (DEGs) were identified by comparing normal and HT groups in GSE138198 and HRA001684. The intersection of these DEGs with PCD-related genes led to the identification of 17 PCD-related DEGs(PCDDEGs).

RESULTS

AUC scores revealed that DCs in HT exhibited significantly elevated levels of necroptosis, ferroptosis, pyroptosis, autophagy, and PANoptosis, expressing six key PCDDEGs: TNFAIP3, CYBB, PTPN6, STAT1, TGFB1, and NLRP3. These genes displayed an AUC>0.8 for HT in the GSE29315, GSE138198, and HRA001684 datasets, confirming their diagnostic accuracy. Moreover, their expression was positively correlated with the serum levels of thyroid peroxidase and thyroglobulin antibodies, while the expression of all PCDDEGs was negatively correlated with the abundance of thyroid follicular epithelial cells. qRT-PCR, WB, IHC, and IF experiments further confirmed the differences in PCDDEGs gene and protein levels in HT patients.

DISCUSSION

These findings highlight the crucial role of DCs in mediating PCD within the thyroid tissues of HT patients. The identified PCDDEGs-TNFAIP3, CYBB, PTPN6, STAT1, TGFB1, and NLRP3-may significantly contribute to HT pathogenesis through PCD pathways.

Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

Identification of a prognosis-related phagocytosis regulator gene signature in medulloblastoma

Objectives

The aims of this study were to screen for phagocytosis regulator-related genes in tissue samples from children with medulloblastoma (MB) and to construct a prognostic model based on those genes.

Methods

Differentially expressed genes between the MB and control groups were identified using the GSE50161 dataset from the Gene Expression Omnibus database. Prognosis-related phagocytosis regulator genes were selected from the GSE85217 dataset. Intersecting genes of the two datasets (differentially expressed prognosis-related phagocytosis regulator genes) were submitted to unsupervised cluster analysis to identify disease subtypes, after which the association between the subtypes and the immune microenvironment was analyzed. A prognostic risk score model was constructed, and functional, immune-related, and drug sensitivity analyses were performed.

Results

In total, 23 differentially expressed prognosis-related phagocytosis regulator genes were identified, from which two disease subtypes (clusters 1 and 2) were classified. The prognoses of the patients in cluster 2 were significantly worse than those of the patients in cluster 1. The immune microenvironment differed significantly between the two subtypes. Finally, 10 genes (FAM81A, EZR, NDUFB9, RCOR1, FOXO4, NHLRC2, KIF23, PTPN6, SMAGP, and MED13) were selected to establish the prognostic risk score model. The prognosis in the low-risk group was better than that in the high-risk group. The model genes NDUFB9 and PTPN6 were positively correlated with M2 macrophages.

Conclusion

Ten key phagocytosis regulator genes were screened to construct a prognostic model for MB. These genes may serve as key biomarkers for predicting the prognosis of patients with this type of brain cancer.

Identification and analysis of biomarkers associated with oxidative stress and ferroptosis in recurrent miscarriage

Recurrent miscarriage (RM) has a huge impact on women. Both oxidative stress and ferroptosis play an important role in the pathogenesis of RM. Hence, it was vital to screen the ferroptosis oxidation-related biomarkers for the diagnosis and treatment of RM. We introduced transcript data to screen out differentially expressed genes (DEGs) in RM. Ferroptosis oxidation-related differentially expressed genes were obtained by overlapping DEGs and oxidative stress related genes with correlations >0.9 with ferroptosis-related genes. Least Absolute Shrinkage and Selectionator operator regression and support vector machine based recursive feature elimination algorithm were implemented to screen feature genes. The biomarkers associated with ferroptosis oxidation were screened via receiver operating characteristic curve analysis. We finally analyzed the competing endogenous RNAs regulatory network and potential drugs of biomarkers. We identified 1047 DEGs in RM. Then, 9 ferroptosis oxidation-related differentially expressed genes were obtained via venn diagram. Subsequently, 8 feature genes (PTPN6, GJA1, HMOX1, CPT1A, CREB3L1, SNCA, EPAS1, and TGM2) were identified via machine learning. Moreover, 4 biomarkers associated with ferroptosis oxidation, including PTPN6, GJA1, CPT1A, and CREB3L1, were screened via receiver operating characteristic curve analysis. We constructed the '227 long noncoding RNAs-4 mRNAs-36 microRNAs' network, in which hsa-miR-635 was associated with CREB3L1 and PTPN6. There were 11 drugs with therapeutic potential on 3 biomarkers associated with ferroptosis oxidation. We also observed higher expression of CPT1A and CREB3L1 in RM group compared to the healthy control group by quantitative real-time reverse transcription polymerase chain reaction. Overall, we obtained 4 biomarkers (PTPN6, GJA1, CPT1A, and CREB3L1) associated with ferroptosis and oxidative stress, which laid a theoretical foundation for the diagnosis and treatment of RM.

YTHDC1 inhibits osteoclast differentiation to alleviate osteoporosis by enhancing PTPN6 messenger RNA stability in an m6A-hUR-dependent manner

YTHDC1 has been confirmed to mediate osteoporosis (OP) progression by regulating osteogenic differentiation. However, whether YTHDC1 mediates osteoclast differentiation and its molecular mechanism remains unclear. Quantitative real-time polymerase chain reaction and Western blot analysis were performed to detect the levels of YTHDC1, PTPN6, NFATc1, TRAP, RUNX2, alkaline phosphatase, and HUR. YTHDC1 knockout mice was constructed by CRISPR/Cas9 system, and the OP mice model was established by ovariectomy. Hematoxylin and eosin staining and micro-computed tomography were used to evaluate bone formation and bone mass. Mouse primary bone marrow macrophage cells were isolated and induced into osteoclasts. TRAP-positive cells were detected using TRAP staining. MeRIP-qPCR, RIP-qPCR assay, RNA affinity isolation assay, and co-immunoprecipitation assay were used to confirm the interactions among YTHDC1, PTPN6, and HUR. YTHDC1 expression was reduced and positively correlated with lumbar bone mineral density in OP patients. In the ovariectomy model of YTHDC1 knockout mice, bone formation was reduced, bone histomorphology was changed, and osteoclastic-related factor (NFATc1 and TRAP) levels were enhanced. Overexpression YTHDC1 inhibited osteoclast differentiation. YTHDC1 increased PTPN6 messenger RNA stability in an m6A-dependent manner. Moreover, YTHDC1 interacted with HUR to positively regulate PTPN6 expression. PTPN6 knockdown promoted osteoclast differentiation, and this effect was reversed by overexpressing HUR or YTHDC1. YTHDC1 was involved in regulating OP progression through inhibiting osteoclast differentiation by enhancing PTPN6 messenger RNA stability in an m6A-HUR-dependent manner.

DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge

The high-throughput genomic and proteomic scanning approaches allow investigators to measure the quantification of genome-wide genes (or gene products) for certain disease conditions, which plays an essential role in promoting the discovery of disease mechanisms. The high-throughput approaches often generate a large gene list of interest (GOIs), such as differentially expressed genes/proteins. However, researchers have to perform manual triage and validation to explore the most promising, biologically plausible linkages between the known disease genes and GOIs (disease signals) for further study. Here, to address this challenge, we proposed a network-based strategy DDK-Linker to facilitate the exploration of disease signals hidden in omics data by linking GOIs to disease knowns genes. Specifically, it reconstructed gene distances in the protein-protein interaction (PPI) network through six network methods (random walk with restart, Deepwalk, Node2Vec, LINE, HOPE, Laplacian) to discover disease signals in omics data that have shorter distances to disease genes. Furthermore, benefiting from the establishment of knowledge base we established, the abundant bioinformatics annotations were provided for each candidate disease signal. To assist in omics data interpretation and facilitate the usage, we have developed this strategy into an application that users can access through a website or download the R package. We believe DDK-Linker will accelerate the exploring of disease genes and drug targets in a variety of omics data, such as genomics, transcriptomics and proteomics data, and provide clues for complex disease mechanism and pharmacological research. DDK-Linker is freely accessible at http://ddklinker.ncpsb.org.cn/.

Cell-Specific Gene Networks and Drivers in Rheumatoid Arthritis Synovial Tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18,16,19,11 key regulators of fibroblast-like synoviocyte (FLS), T cells, B cells, and monocyte signatures and networks, respectively, in RA synovial tissues. Interestingly, FLS and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (synovial B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of NKT cell and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected KDG, TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

Dysregulation of testis mRNA expression levels in hatchery-produced vs wild greater amberjack Seriola dumerili

Reproductive dysfunctions have been recently documented in male greater amberjack Seriola dumerili caught from the wild and reared in captivity. In the present study, we compared testis transcriptome in wild fish (WILD), hatchery-produced fish with apparently normal spermatogenesis (Normal Farmed; NormalF) and hatchery-produced fish with evident reproductive dysfunction (Dysfunctional Farmed; DysF). Gene expression analysis identified 2157, 1985 and 74 differentially expressed genes (DEGs) in DysF vs WILD, NormalF vs DysF and NormalF vs WILD comparisons, respectively. In DysF, a dysregulation of several interconnected biological processes, including cell assembly, steroidogenesis and apoptosis was found. Gene enrichment of progesterone-mediated oocyte maturation, oocyte meiosis and cell cycle pathways were identified in the DysF vs NormalF comparison. Most of the DEGs involved in the enriched pathways were downregulated in DysF. The comparison of NormalF vs WILD showed that most of the DEGs were downregulated in NormalF, including a gene that encodes for a regulatory protein with a protective role in apoptosis regulation (ptpn6), indicating that spermatogenesis was dysfunctional also in the apparently "normal" hatchery-produced fish. Hence, rearing of male greater amberjack in captivity, from eggs produced by captive breeders, did not prevent the appearance of reproductive dysfunctions, and these dysfunctions involved several biological processes and metabolic pathways.

A novel murine model of pyoderma gangrenosum reveals that inflammatory skin-gut crosstalk is mediated by IL-1β-primed neutrophils

Pyoderma gangrenosum (PG) is a debilitating skin condition often accompanied by inflammatory bowel disease (IBD). Strikingly, ~40% of patients that present with PG have underlying IBD, suggesting shared but unknown mechanisms of pathogenesis. Impeding the development of effective treatments for PG is the absence of an animal model that exhibits features of both skin and gut manifestations. This study describes the development of the first experimental drug-induced mouse model of PG with concomitant intestinal inflammation. Topical application of pyrimidine synthesis inhibitors on wounded mouse skin generates skin ulcers enriched in neutrophil extracellular traps (NETs) as well as pro-inflammatory cellular and soluble mediators mimicking human PG. The mice also develop spontaneous intestinal inflammation demonstrated by histologic damage. Further investigations revealed increased circulating low density IL-1β primed neutrophils that undergo enhanced NETosis at inflamed tissue sites supported by an increase in circulatory citrullinated histone 3, a marker of aberrant NET formation. Granulocyte depletion dampens the intestinal inflammation in this model, further supporting the notion that granulocytes contribute to the skin-gut crosstalk in PG mice. We anticipate that this novel murine PG model will enable researchers to probe common disease mechanisms and identify more effective targets for treatment for PG patients with IBD.

Hereditable variants of classical protein tyrosine phosphatase genes: Will they prove innocent or guilty?

Protein tyrosine phosphatases, together with protein tyrosine kinases, control many molecular signaling steps that control life at cellular and organismal levels. Impairing alterations in the genes encoding the involved proteins is expected to profoundly affect the quality of life-if compatible with life at all. Here, we review the current knowledge on the effects of germline variants that have been reported for genes encoding a subset of the protein tyrosine phosphatase superfamily; that of the thirty seven classical members. The conclusion must be that the newest genome research tools produced an avalanche of data that suggest 'guilt by association' for individual genes to specific disorders. Future research should face the challenge to investigate these accusations thoroughly and convincingly, to reach a mature genotype-phenotype map for this intriguing protein family.

Construction of lncRNA and mRNA co-expression network associated with nasopharyngeal carcinoma progression

Nasopharyngeal carcinoma is a type of head and neck cancer with a high incidence in men. In the past decades, the survival rate of NPC has remained around 70%, but it often leads to treatment failure due to its distant metastasis or recurrence. The lncRNA-mRNA regulatory network has not been fully elucidated. We downloaded the NPC-related gene expression datasets GSE53819 and GSE12452 from the Gene Expression Omnibus database; GSE53819 included 18 NPC tissues and 18 normal tissues, and GSE12452 included 31 NPC tissues and 10 normal tissues. Weighted gene co-expression network analysis was performed on mRNA and lncRNA to screen out modules that were highly correlated with tumor progression. The two datasets were subjected to differential analysis after removing batch effects, and then Venn diagrams were used to screen for overlapping genes in the module genes and differential genes. The lncRNA-mRNA co-expression network was then constructed, and key mRNAs were identified by MCODE analysis and expression analysis. GSEA analysis and qRT-PCR were performed on key mRNAs. Through a series of analyses, we speculated that BTK, CD72, PTPN6, and VAV1 may be independent predictors of the prognosis of NPC patients.Taken together, our study provides potential candidate biomarkers for NPC diagnosis, prognosis, or precise treatment.

Critical roles of PTPN family members regulated by non-coding RNAs in tumorigenesis and immunotherapy

Since tyrosine phosphorylation is reversible and dynamic in vivo, the phosphorylation state of proteins is controlled by the opposing roles of protein tyrosine kinases (PTKs) and protein tyrosine phosphatase (PTPs), both of which perform critical roles in signal transduction. Of these, intracellular non-receptor PTPs (PTPNs), which belong to the largest class I cysteine PTP family, are essential for the regulation of a variety of biological processes, including but not limited to hematopoiesis, inflammatory response, immune system, and glucose homeostasis. Additionally, a substantial amount of PTPNs have been identified to hold crucial roles in tumorigenesis, progression, metastasis, and drug resistance, and inhibitors of PTPNs have promising applications due to striking efficacy in antitumor therapy. Hence, the aim of this review is to summarize the role played by PTPNs, including PTPN1/PTP1B, PTPN2/TC-PTP, PTPN3/PTP-H1, PTPN4/PTPMEG, PTPN6/SHP-1, PTPN9/PTPMEG2, PTPN11/SHP-2, PTPN12/PTP-PEST, PTPN13/PTPL1, PTPN14/PEZ, PTPN18/PTP-HSCF, PTPN22/LYP, and PTPN23/HD-PTP, in human cancer and immunotherapy and to comprehensively describe the molecular pathways in which they are implicated. Given the specific roles of PTPNs, identifying potential regulators of PTPNs is significant for understanding the mechanisms of antitumor therapy. Consequently, this work also provides a review on the role of non-coding RNAs (ncRNAs) in regulating PTPNs in tumorigenesis and progression, which may help us to find effective therapeutic agents for tumor therapy.