Journal of Translational Medicine最新文献

Background: Extracellular vesicles (EVs) represent a sophisticated mechanism of intercellular communication that is implicated in health and disease. Specifically, the role of EVs in metabolic regulation and their implications in metabolic pathologies, such as obesity and its comorbidities, remain unclear.

Methods: Extracellular vesicles (EVs) were isolated through serial ultracentrifugation from murine adipocytes treated with palmitate or oleic acid, whole visceral and subcutaneous adipose tissue (obesesomes) of bariatric surgery obese donors, and human hepatocytes under steatosis (steatosomes) for functional in vitro experiments. Functional effects on inflammation and glucose and lipid metabolism of target cells (human and murine macrophages and hepatocytes) were assessed using ELISA, RT-PCR, and immunodetection. Isolated EVs from human steatotic (steatosomes) and control hepatocytes (hepatosomes) were characterized for quantity, size, and tetraspanin profile by NTA and Single Particle Interferometric Reflectance Imaging Sensor (SP-IRIS), and their protein cargo analyzed by qualitative (DDA) and quantitative (DIA-SWATH) proteomics using LC-MS/MS. Proteins identified by proteomics were validated by capturing EVs on functionalized chips by SP-IRIS.

Results and conclusions: In this study, we investigated the role of EVs in the local communication between obese adipocytes and immune cells within adipose tissue, and the interaction of steatotic and healthy hepatocytes in the context of fatty liver disease progression. Furthermore, we analyzed obese adipose tissue-to-liver interactions through EV-obesesomes to elucidate their role in obesity-associated hepatic metabolic dysregulation. Our findings reveal that obesesomes promote inflammation and the secretion of pro-inflammatory cytokines upon interaction with macrophages, exerting a significant impact on reducing insulin resistance and altering lipid and glucose metabolism upon interaction with hepatocytes; in both cases, EVs from palmitate-loaded adipocytes and obesesomes from human visceral adipose depots demonstrated the most deleterious effect. Additionally, EVs secreted by steatotic hepatocytes (steatosomes) induced insulin resistance and altered lipid and glucose metabolism in healthy hepatocytes, suggesting their involvement in MASLD development. Proteomic analysis of steatosomes revealed that these vesicles contain liver disease-associated proteins, rendering them significant repositories of real-time biomarkers for the early stages and progression of MASLD.

Background: Cystinosis is a rare, incurable lysosomal storage disease caused by mutations in the CTNS gene encoding the cystine transporter cystinosin, which leads to lysosomal cystine accumulation in all cells of the body. Patients with cystinosis display signs of podocyte damage characterized by extensive loss of podocytes into the urine at early disease stages, glomerular proteinuria, and the development of focal segmental glomerulosclerosis (FSGS) lesions. Although standard treatment with cysteamine decreases cellular cystine levels, it neither reverses glomerular injury nor prevents the loss of podocytes. Thus, pathogenic mechanisms other than cystine accumulation are involved in podocyte dysfunction in cystinosis.

Methods: We used immortalized patient-derived cystinosis, healthy, and CTNS knockdown podocytes to investigate podocyte dysfunction in cystinosis. The results were validated in our newly in-house developed fluorescent ctns^-/-[Tg(fabp10a:gc-EGFP)] zebrafish larvae model. To understand impaired podocyte functionality, static and dynamic permeability assays, tracer-metabolomic analysis, flow cytometry, western blot, and chemical and dynamic redox-sensing fluorescent probes were used.

Results: In the current study, we discovered that cystinosis podocytes demonstrate increased ferroptotic cell death caused by mitochondrial reactive oxygen species (ROS)-driven membrane lipid peroxidation. Moreover, cystinosis cells present a fragmented mitochondrial network with impaired tricarboxylic acid cycle (TCA) cycle and energy metabolism. Targeting mitochondrial ROS and lipid peroxidation improved podocyte function in vitro and rescued proteinuria in vivo in cystinosis zebrafish larvae.

Conclusions: Mitochondrial ROS contribute to podocyte injury in cystinosis by driving lipid peroxidation and ferroptosis, which in turn lead to podocyte detachment. This finding adds cystinosis to the list of podocytopathies associated with mitochondrial dysfunction. The identified mechanisms reveal new therapeutic targets and highlight lipid peroxidation as an exploitable vulnerability of cystinosis podocytes.

Background: Pulmonary fibrosis (PF) severely impacts both the survival and quality of life of patients with acute respiratory distress syndrome (ARDS) and remains a leading cause of late-stage ARDS-related mortality. The role of epithelial-mesenchymal transition (EMT) in alveolar epithelial cells (AECs) is pivotal in the development of PF.

Methods: This study explored the modulation of mitochondrial dynamics and the induction of EMT by pyruvate kinase M2 (PKM2) in AECs, aiming to identify new strategies for the prevention and treatment of sepsis-associated PF.

Results: The results demonstrated that exposure to LPS increased the levels of PKM2 and the mitochondrial fission marker dynamin-related protein-1 (DRP1), while reducing the levels of the mitochondrial fusion marker mitofusin-2 (MFN2) and the epithelial marker E-cadherin. Moreover, the mesenchymal markers α-SMA and vimentin were upregulated. Treatment with shikonin effectively reversed these alterations, restoring the balance of mitochondrial dynamics, reversing EMT markers, and alleviating the severity of sepsis-associated PF.

Conclusions: This study identified PKM2 as a crucial regulator of mitochondrial dynamics and EMT in AECs during sepsis-associated PF. Targeting PKM2 activity offers a promising strategy for developing treatments to mitigate the progression of sepsis-associated PF.

Background: Tuberculosis (TB) is a major global health issue. Early diagnosis of TB is still a challenge. Studies are seeking non-sputum biomarker-based TB test. Emerging evidence indicates potential significance of blood microbiome signatures for diseases. However, blood microbiome RNA profiles are unknown in TB. We aimed to characterize the blood microbiome of TB patients and identify Mycobacterium tuberculosis (Mtb) genome-derived small RNA molecules to serve as diagnostic biomarkers for TB.

Methods: RNA sequencing data of the blood from TB patients and healthy controls were retrieved from the NCBI-SRA database for analyzing the blood microbiome and identifying rRNA-derived small RNA (rsRNA) of Mtb. Small RNA-seq was performed on plasma exosomes from TB patients and healthy controls. The levels of the candidate Mtb rsRNAs were determined by real-time quantitative reverse transcription PCR (RT-qPCR) on plasma from a separate cohort of 73 TB patients and 62 healthy controls.

Results: The blood microbiome of TB patients consisted of RNA signals from bacteria, fungi, archaea, and viruses, with bacteria accounting for more than 97% of the total. Reduced blood microbial diversity and abundance of 6 Mycobacterium-associated bacterial genera, including Mycobacterium, Priestia, Nocardioides, Agrobacterium, Bradyrhizobium, and Escherichia, were significantly altered in the blood of TB patients. A diagnostic model for TB based on the 6 genera achieved an area under the curve (AUC) of 0.8945. rsRNAs mapped to the Mtb genome were identified from blood and plasma exosomes of TB patients. RT-qPCR results showed that 2 Mtb-derived rsRNAs, 16 S-L1 and 16 S-L2, could be used as diagnostic biomarkers to differentiate TB patients from healthy controls, with a high co-diagnostic efficacy (AUC = 0.7197).

Conclusions: A panel of blood microbiome signatures and Mtb-derived rsRNAs can serve as blood biomarkers for TB diagnosis.

Background: Cancer-associated fibroblasts (CAFs) are key components of the hepatocellular carcinoma (HCC) tumor microenvironment (TME). regulating tumor proliferation, metastasis, therapy resistance, immune evasion via diverse mechanisms. A deeper understanding of the l diversity of CAFs is essential for predicting patient prognosis and guiding treatment strategies.

Methods: We examined the diversity of CAFs in HCC by integrating single-cell, bulk, and spatial transcriptome analyses.

Results: Using a training cohort of 88 HCC single-cell RNA sequencing (scRNA-seq) samples and a validation cohort of 94 samples, encompassing over 1.2 million cells, we classified three fibroblast subpopulations in HCC: HLA-DRB1 + CAF, MMP11 + CAF, and VEGFA + CAF based on highly expressed genes of which, which are primarily located in normal tissue, tumor boundaries, and tumor interiors, respectively. Cell trajectory analysis revealed that VEGFA + CAFs are at the terminal stage of differentiation, which, notably, is tumor-specific. VEGFA + CAFs were significantly associated with patient survival, and the hypoxic microenvironment was found to be a major factor inducing VEGFA + CAFs. Through cellular communication with capillary endothelial cells (CapECs), VEGFA + CAFs promoted intra-tumoral angiogenesis, facilitating tumor progression and metastasis. Additionally, a machine learning model developed using high-expression genes from VEGFA + CAFs demonstrated high accuracy in predicting prognosis and sorafenib response in HCC patients.

Conclusions: We characterized three fibroblast subpopulations in HCC and revealed their distinct spatial distributions within the tumor. VEGFA + CAFs, which was induced by hypoxic TME, were associated with poorer prognosis, as they promote tumor angiogenesis through cellular communication with CapECs. Our findings provide novel insights and pave the way for individualized therapy in HCC patients.

Background: Airway epithelial cells constitute the first line of defense against external noxious stimuli and play crucial roles in the release of epithelial inflammatory cytokines (IL33, IL25 and TSLP), initiating airway allergic inflammatory diseases such as asthma. IL33 plays critical physiological processes in T2-endotype asthma. However, the mechanisms by which allergen exposure triggers IL33 release from airway epithelial cells remain unclear.

Methods: Integrated bioinformatic analysis and transcriptional analysis of bulk RNA-seq and single cell RNA-seq (scRNA-seq) data were used to identify core genes and determine the internal gene network associated with IL33. The expression of EGR1 was subsequently analyzed in vitro in the BEAS-2B cell line and in vivo in a house dust mite (HDM)-induced mouse asthma model. The functional experiments of EGR1 were investigated in vitro via siRNA knockdown and over-expressed plasmid. Chromatin immunoprecipitation (ChIP)-PCR and dual-luciferase reporter assay validation were subsequently performed to investigate the mechanisms by which EGR1 regulates IL33 secretion.

Results: Bulk RNA-seq and scRNA-seq data identified EGR1 as an epithelial cell-derived gene implicated in IL33 expressions in asthma. The comprehensive analysis of multiple datasets indicated that the high EGR1 expression in epithelial cells may suggest a mechanistic basis of T2-endotype childhood asthma. Moreover, we verified that the expressions of EGR1 in airway epithelial cells were elevated both in vitro and in vivo asthma models. EGR1 regulated the production of IL33. Ultimately, ChIP and luciferase reporter assays confirmed that transcription factor EGR1 directly regulate the transcription of IL33 mRNA.

Conclusions: Our integrated bioinformatic analysis elucidated that EGR1 directly regulates the production of IL33 in T2-asthma and provide insights underlying the progression of asthma.

Background: Immune checkpoint inhibitors (ICIs) are emerging promising agents for the treatment of patients with esophageal squamous cell carcinoma (ESCC), however, there are only a small proportion respond to ICI therapy. Therefore, selecting candidate patients who will benefit the most from these drugs is critical. However, validated biomarkers for predicting immunotherapy response and overall survival are lacking. As the fundamental principle of ICI therapy is T cell-mediated tumor killing (TTK), we aimed to develop a unique TTK-related gene prognostic index (TTKPI) for predicting survival outcomes and responses to immune-based therapy in ESCC patients.

Methods: Transcriptomic and clinical information of ESCC patients were from the GSE53625, GSE53624, GSE47404 and TCGA datasets. TTK-related genes were from the TISIDB database. The LASSO Cox regression model was employed to create the TTKPI. The prediction potential of the TTKPI was evaluated using the KM curve and time-dependent ROC curve analysis. Finally, the relationship between TTKPI and immunotherapy efficacy was investigated in clinical trials of ICIs (GSE91061, GSE135222, IMvigor210 cohort). The role of KIF11 in accelerating tumor progression was validated via a variety of functional experiments, including western blot, CCK-8, colony formation, wound healing scratch, and xenograft tumor model. The KIF11 expression was detected by multiplex fluorescent immunohistochemistry on tissue microarray from ESCC patients.

Results: We constructed the TTKPI based on 8 TTK-related genes. The TTKPI low-risk patients exhibited better overall survival. TTKPI was significantly and positively correlated with the main immune checkpoint molecules levels. Furthermore, the low-risk patients were more prone to reap the benefits of immunotherapy in the cohort undergoing anti-PD-L1 therapy. Moreover, we performed functional experiments on KIF11, which ranked as the most significant prognostic risk gene among the 8 TTK-related genes. Our findings identified that KIF11 knockdown significantly hindered cell proliferation and mobility in ESCC cells. The KIF11 expression was negatively related with CD8⁺ T cell infiltration in ESCC patient samples.

Conclusions: The TTKPI is a promising biomarker for accurately determining survival and predicting the effectiveness of immunotherapy in ESCC patients. This risk indicator can help patients receive timely and precise early intervention, thereby advancing personalized medicine and facilitating precise immuno-oncology research. KIF11 plays a crucial role in driving tumor proliferation and migration and may act as a potential tumor biomarker of ESCC.

Background: Hepatocellular carcinoma (HCC) treatment remains challenging, particularly for immune checkpoint inhibitors (ICIs) non-response patients. Spatial transcriptome (ST) data and machine learning algorithms offer new insights into understanding HCC heterogeneity and ICIs resistance mechanisms.

Methods: Utilizing ST data from HCC patients on ICIs treatment, we analyzed pathway activity and immune infiltration. We combined 167 machine learning models to develop a G2M-checkpoint related signature (G2MRS) based on differential gene expression. The four cohorts and in-house cohort was used to validate G2MRS, and KPNA2's role was further examined through in vitro experiments in two different liver cancer cell lines.

Results: Our analysis revealed a distinct suppressive immune barrier structure (SIBS) in ICIs non-response patients, associated with upregulated G2M-checkpoint levels. G2MRS, consisting of KPNA2, CENPA, and UCK2, accurately predicted HCC prognosis and ICIs response. Further in vitro experiments demonstrated KPNA2's role in regulating migration, proliferation, and apoptosis in liver cancer.

Conclusions: This study highlights the importance of spatial heterogeneity and machine learning in refining HCC prognosis and ICIs response prediction. G2MRS and KPNA2 emerge as promising biomarkers for personalized HCC management.

Background: Germline mutations in numerous genes, particularly tumor suppressor genes, markedly heighten the risk of various cancers, including lung cancer, which is the leading cause of cancer-related deaths worldwide. Despite extensive research on well-known genes like BRCA1, BRCA2, and mismatch repair genes, many genetic factors contributing to cancer susceptibility remain unidentified.

Methods: This study reviewed sequencing data from 4,934 Chinese lung cancer patients. Matched white blood cell samples were sequenced using WES or gene panels to identify germline mutations. Analysis included statistical tests to compare patient demographics, clinical characteristics, and somatic mutation profiles.

Results: Among the cohort, 89 patients carried pathogenic/likely pathogenic (P/LP) germline mutations in 20 known cancer susceptibility genes, with ATM, BRCA2, and CHEK2 being the most common. TP53 mutations were linked to early-onset lung cancer, while ATM mutations correlated with late-onset and higher PD-L1 expression, suggesting immunotherapy benefits. Germline mutations were more prevalent in younger patients and females. Somatic mutation profiles showed similarities in common mutations but differences in MTOR (p = 0.044) and MSH6 (p = 0.018) mutations in P/LP carriers. GO and KEGG analyses indicated distinct biological processes and pathways in patients with P/LP germline mutations. Gene exclusivity analysis revealed correlations and mutual exclusivity between specific germline and somatic mutations. Comparative analysis with the gnomAD database showed a higher prevalence of these mutations in lung cancer patients compared to the general and East Asian populations, suggesting an association with increased lung cancer risk in the Chinese cohort.

Conclusion: This study underscores the prevalence of germline mutations in Chinese lung cancer patients, identifying significant associations with clinical characteristics and somatic mutation profiles. The findings highlight the importance of considering germline mutations in lung cancer treatment and the potential benefits of personalized therapy based on genetic susceptibility.