Scutellarin alleviates renal ischemia-reperfusion injury by inhibiting the MAPK pathway and pro-inflammatory macrophage polarization.

Renal ischemia-reperfusion injury (IRI) is an integral process in renal transplantation, which results in compromised graft survival. Macrophages play an important role in both the early inflammatory period and late fibrotic period in response to IRI. In this study, we investigated whether scutellarin (SCU) could protect against renal IRI by regulating macrophage polarization. Mice were given SCU (5-50 mg/kg) by gavage 1 h earlier, followed by a unilateral renal IRI. Renal function and pathological injury were assessed 24 h after reperfusion. The results showed that administration of 50 mg/kg SCU significantly improved renal function and renal pathology in IRI mice. In addition, SCU alleviated IRI-induced apoptosis. Meanwhile, it reduced macrophage infiltration and inhibited pro-inflammatory macrophage polarization. Moreover, in RAW 264.7 cells and primary bone marrow-derived macrophages (BMDMs) exposed to SCU, we found that 150 µM SCU inhibited these cells to polarize to an inflammatory phenotype induced by lipopolysaccharide (LPS) and interferon-γ (IFN-γ). However, SCU has no influence on anti-inflammatory macrophage polarization in vivo and in vitro induced by in interleukin-4 (IL-4). Finally, we explored the effect of SCU on the activation of the mitogen-activated protein kinase (MAPK) pathway both in vivo and in vitro. We found that SCU suppressed the activation of the MAPK pathway, including the extracellular signal-regulated kinase (ERK), Jun N-terminal kinase (JNK), and p38. Our results demonstrated that SCU protects the kidney against IRI by inhibiting macrophage infiltration and polarization toward pro-inflammatory phenotype via the MAPK pathway, suggesting that SCU may be therapeutically important in treatment of IRI.

Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies.

BACKGROUND: While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter- and intra-observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic malignancies is both time-consuming and labor-intensive. PURPOSE: The purpose of this study was to develop a deep learning model of pelvic LNRs delineation for patients with pelvic cancers. METHODS: Planning computed tomography (CT) studies of 160 patients with pelvic primary malignancies (including rectal, prostate, and cervical cancer) were retrospectively collected and divided into training set (n = 120) and testing set (n = 40). Six pelvic LNRs, including abdominal presacral, pelvic presacral, internal iliac nodes, external iliac nodes, obturator nodes, and inguinal nodes were delineated by two radiation oncologists as ground truth (Gt) contours. The cascaded multi-heads U-net (CMU-net) was constructed based on the Gt contours from training cohort, which was subsequently verified in the testing cohort. The automatic delineation of six LNRs (Auto) was evaluated using dice similarity coefficient (DSC), average surface distance (ASD), 95th percentile Hausdorff distance (HD95), and a 7-point scale score. RESULTS: In the testing set, the DSC of six pelvic LNRs by CMU-net model varied from 0.851 to 0.942, ASD from 0.381 to 1.037 mm, and HD95 from 2.025 to 3.697 mm. No significant differences were founded in these three parameters between postoperative and preoperative cases. 95.9% and 96.2% of auto delineations by CMU-net model got a score of 1-3 by two expert radiation oncologists, respectively, meaning only minor edits needed. CONCLUSIONS: The CMU-net was successfully developed for automated delineation of pelvic LNRs for pelvic malignancies radiotherapy with improved contouring efficiency and highly consistent, which might justify its implementation in radiotherapy work flow.

Dual-specificity phosphatase 26 protects against kidney injury caused by ischaemia-reperfusion through restraint of TAK1-JNK/p38-mediated apoptosis and inflammation of renal tubular epithelial cells.

Dual-specificity phosphatase 26 (DUSP26) acts as a pivotal player in the transduction of signalling cascades with its dephosphorylating activity. Currently, DUSP26 attracts extensive attention due to its particular function in several pathological conditions. However, whether DUSP26 plays a role in kidney ischaemia-reperfusion (IR) injury is unknown. Aims of the current work were to explore the relevance of DUSP26 in kidney IR damage. DUSP26 levels were found to be decreased in renal tubular epithelial cells following hypoxia-reoxygenation (HR) and kidney samples subjected to IR treatments. DUSP26-overexpressed renal tubular epithelial cells exhibited protection against HR-caused apoptosis and inflammation, while DUSP26-depleted renal tubular epithelial cells were more sensitive to HR damage. Upregulation of DUSP26 in rat kidneys by infecting adenovirus expressing DUSP26 markedly ameliorated kidney injury caused by IR, while also effectively reducing apoptosis and inflammation. The mechanistic studies showed that the activation of transforming growth factor-ß-activated kinase 1 (TAK1)-JNK/p38 MAPK, contributing to kidney injury under HR or IR conditions, was restrained by increasing DUSP26 expression. Pharmacological restraint of TAK1 markedly diminished DUSP26-depletion-exacebated effects on JNK/p38 activation and HR injury of renal tubular cells. The work reported a renal-protective function of DUSP26, which protects against IR-related kidney damage via the intervention effects on the TAK1-JNK/p38 axis. The findings laid a foundation for understanding the molecular pathogenesis of kidney IR injury and provide a prospective target for treating this condition.

Comprehensive assessment of drought resistance and recovery in kiwifruit genotypes using multivariate analysis.

As global climate change persists, ongoing warming exposes plants, including kiwifruit, to repeated cycles of drought stress and rewatering, necessitating the identification of drought-resistant genotypes for breeding purposes. To better understand the physiological mechanisms underlying drought resistance and recovery in kiwifruit, moderate (40-45% field capacity) and severe (25-30% field capacity) drought stresses were applied, followed by rewatering (80-85% field capacity) to eight kiwifruit rootstocks in this study. We then conducted a multivariate analysis of 20 indices for the assessment of drought resistance and recovery capabilities. Additionally, we identified four principal components, each playing a vital role in coping with diverse water conditions. Three optimal indicator groups were pinpointed, enhancing precision in kiwifruit drought resistance and recovery assessment and simplifying the evaluation system. Finally, MX-1 and HW were identified as representative rootstocks for future research on kiwifruit's responses to moderate and severe drought stresses. This study not only enhances our understanding of the response mechanisms of kiwifruit rootstocks to progressive drought stress and recovery but also provides theoretical guidance for reliable screening of drought-adaptive kiwifruit genotypes.

Causal association and mediating effect of blood biochemical metabolic traits and brain image-derived endophenotypes on Alzheimer's disease.

Background: Recent genetic evidence supports that circulating biochemical and metabolic traits (BMTs) play a causal role in Alzheimer's disease (AD), which might be mediated by changes in brain structure. Here, we leveraged publicly available genome-wide association study data to investigate the intrinsic causal relationship between blood BMTs, brain image-derived phenotypes (IDPs) and AD. Methods: Utilizing the genetic variants associated with 760 blood BMTs and 172 brain IDPs as the exposure and the latest AD summary statistics as the outcome, we analyzed the causal relationship between blood BMTs and brain IDPs and AD by using a two-sample Mendelian randomization (MR) method. Additionally, we used two-step/mediation MR to study the mediating effect of brain IDPs between blood BMTs and AD. Results: Twenty-five traits for genetic evidence supporting a causal association with AD were identified, including 12 blood BMTs and 13 brain IDPs. For BMTs, glutamine consistently reduced the risk of AD in 3 datasets. For IDPs, specific alterations of cortical thickness (atrophy in frontal pole and insular lobe, and incrassation in superior parietal lobe) and subcortical volume (atrophy in hippocampus and its subgroups, left accumbens and left choroid plexus, and expansion in cerebral white matter) are vulnerable to AD. In the two-step/mediation MR analysis, superior parietal lobe, right hippocampal fissure and left accumbens were identified to play a potential mediating role among three blood BMTs and AD. Conclusions: The results obtained in our study suggest that 12 circulating BMTs and 13 brain IDPs play a causal role in AD. Importantly, a subset of BMTs exhibit shared genetic architecture and potentially causal relationships with brain structure, which may contribute to the alteration of brain IDPs in AD.

The effect of insulin resistance in the association between obesity and hypertension incidence among Chinese middle-aged and older adults: data from China health and retirement longitudinal study (CHARLS).

Background and aims: Obesity and insulin resistance are well-known important risk factors for hypertension. This study aimed to investigate the mediating effect of the triglyceride-glucose index (TyG) in the association between Chinese visceral obesity index (CVAI) and hypertension among Chinese middle-aged and older adults. Methods: A total of 10,322 participants aged 45 years and older from CHARLS (2011-2018) were included. Baseline data were collected in 2011 and hypertension incidence data were gathered during follow-up in 2013, 2015 and 2018. Multivariate logistic regression models were constructed to investigate the association of CVAI and TyG with the incidence of hypertension. Additionally, mediation analyses were conducted to evaluate the mediating role of the TyG index in the relationship between CVAI and hypertension. Subgroup analysis was also performed. Results: A total of 2,802 participants developed hypertension during the follow-up period. CVAI and TyG index were independently and significantly associated with hypertension incidence. Increasing quartiles of CVAI and TyG index were associated with high hypertension incidence in middle-aged and older adults. The TyG index was identified as a mediator in the relationship between CVAI and hypertension incidence, with a mediation effect (95% confidence interval) was 12.38% (6.75, 31.81%). Conclusion: Our study found that CVAI and TyG were independently associated with hypertension incidence. TyG played a partial mediating effect in the positive association between CVAI and hypertension incidence.

Single-cell sequencing reveals the evolution of immune molecules across multiple vertebrate species.

INTRODUCTION: Both innate and adaptive immune system undergo evolution from low to high vertebrates. Due to the limitation of conventional approaches in identifying broader spectrum of immune cells and molecules from various vertebrates, it remains unclear how immune molecules evolve among vertebrates. OBJECTIVES: Here, we utilized carry out comparative transcriptome analysis in various immune cells across seven vertebrate species. METHODS: Single-cell RNA sequencing (scRNA-seq). RESULTS: We uncovered both conserved and species-specific profiling of gene expression in innate and adaptive immunity. Macrophages exhibited highly-diversified genes and developed sophisticated molecular signaling networks along with evolution, indicating effective and versatile functions in higher species. In contrast, B cells conservatively evolved with less differentially-expressed genes in analyzed species. Interestingly, T cells represented a dominant immune cell populations in all species and unique T cell populations were identified in zebrafish and pig. We also revealed compensatory TCR cascade components utilized by different species. Inter-species comparison of core gene programs demonstrated mouse species has the highest similarity in immune transcriptomes to human. CONCLUSIONS: Therefore, our comparative study reveals gene transcription characteristics across multiple vertebrate species during the evolution of immune system, providing insights for species-specific immunity as well as the translation of animal studies to human physiology and disease.

A SwinTransformer-Based Segmentation Framework With Self-Supervised Strategy for Post-Operative Prostate Cancer Radiotherapy.

Radical prostatectomy (prostate removal) is a standard treatment for clinically localized prostate cancer and is often followed by postoperative radiotherapy. Postoperative radiotherapy requires accurate delineation of the clinical target volume (CTV) and lymph node drainage area (LNA) on computed tomography (CT) images. However, the CTV contour cannot be determined by the simple prostate expansion after resection of the prostate in the CT image. Constrained by this factor, the manual delineation process in postoperative radiotherapy is more time-consuming and challenging than in radical radiotherapy. In addition, CTV and LNA have no boundaries that can be distinguished by pixel values in CT images, and existing automatic segmentation models cannot get satisfactory results. Radiation oncologists generally determine CTV and LNA profiles according to clinical consensus and guidelines regarding surrounding organs at risk (OARs). In this work, we design a cascade segmentation block to explicitly establish correlations between CTV, LNA, and OARs, leveraging OARs features to guide CTV and LNA segmentation. Furthermore, inspired by the success of the self-attention mechanism and self-supervised learning, we adopt SwinTransformer as our backbone and propose a pure SwinTransformer-based segmentation network with self-supervised learning strategies. We performed extensive quantitative and qualitative evaluations of the proposed method. Compared to other competitive segmentation models, our model shows higher dice scores with minor standard deviations, and the detailed visualization results are more consistent with the ground truth. We believe this work can provide a feasible solution to this problem, making the postoperative radiotherapy process more efficient.

Expanding causal genes for Parkinson's disease via multi-omics analysis.

Genome­wide association studies (GWASs) have revealed numerous loci associated with Parkinson's disease (PD). However, some potential causal/risk genes were still not revealed and no etiological therapies are available. To find potential causal genes and explore genetically supported drug targets for PD is urgent. By integrating the expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) datasets from multiple tissues (blood, cerebrospinal fluid (CSF) and brain) and PD GWAS summary statistics, a pipeline combing Mendelian randomization (MR), Steiger filtering analysis, Bayesian colocalization, fine mapping, Protein-protein network and enrichment analysis were applied to identify potential causal genes for PD. As a result, GPNMB displayed a robust causal role for PD at the protein level in the blood, CSF and brain, and transcriptional level in the brain, while the protective role of CD38 (in brain pQTL and eQTL) was also identified. We also found inconsistent roles of DGKQ on PD between protein and mRNA levels. Another 9 proteins (CTSB, ARSA, SEC23IP, CD84, ENTPD1, FCGR2B, BAG3, SNCA, FCGR2A) were associated with the risk for PD based on only a single pQTL after multiple corrections. We also identified some proteins' interactions with known PD causative genes and therapeutic targets. In conclusion, this study suggested GPNMB, CD38, and DGKQ may act in the pathogenesis of PD, but whether the other proteins involved in PD needs more evidence. These findings would help to uncover the genes underlying PD and prioritize targets for future therapeutic interventions.

Predicting Rectal Cancer Response to Total Neoadjuvant Treatment Using an Artificial Intelligence Model Based on Magnetic Resonance Imaging and Clinical Data.

PURPOSE: To develop a model for predicting response to total neoadjuvant treatment (TNT) for patients with locally advanced rectal cancer (LARC) based on baseline magnetic resonance imaging (MRI) and clinical data using artificial intelligence methods. METHODS: Baseline MRI and clinical data were curated from patients with LARC and analyzed using logistic regression (LR) and deep learning (DL) methods to predict TNT response retrospectively. We defined two groups of response to TNT as pathological complete response (pCR) versus non-pCR (Group 1), and high sensitivity [tumor regression grade (TRG) 0 and TRG 1] versus moderate sensitivity (TRG 2 or patients with TRG 3 and a reduction in tumor volume of at least 20% compared to baseline) versus low sensitivity (TRG 3 and a reduction in tumor volume <20% compared to baseline) (Group 2). We extracted and selected clinical and radiomic features on baseline T2WI. Then we built LR models and DL models. Receiver operating characteristic (ROC) curves analysis was performed to assess predictive performance of models. RESULTS: Eighty-nine patients were assigned to the training cohort, and 29 patients were assigned to the testing cohort. The area under receiver operating characteristics curve (AUC) of LR models, which were predictive of high sensitivity and pCR, were 0.853 and 0.866, respectively. Whereas the AUCs of DL models were 0.829 and 0.838, respectively. After 10 rounds of cross validation, the accuracy of the models in Group 1 is higher than in Group 2. CONCLUSION: There was no significant difference between LR model and DL model. Artificial Intelligence-based radiomics biomarkers may have potential clinical implications for adaptive and personalized therapy.

Systematic druggable genome-wide Mendelian randomisation identifies therapeutic targets for Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is the leading cause of dementia. Currently, there are no effective disease-modifying treatments for AD. Mendelian randomisation (MR) has been widely used to repurpose licensed drugs and discover novel therapeutic targets. Thus, we aimed to identify novel therapeutic targets for AD and analyse their pathophysiological mechanisms and potential side effects. METHODS: A two-sample MR integrating the identified druggable genes was performed to estimate the causal effects of blood and brain druggable expression quantitative trait loci (eQTLs) on AD. A repeat study was conducted using different blood and brain eQTL data sources to validate the identified genes. Using AD markers with available genome-wide association studies data, we evaluated the causal relationship between established AD markers to explore possible mechanisms. Finally, the potential side effects of the druggable genes for AD treatment were assessed using a phenome-wide MR. RESULTS: Overall, 5883 unique druggable genes were aggregated; 33 unique potential druggable genes for AD were identified in at least one dataset (brain or blood), and 5 were validated in a different dataset. Among them, three prior druggable genes (epoxide hydrolase 2 (EPHX2), SERPINB1 and SIGLEC11) reached significant levels in both blood and brain tissues. EPHX2 may mediate the pathogenesis of AD by affecting the entire hippocampal volume. Further phenome-wide MR analysis revealed no potential side effects of treatments targeting EPHX2, SERPINB1 or SIGLEC11. CONCLUSIONS: This study provides genetic evidence supporting the potential therapeutic benefits of targeting the three druggable genes for AD treatment, which will be useful for prioritising AD drug development.

Identifying novel genes for amyotrophic lateral sclerosis by integrating human brain proteomes with genome-wide association data.

BACKGROUND: Genome-Wide Association Studies (GWAS) have identified numerous risk genes for Amyotrophic Lateral Sclerosis (ALS); however, the mechanisms by which these loci confer ALS risk are uncertain. This study aims to identify novel causal proteins in the brains of patients with ALS using an integrative analytical pipeline. METHODS: Using the datasets of Protein Quantitative Trait Loci (pQTL) (NpQTL1 = 376, NpQTL2 = 152), expression QTL (eQTL) (N = 452), and the largest ALS GWAS (NALS=27,205, NControls = 110,881), we performed a systematic analytical pipeline including Proteome-Wide Association Study (PWAS), Mendelian Randomization (MR), Bayesian colocalization, and Transcriptome-Wide Association Study (TWAS) to identify novel causal proteins for ALS in the brain. RESULTS: Using PWAS, we found that the altered protein abundance of 12 genes in the brain was associated with ALS. Three genes (SCFD1, SARM1 and CAMLG) were identified as lead causal genes for ALS with solid evidence (False discovery rate < 0.05, in MR analysis; PPH4 > 80% for Bayesian colocalization). Specifically, an increased abundance of SCFD1 and CAMLG led to an increased risk of ALS, whereas a higher abundance of SARM1 led to a decreased risk of developing ALS. TWAS showed that SCFD1 and CAMLG were related to ALS at the transcriptional level. CONCLUSIONS: SCFD1, CAMLG, and SARM1 exhibited robust associations and causality with ALS. The study findings provide novel clues for identifying potential therapeutic targets in ALS. Further studies are required to explore the mechanisms underlying the identified genes.

Segmentation of rectal tumor from multi-parametric MRI images using an attention-based fusion network.

Accurate segmentation of rectal tumors is the most crucial task in determining the stage of rectal cancer and developing suitable therapies. However, complex image backgrounds, irregular edge, and poor contrast hinder the related research. This study presents an attention-based multi-modal fusion module to effectively integrate complementary information from different MRI images and suppress redundancy. In addition, a deep learning-based segmentation model (AF-UNet) is designed to achieve accurate segmentation of rectal tumors. This model takes multi-parametric MRI images as input and effectively integrates the features from different multi-parametric MRI images by embedding the attention fusion module. Finally, three types of MRI images (T2, ADC, DWI) of 250 patients with rectal cancer were collected, with the tumor regions delineated by two oncologists. The experimental results show that the proposed method is superior to the most advanced image segmentation method with a Dice coefficient of [Formula: see text], which is also better than other multi-modal fusion methods. Framework of the AF-UNet. This model takes multi-modal MRI images as input, and integrates complementary information using attention mechanism and suppresses redundancy.

Identification of distinct immune infiltration and potential biomarkers in patients with liver ischemia-reperfusion injury.

AIMS: To identify alterations of specific gene expression, immune infiltration components, and potential biomarkers in liver ischemia-reperfusion injury (IRI) following liver transplantation (LT). MATERIALS AND METHODS: GSE23649 and GSE151648 datasets were obtained from the Gene Expression Omnibus (GEO) database. To determine the differentially expressed genes (DEGs), we utilized the R package "limma". We also identify the infiltration of different immune cells through single-sample gene-set enrichment analysis (ssGSEA). Furthermore, we utilized LASSO logistic regression to select feature genes and Spearman's rank correlation analysis to determine the correlation between these genes and infiltrating immune cells. Finally, the significance of these feature genes was confirmed using a mouse model of hepatic IRI. KEY FINDINGS: A total of 17 DEGs were acquired, most of which were associated with inflammation, apoptosis, cell proliferation, immune disorders, and cellular response. 28 immune cell types were determined using ssGSEA. 5 feature genes (ADM, KLF6, SERPINE1, SLC20A1, and HBB) were screened using LASSO analysis, but the HBB gene was ultimately excluded due to the lack of statistical significance in the GSE151648 dataset. These 4 feature genes were predominantly related to immune cells. Finally, 15 significantly distinctive types of immune cells between the control and IRI groups were verified. SIGNIFICANCE: We unveiled that macrophages, dendritic cells (DCs), neutrophils, CD4 T cells, and other immune cells infiltrated the IRI that occurred after LT. Moreover, we identified ADM, KLF6, SERPINE1, and SLC20A1 as potential biological biomarkers underlying IRI post-transplant, which may improve the diagnosis and prognosis of this condition.

Effects of Chinese fir planting and phosphorus addition on soil microbial biomass and extracellular enzyme activities.

Plants can alter soil microbial biomass and extracellular enzyme activities related with carbon (C), nitrogen (N), and phosphorus (P), through litter and root exudates, with consequences on soil carbon, nitrogen and phosphorus (P) cycling. However, it is not well known how the changes in soil phosphorus availability affect the relationships between plants and soil microorganisms. In this study, a factorial experiment was conducted to investigate the effects of Chinese fir (Cunninghamia lanceolata) planting and different levels of P addition (0, 1.95, 3.9, 7.8 and 15.6 g P·m-2·a-1) on soil microbial biomass and extracellular enzyme activities. The results showed that planting Chinese fir planting significantly altered soil microbial biomass and C- and N- and P-related extracellular enzyme activities, but the effects were dependent on P addition levels. Without P addition, Chinese fir planting significantly reduced soil nutrient availability and pH, which led to the aggravation of P limitation and lower soil microbial biomass. P addition relieved P limitation, and reduced soil acid phosphatase (ACP) activities by 30.0%, 30.5%, 35.3% and 47.1% with the increasing P addition level (1.95, 3.9, 7.8 and 15.6 g P·m-2·a-1). Under three P addition levels (1.95, 3.9 and 7.8 g P·m-2·a-1), the negative effects of Chinese fir planting on soil microbial growth were alleviated. Under the high P addition level (15.6 g P·m-2·a-1), the negative effects of Chinese fir planting on soil microbial growth occurred again due to soil N limitation. Taken together, Chinese fir planting and soil P availability generally affected soil microbial biomass and extracellular enzyme activities, and changed P limitation.

Lipid metabolism-related miRNAs with potential diagnostic roles in prostate cancer.

BACKGROUND: Prostate cancer (PCa), the second most prevalent solid tumor among men worldwide, has caused greatly increasing mortality in PCa patients. The effects of lipid metabolism on tumor growth have been explored, but the mechanistic details of the association of lipid metabolism disorders with PCa remain largely elusive. METHODS: The RNA sequencing data of the GSE45604 and The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD) datasets were extracted from the Gene Expression Omnibus (GEO) and UCSC Xena databases, respectively. The Molecular Signatures Database (MSigDB) was utilized to identify lipid metabolism-related genes. The limma R package was used to identify differentially expressed lipid metabolism-related genes (DE-LMRGs) and differentially expressed microRNAs (DEMs). Moreover, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), and support vector machine-recursive feature elimination (SVM-RFE) were applied to select signature miRNAs and construct a lipid metabolism-related diagnostic model. The expression levels of selected differentially expressed lipid metabolism-related miRNAs (DE-LMRMs) in PCa and benign prostate hyperplasia (BPH) specimens were verified using quantitative real-time polymerase chain reaction (qRT‒PCR). Furthermore, a transcription factor (TF)-miRNA‒mRNA network was constructed. Eventually, Kaplan‒Meier (KM) curves were plotted to illustrate the associations between signature miRNA-related mRNAs and TFs and overall survival (OS) along with biochemical recurrence-free survival (BCR). RESULTS: Forty-seven LMRMs were screened based on the correlation analysis of 29 DE-LMRGs and 56 DEMs, in which 27 LMRMs were stably expressed in the GSE45604 dataset. Subsequently, receiver operating characteristic (ROC) curves and machine learning methods were employed to develop a lipid metabolism-related diagnostic signature, which may be of diagnostic value for PCa patients. qRT‒PCR results showed that all seven key DE-LMRMs were differentially expressed between PCa and BPH tissues. Eventually, a TF-miRNA‒mRNA network was constructed. CONCLUSIONS: These results suggested that 7 key diagnostic miRNAs were closely related to PCa pathological processes and provided new targets for the diagnosis and treatment of PCa. Moreover, CLIC6 and SCNN1A linked to miR-200c-3p had good prognostic potential and provided valuable insights into the pathogenesis of PCa.

Identifying novel proteins underlying loneliness by integrating GWAS summary data with human brain proteomes.

Enduring loneliness is associated with mental disorders and physical diseases. Although genome-wide association studies (GWAS) have identified risk loci associated with loneliness, how these loci confer the risk remains largely unknown. In the current study, we aimed to investigate key proteins underlying loneliness in the brain by integrating human brain proteomes and transcriptomes with loneliness GWAS to perform a discovery proteome-wide association study (PWAS), followed by a confirmatory PWAS, transcriptome-wide association analysis (TWAS), Mendelian randomization (MR), Steigering filtering analysis and Bayesian colocalization analysis. Moreover, given the fact that loneliness is associated with mental disorders, we explored the shared genetic architecture between loneliness and mental disorders. Totally, we identified 18 genes to be associated with loneliness via their cis-regulated brain protein abundance. Eleven of the 18 genes (61.1%) were replicated in the confirmatory PWAS, and mRNA levels of 4 genes were further validated to be associated with loneliness.MR and genetic colocalization analysis further confirmed that the increased protein abundance of ALDH2 and ICA1L was protective against loneliness, while the increased protein abundance of GPX1 was a risk for developing loneliness. Furthermore, we found genetic correlations, bidirectional causal associations and overlapping phenotype-associated protein profiles between loneliness and mental disorders including major depression and schizophrenia. In summary, our findings provided clues about the brain-related molecular basis underlying loneliness, which warrants further investigation.

Identifying novel proteins underlying schizophrenia via integrating pQTLs of the plasma, CSF, and brain with GWAS summary data.

BACKGROUND: Schizophrenia (SCZ) is a chronic and severe mental illness with no cure so far. Mendelian randomization (MR) is a genetic method widely used to explore etiologies of complex traits. In the current study, we aimed to identify novel proteins underlying SCZ with a systematic analytical approach. METHODS: We integrated protein quantitative trait loci (pQTLs) of the brain, cerebrospinal fluid (CSF), and plasma with the latest and largest SCZ genome-wide association study (GWAS) via a systematic analytical framework, including two-sample MR analysis, Steiger filtering analysis, and Bayesian colocalization analysis. RESULTS: The genetically determined protein level of C4A/C4B (OR = 0.70, p = 1.66E-07) in the brain and ACP5 (OR = 0.42, p = 3.73E-05), CNTN2 (OR = 0.62, p = 2.57E-04), and PLA2G7 (OR = 0.71, p = 1.48E-04) in the CSF was associated with a lower risk of SCZ, while the genetically determined protein level of TIE1 (OR = 3.46, p = 4.76E-05), BCL6 (OR = 3.63, p = 1.59E-07), and MICB (OR = 4.49, p = 2.31E-11) in the CSF were associated with an increased risk for SCZ. Pathway enrichment analysis indicated that genetically determined proteins suggestively associated with SCZ were enriched in the biological process of the immune response. CONCLUSION: In conclusion, we identified one protein in the brain and six proteins in the CSF that showed supporting evidence of being potentially associated with SCZ, which could provide insights into future mechanistic studies to find new treatments for the disease. Our results also supported the important role of neuroinflammation in the pathogenesis of SCZ.

Peripheral level of CD33 and Alzheimer's disease: a bidirectional two-sample Mendelian randomization study.

Increased expression of CD33 in the brain has been suggested to be associated with increased amyloid plaque burden, while the peripheral level of CD33 in Alzheimer's disease (AD) patients and its role in AD remain unclear. The current study aimed to systematically explore the bidirectional relationship between peripheral CD33 and AD. Genome-wide association study (GWAS) datasets of AD (Ncases: 21982; Ncontrols: 41944), blood CD33 mRNA level, the plasma CD33 protein level, and CD33 expression on immune-cell subtypes were obtained from GWASs conducted in the European population. Eligible IVs were extracted from the GWASs. MR estimates were calculated by inverse-variance weighting (IVW) and other sensitivity analyses. The main statistical analyses were conducted using TwoSampleMR (v.0.5.5) in R package (V.4.1.2).In the forward MR analysis (CD33 as exposure and AD as outcome), the IVW results indicated that elevated blood CD33 mRNA level (OR [95% CI] = 1.156[1.080, 1.238], p = 3.25e-05), elevated serum CD33 protein level (OR [95% CI] = 1.08 [1.031, 1.139], p = 1.6e-03) and increased CD33's expression on immune cell subtypes (p < 0.05) were all leading to a higher risk of AD. And sensitivity analyses supported these findings. While the reverse MR analysis (AD as exposure and CD33 as outcome) indicated that AD was not leading to the elevation of CD33's protein level in the blood (p > 0.05). In conclusion, our results indicated that elevated peripheral expression of CD33 was causal to the development of AD. Future studies are needed to work on developing CD33 as a biomarker and therapeutic target in AD.