Circulating EVs long RNA-based subtyping and deconvolution enable prediction of immunogenic signatures and clinical outcome for PDAC

Potential of gut microbiota metabolites in treating COPD: network pharmacology and Mendelian randomization approaches

Objective

The gut microbiota and its metabolites exert a significant influence on COPD, yet the underlying mechanisms remain elusive. We aim to holistically evaluate the role and mechanisms of the gut microbiota and its metabolites in COPD through network pharmacology and Mendelian randomization approaches.

Methods

Employing network pharmacology, we identified the gut microbiota and its metabolites' impact on COPD-related targets, elucidating the complex network mechanisms involving the gut microbiota, its metabolites, targets, and signaling pathways in relation to COPD. Further, promising gut microbiota metabolites and microbiota were pinpointed, with their causal relationships inferred through Mendelian randomization.

Results

A complex biological network was constructed, comprising 39 gut microbiota, 20 signaling pathways, 19 targets, and 23 metabolites associated with COPD. Phenylacetylglutamine emerged as a potentially promising metabolite for COPD treatment, with Mendelian randomization analysis revealing a causal relationship with COPD.

Conclusion

This study illuminates the intricate associations between the gut microbiota, its metabolites, and COPD. Phenylacetylglutamine may represent a novel avenue for COPD treatment. These findings could aid in identifying individuals at high risk for COPD, offering insights into early prevention and treatment strategies.

StructmRNA a BERT based model with dual level and conditional masking for mRNA representation

In this study, we introduce StructmRNA, a new BERT-based model that was designed for the detailed analysis of mRNA sequences and structures. The success of DNABERT in understanding the intricate language of non-coding DNA with bidirectional encoder representations is extended to mRNA with StructmRNA. This new model uses a special dual-level masking technique that covers both sequence and structure, along with conditional masking. This enables StructmRNA to adeptly generate meaningful embeddings for mRNA sequences, even in the absence of explicit structural data, by capitalizing on the intricate sequence-structure correlations learned during extensive pre-training on vast datasets. Compared to well-known models like those in the Stanford OpenVaccine project, StructmRNA performs better in important tasks such as predicting RNA degradation. Thus, StructmRNA can inform better RNA-based treatments by predicting the secondary structures and biological functions of unseen mRNA sequences. The proficiency of this model is further confirmed by rigorous evaluations, revealing its unprecedented ability to generalize across various organisms and conditions, thereby marking a significant advance in the predictive analysis of mRNA for therapeutic design. With this work, we aim to set a new standard for mRNA analysis, contributing to the broader field of genomics and therapeutic development.

Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model

Introduction

Immune dysregulation plays a major role in cancer progression. The quantification of lymphocytic spatial inflammation may enable spatial system biology, improve understanding of therapeutic resistance, and contribute to prognostic imaging biomarkers.

Methods

In this paper, we propose a knowledge-guided deep learning framework to measure the lymphocytic spatial architecture on human H&E tissue, where the fidelity of training labels is maximized through single-cell resolution image registration of H&E to IHC. We demonstrate that such an approach enables pixel-perfect ground-truth labeling of lymphocytes on H&E as measured by IHC. We then experimentally validate our technique in a genetically engineered, immune-compromised Rag2 mouse model, where Rag2 knockout mice lacking mature lymphocytes are used as a negative experimental control. Such experimental validation moves beyond the classical statistical testing of deep learning models and demonstrates feasibility of more rigorous validation strategies that integrate computational science and basic science.

Results

Using our developed approach, we automatically annotated more than 111,000 human nuclei (45,611 CD3/CD20 positive lymphocytes) on H&E images to develop our model, which achieved an AUC of 0.78 and 0.71 on internal hold-out testing data and external testing on an independent dataset, respectively. As a measure of the global spatial architecture of the lymphocytic microenvironment, the average structural similarity between predicted lymphocytic density maps and ground truth lymphocytic density maps was 0.86 ± 0.06 on testing data. On experimental mouse model validation, we measured a lymphocytic density of 96.5 ± %1% in a Rag2+/- control mouse, compared to an average of 16.2 ± %5% in Rag2-/- immune knockout mice (p<0.0001, ANOVA-test).

Discussion

These results demonstrate that CD3/CD20 positive lymphocytes can be accurately detected and characterized on H&E by deep learning and generalized across species. Collectively, these data suggest that our understanding of complex biological systems may benefit from computationally-derived spatial analysis, as well as integration of computational science and basic science.

Artificial intelligence-based evaluation of prognosis in cirrhosis

Cirrhosis represents a significant global health challenge, characterized by high morbidity and mortality rates that severely impact human health. Timely and precise prognostic assessments of liver cirrhosis are crucial for improving patient outcomes and reducing mortality rates as they enable physicians to identify high-risk patients and implement early interventions. This paper features a thorough literature review on the prognostic assessment of liver cirrhosis, aiming to summarize and delineate the present status and constraints associated with the application of traditional prognostic tools in clinical settings. Among these tools, the Child-Pugh and Model for End-Stage Liver Disease (MELD) scoring systems are predominantly utilized. However, their accuracy varies significantly. These systems are generally suitable for broad assessments but lack condition-specific applicability and fail to capture the risks associated with dynamic changes in patient conditions. Future research in this field is poised for deep exploration into the integration of artificial intelligence (AI) with routine clinical and multi-omics data in patients with cirrhosis. The goal is to transition from static, unimodal assessment models to dynamic, multimodal frameworks. Such advancements will not only improve the precision of prognostic tools but also facilitate personalized medicine approaches, potentially revolutionizing clinical outcomes.

Genetic prediction of blood metabolites mediating the relationship between gut microbiota and Alzheimer's disease: a Mendelian randomization study

Background

Observational studies have suggested an association between gut microbiota and Alzheimer's disease (AD); however, the causal relationship remains unclear, and the role of blood metabolites in this association remains elusive.

Purpose

To elucidate the causal relationship between gut microbiota and AD and to investigate whether blood metabolites serve as potential mediators.

Materials and methods

Univariable Mendelian randomization (UVMR) analysis was employed to assess the causal relationship between gut microbiota and AD, while multivariable MR (MVMR) was utilized to mitigate confounding factors. Subsequently, a two-step mediation MR approach was employed to explore the role of blood metabolites as potential mediators. We primarily utilized the inverse variance-weighted method to evaluate the causal relationship between exposure and outcome, and sensitivity analyses including Contamination mixture, Maximum-likelihood, Debiased inverse-variance weighted, MR-Egger, Bayesian Weighted Mendelian randomization, and MR pleiotropy residual sum and outlier were conducted to address pleiotropy.

Results

After adjustment for reverse causality and MVMR correction, class Actinobacteria (OR: 1.03, 95% CI: 1.01-1.06, p = 0.006), family Lactobacillaceae (OR: 1.03, 95% CI: 1.00-1.05, p = 0.017), genus Lachnoclostridium (OR: 1.03, 95% CI: 1.00-1.06, p = 0.019), genus Ruminiclostridium9 (OR: 0.97, 95% CI: 0.94-1.00, p = 0.027) and genus Ruminiclostridium6 (OR: 1.03, 95% CI: 1.01-1.05, p = 0.009) exhibited causal effects on AD. Moreover, 1-ribosyl-imidazoleacetate levels (-6.62%), Metabolonic lactone sulfate levels (2.90%), and Nonadecanoate (19:0) levels (-12.17%) mediated the total genetic predictive effects of class Actinobacteria on AD risk. Similarly, 2-stearoyl-GPE (18:0) levels (-9.87%), Octadecanedioylcarnitine (C18-DC) levels (4.44%), 1-(1-enyl-stearoyl)-2-oleoyl-GPE (p-18:0/18:1) levels (38.66%), and X-23639 levels (13.28%) respectively mediated the total genetic predictive effects of family Lactobacillaceae on AD risk. Furthermore, Hexadecanedioate (C16-DC) levels (5.45%) mediated the total genetic predictive effects of genus Ruminiclostridium 6 on AD risk; Indole-3-carboxylate levels (13.91%), X-13431 levels (7.08%), Alpha-ketoglutarate to succinate ratio (-13.91%), 3-phosphoglycerate to glycerate ratio (15.27%), and Succinate to proline ratio (-14.64%) respectively mediated the total genetic predictive effects of genus Ruminiclostridium 9 on AD risk.

Conclusion

Our mediation MR analysis provides genetic evidence suggesting the potential mediating role of blood metabolites in the causal relationship between gut microbiota and AD. Further large-scale randomized controlled trials are warranted to validate the role of blood metabolites in the specific mechanisms by which gut microbiota influence AD.

Gut microbiota linked to hydrocephalus through inflammatory factors: a Mendelian randomization study

Background

The gut microbiota (GM) has been implicated in neurological disorders, but the relationship with hydrocephalus, especially the underlying mechanistic pathways, is unclear. Using Mendelian randomization (MR), we aim to discover the mediating role of inflammatory factors in the relationship between GM and hydrocephalus.

Methods

After removing confounders, univariable and multivariable MR analyses were performed using summary statistics to assess the causal relationships between GM, inflammatory factors (IL-17A and IL-27), and types of hydrocephalus. Meta-analyses were used to reconcile the differences in MR results between different hydrocephalus sources. Finally, mediator MR analyses were applied to determine the mediating effect of inflammatory factors. Various sensitivity analysis methods were employed to ensure the reliability and stability of the results.

Results

After correction for P-values, Firmicutes (phylum) (OR, 0.34; 95%CI, 0.17-0.69; P = 2.71E-03, P FDR = 2.44E-02) significantly reduced the risk of obstructive hydrocephalus. The remaining 18 different taxa of GM had potential causal relationships for different types of hydrocephalus. In addition, Firmicutes (phylum) decreased the risk of obstructive hydrocephalus by increasing levels of IL-17A (mediating effect = 21.01%), while Eubacterium ruminantium group (genus) increased the risk of normal-pressure hydrocephalus by decreasing levels of IL-27 (mediating effect = 7.48%).

Conclusion

We reveal the connection between GM, inflammatory factors (IL-17A and IL-27), and hydrocephalus, which lays the foundation for unraveling the mechanism between GM and hydrocephalus.

Identification of biomarkers for abdominal aortic aneurysm in Behçet's disease via mendelian randomization and integrated bioinformatics analyses

Behçet's disease (BD) is a complex autoimmune disorder impacting several organ systems. Although the involvement of abdominal aortic aneurysm (AAA) in BD is rare, it can be associated with severe consequences. In the present study, we identified diagnostic biomarkers in patients with BD having AAA. Mendelian randomization (MR) analysis was initially used to explore the potential causal association between BD and AAA. The Limma package, WGCNA, PPI and machine learning algorithms were employed to identify potential diagnostic genes. A receiver operating characteristic curve (ROC) for the nomogram was constructed to ascertain the diagnostic value of AAA in patients with BD. Finally, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were conducted. The MR analysis indicated a suggestive association between BD and the risk of AAA (odds ratio [OR]: 1.0384, 95% confidence interval [CI]: 1.0081-1.0696, p = 0.0126). Three hub genes (CD247, CD2 and CCR7) were identified using the integrated bioinformatics analyses, which were subsequently utilised to construct a nomogram (area under the curve [AUC]: 0.982, 95% CI: 0.944-1.000). Finally, the immune cell infiltration assay revealed that dysregulation immune cells were positively correlated with the three hub genes. Our MR analyses revealed a higher susceptibility of patients with BD to AAA. We used a systematic approach to identify three potential hub genes (CD247, CD2 and CCR7) and developed a nomogram to assist in the diagnosis of AAA among patients with BD. In addition, immune cell infiltration analysis indicated the dysregulation in immune cell proportions.

Roles of gut microbiota in androgenetic alopecia: insights from Mendelian randomization analysis

Background

Androgenetic alopecia (AGA) is the most common type of androgen-associated hair loss. Previous studies have indicated an association between the gut microbiota and AGA. To delve deeper, we executed a two-sample Mendelian randomization (MR) analysis to investigate the potential causal relationship between the gut microbiota and AGA.

Methods

A two-sample MR investigation was utilized to delve into the intricate interplay between gut microbiota and AGA. Information regarding 211 gut microbial taxa was sourced from the MiBioGen consortium. The summary statistics of the genome-wide association studies (GWAS) for AGA were obtained from the FinnGen biobank, which included 195 cases and 201,019 controls. Various analytical approaches, including Inverse Variance Weighting (IVW), Weighted Median, MR-Egger, Weighted Mode, and Simple Mode were employed to evaluate the causal impact of gut microbiota on AGA. Sensitivity analyses were subsequently conducted to affirm the robustness of the findings.

Results

A two-sample MR investigation unveiled the genus Olsenella, genus Ruminococcaceae UCG-004, and genus Ruminococcaceae UCG-010 were identified as risk factors associated with AGA. In contrast, the family Acidaminococcaceae and genus Anaerofilum, along with the genus Ruminiclostridium 9, demonstrated a protective effect. The sensitivity analyses provided additional assurance that the findings of the current study were less susceptible to the influence of confounding variables and biases.

Conclusion

The MR study has established a link between specific gut microbiota and AGA, offering evidence for the identification of more precisely targeted probiotics. This discovery has the potential to aid in the prevention, control, and reversal of AGA progression.

WNT/β-catenin regulatory roles on PD-(L)1 and immunotherapy responses

Dysregulation of WNT/β-catenin is a hallmark of many cancer types and a key mediator of metastasis in solid tumors. Overactive β-catenin signaling hampers dendritic cell (DC) recruitment, promotes CD8+ T cell exclusion and increases the population of regulatory T cells (Tregs). The activity of WNT/β-catenin also induces the expression of programmed death-ligand 1 (PD-L1) on tumor cells and promotes programmed death-1 (PD-1) upregulation. Increased activity of WNT/β-catenin signaling after anti-PD-1 therapy is indicative of a possible implication of this signaling in bypassing immune checkpoint inhibitor (ICI) therapy. This review is aimed at giving a comprehensive overview of the WNT/β-catenin regulatory roles on PD-1/PD-L1 axis in tumor immune ecosystem, discussing about key mechanistic events contributed to the WNT/β-catenin-mediated bypass of ICI therapy, and representing inhibitors of this signaling as promising combinatory regimen to go with anti-PD-(L)1 in cancer immunotherapy. Ideas presented in this review imply the synergistic efficacy of such combination therapy in rendering durable anti-tumor immunity.

Acute Kidney Injury by Ischemia/Reperfusion and Extracellular Vesicles

Acute kidney injury (AKI) is often caused by ischemia-reperfusion injury (IRI). IRI significantly affects kidney metabolism, which elicits pro-inflammatory responses and kidney injury. The ischemia/reperfusion of the kidney is associated with transient high mitochondrial-derived reactive oxygen species (ROS) production rates. Excessive mitochondrial-derived ROS damages cellular components and, together with other pathogenic mechanisms, elicits a range of acute injury mechanisms that impair kidney function. Mitochondrial-derived ROS production also stimulates epithelial cell secretion of extracellular vesicles (EVs) containing RNAs, lipids, and proteins, suggesting that EVs are involved in AKI pathogenesis. This literature review focuses on how EV secretion is stimulated during ischemia/reperfusion and how cell-specific EVs and their molecular cargo may modify the IRI process. Moreover, critical pitfalls in the analysis of kidney epithelial-derived EVs are described. In particular, we will focus on how the release of kidney epithelial EVs is affected during tissue analyses and how this may confound data on cell-to-cell signaling. By increasing awareness of methodological pitfalls in renal EV research, the risk of false negatives can be mitigated. This will improve future EV data interpretation regarding EVs contribution to AKI pathogenesis and their potential as biomarkers or treatments for AKI.

Plasma Extracellular Vesicle Long RNA in Diagnosis and Prediction in Small Cell Lung Cancer

(1) Introduction: The aim of this study was to identify the plasma extracellular vesicle (EV)-specific transcriptional profile in small-cell lung cancer (SCLC) and to explore the application value of plasma EV long RNA (exLR) in SCLC treatment prediction and diagnosis. (2) Methods: Plasma samples were collected from 57 SCLC treatment-naive patients, 104 non-small-cell lung cancer (NSCLC) patients and 59 healthy participants. The SCLC patients were divided into chemo-sensitive and chemo-refractory groups based on the therapeutic effects. The exLR profiles of the plasma samples were analyzed by high-throughput sequencing. Bioinformatics approaches were used to investigate the differentially expressed exLRs and their biofunctions. Finally, a t-signature was constructed using logistic regression for SCLC treatment prediction and diagnosis. (3) Results: We obtained 220 plasma exLRs profiles in all the participants. Totals of 5787 and 1207 differentially expressed exLRs were identified between SCLC/healthy controls, between the chemo-sensitive/chemo-refractory groups, respectively. Furthermore, we constructed a t-signature that comprised ten exLRs, including EPCAM, CCNE2, CDC6, KRT8, LAMB1, CALB2, STMN1, UCHL1, HOXB7 and CDCA7, for SCLC treatment prediction and diagnosis. The exLR t-score effectively distinguished the chemo-sensitive from the chemo-refractory group (p = 9.268 × 10^-9) with an area under the receiver operating characteristic curve (AUC) of 0.9091 (95% CI: 0.837 to 0.9811) and distinguished SCLC from healthy controls (AUC: 0.9643; 95% CI: 0.9256-1) and NSCLC (AUC: 0.721; 95% CI: 0.6384-0.8036). (4) Conclusions: This study firstly characterized the plasma exLR profiles of SCLC patients and verified the feasibility and value of identifying biomarkers based on exLR profiles in SCLC diagnosis and treatment prediction.

An estimate of extracellular vesicle secretion rates of human blood cells

Extracellular vesicles (EVs) have been implicated in the intercellular transfer of RNA and proteins through cellular secretion into the extracellular space. In blood plasma, circulating EVs are mainly derived from blood cells; however, factors that control plasma EV abundance are largely unknown. Here, we estimate the EV secretion rates for blood cell types using reported values for cell-specific plasma EV abundances and their parental cell's ubiquity in healthy humans. While we found that plasma contains on average ∼2 plasma EVs/cell, the cell-specific EV-to-cell ratio spanned four orders of magnitude from 0.13 ± 0.1 erythrocyte-derived EVs/erythrocyte to (1.9 ± 1.3) × 103 monocyte-derived EVs/monocyte. The steady-state plasma EV level was maintained by an estimated plasma EV secretion rate of (1.5 ± 0.4) ×  1012 EVs/min. The cell-specific secretion rate estimates were highest for monocytes (45 ± 21 EVs/cell/min) and lowest for erythrocytes ((3.2 ± 3.0) ×  10-3 EVs/cell/min). The estimated basal cell-specific EV secretion rates were not significantly correlated to the cell's lifespan or size; however, we observed a highly significant correlation to cellular mitochondrial enzyme activities. Together, our analysis indicates that cell-specific mitochondrial metabolism, for example, via reactive oxygen species, affects plasma EV abundance through increased secretion rates, and the results provide a resource for understanding EV function in human health and disease.

Analysis of 5-Methylcytosine Regulators and DNA Methylation-Driven Genes in Colon Cancer

Background: Epigenetic-driven events are important molecular mechanisms of carcinogenesis. The 5-methylcytosine (5mC) regulators play important roles in the methylation-driven gene expression. However, the effect of the 5mC regulators on the oncogenic pathways in colon cancer (CC) remains unclear. Also, the clinical value of such epigenetic-driven events needs further research.

Methods: The transcriptome and matching epigenetic data were obtained from The Cancer Genome Atlas dataset. The gene set variation analysis identified the oncogenic pathways adjusted by 5mC regulators. The “edgeR” and “methylmix” package identified the differential expression genes of DNA methylation-driven genes. The correlation between 5mC regulators or transcription factors and shortlisted genes was investigated by calculating the Spearman's rank correlation coefficient. Among them, the genes related to diagnosis were screened out based on differential gene expression in extracellular vesicles (EVs) by the “limma” package and histology by immunohistochemistry. Then, a risk signature was constructed by fitting the generalized linear model and validated by the receiver operating characteristic curve.

Results: MYC targets pathway and phosphatidylinositol-3-kinase–AKT–mammalian target of rapamycin signaling pathway were identified as the hallmark-related pathways associated with 5mC regulators. Also, the P53 pathway was subject to the influence of regulators' expression. A five methylation-driven gene signature (FIRRE, MYBL2, TGFBI, AXIN2, and SLC35D3) was developed as the biomarker for CC diagnosis. Meanwhile, those genes positively related to 5mC regulators and interacted with their relevant or transcription factors.

Conclusion: In general, 5mC regulators are positively related to each other and DNA methylation-driven genes, with the relationship of multiple active and inhibitory pathways related to cancer. Meanwhile, the signature (FIRRE, MYBL2, TGFBI, AXIN2, and SLC35D3) can prefigure prospective diagnosis in CC.