The DisGeNET knowledge platform for disease genomics: 2019 update

TFTG: A comprehensive database for human transcription factors and their targets

Transcription factors (TFs) are major contributors to gene transcription, especially in controlling cell-specific gene expression and disease occurrence and development. Uncovering the relationship between TFs and their target genes is critical to understanding the mechanism of action of TFs. With the development of high-throughput sequencing techniques, a large amount of TF-related data has accumulated, which can be used to identify their target genes. In this study, we developed TFTG (Transcription Factor and Target Genes) database (http://tf.liclab.net/TFTG), which aimed to provide a large number of available human TF-target gene resources by multiple strategies, besides performing a comprehensive functional and epigenetic annotations and regulatory analyses of TFs. We identified extensive available TF-target genes by collecting and processing TF-associated ChIP-seq datasets, perturbation RNA-seq datasets and motifs. We also obtained experimentally confirmed relationships between TF and target genes from available resources. Overall, the target genes of TFs were obtained through integrating the relevant data of various TFs as well as fourteen identification strategies. Meanwhile, TFTG was embedded with user-friendly search, analysis, browsing, downloading and visualization functions. TFTG is designed to be a convenient resource for exploring human TF-target gene regulations, which will be useful for most users in the TF and gene expression regulation research.

Network-based analysis of heterogeneous patient-matched brain and extracranial melanoma metastasis pairs reveals three homogeneous subgroups

Melanoma, the deadliest form of skin cancer, can metastasize to different organs. Molecular differences between brain and extracranial melanoma metastases are poorly understood. Here, promoter methylation and gene expression of 11 heterogeneous patient-matched pairs of brain and extracranial metastases were analyzed using melanoma-specific gene regulatory networks learned from public transcriptome and methylome data followed by network-based impact propagation of patient-specific alterations. This innovative data analysis strategy allowed to predict potential impacts of patient-specific driver candidate genes on other genes and pathways. The patient-matched metastasis pairs clustered into three robust subgroups with specific downstream targets with known roles in cancer, including melanoma (SG1: RBM38, BCL11B, SG2: GATA3, FES, SG3: SLAMF6, PYCARD). Patient subgroups and ranking of target gene candidates were confirmed in a validation cohort. Summarizing, computational network-based impact analyses of heterogeneous metastasis pairs predicted individual regulatory differences in melanoma brain metastases, cumulating into three consistent subgroups with specific downstream target genes.

drexml: A command line tool and Python package for drug repurposing

We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation.

Identification of novel membrane markers in circulating tumor cells of mesenchymal state in breast cancer

Cancer metastasis is a major cause of cancer-related deaths worldwide. The ability to detect and monitor circulating tumor cells (CTCs) offers a promising approach to early detection and management of metastasis. Although studies on epithelial markers for CTC detection are actively underway, the discovery of mesenchymal markers has not been studied sufficiently. In this study, we developed a new pipeline to identify membrane markers in CTCs of mesenchymal state in breast cancer based on expression profiles of the 310 CTC samples. From the total CTC samples, only CTC samples in the mesenchymal state were collected by employing hierarchical clustering. In samples belonging to the mesenchymal state, we calculated the correlation coefficients between 1995 membrane genes and ZEB2, which was determined as the key mesenchymal signature, allowing the 84 positively correlated genes. Furthermore, to ensure clinical significance, Kaplan-Meier analysis were performed on the 124 breast cancer patients, resulting in the 14 genes predicting prognosis. By exploring genes commonly identified in the both analyses, F11R and PTGIR were characterized as membrane markers in CTCs of mesenchymal state in breast cancer, which were evaluated by enriched terms, literature evidence, and relevant molecular pathways. We expect that the results will be helpful to more effective strategies for metastasis management.

Deciphering molecular basis of pesticide-induced recurrent pregnancy loss: insights from transcriptomics analysis

Recent studies have revealed a notable connection between pesticide exposure and Recurrent Pregnancy Loss (RPL), yet the precise molecular underpinning of this toxicity remains elusive. Through the alignment of Differentially Expressed Genes (DEGs) of healthy and RPL patients with the target genes of 9 pesticide components, we identified a set of 12 genes responsible for RPL etiology. Interestingly, biological process showed that besides RPL, those 12 genes also associated with preeclampsia and cardiovascular disease. Enrichment analysis showed the engagement of these genes associated with essential roles in the molecular transport of small molecules, as well as the aldosterone-regulated sodium reabsorption, endocrine and other factor-regulated calcium reabsorption, mineral absorption, ion homeostasis, and ion transport by P-type ATPases. Notably, the crosstalk targets between pesticide components played crucial roles in influencing RPL results, suggesting a role in attenuating pesticide agents that contribute to RPL. It is important to note that non-significant concentration of the pesticide components observed in both control and RPL samples should not prematurely undermine the potential for pesticides to induce RPL in humans. This study emphasizes the complexity of pesticide induced RPL and highlights avenues for further research and precautionary measures.

Decoding common genetic alterations between Barrett's esophagus and esophageal adenocarcinoma: A bioinformatics analysis

Background

Esophageal adenocarcinoma (EAC) is a common cancer with a poor prognosis in advanced stages. Therefore, early EAC diagnosis and treatment have gained attention in recent decades. It has been found that various pathological changes, particularly Barrett's Esophagus (BE), can occur in the esophageal tissue before the development of EAC. In this study, we aimed to identify the molecular contributor in BE to EAC progression by detecting the essential regulatory genes that are differentially expressed in both BE and EAC.

Materials and methods

We conducted a comprehensive bioinformatics analysis to detect BE and EAC-associated genes. The common differentially expressed genes (DEGs) and common single nucleotide polymorphisms (SNPs) were detected using the GEO and DisGeNET databases, respectively. Then, hub genes and the top modules within the protein-protein interaction network were identified. Moreover, the co-expression network of the top module by the HIPPIE database was constructed. Additionally, the gene regulatory network was constructed based on miRNAs and circRNAs. Lastly, we inspected the DGIdb database for possible interacted drugs.

Results

Our microarray dataset analysis identified 92 common DEGs between BE and EAC with significant enrichment in skin and epidermis development genes. The study also identified 22 common SNPs between BE and EAC. The top module of PPI network analysis included SCEL, KRT6A, SPRR1A, SPRR1B, SPRR3, PPL, SPRR2B, EVPL, and CSTA. We constructed a ceRNA network involving three specific mRNAs, 23 miRNAs, and 101 selected circRNAs. According to the results from the DGIdb database, TD101 was found to interact with the KRT6A gene.

Conclusion

The present study provides novel potential candidate genes that may be involved in the molecular association between Esophageal adenocarcinoma and Barrett's Esophagus, resulting in developing the diagnostic tools and therapeutic targets to prevent progression of BE to EAC.

Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach

Cervical cancer is one of the most severe threats to women worldwide and holds fourth rank in lethality. It is estimated that 604, 127 cervical cancer cases have been reported in 2020 globally. With advancements in high throughput technologies and bioinformatics, several cervical candidate genes have been proposed for better therapeutic strategies. In this paper, we intend to prioritize the candidate genes that are involved in cervical cancer progression through a fractal time series-based cross-correlations approach. we apply the chaos game representation theory combining a two-dimensional multifractal detrended cross-correlations approach among the known and candidate genes involved in cervical cancer progression to prioritize the candidate genes. We obtained 16 candidate genes that showed cross-correlation with known cancer genes. Functional enrichment analysis of the candidate genes shows that they involve GO terms: biological processes, cell-cell junction assembly, cell-cell junction organization, regulation of cell shape, cortical actin cytoskeleton organization, and actomyosin structure organization. KEGG pathway analysis revealed genes' role in Rap1 signaling pathway, ErbB signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, Acute myeloid leukemia, chronic myeloid leukemia, Breast cancer, Thyroid cancer, Bladder cancer, and Gastric cancer. Further, we performed survival analysis and prioritized six genes CDH2, PAIP1, BRAF, EPB41L3, OSMR, and RUNX1 as potential candidate genes for cervical cancer that has a crucial role in tumor progression. We found that our study through this integrative approach an efficient tool and paved a new way to prioritize the candidate genes and these genes could be evaluated experimentally for potential validation. We suggest this may be useful in analyzing the nucleotide sequences and protein sequences for clustering, classification, class affiliation, etc.

Targeting Metabolic Syndrome Pathways: Carrot microRNAs As Potential Modulators

Metabolic syndrome is a condition characterized by metabolic alterations that culminate in chronic noncommunicable diseases of high morbidity and mortality, such as cardiovascular diseases, type 2 diabetes, nonalcoholic fatty liver disease, and colon cancer. Developing new therapeutic strategies with a multifactorial approach is important since current therapies focus on only one or two components of the metabolic syndrome. In this sense, plant-based gene regulation represents an innovative strategy to prevent or modulate human metabolic pathologies, including metabolic syndrome. Here, using a computational and systems biology approach, it was found that carrot microRNAs can modulate key BMPs/SMAD signaling members, C/EBPs, and KLFs involved in several aspects associated with metabolic syndrome, including the hsa04350:TGF-beta signaling pathway, hsa04931:insulin resistance, hsa04152:AMPK signaling pathway, hsa04933:AGE-RAGE signaling pathway in diabetic complications, hsa04010:MAPK signaling pathway, hsa04350:TGF-beta signaling pathway, hsa01522:endocrine resistance, and hsa04910:insulin signaling pathway. These data demonstrated the potential applications of carrot microRNAs as effective food-based therapeutics for obesity and associated metabolic diseases.

Role of ribosomal pathways and comorbidity in COVID-19: Insight from SARS-CoV-2 proteins and host proteins interaction network analysis

The COVID-19 pandemic has become a significant global issue in terms of public health. While it is largely associated with respiratory complications, recent reports indicate that patients also experience neurological symptoms and other health issues. The objective of this study is to examine the network of protein-protein interactions (PPI) between SARS-CoV-2 proteins and human host proteins, pinpoint the central genes within this network implicated in disease pathology, and assess their viability as targets for drug development. The study adopts a network-based approach to construct a network of 29 SARS-CoV-2 proteins interacting with 2896 host proteins, with 176 host genes being identified as interacting genes with all the viral proteins. Gene ontology and pathway analysis of these host proteins revealed their role in biological processes such as translation, mRNA splicing, and ribosomal pathways. We further identified EEF2, RPS3, RPL9, RPS16, and RPL11 as the top 5 most connected hub genes in the disease-causing network, with significant interactions among each other. These hub genes were found to be involved in ribosomal pathways and cytoplasmic translation. Further a disease-gene interaction was also prepared to investigate the role of hub genes in other disorders and to understand the condition of comorbidity in COVID-19 patients. We also identified 13 drug molecules having interactions with all the hub genes, and estradiol emerged as the top potential drug target for the COVID-19 patients. Our study provides valuable insights using the protein-protein interaction network of SARS-CoV-2 proteins with host proteins and highlights the molecular basis of manifestation of COVID-19 and proposes drug for repurposing. As the pandemic continues to evolve, it is anticipated that investigating SARS-CoV-2 proteins will remain a critical area of focus for researchers globally, particularly in addressing potential challenges posed by specific SARS-CoV-2 variants in the future.

Bioinformatics-based analysis of the dialog between COVID-19 and RSA

Pregnant women infected with SARS-CoV-2 in early pregnancy may face an increased risk of miscarriage due to immune imbalance at the maternal-fetal interface. However, the molecular mechanisms underlying the crosstalk between COVID-19 infection and recurrent spontaneous abortion (RSA) remain poorly understood. This study aimed to elucidate the transcriptomic molecular dialog between COVID-19 and RSA. Based on bioinformatics analysis, 307 common differentially expressed genes were found between COVID-19 (GSE171110) and RSA (GSE165004). Common DEGs were mainly enriched in ribosome-related and cell cycle-related signaling pathways. Using degree algorithm, the top 10 hub genes (RPS27A, RPL5, RPS8, RPL4, RPS2, RPL30, RPL23A, RPL31, RPL26, RPL37A) were selected from the common DEGs based on their scores. The results of the qPCR were in general agreement with the results of the raw letter analysis. The top 10 candidate drugs were also selected based on P-values. In this study, we provide molecular markers, signaling pathways, and small molecule compounds that may associate COVID-19. These findings may increase the accurate diagnosis and treatment of COVID-19 patients.

Network pharmacology-based analysis on the key mechanisms of Yiguanjian acting on chronic hepatitis

Chronic hepatitis (CH) encompasses a prevalent array of liver conditions that significantly contribute to global morbidity and mortality. Yiguanjian (YGJ) is a classical traditional Chinese medicine with a long history of medicinal as a treatment for CH. Although it has been reported that YGJ can reduce liver inflammation, the intricate mechanism requires further elucidation. We used network pharmacology approaches in this work, such as gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and network-based analysis of protein-protein interactions (PPIs), to clarify the pharmacological constituents, potential therapeutic targets, and YGJ signaling pathways associated with CH. Employing the random walk restart (RWR) algorithm, we identified GNAS, GNB1, CYP2E1, SFTPC, F2, MAPK3, PLG, SRC, HDAC1, and STAT3 as pivotal targets within the PPI network of YGJ-CH. YGJ attenuated liver inflammation and inhibited GNAS/STAT3 signaling in vivo. In vitro, we overexpressed the GNAS gene further to verify the critical role of GNAS in YGJ treatment. Our findings highlight GNAS/STAT3 as a promising therapeutic target for CH, providing a basis and direction for future investigations.

Network pharmacology and in vivo experiment-based strategy for investigating the mechanism of chronic prostatitis/chronic pelvic pain syndrome in QianLieJinDan tablets

Background

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is a common urinary system disease that is prone to recurrence. It typically leads to varying degrees of pelvic pain and discomfort, as well as symptoms related to the urinary system in affected patients. QianLieJinDan tablets (QLJD), a traditional Chinese medicine, have shown promising therapeutic effects on CP/CPPS in clinical practice, but the underlying mechanisms of QLJD in treating CP/CPPS have not been determined.

Objective

To reveal the phytochemical characterization and multitarget mechanism of QLJD on CP/CPPS.

Methods

The concentrations of the components of QLJD were determined using UHPLC-Q Exactive Orbitrap-MS. Utilizing network pharmacology approaches, the potential components, targets, and pathways involved in the treatment of CP/CPPS caused by QLJD were screened. Molecular docking calculations were employed to assess the affinity between the components of the QLJD and potential targets, revealing the optimal molecular conformation and binding site. Finally, the therapeutic efficacy and potential underlying mechanisms of QLJD were investigated through pharmacological experiments.

Results

In this study, a total of 35 components targeting 29 CP-related genes were identified, among which quercetin, baicalin, icariin, luteolin, and gallic acid were the major constituents. Enrichment analysis revealed that the potential targets were involved mainly in the regulation of cytokines, cell proliferation and apoptosis, and the oxidative stress response and were primarily associated with the cytokine‒cytokine receptor interaction pathway, the IL-17 signaling pathway, the Th17 cell differentiation pathway, and the JAK-STAT signaling pathway. In vivo experiments demonstrated that QLJD effectively attenuated the infiltration of CD3+ T cells and the expression of ROS in a CP/CPPS model rat prostate tissue. Furthermore, through the inhibition of IL-6 and STAT3 expression, QLJD reduced the differentiation of Th17 cells, thereby ameliorating pathological injury and prostatic index in prostate tissue.

Conclusion

The potential of QLJD as an anti-CP/CPPS agent lies in its ability to interfere with the expression of IL-6 and STAT3, inhibit Th17 cell differentiation, reduce inflammatory cell infiltration in rat prostate tissue, and alleviate oxidative stress damage through its multi-component, multi-target, and multi-pathway effects.

A single dose of cocaine rewires the 3D genome structure of midbrain dopamine neurons

Midbrain dopamine neurons (DNs) respond to a first exposure to addictive drugs and play key roles in chronic drug usage 1-3 . As the synaptic and transcriptional changes that follow an acute cocaine exposure are mostly resolved within a few days 4,5 , the molecular changes that encode the long-term cellular memory of the exposure within DNs remain unknown. To investigate whether a single cocaine exposure induces long-term changes in the 3D genome structure of DNs, we applied Genome Architecture Mapping and single nucleus transcriptomic analyses in the mouse midbrain. We found extensive rewiring of 3D genome architecture at 24 hours past exposure which remains or worsens by 14 days, outlasting transcriptional responses. The cocaine-induced chromatin rewiring occurs at all genomic scales and affects genes with major roles in cocaine-induced synaptic changes. A single cocaine exposure triggers extensive long-lasting changes in chromatin condensation in post-synaptic and post-transcriptional regulatory genes, for example the unfolding of Rbfox1 which becomes most prominent 14 days post exposure. Finally, structurally remodeled genes are most expressed in a specific DN sub-type characterized by low expression of the dopamine auto-receptor Drd2 , a key feature of highly cocaine-sensitive cells. These results reveal an important role for long-lasting 3D genome remodelling in the cellular memory of a single cocaine exposure, providing new hypotheses for understanding the inception of drug addiction and 3D genome plasticity.

Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality

BACKGROUND

Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality.

METHODS

CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis.

RESULTS

A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64-10.25]) and ovarian cancer in UKBB (5.53[2.08-8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97-2.96]) compared to females (1.84[1.44-2.37]).

CONCLUSION

We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

Exploring the molecular mechanism of Epimedium for the treatment of ankylosing spondylitis based on network pharmacology, molecular docking, and molecular dynamics simulations

Ankylosing spondylitis (AS) is a rheumatic disease that causes inflammation and bone formation in the spine. Despite significant advances in treatment, adverse side effects have triggered research into natural compounds. Epimedium (EP) is a traditional Chinese herb with a variety of pharmacological activities, including antirheumatic, anti-inflammatory, and immunomodulatory activities; however, its direct effects on AS treatment and the underlying molecular mechanisms have not been systematically studied. Thus, here, we used network pharmacology, molecular docking, and molecular dynamics simulations to explore the targets of EP for treating AS. We constructed an interaction network to elucidate the complex relationship between EP and AS. Sixteen active ingredients in EP were screened; 80 potential targets were identified. In particular, 8-(3-methylbut-2-enyl)-2-phenylchromone, anhydroicaritin, and luteolin were the core components and TNF, IL-6, IL-1β, MMP9, and PTGS2 were the core targets. The GO and KEGG analyses indicated that EP may modulate multiple biological processes and pathways, including the AGE-RAGE, TNF, NF-κB/MAPK, and TLR signaling pathways, for AS treatment. Molecular docking and molecular dynamics simulations showed good affinity between the active components and core targets of EP, with stable binding within 100 nanoseconds. In particular, 8-(3-methylbut-2-enyl)-2-phenylchromone possessed the highest free energy of binding to PTGS2 and TNF (-115.575 and - 87.676 kcal/mol, respectively). Thus, EP may affect AS through multiple pathways, including the alleviation of inflammation, oxidative stress, and immune responses. In summary, we identified the active components and potential targets of EP, highlighting new strategies for the further experimental validation and exploration of lead compounds for treating AS.

Pervasive structural heterogeneity rewires glioblastoma chromosomes to sustain patient-specific transcriptional programs

Glioblastoma multiforme (GBM) encompasses brain malignancies marked by phenotypic and transcriptional heterogeneity thought to render these tumors aggressive, resistant to therapy, and inevitably recurrent. However, little is known about how the spatial organization of GBM genomes underlies this heterogeneity and its effects. Here, we compile a cohort of 28 patient-derived glioblastoma stem cell-like lines (GSCs) known to reflect the properties of their tumor-of-origin; six of these were primary-relapse tumor pairs from the same patient. We generate and analyze 5 kbp-resolution chromosome conformation capture (Hi-C) data from all GSCs to systematically map thousands of standalone and complex structural variants (SVs) and the multitude of neoloops arising as a result. By combining Hi-C, histone modification, and gene expression data with chromatin folding simulations, we explain how the pervasive, uneven, and idiosyncratic occurrence of neoloops sustains tumor-specific transcriptional programs via the formation of new enhancer-promoter contacts. We also show how even moderately recurrent neoloops can relate to patient-specific vulnerabilities. Together, our data provide a resource for dissecting GBM biology and heterogeneity, as well as for informing therapeutic approaches.

Causal relationship between inflammatory proteins and glioblastoma: a two-sample bi‑directional mendelian randomization study

Background: Observational studies have indicated a potential correlation between glioblastoma and circulating inflammatory proteins. Further investigation is required to establish a causal relationship between these two factors. Methods: We performed a Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary of 91 circulating inflammation-related proteins (N = 14,824) to assess their causal impact on glioblastoma. The GWAS summary data for glioblastoma included 243 cases and 287,137 controls. The inverse variance weighted (IVW) method was used as the primary analytical method to assess causality. Four additional MR methods [simple mode, MR-Egger, weighted median, and weighted mode] were used to supplement the IVW results. Furthermore, several sensitivity analyses were performed to assess heterogeneity, horizontal pleiotropy, and stability. Reverse MR analysis was also performed. glioblastoma transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed to validate the findings obtained through MR, while pathway and functional enrichment analyses were conducted to predict the potential underlying mechanisms. Results: Our findings from employing the inverse variance weighted method in our forward MR analysis provide robust evidence supporting a potential association between glioblastoma and elevated levels of Cystatin D, as well as decreased levels of fibroblast growth factor 21 (FGF21) in the circulation. Moreover, our reverse MR analysis revealed that glioblastoma may contribute to increased concentrations of C-X-C motif chemokine 9 (CXCL9) and Interleukin-33 (IL-33) in the bloodstream. Transcriptomic analysis showed that FGF21 expression was inversely associated with the risk of developing glioblastoma, whereas an increased risk was linked to elevated levels of CXCL9 and IL-33. Pathway and functional enrichment analyses suggested that Cystatin D might exert its effects on glioblastoma through intracellular protein transport, whereas FGF21 might affect glioblastoma via glucose response mechanisms. Conclusion: These results indicate that FGF21 is a significant factor in glioblastoma susceptibility. Glioblastoma also affects the expression of inflammatory proteins such as C-X-C motif chemokine 9 and Interleukin-33, providing new insights into the mechanisms of glioblastoma genesis and clinical research.

Cornuside alleviates psoriasis-like skin lesions in mice by relieving inflammatory effects

Psoriasis is a chronic inflammatory skin disease substantially affecting the quality of life, with no complete cure owing to its complex pathogenesis. Cornuside, a major bioactive compound present in Cornus officinalis Sieb. et Zucc., which is a well-known traditional Chinese medicine with a variety of biological and pharmacological activities, such as anti-apoptotic, antioxidant, and anti-inflammatory properties. However, its effects on psoriasis remain unclear. Our preliminary analysis of network pharmacology showed that cornuside may be involved in psoriasis by regulating the inflammatory response and IL-17 signaling pathway. Thus, we investigated the protective role and mechanism of cornuside in the pathogenesis of psoriasis in an imiquimod (IMQ)-induced psoriasis mouse model. In-vivo experiments demonstrated that cornuside-treated mice had reduced skin erythema, scales, thickness, and inflammatory infiltration. The Psoriasis Area Severity Index score was significantly lower than that of the IMQ group. Flow cytometry analysis indicated that cornuside effectively inhibited Th1- and Th17-cell infiltration and promoted aggregation of Th2 cells in skin tissues. Cornuside also inhibited the infiltration of macrophages to the skin. Furthermore, in-vitro experiments indicated that cornuside also decreased the polarization of M1 macrophages and reduced the levels of associated cytokines. Western blotting demonstrated that cornuside suppressed the phosphorylation of c-Jun N-terminal kinase (JNK) and extracellular receptor kinase (ERK) in bone marrow-derived macrophages. Our findings indicate that cornuside has a protective effect against IMQ-induced psoriasis by inhibiting M1 macrophage polarization through the ERK and JNK signaling pathways and modulating the infiltration of immune cells as well as the expression of inflammatory factors.

Data-Driven Characterization of Genetic Variability in Disease Pathways and Pesticide-Induced Nervous System Disease in the United States Population

BACKGROUND

Genetic susceptibility to chemicals is incompletely characterized. However, nervous system disease development following pesticide exposure can vary in a population, implying some individuals may have higher genetic susceptibility to pesticide-induced nervous system disease.

OBJECTIVES

We aimed to build a computational approach to characterize single-nucleotide polymorphisms (SNPs) implicated in chemically induced adverse outcomes and used this framework to assess the link between differential population susceptibility to pesticides and human nervous system disease.

METHODS

We integrated publicly available datasets of Chemical-Gene, Gene-Pathway, and SNP-Disease associations to build Chemical-Pathway-Gene-SNP-Disease linkages for humans. As a case study, we integrated these linkages with spatialized pesticide application data for the US from 1992 to 2018 and spatialized nervous system disease rates for 2018. Through this, we characterized SNPs that may be important in states with high disease occurrence based on the pesticides used there.

RESULTS

We found that the number of SNP hits per pesticide in US states positively correlated with disease incidence and prevalence for Alzheimer's disease, Parkinson disease, and multiple sclerosis. We performed frequent itemset mining to differentiate pesticides used over time in states with high and low disease occurrence and found that only 19% of pesticide sets overlapped between 10 states with high disease occurrence and 10 states with low disease occurrence rates, and more SNPs were implicated in pathways in high disease occurrence states. Through a cross-validation of subsets of five high and low disease occurrence states, we characterized SNPs, genes, pathways, and pesticides more frequently implicated in high disease occurrence states.

DISCUSSION

Our findings support that pesticides contribute to nervous system disease, and we developed priority lists of SNPs, pesticides, and pathways for further study. This data-driven approach can be adapted to other chemicals, diseases, and locations to characterize differential population susceptibility to chemical exposures. https://doi.org/10.1289/EHP14108.

Rheum palmatum L. and Salvia miltiorrhiza Bge. Alleviates Acute Pancreatitis by Regulating Th17 Cell Differentiation: An Integrated Network Pharmacology Analysis, Molecular Dynamics Simulation and Experimental Validation

OBJECTIVE

To identify the core targets of Rheum palmatum L. and Salvia miltiorrhiza Bge., (Dahuang-Danshen, DH-DS) and the mechanism underlying its therapeutic efficacy in acute pancreatitis (AP) using a network pharmacology approach and validate the findings in animal experiments.

METHODS

Network pharmacology analysis was used to elucidate the mechanisms underlying the therapeutic effects of DH-DS in AP. The reliability of the results was verified by molecular docking simulation and molecular dynamics simulation. Finally, the results of network pharmacology enrichment analysis were verified by immunohistochemistry, Western blot analysis and real-time quantitative PCR, respectively.

RESULTS

Sixty-seven common targets of DH-DS in AP were identified and mitogen-activated protein kinase 3 (MAPK3), Janus kinase 2 (JAK2), signal transducer and activator of transcription 3 (STAT3), protein c-Fos (FOS) were identified as core targets in the protein interaction (PPI) network analysis. Gene ontology analysis showed that cellular response to organic substance was the main functions of DH-DS in AP, and Kyoto Encyclopedia of Genes and Genomes analysis showed that the main pathway included Th17 cell differentiation. Molecular docking simulation confirmed that DH-DS binds with strong affinity to MAPK3, STAT3 and FOS. Molecular dynamics simulation revealed that FOS-isotanshinone II and STAT3-dan-shexinkum d had good binding capacity. Animal experiments indicated that compared with the AP model group, DH-DS treatment effectively alleviated AP by inhibiting the expression of interleukin-1β, interleukin-6 and tumor necrosis factor-α, and blocking the activation of Th17 cell differentiation (P<0.01).

CONCLUSION

DH-DS could inhibit the expression of inflammatory factors and protect pancreatic tissues, which would be functioned by regulating Th17 cell differentiation-related mRNA and protein expressions.

Strategic pathway analysis for dual management of epilepsy and comorbid depression: a systems biology perspective

Depression is a common psychiatric comorbidity among patients with epilepsy (PWE), affecting more than a third of PWE. Management of depression may improve quality of life of epileptic patients. Unfortunately, available antidepressants worsen epilepsy by reducing the seizure threshold. This situation demands search of new safer target for combined directorate of epilepsy and comorbid depression. A system biology approach may be useful to find novel pathways/markers for the cure of both epilepsy and associated depression via analyzing available genomic and proteomic information. Hence, the system biology approach using curated 64 seed genes involved in temporal lobe epilepsy and mental depression was applied. The interplay of 600 potential proteins was revealed by the Disease Module Detection (DIAMOnD) Algorithm for the treatment of both epilepsy and comorbid depression using these seed genes. The gene enrichment analysis of seed and diamond genes through DAVID suggested 95 pathways. Selected pathways were refined based on their syn or anti role in epilepsy and depression. In conclusion, total 8 pathways and 27 DIAMOnD genes/proteins were finally deduced as potential new targets for modulation of selected pathways to manage epilepsy and comorbid depression.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00208-1.

State of the Interactomes: an evaluation of molecular networks for generating biological insights

Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

First Transcriptome Analysis of Hepatoblastoma in Brazil: Unraveling the Pivotal Role of Noncoding RNAs and Metabolic Pathways

Hepatoblastoma stands as the most prevalent liver cancer in the pediatric population. Characterized by a low mutational burden, chromosomal and epigenetic alterations are key drivers of its tumorigenesis. Transcriptome analysis is a powerful tool for unraveling the molecular intricacies of hepatoblastoma, shedding light on the effects of genetic and epigenetic changes on gene expression. In this study conducted in Brazilian patients, an in-depth whole transcriptome analysis was performed on 14 primary hepatoblastomas, compared to control liver tissues. The analysis unveiled 1,492 differentially expressed genes (1,031 upregulated and 461 downregulated), including 920 protein-coding genes (62%). Upregulated biological processes were linked to cell differentiation, signaling, morphogenesis, and development, involving known hepatoblastoma-associated genes (DLK1, MEG3, HDAC2, TET1, HMGA2, DKK1, DKK4), alongside with novel findings (GYNG4, CDH3, and TNFRSF19). Downregulated processes predominantly centered around oxidation and metabolism, affecting amines, nicotinamides, and lipids, featuring novel discoveries like the repression of SYT7, TTC36, THRSP, CCND1, GCK and CAMK2B. Two genes, which displayed a concordant pattern of DNA methylation alteration in their promoter regions and dysregulation in the transcriptome, were further validated by RT-qPCR: the upregulated TNFRSF19, a key gene in the embryonic development, and the repressed THRSP, connected to lipid metabolism. Furthermore, based on protein-protein interaction analysis, we identified genes holding central positions in the network, such as HDAC2, CCND1, GCK, and CAMK2B, among others, that emerged as prime candidates warranting functional validation in future studies. Notably, a significant dysregulation of non-coding RNAs (ncRNAs), predominantly upregulated transcripts, was observed, with 42% of the top 50 highly expressed genes being ncRNAs. An integrative miRNA-mRNA analysis revealed crucial biological processes associated with metabolism, oxidation reactions of lipids and carbohydrates, and methylation-dependent chromatin silencing. In particular, four upregulated miRNAs (miR-186, miR-214, miR-377, and miR-494) played a pivotal role in the network, potentially targeting multiple protein-coding transcripts, including CCND1 and CAMK2B. In summary, our transcriptome analysis highlighted disrupted embryonic development as well as metabolic pathways, particularly those involving lipids, emphasizing the emerging role of ncRNAs as epigenetic regulators in hepatoblastomas. These findings provide insights into the complexity of the hepatoblastoma transcriptome and identify potential targets for future therapeutic interventions.

GLIMS: A two-stage gradual-learning method for cancer genes prediction using multi-omics data and co-splicing network

Identifying cancer genes is vital for cancer diagnosis and treatment. However, because of the complexity of cancer occurrence and limited cancer genes knowledge, it is hard to identify cancer genes accurately using only a few omics data, and the overall performance of existing methods is being called for further improvement. Here, we introduce a two-stage gradual-learning strategy GLIMS to predict cancer genes using integrative features from multi-omics data. Firstly, it uses a semi-supervised hierarchical graph neural network to predict the initial candidate cancer genes by integrating multi-omics data and protein-protein interaction (PPI) network. Then, it uses an unsupervised approach to further optimize the initial prediction by integrating the co-splicing network in post-transcriptional regulation, which plays an important role in cancer development. Systematic experiments on multi-omics cancer data demonstrated that GLIMS outperforms the state-of-the-art methods for the identification of cancer genes and it could be a useful tool to help advance cancer analysis.

Artificial Intelligence and Computational Biology in Gene Therapy: A Review

One of the trending fields in almost all areas of science and technology is artificial intelligence. Computational biology and artificial intelligence can help gene therapy in many steps including: gene identification, gene editing, vector design, development of new macromolecules and modeling of gene delivery. There are various tools used by computational biology and artificial intelligence in this field, such as genomics, transcriptomic and proteomics data analysis, machine learning algorithms and molecular interaction studies. These tools can introduce new gene targets, novel vectors, optimized experiment conditions, predict the outcomes and suggest the best solutions to avoid undesired immune responses following gene therapy treatment.

Immune response stability to the SARS-CoV-2 mRNA vaccine booster is influenced by differential splicing of HLA genes

Many molecular mechanisms that lead to the host antibody response to COVID-19 vaccines remain largely unknown. In this study, we used serum antibody detection combined with whole blood RNA-based transcriptome analysis to investigate variability in vaccine response in healthy recipients of a booster (third) dose schedule of the mRNA BNT162b2 vaccine against COVID-19. The cohort was divided into two groups: (1) low-stable individuals, with antibody concentration anti-SARS-CoV IgG S1 below 0.4 percentile at 180 days after boosting vaccination; and (2) high-stable individuals, with antibody values greater than 0.6 percentile of the range in the same period (median 9525 [185-80,000] AU/mL). Differential gene expression, expressed single nucleotide variants and insertions/deletions, differential splicing events, and allelic imbalance were explored to broaden our understanding of the immune response sustenance. Our analysis revealed a differential expression of genes with immunological functions in individuals with low antibody titers, compared to those with higher antibody titers, underscoring the fundamental importance of the innate immune response for boosting immunity. Our findings also provide new insights into the determinants of the immune response variability to the SARS-CoV-2 mRNA vaccine booster, highlighting the significance of differential splicing regulatory mechanisms, mainly concerning HLA alleles, in delineating vaccine immunogenicity.

Chinese medicine in the treatment of non-alcoholic fatty liver disease based on network pharmacology: a review

Non-alcoholic fatty liver disease (NAFLD) is a clinicopathological syndrome characterized by abnormalities in hepatic fat deposition, the incidence of which has been increasing year by year in recent years. It has become the largest chronic liver disease globally and one of the important causes of cirrhosis and even primary liver cancer formation. The pathogenesis of NAFLD has not yet been fully clarified. Modern medicine lacks targeted clinical treatment protocols for NAFLD, and most drugs lack efficacy and have high side effects. In contrast, Traditional Chinese Medicine (TCM) has significant advantages in the treatment and prevention of NAFLD, which have been widely recognized by scholars around the world. In recent years, through the establishment of a "medicine-disease-target-pathway" network relationship, network pharmacology can explore the molecular basis of the role of medicines in disease prevention and treatment from various perspectives, predicting the pharmacological mechanism of the corresponding medicines. This approach is compatible with the holistic view and treatment based on pattern differentiation of TCM and has been widely used in TCM research. In this paper, by searching relevant databases such as PubMed, Web of Science, and Embase, we reviewed and analyzed the relevant signaling pathways and specific mechanisms of action of single Chinese medicine, Chinese medicine combinations, and Chinese patent medicine for the treatment of NAFLD in recent years. These related studies fully demonstrated the therapeutic characteristics of TCM with multi-components, multi-targets, and multi-pathways, which provided strong support for the exact efficacy of TCM exerted in the clinic. In conclusion, we believe that network pharmacology is more in line with the TCM mindset of treating diseases, but with some limitations. In the future, we should eliminate the potential risks of false positives and false negatives, clarify the interconnectivity between components, targets, and diseases, and conduct deeper clinical or experimental studies.

PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis

Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA). Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells. Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.

Transcriptomic analysis reveals sex-specific patterns in the hippocampus in Alzheimer's disease

Background

The hippocampus, vital for memory and learning, is among the first brain regions affected in Alzheimer's Disease (AD) and exhibits adult neurogenesis. Women face twice the risk of developing AD compare to men, making it crucial to understand sex differences in hippocampal function for comprehending AD susceptibility.

Methods

We conducted a comprehensive analysis of bulk mRNA postmortem samples from the whole hippocampus (GSE48350, GSE5281) and its CA1 and CA3 subfields (GSE29378). Our aim was to perform a comparative molecular signatures analysis, investigating sex-specific differences and similarities in the hippocampus and its subfields in AD. This involved comparing the gene expression profiles among: (a) male controls (M-controls) vs. female controls (F-controls), (b) females with AD (F-AD) vs. F-controls, (c) males with AD (M-AD) vs. M-controls, and (d) M-AD vs. F-AD. Furthermore, we identified AD susceptibility genes interacting with key targets of menopause hormone replacement drugs, specifically the ESR1 and ESR2 genes, along with GPER1.

Results

The hippocampal analysis revealed contrasting patterns between M-AD vs. M-controls and F-AD vs. F-controls, as well as M-controls vs. F-controls. Notably, BACE1, a key enzyme linked to amyloid-beta production in AD pathology, was found to be upregulated in M-controls compared to F-controls in both CA1 and CA3 hippocampal subfields. In M-AD vs. M-controls, the GABAergic synapse was downregulated, and the Estrogen signaling pathway was upregulated in both subfields, unlike in F-AD vs. F-controls. Analysis of the whole hippocampus also revealed upregulation of the GABAergic synapse in F-AD vs. F-controls. While direct comparison of M-AD vs. F-AD, revealed a small upregulation of the ESR1 gene in the CA1 subfield of males. Conversely, F-AD vs. F-controls exhibited downregulation of the Dopaminergic synapse in both subfields, while the Calcium signaling pathway showed mixed regulation, being upregulated in CA1 but downregulated in CA3, unlike in M-AD vs. M-controls. The upregulated Estrogen signaling pathway in M-AD, suggests a compensatory response to neurodegenerative specifically in males with AD. Our results also identified potential susceptibility genes interacting with ESR1 and ESR2, including MAPK1, IGF1, AKT1, TP53 and CD44.

Conclusion

These findings underscore the importance of sex-specific disease mechanisms in AD pathogenesis. Region-specific analysis offers a more detailed examination of localized changes in the hippocampus, enabling to capture sex-specific molecular patterns in AD susceptibility and progression.

Kaempferol-induced mitochondrial damage promotes NF-κB-NLRP3-caspase-1 signaling axis-mediated pyroptosis in gastric cancer cells

GC is a gastrointestinal tumor with high morbidity and mortality. Owing to the high rate of postoperative recurrence associated with GC, the effectiveness of radiotherapy and chemotherapy may be compromised by the occurrence of severe undesirable side effects. In light of these circumstances, KP, a flavonoid abundantly present in diverse herbal and fruit sources, emerges as a promising therapeutic agent with inherent anti-tumor properties. This study endeavors to demonstrate the therapeutic potential of KP in the context of GC while unraveling the intricate underlying mechanisms. Notably, our investigations unveil that KP stimulation effectively promotes the activation of NLRP3 inflammatory vesicles within AGS cells by engaging the NF-κB signaling pathway. Consequently, the signal cascade triggers the cleavage of Caspase-1, culminating in the liberation of IL-18. Furthermore, we ascertain that KP facilitate AGS cell pyroptosis by inducing mitochondrial damage. Collectively, our findings showcase KP as a compelling candidate for the treatment of GC-related diseases, heralding new possibilities for future therapeutic interventions.

Mendelian randomization and transcriptome analysis identified immune-related biomarkers for osteoarthritis

Background

The immune microenvironment assumes a significant role in the pathogenesis of osteoarthritis (OA). However, the current biomarkers for the diagnosis and treatment of OA are not satisfactory. Our study aims to identify new OA immune-related biomarkers to direct the prevention and treatment of OA using multi-omics data.

Methods

The discovery dataset integrated the GSE89408 and GSE143514 datasets to identify biomarkers that were significantly associated with the OA immune microenvironment through multiple machine learning methods and weighted gene co-expression network analysis (WGCNA). The identified signature genes were confirmed using two independent validation datasets. We also performed a two-sample mendelian randomization (MR) study to generate causal relationships between biomarkers and OA using OA genome-wide association study (GWAS) summary data (cases n = 24,955, controls n = 378,169). Inverse-variance weighting (IVW) method was used as the main method of causal estimates. Sensitivity analyses were performed to assess the robustness and reliability of the IVW results.

Results

Three signature genes (FCER1G, HLA-DMB, and HHLA-DPA1) associated with the OA immune microenvironment were identified as having good diagnostic performances, which can be used as biomarkers. MR results showed increased levels of FCER1G (OR = 1.118, 95% CI 1.031-1.212, P = 0.041), HLA-DMB (OR = 1.057, 95% CI 1.045 -1.069, P = 1.11E-21) and HLA-DPA1 (OR = 1.030, 95% CI 1.005-1.056, P = 0.017) were causally and positively associated with the risk of developing OA.

Conclusion

The present study identified the 3 potential immune-related biomarkers for OA, providing new perspectives for the prevention and treatment of OA. The MR study provides genetic support for the causal effects of the 3 biomarkers with OA and may provide new insights into the molecular mechanisms leading to the development of OA.

Chemotherapeutic potential of betanin/capecitabine combination targeting colon cancer: experimental and bioinformatic studies exploring NFκB and cyclin D1 interplay

Introduction: Betanin (C₂₄H₂₆N₂O₁₃) is safe to use as food additives approved by the FDA with anti-inflammatory and anticancer effects in many types of cancer cell lines. The current experiment was designed to test the chemotherapeutic effect of the combination of betanin with the standard chemotherapeutic agent, capecitabine, against chemically induced colon cancer in mice. Methods: Bioinformatic approach was designed to get information about the possible mechanisms through which the drugs may control cancer development. Five groups of mice were assigned as, (i) saline, (ii) colon cancer, (iii) betanin, (iv) capecitabine and (v) betanin/capecitabine. Drugs were given orally for a period of six weeks. Colon tissues were separated and used for biological assays and histopathology. Results: In addition, the mRNA expression of TNF-α (4.58-fold), NFκB (5.33-fold), IL-1β (4.99-fold), cyclin D1 (4.07-fold), and IL-6 (3.55-fold) and protein levels showed several folds increases versus the saline group. Tumor histopathology scores in the colon cancer group (including cryptic distortion and hyperplasia) and immunostaining for NFκB (2.94-fold) were high while periodic-acid Schiff staining demonstrated poor mucin content (33% of the saline group). These pathologic manifestations were reduced remarkably in betanin/capecitabine group. Conclusion: Collectively, our findings demonstrated the usefulness of betanin/capecitabine combination in targeting colon cancer and highlighted that betanin is a promising adjuvant therapy to capecitabine in treating colon cancer patients.

Uropathogenic Escherichia coli infection: innate immune disorder, bladder damage, and Tailin Fang II

Background

Uropathogenic Escherichia coli (UPEC) activates innate immune response upon invading the urinary tract, whereas UPEC can also enter bladder epithelial cells (BECs) through interactions with fusiform vesicles on cell surfaces and subsequently escape from the vesicles into the cytoplasm to establish intracellular bacterial communities, finally evading the host immune system and leading to recurrent urinary tract infection (RUTI). Tailin Fang II (TLF-II) is a Chinese herbal formulation composed of botanicals that has been clinically proven to be effective in treating urinary tract infection (UTI). However, the underlying therapeutic mechanisms remain poorly understood.

Methods

Network pharmacology analysis of TLF-II was conducted. Female Balb/C mice were transurethrally inoculated with UPEC CFT073 strain to establish the UTI mouse model. Levofloxacin was used as a positive control. Mice were randomly divided into four groups: negative control, UTI, TLF-II, and levofloxacin. Histopathological changes in bladder tissues were assessed by evaluating the bladder organ index and performing hematoxylin-eosin staining. The bacterial load in the bladder tissue and urine sample of mice was quantified. Activation of the TLR4-NF-κB pathway was investigated through immunohistochemistry and western blotting. The urinary levels of interleukin (IL)-1β and IL-6 and urine leukocyte counts were monitored. We also determined the protein expressions of markers associated with fusiform vesicles, Rab27b and Galectin-3, and levels of the phosphate transporter protein SLC20A1. Subsequently, the co-localization of Rab27b and SLC20A1 with CFT073 was examined using confocal fluorescence microscopy.

Results

Data of network pharmacology analysis suggested that TLF-II could against UTI through multiple targets and pathways associated with innate immunity and inflammation. Additionally, TLF-II significantly attenuated UPEC-induced bladder injury and reduced the bladder bacterial load. Meanwhile, TLF-II inhibited the expression of TLR4 and NF-κB on BECs and decreased the urine levels of IL-1β and IL-6 and urine leukocyte counts. TLF-II reduced SLC20A1 and Galectin-3 expressions and increased Rab27b expression. The co-localization of SLC20A1 and Rab27b with CFT073 was significantly reduced in the TLF-II group.

Conclusion

Collectively, innate immunity and bacterial escape from fusiform vesicles play important roles in UPEC-induced bladder infections. Our findings suggest that TLF-II combats UPEC-induced bladder infections by effectively mitigating bladder inflammation and preventing bacterial escape from fusiform vesicles into the cytoplasm. The findings suggest that TLF-II is a promising option for treating UTI and reducing its recurrence.

Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis

Genome-wide association studies (GWAS) have associated 76 loci with the risk of developing melanoma. However, understanding the molecular basis of such associations has remained a challenge because most of these loci are in non-coding regions of the genome. Here, we integrated data on epigenomic markers, three-dimensional (3D) genome organization, and expression quantitative trait loci (eQTL) from melanoma-relevant tissues and cell types to gain novel insights into the mechanisms underlying melanoma risk. This integrative approach revealed a total of 151 target genes, both near and far away from the risk loci in linear sequence, with known and novel roles in the etiology of melanoma. Using protein-protein interaction networks, we identified proteins that interact-directly or indirectly-with the products of the target genes. The interacting proteins were enriched for known melanoma driver genes. Further integration of these target genes into tissue-specific gene regulatory networks revealed patterns of gene regulation that connect melanoma to its comorbidities. Our study provides novel insights into the biological implications of genetic variants associated with melanoma risk.

Network pharmacology and molecular docking to elucidate the common mechanism of hydroxychloroquine treatment in lupus nephritis and IgA nephropathy

OBJECTIVE

Hydroxychloroquine (HCQ), characterized by a broad effect on immune regulation, has been widely used in the treatment of autoimmune glomerulonephritis such as lupus nephritis (LN) and immunoglobulin A nephropathy (IgAN). The current research investigates whether HCQ plays a role in the treatment of LN and IgAN through common mechanisms since the pathogenesis of both LN and IgAN is closely related to immune complex deposition, complement activation, and ultimately inflammation.

METHODS

Seventy-two common targets were obtained related to the common mechanism of HCQ treatment of LN and IgAN. Targets associated with LN and IgAN were collected based on DisGeNET, GeneCards, and OMIM databases. Possible HCQ targets were obtained from the PubChem database and PharmMapper databases. The overlapping targets of HCQ ingredients, IgAN, and LN were discovered via the Venn 2.1.0 online platform. Through the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. Cytoscape (v3.9.1) was used to build a protein-protein interaction (PPI) network. Molecular docking was performed by using AutoDockTools 1.5.6 software and PyMol software to match the binding activity between HCQ and the 10 core targets.

RESULTS

The results showed that core targets (including MMP 2, PPARG, IL-2, MAPK14, MMP 9, and SRC), three signaling pathways (including the PI3K-Akt, AGE-RAGE, and MAPK), and cell differentiation (including Th1, Th2, and Th17) might be related to the body's immunity and inflammation. These results suggested that HCQ might act on targets and pathways involved in inflammation and immune regulation to exert a common effect on the treatment of LN and IgAN.

CONCLUSIONS

The current study provided new evidence for the protective mechanism and clinical utility of HCQ against LN and IgAN.

Mechanism of Bazi Bushen capsule in delaying the senescence of mesenchymal stem cells based on network pharmacology and experimental validation

Ageing is becoming an increasingly serious problem; therefore, there is an urgent need to find safe and effective anti-ageing drugs.

Aims

To investigate the effects of Bazi Bushen capsule (BZBS) on the senescence of mesenchymal stem cells (MSCs) and explore its mechanism of action.

Methods

Network pharmacology was used to predict the targets of BZBS in delaying senescence in MSCs. For in vitro studies, MSCs were treated with D-gal, BZBS, and NMN, and cell viability, cell senescence, stemness-related genes, and cell cycle were studied using cell counting kit-8 (CCK-8) assay, SA-β-galactosidase (SA-β-gal) staining, Quantitative Real-Time PCR (qPCR) and flow cytometry (FCM), respectively. Alkaline phosphatase (ALP), alizarin red, and oil red staining were used to determine the osteogenic and lipid differentiation abilities of MSCs. Finally, the expression of senescence-related genes and cyclin-related factors was detected by qPCR and western blotting.

Results

Network pharmacological analysis suggested that BZBS delayed cell senescence by interfering in the cell cycle. Our in vitro studies suggested that BZBS could significantly increase cell viability (P < 0.01), decrease the quantity of β-galactosidase+ cells (P < 0.01), downregulate p16 and p21 (P < 0.05, P < 0.01), improve adipogenic and osteogenic differentiation, and upregulate Nanog, OCT4 and SOX2 genes (P < 0.05, P < 0.01) in senescent MSCs. Moreover, BZBS significantly reduced the proportion of senescent MSCs in the G0/G1 phase (P < 0.01) and enhanced the expression of CDK4, Cyclin D1, and E2F1 (P < 0.05, P < 0.01, respectively). Upon treatment with HY-50767A, a CDK4 inhibitor, the upregulation of E2F1 was no longer observed in the BZBS group.

Conclusions

BZBS can protect MSCs against D-gal-induced senescence, which may be associated with cell cycle regulation via the Cyclin D1/CDK4/E2F1 signalling pathway.

Immediate-Early Genes as Influencers in Genetic Networks and their Role in Alzheimer's Disease

Immediate-early genes (IEGs) are a class of activity-regulated genes (ARGs) that are transiently and rapidly activated in the absence of de novo protein synthesis in response to neuronal activity. We explored the role of IEGs in genetic networks to pinpoint potential drug targets for Alzheimer's disease (AD). Using a combination of network analysis and genome-wide association study (GWAS) summary statistics we show that (1) IEGs exert greater topological influence across different human and mouse gene networks compared to other ARGs, (2) ARGs are sparsely involved in diseases and significantly more mutational constrained compared to non-ARGs, (3) Many AD-linked variants are in ARGs gene regions, mainly in MARK4 near FOSB, with an AD risk eQTL that increases MARK4 expression in cortical areas, (4) MARK4 holds an influential place in a dense AD multi-omic network and a high AD druggability score. Our work on IEGs' influential network role is a valuable contribution to guiding interventions for diseases marked by dysregulation of their downstream targets and highlights MARK4 as a promising underexplored AD-target.

Human cytomegalovirus (CMV) dysregulates neurodevelopmental pathways in cerebral organoids

Human cytomegalovirus (CMV) infection is the leading non-genetic aetiology of congenital malformation in developed countries, causing significant fetal neurological injury. This study investigated potential CMV pathogenetic mechanisms of fetal neural malformation using in vitro human cerebral organoids. Cerebral organoids were permissive to CMV replication, and infection dysregulated cellular pluripotency and differentiation pathways. Aberrant expression of dual-specificity tyrosine phosphorylation-regulated kinases (DYRK), sonic hedgehog (SHH), pluripotency, neurodegeneration, axon guidance, hippo signalling and dopaminergic synapse pathways were observed in CMV-infected organoids using immunofluorescence and RNA-sequencing. Infection with CMV resulted in dysregulation of 236 Autism Spectrum Disorder (ASD)-related genes (p = 1.57E-05) and pathways. This notable observation suggests potential links between congenital CMV infection and ASD. Using DisGeNET databases, 103 diseases related to neural malformation or mental disorders were enriched in CMV-infected organoids. Cytomegalovirus infection-related dysregulation of key cerebral cellular pathways potentially provides important, modifiable pathogenetic mechanisms for congenital CMV-induced neural malformation and ASD.

Identification of Dalbergiae Odoriferae Lignum active ingredients and potential mechanisms in the treatment of adriamycin-induced cardiotoxicity based on network pharmacology and experimental verification

The purpose of this study is to investigate the ingredients and mechanisms through which Dalbergiae Odoriferae Lignum (DOL) reduces adriamycin-induced cardiotoxicity. DOL's ingredients and drug targets were acquired from Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), and adriamycin-induced cardiotoxicity disease targets were gathered from GeneCards and National Center for Biotechnology Information (NCBI). The therapeutic targets of DOL against adriamycin-induced cardiotoxicity were identified by intersecting drug and disease targets. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted using R. Subsequently, core targets were determined and used for molecular docking with DOL ingredients. In vitro and in vivo experiments validated DOL's primary ingredients against adriamycin-induced cardiotoxicity efficacy. Western blot and immunohistochemistry verified its impact on target protein. After intersecting 530 drug targets and 51 disease targets, 19 therapeutic targets for DOL alleviated adriamycin-induced cardiotoxicity were received. Molecular docking demonstrated that DOL primary ingredient formononetin had a robust binding affinity for nitric oxide synthase 3 (NOS3). Experimental results showed that formononetin effectively mitigated adriamycin-induced cardiotoxicity. Additionally, western blot and immunohistochemistry showed that formononetin improved NOS3 expression. The network pharmacology and experimentation suggest that the primary ingredient of DOL, formononetin, may target NOS3 to act as a therapeutic agent for adriamycin-induced cardiotoxicity.

Deciphering the molecular and clinical characteristics of TREM2, HCST, and TYROBP in cancer immunity: A comprehensive pan-cancer study

Background

Hematopoietic cell signal transducer (HCST) and tyrosine kinase-binding protein (TYROBP) are triggering receptors expressed on myeloid cells 2 (TREM2), which are pivotal in the immune response to disease. Despite growing evidence underscoring the significance of TREM2, HCST, and TYROBP in certain forms of tumorigenesis, a comprehensive pan-cancer analysis of these proteins is lacking.

Methods

Multiple databases were synthesized to investigate the relationship between TREM2, HCST, TYROBP, and various cancer types. These include prognosis, methylation, regulation by long non-coding RNAs and transcription factors, immune signatures, pathway activity, microsatellite instability (MSI), tumor mutational burden (TMB), single-cell transcriptome profiling, and drug sensitivity.

Results

TREM2, HCST, and TYROBP displayed extensive somatic changes across numerous tumors, and their mRNA expression and methylation levels influenced patient outcomes across multiple cancer types. long non-coding RNA (lncRNA) -messenger RNA (mRNA) and TF-mRNA regulatory networks involving TREM2, HCST, and TYROBP were identified, with lncRNA MEG3 and the transcription factor SIP1 emerging as potential key regulators. Further immune analyses indicated that TREM2, HCST, and TYROBP play critical roles in immune-related pathways and macrophage differentiation, and may be significantly associated with TGF-β and SMAD9. Furthermore, the expression of TREM2, HCST, and TYROBP correlated with the immunotherapy markers TMB and MSI, and influenced sensitivity to immune-targeted drugs, thereby indicating their potential as predictors of immunotherapy outcomes.

Conclusion

This study offers valuable insights into the roles of TREM2, HCST, and TYROBP in tumor immunotherapy, suggesting their potential as prognostic markers and therapeutic targets for various cancers.

Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity

There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.

Single-cell division tracing and transcriptomics reveal cell types and differentiation paths in the regenerating lung

Understanding the molecular and cellular processes involved in lung epithelial regeneration may fuel the development of therapeutic approaches for lung diseases. We combine mouse models allowing diphtheria toxin-mediated damage of specific epithelial cell types and parallel GFP-labeling of functionally dividing cells with single-cell transcriptomics to characterize the regeneration of the distal lung. We uncover cell types, including Krt13+ basal and Krt15+ club cells, detect an intermediate cell state between basal and goblet cells, reveal goblet cells as actively dividing progenitor cells, and provide evidence that adventitial fibroblasts act as supporting cells in epithelial regeneration. We also show that diphtheria toxin-expressing cells can persist in the lung, express specific inflammatory factors, and transcriptionally resemble a previously undescribed population in the lungs of COVID-19 patients. Our study provides a comprehensive single-cell atlas of the distal lung that characterizes early transcriptional and cellular responses to concise epithelial injury, encompassing proliferation, differentiation, and cell-to-cell interactions.

Nobiletin alleviates atherosclerosis by inhibiting lipid uptake via the PPARG/CD36 pathway

BACKGROUND

Atherosclerosis (AS) is a persistent inflammatory condition triggered and exacerbated by several factors including lipid accumulation, endothelial dysfunction and macrophages infiltration. Nobiletin (NOB) has been reported to alleviate atherosclerosis; however, the underlying mechanism remains incompletely understood.

METHODS

This study involved comprehensive bioinformatic analysis, including multidatabase target prediction; GO and KEGG enrichment analyses for function and pathway exploration; DeepSite and AutoDock for drug binding site prediction; and CIBERSORT for immune cell involvement. In addition, target intervention was verified via cell scratch assays, oil red O staining, ELISA, flow cytometry, qRT‒PCR and Western blotting. In addition, by establishing a mouse model of AS, it was demonstrated that NOB attenuated lipid accumulation and the extent of atherosclerotic lesions.

RESULTS

(1) Altogether, 141 potentially targetable genes were identified through which NOB could intervene in atherosclerosis. (2) Lipid and atherosclerosis, fluid shear stress and atherosclerosis may be the dominant pathways and potential mechanisms. (3) ALB, AKT1, CASP3 and 7 other genes were identified as the top 10 target genes. (4) Six genes, including PPARG, MMP9, SRC and 3 other genes, were related to the M0 fraction. (5) CD36 and PPARG were upregulated in atherosclerosis samples compared to the normal control. (6) By inhibiting lipid uptake in RAW264.7 cells, NOB prevents the formation of foam cell. (7) In RAW264.7 cells, the inhibitory effect of oxidized low-density lipoprotein on foam cells formation and lipid accumulation was closely associated with the PPARG signaling pathway. (8) In vivo validation showed that NOB significantly attenuated intra-arterial lipid accumulation and macrophage infiltration and reduced CD36 expression.

CONCLUSIONS

Nobiletin alleviates atherosclerosis by inhibiting lipid uptake via the PPARG/CD36 pathway.

Potential of Gut Microbial Metabolites in Treating Osteoporosis and Obesity: A Network Pharmacology and Bioinformatics Approach

BACKGROUND The gut microbial metabolites demonstrate significant activity against metabolic diseases including osteoporosis (OP) and obesity, but active compounds, targets, and mechanisms have not been fully identified. Hence, the current investigation explored the mechanisms of active metabolites and targets against OP and obesity by using network pharmacology approaches. MATERIAL AND METHODS The gutMGene database was used to collect gut microbial targets-associated metabolites; DisGeNET and OMIM databases were used to identify targets relevant to OP and obesity. A total of 63 and 89 overlapped targets were considered the final OP and obesity targets after creating a Venn diagram of metabolites-related targets and disease-related targets. Furthermore, the top 20% of degrees, betweenness, and closeness were used to form the sub-network of protein-protein interaction of these targets. Finally, the biotransformation-increased receptors and biological mechanisms were identified and validated using ADMET properties analysis, molecular docking, and molecular dynamic simulation. RESULTS GO, KEGG pathway analysis, and protein-protein interactions were performed to establish metabolites and target networks. According to the enrichment analysis, OP and obesity are highly linked to the lipid and atherosclerosis pathways. Moreover, ADMET analysis depicts that the major metabolites have drug-likeliness activity and no or less toxicity. Following that, the molecular docking studies showed that compound K and TP53 target have a remarkable negative affinity (-8.0 kcal/mol) among all metabolites and targets for both diseases. Finally, the conformity of compound K against the targeted protein TP53 was validated by 250ns MD simulation. CONCLUSIONS Therefore, we summarized that compound K can regulate TP53 and could be developed as a therapy option for OP and obesity.

Predicting drug outcome of population via clinical knowledge graph

Optimal treatments depend on numerous factors such as drug chemical properties, disease biology, and patient characteristics to which the treatment is applied. To realize the promise of AI in healthcare, there is a need for designing systems that can capture patient heterogeneity and relevant biomedical knowledge. Here we present PlaNet, a geometric deep learning framework that reasons over population variability, disease biology, and drug chemistry by representing knowledge in the form of a massive clinical knowledge graph that can be enhanced by language models. Our framework is applicable to any sub-population, any drug as well drug combinations, any disease, and to a wide range of pharmacological tasks. We apply the PlaNet framework to reason about outcomes of clinical trials: PlaNet predicts drug efficacy and adverse events, even for experimental drugs and their combinations that have never been seen by the model. Furthermore, PlaNet can estimate the effect of changing population on the trial outcome with direct implications on patient stratification in clinical trials. PlaNet takes fundamental steps towards AI-guided clinical trials design, offering valuable guidance for realizing the vision of precision medicine using AI.

Comprehensive mapping of the AOP-Wiki database: identifying biological and disease gaps

Introduction: The Adverse Outcome Pathway (AOP) concept facilitates rapid hazard assessment for human health risks. AOPs are constantly evolving, their number is growing, and they are referenced in the AOP-Wiki database, which is supported by the OECD. Here, we present a study that aims at identifying well-defined biological areas, as well as gaps within the AOP-Wiki for future research needs. It does not intend to provide a systematic and comprehensive summary of the available literature on AOPs but summarizes and maps biological knowledge and diseases represented by the already developed AOPs (with OECD endorsed status or under validation). Methods: Knowledge from the AOP-Wiki database were extracted and prepared for analysis using a multi-step procedure. An automatic mapping of the existing information on AOPs (i.e., genes/proteins and diseases) was performed using bioinformatics tools (i.e., overrepresentation analysis using Gene Ontology and DisGeNET), allowing both the classification of AOPs and the development of AOP networks (AOPN). Results: AOPs related to diseases of the genitourinary system, neoplasms and developmental anomalies are the most frequently investigated on the AOP-Wiki. An evaluation of the three priority cases (i.e., immunotoxicity and non-genotoxic carcinogenesis, endocrine and metabolic disruption, and developmental and adult neurotoxicity) of the EU-funded PARC project (Partnership for the Risk Assessment of Chemicals) are presented. These were used to highlight under- and over-represented adverse outcomes and to identify and prioritize gaps for further research. Discussion: These results contribute to a more comprehensive understanding of the adverse effects associated with the molecular events in AOPs, and aid in refining risk assessment for stressors and mitigation strategies. Moreover, the FAIRness (i.e., data which meets principles of findability, accessibility, interoperability, and reusability (FAIR)) of the AOPs appears to be an important consideration for further development.

Exposure to Airborne PM2.5 Water-Soluble Inorganic Ions Induces a Wide Array of Reproductive Toxicity

Water-soluble inorganic ions (WSIIs, primarily NH4+, SO42-, and NO3-) are major components in ambient PM2.5, but their reproductive toxicity remains largely unknown. An animal study was conducted where parental mice were exposed to PM2.5 WSIIs or clean air during preconception and the gestational period. After delivery, all maternal and offspring mice lived in a clean air environment. We assessed reproductive organs, gestation outcome, birth weight, and growth trajectory of the offspring mice. In parallel, we collected birth weight and placenta transcriptome data from 150 mother-infant pairs from the Rhode Island Child Health Study. We found that PM2.5 WSIIs induced a broad range of adverse reproductive outcomes in mice. PM2.5 NH4+, SO42-, and NO3- exposure reduced ovary weight by 24.22% (p = 0.005), 14.45% (p = 0.048), and 16.64% (p = 0.022) relative to the clean air controls. PM2.5 SO42- exposure reduced the weight of testicle by 5.24% (p = 0.025); further, mice in the PM2.5 SO42- exposure group had 1.81 (p = 0.027) fewer offspring than the control group. PM2.5 NH4+, SO42-, and NO3- exposure all led to lower birth than controls. In mice, 557 placenta genes were perturbed by exposure. Integrative analysis of mouse and human data suggested hypoxia response in placenta as an etiological mechanism underlying PM2.5 WSII exposure's reproductive toxicity.

Pan-cancer molecular signatures connecting aspartate transaminase (AST) to cancer prognosis, metabolic and immune signatures

Background

Serum aspartate transaminase (sAST) level is used routinely in conjunction with other clinical assays to assess liver health and disease. Increasing evidence suggests that sAST is associated with all-cause mortality and has prognostic value in several cancers, including gastrointestinal and urothelial cancers. Here, we undertake a systems approach to unravel molecular connections between AST and cancer prognosis, metabolism, and immune signatures at the transcriptomic and proteomic levels.

Methods

We mined public gene expression data across multiple normal and cancerous tissues using the Genotype Tissue Expression (GTEX) resource and The Cancer Genome Atlas (TCGA) to assess the expression of genes encoding AST isoenzymes (GOT1 and GOT2) and their association with disease prognosis and immune infiltration signatures across multiple tumors. We examined the associations between AST and previously reported pan-cancer molecular subtypes characterized by distinct metabolic and immune signatures. We analyzed human protein-protein interaction networks for interactions between GOT1 and GOT2 with cancer-associated proteins. Using public databases and protein-protein interaction networks, we determined whether the subset of proteins that interact with AST (GOT1 and GOT2 interactomes) are enriched with proteins associated with specific diseases, miRNAs and transcription factors.

Results

We show that AST transcript isoforms (GOT1 and GOT2) are expressed across a wide range of normal tissues. AST isoforms are upregulated in tumors of the breast, lung, uterus, and thymus relative to normal tissues but downregulated in tumors of the liver, colon, brain, kidney and skeletal sarcomas. At the proteomic level, we find that the expression of AST is associated with distinct pan-cancer molecular subtypes with an enrichment of specific metabolic and immune signatures. Based on human protein-protein interaction data, AST physically interacts with multiple proteins involved in tumor initiation, suppression, progression, and treatment. We find enrichments in the AST interactomes for proteins associated with liver and lung cancer and dermatologic diseases. At the regulatory level, the GOT1 interactome is enriched with the targets of cancer-associated miRNAs, specifically mir34a - a promising cancer therapeutic, while the GOT2 interactome is enriched with proteins that interact with cancer-associated transcription factors.

Conclusions

Our findings suggest that perturbations in the levels of AST within specific tissues reflect pathophysiological changes beyond tissue damage and have implications for cancer metabolism, immune infiltration, prognosis, and treatment personalization.

Pregnancy-specific beta-1-glycoprotein 6 is a potential novel diagnostic biomarker of placenta accreta spectrum

Early diagnosis is essential for the safer perinatal management of placenta accreta spectrum (PAS). We used transcriptome analysis to investigate diagnostic maternal serum biomarkers and the mechanisms of PAS development. We analyzed eight formalin-fixed paraffin-embedded placental specimens from two placenta increta and three placenta percreta cases who underwent cesarean hysterectomy at Sapporo Medical University Hospital between 2013 and 2019. Invaded placental regions were isolated from the uterine myometrium and RNA was extracted. The transcriptome difference between normal placenta and PAS was analyzed by microarray analysis. The PAS group showed markedly decreased expression of placenta-specific genes such as LGALS13 and the pregnancy-specific beta-1-glycoprotein (PSG) family. Term enrichment analysis revealed changes in genes related to cellular protein catabolic process, female pregnancy, autophagy, and metabolism of lipids. From the highly dysregulated genes in the PAS group, we investigated the expression of PSG family members, which are secreted into the intervillous space and can be detected in maternal serum from the early stage of pregnancy. The gene expression level of PSG6 in particular was progressively decreased from placenta increta to percreta. The PSG family, especially PSG6, is a potential biomarker for PAS diagnosis.

Stress granule-mediated RNA regulatory mechanism in Alzheimer's disease

Living organisms experience a range of stresses. To cope effectively with these stresses, eukaryotic cells have evolved a sophisticated mechanism involving the formation of stress granules (SGs), which play a crucial role in protecting various types of RNA species under stress, such as mRNAs and long non-coding RNAs (lncRNAs). SGs are non-membranous cytoplasmic ribonucleoprotein (RNP) granules, and the RNAs they contain are translationally stalled. Importantly, SGs have been thought to contribute to the pathophysiology of neurodegenerative diseases, including Alzheimer's disease (AD). SGs also contain multiple RNA-binding proteins (RBPs), several of which have been implicated in AD progression. SGs are transient structures that dissipate after stress relief. However, the chronic stresses associated with aging lead to the persistent formation of SGs and subsequently to solid-like pathological SGs, which could impair cellular RNA metabolism and also act as a nidus for the aberrant aggregation of AD-associated proteins. In this paper, we provide a comprehensive summary of the physical basis of SG-enriched RNAs and SG-resident RBPs. We then review the characteristics of AD-associated gene transcripts and their similarity to the SG-enriched RNAs. Furthermore, we summarize and discuss the functional implications of SGs in neuronal RNA metabolism and the aberrant aggregation of AD-associated proteins mediated by SG-resident RBPs in the context of AD pathogenesis. Geriatr Gerontol Int 2024; 24: 7-14.