Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines.

This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools , status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Health's (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.

Adiponectin mRNA Conjugated with Lipid Nanoparticles Specifically Targets the Pathogenesis of Type 2 Diabetes.

Type 2 diabetes (T2D) is a widespread health condition both in the United States and around the world, with insulin resistance playing a critical role in its development. Effective treatment strategies are essential for managing T2D and mitigating associated risks. Adiponectin (APN), secreted by adipocytes, exhibits an inverse correlation with obesity-related adiposity, and its levels are negatively associated with insulin resistance and body mass index. This study aimed to enhance endogenous APN levels in a diet-induced obese (DIO) mouse model using lipid nanoparticles (LNP) as safe delivery agents for APN mRNA conjugates. The results indicate that APN-mRNA-LNP administration successfully induced APN synthesis in various tissues, including muscle, liver, kidney, pancreas, and adipose cells. This induction was associated with several positive outcomes, such as preventing diet-induced body weight gain, improving hyperglycemia by promoting Glut-4 expression, alleviating diabetic nephropathy symptoms by blocking the EGFR pathway, and reducing pro-inflammatory cytokine production. In addition, the treatment demonstrated enhanced insulin sensitivity by activating DGKd and inhibiting PKCε. This resulted in reactivation of insulin receptors in insulin target tissues and stimulation of insulin secretion from pancreatic beta cells. The findings of the present study highlight the potential of APN-mRNA-LNP-based nucleic acid therapy as a treatment for type 2 diabetes, offering a comprehensive approach to addressing its complexities.

Pharmacogenomics of coronary artery response to intravenous gamma globulin in kawasaki disease.

Kawasaki disease (KD) is a multisystem inflammatory illness of infants and young children that can result in acute vasculitis. The mechanism of coronary artery aneurysms (CAA) in KD despite intravenous gamma globulin (IVIG) treatment is not known. We performed a Whole Genome Sequencing (WGS) association analysis in a racially diverse cohort of KD patients treated with IVIG, both using AHA guidelines. We defined coronary aneurysm (CAA) (N = 234) as coronary z ≥ 2.5 and large coronary aneurysm (CAA/L) (N = 92) as z ≥ 5.0. We conducted logistic regression models to examine the association of genetic variants with CAA/L during acute KD and with persistence >6 weeks using an additive model between cases and 238 controls with no CAA. We adjusted for age, gender and three principal components of genetic ancestry. The top significant variants associated with CAA/L were in the intergenic regions (rs62154092 p < 6.32E-08 most significant). Variants in SMAT4, LOC100127, PTPRD, TCAF2 and KLRC2 were the most significant non-intergenic SNPs. Functional mapping and annotation (FUMA) analysis identified 12 genomic risk loci with eQTL or chromatin interactions mapped to 48 genes. Of these NDUFA5 has been implicated in KD CAA and MICU and ZMAT4 has potential functional implications. Genetic risk score using these 12 genomic risk loci yielded an area under the receiver operating characteristic curve (AUC) of 0.86. This pharmacogenomics study provides insights into the pathogenesis of CAA/L in IVIG-treated KD and shows that genomics can help define the cause of CAA/L to guide management and improve risk stratification of KD patients.

Establishment of a novel cellular model for Alzheimer's disease in vitro studies.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by memory loss, cognitive impairment, and behavioral and psychological symptoms of dementia. The limited efficacy of drugs for the treatment of neurodegenerative diseases reflects their complex etiology and pathogenesis. A novel in vitro model may help to bridge the gap between existing preclinical animal models and human clinical trials, thus identifying promising therapeutic targets that can be explored in upcoming clinical trials. By assisting in the identification of the mechanism of action and potential dangers, in vitro testing can also shorten the time and expense of translation. AIM: As a result of these factors, our objective is to develop a powerful and informative cellular model of AD within a short period of time. Through triggering the MAPK and NF-κß signaling pathways with the aid of small chemical compounds (PAF C-16 and BetA), respectively, in mouse microglial (SIM-A9) and neuroblast Neuro-2a (N2a) cell lines. RESULTS: PAF C-16, initiated an activation effect at a concentration of 3.12 nM to 25 nM in the SIM-A9 and N2a cell lines after 72 h. BetA, activated the NF-κß pathway with a concentration of 12.5 nM to 25 nM in the SIM-A9 and N2a cell lines after 72 h. The combination of the activator chemicals provided suitable activation for MEK1/2-ERK and NF-κß in more than three subcultures. Activators significantly initiate APP and MAPT gene expression, as well as the expression of proteins APP, ß. Amyloid, tau, and p-tau. The activation of the targeted pathways leads to significant morphological changes. CONCLUSION: We can infer that the MEK1/2-ERK and NF-κß pathways, respectively, are directly activated by the PAF C-16 and BetA chemicals. The activation of MEK1/2-ERK pathway results in the activation of the APP gene, which in turn activates the ß. Amyloid protein, which in turn results in plaque. Furthermore, NF-κß activation results in the activation of the MAPT gene, which leads to Tau and p-Tau protein activation, which ultimately results in tangles. This can be put into practice in just three days, with a high level of activity and stability that is passed down to the next three generations (subculture), with significant morphological changes. In microglial and neuroblast cell lines, we were successful in creating a novel AD-cell model.

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.

Explorative Discovery of Gene Signatures and Clinotypes in Glioblastoma Cancer Through GeneTerrain Knowledge Map Representation.

This study introduces the GeneTerrain Knowledge Map Representation (GTKM), a novel method for visualizing gene expression data in cancer research. GTKM leverages protein-protein interactions to graphically display differentially expressed genes (DEGs) on a 2-dimensional contour plot, offering a more nuanced understanding of gene interactions and expression patterns compared to traditional heatmap methods. The research demonstrates GTKM's utility through four case studies on glioblastoma (GBM) datasets, focusing on survival analysis, subtype identification, IDH1 mutation analysis, and drug sensitivities of different tumor cell lines. Additionally, a prototype website has been developed to showcase these findings, indicating the method's adaptability for various cancer types. The study reveals that GTKM effectively identifies gene patterns associated with different clinical outcomes in GBM, and its profiles enable the identification of sub-gene signature patterns crucial for predicting survival. The methodology promises significant advancements in precision medicine, providing a powerful tool for understanding complex gene interactions and identifying potential therapeutic targets in cancer treatment.

Mondrian Map: Piet Mondrian's Abstraction for Differential Pathway Analysis.

Motivation: Visualizing complex biological networks is a significant challenge, as traditional tools often struggle to represent data clearly and intuitively. Drawing inspiration from Piet Mondrian's abstract art style, we introduce Mondrian Map, a novel visualization tool that transforms the complexity of biological networks into a more organized, meaningful, and aesthetically appealing form. Results: In our case study of glioma progression, Mondrian Map reveals distinct pathway patterns across multiple patient profiles at different time points. Afterwards, the significance of these distinctive pathways in glioblastoma multiforme development is validated by recent literature. Mondrian Map's visually intuitive representation of complex biological networks enables researchers to easily identify crucial pathways and potential therapeutic targets. Moreover, The potential applications of Mondrian Map extend beyond biology, making it a versatile tool across domains. Availability and Implementation: The code was implemented in Python version 3.10 and is available through GitHub (github.com/fuad021/mondrian-map). The datasets used in this paper were retrieved from Glioma Longitudinal AnalySiS (GLASS) consortium.

An NLP-based technique to extract meaningful features from drug SMILES.

NLP is a well-established field in ML for developing language models that capture the sequence of words in a sentence. Similarly, drug molecule structures can also be represented as sequences using the SMILES notation. However, unlike natural language texts, special characters in drug SMILES have specific meanings and cannot be ignored. We introduce a novel NLP-based method that extracts interpretable sequences and essential features from drug SMILES notation using N-grams. Our method compares these features to Morgan fingerprint bit-vectors using UMAP-based embedding, and we validate its effectiveness through two personalized drug screening (PSD) case studies. Our NLP-based features are sparse and, when combined with gene expressions and disease phenotype features, produce better ML models for PSD. This approach provides a new way to analyze drug molecule structures represented as SMILES notation, which can help accelerate drug discovery efforts. We have also made our method accessible through a Python library.

Photo-neuro-immuno-endocrinology: How the ultraviolet radiation regulates the body, brain, and immune system.

Ultraviolet radiation (UVR) is primarily recognized for its detrimental effects such as cancerogenesis, skin aging, eye damage, and autoimmune disorders. With exception of ultraviolet B (UVB) requirement in the production of vitamin D3, the positive role of UVR in modulation of homeostasis is underappreciated. Skin exposure to UVR triggers local responses secondary to the induction of chemical, hormonal, immune, and neural signals that are defined by the chromophores and extent of UVR penetration into skin compartments. These responses are not random and are coordinated by the cutaneous neuro-immuno-endocrine system, which counteracts the action of external stressors and accommodates local homeostasis to the changing environment. The UVR induces electrical, chemical, and biological signals to be sent to the brain, endocrine and immune systems, as well as other central organs, which in concert regulate body homeostasis. To achieve its central homeostatic goal, the UVR-induced signals are precisely computed locally with transmission through nerves or humoral signals release into the circulation to activate and/or modulate coordinating central centers or organs. Such modulatory effects will be dependent on UVA and UVB wavelengths. This leads to immunosuppression, the activation of brain and endocrine coordinating centers, and the modification of different organ functions. Therefore, it is imperative to understand the underlying mechanisms of UVR electromagnetic energy penetration deep into the body, with its impact on the brain and internal organs. Photo-neuro-immuno-endocrinology can offer novel therapeutic approaches in addiction and mood disorders; autoimmune, neurodegenerative, and chronic pain-generating disorders; or pathologies involving endocrine, cardiovascular, gastrointestinal, or reproductive systems.

Anti-inflammatory cerium-containing nano-scaled mesoporous bioactive glass for promoting regenerative capability of dental pulp cells.

AIMS: This study aimed to investigate the anti-inflammatory and odontoblastic effects of cerium-containing mesoporous bioactive glass nanoparticles (Ce-MBGNs) on dental pulp cells as novel pulp-capping agents. METHODOLOGY: Ce-MBGNs were synthesized using a post-impregnation strategy based on the antioxidant properties of Ce ions and proposed the first use of Ce-MBGNs for pulp-capping application. The biocompatibility of Ce-MBGNs was analysed using the CCK-8 assay and apoptosis detection. Additionally, the reactive oxygen species (ROS) scavenging ability of Ce-MBGNs was measured using the 2,7-Dichlorofuorescin Diacetate (DCFH-DA) probe. The anti-inflammatory effect of Ce-MBGNs on THP-1 cells was further investigated using flow cytometry and quantitative real-time polymerase chain reaction (RT-qPCR). Moreover, the effect of Ce-MBGNs on the odontoblastic differentiation of the dental pulp cells (DPCs) was assessed by combined scratch assays, RT-qPCR, western blotting, immunocytochemistry, Alizarin Red S staining and tissue-nonspecific alkaline phosphatase staining. Analytically, the secretions of tumour necrosis factor-α (TNF-α) and interleukin-1ß (IL-1ß) were detected with enzyme-linked immunosorbent assay (ELISA). RESULTS: Ce-MBGNs were confirmed to effectively scavenge ROS in THP-1-derived macrophages and DPCs. Flow cytometry and RT-qPCR assays revealed that Ce-MBGNs significantly inhibited the M1 polarization of macrophages (Mφ). Furthermore, the protein levels of TNF-α and IL-1ß were downregulated in THP-1-derived macrophages after stimulation with Ce-MBGNs. With a step-forward virtue of promoting the odontoblastic differentiation of DPCs, we further confirmed that Ce-MBGNs could regulate the formation of a conductive immune microenvironment with respect to tissue repair in DPCs, which was mediated by macrophages. CONCLUSIONS: Ce-MBGNs protected cells from self-produced oxidative damage and exhibited excellent immunomodulatory and odontoblastic differentiation effects on DPCs. As a pulp-capping agent, this novel biomaterial can exert anti-inflammatory effects and promote restorative dentine regeneration in clinical treatment. We believe that this study will stimulate further correlative research on the development of advanced pulp-capping agents.

Pharmacogenomics of Coronary Artery Response to Intravenous Gamma Globulin in Kawasaki Disease.

Background: Kawasaki disease (KD) is a multisystem inflammatory illness of infants and young children that can result in acute vasculitis. The pathological walls of afflicted coronary arteries show propensity for forming thrombosis and aneurysms. The mechanism of coronary artery aneurysms (CAA) despite intravenous gamma globulin (IVIG) treatment is not known. Methods: We performed a Whole Genome Sequencing (WGS) association analysis in a racially diverse cohort of KD patients treated with IVIG, both using AHA guidelines. We defined coronary aneurysm (CAA) (N = 234) as coronary z>2.5 and large coronary aneurysm (CAA/L) (N = 92) as z>5.0. We conducted logistic regression models to examine the association of genetic variants with CAA/L during acute KD and with persistence >6 weeks using an additive model between cases and 238 controls with no CAA. We adjusted for age, gender and three principal components of genetic ancestry. We performed functional mapping and annotation (FUMA) analysis and further assessed the predictive risk score of genomic risk loci using the area under the receiver operating characteristic curve (AUC). Results: The top significant variants associated with CAA/L were in the intergenic regions (rs62154092 p<6.32E-08 most significant). Variants in SMAT4, LOC100127 , PTPRD, TCAF2 and KLRC2 were the most significant non-intergenic SNPs. FUMA identified 12 genomic risk loci with eQTL or chromatin interactions mapped to 48 genes. Of these NDUFA5 has been implicated in KD CAA and MICU and ZMAT4 has potential functional implications. Genetic risk score using these 12 genomic risk loci yielded an AUC of 0.86. Conclusions: This pharmacogenomics study provides insights into the pathogenesis of CAA/L in IVIG-treated KD patients. We have identified multiple novel SNPs associated with CAA/L and related genes with potential functional implications. The study shows that genomics can help define the cause of CAA/L to guide management and improve risk stratification of KD patients.

Antidiabetic features of AdipoAI, a novel AdipoR agonist.

Adiponectin is an antidiabetic endogenous adipokine that plays a protective role against the unfavorable metabolic sequelae of obesity. Recent evidence suggests a sinister link between hypoadiponectinemia and development of insulin resistance/type 2 diabetes (T2D). Adiponectin's insulin-sensitizing property is mediated through the specific adiponectin receptors R1 and R2, which activate the AMP-activated protein kinase (AMPK) and peroxisome proliferator-activated receptor (PPAR) α pathways. AdipoAI is a novel synthetic analogue of endogenous adiponectin with possibly similar pharmacological effects. Thus, there is a need of orally active small molecules that activate Adipoq subunits, and their downstream signaling, which could ameliorate obesity related type 2 diabetes. In the study we aim to investigate the effects of AdipoAI on obesity and T2D. Through in-vitro and in-vivo analyses, we investigated the antidiabetic potentials of AdipoAI and compared it with AdipoRON, another orally active adiponectin receptors agonist. Our results showed that in-vitro treatment of AdipoAI (0-5 µM) increased adiponectin receptor subunits AdipoR1/R2 with increase in AMPK and APPL1 protein expression in C2C12 myotubes. Similarly, in-vivo, oral administration of AdipoAI (25 mg/kg) observed similar effects as that of AdipoRON (50 mg/kg) with improved control of blood glucose and insulin sensitivity in diet-induced obesity (DIO) mice models. Further, AdipoAI significantly reduced epididymal fat content with decrease in inflammatory markers and increase in PPAR-α and AMPK levels and exhibited hepatoprotective effects in liver. Further, AdipoAI and AdipoRON also observed similar results in adipose tissue. Thus, our results suggest that low doses of orally active small molecule agonist of adiponectin AdipoAI can be a promising therapeutic target for obesity and T2D.

A novel lncRNA-mediated epigenetic regulatory mechanism in periodontitis.

Periodontitis is a highly prevalent chronic inflammatory disease with an exaggerated host immune response, resulting in periodontal tissue destruction and potential tooth loss. The long non-coding RNA, LncR-ANRIL, located on human chromosome 9p21, is recognized as a genetic risk factor for various conditions, including atherosclerosis, periodontitis, diabetes, and cancer. LncR-APDC is an ortholog of ANRIL located on mouse genome chr4. This study aims to comprehend the regulatory role of lncR-APDC in periodontitis progression. Our experimental findings, obtained from lncR-APDC gene knockout (KO) mice with induced experimental periodontitis (EP), revealed exacerbated bone loss and disrupted pro-inflammatory cytokine regulation. Downregulation of osteogenic differentiation occurred in bone marrow stem cells harvested from lncR-APDC-KO mice. Furthermore, single-cell RNA sequencing of periodontitis gingival tissue revealed alterations in the proportion and function of immune cells, including T and B cells, macrophages, and neutrophils, due to lncR-APDC silencing. Our findings also unveiled a previously unidentified epithelial cell subset that is distinctively presenting in the lncR-APDC-KO group. This epithelial subset, characterized by the positive expression of Krt8 and Krt18, engages in interactions with immune cells through a variety of ligand-receptor pairs. The expression of Tff2, now recognized for its role in chronic inflammatory conditions, exhibited a notable increase across various tissue and cell types in lncR-APDC deficient mice. Additionally, our investigation revealed the potential for a direct binding interaction between lncR-APDC and Tff2. Intra-gingival administration of AAV9-lncR-APDC was shown to have therapeutic effects in the EP model. In conclusion, our results suggest that lncR-APDC plays a critical role in the progression of periodontal disease and holds therapeutic potential for periodontitis. Furthermore, the presence of the distinctive epithelial subpopulation and significantly elevated Tff2 levels in the lncR-APDC-silenced EP model offer new perspectives on the epigenetic regulation of periodontitis pathogenesis.

Long non-coding RNA APDC plays important regulatory roles in metabolism of bone and adipose tissues.

The long noncoding RNA (lncR) ANRIL in the human genome is an established genetic risk factor for atherosclerosis, periodontitis, diabetes, and cancer. However, the regulatory role of lncR-ANRIL in bone and adipose tissue metabolism remains unclear. To elucidate the function of lncRNA ANRIL in a mouse model, we investigated its ortholog, AK148321 (referred to as lncR-APDC), located on chr4 of the mouse genome, which is hypothesized to have similar biological functions to ANRIL. We initially revealed that lncR-APDC in mouse bone marrow cells (BMSCs) and lncR-ANRIL in human osteoblasts (hFOBs) are both increased during early osteogenesis. Subsequently, we examined the osteogenesis, adipogenesis, osteoclastogenesis function with lncR-APDC deletion/overexpression cell models. In vivo, we compared the phenotypic differences in bone and adipose tissue between APDC-KO and wild-type mice. Our findings demonstrated that lncR-APDC deficiency impaired osteogenesis while promoting adipogenesis and osteoclastogenesis. Conversely, the overexpression of lncR-APDC stimulated osteogenesis, but impaired adipogenesis and osteoclastogenesis. Furthermore, KDM6B was downregulated with lncR-APDC deficiency and upregulated with overexpression. Through binding-site analysis, we identified miR-99a as a potential target of lncR-APDC. The results suggest that lncR-APDC exerts its osteogenic function via miR-99a/KDM6B/Hox pathways. Additionally, osteoclasto-osteogenic imbalance was mediated by lncR-APDC through MAPK/p38 and TLR4/MyD88 activation. These findings highlight the pivotal role of lncR-APDC as a key regulator in bone and fat tissue metabolism. It shows potential therapeutic for addressing imbalances in osteogenesis, adipogenesis, and osteoclastogenesis.

Deep learning structural insights into heterotrimeric alternatively spliced P2X7 receptors.

P2X7 receptors (P2X7Rs) are membrane-bound ATP-gated ion channels that are composed of three subunits. Different subunit structures may be expressed due to alternative splicing of the P2RX7 gene, altering the receptor's function when combined with the wild-type P2X7A subunits. In this study, the application of the deep-learning method, AlphaFold2-Multimer (AF2M), for the generation of trimeric P2X7Rs was validated by comparing an AF2M-generated rat wild-type P2X7A receptor with a structure determined by cryogenic electron microscopy (cryo-EM) (Protein Data Bank Identification: 6U9V). The results suggested AF2M could firstly, accurately predict the structures of P2X7Rs and secondly, accurately identify the highest quality model through the ranking system. Subsequently, AF2M was used to generate models of heterotrimeric alternatively spliced P2X7Rs consisting of one or two wild-type P2X7A subunits in combination with one or two P2X7B, P2X7E, P2X7J, and P2X7L splice variant subunits. The top-ranking models were deemed valid based on AF2M's confidence measures, stability in molecular dynamics simulations, and consistent flexibility of the conserved regions between the models. The structure of the heterotrimeric receptors, which were missing key residues in the ATP binding sites and carboxyl terminal domains (CTDs) compared to the wild-type receptor, help to explain their observed functions. Overall, the models produced in this study (available as supplementary material) unlock the possibility of structure-based studies into the heterotrimeric P2X7Rs.

Combination of High-Resolution Structures for the B Cell Receptor and Co-Receptors Provides an Understanding of Their Interactions with Therapeutic Antibodies.

B cells are central to the adaptive immune response, providing long lasting immunity after infection. B cell activation is mediated by a cell surface B cell receptor (BCR) following recognition of an antigen. BCR signaling is modulated by several co-receptors including CD22 and a complex that contains CD19 and CD81. Aberrant signaling through the BCR and co-receptors promotes the pathogenesis of several B cell malignancies and autoimmune diseases. Treatment of these diseases has been revolutionized by the development of monoclonal antibodies that bind to B cell surface antigens, including the BCR and its co-receptors. However, malignant B cells can escape targeting by several mechanisms and until recently, rational design of antibodies has been limited by the lack of high-resolution structures of the BCR and its co-receptors. Herein we review recently determined cryo-electron microscopy (cryo-EM) and crystal structures of the BCR, CD22, CD19 and CD81 molecules. These structures provide further understanding of the mechanisms of current antibody therapies and provide scaffolds for development of engineered antibodies for treatment of B cell malignancies and autoimmune diseases.

The adiponectin receptor agonist AdipoAI attenuates periodontitis in diabetic rats by inhibiting gingival fibroblast-induced macrophage migration.

BACKGROUND AND PURPOSE: Low-grade inflammation, a common feature of both diabetes and periodontitis, partly accounts for the complexity and refractoriness of diabetes-associated periodontitis. Adiponectin (APN), the most abundant adipokine in human blood, has been widely reported to have anti-inflammatory functions. Herein, we investigated the ability of an APN receptor agonist, AdipoAI, to alleviate diabetes-associated periodontitis. Furthermore, we revealed the possible mechanism underlying its anti-inflammatory effects. EXPERIMENTAL APPROACH: The maxillary first molar of Zucker diabetic fatty (ZDF) rats was ligated to construct a diabetes-associated periodontitis model, and rats were administered AdipoAI by gavage. We examined diabetes-related indexes, pathological changes in insulin target organs, alveolar bone resorption and systemic and local inflammation. In vitro, transwell assays were used to evaluate monocyte/macrophage migration induced by human gingival fibroblasts (hGFs) with/without AdipoAI treatment. Additionally, we examined chemokine expression levels in hGFs and hGF-induced monocyte/macrophage migration upon siRNA knockdown of Adiponectin receptor expression. Expression of Adipo1/Adipo2 receptors and inflammation-related signalling pathways were examined by IHC and WB, followed by confirmation with an NF-κB P65 inhibitor (BAY 11-7082). KEY RESULTS: AdipoAI lowered fasting blood glucose and serum insulin in ZDF rats and alleviated inflammation in insulin target tissues. Locally, AdipoAI reduced alveolar bone absorption and gingival inflammation. Mechanistically, AdipoAI inhibited hGF-induced monocyte/macrophage migration by reducing CCL2 secretion. In hGFs, AdipoAI attenuated LPS-induced activation of NF-κB P65 and CCL2 expression, which was dependent on the Adipo receptor 1. CONCLUSION AND IMPLICATIONS: AdipoAI, with its ability to alleviate inflammatory damage in tissues, is a candidate for diabetes-associated periodontitis treatment.

Binding and Dynamics Demonstrate the Destabilization of Ligand Binding for the S688Y Mutation in the NMDA Receptor GluN1 Subunit.

Encephalopathies are brain dysfunctions that lead to cognitive, sensory, and motor development impairments. Recently, the identification of several mutations within the N-methyl-D-aspartate receptor (NMDAR) have been identified as significant in the etiology of this group of conditions. However, a complete understanding of the underlying molecular mechanism and changes to the receptor due to these mutations has been elusive. We studied the molecular mechanisms by which one of the first mutations within the NMDAR GluN1 ligand binding domain, Ser688Tyr, causes encephalopathies. We performed molecular docking, randomly seeded molecular dynamics simulations, and binding free energy calculations to determine the behavior of the two major co-agonists: glycine and D-serine, in both the wild-type and S688Y receptors. We observed that the Ser688Tyr mutation leads to the instability of both ligands within the ligand binding site due to structural changes associated with the mutation. The binding free energy for both ligands was significantly more unfavorable in the mutated receptor. These results explain previously observed in vitro electrophysiological data and provide detailed aspects of ligand association and its effects on receptor activity. Our study provides valuable insight into the consequences of mutations within the NMDAR GluN1 ligand binding domain.

AdipoAI suppresses osteoclastogenesis by activating AdipoR1/APPL1: An in vivo experimental study in diabetes-associated peri-implantitis.

AIM: Diabetics experience severe peri-implant inflammatory bone damage. We aimed to provide powerful evidence supporting the novel adiponectin receptor agonist AdipoAI in treating diabetes-associated peri-implantitis. MATERIALS AND METHODS: Twenty-four ZDF-Leprfa/Crl rats were randomly allocated to three groups (N = 8). After feeding with a high-fat diet to establish diabetic rats, experimental peri-implantitis was induced by implanting titanium rods (1.5 mm diameter and 20 mm length) contaminated with Staphylococcus aureus into the femurs. Radiographic evaluation, microCT, histological analyses and qRT-PCR were used to detect inflammatory infiltration and bone destruction. In vitro, the inhibition by AdipoAI of osteoclastogenesis, including the number and function of osteoclasts, was investigated by TRAP staining, immunofluorescence, qRT-PCR and Western blotting. Immunofluorescence, qRT-PCR and Western blotting were also utilized to explore AdipoR1, APPL1, NF-κB and Wnt5a-Ror2 signalling molecules in this process. One-way ANOVA with Tukey's post hoc test was used to compare the data. RESULTS: AdipoAI reduced inflammation and bone destruction caused by peri-implantitis in diabetic rats, which were manifested by a reduction in F4/80-positive macrophage infiltration by 72%, the number of osteoclasts by 58% and the levels of cytokines (p < .05) in disease group. In vitro, 1 µM AdipoAI decreased the number of osteoclasts to 51%, inhibited F-actin ring formation and reduced the levels of related markers (p < .05). Mechanistically, AdipoAI activated AdipoR1/APPL1 and conversely suppressed the phosphorylation of IκB-α, nuclear translocation of P65 and the Wnt5a-Ror2 signalling pathway (p < .05). CONCLUSIONS: AdipoAI suppressed osteoclastogenesis in diabetes-associated peri-implantitis by inhibiting the NF-κB and Wnt5a-Ror2 pathways via the AdipoR1/APPL1 axis.

Analysis of cardiac single-cell RNA-sequencing data can be improved by the use of artificial-intelligence-based tools.

Single-cell RNA sequencing (scRNAseq) enables researchers to identify and characterize populations and subpopulations of different cell types in hearts recovering from myocardial infarction (MI) by characterizing the transcriptomes in thousands of individual cells. However, the effectiveness of the currently available tools for processing and interpreting these immense datasets is limited. We incorporated three Artificial Intelligence (AI) techniques into a toolkit for evaluating scRNAseq data: AI Autoencoding separates data from different cell types and subpopulations of cell types (cluster analysis); AI Sparse Modeling identifies genes and signaling mechanisms that are differentially activated between subpopulations (pathway/gene set enrichment analysis), and AI Semisupervised Learning tracks the transformation of cells from one subpopulation into another (trajectory analysis). Autoencoding was often used in data denoising; yet, in our pipeline, Autoencoding was exclusively used for cell embedding and clustering. The performance of our AI scRNAseq toolkit and other highly cited non-AI tools was evaluated with three scRNAseq datasets obtained from the Gene Expression Omnibus database. Autoencoder was the only tool to identify differences between the cardiomyocyte subpopulations found in mice that underwent MI or sham-MI surgery on postnatal day (P) 1. Statistically significant differences between cardiomyocytes from P1-MI mice and mice that underwent MI on P8 were identified for six cell-cycle phases and five signaling pathways when the data were analyzed via Sparse Modeling, compared to just one cell-cycle phase and one pathway when the data were analyzed with non-AI techniques. Only Semisupervised Learning detected trajectories between the predominant cardiomyocyte clusters in hearts collected on P28 from pigs that underwent apical resection (AR) on P1, and on P30 from pigs that underwent AR on P1 and MI on P28. In another dataset, the pig scRNAseq data were collected after the injection of CCND2-overexpression Human-induced Pluripotent Stem Cell-derived cardiomyocytes (CCND2hiPSC) into injured P28 pig heart; only the AI-based technique could demonstrate that the host cardiomyocytes increase proliferating by through the HIPPO/YAP and MAPK signaling pathways. For the cluster, pathway/gene set enrichment, and trajectory analysis of scRNAseq datasets generated from studies of myocardial regeneration in mice and pigs, our AI-based toolkit identified results that non-AI techniques did not discover. These different results were validated and were important in explaining myocardial regeneration.