The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Heart-on-a-Chip Model of Epicardial-Myocardial Interaction in Ischemia Reperfusion Injury.

Epicardial cells (EPIs) form the outer layer of the heart and play an important role in development and disease. Current heart-on-a-chip platforms still do not fully mimic the native cardiac environment due to the absence of relevant cell types, such as EPIs. Here, using the Biowire II platform, engineered cardiac tissues with an epicardial outer layer and inner myocardial structure are constructed, and an image analysis approach is developed to track the EPI cell migration in a beating myocardial environment. Functional properties of EPI cardiac tissues improve over two weeks in culture. In conditions mimicking ischemia reperfusion injury (IRI), the EPI cardiac tissues experience less cell death and a lower impact on functional properties. EPI cell coverage is significantly reduced and more diffuse under normoxic conditions compared to the post-IRI conditions. Upon IRI, migration of EPI cells into the cardiac tissue interior is observed, with contributions to alpha smooth muscle actin positive cell population. Altogether, a novel heart-on-a-chip model is designed to incorporate EPIs through a formation process that mimics cardiac development, and this work demonstrates that EPI cardiac tissues respond to injury differently than epicardium-free controls, highlighting the importance of including EPIs in heart-on-a-chip constructs that aim to accurately mimic the cardiac environment.

Endothelial extracellular vesicles enhance vascular self-assembly in engineered human cardiac tissues.

The fabrication of complex and stable vasculature in engineered cardiac tissues represents a significant hurdle towards building physiologically relevant models of the heart. Here, we implemented a 3D model of cardiac vasculogenesis, incorporating endothelial cells (EC), stromal cells, and human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (CM) in a fibrin hydrogel. The presence of CMs disrupted vessel formation in 3D tissues, resulting in the upregulation of endothelial activation markers and altered extracellular vesicle (EV) signaling in engineered tissues as determined by the proteomic analysis of culture supernatant. miRNA sequencing of CM- and EC-secreted EVs highlighted key EV-miRNAs that were postulated to play differing roles in cardiac vasculogenesis, including the let-7 family and miR-126-3p in EC-EVs. In the absence of CMs, the supplementation of CM-EVs to EC monolayers attenuated EC migration and proliferation and resulted in shorter and more discontinuous self-assembling vessels when applied to 3D vascular tissues. In contrast, supplementation of EC-EVs to the tissue culture media of 3D vascularized cardiac tissues mitigated some of the deleterious effects of CMs on vascular self-assembly, enhancing the average length and continuity of vessel tubes that formed in the presence of CMs. Direct transfection validated the effects of the key EC-EV miRNAs let-7b-5p and miR-126-3p in improving the maintenance of continuous vascular networks. EC-EV supplementation to biofabricated cardiac tissues and microfluidic devices resulted in tissue vascularization, illustrating the use of this approach in the engineering of enhanced, perfusable, microfluidic models of the myocardium.

Bridging the gap between in vitro and in vivo models: a way forward to clinical translation of mitochondrial transplantation in acute disease states.

Mitochondrial transplantation and transfer are being explored as therapeutic options in acute and chronic diseases to restore cellular function in injured tissues. To limit potential immune responses and rejection of donor mitochondria, current clinical applications have focused on delivery of autologous mitochondria. We recently convened a Mitochondrial Transplant Convergent Working Group (CWG), to explore three key issues that limit clinical translation: (1) storage of mitochondria, (2) biomaterials to enhance mitochondrial uptake, and (3) dynamic models to mimic the complex recipient tissue environment. In this review, we present a summary of CWG conclusions related to these three issues and provide an overview of pre-clinical studies aimed at building a more robust toolkit for translational trials.

Optimization of differential filtration-based mitochondrial isolation for mitochondrial transplant to cerebral organoids.

BACKGROUND: Mitochondrial dysfunction is involved in several diseases ranging from genetic mitochondrial disorders to chronic metabolic diseases. An emerging approach to potentially treat mitochondrial dysfunction is the transplantation of autologous live mitochondria to promote cell regeneration. We tested the differential filtration-based mitochondrial isolation protocol established by the McCully laboratory for use in cellular models but found whole cell contaminants in the mitochondrial isolate. METHODS: Therefore, we explored alternative types of 5-µm filters (filters A and B) for isolation of mitochondria from multiple cell lines including HEK293 cells and induced pluripotent stem cells (iPSCs). MitoTracker™ staining combined with flow cytometry was used to quantify the concentration of viable mitochondria. A proof-of-principle mitochondrial transplant was performed using mitoDsRed2-tagged mitochondria into a H9-derived cerebral organoid. RESULTS: We found that filter B provided the highest quality mitochondria as compared to the 5-µm filter used in the original protocol. Using this method, mitochondria were also successfully isolated from induced pluripotent stem cells. To test for viability, mitoDsRed2-tagged mitochondria were isolated and transplanted into H9-derived cerebral organoids and observed that mitochondria were engulfed as indicated by immunofluorescent co-localization of TOMM20 and MAP2. CONCLUSIONS: Thus, use of filter B in a differential filtration approach is ideal for isolating pure and viable mitochondria from cells, allowing us to begin evaluating long-term integration and safety of mitochondrial transplant using cellular sources.

Evaluation of postmortem microarray data in bipolar disorder using traditional data comparison and artificial intelligence reveals novel gene targets.

Large-scale microarray studies on post-mortem brain tissues have been utilized to investigate the complex molecular pathology of bipolar disorder. However, a major challenge in characterizing the dysregulation of gene expression in patients with bipolar disorder includes the lack of convergence between different studies, limiting comprehensive understanding from individual results. In this study, we aimed to identify genes that are both validated in published literature and are important classification features of unsupervised machine learning analysis of Stanley Brain Bank microarray database, followed by augmented intelligence method to identify distinct patient molecular subgroups. Through combining traditional literature approaches and machine learning, we identified TBL1XR1, SMARCA2, and CHMP5 to be replicated in 3 of the 4 studies included our analysis. The expression of these genes segregated unique subgroups of patients with bipolar disorder. Our study suggests the involvement of PPARγ pathway regulation in patients with bipolar disorder.