Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria.

Accumulating evidence suggests that cardiovascular disease (CVD) is associated with an altered gut microbiome. Our understanding of the underlying mechanisms has been hindered by lack of matched multi-omic data with diagnostic biomarkers. To comprehensively profile gut microbiome contributions to CVD, we generated stool metagenomics and metabolomics from 1,429 Framingham Heart Study participants. We identified blood lipids and cardiovascular health measurements associated with microbiome and metabolome composition. Integrated analysis revealed microbial pathways implicated in CVD, including flavonoid, γ-butyrobetaine, and cholesterol metabolism. Species from the Oscillibacter genus were associated with decreased fecal and plasma cholesterol levels. Using functional prediction and in vitro characterization of multiple representative human gut Oscillibacter isolates, we uncovered conserved cholesterol-metabolizing capabilities, including glycosylation and dehydrogenation. These findings suggest that cholesterol metabolism is a broad property of phylogenetically diverse Oscillibacter spp., with potential benefits for lipid homeostasis and cardiovascular health.

standR: spatial transcriptomic analysis for GeoMx DSP data.

To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cells in situ. State-of-the-art spatial profiling technologies, such as the Nanostring GeoMx Digital Spatial Profiler (DSP), now allow quantitative spatially resolved measurement of the transcriptome in tissues. However, the bioinformatics pipelines currently used to analyse GeoMx data often fail to successfully account for the technical variability within the data and the complexity of experimental designs, thus limiting the accuracy and reliability of the subsequent analysis. Carefully designed quality control workflows, that include in-depth experiment-specific investigations into technical variation and appropriate adjustment for such variation can address this issue. Here, we present standR, an R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data. With four case studies from previously published experiments, we demonstrate how the standR workflow can enhance the statistical power of GeoMx DSP data analysis and how the application of standR enables scientists to develop in-depth insights into the biology of interest.

StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling.

The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients' relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network's topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm's inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients' pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.

vissE.cloud: a webserver to visualise higher order molecular phenotypes from enrichment analysis.

Gene-set analysis (GSA) dominates the functional interpretation of omics data and downstream hypothesis generation. Despite its ability to summarise thousands of measurements into semantically interpretable components, GSA often results in hundreds of significantly enriched gene-sets. However, summarisation and effective visualisation of GSA results to facilitate hypothesis generation is still lacking. While some webservers provide gene-set visualization tools, there is still a need for tools that can effectively summarize and guide exploration of GSA results. To enable versatility, webservers accept gene lists as input, however, none provide end-to-end solutions for emerging data types such as single-cell and spatial omics. Here, we present vissE.Cloud, a webserver for end-to-end gene-set analysis, offering gene-set summarisation and highly interactive visualisation. vissE.Cloud uses algorithms from our earlier R package vissE to summarise GSA results by identifying biological themes. We maintain versatility by allowing analysis of gene lists, as well as, analysis of raw single-cell and spatial omics data, including CosMx and Xenium data, making vissE.Cloud the first webserver to provide end-to-end gene-set analysis of sub-cellular localised spatial data. Structuring the results hierarchically allows swift interactive investigations of results at the gene, gene-set, and clusters level. vissE.Cloud is freely available at https://www.vissE.Cloud.

Inhibition of the angiotensin II type 2 receptor AT2R is a novel therapeutic strategy for glioblastoma.

Glioblastoma (GBM) is an aggressive malignant primary brain tumor with limited therapeutic options. We show that the angiotensin II (AngII) type 2 receptor (AT2R) is a therapeutic target for GBM and that AngII, endogenously produced in GBM cells, promotes proliferation through AT2R. We repurposed EMA401, an AT2R antagonist originally developed as a peripherally restricted analgesic, for GBM and showed that it inhibits the proliferation of AT2R-expressing GBM spheroids and blocks their invasiveness and angiogenic capacity. The crystal structure of AT2R bound to EMA401 was determined and revealed the receptor to be in an active-like conformation with helix-VIII blocking G-protein or ß-arrestin recruitment. The architecture and interactions of EMA401 in AT2R differ drastically from complexes of AT2R with other relevant compounds. To enhance central nervous system (CNS) penetration of EMA401, we exploited the crystal structure to design an angiopep-2-tethered EMA401 derivative, A3E. A3E exhibited enhanced CNS penetration, leading to reduced tumor volume, inhibition of proliferation, and increased levels of apoptosis in an orthotopic xenograft model of GBM.

Dimensionality Engineering of Magnetic Anisotropy from the Anomalous Hall Effect in Synthetic SrRuO3 Crystals.

Magnetic anisotropy in atomically thin correlated heterostructures is essential for exploring quantum magnetic phases for next-generation spintronics. Whereas previous studies have mostly focused on van der Waals systems, here we investigate the impact of dimensionality of epitaxially grown correlated oxides down to the monolayer limit on structural, magnetic, and orbital anisotropies. By designing oxide superlattices with a correlated ferromagnetic SrRuO3 and nonmagnetic SrTiO3 layers, we observed modulated ferromagnetic behavior with the change of the SrRuO3 thickness. Especially, for three-unit-cell-thick layers, we observe a significant 1500% improvement of the coercive field in the anomalous Hall effect, which cannot be solely attributed to the dimensional crossover in ferromagnetism. The atomic-scale heterostructures further reveal the systematic modulation of anisotropy for the lattice structure and orbital hybridization, explaining the enhanced magnetic anisotropy. Our findings provide valuable insights into engineering the anisotropic hybridization of synthetic magnetic crystals, offering a tunable spin order for various applications.

Defining the relationship between cellular and extracellular vesicle (EV) content in breast cancer via an integrative multi-omic analysis.

Much recent research has been dedicated to exploring the utility of extracellular vesicles (EVs) as circulating disease biomarkers. Underpinning this work is the assumption that the molecular cargo of EVs directly reflects the originating cell. Few attempts have been made, however, to empirically validate this on the -omic level. To this end, we have performed an integrative multi-omic analysis of a panel of breast cancer cell lines and corresponding EVs. Whole transcriptome analysis validated that the cellular transcriptome remained stable when cultured cells are transitioned to low serum or serum-free medium for EV collection. Transcriptomic profiling of the isolated EVs indicated a positive correlation between transcript levels in cells and EVs, including disease-associated transcripts. Analysis of the EV proteome verified that HER2 protein is present in EVs, however neither the estrogen (ER) nor progesterone (PR) receptor proteins are detected regardless of cellular expression. Using multivariate analysis, we derived an EV protein signature to infer cellular patterns of ER and HER2 expression, though the ER protein could not be directly detected. Integrative analyses affirmed that the EV proteome and transcriptome captured key phenotypic hallmarks of the originating cells, supporting the potential of EVs for non-invasive monitoring of breast cancers.

vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis.

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and can lead to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis and visualisation tool that organises information into semantic categories and provides various visualisation modules to characterise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissE's versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, empowering biologists during molecular discovery.

Biallelic TYR and TKFC variants in Egyptian patients with OCA1 and new expanded TKFC features.

BACKGROUND: Oculocutaneous albinism type1 (OCA1) is caused by the TYR gene's homozygous and compound heterozygous variants. TKFC gene variants cause triokinase & FMN cyclase deficiency syndrome with variable multisystemic disorders. OBJECTIVES: To determine the potential disease-causing variants in two deceased patients presenting atypical OCA1 features by demonstrating three generations for a single family. The two deceased neonates had severe skeletal abnormalities and fatal hypertrophic cardiomyopathy. We also explored the potential mechanisms for the causative relationship between TKFC and multisystem disorders. PATIENTS AND METHODS: Due to the new emerging symptoms that weren't reported before with the TYR gene, the following methods were performed: Sanger sequencing for the TYR gene, followed by whole exome sequencing, co-segregation, and computational analyses. RESULTS: Extensive parental consanguinity was found, and consequently an autosomal recessive mode of inheritance was prioritized. Upon performing sequencing and segregation data, the following has been confirmed: positive co-segregation of nonsense homozygous NM_000372.5:c.346C > T p.(Arg116*) variant in TYR gene and multisystem disease-missense homozygous NM_015533.4:c.598G > A p.(Val200Ile) variant in TKFC gene in the two affected index patients who deceased due to hypertrophic cardiomyopathy. Using computational analysis, we found that c.598G > A p.(Val200Ile) pathogenicity has led to the failure of L2-K1 active site closure due to the potential differential fluctuation between valine and isoleucine residues. Subsequently, disruption of endogenous DHA phosphorylation was found. Two potential mechanisms exploring the causative relationship between TKFC gene and multisystem disorders have been suggested. CONCLUSIONS: This study presented a first family with the co-existence of biallelic variants in TYR and TKFC genes associating severe skeletal abnormalities and lethal hypertrophic cardiomyopathy. Neither of these genes would have been pursued in the standard genetic counseling. Such discovery is paving the way for more efficient genetic counseling. Comparing TKFC results with literature data showed that our relevant expanded TKFC variant is the 3rd worldwide.

Global and regional governance of One Health and implications for global health security.

The apparent failure of global health security to prevent or prepare for the COVID-19 pandemic has highlighted the need for closer cooperation between human, animal (domestic and wildlife), and environmental health sectors. However, the many institutions, processes, regulatory frameworks, and legal instruments with direct and indirect roles in the global governance of One Health have led to a fragmented, global, multilateral health security architecture. We explore four challenges: first, the sectoral, professional, and institutional silos and tensions existing between human, animal, and environmental health; second, the challenge that the international legal system, state sovereignty, and existing legal instruments pose for the governance of One Health; third, the power dynamics and asymmetry in power between countries represented in multilateral institutions and their impact on priority setting; and finally, the current financing mechanisms that predominantly focus on response to crises, and the chronic underinvestment for epidemic and emergency prevention, mitigation, and preparedness activities. We illustrate the global and regional dimensions to these four challenges and how they relate to national needs and priorities through three case studies on compulsory licensing, the governance of water resources in the Lake Chad Basin, and the desert locust infestation in east Africa. Finally, we propose 12 recommendations for the global community to address these challenges. Despite its broad and holistic agenda, One Health continues to be dominated by human and domestic animal health experts. Substantial efforts should be made to address the social-ecological drivers of health emergencies including outbreaks of emerging, re-emerging, and endemic infectious diseases. These drivers include climate change, biodiversity loss, and land-use change, and therefore require effective and enforceable legislation, investment, capacity building, and integration of other sectors and professionals beyond health.