The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

Designing host-associated microbiomes using the consumer/resource model.

A key step towards rational microbiome engineering is the in silico sampling of realistic microbial communities that correspond to desired host phenotypes, and vice versa. This remains challenging due to a lack of generative models that simultaneously model compositions of host-associated microbiomes and host phenotypes. To that end, we present a machine learning model based on the consumer/resource (C/R) framework. In the model, variation in microbial ecosystem composition arises due to differences in the availability of effective resources (latent variables) while species' resource preferences remain conserved. Variation in the same latent variables is used to model phenotypic variation across hosts. In silico microbiomes generated by our model accurately reproduce universal and dataset-specific statistics of bacterial communities. The model allows us to address two salient questions in microbiome design: (1) which host phenotypes maximally constrain the composition of the host-associated microbiome? and (2) what are plausible microbiome compositions corresponding to user-specified host phenotypes? Thus, our model aids the design and analysis of microbial communities associated with host phenotypes of interest.

Toward Grading Subarachnoid Hemorrhage Risk Prediction: A Machine Learning-Based Aneurysm Rupture Score.

OBJECTIVE: Existing approaches neither provide an accurate prediction of subarachnoid hemorrhage (SAH) nor offer a quantitative comparison among a group of its risk factors. To evaluate the population, hypertension, age, size, earlier subarachnoid hemorrhage, and location (PHASES) and unruptured intracranial aneurysm treatment score (UIATS) scores and develop an Artificial Intelligence-based 5-year and lifetime aneurysmal rupture criticality prediction (ARCP) score for a set of risk factors. METHODS: We design various location-specific and ensemble learning models to develop lifetime rupture risk, employ the longitudinal data to develop a linear regression-based model to predict an aneurysm's growth score, and use the Apriori algorithm to identify risk factors strongly associated with SAH. We develop ARCP by integrating output of Apriori algorithm and ML models and compare with PHASES and UIATS scores along with the scores of a multidisciplinary team of neurosurgeons. RESULTS: The PHASES and UIATS scores show sensitivities of 22% and 35% and specificities of 76% and 79%, respectively. Location-specific models show precision and recall of 93% and 90% for the middle cerebral artery, 83% and 80% for the anterior communicating artery, and 80% and 80% for the supraclinoid internal carotid artery, respectively. The ensemble method shows both precision and recall of 80%. The validation of the models shows that ARCP performs better than our control group of neurosurgeons. Data-driven knowledge produces comparisons among 61 risk factor combinations, 11 ranked minor, 8 moderate, and 41 severe, and 1 of which is a critical factor. CONCLUSIONS: The PHASES and UIATS are weak predictors, and the ARCP score can identify, and grade, risk factors associated with SAH.

In silico, in vitro and in vivo characterization of host-associated Latilactobacillus curvatus strains for potential probiotic applications in farmed Atlantic salmon (Salmo salar).

Salmon aquaculture is the fastest growing animal protein production system in the world; however, intensive farming leads to poor weight gain, stress, and disease outbreaks. Probiotics offer the potential to enhance growth performance and feed efficiency in Atlantic salmon, as well as immunostimulate fish against common pathogens, benefitting farmers and consumers with more efficient production. Here, we isolated and identified 900 native microbial isolates including 18 Lactobacilli from the farmed salmon intestines. Based on whole-genome sequencing and phylogenetic analysis, the Lactobacillus candidates belonged to Latilactobacillus curvatus (L. curvatus) species and formed two distinct phylogenetic groups. Using bioinformatics and in vitro analyses, we selected two candidates L. curvatus ATCC PTA-127116 and L. curvatus ATCC PTA-127117, which showed desirable safety and probiotic properties. The two L. curvatus candidates were evaluated for safety and efficacy (higher final weight) in Atlantic salmon alongside spore-forming Bacilli isolated from salmon, poultry, and swine. All the tested candidates were safe to salmon with no adverse effects. While we did not see efficacy in any Bacillus supplemented groups, compared to untreated group, the group administered with the two L. curvatus strains consortium in feed for seven weeks in freshwater showed indicators of improvement in final body weight by 4.2%. Similarly, the two L. curvatus candidates were also evaluated for safety and efficacy in Atlantic salmon in saltwater; the group administered with the two L. curvatus strains consortium in feed for 11 weeks showed indicators of improvement in final body weight by 4.7%. Comprehensive metabolomics analyses in the presence of different prebiotics and/or additives identified galactooligosaccharide as a potential prebiotic to enhance the efficacy of two L. curvatus candidates. All together, these data provide comprehensive genomic, phenotypic and metabolomic evidence of safety and desirable probiotic properties as well as indicators of in vivo efficacy of two novel endogenous L. curvatus candidates for potential probiotic applications in Atlantic salmon. The in vivo findings need to be confirmed in larger performance studies, including field trials.

Introducing the Rupture Criticality Index to Compare Risk Factor Combinations Associated With Aneurysmal Rupture.

BACKGROUND: Relative risk is insufficient to guide treatment decision-making for unruptured intracranial aneurysms. Our objective was to introduce a novel risk assessment methodology called the Rupture Criticality Index (RCI), which allows for concurrent evaluation of groups of risk factors (RFs). METHODS: From a retrospective database of saccular aneurysms, we identify 915 patients and delineate 50 potential RFs for aneurysms in 11 unique locations. RF combinations for multivariable analysis were defined by aneurysm size, location, and a third variable from the study design. Data analysis was performed by applying frequency distribution methods to define the RCI of each RF combination. RESULTS: RF combinations at greatest risk were small (4.8-8.2 mm) or medium (8.3-14.5 mm) anterior communicating aneurysms (ACoA) in male individuals (RCI 9.87-10), small ACoA in those ≤37 years or 38-55 years (RCI 8.67-8.99), medium basilar tip aneurysms (BTAs) in male individuals (RCI 10), and large (14.6-22.5 mm) BTA in Caucasians or those aged 38-55 years (RCI 9.25, 9.35, respectively). CONCLUSIONS: We introduce the concept of RCI and compare how RF combinations are associated with aneurysmal rupture. This novel approach to aneurysmal rupture identifies high-risk clinical presentations and can be used to guide clinical decision-making in patients with non-traditional risks.