Palatal segment contributions to midfacial growth in three inbred mouse strains.

Following facial prominence fusion, anterior-posterior (A-P) elongation of the palate is a critical aspect of palatogenesis and integrated midfacial elongation. Reciprocal epithelial-mesenchymal interactions drive secondary palate elongation and periodic signaling center formation within the rugae growth zone (RGZ). However, the relationship between RGZ dynamics and the morphogenetic behavior of underlying palatal bone mesenchymal precursors has remained enigmatic. Our results indicate that cellular activity at the RGZ simultaneously drives rugae formation and elongation of the maxillary bone primordium within the anterior secondary palate, which more than doubles in length prior to palatal shelf fusion. The first formed palatal ruga, found just posterior to the RGZ, represents a consistent morphological boundary between anterior and posterior secondary palate bone precursors, being found at the future maxillary-palatine suture. These results suggest a model where changes in RGZ-driven A-P growth of the anterior secondary palate may produce interspecies and intraspecies differences in facial prognathism and differences in the proportional contribution of palatal segment-associated bones to total palate length. An ontogenetic comparison of three inbred mouse strains indicated that while RGZ-driven growth of the anterior secondary palate is critical for early midfacial outgrowth, subtle strain-specific bony contributions to adult palate length are not present until after this initial palatal growth period. This multifaceted illustration of normal midfacial growth dynamics confirms a one-to-one relationship between palatal segments and upper jaw bones during the earliest stages of palatal growth, which may serve as the basis for evolutionary change in upper jaw morphology. Additionally, identified mouse strain-specific differences in palatal segment elongation provide a useful foundation for understanding the impact of background genetic effects on facial morphogenesis.

Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status.

BACKGROUND: There is still more to learn about the pathobiology of COVID-19. A multi-omic approach offers a holistic view to better understand the mechanisms of COVID-19. We used state-of-the-art statistical learning methods to integrate genomics, metabolomics, proteomics, and lipidomics data obtained from 123 patients experiencing COVID-19 or COVID-19-like symptoms for the purpose of identifying molecular signatures and corresponding pathways associated with the disease. RESULTS: We constructed and validated molecular scores and evaluated their utility beyond clinical factors known to impact disease status and severity. We identified inflammation- and immune response-related pathways, and other pathways, providing insights into possible consequences of the disease. CONCLUSIONS: The molecular scores we derived were strongly associated with disease status and severity and can be used to identify individuals at a higher risk for developing severe disease. These findings have the potential to provide further, and needed, insights into why certain individuals develop worse outcomes.

Multi-omic analysis reveals enriched pathways associated with COVID-19 and COVID-19 severity.

COVID-19 is a disease characterized by its seemingly unpredictable clinical outcomes. In order to better understand the molecular signature of the disease, a recent multi-omics study was done which looked at correlations between biomolecules and used a tree- based machine learning approach to predict clinical outcomes. This study specifically looked at patients admitted to the hospital experiencing COVID-19 or COVID-19 like symptoms. In this paper we examine the same multi-omics data, however we take a different approach, and we identify stable molecules of interest for further pathway analysis. We used stability selection, regularized regression models, enrichment analysis, and principal components analysis on proteomics, metabolomics, lipidomics, and RNA sequencing data, and we determined key molecules and biological pathways in disease severity, and disease status. In addition to the individual omics analyses, we perform the integrative method Sparse Multiple Canonical Correlation Analysis to analyse relationships of the different view of data. Our findings suggest that COVID-19 status is associated with the cell cycle and death, as well as the inflammatory response. This relationship is reflected in all four sets of molecules analyzed. We further observe that the metabolic processes, particularly processes to do with vitamin absorption and cholesterol are implicated in COVID-19 status and severity.

Automated syndrome diagnosis by three-dimensional facial imaging.

PURPOSE: Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces. METHODS: We analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images. RESULTS: Unrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative. CONCLUSION: Deep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance.