Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities.

Schizophrenia and bipolar disorder are characterized by highly similar neuropsychological signatures, implying shared neurobiological mechanisms between these two disorders. These disorders also have comorbidities, such as type 2 diabetes mellitus (T2DM). To date, an understanding of the mechanisms that mediate the link between these two disorders remains incomplete. In this work, we identify and investigate shared patterns across multiple schizophrenia, bipolar disorder and T2DM gene expression datasets through multiple strategies. Firstly, we investigate dysregulation patterns at the gene-level and compare our findings against disease-specific knowledge graphs (KGs). Secondly, we analyze the concordance of co-expression patterns across datasets to identify disease-specific as well as common pathways. Thirdly, we examine enriched pathways across datasets and disorders to identify common biological mechanisms between them. Lastly, we investigate the correspondence of shared genetic variants between these two disorders and T2DM as well as the disease-specific KGs. In conclusion, our work reveals several shared candidate genes and pathways, particularly those related to the immune system, such as TNF signaling pathway, IL-17 signaling pathway and NF-kappa B signaling pathway and nervous system, such as dopaminergic synapse and GABAergic synapse, which we propose mediate the link between schizophrenia and bipolar disorder and its shared comorbidity, T2DM.

Common data model for COVID-19 datasets.

MOTIVATION: A global medical crisis like the coronavirus disease 2019 (COVID-19) pandemic requires interdisciplinary and highly collaborative research from all over the world. One of the key challenges for collaborative research is a lack of interoperability among various heterogeneous data sources. Interoperability, standardization and mapping of datasets are necessary for data analysis and applications in advanced algorithms such as developing personalized risk prediction modeling. RESULTS: To ensure the interoperability and compatibility among COVID-19 datasets, we present here a common data model (CDM) which has been built from 11 different COVID-19 datasets from various geographical locations. The current version of the CDM holds 4639 data variables related to COVID-19 such as basic patient information (age, biological sex and diagnosis) as well as disease-specific data variables, for example, Anosmia and Dyspnea. Each of the data variables in the data model is associated with specific data types, variable mappings, value ranges, data units and data encodings that could be used for standardizing any dataset. Moreover, the compatibility with established data standards like OMOP and FHIR makes the CDM a well-designed CDM for COVID-19 data interoperability. AVAILABILITY AND IMPLEMENTATION: The CDM is available in a public repo here: https://github.com/Fraunhofer-SCAI-Applied-Semantics/COVID-19-Global-Model. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The COVID-19 Ontology.

MOTIVATION: The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development. RESULTS: The ontology comprises 2270 classes of concepts and 38 987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19. The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute. AVAILABILITYAND IMPLEMENTATION: COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The efficacy of sulfated polysaccharides from Padina tetrastromatica in modulating the immune functions of RAW 264.7 cells.

Immunomodulation is a collective term of immunostimulation and immunosuppression. Immunotherapy by means of immunomodulation is gaining additional significance as the frequency of mutant microbes as well as cancer cases are increasing in the present time. A wide population of marine vegetation has contributed to the traditional and modern therapeutic regimens owing to the abundance of bioactive molecules. Among the seaweed born macromolecules, the sulfated polysaccharides (SPS) from marine macro algae were reported to exhibit excellent biological activities in addition to their structural and nutritional roles. Interestingly, SPS from the marine brown algae Padina tetrastromatica is not yet explored for their immunomodulatory potential. In the present study, SPS were extracted by ethanol precipitation, purified using DEAE cellulose column chromatography, and named as Ethanolic Sulfated Polysaccharide-Column Purified (ESPs-CP). The study includes, evaluation of macrophage proliferation, prostaglandin and nitric oxide production, COX-2, 5-LOX, and iNOS estimation and gene expression studies in RAW 264.7 cells. ESPs-CP strongly stimulated macrophage proliferation and production of prostaglandin and nitric oxide. They also increased COX-2, 5-LOX, and iNOS concentration in macrophages, which was comparable to that of LPS stimulated macrophages. Increase of prostaglandin and NO production may be due to increased expression of COX-2 and iNOS as observed in gene expression studies. The mRNA expression of pro- inflammatory cytokines such as IL-6, IL-1ß, TNF-α and anti-inflammatory cytokines IL-10 and TGF-ß were also enhanced by ESPs-CP. The evaluations signify the possibilities of SPS as potent immunostimulators during immune deficiencies.

PEG-penetrated chitosan-alginate co-polysaccharide-based partially and fully cross-linked hydrogels as ECM mimic for tissue engineering applications.

The emerging strategy of tissue engineering for the management of end-stage organ failure and associated complications mainly relies on ECM mimicking scaffolds for neo-tissue genesis. In the current study, novel polyethylene glycol interpenetrated cross-linked hydrogel scaffold based on a co-polysaccharide (PIAC) synthesized from two marine heteropolysaccharides, alginate and chitosan, was designed. Partially cross-linked (PIAC-P) and fully cross-linked hydrogels (PIAC-F) were prepared. The physiochemical evaluations of both the hydrogels revealed the presence of alginate fraction and extensive -OH groups on the surface, sufficient water content and water holding capacity. The porosity and bulk density were also appreciable. The scaffolds were hemocompatible and were able to adsorb appreciable plasma proteins on to the surface. MTT assay on hydrogel extracts and direct contact assay showed the nontoxic effects of fibroblast cells upon contact with the hydrogel. Live/dead assay using ethidium bromide/acridine orange cocktail on fibroblast cells grown on the hydrogels after 5 days of initial seeding displayed green nucleus revealing the non-apoptotic cells. PIAC-P hydrogels were superior to certain aspects due to the availability of free functional groups than PIAC-F where most of these groups were utilized for cross-linking. The biological evaluations confirmed the healthy being and 3D growth of fibroblasts on the porous networks of both the hydrogels. The present hydrogel can form an ECM mimic and can form a potent candidate for various tissue engineering applications.