The Fourth Bioelectronic Medicine Summit "Technology Targeting Molecular Mechanisms": current progress, challenges, and charting the future.

There is a broad and growing interest in Bioelectronic Medicine, a dynamic field that continues to generate new approaches in disease treatment. The fourth bioelectronic medicine summit "Technology targeting molecular mechanisms" took place on September 23 and 24, 2020. This virtual meeting was hosted by the Feinstein Institutes for Medical Research, Northwell Health. The summit called international attention to Bioelectronic Medicine as a platform for new developments in science, technology, and healthcare. The meeting was an arena for exchanging new ideas and seeding potential collaborations involving teams in academia and industry. The summit provided a forum for leaders in the field to discuss current progress, challenges, and future developments in Bioelectronic Medicine. The main topics discussed at the summit are outlined here

Moving towards making (quantitative) structure-activity relationships ((Q)SARs) for toxicity-related endpoints findable, accessible, interoperable and reusable (FAIR)

(Quantitative) structure-activity relationships ((Q)SARs) are widely used in chemical safety assessment to predict toxicological effects. Many thousands of (Q)SAR models have been developed and published, however, few are easily available to use. This investigation has applied previously developed Findability, Accessibility, Interoperability, and Reuse (FAIR) Principles for in silico models to six published, different, machine learning (ML) (Q)SARs for the same toxicity dataset (inhibition of growth to Tetrahymena pyriformis). The majority of principles were met, however, there are still gaps in making (Q)SARs FAIR. This study has enabled insights into, and recommendations for, the FAIRification of (Q)SARs including areas where more work and effort may be required. For instance, there is still a need for (Q)SARs to be associated with a unique identifier and full data / metadata for toxicological activity or endpoints, molecular properties and descriptors, as well as model description to be provided in a standardised manner. A number of solutions to the challenges were identified, such as building on the QSAR Model Reporting Format (QMRF) and the application of QSAR Assessment Framework (QAF). This study also demonstrated that resources such as the QSAR Databank (QsarDB, www.qsardb.org) are valuable in storing ML QSARs in a searchable database and also provide a Digital Object Identifier (DOI). Many activities related to FAIR are currently underway and (Q)SAR modellers should be encouraged to utilise these to move towards the easier access and use of models. Enabling FAIR computational toxicology models will support the overall progress towards animal free chemical safety assessment

Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity

Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/

The Findable, Accessible, Interoperable, Reusable (FAIR) Lite Principles to ensure utility of computational toxicology models

A broad range of computational models are available for animal-free chemical safety assessment. The models are used to predict a variety of endpoints, including adverse effects or apical endpoints, toxicokinetic properties and exposure, often from chemical structure or in vitro inputs alone. To support their wider use, such models need to be Findable, Accessible, Interoperable, Reusable (FAIR). This study has reevaluated the existing FAIR principles applied to quantitative structure-activity relationships (QSARs) in order to adapt these principles to a wider range of computational models. Despite the breadth and variety of approaches, many computational models comprise common components including the training series, information about the modelling engine and the model itself. As a result, a refined set of four FAIR Lite principles is proposed based on the methodological foundations of computational toxicology which are unambiguously understood by practitioners such as developers and end-users. To this end, it is proposed that to comply with the original , a computational toxicology model should be associated with (i) a globally unique identifier for model citation; (ii) the capture and curation of the model; (iii) the metadata for the dependent and independent variables and, where possible, data; and (iv) storage in a searchable and interoperable platform. The FAIR Lite principles are mapped onto the original FAIR principles applied to QSARs, thereby demonstrating that a simpler checklist approach covers all aspects

Pathogenic, cultural, morphological and molecular variability among eight isolates of Alternaria solani, causing early blight of tomato

Among the fungal diseases infecting tomato crops, early blight caused by Alternaria solani (Ellis and Martin) Jones and Grout is one of the most catastrophic disease causing accountable losses. Further, all of the tomato cultivars presently under cultivation have succumb more or less to early blight disease. Therefore, the present studies were undertaken for the pathogenic, cultural, morphological and molecular variability among the isolates of A. solani. The results reveal all of the eight isolates of A. solani as pathogenic to tomato (Cv. Pusa Ruby) and showed variability amongst them. The test isolates could grow better on the basic culture medium potato dextrose agar; however, highest mycelial growth was recorded on the isolate AsLt (88.50 mm), followed by AsBd (82.36 mm) and AsHl (78.40 mm), with excellent sporulation. All of the eight test isolates exhibited a wide range of variability in respect of their mycelial and conidial dimensions and septation. RAPD-PCR analysis of the four most virulent A. solani isolates, using 13 OPA primers revealed that the isolates AsBd (Beed) and AsLt (Latur) were closely related with 85% genetic similarity whereas, the isolates AsHl (Hingoli) and AsJl (Jalna) were closely related with 50% genetic similarity, but distinct from that of AsLt and AsBd isolates. Key words: Tomato, Alternaria solani, isolates, pathogenic, molecular variability, virulent, primers

Brain region-specific alterations in the gene expression of cytokines, immune cell markers and cholinergic system components during peripheral endotoxin-induced inflammation

Inflammatory conditions characterized by excessive peripheral immune responses are associated with diverse alterations in brain function, and brain-derived neural pathways regulate peripheral inflammation. Important aspects of this bidirectional peripheral immune-brain communication, including the impact of peripheral inflammation on brain region-specific cytokine responses, and brain cholinergic signaling (which plays a role in controlling peripheral cytokine levels), remain unclear. To provide insight, we studied gene expression of cytokines, immune cell markers and brain cholinergic system components in the cortex, cerebellum, brainstem, hippocampus, hypothalamus, striatum and thalamus in mice after an intraperitoneal lipopolysaccharide injection. Endotoxemia was accompanied by elevated serum levels of interleukin (IL)-1β, IL-6 and other cytokines and brain region-specific increases in Il1b (the highest increase, relative to basal level, was in cortex; the lowest increase was in cerebellum) and Il6 (highest increase in cerebellum; lowest increase in striatum) mRNA expression. Gene expression of brain Gfap (astrocyte marker) was also differentially increased. However, Iba1 (microglia marker) mRNA expression was decreased in the cortex, hippocampus and other brain regions in parallel with morphological changes, indicating microglia activation. Brain choline acetyltransferase (Chat ) mRNA expression was decreased in the striatum, acetylcholinesterase (Ache) mRNA expression was decreased in the cortex and increased in the hippocampus, and M1 muscarinic acetylcholine receptor (Chrm1) mRNA expression was decreased in the cortex and the brainstem. These results reveal a previously unrecognized regional specificity in brain immunoregulatory and cholinergic system gene expression in the context of peripheral inflammation and are of interest for designing future antiinflammatory approaches