Biology subjects

Might, M.

Publications and source records attributed to Might, M..

4 recordsLinked to original sources

bioRxiv2025

Anti-Parkinsonian Drugs Rescue Locomotor Deficits in JIP3 Knockout Zebrafish: Implications for Treating Patients with MAPK8IP3-related Neurodevelopmental Disorders

MAPK8IP3-related neurodevelopmental disorders are a spectrum of rare conditions caused by de novo mutations in the MAPK8IP3 gene that encodes the JIP3 protein. These disorders are associated with a spectrum of neurodevelopmental symptoms that manifest in children and cause brain abnormalities, profound intellectual disabilities, movement disorders, and developmental delays. JIP3 is required for axonal transport of proteins and organelles between the soma and the synaptic terminal of neurons, a process critical for normal brain development and function. Homozygous loss-of-function mutations in JIP3 lead to impaired axonal transport and aggregation of cargo, which result in axonal swelling and stunted elongation. Despite these severe outcomes, disease mechanisms are poorly understood, and no current treatments are available. Here we conduct thorough morphological, behavioral, and motility phenotyping in the JIP3 knockout zebrafish and identify locomotor deficits and morphological abnormalities. To identify treatment options, we used insights from expert clinicians and the artificial intelligence tool, mediKanren, to identify drug candidates hypothesized to improve patient symptoms or compensate for the loss of JIP3 at the molecular level. We then prioritized drugs that are FDA-approved, safe for children, and readily available. These collective efforts identified amantadine and levodopa as candidate therapies and rescued motor phenotypes associated with JIP3 loss-of-function in zebrafish.

neuroscience↗

bioRxiv2021

Water-soluble tocopherol derivatives inhibit SARS-CoV-2 RNA-dependent RNA polymerase

The recent emergence of a novel coronavirus, SARS-CoV-2, has led to the global pandemic of the severe disease COVID-19 in humans. While efforts to quickly identify effective antiviral therapies have focused largely on repurposing existing drugs1-4, the current standard of care, remdesivir, remains the only authorized antiviral intervention of COVID-19 and provides only modest clinical benefits5. Here we show that water-soluble derivatives of -tocopherol have potent antiviral activity and synergize with remdesivir as inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). Through an artificial-intelligence-driven in silico screen and in vitro viral inhibition assay, we identified D--tocopherol polyethylene glycol succinate (TPGS) as an effective antiviral against SARS-CoV-2 and {beta}-coronaviruses more broadly that also displays strong synergy with remdesivir. We subsequently determined that TPGS and other water-soluble derivatives of -tocopherol inhibit the transcriptional activity of purified SARS-CoV-2 RdRp and identified affinity binding sites for these compounds within a conserved, hydrophobic interface between SARS-CoV-2 nonstructural protein 7 and nonstructural protein 8 that is functionally implicated in the assembly of the SARS-CoV-2 RdRp6. In summary, we conclude that solubilizing modifications to -tocopherol allow it to interact with the SARS-CoV-2 RdRp, making it an effective antiviral molecule alone and even more so in combination with remdesivir. These findings are significant given that many tocopherol derivatives, including TPGS, are considered safe for humans, orally bioavailable, and dramatically enhance the activity of the only approved antiviral for SARS-CoV-2 infection7-9.

microbiology↗

bioRxiv2019

Structured Reviews for Data and Knowledge Driven Research

MotivationHypothesis generation is a critical step in research and a cornerstone in the rare disease field. Research is most efficient when those hypotheses are based on the entirety of knowledge known to date. Systematic review articles are commonly used in biomedicine to summarize existing knowledge and contextualize experimental data. But the information contained within review articles is typically only expressed as free-text, which is difficult to use computationally. Researchers struggle to navigate, collect and remix prior knowledge as it is scattered in several silos without seamless integration and access. This lack of a structured information framework hinders research by both experimental and computational scientists.\n\nResultsTo better organize knowledge and data, we built a structured review article that is specifically focused on NGLY1 Deficiency, an ultra-rare genetic disease first reported in 2012. We represented this structured review as a knowledge graph, and then stored this knowledge graph in a Neo4j database to simplify dissemination, querying, and visualization of the network. Relative to free-text, this structured review better promotes the principles of findability, accessibility, interoperability, and reusability (FAIR). In collaboration with domain experts in NGLY1 Deficiency, we demonstrate how this resource can improve the efficiency and comprehensiveness of hypothesis generation. We also developed a read-write interface that allows domain experts to contribute FAIR structured knowledge to this community resource. In contrast to traditional free-text review articles, this structured review exists as a living knowledge graph that is curated by humans and accessible to computational analyses. Finally, we have generalized this workflow into modular and repurposable components that can be applied to other domain areas. This NGLY1 Deficiency-focused network is publicly available at http://ngly1graph.org/.\n\nAvailability and implementationSource code and network data files are at: https://github.com/SuLab/ngly1-graph and https://github.com/SuLab/bioknowledge-reviewer.\n\nContactasu@scripps.edu

bioinformatics↗

bioRxiv2018

A graph-based algorithm for RNA-seq data normalization

The use of RNA-sequencing has garnered much attention in the recent years for characterizing and understanding various biological systems. However, it remains a major challenge to gain insights from a large number of RNA-seq experiments collectively, due to the normalization problem. Current normalization methods are based on assumptions that fail to hold when RNA-seq profiles become more abundant and heterogeneous. We present a normalization procedure that does not rely on these assumptions, or on prior knowledge about the reference transcripts in those conditions. This algorithm is based on a graph constructed from intrinsic correlations among RNA-seq transcripts and seeks to identify a set of densely connected vertices as references. Application of this algorithm on our benchmark data showed that it can recover the reference transcripts with high precision, thus resulting in high-quality normalization. As demonstrated on a real data set, this algorithm gives good results and is efficient enough to be applicable to real-life data.\n\n2012 ACM Subject ClassificationApplied computing [->] Computational transcriptomics, Applied computing [->] Bioinformatics\n\nDigital Object Identifier10.4230/LIPIcs.WABI.2018.xxx\n\nFundingThis material was based on research supported by the National Heart, Lung, and Blood Institute (NHLBI)-NIH sponsored Programs of Excellence in Glycosciences [grant number HL107152 to B.K.], and partially by NSF [CAREER grant 1350344 to M.M.]. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.

bioinformatics↗