Biology subjects

Yoo, R.

Publications and source records attributed to Yoo, R..

6 recordsLinked to original sources

bioRxiv2021

NBI-921352, a First-in-Class, Na V 1.6 Selective, Sodium Channel Inhibitor That Prevents Seizures in Scn8a Gain-of- Function Mice, and Wild-Type Mice and Rats

NBI-921352 (formerly XEN901) is a novel sodium channel inhibitor designed to specifically target NaV1.6 channels. Such a molecule provides a precision-medicine approach to target SCN8A-related epilepsy syndromes (SCN8A-RES), where gain-of-function (GoF) mutations lead to excess NaV1.6 sodium current, or other indications where NaV1.6 mediated hyper-excitability contributes to disease (Gardella and Moller, 2019; Johannesen et al., 2019; Veeramah et al., 2012). NBI-921352 is a potent inhibitor of NaV1.6 (IC50 0.051 {micro}M), with exquisite selectivity over other sodium channel isoforms (selectivity ratios of 756X for NaV1.1, 134X for NaV1.2, 276X for NaV1.7, and >583X for NaV1.3, NaV1.4, and NaV1.5). NBI-921352 is a state-dependent inhibitor, preferentially inhibiting activated (inactivated or open) channels. The state dependence leads to potent stabilization of inactivation, inhibiting NaV1.6 currents, including resurgent and persistent NaV1.6 currents, while sparing the closed/rested channels. The isoform-selective profile of NBI-921352 led to a robust inhibition of action-potential firing in glutamatergic excitatory pyramidal neurons, while sparing fast-spiking inhibitory interneurons, where NaV1.1 predominates. Oral administration of NBI-921352 prevented electrically induced seizures in a Scn8a GoF mouse, as well as in wild-type mouse and rat seizure models. NBI-921352 was effective in preventing seizures at lower brain and plasma concentrations than commonly prescribed sodium channel inhibitor antiseizure medicines (ASMs) carbamazepine, phenytoin, and lacosamide. NBI-921352 was well tolerated at higher multiples of the effective plasma and brain concentrations than those ASMs. NBI-921352 is entering phase II proof-of-concept trials for the treatment of SCN8A-developmental epileptic encephalopathy (SCN8A-DEE) and adult focal-onset seizures.

pharmacology and toxicology↗

bioRxiv2021

Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal thermoacidophile Sulfolobus acidocaldarius

Dynamic cellular responses to environmental constraints are coordinated by the transcriptional regulatory network (TRN), which modulates gene expression. This network controls most fundamental cellular responses, including metabolism, motility, and stress responses. Here, we apply independent component analysis, an unsupervised machine learning approach, to 95 high-quality Sulfolobus acidocaldarius RNA-seq datasets and extract 45 independently modulated gene sets, or iModulons. Together, these iModulons contain 755 genes (32% of the genes identified on the genome) and explain over 70% of the variance in the expression compendium. We show that 5 modules represent the effects of known transcriptional regulators, and hypothesize that most of the remaining modules represent the effects of uncharacterized regulators. Further analysis of these gene sets results in: (1) the prediction of a DNA export system composed of 5 uncharacterized genes, (2) expansion of the LysM regulon, and (3) evidence for an as-yet-undiscovered global regulon. Our approach allows for a mechanistic, systems-level elucidation of an extremophiles responses to biological perturbations, which could inform research on gene-regulator interactions and facilitate regulator discovery in S. acidocaldarius. We also provide the first global TRN for S. acidocaldarius. Collectively, these results provide a roadmap towards regulatory network discovery in archaea.

systems biology↗

bioRxiv2021

Machine learning of all Mycobacterium Tuberculosis H37Rv RNA-seq data reveals complex interplay between metabolism, stress response, and infection

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, a systems level characterization and analysis of interactions among the controlling transcription factors remains to be achieved. Here, we applied an unsupervised machine learning method to modularize the M. tuberculosis transcriptome and describe the role of transcription factors (TFs) in the TRN. By applying Independent Component Analysis (ICA) to over 650 transcriptomic samples, we obtained 80 independently modulated gene sets known as "iModulons," many of which correspond to known regulons. These iModulons explain 61% of the variance in the organisms transcriptional response. We show that iModulons: 1) reveal the function of previously unknown regulons, 2) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and virulence events, and 3) identify intrinsic clusters of transcriptional regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation, and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.

systems biology↗

bioRxiv2021

Mining all publicly available expression data to compute dynamic microbial transcriptional regulatory networks

We are firmly in the era of biological big data. Millions of omics datasets are publicly accessible and can be employed to support scientific research or build a holistic view of an organism. Here, we introduce a workflow that converts all public gene expression data for a microbe into a dynamic representation of the organisms transcriptional regulatory network. This five-step process walks researchers through the mining, processing, curation, analysis, and characterization of all available expression data, using Bacillus subtilis as an example. The resulting reconstruction of the B. subtilis regulatory network can be leveraged to predict new regulons and analyze datasets in the context of all published data. The results are hosted at https://imodulondb.org/, and additional analyses can be performed using the PyModulon Python package. As the number of publicly available datasets increases, this pipeline will be applicable to a wide range of microbial pathogens and cell factories.

bioinformatics↗

bioRxiv2020

Co-targeting myelin inhibitors and CSPGs enhances sensory axon regeneration within, but not into, the spinal cord

A major barrier to intraspinal regeneration after dorsal root (DR) injury is the DR entry zone (DREZ), the CNS/PNS interface. DR axons stop regenerating at the DREZ, even if regenerative capacity is increased by a nerve conditioning lesion. This potent blockade has long been attributed to myelin-associated inhibitors and CSPGs, but incomplete lesions and conflicting reports have prevented conclusive agreement. Here we evaluated DR regeneration in mice, using novel strategies to facilitate complete lesions and analyses, selective tracing of proprioceptive and mechanoreceptive axons, and the first simultaneous targeting of Nogo/Reticulon-4, MAG, OMgp, CSPGs and GDNF. Co-eliminating myelin inhibitors and CSPGs elicited regeneration of only a few conditioning-lesioned DR axons across the DREZ. Their absence, however, markedly and synergistically enhanced regeneration of GDNF-stimulated axons, highlighting the importance of sufficiently elevating intrinsic growth capacity. We also conclude that myelin inhibitors and CSPGs are not the primary mechanism stopping axons at the DREZ.

neuroscience↗

bioRxiv2020

Synthesis of the novel transporter YdhC, is regulated by the YdhB transcription factor controlling adenosine and adenine uptake.

The YdhB transcriptional factor, re-named here AdnB, homologous to the allantoin regulator, AllS, was shown to regulate ydhC gene expression in Escherichia coli, which is divergently transcribed from adnB, and this gene arrangement is conserved in many Protreobacteria. The predicted consensus DNA binding sequence for YdhB is also conserved in Entrobacterial genomes. RNA-seq data confirmed the activation predicted due to the binding of AdnB as shown by Chip-Exo results. Fluorescent polarization experiments revealed binding of YdhB to the predicted binding site upstream of ydhC in the presence of 0.35 mM adenine, but not in its absence. The E. coli MG1655, strain lacking the ydhB gene, showed a lower level of ydhC mRNA in cells grown in M9-glucose supplemented with 2 mM adenosine. Adenosine and adenine are products of purine metabolism and provide sources of ammonium for many organisms. They are utilized under nitrogen starvation conditions as single nitrogen sources. Deletion of either the ydhC or the ydhB gene leads to a substantially decreased growth rate for E. coli in minimal M9 medium with glycerol as the carbon source and adenosine or adenine as the single nitrogen source. The ydhC mutant showed increased resistance to Paromomycine, Sulfathiazole and Sulfamethohazole using Biolog plates. We provide evidence that YdhB, (a novel LysR family regulator) activates expression of the ydhC gene, encoding a novel adenosine/adenine transporter in E. coli. The YdhB binding consensus for different groups of Enterobacteria was predicted.

microbiology↗