Amperometric sensors have been developed for hydrogen peroxide, choline, and acetylcholine by immobilization of horseradish peroxidase, (HRP), choline oxidase, and acetylcholinesterase in a cross-linked redox polymer deposited on glassy carbon electrodes. Peroxide sensors, prepared by immobilization of HRP alone, gave detection limits of 10 nM and a linear response up to ca. 1 mM. Coimmobilization of HRP and glucose oxidase was used to establish the feasibility of highly efficient bienzyme sensors at low substrate levels. Replacing glucose oxidase with choline oxidase produced sensors with submicromolar detection limits and a linear response up to 0.8 mM. Addition of acetylcholinesterase to the sensors generated a relatively small response to acetylcholine that demonstrates the feasibility of trienzyme sensors. At low substrate concentrations, no loss in sensitivity during a 1-day experiment was observed. The response times of these sensors are all less than 30 s with 2-s response times achieved in some cases.
Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical–molecular–biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.
The natural ability of stem cells to self-organize into functional tissue has been harnessed for the production of functional human intestinal organoids. Although dynamic mechanical forces play a central role in intestinal development and morphogenesis, conventional methods for the generation of intestinal organoids have relied solely on biological factors. Here, we show that the incorporation of uniaxial strain, by using compressed nitinol springs, in human intestinal organoids transplanted into the mesentery of mice induces growth and maturation of the organoids. Assessment of morphometric parameters, transcriptome profiling, and functional assays of the strain-exposed tissue revealed higher similarities to native human intestine, with regards to tissue size and complexity, and muscle tone. Our findings suggest that the incorporation of physiologically relevant mechanical cues during the development of human intestinal tissue enhances its maturation and enterogenesis.
High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. examining compounds with beneficial effects in models of Huntington's Disease, we found common MoAs for compounds with unrelated structures, connectivity scores, and binding targets. the approach also predicted highly divergent MoAs for two FDA-approved antihistamines. We experimentally validated these effects, demonstrating that one antihistamine activates autophagy, while the other targets bioenergetics. the use of multiple omics was essential, as some MoAs were virtually undetectable in specific assays. Our approach does not require reference compounds or large databases of experimental data in related systems and thus can be applied to the study of agents with uncharacterized MoAs and to rare or understudied diseases. Unknown modes of action of drug candidates can lead to unpredicted consequences on effectiveness and safety. Computational methods, such as the analysis of gene signatures, and high-throughput experimental methods have accelerated the discovery of lead compounds that affect a specific target or phenotype 1-3. However, these advances have not dramatically changed the rate of drug approvals. Between 2000 and 2015, 86% of drug candidates failed to earn FDA approval, with toxicity or a lack of efficacy being common reasons for their clinical trial termination 4,5. Even compounds identified for binding to a specific target can have complex downstream functional consequences, or modes of action (MoAs) 6. Understanding the MoAs of compounds remains a crucial challenge in increasing the success rate of clinical trials and drug repurposing efforts 4,6. Computational approaches have contributed to the discovery of MoAs. Using the Connectivity Map data, tools like MANTRA can predict MoAs of new compounds based on their gene expression similarity to reference compounds with known MoAs 7. To combat antibiotic resistance, reference compounds were also used to infer MoAs of uncharacterized antimicrobial compounds by comparing their untargeted metabolomic profiles in bacteria 8. From human cancer cell lines, basal gene expression signatures were correlated with sensitivity patterns of compounds to identify previously unknown activation mechanisms and compound binding targets 9. Similarly, gene expression profiles of human lymphoma cells treated with anti-cancer drugs were compared using the gene regulatory network-based DeMAND algorithm to predict novel targets and unexpected similarities between the drugs 10. However, all of these methods require prior context-specific knowledge, such as data from reference compounds with known MoAs, sensitivity data, or gene-regulatory interactions. More general approaches to discover MoAs are urgently needed. In...
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