Biology subjects

Lee, D.

Publications and source records attributed to Lee, D..

At least 253 records · Page 14Linked to original sources

bioRxiv2020

Improvements to multi-offset adaptive optics scanning laser ophthalmoscopy for in vivo imaging of individual retinal ganglion cell layer neurons in humans

Previous work has shown that multi-offset detection in adaptive optics scanning laser ophthalmoscopy (AOSLO) can be used to image retinal ganglion cells (RGCs) in monkeys and humans. However, though images of RGCs in anesthetized monkeys with high light levels produced high contrast images of RGCs, images from humans failed to reach the same contrast due to several drawbacks in the previous dual-wavelength multi-offset approach. Our aim here was to design and build a multi-offset detection pattern for humans at safe light levels that could reveal the retinal ganglion cell layer neurons with a contrast, robustness and acquisition time approaching results only previously obtained in monkeys. Here, we present a new imaging system using only one light source, compared to the previous dual-wavelength used on monkeys. Our single-wavelength solution allows for increased light power and eliminates problematic chromatic aberrations. Then, we demonstrate that a radial multi-offset detection pattern with an offset distance of 8-10 Airy Disk Diameter (ADD) is optimal to detect photons multiply scattered in all directions from RGCs thereby enhancing their contrast. This new setup and image processing pipeline led to improved imaging of retinal ganglion cells using multi-offset imaging in AOSLO.

bioengineering↗

bioRxiv2020

Transient neuronal suppression for exploitation of new sensory evidence

In noisy but stationary environments, decisions should be based on the temporal integration of sequentially sampled evidence. This strategy has been supported by many behavioral studies and is qualitatively consistent with neural activity in multiple brain areas. By contrast, decision-making in the face of non-stationary sensory evidence remains poorly understood. Here, we trained monkeys to identify the dominant color of a dynamically refreshed bicolor patch that becomes informative after a variable delay. Animals' behavioral responses were briefly suppressed after evidence changes, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to that frequently observed after stimulus onset. Generalized drift-diffusion models revealed consistency of behavior and neural activity with brief suppression of motor output, but not with pausing or resetting of evidence accumulation. These results suggest that momentary arrest of motor preparation is an important component of dynamic perceptual decision making.

neuroscience↗

bioRxiv2020

Chirality-dependent anti-inflammatory effect of glutathione after spinal cord injury

Neuroinflammation forms a glial scar following a spinal cord injury (SCI). The injured axon cannot regenerate across the scar, suggesting permanent paraplegia. In this study, we report that d-chiral glutathione (D-GSH) suppresses the inflammatory response after SCI and leads to axon regeneration of the injured spinal cord to a greater extent than l-chiral glutathione (L-GSH). After SCI, axon regrowth in D-GSH-treated rats was significantly increased compared to that in L-GSH-treated rats (***p < 0.001). Secondary damage and motor function were significantly improved in D-GSH-treated rats compared to those outcomes in L-GSH-treated rats (**p < 0.01). Moreover, D-GSH significantly decreased pro-inflammatory cytokines and glial scar via inhibition of the mitogen-activated protein kinase (MAPK) signaling pathway compared to L-GSH (***p < 0.001). In primary cultured macrophages, we found that D-GSH undergoes more intracellular interaction with activated macrophages than L-GSH (***p < 0.001). These findings reveal a potential new regenerative function of chiral GSH in SCI and suggest that chiral GSH has therapeutic potential as a treatment of other diseases.

neuroscience↗

bioRxiv2020

The epithelial splicing regulator ESRP2 is epigenetically repressed by DNA hypermethylation in Wilms tumour and acts as a tumour suppressor

Wilms tumour (WT), a childhood kidney cancer with embryonal origins, has been extensively characterised for genetic and epigenetic alterations, but a proportion of WTs still lack identifiable abnormalities. To uncover DNA methylation changes critical for WT pathogenesis, we compared the epigenome of fetal kidney with two WT cell lines, using methyl-CpG immunoprecipitation. We filtered our results to remove common cancer-associated epigenetic changes, and to enrich for genes involved in early kidney development. This identified four candidate genes that were hypermethylated in WT cell lines compared to fetal kidney, of which ESRP2 (epithelial splicing regulatory protein 2), was the most promising gene for further study. ESRP2 was commonly repressed by DNA methylation in WT, and this was shown to occur early in WT development (in nephrogenic rests). ESRP2 expression could be reactivated by DNA methyltransferase inhibition in WT cell lines. When ESRP2 was overexpressed in WT cell lines, it acted as an inhibitor of cellular proliferation in vitro, and in vivo it suppressed tumour growth of orthotopic xenografts in nude mice. RNA-seq of the ESRP2-expressing WT cell lines identified several novel splicing targets, in addition to well-characterised targets of ESRP2. We propose a model in which the mesenchymal to epithelial transition that is essential for early kidney development, can be disrupted in to generate WT, either by genetic abnormalities such as WT1 mutations, or by epigenetic defects, such as ESRP2 methylation.

cancer biology↗

bioRxiv2020

Protective Effects of STI-2020 Antibody Delivered Post-Infection by the Intranasal or Intravenous Route in a Syrian Golden Hamster COVID-19 Model

We have previously reported that the SARS-CoV-2 neutralizing antibody, STI-2020, potently inhibits cytopathic effects of infection by genetically diverse clinical SARS-CoV-2 pandemic isolates in vitro, and has demonstrated efficacy in a hamster model of COVID-19 when administered by the intravenous route immediately following infection. We now have extended our in vivo studies of STI-2020 to include disease treatment efficacy, profiling of biodistribution of STI-2020 in mice when antibody is delivered intranasally (IN) or intravenously (IV), as well as pharmacokinetics in mice following IN antibody administration. Importantly, SARS-CoV-2-infected hamsters were treated with STI-2020 using these routes, and treatment effects on severity and duration of COVID-19-like disease in this model were evaluated. In SARS-CoV-2 infected hamsters, treatment with STI-2020 12 hours post-infection using the IN route led to a decrease in severity of clinical disease signs and a more robust recovery during 9 days of infection as compared to animals treated with an isotype control antibody. Treatment via the IV route using the same dose and timing regimen resulted in a decrease in the average number of consecutive days that infected animals experienced weight loss, shortening the duration of disease and allowing recovery to begin more rapidly in STI-2020 treated animals. Following IN administration in mice, STI-2020 was detected within 10 minutes in both lung tissue and lung lavage. The half-life of STI-2020 in lung tissue is approximately 25 hours. We are currently investigating the minimum effective dose of IN-delivered STI-2020 in the hamster model as well as establishing the relative benefit of delivering neutralizing antibodies by both IV and IN routes.

microbiology↗

bioRxiv2020

Hericerin derivatives from Hericium erinaceus exert BDNF-like neurotrophic activity in central hippocampal neurons and enhance memory

The traditional medicinal mushroom Hericium erinaceus has long been known for enhancing the peripheral nerve regeneration through targeting nerve growth factor (NGF) neurotrophic activity. It was also reported to protect against ageing-dependent cognitive decline in wildtype and in Alzheimers disease mouse models suggesting a yet to be defined action on neurons of the central nervous system. Here, we purified and identified biologically active compounds from H. erinaceus, based on their ability to promote neurite outgrowth in hippocampal neurons. N-de phenylethyl isohericerin (NDPIH), an isoindoline compound from this mushroom together with its hydrophobic derivative hericene A, were highly potent in inducing extensive axon outgrowth and neurite branching in the absence of serum demonstrating high neurotropic activity. NDPIH also induced enlarged growth cones suggestive of a brain-derived neurotrophic factor (BDNF)-like activity. Pharmacological inhibition of tropomyosin receptor kinase B (TrkB) by ANA12 prevented NDPIH-induced neurotrophic activity providing evidence that NDPIH acts via TrkB receptors to mediate its neurotrophic effect in central neurons. Finally, in vivo treatment with H. erinaceus crude extract and hericene A significantly increased BDNF and downstream pathway and enhanced learning and memory in the novel object recognition memory test. Our results suggest that hericene A can promote BDNF-like activity in neurons in vitro and in vivo thereby enhancing recognition memory.

neuroscience↗

bioRxiv2020

Value Certainty in Diffusion Decision Models

The drift-diffusion model (DDM) is widely used and broadly accepted for its ability to account for binary choices (in both the perceptual and preferential domains) and response times (RT), as a function of the stimulus or the choice alternative (or option) values. The DDM is built on an evidence accumulation-to-bound concept, where, in the value domain, a decision maker repeatedly samples the mental representations of the values of the available options until satisfied that there is enough evidence (or support) in favor of one option over the other. As the signals that drive the evidence are derived from value estimates that are not known with certainty, repeated sequential samples are necessary to average out noise. The classic DDM does not allow for different options to have different levels of precision in their value representations. However, recent studies have shown that decision makers often report levels of certainty regarding value estimates that vary across choice options. There is therefore a need to extend the DDM to include an option-specific value certainty component. We present several such DDM extensions and validate them against empirical data from four previous studies. The data support best a DDM version in which the drift of the accumulation is based on a sort of signal-to-noise ratio of value for each option (rather than a mere accumulation of samples from the corresponding value distributions). This DDM variant accounts for the impact of value certainty on both choice consistency and response time present in the empirical data.

neuroscience↗

bioRxiv2020

Coherence Shifts in Attribute Evaluations

In five experiments, people repeatedly judged individual options with respect to both overall value and attribute values. When required to choose between two snacks, each differing in two attributes (pleasure and nutrition), peoples assessments of value shifted from pre- to post-choice in the direction that spread the alternatives further apart so as to favor the winner, thereby increasing confidence in the choice. This shift was observed not only for ratings of overall value, but also for each of the two individual attributes. The magnitude of the coherence shift increased with choice difficulty as measured by the difference in initial ratings of overall value for the two options, as well as with a measure of attribute disparity (the degree to which individual attributes "disagree" with one another as to which option is superior). In Experiments 2-5, tasks other than explicit choice generated the same qualitative pattern of value changes, confidence, and response time. These findings support the hypothesis that active consideration of options, whether or not explicitly related to value, automatically refines the mental value representations for the options, which in turn allows them to be more precisely distinguished when later included in a value-based choice set.

neuroscience↗

bioRxiv2020

Role of neutrophil extracellular traps in regulation of lung cancer invasion and metastasis: Structural Insights from a Computational Model

Lung cancer is one of the leading causes of cancer-related deaths worldwide and is characterized by hijacking immune system for active growth and aggressive metastasis. Neutrophils, which in their original form should establish immune activities to the tumor as a first line of defense, are undermined by tumor cells to promote tumor invasion in several ways. In this study, we investigate the mutual interactions between the tumor cells and the neutrophils that facilitate tumor invasion by developing a mathematical model that involves taxis-reaction-diffusion equations for the critical components in the interaction. These include the densities of tumor and neutrophils, and the concentrations of signaling molecules and structure such as neutrophil extracellular traps (NETs). We apply the mathematical model to a Boyden invasion assay used in the experiments to demonstrate that the tumor-associated neutrophils can enhance tumor cell invasion by secreting the neutrophil elastase. We show that the model can both reproduce the major experimental observation on NET-mediated cancer invasion and make several important predictions to guide future experiments with the goal of the development of new anti-tumor strategies. Moreover, using this model, we investigate the fundamental mechanism of NET-mediated invasion of cancer cells and the impact of internal and external heterogeneity on the migration patterning of tumour cells and their response to different treatment schedules. Author summaryWhen cancer patients are diagnosed with tumours at a primary site, the cancer cells are often found in the blood or already metastasized to the secondary sites in other organs. These metastatic cancer cells are more resistant to major anti-cancer therapies, and lead to the low survival probability. Until recently, the role of neutrophils, specifically tumor-associated neutrophils as a member of complex tumor microenvironment, has been ignored for a long time due to technical difficulties in tumor biology but these neutrophils are emerging as an important player in regulation of tumor invasion and metastasis. The mutual interaction between a tumor and neutrophils from bone marrow or in blood induces the critical transition of the naive form, called the N1 type, to the more aggressive phenotype, called the N2 TANs, which then promotes tumor invasion. In this article, we investigate how stimulated neutrophils with different N1 and N2 landscapes shape the metastatic potential of the lung cancers. Our simulation framework is designed for boyden invasion chamber in experiments and based on a mathematical model that describes how tumor cells interact with neutrophils and N2 TANs can promote tumor cell invasion. We demonstrate that the efficacy of anti-tumor (anti-invasion) drugs depend on this critical communication and N1 [->] N2 landscapes of stimulated neutrophils.

cancer biology↗

bioRxiv2020

Balancing selection maintains ancient genetic diversity in C. elegans

Across diverse taxa, selfing species have evolved independently from outcrossing species thousands of times. The transition from outcrossing to selfing significantly decreases the effective population size, effective recombination rate, and heterozygosity within a species. These changes lead to a reduction in genetic diversity, and therefore adaptive potential, by intensifying the effects of random genetic drift and linked selection. Within the nematode genus Caenorhabditis, selfing has evolved at least three times and all three species, including in the model organism Caenorhabditis elegans, show substantially reduced genetic diversity relative to outcrossing species. Selfing and outcrossing Caenorhabditis species are often found in the same niches, but we still do not know how selfing species with limited genetic diversity can adapt to these environments. Here, we examine the whole-genome sequences from 609 wild C. elegans strains isolated worldwide and show that genetic variation is concentrated in punctuated hyper-divergent regions that cover 20% of the C. elegans reference genome. These regions are enriched in environmental response genes that mediate sensory perception, pathogen response, and xenobiotic stress response. Population genomic evidence suggests that genetic diversity in these regions has been maintained by long-term balancing selection. Using long-read genome assemblies for 15 wild strains, we show that hyper-divergent haplotypes contain unique sets of genes and show levels of divergence comparable to levels found between Caenorhabditis species that diverged millions of years ago. These results provide an example for how species can avoid the evolutionary "dead end" associated with selfing.

evolutionary biology↗

bioRxiv2020

Development of a genetically-encoded oxytocin sensor

Oxytocin (OXT) is a neuropeptide originating in the paraventricular nucleus (PVN) of the hypothalamus, with a role in influencing various social behaviors. However, pinpointing its actions only during the time animals are performing specific behaviors has been difficult to study. Here we developed an optogenetic gene expression system designed to selectively label neuronal populations activated by OXT in the presence of blue-light, named "OXTR-iTango2". The OXTR-iTango2 was capable of inducing gene expression of a reporter gene in both human embryonic kidney (HEK) cells and neurons in a quantitative manner. In vivo expression of OXTR-iTango2 selectively labeled OXT-sensitive neurons in a blue-light dependent manner. Furthermore, we were able to detect a subset of dopamine (DA) neurons in the ventral tegmental area (VTA) that receive OXT activation during social interaction. Thus, we provide a genetically-encoded, scalable optogenetic toolset to target neural circuits activated by OXT in behaving animals with a high temporal resolution.

neuroscience↗

bioRxiv2020

An empirical test of the role of value certainty in decision making

Most contemporary models of value-based decisions are built on value estimates that are typically self-reported by the decision maker. Such models have been successful in accounting for choice accuracy and response time, and more recently choice confidence. The fundamental driver of such models is choice difficulty, which is almost always defined as the absolute value difference between the subjective value ratings of the options in a choice set. Yet a decision maker is not necessarily able to provide a value estimate with the same degree of certainty for each option that he encounters. We propose that choice difficulty is determined not only by absolute value distance of choice options, but also by their value certainty. In this study, we first demonstrate the reliability of the concept of an option-specific value certainty using three different experimental measures. We then demonstrate the influence that value certainty has on choice, including accuracy (consistency), choice confidence, response time, and choice-induced preference change (i.e., the degree to which value estimates change from pre- to post-choice evaluation). We conclude with a suggestion of how popular contemporary models of choice (e.g., race model, drift-diffusion model) could be improved by including option-specific value certainty as one of their inputs.

neuroscience↗

bioRxiv2020

NIMBus: a Negative Binomial Regression based Integrative Method for Mutation Burden Analysis

BackgroundIdentifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues. ResultsWe developed a Negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma-Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods. ConclusionNIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

bioinformatics↗

bioRxiv2020

DiNeR: a Differential Graphical Model for analysis of co-regulation Network Rewiring

BackgroundDuring transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. "co-binding changes") can affect the co-regulating associations between TFs (i.e. "rewiring the co-regulator network"). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied. MethodsTo address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease. ResultsWe assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes. ConclusionsOur method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators.

bioinformatics↗

bioRxiv2020

A screen of FDA-approved drugs identifies inhibitors of Protein Tyrosine Phosphatase 4A3 (PTP4A3 or PRL-3)

Protein tyrosine phosphatase 4A3 (PTP4A3 or PRL-3) is highly expressed in a variety of cancers, where it promotes tumor cell migration and metastasis leading to poor prognosis. Despite its clinical significance, there are currently no viable options to target PRL-3 in vivo. Here, we screened 1,443 FDA-approved drugs for their ability to inhibit the activity of the PRL phosphatase family. We identified five specific inhibitors for PRL-3 as well as one selective inhibitor of PRL-2. Additionally, we found nine drugs that broadly and significantly suppressed PRL activity. Two of these broad PRL inhibitors, Salirasib and Candesartan blocked PRL-3-induced migration in human embryonic kidney (HEK) cells with no negative impact on cell viability. Both drugs prevented migration of PRL-3 expressing human colorectal cancer cells to a similar degree as the research-grade PRL inhibitor, Thienopyridone, and are selective towards PRLs over other phosphatases. In silico modeling revealed that Salirasib binds a putative allosteric site near the WPD loop of PRL-3, while Candesartan binds a potentially novel targetable site adjacent to the CX5R motif. Inhibitor binding at either of these sites is predicted to trap PRL-3 in a closed conformation, preventing substrate binding and inhibiting function.

cancer biology↗

bioRxiv2020

DNA micro-disk for the management of DNA-based data storage with index and write-once-read-many (WORM) memory features

DNA-based data storage has attracted attention because of its higher physical density of the data and longer retention time than those of conventional digital data storage1-7. However, previous DNA-based data storage lacked index features and the data quality of storage after a single access is not preserved, obstructing its industrial use. Here, we propose DNA micro-disks, quick response (QR)-coded micro-sized disks that harbour data-encoded DNA molecules for the efficient management of DNA-based data storage. We demonstrate the two major features that previous DNA-based data storage studies could not achieve. One feature is accessing data items efficiently by indexing the data-encoded DNA library. Another is achieving write-once-read-many (WORM) memory through the immobilization of DNA molecules on the disk and their enrichment through in situ DNA production. Through these features, the reliability of DNA-based data storage was increased by allowing multiple accession of data-encoded DNA without data loss.

bioengineering↗

bioRxiv2020

A Sec14-like Phosphatidylinositol Transfer Protein Paralog Defines a Novel Class of Heme-binding Proteins With An Unusual Heme Coordination Mechanism

Yeast Sfh5 is an unusual member of the Sec14-like phosphatidylinositol transfer protein (PITP) family. Whereas PITPs are defined by their abilities to transfer phosphatidylinositol between membranes in vitro, and to stimulate phosphoinositide signaling in vivo, Sfh5 does not exhibit these activities. Rather, Sfh5 is a redox-active penta-coordinate high spin FeIII heme-binding protein with an unusual heme-binding arrangement that involves a co-axial tyrosine/histidine coordination strategy and a complex electronic structure connecting the open shell iron d-orbitals with three aromatic ring systems. That Sfh5 is not a PITP is supported by demonstrations that heme is not a readily exchangeable ligand, and that phosphatidylinositol-exchange activity is resuscitated in heme binding-deficient Sfh5 mutants. The collective data identify Sfh5 as the prototype of a new class of fungal hemoproteins, and emphasize the versatility of the Sec14-fold as scaffold for translating the binding of chemically distinct ligands to the control of diverse sets of cellular activities.

biochemistry↗

bioRxiv2020

Bayesian Structural Time Series for Mobile Health and Sensor Data: A Flexible Modeling Framework for Evaluating Interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g. wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures as well as knowledge of the temporal and spatial properties of the data. Thus, biomedical sensor data need to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. We further show how this framework corrects for covariates to provide accurate assessments of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool around a specific Google implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the resulting software implementation to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity. In particular, we show how the framework is able to evaluate an exercise interventions effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

bioinformatics↗