Biology subjects

Chan, J.

Publications and source records attributed to Chan, J..

9 recordsLinked to original sources

bioRxiv2018

Macaque anterior cingulate cortex deactivation impairs performance and alters lateral prefrontal oscillatory activities in a rule-switching task

In primates, both the dorsal anterior cingulate cortex (dACC) and the dorsolateral prefrontal cortex (dlPFC) are key regions of the frontoparietal cognitive control network. To study the role of the dACC and its communication with the dlPFC in cognitive control, we recorded the local field potentials from the dlPFC before and during the reversible deactivation of the dACC, in macaque monkeys engaging in uncued switches between two stimulus-response rules. Cryogenic dACC deactivation impaired response accuracy during rule-maintenance, but not rule-switching, which coincided with a reduction in the correct-error difference in dlPFC beta activities specifically during maintenance of the more challenging rule. During both rule switching and maintenance, dACC deactivation prolonged the animals reaction time and reduced task-related theta/alpha activities in the dlPFC; it also weakened dlPFC theta-gamma phase-amplitude modulation. Thus, the dACC and its interaction with the dlPFC plays a critical role in the maintenance of a new, challenging rule.

neuroscience↗

bioRxiv2018

An efficient timer and sizer of protein motions reveals the time scales of functional dynamics in the ribosome

The clock of life ticks as fast as how efficiently proteins could perform their functional dynamics. Protein complexes execute functions via several large-scale intrinsic motions across multiple conformational states, which occur at a timescale of nano-to milliseconds for well-folded proteins. Computationally expensive molecular dynamics (MD) simulation has been the only theoretical tool to time and size these motions, though barely to their slowest ends. Here, we convert a simple elastic network model (ENM), which takes a few seconds (ubiquitin) to hours (ribosome) for the analysis, into a molecular timer and sizer to gauge the slowest functional motions of proteins. Quasi-harmonic analysis, fluctuation-profile matching (FPM) and the Wiener-Khintchine theorem (WKT) are used to define the \"time-periods\", t, for anharmonic principal components (PCs) which are validated by NMR order parameters. The PCs with their respective \"time-periods\" are mapped to the eigenvalues ({lambda}ENM) of the corresponding ENM modes. Thus, the power laws t(ns) = 86.9{lambda}ENM-1.9 and {sigma}2([A]2) = 46.1 {lambda}ENM-2.5 are established allowing the characterization of the time scales of Nuclear Magnetic Resonance (NMR)-solved conformers, crystallographic anisotropic displacement parameters, and important ribosomal motions, as well as motional sizes of the latter.\n\nAuthor SummaryThe time scale of biological processes is governed by protein functional dynamics that often corresponds to the largest conformational spread and the longest time scales among all possible motions. Current simulation methodologies cannot reach the slowest, often functional, motions especially for supramolecular machineries. Borrowing the spring-bead model used in polymer physics since 60s, the efficient elastic network model (ENM), introduced in 90s, captured all modes of protein motions but largely underestimated the time scales of slowest modes due to its harmonic approximation.\n\nHere we map water-damped modes sampled by MD simulations to corresponding ones in ENM and thereby establish 2 power laws that describe the authentic time scales and sizes for the slowest anharmonic modes as functions of ENM eigenvalues. With that, we adequately describe the sizes and time scales for three proteins, confirmed by NMR spectroscopy, and ribosome that contains ~0.2 million heavy atoms (~ 20 thousands coarse-grained nodes).

biophysics↗

bioRxiv2018

Regulation of neural stem cell fate by the transcriptional repressor Capicua

Capicua (Cic) is a transcriptional repressor mutated in the brain cancer oligodendroglioma. Despite its cancer link, little is known of Cics function in the brain. Here, we investigated the relationship between Cic expression and cell type specification in the brain. Cic is strongly expressed in astrocytic and neuronal lineage cells but is more weakly expressed in stem cells and oligodendroglial lineage cells. Using a new conditional Cic knockout mouse, we show that forebrain-specific Cic deletion increases proliferation and self-renewal of neural stem cells. Furthermore, Cic loss biases neural stem cells toward glial lineage selection, expanding the pool of oligodendrocyte precursor cells (OPCs). These proliferation and lineage selection effects in the developing brain are dependent on de-repression of Ets transcription factors. In patient-derived oligodendroglioma cells, CIC re-expression or ETV5 blockade decreases lineage bias, proliferation, self-renewal and tumorigenicity. Our results identify Cic is an important regulator of cell fate in neurodevelopment and oligodendroglioma, and suggest that its loss contributes to oligodendroglioma by promoting proliferation and an OPC-like identity via Ets overactivity.

cancer biology↗

bioRxiv2018

Characterizing Subpopulations with Better Response to Treatment Using Observational Data - an Epilepsy Case Study

Electronic health records and health insurance claims, providing observational data on millions of patients, offer great opportunities, and challenges, for population health studies. The objective of this study is identifying subpopulations that are likely to benefit from a given treatment using observational data. We refer to these subpopulations as \"better responders\" and focus on characterizing these using linear scores with a limited number of variables. Building upon well-established causal inference techniques for analyzing observational data, we propose two algorithms that generate such scores for identifying better responders, as well as methods for evaluating and comparing these scores. We applied our methodology to a large dataset of ~135,000 epilepsy patients derived from claims data. Out of this sample, 85,000 were used to characterize subpopulations with better response to next-generation (\"Newer\") anti-epileptic drugs (AEDs), compared to an alternative treatment by first-generation (\"Older\") AEDs. The remaining 50,000 epilepsy patients were then used to evaluate our scores. Our results demonstrate the ability of our scores to identify large subpopulations of epilepsy patients with significantly better response to newer AEDs.

epidemiology↗

bioRxiv2018

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks

An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables. These challenges are traditionally tackled by likelihood-free methods that use scientific simulators to generate datasets and reduce them to hand-designed, permutation-invariant summary statistics, often leading to inaccurate inference. In this work, we develop an exchangeable neural network that performs summary statistic-free, likelihood-free inference. Our frame-work can be applied in a black-box fashion across a variety of simulation-based tasks, both within and outside biology. We demonstrate the power of our approach on the recombination hotspot testing problem, outperforming the state-of-the-art.

evolutionary biology↗

bioRxiv2017

Nuclear Abl Drives Extracellular-Vesicle Transfer of miR-34c to Induce Bystander Effects of Radiation

SUMMARYIonizing radiation stimulates nuclear accumulation of Abl tyrosine kinase that is required for directly irradiated cells to produce microRNA-34c-containing extracellular vesicles, which transfer the microRNA into non-irradiated cells to induce reactive oxygen species and bystander DNA damage.\n\nABSTRACTIonizing radiation (IR) activates an array of DNA damage response (DDR) that includes the induction of bystander effects (BE) in cells not targeted by radiation. How DDR pathways in irradiated cells stimulate BE in non-targeted cells is mostly unknown. We show here that extracellular vesicles from irradiated cells (EV-IR) induce reactive oxygen species (ROS) and DNA damage when internalized by un-irradiated cells. We found that EV-IR from Abl-NLS-mutated cells could not induce ROS or DNA damage, and restoration of nuclear Abl rescued those defects. Expanding a previous finding that Abl stimulates miR-34c expression, we show here that nuclear Abl also drives the vesicular secretion of miR-34c. Ectopic miR-34c expression, without irradiation, generated EV-miR-34c capable of inducing ROS and DNA damage. Furthermore, EV-IR from miR34-knockout cells could not induce ROS and raised {gamma}H2AX to lesser extent than EV-IR from miR34-wild type cells. These results establish a novel role for the Abl-miR-34c DDR pathway in stimulating radiation-induced bystander effects.

cell biology↗

bioRxiv2017

The Anatomical Tracings of Lesions After Stroke (ATLAS) Dataset - Release 1.1

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

neuroscience↗

bioRxiv2017

Type 2 diabetes promotes cell centrosome amplification and the role of AKT-ROS-dependent signalling of ROCK1 and 14-3-3σ

Type2 diabetes is associated with oxidative stress which can cause cell centrosome amplification. The study investigated centrosome amplification in type 2 diabetes and the underlying mechanisms. We found that centrosome amplification was increased in the peripheral blood mononuclear cells (PBMC) from the type 2 diabetic patients, which correlated with the levels of fasting blood glucose and HbA1c. High glucose, insulin and palmitic acid, alone or in combinations, induced ROS production and centrosome amplification. Together, they increased AKT activation as well as the expression, binding and centrosome translation of ROCK1 and 14-3-3{sigma}. Results from further analyses showed that AKT-ROS-dependent upregulations of expression, binding and centrosome translocation of ROCK1 and 14-3-3{sigma} was the molecular pathway underlying the centrosome amplification induced by high glucose, insulin and palmitic acid. Moreover, the increases in AKT activation and ROS production as well as expression, binding and centrosome distribution of ROCK1 and 14-3-3{sigma} were confirmed in the PBMC from the patients with type 2 diabetes. In conclusion, our results show that type 2 diabetes promotes cell centrosome amplification, and suggest that the diabetic pathophysiological factors-activated AKT-ROS-dependent signalling of ROCK1 and 14-3-3{sigma} is the underlying molecular mechanism.

cell biology↗

bioRxiv2017

Phenotype and gene ontology enrichment as guides for disease modeling in C. elegans

Genome-wide experiments have the capacity to generate massive amounts of unbiased data about an organism. In order to interpret this data, dimensionality reduction techniques are required. One approach is to annotate genes using controlled languages and to test experimental datasets for term enrichment using probabilistic methods. Although gene, phenotype and anatomy ontologies exist for C. elegans, no unified software offers enrichment analyses of all the ontologies using the same methodology. Here, we present the WormBase Enrichment Suite, which offers users the ability to test all nematode ontologies simultaneously. We show that the WormBase Enrichment Suite provides valuable insight into different biological problems. Briefly, we show that phenotype enrichment analysis (PEA) can help researchers identify disease phenologs, phenotypes that are homologous across species, which can inform disease modeling in C. elegans. The WormBase Enrichment Suite analysis can also shed light on RNA-seq datasets by showing what molecular functions are enriched, which phenotypes these functions are implicated in and what tissues are overrepresented in the dataset. Finally, we explore the phenotype-anatomy relationship, showing that a small subset of highly specific tissues are disproportionately likely to cause an Egl phenotype, but inferring tissue expression from an Egl phenotype is limited to the largest tissues.

bioinformatics↗