Biology subjects

Wang, A.

Publications and source records attributed to Wang, A..

12 recordsLinked to original sources

bioRxiv2026

Aberrant neuronal cell cycle re-entry induces late-onset Alzheimer's disease relevant neuropathological and gene expression changes

Aberrant neuronal cell cycle re-entry (NCCR) is an alternative pathogenic mechanism in Alzheimer disease (AD) that has gained substantial support in the literature. The pathogenic role of ectopic NCCR is supported by our past work demonstrating that SV40T-mediated NCCR in adult mice can induce numerous pathologies associated with AD. Since NCCR is chronically induced for an extended period in the mouse model which gives rise to numerous pathologies including neuroinflammation, many of these neuropathological changes could simultaneously participate in driving disease progression. We hypothesized that the NCCR is a primary pathogenic driver and that halting this disease process at a later age could be sufficient for preventing the progression of AD-related pathologies. Here we show that modulation of NCCR at a later age prevents the progression of AD pathologies, including Abeta; and tau pathologies. Furthermore, functional genomics analysis demonstrates the late-onset AD (LOAD)-relevance of NCCR. Our findings suggest that our NCCR mouse model could help identify novel therapeutic targets that could aid in preventing AD progression.

neuroscience↗

bioRxiv2018

Using SMOG 2 to simulate complex biomolecular assemblies

Over the last 20 years, the application of structure-based (G[o]-like) models has ranged from protein folding with coarse-grained models to all-atom representations of large-scale molecular assemblies. While there are many variants that may be employed, the common feature of these models is that some (or all) of the stabilizing energetic interactions are defined based on knowledge of a particular experimentally-obtained conformation. With the generality of this approach, there was a need for a versatile computational platform for designing and implementing this class of models. To this end, the SMOG 2 software package provides an easy-to-use interface, where the user has full control of the model parameters. This software allows the user to edit XML-formatted files in order to provide definitions of new structure-based models. SMOG 2 reads these \"template\" files and maps the interactions onto specific structures, which are provided in PDB format. The force field files produced by SMOG 2 may then be used to perform simulations with a variety of popular molecular dynamics suites. In this chapter, we describe some of the key features of the SMOG 2 package, while providing examples and strategies for applying these techniques to complex (often large-scale) molecular assemblies, such as the ribosome.

biophysics↗

bioRxiv2018

Interpreting turbidity measurements for vesicle studies

Spectrophotometers are routinely used to assess the turbidity of vesicle solutions. Here we provide guidelines for interpreting turbidity measurements of vesicle samples, and highlight potential pitfalls of the approach. We use an exact solution for core-shell scatterers to model and calculate how samples of vesicles scatter light, and provide a comprehensive overview of how the turbidity of vesicle samples can change with vesicle size, contents, and composition. Surprisingly, we find that vesicle lamellarity has a large effect on sample turbidity, while unilamellar vesicles of different sizes have similar turbidity. We use our model in conjunction with experimental data to measure the thickness of oleic acid vesicle membranes and find excellent agreement with values determined by cryo-TEM. We also calculate the effects of potential errors in measurement from forward scattering and multiple scattering.

biophysics↗

bioRxiv2018

Pancreatic islet chromatin accessibility and conformation defines distal enhancer networks of type 2 diabetes risk

The gene targets of enhancer activity in pancreatic islets are largely unknown, impeding discovery of islet regulatory networks involved in type 2 diabetes (T2D) risk. We mapped chromatin state, accessibility and conformation using ChIP-seq, ATAC-seq and Hi-C in human pancreatic islets, which we integrated with T2D genetic fine-mapping and islet expression QTL data. Active islet regulatory elements preferentially interacted with other active elements, often at distances over 1MB, and we identified target genes for thousands of distal islet enhancers. A third of T2D risk signals mapped in islet enhancers, and target genes regulated by these signals were specifically involved in processes related to protein transport and secretion. Among implicated target genes of T2D islet enhancer signals with no prior known role in islet function, we demonstrated that reduced IGF2BP2 activity in mouse islets leads to impaired glucose-stimulated insulin secretion. These results link distal islet enhancer regulation of protein secretion and transport to genetic risk of T2D, and highlight the utility of high-throughput chromatin conformation maps to uncover the gene regulatory networks of complex disease.

genomics↗

bioRxiv2018

Recombinant expression of Proteorhodopsin and biofilm regulators in Escherichia coli for nanoparticle binding and removal in a wastewater treatment model

The small size of nanoparticles is both an advantage and a problem. Their high surface-area-to-volume ratio enables novel medical, industrial, and commercial applications. However, their small size also allows them to evade conventional filtration during water treatment, posing health risks to humans, plants, and aquatic life. This project aims to remove nanoparticles during wastewater treatment using genetically modified Escherichia coli in two ways: 1) binding citrate-capped nanoparticles with the membrane protein Proteorhodopsin, and 2) trapping nanoparticles using Escherichia coli biofilm produced by overexpressing two regulators: OmpR234 and CsgD. We demonstrate experimentally that Escherichia coli expressing Proteorhodopsin binds to 60 nm citrate-capped silver nanoparticles. We also successfully upregulate biofilm production and show that Escherichia coli biofilms are able to trap 30 nm gold particles. Finally, both Proteorhodopsin and biofilm approaches are able to bind and remove nanoparticles in simulated wastewater treatment tanks. We envision integrating our trapping system in both rural and urban wastewater treatment plants to efficiently capture all nanoparticles before treated water is released into the environment.\n\nFinancial DisclosureThis work was funded by the Taipei American School. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\n\nCompeting InterestsThe authors have declared that no competing interests exist.\n\nEthics StatementN/A\n\nData AvailabilityYes - all data are fully available without restriction. Sequences for the plasmids used in this study are available through the Registry of Standard Biological Parts. Links to raw data are included in Supplementary Information.

synthetic biology↗

bioRxiv2018

Early identity recognition of familiar faces is not dependent on holistic processing

It is widely accepted that holistic processing is critical for early face recognition, but recent work has suggested a larger role for feature-based processing. The earliest step in familiar face recognition is thought to be matching a perceptual representation of a familiar face to a stored representation of that face, which is thought to be indexed by the N250r event-related potential (ERP). In the current face priming studies, we investigated whether this perceptual representation can be effectively activated by feature-based processing. In the first experiment, prime images were familiar whole faces, isolated eyes, or isolated mouths. Whole faces and isolated eyes, but not isolated mouths, effectively modulated the N250r. In the second experiment, prime images were familiar whole faces presented either upright or inverted. Inverted face primes were no less effective than upright face primes in modulating the N250r. Together, the results of these studies indicate that activation of the earliest face recognition processes is not dependent on holistic processing of a typically configured face. Rather, feature-based processing can effectively activate the perceptual memory of a familiar face. However, not all features are effective primes as we found eyes, but not mouths, were effective in activating early face recognition.\n\nHighlightsO_LIHolistic processing is not necessary for early identity recognition of familiar faces.\nC_LIO_LIInverted faces and isolated features can effectively activate the perceptual memory of a familiar face.\nC_LIO_LIThis effectiveness was observed for eyes, but not mouths.\nC_LI

neuroscience↗

bioRxiv2018

Soil microbial habitats in an extreme desert Mars-analogue environment

Sediments in the hyper-arid core of the Atacama Desert are a terrestrial analogue to Mars regolith. Understanding the distribution and drivers of microbial life in the sediment may give critical clues on how to search for biosignatures on Mars. Here, we identify the spatial distribution of highly specialised bacterial communities in previously unexplored depth horizons of subsurface sediments. We deployed an autonomous rover in a mission-relevant Martian drilling scenario with manual sample validation. Subsurface communities were delineated by depth related to sediment moisture. Geochemical analysis indicated soluble salts and minerology that influenced water bio-availability, particularly in deeper sediments. Colonization was also patchy and uncolonized sediment was associated with indicators of extreme osmotic challenge. The study identifies linkage between biocomplexity, moisture and geochemistry in Mars-like sediments at the limit of habitability and demonstrates feasibility of the rover-mounted drill for future Mars sample recovery.

microbiology↗

bioRxiv2017

Housekeeping genes, revisited at the single-cell level

BackgroundSingle-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level are intrinsically stochastic and noisy. Yet, on cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how their expression stability can be assessed? We have developed a computational framework for ranking expression stability of genes in single cells. Here we evaluate the proposed framework and characterize SEGs derived from two scRNA-seq datasets that profile early human and mouse development.\n\nResultsHere, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets are highly reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across 10 diverse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in individual cells.\n\nConclusionsSEGs identified in this study have immediate utility both for understanding variation/stability of single-cell transcriptomes and for practical applications including scRNA-seq data normalization, the proposed framework can be applied to identify genes with stable expression in other scRNA-seq datasets.

bioinformatics↗

bioRxiv2017

ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy

Existing1drug discovery process follows a reductionist model of \"one-drug-one-gene-one-disease,\" which is not adequate to tackle complex diseases that involve multiple malfunctioned genes. The availability of big omics data offers new opportunities to transform the drug discovery process into a new paradigm of systems pharmacology that focuses on designing drugs to target molecular interaction networks instead of a single gene. Here, we develop a reliable multi-rank, multi-layered recommender system, ANTENNA, to mine large-scale chemical genomics and disease association data for the prediction of novel drug-gene-disease associations. ANTENNA integrates a novel tri-factorization based dual-regularized weighted and imputed One Class Collaborative Filtering (OCCF) algorithm, tREMAP, with a statistical framework that is based on Random Walk with Restart and can assess the reliability of a specific prediction. In the benchmark study, tREMAP clearly outperforms the single rank OCCF. We apply ANTENNA to a real-world problem: repurposing old drugs for new clinical indications that have yet had an effective treatment. We discover that FDA-approved drug diazoxide can inhibit multiple kinase genes whose malfunction is responsible for many diseases including cancer, and kill triple negative breast cancer (TNBC) cells effectively at a low concentration (IC50 = 0.87 M). The TNBC is a deadly disease that currently does not have effective targeted therapies. Our finding demonstrates the power of big data analytics in drug discovery, and has a great potential toward developing a targeted therapy for the effective treatment of TNBC.

bioinformatics↗

bioRxiv2017

In vivo Imaging β-cell Function Reveals Two Waves of β-cell Maturation

The insulin-secreting cells generated from stem cells in vitro are less glucose responsive than primary {beta}-cells. To search for the missing ingredients that are needed for {beta}-cell maturation, we have longitudinally monitored function of every {beta}-cell in Tg (ins:Rcamp1.07) zebrafish embryos with a newly-invented two-photon light-sheet microscope. We have shown that {beta}-cell maturation begins from the islet mantle and propagates to the islet core during the hatching period, coordinated by the islet vascularization. Lower concentration of glucose is optimal to initiate {beta}-cell maturation, while increased glucose delivery to every cell through microcirculation is required for functional boosting of the {beta}-cells. Both the initiation and the boosting of {beta}-cell maturation demands activation of calcineurin/NFAT by glucose. Calcineurin activator combined with glucose promotes mouse neonatal {beta}-cells cultured in vitro to mature to a functional state similar to adult {beta}-cells, suggesting a new strategy for improving stem cell-derived {beta}-like cell function in vitro.

developmental biology↗

bioRxiv2017

Assessing Inhibitors Of Mutant Isocitrate Dehydrogenase Using A Suite Of Pre-Clinical Discovery Assays

Isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) are key metabolic enzymes that are mutated in a variety of cancers to confer a gain-of-function activity resulting in the accumulation and secretion of an oncometabolite, D-2-hydroxyglutarate (2-HG). Accumulation of 2-HG can result in epigenetic dysregulation and a block in cellular differentiation, suggesting these mutations play a role in neoplasia. Based on its potential as a cancer target, a number of small molecule inhibitors have been developed to specifically inhibit mutant forms of IDH (mIDH1 and mIDH2). Here, a panel of mIDH inhibitors were systematically profiled using biochemical, cell-based, and tier-one ADME techniques. We quantified the biochemical effect of each inhibitor on mIDH1 (R132H and R132C) and mIDH2 (R172Q). The effect of these inhibitors on 2-HG concentrations in seven cell lines representing five different IDH1 mutations in both 2D and 3D cell cultures was assessed. Target engagement of these inhibitors was analyzed utilizing cellular thermal shift assays (CETSA), the effects of inhibitors on reversing 2-HG-induced block on leukemic cellular differentiation. We conclude from our mIDH1 assay panel that AG-120 and a Novartis inhibitor exhibited excellent activity in all biochemical and most cellular assays. While AG-120 has superior DMPK properties, it lacks efficacy a leukemic differentiation model. In conclusion, we present a comprehensive suite of in vitro preclinical drug development assays that can be used as a tool-box to identify lead compounds for mIDH drug discovery programs, as well as what we believe is the most comprehensive publically available dataset on the top mIDH inhibitors.

cancer biology↗

bioRxiv2017

Generation And Comparative Analysis Of Full-Length Transcriptomes In Sweetpotato And Its Putative Wild Ancestor I. trifida

Sweetpotato [Ipomoea batatas (L.) Lam.] is one of the most important crops in many developing countries and provides a candidate source of bioenergy. However, neither high-quality reference genome nor large-scale full-length cDNA sequences for this outcrossing hexaploid are still lacking, which in turn impedes progress in research studies in sweetpotato functional genomics and molecular breeding. In this study, we apply a combination of second- and third-generation sequencing technologies to sequence full-length transcriptomes in sweetpotato and its putative ancestor I. trifida. In total, we obtained 53,861/51,184 high-quality transcripts, which includes 34,963/33,637 putative full-length cDNA sequences, from sweetpotato/I. trifida. Amongst, we identified 104,540/94,174 open reading frames, 1476/1475 transcription factors, 25,315/27,090 simple sequence repeats, 417/531 long non-coding RNAs out of the sweetpotato/I. trifida dataset. By utilizing public available genomic contigs, we analyzed the gene features (including exon number, exon size, intron number, intron size, exon-intron structure) of 33,119 and 32,793 full-length transcripts in sweetpotato and I. trifida, respectively. Furthermore, comparative analysis between our transcript datasets and other large-scale cDNA datasets from different plant species enables us assessing the quality of public datasets, estimating the genetic similarity across relative species, and surveyed the evolutionary pattern of genes. Overall, our study provided fundamental resources of large-scale full-length transcripts in sweetpotato and its putative ancestor, for the first time, and would facilitate structural, functional and comparative genomics studies in this important crop.

plant biology↗