Biology subjects

Le, H.

Publications and source records attributed to Le, H..

26 records · Page 2Linked to original sources

bioRxiv2022

Preclinical assessment of Fibroblast Activation Protein-specific MRI as an alternative to PSMA for image-guided focal therapy of prostate cancer

Accurate and precise delineation of gross tumour volumes remains a barrier to radiotherapy dose escalation and boost dosing in the treatment of solid tumours, such as prostate cancer. Magnetic resonance imaging of tumour molecular targets has the power to enable focal dose boosting, particularly when combined with technological advances such as MRI-LINAC. Fibroblast activation protein (FAP) is a transmembrane protein overexpressed in stromal components of >90% of epithelial carcinomas. Herein we compare targeted MRI of gold standard PSMA with FAP in the delineation of orthotopic tumours in a mouse model of prostate cancer. Control (no ligand), FAP and PSMA-targeting iron oxide nanoparticles were prepared with modification of an MRI agent (FerroTrace). Mice with orthotopic LNCaP tumours underwent T2-weighted 3D MRI 24 hours after intravenous injection of contrast agents. FAP and PSMA nanoparticles produced contrast enhancement on MRI when compared to control nanoparticles, which was most pronounced on the tumour periphery. FAP-targeted MRI increased the proportion of tumour contrast enhancing black pixels by 13.37% when compared to PSMA. Furthermore, analysis of changes in R2 values between healthy prostates and LNCaP tumours indicated an increase in contrast enhancing pixels in the tumour border of 15%, when targeting FAP, in contrast to PSMA This study demonstrates preclinical feasibility of PSMA and FAP-targeted MRI which can enable targeted image-guided focal therapy of localized prostate cancer.

bioengineering↗

bioRxiv2022

A biphasic Deep Semi-supervised framework for Suptype Classification and biomarker discovery

To take full advantage of the unprecedented development of -omics technologies and generate further biological insights into human disease, it is a pressing need to develop novel computational methods for integrative analysis of multi-omics data. Here we proposed a biphasic Deep Semi-supervised multi-omics integration framework for Subtype Classification and biomarker discovery, DeepSSC. In phase 1, each denoising autoencoder was used to extract a compact representation for each -omics data, and then they were concatenated and put into a feed-forward neural network for subtype classification. In phase 2, our Biomarker Gene Identification procedure leveraged that neural network classifier to render subtype-specific important biomarkers. We also validated our given results on independent dataset. We demonstrated that DeepSSC exhibited better performance over other state-of-the-art techniques concerning classification tasks. As a result, DeepSSC successfully detected well-known biomarkers and hinted at novel candidates from different -omics data types related to the investigated biomedical problems.

bioinformatics↗

bioRxiv2020

Chromosome-level Genome Assembly of a Regenerable Maize Inbred Line A188

The highly embryogenic and transformable maize inbred line A188 is an attractive model for analyzing maize gene function. Here we constructed a chromosome-level genome assembly of A188 using long reads and optical maps. Genome comparison of A188 with the reference line B73 identified pervasive structural variation, including a 1.8 Mb duplication on the Gametophyte factor1 locus for unilateral cross-incompatibility and six inversions of 0.7 Mb or greater. Increased copy number of the gene, carotenoid cleavage dioxygenase 1 (ccd1) in A188 is associated with elevated expression during seed development. High ccd1 expression together with low expression of yellow endosperm 1 (y1) condition reduced carotenoid accumulation, which accounts for the white seed phenotype of A188 that contrasts with the yellow seed of B73 that has high expression of y1 and low expression of the single-copy ccd1. Further, transcriptome and epigenome analyses with the A188 reference genome revealed enhanced expression of defense pathways and altered DNA methylation patterns of embryonic callus.

genomics↗

bioRxiv2020

Mapping Cognitive Brain Functions at Scale

A fundamental set of cognitive abilities enable humans to efficiently process goal-relevant information, suppress irrelevant distractions, maintain information in working memory, and act flexibly in different behavioral contexts. Yet, studies of human cognition and their underlying neural mechanisms usually evaluate these cognitive constructs in silos, instead of comprehensively in-tandem within the same individual. Here, we developed a scalable, mobile platform, "BrainE" (short for Brain Engagement), to rapidly assay several essential aspects of cognition simultaneous with wireless electroencephalography (EEG) recordings. Using BrainE, we rapidly assessed five aspects of cognition including (1) selective attention, (2) response inhibition, (3) working memory, (4) flanker interference and (5) emotion interference processing, in 102 healthy young adults. We evaluated stimulus encoding in all tasks using the EEG neural recordings, and isolated the cortical sources of the spectrotemporal EEG dynamics. Additionally, we used BrainE in a two-visit study in 24 young adults to investigate the reliability of the neuro-cognitive data as well as its plasticity to transcranial magnetic stimulation (TMS). We found that stimulus encoding on multiple cognitive tasks could be rapidly assessed, identifying common as well as distinct task processes in both sensory and cognitive control brain regions. Event related synchronization (ERS) in the theta (3-7 Hz) and alpha (8-12 Hz) frequencies as well as event related desynchronization (ERD) in the beta frequencies (13-30 Hz) were distinctly observed in each task. The observed ERS/ERD effects were overall anticorrelated. The two-visit study confirmed high test-retest reliability for both cognitive and neural data, and neural responses showed specific TMS protocol driven modulation. We also show that the global cognitive neural responses are sensitive to mental health symptom self-reports. This first study with the BrainE platform showcases its utility in studying neuro-cognitive dynamics in a rapid and scalable fashion. HighlightsO_LIRapid and scalable EEG recordings reveal common and distinct cortical activations across five core cognitive tasks. C_LIO_LIData acquired across visits one-week-apart show high test-retest reliability for both cognitive and neural measurements. C_LIO_LIEvoked neural responses during emotion interference processing demonstrate specific short-term plasticity driven by type of neurostimulation. C_LIO_LICognitively evoked neural responses are sensitive to variations in mental health symptoms. C_LI

neuroscience↗

bioRxiv2019

The Degree Distribution of Human Brain Functional Connectivity is Generalized Pareto: A Multi-Scale Analysis

Are degree distributions of human brain functional connectivity networks heavy-tailed? Initial claims based on least-square fitting suggested that brain functional connectivity networks obey power law scaling in their degree distributions. This interpretation has been challenged on methodological grounds. Subsequently, estimators based on maximum-likelihood and non-parametric tests involving surrogate data have been proposed. No clear consensus has emerged as results especially depended on data resolution. To identify the underlying topological distribution of brain functional connectivity calls for a closer examination of the relationship between resolution and statistics of model fitting. In this study, we analyze high-resolution functional magnetic resonance imaging (fMRI) data from the Human Connectome Project to assess its degree distribution across resolutions. We consider resolutions from one thousand to eighty thousand regions of interest (ROIs) and test whether they follow a heavy or short-tailed distribution. We analyze power law, exponential, truncated power law, log-normal, Weibull and generalized Pareto probability distributions. Notably, the Generalized Pareto distribution is of particular interest since it interpolates between heavy-tailed and short-tailed distributions, and it provides a handle on estimating the tails heaviness or shortness directly from the data. Our results show that the statistics support the short-tailed limit of the generalized Pareto distribution, rather than a power law or any other heavy-tailed distribution. Working across resolutions of the data and performing cross-model comparisons, we further establish the overall robustness of the generalized Pareto model in explaining the data. Moreover, we account for earlier ambiguities by showing that down-sampling the data systematically affects statistical results. At lower resolutions models cannot easily be differentiated on statistical grounds while their plausibility consistently increases up to an upper bound. Indeed, more power law distributions are reported at low resolutions (5K) than at higher ones (50K or 80K). However, we show that these positive identifications at low resolutions fail cross-model comparisons and that down-sampling data introduces the risk of detecting spurious heavy-tailed distributions. This dependence of the statistics of degree distributions on sampling resolution has broader implications for neuroinformatic methodology, especially, when several analyses rely on down-sampled data, for instance, due to a choice of anatomical parcellations or measurement technique. Our findings that node degrees of human brain functional networks follow a short-tailed distribution have important implications for claims of brain organization and function. Our findings do not support common simplistic representations of the brain as a generic complex system with optimally efficient architecture and function, modeled with simple growth mechanisms. Instead these findings reflect a more nuanced picture of a biological system that has been shaped by longstanding and pervasive developmental and architectural constraints, including wiring-cost constraints on the centrality architecture of individual nodes.

neuroscience↗

bioRxiv2019

LARP7 is a BRCA1 ubiquitinase substrate and regulates genome stability and tumorigenesis

Attenuated DNA repair leads to genomic instability and tumorigenesis. BRCA1/BARD1 are the best known tumor suppressors that promote homology recombination (HR) and arrest cell cycle at G2/M checkpoint. As E3 ubiquitin ligases, their ubiquitinase activity has been known to involve in the HR and tumor suppression, but the mechanism remains ambiguous. Here, we demonstrated upon genotoxic stress, BRCA1 together with BARD1 catalyzed the K48 ployubiquitination on LARP7, a 7SK RNA binding protein known to control RNAPII pausing, and thereby degraded it through 26S ubiquitin-proteasome pathway. Depleting LARP7 suppressed the expression of CDK1 complex, arrested cell at G2/M DNA damage checkpoint and reduced BRCA2 phosphorylation which thereby facilitated RAD51 recruitment to damaged DNA to enhance HR. Importantly, LARP7 depletion observed in breast patients lead to the chemoradiotherapy resistance both in vitro and in vivo. Together, this study unveils a mechanism by which BRCA1/BARD1 utilizes their E3 ligase activity to control HR and cell cycle, and highlights LARP7 as a potential target for cancer prevention and therapy.\n\nHighlightsO_LIDNA damage response downregulates LARP7 through BRCA1/BARD1\nC_LIO_LIBRCA1/BARD1 catalyzes the K48 polyubiquitination on LARP7\nC_LIO_LILARP7 promotes G2/M cell cycle transition and tumorigenesis via CDK1 complex\nC_LIO_LILARP7 disputes homology-directed repair that leads to tumor therapy resistance\nC_LI

molecular biology↗

bioRxiv2019

GraphDTA: prediction of drug-target binding affinity using graph convolutional networks

The development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug--target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug--target affinity. We show that graph neural networks not only predict drug--target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drug--target binding affinity prediction, and that representing drugs as graphs can lead to further improvements. Availability of data and materialsThe proposed models are implemented in Python. Related data, pre-trained models, and source code are publicly available at https://github.com/thinng/GraphDTA. All scripts and data needed to reproduce the post-hoc statistical analysis are available from https://doi.org/10.5281/zenodo.3603523. ContactThin.Nguyen@deakin.edu.au

bioinformatics↗

bioRxiv2019

Functional arrays of human pluripotent stem cell-derived cardiac microtissues

To accelerate the cardiac drug discovery pipeline, we set out to develop a platform that would be amenable to standard multiwell-plate manipulations and be capable of quantifying tissue-level functions such as contractile force. We report a 96-well-based array of 3D human pluripotent stem cell (hPSC)-derived cardiac microtissues - termed Cardiac MicroRings (CaMiRi) - in custom printed multiwell plates capable of contractile force measurement. Within each well, two elastomeric microcantilevers are situated above a ramp. The wells are seeded with cell-laden collagen which, in response to the slope of the ramp, self-organizes around tip-gated microcantilevers to form contracting CaMiRi. The contractile force exerted by the CaMiRi is measured and calculated using the deflection of the cantilevers. Platform responses were robust and comparable across wells and we used it to determine an optimal tissue formulation. We validated contractile force response of CaMiRi using selected cardiotropic compounds with known effects. Additionally, we developed automated protocols for CaMiRi seeding, image acquisition, and analysis to enable measurement of contractile force with increased throughput. The unique tissue fabrication properties of the platform, and the consequent effects on tissue function, were demonstrated upon adding hPSC-derived epicardial cells to the system. This platform represents an open-source contractile force screening system useful for drug screening and tissue engineering applications.

bioengineering↗