Biology subjects

Tu, D.

Publications and source records attributed to Tu, D..

10 recordsLinked to original sources

bioRxiv2026

Potent substoichiometric inhibition of alpha-synuclein aggregation by de novo oligomer-binding proteins

The intrinsically disordered, non-amyloid-{beta} component domain of -synuclein (Syn) drives aggregation in Parkinson's disease but lacks a stable epitope for structure-based drug design. We used deep learning-based protein design to generate de novo binders capturing this region in a {beta}-strand conformation. Three designs bound Syn with nanomolar affinities, with solution NMR spectroscopy supporting the intended fold. All three suppressed fibril formation at substoichiometric ratios, and fitting of aggregation kinetics resolved the microscopic steps inhibited by each. Notably, one binder preferentially engaged oligomers and inhibited secondary nucleation, both closely linked to toxicity. In cells, all three suppressed seeded Syn assembly, with the oligomer binder being the most effective. Together, these findings establish a set of molecules that target distinct aggregation intermediates and mechanistically reshape Syn assembly.

biophysics↗

bioRxiv2024

The role of auxin-mediated gene activation in the bryophyte, Physcomitrium patens

Perception and response to the hormone auxin is critical to plant growth and development. Expression of auxin-response genes is tightly regulated via known mechanisms of both activation and repression. Across the plant lineage, auxin-response gene induction is performed by AUXIN-REPSONSE FACTOR (ARF) activating transcription factors. Conversely, AUXIN/INDOLE ACETIC ACID proteins repress expression. Studies of gain-of-function constitutive-repression lines and ARF loss-of-function mutants have advanced the field. Yet, there is a need for a comparative study of aberrant auxin-signaling mutants to understand the developmental consequences of constitutive repression versus the absence of auxin-mediated gene induction. Using CRISPR/Cas9 gene-editing tools, we mutated each activating ARF gene in the model bryophyte, Physcomitrium patens. The resulting septuple loss-of-function mutant line (arfasept) has severe developmental phenotypes and a diminished ability to respond to exogenous auxin. However, phenotypic analysis revealed that the arfasept line is not as severely affected as the constitutive-repression lines. Expression analysis of several auxin-response genes demonstrate that auxin-mediated gene induction is abolished in both arfasept and constitutive-repression lines but that basal expression levels are higher in the arfaseptlines. Our results suggest that the expression of auxin-regulated genes important for developmental progression is maintained, albeit at reduced levels, in the absence of ARFs. HighlightResearchers used CRIPSR/Cas9 to produce a septuple Auxin Response Factor mutant in the model bryophyte Physcomitrium patens revealing the consequences of the complete loss of auxin-mediated gene activation on development.

plant biology↗

bioRxiv2024

Mapping individual differences in intermodal coupling in neurodevelopment

Within-individual coupling between measures of brain structure and function evolves in development and may underlie differential risk for neuropsychiatric disorders. Despite increasing interest in the development of structure-function relationships, rigorous methods to quantify and test individual differences in coupling remain nascent. In this article, we explore and address gaps in approaches for testing and spatially localizing individual differences in intermodal coupling, including a new method, called CEIDR. CEIDR controls false positives in individual differences in intermodal correlations that arise from mean and variance heterogeneity and improves statistical power by adopting adaptive cluster enhancement. Through a comparison across different approaches to testing individual differences in intermodal coupling, we delineate subtle differences in the hypotheses they test, which may ultimately lead researchers to arrive at different results. Finally, we illustrate these differences in two applications to brain development using data from the Philadelphia Neurodevelopmental Cohort.

neuroscience↗

bioRxiv2023

Regression and Alignment for Functional Data and Network Topology

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of pre-processing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales--from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

neuroscience↗

bioRxiv2022

A Clade-D Auxin Response Factor is a Major Regulator of Auxin Signaling in Physcomitrium patens.

Auxin Response Factors (ARFs) are a family of transcription factors that are responsible for regulating gene expression in response to changes in auxin level. The analysis of ARF sequence and activity indicates that there are two major groups-activators and repressors. One clade of ARFs, clade-D, is sister to clade-A activating ARFs, but are unique in that they lack a DNA binding domain. Clade-D ARFs are present in lycophytes and bryophytes but absent in pteridophytes and spermatophytes. The transcriptional activity of clade-D ARFs, as well as how they regulate gene expression, is not well understood. Here, we report that clade-D ARFs are transcriptional activators in the model bryophyte P. patens and have a major role in the development of this species. {Delta}arfd1,d2 protonemata exhibit a delay in filament branching, as well as a delay in a key cell differentiation event. Additionally, leafy gametophore development in {Delta}arfd1,d2 lines lag behind wild-type. We present evidence that ARFd1 interacts with activating, but not repressing, ARFs. An ARFd1 hypomorph, arfd1T653L, cannot multimerize. Therefore, we propose a model by which clade-D ARFs enhance gene expression by oligomerizing to DNA-bound archetypal ARFs.

plant biology↗

bioRxiv2022

Two-Stage Multivariate Mendelian Randomization on Multiple Outcomes with Mixed Distributions

SO_SCPLOWUMMARYC_SCPLOWIn clinical research, it is of importance to study whether certain clinical factors or exposures have causal effects on clinical and patient reported outcomes like toxicities, quality of life, and self-reported symptoms, which can help improve patient care. Usually, such outcomes are recorded as multiple variables with different distributions. Mendelian randomization is a commonly used technique for causal inference with the help of genetic instrumental variables to deal with observed and unobserved confounders. Nevertheless, the current methodology of Mendelian randomization on multiple outcomes only focuses on one outcome at a time, meaning that it does not consider the correlation structure of multiple outcomes, which may lead to loss of statistical power. In situations with multiple outcomes of interest, especially when there are mixed correlated outcomes with multiple distributions, it is much more desirable to jointly analyze them with a multivariate approach. Some multivariate methods have been proposed to model mixed outcomes, however, they do not incorporate instrumental variables and cannot handle unmeasured confounders. To overcome the above challenges, we propose a two-stage multivariate Mendelian Randomization Method (MRMO), that can perform multivariate analysis on mixed outcomes using instrumental variables. We demonstrate that our proposed MRMO algorithm can gain power over the existing univariate method through simulation studies and a clinical application on a randomized Phase III clinical trial study on colorectal cancer patients.

genetics↗

bioRxiv2022

Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence

With the increasing availability of neuroimaging data from multiple modalities--each providing a different lens through which to study brain structure or function--new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.

neuroscience↗

bioRxiv2022

CoCoA: Conditional Correlation Models with Association Size

SO_SCPLOWUMMARYC_SCPLOWMany scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose CoCoA (Conditional Correlation Model with Association Size), a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale, while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semi-parametric estimators adapted from genome association studies, and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modelling and partitioned correlation analyses.

bioinformatics↗

bioRxiv2020

Multi-Modal Biomarkers of Cerebral Edema in Low Resolution MRI

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images (MRI). These methods, however, may not translate to low resolution images acquired on MRI scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria were imaged using low-field 0.35 Tesla MRI. We integrate multi-atlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to cerebral malaria.

neuroscience↗

bioRxiv2017

The prognostic effects of somatic mutations in ER-positive breast cancer

More than 50 genes are recurrently affected by somatic mutation in estrogen receptor positive (ER+) breast cancer but prognostic effects have not been definitively established. Primary tumor DNA was therefore subjected to targeted sequencing from 625 postmenopausal (UBC-TAM series) and 328 premenopausal (MA12 trial) hormone receptor-positive (HR+) patients. Independent validation of prognostic interactions was achieved using independent data from the METABRIC study. Associations between MAP3K1 and PIK3CA with luminal A status and TP53 mutations with Luminal B/non-luminal tumors were observed, validating the methodological approach. In UBC-TAM, NF1 frame-shift nonsense (FS/NS) mutation was validated as a poor outcome driver. For MA12, poor outcome associated with PIK3R1 mutation was similarly validated. DDR1 mutations were strongly associated with poor prognosis in UBC-TAM despite stringent false-discovery correction (q=0.0003). In conclusion, uncommon recurrent somatic mutations should be further explored to create a more complete explanation of the highly variable outcomes that typify ER+ breast cancer.

genomics↗