Biology subjects

Dang, X.

Publications and source records attributed to Dang, X..

9 recordsLinked to original sources

bioRxiv2026

SubcellPert: Benchmarking Subcellular Perturbation Prediction

Perturbations change both RNA abundance and intracellular organization, but gene-count predictors omit the positions needed to model spatial responses. To address this limitation, we introduce \textbf{\textit{SubcellPert}}, a benchmark for perturbation-response prediction at single-RNA-molecule resolution. It comprises 14 imaging datasets with 13.88 million cells and 3.65 billion RNA molecules. Given source observations and a directed condition change, models generate target RNA populations in normalized two-dimensional coordinates. Two independently trained tasks separate abundance from localization: \textit{Oracle} predicts coordinates given exact target counts; \textit{Joint} predicts both. Eight baselines span source resampling, classical transport, and frozen foundation-model adapters. Fixed perturbation and cellular-context holdouts evaluate abundance error, target-gene coverage, and conditional spatial error. Through within-dataset analyses, we find context-dependent gains from spatial features in condition decoding. Interpreting spatial responses requires distinguishing changes within spatial clusters from population composition, and RNA fractions from enrichment relative to compartment geometry. Through comprehensive experiments, we find that Gaussian transport achieves the lowest Oracle spatial error on eight of eleven cellular-context OOD datasets. Audits show that better centroid accuracy can accompany worse radial distributions, and that 19 of 190 Joint adapter fits retain source counts after validation. SubcellPert opens a spatial frontier for virtual cell research, laying the groundwork for predictive models that capture both the molecular composition and intracellular organization of cellular responses. The code is available at \url{https://github.com/king159/SubcellPert}.

bioinformatics↗

bioRxiv2026

A Deep Learning Framework for Spatiotemporal Modeling of Visual Task fMRI

Characterizing the dynamic coordination of distributed brain regions during cognitive tasks remains challenging, as traditional fMRI analysis focuses on localized activations without revealing the underlying information flow that drives them. Here, we propose STREAM (Spatiotemporal Representation for Effective connectivity Analysis Model), a deep-learning framework that learns neural transition functions in task-fMRI to characterize effective connectivity and whole-brain information flow. Applied to visual category processing in 1074 participants, STREAM accurately reconstructs activation maps while further revealing that traditional activation regions are primarily driven by incoming signals. Moreover, the Default Mode Network acts as a high-level regulatory hub with extensive outgoing influence, challenging its passive characterization. Additionally, category-specific communication emerges from dynamic reconfiguration of signaling patterns among key hubs rather than static pathways. These findings establish a novel computational paradigm that uncovers directional signaling mechanisms driving local dynamics in task-fMRI, revealing how the brain flexibly reconfigures functional architecture for complex cognition.

neuroscience↗

bioRxiv2023

Imidacloprid disrupts larval molting regulation and nutrient energy metabolism, causing developmental delay in honey bee Apis mellifera

Imidacloprid is a global health threat that severely poisons the economically and ecologically important honeybee pollinator, Apis mellifera. However, its effects on developing bee larvae remain largely unexplored. Our pilot study showed that imidacloprid causes developmental delay in bee larvae, but the underlying toxicological mechanisms remain incompletely understood. In this study, we exposed bee larvae to imidacloprid at environmentally relevant concentrations of 0.7, 1.2, 3.1, and 377 ppb. There was a marked dose-dependent delay in larval development, characterized by reductions in body mass, width, and growth index. However, imidacloprid did not affect on larval survival and food consumption. The primary toxicological effects induced by elevated concentrations of imidacloprid (377 ppb) included inhibition of neural transmission gene expression, induction of oxidative stress, gut structural damage, and apoptosis, inhibition of developmental regulatory hormones and genes, suppression of gene expression levels involved in proteolysis, amino acid transport, protein synthesis, carbohydrate catabolism, oxidative phosphorylation, and glycolysis energy production. In addition, we found that the larvae may use antioxidant defenses and P450 detoxification mechanisms to mitigate the effects of imidacloprid. Ultimately, this study provides the first evidence that environmentally exposed imidacloprid can affect the growth and development of bee larvae by disrupting molting regulation and limiting the metabolism and utilization of dietary nutrients and energy. These findings have broader implications for studies assessing pesticide hazards in other juvenile animals.

zoology↗

bioRxiv2022

Multi-modal multi-resolution atlas of the human neonatal cerebral cortex based on microstructural similarity

The neonatal period is a critical window for the development of the human brain and may hold implications for the long-term development of cognition and disorders. Multi-modal connectome studies have revealed many important findings underlying the adult brain but related studies were rare in the early human brain. One potential challenge is the lack of an appropriate and unbiased parcellation that combines structural and functional information in this population. Using 348 multi-modal MRI datasets from the developing human connectome project, we found that the information fused from the structural, diffusion, and functional MRI was relatively stable across MRI features and showed high reproducibility at the group level. Therefore, we generated automated multi-resolution parcellations (300 - 500 parcels) based on the similarity across multi-modal features using a gradient-based parcellation algorithm. In addition, to acquire a parcellation with high interpretability, we provided a manually delineated parcellation (210 parcels), which was approximately symmetric, and the adjacent areas around each boundary were statistically different in terms of the integrated similarity metric and at least one kind of original features. Overall, the present study provided multi-resolution and neonate-specific parcellations of the cerebral cortex based on multi-modal MRI properties, which may facilitate future studies of the human connectome in the early development period.

neuroscience↗

bioRxiv2022

A human mitofusin 2 mutation causes mitophagic cardiomyopathy

Cardiac muscle has the highest mitochondrial density of any human tissue, but mitochondrial dysfunction is not a recognized cause of isolated cardiomyopathy. Here, we determined that the rare mitofusin (MFN) 2 R400Q mutation is ~20x over-represented in clinical cardiomyopathy, whereas this specific mutation is not reported as a cause of the MFN2 mutant-induced peripheral neuropathy, Charcot-Marie-Tooth disease type 2A (CMT2A). Accordingly, we interrogated the enzymatic, biophysical and functional characteristics of MFN2 Q400 versus wild-type and representative CMT2A-causing MFN2 mutants. All MFN2 mutants we studied suppressed mitochondrial fusion, the canonical MFN2 function. Compared to CMT2A mutants MFN2 R94Q and T105M that lacked catalytic GTPase activity and exhibited normal activation-induced changes in conformation, MFN2 Q400 had normal GTPase activity with impaired conformational shifting. GTPase-defective MFN2 mutants, but not MFN2 Q400, suppressed mitochondrial motility, provoked mitochondrial depolarization and reduced mitochondrial respiration. By contrast, MFN2 Q400 was uniquely defective in recruiting Parkin to mitochondria. CRISPR editing of the R400Q mutation into the mouse Mfn2 gene induced perinatal cardiomyopathy with no other organ involvement. RNA sequencing and metabolomics of cardiomyopathic Mfn2 Q400 hearts revealed signature abnormalities recapitulating experimental mitophagic cardiomyopathy. Indeed, cardiomyoblasts expressing MFN2 Q400 exhibited multiple mitophagy defects, but normal mitochondrial respiration. MFN2 Q400 is the first known natural mitophagy- and shape change-defective MFN2 mutant. Its unique profile of dysfunction evokes mitophagic cardiomyopathy, suggesting a mechanism for its enrichment in clinical cardiomyopathy.

developmental biology↗

bioRxiv2022

Development of visual cortex in human neonates are selectively modified by postnatal experience

Experience-dependent cortical plasticity is a pivotal process of human brain development and essential for the formation of most cognitive functions. Although studies found that early visual experience could influence the endogenous development of visual cortex in animals, little is known about such impact on human infants. Using the multi-modal MRI data from developing human connectome project, we revealed the early structural and functional maps in the ventral visual cortex and their development across the first month of age. Particularly, we found the postnatal experience could modulate both the cortical morphology in ventral visual cortex and the functional circuit between bilateral primary visual cortices. But the cortical myelination and overall functional circuits of ventral cortex, particularly that of the high-order visual cortex, developed without significant influence of postnatal experience in such early period. These experience-dependent cortical properties were further validated in the preterm-born infants who have longer postnatal time but immature cortical development at birth. The results confirmed that the development of cortical thickness was dominated by the postnatal experience but the functional circuit might be determined by the overall maturity of visual cortex. These data suggest in human newborns that early postnatal experience shapes the structural and functional development of the visual cortex in selective and organized pattern.

neuroscience↗

bioRxiv2020

A phenotype-specific framework for identifying the eye abnormalities causative nonsynonymous-variants

The most important role of variant pathogenicity predictors is to identify the disease-phenotype causative variant in studying monogenic diseases. In the last decade, machine-learning based predictors exhibited a relatively accurate performance for distinguishing the pathogenic variants and contributed a significant role for all disease-spectrums. Yet, few predictors can investigate the phenotypic significance of variants. Here we presented a phenotype-specific framework aimed to directly point out the phenotypic significance of predicted candidates, and showed its advancing performance in eye abnormalities. By training on eye-abnormalities causative variants, our method presented 96.2% accuracy, 96.1% precision, 93.4% recall for pathogenicity identification. Inconsistent with the modeling performance, identifying the single phenotype-causative variant from various sequencing variants is challenging for all predictors. Underlying the phenotype-oriented, our method significantly promoted the precision and reduced the cost for identifying the single causative variant from thousands of candidates. These advances highlight the significance of the phenotype-specific training method for studying disease.

bioinformatics↗

bioRxiv2019

Gene pathogenicity prediction of Mendelian diseases via the Random Forest algorithm

The study of Mendelian diseases and the identification of their causative genes are of great significance in the field of genetics. The evaluation of the pathogenicity of genes and the total number of Mendelian disease genes are both important questions worth studying. However, very few studies have addressed these issues to date, so we attempt to answer them in this study. We calculated gene pathogenicity prediction (GPP) score by a machine learning approach (random forest algorithm) to evaluate the pathogenicity of genes. When we applied the GPP score to the testing gene set, we obtained accuracy of 80%, recall of 93% and area under the curve (AUC) of 0.87. Our results estimated that a total of 10,399 protein-coding genes were Mendelian disease genes. Furthermore, we found the GPP score was positively correlated with the severity of disease. Our results indicate that GPP score may provide a robust and reliable guideline to predict the pathogenicity of protein-coding genes. To our knowledge, this is the first trial to estimate the total number of Mendelian disease genes.

genetics↗

bioRxiv2018

Functional importance of JMY expression by Sertoli cells in mediating mouse spermatogenesis

Sertoli cells are crucial for spermatogenesis in the seminiferous epithelium because their actin cytoskeleton supports vesicle transport, cell junction, protein anchoring and spermiation. Here, we show that junction-mediating and regulatory protein (JMY), an actin regulating protein, also affects endocytic vesicle trafficking and Sertoli cell junction remodeling since disruption of these functions induced male subfertility in Sertoli cell-specific Jmy knockout mice. Specifically, these mice have: a) impaired BTB integrity and spermatid adhesion in the seminiferous tubules; b) high incidence of sperm structural deformity; c) reduced sperm count and poor sperm motility. Moreover, the cytoskeletal integrity in Sertoli cell-specific Jmy knockout mice was compromised along with endocytic vesicular trafficking. These effects impaired junctional protein recycling and reduced Sertoli cell junctions. In addition, JMY interaction with -actinin1 and Sorbs2 was related to JMY activity and in turn actin cytoskeletal organization. In summary, JMY affects control of spermatogenesis through regulating actin filament organization and endocytic vesicle trafficking in Sertoli cells.

cell biology↗