Biology subjects

Dang, P.

Publications and source records attributed to Dang, P..

12 recordsLinked to original sources

bioRxiv2026

A kinetic-aware approach to infer metabolic variations and flux using transcriptomics and metabolomics data

Assessing metabolic variations and flux quantities enable systematic understandings of metabolic shifts, reprogramming, adaptation and interactions in human diseases. However, omics-based estimation of metabolic flux and its variation remains challenging due to several fundamental limitations: the need for disease and tissue context specific metabolic model; nonlinear enzyme kinetic model that links enzyme and substrate changes to reaction flux; partial, unpaired, and snap-shot measurements across omics modalities; and uncertainty in computational prediction. Here, we present Michaelis-Menten model-based Flux Estimation Analysis (mmFEA), a Monte Carlo framework for estimating condition-specific flux changes by integrating paired or unpaired metabolomics and transcriptomics (or proteomics) data. mmFEA separates each reaction-rate change into enzyme- and substrate-associated components and assess reaction rate using Michaelis-Menten kinetics equation. A baseline metabolite saturation rate is introduced by integrating protein language model predicted kinetic parameters and human baseline level metabolic concentration to enable kinetic-aware integration of unpaired substrate and enzyme level measurements. Distribution of metabolic flux and variations between conditions are further computed using MCMC sampling by treating Michaelis-Menten-derived marginal flux distribution as prior and coherency in flux balance as likelihood. To benchmark mmFEA, we generated an in-house multi-omics data set including transcriptomics, metabolomics, metabolic activity functional assay, and CRISPR screening data using pancreatic cancer cell line system treated by APEX1 inhibitors. We demonstrated that mmFEA could accurately capture experimentally observed metabolic changes and achieved a better performance than all baseline methods. Our analysis revealed the necessity in using both substrate and enzyme modality and kinetic aware model in metabolic flux assessment. Further analysis using independent pancreatic cancer cohorts further validated the robustness of mmFEA, supporting integration of condition-linked unpaired data. Pan-cancer and spatial multi-omic applications demonstrated the use of mmFEA for resolving context-dependent metabolic variation when direct flux measurements are unavailable. Together, mmFEA provides a mechanistically grounded framework for estimating relative metabolic flux changes and their uncertainty from heterogeneous omics data.

systems biology↗

bioRxiv2026

Semantic category and presentation frequency-based expectations are associated with distinct neural prediction effects.

Past work has indicated that expectation can modulate neural responses to visual stimuli, but it is unclear whether these effects remain consistent across different types of unexpected stimuli. Here, we measured and compared neural prediction effects associated with semantic category and presentation frequency-based expectations in real-world object stimuli. Participants (n = 35) viewed real-world object images in rapid serial visual presentation (RSVP) streams. Semantically unexpected stimuli occurred when a stimulus was presented in a semantically incongruent stream. Low-frequency violations occurred when a rarely presented stimulus was displayed in a semantically congruent stream. Multivariate pattern analysis of electroencephalography (EEG) was used to quantify and compare the degree of information represented in neural activity for stimuli in different prediction conditions. Semantically expected stimuli yielded lower decoding accuracy relative to random (unpredictable) stimuli (125-313 ms post-onset) while semantically unexpected stimuli exhibited increased decoding accuracy (199-238 ms & 523-559 ms). Low-frequency violations yielded decoding accuracy which was not significantly different from semantically expected stimuli. Exploratory analyses indicated that dissimilarity between expected and presented stimuli quantified in terms of higher-level stimulus features, but not low-level visual features, modulated the observed neural prediction effects. These results demonstrate that different types of prediction violations have distinct modulatory effects on neural responses, providing novel insight into the neural implementation of predictive processing.

neuroscience↗

bioRxiv2026

MetaKnogic-Alpha: A Hyper-Relational Knowledge Base for Grounded Metabolic Reasoning

The exponential trajectory of biomedical literature has precipitated a fundamental "synthesis gap" in metabolic research, where critical mechanistic insights remain fragmented across hundreds of thousands of disjointed full-text articles, preventing the consolidation of a global mechanistic view. Here, we present MetaKnogic-Alpha, a foundational mechanistic knowledge substrate designed to bridge this gap by transforming unstructured literature into a navigable, logic-based resource. MetaKnogic-Alpha synthesizes over 100K full-text articles into a hyper-relational hypergraph structure, preserving the n-ary relational logic inherent in complex metabolic pathways. To ensure biological rigor, we implemented a hierarchical discovery protocol: an autonomous reasoning agent first enriches query nomenclature for domain-specific precision, followed by a multi-hop topological expansion within the hypergraph to surface functional neighbors, such as enzymatic co-factors and distal regulators, often lost in traditional search paradigms. Crucially, the system subjects all literature-derived insights to a deterministic biochemical grounding against a curated metabolic reaction network, significantly mitigating the risk of probabilistic hallucinations common in standalone generative models. In rigorous benchmarking, MetaKnogic-Alpha achieved a mechanistic accuracy of 0.98 in scenarios where supporting evidence was present, providing a robustly attributable audit trail back to the primary literature via PubMed Central Identifiers. We designate this primary release as "alpha" to establish the foundational architectural logic for a burgeoning million-scale resource. By compressing the synthesis of thousands of papers from a multi-month manual effort into several hours of automated discovery, MetaKnogic-Alpha serves as a high-fidelity research companion that augments the human experts ability to resolve complex metabolic interactions and identify novel therapeutic drivers in precision oncology.

bioinformatics↗

bioRxiv2026

Transcriptomics-based modeling of methionine metabolism effectively estimates sample-wise DNA methylation activity and epigenetic aging

DNA methylation is a central epigenetic modification that regulates gene expression, maintains genomic stability, and guides cellular differentiation. However, direct measurements of DNA methylation, such as whole-genome bisulfite sequencing or DNA methylation arrays, are costly and require substantial DNA input, limiting their scalability for large cohorts and their applicability to emerging modalities such as single-cell and spatially resolved transcriptomics. In this study, motivated by the fact that DNA methylation is fundamentally a metabolic process, we investigate whether sample-wise DNA methylation activity can be inferred directly from transcriptomic profiles of genes involved in methionine and one-carbon metabolism. We show that a compact metabolic model comprising seven core reaction steps and 98 genes accurately predicts total DNA methylation activity across matched transcriptomic and methylation datasets from CCLE, TCGA, GTEx, and an independent single-cell multi-omics data set. Building on this framework, we develop Total DNA Methylation Activity (TDMA), a physics-informed neural network-based score that enables robust estimation of DNA methylation activity from bulk, single-cell, and spatial transcriptomics data. We demonstrate that TDMA captures methylation-dependent transcriptional regulation and identifies genes and pathways under epigenetic control. Applying TDMA to GTEx, we further reveal strong associations between the predicted total methylation activity, chronological aging, and established epigenetic clocks. We also demonstrated that TDMA can serve as a transcriptomics-derived epigenetic clock and highlights age-dependent roles of folate and glutathione metabolism in epigenetic aging. Applying TDMA to single cell and spatial transcriptomics data collected from pancreatic adenocarcinoma (PDAC), we identified that methionine metabolism and DNA methylation regulates T cell cytotoxicity in the tumor microenvironment of PDAC. Together, this work establishes a scalable, modality-agnostic framework for estimating DNA methylation activity from transcriptomics and provides new insights into the metabolic regulation of epigenetic aging.

bioinformatics↗

bioRxiv2025

Mitochondrial DNA Mutations Determine Favourable Molecular Responses to Targeted Kinase Inhibitor Therapy and Impair Oxidative Phosphorylation

Somatic mutations in mitochondrial DNA (mtDNA) are not typically considered key oncogenic drivers of cancer, primarily because of a high synonymous to non-synonymous variant ratio. Here, we surveyed 248 matched diagnosis and remission samples from patients with chronic myeloid leukemia (CML) and found a 75% had mitochondrial mutations with a median number of 2 mutations per patient. mtDNA mutations were predominantly non-synonymous, enriched in the D-loop control region, and likely originated from replication and transcriptional errors. Functionally, mtDNA mutations were associated with reduced oxidative phosphorylation (OXPHOS), as measured by Seahorse analyser. This metabolic vulnerability could be phenocopied by treatment with the complex I inhibitor IACS-10759 in combination with the targeted tyrosine kinase inhibitor (TKI) imatinib, which significantly reduced the colony-forming potential of TKI resistant leukemic stem/progenitor cells (LSPCs). Strikingly, we show that mtDNA mutations were associated with increased sensitivity to imatinib therapy in the clinic. Patients with [≥]3 mutations and patients with mutations in the D-loop showed significantly higher cumulative incidence of major molecular response at 24 months (90% vs. 68%, p = 0.004, and 89% vs 68%, p = 0.004 respectively). Single-cell RNA sequencing further revealed enrichment in non-synonymous mtDNA variants in LSPCs from TKI-sensitive patients, while TKI-resistant cells exhibited upregulated gene signatures related to glycerolipid and phospholipid metabolism and mitochondrial biogenesis. Together, our findings demonstrate that mtDNA mutations are key determinants of sensitivity to targeted therapy, rather than oncogenic drivers of leukemogenesis. Mechanistically, non-synonymous mtDNA mutations appear to restrict mitochondrial metabolic plasticity, with widespread implications for precision oncology.

cancer biology↗

bioRxiv2024

A tumor-secreted protein utilizes glucagon release to cause host wasting

Tumor-host interaction plays a critical role in malignant tumor-induced organ wasting across multiple species. Despite known regulation of regional wasting of individual peripheral organs by tumors, whether and how tumors utilize critical host catabolic hormone(s) to simultaneously induce systemic host wasting, however, is largely unknown. Using the conserved yki3SA-tumor model in Drosophila, we discovered that tumors increase the production of adipokinetic hormone (Akh), a glucagon-like catabolic hormone, to cause systemic host wasting, including muscle dysfunction, lipid loss, hyperglycemia, and ovary atrophy. We next integrated RNAi screening and Gal4-LexA dual expression system to identify that yki3SA-gut tumors secrete Pvf1 to remotely activate its receptor Pvr in Akh-producing cells (APCs), ultimately promoting Akh production. The underlying molecular mechanisms involved the Pvf1-Pvr axis that triggers Mmp2-dependent ECM remodeling of APCs and enhances innervation from the excitatory cholinergic neurons. Interestingly, we also confirmed the similar mechanisms governing tumor-induced glucagon release and organ wasting in mammals. Blockade of either glucagon or PDGFR (homolog of Pvr) action efficiently ameliorated organ wasting in the presence of malignant tumors. Therefore, our results demonstrate that tumors remotely promote neural-associated Akh/glucagon production via Pvf1-Pvr axis to cause systemic host wasting.

physiology↗

bioRxiv2024

Violated predictions enhance the representational fidelity of visual features in perception

Predictive coding theories argue that recent experience establishes expectations that generate prediction errors when violated. In humans, brain imaging studies have revealed unique signatures of violated predictions in sensory cortex, but the perceptual consequences of these effects remain unknown. We had observers perform a dual-report task on the orientation of a briefly presented target grating within predictable or random sequences, while we recorded pupil size as an index of surprise. Observers first made a speeded response to categorize the orientation of the target grating (clockwise or counterclockwise from vertical), then reproduced its orientation without time pressure by rotating a bar. This allowed us to separately assess response speed and precision for the same stimuli. Critically, on half the trials, the target orientation deviated from the spatiotemporal structure established by the preceding gratings. Observers responded faster and more accurately to unexpected gratings, and pupillometry provided physiological evidence of observers surprise in response to these events. In a second experiment, we cued the spatial location and timing of the grating and found the same pattern of results, demonstrating that unexpected orientation information is sufficient to produce faster and more precise responses, even when the location and timing of the relevant stimuli are fully expected. These findings indicate that unexpected events are prioritized by the visual system both in terms of processing speed and representational fidelity.

neuroscience↗

bioRxiv2022

Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer.

Glucose and glutamine are major carbon and energy sources that promote the fast proliferation of cancer cells. Metabolic shifs observed on cell line or mouse models may not reflect the general metabolic shifts in real human cancer tissue. In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including glycolytic pathway, production of lactate, TCA cycle, nucleic acids synthesis, glutaminolysis, glutaminate, glutamine and glutathione metabolism, amino acids synthesis, in 11 cancer subtypes and 9 matched adjacent normal tissue types, by using TCGA tissue transcriptomics data. Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of TCA cycle, i.e., Warbug effect in the analyzed cancer types. However, consistently increased lactate production and second half of TCA cycle were only seen in certain cancer types. More interestingly, we did not see cancer tissues have highly shifted glutaminolysis compared to their adjacent normal controls. A systems biology model of metabolic shifts in through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes, (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls, and (3) the different shifts happened to tissue specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. This study strongly suggests the possibility to have a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.

bioinformatics↗

bioRxiv2022

Longitudinal characterization of circulating extracellular vesicles and small RNA during simian immunodeficiency virus infection and antiretroviral therapy

ObjectivesLatent infection by human immunodeficiency virus (HIV) hinders viral eradication despite effective antiretroviral treatment (ART), Amongst proposed contributors to viral latency are cellular small RNAs that have also been proposed to shuttle between cells in extracellular vesicles (EVs). Thus, we profiled EV small RNAs during different infection phases to understand the potential relationship between these EV-associated small RNAs and viral infection. DesignA well characterized simian immunodeficiency virus (SIV)/macaque model of HIV was used to profile EV-enriched blood plasma fractions harvested during pre-infection, acute infection, latent infection/ART treatment, and rebound after ART interruption. MethodsMeasurement of EV concentration, size distribution, and morphology was complemented with qPCR array for small RNA expression, followed by individual qPCR validations. Iodixanol density gradients were used to separate EV subtypes and virions. ResultsPlasma EV particle counts correlated with viral load and peaked during acute infection. However, SIV gag RNA detection showed that virions did not fully explain this peak. EV microRNAs miR-181a, miR-342-3p, and miR-29a decreased with SIV infection and remained downregulated in latency. Interestingly, small nuclear RNA U6 had a tight association with viral load peak. ConclusionsThis study is the first to monitor how EV concentration and EV small RNA expression change dynamically in acute viral infection, latency, and rebound in a carefully controlled animal model. These changes may also reveal regulatory roles in retroviral infection and latency.

microbiology↗

bioRxiv2020

scFEA: A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

The metabolic heterogeneity, and metabolic interplay between cells and their microenvironment have been known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single cell metabolomics technology, we are yet to establish systematic understanding of intra-tissue metabolic heterogeneity and cooperation phenomena among cell populations. To mitigate this knowledge gap, we developed a novel computational method, namely scFEA (single cell Flux Estimation Analysis), to infer single cell fluxome from single cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a comprehensively reconstructed human metabolic map into a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multi-layer neural networks to fully capitulate the non-linear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq dataset with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this dataset demonstrated the consistency between predicted flux and metabolic imbalance with the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics datasets and identified context and cell group specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analysis including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

bioinformatics↗

bioRxiv2020

Characterization of novel Actinobacteriophage Giantsbane reveals potential genomic recombination hotspot and unexpected intra-cluster similarities

Bacteriophages that infect Arthrobacter, a genus of bacteria which play key ecological roles in soil, warrant further study. Giantsbane, a novel Actinobacteriophage, was isolated using Arthrobacter globiformis as a host. Transmission electron microscopy and whole-genome sequencing revealed a Siphoviridae morphology and a genome length of 56,734 bp. Genome annotation identified 94 putative genes, such as a duplicated major tail protein and a major capsid and protease fusion protein. No genes were associated with lysogeny, indicating a lytic phage. Giantsbane was assigned to the phage cluster AU. Batch average nucleotide identity analysis and phylogenetic networks constructed from shared genes revealed unexpected nucleotide and gene content similarities within cluster AU. These findings have resulted in the creation of two new AU subclusters and the resubclustering of three AU bacteriophages. Analysis using Phamerator and MEME identified repeated motifs and a gene cassette present in all evaluated cluster AU phages which may promote recombination. These findings offer the first intra-cluster analysis of cluster AU phages and further our understanding of the relationships between closely related bacteriophages.

genomics↗

bioRxiv2019

Retinal ganglion cells harboring the OPTN(E50K) glaucoma mutation exhibit neurodegenerative phenotypes when derived from hPSC-derived three dimensional retinal organoids

Retinal ganglion cells (RGCs) serve as the primary connection between the eye and the brain, with this connection disrupted in glaucoma. Numerous cellular mechanisms have been associated with glaucomatous neurodegeneration, and useful models of glaucoma allow for the precise analysis of degenerative phenotypes. Human pluripotent stem cells (hPSCs) serve as powerful tools for studying human neurodegenerative diseases, particularly cellular mechanisms underlying degeneration. Thus, efforts were initially focused upon the use of hPSCs with an E50K mutation in the Optineurin (OPTN) gene. CRISPR/Cas9 gene editing was used to introduce the OPTN(E50K) mutation into existing lines of hPSCs, as well as the generation of isogenic control lines from OPTN(E50K) patient-derived hPSC lines. OPTN(E50K) RGCs exhibited numerous neurodegenerative deficits, including neurite retraction, autophagy dysfunction, apoptosis, and increased excitability. The results of this study provide an extensive analysis of the OPTN(E50K) mutation in hPSC-derived RGCs, with the opportunity to develop novel treatments for glaucoma.

neuroscience↗