Biology subjects

Pua, H. H.

Publications and source records attributed to Pua, H. H..

6 recordsLinked to original sources

bioRxiv2026

Tissue-aware elastic net decomposition reveals shared and lineage-specific drug response biomarkers

MotivationComputational models that predict cancer drug response from genomic features are central to biomarker discovery, yet a recent audit found data leakage in 72% of 32 published methods, and complex models offer little interpretability while only modestly exceeding simple baselines under honest evaluation. Tissue lineage is a largely untapped source of legitimate inductive bias, but existing tissue-aware methods neither separate pan-cancer from lineage-specific signal nor report leakage-free performance. ResultsWe introduce the Data Shared Elastic Net (DSEN), a tissue-aware regression that decomposes each drugs model into a shared coefficient block common to all lineages and tissue-specific deviation blocks. Under leakage-free cross-validation across 265 drugs, 1,462 cell lines and 31 tissue lineages, DSEN improved mean squared error over a standard elastic net for 92.5% of drugs (mean 4.95%) while selecting 58% fewer stable shared features. Shared coefficients generalized to held-out tissues (59% tissue-level win rate) and recurrently recovered transferable pathway modules (p53, MAPK), whereas tissue blocks captured lineage markers such as the skin MITF /S100B program. The closest tissue-aware comparator, TG-LASSO, performed worse than the tissue-agnostic baseline (-13.8% mean MSE). Ablation shows tissue-aware modeling helps most when features are scarce, with no single modality dominating. Availability and implementationhttps://github.com/AsiaeeLab/tissue-aware-drug-response. Contactamir.asiaeetaheri@vumc.org

bioinformatics↗

bioRxiv2026

A shape-constrained regression and wild bootstrap framework for reproducible drug synergy testing

High-throughput drug combination screens require methods to identify synergistic pairs, yet widely used synergy scores lack statistical inference and can fail when parametric dose-response fits do not converge. We present SIR (Synergy via Isotonic Regression), a nonparametric framework that defines interaction as deviation from a monotone-additive null, fit by 2D isotonic regression. A degrees-of-freedom-corrected wild bootstrap yields calibrated p-values for each dose-response matrix. On DrugCombDB, SIR interaction surfaces achieve higher replicate concordance (median correlation 0.91 across 1,839 replicate pairs) than all baselines (0.53-0.74), while avoiding Loewes 20.9% and ZIPs 3.6% failure rates. The fitted surface also predicts missing wells (median holdout RMSE 0.040). By replacing heuristic scores with calibrated effect sizes and p-values, SIR enables principled hit calling and error-rate control in large screens.

bioinformatics↗

bioRxiv2026

Widespread data leakage inflates performance estimates in cancer drug response prediction

Drug response prediction models guide biomarker discovery, yet their reported accuracy depends on rigorous cross-validation (CV). We show that supervised feature screening applied to all samples before CV, a widespread practice, introduces data leakage that systematically underestimates prediction error. Across 265 drugs and 1,462 cancer cell lines, leakage-free CV raises mean squared error by 16.6% on average, with near-zero feature overlap between leaked and corrected pipelines (mean Jaccard 0.18; 36.2% of drugs share no features). Despite selecting five times more features, the leaked pipeline recovers known drug targets at nearly the same rate as the corrected pipeline, indicating that the inflated feature sets capture statistical artifacts rather than biological signal. A code-level audit of 32 published methods (2017-2024) confirms leakage in 23 (72%), spanning five distinct modes and cited over 3,000 times. The magnitude of inflation from this single leakage mode is comparable to improvement margins typically claimed over elastic net baselines, raising the possibility that some reported advances reflect evaluation artifacts. We provide a leakage taxonomy, audit guide and reference implementation for leakage-free evaluation.

bioinformatics↗

bioRxiv2025

RNY1 partitions into extracellular vesicles and ribonucleoprotein particles during airway inflammation to regulate macrophage programming

YRNAs are small noncoding RNAs that are abundant in both cells and biofluids. Prior research has shown that the secretion of extracellular YRNAs (exYRNAs) changes in response to inflammatory stimuli. However, the mechanisms by which exYRNA packaging and dynamics in biofluids regulate inflammation remain poorly understood. In this study, we found that one YRNA species, RNY1, increased in airway fluid during allergen-induced lung inflammation and correlated with neutrophil infiltration. Using RNase sensitivity assays and size exclusion chromatography, we determined that RNY1 was present in airway fluid extracellular vesicles (EVs) and ribonucleoproteins (RNPs), while another YRNA species, RNY3, was present only in EVs. Both the EV and RNP-containing fractions of airway fluid had a unique ability to program cellular inflammation. Airway fluid EVs increased expression of an alternative activation program in macrophages including Arg1, Ym1, Il10, and Il6, while RNPs induced gene expression more consistent with a classic pro-inflammatory phenotype. RNY1 contributed to the programming of macrophages by airway EVs, as macrophages treated with EVs isolated from RNY1-/- mice demonstrated lower induction of Arg1 and Ym1. Together, these results define the form and function of exYRNAs in lung biofluid and support their role in communicating signals during inflammation.

immunology↗

bioRxiv2024

Th2 cell extracellular vesicles promote eosinophil survival through the cytokine cargo IL-3 and prolong airway eosinophilia.

BackgroundExtracellular vesicles (EVs) mediate intercellular communication during immune responses. EVs are abundant in respiratory biofluids, and the composition of EVs in the lung changes during inflammation. ObjectiveWe aimed to quantify the contribution of T cells to airway EVs in allergic lung inflammation and ascertain their function during a type 2 inflammatory response. MethodsGenetic membrane tagging was combined with single vesicle flow cytometry to quantify T cell EVs in the airways of mice challenged with ovalbumin or house dust mite. EVs were purified from T helper type 2 (Th2) cell cultures and their functions on eosinophils assessed by flow cytometry and RNA sequencing. Th2 cell EVs were instilled into the lungs of mice to determine effects on lung eosinophilia. Finally, the function of an EV protein cargo was tested using inhibitors and blocking antibodies. ResultsT cell EVs are increased in the airways of mice with induced allergic inflammation. EVs secreted by Th2 cells inhibit apoptosis and induce activating pathways in eosinophils in vitro. This effect depends on re-stimulation through the T cell receptor. Th2 cell EVs prolong eosinophilia in vivo during allergic airway inflammation. Th2 cell EVs carry a potent form of the cytokine IL-3 on their surfaces, which inhibits apoptosis by activating Jak1/2-dependent pro-survival programs in eosinophils. ConclusionTh2 cell EVs promote eosinophil survival and prolong eosinophilia during allergic airway inflammation. This function depends on the EV cargo IL-3, supporting a role for EVs as vehicles of cytokine-based communication in lung inflammation. Key MessagesO_LIT cells secrete extracellular vesicles in the airway during allergic lung inflammation. C_LIO_LITh2 cell extracellular vesicles inhibit eosinophil apoptosis and prolong airway eosinophilia during allergic lung inflammation. C_LIO_LIIL-3 carried on Th2 cell EVs is a functional cargo, supporting a role for cytokine-carrying EVs as drivers of type 2 inflammation. C_LI Capsule summaryThis study supports that T cell extracellular vesicles may be important drivers of eosinophilic inflammation through the cytokine cargo IL-3, offering new insights into pro-inflammatory signaling in the allergic lung of patients with asthma.

immunology↗

bioRxiv2022

Epithelial Outgrowth Through Mesenchymal Rings Drives Alveologenesis

Determining how alveoli are formed and maintained is critical to understanding lung organogenesis and regeneration after injury. While technological barriers have heretofore limited real-time observation of alveologenesis, we have now used scanned oblique plane illumination microscopy of living lung slices to observe specific cellular behaviors at high resolution over several days. Contrary to the prevailing paradigm that alveoli form by airspace subdivision via ingrowing septa, we find that alveoli form by ballooning epithelial outgrowth supported by stable mesenchymal ring structures. Our systematic analysis allowed creation of a computational model of finely-timed cellular structural changes that drive alveologenesis under normal conditions or with perturbed intercellular Wnt signaling. This new paradigm and platform can be leveraged for mechanistic studies and screening for therapies to promote lung regeneration. One-Sentence SummaryLong-term live analysis of neonatal lungs supports a dynamic epithelial outgrowth model for alveologenesis.

developmental biology↗