Biology subjects

Kan, K.

Publications and source records attributed to Kan, K..

5 recordsLinked to original sources

bioRxiv2026

Systematic In Silico Off-Target Assessment of siRNAs: Integrated Tissue-Specific Scoring and Cross-Species Preclinical Model Selection with TargetSureR

Small interfering RNAs (siRNAs) have become a transformative class of nucleic acid therapeutics for clinical disease treatment, yet sequence-dependent off-target silencing continues to pose a major safety barrier that hinders their preclinical refinement and large-scale translational application. Existing bioinformatics tools only support partial off-target evaluation, either focusing on basic sequence optimization or simple seed-region scanning, and fail to deliver systematic, multi-dimensional and reproducible safety assessment for siRNA lead screening. To fill this gap, we developed TargetSureR, a lightweight, modular and CRAN-compatible open-source R package dedicated to full-process siRNA off-target risk profiling. This tool accommodates dual sequence-based and precomputed position-based inputs, integrates GTEx multi-tissue expression data and curated cancer, adverse-event and immune gene panels, and establishes a seven-dimensional scoring framework to stratify off-target risks into four hierarchical tiers. It further enables tissue-specific safety characterization and quantitative cross-species model selection, with an Ensembl API fallback mechanism ensuring high transcript annotation resolution. Built purely in R with no external shell dependencies, TargetSureR provides a standardized, robust and user-friendly workflow for systematic siRNA preclinical safety evaluation, and is freely available at https://github.com/nishuai/TargetSureR.

bioinformatics↗

bioRxiv2026

CESAR: High-Sensitivity Detection of Copy Number Variations in ctDNA Using Segmentation and Anchor Recalibration

BackgroundDetecting copy number variations (CNVs) in circulating tumor DNA (ctDNA) is crucial for the companion diagnosis and resistance monitoring of various solid tumors (e.g., NSCLC, Glioblastoma). However, when tumor-derived DNA fractions are extremely low (often <1%), traditional depth-based methods frequently fail due to non-linear sequencing depth fluctuations and probe-specific capture biases inherent to targeted Next-Generation Sequencing (NGS). MethodsWe developed CESAR (CNV Estimation with Segmentation and Anchor Recalibration), a computational tool for tumor-only CNV detection in targeted NGS panels. CESAR uses Circular Binary Segmentation (CBS) to re-partition target regions according to relative capture efficiency, then applies a data-driven "anchor" selection procedure that, for each target segment, identifies a personalized set of co-varying genomic segments. By selecting the anchor set that minimizes the coefficient of variation (CV) of the anchor-recalibrated depth ratio across a panel of normals, CESAR recalibrates the per-segment baseline and suppresses probe-specific technical noise. Copy-number status is then called from the deviation of the observed ratio against this trained baseline. ResultsUsing standard DNA reference materials, CESAR identified amplifications of MET, ERBB2, and EGFR at low tumor fractions. CESAR resolved focal alterations as subtle as 2.18 copies (a 1.09-fold change relative to the diploid baseline) while reporting no false-positive amplifications in diploid control regions. Applied to a clinical cohort of nine NSCLC ctDNA samples profiled on a 94-gene panel, CESAR successfully separated three MET-amplified samples from six matched negatives, where the standard CNVkit-based pipeline failed to distinguish them. In head-to-head benchmarking on identical data, CESAR reduced technical variance relative to the widely used CNVkit and more reproducibly resolved low-level copy-number gains, particularly in the depth-heterogeneous MET amplicon. ConclusionsCESAR provides a stable and sensitive framework for tumor-only CNV calling in liquid biopsies. On reference standards it outperforms CNVkit in bias and reproducibility, and on a clinical NSCLC cohort it recovered MET CNV abnormalities that a standard CNVkit pipeline missed. Validation on larger and more diverse cohorts is warranted.

bioinformatics↗

bioRxiv2025

LISA: A Comprehensive R Package for Lentiviral Integration Site Analysis in Gene Therapy Safety Assessment

BackgroundGene therapy using integrating viral vectors has shown remarkable clinical success, but the random integration of vectors into the host genome poses potential safety concerns, particularly insertional mutagenesis. Common Integration Sites (CIS) near oncogenes significantly increase the risk of clonal expansion and malignant transformation. Current tools for integration site (IS) analysis lack comprehensive functionality for safety assessment, particularly in identifying risk-associated genomic regions and tracking clonal dynamics over time. ResultsWe developed LISA (Longitudinal Integration Site Analysis), a comprehensive R package specifically designed for gene therapy safety evaluation. LISA incorporates a novel approach that trains integration site profiles using cumulative curves derived from normal cell infusion samples and normal patient samples, with subsequent statistical testing to distinguish these normal samples from those with exceptional clonal expansion, thereby enabling effective identification of dominant clone formation. For longitudinal studies, LISA monitors fluctuations in clonal dynamics via Richness and Evenness metrics, and integrates this cumulative curve-based statistical testing approach into its early warning system to detect potentially dominant clones. Additionally, LISA features an optimized CIS detection algorithm with O (n log n) time complexity, achieved through chromosome-position encoding and a seed-site strategy, which facilitates efficient analysis of large-scale clinical datasets. The package also provides systematic functional annotations, including annotations for Safe Harbor regions, enhancer/promoter elements, and proximity to three curated gene sets: adverse event-associated genes (AE genes, n=36), cancer-related genes (n=980), and immune-related genes (n=2,485). Furthermore, it encompasses six categories of interactive visualizations and has been validated using real clinical data from gene therapy trials.

bioinformatics↗

bioRxiv2023

FOXA2 is essential for maintaining the urea cycle in acute liver failure

Hepatic encephalopathy is a lethal complication of acute liver failure (ALF), and is caused by hyperammonemia. Ammonia clearance by the liver requires an intact and complete urea cycle comprising six enzymes, including the rate-limiting enzyme carbamoyl phosphate synthetase I (CPS1). To date, the detailed regulation of CPS1 transcription in order to maintain urea cycle in physiological condition and ALF remains largely unknown. This study scrutinizes the role of pioneer factor forkhead box A 2 (FOXA2) in the regulation of CPS1 transcription, urea cycle performance and hyperammonemia. Physiologically, CPS1 transcription requires FOXA2 to maintain chromatin accessibility on its enhancers, which is essential for CCAAT enhancer-binding protein-alpha (C/EBP) binding to activate gene transcription. In ALF, hepatic C/EBP expression is inhibited by inflammatory mediators such as TGF-{beta} and TNF-. In this setting, retinoic acid receptor synergizes with FOXA2 to maintain CPS1 transcriptions. Once ALF patients suffer from massive hepatic necrosis, liver progenitor cells initiate a transcription network comprising FOXA2 and C/EBP to perform the urea cycle and prevent hyperammonemia. In ALF, hepatic encephalopathy occurs in patients lacking hepatic FOXA2 expression. In mice with acetaminophen-induced ALF, injection of Foxa2-AAV8 maintains urea cycle and prevents hyperammonemia. Taken together, FOXA2 is essential for maintaining the urea cycle. Pharmaceutical induction of hepatic FOXA2 expression might represent a novel approach to treat hepatic encephalopathy in ALF. One Sentence SummaryPioneer factor FOXA2 synergizes with C/EBP or RAR to maintain urea cycle in acute liver failure

molecular biology↗

bioRxiv2023

Insulin determines the effects of TGF-beta on HNF4alpha transcription and epithelial-to-mesenchymal transition in hepatocytes

To date, epithelial-to-mesenchymal transition (EMT) has been observed in cultured hepatocytes, but not in vivo. TGF-{beta} is supposed to initiate EMT in hepatocytes by inhibiting HNF4 through the SMAD2/3 complex. We report that TGF-{beta} does not directly inhibit HNF4, but contributes to its transcriptional regulation by SMAD2/3 recruiting acetyltransferase CBP/p300 to the HNF4 promoter. The recruitment of CBP/p300 is indispensable for C/EBPa binding, another essential requirement for constitutive HNF4 expression in hepatocytes. In contrast to the observed induction of HNF4, SMAD2/3 inhibits C/EBP transcription. Therefore, long-term TGF-{beta} incubation results in C/EBP depletion, which abrogates HNF4 expression. Intriguingly, SMAD2/3 inhibitory binding to the C/EBP promoter is abolished by insulin. Thus, maintaining a high insulin concentration in culture medium ensures constitutive HNF4 and thereby prevents TGF-{beta}-induced hepatocyte EMT. Furthermore, insulin inhibits TGF-{beta}-induced SMAD2/3 binding to the promoters of core EMT transcription factors e.g., SNAI1. SNAI1 transcription requires both SMAD2/3 and FOXO1 in nuclei. Insulin inhibits SNAI1 transcription through impeding SMAD2/3 binding to its promoter and inducing FOXO1 phosphorylation. Hence, insulin is the key factor that prevents TGF-{beta}-induced EMT in hepatocytes.

cell biology↗