Biology subjects

Lin, H.

Publications and source records attributed to Lin, H..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Acute Degradation of Pumilio Proteins Uncovers a Biphasic Post-transcriptional Regulatory Hierarchy Controlling Embryonic Stem Cell Fate Decisions

Post-transcriptional regulation is critical for mammalian embryogenesis yet has been underexplored. We previously showed that RNA-binding Pumilio proteins (Pum1/2) are essential for early mouse embryogenesis and embryonic stem cell (ESC) functions. Here, using acute protein degradation systems combined with time-resolved RNA-seq and eCLIP, we delineate a two-phase regulatory hierarchy modulated by Pum1/2 in mouse ESCs. The first phase, occurring within 10 hours of Pum1/2 depletion, is predominantly the stabilization of over 100 Pum1/2-target mRNAs, while the second phase, occurring in subsequent 66 hours, propagates to over 1,000 mRNAs mostly through indirect regulatory effects. Functionally, Pum1/2 depletion delays transition from naive to formative pluripotency, impairs neuroectoderm differentiation, and enhances germline specification. Mechanistically, Pum1/2 directly repress mRNAs encoding PRC2 subunits, including Suz12, thereby constraining H3K27me3 deposition at neuroectodermal gene loci. These findings establish Pum1/2 as biphasic post-transcriptional regulators of pluripotency and lineage balance and link RNA stability control to chromatin-mediated silencing. HIGHLIGHTSO_LIAcute Pum1/2 degradation provides temporal resolution for profiling post-transcriptional regulation. C_LIO_LIPum1/2 destabilize more than 100 direct targets and modulate a biphasic network of over 1,000 mRNAs. C_LIO_LIPum1/2 loss delays pluripotency transition, suppresses neuroectoderm, and promotes germline fate. C_LIO_LIPum1/2 directly regulate Suz12 mRNA decay to modulate PRC2-mediated repression. C_LI

cell biology↗

bioRxiv2026

Sub-stoichiometric Degradation is Dispensable for Potent PROTACs: A Case Study for Irreversible Covalent BTK Degraders

Proteolysis-targeting chimeras (PROTACs) represent a transformative therapeutic modality, yet the viability of covalent PROTACs remains debated, as irreversible binding seemingly contradicts the catalytic mechanism central to their function. Here, we develop and characterize PSIRC3, a highly potent covalent PROTAC for Brutons tyrosine kinase (BTK) that addresses this ambiguity. PSIRC3 induces potent and selective BTK degradation with a sub-nanomolar DC50 of 0.75 nM and a Dmax greater than 85%, while its non-covalent counterpart is completely inactive. This degradation activity is strictly dependent on covalent bond formation with the Cys481 residue, as evidenced by a total loss of efficacy against the C481S BTK mutant. PSIRC3 acts with remarkable speed, achieving maximum BTK degradation within 30 minutes, a kinetic profile linked to rapid cell permeation and efficient ternary complex formation. In vivo, a single administration of PSIRC3 leads to substantial BTK degradation in both PBMCs (>80%) and splenocytes (>50%). Computational modeling, parameterized with experimental data, reveals that degradation efficacy is governed by a delicate balance between E3 ligase and target protein affinities. Specifically, excessively high E3 affinity is detrimental by inducing a hook effect, while higher target affinity is generally beneficial. Our findings provide strong evidence that covalent engagement can drive potent and selective protein degradation, challenging the prevailing notion that catalytic turnover is indispensable for PROTAC efficacy. This work establishes a new benchmark for covalent degraders and opens new avenues for targeting previously intractable proteins.

biochemistry↗

bioRxiv2026

eScreen: a deep learning framework for functionally decoding the regulatory genome at single-nucleotide resolution

The human genome is densely populated with cis-regulatory elements (CREs), yet deciphering their functional regulatory syntax and combinational logic remains a fundamental challenge. Here, we integrate 379 genome-scale CRISPR screen experiments, encompassing 21 million perturbations across 23 cell types, to construct a compendium of 41,239 high-confidence functional CREs from 530,527 candidates. Leveraging this resource, we develop eScreen, a deep learning model built on the StripedHyena2 architecture to functionally decode the regulatory genome at single-nucleotide resolution. eScreen achieves three primary functions: (1) predicts genome-wide cell-type-specific CRE functional activity with high accuracy, outperforming existing models; (2) provides mechanistic interpretation of regulatory syntax at single-nucleotide resolution; (3) dissects the functional organization of enhancer clusters through in silico perturbation analysis. We perform multiple independent CRISPR knockout, CRISPR interference (CRISPRi), and base editing screens to validate these functions of eScreen both at scale and on individual cases. Furthermore, we provide an interactive web server (https://escreen.huanglabxmu.com/) for the community to access the integrated CRISPR screen resources and eScreen functions. Collectively, our work establishes a highly precise and convenient tool to decode the causal effects of the regulatory genome.

genomics↗

bioRxiv2026

Efficient and scalable Python implementation of ANCOM-BC for omics differential abundance testing

ANCOM-BC (Analysis of Compositions of Microbiomes with Bias Correction) is a critical algorithm for differential abundance analysis. Designed for handling compositional data, it features the ability to estimate the unknown sampling fractions and correct the bias induced by their differences. While the original R package is robust for standard dataset sizes, the increasing magnitude of modern multi-omics studies presents computational challenges, often resulting in runtime bottleneck for high-dimensional data. Here, we present a native Python implementation of ANCOM-BC in the scikit-bio package that significantly accelerates the workflow, enabling its application to large-scale datasets. By leveraging the vectorization with NumPy and numerical optimization in SciPy, we enhanced the efficiency without compromising the statistical accuracy of the method. The benchmarking results revealed a dramatic increase in performance in both simulated and real datasets. We demonstrated that our Python implementation generates test results that match the original R package, while achieving a 100-fold acceleration in runtime and an 87% reduction in memory consumption. This new implementation allows for the analysis of datasets that are significantly larger than previously possible, and enables applications to alternative omics data types and complex statistical models that vary greatly by the number of samples, features, and covariates.

bioinformatics↗

bioRxiv2026

mRNAfold: Co-optimization of Global Stability, Local Structure, and Codon Choice via Suboptimal Folding

mRNA medicines hold great promise, but designing sequences with high translation efficiency, robust in-solution stability, and manufacturability remains a major challenge due to the vast combinatorial space of synonymous coding sequences. Computational approaches such as mRNA folding algorithms have emerged as powerful tools by co-optimizing for in-solution stability and translation efficiency, yet current methods face important limitations. Here, we present "mRNAfold", an improved mRNA folding algorithm and software package that addresses these gaps by enabling efficient exploration of diverse near-optimal solutions, incorporating untranslated regions (UTRs), parallel execution, and supporting tunable control over local structural features across the mRNA. Thermodynamically optimized mRNAs from mRNAfold were more stable ({approx} 2-fold) in-solution than those generated by simple GC maximization for the same encoded protein. In addition, mRNAs designed to vary local structure near the start codon while maintaining consistent structure and codon optimality elsewhere showed a complex relationship between local structure near the start codon and protein production in cells. We observed no impact of structure in the start codon region for a set of mRNAs with high codon optimality, but it did impact protein production for a set of mRNAs with lower codon optimality. Together, these results underscore the potential of structure-aware, multi-objective design to improve mRNA medicines and offer a framework for exploring how sequence, structure, and expression are interrelated.

bioinformatics↗

bioRxiv2026

Healthcare Infrastructure Shapes Evolutionary Trade-offs and Geographic Dissemination of Multidrug-Resistant Acinetobacter baumannii

Antimicrobial-resistant pathogens pose an existential threat to modern medicine, yet the evolutionary forces driving their adaptation in healthcare systems remain largely unexplored. We revealed that hospital network architecture functions as a primary selective pressure, driving pathogen evolution through infrastructure-dependent virulence-transmission trade-offs. Phylogenomic analysis of 5,023 Acinetobacter baumannii isolates across Chinas centralized healthcare system identifies two co-existing evolutionary strategies: a virulence-optimized clade (ESL2.4) that spread slowly (20.4 km per year) in low-connectivity hospitals, and a transmission-optimized clade (ESL2.5) that disseminate rapidly (65.2 km per year) through mega-city healthcare hubs, likely attributed to its capsule conversion and increased upper respiratory colonization. Comparative analysis with European A. baumannii populations demonstrates that healthcare connectivity, not geography, governs pathogen distribution through convergent genomic adaptations. Our simulation suggests competitive asymmetries of the two clades, following the ecotype principle and enabling stable coexistence. The COVID-19 pandemic provided a natural experiment validating these mechanisms: outpatient restrictions reduced transmission-optimized lineage spread by 89%, while virulence-optimized lineage persisted through inpatient networks. These findings establish healthcare infrastructure as a critical evolutionary driver with immediate implications for predicting and controlling antimicrobial resistance emergence across diverse healthcare systems.

microbiology↗

bioRxiv2026

EssTFNet: Integration of Adaptive Time - Frequency and DNA Language Models for Interpretable Human Essential Gene Prediction

Essential genes are defined as those that are indispensable for an organisms survival. The loss of function of these genes results in cell death or an inability to complete the normal life cycle. Research on essential genes is pivotal in elucidating the origin and evolution of life, as well as in identifying potential therapeutic targets. Therefore, accurately predicting essential genes is of great scientific importance and has many applications in basic research and the biomedical field. In this study, we propose EssTFNet, a novel and interpretable deep learning framework that combines adaptive time-frequency analysis with a DNA language model to achieve accurate prediction of human essential genes while enabling mechanistic biological interpretation. Specifically, EssTFNet leverages the architecture of ATFNet, which innovatively maps DNA and protein sequences into equivalent time-series signals to extract periodic and non-stationary features, thereby enhancing the models capacity to capture complex sequence patterns. Through effective feature selection and architectural optimization, EssTFNet strikes a favorable balance among prediction accuracy, model interpretability, and cross-tissues generalization capability, significantly outperforming current mainstream sequence-based deep learning methods with an AUC of 0.8336 and an AUPR of 0.8212. Additionally, the DeepLIFT attribution method was employed to identify functional motifs closely associated with gene essentiality, offering valuable insights for experimental validation. For the convenience of researchers, we have developed an easy-to-use web server and made it along with the source code in a GitHub repository: https://github.com/QIANJINYDX/EssTFNet. Overall, this study presents a potentially useful methodological framework for human essential genes prediction, which could provide valuable insights for future research and applications in this field.

bioinformatics↗

bioRxiv2025

Super-CUT&Tag: A Sensitive, Spatially Resolved Approach for Epigenomic Profiling in Tissue Sections

Spatial profiling of protein-DNA interactions is essential for understanding gene regulatory processes, but current methods suffer from low sensitivity, limited resolution, and complex workflows. Here we introduce Super-CUT&Tag (Solid-phase interface embedded with an Ultra-dense DNA barcode array to Profile Epigenomic landscapes for spatially Resolved CUT&Tag), a robust spatial epigenomic method that directly transfers in situ protein-DNA interactions from tissue sections onto a pre-fabricated spatially barcoded array. Incorporating 3D polyamidoamine (PAMAM) dendrimers markedly improves chromatin capture efficiency, achieving several-fold higher sensitivity compared to current spatial CUT&Tag approaches. Using Super-CUT&Tag, we profile H3K27ac spatial landscape and uncover previously hidden active-chromatin heterogeneity across mouse embryonic tissues and stages. Integration with spatial transcriptomics further reveals spatiotemporal enhancer regulation dynamics of Neurod2 during corticogenesis. By overcoming key bottlenecks in sensitivity, resolution and usability, Super-CUT&Tag establishes a powerful platform for spatial epigenomic profiling in complex tissues.

genetics↗

bioRxiv2025

ATRX Loss Predicts Poor Outcomes and Reveals a Therapeutic Vulnerability to TEAD Inhibition in Soft Tissue Sarcomas

ATRX is one of the most frequently altered genes in sarcoma and encodes an ATP-dependent chromatin remodeler implicated in maintaining heterochromatin. However, ATRX alterations have not been leveraged for sarcoma treatment. We observed loss of ATRX protein in 14% of soft tissue leiomyosarcoma (STLMS, n =127), 53% of uterine leiomyosarcoma (ULMS, n = 95), 37% of undifferentiated pleomorphic sarcoma (UPS, n = 82), and 8% of dedifferentiated liposarcoma (DDLPS, n = 84). ATRX loss was associated with significantly worse outcomes in ULMS, UPS, and DDLPS. ATRX knockout in sarcoma cells increased proliferation in cooperation with TP53 deletion. ATRX knockout led to chromatin de-repression and enrichment of PRDM4 and NFIX transcription factor (TF) motifs. PRDM4 and NFIX knockdown in ATRX-mutant sarcoma lines resulted in reduced proliferation and invasion suggesting epistatic relationship. Consistent with the known functional relationship between PRMD4 and YAP1, we observed that ATRX/TP53 KO cells were more sensitive to the TEAD inhibitor VT103 compared to TP53 KO and ATRX WT controls. Overall, our results identify ATRX loss as a prognostic factor of worse outcomes, implicate the ATRX-PRDM4-YAP1 axis as a novel underlying mechanism, and suggest use of TEAD inhibition as a potential therapeutic strategy for ATRX-deficient sarcomas. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC="FIGDIR/small/689992v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@a759ecorg.highwire.dtl.DTLVardef@100c580org.highwire.dtl.DTLVardef@1a675c9org.highwire.dtl.DTLVardef@17f1f91_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Physiological Significance of Bacterial Mn(II) Oxidation

Bacterial oxidation of Mn(II) to Mn(III/IV) oxides is widespread in nature and proceeds markedly faster than abiotic oxidation. However, the physiological role of this process remains unclear. Building on recent evidence that environmental stress determines bacterial Mn(II)-oxidation activity, we used Pseudomonas putida KT2440--a known Mn(II)-oxidizing bacterium--to investigate the potential drivers and benefits of this reaction. Using RNA-seq, we observed up-regulation of starvation-related two-component system genes during Mn(II) oxidation, supporting the idea that nutrient limitation acts as a trigger. This led us to hypothesize that introducing an external antagonistic stressor could further accelerate the onset and rate of Mn(II) oxidation. To test this, we established a synthetic microbial community in which KT2440 was confronted with Pseudomonas aeruginosa PAO1, which possesses both contact-dependent and diffusible antagonistic systems. Across multiple assays measuring oxidation initiation time, manganese oxide accumulation, and bacterial survival, the presence of PAO1 significantly accelerated Mn(II) oxidation by KT2440. More importantly, Mn(II) oxidation strongly enhanced KT2440 survival under antagonistic pressure. Correlation analysis indicated a positive relationship between survival rate and oxidation intensity. Fluorescence microscopy and agent-based modeling further confirmed that this protective effect is contact-dependent. By integrating these results with earlier findings, we propose that the physiological significance of bacterial Mn(II) oxidation lies in enhancing the survival fitness of the oxidizer under stressful conditions.

microbiology↗

bioRxiv2025

The Human-In-the-Loop Drug Design Framework with Equivariant Rectified Flow

Advancements in AI-based drug design often face obstacles due to incomplete datasets, hindering progress to clinical trials. Human experts bring invaluable expertise and nuanced contextual understanding to drug design. There are two main difficulties in integrating human knowledge into the drug development process. First, human annotations are costly, and traditional machine learning algorithms require a large number of samples to be effective. Second, human experts are unable to accurately describe their expertise using natural language, nor can they precisely specify what kind of molecule is needed before seeing the generated molecules. To address these problems, we propose a new platform, called HIL-DD, for experts to infuse their experience by selecting molecules generated by AI that meet their criteria, or discarding those that do not. The core generative technology utilizes an Equivariant Rectified Flow Model (ERFM), which offers faster generation speeds than conventional diffusion models, enabling efficient human-AI collaboration. More importantly, we provide a user-friendly interface to ensure smooth and effective collaboration between human experts and AI systems. Rigorous experiments demonstrate that our system can produce 3D molecules that align with expert expectations in minimal interactive sessions. These generated molecules maintain drug-like qualities comparable to those created by current state-of-the-art models.

bioinformatics↗

bioRxiv2025

A Global Discovery of Antimicrobial Peptides in Deep-Sea Microbiomes Driven by an ESM-2 and Transformer-based Dual-Engine Framework

The global crisis of multidrug-resistant pathogens necessitates innovative antimicrobial peptide (AMP) discovery. However, current AMP predictors are severely compromised by three overlooked systematic data biases, including sequence length imbalance, N-terminal methionine artifacts, and lack of microbial optimization. To overcome these fundamental bottlenecks, we developed XAMP, a dual-engine predictor integrating XAMP-E (ESM-2-based for high accuracy) and XAMP-T (a one-layer Transformer for high speed), trained on rigorously debiased datasets. Consequently, XAMP achieved a median AUC of 0.972, representing an improvement of up to 21.9% in AUC while operating 5 to 40 times faster. Beyond accurate classification, we found that AMPs inherently possess a significantly higher net charge (+3.9 vs. +0.5) despite similar hydrophobicity, along with distinct cooperative patterns such as W-K/R pairs. Applying this pipeline to 238 deep-sea metagenomes (>1000m depth), we identified 2,355 promising AMP candidates from microbial dark matter, and metaproteomic analysis confirmed in-situ expression for a subset of these candidates. Notably, these deep-sea AMPs exhibited a significant 1.53-to 2.93-fold enrichment in K/R/W/Y residues compared to known AMPs. Experimental validation demonstrated that six synthesized peptides exhibit potent, broad-spectrum activity against ESKAPE pathogens, with particular efficacy against Gram-negative bacteria that dominate deep-sea ecosystems and pose major clinical challenges. This study establishes that correcting algorithmic data biases is a prerequisite for reliable mining, providing a robust framework to unlock the therapeutic potential hidden within microbial dark matter, offering new avenues to combat the antibiotic resistance crisis.

bioinformatics↗

bioRxiv2025

Mid-Infrared Photothermal Mesoscopy with Millimeter Field of View and Sub-micron Spatial Resolution

By optically sensing mid-infrared absorption through a visible probe beam, mid-infrared photothermal (MIP) microscopy has emerged as a powerful tool for chemical imaging with micromolar sensitivity and submicron spatial resolution. The adoption of spatially multiplexed camera-based widefield detection further enhanced the imaging speed. However, current widefield MIP systems suffer from a small field of view (FOV)--typically tens of square micrometers, which constrains their utility in large-area tissue imaging applications. Here, we report a laser-scan MIP mesoscope that achieves millimeter-scale FOV while preserving submicron resolution. By leveraging an all-reflective laser scanning architecture, low-magnification and medium numerical-aperture objectives, and a defocused signal collection scheme, our MIP mesoscope achieves a 1.2 x 1.2 mm2 FOV, 650 nm lateral resolution, and microsecond-scale pixel dwell time. In vivo chemical imaging of whole Caenorhabditis elegans and high-throughput detection of beta-amyloids in both mouse and human brain tissues associated with Alzheimers disease are demonstrated.

bioengineering↗

bioRxiv2025

PIWIL3-piRNA Pathway Is Essential for Rabbit Oogenesis and Embryogenesis via Broad Regulation of the Transcriptome and Proteome

Female infertility represents a significant challenge in reproductive medicine. Although it is known to be primarily due to oogenic and early embryonic failures, the molecular mechanisms underlying these failures remain elusive. The Piwi-piRNA pathway is crucial for gametogenesis in diverse organisms. Yet, its role in mammalian female fertility is unclear. This is partly because most studies are done in mice, which contain only PIWIL1, PIWIL2, and PIWIL4 that are essential for spermatogenesis but not for oogenesis. PIWIL3 emerges in higher mammals and is highly expressed in human oocytes. Interestingly, female PIWIL3-knockout golden hamsters exhibit only reduced fertility without detectable defects in oogenesis. This has left the function of PIWIL3 in higher mammals, including humans, unexplored. Here, we discovered that PIWIL3 in the rabbit (Oryctolagus cuniculus) shares high homology with human PIWIL3. It is the predominant PIWI protein in oocytes in both species. Using CRISPR-cas9-mediated knockout, we demonstrated that rabbit PIWIL3 is essential for female fertility. Its loss leads to severe defects in oogenesis. Moreover, embryos lacking maternal PIWIL3 arrest developmentally at the 8-cell stage. Mechanistically, rabbit PIWIL3 binds [~]18-nucleotide piRNAs, mirroring the behavior of human PIWIL3, and is critical for piRNA biogenesis. Moreover, it regulates transcriptomic and proteomic landscapes and silences a broad array of transposons during late oogenesis yet activates another set of transposons during early embryogenesis. These findings establish PIWIL3 as a pivotal dual regulator of gene expression and transposon activity, which is essential for oogenesis and early embryogenesis in non-rodent mammals, potentially including humans.

developmental biology↗

bioRxiv2025

Inhibition of CGRP receptor ameliorates AD pathology by reprogramming lipid metabolism through HDAC11/LXRβ/ABCA1 signaling

The Calcitonin gene-related peptide (CGRP) receptor has gained attention in Alzheimers Disease (AD) research due to its involvement in regulating neuroinflammation. However, its role and mechanism in AD pathology remain unclear. Here, we demonstrate that CALCRL, a core component of the CGRP receptor, is upregulated in the hippocampus of AD dementia patients and 5xFAD mice. Knockout of the CGRP receptor ligand Calca or pharmacological blockade using Rimegepant (Rim), reduces soluble A{beta}1-42 oligomer-induced neuronal death and glial inflammation. Rim treatment also rescues neurobehavioral impairments, neurodegeneration, and lipid metabolism dysfunction in 5xFAD mice. Mechanistically, these effects are mediated through HDAC11 inhibition, which enhances LXR{beta} acetylation and ABCA1 expression, promoting the reprogramming of neuronal lipid metabolism. Importantly, this CALCRL/HDAC11/LXR{beta}/ABCA1 axis is conserved across both humans and mice. Our findings uncover a novel mechanism underlying AD pathogenesis and highlight the therapeutic potential of targeting CGRP signaling in AD. HIGHLIGHTSInhibition of CGRP receptor ameliorates disease pathology in models of AD HDAC11 mediates CGRP receptor function in AD HDAC11 is a pivotal regulator in lipid metabolism by LXR{beta}/ABCA1 signaling The HDAC11/LXR{beta}/ABCA1 axis is conserved in AD humans and mice

neuroscience↗

bioRxiv2025

Ultra-High-Throughput LC-MS Method for Targeted Protein Degradation Compound Screening Using the Orbitrap Astral Mass Spectrometer

High-throughput proteomics analysis is crucial for targeted protein degradation (TPD) compound screening. Here, we developed a 300 sample per day (SPD) LC-MS method using the Orbitrap Astral mass spectrometer for ultra-high-throughput TPD compound screening. We identified close to 8,000 protein groups from a single cell line with a coefficient of variance (CV) of less than 10%, highlighting the deep proteome coverage and method reproducibility even at 300 SPD. This high degree of precision provides the statistical confidence to detect subtle, yet significant, changes in protein abundance that were previously challenging to quantify in high-throughput workflows. To evaluate the quantitation accuracy of this method, we further mixed the digests from two or three species at different ratios. Our three-proteome mixture results demonstrated highly accurate quantitation for proteins with both small and large fold changes. Moreover, our two-proteome mixture experiment, where 20 to 160 ng of yeast digest was spiked into 200 ng of HeLa digest, showed an R2 of 0.999 for the yeast proteome, underscoring the quantitation accuracy of the method. Utilizing this workflow, we studied dose-dependent protein degradation patterns induced by pomalidomide, iberdomide, and mezigdomide. Our results indicate that mezigdomide may possess enhanced efficacy in T cells by degrading additional proteins such as IKZF2, thereby boosting anti-cancer immunity. Together, we developed an ultra-high-throughput LC-MS method with excellent proteome coverage and quantitation accuracy that is highly suitable for chemoproteomics screening of drug libraries.

biochemistry↗

bioRxiv2025

TAFFISH: A lightweight, modular, and containerized workflow framework for reproducible bioinformatics analyses

SummaryBioinformatics analyses often rely on shell commands and small shell scripts whose executable context is difficult to preserve, inspect and reuse. TAFFISH addresses this gap by packaging command-line tool calls and lightweight shell flows as installable, versioned and inspectable executable units. Through a curated public Hub, TAFFISH indexes command interfaces, execution backends, platform constraints, release metadata and smoke-test/validation records. Together, these components provide a command-level reproducibility layer that works directly in ordinary shells and can also be invoked from existing workflow systems. Availability and ImplementationTAFFISH is implemented in Common Lisp and released under Apache-2.0. TAFFISH version 0.10.1, together with the source code, documentation and public Hub resources, is available at https://github.com/taffish/taffish, https://taffish.com, https://taffish.github.io and https://github.com/taffish/. The submitted software release, frozen Hub snapshot, test data, command logs, checksums and reproducibility package are archived at https://doi.org/10.5281/zenodo.21054185. The authors will maintain the software and public resources for at least two years after publication. ContactHao Lyu: hao.lyu@uestc.edu.cn; Hao Lin: hlin@uestc.edu.cn; Kejun Deng: dengkj@uestc.edu.cn Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2025

Evaluation of Antigen Expression and Early Immune Response following Cutaneous Suction-mediated DNA Delivery

Suction-based in vivo cutaneous DNA transfection is a newly developed, cost-effective method that produces high transfection efficiency. This method has shown robust immunogenic responses following SARS-CoV-2 DNA vaccination in both pre-clinical studies and clinical trials. The current work investigates suction-based transfection and immune activation on a detailed, cellular level. The spatiotemporal patterns of antigen expression in rat skin following suction-induced delivery of a pEGFP-N1 plasmid and a SARS-CoV-2 DNA vaccine are evaluated via immunofluorescence staining, which demonstrates early and prolonged expression. The epidermis is identified as the primary location of transfection, and the transfected cells are primarily epidermal keratinocytes. Early immune response is assessed by detection of antigen presenting cells (APCs) following suction-induced DNA vaccination.

bioengineering↗