bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.06.25.734422

BIRD-Seq: B2 Protein Integrated End-to-End Pipeline for dsRNA Detection and Nanopore Sequencing for Virus Monitoring

Abstract

Double-stranded RNA (dsRNA) is a near-universal hallmark of active viral infection. Despite its role as a pan-viral replication intermediate, dsRNA-centred technologies for virus monitoring remain scarce and largely rely on monoclonal antibody-based approaches that, while highly sensitive, are costly and difficult to engineer, scale, or integrate with downstream assays. Here, we present a modular and antibody-free pipeline for quantitative and qualitative dsRNA analysis built around an engineered B2 protein from Flock House virus with nanomolar-range binding affinity. The pipeline is compatible with absorbance- or luminescence-based measurement formats. In a sandwich assay configuration (Sand-BIRD), sub-ng mL-1 quantification of dsRNA is achieved, comparable to the gold standard J2 monoclonal antibody, directly from crude biological samples without RNA extraction. Sand-BIRD reliably detects viral infection in both plant samples (Tomato bushy stunt virus and Grapevine fanleaf virus) and mosquitoes (West Nile virus and Dengue virus) with commercial-grade reliability. dsRNA eluted from positive samples were further processed directly by Oxford Nanopore direct sequencing, enabling identification of virus species without prior sequence knowledge or total RNA extraction. Together, this work establishes an end-to-end, sequence-agnostic workflow for direct RNA-duplex quantification and sequencing (BIRD-Seq), which has compelling potential for emerging infectious disease surveillance and next-generation point-of-care (PoC) diagnostics. Technology ReadinessBIRD-Seq is an integrated pipeline for agnostic dsRNA detection and sequencing designed for broad-spectrum virus monitoring. A Technology Readiness Level (TRL) 5 under NASAs classification framework has been reached as BIRD-Seq has been validated in laboratory-relevant environments using real-world samples, including virus-infected plants and mosquitoes. The ELISA-based sensing platform employs engineered variants of the B2 protein (from Flock House virus) in a sandwich assay format for dsRNA capture and detection, achieving sensitivity comparable to the gold-standard J2 monoclonal antibody. Unlike traditional antibody-based methods, the B2 protein offers key practical advantages: straightforward production in bacterial expression systems, high versatility, and reduced manufacturing costs, as well as direct compatibility with crude extract monitoring, eliminating the need for RNA extraction. Captured B2/RNA duplexes can then be directly eluted from the ELISA microplates and subjected to downstream nanopore direct RNA sequencing, providing both quantitative and qualitative information on the underlying virus infection, a capacity enabled by the near-universal nature of dsRNA as a pathogen-associated molecular pattern. That said, further validation on large-scale field-collected and clinical samples will be essential before widespread deployment can be envisioned. While the B2 sandwich assay offers favorable cost-efficiency over antibody-based alternatives, the relatively high cost of Oxford Nanopore direct RNA sequencing remains an important economic constraint. Nevertheless, the growing importance of dsRNA detection across virus sensing, mRNA vaccine development, innate immunity research, and human disease diagnostics, combined with the increasing role of portable long-read sequencing in emerging infectious disease (EIDs) surveillance, positions BIRD-Seq as an innovative and competitive diagnostic platform. Highlights- Double-stranded RNA (dsRNA) is one of the critical pathogen-associated molecular patterns for viral invasion in the host. A protein-based sandwich assay for dsRNA detection in crude biological samples with a sub-ng mL-1 order detection limit was developed, achieving similar sensing efficiency in comparison to expensive and proprietary monoclonal antibody-based ELISA methods. - The quantitative detection of RNA duplex is coupled with an Oxford nanopore direct dsRNA sequencing method for virus species identification and qualitative analysis. - This is one of the very first dsRNA-centered end-to-end workflows for virus monitoring and sequencing, validated for both infected plant and animal samples.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xhurxhi, A. N., Sahu, S., Scheer, H., Miott, E. F., Alioua, A., Clesse, D., Monsion, B., Pompon, J., Szunerits, S., Blevins, T., Ritzenthaler, C.. 2026-06-26. BIRD-Seq: B2 Protein Integrated End-to-End Pipeline for dsRNA Detection and Nanopore Sequencing for Virus Monitoring. https://doi.org/10.64898/2026.06.25.734422

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Single-Component Photo-Crosslinked Lipoic Acid Functionalized Mucin Hydrogels: Dynamic Mucus Mimetics for Antiviral Protection

Native mucus protects epithelial surfaces through viscoelastic networks of disulfide-linked mucins. However, commercially available mucins often fail to reconstitute comparable mechanical and barrier properties, while synthetic and hybrid mucus mimetics require polymeric crosslinkers that may alter mucus-like composition. Inspired by the native architecture, we directly functionalized porcine gastric and bovine submaxillary mucins with -lipoic acid to develop a single-component, photo-crosslinkable hydrogels. Upon UV irradiation (365 nm), the modified mucins rapidly form disulfide-crosslinked networks without added photo-initiator/ polymeric crosslinkers, two orders of magnitude faster as compared with PEG-SH approaches. The resulting mucin-lipoic acid (MUC-LA) hydrogels exhibit mucus-relevant properties, including tunable viscoelasticity, redox responsiveness, self-healing, and size-dependent restrictions on nanoparticle transport. In transwell-based infection models, MUC-LA hydrogels (2-3%) mimicking the healthy airway mucus (G' {approx} 10-20 Pa) suppress epithelial infection by SARS-CoV-2 and influenza A virus. Crosslinked MUC-LA provides greater protection against SARS-CoV-2 than the non-crosslinked, whereas both formulations suppress influenza A infection, suggesting distinct contributions from network formation and mucin-associated interactions. These findings establish a dynamic, mucus-mimetic platform integrating rapid photo-gelation, controllable mechanics, selective transport, and antiviral barrier functionality. MUC-LA hydrogels offer opportunities to investigate mucosal transport and host-pathogen interactions, with potential applications in drug delivery, and as biomaterials.

bioengineering↗

Population-level emulation enables fast Bayesian calibration of cardiac digital twins

Purpose: Patient-specific cardiac digital twins have the potential to guide individualized therapy, but generating these in a routine clinical workflow remains challenging. This is especially true when the twins must carry quantified uncertainty: Bayesian calibration requires thousands of simulations per patient, and the emulators that reduce this cost are typically trained one patient at a time. We aimed to train a single emulator across a patient cohort, so that a new patient can be calibrated in minutes without new simulations. Methods: We trained a population-level Gaussian process emulator whose inputs included a 12-dimensional latent encoding of the clinical features of each patient, so that a single emulator represents the whole cohort. We compared spending simulation budgets on resampling the real cohort with augmenting the cohort with digital chimeras, synthetic patients interpolated between real patients. Seven parameters of a closed-loop heart and circulation model were calibrated by Markov chain Monte Carlo to ten clinical biomarkers in 26 patients with left bundle branch block, each using an emulator trained without that patient, and the calibrated posteriors were compared against the full simulator. Results: A single emulator, trained without the held-out patient, calibrated the digital twin of each patient in 59.7-63.0 s per patient, versus 15 h with the simulator. Resampling the real cohort was the more efficient training strategy, bringing every output to R2 greater than or equal to 0.9, which augmentation did not achieve for the same simulation budget. For held-out patients, both strategies performed comparably (median leave-one-out normalized root-mean-square error 11.0% vs. 10.7%; p = 0.745), and error rose with distance from the cohort centroid (Spearman rho = +0.406). The calibrated models showed no cohort-wide bias (median standardized residual -0.12 against the clinical targets), and 95% predictive intervals contained 78.5% of targets, with the largest misfit in systolic blood pressure. Conclusion: Removing the need for new simulations for every new patient is a meaningful step toward uncertainty-aware cardiac digital twins that fit within a routine clinical workflow. Because the simulator, surrogate and sampler are interchangeable, the approach used here can be applied to other (cardiac) models and cohorts.

bioengineering↗

Multi-Structure Segmentation of Type B Aortic Dissection from CT Images with U-Net

Type B aortic dissection (TBAD) is a life-threatening condition in which a tear in the intimal layer separates the aortic wall into a true and a false lumen. Assessment of the lumen, the intimal flap, and false-lumen thrombosis from CT images is important for risk analysis and treatment planning. This work applied U-Net-based deep neural networks to the automatic segmentation of these structures in TBAD. We collected 46 CT scans from 20 patients, with voxel-wise annotations of three structures: the aortic lumen, the intimal flap, and the thrombus. We explored four models: two convolutional networks, nnU-Net 2D and 3D, and two transformer-based architectures, TransUNet 2D and 3D. We observed that (1) for both nnU-Net and TransUNet, the 3D models outperformed their 2D counterparts on all three structures. The difference was small for the aortic lumen but large for the thin flap and the thrombus. The best model, nnU-Net 3D, reached a DSC of 93.05% for the lumen, 50.22% for the flap, and 69.94% for the thrombus. (2) The intimal flap was the most difficult structure for all models. Even the best 3D models showed large thrombus boundary errors in a few scans. These results provide a baseline for developing more advanced models for accurate segmentation of these structures in TBAD.

bioengineering↗