Precise spatial restriction of transgene expression is essential for safe in vivo production of bioactive proteins; however, regulatory elements that confer tissue specificity often span large genomic regions and are incompatible with size-limited viral vectors. Here, we report a compact synthetic promoter-feedback circuit that enables enhanced, oviduct-restricted transgene expression in chickens. By combining a minimal ovalbumin promoter (<200bp) with a tetracycline-responsive element (TRE)-tTA positive feedback loop, we established a viral vector-compatible expression module that achieves enhanced transcriptional output while preserving tissue specificity. This system was validated in vitro and in genetically modified chickens. As a functional demonstration, chickens expressing transforming growth factor beta 1 (TGF-β1) specifically in egg white were generated. TGF-β1 accumulated predominantly as a presumptive latent/pro-form consisting of the latency-associated peptide (LAP)/pro-domain and the mature TGF-β1 dimer, at a semi-quantitatively estimated level of approximately 110µg/mL, and remained detectable for over 2 years. Oral administration of TGF-β1-containing egg white significantly attenuated inflammation in a murine DSS-induced colitis model. Together, these findings establish a modular, size-efficient transcriptional enhancement platform that enables robust tissue-restricted expression in vivo. This strategy provides a generalizable framework for overcoming regulatory size constraints in viral vector-mediated gene delivery and expands the potential for producing edible biopharmaceuticals in transgenic livestock.
Yoshinori Kawabe, Takayuki Otsubo, Reina Obata et al.· New Biotechnology· 0 citations
Disease severity is often annotated using a small number of discrete severity levels with an inherent order, yet such labels are subjective and often corrupted by label noise biased toward adjacent levels. Conventional methods for learning with noisy labels typically treat label noise as random class flips, overlooking the ordinal structure of these misannotations. We propose Ordinal Negative Label Learning (Ord-NLL), an extension of negative label learning that explicitly incorporates ordinal structure into negative-label sampling. Ord-NLL constructs an ordinal negative-label distribution that assigns higher sampling probability to levels farther from the observed label, thereby encouraging learning that respects ordinal relationships. The method uses a single-term objective derived theoretically and requires neither the noise rate nor the label-transition matrix, making it practical when prior knowledge about the noise process is unavailable. We further introduce Ord-NLL+, which leverages the confidence estimates produced by Ord-NLL for sample selection and retraining. Experiments on two multi-expert ulcerative colitis (UC) endoscopic-image datasets under two ordinal-noise models show that Ord-NLL is competitive with or superior to strong baselines while reducing mean absolute error, and that Ord-NLL+ often yields further gains. Across controlled ordinal-noise experiments, Ord-NLL and Ord-NLL + consistently outperformed conventional NLL and remained competitive with strong noisy-label baselines. In a representative severe-noise setting, Ord-NLL + achieved 0.715 accuracy, 0.305 MAE, and 0.625 macro-F1, outperforming both conventional NLL and the best competing baseline. These results suggest that explicitly incorporating ordinal structure into negative-label learning is an effective strategy for robust severity estimation under noisy ordinal annotations.
Shumpei Takezaki, K. Shiku, S. Harada et al.· IEEE Access· 0 citations
