Menon, A. V. | bioRxiv Science

bioRxiv2023

Sea squirt-inspired bio-derived tissue sealants

Sea squirts or tunicates bodies are composed of cellulose nanofibers and gallol- functionalized proteins. These sea creatures are known to heal their injuries under seawater by forming crosslinks between gallols and functional groups from other proteins in their bodies. Inspired by their wound healing mechanism, herein, we have developed a tissue sealant using zein (a plant-based protein) and tannic acid (gallol-containing polyphenol). Except for fibrin- based sealants, most commercial surgical adhesives, and sealants available today are derived from petroleum products that compromise their biodegradability. They often have complicated and multi-step synthesis processes that ultimately affect their affordability. To overcome this challenge, we ensured that these sea squirt-inspired tissue sealants are bio-based, easily synthesized, and low-cost. The sealants were studied on their own and with a food-grade enzyme transglutaminase. The adhesion performances of the sealants were found to be higher than physiological pressures in seven out of nine different tissue substrates studied here. Their performance was also better than or on par with the FDA-approved fibrin sealant Tisseel. Ex vivo models demonstrate instant sealing of leaking wounds in less than a minute. The sealants were not only cytocompatible but also showed complete wound healing on par with sutures and Tisseel when applied in vivo on skin incisions in rats. Overall, these sea squirt-inspired bio-based sealants show great potential to replace currently available wound closure methods.

bioengineering↗

bioRxiv2020

Ultra-fast Prediction of Somatic Structural Variations by Reduced Read Mapping via Pan-Genome k-mer Sets

Genome rearrangements often result in copy number alterations of cancer-related genes and cause the formation of cancer-related fusion genes. Current structural variation (SV) callers, however, still produce massive numbers of false positives (FPs) and require high computational costs. Here, we introduce an ultra-fast and high-performing somatic SV detector, called ETCHING, that significantly reduces the mapping cost by filtering reads matched to pan-genome and normal k-mer sets. To reduce the number of FPs, ETCHING takes advantage of a Random Forest classifier that utilizes six breakend-related features. We systematically benchmarked ETCHING with other SV callers on reference SV materials, validated SV biomarkers, tumor and matched-normal whole genomes, and tumor-only targeted sequencing datasets. For all datasets, our SV caller was much faster ([≥]15X) than other tools without compromising performance or memory use. Our approach would provide not only the fastest method for largescale genome projects but also an accurate clinically practical means for real-time precision medicine.

bioinformatics↗