Biology subjects

Aihara, G.

Publications and source records attributed to Aihara, G..

3 recordsLinked to original sources

bioRxiv2026

High temperature ligation enables in vitro assembly of eukaryotic genes

Our ability to efficiently construct synthetic DNA products is essential to studying and engineering biology. There is an abundance of widely adopted DNA assembly technologies suitable for generating synthetic constructs from diverse DNA inputs, but these techniques suffer from low specificity of connection during assembly, which decreases the accuracy, efficiency, and throughput of construction. Here we propose a broadly applicable paradigm for DNA assembly, High Temperature (HighT) assembly, that lowers misconnection rates by ligating complementary single strand DNA overhangs at reaction temperatures significantly higher than their predicted melting temperatures. By expanding the concept of melting temperature to demarcate smaller association fractions of DNA duplexes, we characterize HighT and demonstrate 40 percentage points increase in accuracy, and 4 orders of magnitude increase in yield of transformants when applying the technique to conventional assembly technologies. HighT is then applied to develop cloning pipelines for the development of efficient assembly-to-integration protocols, the construction of specific low-abundance isoforms of medically relevant proteins from human gDNA and oligos, and the de novo construction of a 10 kb, GC rich genomic region from the functionally extinct Northern White Rhinoceros created entirely in vitro.

molecular biology↗

bioRxiv2026

Unbiased and scalable reduction of diverse bacterial genomes

The genome is a complex, integrated system where the functions and regulatory interactions of its many components remain poorly understood. Genome minimization aims to reduce genomic complexity by removing non-essential elements to reveal the fundamental building blocks of cellular life. However, current minimization strategies are often slow and species-specific due to a reliance on prior information, and limited to producing single, isolated strains, which obscures the diverse ways a genome can adapt to large-scale DNA removal. Here we show the development and application of Stochastic Lineage-based Iterative Minimization (SLIM) a modular, high-throughput platform for unbiased genome reduction across phylogenetically diverse bacteria. We apply SLIM to generate a library of genome-reduced Escherichia coli lineages. We then interrogate the lineages, identifying both universal and lineage-specific transcriptional and translational reprogramming in response to deletions. We demonstrate that these expression dynamics drive environment-dependent fitness, allowing us to pinpoint a single gene deletion in one genome-reduced lineage as the driver of a measurable environmental growth defect. Beyond E. coli, we successfully deploy SLIM in phylogenetically distinct bacterial taxa to rapidly reduce the genomes of Shigella flexneri and Pseudomonas putida, distinct genus and order respectively from E. coli, without species-specific optimization. Our results establish a scalable, generalizable framework for navigating the vast landscape of minimized genomes, providing a powerful new tool for functional discovery and the rational design of synthetic genomic chassis.

synthetic biology↗

bioRxiv2024

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

MotivationSpatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. ResultsTo enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce resource requirements while maintaining high performance. We further integrate SEraster with existing analysis tools to characterize cell-type spatial cooccurrence. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as cooccurring cell-types that recapitulate expected organ structures. Availability and implementationSource code is available on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster.

bioinformatics↗