Biology subjects

Jang, J. H.

Publications and source records attributed to Jang, J. H..

3 recordsLinked to original sources

bioRxiv2025

Unveiling differential responses to UVB (305 nm) and UVC (275 nm) in cacao-infecting Colletotrichum gloeosporioides and Pestalotiopsis sp.

Sustainable control of microbial pathogens requires alternatives to chemicals, but physical methods like Ultraviolet-C (UVC) show variable efficacy linked to poorly understood pathogen-specific responses. Here, we investigate differential UVB/UVC responses in plant pathogenic fungi (Colletotrichum gloeosporioides, Pestalotiopsis sp.). Using hyperspectral imaging and machine learning, we dissect the physiological underpinnings of UV sensitivity. UVC proves more potent than UVB, with Pestalotiopsis sp. showing significantly higher resistance than C. gloeosporioides isolates. Crucially, hyperspectral signatures correlated with resistance, revealing UVC-induced photopigment changes, biochemical disruption, and oxidative stress markers in sensitive isolates, contrasting with minimal perturbation in the resistant isolate. Machine learning accurately decoded these complex phenotypes for classification. This understanding enabled enhanced inactivation via optimized pulsed UVC and synergistic sonication. We link distinct physiological states, non-invasively detected via hyperspectral imaging, to fungal UV resistance, demonstrating how integrating advanced phenotyping and machine learning provides a mechanistic basis for optimizing physical pathogen controls.

microbiology↗

bioRxiv2025

Genome-wide association mapping and predictive modeling of wet bean mass in a diverse cacao collection

Improving cacao yield, a key objective in post-domestication crop improvement, remains a primary goal for breeders, but progress is often hindered by the confounding effects of population structure. To overcome this, we analyzed 346 diverse cacao accessions using an ML-based association mapping framework (with and without population structure adjustment) and a phenotype-only ML prediction of yield. By correcting for population structure, our Bootstrap Forest-based GWAS revealed association signals that showed consistent enrichment for ribosome and protein-synthesis functions, and a recurrent subset of SNPs with high importance appeared across multiple yield components, including pod index and seed number. In parallel, a Neural Network model was utilized to identify cotyledon mass and length as the most powerful predictors for total wet bean mass (R{superscript 2} = 0.715 by repeated five-fold cross-validation), suggesting a practical, low-cost screening proxy for breeding). Collectively, this study delivers a robust genetic framework and a novel predictive tool to accelerate the development of high-yielding cacao varieties through the early identification of elite clones.

genomics↗

bioRxiv2024

Single laboratory evaluation of the (Q20+) nanopore sequencing kit for bacterial outbreak investigations

This study aimed to evaluate the potential of Oxford Nanopore Technologies (ONT) GridION with Q20+ chemistry as a rapid and accurate method for identifying and clustering foodborne pathogens. The study focuses on assessing whether ONT Q20+ technology could offer near real-time pathogen identification, including SNP differences, serotypes, and antimicrobial resistance genes, to overcome the drawbacks of existing methodologies. This pilot study evaluated different combinations of two DNA extraction methods (Maxwell RSC Cultured Cell DNA kit, and Monarch high molecular weight extraction kits) and two ONT library preparation protocols (ligation and the rapid barcoding sequencing kit) using five well-characterized strains representing diverse foodborne pathogens. The results showed that any combination of extraction and sequencing kits produced high-quality closed bacterial genomes. However, there were variations in assembly length and genome completeness based on different combinations of methods, indicating the need for further optimization. in silico analyses demonstrated that the Q20+ nanopore sequencing chemistry accurately identified species, genotyped, and detected virulence factors comparable to Illumina sequencing. Phylogenomic clustering methods showed that ONT assemblies clustered with reference genomes, although some indels and SNP differences were observed. There were also differences on SNP accuracy among the different species. The observed SNP differences were likely due to sequencing and analysis processes rather than genetic variations in the sampled bacteria. The study also compared a change in the basecaller model with the previous model (SUP 4Khz 260 bps) and found no significant difference in accuracy (SUP 5Khz 400 bps). In conclusion, the evaluation of ONT Q20+ nanopore sequencing chemistry demonstrated its potential as an alternative for rapid and comprehensive bacterial genome analysis in outbreak investigations. However, further research, verification studies, and optimization efforts are needed to address the observed limitations to adopt and fully realize the impact of nanopore sequencing on public health outcomes and more efficient responses to foodborne disease threats.

genomics↗