Biology subjects

Chhetri, H. B.

Publications and source records attributed to Chhetri, H. B..

3 recordsLinked to original sources

bioRxiv2025

Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis

Genetic mapping is a powerful tool for eukaryotic genetics that has only been applied to bacteria in limited circumstances. Quantitative trait locus (QTL) mapping generally relies on sexual recombination to break linkages between genes, yet bacteria rarely undergo sufficient homologous recombination to generate suitable mapping populations. In this work, we used iterative biparental genome shuffling by protoplast fusion in Bacillus subtilis to generate a population of bacteria with substantial random recombination throughout their genomes. Individual shuffled progeny were arrayed in well plates, resequenced, and characterized for a range of complex phenotypes including spore germination and swarming motility. Genetic mapping of the resulting phenotypes identified high-confidence QTLs of moderate size ([~]10 kb), and these associations were validated through targeted genetic swaps. This B. subtilis QTL population can easily be used to map additional phenotypes, and the general approach for QTL mapping is applicable in a wide range of bacteria. One-Sentence SummaryBacterial genome shuffling mimics sexual recombination and enables genetic mapping of complex phenotypes to causal alleles.

microbiology↗

bioRxiv2025

Which plant traits increase soil carbon sequestration? Empirical evidence from a long-term poplar genetic diversity trial

Breeding or engineering crops to increase soil organic carbon (SOC) storage is a potential route to land-based carbon dioxide removal on working agricultural lands. However, due to limited observational datasets plus shifting paradigms of SOC stabilization, there is a lack of understanding of which plant traits to target for SOC enhancement or the ultimate sequestration potential of such measures. Existing long-term common gardens of genetically diverse plant populations may provide an opportunity to evaluate biological controls on SOC outcomes, separate from environmental or management variability. Here we report on soil carbon and root chemical data collected for 24 genotypes within a 13-year-old common garden in northwestern Oregon planted with over a thousand natural variants of Populus trichocarpa. Fractionating surface soil (0-15 cm) revealed substantial variation in stocks of mineral-associated organic matter (MAOM; 18-67 tonnes C/ha) and particulate organic matter (POM; 2-22 tonnes C/ha). Tree genotype explained 24 and 26% of the MAOM and POM stock variability, respectively, after controlling for background variability. We found minimal association between SOC concentration and either aboveground tree productivity or conventional metrics of root biomass recalcitrance (C/N ratios and lignin content). However, root elemental content appeared influential for MAOM-C, which showed a strong positive association with root aluminum (Al), and strong negative association with root boron (B) and magnesium (Mg). Furthermore, root concentrations of these elements were highly heritable (57-78%) and not simply a reflection of background variation in bulk soil elemental concentrations. We estimate that surface SOC stocks under these 24 genotypes have diverged at rates of up to 1.2-4.3 tonnes C/ha/yr. These results suggest that genetic diversity trials have value for elucidating biological controls on SOC dynamics, and that traits associated with root elemental content may be an important target for enhancing SOC.

ecology↗

bioRxiv2021

Climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics

Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on earth at high geospatial resolution. The results are captured as networks, in which edges between geolocations are defined if their historical climates exceed a similarity threshold. We then apply Markov clustering and our novel Correlation of Correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37 x 1018 operations/sec) and one of the largest (168.7 x 1021 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Correlation and network analysis methods of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.

systems biology↗