Dawn M. Klingeman

A natural temperature, elevation, and rainfall gradient over the Cascade Mountains drives changes in Populus trichocarpa stress response and microbial community structure

The study examines how climate gradients affect Populus trichocarpa stress responses and microbial communities across different soil niches.

Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis

This study uses genome shuffling in Bacillus subtilis to map quantitative trait loci for complex phenotypes, identifying high-confidence QTLs of moderate size.

Parallel tracking by sequencing reveals the impact of restriction modification systems on transfer of an integrative and conjugative element

The paper develops ICE-seq, a sequencing-based method to track ICE transfer in complex communities, showing RM systems can alter transfer rates by up to 10,000-fold.

Combinatorial pathway engineering using multiplex Serine recombinase-Assisted Genome Engineering (mSAGE)

The paper introduces mSAGE, a method for multiplexed DNA insertion in Pseudomonas putida, and uses it to construct a combinatorial gene library for isophthalate degradation.

When good guides go bad: empirical evaluation of all unique Cas9 targets in E. coli reveal widespread functionality and rules for gRNA design

The paper evaluates nearly 500,000 unique gRNAs in E. coli, finding most are functional and identifying rules to improve gRNA design.

BONCAT-Live for isolation and cultivation of active environmental bacteria

BONCAT-Live isolates and cultivates active bacteria from environmental samples in response to specific stimuli, enabling study of their metabolic and adaptive responses.

Multilayered regulation by RNA thermometers enables precise control of Cas9 expression

The paper demonstrates that RNA thermometers can reduce Cas9-induced cell death by controlling its expression through temperature-dependent regulation.

Quantum biological insights into CRISPR-Cas9 sgRNA efficiency from explainable-AI driven feature engineering

This paper develops a novel feature set and public resource for predicting CRISPR-Cas9 sgRNA efficiency using quantum chemical tensors and explainable-AI methods.