Feedstock variability impacts the bioconversion of sugar and lignin streams derived from corn stover by Clostridium tyrobutyricum and engineered Pseudomonas putida.

Feedstock variability represents a challenge in lignocellulosic biorefineries, as it can influence both lignocellulose deconstruction and microbial conversion processes for biofuels and biochemicals production. The impact of feedstock variability on microbial performance remains underexplored, and predictive tools for microbial behaviour are needed to mitigate risks in biorefinery scale-up. Here, twelve batches of corn stover were deconstructed via deacetylation, mechanical refining, and enzymatic hydrolysis to generate lignin-rich and sugar streams.

Two pathways to understanding electron transfer in reaction centers from photosynthetic bacteria: A comparison of Rhodobacter sphaeroides and Rhodobacter capsulatus mutants.

The rates, yields, mechanisms and directionality of electron transfer (ET) are explored in twelve pairs of Rhodobacter (R.) sphaeroides and R. capsulatus mutant RCs designed to defeat ET from the excited primary donor (P*) to the A-side cofactors and re-direct ET to the normally inactive mirror-image B-side cofactors.

High Yield of B-Side Electron Transfer at 77 K in the Photosynthetic Reaction Center Protein from .

The primary electron transfer (ET) processes at 295 and 77 K are compared for the reaction center (RC) pigment-protein complex from 13 mutants including a wild-type control. The engineered RCs bear mutations in the L and M polypeptides that largely inhibit ET from the excited state P* of the primary electron donor (P, a bacteriochlorophyll dimer) to the normally photoactive A-side cofactors and enhance ET to the C-symmetry related, and normally photoinactive, B-side cofactors. P* decay is multiexponential at both temperatures and modeled as arising from subpopulations that differ in contributions of two-step ET ( P* → PB → PH), one-step superexchange ET ( P* → PH), and P* → ground state.

Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network.

Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida KT2440, an important organism for bioproduction.

Hidden vibronic and excitonic structure and vibronic coherence transfer in the bacterial reaction center.

We report two-dimensional electronic spectroscopy (2DES) experiments on the bacterial reaction center (BRC) from purple bacteria, revealing hidden vibronic and excitonic structure. Through analysis of the coherent dynamics of the BRC, we identify multiple quasi-resonances between pigment vibrations and excitonic energy gaps, and vibronic coherence transfer processes that are typically neglected in standard models of photosynthetic energy transfer and charge separation. We support our assignment with control experiments on bacteriochlorophyll and simulations of the coherent dynamics using a reduced excitonic model of the BRC.