Bourns College of Engineering

CRISPR-based high-throughput genome-wide loss-of-function screens are a valuable approach to functional genetics and strain engineering. The yeast Komagataella phaffii is a host of particular interest in the biopharmaceutical industry and as a metabolic engineering host for proteins and metabolites. Here, we design and validate a highly active 6-fold coverage genome-wide sgRNA library for this biotechnologically important yeast containing 30,848 active sgRNAs targeting over 99% of its coding sequences. Conducting fitness screens in the absence of functional non-homologous end joining (NHEJ), the dominant DNA repair mechanism in K. phaffii, provides a quantitative means to assess the activity of each sgRNA in the library. This approach allows for the experimental validation of each guide's targeting activity, leading to more precise screening outcomes. We used this approach to conduct growth screens with glucose as the sole carbon source and identify essential genes. Comparative analysis of the called gene sets identified a core set of K. phaffii essential genes, many of which relate to metabolic engineering targets, including protein production, secretion, and glycosylation. The high activity, genome-wide CRISPR library developed here enables functional genomic screening in K. phaffii, applied here to gene essentiality classification, and promises to enable other genetic screens.

Plant natural products (PNPs) hold significant pharmaceutical importance. The sessile nature of plants has led to the evolution of chemical defense mechanisms over millions of years to combat environmental challenges, making it a crucial and essential defense weapon. Despite their importance, the abundance of these bioactive molecules in plants is typically low, and conventional methods are time-consuming for enhancing production. Moreover, there is a pressing need for novel drug leads, exemplified by the shortage of antibiotics and anticancer drugs. Understanding how plants respond to stress and regulate metabolism to produce these molecules presents an opportunity to explore new avenues for discovering compounds that are typically under the detection limit or not naturally produced. Additionally, this knowledge can contribute to the advancement of plant engineering, enabling the development of new chassis for the biomanufacturing of these valuable molecules. In this perspective, we explore the intricate regulation of PNP biosynthesis in plants, and discuss the biotechnology strategies that have been and can be utilized for the discovery and production enhancement of PNPs in plants.

We present an open-source software package, TRAVOLTA (Terrific Refinements to Accelerate, Validate, and Optimize Large Time-dependent Algorithms), for carrying out massively parallelized quantum optimal control calculations on GPUs. The TRAVOLTA software package is a significant overhaul of our previous NIC-CAGE algorithm and also includes algorithmic improvements to the gradient ascent procedure to enable faster convergence. We examine three different variants of GPU parallelization to assess their performance in constructing optimal control fields in a variety of quantum systems. In addition, we provide several examples with extensive benchmarks of our GPU-enhanced TRAVOLTA code to show that it generates the same results as previous CPU-based algorithms but with a speedup that is more than ten times faster. Our GPU enhancements and algorithmic improvements enable large quantum optimal control calculations that can be efficiently and routinely executed on modern multi-core computational hardware. Program summary: Program Title: TRAVOLTA CPC Library link to program files: https://doi.org/10.17632/grwppm37rn.1 Licensing provisions: GNU General Public License 3 Programming language: C++, openBLAS, and CUDA Supplementary material: Brief review of LU decomposition, raw numerical values used to generate Fig. 6 in the main text, and input examples for the TRAVOLTA software package. Nature of problem: The TRAVOLTA software package utilizes GPU accelerated routines and new algorithmic improvements to compute optimized electric fields that can drive a system from a known initial vibrational eigenstate to a specified final quantum state with a large (≈1) transition probability. Solution method: Quantum control, GPU acceleration, analytic gradients, Crank-Nicolson propagation, and gradient ascent optimization.

Fluoroalkylether substances (ether PFAS) constitute a large group of emerging PFAS with uncertain environmental fate. Among them, GenX is the well-known alternative to perfluorooctanoic acid and one of the six proposed PFAS to be regulated by the U.S. Environmental Protection Agency. This study investigated the structure-biodegradability relationship for 12 different ether PFAS with a carboxylic acid headgroup in activated sludge communities. Only polyfluorinated ethers with at least one -CH₂- moiety adjacent to or a C=C bond in the proximity of the ether bond underwent active biotransformation via oxidative and hydrolytic O-dealkylation. The bioreactions at ether bonds led to the formation of unstable fluoroalcohol intermediates subject to spontaneous defluorination. We further demonstrated that this aerobic biotransformation/defluorination could complement the advanced reduction process in a treatment train system to achieve more cost-effective treatment for GenX and other recalcitrant perfluorinated ether PFAS. These findings provide essential insights into the environmental fate of ether PFAS, the design of biodegradable alternative PFAS, and the development of cost-effective ether PFAS treatment strategies.

Key message

Atmospheric simulation chambers continue to be indispensable tools for research in the atmospheric sciences. Insights from chamber studies are integrated into atmospheric chemical transport models, which are used for science-informed policy decisions. However, a centralized data management and access infrastructure for their scientific products had not been available in the United States and many parts of the world. ICARUS (Integrated Chamber Atmospheric data Repository for Unified Science) is an open access, searchable, web-based infrastructure for storing, sharing, discovering, and utilizing atmospheric chamber data [https://icarus.ucdavis.edu]. ICARUS has two parts: a data intake portal and a search and discovery portal. Data in ICARUS are curated, uniform, interactive, indexed on popular search engines, mirrored by other repositories, version-tracked, vocabulary-controlled, and citable. ICARUS hosts both legacy data and new data in compliance with open access data mandates. Targeted data discovery is available based on key experimental parameters, including organic reactants and mixtures that are managed using the PubChem chemical database, oxidant information, nitrogen oxide (NOx) content, alkylperoxy radical (RO₂) fate, seed particle information, environmental conditions, and reaction categories. A discipline-specific repository such as ICARUS with high amounts of metadata works to support the evaluation and revision of atmospheric model mechanisms, intercomparison of data and models, and the development of new model frameworks that can have more predictive power in the current and future atmosphere. The open accessibility and interactive nature of ICARUS data may also be useful for teaching, data mining, and training machine learning models.

We present a new implementation of real-time time-dependent density functional theory (RT-TDDFT) for calculating excited-state dynamics of periodic systems in the open-source Python-based PySCF software package. Our implementation uses Gaussian basis functions in a velocity gauge formalism and can be applied to periodic surfaces, condensed-phase, and molecular systems. As representative benchmark applications, we present optical absorption calculations of various molecular and bulk systems and a real-time simulation of field-induced dynamics of a (ZnO)₄ molecular cluster on a periodic graphene sheet. We present representative calculations on optical response of solids to infinitesimal external fields as well as real-time charge-transfer dynamics induced by strong pulsed laser fields. Due to the widespread use of the Python language, our RT-TDDFT implementation can be easily modified and provides a new capability in the PySCF code for real-time excited-state calculations of chemical and material systems.

Specific-ion selectivity is a highly desirable feature for the next generation of membranes. However, existing membranes rely on differences in charge, size and hydration energy, which limits their ability to target individual ion species. Here we demonstrate a nanocomposite ion-exchange membrane material that enables a reverse-selective transport mechanism that can selectively pass a single ion species. We demonstrate this transport mechanism with phosphate ions selectively transporting across negatively charged cation exchange membranes. Selective transport is enabled by the in situ growth of hydrous manganese oxide nanoparticles throughout a cation exchange membrane that provide a diffusion pathway via phosphate-specific, reversible outer-sphere interactions. On incorporating the hydrous manganese oxide nanoparticles, the membrane's phosphate flux increased by a factor of 27 over an unmodified cation exchange membrane, and the selectivity of phosphorous over sulfate, nitrate and chloride reaches 47, 100 and 20, respectively. By pairing ion-specific outer-sphere interactions between the target ions and appropriate nanoparticles, these nanocomposite ion-exchange materials can, in principle, achieve selective transport for a range of ions.

Crassulacean acid metabolism (CAM) plants exhibit elevated drought and heat tolerance compared to C₃ and C₄ plants through an inverted pattern of day/night stomatal closure and opening for CO₂ assimilation. However, the molecular responses to water-deficit conditions remain unclear in obligate CAM species. In this study, we presented genome-wide transcription sequencing analysis using leaf samples of an obligate CAM species Kalanchoë fedtschenkoi under moderate and severe drought treatments at two-time points of dawn (2-h before the start of light period) and dusk (2-h before the dark period). Differentially expressed genes were identified in response to environmental drought stress and a whole genome wide co-expression network was created as well. We found that the expression of CAM-related genes was not regulated by drought stimuli in K. fedtschenkoi. Our comparative analysis revealed that CAM species (K. fedtschenkoi) and C₃ species (Arabidopsis thaliana, Populus deltoides 'WV94') share some common transcriptional changes in genes involved in multiple biological processes in response to drought stress, including ABA signaling and biosynthesis of secondary metabolites.

To supply adequate food, the ongoing and unrestrained administration of nitrogen fertilizer to agricultural fields is polluting the climate and living organisms. On the other hand, the agriculture sector urgently needs a technological upgrade to effectively confront hunger and poverty. Here, we report a rapid synthesis of zinc and magnesium-doped hydroxyapatite-urea nanohybrids for slow release and delivery of nitrogen to wheat and rice crops. Nanohybrids slowly release nitrogen for up to six weeks compared to the burst release of nitrogen from urea, and their use substantially reduces, by at least 3.8 times, ammonia emissions into the environment compared with that of urea fertilizer. A half‑nitrogen dose applied as multi-nutrient complexed nanohybrids maintained crop growth, yield, and nutritional compositions in wheat and subsequent rice crops. Nanohybrids enhanced the wheat crop yield and nitrogen uptake by 22.13% and 58.30%, respectively. The synthesized nitrogen nanohybrids remained in the soil for two continuous crop cycles, reduced ammonia volatilization, and achieved nitrogen delivery to the crops. Additionally, soil dehydrogenase activity (534.55% above control) and urease activities (81.82% above control) suggest that nanohybrids exhibited no adverse impact on soil microorganisms. Our comprehensive study demonstrates the advantages of 'doping' as a method for tailoring hydroxyapatite nanoparticles properties for extended agricultural and environmental applications. The use of nanohybrids substantially reduced greenhouse gas emissions and enabled the reduction, by half, of nitrogen inputs into the agricultural fields. This study, therefore, reports a novel nano-enabled platform of engineered hydroxyapatite-urea nanohybrids as a nitrogen fertilizer for efficient nitrogen delivery that results in improved crop growth while minimizing environmental pollution.