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Open Access Publications from the University of California

This series is home to publications and data sets from the Bourns College of Engineering at the University of California, Riverside.

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Center for Environmental Research and Technology

Cover page of High-fidelity, hyper-accurate, and evolved mutants rewire atomic-level communication in CRISPR-Cas9.

High-fidelity, hyper-accurate, and evolved mutants rewire atomic-level communication in CRISPR-Cas9.

(2024)

The high-fidelity (HF1), hyper-accurate (Hypa), and evolved (Evo) variants of the CRISPR-associated protein 9 (Cas9) endonuclease are critical tools to mitigate off-target effects in the application of CRISPR-Cas9 technology. The mechanisms by which mutations in recognition subdomain 3 (Rec3) mediate specificity in these variants are poorly understood. Here, solution nuclear magnetic resonance and molecular dynamics simulations establish the structural and dynamic effects of high-specificity mutations in Rec3, and how they propagate the allosteric signal of Cas9. We reveal conserved structural changes and dynamic differences at regions of Rec3 that interface with the RNA:DNA hybrid, transducing chemical signals from Rec3 to the catalytic His-Asn-His (HNH) domain. The variants remodel the communication sourcing from the Rec3 α helix 37, previously shown to sense target DNA complementarity, either directly or allosterically. This mechanism increases communication between the DNA mismatch recognition helix and the HNH active site, shedding light on the structure and dynamics underlying Cas9 specificity and providing insight for future engineering principles.

Cover page of Drug target prediction through deep learning functional representation of gene signatures.

Drug target prediction through deep learning functional representation of gene signatures.

(2024)

Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institutes L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

Cover page of An alpha-helical lid guides the target DNA toward catalysis in CRISPR-Cas12a.

An alpha-helical lid guides the target DNA toward catalysis in CRISPR-Cas12a.

(2024)

CRISPR-Cas12a is a powerful RNA-guided genome-editing system that generates double-strand DNA breaks using its single RuvC nuclease domain by a sequential mechanism in which initial cleavage of the non-target strand is followed by target strand cleavage. How the spatially distant DNA target strand traverses toward the RuvC catalytic core is presently not understood. Here, continuous tens of microsecond-long molecular dynamics and free-energy simulations reveal that an α-helical lid, located within the RuvC domain, plays a pivotal role in the traversal of the DNA target strand by anchoring the crRNA:target strand duplex and guiding the target strand toward the RuvC core, as also corroborated by DNA cleavage experiments. In this mechanism, the REC2 domain pushes the crRNA:target strand duplex toward the core of the enzyme, while the Nuc domain aids the bending and accommodation of the target strand within the RuvC core by bending inward. Understanding of this critical process underlying Cas12a activity will enrich fundamental knowledge and facilitate further engineering strategies for genome editing.

Cover page of On-Site Fluorescent Detection of Sepsis-Inducing Bacteria using a Graphene-Oxide CRISPR-Cas12a (GO-CRISPR) System.

On-Site Fluorescent Detection of Sepsis-Inducing Bacteria using a Graphene-Oxide CRISPR-Cas12a (GO-CRISPR) System.

(2024)

Sepsis is an extremely dangerous medical condition that emanates from the bodys response to a pre-existing infection. Early detection of sepsis-inducing bacterial infections can greatly enhance the treatment process and potentially prevent the onset of sepsis. However, current point-of-care (POC) sensors are often complex and costly or lack the ideal sensitivity for effective bacterial detection. Therefore, it is crucial to develop rapid and sensitive biosensors for the on-site detection of sepsis-inducing bacteria. Herein, we developed a graphene oxide CRISPR-Cas12a (GO-CRISPR) biosensor for the detection of sepsis-inducing bacteria in human serum. In this strategy, single-stranded (ssDNA) FAM probes were quenched with single-layer graphene oxide (GO). Target-activated Cas12a trans-cleavage was utilized for the degradation of the ssDNA probes, detaching the short ssDNA probes from GO and recovering the fluorescent signals. Under optimal conditions, we employed our GO-CRISPR system for the detection of Salmonella Typhimurium (S. Typhimurium) with a detection sensitivity of as low as 3 × 103 CFU/mL in human serum, as well as a good detection specificity toward other competing bacteria. In addition, the GO-CRISPR biosensor exhibited excellent sensitivity to the detection of S. Typhimurium in spiked human serum. The GO-CRISPR system offers superior rapidity for the detection of sepsis-inducing bacteria and has the potential to enhance the early detection of bacterial infections in resource-limited settings, expediting the response for patients at risk of sepsis.

Cover page of Identification of Candidate Genes Controlling Red Seed Coat Color in Cowpea (Vigna unguiculata [L.] Walp)

Identification of Candidate Genes Controlling Red Seed Coat Color in Cowpea (Vigna unguiculata [L.] Walp)

(2024)

Seed coat color is an important consumer-related trait of the cowpea (Vigna unguiculata [L.] Walp.) and has been a subject of study for over a century. Utilizing newly available resources, including mapping populations, a high-density genotyping platform, and several genome assemblies, the red seed coat color has been mapped to two loci, Red-1 (R-1) and Red-2 (R-2), on Vu03 and Vu07, respectively. A gene model (Vigun03g118700) encoding a dihydroflavonol 4-reductase, a homolog of anthocyanidin reductase 1, which catalyzes the biosynthesis of epicatechin from cyanidin, has been identified as a candidate gene for R-1. Possible causative variants have also been identified for Vigun03g118700. A gene model on Vu07 (Vigun07g118500), with predicted nucleolar function and high relative expression in the developing seed, has been identified as a candidate for R-2. The observed red color is believed to be the result of a buildup of cyanidins in the seed coat.

Cover page of Reverse metabolomics for the discovery of chemical structures from humans

Reverse metabolomics for the discovery of chemical structures from humans

(2024)

Determining the structure and phenotypic context of molecules detected in untargeted metabolomics experiments remains challenging. Here we present reverse metabolomics as a discovery strategy, whereby tandem mass spectrometry spectra acquired from newly synthesized compounds are searched for in public metabolomics datasets to uncover phenotypic associations. To demonstrate the concept, we broadly synthesized and explored multiple classes of metabolites in humans, including N-acyl amides, fatty acid esters of hydroxy fatty acids, bile acid esters and conjugated bile acids. Using repository-scale analysis1,2, we discovered that some conjugated bile acids are associated with inflammatory bowel disease (IBD). Validation using four distinct human IBD cohorts showed that cholic acids conjugated to Glu, Ile/Leu, Phe, Thr, Trp or Tyr are increased in Crohn's disease. Several of these compounds and related structures affected pathways associated with IBD, such as interferon-γ production in CD4+ T cells3 and agonism of the pregnane X receptor4. Culture of bacteria belonging to the Bifidobacterium, Clostridium and Enterococcus genera produced these bile amidates. Because searching repositories with tandem mass spectrometry spectra has only recently become possible, this reverse metabolomics approach can now be used as a general strategy to discover other molecules from human and animal ecosystems.

Cover page of A Comparative Study of Ex-Vivo Murine Pulmonary Mechanics Under Positive- and Negative-Pressure Ventilation.

A Comparative Study of Ex-Vivo Murine Pulmonary Mechanics Under Positive- and Negative-Pressure Ventilation.

(2024)

Increased ventilator use during the COVID-19 pandemic resurrected persistent questions regarding mechanical ventilation including the difference between physiological and artificial breathing induced by ventilators (i.e., positive- versus negative-pressure ventilation, PPV vs NPV). To address this controversy, we compare murine specimens subjected to PPV and NPV in ex vivo quasi-static loading and quantify pulmonary mechanics via measures of quasi-static and dynamic compliances, transpulmonary pressure, and energetics when varying inflation frequency and volume. Each investigated mechanical parameter yields instance(s) of significant variability between ventilation modes. Most notably, inflation compliance, percent relaxation, and peak pressure are found to be consistently dependent on the ventilation mode. Maximum inflation volume and frequency note varied dependencies contingent on the ventilation mode. Contradictory to limited previous clinical investigations of oxygenation and end-inspiratory measures, the mechanics-focused comprehensive findings presented here indicate lung properties are dependent on loading mode, and importantly, these dependencies differ between smaller versus larger mammalian species despite identical custom-designed PPV/NPV ventilator usage. Results indicate that past contradictory findings regarding ventilation mode comparisons in the field may be linked to the chosen animal model. Understanding the differing fundamental mechanics between PPV and NPV may provide insights for improving ventilation strategies and design to prevent associated lung injuries.

Cover page of Physics-informed UNets for discovering hidden elasticity in heterogeneous materials

Physics-informed UNets for discovering hidden elasticity in heterogeneous materials

(2023)

Soft biological tissues often have complex mechanical properties due to variation in structural components. In this paper, we develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) to infer the spatial distributions of mechanical parameters from strain maps as input images, normal stress boundary conditions, and domain physics information. We show superior performance - both in terms of accuracy and computational cost - by El-UNet compared to fully-connected physics-informed neural networks in estimating unknown parameters and stress distributions for isotropic linear elasticity. We characterize different variations of El-UNet and propose a self-adaptive spatial loss weighting approach. To validate our inversion models, we performed various finite-element simulations of isotropic domains with heterogenous distributions of material parameters to generate synthetic data. El-UNet is faster and more accurate than the fully-connected physics-informed implementation in resolving the distribution of unknown fields. Among the tested models, the self-adaptive spatially weighted models had the most accurate reconstructions in equal computation times. The learned spatial weighting distribution visibly corresponded to regions that the unweighted models were resolving inaccurately. Our work demonstrates a computationally efficient inversion algorithm for elasticity imaging using convolutional neural networks and presents a potential fast framework for three-dimensional inverse elasticity problems that have proven unachievable through previously proposed methods.

Cover page of Unveiling the RNA-mediated allosteric activation discloses functional hotspots in CRISPR-Cas13a.

Unveiling the RNA-mediated allosteric activation discloses functional hotspots in CRISPR-Cas13a.

(2024)

Cas13a is a recent addition to the CRISPR-Cas toolkit that exclusively targets RNA, which makes it a promising tool for RNA detection. It utilizes a CRISPR RNA (crRNA) to target RNA sequences and trigger a composite active site formed by two Higher Eukaryotes and Prokaryotes Nucleotide (HEPN) domains, cleaving any solvent-exposed RNA. In this system, an intriguing form of allosteric communication controls the RNA cleavage activity, yet its molecular details are unknown. Here, multiple-microsecond molecular dynamics simulations are combined with graph theory to decipher this intricate activation mechanism. We show that the binding of a target RNA acts as an allosteric effector, by amplifying the communication signals over the dynamical noise through interactions of the crRNA at the buried HEPN1-2 interface. By introducing a novel Signal-to-Noise Ratio (SNR) of communication efficiency, we reveal critical allosteric residues-R377, N378, and R973-that rearrange their interactions upon target RNA binding. Alanine mutation of these residues is shown to select target RNA over an extended complementary sequence beyond guide-target duplex for RNA cleavage, establishing the functional significance of these hotspots. Collectively our findings offer a fundamental understanding of the Cas13a mechanism of action and pave new avenues for the development of highly selective RNA-based cleavage and detection tools.

Cover page of Beyond Conventional Density Functional Theory: Advanced Quantum Dynamical Methods for Understanding Degradation of Per- and Polyfluoroalkyl Substances.

Beyond Conventional Density Functional Theory: Advanced Quantum Dynamical Methods for Understanding Degradation of Per- and Polyfluoroalkyl Substances.

(2024)

Computational chemistry methods, such as density functional theory (DFT), have now become more common in environmental research, particularly for simulating the degradation of per- and polyfluoroalkyl substances (PFAS). However, the vast majority of PFAS computational studies have focused on conventional DFT approaches that only probe static, time-independent properties of PFAS near stationary points on the potential energy surface. To demonstrate the rich mechanistic information that can be obtained from time-dependent quantum dynamics calculations, we highlight recent studies using these advanced techniques for probing PFAS systems. We briefly discuss recent applications ranging from ab initio molecular dynamics to DFT-based metadynamics and real-time time-dependent DFT for probing PFAS degradation in various reactive environments. These quantum dynamical approaches provide critical mechanistic information that cannot be gleaned from conventional DFT calculations. We conclude with a perspective of promising research directions and recommend that these advanced quantum dynamics simulations be more widely used by the environmental research community to directly probe PFAS degradation dynamics and other environmental processes.