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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Geographic location and year determine virulence, and year determines genetic change, in populations of Neopseudocercosporella capsellae

White leaf spot ( Neopseudocercosporella capsellae ) can be severe and problematic worldwide across both horticultural and oilseed Brassicaceae, including susceptible rapeseed. In this study, 82 isolates from 2015 and 106 isolates from across Australia in 2016 were first assessed for their virulence against three different rapeseed ( Brassica napus ) cultivars. For both years there were significant ( P < 0.001) differences. Also, there were significant (all P < 0.001) differences between isolates in each year, and between cultivars. For 2016 isolates, there were also significant differences ( P < 0.001) between isolates across three different Australian states, and a significant interaction ( P < 0.001) between isolates with cultivars. Of the three Australian states, isolates from Victoria were most virulent. Among tested cultivars, cv. Scoop was most susceptible. Subsequently, phylogenetic analysis of 114 of these same 2015 and 2016 isolates showed current isolates clustered separately from the majority of 2005 N. capsellae isolates collected from Western Australia a decade earlier, confirming significant genetic change within N. capsellae populations over the past decade. However, isolate clusters showed no association with geographical location. The results suggest that phylogenetic association among 2005 and 2015–2016 N. capsellae isolates is complementary with pathogenicity variations explained by geographically different N. capsellae pathogen populations. Neopseudocercosporella capsellae populations are evolving rapidly, challenging management through host resistance at a time of increasing incidence and severity of white leaf spot disease over the past decade. The outcome is well illustrated by cv. Scoop, previously resistant to 2005 isolates but moderately susceptible to 2015 and highly susceptible to 2016 isolates.

Murtza, T.↗
DOE OSTI2025

Comparative transcriptomics uncovers poplar and fungal genetic determinants of ectomycorrhizal compatibility

Ectomycorrhizal symbiosis supports tree growth and is crucial for nutrient cycling and temperate and boreal ecosystems functioning. The establishment of functional ectomycorrhiza (ECM) first requires the association of compatible partners. However, host and fungal genetic determinants governing mycorrhizal compatibility are unknown. To identify such factors in poplar and its fungal associates, we mined existing and de novo tree and fungal transcriptional datasets. We identified co-expressed genes enabling ECM symbiosis at early and mature stages of the interaction. These sets of genes can be divided into general fungal-sensing and ECM-specific components. We highlight the importance of fungal modulation of plant JA-related defenses and the regulation of secretory pathways for ECM compatibility, including upregulation of key fungal small secreted proteins, the downregulation of plant secreted peroxidases, and the downregulation of plant cell wall remodeling proteins concomitantly with the upregulation of fungal glycosyl hydrolases acting on pectin. Not only gene regulation, but also its temporal scale and dynamics seem to play a crucial role for mycorrhizal compatibility. The expression profile of the host Common Symbiosis Pathway and nutrient transporters was also studied, revealing constitutive levels of expression and moderate upregulation in compatible ECM interactions. Overall, these results underscore the importance of novel biological functions during the establishment of ECM symbiosis, help us gain insights into the molecular events determining mycorrhiza compatibility, and serve as a data-rich transcriptomic resource to open new research questions in the field.

Marqués‐Gálvez, José Eduardo↗
DOE OSTI2022

Analysis of Salmonella enterica Isolated from a Mixed-Use Watershed in Georgia, USA: Antimicrobial Resistance, Serotype Diversity, and Genetic Relatedness to Human Isolates

As the cases of Salmonella enterica infections associated with contaminated water are increasing, this study was conducted to address the role of surface water as a reservoir of S. enterica serotypes. We sampled rivers and streams (n = 688) over a 3-year period (2015 to 2017) in a mixed-use watershed in Georgia, USA, and 70.2% of the total stream samples tested positive for Salmonella. A total of 1,190 isolates were recovered and characterized by serotyping, antimicrobial susceptibility testing, and pulsed-field gel electrophoresis (PFGE). A wide range of serotypes was identified, including those commonly associated with humans and animals, with S. enterica serotype Muenchen being predominant (22.7%) and each serotype exhibiting a high degree of strain diversity by PFGE. About half (46.1%) of the isolates had PFGE patterns indistinguishable from those of human clinical isolates in the CDC PulseNet database. A total of 52 isolates (4.4%) were resistant to antimicrobials, out of which 43 isolates were multidrug resistant (MDR; resistance to two or more classes of antimicrobials). These 52 resistant Salmonella isolates were screened for the presence of antimicrobial resistance genes, plasmid replicons, and class 1 integrons, out of which four representative MDR isolates were selected for whole-genome sequencing analysis. The results showed that 28 MDR isolates resistant to 10 antimicrobials had blacmy-2 on an A/C plasmid. Persistent contamination of surface water with a high diversity of Salmonella strains, some of which are drug resistant and genetically indistinguishable from human isolates, supports a role of environmental surface water as a reservoir for and transmission route of this pathogen.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Insights into genetic determinants of volatile fatty acid catabolism in Cupriavidus necator H16

The soil bacterium Cupriavidus necator H16 is a promising host for upgrading waste-derived volatile fatty acids (VFAs) into renewable biochemicals. While bacterial VFA metabolic pathways are well understood, the C. necator genome encodes multiple enzymes for each catabolic step, and the degree of substrate specificity among these homologs is currently unknown. To gain insight into the catabolism of VFA substrates in C. necator, we performed transcriptomics on cells grown with acetate, propionate, butyrate, valerate, or hexanoate as the sole source of carbon and energy. These data revealed that C. necator upregulates multiple sets of genes putatively involved in substrate activation and β-oxidation in response to VFAs. To better understand this redundancy, we performed biochemical and genetic deletion studies of acyl-CoA synthetase enzymes upregulated during growth on VFA substrates. These results demonstrated the functional redundancy of the C. necator VFA catabolism and led to the identification of a gene cluster, H16_B1332-H16_B1337, that contains several genes that are important for the efficient catabolism of hexanoate. Constitutive expression of a second copy of these hexanoate catabolism genes did not improve growth of C. necator on hexanoate, suggesting that other factors (e.g., redox, transport, or toxicity) may be limiting for growth. Collectively, this work provides new insight into how C. necator uses metabolic regulation to effectively utilize VFA substrates and uncovers the important role of the gene cluster H16_B1332-H16_B1337 in the catabolism of hexanoate.

09 BIOMASS FUELS↗
DOE OSTI2020

Genetic Redundancy in Iron and Manganese Transport in the Metabolically Versatile Bacterium Rhodopseudomonas palustris TIE-1

The purple nonsulfur bacterium Rhodopseudomonas palustris TIE-1 can produce useful biochemicals such as bioplastics and biobutanol. Production of such biochemicals requires intracellular electron availability, which is governed by the availability and the transport of essential metals such as iron (Fe). Because of the distinct chemical properties of ferrous [Fe(II)] and ferric iron [Fe(III)], different systems are required for their transport and storage in bacteria. Although Fe(III) transport systems are well characterized, we know much less about Fe(II) transport systems except for the FeoAB system. Iron transporters can also import manganese (Mn). We studied Fe and Mn transport by five putative Fe transporters in TIE-1 under metal-replete, metal-depleted, oxic, and anoxic conditions. We observed that by overexpressing feoAB, efeU, and nramp1AB, the intracellular concentrations of Fe and Mn can be enhanced in TIE-1 under oxic and anoxic conditions, respectively. The deletion of a single gene/operon does not attenuate Fe or Mn uptake in TIE-1 regardless of the growth conditions used. This indicates that genetically dissimilar yet functionally redundant Fe transporters in TIE-1 can complement each other. Relative gene expression analysis shows that feoAB and efeU are expressed during Fe and Mn depletion under both oxic and anoxic conditions. The promoters of these transporter genes contain a combination of Fur and Fnr boxes, suggesting that their expression is regulated by both Fe and oxygen availability. The findings from this study will help us modulate intracellular Fe and Mn concentrations, ultimately improving TIE-1’s ability to produce desirable biomolecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Genetic, Genomic, and Transcriptomic Studies of Pyruvate Metabolism in Methanosarcina barkeri Fusaro

Pyruvate, a central intermediate in the carbon fixation pathway of methanogenic archaea, is rarely used as an energy source by these organisms. The sole exception to this rule is a genetically uncharacterized Methanosarcina barkeri mutant capable of using pyruvate as a sole energy and carbon source (the Pyr+ phenotype). Here, we provide evidence that suggests that the Pyr+ mutant is able to metabolize pyruvate by overexpressing pyruvate ferredoxin oxidoreductase (por) and mutating genes involved in central carbon metabolism. Genomic analysis showed that the Pyr+ strain has two mutations localized to Mbar_A1588, the biotin protein ligase subunit of the pyruvate carboxylase (pyc) operon, and Mbar_A2165, a putative transcriptional regulator. Mutants expressing the Mbar_A1588 mutation showed no growth defect compared to the wild type (WT), yet the strains lacked pyc activity. Recreation of the Mbar_A2165 mutation resulted in a 2-fold increase of Por activity and gene expression, suggesting a role in por transcriptional regulation. Further transcriptomic analysis revealed that Pyr+ strains also overexpress the gene encoding phosphoenolpyruvate carboxylase, indicating the presence of a previously uncharacterized route for synthesizing oxaloacetate in M. barkeri and explaining the unimpaired growth in the absence of Pyc. Surprisingly, stringent repression of the por operon was lethal, even when the media were supplemented with pyruvate and/or Casamino Acids, suggesting that por plays an unidentified essential function in M. barkeri.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

mSphere of Influence: Comprehensive Genetic Analysis

Jason M. Peters works in the fields of antibiotic resistance and biofuel production. In this mSphere of Influence article, he reflects on how the paper “A global genetic interaction network maps a wiring diagram of cellular function” by Costanzo et al. (Science 353:aaf1420, 2016, https://doi.org/10.1126/science.aaf1420 ) has impacted his work by highlighting the power of gene networks to uncover new biology.

Peters, Jason M.↗
DOE OSTI2024

Multifactorial genetic control and magnesium levels govern the production of a Streptomyces antibiotic with unusual cell density dependence

Streptomyces bacteria are renowned both for their antibiotic production capabilities and for their cryptic metabolic potential. Their metabolic repertoire is subject to stringent genetic control, with many of the associated biosynthetic gene clusters being repressed by the conserved nucleoid-associated protein Lsr2. In an effort to stimulate new antibiotic production in wild Streptomyces isolates, we leveraged the activity of an Lsr2 knockdown construct and successfully enhanced antibiotic production in the wild Streptomyces isolate WAC07094. We determined that this new activity stemmed from increased levels of the angucycline-like family member saquayamycin. Saquayamycin has both antibiotic and anti-cancer activities, and intriguingly, beyond Lsr2-mediated repression, we found saquayamycin production was also suppressed at high density on solid or in liquid growth media; its levels were greatest in low-density cultures. This density-dependent control was exerted at the level of the cluster-situated regulatory gene sqnR and was mediated in part through the activity of the PhoRP two-component regulatory system, where deleting phoRP led to both constitutive antibiotic production and sqnR expression. This suggests that PhoP functions to repress the expression of sqnR at high cell density. We further discovered that magnesium supplementation could alleviate this density dependence, although its action was independent of PhoP. Finally, we revealed that the nitrogen-responsive regulators GlnR and AfsQ1 could relieve the repression exerted by Lsr2 and PhoP. Intriguingly, we found that this low density-dependent production of saquayamycin was not unique to WAC07094; saquayamycin production by another wild isolate also exhibited low-density activation, suggesting that this spatial control may serve an important ecological function in their native environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Multi-strain analysis of Pseudomonas putida reveals the metabolic and genetic diversity of the species

Pseudomonas putida is a gram-negative bacterial species increasingly utilized in biotechnology due to its robust growth, ability to degrade aromatic compounds, solvent tolerance, and genetic tractability. In this study, we report a comprehensive multi-strain analysis of 164 P. putida strains based on the reconstruction of a pan-putida metabolic network and the formulation of strain-specific genome-scale metabolic models (GEMs). We performed whole-genome sequencing and hybrid assembly for 40 strains, contributing a ~8% increase to the available genomic data for P. putida . Furthermore, high-throughput phenotypic profiling using the Biolog phenotype microarray system for 24 strains on 190 unique carbon sources, along with 15 aromatic compounds not present on Biolog plates, yielded 4,920 unique strain-phenotype measurements. These data were leveraged to curate GEMs for 24 representative strains, including a refined model for strain KT2440, which comprised 1,480 genes and 2,191 metabolites, achieving a prediction accuracy of 91.2% in carbon utilization. Systematic comparison of genomes and GEMs revealed both conserved core pathways and significant allelic and functional divergence across strains, highlighting strain-specific variation in aromatic degradation. While pathways for protocatechuate and phenylacetate degradation were widely conserved, metabolic capabilities for compounds such as ferulate, phenol, and cresols varied markedly, suggesting adaptation to distinct ecological niches. Alleleome analysis of enzymes, such as PcaI and PcaJ, revealed distinct, functionally similar clades, indicating possible convergent evolution or horizontal gene transfer. These results provide computable resources and informative models for selecting P. putida strains with desired traits for biomanufacturing and bioremediation and offer insights into the evolution and phylogeny of the P. putida species.

aromatics utilization↗
DOE OSTI2015

Genetic Background Modulates lncRNA-Coordinated Tissue Response to Low Dose Ionizing Radiation

Long noncoding RNAs (lncRNAs) are emerging as key regulators of diverse cell functions and processes. However, the relevance of lncRNAs in the cell and tissue response to ionizing radiation has not yet been characterized. Here we used microarray profiling to determine lncRNA and mRNA expression in mammary glands of BALB/c and SPRET/EiJ mice after low-dose ionizing radiation (LDIR) exposure. We found that unirradiated mammary tissues of these strains differed significantly in baseline expressions of 290 lncRNAs. LDIR exposure (10 cGy) induced a significant change in the expression of many lncRNAs. The vast majority of lncRNAs identified to be differentially expressed after LDIR in either BALB/c or SPRET/EiJ had a significantly correlated expression pattern with at least one LDIR responsive mRNA. Functional analysis revealed that the response to LDIR in BALB/c mice is highly dynamic with enrichment for genes involved in tissue injury, inflammatory responses, and mammary gland development at 2, 4, and 8 weeks after LDIR, respectively. Our study demonstrates that genetic background strongly influences the expression of lncRNAs and their response to radiation and that lncRNAs may coordinate the tissue response to LDIR exposure via regulation of coding mRNAs.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2016

Generate Optimized Genetic Rhythm for Enzyme Expression in Non-native systems

Most amino acids are represented by more than one codon, resulting in redundancy in the genetic code. Silent codon substitutions that do not alter the amino acid sequence still have an effect on protein expression. We have developed an algorithm, GoGREEN, to enhance the expression of foreign proteins in a host organism. GoGREEN selects codons according to frequency patterns seen in the gene of interest using the codon usage table from the host organism. GoGREEN is also designed to accommodate gaps in the sequence.This software takes for input (1) the aligned protein sequences for genes the user wishes to express, (2) the codon usage table for the host organism, (3) and the DNA sequence for the target protein found in the host organism. The program will select codons based on codon usage patterns for the target DNA sequence. The program will also select codons for “gaps” found in the aligned protein sequences using the codon usage table from the host organism.

Source record↗
DOE OSTI2022

Sex Determination and Ovarian Development in Reptiles and Amphibians: From Genetic Pathways to Environmental Influences

Background: Reptiles and amphibians provide untapped potential for discovering how a diversity of genetic pathways and environmental conditions are incorporated into developmental processes that can lead to similar functional outcomes. These groups display a multitude of reproductive strategies, and whereas many attributes are conserved within groups and even across vertebrates, several aspects of sexual development show considerable variation. Summary: Here, in this review, we focus our attention on the development of the reptilian and amphibian ovary. First, we review and describe the events leading to ovarian development, including sex determination and ovarian maturation, through a comparative lens. We then describe how these events are influenced by environmental factors, focusing on temperature and exposure to anthropogenic chemicals. Lastly, we identify critical knowledge gaps and future research directions that will be crucial to moving forward in our understanding of ovarian development and the influences of the environment in reptiles and amphibians. Key Messages: Reptiles and amphibians provide excellent models for understanding the diversity of sex determination strategies and reproductive development. However, a greater understanding of the basic biology of these systems is necessary for deciphering the adaptive and potentially disruptive implications of embryo-by-environment interactions in a rapidly changing world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

An emerging multi-omic understanding of the genetics of opioid addiction

Opioid misuse, addiction, and associated overdose deaths remain global public health crises. Despite the tremendous need for pharmacological treatments, current options are limited in number, use, and effectiveness. Fundamental leaps forward in our understanding of the biology driving opioid addiction are needed to guide development of more effective medication-assisted therapies. This Review focuses on the omics-identified biological features associated with opioid addiction. Recent GWAS have begun to identify robust genetic associations, including variants in OPRM1, FURIN, and the gene cluster SCAI/PPP6C/RABEPK. An increasing number of omics studies of postmortem human brain tissue examining biological features (e.g., histone modification and gene expression) across different brain regions have identified broad gene dysregulation associated with overdose death among opioid misusers. Drawn together by meta-analysis and multi-omic systems biology, and informed by model organism studies, key biological pathways enriched for opioid addiction–associated genes are emerging, which include specific receptors (e.g., GABAB receptors, GPCR, and Trk) linked to signaling pathways (e.g., Trk, ERK/MAPK, orexin) that are associated with synaptic plasticity and neuronal signaling. Studies leveraging the agnostic discovery power of omics and placing it within the context of functional neurobiology will propel us toward much-needed, field-changing breakthroughs, including identification of actionable targets for drug development to treat this devastating brain disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The Application of a Genetic Algorithm to the Optimization of a Mesoscale Model for Emergency Response

Besides solving the equations of momentum, heat, and moisture transport on the model grid, mesoscale weather models must account for subgrid-scale processes that affect the resolved model variables. These are simulated with model parameterizations, which often rely on values preset by the user. Such “free” model parameters, along with others set to initialize the model, are often poorly constrained, requiring that a user select each from a range of plausible values. Finding the values to optimize any forecasting tool can be accomplished with a search algorithm, and one such process—the genetic algorithm (GA)—has become especially popular. As applied to modeling, GAs represent a Darwinian process: an ensemble of simulations is run with a different set of parameter values for each member, and the members subsequently judged to be most accurate are selected as “parents” who pass their parameters onto a new generation. At the U.S. Department of Energy’s Savannah River Site in South Carolina, we are applying a GA to the Regional Atmospheric Modeling System (RAMS) mesoscale weather model, which supplies input to a model to simulate the dispersion of an airborne contaminant as part of the site’s emergency response preparations. An ensemble of forecasts is run each day, weather data are used to “score” the individual members of the ensemble, and the parameters from the best members are used for the next day’s forecasts. As meteorological conditions change, the parameters change as well, maintaining a model configuration that is best adapted to atmospheric conditions. Significance Statement We wanted to develop a forecasting system by which a weather model is run over the Savannah River Site each day and repeatedly adjusted according to how well it performed the previous day. To run the model, a series of values (parameters) must be set to control how the model will calculate winds, temperatures, and other desired variables. Each day the model was run several times using different combinations of these parameters and later compared with observed meteorological conditions. Parameters that produced the most accurate forecasts were preferentially reused to create the forecasts for the next day. The process was tested for the summer of 2020 and exhibited lower errors than forecasts produced by the model using default values of the parameters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization

In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computational cost associated with engine design optimization. However, such methods still suffer from drawbacks. One main disadvantage is that the default machine learning (ML) hyperparameters are often severely suboptimal for a given problem. This has often been addressed by manually trying out different hyperparameter settings, but this solution is ineffective in case of a high-dimensional hyperparameter space. Besides this problem, the amount of data needed for training is also not known a priori. In response to these issues that need to be addressed, the present work describes and validates an automated active learning approach, AutoML-GA, for surrogate-based optimization of internal combustion engines. In this approach, a Bayesian optimization technique is used to find the best machine learning hyperparameters based on an initial dataset obtained from a small number of CFD simulations. Subsequently, a genetic algorithm is employed to locate the design optimum on the ML surrogate surface. In the vicinity of the design optimum, the solution is refined by repeatedly running CFD simulations at the projected optima and adding the newly obtained data to the training dataset. It is demonstrated that AutoML-GA leads to a better optimum with a lower number of CFD simulations, compared to the use of default hyperparameters. The proposed framework offers the advantage of being a more hands-off approach that can be readily utilized by researchers and engineers in industry who do not have extensive machine learning expertise.

Owoyele, Opeoluwa↗
DOE OSTI2025

Seed coat transcriptomic profiling of 5-593, a genotype important for genetic studies of seed coat color and patterning in common bean ( Phaseolus vulgaris L.)

Common bean (Phaseolus vulgaris L.) market classes have distinct seed coat colors, which are directly related to the diverse flavonoids found in the mature seed coat. To understand and elucidate the molecular mechanisms underlying the regulation of seed coat color, RNA-Seq data was collected from the black bean 5-593 and used for a differential gene expression and enrichment analysis from four different seed coat color development stages. 5-593 carries dominant alleles for 10 of the 11 major genes that control seed coat color and expression and has historically been used to develop introgression lines used for seed coat genetic analysis. Pairwise comparison among the four stages identified 6,294 differentially expressed genes (DEGs) varying from 508 to 5,780 DEGs depending on the compared stages. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that phenylpropanoid biosynthesis, flavonoid biosynthesis, and plant hormone signal transduction comprised the principal pathways expressed during bean seed coat pigment development. Transcriptome analysis suggested that most structural genes for flavonoid biosynthesis and some potential regulatory genes were significantly differentially expressed. Further studies detected 29 DEGs as important candidate genes governing the key enzymatic flavonoid biosynthetic pathways for common bean seed coat color development. Additionally, four gene models, Pv5-593.02G016100, 593.02G078700, Pv5-593.02G090900, and Pv5-593.06G121300, encode MYB-like transcription factor family protein were identified as strong candidate regulatory genes in anthocyanin biosynthesis which could regulate the expression levels of some important structural genes in flavonoid biosynthesis pathway. These findings provide a framework to draw new insights into the molecular networks underlying common bean seed coat pigment development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Expanding the genetic toolkit: adenine and cytosine base editors for gene disruption in Aspergillus niger

Despite revolutionizing fungal genetic engineering, conventional CRISPR/Cas9-mediated knockouts rely on DNA double-strand breaks (DSBs), which can cause unwanted insertions and deletions, chromosomal abnormalities, and cytotoxicity. Base editors such as adenine base editors (ABEs), which convert A‧T to G‧C, and cytosine base editors (CBEs), which convert C‧G to T‧A, offer a safer alternative by enabling predictable, target-specific single-nucleotide changes without introducing DSBs. To overcome the limitations of traditional genome editing in filamentous fungi, we developed efficient base-editing systems in Aspergillus niger . For the first time, we constructed an ABE in A. niger , achieving up to 80% editing efficiency and inducing predictable A-to-G mutations at the intended intron sites, disrupting gene function through mRNA mis-splicing. We also developed a highly efficient CBE system, capable of introducing premature stop codons with 50–100% efficiency. To broaden the editing scope, we implemented a Cas9-NG variant recognizing a relaxed PAM sequence requiring only a single guanine (G), enabling editing at start codons and splice sites. Leveraging this expanded scope, we established gene disruption approaches by targeting start codons via ABE-mediated A-to-G conversions (ATG-to-GTG and ATG-to-ACG) and CBE-mediated C-to-T conversion (ATG-to-ATA). Additionally, our base-editing systems enable multiplex gRNA delivery and marker-free editing of multiple genes. Collectively, the scope-expanding strategies increase the number of genes targetable for disruption by base-editing in A. niger by 26.3% and enable near-complete coverage of 96% of the coding genes. Overall, this work demonstrates the potential of ABE and CBE systems as versatile, efficient, and safer alternatives to DSBs-based gene disruption in filamentous fungi.

Aspergillus↗