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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2023

Plant Cell Biology International Meeting 2022

There is a strong need to develop the knowledge base that will enable the rational design of improved crop species for sustainable agriculture and biomass production. To attain this goal, researchers must learn how plant cells integrate metabolism and cellular dynamics during growth and in response to environmental challenges. Meeting these goals require data sharing and the development and training of faculty, post-doctoral fellows, graduate and undergraduate students in state-of-the-art quantitative integrative cell biology methods. The inaugural Plant Cell Biology International (PCBI) meeting capitalized on the interaction between two well-established plant cell biology communities across the Atlantic, the Midwest Plant Cell Dynamics (PCD) and the European Network for Plant Endomembrane Research (ENPER) and provided a framework to meet these goals. The conference took place in person successfully during August 1-5th 2022 in Crete, Greece. The conference offered opportunities for undergraduate and graduate students, postdocs, young investigators across the globe to present their results and interact across different disciplines. The conference provided an open and inclusive forum for oral presentations that allowed all labs to share research results. It was attended over 80 % by students, postdocs and young investigators. Workshops on best practices for image acquisition and processing pipelines, publication guidelines by scientific journal editors, professional development and well-being benefited the participants at large. Overall, this was the inaugural joint Plant Cell Biology International conference that for the first time officially brought together plant cell biologists to discuss opportunities and challenges in the field to gain a mechanistic understanding of complex biological processes and advance sustainable agriculture. Due to its success, it was rescheduled for 2025 in Crete, to continue strengthening the plant cell biology community and exchanging knowledge and expertise across the globe.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Parallel String Graph Construction and Transitive Reduction for De Novo Genome Assembly

One of the most computationally intensive tasks in computational biology is de novo genome assembly, the decoding of the sequence of an unknown genome from redundant and erroneous short sequences. A common assembly paradigm identifies overlapping sequences, simplifies their layout, and creates consensus. Despite many algorithms developed in the literature, the efficient assembly of large genomes is still an open problem. In this work, we introduce new distributed-memory parallel algorithms for overlap detection and layout simplification steps of de novo genome assembly, and implement them in the diBELLA 2D pipeline. Our distributed memory algorithms for both overlap detection and layout simplification are based on linear-algebra operations over semirings using 2D distributed sparse matrices. Our layout step consists of performing a transitive reduction from the overlap graph to a string graph. We provide a detailed communication analysis of the main stages of our new algorithms. diBELLA 2D achieves near linear scaling with over 80% parallel efficiency for the human genome, reducing the runtime for overlap detection by 1.2-1.3× for the human genome and 1.5-1.9× for C.elegans compared to the state-of-the-art. Our transitive reduction algorithm outperforms an existing distributed-memory implementation by 10.5-13.3× for the human genome and 18-29× for the C. elegans. Our work paves the way for efficient de novo assembly of large genomes using long reads in distributed memory.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB

Abstract Motivation Membrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task. Results We present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D. Availability and implementation Annotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Development of QSAR models to predict blood-brain barrier permeability

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington’s Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80–83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70–72% in negative predictivity, and 78–86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data Mining of Groundwater to Identify MAGs with Methane, Propane and Toluene Monooxygenases

Whole genome sequencing datasets, involving more than 600 groundwater samples, from nine countries, were analyzed to identify metagenome assembled genomes (MAGs) containing full operons for propane monooxygenase, soluble methane monooxygease, toluene monooxygenase and particulate ammonia/methane monooxygenase. The enzymes encoded by these genes are a focus of interest because of their ability to degrade common groundwater contaminants. Due to the large amount of data, sequence analyses involved more than 80 individual KBase narratives. The approach followed the KBase tutorial called "Metagenome-Assembled Genome Extraction from a Compost Microbiome Enrichment" The generated MAGs were exported from each individual narrative into separate summary KBase narratives for each monooxygenase. Three KBase narratives were generated for particulate ammonia/methane monooxygenase, due to the large number of MAGs identified.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Bringing function to structure: Root–soil interactions shaping phosphatase activity throughout a soil profile in Puerto Rico

Abstract Large areas of highly productive tropical forests occur on weathered soils with low concentrations of available phosphorus (P). In such forests, root and microbial production of acid phosphatase enzymes capable of mineralizing organic phosphorus is considered vital to increasing available P for plant uptake. We measured both root and soil phosphatase throughout depth and alongside a variety of root and soil factors to better understand the potential of roots and soil biota to increase P availability and to constrain estimates of the biochemical mineralization within ecosystem models. We measured soil phosphatase down to 1 m, root phosphatase to 30 cm, and collected data on fine‐root mass density, specific root length, soil P, bulk density, and soil texture using soil cores in four tropical forests within the Luquillo Experimental Forest in Puerto Rico. We found that soil phosphatase decreased with soil depth, but not root phosphatase. Furthermore, when both soil and root phosphatase were expressed per soil volume, soil phosphatase was 100‐fold higher that root phosphatase. Both root and soil factors influenced soil and root phosphatase. Soil phosphatase increased with fine‐root mass density and organic P, which together explained over 50% of the variation in soil phosphatase. Over 80% of the variation in root phosphatase per unit root mass was attributed to specific root length (positive correlation) and available (resin) P (negative correlation). Synthesis : Fine‐root traits and soil P data are necessary to understand and represent soil and root phosphatase activity throughout the soil column and across sites with different soil conditions and tree species. These findings can be used to parameterize or benchmark estimates of biochemical mineralization in ecosystem models that contain fine‐root biomass and soil P distributions throughout depth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Structural characterization of strawberry pomace

Strawberries are a nutrient dense food rich in vitamins, minerals, non-nutrient antioxidant phenolics, and fibers. Strawberry fiber bioactive structures are not well characterized and limited information is available about the interaction between strawberry fiber and phenolics. Therefore, we analyzed commercial strawberry pomace in order to provide a detailed carbohydrate structural characterization, and to associate structures with functions. The pomace fraction, which remained after strawberry commercial juice extraction, contained mostly insoluble (49.1 % vs. 5.6 % soluble dietary fiber) dietary fiber, with pectin, xyloglucan, xylan, β-glucan and glucomannan polysaccharides; glucose, fructose, xylose, arabinose, galactose, fucose and galacturonic acid free carbohydrates; protein (15.6 %), fat (8.34 %), and pelargonidin 3-glucoside (562 μg/g). Oligosaccharides from fucogalacto-xyloglucan, methyl-esterified rhamnogalacturonan I with branched arabinogalacto-side chains, rhamnogalacturonan II, homogalacturonan and β-glucan were detected by MALDI-TOF MS, NMR and glycosyl-linkage analysis. Previous reports suggest that these oligosaccharide and polysaccharide structures have prebiotic, bacterial pathogen anti-adhesion, and cholesterol-lowering activity, while anthocyanins are well-known antioxidants. A strawberry pomace microwave acid-extracted (10 min, 80 °C) fraction had high molar mass (2376 kDa) and viscosity (3.75 dL/g), with an extended rod shape. A random coil shape, that was reported previously to bind to phenolic compounds, was observed for other strawberry microwave-extracted fractions. These strawberry fiber structural details suggest that they can thicken foods, while the polysaccharide and polyphenol interaction indicates great potential as a multiple-function bioactive food ingredient important for gut and metabolic health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions

The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R . palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R . palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R . palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R . palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R . palustris Bis A53 ( i DT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. i DT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Dynamics and activity of an ammonia-oxidizing archaea bloom in South San Francisco Bay

Abstract Transient or recurring blooms of ammonia-oxidizing archaea (AOA) have been reported in several estuarine and coastal environments, including recent observations of AOA blooms in South San Francisco Bay. Here, we measured nitrification rates, quantified AOA abundance, and analyzed both metagenomic and metatranscriptomic data to examine the dynamics and activity of nitrifying microorganisms over the course of an AOA bloom in South San Francisco Bay during the autumn of 2018 and seasonally throughout 2019. Nitrification rates were correlated with AOA abundance in quantitative polymerase chain reaction (PCR) data, and both increased several orders of magnitude between the autumn AOA bloom and spring and summer seasons. From bloom samples, we recovered an extremely abundant, high-quality Candidatus Nitrosomarinus catalina-like AOA metagenome-assembled genome that had high transcript abundance during the bloom and expressed >80% of genes in its genome. We also recovered a putative nitrite-oxidizing bacteria metagenome-assembled genome from within the Nitrospinaceae that was of much lower abundance and had lower transcript abundance than AOA. During the AOA bloom, we observed increased transcript abundance for nitrogen uptake and oxidative stress genes in non-nitrifier metagenome-assembled genomes. This study confirms AOA are not only abundant but also highly active during blooms oxidizing large amounts of ammonia to nitrite—a key intermediate in the microbial nitrogen cycle—and producing reactive compounds that may impact other members of the microbial community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Potential virus-mediated nitrogen cycling in oxygen-depleted oceanic waters

Viruses play an important role in the ecology and biogeochemistry of marine ecosystems. Beyond mortality and gene transfer, viruses can reprogram microbial metabolism during infection by expressing auxiliary metabolic genes (AMGs) involved in photosynthesis, central carbon metabolism, and nutrient cycling. While previous studies have focused on AMG diversity in the sunlit and dark ocean, less is known about the role of viruses in shaping metabolic networks along redox gradients associated with marine oxygen minimum zones (OMZs). Here, we analyzed relatively quantitative viral metagenomic datasets that profiled the oxygen gradient across Eastern Tropical South Pacific (ETSP) OMZ waters, assessing whether OMZ viruses might impact nitrogen (N) cycling via AMGs. Identified viral genomes encoded six N-cycle AMGs associated with denitrification, nitrification, assimilatory nitrate reduction, and nitrite transport. The majority of these AMGs (80%) were identified in T4-like Myoviridae phages, predicted to infect Cyanobacteria and Proteobacteria, or in unclassified archaeal viruses predicted to infect Thaumarchaeota. Four AMGs were exclusive to anoxic waters and had distributions that paralleled homologous microbial genes. Together, these findings suggest viruses modulate N-cycling processes within the ETSP OMZ and may contribute to nitrogen loss throughout the global oceans thus providing a baseline for their inclusion in the ecosystem and geochemical models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Early prediction of antigenic transitions for influenza A/H3N2

Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. The eventual fate of a new cluster depends on its initial epidemiological growth rate––which is a function of mutational load and population susceptibility to the cluster––along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ~80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Characterization and structural analysis of a thermophilic GH11 xylanase from compost metatranscriptome

Xylanase is efficient for xylan degradation and widely applied in industries. We found a GH11 family xylanase (Xyn11A) with high thermostability and catalytic activity from compost metatranscriptome. This xylanase has the optimal reaction temperature at 80 °C with the activity of 2907.3 U/mg. The X-ray crystallographic structure shows a typical “right hand” architecture, which is the characteristics of the GH11 family enzymes. Comparing it with the mesophilic XYN II, a well-studied GH11 xylanase from Trichoderma reesei, Xyn11A is more compact with more H-bonds. Our mutagenic results show that the electrostatic interactions in the thumb and palm region of Xyn11A could result in its high thermostability and activity. Introducing a disulfide bond at the N-terminus further increased its optimal reaction temperature to 90 °C with augmented activity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A Long Non-coding RNA, LOC157273, Is an Effector Transcript at the Chromosome 8p23.1-PPP1R3B Metabolic Traits and Type 2 Diabetes Risk Locus

Causal transcripts at genomic loci associated with type 2 diabetes (T2D) are mostly unknown. The chr8p23.1 variant rs4841132, associated with an insulin-resistant diabetes risk phenotype, lies in the second exon of a long non-coding RNA (lncRNA) gene, LOC157273, located 175 kilobases from PPP1R3B, which encodes a key protein regulating insulin-mediated hepatic glycogen storage in humans. We hypothesized that LOC157273 regulates expression of PPP1R3B in human hepatocytes. We tested our hypothesis using Stellaris fluorescent in situ hybridization to assess subcellular localization of LOC157273; small interfering RNA (siRNA) knockdown of LOC157273, followed by RT-PCR to quantify LOC157273 and PPP1R3B expression; RNA-seq to quantify the whole-transcriptome gene expression response to LOC157273 knockdown; and an insulin-stimulated assay to measure hepatocyte glycogen deposition before and after knockdown. We found that siRNA knockdown decreased LOC157273 transcript levels by approximately 80%, increased PPP1R3B mRNA levels by 1.7-fold, and increased glycogen deposition by >50% in primary human hepatocytes. An A/G heterozygous carrier (vs. three G/G carriers) had reduced LOC157273 abundance due to reduced transcription of the A allele and increased PPP1R3B expression and glycogen deposition. We show that the lncRNA LOC157273 is a negative regulator of PPP1R3B expression and glycogen deposition in human hepatocytes and a causal transcript at an insulin-resistant T2D risk locus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Proteome-scale Structure Prediction Data - Pseudodesulfovibrio mercurii

The number of proteins predicted for Pseudodesulfovibrio mercurii is 3,446, each of which have five predicted structures from an AlphaFold run, as well as structural alignment results using the TMscore-based structural alignment method within the APoc program. Specifically, AlphaFold outputs the atoms and coordinates of the protein model in human-readable PDB files and quantitative prediction metrics in Python PICKLE files. The 5 models have been ranked based on the predicted TM-score (pTMS), a quantitative confidence metric output by AlphaFold that reports on protein model quality. The top ranked model has undergone an energy minimization calculation to relax and remove any potential clashes in the atomic coordinates. Structural alignment results are stored in two files for each protein; the top ranked model (as discussed above) is used for all alignment analyses. Both are compressed gzip files that, once unpacked, are human readable. The first file is the TMalign score results and contains the quantitative metrics for the top alignments between the predicted structure and experimental structures from the PDB70, a curated non-redundant database of about 80,000 experimental structures developed by the Soding lab. Each data point in this file is directly associated with one experimental structure; PDB ID and brief meta-data about the protein taken from the PDB70 file are reported alongside the quantitative metrics. The second results file contains the raw results associated with each alignment reported in the score results file. Specifically, the translation and rotation arrays for each alignment are provided so that the structural alignment can be recreated. Additionally, residue-level scores are reported to quantify the closeness of the aligned residues between the predicted and experimental models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Viral elements and their potential influence on microbial processes along the permanently stratified Cariaco Basin redoxcline

Little is known about viruses in oxygen-deficient water columns (ODWCs). In surface ocean waters, viruses are known to act as gene vectors among susceptible hosts. Some of these genes may have metabolic functions and are thus termed auxiliary metabolic genes (AMGs). AMGs introduced to new hosts by viruses can enhance viral replication and/or potentially affect biogeochemical cycles by modulating key microbial pathways. Here we identify 748 viral populations that cluster into 94 genera along a vertical geochemical gradient in the Cariaco Basin, a permanently stratified and euxinic ocean basin. The viral communities in this ODWC appear to be relatively novel as 80 of these viral genera contained no reference viral sequences, likely due to the isolation and unique features of this system. We identify viral elements that encode AMGs implicated in distinctive processes, such as sulfur cycling, acetate fermentation, signal transduction, [Fe–S] formation, and N-glycosylation. These AMG-encoding viruses include two putative Mu-like viruses, and viral-like regions that may constitute degraded prophages that have been modified by transposable elements. Our results provide an insight into the ecological and biogeochemical impact of viruses oxygen-depleted and euxinic habitats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Subsampling and DNA pooling can increase gains through genomic selection in switchgrass

Genomic selection (GS) can accelerate breeding cycles in perennial crops such as the bioenergy grass switchgrass (Panicum virgatum L.). The sequencing costs of GS can be reduced by pooling DNA samples in the training population (TP), only sequencing TP phenotypic outliers, or pooling candidate population (CP) samples. These strategies were simulated for two traits (spring vigor and anthesis date) in three breeding populations. Sequencing only the outlier 50% of the TP phenotype distribution resulted in a penalty of <5% of the predictive ability, measured using cross-validation. Predictive ability also decreased when sequencing progressively fewer TP DNA pools, but TPs constructed from only two phenotypically contrasting DNA samples retained a mean of >80% predictive ability relative to individual TP sequencing. Novel group testing methods allowed greater than one CP individual to be screened per sequenced DNA sample but resulted in a predictive ability penalty. To determine the impact of reduced sequencing, genetic gain was calculated for seven GS scenarios with variable sequencing budgets. Reduced TP sequencing and most CP pooling methods were superior to individual sequence-based GS when sequencing resources were restricted (2,000 DNA samples per 5-yr cycle). Only one scenario was superior to individual sequencing when sequencing budgets were large (8,000 DNA samples per 5-yr cycle). This study highlights multiple routes for reduced sequencing costs in GS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Population genomics provides insights into the genetic basis of adaptive evolution in the mushroom-forming fungus Lentinula edodes

Introduction: Mushroom-forming fungi comprise diverse species that develop complex multicellular structures. In cultivated species, both ecological adaptation and artificial selection have driven genome evolution. However, little is known about the connections among genotype, phenotype and adaptation in mushroom-forming fungi. Objectives: This study aimed to (1) uncover the population structure and demographic history of Lentinula edodes, (2) dissect the genetic basis of adaptive evolution in L. edodes, and (3) determine if genes related to fruiting body development are involved in adaptive evolution. Methods: We analyzed genomes and fruiting body-related traits (FBRTs) in 133 L. edodes strains and conducted RNA-seq analysis of fruiting body development in the YS69 strain. Combined methods of genomic scan for divergence, genome-wide association studies (GWAS), and RNA-seq were used to dissect the genetic basis of adaptive evolution. Results: We detected three distinct subgroups of L. edodes via single nucleotide polymorphisms, which showed robust phenotypic and temperature response differentiation and correlation with geographical distribution. Demographic history inference suggests that the subgroups diverged 36,871 generations ago. Moreover, L. edodes cultivars in China may have originated from the vicinity of Northeast China. A total of 942 genes were found to be related to genetic divergence by genomic scan, and 719 genes were identified to be candidates underlying FBRTs by GWAS. Integrating results of genomic scan and GWAS, 80 genes were detected to be related to phenotypic differentiation. A total of 364 genes related to fruiting body development were involved in genetic divergence and phenotypic differentiation. Conclusion: Adaptation to the local environment, especially temperature, triggered genetic divergence and phenotypic differentiation of L. edodes. A general model for genetic divergence and phenotypic differentiation during adaptive evolution in L. edodes, which involves in signal perception and transduction, transcriptional regulation, and fruiting body morphogenesis, was also integrated here.

59 BASIC BIOLOGICAL SCIENCES↗