Glucose-\u3ci\u3eABL1\u3c/i\u3e-TOR Signaling Modulates Cell Cycle Tuning to Control Terminal Appressorial Cell Differentiation

The conserved target of rapamycin (TOR) pathway integrates growth and development with available nutrients, but how cellular glucose controls TOR function and signaling is poorly understood. Here, we provide functional evidence from the devastating rice blast fungus Magnaporthe oryzae that glucose can mediate TOR activity via the product of a novel carbon- responsive gene, ABL1, in order to tune cell cycle progression during infection-related development. Under nutrient-free conditions, wild type (WT) M. oryzae strains form terminal plant-infecting cells (appressoria) at the tips of germ tubes emerging from three-celled spores (conidia). WT appressorial development is accompanied by one round of mitosis followed by autophagic cell death of the conidium. In contrast, Δabl1 mutant strains undergo multiple rounds of accelerated mitosis in elongated germ tubes, produce few appressoria, and are abolished for autophagy. Treating WT spores with glucose or 2-deoxyglucose phenocopied Δabl1. Inactivating TOR in Δabl1 mutants or glucose-treated WT strains restored appressorium formation by promoting mitotic arrest at G1/G0 via an appressorium- and autophagy-inducing cell cycle delay at G2/M. Collectively, this work uncovers a novel glucose- ABL1-TOR signaling axis and shows it engages two metabolic checkpoints in order to modulate cell cycle tuning and mediate terminal appressorial cell differentiation. We thus provide new molecular insights into TOR regulation and cell development in response to glucose

TOR-autophagy branch signaling via Imp1 dictates plant-microbe biotrophic interface longevity

Like other intracellular eukaryotic phytopathogens, the devastating rice blast fungus Magnaporthe (Pyricularia) oryzae first infects living host cells by elaborating invasive hyphae (IH) surrounded by a plant-derived membrane. This forms an extended biotrophic interface enclosing an apoplastic compartment into which fungal effectors can be deployed to evade host detection. M. oryzae also forms a focal, plant membrane-rich structure, the biotrophic interfacial complex (BIC), that accumulates cytoplasmic effectors for translocation into host cells. Molecular decision-making processes integrating fungal growth and metabolism in host cells with interface function and dynamics are unknown. Here, we report unanticipated roles for the M. oryzae Target-of-Rapamycin (TOR) nutrient-signaling pathway in mediating plant-fungal biotrophic interface membrane integrity. Through a forward genetics screen for M. oryzae mutant strains resistant to the specific TOR kinase inhibitor rapamycin, we discovered IMP1 encoding a novel vacuolar protein required for membrane trafficking, VATPase assembly, organelle acidification and autophagy induction. During infection, Δimp1 deletants developed intracellular IH in the first infected rice cell following cuticle penetration. However, fluorescently labeled effector probes revealed that interface membrane integrity became compromised as biotrophy progressed, abolishing the BIC and releasing apoplastic effectors into host cytoplasm. Growth between rice cells was restricted. TOR-independent autophagy activation in Δimp1 deletants (following infection) remediated interface function and cell-to-cell growth. Autophagy inhibition in wild type (following infection) recapitulated Δimp1. In addition to vacuoles, Imp1GFP localized to IH membranes in an autophagy-dependent manner. Collectively, our results suggest TOR-Imp1-autophagy branch signaling mediates membrane homeostasis to prevent catastrophic erosion of the biotrophic interface, thus facilitating fungal growth in living rice cells. The significance of this work lays in elaborating a novel molecular mechanism of infection stressing the dominance of fungal metabolism and metabolic control in sustaining long-term plant-microbe interactions. This work also has implications for understanding the enigmatic biotrophy to necrotrophy transition

Electrical Behavior Association Mining for Household ShortTerm Energy Consumption Forecasting

Accurate household short-term energy consumption forecasting (STECF) is crucial for home energy management, but it is technically challenging, due to highly random behaviors of individual residential users. To improve the accuracy of STECF on a day-ahead scale, this paper proposes an novel STECF methodology that leverages association mining in electrical behaviors. First, a probabilistic association quantifying and discovering method is proposed to model the pairwise behaviors association and generate associated clusters. Then, a convolutional neural network-gated recurrent unit (CNN-GRU) based forecasting is provided to explore the temporal correlation and enhance accuracy. The testing results demonstrate that this methodology yields a significant enhancement in the STECF.Comment: 3 figures and 4 tables; This manuscript is submitted for possible publicatio

Comparative genomics of five Valsa species gives insights on their pathogenicity evolution

Valsa is a genus of ascomycetes within the Valsaceae family. This family includes many wood destructive pathogens such as the well known Valsa mali and Valsa pyri which cause canker diseases in fruit trees and threaten the global fruit production. Lack of genomic information of this family is impeding our understandings about their evolution and genetic basis of their pathogenicity divergence. Here, we report genome assemblies of Valsa malicola, Valsa persoonii, and Valsa sordida which represent close relatives of Valsa mali and Valsa pyri with different host preferences. Comparative genomics analysis revealed that segmental rearrangements, inversions, and translocations frequently occurred among Valsa spp. genomes. Gene families that exhibited gene copy expansions tended to be associated with secondary metabolism, transmembrane transport, and pyrophosphatase activities. Orthologous genes in regions lost synteny exhibited significantly higher rate of synonymous substitution (KS) than those in regions retained synteny. Moreover, among these genes, membrane transporter families associated with antidrug (MFS, DHA) activities and nutrient transportation (SP and APCs) activities were significantly over-represented. Lineage specific synonymous substitution (KS) and nonsynonymous substitution (KA) analysis based on the phylogeny constructed from 11 fungal species identified a set of genes with selection signatures in Valsa clade and these genes were significantly enriched in functions associated with fatty acid beta-oxidation, DNA helicase activity, and ATPase activity. Furthermore, unique genes that possessed or retained by each of the five Valsa species are more likely part of the secondary metabolic (SM) gene clusters. SM gene clusters conserved across five Valsa species showed various degrees of diversification in both identity and completeness. All 11 syntenically conserved SM clusters showed differential expression during the infection of apple branch with Valsa mali suggesting involvements of secondary metabolism in the pathogenicity of Valsa species

SNP discovery in proso millet (\u3ci\u3ePanicum miliaceum\u3c/i\u3e L.) using lowpass genome sequencing

Domesticated ~10,000 years ago in northern China, Proso millet (Panicum miliaceum L.) is a climate-resilient and human health-promoting cereal crop. The genome size of this self-pollinated allotetraploid is 923 Mb. Proso millet seeds are an important part of the human diet in many countries. In the USA, its use is restricted to the birdseed and pet food market. Proso millet is witnessing gradual demand in the global human health and wellness food market owing to its health-promoting properties such as low glycemic index and gluten-free. The breeding efforts for developing improved proso millet cultivars are hindered by the dearth of genomic resources available to researchers. The publication of the reference genome and availability of costeffective NGS methodologies could lead to the identification of high-quality genetic variants, which can be incorporated into breeding pipelines. Here, we report the identification of single-nucleotide polymorphisms (SNPs) by low-pass (1x) genome sequencing of 85 diverse proso millet accessions from 23 different countries. The 2 x 150 bp Illumina paired-end reads generated after sequencing were aligned to the proso millet reference genome. The resulting sequence alignment information was used to call SNPs. We obtained 972,863 bi-allelic SNPs after quality filtering of the raw SNPs. These SNPs were used to assess the population structure and phylogenetic relationships among the accessions. Most of the accessions were found to be highly inbred with heterozygosity ranging between .05 and .20. Principal component analysis (PCA) showed that PC1 (principal component) and PC2 explained 19% of the variability in the population. PCA also clustered all the genotypes into three groups. A neighbor-joining tree clustered the genotypes into four distinct groups exhibiting diverse representation within the population. The SNPs identified in our study could be used for molecular breeding and genetics research (e.g., genetic and association mapping, and population genetics) in proso millet after proper validation

Scheduling of EV Battery Swapping, I: Centralized Solution

We formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best battery station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of EVs’ travel distance and electricity generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints, and ac power flow equations. To deal with the nonconvexity of power flow equations and the binary nature of station assignments, we propose a solution based on second-order cone programming (SOCP) relaxation of optimal power flow and generalized Benders decomposition. When the SOCP relaxation is exact, this approach computes a global optimum. We evaluate the performance of the proposed algorithm through simulations. The algorithm requires global information and is suitable for cases where the distribution grid, battery stations, and EVs are managed centrally by the same operator. In Part II of this paper, we develop distributed solutions for cases where they are operated by different organizations that do not share private information

Genome-wide analysis of genes encoding core components of the ubiquitin system in soybean (\u3ci\u3eGlycine max\u3c/i\u3e) reveals a potential role for ubiquitination in host immunity against soybean cyst nematode

Background: Ubiquitination is a major post-translational protein modification that regulates essentially all cellular and physiological pathways in eukaryotes. The ubiquitination process typically involves three distinct classes of enzymes, ubiquitin-activating enzyme (E1), ubiquitin-conjugating enzyme (E2) and ubiquitin ligase (E3). To date, a comprehensive identification and analysis of core components comprising of the whole soybean (Glycine max) ubiquitin system (UBS) has not been reported. Results: We performed a systematic, genome-wide analysis of genes that encode core members of the soybean UBS in this study. A total of 1431 genes were identified with high confidence to encode putative soybean UBS components, including 4 genes encoding E1s, 71 genes that encode the E2s, and 1356 genes encoding the E3-related components. Among the E3-encoding genes, 760 encode RING-type E3s, 124 encode U-box domain-containing E3s, and 472 encode F-box proteins. To find out whether the identified soybean UBS genes encode active enzymes, a set of genes were randomly selected and the enzymatic activities of their recombinant proteins were tested. Thioester assays indicated proteins encoded by the soybean E1 gene GmUBA1 and the majority of selected E2 genes are active E1 or E2 enzymes, respectively. Meanwhile, most of the purified RING and U-box domain-containing proteins displayed E3 activity in the in vitro ubiquitination assay. In addition, 1034 of the identified soybean UBS genes were found to express in at least one of 14 soybean tissues examined and the transcript level of 338 soybean USB genes were significantly changed after abiotic or biotic (Fusarium oxysporum and Rhizobium strains) stress treatment. Finally, the expression level of a large number of the identified soybean UBS-related genes was found significantly altered after soybean cyst nematode (SCN) treatment, suggesting the soybean UBS potentially plays an important role in soybean immunity against SCN. Conclusions: Our findings indicate the presence of a large and diverse number of core UBS proteins in the soybean genome, which suggests that target-specific modification by ubiquitin is a complex and important part of cellular and physiological regulation in soybean. We also revealed certain members of the soybean UBS may be involved in immunity against soybean cyst nematode (SCN). This study sets up an essential foundation for further functional characterization of the soybean UBS in various physiological processes, such as host immunity against SCN

Scheduling of EV Battery Swapping, I: Centralized Solution

We formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best battery station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of EVs’ travel distance and electricity generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints, and ac power flow equations. To deal with the nonconvexity of power flow equations and the binary nature of station assignments, we propose a solution based on second-order cone programming (SOCP) relaxation of optimal power flow and generalized Benders decomposition. When the SOCP relaxation is exact, this approach computes a global optimum. We evaluate the performance of the proposed algorithm through simulations. The algorithm requires global information and is suitable for cases where the distribution grid, battery stations, and EVs are managed centrally by the same operator. In Part II of this paper, we develop distributed solutions for cases where they are operated by different organizations that do not share private information

Association mapping across a multitude of traits collected in diverse environments in maize

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction