Identifying Artifacts from Large Library Docking.

While large library docking has discovered potent ligands for multiple targets, as the libraries have grown, the very top of the hit-lists can become populated with artifacts that cheat our scoring functions. Though these cheating molecules are rare, they become ever-more dominant with library growth. Here, we investigate rescoring top-ranked molecules from docking screens with orthogonal methods to identify these artifacts, exploring implicit solvent models and absolute binding free energy perturbation (AB-FEP) as cross-filters. In retrospective studies, this approach deprioritized high-ranking non-binders for nine targets while leaving true ligands relatively unaffected. We tested the method prospectively against results from large library docking AmpC ß-lactamase. From the very top of the docking hit lists, we prioritized 128 molecules for synthesis and experimental testing, a mixture of 39 molecules that rescoring flagged as likely cheaters and another 89 that were plausible true actives. None of the 39 predicted cheating compounds inhibited AmpC up to 200 µ M in enzyme assays, while 57% of the 89 plausible true actives did do so, with 19 of them inhibiting the enzyme with apparent K i values better than 50 µ M . As our libraries continue to grow, a strategy of catching docking artifacts by rescoring with orthogonal methods may find wide use in the field.

Defining context-dependent m6A RNA methylomes in Arabidopsis.

N6-Methyladenosine (m6A) prevalently occurs on cellular RNA across almost all kingdoms of life. It governs RNA fate and is essential for development and stress responses. However, the dynamic, context-dependent m6A methylomes across tissues and in response to various stimuli remain largely unknown in multicellular organisms. Here, we generate a comprehensive census that identifies m6A methylomes in 100 samples during development or following exposure to various external conditions in Arabidopsis thaliana. We demonstrate that m6A is a suitable biomarker to reflect the developmental lineage, and that various stimuli rapidly affect m6A methylomes that constitute the regulatory network required for an effective response to the stimuli. Integrative analyses of the census and its correlation with m6A regulators identify multiple layers of regulation on highly context-dependent m6A modification in response to diverse developmental and environmental stimuli, providing insights into m6A modification dynamics in the myriad contexts of multicellular organisms.

Stenotrophomonas maltophilia Isolated from the Gut Symbiotic Community of the Plastic-Eating Tenebrio molitor.

Polyvinyl chloride (PVC) waste is a major environmental challenge. In this study, we found that a PVC-eating insect, Tenebrio molitor, could survive by consuming PVC as a dietary supplement. To understand the gut symbiotic community, metagenomic analysis was performed to reveal the biodiversity of a symbiotic community in the midgut of Tenebrio molitor. Among them, seven genera were enriched from the midgut of the insect under culture conditions with PVC as carbon source. A strain of Stenotrophomonas maltophilia was isolated from the midgut symbiotic community of the plastic-eating Tenebrio molitor. To unravel the functional gene for the biodegradation enzyme, we sequenced the whole genome of Stenotrophomonas maltophilia and found that orf00390, annotated as a hydrolase, was highly expressed in the PVC culture niche.

TMPRSS2 Inhibitor Discovery Facilitated through an In Silico and Biochemical Screening Platform.

The COVID-19 pandemic has highlighted the need for new antiviral approaches because many of the currently approved drugs have proven ineffective against mitigating SARS-CoV-2 infections. The host transmembrane serine protease TMPRSS2 is a promising antiviral target because it plays a role in priming the spike protein before viral entry occurs for the most virulent variants. Further, TMPRSS2 has no established physiological role, thereby increasing its attractiveness as a target for antiviral agents. Here, we utilize virtual screening to curate large libraries into a focused collection of potential inhibitors. Optimization of a recombinant expression and purification protocol for the TMPRSS2 peptidase domain facilitates subsequent biochemical screening and characterization of selected compounds from the curated collection in a kinetic assay. In doing so, we identify new noncovalent TMPRSS2 inhibitors that block SARS-CoV-2 infectivity in a cellular model. One such inhibitor, debrisoquine, has high ligand efficiency, and an initial structure-activity relationship study demonstrates that debrisoquine is a tractable hit compound for TMPRSS2.

pyCHARMM: Embedding CHARMM Functionality in a Python Framework.

CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. We anticipate that pyCHARMM will be a robust platform for the development of comprehensive and complex workflows utilizing Python and its extensive functionality as well as an optimal platform for users to learn molecular modeling methods and practices within a Python-friendly environment such as Jupyter Notebooks.

Recent advances in the plant epitranscriptome.

Chemical modifications of RNAs, known as the epitranscriptome, are emerging as widespread regulatory mechanisms underlying gene regulation. The field of epitranscriptomics advances recently due to improved transcriptome-wide sequencing strategies for mapping RNA modifications and intensive characterization of writers, erasers, and readers that deposit, remove, and recognize RNA modifications, respectively. Herein, we review recent advances in characterizing plant epitranscriptome and its regulatory mechanisms in post-transcriptional gene regulation and diverse physiological processes, with main emphasis on N6-methyladenosine (m6A) and 5-methylcytosine (m5C). We also discuss the potential and challenges for utilization of epitranscriptome editing in crop improvement.

Knockdown of Long Noncoding RNA 01124 Inhibits the Malignant Behaviors of Colon Cancer Cells via miR-654-5p/HAX-1.

Background: Previous studies have shown that long noncoding RNAs (lncRNAs) play a key role in cancer, including colon cancer (CC). However, the exact role of long noncoding RNA 01124 (LINC01124) in CC and its mechanisms of action remain unknown. In this study, we investigated the functional effects and the possible mechanism of LINC01124 in CC. Methods: We first determined the expression of LINC01124 in CC tissues (The Cancer Genome Atlas (TCGA) database) and cell lines (quantitative real-time polymerase chain reaction (qRT-PCR)). Functional analysis via Cell Counting Kit-8 (CCK-8), colony formation, cell cycle, wound healing and Transwell assays were performed, and a mechanistic experiment was performed with the western blotting. The function of LINC01124 was also determined in vivo using nude BALB/c mice. Results: The results showed that LINC01124 was upregulated in CC tissues and cell lines. Functional studies showed that knockdown of LINC01124 significantly suppressed the proliferation, migration, and invasion of colon cancer cells in vitro and in vivo. Subsequent mechanistic experiments indicated that LINC01124 acted as a sponge to suppress microRNA 654-5p, which targeted HAX-1. Downregulation of LINC01124 decreased the expression of HAX-1, and overexpression of the miR-654-5p inhibitor attenuated the sh-LINC01124-induced inhibition of CC cell proliferation, migration, and invasion. Conclusion: Collectively, this study revealed that the knockdown of LINC01124 inhibited the malignant behaviors of CC via the miR-654-5p/HAX-1 axis, suggesting that LINC01124 might be a therapeutic target for CC treatment.

Covalent docking in CDOCKER.

Targeted covalent inhibitors (TCIs) are considered to be an important component in the toolbox of drug discovery and about 30% of currently marketed drugs are TCIs. Although these drugs raise concerns about toxicity, their high potencies and prolonged effects result in less-frequent drug dosing and wide therapeutic margins for patients. This leads to increased interests in developing new computational methods to identify novel covalent inhibitors. The implementation of successful in silico docking algorithms have the potential to provide significant savings of time and money in the discovery of lead compounds. In this paper, we describe the implementation and testing of a covalent docking methodology in Rigid CDOCKER and the optimization of the corresponding physics-based scoring function with an additional customizable covalent bond grid potential which represents the free energy change of bond formation between the ligand and the receptor. We optimize the covalent bond grid potential for different common covalent bond formation reaction in TCIs. The average runtime for docking one covalent compound is 15 minutes which is comparable or faster than other well-established covalent docking methods. We demonstrate comparable top rank accuracy compared with other covalent docking algorithms using the pose prediction benchmark dataset for covalent docking algorithms developed by the Keseru group. Finally, we construct a retrospective virtual screening benchmark dataset containing 8 different receptor targets with different covalent bond formation reactions. To our knowledge, this is the largest dataset for benchmarking covalent docking methods. We show that our new covalent docking algorithm has the ability to identify lead compounds among a large chemical space. The largest AUC value is 0.909 for the target receptor CATK and the warhead chemistry of the covalent inhibitors is addition to the aldehyde functionality.

Brassica evolution of essential BnaFtsH1 genes involved in the PSII repair cycle and loss of FtsH5.

The PSII repair cycle is an important part of photosynthesis and is essential for high photosynthetic efficiency. The study of essential genes in Brassica napus provides significant potential for the improvement of gene editing technology and molecular breeding design. Previously, we identified a B. napus lethal mutant (7-521Y), which was controlled by two recessive genes (cyd1 and cyd2). BnaC06.FtsH1 was identified as a CYD1 target gene through functional verification. In the present study, we employed fine-mapping, genetic complementation, and CRISPR/Cas9 experiments to identify BnaA07.FtsH1 as the target gene of CYD2, functioning similarly to BnaC06.FtsH1. By analyzing CRISPR/Cas9 T1 generation plants of the Westar variety, we found that the copy number of FtsH1 was positively correlated with its biomass accumulation. Transcriptome analysis of cotyledons revealed differences in the expression of photosynthesis antenna and structural proteins between the mutant and complementary seedlings. Phylogenetic and chromosome linear analyses, based on 15 sequenced cruciferous species, revealed that Brassica alone had lost FtsH5 during evolution. This may be related to the fact that FtsH5 was located at the end of chromosome ABK8 in the ancestor species. Cloning and identification of BnaFtsH1s provide a deeper understanding of PSII repair cycle mechanisms and offer new insights for the improvement of photosynthetic efficiency and molecular breeding design in B. napus.

Morphological Characterization and Transcriptome Analysis of New Dwarf and Narrow-Leaf (dnl2) Mutant in Maize.

Lodging is the primary factor limiting high yield under a high plant density. However, an optimal plant height and leaf shape can effectively decrease the lodging risk. Here we studied an ethyl methanesulfonate (EMS)-induced dwarf and a narrow-leaf mutant, dnl2. Gene mapping indicated that the mutant was controlled by a gene located on chromosome nine. Phenotypic and cytological observations revealed that dnl2 showed inhibited cell growth, altered vascular bundle patterning, and disrupted secondary cell wall structure when compared with the wild-type, which could be the direct cause of the dwarf and narrow-leaf phenotype. The phytohormone levels, especially auxin and gibberellin, were significantly decreased in dnl2 compared to the wild-type plants. Transcriptome profiling of the internodes of the dnl2 mutant and wild-type revealed a large number of differentially expressed genes enriched in the cell wall biosynthesis, remodeling, and hormone biosynthesis and signaling pathways. Therefore, we suggest that crosstalk between hormones (the altered vascular bundle and secondary cell wall structure) may contribute to the dwarf and narrow-leaf phenotype by influencing cell growth. These results provide a foundation for DNL2 gene cloning and further elucidation of the molecular mechanism of the regulation of plant height and leaf shape in maize.

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy.

The binding of small-molecule ligands to protein or nucleic acid targets is important to numerous biological processes. Accurate prediction of the binding modes between a ligand and a macromolecule is of fundamental importance in structure-based structure-function exploration. When multiple ligands with different sizes are docked to a target receptor, it is reasonable to assume that the residues in the binding pocket may adopt alternative conformations upon interacting with the different ligands. In addition, it has been suggested that the entropic contribution to binding can be important. However, only a few attempts to include the side chain conformational entropy upon binding within the application of flexible receptor docking methodology exist. Here, we propose a new physics-based scoring function that includes both enthalpic and entropic contributions upon binding by considering the conformational variability of the flexible side chains within the ensemble of docked poses. We also describe a novel hybrid searching algorithm that combines both molecular dynamics (MD)-based simulated annealing and genetic algorithm crossovers to address the enhanced sampling of the increased search space. We demonstrate improved accuracy in flexible cross-docking experiments compared with rigid cross-docking. We test our developments by considering five protein targets, thrombin, dihydrofolate reductase(DHFR), T4 L99A, T4 L99A/M102Q, and PDE10A, which belong to different enzyme classes with different binding pocket environments, as a representative set of diverse ligands and receptors. Each target contains dozens of different ligands bound to the same binding pocket. We also demonstrate that this flexible docking algorithm may be applicable to RNA docking with a representative riboswitch example. Our findings show significant improvements in top ranking accuracy across this set, with the largest improvement relative to rigid, 23.64%, occurring for ligands binding to DHFR. We then evaluate the ability to identify lead compounds among a large chemical space for the proposed flexible receptor docking algorithm using a subset of the DUD-E containing receptor targets MCR, GCR, and ANDR. We demonstrate that our new algorithms show improved performance in modeling flexible binding site residues compared to DOCK. Finally, we select the T4 L99A and T4 L99A/M102Q decoy sets, containing dozens of binders and experimentally validated nonbinders, to test our approach in distinguishing binders from nonbinders. We illustrate that our new algorithms for searching and scoring have superior performance to rigid receptor CDOCKER as well as AutoDock Vina. Finally, we suggest that flexible CDOCKER is sufficiently fast to be utilized in high-throughput docking screens in the context of hierarchical approaches.

CRMP5 regulates cell proliferation and development of colorectal cancer via MAPK-dependent signaling.

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Collapsin response mediator protein 5 (CRMP5) belongs to a family of five cytosolic proteins that serve a major role in neural development. CRMP5 has been identified as a biomarker of numerous cancer types, including lung cancer and glioblastoma. However, the role of CRMP5 in CRC remains unclear. In the present study, CRMP5 was characterized as a novel biomarker of poor survival in CRC. CRMP5-overexpression in CRC cells promoted cell proliferation and migration while CPMP5-knockdown decreased cell growth and migration. A novel mechanism was uncovered, by which CRMP5 regulates MAPK signaling to drive CRC cell proliferation and development. Furthermore, CRMP5-overexpression induced chemotherapy resistance and tumor recurrence in CRC. Taken together, these results demonstrated the important role of CRMP5 in the development and proliferation of CRC cells and suggested that CRMP5 may be a novel therapeutic target for CRC.

Identifying quantitative trait loci for the general combining ability of yield-relevant traits in maize.

Maize is the most important staple crop worldwide. Many of its agronomic traits present with a high level of heterosis. Combining ability was proposed to exploit the rule of heterosis, and general combining ability (GCA) is a crucial measure of parental performance. In this study, a recombinant inbred line population was used to construct testcross populations by crossing with four testers based on North Carolina design II. Six yield-relevant traits were investigated as phenotypic data. GCA effects were estimated for three scenarios based on the heterotic group and the number of tester lines. These estimates were then used to identify quantitative trait loci (QTL) and dissect genetic basis of GCA. A higher heritability of GCA was obtained for each trait. Thus, testing in early generation of breeding may effectively select candidate lines with relatively superior GCA performance. The GCA QTL detected in each scenario was slightly different according to the linkage mapping. Most of the GCA-relevant loci were simultaneously detected in all three datasets. Therefore, the genetic basis of GCA was nearly constant although discrepant inbred lines were appointed as testers. In addition, favorable alleles corresponding to GCA could be pyramided via marker-assisted selection and made available for maize hybrid breeding.

TMPRSS2 inhibitor discovery facilitated through an in silico and biochemical screening platform.

The COVID-19 pandemic has highlighted the need for new antiviral targets, as many of the currently approved drugs have proven ineffective against mitigating SARS-CoV-2 infections. The host transmembrane serine protease TMPRSS2 is a highly promising antiviral target, as it plays a direct role in priming the spike protein before viral entry occurs. Further, unlike other targets such as ACE2, TMPRSS2 has no known biological role. Here we utilize virtual screening to curate large libraries into a focused collection of potential inhibitors. Optimization of a recombinant expression and purification protocol for the TMPRSS2 peptidase domain facilitates subsequent biochemical screening and characterization of selected compounds from the curated collection in a kinetic assay. In doing so, we demonstrate that serine protease inhibitors camostat, nafamostat, and gabexate inhibit through a covalent mechanism. We further identify new non-covalent compounds as TMPRSS2 protease inhibitors, demonstrating the utility of a combined virtual and experimental screening campaign in rapid drug discovery efforts.

Fine Mapping and Identification of BnaC06.FtsH1, a Lethal Gene That Regulates the PSII Repair Cycle in Brassica napus.

Photosystem II (PSII) is an important component of the chloroplast. The PSII repair cycle is crucial for the relief of photoinhibition and may be advantageous when improving stress resistance and photosynthetic efficiency. Lethal genes are widely used in the efficiency detection and method improvement of gene editing. In the present study, we identified the naturally occurring lethal mutant 7-521Y with etiolated cotyledons in Brassica napus, controlled by double-recessive genes (named cyd1 and cyd2). By combining whole-genome resequencing and map-based cloning, CYD1 was fine-mapped to a 29 kb genomic region using 15,167 etiolated individuals. Through cosegregation analysis and functional verification of the transgene, BnaC06.FtsH1 was determined to be the target gene; it encodes an filamentation temperature sensitive protein H 1 (FtsH1) hydrolase that degrades damaged PSII D1 in Arabidopsis thaliana. The expression of BnaC06.FtsH1 was high in the cotyledons, leaves, and flowers of B. napus, and localized in the chloroplasts. In addition, the expression of EngA (upstream regulation gene of FtsH) increased and D1 decreased in 7-521Y. Double mutants of FtsH1 and FtsH5 were lethal in A. thaliana. Through phylogenetic analysis, the loss of FtsH5 was identified in Brassica, and the remaining FtsH1 was required for PSII repair cycle. CYD2 may be a homologous gene of FtsH1 on chromosome A07 of B. napus. Our study provides new insights into lethal mutants, the findings may help improve the efficiency of the PSII repair cycle and biomass accumulation in oilseed rape.

lncRNA LINC00963 downregulation regulates colorectal cancer tumorigenesis and progression via the miR­10b/FGF13 axis.

Long non-coding RNAs (lncRNAs) serve a key role in different types of cancer, including colorectal cancer (CRC). The exact roles and mechanisms underlying lncRNA00963 [long intergenic non­protein coding RNA 963 (LINC00963)] in CRC are not completely understood. The present study aimed to identify the effects and mechanisms underlying LINC00963 in CRC. Firstly, the LINC00963 expression was detected using reverse transcription­quantitative PCR and the results demonstrated that LINC00963 expression levels were significantly increased in CRC tissues and cell lines compared with healthy tissues and HpoEpiC cells, respectively. Online database analysis indicated that high levels of LINC00963 were associated with low survival rates. The results of functional experiments, such as CCK­8 assay, colony formation assay, wound healing assay and Transwell invasion assay, indicated that LINC00963 knockdown significantly inhibited CRC cell proliferation, colony formation, migration and invasion compared with the small interfering RNA (si)­negative control (NC) group. Furthermore, the luciferase reporter indicated that LINC00963 competitively regulated microRNA (miR)­10b by targeting fibroblast growth factor 13 (FGF13). Compared with si­NC, LINC00963 knockdown decreased the expression levels of FGF13, vimentin and N­cadherin, and increased the expression of E­cadherin as detecting by western blotting. miR­10b inhibitors partly attenuated si­LINC00963­induced inhibition of CRC cell proliferation, migration and invasion. Collectively, the results of the present study suggested a potential role of the LINC00963/miR-10b/FGF13 axis in the tumorigenesis and progression of CRC, indicating a novel lncRNA-based diagnostic or therapeutic target for CRC.

Genome-Wide Identification of mRNAs, lncRNAs, and Proteins, and Their Relationship With Sheep Fecundity.

The exploration of multiple birth-related genes has always been a significant focus in sheep breeding. This study aimed to find more genes and proteins related to the litter size in sheep. Ovarian specimens of Small Tail Han sheep (multiple births) and Xinji Fine Wool sheep (singleton) were collected during the natural estrus cycle. Transcriptome and proteome of ovarian specimens were analyzed. The transcriptome results showed that "steroid hormone biosynthesis" and "ovarian steroidogenesis" were significantly enriched, in which HSD17B1 played an important role. The proteome data also confirmed that the differentially expressed proteins (DEPs) were enriched in the ovarian steroidogenesis pathway, and the CYP17A1 was the candidate DEP. Furthermore, lncRNA MSTRG.28645 was highly expressed in Small Tailed Han sheep but lowly expressed in Xinji fine wool sheep. In addition, MSTRG.28645, a hub gene in the co-expression network between mRNAs and lncRNAs, was selected as one of the candidate genes for subsequent verification. Expectedly, the overexpression and interference of HSD17B1 and MSTRG.28645 showed a significant effect on hormone secretion in granulosa cells. Therefore, this study confirmed that HSD17B1 and MSTRG.28645 might be potential genes related to the fecundity of sheep. It was concluded that both HSD17B1 and MSTRG.28645 were critical regulators in the secretion of hormones that affect the fecundity of the sheep.

Accelerated CDOCKER with GPUs, Parallel Simulated Annealing, and Fast Fourier Transforms.

Fast Fourier transform (FFT)-based protein ligand docking together with parallel simulated annealing for both rigid and flexible receptor docking are implemented on graphical processing unit (GPU) accelerated platforms to significantly enhance the throughput of the CDOCKER and flexible CDOCKER - the docking algorithms in the CHARMM program for biomolecule modeling. The FFT-based approach for docking, first applied in protein-protein docking to efficiently search for the binding position and orientation of proteins, is adapted here to search ligand translational and rotational spaces given a ligand conformation in protein-ligand docking. Running on GPUs, our implementation of FFT docking in CDOCKER achieves a 15 000 fold speedup in the ligand translational and rotational space search in protein-ligand docking problems. With this significant speedup it becomes practical to exhaustively search ligand translational and rotational space when docking a rigid ligand into a protein receptor. We demonstrate in this paper that this provides an efficient way to calculate an upper bound for docking accuracy in the assessment of scoring functions for protein-ligand docking, which can be useful for improving scoring functions. The parallel molecular dynamics (MD) simulated annealing, also running on GPUs, aims to accelerate the search algorithm in CDOCKER by running MD simulated annealing in parallel on GPUs. When utilized as part of the general CDOCKER docking protocol, acceleration in excess of 20 times is achieved. With this acceleration, we demonstrate that the performance of CDOCKER for redocking is significantly improved compared with three other popular protein-ligand docking programs on two widely used protein ligand complex data sets: the Astex diverse set and the SB2012 test set. The flexible CDOCKER is similarly improved by the parallel MD simulated annealing on GPUs. Based on the results presented here, we suggest that the accelerated CDOCKER platform provides a highly competitive docking engine for both rigid-receptor and flexible-receptor docking studies and will further facilitate continued improvement in the physics-based scoring function employed in CDOCKER docking studies.

Genetic mapping and genomic selection for maize stalk strength.

BACKGROUND: Maize is one of the most important staple crops and is widely grown throughout the world. Stalk lodging can cause enormous yield losses in maize production. However, rind penetrometer resistance (RPR), which is recognized as a reliable measurement to evaluate stalk strength, has been shown to be efficient and useful for improving stalk lodging-resistance. Linkage mapping is an acknowledged approach for exploring the genetic architecture of target traits. In addition, genomic selection (GS) using whole genome markers enhances selection efficiency for genetically complex traits. In the present study, two recombinant inbred line (RIL) populations were utilized to dissect the genetic basis of RPR, which was evaluated in seven growth stages. RESULTS: The optimal stages to measure stalk strength are the silking phase and stages after silking. A total of 66 and 45 quantitative trait loci (QTL) were identified in each RIL population. Several potential candidate genes were predicted according to the maize gene annotation database and were closely associated with the biosynthesis of cell wall components. Moreover, analysis of gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway further indicated that genes related to cell wall formation were involved in the determination of RPR. In addition, a multivariate model of genomic selection efficiently improved the prediction accuracy relative to a univariate model and a model considering RPR-relevant loci as fixed effects. CONCLUSIONS: The genetic architecture of RPR is highly genetically complex. Multiple minor effect QTL are jointly involved in controlling phenotypic variation in RPR. Several pleiotropic QTL identified in multiple stages may contain reliable genes and can be used to develop functional markers for improving the selection efficiency of stalk strength. The application of genomic selection to RPR may be a promising approach to accelerate breeding process for improving stalk strength and enhancing lodging-resistance.

Improving Genomic Selection With Quantitative Trait Loci and Nonadditive Effects Revealed by Empirical Evidence in Maize.

Genomic selection (GS), a tool developed for molecular breeding, is used by plant breeders to improve breeding efficacy by shortening the breeding cycle and to facilitate the selection of candidate lines for creating hybrids without phenotyping in various environments. Association and linkage mapping have been widely used to explore and detect candidate genes in order to understand the genetic mechanisms of quantitative traits. In the current study, phenotypic and genotypic data from three experimental populations, including data on six agronomic traits (e.g., plant height, ear height, ear length, ear diameter, grain yield per plant, and hundred-kernel weight), were used to evaluate the effect of trait-relevant markers (TRMs) on prediction accuracy estimation. Integrating information from mapping into a statistical model can efficiently improve prediction performance compared with using stochastically selected markers to perform GS. The prediction accuracy can reach plateau when a total of 500-1,000 TRMs are utilized in GS. The prediction accuracy can be significantly enhanced by including nonadditive effects and TRMs in the GS model when genotypic data with high proportions of heterozygous alleles and complex agronomic traits with high proportion of nonadditive variancein phenotypic variance are used to perform GS. In addition, taking information on population structure into account can slightly improve prediction performance when the genetic relationship between the training and testing sets is influenced by population stratification due to different allele frequencies. In conclusion, GS is a useful approach for prescreening candidate lines, and the empirical evidence provided by the current study for TRMs and nonadditive effects can inform plant breeding and in turn contribute to the improvement of selection efficiency in practical GS-assisted breeding programs.