PPGR: a comprehensive perennial plant genomes and regulation database.

Perennial woody plants hold vital ecological significance, distinguished by their unique traits. While significant progress has been made in their genomic and functional studies, a major challenge persists: the absence of a comprehensive reference platform for collection, integration and in-depth analysis of the vast amount of data. Here, we present PPGR (Resource for Perennial Plant Genomes and Regulation; https://ngdc.cncb.ac.cn/ppgr/) to address this critical gap, by collecting, integrating, analyzing and visualizing genomic, gene regulation and functional data of perennial plants. PPGR currently includes 60 species, 847 million protein-protein/TF (transcription factor)-target interactions, 9016 transcriptome samples under various environmental conditions and genetic backgrounds. Noteworthy is the focus on genes that regulate wood production, seasonal dormancy, terpene biosynthesis and leaf senescence representing a wealth of information derived from experimental data, literature mining, public databases and genomic predictions. Furthermore, PPGR incorporates a range of multi-omics search and analysis tools to facilitate browsing and application of these extensive datasets. PPGR represents a comprehensive and high-quality resource for perennial plants, substantiated by an illustrative case study that demonstrates its capacity in unraveling gene functions and shedding light on potential regulatory processes.

Genetic architecture of the metabolic pathway of salicylic acid biosynthesis in Populus.

Salicylic acid (SA) is a vital hormone for adaptive responses to biotic and abiotic stresses, which facilitates growth-immunity trade-offs in plants. However, the genetic regulatory networks underlying the metabolic pathway of SA biosynthesis in perennial species remain unclear. Here, we integrated genome-wide association study (GWAS) with metabolite and expression profiling methodologies to dissect the genetic architecture of SA biosynthesis in Populus. First, we quantified nine intermediate metabolites of SA biosynthesis in 300 unrelated Populus tomentosa Carr. individuals. Then, we used a systematic genetic strategy to identify candidate genes for constructing the genetic regulatory network of SA biosynthesis. We focused on WRKY70, an efficient transcription factor, as the key causal gene in the regulatory network, and combined the novel genes coordinating the accumulation of SA. Finally, we identified eight GWAS signals and eight expression quantitative trait loci situated in a selective sweep, and showed the presence of large allele frequency differences among the three geographic populations, revealing that candidate genes subject to selection were involved in SA biosynthesis. This study provides an integrated strategy for dissecting the genetic architecture of the metabolic pathway of SA biosynthesis in Populus, thereby enhancing our understanding of genetic regulation of SA biosynthesis in trees, and accelerating marker-assisted breeding efforts toward high-resistance elite varieties of Populus.

Genetic interactions among Pto-miR319 family members and their targets influence growth and wood properties in Populus tomentosa.

MicroRNAs (miRNAs) play crucial roles in all aspects of plant growth and development, but the genetic interactions of miRNAs and their target genes in woody plants are largely unknown. Here, we integrated association genetics and expression profiling to decipher the allelic variations and interactions of the Pto-MIR319 family of miRNAs and 12 putative Pto-miR319 target genes related to wood formation in 435 unrelated individuals of Populus tomentosa Carrière (Chinese white poplar). Expression pattern analysis showed that among all pairings between expressions of pre-miRNA of Pto-MIR319 members and targets, 70.0% showed negative correlation of expression levels (r = - 0.944 to 0.674, P < 0.01) in eight tissues and organs of poplar, suggesting that Pto-miR319 may participate in the regulatory network of wood formation. Single SNP-based association studies identified 137 significant associations (P < 0.01, Q < 0.1), representing 126 unique SNPs from Pto-MIR319 members and their targets, with 10 tree growth traits, revealing that these genetic factors have common roles related to wood formation. Epistasis analysis uncovered 105 significant SNP-SNP associations (P < 0.01) influencing the 10 traits, demonstrating the close genetic interactions between Pto-MIR319 family members and the 12 Pto-miR319 target genes. Notably, one common SNP, in the precursor region of Pto-MIR319e, affected the stability of Pto-MIR319e's secondary structure by altering the stem-loop structure and minimum free energy, contributing to variations in the expression of Pto-MIR319e and Pto-miR319e target genes. This study enriches the understanding of the functions of miR319 family miRNAs in poplar and exemplifies a feasible approach to exploring the genetic effects underlying miRNA-mRNA interactions related to complex traits in trees.

Genetic dissection of the gene coexpression network underlying photosynthesis in Populus.

Photosynthesis is a key reaction that ultimately generates the carbohydrates needed to form woody tissues in trees. However, the genetic regulatory network of protein-encoding genes (PEGs) and regulatory noncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), underlying the photosynthetic pathway is unknown. Here, we integrated data from coexpression analysis, association studies (additive, dominance and epistasis), and expression quantitative trait nucleotide (eQTN) mapping to dissect the causal variants and genetic interaction network underlying photosynthesis in Populus. We initially used 30 PEGs, 6 miRNAs and 12 lncRNAs to construct a coexpression network based on the tissue-specific gene expression profiles of 15 Populus samples. Then, we performed association studies using a natural population of 435 unrelated Populus tomentosa individuals, and identified 72 significant associations (P ≤ 0.001, q ≤ 0.05) with diverse additive and dominance patterns underlying photosynthesis-related traits. Analysis of epistasis and eQTNs revealed that the complex genetic interactions in the coexpression network contribute to phenotypes at various levels. Finally, we demonstrated that heterologously expressing the most highly linked gene (PtoPsbX1) in this network significantly improved photosynthesis in Arabidopsis thaliana, pointing to the functional role of PtoPsbX1 in the photosynthetic pathway. This study provides an integrated strategy for dissecting a complex genetic interaction network, which should accelerate marker-assisted breeding efforts to genetically improve woody plants.

Long noncoding RNA MEG3 deteriorates inflammatory damage by downregulating microRNA-101a.

Valvulopathy is a familiar heart disease, which fearfully harms the health of the body. We studied the effects and mechanism of long noncoding RNA maternally expressed gene 3 (lncMEG3) on MVICs cell in inflammatory damage. Cell Counting Kit-8 and flow cytometry were respectively used to detect the effect of tumor necrosis factor α (TNF-α), MEG3 and microRNA (miR)-101a on cell viability and apoptosis. Moreover, MEG3 and miR-101a expression were changed by cell transfection and investigated by reverse transcription-quantitative polymerase chain reaction. Furthermore, Western blot was used to investigate the levels of Bax, pro-caspase-3, cleaved-caspase-3, pro-caspase-9, cleaved-caspase-9, interleukin (IL)-1ß, IL-6 and related-proteins of cell pathways. Otherwise, the levels of IL-1ß and IL-6 were also investigated by enzyme-linked immunosorbent assay kit. Reactive oxygen species (ROS) was examined by ROS assay. We found TNF-α caused inflammatory damage and upregulated MEG3. MEG3 was overexpressed and silenced in cells. Besides, MEG3 deteriorated inflammatory damage. Furthermore, MEG3 negatively regulated miR-101a and miR-101a mimic could reverse the effect of pc-MEG3. Besides, MEG3 enhanced the JNK and NF-κB pathways by downregulating miR-101a. In conclusion, MEG3 deteriorated cell inflammatory damage by downregulating miR-101a via JNK and NF-κB pathways.

Retracted: Tetramethylpyrazine partially relieves hypoxia-caused damage of cardiomyocytes H9c2 by downregulation of miR-449a.

Inadequate oxygen supply is probably one of the most important pathophysiological mechanisms of cardiomyocyte damage in ischemic heart disease. Tetramethylpyrazine (TMP, also known as ligustrazine) is the main active ingredient isolated from the rhizome of Ligusticum chuanxiong Hort. A previous study reported that the TMP could exert cardioprotective activity. This study aimed to explore the molecular mechanism of the protective effects of TMP on cardiomyocyte damage caused by hypoxia. The viability and apoptosis of cardiomyocytes H9c2 were detected using cell counting kit-8 assay and annexin V-FITC/PI staining, respectively. Quantitative reverse transcription polymerase chain reaction was conducted to measure the expression level of microRNA-449a (miR-449a). Cell transfection was performed to upregulate the expression level of miR-449a or downregulate the expression level of sirtuin 1 (Sirt1). The protein expression levels of Sirt1 and key factors involved in cell apoptosis and phosphatidylinositol 3-kinase/protein kinase 3 (PI3K/AKT) pathway were evaluated using western blot analysis. We found that the hypoxia incubation inhibited H9c2 viability, induced cell apoptosis, and inactivated the PI3K/AKT pathway. TMP treatment partially relieved the hypoxia-caused H9c2 cell viability loss and apoptosis, as well as reversed the hypoxia-caused inactivation of the PI3K/AKT pathway. Moreover, TMP partially alleviated the upregulation of miR-449a in H9c2 cells caused by hypoxia. Overexpression of miR-449a weakened the effects of TMP on hypoxia-treated H9c2 cells. Furthermore, Sirt1 was a target gene of miR-449a. Knockdown of Sirt1 also weakened the effects of TMP on hypoxia-treated H9c2 cells. In conclusion, TMP partially relieved hypoxia-caused cardiomyocytes H9c2 viability loss and apoptosis at least through downregulating miR-499a, upregulating Sirt1, and then activating the PI3K/AKT pathway.

Association Genetics in Populus Reveal the Allelic Interactions of Pto-MIR167a and Its Targets in Wood Formation.

MicroRNAs (miRNAs) play crucial regulatory roles in plant growth and development by interacting with RNA molecules, including messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs); however, the genetic networks of miRNAs and their targets influencing the phenotypes of perennial trees remain to be investigated. Here, we integrated expression profiling and association analysis of underlying physiology and expression traits to dissect the allelic variations and genetic interactions of Pto-MIR167a and its targets, sponge lncRNA ARFRL, and Pto-ARF8, in 435 unrelated individuals of Populus tomentosa. Tissue-specific expression analysis in eight tissues, including stem, leaf, root, and shoot apex, revealed negative correlations between Pto-MIR167a and lncRNA ARFRL and Pto-ARF8 (r = -0.60 and -0.61, respectively, P < 0.01), and a positive correlation between sponge lncRNA ARFRL and Pto-ARF8 (r = 0.90, P < 0.01), indicating their potential regulatory roles in tree growth and wood formation. Single nucleotide polymorphism (SNP)-based association studies detected 53 significant associations (P < 0.01, Q < 0.1) representing 41 unique SNPs from the three genes and six traits, suggesting their potential roles in wood formation. Epistasis uncovered 88 pairwise interactions for 10 traits, which provided substantial evidence for genetic interactions among Pto-MIR167a, lncRNA ARFRL, and Pto-ARF8. Using gene expression-based association mapping, we also examined SNPs within the three genes that influence phenotypes by regulating the expression of Pto-ARF8. Interestingly, SNPs in the precursor region of Pto-MIR167a altered its secondary structure stability and transcription, thereby affecting the expression of its targets. In summary, we elucidated the genetic interactions between Pto-MIR167a and its targets, sponge lncRNA ARFRL, and Pto-ARF8, in tree growth and wood formation, and provide a feasible method for further investigation of multi-factor genetic networks influencing phenotypic variation in the population genetics of trees.

Conserved noncoding sequences conserve biological networks and influence genome evolution.

Comparative genomics approaches have identified numerous conserved cis-regulatory sequences near genes in plant genomes. Despite the identification of these conserved noncoding sequences (CNSs), our knowledge of their functional importance and selection remains limited. Here, we used a combination of DNA methylome analysis, microarray expression analyses, and functional annotation to study these sequences in the model tree Populus trichocarpa. Methylation in CG contexts and non-CG contexts was lower in CNSs, particularly CNSs in the 5'-upstream regions of genes, compared with other sites in the genome. We observed that CNSs are enriched in genes with transcription and binding functions, and this also associated with syntenic genes and those from whole-genome duplications, suggesting that cis-regulatory sequences play a key role in genome evolution. We detected a significant positive correlation between CNS number and protein interactions, suggesting that CNSs may have roles in the evolution and maintenance of biological networks. The divergence of CNSs indicates that duplication-degeneration-complementation drives the subfunctionalization of a proportion of duplicated genes from whole-genome duplication. Furthermore, population genomics confirmed that most CNSs are under strong purifying selection and only a small subset of CNSs shows evidence of adaptive evolution. These findings provide a foundation for future studies exploring these key genomic features in the maintenance of biological networks, local adaptation, and transcription.

Computational Prediction of RNA-Binding Proteins and Binding Sites.

Proteins and RNA interaction have vital roles in many cellular processes such as protein synthesis, sequence encoding, RNA transfer, and gene regulation at the transcriptional and post-transcriptional levels. Approximately 6%-8% of all proteins are RNA-binding proteins (RBPs). Distinguishing these RBPs or their binding residues is a major aim of structural biology. Previously, a number of experimental methods were developed for the determination of protein-RNA interactions. However, these experimental methods are expensive, time-consuming, and labor-intensive. Alternatively, researchers have developed many computational approaches to predict RBPs and protein-RNA binding sites, by combining various machine learning methods and abundant sequence and/or structural features. There are three kinds of computational approaches, which are prediction from protein sequence, prediction from protein structure, and protein-RNA docking. In this paper, we review all existing studies of predictions of RNA-binding sites and RBPs and complexes, including data sets used in different approaches, sequence and structural features used in several predictors, prediction method classifications, performance comparisons, evaluation methods, and future directions.

Systematic functional genomics resource and annotation for poplar.

Poplar, as a model species for forestry research, has many excellent characteristics. Studies on functional genes have provided the foundation, at the molecular level, for improving genetic traits and cultivating elite lines. Although studies on functional genes have been performed for many years, large amounts of experimental data remain scattered across various reports and have not been unified via comprehensive statistical analysis. This problem can be addressed by employing bioinformatic methodology and technology to gather and organise data to construct a Poplar Functional Gene Database, containing data on 207 poplar functional genes. As an example, the authors investigated genes of Populus euphratica involved in the response to salt stress. Four small cDNA libraries were constructed and treated with 300 mM NaCl or pure water for 6 and 24 h. Using high-throughput sequencing, they identified conserved and novel miRNAs that were differentially expressed. Target genes were next predicted and detailed functional information derived using the Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes pathway analysis. This information provides a primary visual schema allowing us to understand the dynamics of the regulatory gene network responding to salt stress in Populus.

An overview of the prediction of protein DNA-binding sites.

Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications.

Genome-wide analysis of salt-responsive and novel microRNAs in Populus euphratica by deep sequencing.

BACKGROUND: Populus euphratica is a representative model woody plant species for studying resistance to abiotic stresses such as drought and salt. Salt stress is one of the most common environmental factors that affect plant growth and development. MicroRNAs (miRNAs) are small, noncoding RNAs that have important regulatory functions in plant growth, development, and response to abiotic stress. RESULTS: To investigate the miRNAs involved in the salt-stress response, we constructed four small cDNA libraries from P. euphratica plantlets treated with or without salt (300 mM NaCl, 3 days) in either the root or leaf. Using high-throughput sequencing to identify miRNAs, we found 164 conserved miRNAs belonging to 44 families. Of these, 136 novel miRNAs were from the leaf, and 128 novel miRNAs were from the root. In response to salt stress, 95 miRNAs belonging to 46 conserved miRNAs families changed significantly, with 56 miRNAs upregulated and 39 miRNAs downregulated in the leaf. A comparison of the leaf and root tissues revealed 155 miRNAs belonging to 63 families with significantly altered expression, including 84 upregulated and 71 downregulated miRNAs. Furthermore, 479 target genes in the root and 541 targets of novel miRNAs in the leaf were predicted, and functional information was annotated using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. CONCLUSIONS: This study provides a novel visual field for understanding the regulatory roles of miRNAs in response to salt stress in Populus.

Multiallelic epistatic model for an out-bred cross and mapping algorithm of interactive quantitative trait loci.

BACKGROUND: Genetic mapping has proven to be powerful for studying the genetic architecture of complex traits by characterizing a network of the underlying interacting quantitative trait loci (QTLs). Current statistical models for genetic mapping were mostly founded on the biallelic epistasis of QTLs, incapable of analyzing multiallelic QTLs and their interactions that are widespread in an outcrossing population. RESULTS: Here we have formulated a general framework to model and define the epistasis between multiallelic QTLs. Based on this framework, we have derived a statistical algorithm for the estimation and test of multiallelic epistasis between different QTLs in a full-sib family of outcrossing species. We used this algorithm to genomewide scan for the distribution of multiallelic epistasis for a rooting ability trait in an outbred cross derived from two heterozygous poplar trees. The results from simulation studies indicate that the positions and effects of multiallelic QTLs can well be estimated with a modest sample and heritability. CONCLUSIONS: The model and algorithm developed provide a useful tool for better characterizing the genetic control of complex traits in a heterozygous family derived from outcrossing species, such as forest trees, and thus fill a gap that occurs in genetic mapping of this group of important but underrepresented species.

Comparison of linear gap penalties and profile-based variable gap penalties in profile-profile alignments.

Profile-profile alignment algorithms have proven powerful for recognizing remote homologs and generating alignments by effectively integrating sequence evolutionary information into scoring functions. In comparison to scoring function, the development of gap penalty functions has rarely been addressed in profile-profile alignment algorithms. Although indel frequency profiles have been used to construct profile-based variable gap penalties in some profile-profile alignment algorithms, there is still no fair comparison between variable gap penalties and traditional linear gap penalties to quantify the improvement of alignment accuracy. We compared two linear gap penalty functions, the traditional affine gap penalty (AGP) and the bilinear gap penalty (BGP), with two profile-based variable gap penalty functions, the Profile-based Gap Penalty used in SP(5) (SPGP) and a new Weighted Profile-based Gap Penalty (WPGP) developed by us, on some well-established benchmark datasets. Our results show that profile-based variable gap penalties get limited improvements than linear gap penalties, whether incorporated with secondary structure information or not. Secondary structure information appears less powerful to be incorporated into gap penalties than into scoring functions. Analysis of gap length distributions indicates that gap penalties could stably maintain corresponding distributions of gap lengths in their alignments, but the distribution difference from reference alignments does not reflect the performance of gap penalties. There is useful information in indel frequency profiles, but it is still not good enough for improving alignment accuracy when used in profile-based variable gap penalties. All of the methods tested in this work are freely accessible at http://protein.cau.edu.cn/gppat/.

MetaDBSite: a meta approach to improve protein DNA-binding sites prediction.

BACKGROUND: Protein-DNA interactions play an important role in many fundamental biological activities such as DNA replication, transcription and repair. Identification of amino acid residues involved in DNA binding site is critical for understanding of the mechanism of gene regulations. In the last decade, there have been a number of computational approaches developed to predict protein-DNA binding sites based on protein sequence and/or structural information. RESULTS: In this article, we present metaDBSite, a meta web server to predict DNA-binding residues for DNA-binding proteins. MetaDBSite integrates the prediction results from six available online web servers: DISIS, DNABindR, BindN, BindN-rf, DP-Bind and DBS-PRED and it solely uses sequence information of proteins. A large dataset of DNA-binding proteins is constructed from the Protein Data Bank and it serves as a gold-standard benchmark to evaluate the metaDBSite approach and the other six predictors. CONCLUSIONS: The comparison results show that metaDBSite outperforms single individual approach. We believe that metaDBSite will become a useful and integrative tool for protein DNA-binding residues prediction. The MetaDBSite web-server is freely available at http://projects.biotec.tu-dresden.de/metadbsite/ and http://sysbio.zju.edu.cn/metadbsite.

TIM-Finder: a new method for identifying TIM-barrel proteins.

BACKGROUND: The triosephosphate isomerase (TIM)-barrel fold occurs frequently in the proteomes of different organisms, and the known TIM-barrel proteins have been found to play diverse functional roles. To accelerate the exploration of the sequence-structure protein landscape in the TIM-barrel fold, a computational tool that allows sensitive detection of TIM-barrel proteins is required. RESULTS: To develop a new TIM-barrel protein identification method in this work, we consider three descriptors: a sequence-alignment-based descriptor using PSI-BLAST e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. With the assistance of Support Vector Machine (SVM), the three descriptors were combined to obtain a new method with improved performance, which we call TIM-Finder. When tested on the whole proteome of Bacillus subtilis, TIM-Finder is able to detect 194 TIM-barrel proteins at a 99% confidence level, outperforming the PSI-BLAST search as well as one existing fold recognition method. CONCLUSIONS: TIM-Finder can serve as a competitive tool for proteome-wide TIM-barrel protein identification. The TIM-Finder web server is freely accessible at http://202.112.170.199/TIM-Finder/.

DescFold: a web server for protein fold recognition.

BACKGROUND: Machine learning-based methods have been proven to be powerful in developing new fold recognition tools. In our previous work [Zhang, Kochhar and Grigorov (2005) Protein Science, 14: 431-444], a machine learning-based method called DescFold was established by using Support Vector Machines (SVMs) to combine the following four descriptors: a profile-sequence-alignment-based descriptor using Psi-blast e-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. In this work, we focus on the improvement of DescFold by incorporating more powerful descriptors and setting up a user-friendly web server. RESULTS: In seeking more powerful descriptors, the profile-profile alignment score generated from the COMPASS algorithm was first considered as a new descriptor (i.e., PPA). When considering a profile-profile alignment between two proteins in the context of fold recognition, one protein is regarded as a template (i.e., its 3D structure is known). Instead of a sequence profile derived from a Psi-blast search, a structure-seeded profile for the template protein was generated by searching its structural neighbors with the assistance of the TM-align structural alignment algorithm. Moreover, the COMPASS algorithm was used again to derive a profile-structural-profile-alignment-based descriptor (i.e., PSPA). We trained and tested the new DescFold in a total of 1,835 highly diverse proteins extracted from the SCOP 1.73 version. When the PPA and PSPA descriptors were introduced, the new DescFold boosts the performance of fold recognition substantially. Using the SCOP_1.73_40% dataset as the fold library, the DescFold web server based on the trained SVM models was further constructed. To provide a large-scale test for the new DescFold, a stringent test set of 1,866 proteins were selected from the SCOP 1.75 version. At a less than 5% false positive rate control, the new DescFold is able to correctly recognize structural homologs at the fold level for nearly 46% test proteins. Additionally, we also benchmarked the DescFold method against several well-established fold recognition algorithms through the LiveBench targets and Lindahl dataset. CONCLUSIONS: The new DescFold method was intensively benchmarked to have very competitive performance compared with some well-established fold recognition methods, suggesting that it can serve as a useful tool to assist in template-based protein structure prediction. The DescFold server is freely accessible at http://202.112.170.199/DescFold/index.html.