Insights into the biodegradation of polycaprolactone through genomic analysis of two plastic-degrading Rhodococcus bacteria

Polycaprolactone (PCL) is an aliphatic polyester often utilized as a model to investigate the biodegradation potential of bacteria and the involved catabolic enzymes. This study aims to characterize PCL biodegradative metabolic potential and correlate it to genomic traits of two plastic-degrading bacteria-Rhodococcus erythropolis D4 strain, a new isolate from plastic-rich organic waste treatment plant, and Rhodococcus opacus R7, known for its relevant biodegradative potential on polyethylene and similar compounds. After preliminary screening for bacteria capable of hydrolyzing tributyrin and PCL, the biodegradation of PCL was evaluated in R. erythropolis D4 and R. opacus R7 by measuring their growth and the release of PCL catabolism products up to 42 days. After 7 days, an increase of at least one order of magnitude of cell number was observed. GC-MS analyses of 28-day culture supernatants showed an increase in carboxylic acids in both Rhodococcus cultures. Furthermore, hydrolytic activity (~5 U mg-1) on short/medium-chain p-nitrophenyl esters was detected in their supernatant. Finally, a comparative genome analysis was performed between two Rhodococcus strains. A comparison with genes annotated in reference strains revealed hundreds of gene products putatively related to polyester biodegradation. Based on additional predictive analysis of gene products, gene expression was performed on a smaller group of genes, revealing that exposure to PCL elicits the greatest increase in transcription for a single gene in strain R7 and two genes, including that encoding a putative lipase, in strain D4. This work exhibits a multifaceted experimental approach to exploit the broad potential of Rhodococcus strains in the field of plastic biodegradation.

Bilirubin-Induced Transcriptomic Imprinting in Neonatal Hyperbilirubinemia

Recent findings indicated aberrant epigenetic control of the central nervous system (CNS) development in hyperbilirubinemic Gunn rats as an additional cause of cerebellar hypoplasia, the landmark of bilirubin neurotoxicity in rodents. Because the symptoms in severely hyperbilirubinemic human neonates suggest other regions as privileged targets of bilirubin neurotoxicity, we expanded the study of the potential impact of bilirubin on the control of postnatal brain development to regions correlating with human symptoms. Histology, transcriptomic, gene correlation, and behavioral studies were performed. The histology revealed widespread perturbation 9 days after birth, restoring in adulthood. At the genetic level, regional differences were noticed. Bilirubin affected synaptogenesis, repair, differentiation, energy, extracellular matrix development, etc., with transient alterations in the hippocampus (memory, learning, and cognition) and inferior colliculi (auditory functions) but permanent changes in the parietal cortex. Behavioral tests confirmed the presence of a permanent motor disability. The data correlate well both with the clinic description of neonatal bilirubin-induced neurotoxicity, as well as with the neurologic syndromes reported in adults that suffered neonatal hyperbilirubinemia. The results pave the way for better deciphering the neurotoxic features of bilirubin and evaluating deeply the efficacy of new therapeutic approaches against the acute and long-lasting sequels of bilirubin neurotoxicity.

Gene Coexpression Analysis Identifies Genes Associated with Chlorophyll Content and Relative Water Content in Pearl Millet

Pearl millet is a significant crop that is tolerant to abiotic stresses and is a staple food of arid regions. However, its underlying mechanisms of stress tolerance are not fully understood. Plant survival is regulated by the ability to perceive a stress signal and induce appropriate physiological changes. Here, we screened for genes regulating physiological changes such as chlorophyll content (CC) and relative water content (RWC) in response to abiotic stress by using "weighted gene coexpression network analysis" (WGCNA) and clustering changes in physiological traits, i.e., CC and RWC associated with gene expression. Genes' correlations with traits were defined in the form of modules, and different color names were used to denote a particular module. Modules are groups of genes with similar patterns of expression, which also tend to be functionally related and co-regulated. In WGCNA, the dark green module (7082 genes) showed a significant positive correlation with CC, and the black (1393 genes) module was negatively correlated with CC and RWC. Analysis of the module positively correlated with CC highlighted ribosome synthesis and plant hormone signaling as the most significant pathways. Potassium transporter 8 and monothiol glutaredoxin were reported as the topmost hub genes in the dark green module. In Clust analysis, 2987 genes were found to display a correlation with increasing CC and RWC. Furthermore, the pathway analysis of these clusters identified the ribosome and thermogenesis as positive regulators of RWC and CC, respectively. Our study provides novel insights into the molecular mechanisms regulating CC and RWC in pearl millet.

A cofunctional grouping-based approach for non-redundant feature gene selection in unannotated single-cell RNA-seq analysis

Feature gene selection has significant impact on the performance of cell clustering in single-cell RNA sequencing (scRNA-seq) analysis. A well-rounded feature selection (FS) method should consider relevance, redundancy and complementarity of the features. Yet most existing FS methods focus on gene relevance to the cell types but neglect redundancy and complementarity, which undermines the cell clustering performance. We develop a novel computational method GeneClust to select feature genes for scRNA-seq cell clustering. GeneClust groups genes based on their expression profiles, then selects genes with the aim of maximizing relevance, minimizing redundancy and preserving complementarity. It can work as a plug-in tool for FS with any existing cell clustering method. Extensive benchmark results demonstrate that GeneClust significantly improve the clustering performance. Moreover, GeneClust can group cofunctional genes in biological process and pathway into clusters, thus providing a means of investigating gene interactions and identifying potential genes relevant to biological characteristics of the dataset. GeneClust is freely available at https://github.com/ToryDeng/scGeneClust.

Reconstructing horizontal gene flow network to understand prokaryotic evolution

Horizontal gene transfer (HGT) is a major source of phenotypic innovation and a mechanism of niche adaptation in prokaryotes. Quantification of HGT is critical to decipher its myriad roles in microbial evolution and adaptation. Advances in genome sequencing and bioinformatics have augmented our ability to understand the microbial world, particularly the direct or indirect influence of HGT on diverse life forms. Methods for detecting HGT can be classified into phylogenetic-based and parametric or composition-based approaches. Here, we exploited the complementary strengths of both the approaches to construct a high confidence horizontal gene flow network. Our network is unique in its ability to detect the transfer of native genes of a genome to genomes from other taxa, thus establishing donor and recipient organisms (taxa), rather than through a post hoc analysis as is the practice with several other approaches. The scale-free horizontal gene flow network presented here provides new insights into modes of transfer for the exchange of genetic information and also illuminates differential gene flow across phyla.

Reconstructing horizontal gene flow network to understand prokaryotic evolution

Horizontal gene transfer (HGT) is a major source of phenotypic innovation and a mechanism of niche adaptation in prokaryotes. Quantification of HGT is critical to decipher its myriad roles in microbial evolution and adaptation. Advances in genome sequencing and bioinformatics have augmented our ability to understand the microbial world, particularly the direct or indirect influence of HGT on diverse life forms. Methods for detecting HGT can be classified into phylogenetic-based and parametric or composition-based approaches. Here, we exploited the complementary strengths of both the approaches to construct a high confidence horizontal gene flow network. Our network is unique in its ability to detect the transfer of native genes of a genome to genomes from other taxa, thus establishing donor and recipient organisms (taxa), rather than through a post hoc analysis as is the practice with several other approaches. The scale-free horizontal gene flow network presented here provides new insights into modes of transfer for the exchange of genetic information and also illuminates differential gene flow across phyla.

Mapping Strengths and Weaknesses of Different Clustering Approaches to Deciphering Bacterial Chimerism

Bacterial genomes are chimeras of DNA of different ancestries. Deconstructing chimeric genomes is central to understanding the evolutionary trajectories of their disparate components and thus the organisms as a whole in the light of their evolutionary contexts. Of specific interest is to delineate and quantify native (vertically inherited) and alien (horizontally acquired) components of bacterial genomes and also specify genomic fractions that represent different donor sources. An agglomerative clustering procedure that prioritizes grouping of proximal similar genomic segments has previously been invoked for this purpose in conjunction with a recursive segmentation procedure. Surprisingly, however, the relative strengths and weaknesses of different clustering approaches to deciphering bacterial chimerism have not yet been investigated, despite the need to robustly interpret tens of thousands of completely sequenced bacterial genomes and nearly complete genome assemblies available in the public databases. To bridge this knowledge gap and develop more robust approaches, we assessed different clustering methods, including segment order based (proximal) clustering, hierarchical clustering, affinity propagation clustering, and a novel network clustering approach on chimeric genomes modeled after bacterial genomes representing a broad spectrum of compositional complexity. Although segment order-based clustering and network clustering compared favorably with the other approaches in discriminating between native and alien DNA at genome optimized settings, network clustering did consistently better than other methods at parametric settings optimized on all test genomes together. Segment order-based clustering and hierarchical clustering outperformed other methods in alien DNA identification while preserving donor identity in the genomes. Our study highlights the strengths and weaknesses of different approaches and suggests how this can be leveraged to achieve a more robust deconstruction of bacterial chimerism.

A novel group of genes that cause endocrine resistance in breast cancer identified by dynamic gene expression analysis

Breast cancer (BC) is the most common type of cancer diagnosed in women. Among female cancer deaths, BC is the second leading cause of death worldwide. For estrogen receptor-positive (ER-positive) breast cancers, endocrine therapy is an effective therapeutic approach. However, in many cases, an ER-positive tumor becomes unresponsive to endocrine therapy, and tumor regrowth occurs after treatment. While some genetic mutations contribute to resistance in some patients, the underlying causes of resistance to endocrine therapy are mostly undetermined. In this study, we utilized a recently developed statistical approach to investigate the dynamic behavior of gene expression during the development of endocrine resistance and identified a novel group of genes whose time course expression significantly change during cell modelling of endocrine resistant BC development. Expression of a subset of these genes was also differentially expressed in microarray analysis of endocrine-resistant and endocrine-sensitive tumor samples. Surprisingly, a subset of those genes was also differentially genes expressed in triple-negative breast cancer (TNBC) as compared with ER-positive BC. The findings suggest shared genetic mechanisms may underlie the development of endocrine resistant BC and TNBC. Our findings identify 34 novel genes for further study as potential therapeutic targets for treatment of endocrine-resistant BC and TNBC.

Hormonal Regulation and Crosstalk of Auxin/Cytokinin Signaling Pathways in Potatoes In Vitro and in Relation to Vegetation or Tuberization Stages

Auxins and cytokinins create versatile regulatory network controlling virtually all aspects of plant growth and development. These hormonal systems act in close contact, synergistically or antagonistically, determining plant phenotype, resistance and productivity. However, the current knowledge about molecular interactions of these systems is still scarce. Our study with potato plants aimed at deciphering potential interactions between auxin and cytokinin signaling pathways at the level of respective gene expression. Potato plants grown on sterile medium with 1.5% (vegetation) or 5% (tuberization) sucrose were treated for 1 h with auxin or cytokinin. Effects of these two hormones on expression profiles of genes belonging to main signaling pathways of auxin and cytokinin were quantified by RT-qPCR. As a result, several signaling genes were found to respond to auxin and/or cytokinin by up- or down-regulation. The observed effects were largely organ-specific and depended on sucrose content. Auxin strongly reduced cytokinin perception apparatus while reciprocal cytokinin effect was ambiguous and sucrose-dependent. In many cases, functional clustering of genes of the same family was observed. Promoters in some clusters are enriched with canonic hormone-response cis-elements supporting their direct sensitivity to hormones. Collectively, our data shed new light on the crosstalk between auxin- and cytokinin signaling pathways.

A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity

The human genetic dissection of clinical phenotypes is complicated by genetic heterogeneity. Gene burden approaches that detect genetic signals in case-control studies are underpowered in genetically heterogeneous cohorts. We therefore developed a genome-wide computational method, network-based heterogeneity clustering (NHC), to detect physiological homogeneity in the midst of genetic heterogeneity. Simulation studies showed our method to be capable of systematically converging genes in biological proximity on the background biological interaction network, and capturing gene clusters harboring presumably deleterious variants, in an efficient and unbiased manner. We applied NHC to whole-exome sequencing data from a cohort of 122 individuals with herpes simplex encephalitis (HSE), including 13 individuals with previously published monogenic inborn errors of TLR3-dependent IFN-α/β immunity. The top gene cluster identified by our approach successfully detected and prioritized all causal variants of five TLR3 pathway genes in the 13 previously reported individuals. This approach also suggested candidate variants of three reported genes and four candidate genes from the same pathway in another ten previously unstudied individuals. TLR3 responsiveness was impaired in dermal fibroblasts from four of the five individuals tested, suggesting that the variants detected were causal for HSE. NHC is, therefore, an effective and unbiased approach for unraveling genetic heterogeneity by detecting physiological homogeneity.

Amino Acid k-mer Feature Extraction for Quantitative Antimicrobial Resistance (AMR) Prediction by Machine Learning and Model Interpretation for Biological Insights

Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide k-mers have been proposed for presenting DNA sequences to the model. However, there are trade-offs between interpretability, computational complexity and accuracy for different feature extraction methods. In this study, we have proposed a new feature extraction method, counting amino acid k-mers or oligopeptides, which provides easier model interpretation compared to counting nucleotide k-mers and reaches the same or even better accuracy in comparison with different methods. Additionally, we have trained machine learning algorithms using different feature extraction methods and compared the results in terms of accuracy, model interpretability and computational complexity. We have built a new feature selection pipeline for extraction of important features so that new AMR determinants can be discovered by analyzing these features. This pipeline allows the construction of models that only use a small number of features and can predict resistance accurately.

CTCF-Mediated Genome Architecture Regulates the Dosage of Mitotically Stable Mono-allelic Expression of Autosomal Genes

The mechanisms that guide the clonally stable random mono-allelic expression of autosomal genes remain enigmatic. We show that (1) mono-allelically expressed (MAE) genes are assorted and insulated from bi-allelically expressed (BAE) genes through CTCF-mediated chromatin loops; (2) the cell-type-specific dynamics of mono-allelic expression coincides with the gain and loss of chromatin insulator sites; (3) dosage of MAE genes is more sensitive to the loss of chromatin insulation than that of BAE genes; and (4) inactive alleles of MAE genes are significantly more insulated than active alleles and are de-repressed upon CTCF depletion. This alludes to a topology wherein the inactive alleles of MAE genes are insulated from the spatial interference of transcriptional states from the neighboring bi-allelic domains via CTCF-mediated loops. We propose that CTCF functions as a typical insulator on inactive alleles, but facilitates transcription through enhancer-linking on active allele of MAE genes, indicating widespread allele-specific regulatory roles of CTCF.

Genome-Wide Identification of NBS-Encoding Resistance Genes in Sunflower (Helianthus annuus L.)

Nucleotide Binding Site—Leucine-Rich Repeat (NBS-LRR) genes encode disease resistance proteins involved in plants’ defense against their pathogens. Although sunflower is affected by many diseases, only a few molecular details have been uncovered regarding pathogenesis and resistance mechanisms. Recent availability of sunflower whole genome sequences in publicly accessible databases allowed us to accomplish a genome-wide identification of Toll-interleukin-1 receptor-like Nucleotide-binding site Leucine-rich repeat (TNL), Coiled Coil (CC)-NBS-LRR (CNL), Resistance to powdery mildew8 (RPW8)-NBS-LRR (RNL) and NBS-LRR (NL) protein encoding genes. Hidden Markov Model (HMM) profiling of 52,243 putative protein sequences from sunflower resulted in 352 NBS-encoding genes, among which 100 genes belong to CNL group including 64 genes with RX_CC like domain, 77 to TNL, 13 to RNL, and 162 belong to NL group. We also identified signal peptides and nuclear localization signals present in the identified genes and their homologs. We found that NBS genes were located on all chromosomes and formed 75 gene clusters, one-third of which were located on chromosome 13. Phylogenetic analyses between sunflower and Arabidopsis NBS genes revealed a clade-specific nesting pattern in CNLs, with RNLs nested in the CNL-A clade, and species-specific nesting pattern for TNLs. Surprisingly, we found a moderate bootstrap support (BS = 50%) for CNL-A clade being nested within TNL clade making both the CNL and TNL clades paraphyletic. Arabidopsis and sunflower showed 87 syntenic blocks with 1049 high synteny hits between chromosome 5 of Arabidopsis and chromosome 6 of sunflower. Expression data revealed functional divergence of the NBS genes with basal level tissue-specific expression. This study represents the first genome-wide identification of NBS genes in sunflower paving avenues for functional characterization and potential crop improvement.

A Robust Manifold Graph Regularized Nonnegative Matrix Factorization Algorithm for Cancer Gene Clustering

Detecting genomes with similar expression patterns using clustering techniques plays an important role in gene expression data analysis. Non-negative matrix factorization (NMF) is an effective method for clustering the analysis of gene expression data. However, the NMF-based method is performed within the Euclidean space, and it is usually inappropriate for revealing the intrinsic geometric structure of data space. In order to overcome this shortcoming, Cai et al. proposed a novel algorithm, called graph regularized non-negative matrices factorization (GNMF). Motivated by the topological structure of the GNMF-based method, we propose improved graph regularized non-negative matrix factorization (GNMF) to facilitate the display of geometric structure of data space. Robust manifold non-negative matrix factorization (RM-GNMF) is designed for cancer gene clustering, leading to an enhancement of the GNMF-based algorithm in terms of robustness. We combine the l2,1-norm NMF with spectral clustering to conduct the wide-ranging experiments on the three known datasets. Clustering results indicate that the proposed method outperforms the previous methods, which displays the latest application of the RM-GNMF-based method in cancer gene clustering.

Comparative Genomics of Non-TNL Disease Resistance Genes from Six Plant Species

Disease resistance genes (R genes), as part of the plant defense system, have coevolved with corresponding pathogen molecules. The main objectives of this project were to identify non-Toll interleukin receptor, nucleotide-binding site, leucine-rich repeat (nTNL) genes and elucidate their evolutionary divergence across six plant genomes. Using reference sequences from Arabidopsis, we investigated nTNL orthologs in the genomes of common bean, Medicago, soybean, poplar, and rice. We used Hidden Markov Models for sequence identification, performed model-based phylogenetic analyses, visualized chromosomal positioning, inferred gene clustering, and assessed gene expression profiles. We analyzed 908 nTNL R genes in the genomes of the six plant species, and classified them into 12 subgroups based on the presence of coiled-coil (CC), nucleotide binding site (NBS), leucine rich repeat (LRR), resistance to Powdery mildew 8 (RPW8), and BED type zinc finger domains. Traditionally classified CC-NBS-LRR (CNL) genes were nested into four clades (CNL A-D) often with abundant, well-supported homogeneous subclades of Type-II R genes. CNL-D members were absent in rice, indicating a unique R gene retention pattern in the rice genome. Genomes from Arabidopsis, common bean, poplar and soybean had one chromosome without any CNL R genes. Medicago and Arabidopsis had the highest and lowest number of gene clusters, respectively. Gene expression analyses suggested unique patterns of expression for each of the CNL clades. Differential gene expression patterns of the nTNL genes were often found to correlate with number of introns and GC content, suggesting structural and functional divergence.

Gene expression atlas of pigeonpea and its application to gain insights into genes associated with pollen fertility implicated in seed formation

Pigeonpea (Cajanus cajan) is an important grain legume of the semi-arid tropics, mainly used for its protein rich seeds. To link the genome sequence information with agronomic traits resulting from specific developmental processes, a Cajanus cajan gene expression atlas (CcGEA) was developed using the Asha genotype. Thirty tissues/organs representing developmental stages from germination to senescence were used to generate 590.84 million paired-end RNA-Seq data. The CcGEA revealed a compendium of 28 793 genes with differential, specific, spatio-temporal and constitutive expression during various stages of development in different tissues. As an example to demonstrate the application of the CcGEA, a network of 28 flower-related genes analysed for cis-regulatory elements and splicing variants has been identified. In addition, expression analysis of these candidate genes in male sterile and male fertile genotypes suggested their critical role in normal pollen development leading to seed formation. Gene network analysis also identified two regulatory genes, a pollen-specific SF3 and a sucrose-proton symporter, that could have implications for improvement of agronomic traits such as seed production and yield. In conclusion, the CcGEA provides a valuable resource for pigeonpea to identify candidate genes involved in specific developmental processes and to understand the well-orchestrated growth and developmental process in this resilient crop.

CNL Disease Resistance Genes in Soybean and Their Evolutionary Divergence

Disease resistance genes (R-genes) encode proteins involved in detecting pathogen attack and activating downstream defense molecules. Recent availability of soybean genome sequences makes it possible to examine the diversity of gene families including disease-resistant genes. The objectives of this study were to identify coiled-coil NBS-LRR (= CNL) R-genes in soybean, infer their evolutionary relationships, and assess structural as well as functional divergence of the R-genes. Profile hidden Markov models were used for sequence identification and model-based maximum likelihood was used for phylogenetic analysis, and variation in chromosomal positioning, gene clustering, and functional divergence were assessed. We identified 188 soybean CNL genes nested into four clades consistent to their orthologs in Arabidopsis. Gene clustering analysis revealed the presence of 41 gene clusters located on 13 different chromosomes. Analyses of the K s-values and chromosomal positioning suggest duplication events occurring at varying timescales, and an extrapericentromeric positioning may have facilitated their rapid evolution. Each of the four CNL clades exhibited distinct patterns of gene expression. Phylogenetic analysis further supported the extrapericentromeric positioning effect on the divergence and retention of the CNL genes. The results are important for understanding the diversity and divergence of CNL genes in soybean, which would have implication in soybean crop improvement in future.

Neighboring Genes Show Correlated Evolution in Gene Expression

When considering the evolution of a gene’s expression profile, we commonly assume that this is unaffected by its genomic neighborhood. This is, however, in contrast to what we know about the lack of autonomy between neighboring genes in gene expression profiles in extant taxa. Indeed, in all eukaryotic genomes genes of similar expression-profile tend to cluster, reflecting chromatin level dynamics. Does it follow that if a gene increases expression in a particular lineage then the genomic neighbors will also increase in their expression or is gene expression evolution autonomous? To address this here we consider evolution of human gene expression since the human-chimp common ancestor, allowing for both variation in estimation of current expression level and error in Bayesian estimation of the ancestral state. We find that in all tissues and both sexes, the change in gene expression of a focal gene on average predicts the change in gene expression of neighbors. The effect is highly pronounced in the immediate vicinity (<100 kb) but extends much further. Sex-specific expression change is also genomically clustered. As genes increasing their expression in humans tend to avoid nuclear lamina domains and be enriched for the gene activator 5-hydroxymethylcytosine, we conclude that, most probably owing to chromatin level control of gene expression, a change in gene expression of one gene likely affects the expression evolution of neighbors, what we term expression piggybacking, an analog of hitchhiking.

A novel bioinformatics pipeline to discover genes related to arbuscular mycorrhizal symbiosis based on their evolutionary conservation pattern among higher plants

BACKGROUND

Genes involved in arbuscular mycorrhizal (AM) symbiosis have been identified primarily by mutant screens, followed by identification of the mutated genes (forward genetics). In addition, a number of AM-related genes has been identified by their AM-related expression patterns, and their function has subsequently been elucidated by knock-down or knock-out approaches (reverse genetics). However, genes that are members of functionally redundant gene families, or genes that have a vital function and therefore result in lethal mutant phenotypes, are difficult to identify. If such genes are constitutively expressed and therefore escape differential expression analyses, they remain elusive. The goal of this study was to systematically search for AM-related genes with a bioinformatics strategy that is insensitive to these problems. The central element of our approach is based on the fact that many AM-related genes are conserved only among AM-competent species.

RESULTS

Our approach involves genome-wide comparisons at the proteome level of AM-competent host species with non-mycorrhizal species. Using a clustering method we first established orthologous/paralogous relationships and subsequently identified protein clusters that contain members only of the AM-competent species. Proteins of these clusters were then analyzed in an extended set of 16 plant species and ranked based on their relatedness among AM-competent monocot and dicot species, relative to non-mycorrhizal species. In addition, we combined the information on the protein-coding sequence with gene expression data and with promoter analysis. As a result we present a list of yet uncharacterized proteins that show a strongly AM-related pattern of sequence conservation, indicating that the respective genes may have been under selection for a function in AM. Among the top candidates are three genes that encode a small family of similar receptor-like kinases that are related to the S-locus receptor kinases involved in sporophytic self-incompatibility.

CONCLUSIONS

We present a new systematic strategy of gene discovery based on conservation of the protein-coding sequence that complements classical forward and reverse genetics. This strategy can be applied to diverse other biological phenomena if species with established genome sequences fall into distinguished groups that differ in a defined functional trait of interest.

Genome analysis of poplar LRR-RLP gene clusters reveals RISP, a defense-related gene coding a candidate endogenous peptide elicitor

In plants, cell-surface receptors control immunity and development through the recognition of extracellular ligands. Leucine-rich repeat receptor-like proteins (LRR-RLPs) constitute a large multigene family of cell-surface receptors. Although this family has been intensively studied, a limited number of ligands has been identified so far, mostly because methods used for their identification and characterization are complex and fastidious. In this study, we combined genome and transcriptome analyses to describe the LRR-RLP gene family in the model tree poplar (Populus trichocarpa). In total, 82 LRR-RLP genes have been identified in P. trichocarpa genome, among which 66 are organized in clusters of up to seven members. In these clusters, LRR-RLP genes are interspersed by orphan, poplar-specific genes encoding small proteins of unknown function (SPUFs). In particular, the nine largest clusters of LRR-RLP genes (47 LRR-RLPs) include 71 SPUF genes that account for 59% of the non-LRR-RLP gene content within these clusters. Forty-four LRR-RLP and 55 SPUF genes are expressed in poplar leaves, mostly at low levels, except for members of some clusters that show higher and sometimes coordinated expression levels. Notably, wounding of poplar leaves strongly induced the expression of a defense SPUF gene named Rust-Induced Secreted protein (RISP) that has been previously reported as a marker of poplar defense responses. Interestingly, we show that the RISP-associated LRR-RLP gene is highly expressed in poplar leaves and slightly induced by wounding. Both gene promoters share a highly conserved region of ~300 nucleotides. This led us to hypothesize that the corresponding pair of proteins could be involved in poplar immunity, possibly as a ligand/receptor couple. In conclusion, we speculate that some poplar SPUFs, such as RISP, represent candidate endogenous peptide ligands of the associated LRR-RLPs and we discuss how to investigate further this hypothesis.

Transcription factories

There is considerable evidence that transcription does not occur homogeneously or diffusely throughout the nucleus, but rather at a number of specialized, discrete sites termed transcription factories. The factories are composed of ~4–30 RNA polymerase molecules, and are associated with many other molecules involved in transcriptional activation and mRNA processing. Some data suggest that the polymerase molecules within a factory remain stationary relative to the transcribed DNA, which is thought to be reeled through the factory site. There is also some evidence that transcription factories could help organize chromatin and nuclear structure, contributing to both the formation of chromatin loops and the clustering of active and co-regulated genes.