A Model-Based Approach for Identifying Functional Intergenic Transcribed Regions and Noncoding RNAs

Evaluation of efficacy of non-coding RNA in abiotic stress management of field crops: Current status and future prospective

Abiotic stresses are responsible for the major losses in crop yield all over the world. Stresses generate harmful ROS which can impair cellular processes in plants. Therefore, plants have evolved antioxidant systems in defence against the stress-induced damages. The frequency of occurrence of abiotic stressors has increased several-fold due to the climate change experienced in recent times and projected for the future. This had particularly aggravated the risk of yield losses and threatened global food security. Non-coding RNAs are the part of eukaryotic genome that does not code for any proteins. However, they have been recently found to have a crucial role in the responses of plants to both abiotic and biotic stresses. There are different types of ncRNAs, for example, miRNAs and lncRNAs, which have the potential to regulate the expression of stress-related genes at the levels of transcription, post-transcription, and translation of proteins. The lncRNAs are also able to impart their epigenetic effects on the target genes through the alteration of the status of histone modification and organization of the chromatins. The current review attempts to deliver a comprehensive account of the role of ncRNAs in the regulation of plants' abiotic stress responses through ROS homeostasis. The potential applications ncRNAs in amelioration of abiotic stresses in field crops also have been evaluated.

Variants in interferon gamma inducible protein 16 (IFI16) and absent in melanoma 2 (AIM2) genes that modulate inflammatory response are associated with periodontitis

OBJECTIVE

Evaluate the association of genetic variants of the interferon gamma inducible protein 16 (IFI16) and absent in melanoma 2 (AIM2) genes with periodontitis.

METHODS

The study involved 117 individuals with periodontitis and 389 without periodontitis, all Brazilians, miscegenated. Individuals with periodontitis presented at least 4 teeth with ≥ 1 site with probing depth ≥ 4 mm; clinical attachment level ≥ 3 mm on the same site and bleeding upon stimulus. Genotyping was performed using the Infinium Multi-Ethnic AMR/AFR-8 Bead Chip focused on Hispanic and African American populations with approximately 2 million markers of the human genome. Multivariate logistic regression was performed to identify associations in additive, dominant and recessive models adjusted for covariates age, obesity, mouth breathing, flossing, asthma, and ancestry.

RESULTS

In IFI16, the rs75985579-A is positively associated with periodontitis in the additive (Odds Ratio adjusted (ORadjusted) 2.65, 95% confidence interval (CI):1.25-5.60, p value: 0.007) and dominant models (ORadjusted 2.56, 95%CI:1.13-5.81, p value: 0.017). In AIM2, the rs76457189-G, is associated negatively with periodontitis in two genetic models evaluated, additive (ORadjusted 0.21, 95%CI:0.05-0.94, p value: 0.022) and dominant (ORadjusted 0.21, 95%CI:0.05-0.94, p value: 0.022).

CONCLUSIONS

These results have shown that variants in the IFI16 and AIM2 genes are associated with periodontitis. Individuals with at least one A (adenine) allele of the rs75985579 (IFI16) are more than twice as likely to have periodontitis, while individuals with the G (guanine) allele of rs76457189 (AIM2) are less likely to be diagnosed with periodontitis, providing a negative association with periodontitis.

Unscheduled epigenetic modifications cause genome instability and sterility through aberrant R-loops following starvation

During starvation, organisms modify both gene expression and metabolism to adjust to the energy stress. We previously reported that Caenorhabditis elegans lacing AMP-activated protein kinase (AMPK) exhibit transgenerational reproductive defects associated with abnormally elevated trimethylated histone H3 at lysine 4 (H3K4me3) levels in the germ line following recovery from acute starvation. Here, we show that these H3K4me3 marks are significantly increased at promoters, driving aberrant transcription elongation resulting in the accumulation of R-loops in starved AMPK mutants. DNA-RNA immunoprecipitation followed by high-throughput sequencing (DRIP-seq) analysis demonstrated that a significant proportion of the genome was affected by R-loop formation. This was most pronounced in the promoter-transcription start site regions of genes, in which the chromatin was modified by H3K4me3. Like H3K4me3, the R-loops were also found to be heritable, likely contributing to the transgenerational reproductive defects typical of these mutants following starvation. Strikingly, AMPK mutant germ lines show considerably more RAD-51 (the RecA recombinase) foci at sites of R-loop formation, potentially sequestering them from their roles at meiotic breaks or at sites of induced DNA damage. Our study reveals a previously unforeseen role of AMPK in maintaining genome stability following starvation. The downstream effects of R-loops on DNA damage sensitivity and germline stem cell integrity may account for inappropriate epigenetic modification that occurs in numerous human disorders, including various cancers.

Identification and differential analysis of noncoding RNAs in response to drought in Phyllostachys aureosulcata f. spectabilis

The role of noncoding RNAs (ncRNAs) in plant resistance to abiotic stresses is increasingly being discovered. Drought stress is one of the most common stresses that affecting plant growth, and high intensity drought has a significant impact on the normal growth of plants. In this study, a high-throughput sequencing was performed on plant tissue samples of Phyllostachys aureosulcata f. spectabilis C. D. Chu et C. S. Chao by drought treatment for 0, 2, 4 and 6 days. The sequencing results were analysed bioinformatically. We detected 336,946 RNAs among all 12 samples, including 192,098 message RNAs (mRNAs), 142,761 long noncoding RNAs (lncRNAs), 1,670 circular RNAs (circRNAs), and 417 microRNAs (miRNAs). We detected 2,419 differentially expressed (DE) ncRNAs, including 213 DE circRNAs, 2,088 DE lncRNAs and 118 DE miRNAs. Then, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to functionally predict DE ncRNAs. The results showed that most DE ncRNAs are involved in the response to drought stress, mainly in biochemical reactions involved in some metabolites, as well as in organelle activities. In addition, we validated two random circRNAs and demonstrated their circularity. We also found a stable internal reference gene available for Phyllostachys aureosulcata f. spectabilis and validated the accuracy of this experiment by quantitative real-time polymerase chain reaction (qRT-PCR).

Genome-Wide ChIP-seq and RNA-seq Analyses of STAT3 Target Genes in TLRs Activated Human Peripheral Blood B Cells

Toll like receptors (TLRs) induced response plays a vital role in B-cell development and activation, in which TLR7-mediated and TLR9-mediated response interact together and play antagonistic or cooperative roles at different situations. Previous studies showed that the transcription factor signal transducer and activator of transcription (STAT) 3 was one of the key transcriptional factors (TFs) needed for both TLR7 and TLR9 signaling in B cell, and patients with autosomal dominant hyper IgE syndromes (AD-HIES) due to STAT3 mutations having defective TLRs response in B cells. However, how STAT3 affects its target genes and the downstream signaling pathways in B cell upon TLRs stimulation remains unclarified on a genome-wide level. ChIP-seq and RNA-seq was used in this study to identify the STAT3 targets in response to TLRs stimulation in human B cell. STAT3 ChIP-seq results showed a total of 611 and 2,289 differential STAT3-binding sites in human B cell after TLR7 and TLR9 agonists stimulation, respectively. RNA-seq results showed 1,186 and 1,775 differentially expressed genes after TLR7 and TLR9 activation, respectively. We identified 47 primary STAT3 target genes after TLR7 activation and 189 target genes after TLR9 activation in B cell by integration of STAT3 ChIP-seq and RNA-seq data. Among these STAT3 primary targets, we identified 7 TFs and 18 TFs for TLR7 and TLR9 response, respectively. Besides, we showed that STAT3 might regulate TLR9, but not TLR7 response in B cells through directly regulating integrin signaling pathway, which might further affect the antagonism between TLR7 and TLR9 signaling in B cell. Our study provides insights into the molecular mechanism of human TLRs response in B cell and how it can be regulated, which helps to better understand and modulate TLR-mediated pathogenic immune responses in B cell.

Characterization of novel pollen-expressed transcripts reveals their potential roles in pollen heat stress response in Arabidopsis thaliana

Arabidopsis pollen transcriptome analysis revealed new intergenic transcripts of unknown function, many of which are long non-coding RNAs, that may function in pollen-specific processes, including the heat stress response. The male gametophyte is the most heat sensitive of all plant tissues. In recent years, long noncoding RNAs (lncRNAs) have emerged as important components of cellular regulatory networks involved in most biological processes, including response to stress. While examining RNAseq datasets of developing and germinating Arabidopsis thaliana pollen exposed to heat stress (HS), we identified 66 novel and 246 recently annotated intergenic expressed loci (XLOCs) of unknown function, with the majority encoding lncRNAs. Comparison with HS in cauline leaves and other RNAseq experiments indicated that 74% of the 312 XLOCs are pollen-specific, and at least 42% are HS-responsive. Phylogenetic analysis revealed that 96% of the genes evolved recently in Brassicaceae. We found that 50 genes are putative targets of microRNAs and that 30% of the XLOCs contain small open reading frames (ORFs) with homology to protein sequences. Finally, RNAseq of ribosome-protected RNA fragments together with predictions of periodic footprint of the ribosome P-sites indicated that 23 of these ORFs are likely to be translated. Our findings indicate that many of the 312 unknown genes might be functional and play a significant role in pollen biology, including the HS response.

Integrated Transcriptome, microRNA, and Phytochemical Analyses Reveal Roles of Phytohormone Signal Transduction and ABC Transporters in Flavor Formation of Oolong Tea (Camellia sinensis) during Solar Withering

The unique aroma and flavor of oolong tea develop during the withering stage of postharvest processing. We explored the roles of miRNA-related regulatory networks during tea withering and their effects on oolong tea quality. We conducted transcriptome and miRNA analyses to identify differentially expressed (DE) miRNAs and target genes among fresh leaves, indoor-withered leaves, and solar-withered leaves. We identified 32 DE-miRNAs and 41 target genes involved in phytohormone signal transduction and ABC transporters. Further analyses indicated that these two pathways regulated the accumulation of flavor-related metabolites during tea withering. Flavonoid accumulation was correlated with the miR167d_1-ARF-GH3, miR845-ABCC1-3/ABCC2, miR166d-5p_1-ABCC1-2, and miR319c_3-PIF-ARF modules. Terpenoid content was correlated with the miR171b-3p_2-DELLA-MYC2 and miR166d-5p_1-ABCG2-MYC2 modules. These modules inhibited flavonoid biosynthesis and enhanced terpenoid biosynthesis in solar-withered leaves. Low auxin and gibberellic acid contents and circRNA-related regulatory networks also regulated the accumulation of flavor compounds in solar-withered leaves. Our analyses reveal how solar withering produces high-quality oolong tea.

Expression Partitioning of Duplicate Genes at Single Cell Resolution in Arabidopsis Roots

Gene duplication is a key evolutionary phenomenon, prevalent in all organisms but particularly so in plants, where whole genome duplication (WGD; polyploidy) is a major force in genome evolution. Much effort has been expended in attempting to understand the evolution of duplicate genes, addressing such questions as why some paralog pairs rapidly return to single copy status whereas, in other pairs, both paralogs are retained and may diverge in expression pattern or function. The effect of a gene - its site of expression and thus the initial locus of its function - occurs at the level of a cell comprising a single cell type at a given state of the cell's development. Using Arabidopsis thaliana single cell transcriptomic data we categorized patterns of expression for 11,470 duplicate gene pairs across 36 cell clusters comprising nine cell types and their developmental states. Among these 11,470 pairs, 10,187 (88.8%) had at least one copy expressed in at least one of the 36 cell clusters. Pairs produced by WGD more often had both paralogs expressed in root cells than did pairs produced by small scale duplications. Three quarters of gene pairs expressed in the 36 cell clusters (7,608/10,187) showed extreme expression bias in at least one cluster, including 352 cases of reciprocal bias, a pattern consistent with expression subfunctionalization. More than twice as many pairs showed reciprocal expression bias between cell states than between cell types or between roots and leaves. A group of 33 gene pairs with reciprocal expression bias showed evidence of concerted divergence of gene networks in stele vs. epidermis. Pairs with both paralogs expressed without bias were less likely to have paralogs with divergent mutant phenotypes; such bias-free pairs showed evidence of preservation by maintenance of dosage balance. Overall, we found considerable evidence of shifts in gene expression following duplication, including in >80% of pairs encoding 7,653 genes expressed ubiquitously in all root cell types and states for which we inferred the polarity of change.

Genes and gene models, an important distinction

Genome sequencing has fundamentally changed how plant biologists think about genes. All or nearly all genes can ultimately be associated with a gene model. However, many gene models appear to play little or no role in the traits of an organism. A range of structural, molecular, population and evolutionary features all show a separation between genes with known phenotypes and the overall set of annotated gene models. These different features could be combined to develop models to distinguish the genes that determine the traits of plants from the subset gene other annotated gene models which are unlikely to play a role in doing so. Efforts to identify the subset of annotated gene models likely involved in specifying the characteristics of plants would help aid a wide range of researchers.

A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes

How do the cells of an organism—each with an identical genome—give rise to tissues of incredible phenotypic diversity? Key to answering this question is the transcriptome: the set of genes expressed in a given tissue. We would clearly benefit from the ability to identify qualitative differences in expression (whether a gene is active or inactive in a given tissue/species). Inferring the expression state of genes is surprisingly difficult, owing to the complex biological processes that give rise to transcriptomes and to the vagaries of techniques used to generate transcriptomic datasets. We develop a hierarchical Bayesian mixture model that—by describing those biological and technical processes—allows us to infer the expression state of genes from replicate transcriptomic datasets.

Transcriptomes are key to understanding the relationship between genotype and phenotype. The ability to infer the expression state (active or inactive) of genes in the transcriptome offers unique benefits for addressing this issue. For example, qualitative changes in gene expression may underly the origin of novel phenotypes, and expression states are readily comparable between tissues and species. However, inferring the expression state of genes is a surprisingly difficult problem, owing to the complex biological and technical processes that give rise to observed transcriptomic datasets. Here, we develop a hierarchical Bayesian mixture model that describes this complex process and allows us to infer expression state of genes from replicate transcriptomic libraries. We explore the statistical behavior of this method with analyses of simulated datasets—where we demonstrate its ability to correctly infer true (known) expression states—and empirical-benchmark datasets, where we demonstrate that the expression states inferred from RNA-sequencing (RNA-seq) datasets using our method are consistent with those based on independent evidence. The power of our method to correctly infer expression states is generally high and remarkably, approaches the maximum possible power for this inference problem. We present an empirical analysis of primate-brain transcriptomes, which identifies genes that have a unique expression state in humans. Our method is implemented in the freely available R package zigzag.

The cis-regulatory codes of response to combined heat and drought stress in Arabidopsis thaliana

Plants respond to their environment by dynamically modulating gene expression. A powerful approach for understanding how these responses are regulated is to integrate information about cis-regulatory elements (CREs) into models called cis-regulatory codes. Transcriptional response to combined stress is typically not the sum of the responses to the individual stresses. However, cis-regulatory codes underlying combined stress response have not been established. Here we modeled transcriptional response to single and combined heat and drought stress in Arabidopsis thaliana. We grouped genes by their pattern of response (independent, antagonistic and synergistic) and trained machine learning models to predict their response using putative CREs (pCREs) as features (median F-measure = 0.64). We then developed a deep learning approach to integrate additional omics information (sequence conservation, chromatin accessibility and histone modification) into our models, improving performance by 6.2%. While pCREs important for predicting independent and antagonistic responses tended to resemble binding motifs of transcription factors associated with heat and/or drought stress, important synergistic pCREs resembled binding motifs of transcription factors not known to be associated with stress. These findings demonstrate how in silico approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization.

Genome-Phenome Wide Association in Maize and Arabidopsis Identifies a Common Molecular and Evolutionary Signature

Linking natural genetic variation to trait variation can help determine the functional roles ofdifferent genes. Variations of one or several traits are often assessed separately. High-throughput phenotyping and data mining can capture dozens or hundreds of traits from the same individuals. Here, we test the association between markers within a gene and many traits simultaneously. This genome-phenome wide association study (GPWAS) is both a multi-marker and multi-trait test. Genes identified using GPWAS with 260 phenotypic traits in maize were enriched for genes independently linked to phenotypic variation. Traits associated with classical mutants were consistent with reported phenotypes for mutant alleles. Genes linked to phenomic variation in maize using GPWAS shared molecular, population genetic, and evolutionary features with classical mutants in maize. Genes linked to phenomic variation in Arabidopsis using GPWAS are significantly enriched in genes with known loss-of-function phenotypes. GPWAS may be an effective strategy to identify genes in which loss-of-function alleles produce mutant phenotypes. The shared signatures present in classical mutants and genes identified using GPWAS may be markers for genes with a role in specifying plant phenotypes generally or pleiotropy specifically.

Evolutionary characteristics of intergenic transcribed regions indicate rare novel genes and widespread noisy transcription in the Poaceae

Extensive transcriptional activity occurring in intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To address this, we evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions (ITRs) in four Poaceae species. Among 43,301 ITRs across the four species, 34,460 (80%) are species-specific. ITRs found across species tend to be more divergent in expression and have more recent duplicates compared to annotated genes. To assess if ITRs are functional (under selection), machine learning models were established in Oryza sativa (rice) that could accurately distinguish between phenotype genes and pseudogenes (area under curve-receiver operating characteristic = 0.94). Based on the models, 584 (8%) and 4391 (61%) rice ITRs are classified as likely functional and nonfunctional with high confidence, respectively. ITRs with conserved expression and ancient retained duplicates, features that were not part of the model, are frequently classified as likely-functional, suggesting these characteristics could serve as pragmatic rules of thumb for identifying candidate sequences likely to be under selection. This study also provides a framework to identify novel genes using comparative transcriptomic data to improve genome annotation that is fundamental for connecting genotype to phenotype in crop and model systems.

Plant Noncoding RNAs: Hidden Players in Development and Stress Responses

A large and significant portion of eukaryotic transcriptomes consists of noncoding RNAs (ncRNAs) that have minimal or no protein-coding capacity but are functional. Diverse ncRNAs, including both small RNAs and long ncRNAs (lncRNAs), play essential regulatory roles in almost all biological processes by modulating gene expression at the transcriptional and posttranscriptional levels. In this review, we summarize the current knowledge of plant small RNAs and lncRNAs, with a focus on their biogenesis, modes of action, local and systemic movement, and functions at the nexus of plant development and environmental responses. The complex connections among small RNAs, lncRNAs, and small peptides in plants are also discussed, along with the challenges of identifying and investigating new classes of ncRNAs.

We simply cannot go on being so vague about 'function'

Function is an onerous concept, as the recent study by Steven Salzberg and colleagues demonstrates. We should be careful and always specific in using the ‘F-word’.