Machine learning analyses of methylation profiles uncovers tissue-specific gene expression patterns in wheat

Genome-wide characterization and expression analysis of the CINNAMYL ALCOHOL DEHYDROGENASE gene family in Triticum aestivum

BACKGROUND

CINNAMYL ALCOHOL DEHYDROGENASE (CAD) catalyzes the NADPH-dependent reduction of cinnamaldehydes into cinnamyl alcohols and is a key enzyme found at the final step of the monolignol pathway. Cinnamyl alcohols and their conjugates are subsequently polymerized in the secondary cell wall to form lignin. CAD genes are typically encoded by multi-gene families and thus traditionally organized into general classifications of functional relevance.

RESULTS

In silico analysis of the hexaploid Triticum aestivum genome revealed 47 high confidence TaCAD copies, of which three were determined to be the most significant isoforms (class I) considered bone fide CADs. Class I CADs were expressed throughout development both in RNAseq data sets as well as via qRT-PCR analysis. Of the 37 class II TaCADs identified, two groups were observed to be significantly co-expressed with class I TaCADs in developing tissue and under chitin elicitation in RNAseq data sets. These co-expressed class II TaCADs were also found to be phylogenetically unrelated to a separate clade of class II TaCADs previously reported to be an influential resistance factor to pathogenic fungal infection. Lastly, two groups were phylogenetically identified as class III TaCADs, which possess distinct conserved gene structures. However, the lack of data supporting their catalytic activity for cinnamaldehydes and their bereft transcriptional presence in lignifying tissues challenges their designation and function as CADs.

CONCLUSIONS

Taken together, our comprehensive transcriptomic analyses suggest that TaCAD genes contribute to overlapping but nonredundant functions during T. aestivum growth and development across a wide variety of agroecosystems and provide tolerance to various stressors.

Regulation of plant epigenetic memory in response to cold and heat stress: towards climate resilient agriculture

Plants have evolved to adapt and grow in hot and cold climatic conditions. Some also adapt to daily and seasonal temperature changes. Epigenetic modifications play an important role in regulating plant tolerance under such conditions. DNA methylation and post-translational modifications of histone proteins influence gene expression during plant developmental stages and under stress conditions, including cold and heat stress. While short-term modifications are common, some modifications may persist and result in stress memory that can be inherited by subsequent generations. Understanding the mechanisms of epigenomes responding to stress and the factors that trigger stress memory is crucial for developing climate-resilient agriculture, but such an integrated view is currently limited. This review focuses on the plant epigenetic stress memory during cold and heat stress. It also discusses the potential of machine learning to modify stress memory through epigenetics to develop climate-resilient crops.

Epigenetic stress memory: A new approach to study cold and heat stress responses in plants

Understanding plant stress memory under extreme temperatures such as cold and heat could contribute to plant development. Plants employ different types of stress memories, such as somatic, intergenerational and transgenerational, regulated by epigenetic changes such as DNA and histone modifications and microRNAs (miRNA), playing a key role in gene regulation from early development to maturity. In most cases, cold and heat stresses result in short-term epigenetic modifications that can return to baseline modification levels after stress cessation. Nevertheless, some of the modifications may be stable and passed on as stress memory, potentially allowing them to be inherited across generations, whereas some of the modifications are reactivated during sexual reproduction or embryogenesis. Several stress-related genes are involved in stress memory inheritance by turning on and off transcription profiles and epigenetic changes. Vernalization is the best example of somatic stress memory. Changes in the chromatin structure of the Flowering Locus C (FLC) gene, a MADS-box transcription factor (TF), maintain cold stress memory during mitosis. FLC expression suppresses flowering at high levels during winter; and during vernalization, B3 TFs, cold memory cis-acting element and polycomb repressive complex 1 and 2 (PRC1 and 2) silence FLC activation. In contrast, the repression of SQUAMOSA promoter-binding protein-like (SPL) TF and the activation of Heat Shock TF (HSFA2) are required for heat stress memory. However, it is still unclear how stress memory is inherited by offspring, and the integrated view of the regulatory mechanisms of stress memory and mitotic and meiotic heritable changes in plants is still scarce. Thus, in this review, we focus on the epigenetic regulation of stress memory and discuss the application of new technologies in developing epigenetic modifications to improve stress memory.

Deciphering the molecular basis of tissue-specific gene expression in plants: Can synthetic biology help?

Gene expression differences between distinct cell types are orchestrated by specific sets of transcription factors and epigenetic regulators acting upon the genome. In plants, the mechanisms underlying tissue-specific gene activity remain largely unexplored. Although transcriptional and epigenetic profiling of individual organs, tissues, and more recently, of single cells can easily detect the molecular signatures of different biological samples, how these unique cell identities are established at the mechanistic level is only beginning to be decoded. Computational methods, including machine learning, used in combination with experimental approaches, enable the identification and validation of candidate cis-regulatory elements driving cell-specific expression. Synthetic biology shows great promise not only as a means of testing candidate DNA motifs but also for establishing the general rules of nature driving promoter architecture and for the rational design of genetic circuits in research and agriculture to confer tissue-specific expression to genes or molecular pathways of interest.

Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach

Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.

Comparative methylation and RNA-seq expression analysis in CpG context to identify genes involved in Backfat vs. Liver diversification in Nanchukmacdon Pig

BACKGROUND

DNA methylation and demethylation at CpG islands is one of the main regulatory factors that allow cells to respond to different stimuli. These regulatory mechanisms help in developing tissue without affecting the genomic composition or undergoing selection. Liver and backfat play important roles in regulating lipid metabolism and control various pathways involved in reproductive performance, meat quality, and immunity. Genes inside these tissue store a plethora of information and an understanding of these genes is required to enhance tissue characteristics in the future generation.

RESULTS

A total of 16 CpG islands were identified, and they were involved in differentially methylation regions (DMRs) as well as differentially expressed genes (DEGs) of liver and backfat tissue samples. The genes C7orf50, ACTB and MLC1 in backfat and TNNT3, SIX2, SDK1, CLSTN3, LTBP4, CFAP74, SLC22A23, FOXC1, GMDS, GSC, GATA4, SEMA5A and HOXA5 in the liver, were categorized as differentially-methylated. Subsequently, Motif analysis for DMRs was performed to understand the role of the methylated motif for tissue-specific differentiation. Gene ontology studies revealed association with collagen fibril organization, the Bone Morphogenetic Proteins (BMP) signaling pathway in backfat and cholesterol biosynthesis, bile acid and bile salt transport, and immunity-related pathways in methylated genes expressed in the liver.

CONCLUSIONS

In this study, to understand the role of genes in the differentiation process, we have performed whole-genome bisulfite sequencing (WGBS) and RNA-seq analysis of Nanchukmacdon pigs. Methylation and motif analysis reveals the critical role of CpG islands and transcriptional factors binding site (TFBS) in guiding the differential patterns. Our findings could help in understanding how methylation of certain genes plays an important role and can be used as biomarkers to study tissue specific characteristics.

Developmental Methylome of the Medicinal Plant Catharanthus roseus Unravels the Tissue-Specific Control of the Monoterpene Indole Alkaloid Pathway by DNA Methylation

Catharanthus roseus produces a wide spectrum of monoterpene indole alkaloids (MIAs). MIA biosynthesis requires a tightly coordinated pathway involving more than 30 enzymatic steps that are spatio-temporally and environmentally regulated so that some MIAs specifically accumulate in restricted plant parts. The first regulatory layer involves a complex network of transcription factors from the basic Helix Loop Helix (bHLH) or AP2 families. In the present manuscript, we investigated whether an additional epigenetic layer could control the organ-, developmental- and environmental-specificity of MIA accumulation. We used Whole-Genome Bisulfite Sequencing (WGBS) together with RNA-seq to identify differentially methylated and expressed genes among nine samples reflecting different plant organs and experimental conditions. Tissue specific gene expression was associated with specific methylation signatures depending on cytosine contexts and gene parts. Some genes encoding key enzymatic steps from the MIA pathway were found to be simultaneously differentially expressed and methylated in agreement with the corresponding MIA accumulation. In addition, we found that transcription factors were strikingly concerned by DNA methylation variations. Altogether, our integrative analysis supports an epigenetic regulation of specialized metabolisms in plants and more likely targeting transcription factors which in turn may control the expression of enzyme-encoding genes.