Motif Prediction Research Articles

StoatyDive: Evaluation and classification of peak profiles for sequencing data.

BackgroundThe prediction of binding sites (peak-calling) is a common task in the data analysis of methods such as cross-linking immunoprecipitation in combination with high-throughput sequencing (CLIP-Seq). The predicted binding sites are often further analyzed to predict sequence motifs or structure patterns. When looking at a typical result of such high-throughput experiments, the obtained peak profiles differ largely on a genomic level. Thus, a tool is missing that evaluates and classifies the predicted peaks on the basis of their shapes. We hereby present StoatyDive, a tool that can be used to filter for specific peak profile shapes of sequencing data such as CLIP.FindingsWith StoatyDive we are able to classify peak profile shapes from CLIP-seq data of the histone stem-loop-binding protein (SLBP). We compare the results to existing tools and show that StoatyDive finds more distinct peak shape clusters for CLIP data. Furthermore, we present StoatyDive’s capabilities as a quality control tool and as a filter to pick different shapes based on biological or technical questions for other CLIP data from different RNA binding proteins with different biological functions and numbers of RNA recognition motifs. We finally show that proteins involved in splicing, such as RBM22 and U2AF1, have potentially sharper-shaped peaks than other RNA binding proteins.ConclusionStoatyDive finally fills the demand for a peak shape clustering tool for CLIP-Seq data that fine-tunes downstream analysis steps such as structure or sequence motif predictions and that acts as a quality control.

Discovery of a Low Thermal Conductivity Oxide Guided by Probe Structure Prediction and Machine Learning

AbstractWe report the aperiodic titanate Ba10Y6Ti4O27 with a room‐temperature thermal conductivity that equals the lowest reported for an oxide. The structure is characterised by discontinuous occupancy modulation of each of the sites and can be considered as a quasicrystal. The resulting localisation of lattice vibrations suppresses phonon transport of heat. This new lead material for low‐thermal‐conductivity oxides is metastable and located within a quaternary phase field that has been previously explored. Its isolation thus requires a precisely defined synthetic protocol. The necessary narrowing of the search space for experimental investigation was achieved by evaluation of titanate crystal chemistry, prediction of unexplored structural motifs that would favour synthetically accessible new compositions, and assessment of their properties with machine‐learning models.

Prediction of crystal structures and motifs in the Fe–Mg–O system at Earth’s core pressures

Fe, Mg, and O are among the most abundant elements in terrestrial planets. While the behavior of the Fe–O, Mg–O, and Fe–Mg binary systems under pressure have been investigated, there are still very few studies of the Fe–Mg–O ternary system at relevant Earth’s core and super-Earth’s mantle pressures. Here, we use the adaptive genetic algorithm (AGA) to study ternary Fe x Mg y O z phases in a wide range of stoichiometries at 200 GPa and 350 GPa. We discovered three dynamically stable phases with stoichiometries FeMg2O4, Fe2MgO4, and FeMg3O4 with lower enthalpy than any known combination of Fe–Mg–O high-pressure compounds at 350 GPa. With the discovery of these phases, we construct the Fe–Mg–O ternary convex hull. We further clarify the composition- and pressure-dependence of structural motifs with the analysis of the AGA-found stable and metastable structures. Analysis of binary and ternary stable phases suggest that O, Mg, or both could stabilize a BCC iron alloy at inner core pressures.

Systematic analysis of differentially expressed ZmMYB genes related to drought stress in maize.

The online version contains supplementary material available at 10.1007/s12298-021-01013-2.

Genome-wide identification and characterization of PIF genes and their response to high temperature stress in potato

<p id="C3">Phytochrome interacting factors (PIFs) belong to the basic helix-loop-helix (bHLH) transcription factor family, which integrates external environmental signals such as light and temperature with plant endogenous signal pathways, and then form a complex signal transduction network to precisely regulate the growth and development of plants. Up to now, there are few studies of PIF family genes in potato. Identification and analysis of StPIF family members will help to further improve the yield and quality of potatoes. In this study, BlastP analysis was performed in the potato genome database using <italic>Arabidopsis</italic> PIFs family member protein sequences as source sequences and seven StPIFs family members were identified. Systemic evolution, chromosome distribution, replication events, protein physicochemical properties, gene structure, motif prediction, promoter cis-acting elements, gene expression pattern, and the response to high temperature stress were also conducted. These results showed that all members of the StPIFs gene family contain Motif 1 (bHLH domain) and Motif 2 (APB domain); multiple cis-regulatory elements involved in light response, hormones, drought, low temperature, and circadian rhythm as well as defense and stress response regulatory elements were predicted in the promoter regions of the <italic>StPIF</italic> genes. Results of gene expression patterns and high temperature stress response at budding stage revealed that StPIFs family members had obvious tissue expression specificity, indicating their functional differentiation, and most <italic>StPIFs</italic> members had obvious responses to biotic stress and abiotic stresses including high temperature. In conclusion, these foundlings greatly enriched the understanding of the members of the StPIF family, and laid a theoretical foundation for further exploring the functions of <italic>StPIF</italic>s genes in responses to biotic stress and abiotic stress during potato growth period including high temperature at tuber stage.

Bayesian Markov models improve the prediction of binding motifs beyond first order.

Transcription factors (TFs) regulate gene expression by binding to specific DNA motifs. Accurate models for predicting binding affinities are crucial for quantitatively understanding of transcriptional regulation. Motifs are commonly described by position weight matrices, which assume that each position contributes independently to the binding energy. Models that can learn dependencies between positions, for instance, induced by DNA structure preferences, have yielded markedly improved predictions for most TFs on in vivo data. However, they are more prone to overfit the data and to learn patterns merely correlated with rather than directly involved in TF binding. We present an improved, faster version of our Bayesian Markov model software, BaMMmotif2. We tested it with state-of-the-art motif discovery tools on a large collection of ChIP-seq and HT-SELEX datasets. BaMMmotif2 models of fifth-order achieved a median false-discovery-rate-averaged recall 13.6% and 12.2% higher than the next best tool on 427 ChIP-seq datasets and 164 HT-SELEX datasets, respectively, while being 8 to 1000 times faster. BaMMmotif2 models showed no signs of overtraining in cross-cell line and cross-platform tests, with similar improvements on the next-best tool. These results demonstrate that dependencies beyond first order clearly improve binding models for most TFs.

On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction.

Direct-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. We present RADI/raDIMod, a variation of the original DCA algorithm that groups chemically equivalent residues combined with super-secondary structure motifs to model protein structures. Interestingly, the simplification produced by grouping amino acids into only two groups (polar and non-polar) is still representative of the physicochemical nature that characterizes the protein structure and it is in line with the role of hydrophobic forces in protein-folding funneling. As a result of a compressed alphabet, the number of sequences required for the multiple sequence alignment is reduced. The number of long-range contacts predicted is limited; therefore, our approach requires the use of neighboring sequence-positions. We use the prediction of secondary structure and motifs of super-secondary structures to predict local contacts. We use RADI and raDIMod, a fragment-based protein structure modelling, achieving near native conformations when the number of super-secondary motifs covers >30–50% of the sequence. Interestingly, although different contacts are predicted with different alphabets, they produce similar structures.

Deep-BSC: Predicting Raw DNA Binding Pattern in Arabidopsis Thaliana

Background: With the rapid development of the sequencing methods in recent years, binding sites have been systematically identified in such projects as Nested-MICA and MEME. Prediction of DNA motifs with higher accuracy and precision has been a very important task for bioinformaticians. Nevertheless, experimental approaches are still time-consuming for big data set, making computational identification of binding sites indispensable. Objective: To facilitate the identification of the binding site, we proposed a deep learning architecture, named Deep-BSC (Deep-Learning Binary Search Classification), to predict binding sites in a raw DNA sequence with more precision and accuracy. Methods: Our proposed architecture purely relies on the raw DNA sequence to predict the binding sites for protein by using a convolutional neural network (CNN). We trained our deep learning model on binding sites at the nucleotide level. DNA sequence of A. thaliana is used in this study because it is a model plant. Results: The results demonstrate the effectiveness and efficiency of our method in the classification of binding sites against random sequences, using deep learning. We construct a CNN with different layers and filters to show the usefulness of max-pooling technique in the proposed method. To gain the interpretability of our approach, we further visualized binding sites in the saliency map and successfully identified similar motifs in the raw sequence. The proposed computational framework is time and resource efficient. Conclusion: Deep-BSC enables the identification of binding sites in the DNA sequences via a highly accurate CNN. The proposed computational framework can also be applied to problems such as operator, repeats in the genome, DNA markers, and recognition sites for enzymes, thereby promoting the use of Deep-BSC method in life sciences.

Robust Transcription Factor Binding Site Prediction Using Deep Neural Networks

Aims: Robust and more accurate method for identifying transcription factor binding sites (TFBS) for gene expression. Background: Deep neural networks (DNNs) have shown promising growth in solving complex machine learning problems. Conventional techniques are comfortably replaced by DNNs in computer vision, signal processing, healthcare, and genomics. Understanding DNA sequences is always a crucial task in healthcare and regulatory genomics. For DNA motif prediction, choosing the right dataset with a sufficient number of input sequences is crucial in order to design an effective model. Objective: Designing a new algorithm which works on different dataset while an improved performance for TFBS prediction. Methods: With the help of Layerwise Relevance Propagation, the proposed algorithm identifies the invariant features with adaptive noise patterns. Results: The performance is compared by calculating various metrics on standard as well as recent methods and significant improvement is noted. Conclusion: By identifying the invariant and robust features in the DNA sequences, the classification performance can be increased.

Interaction of transcription factor AP-2 gamma with proto-oncogene PELP1 promotes tumorigenesis by enhancing RET signaling.

A significant proportion of estrogen receptor-positive (ER+) breast cancer (BC) initially responds to endocrine therapy but eventually evolves into therapy-resistant BC. Transcription factor AP-2 gamma (TFAP2C) is a known regulator of ER activity, and high expression of TFAP2C is associated with a decreased response to endocrine therapies. PELP1 is a nuclear receptor coregulator, commonly overexpressed in BC, and its levels are correlated with poorer survival. In this study, we identified PELP1 as a novel interacting protein of TFAP2C. RNA-seq analysis of PELP1 knockdown BC cells followed by transcription factor motif prediction pointed to TFAP2C being enriched in PELP1-regulated genes. Gene set enrichment analysis (GSEA) revealed that the TFAP2C-PELP1 axis induced a subset of common genes. Reporter gene assays confirmed PELP1 functions as a coactivator of TFAP2C. Mechanistic studies showed that PELP1-mediated changes in histone methylation contributed to increased expression of the TFAP2C target gene RET. Furthermore, the TFAP2C-PELP1 axis promoted the activation of the RET signaling pathway, which contributed to downstream activation of AKT and ERK pathways in ER+ BC cells. Concomitantly, knockdown of PELP1 attenuated these effects mediated by TFAP2C. Overexpression of TFAP2C contributed to increased cell proliferation and therapy resistance in ER+ BC models, while knockdown of PELP1 mitigated these effects. Utilizing ZR75-TFAP2C xenografts with or without PELP1 knockdown, we provided genetic evidence that endogenous PELP1 is essential for TFAP2C-driven BC progression invivo. Collectively, our studies demonstrated that PELP1 plays a critical role in TFAP2C transcriptional and tumorigenic functions in BC and blocking the PELP1-TFAP2C axis could have utility for treating therapy resistance.

Reversibility of digit loss revisited: Limb diversification in Bachia lizards (gymnophthalmidae).

Strict interpretations of the Dollo's Law lead to postulation that trait loss is irreversible and organisms never recover ancestral phenotypes. Dollo, however, admitted the possibility of reversals in trait loss when predicted differences between reversed (derived) and ancestral forms. Phenotypic signatures from reversals are expected, as the historical context of a reversal in trait loss differs from the initial setting where the trait originally evolved. This article combines morphological and molecular information for Bachia scolecoides to discuss phenotypic and genetic patterns established during processes that reversed digit loss in Gymnophthalmidae (also termed microteiid lizards). Results suggest that pathways leading to the derived tetradactyl state of B. scolecoides comprise particularities in their origin and associated processes. Autopodial bones of B. scolecoides lack digit identity, and muscle anatomy is very similar between manus and pes. Gymnophthalmidae sequence patterns in the limb-specific sonic hedgehog enhancer (ZRS) suggest that regulation of shh expression did not degenerate in Bachia, given the prediction of similar motifs despite mutations specific to Bachia. Persistence of developmental mechanisms might explain intermittent character expression leading to reversals of digit loss, as ZRS signaling pathways remain active during the development of at least one pair of appendices in Bachia, especially if some precursors persisted at early stages. Patterns of ZRS sequences suggest that irreversibility of trait loss might be lineage-specific (restricted to Gymnophthalmini) and contingent to the type of signature established. These results provide insights regarding possible mechanisms that may allow reactivation of developmental programs in specific regions of the embryo.

Inferring the structures of signaling motifs from paired dynamic traces of single cells.

Individual cells show variability in their signaling dynamics that often correlates with phenotypic responses, indicating that cell-to-cell variability is not merely noise but can have functional consequences. Based on this observation, we reasoned that cell-to-cell variability under the same treatment condition could be explained in part by a single signaling motif that maps different upstream signals into a corresponding set of downstream responses. If this assumption holds, then repeated measurements of upstream and downstream signaling dynamics in a population of cells could provide information about the underlying signaling motif for a given pathway, even when no prior knowledge of that motif exists. To test these two hypotheses, we developed a computer algorithm called MISC (Motif Inference from Single Cells) that infers the underlying signaling motif from paired time-series measurements from individual cells. When applied to measurements of transcription factor and reporter gene expression in the yeast stress response, MISC predicted signaling motifs that were consistent with previous mechanistic models of transcription. The ability to detect the underlying mechanism became less certain when a cell’s upstream signal was randomly paired with another cell’s downstream response, demonstrating how averaging time-series measurements across a population obscures information about the underlying signaling mechanism. In some cases, motif predictions improved as more cells were added to the analysis. These results provide evidence that mechanistic information about cellular signaling networks can be systematically extracted from the dynamical patterns of single cells.

Identification and characterization of the LRR repeats in plant LRR-RLKs

BackgroundLeucine-rich-repeat receptor-like kinases (LRR-RLKs) play central roles in sensing various signals to regulate plant development and environmental responses. The extracellular domains (ECDs) of plant LRR-RLKs contain LRR motifs, consisting of highly conserved residues and variable residues, and are responsible for ligand perception as a receptor or co-receptor. However, there are few comprehensive studies on the ECDs of LRR-RLKs due to the difficulty in effectively identifying the divergent LRR repeats.ResultsIn the current study, an efficient LRR motif prediction program, the “Phyto-LRR prediction” program, was developed based on the position-specific scoring matrix algorithm (PSSM) with some optimizations. This program was trained by 16-residue plant-specific LRR-highly conserved segments (HCS) from LRR-RLKs of 17 represented land plant species and a database containing more than 55,000 predicted LRRs based on this program was constructed. Both the prediction tool and database are freely available at http://phytolrr.com/ for website usage and at http://github.com/phytolrr for local usage. The LRR-RLKs were classified into 18 subgroups (SGs) according to the maximum-likelihood phylogenetic analysis of kinase domains (KDs) of the sequences. Based on the database and the SGs, the characteristics of the LRR motifs in the ECDs of the LRR-RLKs were examined, such as the arrangement of the LRRs, the solvent accessibility, the variable residues, and the N-glycosylation sites, revealing a comprehensive profile of the plant LRR-RLK ectodomains.ConclusionThe “Phyto-LRR prediction” program is effective in predicting the LRR segments in plant LRR-RLKs, which, together with the database, will facilitate the exploration of plant LRR-RLKs functions. Based on the database, comprehensive sequential characteristics of the plant LRR-RLK ectodomains were profiled and analyzed.

Genome-wide identification and characterization of small auxin-up RNA (SAUR) gene family in plants: evolution and expression profiles during normal growth and stress response

BackgroundAuxin is critical to plant growth and development, as well as stress responses. Small auxin-up RNA (SAUR) is the largest family of early auxin responsive genes in higher plants. However, the function of few SAUR genes is known owing to functional redundancy among the many family members.ResultsIn this study, we conducted a phylogenetic analysis using protein sequences of 795 SAURs from Anthoceros angustus, Marchantia polymorpha, Physcomitrella patens, Selaginella moellendorffii, Ginkgo biloba, Gnetum montanum, Amborella trichopoda, Arabidopsis thaliana, Oryza sativa, Zea mays, Glycine max, Medicago truncatula and Setaria italica. The phylogenetic trees showed that the SAUR proteins could be divided into 10 clades and three subfamilies, and that SAUR proteins of three bryophyte species were only located in subfamily III, which suggested that they may be ancestral. From bryophyta to anthophyta, SAUR family have appeared very large expansion. The number of SAUR gene in Fabaceae species was considerably higher than that in other plants, which may be associated with independent whole genome duplication event in the Fabaceae lineages. The phylogenetic trees also showed that SAUR genes had expanded independently monocotyledons and dicotyledons in angiosperms. Conserved motif and protein structure prediction revealed that SAUR proteins were highly conserved among higher plants, and two leucine residues in motif I were observed in almost all SAUR proteins, which suggests the residues plays a critical role in the stability and function of SAUR proteins. Expression analysis of SAUR genes using publicly available RNA-seq data from rice and soybean indicated functional similarity of members in the same clade, which was also further confirmed by qRT-PCR. Summarization of SAUR functions also showed that SAUR functions were usually consistent within a subclade.ConclusionsThis study provides insights into the evolution and function of the SAUR gene family from bryophyta to anthophyta, particularly in Fabaceae plants. Future investigation to understand the functions of SAUR family members should employ a clade as the study unit.

Expansion and re-classification of the extracytoplasmic function (ECF) σ factor family

Extracytoplasmic function σ factors (ECFs) represent one of the major bacterial signal transduction mechanisms in terms of abundance, diversity and importance, particularly in mediating stress responses. Here, we performed a comprehensive phylogenetic analysis of this protein family by scrutinizing all proteins in the NCBI database. As a result, we identified an average of ∼10 ECFs per bacterial genome and 157 phylogenetic ECF groups that feature a conserved genetic neighborhood and a similar regulation mechanism. Our analysis expands previous classification efforts ∼50-fold, enriches many original ECF groups with previously unclassified proteins and identifies 22 entirely new ECF groups. The ECF groups are hierarchically related to each other and are further composed of subgroups with closely related sequences. This two-tiered classification allows for the accurate prediction of common promoter motifs and the inference of putative regulatory mechanisms across subgroups composing an ECF group. This comprehensive, high-resolution description of the phylogenetic distribution of the ECF family, together with the massive expansion of classified ECF sequences and an openly accessible data repository called ‘ECF Hub’ (https://www.computational.bio.uni-giessen.de/ecfhub), will serve as a powerful hypothesis-generator to guide future research in the field.

Computational identification of fertility functions of bovine Reprimo gene

Improvement in fertility is possible through gene assisted selection. Cattle fertility and genes underlying it should be thoroughly studied and exploited to find solution to declining cattle fertility. Reprimo (RPRM) gene is a pleiotropic gene involved in suppression of cancer, regulation of mitotic cell cycle, cell cycle arrest and regulation of survival. Comparison of protein tertiary structures is important in inferring functional characteristics of new proteins. This study used computational approach to identify some fertility functions of bovine RPRM gene using motif prediction and protein structure comparison. Amino acid sequences of bovine RPRM gene and some other cattle fertility genes were retrieved from GenBank. Motifs in the amino acid sequence of bovine RPRM gene were predicted using PROSITE software. The domain structure of bovine RPRM protein was predicted using simple modular architecture research tool (SMART). Protein tertiary structures (3D structures) of bovine RPRM gene and other cattle fertility genes were predicted with Phyre2 software. To have structural and functional similarity, it has been found that protein structure after superimposition should have Root Mean Square Deviation (RMSD) value less than or equal to 2Å. The protein 3D structures of other cattle fertility genes were superposed against the protein 3D structure of bovine RPRM gene using SuperPose web server and the proteins with RMSD value of 2Å or less were predicted as proteins with similar functions and structures as bovine RPRM gene. The predicted motifs (N-glycosylation site, N-myristoylation site, and cAMP and cGMP-dependent protein kinase phosphorylation site) and protein structure comparison revealed that, bovine RPRM gene and bovine growth hormone gene have the same fertility function with alpha carbon and backbone root mean square deviations of 1.94 Å and 1.81 Å, respectively. It follows therefore that other fertility functions of bovine RPRM gene included sexual maturation, steroidogenesis, gametogenesis, gonadal differentiation and gonadotrophin secretion which are the functions of growth hormone gene.

Investigation of a Truncated Aptamer for Ofloxacin Detection Using a Rapid FRET-Based Apta-Assay

In this work, we describe the use of a new truncated aptamer for the determination of ofloxacin (OFL), being a principal quinolone commonly used in both human and animal healthcare. Since the affinity of a 72-mer ssDNA sequence has been previously described without further investigations, this paper demonstrates the first computational prediction of the binding motif between this aptamer and OFL through in silico molecular docking studies. Besides, we suggest the application of the characterized recognition mechanism in a simple FRET (Förster Resonance Energy Transfer) pattern for the rapid aptasensing of the quinolone of interest. Accordingly, our approach harnesses the fluorescence quenching of the fluorescein-tagged aptamer (FAM-APT) induced by its partial hybridization to a tetramethyl rhodamine-labelled complementary ssDNA (TAMRA-cDNA). In such a structure, dye labels brought into close proximity act as a FRET pair. Upon ofloxacin addition, an affinity competition occurs to form a more stable FAM-APT/OFL complex, thus unquenching the FAM-APT signal. Interestingly, the recovered fluorescence intensity was found to correlate well with the antibiotic’s concentrations in the range of 0.2–200 μM in HEPES buffer, with a linear response that ranged between 0.2 and 20 μM. The rapid apta-assay achieved limits of detection and quantification of 0.12 and 0.40 μM, respectively. The truncated aptamer has also shown an improved specificity toward OFL than other quinolones, compared to the original full-length aptamer described in previous works. Finally, the practical application of the developed apta-assay was successfully confirmed to detect OFL quinolone in spiked milk samples, with satisfactory recoveries ranging between 97.4% and 111.4%.

Functional motif prediction of non-structural proteins for serotype-specific differential diagnosis of dengue infection

Background: Dengue virus (DENV) causes abrupt outbreak with high morbidity rate leading to Dengue Hemorrhagic Fever or Dengue Shock Syndrome. This year India recorded 50,000 dengue cases. DENV exist as four serotypes (1,2,3,4) and all are prevalent among Indian population. DENV genome has a single ORF of 10Kb that encodes three structural and seven non-structural proteins. Non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5) were analyzed to select an antigen candidate to differentially diagnose that are prevalent in India. Methods and materials: DENV1 Western Pacific Strain, DENV2 New Guinea C strain and DENV4 341750 strain were obtained from USFDA. The sequences of Indian strains were retrieved from ViPR database. Similarity search between the Indian and in test strains analyzed by non-structural proteins. MEGA X by Nearest-Neighbor Interchange procedure by Maximum-Likelihood method was used. Functional motifs were predicted through online tool Eukaryotic Linear Motif (ELM) with 0.1 motif probability cutoff and conserved regions of predicted motifs for all four serotypes. Results: DEN1WP, DENV2NGC and DENV4 341750 strains were found closely related to Indian strains. Meanwhile amino acid substitutions within three strains found to for non-structural proteins at positions 127/352 for NS1, 165/219 for NS2A, 68/130 for NS2B, 186/619 for NS3, 65/127 for NS4A, 73/249 for NS4B, 271/901 for NS5. The functional motifs analysis resulted in 43, 40 and 48 sites in Non-structural protein 1 region for DENV1, DENV2 and DENV4 respectively. This include “KFQPESP” motif sequence at 33–39 residues in DENV2, DENV4 and “PESPSK” motif sequence at 36–41 residues in DENV2, which are class I and class II SH3 domain-binding motifs. Sequence at 35–41 residues are “QADSPKR” in DENV1 and DENV3(DEL_72), which are protein kinase (MAP Kinase) domain-binding motifs. Conclusion: DENV1, DENV2 and DENV4 can be discriminated based on their conserved regions that act as functional motifs that could bind to human receptor domains (class I SH3, class II SH3 and MAP Kinase). The study resulted in NS1 antigen, which will be useful in detecting serotype-specific dengue infection among the affected patients. Further, aim of our project is to express NS1 antigen in plant host which is a cost-effective robust platform.

Genome-Wide Identification and Analysis of GHMP Kinase Gene Superfamily in Bread Wheat (Triticum aestivum L.)

The GHMP (galactokinase, homoserine kinase, mevalonate kinase and phosphomevalonate kinase) kinase gene superfamily is a unique class of ATP-dependent enzymes. It plays a role in the metabolism of carbohydrate, biosynthesis of isoprenes, amino acids and nucleotide sugars. However, this important gene family has not been characterized in wheat (Triticum aestivum L.). In this study, total forty-seven putative GHMP kinase sequences were identified in wheat (Triticum aestivum L.) genome. Physicochemical properties, motif prediction, conserved domain and exon-intron configuration analyses revealed that these sequences were distributed in 10 subfamilies. Promoter analysis of GHMP kinase sequences showed that the cis-acting regulatory elements are involved in diverse functions such as light, stress, hormone and metabolism responsiveness, tissue-specific activation, circadian control, binding site and cell cycle regulation. Phylogenetic relationship with other monocot species has further strengthened the subfamily grouping pattern. The heat map presented by transcriptomic data analysis revealed that 25 and 12 genes showed significant differential expression against the abiotic and biotic stresses, respectively. The expression of selected genes was validated under abiotic stress conditions viz., heat, drought and their combination by qRT-PCR analysis. In conclusion, the present study provides novel information, thus enhances our understanding of the GHMP kinase gene family in wheat. The information obtained will be valuable in selecting potential candidate genes in wheat for developing cultivars with improved tolerance against biotic and abiotic stresses.

Perinatal protein malnutrition results in genome-wide disruptions of 5-hydroxymethylcytosine at regions that can be restored to control levels by an enriched environment

ABSTRACT Maternal malnutrition remains one of the major adversities affecting brain development and long-term mental health outcomes, increasing the risk to develop anxiety and depressive disorders. We have previously shown that malnutrition-induced anxiety-like behaviours can be rescued by a social and sensory stimulation (enriched environment) in male mice. Here, we expand these findings to adult female mice and profiled genome-wide ventral hippocampal 5hmC levels related to malnutrition-induced anxiety-like behaviours and their rescue by an enriched environment. This approach revealed 508 differentially hydroxymethylated genes associated with protein malnutrition and that several genes (N = 34) exhibited a restored 5hmC abundance to control levels following exposure to an enriched environment, including genes involved in neuronal functions like dendrite outgrowth, axon guidance, and maintenance of neuronal circuits (e.g. Fltr3, Itsn1, Lman1, Lsamp, Nav, and Ror1) and epigenetic mechanisms (e.g. Hdac9 and Dicer1). Sequence motif predictions indicated that 5hmC may be modulating the binding of transcription factors for several of these transcripts, suggesting a regulatory role for 5hmC in response to perinatal malnutrition and exposure to an enriched environment. Together, these findings establish a role for 5hmC in early-life malnutrition and reveal genes linked to malnutrition-induced anxious behaviours that are mitigated by an enriched environment.