Transcriptomic Studies in Non-Model Plants: Case of Pisum sativum L. and Medicago lupulina L.

Transcriptomics is a dynamically developing branch of biology highly important for geneticists and molecular ecologists alike. A large number of studies concerning differential gene expression, mapping of genes and quantitative trait loci (QTL), analysis of genotyping variations and so on has been conducted recently on several non‐model plants using next‐generation sequencing techniques. One example of non‐model legumes is garden pea (Pisum sativum L.), a valuable pulse crop capable of forming nitrogen‐fixing nodules and arbuscular mycorrhiza. Adaptation of standardised RNA‐seq approaches and data analysis developed for model plants to P. sativum should facilitate both studying of pea molecular genetics and breeding of new cultivars possessing agriculturally important traits. Another non‐model legume is black medick Medicago lupulina L. (a close relative of model legume plant barrel medick, Medicago truncatula Gaertn.), for which unique genetic lines almost obligatory dependent on arbuscular mycorrhiza symbiosis formation have been obtained. Such lines show promise as the perfect model for studying the genetic bases of arbuscular mycorrhiza development. In this chapter, we give a brief description of the current developments in the field of garden pea and black medick transcriptomics. Our aim is to provide a quick start guide to the non‐expert researchers for next‐generation sequencing (NGS)‐based transcriptome analysis

Concept, opportunities and challenges of urban tourism in the Arab world: Case studies of Dubai, Cairo and Amman

This paper aims to provide a better understanding of the current situation of urban tourism with referring to the experiences of the Arab World. By using bibliographic documentation and descriptive-analytic method, this paper addresses the main opportunities, impediments, and challenges of urban tourism in three Arab cities (Dubai, Cairo and Amman). This paper indicates that the three cities have many opportunities stemming from their location and their rich tourism resources. Moreover, urban tourism in these cities should confront some internal (country based) and external (global and regional) challenges such as seasonality, pollution, congestion, competition, funding and instability. Some recommendations and policy implications were suggested. The paper concludes, by arguing, that common internal and external challenges need to be addressed in a systematic manner within the broader cultural and tourism policy context in which urban tourism is now implicated. There is a lack of literature on urban tourism within the Arab countries, which is attributed to that tourism in the Arab world is heritage dominated type. Therefore, this research also attempts to bridge the gap in the existing literature about urban tourism in the Arab cities

Mapping-by-sequencing using NGS-based 3′-MACE-Seq reveals a new mutant allele of the essential nodulation gene Sym33 (IPD3) in pea (Pisum sativum L.)

Large collections of pea symbiotic mutants were accumulated in the 1990s, but the causal genes for a large portion of the mutations are still not identified due to the complexity of the task. We applied a Mapping-by-Sequencing approach including Bulk Segregant Analysis and Massive Analysis of cDNA Ends (MACE-Seq) sequencing technology for genetic mapping the Sym11 gene of pea which controls the formation of symbioses with both nodule bacteria and arbuscular-mycorrhizal fungi. For mapping we developed an F2-population from the cross between pea line N24 carrying the mutant allele of sym11 and the wild type NGB1238 (=JI0073) line. Sequencing libraries were prepared from bulks of 20 plants with mutant and 12 with wild-type phenotype. MACE-Seq differential gene expression analysis between mutant-phenotype and wild-type-phenotype bulks revealed 2,235 genes, of which 514 (23%) were up-regulated and 1,721 (77%) were down-regulated in plant roots inoculated with rhizobia as a consequence of sym11 mutation. MACE-Seq also detected single nucleotide variants between bulks in 217 pea genes. Using a novel mathematical model we calculated the recombination frequency (RF) between the Sym11 gene and these 217 polymorphic genes. Six genes with the lowest RF were converted into CAPS or dCAPS markers and genetically mapped on the complete mapping population of 108 F2-plants which confirmed their tight linkage to Sym11 and to each other. The Medicago truncatula Gaertn. (Mt) homologs of these genes are located in a distinct region of Mt chromosome 5, which corresponds to linkage group I of pea. Among 94 candidate genes from this region only one was down-regulated—the pea Sym33 homolog of the Mt IPD3 gene which is essential for nodulation. Sequencing of the Sym33 allele of the N24 (sym11) mutant revealed a single nucleotide deletion (c.C319del) in its third exon resulting in a codon shift in the open reading frame and premature translation termination. Thus, we identified a novel mutant allele sym33-4 most probably responsible for the mutant phenotype of the N24 (sym11) line, thereby demonstrating that mapping by MACE-Seq can be successfully used for genetic mapping of mutations and identification of candidate genes in pea

De Novo Assembly of the Pea (Pisum sativum L.) Nodule Transcriptome

The large size and complexity of the garden pea (Pisum sativum L.) genome hamper its sequencing and the discovery of pea gene resources. Although transcriptome sequencing provides extensive information about expressed genes, some tissue-specific transcripts can only be identified from particular organs under appropriate conditions. In this study, we performed RNA sequencing of polyadenylated transcripts from young pea nodules and root tips on an Illumina GAIIx system, followed by de novo transcriptome assembly using the Trinity program. We obtained more than 58,000 and 37,000 contigs from “Nodules” and “Root Tips” assemblies, respectively. The quality of the assemblies was assessed by comparison with pea expressed sequence tags and transcriptome sequencing project data available from NCBI website. The “Nodules” assembly was compared with the “Root Tips” assembly and with pea transcriptome sequencing data from projects indicating tissue specificity. As a result, approximately 13,000 nodule-specific contigs were found and annotated by alignment to known plant protein-coding sequences and by Gene Ontology searching. Of these, 581 sequences were found to possess full CDSs and could thus be considered as novel nodule-specific transcripts of pea. The information about pea nodule-specific gene sequences can be applied for gene-based markers creation, polymorphism studies, and real-time PCR

Selection Signatures in the First Exon of Paralogous Receptor Kinase Genes from the Sym2 Region of the Pisum sativum L. Genome

During the initial step of the symbiosis between legumes (Fabaceae) and nitrogen-fixing bacteria (rhizobia), the bacterial signal molecule known as the Nod factor (nodulation factor) is recognized by plant LysM motif-containing receptor-like kinases (LysM-RLKs). The fifth chromosome of barrel medic (Medicago truncatula Gaertn.) contains a cluster of paralogous LysM-RLK genes, one of which is known to participate in symbiosis. In the syntenic region of the pea (Pisum sativum L.) genome, three genes have been identified: PsK1 and PsSym37, two symbiosis-related LysM-RLK genes with known sequences, and the unsequenced PsSym2 gene which presumably encodes a LysM-RLK and is associated with increased selectivity to certain Nod factors. In this work, we identified a new gene encoding a LysM-RLK, designated as PsLykX, within the Sym2 genomic region. We sequenced the first exons (corresponding to the protein receptor domain) of PsSym37, PsK1, and PsLykX from a large set of pea genotypes of diverse origin. The nucleotide diversity of these fragments was estimated and groups of haplotypes for each gene were revealed. Footprints of selection pressure were detected via comparative analyses of SNP distribution across the first exons of these genes and their homologs MtLYK2, MtLYK3, and MtLYK4 from M. truncatula retrieved from the Medicago Hapmap project. Despite the remarkable similarity among all the studied genes, they exhibited contrasting selection signatures, possibly pointing to diversification of their functions. Signatures of balancing selection were found in LysM1-encoding parts of PsSym37 and PsK1, suggesting that the diversity of these parts may be important for pea LysM-RLKs. The first exons of PsSym37 and PsK1 displayed signatures of purifying selection, as well as MtLYK2 of M. truncatula. Evidence of positive selection affecting primarily LysM domains was found in all three investigated M. truncatula genes, as well as in the pea gene PsLykX. The data suggested that PsLykX is a promising candidate for PsSym2, which has remained elusive for more than 30 years

Neural Networks in Forecasting Disease Dynamics

Introduction.In recent years, computer technologies are more and more widely used in medicine. Thus, medical neuro‑ informatics solves diagnostic and forecasting tasks using neural networks.Materials and methods. Using the example of erysipelas, the possibility of forecasting the course and outcome of the dis‑ ease is demonstrated. A retrospective study of the medical histories of patients treated for erysipelas at the Ufa Clinical Hospital No.8 during 2006–2015 was carried out. Modern statistical packages and the MATLAB environment were used.Results and discussion.The conducted comparative analysis showed a 3-layer recurring network of direct distribution to be the most suitable neural network architecture. The optimal structure of the neural network was found to be: 27–6–1 (i.e. 27 neurons are used at the entrance; 6 — in a hidden layer; 1 — in the output layer). The best convergence of the network learning process is provided by the quasi-Newton and conjugated gradient algorithms. In order to assess the effectiveness of the proposed neural network in predicting the dynamics of inflammation, a comparative analysis was carried out using a number of conventional methods, such as exponential smoothing, moving average, least squares and group data handling.Conclusion.The proposed neural network based on approximation and extrapolation of variations in the patient’s medi‑ cal history over fixed time window segments (within the ‘sliding time window’) can be successfully used for forecasting the development and outcome of erysipelas

Association Study of Symbiotic Genes in Pea (Pisum sativum L.) Cultivars Grown in Symbiotic Conditions

In garden pea (Pisum sativum L.), several symbiotic genes are known to control the development of mutualistic symbioses with nodule bacteria (NB) and arbuscular mycorrhizal fungi (AMF). Here, we studied whether the allelic state of the symbiotic genes was associated with the growth parameters of pea plants under single inoculation with NB and under double inoculation with NB + AMF. Using different statistical methods, we analyzed the dataset obtained from a pot experiment that involved 99 pea cultivars, 10 of which were characterized as having shortened internodes due to the presence of the natural mutation p.A229T in the developmental gene Le. The plant’s habitus strongly influenced most of the studied growth and yield parameters and the effectiveness of the symbiotic interactions under NB and NB + AMF inoculation. Double inoculation had different effects on Le+ (normal) and le− (dwarf) plants with regard to nitrogen and phosphorus content in seeds. Regardless of the Le-status of plants, allelic states of the symbiotic gene LykX encoding the putative receptor of Nod factors (bacterial signal molecules) were shown to be associated with seed number, thousand-seed weight, and pod number at the level of FDR < 0.001, whereas associations of allelic states of the other studied symbiotic genes were less significant

Pipeline of the PMD2 development.

<p>Initial and resulting datasets are placed in blue octagons. Operations are placed in peach-colored rectangles. Steps performed previously by Tayeh and colleagues in 2015 and described in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186713#pone.0186713.ref027" target="_blank">27</a>] are indicated by red arrows. Steps performed in the course of the present study are indicated by blue arrows. MtGEA–<i>Medicago truncatula</i> Gene Expression Atlas.</p

Marker distribution across the artificial LGs presented in PMD1.

<p>Marker distribution across the artificial LGs presented in PMD1.</p