Genome and transcriptome based comparative analysis of Tilletia indica to decipher the causal genes for pathogenicity of Karnal bunt in wheat.

Tilletia indica Mitra causes Karnal bunt (KB) in wheat by pathogenic dikaryophase. The present study is the first to provide the draft genomes of the dikaryon (PSWKBGD-3) and its two monosporidial lines (PSWKBGH-1 and 2) using Illumina and PacBio reads, their annotation and the comparative analyses among the three genomes by extracting polymorphic SSR markers. The trancriptome from infected wheat grains of the susceptible wheat cultivar WL711 at 24 h, 48h, and 7d after inoculation of PSWKBGH-1, 2 and PSWKBGD-3 were also isolated. Further, two transcriptome analyses were performed utilizing T. indica transcriptome to extract dikaryon genes responsible for pathogenesis, and wheat transcriptome to extract wheat genes affected by dikaryon involved in plant-pathogen interaction during progression of KB in wheat. A total of 54, 529, and 87 genes at 24hai, 48hai, and 7dai, respectively were upregulated in dikaryon stage while 21, 35, and 134 genes of T. indica at 24hai, 48hai, and 7dai, respectively, were activated only in dikaryon stage. While, a total of 23, 17, and 52 wheat genes at 24hai, 48hai, and 7dai, respectively were upregulated due to the presence of dikaryon stage only. The results obtained during this study have been compiled in a web resource called TiGeR ( http://backlin.cabgrid.res.in/tiger/ ), which is the first genomic resource for T. indica cataloguing genes, genomic and polymorphic SSRs of the three T. indica lines, wheat and T. indica DEGs as well as wheat genes affected by T. indica dikaryon along with the pathogenecity related proteins of T. indica dikaryon during incidence of KB at different time points. The present study would be helpful to understand the role of dikaryon in plant-pathogen interaction during progression of KB, which would be helpful to manage KB in wheat, and to develop KB-resistant wheat varieties.

Integration of miRNA dynamics and drought tolerant QTLs in rice reveals the role of miR2919 in drought stress response.

To combat drought stress in rice, a major threat to global food security, three major quantitative trait loci for 'yield under drought stress' (qDTYs) were successfully exploited in the last decade. However, their molecular basis still remains unknown. To understand the role of secondary regulation by miRNA in drought stress response and their relation, if any, with the three qDTYs, the miRNA dynamics under drought stress was studied at booting stage in two drought tolerant (Sahbaghi Dhan and Vandana) and one drought sensitive (IR 20) cultivars. In total, 53 known and 40 novel differentially expressed (DE) miRNAs were identified. The primary drought responsive miRNAs were Osa-MIR2919, Osa-MIR3979, Osa-MIR159f, Osa-MIR156k, Osa-MIR528, Osa-MIR530, Osa-MIR2091, Osa-MIR531a, Osa-MIR531b as well as three novel ones. Sixty-one target genes that corresponded to 11 known and 4 novel DE miRNAs were found to be co-localized with the three qDTYs, out of the 1746 target genes identified. We could validate miRNA-mRNA expression under drought for nine known and three novel miRNAs in eight different rice genotypes showing varying degree of tolerance. From our study, Osa-MIR2919, Osa-MIR3979, Osa-MIR528, Osa-MIR2091-5p and Chr01_11911S14Astr and their target genes LOC_Os01g72000, LOC_Os01g66890, LOC_Os01g57990, LOC_Os01g56780, LOC_Os01g72834, LOC_Os01g61880 and LOC_Os01g72780 were identified as the most promising candidates for drought tolerance at booting stage. Of these, Osa-MIR2919 with 19 target genes in the qDTYs is being reported for the first time. It acts as a negative regulator of drought stress tolerance by modulating the cytokinin and brassinosteroid signalling pathway.

Comparative transcriptome profiling of near isogenic lines PBW343 and FLW29 to unravel defense related genes and pathways contributing to stripe rust resistance in wheat.

Stripe rust (Sr), caused by Puccinia striiformis f. sp. tritici (Pst), is the most devastating disease that poses serious threat to the wheat-growing nations across the globe. Developing resistant cultivars is the most challenging aspect in wheat breeding. The function of resistance genes (R genes) and the mechanisms by which they influence plant-host interactions are poorly understood. In the present investigation, comparative transcriptome analysis was carried out by involving two near-isogenic lines (NILs) PBW343 and FLW29. The seedlings of both the genotypes were inoculated with Pst pathotype 46S119. In total, 1106 differentially expressed genes (DEGs) were identified at early stage of infection (12 hpi), whereas expressions of 877 and 1737 DEGs were observed at later stages (48 and 72 hpi) in FLW29. The identified DEGs were comprised of defense-related genes including putative R genes, 7 WRKY transcriptional factors, calcium, and hormonal signaling associated genes. Moreover, pathways involved in signaling of receptor kinases, G protein, and light showed higher expression in resistant cultivar and were common across different time points. Quantitative real-time PCR was used to further confirm the transcriptional expression of eight critical genes involved in plant defense mechanism against stripe rust. The information about genes are likely to improve our knowledge of the genetic mechanism that controls the stripe rust resistance in wheat, and data on resistance response-linked genes and pathways will be a significant resource for future research.

Post-COVID-19 rhino-orbito-cerebral mucormycosis-A clinico-mycological study from North India.

The second wave of coronavirus disease 2019 (COVID-19), during the early 2021, lead to a devastating outbreak of mucormycosis in India. This study aimed to determine the aetiology, clinical features, comorbidities, and risk factors of rhino-orbito-cerebral mucormycosis (ROCM) and antifungal susceptibility pattern for the isolates. The study included all suspected cases of ROCM in post-COVID-19 patients attending the hospital from May to December 2021. A total of 70 patients were diagnosed with mucormycosis during the study period. The commonest presentations were rhino-orbital and rhino-orbito-cerebral in 35.7% of cases each. Diabetes mellitus was the commonest associated risk factor in 95.7% of all patients, while 78.5% of the patients were treated with corticosteroids in the recent past, and 25.7% presented with active COVID-19 pneumonia. The commonest isolate was Rhizopus arrhizus n = 14, followed by Aspergillus flavus n = 16, A. fumigatus n = 4, A. niger n = 3, Fusarium oxysporumn = 1, and Apophysomyces variabilisn = 1. Fungal species identification was done by phenotypic methods for all the isolates and DNA sequence analysis of 18 isolates, and antifungal susceptibility testing of 30 isolates was performed by commercially prepared HiMIC plate (HiMedia, Mumbai, India) using broth microdilution for amphotericin B, isavuconazole, itraconazole, voriconazole, and posaconazole. The MIC50 and MIC90 of amphotericin B for R. arrhizus strains were 0.25 and 4 µg/ml, respectively; and the MIC50 and MIC90 results for itraconazole, posaconazole, and isavuconazole were 8 and 8, 2 and 2, and 2 and 8 µg/ml, respectively. In vitro data showed that amphotericin B was the most effective antifungal against most species. The commercially available ready-to-use minimum inhibitory concentration plates are user-friendly for performing antifungal susceptibility, which may be useful in choosing appropriate regimens and monitoring emerging resistance.

First report on the utility of pupal case for early determination of CRISPR/Cas9 ribonucleoprotein mediated genomic edits in the oriental fruit fly, Bactrocera dorsalis (Hendel) (Tephritidae: Diptera).

The Oriental fruit fly, Bactrocera dorsalis (Hendel), is a highly invasive pest of quarantine importance affecting the global fruit trade. In managing B. dorsalis, methods like cultural, biological, chemical, sterile insect technique (SIT), and semiochemical-mediated attract-and-kill are in use with varying success. The SIT approach is the method of choice for a chemical-free, long-term suppression of B. dorsalis, followed in many countries across the globe. The nonspecific mutations caused by irradiation affect the overall fitness of flies, thus requiring a more precise method for a heritable, fitness-not-compromising approach. In this regard, CRISPR/Cas9-mediated genome editing enables the creation of mutations at the precise genomic location/s through RNA-guided dsDNA cleavage. Of late, DNA-free editing employing ribonucleoprotein complex (RNP) is preferred to validate the target genes at G0 stage embryos in insects. It requires characterizing genomic edits from adults after completing their life cycle, which may entail a few days to months, depending on longevity. Additionally, edit characterization is required from each individual, as edits are unique. Therefore, all RNP-microinjected individuals must be maintained until the end of their life cycle, irrespective of editing. To overcome this impediment, we predetermine the genomic edits from the shed tissues, such as pupal cases, to maintain only edited individuals. In this study, we have shown the utility of pupal cases from five males and females of B. dorsalis to predetermine the genomic edits, which corroborated the edits from the respective adults.

Fruit transcriptional profiling of the contrasting genotypes for shelf life reveals the key candidate genes and molecular pathways regulating post-harvest biology in cucumber.

Cucumber fruits are perishable in nature and become unfit for market within 2-3 days of harvesting. A natural variant, DC-48 with exceptionally high shelf life was developed and used to dissect the genetic architecture and molecular mechanism for extended shelf life through RNA-seq for first time. A total of 1364 DEGs were identified and cell wall degradation, chlorophyll and ethylene metabolism related genes played key role. Polygalacturunase (PG), Expansin (EXP) and xyloglucan were down regulated determining fruit firmness and retention of fresh green colour was mainly attributed to the low expression level of the chlorophyll catalytic enzymes (CCEs). Gene regulatory networks revealed the hub genes and cross-talk associated with wide variety of the biological processes. Large number of SSRs (21524), SNPs (545173) and InDels (126252) identified will be instrumental in cucumber improvement. A web genomic resource, CsExSLDb developed will provide a platform for future investigation on cucumber post-harvest biology.

Whole genome sequencing of spotted stem borer, Chilo partellus, reveals multiple genes encoding enzymes for detoxification of insecticides.

Spotted stem borer, Chilo partellus, is the most important constraint for increasing the production and productivity of maize and sorghum, the two major coarse cereals in Asia and Africa. The levels of resistance to this pest in the cultivated germplasm are low to moderate, and hence, farmers have to use insecticides for effective control of this pest. However, there is no information on the detoxification mechanisms in C. partellus, which is one of the constraints for deployment of appropriate insecticides to control this pest. The ability to detoxify insecticides varies across insect populations, and hence, we sequenced different populations of C. partellus to identify and understand detoxification mechanisms to devise appropriate strategies for deployment of different insecticides for controlling this pest. Larval samples were sequenced from three different cohorts of C. partellus using the Illumina HiSeq 2500 platform. The data were subjected to identify putative genes that are involved in detoxification on insecticides in our cohort insect species. These studies resulted in identification of 64 cytochrome P450 genes (CYP450s), and 36 glutathione S-transferases genes (GSTs) encoding metabolic detoxification enzymes, primarily responsible for xenobiotic metabolism in insects. A total of 183 circadian genes with > 80% homolog and 11 olfactory receptor genes that mediate chemical cues were found in the C. partellus genome. Also, target receptors related to insecticide action, 4 acetylcholinesterase (AChE), 14 γ-aminobutyric acid (GABA), and 15 nicotinic acetylcholine (nAChR) receptors were detected. This is the first report of whole genome sequencing of C. partellus useful for understanding mode of action of different insecticides, and mechanisms of detoxification and designing target-specific insecticides to develop appropriate strategies to control C. partellus for sustainable crop production.

A Deep Clustering-based Novel Approach for Binning of Metagenomics Data.

Background: One major challenge in binning Metagenomics data is the limited availability of reference datasets, as only 1% of the total microbial population is yet cultured. This has given rise to the efficacy of unsupervised methods for binning in the absence of any reference datasets. Objective: To develop a deep clustering-based binning approach for Metagenomics data and to evaluate results with suitable measures. Methods: In this study, a deep learning-based approach has been taken for binning the Metagenomics data. The results are validated on different datasets by considering features such as Tetra-nucleotide frequency (TNF), Hexa-nucleotide frequency (HNF) and GC-Content. Convolutional Autoencoder is used for feature extraction and for binning; the K-means clustering method is used. Results: In most cases, it has been found that evaluation parameters such as the Silhouette index and Rand index are more than 0.5 and 0.8, respectively, which indicates that the proposed approach is giving satisfactory results. The performance of the developed approach is compared with current methods and tools using benchmarked low complexity simulated and real metagenomic datasets. It is found better for unsupervised and at par with semi-supervised methods. Conclusion: An unsupervised advanced learning-based approach for binning has been proposed, and the developed method shows promising results for various datasets. This is a novel approach for solving the lack of reference data problem of binning in metagenomics.

MetaConClust - Unsupervised Binning of Metagenomics Data using Consensus Clustering.

Background: Binning of metagenomic reads is an active area of research, and many unsupervised machine learning-based techniques have been used for taxonomic independent binning of metagenomic reads. Objective: It is important to find the optimum number of the cluster as well as develop an efficient pipeline for deciphering the complexity of the microbial genome. Methods: Applying unsupervised clustering techniques for binning requires finding the optimal number of clusters beforehand and is observed to be a difficult task. This paper describes a novel method, MetaConClust, using coverage information for grouping of contigs and automatically finding the optimal number of clusters for binning of metagenomics data using a consensus-based clustering approach. The coverage of contigs in a metagenomics sample has been observed to be directly proportional to the abundance of species in the sample and is used for grouping of data in the first phase by MetaConClust. The Partitioning Around Medoid (PAM) method is used for clustering in the second phase for generating bins with the initial number of clusters determined automatically through a consensus-based method. Results: Finally, the quality of the obtained bins is tested using silhouette index, rand Index, recall, precision, and accuracy. Performance of MetaConClust is compared with recent methods and tools using benchmarked low complexity simulated and real metagenomic datasets and is found better for unsupervised and comparable for hybrid methods. Conclusion: This is suggestive of the proposition that the consensus-based clustering approach is a promising method for automatically finding the number of bins for metagenomics data.

Microbiome of Pukzing Cave in India shows high antimicrobial activity against plant and animal pathogens.

Pukzing cave, the largest cave of Mizoram, India was explored for bacterial diversity. Culture dependent method revealed 235 bacterial isolates using three different treatments. Identity of the microbial species was confirmed by 16S rDNA sequencing. The highest bacterial population was recovered from heat treatment (n = 97;41.2%) followed by normal (n = 79;33.6%) and cold treatment (n = 59;25.1%) indicating dominance of moderate thermophiles. Antimicrobial potential of isolates showed 20.4% isolates having antimicrobial ability against tested pathogens. Amplicon sequencing of PKSI, PKSII and NRP specific genes revealed presence of AMP genes in the microbial population. Six microbial pathogens were selected for screening as they are well known for different disease cause organism in various fields such as agriculture and human health. Cave environment harbors unique microbial flora and hypervariable region V4 is more informative. Higher activity of AMP assay against these microbes indicates that cave microbial communities could be potential source of future genomic resources.

BAC-FISH Based Physical Map of Endangered Catfish Clarias magur for Chromosome Cataloguing and Gene Isolation through Positional Cloning.

Construction of a physical chromosome map of a species is important for positional cloning, targeted marker development, fine mapping of genes, selection of candidate genes for molecular breeding, as well as understanding the genome organization. The genomic libraries in the form of bacterial artificial chromosome (BAC) clones are also a very useful resource for physical mapping and identification and isolation of full-length genes and the related cis acting elements. Some BAC-FISH based studies reported in the past were gene based physical chromosome maps of Clarias magur (magur) to understand the genome organization of the species and to establish the relationships with other species in respect to genes' organization and evolution in the past. In the present study, we generated end sequences of the BAC clones and analyzed those end sequences within the scaffolds of the draft genome of magur to identify and map the genes bioinformatically for each clone. A total of 36 clones mostly possessing genes were identified and used in probe construction and their subsequent hybridization on the metaphase chromosomes of magur. This study successfully mapped all 36 specific clones on 16 chromosome pairs, out of 25 pairs of magur chromosomes. These clones are now recognized as chromosome-specific makers, which are an aid in individual chromosome identification and fine assembly of the genome sequence, and will ultimately help in developing anchored genes' map on the chromosomes of C. magur for understanding their organization, inheritance of important fishery traits and evolution of magur with respect to channel catfish, zebrafish and other species.

Metabolomics-Driven Mining of Metabolite Resources: Applications and Prospects for Improving Vegetable Crops.

Vegetable crops possess a prominent nutri-metabolite pool that not only contributes to the crop performance in the fields, but also offers nutritional security for humans. In the pursuit of identifying, quantifying and functionally characterizing the cellular metabolome pool, biomolecule separation technologies, data acquisition platforms, chemical libraries, bioinformatics tools, databases and visualization techniques have come to play significant role. High-throughput metabolomics unravels structurally diverse nutrition-rich metabolites and their entangled interactions in vegetable plants. It has helped to link identified phytometabolites with unique phenotypic traits, nutri-functional characters, defense mechanisms and crop productivity. In this study, we explore mining diverse metabolites, localizing cellular metabolic pathways, classifying functional biomolecules and establishing linkages between metabolic fluxes and genomic regulations, using comprehensive metabolomics deciphers of the plant's performance in the environment. We discuss exemplary reports covering the implications of metabolomics, addressing metabolic changes in vegetable plants during crop domestication, stage-dependent growth, fruit development, nutri-metabolic capabilities, climatic impacts, plant-microbe-pest interactions and anthropogenic activities. Efforts leading to identify biomarker metabolites, candidate proteins and the genes responsible for plant health, defense mechanisms and nutri-rich crop produce are documented. With the insights on metabolite-QTL (mQTL) driven genetic architecture, molecular breeding in vegetable crops can be revolutionized for developing better nutritional capabilities, improved tolerance against diseases/pests and enhanced climate resilience in plants.

Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges.

With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.

mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net.

BACKGROUND: Localization of messenger RNAs (mRNAs) plays a crucial role in the growth and development of cells. Particularly, it plays a major role in regulating spatio-temporal gene expression. The in situ hybridization is a promising experimental technique used to determine the localization of mRNAs but it is costly and laborious. It is also a known fact that a single mRNA can be present in more than one location, whereas the existing computational tools are capable of predicting only a single location for such mRNAs. Thus, the development of high-end computational tool is required for reliable and timely prediction of multiple subcellular locations of mRNAs. Hence, we develop the present computational model to predict the multiple localizations of mRNAs. RESULTS: The mRNA sequences from 9 different localizations were considered. Each sequence was first transformed to a numeric feature vector of size 5460, based on the k-mer features of sizes 1-6. Out of 5460 k-mer features, 1812 important features were selected by the Elastic Net statistical model. The Random Forest supervised learning algorithm was then employed for predicting the localizations with the selected features. Five-fold cross-validation accuracies of 70.87, 68.32, 68.36, 68.79, 96.46, 73.44, 70.94, 97.42 and 71.77% were obtained for the cytoplasm, cytosol, endoplasmic reticulum, exosome, mitochondrion, nucleus, pseudopodium, posterior and ribosome respectively. With an independent test set, accuracies of 65.33, 73.37, 75.86, 72.99, 94.26, 70.91, 65.53, 93.60 and 73.45% were obtained for the respective localizations. The developed approach also achieved higher accuracies than the existing localization prediction tools. CONCLUSIONS: This study presents a novel computational tool for predicting the multiple localization of mRNAs. Based on the proposed approach, an online prediction server "mLoc-mRNA" is accessible at http://cabgrid.res.in:8080/mlocmrna/ . The developed approach is believed to supplement the existing tools and techniques for the localization prediction of mRNAs.

Revelation of candidate genes and molecular mechanism of reproductive seasonality in female rohu (Labeo rohita Ham.) by RNA sequencing.

BACKGROUND: Carp fish, rohu (Labeo rohita Ham.) is important freshwater aquaculture species of South-East Asia having seasonal reproductive rhythm. There is no holistic study at transcriptome level revealing key candidate genes involved in such circannual rhythm regulated by biological clock genes (BCGs). Seasonality manifestation has two contrasting phases of reproduction, i.e., post-spawning resting and initiation of gonadal activity appropriate for revealing the associated candidate genes. It can be deciphered by RNA sequencing of tissues involved in BPGL (Brain-Pituitary-Gonad-Liver) axis controlling seasonality. How far such BCGs of this fish are evolutionarily conserved across different phyla is unknown. Such study can be of further use to enhance fish productivity as seasonality restricts seed production beyond monsoon season. RESULT: A total of ~ 150 Gb of transcriptomic data of four tissues viz., BPGL were generated using Illumina TruSeq. De-novo assembled BPGL tissues revealed 75,554 differentially expressed transcripts, 115,534 SSRs, 65,584 SNPs, 514 pathways, 5379 transcription factors, 187 mature miRNA which regulates candidate genes represented by 1576 differentially expressed transcripts are available in the form of web-genomic resources. Findings were validated by qPCR. This is the first report in carp fish having 32 BCGs, found widely conserved in fish, amphibian, reptile, birds, prototheria, marsupials and placental mammals. This is due to universal mechanism of rhythmicity in response to environment and earth rotation having adaptive and reproductive significance. CONCLUSION: This study elucidates evolutionary conserved mechanism of photo-periodism sensing, neuroendocrine secretion, metabolism and yolk synthesis in liver, gonadal maturation, muscular growth with sensory and auditory perception in this fish. Study reveals fish as a good model for research on biological clock besides its relevance in reproductive efficiency enhancement.

Drought responsiveness in black pepper (Piper nigrum L.): Genes associated and development of a web-genomic resource.

Black pepper (Piper nigrum L.; 2n = 52; Piperaceae), the king of spices, is a perennial, trailing woody flowering vine and has global importance with widespread dietary, medicinal, and preservative uses. It is an economically important germplasm cultivated for its fruit and the major cash crop in >30 tropical countries. Crop production is mainly affected by drought stress. The present study deals with the candidate gene identification from drought-affected black pepper leaf transcriptome generated by Illumina Hiseq2000. It also aims to mine putative molecular markers (namely SSRs, SNPs, and InDels) and generate primers for them. The identification of transcription factors and pathways involved in drought tolerance is also reported here. De novo transcriptome assembly was performed with trinity assembler. In total, 4914 differential expressed genes, 2110 transcriptional factors, 786 domains and 1137 families, 20,124 putative SSR markers, and 259,236 variants were identified. At2g30105 (unidentified gene containing leucine-rich repeats and ubiquitin-like domain), serine threonine protein kinase, Mitogen-activated protein kinase, Nucleotide Binding Site-Leucine Rich Repeat, Myeloblastosis-related proteins, basic helix-loop-helix are all found upregulated and are reported to be associated with plant tolerance against drought condition. All these information are catalogued in the Black Pepper Drought Transcriptome Database (BPDRTDb), freely accessible for academic use at http://webtom.cabgrid.res.in/bpdrtdb/. This database is a good foundation for the genetic improvement of pepper plants, breeding programmes, and mapping population of this crop. Putative markers can also be a reliable genomic resource to develop drought-tolerant variety for better black pepper productivity.

Genome sequence analysis provides insights on genomic variation and late blight resistance genes in potato somatic hybrid (parents and progeny).

Wild Solanum species are the important resources for potato improvement. With the availability of potato genome and sequencing progress, knowledge about genomic resources is essential for novel genes discovery. Hence, the aim of this study was to decipher draft genome sequences of unique potato genotypes i.e. somatic hybrid P8 (J1), wild species S. pinnatisectum (J2), progeny MSH/14-112 (P8 × cv. Kufri Jyoti) (J3), and S. tuberosum dihaploid C-13 (J4). Draft genome sequencing using Illumina platform and reference-based assemblies with the potato genome yielded genome assembly size of 725.01 Mb (J1), 724.95 Mb (J2), 725.01 Mb (J3), and 809.59 Mb (J4). Further, 39,260 (J1), 25,711 (J2), 39,730 (J3) and 30,241 (J4) genes were identified and 17,411 genes were found common in the genotypes particularly late blight resistance genes (R3a, RGA2, RGA3, R1B-16, Rpi-blb2, Rpi and Rpi-vnt1). Gene ontology (GO) analysis showed that molecular function was predominant and signal transduction was major KEGG pathways. Further, gene enrichment analysis revealed dominance of metabolic process (GO: 0008152) in all the samples. Phylogeny analysis showed relatedness with potato and other plant species. Heterozygous single nucleotide polymorphism (SNP) was more than homozygous, and SNP in genic region was more than inter-genic region. Copy number variation (CNV) analysis indicated greater number of deletions than duplications. Sequence diversity and conserved motifs analysis revealed variation for late blight resistance genes. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis showed differential expression of late blight resistance genes. Our study provides insights on genome sequence, structural variation and late blight resistance genes in potato somatic hybrid (parents and progeny) for future research.

Muscle transcriptome signature and gene regulatory network analysis in two divergent lines of a hilly bovine species Mithun (Bos frontalis).

A massive bovine, Bos frontalis, also known as Mithun or Gayal, found at higher altitude is very promising meat and milk animal. For candidate gene and marker discovery, RNA-seq data was generated from longissimus dorsi muscle tissues with Illumina-HiSeq. Such markers can be used in future for genetic gain of traits like feed conversion efficiency (FCE) and average daily gain (ADG). Analysis revealed 297differentially expressed genes (DEGs) having 173 up and 124 down-regulated unigenes. Extensive conservation was found in genic region while comparing with Bos taurus. Analysis revealed 57 pathways having 112 enzymes, 72 transcriptional factors and cofactors, 212 miRNAs regulating 71 DEGs, 25,855 SSRs, mithun-specific 104,822 variants and 7288 indels, gene regulatory network (GRN) having 24 hub-genes and transcriptional factors regulating cell proliferation, immune tolerance and myogenesis. This is first report of muscle transcriptome depicting candidate genes with GRN controlling FCE and ADG. Reported putative molecular markers, candidate genes and hub proteins can be valuable genomic resources for association studies in genetic improvement programme.

Identification and characterization of trait-specific SNPs using ddRAD sequencing in water buffalo.

Single Nucleotide Polymorphism (SNP) is one of the important molecular markers widely used in animal breeding program for improvement of any desirable genetic traits. Considering this, the present study was carried out to identify, annotate and analyze the SNPs related to four important traits of buffalo viz. milk volume, age at first calving, post-partum cyclicity and feed conversion efficiency. We identified 246,495, 168,202, 74,136 and 194,747 genome-wide SNPs related to mentioned traits, respectively using ddRAD sequencing technique based on 85 samples of Murrah Buffaloes. Distribution of these SNPs were highest (61.69%) and lowest (1.78%) in intron and exon regions, respectively. Under coding regions, the SNPs for the four traits were further classified as synonymous (4697) and non-synonymous (3827). Moreover, Gene Ontology (GO) terms of identified genes assigned to various traits. These characterized SNPs will enhance the knowledge of cellular mechanism for enhancing productivity of water buffalo through molecular breeding.

Plant virus interaction mechanism and associated pathways in mosaic disease of small cardamom (Elettaria cardamomum Maton) by RNA-Seq approach.

Small cardamom (Elettaria cardamomum), grown in limited coastal tropical countries is one of the costliest and widely exported agri-produce having global turnover of >10 billion USD. Mosaic/marble disease is one of the major impediments that requires understanding of disease at molecular level. Neither whole genome sequence nor any genomic resources are available, thus RNA seq approach can be a rapid and economical alternative. De novo transcriptome assembly was done with Illumina Hiseq data. A total of 5317 DEGs, 2267 TFs, 114 pathways and 175,952 genic region putative markers were obtained. Gene regulatory network analysis deciphered molecular events involved in marble disease. This is the first transcriptomic report revealing disease mechanism mediated by perturbation in auxin homeostasis and ethylene signalling leading to senescence. The web-genomic resource (SCMVTDb) catalogues putative molecular markers, candidate genes and transcript information. SCMVTDb can be used in germplasm improvement against mosaic disease in endeavour of small cardamom productivity. Availability of genomic resource, SCMVTDb: http://webtom.cabgrid.res.in/scmvtdb/.