Use of an Integrated Multi-Omics Approach To Identify Molecular Mechanisms and Critical Factors Involved in the Pathogenesis of Leptospira.

Leptospirosis, a bacterial zoonosis caused by pathogenic Leptospira spp., is prevalent worldwide and has become a serious threat in recent years. Limited understanding of Leptospira pathogenesis and host response has hampered the development of effective vaccine and diagnostics. Although Leptospira is phagocytosed by innate immune cells, it resists its destruction, and the evading mechanism involved is unclear. In the present study, we used an integrative multi-omics approach to identify the critical molecular factors of Leptospira involved in pathogenesis during interaction with human macrophages. Transcriptomic and proteomic analyses were performed at 24 h postinfection of human macrophages (phorbol-12-myristate-13-acetate differentiated THP-1 cells) with the pathogenic Leptospira interrogans serovar Icterohaemorrhagiae strain RGA (LEPIRGA). Our results identified a total of 1,528 transcripts and 871 proteins that were significantly expressed with an adjusted P value of <0.05. The correlations between the transcriptomic and proteomic data were above average (r = 0.844), suggesting the role of the posttranscriptional processes during host interaction. The conjoint analysis revealed the expression of several virulence-associated proteins such as adhesins, invasins, and secretory and chemotaxis proteins that might be involved in various processes of attachment and invasion and as effectors during pathogenesis in the host. Further, the interaction of bacteria with the host cell (macrophages) was a major factor in the differential expression of these proteins. Finally, eight common differentially expressed RNA-protein pairs, predicted as virulent, outer membrane/extracellular proteins were validated by quantitative PCR. This is the first report using integrated multi-omics approach to identify critical factors involved in Leptospira pathogenesis. Validation of these critical factors may lead to the identification of target antigens for the development of improved diagnostics and vaccines against leptospirosis. IMPORTANCE Leptospirosis is a zoonotic disease of global importance. It is caused by a Gram-negative bacterial spirochete of the genus Leptospira. The current challenge is to detect the infection at early stage for treatment or to develop potent vaccines that can induce cross-protection against various pathogenic serovars. Understanding host-pathogen interactions is important to identify the critical factors involved in pathogenesis and host defense for developing improved vaccines and diagnostics. Utilizing an integrated multi-omics approach, our study provides important insight into the interaction of Leptospira with human macrophages and identifies a few critical factors (such as virulence-associated proteins) involved in pathogenesis. These factors can be exploited for the development of novel tools for the detection, treatment, or prevention of leptospirosis.

Haustorium formation and a distinct biotrophic transcriptome characterize infection of Nicotiana benthamiana by the tree pathogen Phytophthora kernoviae.

Phytophthora species cause some of the most serious diseases of trees and threaten forests in many parts of the world. Despite the generation of genome sequence assemblies for over 10 tree-pathogenic Phytophthora species and improved detection methods, there are many gaps in our knowledge of how these pathogens interact with their hosts. To facilitate cell biology studies of the infection cycle we examined whether the tree pathogen Phytophthora kernoviae could infect the model plant Nicotiana benthamiana. We transformed P. kernoviae to express green fluorescent protein (GFP) and demonstrated that it forms haustoria within infected N. benthamiana cells. Haustoria were also formed in infected cells of natural hosts, Rhododendron ponticum and European beech (Fagus sylvatica). We analysed the transcriptome of P. kernoviae in cultured mycelia, spores, and during infection of N. benthamiana, and detected 12,559 transcripts. Of these, 1,052 were predicted to encode secreted proteins, some of which may function as effectors to facilitate disease development. From these, we identified 87 expressed candidate RXLR (Arg-any amino acid-Leu-Arg) effectors. We transiently expressed 12 of these as GFP fusions in N. benthamiana leaves and demonstrated that nine significantly enhanced P. kernoviae disease progression and diversely localized to the cytoplasm, nucleus, nucleolus, and plasma membrane. Our results show that N. benthamiana can be used as a model host plant for studying this tree pathogen, and that the interaction likely involves suppression of host immune responses by RXLR effectors. These results establish a platform to expand the understanding of Phytophthora tree diseases.

ResCap: plant resistance gene prediction and probe generation pipeline for resistance gene sequence capture.

Summary: The discovery of novel resistance genes (R-genes) is an important component in disease resistance breeding. Nevertheless, R-gene identification from wild species and close relatives of plants is not only a difficult but also a cumbersome process. In this study, ResCap, a support vector machine-based high-throughput R-gene prediction and probe generation pipeline has been developed to generate probes from genomic datasets. ResCap contains two integral modules. The first module identifies the R-genes and R-gene like sequences under four categories containing different domains such as TIR-NBS-LRR (TNL), CC-NBS-LRR (CNL), Receptor-like kinase (RLK) and Receptor-like proteins (RLPs). The second module generates probes from extracted nucleotide sequences of resistance genes to conduct sequence capture (SeqCap) experiments. For the validation of ResCap pipeline, ResCap generated probes were synthesized and a sequence capture experiment was performed to capture expressed resistance genes among six spring barley genotypes. The developed ResCap pipeline in combination with the performed sequence capture experiment has shown to increase precision of R-gene identification while simultaneously allowing rapid gene validation including non-sequenced plants. Availability and implementation: The ResCap pipeline is available at http://rescap.ltj.slu.se/ResCap/. Contact: sandeep.kushwaha@slu.se or sandeep@niab.org.in. Supplementary information: Supplementary materials are available at Bioinformatics Advances online.

Efficient RNA silencing suppression activity of Potato Mop-Top Virus 8K protein is driven by variability and positive selection.

Previously, we investigated the evolution of Potato mop-top virus (PMTV) ORFs. Results indicate that positive selection acts exclusively on an ORF encoding the 8K protein, a weak viral suppressor of RNA silencing (VSR). However, how the extraordinary variability contributes to 8K-mediated RNA silencing suppression remains unknown. Here, we characterized the RNA silencing suppression activity of the 8K protein from seven diverse isolates. We show that 8K encoded by isolate P1 exhibits stronger RNA silencing suppression activity than the 8K protein from six other isolates. Mutational analyses revealed that Ser-50 is critical for these differences. By comparing small RNA profiles we found a lower abundance of siRNAs with U residue at the 5'-terminus after expression of the P1 8K compared to expression of 8K from isolate P125, an isolate with weak VSR activity. These results provide new clues as to the role of positive selection in shaping activities of VSRs.

Draft Genome Sequence for the Tree Pathogen Phytophthora plurivora.

Species from the genus Phytophthora are well represented among organisms causing serious diseases on trees. Phytophthora plurivora has been implicated in long-term decline of woodland trees across Europe. Here we present a draft genome sequence of P. plurivora, originally isolated from diseased European beech (Fagus sylvatica) in Malmö, Sweden. When compared with other sequenced Phytophthora species, the P. plurivora genome assembly is relatively compact, spanning 41 Mb. This is organized in 1,919 contigs and 1,898 scaffolds, encompassing 11,741 predicted genes, and has a repeat content of approximately 15%. Comparison of allele frequencies revealed evidence for tetraploidy in the sequenced isolate. As in other sequenced Phytophthora species, P. plurivora possesses genes for pathogenicity-associated RXLR and Crinkle and Necrosis effectors, predominantly located in gene-sparse genomic regions. Comparison of the P. plurivora RXLR effectors with orthologs in other sequenced species in the same clade (Phytophthora multivora and Phytophthora capsici) revealed that the orthologs were likely to be under neutral or purifying selection.

Genome Sequence Resource for the Oomycete Taro Pathogen Phytophthora colocasiae.

Phytophthora colocasiae is a phytopathogenic oomycete that causes leaf blight and corm rot on taro (Colocasia esculenta), an important staple crop in the tropics. The impact of P. colocasiae is a serious concern for food security in Asian and Oceanic regions. Vietnamese strain 7290 of P. colocasiae was sequenced (Illumina) to assemble a draft genome of 56.6 Mb, comprised of 19,853 scaffolds and 19,984 predicted protein-coding genes. As in other Phytophthora species, P. colocasiae possesses numerous pathogenicity-related genes, such as the RxLR class of effectors. This draft genome sequence of P. colocasiae provides a resource to underpin the first steps in determining the molecular mechanisms of disease development in this pathosystem.

Draft genome of the oomycete pathogen Phytophthora cactorum strain LV007 isolated from European beech (Fagus sylvatica).

Phytophthora cactorum is a broad host range phytopathogenic oomycete. P. cactorum strain LV007 was isolated from a diseased European Beech (Fagus sylvatica) in Malmö, Sweden in 2016. The draft genome of P. cactorum strain LV007 is 67.81 Mb. It contains 15,567 contigs and 21,876 predicted protein-coding genes. As reported for other phytopathogenic Phytophthora species, cytoplasmic effector proteins including RxLR and CRN families were identified. The genome sequence has been deposited at DDBJ/ENA/GenBank under the accession NBIJ00000000. The version described in this paper is version NBIJ01000000.

Draft Genome Sequence of the Mycoparasitic Oomycete Pythium oligandrum Strain CBS 530.74.

The oomycete Pythium oligandrum is a mycoparasite and licenced biological control agent. Here, we report the draft genome sequence of P. oligandrum strain CBS 530.74, which is 36.80 Mb. It contains 341 scaffolds and 11,647 predicted protein-coding genes. As reported for plant-pathogenic Pythium species, RXLR-type effector sequences are absent.

Food Waste to Energy: An Overview of Sustainable Approaches for Food Waste Management and Nutrient Recycling.

Food wastage and its accumulation are becoming a critical problem around the globe due to continuous increase of the world population. The exponential growth in food waste is imposing serious threats to our society like environmental pollution, health risk, and scarcity of dumping land. There is an urgent need to take appropriate measures to reduce food waste burden by adopting standard management practices. Currently, various kinds of approaches are investigated in waste food processing and management for societal benefits and applications. Anaerobic digestion approach has appeared as one of the most ecofriendly and promising solutions for food wastes management, energy, and nutrient production, which can contribute to world's ever-increasing energy requirements. Here, we have briefly described and explored the different aspects of anaerobic biodegrading approaches for food waste, effects of cosubstrates, effect of environmental factors, contribution of microbial population, and available computational resources for food waste management researches.

Draft Genome Sequence of the Mycoparasitic Oomycete Pythium periplocum Strain CBS 532.74.

The oomycete Pythium periplocum is an aggressive mycoparasite of a number of plant pathogenic fungi and oomycetes and therefore has potential as a biological control agent. Here, we report the first draft genome sequence of P. periplocum, which comprises 35.89 Mb. It contains 1,043 scaffolds and 14,399 predicted protein-coding genes.

Potato tuber expression of Arabidopsis WRINKLED1 increase triacylglycerol and membrane lipids while affecting central carbohydrate metabolism.

Tuber and root crops virtually exclusively accumulate storage products in the form of carbohydrates. An exception is yellow nutsedge (Cyperus esculentus) in which tubers have the capacity to store starch and triacylglycerols (TAG) in roughly equal amounts. This suggests that a tuber crop can efficiently handle accumulation of energy dense oil. From a nutritional as well as economic aspect, it would be of interest to utilize the high yield capacity of tuber or root crops for oil accumulation similar to yellow nutsedge. The transcription factor WRINKLED1 from Arabidopsis thaliana, which in seed embryos induce fatty acid synthesis, has been shown to be a major factor for oil accumulation. WRINKLED1 was expressed in potato (Solanum tuberosum) tubers to explore whether this factor could impact tuber metabolism. This study shows that a WRINKLED1 transcription factor could induce triacylglycerol accumulation in tubers of transformed potato plants grown in field (up to 12 nmol TAG/mg dry weight, 1% of dry weight) together with a large increase in polar membrane lipids. The changes in metabolism further affected starch accumulation and composition concomitant with massive increases in sugar content.

NBSPred: a support vector machine-based high-throughput pipeline for plant resistance protein NBSLRR prediction.

UNLABELLED: The nucleotide binding site leucine-rich repeats (NBSLRRs) belong to one of the largest known families of disease resistance genes that encode resistance proteins (R-protein) against the pathogens of plants. Various defence mechanisms have explained the regulation of plant immunity, but still, we have limited understanding about plant defence against different pathogens. Identification of R-proteins and proteins having R-protein-like features across the genome, transcriptome and proteome would be highly useful to develop the global understanding of plant defence mechanisms, but it is laborious and time-consuming task. Therefore, we have developed a support vector machine-based high-throughput pipeline called NBSPred to differentiate NBSLRR and NBSLRR-like protein from Non-NBSLRR proteins from genome, transcriptome and protein sequences. The pipeline was tested and validated with input sequences from three dicot and two monocot plants including Arabidopsis thaliana, Boechera stricta, Brachypodium distachyon Solanum lycopersicum and Zea mays. AVAILABILITY AND IMPLEMENTATION: The NBSPred pipeline is available at http://soilecology.biol.lu.se/nbs/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: sandeep.kushwaha@biol.lu.se.

Captured metagenomics: large-scale targeting of genes based on 'sequence capture' reveals functional diversity in soils.

Microbial enzyme diversity is a key to understand many ecosystem processes. Whole metagenome sequencing (WMG) obtains information on functional genes, but it is costly and inefficient due to large amount of sequencing that is required. In this study, we have applied a captured metagenomics technique for functional genes in soil microorganisms, as an alternative to WMG. Large-scale targeting of functional genes, coding for enzymes related to organic matter degradation, was applied to two agricultural soil communities through captured metagenomics. Captured metagenomics uses custom-designed, hybridization-based oligonucleotide probes that enrich functional genes of interest in metagenomic libraries where only probe-bound DNA fragments are sequenced. The captured metagenomes were highly enriched with targeted genes while maintaining their target diversity and their taxonomic distribution correlated well with the traditional ribosomal sequencing. The captured metagenomes were highly enriched with genes related to organic matter degradation; at least five times more than similar, publicly available soil WMG projects. This target enrichment technique also preserves the functional representation of the soils, thereby facilitating comparative metagenomics projects. Here, we present the first study that applies the captured metagenomics approach in large scale, and this novel method allows deep investigations of central ecosystem processes by studying functional gene abundances.

MetCap: a bioinformatics probe design pipeline for large-scale targeted metagenomics.

BACKGROUND: Massive sequencing of genes from different environments has evolved metagenomics as central to enhancing the understanding of the wide diversity of micro-organisms and their roles in driving ecological processes. Reduced cost and high throughput sequencing has made large-scale projects achievable to a wider group of researchers, though complete metagenome sequencing is still a daunting task in terms of sequencing as well as the downstream bioinformatics analyses. Alternative approaches such as targeted amplicon sequencing requires custom PCR primer generation, and is not scalable to thousands of genes or gene families. RESULTS: In this study, we are presenting a web-based tool called MetCap that circumvents the limitations of amplicon sequencing of multiple genes by designing probes that are suitable for large-scale targeted metagenomics sequencing studies. MetCap provides a novel approach to target thousands of genes and genomic regions that could be used in targeted metagenomics studies. Automatic analysis of user-defined sequences is performed, and probes specifically designed for metagenome studies are generated. To illustrate the advantage of a targeted metagenome approach, we have generated more than 400,000 probes that match more than 300,000 [corrected] publicly available sequences related to carbon degradation, and used these probes for target sequencing in a soil metagenome study. The results show high enrichment of target genes and a successful capturing of the majority of gene families. MetCap is freely available to users from: http://soilecology.biol.lu.se/metcap/ . CONCLUSION: MetCap is facilitating probe-based target enrichment as an easy and efficient alternative tool compared to complex primer-based enrichment for large-scale investigations of metagenomes. Our results have shown efficient large-scale target enrichment through MetCap-designed probes for a soil metagenome. The web service is suitable for any targeted metagenomics project that aims to study several genes simultaneously. The novel bioinformatics approach taken by the web service will enable researchers in microbial ecology to tap into the vast diversity of microbial communities using targeted metagenomics as a cost-effective alternative to whole metagenome sequencing.

Protein interaction network analysis--approach for potential drug target identification in Mycobacterium tuberculosis.

In host-parasite diseases like tuberculosis, non-homologous proteins (enzymes) as drug target are first preference. Most potent drug target can be identified among large number of non-homologous protein through protein interaction network analysis. In this study, the entire promising dimension has been explored for identification of potential drug target. A comparative metabolic pathway analysis of the host Homo sapiens and the pathogen M. tuberculosis H37Rv has been performed with three level of analysis. In first level, the unique metabolic pathways of M. tuberculosis have been identified through its comparative study with H. sapiens and identification of non-homologous proteins has been done through BLAST similarity search. In second level, choke-point analysis has been performed with identified non-homologous proteins of metabolic pathways. In third level, two type of analysis have been performed through protein interaction network. First analysis has been done to find out the most potential metabolic functional associations among all identified choke point proteins whereas second analysis has been performed to find out the functional association of high metabolic interacting proteins to pathogenesis causing proteins. Most interactive metabolic proteins which have highest number of functional association with pathogenesis causing proteins have been considered as potential drug target. A list of 18 potential drug targets has been proposed which are various stages of progress at the TBSGC and proposed drug targets are also studied for other pathogenic strains. As a case study, we have built a homology model of identified drug targets histidinol-phosphate aminotransferase (HisC1) using MODELLER software and various information have been generated through molecular dynamics which will be useful in wetlab structure determination. The generated model could be further explored for insilico docking studies with suitable inhibitors.

PINAT1.0: protein interaction network analysis tool.

UNLABELLED: Cellular processes are regulated by interaction of various proteins i.e. multiprotein complexes and absences of these interactions are often the cause of disorder or disease. Such type of protein interactions are of great interest for drug designing. In host-parasite diseases like Tuberculosis, non-homologous proteins as drug target are first preference. Most potent drug target can be identifying among large number of non-homologous protein through protein interaction network analysis. Drug target should be those non-homologous protein which is associated with maximum number of functional proteins i.e. has highest number of interactants, so that maximum harm can be caused to pathogen only. In present work, Protein Interaction Network Analysis Tool (PINAT) has been developed to identification of potential protein interaction for drug target identification. PINAT is standalone, GUI application software made for protein-protein interaction (PPI) analysis and network building by using co-evolutionary profile. PINAT is very useful for large data PPI study with easiest handling among available softwares. PINAT provides excellent facilities for the assembly of data for network building with visual presentation of the results and interaction score. The software is written in JAVA and provides reliability through transparency with user. AVAILABILITY: PINAT is available at www.manit.ac.in/pinat.