Integrative omics analysis. A study based on Plasmodium falciparum mRNA and protein data

BACKGROUND: Technological improvements have shifted the focus from data generation to data analysis. The availability of large amounts of data from transcriptomics, protemics and metabolomics experiments raise new questions concerning suitable integrative analysis methods. We compare three integrative analysis techniques (co-inertia analysis, generalized singular value decomposition and integrative biclustering) by applying them to gene and protein abundance data from the six life cycle stages of Plasmodium falciparum. Co-inertia analysis is an analysis method used to visualize and explore gene and protein data. The generalized singular value decomposition has shown its potential in the analysis of two transcriptome data sets. Integrative Biclustering applies biclustering to gene and protein data. RESULTS: Using CIA, we visualize the six life cycle stages of Plasmodium falciparum, as well as GO terms in a 2D plane and interpret the spatial configuration. With GSVD, we decompose the transcriptomic and proteomic data sets into matrices with biologically meaningful interpretations and explore the processes captured by the data sets. IBC identifies groups of genes, proteins, GO Terms and life cycle stages of Plasmodium falciparum. We show method-specific results as well as a network view of the life cycle stages based on the results common to all three methods. Additionally, by combining the results of the three methods, we create a three-fold validated network of life cycle stage specific GO terms: Sporozoites are associated with transcription and transport; merozoites with entry into host cell as well as biosynthetic and metabolic processes; rings with oxidation-reduction processes; trophozoites with glycolysis and energy production; schizonts with antigenic variation and immune response; gametocyctes with DNA packaging and mitochondrial transport. Furthermore, the network connectivity underlines the separation of the intraerythrocytic cycle from the gametocyte and sporozoite stages. CONCLUSION: Using integrative analysis techniques, we can integrate knowledge from different levels and obtain a wider view of the system under study. The overlap between method-specific and common results is considerable, even if the basic mathematical assumptions are very different. The three-fold validated network of life cycle stage characteristics of Plasmodium falciparum could identify a large amount of the known associations from literature in only one study

Systems analysis of host-parasite interactions.

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug-resistant parasites necessitates that the research community take an active role in understanding host-parasite infection biology in order to develop improved therapeutics. Recent advances in next-generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host-parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high-throughput -omic data will undoubtedly generate extraordinary insight into host-parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host-parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies

Systems Biology Approaches Towards Immunity against <em>Plasmodium</em>

Malaria is one of the most devastating infectious diseases known to humans. It is caused by unicellular protozoan parasites belonging to the genus Plasmodium. Till date, over 200 species of Plasmodium have been formally described, and each species infects a certain range of hosts. However, the human infection is limited to only five of the species, of which P. falciparum is the most responsible. Due to the emergence of parasite resistance to frontline chemotherapies and mosquito resistance to current insecticides which threaten the control programmes, new antimalarial therapeutics or approaches capable of predicting useful models of how different cells of the innate immune system function, is the need of the hour. Systems Immunology is a relatively recent discipline under Systems Biology to understand the structure and function of the immune system and how the components of the immune system work together as a whole. Thus, this chapter aims to give insight into the approaches of Systems Biology for investigating the immune factors that are formed during Plasmodium falciparum infection in the human body. Here, the numerous experimental and computational works with the ongoing methodologies using Systems Biology approaches along with the interactions of host and pathogen will be discussed

Paving the Way:Contributions of Big Data to Apicomplexan and Kinetoplastid Research

In the age of big data an important question is how to ensure we make the most out of the resources we generate. In this review, we discuss the major methods used in Apicomplexan and Kinetoplastid research to produce big datasets and advance our understanding of Plasmodium, Toxoplasma, Cryptosporidium, Trypanosoma and Leishmania biology. We debate the benefits and limitations of the current technologies, and propose future advancements that may be key to improving our use of these techniques. Finally, we consider the difficulties the field faces when trying to make the most of the abundance of data that has already been, and will continue to be, generated

<i>Plasmodium </i>Condensin Core Subunits SMC2/SMC4 Mediate Atypical Mitosis and Are Essential for Parasite Proliferation and Transmission

Condensin is a multi-subunit protein complex regulating chromosome condensation and segregation during cell division. In Plasmodium spp., the causative agent of malaria, cell division is atypical and the role of condensin is unclear. Here we examine the role of SMC2 and SMC4, the core subunits of condensin, during endomitosis in schizogony and endoreduplication in male gametogenesis. During early schizogony, SMC2/SMC4 localize to a distinct focus, identified as the centromeres by NDC80 fluorescence and chromatin immunoprecipitation sequencing (ChIP-seq) analyses, but do not form condensin I or II complexes. In mature schizonts and during male gametogenesis, there is a diffuse SMC2/SMC4 distribution on chromosomes and in the nucleus, and both condensin I and condensin II complexes form at these stages. Knockdown of smc2 and smc4 gene expression reveals essential roles in parasite proliferation and transmission. The condensin core subunits (SMC2/SMC4) form different complexes and may have distinct functions at various stages of the parasite life cycle

Dissecting the genetic bases of severe malaria resistance using genome-wide and post genomewide study approaches

P. falciparum malaria remains one of the leading public health problems worldwide. The global tally of malaria in 2018 was estimated at 228 million cases and 405, 000 deaths worldwide. African countries disproportionately carry the global burden of malaria accounting for 93% and 94% of cases and deaths, respectively. Even though most infected children recover from P. falciparum malaria, a small subset (~1%) of cases progresses to severe disease and death. Over the last decade, several genome-wide association studies (GWASs) have been conducted in diverse malaria endemic populations to understand the natural host protective immunity against severe malaria that can provide clues for the development of new vaccines and therapeutics. However, beyond identifying association variants, conventional GWAS approaches can't inform the underpinning biological functions. To bridge this gap, we applied various contemporary statistical genetic analytic approaches to malaria GWAS datasets of diverse malaria endemic populations. First, we accessed malaria resistance GWAS datasets of three African populations (N=~11,000) including Kenya, Gambia and Malawi from European Genome Phenome Archive (EGA) through MalariaGEN consortium standard data accession procedures. We explored the challenges of GWAS approaches in the genetically diverse Africa populations and figured out how various advanced statistical genetic methods can be implemented to address these challenges. We investigated single nucleotide polymorphism (SNP) heritability (h2 g) of malaria resistance in the Gambian populations and determined appropriate quality (QC) thresholds to accurately estimate the h2 g in our dataset. Second, we estimated h2 g in the three populations and partitioned the h2 g into chromosomes, allele frequencies and annotations using the genetic relationship-matrix restricted maximum likelihood approaches. We further created African specific reference panel from African population datasets obtained from 1000 Genomes Project and African Genome Variation Project dataset and computed linkage disequilibrium (LD). We used LD information obtained from these reference panels to compute cell-type specific and none cell-type specific enrichments for GWAS-summary statistics meta-analyzed across the three populations. Our results showed for the first time that malaria resistance is polygenic trait with h2 g of ~20% and that the causal variants are overrepresented around protein coding regions of the genome. We further showed that the h2 g is disproportionately concentrated on three chromosomes (chr 5, 11 and 20), suggesting cost-effectiveness of targeting these chromosomes in future malaria genomic sequencing studies. Third, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analyzed across eleven populations in malaria endemic regions in Africa, Asia and Oceania. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping and gene-based association analyses to identify candidate severe malaria resistance genes. We performed network and pathway analyses to investigate their shared biological functions. We further applied rare variant analysis to raw GWAS datasets of three malaria endemic populations including Kenya, Malawi and Gambia and performed various population genetic structures of the identified genes in the three endemic populations and 20 world-wide ethnics. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signaling elements and neuronal systems. Furthermore, our population genetic analysis revealed that the minor allele frequencies (MAF) of the SNPs residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes that are enriched in pathways linked to severe malaria pathogenesis. This highlights the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways. In conclusions, this project showed that malaria resistance trait is mainly a polygenic trait which is influenced by genes and pathways linked to blood stage lifecycle of P. falciparum. These findings constitute the foundations for future experimental studies that can potentially lead to translational medicine including development of new vaccines and therapeutics. However, ‘-omics' studies including those implemented in this study, are limited to single datatype analysis and lack adequate power to explain the complexity of molecular processes and usually lead to identification of correlations than causations. Thus, beyond singe locus analysis, the future direction of malaria resistance requires a paradigm shift from single-omics to multi-stage and multi-dimensional integrative multi-omics studies that combines multiple data types from the human host, the parasite, and the environment. The current biotechnological and statistical advances may eventually lead to the feasibility of systems biology studies and revolutionize malaria research

Probabilistic data integration identifies reliable gametocyte-specific proteins and transcripts in malaria parasites.

Plasmodium gametocytes are the sexual forms of the malaria parasite essential for transmission to mosquitoes. To better understand how gametocytes differ from asexual blood-stage parasites, we performed a systematic analysis of available 'omics data for P. falciparum and other Plasmodium species. 18 transcriptomic and proteomic data sets were evaluated for the presence of curated "gold standards" of 41 gametocyte-specific versus 46 non-gametocyte genes and integrated using Bayesian probabilities, resulting in gametocyte-specificity scores for all P. falciparum genes. To illustrate the utility of the gametocyte score, we explored newly predicted gametocyte-specific genes as potential biomarkers of gametocyte carriage and exposure. We analyzed the humoral immune response in field samples against 30 novel gametocyte-specific antigens and found five antigens to be differentially recognized by gametocyte carriers as compared to malaria-infected individuals without detectable gametocytes. We also validated the gametocyte-specificity of 15 identified gametocyte transcripts on culture material and samples from naturally infected individuals, resulting in eight transcripts that were >1000-fold higher expressed in gametocytes compared to asexual parasites and whose transcript abundance allowed gametocyte detection in naturally infected individuals. Our integrated genome-wide gametocyte-specificity scores provide a comprehensive resource to identify targets and monitor P. falciparum gametocytemia

Data integration in the era of omics: current and future challenges

To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community