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

Advances in Malaria Diagnostic Methods in Resource-Limited Settings: A Systematic Review

Malaria continues to pose a health challenge globally, and its elimination has remained a major topic of public health discussions. A key factor in eliminating malaria is the early and accurate detection of the parasite, especially in asymptomatic individuals, and so the importance of enhanced diagnostic methods cannot be overemphasized. This paper reviewed the advances in malaria diagnostic tools and detection methods over recent years. The use of these advanced diagnostics in lower and lower-middle-income countries as compared to advanced economies has been highlighted. Scientific databases such as Google Scholar, PUBMED, and Multidisciplinary Digital Publishing Institute (MDPI), among others, were reviewed. The findings suggest important advancements in malaria detection, ranging from the use of rapid diagnostic tests (RDTs) and molecular-based technologies to advanced non-invasive detection methods and computerized technologies. Molecular tests, RDTs, and computerized tests were also seen to be in use in resource-limited settings. In all, only twenty-one out of a total of eighty (26%) low and lower-middle-income countries showed evidence of the use of modern malaria diagnostic methods. It is imperative for governments and other agencies to direct efforts toward malaria research to upscale progress towards malaria elimination globally, especially in endemic regions, which usually happen to be resource-limited regions.

Multi-Omics Advancements towards Plasmodium vivax Malaria Diagnosis

Plasmodium vivax malaria is one of the most lethal infectious diseases, with 7 million infections annually. One of the roadblocks to global malaria elimination is the lack of highly sensitive, specific, and accurate diagnostic tools. The absence of diagnostic tools in particular has led to poor differentiation among parasite species, poor prognosis, and delayed treatment. The improvement necessary in diagnostic tools can be broadly grouped into two categories: technologies-driven and omics-driven progress over time. This article discusses the recent advancement in omics-based malaria for identifying the next generation biomarkers for a highly sensitive and specific assay with a rapid and antecedent prognosis of the disease. We summarize the state-of-the-art diagnostic technologies, the key challenges, opportunities, and emerging prospects of multi-omics-based sensors.

Comparison and evaluation of integrative methods for the analysis of multilevel omics data: a study based on simulated and experimental cancer data

Integrative analysis aims to identify the driving factors of a biological process by the joint exploration of data from multiple cellular levels. The volume of omics data produced is constantly increasing, and so too does the collection of tools for its analysis. Comparative studies assessing performance and the biological value of results, however, are rare but in great demand. We present a comprehensive comparison of three integrative analysis approaches, sparse canonical correlation analysis (sCCA), non-negative matrix factorization (NMF) and logic data mining MicroArray Logic Analyzer (MALA), by applying them to simulated and experimental omics data. We find that sCCA and NMF are able to identify differential features in simulated data, while the Logic Data Mining method, MALA, falls short. Applied to experimental data, we show that MALA performs best in terms of sample classification accuracy, and in general, the classification power of prioritized feature sets is high (97.1-99.5% accuracy). The proportion of features identified by at least one of the other methods, however, is approximately 60% for sCCA and NMF and nearly 30% for MALA, and the proportion of features jointly identified by all methods is only around 16%. Similarly, the congruence on functional levels (Gene Ontology, Reactome) is low. Furthermore, the agreement of identified feature sets with curated gene signatures relevant to the investigated disease is modest. We discuss possible reasons for the moderate overlap of identified feature sets with each other and with curated cancer signatures. The R code to create simulated data, results and figures is provided at https://github.com/ThallingerLab/IamComparison.

An in silico Approach for Integrating Phenotypic and Target-based Approaches in Drug Discovery

Phenotypic and target-based approaches are useful methods in drug discovery. The phenotypic approach is an experimental approach for evaluating the phenotypic response. The target-based approach is a rational approach for screening drug candidates targeting a biomolecule that causes diseases. These approaches are widely used for drug discovery. However, two serious problems of target deconvolution and polypharmacology are encountered in these conventional experimental approaches. To overcome these two problems, we developed a new in silico method using a probabilistic framework. This method integrates both the phenotypic and target-based approaches to estimate a relevant network from compound to phenotype. Our method can computationally execute target deconvolution considering polypharmacology and can provide keys for understanding the pathway and mechanism from compound to phenotype, thereby promoting drug discovery.

Mathematically universal and biologically consistent astrocytoma genotype encodes for transformation and predicts survival phenotype

DNA alterations have been observed in astrocytoma for decades. A copy-number genotype predictive of a survival phenotype was only discovered by using the generalized singular value decomposition (GSVD) formulated as a comparative spectral decomposition. Here, we use the GSVD to compare whole-genome sequencing (WGS) profiles of patient-matched astrocytoma and normal DNA. First, the GSVD uncovers a genome-wide pattern of copy-number alterations, which is bounded by patterns recently uncovered by the GSVDs of microarray-profiled patient-matched glioblastoma (GBM) and, separately, lower-grade astrocytoma and normal genomes. Like the microarray patterns, the WGS pattern is correlated with an approximately one-year median survival time. By filling in gaps in the microarray patterns, the WGS pattern reveals that this biologically consistent genotype encodes for transformation via the Notch together with the Ras and Shh pathways. Second, like the GSVDs of the microarray profiles, the GSVD of the WGS profiles separates the tumor-exclusive pattern from normal copy-number variations and experimental inconsistencies. These include the WGS technology-specific effects of guanine-cytosine content variations across the genomes that are correlated with experimental batches. Third, by identifying the biologically consistent phenotype among the WGS-profiled tumors, the GBM pattern proves to be a technology-independent predictor of survival and response to chemotherapy and radiation, statistically better than the patient's age and tumor's grade, the best other indicators, and MGMT promoter methylation and IDH1 mutation. We conclude that by using the complex structure of the data, comparative spectral decompositions underlie a mathematically universal description of the genotype-phenotype relations in cancer that other methods miss.

The core regulatory network in human cells

In order to discover the common characteristics of various cell types in the human body, many researches have been conducted to find the set of genes commonly expressed in various cell types and tissues. However, the functional characteristics of a cell is determined by the complex regulatory relationships among the genes rather than by expressed genes themselves. Therefore, it is more important to identify and analyze a core regulatory network where all regulatory relationship between genes are active across all cell types to uncover the common features of various cell types. Here, based on hundreds of tissue-specific gene regulatory networks constructed by recent genome-wide experimental data, we constructed the core regulatory network. Interestingly, we found that the core regulatory network is organized by simple cascade and has few complex regulations such as feedback or feed-forward loops. Moreover, we discovered that the regulatory links from genes in the core regulatory network to genes in the peripheral regulatory network are much more abundant than the reverse direction links. These results suggest that the core regulatory network locates at the top of regulatory network and plays a role as a 'hub' in terms of information flow, and the information that is common to all cells can be modified to achieve the tissue-specific characteristics through various types of feedback and feed-forward loops in the peripheral regulatory networks. We also found that the genes in the core regulatory network are evolutionary conserved, essential and non-disease, non-druggable genes compared to the peripheral genes. Overall, our study provides an insight into how all human cells share a common function and generate tissue-specific functional traits by transmitting and processing information through regulatory network.

Platform-Independent Genome-Wide Pattern of DNA Copy-Number Alterations Predicting Astrocytoma Survival and Response to Treatment Revealed by the GSVD Formulated as a Comparative Spectral Decomposition

We use the generalized singular value decomposition (GSVD), formulated as a comparative spectral decomposition, to model patient-matched grades III and II, i.e., lower-grade astrocytoma (LGA) brain tumor and normal DNA copy-number profiles. A genome-wide tumor-exclusive pattern of DNA copy-number alterations (CNAs) is revealed, encompassed in that previously uncovered in glioblastoma (GBM), i.e., grade IV astrocytoma, where GBM-specific CNAs encode for enhanced opportunities for transformation and proliferation via growth and developmental signaling pathways in GBM relative to LGA. The GSVD separates the LGA pattern from other sources of biological and experimental variation, common to both, or exclusive to one of the tumor and normal datasets. We find, first, and computationally validate, that the LGA pattern is correlated with a patient's survival and response to treatment. Second, the GBM pattern identifies among the LGA patients a subtype, statistically indistinguishable from that among the GBM patients, where the CNA genotype is correlated with an approximately one-year survival phenotype. Third, cross-platform classification of the Affymetrix-measured LGA and GBM profiles by using the Agilent-derived GBM pattern shows that the GBM pattern is a platform-independent predictor of astrocytoma outcome. Statistically, the pattern is a better predictor (corresponding to greater median survival time difference, proportional hazard ratio, and concordance index) than the patient's age and the tumor's grade, which are the best indicators of astrocytoma currently in clinical use, and laboratory tests. The pattern is also statistically independent of these indicators, and, combined with either one, is an even better predictor of astrocytoma outcome. Recurring DNA CNAs have been observed in astrocytoma tumors' genomes for decades, however, copy-number subtypes that are predictive of patients' outcomes were not identified before. This is despite the growing number of datasets recording different aspects of the disease, and due to an existing fundamental need for mathematical frameworks that can simultaneously find similarities and dissimilarities across the datasets. This illustrates the ability of comparative spectral decompositions to find what other methods miss.

Molecular Profiling of Phagocytic Immune Cells in Anopheles gambiae Reveals Integral Roles for Hemocytes in Mosquito Innate Immunity

The innate immune response is highly conserved across all eukaryotes and has been studied in great detail in several model organisms. Hemocytes, the primary immune cell population in mosquitoes, are important components of the mosquito innate immune response, yet critical aspects of their biology have remained uncharacterized. Using a novel method of enrichment, we isolated phagocytic granulocytes and quantified their proteomes by mass spectrometry. The data demonstrate that phagocytosis, blood-feeding, and Plasmodium falciparum infection promote dramatic shifts in the proteomic profiles of An. gambiae granulocyte populations. Of interest, large numbers of immune proteins were induced in response to blood feeding alone, suggesting that granulocytes have an integral role in priming the mosquito immune system for pathogen challenge. In addition, we identify several granulocyte proteins with putative roles as membrane receptors, cell signaling, or immune components that when silenced, have either positive or negative effects on malaria parasite survival. Integrating existing hemocyte transcriptional profiles, we also compare differences in hemocyte transcript and protein expression to provide new insight into hemocyte gene regulation and discuss the potential that post-transcriptional regulation may be an important component of hemocyte gene expression. These data represent a significant advancement in mosquito hemocyte biology, providing the first comprehensive proteomic profiling of mosquito phagocytic granulocytes during homeostasis blood-feeding, and pathogen challenge. Together, these findings extend current knowledge to further illustrate the importance of hemocytes in shaping mosquito innate immunity and their principal role in defining malaria parasite survival in the mosquito host.

From big data analysis to personalized medicine for all: challenges and opportunities

Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.

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.