Geoffrey H. Siwo

Quantitative trait loci mapping reveals candidate pathways regulating cell cycle duration in Plasmodium falciparum

BackgroundElevated parasite biomass in the human red blood cells can lead to increased malaria morbidity. The genes and mechanisms regulating growth and development of Plasmodiumfalciparum through its erythrocytic cycle are not well understood. We previously showed that strains HB3 and Dd2 diverge in their proliferation rates, and here use quantitative trait loci mapping in 34 progeny from a cross between these parent clones along with integrative bioinformatics to identify genetic loci and candidate genes that control divergences in cell cycle duration.ResultsGenetic mapping of cell cycle duration revealed a four-locus genetic model, including a major genetic effect on chromosome 12, which accounts for 75% of the inherited phenotype variation. These QTL span 165 genes, the majority of which have no predicted function based on homology. We present a method to systematically prioritize candidate genes using the extensive sequence and transcriptional information available for the parent lines. Putative functions were assigned to the prioritized genes based on protein interaction networks and expression eQTL from our earlier study. DNA metabolism or antigenic variation functional categories were enriched among our prioritized candidate genes. Genes were then analyzed to determine if they interact with cyclins or other proteins known to be involved in the regulation of cell cycle.ConclusionsWe show that the divergent proliferation rate between a drug resistant and drug sensitive parent clone is under genetic regulation and is segregating as a complex trait in 34 progeny. We map a major locus along with additional secondary effects, and use the wealth of genome data to identify key candidate genes. Of particular interest are a nucleosome assembly protein (PFL0185c), a Zinc finger transcription factor (PFL0465c) both on chromosome 12 and a ribosomal protein L7Ae-related on chromosome 4 (PFD0960c).

Viral organization of human proteins

Although maps of intracellular interactions are increasingly well characterized, little is known about large-scale maps of host-pathogen protein interactions. The investigation of host-pathogen interactions can reveal features of pathogenesis and provide a foundation for the development of drugs and disease prevention strategies. A compilation of experimentally verified interactions between HIV-1 and human proteins and a set of HIV-dependency factors (HDF) allowed insights into the topology and intricate interplay between viral and host proteins on a large scale. We found that targeted and HDF proteins appear predominantly in rich-clubs, groups of human proteins that are strongly intertwined among each other. These assemblies of proteins may serve as an infection gateway, allowing the virus to take control of the human host by reaching protein pathways and diversified cellular functions in a pronounced and focused way. Particular transcription factors and protein kinases facilitate indirect interactions between HDFs and viral proteins. Discerning the entanglement of directly targeted and indirectly interacting proteins may uncover molecular and functional sites that can provide novel perspectives on the progression of HIV infection and highlight new avenues to fight this virus.

Ten Simple Rules for Organizing a Virtual Conference—Anywhere

1 International Institute of Tropical Agriculture, Nairobi, Kenya, 2 Faculty of Life Sciences, The University of Manchester, Manchester, United Kingdom, 3 Department of Computer and Information Sciences, Covenant University, Ota, Nigeria, 4 Institute of Bioinformatics, Johannes Kepler University, Linz, Austria, 5 Moroccan Society for Bioinformatics Institute, Morocco, 6 South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa, 7 University of Cape Town, Cape Town, South Africa, 8 University of Notre Dame, South Bend, Indiana, United States of America, 9 Biotechnology Unit, University of Buea, Buea, South West Region, Cameroon, 10 International Livestock Research Institute, Nairobi, Kenya, 11 Biosciences Eastern and Central Africa, Nairobi, Kenya, 12 International Center of Insect Physiology and Ecology, Nairobi, Kenya, 13 Bioinformatics Organization, Hudson, Massachusetts, United States of America, 14 Bioinformatics Team, Center for Development of Advanced Computing, Pune University Campus, Pune, India, 15 Harvard School of Public Health, Boston, Massachusetts, United States of America

Chemogenomic profiling of Plasmodium falciparum as a tool to aid antimalarial drug discovery

The spread of Plasmodium falciparum multidrug resistance highlights the urgency to discover new targets and chemical scaffolds. Unfortunately, lack of experimentally validated functional information about most P. falciparum genes remains a strategic hurdle. Chemogenomic profiling is an established tool for classification of drugs with similar mechanisms of action by comparing drug fitness profiles in a collection of mutants. Inferences of drug mechanisms of action and targets can be obtained by associations between shifts in drug fitness and specific genetic changes in the mutants. In this screen, P. falciparum, piggyBac single insertion mutants were profiled for altered responses to antimalarial drugs and metabolic inhibitors to create chemogenomic profiles. Drugs targeting the same pathway shared similar response profiles and multiple pairwise correlations of the chemogenomic profiles revealed novel insights into drugs’ mechanisms of action. A mutant of the artemisinin resistance candidate gene - “K13-propeller” gene (PF3D7_1343700) exhibited increased susceptibility to artemisinin drugs and identified a cluster of 7 mutants based on similar enhanced responses to the drugs tested. Our approach of chemogenomic profiling reveals artemisinin functional activity, linked by the unexpected drug-gene relationships of these mutants, to signal transduction and cell cycle regulation pathways.

A supervised learning approach to the ensemble clustering of genes

High-throughput techniques have become a primary approach to gathering biological data. These data can be used to explore relationships between genes and guide development of drugs and other research. However, the deluge of data contains an overwhelming amount of unknown information about the organism under study. Therefore, clustering is a common first step in the exploratory analysis of high-throughput biological data. We present a supervised learning approach to clustering that utilises known gene-gene interaction data to improve results for already commonly used clustering techniques. The approach creates an ensemble similarity measure that can be used as input to any clustering technique and provides results with increased biological significance while not altering the clustering method.

The future of genomic medicine education in Africa

There are many challenges and opportunities for Africans in the emerging area of genome medicine. In particular, there is a need for investment in local education using real-world African genetic data sets. Cloud-based computing platforms offer one solution for engaging the next generation of biomedical scientists in tackling disease in Africa, and by extension, the world.

Predicting functional and regulatory divergence of a drug resistance transporter gene in the human malaria parasite.

BackgroundThe paradigm of resistance evolution to chemotherapeutic agents is that a key coding mutation in a specific gene drives resistance to a particular drug. In the case of resistance to the anti-malarial drug chloroquine (CQ), a specific mutation in the transporter pfcrt is associated with resistance. Here, we apply a series of analytical steps to gene expression data from our lab and leverage 3 independent datasets to identify pfcrt-interacting genes. Resulting networks provide insights into pfcrt’s biological functions and regulation, as well as the divergent phenotypic effects of its allelic variants in different genetic backgrounds.ResultsTo identify pfcrt-interacting genes, we analyze pfcrt co-expression networks in 2 phenotypic states - CQ-resistant (CQR) and CQ-sensitive (CQS) recombinant progeny clones - using a computational approach that prioritizes gene interactions into functional and regulatory relationships. For both phenotypic states, pfcrt co-expressed gene sets are associated with hemoglobin metabolism, consistent with CQ’s expected mode of action. To predict the drivers of co-expression divergence, we integrate topological relationships in the co-expression networks with available high confidence protein-protein interaction data. This analysis identifies 3 transcriptional regulators from the ApiAP2 family and histone acetylation as potential mediators of these divergences. We validate the predicted divergences in DNA mismatch repair and histone acetylation by measuring the effects of small molecule inhibitors in recombinant progeny clones combined with quantitative trait locus (QTL) mapping.ConclusionsThis work demonstrates the utility of differential co-expression viewed in a network framework to uncover functional and regulatory divergence in phenotypically distinct parasites. pfcrt-associated co-expression in the CQ resistant progeny highlights CQR-specific gene relationships and possible targeted intervention strategies. The approaches outlined here can be readily generalized to other parasite populations and drug resistances.

Shared Molecular Strategies of the Malaria Parasite P. falciparum and the Human Virus HIV-1

We augmented existing computationally predicted and experimentally determined interactions with evolutionarily conserved interactions between proteins of the malaria parasite, P. falciparum, and the human host. In a validation step, we found that conserved interacting host-parasite protein pairs were specifically expressed in host tissues where both the parasite and host proteins are known to be active. We compared host-parasite interactions with experimentally verified interactions between human host proteins and a very different pathogen, HIV-1. Both pathogens were found to use their protein repertoire in a combinatorial manner, providing a broad connection to host cellular processes. Specifically, the two biologically distinct pathogens predominately target central proteins to take control of a human host cell, effectively reaching into diversified cellular host cellular functions. Interacting signaling pathways and a small set of regulatory and signaling proteins were prime targets of both pathogens, suggesting remarkably similar patterns of host-pathogen interactions despite the vast biological differences of both pathogens. Such an identification of shared molecular strategies by the virus HIV-1 and the eukaryotic intracellular pathogen P. falciparum may allow us to illuminate new avenues of disease intervention.

Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach

The Gene Promoter Expression Prediction challenge consisted of predicting gene expression from promoter sequences in a previously unknown experimentally generated data set. The challenge was presented to the community in the framework of the sixth Dialogue for Reverse Engineering Assessments and Methods (DREAM6), a community effort to evaluate the status of systems biology modeling methodologies. Nucleotide-specific promoter activity was obtained by measuring fluorescence from promoter sequences fused upstream of a gene for yellow fluorescence protein and inserted in the same genomic site of yeast Saccharomyces cerevisiae. Twenty-one teams submitted results predicting the expression levels of 53 different promoters from yeast ribosomal protein genes. Analysis of participant predictions shows that accurate values for low-expressed and mutated promoters were difficult to obtain, although in the latter case, only when the mutation induced a large change in promoter activity compared to the wild-type sequence. As in previous DREAM challenges, we found that aggregation of participant predictions provided robust results, but did not fare better than the three best algorithms. Finally, this study not only provides a benchmark for the assessment of methods predicting activity of a specific set of promoters from their sequence, but it also shows that the top performing algorithm, which used machine-learning approaches, can be improved by the addition of biological features such as transcription factor binding sites.

Prediction of fine-tuned promoter activity from DNA sequence

The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring biological systems.