Kellen L. Olszewski

Specific DNA-binding by Apicomplexan AP2 transcription factors

Malaria remains one of the most prevalent infectious diseases worldwide, affecting more than half a billion people annually. Despite many years of research, the mechanisms underlying transcriptional regulation in the malaria-causing Plasmodium spp., and in Apicomplexan parasites generally, remain poorly understood. In Plasmodium, few regulatory elements sufficient to drive gene expression have been characterized, and their cognate DNA-binding proteins remain unknown. This study characterizes the DNA-binding specificities of two members of the recently identified Apicomplexan AP2 (ApiAP2) family of putative transcriptional regulators from Plasmodium falciparum. The ApiAP2 proteins contain AP2 domains homologous to the well characterized plant AP2 family of transcriptional regulators, which play key roles in development and environmental stress response pathways. We assayed ApiAP2 protein–DNA interactions using protein-binding microarrays and combined these results with computational predictions of coexpressed target genes to couple these putative trans factors to corresponding cis-regulatory motifs in Plasmodium. Furthermore, we show that protein–DNA sequence specificity is conserved in orthologous proteins between phylogenetically distant Apicomplexan species. The identification of the DNA-binding specificities for ApiAP2 proteins lays the foundation for the exploration of their role as transcriptional regulators during all stages of parasite development. Because of their origin in the plant lineage, ApiAP2 proteins have no homologues in the human host and may prove to be ideal antimalarial targets.

Host-parasite Interactions Revealed by Plasmodium falciparum Metabolomics

Intracellular pathogens have devised mechanisms to exploit their host cells to ensure their survival and replication. The malaria parasite Plasmodium falciparum relies on an exchange of metabolites with the host for proliferation. Here we describe a mass spectrometry-based metabolomic analysis of the parasite throughout its 48 hr intraerythrocytic developmental cycle. Our results reveal a general modulation of metabolite levels by the parasite, with numerous metabolites varying in phase with the developmental cycle. Others differed from uninfected cells irrespective of the developmental stage. Among these was extracellular arginine, which was specifically converted to ornithine by the parasite. To identify the biochemical basis for this effect, we disrupted the plasmodium arginase gene in the rodent malaria model P. berghei. These parasites were viable but did not convert arginine to ornithine. Our results suggest that systemic arginine depletion by the parasite may be a factor in human malarial hypoargininemia associated with cerebral malaria pathogenesis.

Identification and Genome-Wide Prediction of DNA Binding Specificities for the ApiAP2 Family of Regulators from the Malaria Parasite

The molecular mechanisms underlying transcriptional regulation in apicomplexan parasites remain poorly understood. Recently, the Apicomplexan AP2 (ApiAP2) family of DNA binding proteins was identified as a major class of transcriptional regulators that are found across all Apicomplexa. To gain insight into the regulatory role of these proteins in the malaria parasite, we have comprehensively surveyed the DNA-binding specificities of all 27 members of the ApiAP2 protein family from Plasmodium falciparum revealing unique binding preferences for the majority of these DNA binding proteins. In addition to high affinity primary motif interactions, we also observe interactions with secondary motifs. The ability of a number of ApiAP2 proteins to bind multiple, distinct motifs significantly increases the potential complexity of the transcriptional regulatory networks governed by the ApiAP2 family. Using these newly identified sequence motifs, we infer the trans-factors associated with previously reported plasmodial cis-elements and provide evidence that ApiAP2 proteins modulate key regulatory decisions at all stages of parasite development. Our results offer a detailed view of ApiAP2 DNA binding specificity and take the first step toward inferring comprehensive gene regulatory networks for P. falciparum.

Branched tricarboxylic acid metabolism in Plasmodium falciparum

A central hub of carbon metabolism is the tricarboxylic acid cycle, which serves to connect the processes of glycolysis, gluconeogenesis, respiration, amino acid synthesis and other biosynthetic pathways. The protozoan intracellular malaria parasites (Plasmodium spp.), however, have long been suspected of possessing a significantly streamlined carbon metabolic network in which tricarboxylic acid metabolism plays a minor role. Blood-stage Plasmodium parasites rely almost entirely on glucose fermentation for energy and consume minimal amounts of oxygen, yet the parasite genome encodes all of the enzymes necessary for a complete tricarboxylic acid cycle. Here, by tracing 13C-labelled compounds using mass spectrometry we show that tricarboxylic acid metabolism in the human malaria parasite Plasmodium falciparum is largely disconnected from glycolysis and is organized along a fundamentally different architecture from the canonical textbook pathway. We find that this pathway is not cyclic, but rather is a branched structure in which the major carbon sources are the amino acids glutamate and glutamine. As a consequence of this branched architecture, several reactions must run in the reverse of the standard direction, thereby generating two-carbon units in the form of acetyl-coenzyme A. We further show that glutamine-derived acetyl-coenzyme A is used for histone acetylation, whereas glucose-derived acetyl-coenzyme A is used to acetylate amino sugars. Thus, the parasite has evolved two independent production mechanisms for acetyl-coenzyme A with different biological functions. These results significantly clarify our understanding of the Plasmodium metabolic network and highlight the ability of altered variants of central carbon metabolism to arise in response to unique environments.

A gene expression fingerprint of C. elegans embryonic motor neurons

BackgroundDifferential gene expression specifies the highly diverse cell types that constitute the nervous system. With its sequenced genome and simple, well-defined neuroanatomy, the nematode C. elegans is a useful model system in which to correlate gene expression with neuron identity. The UNC-4 transcription factor is expressed in thirteen embryonic motor neurons where it specifies axonal morphology and synaptic function. These cells can be marked with an unc-4::GFP reporter transgene. Here we describe a powerful strategy, Micro-Array Profiling of C. elegans cells (MAPCeL), and confirm that this approach provides a comprehensive gene expression profile of unc-4::GFP motor neurons in vivo.ResultsFluorescence Activated Cell Sorting (FACS) was used to isolate unc-4::GFP neurons from primary cultures of C. elegans embryonic cells. Microarray experiments detected 6,217 unique transcripts of which ~1,000 are enriched in unc-4::GFP neurons relative to the average nematode embryonic cell. The reliability of these data was validated by the detection of known cell-specific transcripts and by expression in UNC-4 motor neurons of GFP reporters derived from the enriched data set. In addition to genes involved in neurotransmitter packaging and release, the microarray data include transcripts for receptors to a remarkably wide variety of signaling molecules. The added presence of a robust array of G-protein pathway components is indicative of complex and highly integrated mechanisms for modulating motor neuron activity. Over half of the enriched genes (537) have human homologs, a finding that could reflect substantial overlap with the gene expression repertoire of mammalian motor neurons.ConclusionWe have described a microarray-based method, MAPCeL, for profiling gene expression in specific C. elegans motor neurons and provide evidence that this approach can reveal candidate genes for key roles in the differentiation and function of these cells. These methods can now be applied to generate a gene expression map of the C. elegans nervous system.

Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network

Genome‐scale metabolic reconstructions can serve as important tools for hypothesis generation and high‐throughput data integration. Here, we present a metabolic network reconstruction and flux‐balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network accounts for 1001 reactions and 616 metabolites. Enzyme–gene associations were established for 366 genes and 75% of all enzymatic reactions. Compared with other microbes, the P. falciparum metabolic network contains a relatively high number of essential genes, suggesting little redundancy of the parasite metabolism. The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. Moreover, using constraints based on gene‐expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy. Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins. To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small‐molecule inhibitor.

Cell-specific microarray profiling experiments reveal a comprehensive picture of gene expression in the C. elegans nervous system

BackgroundWith its fully sequenced genome and simple, well-defined nervous system, the nematode Caenorhabditis elegans offers a unique opportunity to correlate gene expression with neuronal differentiation. The lineal origin, cellular morphology and synaptic connectivity of each of the 302 neurons are known. In many instances, specific behaviors can be attributed to particular neurons or circuits. Here we describe microarray-based methods that monitor gene expression in C. elegans neurons and, thereby, link comprehensive profiles of neuronal transcription to key developmental and functional properties of the nervous system.ResultsWe employed complementary microarray-based strategies to profile gene expression in the embryonic and larval nervous systems. In the MAPCeL (Microarray Profiling C. elegans cells) method, we used fluorescence activated cell sorting (FACS) to isolate GFP-tagged embryonic neurons for microarray analysis. To profile the larval nervous system, we used the mRNA-tagging technique in which an epitope-labeled mRNA binding protein (FLAG-PAB-1) was transgenically expressed in neurons for immunoprecipitation of cell-specific transcripts. These combined approaches identified approximately 2,500 mRNAs that are highly enriched in either the embryonic or larval C. elegans nervous system. These data are validated in part by the detection of gene classes (for example, transcription factors, ion channels, synaptic vesicle components) with established roles in neuronal development or function. Of particular interest are 19 conserved transcripts of unknown function that are also expressed in the mammalian brain. In addition to utilizing these profiling approaches to define stage-specific gene expression, we also applied the mRNA-tagging method to fingerprint a specific neuron type, the A-class group of cholinergic motor neurons, during early larval development. A comparison of these data to a MAPCeL profile of embryonic A-class motor neurons identified genes with common functions in both types of A-class motor neurons as well as transcripts with roles specific to each motor neuron type.ConclusionWe describe microarray-based strategies for generating expression profiles of embryonic and larval C. elegans neurons. These methods can be applied to particular neurons at specific developmental stages and, therefore, provide an unprecedented opportunity to obtain spatially and temporally defined snapshots of gene expression in a simple model nervous system.

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

BackgroundThe availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes).ResultsWe developed Nearest Neighbor Networks (NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods.ConclusionThe Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large datasets, and its ability to span a wide range of biological functions with high precision.

Co-inhibition of Plasmodium falciparum S-Adenosylmethionine Decarboxylase/Ornithine Decarboxylase Reveals Perturbation-specific Compensatory Mechanisms by Transcriptome, Proteome, and Metabolome Analyses

Polyamines are ubiquitous components of all living cells, and their depletion usually causes cytostasis, a strategy employed for treatment of West African trypanosomiasis. To evaluate polyamine depletion as an anti-malarial strategy, cytostasis caused by the co-inhibition of S-adenosylmethionine decarboxylase/ornithine decarboxylase in Plasmodium falciparum was studied with a comprehensive transcriptome, proteome, and metabolome investigation. Highly synchronized cultures were sampled just before and during cytostasis, and a novel zero time point definition was used to enable interpretation of results in lieu of the developmentally regulated control of gene expression in P. falciparum. Transcriptome analysis revealed the occurrence of a generalized transcriptional arrest just prior to the growth arrest due to polyamine depletion. However, the abundance of 538 transcripts was differentially affected and included three perturbation-specific compensatory transcriptional responses as follows: the increased abundance of the transcripts for lysine decarboxylase and ornithine aminotransferase and the decreased abundance of that for S-adenosylmethionine synthetase. Moreover, the latter two compensatory mechanisms were confirmed on both protein and metabolite levels confirming their biological relevance. In contrast with previous reports, the results provide evidence that P. falciparum responds to alleviate the detrimental effects of polyamine depletion via regulation of its transcriptome and subsequently the proteome and metabolome.

Central carbon metabolism of Plasmodium parasites

The central role of metabolic perturbation to the pathology of malaria, the promise of antimetabolites as antimalarial drugs and a basic scientific interest in understanding this fascinating example of highly divergent microbial metabolism has spurred a major and concerted research effort towards elucidating the metabolic network of the Plasmodium parasites. Central carbon metabolism, broadly comprising the flow of carbon from nutrients into biomass, has been a particular focus due to clear and early indications that it plays an essential role in this network. Decades of painstaking efforts have significantly clarified our understanding of these pathways of carbon flux, and this foundational knowledge, coupled with the advent of advanced analytical technologies, have set the stage for the development of a holistic, network-level model of plasmodial carbon metabolism. In this review we summarize the current state of knowledge regarding central carbon metabolism and suggest future avenues of research. We focus primarily on the blood stages of Plasmodium falciparum, the most lethal of the human malaria parasites, but also integrate results from simian, avian and rodent models of malaria that were a major focus of early investigations into plasmodial metabolism.