The objective was to evaluate the safety and immunogenicity of the AMA1-based malaria vaccine FMP2.1/AS02A in children exposed to seasonal falciparum malaria.
A Phase 1 double blind randomized controlled dose escalation trial was conducted in Bandiagara, Mali, West Africa, a rural town with intense seasonal transmission of Plasmodium falciparum malaria. The malaria vaccine FMP2.1/AS02A is a recombinant protein (FMP2.1) based on apical membrane antigen 1 (AMA1) from the 3D7 clone of P. falciparum, formulated in the Adjuvant System AS02A. The comparator vaccine was a cell-culture rabies virus vaccine (RabAvert®). One hundred healthy Malian children aged 1–6 years were recruited into 3 cohorts and randomized to receive either 10 µg FMP2.1 in 0.1 mL AS02A, or 25 µg FMP2.1 in 0.25 mL AS02A, or 50 µg FMP2.1 50 µg in 0.5 mL AS02A, or rabies vaccine. Three doses of vaccine were given at 0, 1 and 2 months, and children were followed for 1 year. Solicited symptoms were assessed for 7 days and unsolicited symptoms for 30 days after each vaccination. Serious adverse events were assessed throughout the study. Transient local pain and swelling were common and more frequent in all malaria vaccine dosage groups than in the comparator group, but were acceptable to parents of participants. Levels of anti-AMA1 antibodies measured by ELISA increased significantly (at least 100-fold compared to baseline) in all 3 malaria vaccine groups, and remained high during the year of follow up.
The FMP2.1/AS02A vaccine had a good safety profile, was well-tolerated, and induced high and sustained antibody levels in malaria-exposed children. This malaria vaccine is being evaluated in a Phase 2 efficacy trial in children at this site.
ClinicalTrials.gov NCT00358332 [NCT00358332]
Cross-species comparison and functional analysis of over-abundant motifs in an integrated network of yeast transcriptional and protein-protein interaction data showed that the over-abundance of the network motifs does not have any immediate functional or evolutive counterpart.
Cellular functions are regulated by complex webs of interactions that might be schematically represented as networks. Two major examples are transcriptional regulatory networks, describing the interactions among transcription factors and their targets, and protein-protein interaction networks. Some patterns, dubbed motifs, have been found to be statistically over-represented when biological networks are compared to randomized versions thereof. Their function in vitro has been analyzed both experimentally and theoretically, but their functional role in vivo, that is, within the full network, and the resulting evolutionary pressures remain largely to be examined.
We investigated an integrated network of the yeast Saccharomyces cerevisiae comprising transcriptional and protein-protein interaction data. A comparative analysis was performed with respect to Candida glabrata, Kluyveromyces lactis, Debaryomyces hansenii and Yarrowia lipolytica, which belong to the same class of hemiascomycetes as S. cerevisiae but span a broad evolutionary range. Phylogenetic profiles of genes within different forms of the motifs show that they are not subject to any particular evolutionary pressure to preserve the corresponding interaction patterns. The functional role in vivo of the motifs was examined for those instances where enough biological information is available. In each case, the regulatory processes for the biological function under consideration were found to hinge on post-transcriptional regulatory mechanisms, rather than on the transcriptional regulation by network motifs.
The overabundance of the network motifs does not have any immediate functional or evolutionary counterpart. A likely reason is that motifs within the networks are not isolated, that is, they strongly aggregate and have important edge and/or node sharing with the rest of the network.
Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.
Genetic interactions map functional dependencies between genes, under a given phenotype. In the budding yeast Saccharomyces cerevisiae, most genetic interactions have been measured under a single phenotype - growth rate in standard laboratory conditions. Recently, genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We analyzed quantitative genetic interaction networks mapped in yeast under different experimental conditions and phenotypic readouts and found that they provide significant unique information. We next asked if this unique information is complementary. As a measure of complementarity, we asked if combining networks mapped under different experimental conditions could improve gene function prediction. Two genes that genetically interact with a similar set of genes (two genes with similar genetic interaction profiles) are more likely to be in the same pathway or complex and this can be used for gene function prediction. We found that combining multiple genetic interaction profile correlation networks using a simple ‘maximum correlation’ approach improved gene function prediction, demonstrating that the networks provide complementary information. Thus, using diverse phenotypic readouts and experimental conditions will likely increase the amount of information produced by genetic interaction screens.
Protein-protein interaction networks (PINs) are rich sources of information that enable the network properties of biological systems to be understood. A study of the topological and statistical properties of budding yeast and human PINs revealed that they are scale-rich and configured as highly optimized tolerance (HOT) networks that are similar to the router-level topology of the Internet. This is different from claims that such networks are scale-free and configured through simple preferential-attachment processes. Further analysis revealed that there are extensive interconnections among middle-degree nodes that form the backbone of the networks. Degree distributions of essential genes, synthetic lethal genes, synthetic sick genes, and human drug-target genes indicate that there are advantageous drug targets among nodes with middle- to low-degree nodes. Such network properties provide the rationale for combinatorial drugs that target less prominent nodes to increase synergetic efficacy and create fewer side effects.
Genome-wide data on interactions between proteins are now available, and networks of protein interactions are the keys to understanding diseases and finding accurate drug targets. This study revealed that the architectural properties of the backbones of protein interaction networks (PINs) were similar to those of the Internet router-level topology by using statistical analyses of genome-wide budding yeast and human PINs. This type of network is known as a highly optimized tolerance (HOT) network that is robust against failures in its components and that ensures high levels of communication. Moreover, we also found that a large number of the most successful drug-target proteins are on the backbone of the human PIN. We made a list of proteins on the backbone of the human PIN, which may help drug companies to search more efficiently for new drug targets.
Systems biology approaches can reveal intermediary levels of organization between genotype and phenotype that often underlie biological phenomena such as polygenic effects and protein dispensability. An important conceptualization is the module, which is loosely defined as a cohort of proteins that perform a dedicated cellular task. Based on a computational analysis of limited interaction datasets in the budding yeast Saccharomyces cerevisiae, it has been suggested that the global protein interaction network is segregated such that highly connected proteins, called hubs, tend not to link to each other. Moreover, it has been suggested that hubs fall into two distinct classes: “party” hubs are co-expressed and co-localized with their partners, whereas “date” hubs interact with incoherently expressed and diversely localized partners, and thereby cohere disparate parts of the global network. This structure may be compared with altocumulus clouds, i.e., cotton ball–like structures sparsely connected by thin wisps. However, this organization might reflect a small and/or biased sample set of interactions. In a multi-validated high-confidence (HC) interaction network, assembled from all extant S. cerevisiae interaction data, including recently available proteome-wide interaction data and a large set of reliable literature-derived interactions, we find that hub–hub interactions are not suppressed. In fact, the number of interactions a hub has with other hubs is a good predictor of whether a hub protein is essential or not. We find that date hubs are neither required for network tolerance to node deletion, nor do date hubs have distinct biological attributes compared to other hubs. Date and party hubs do not, for example, evolve at different rates. Our analysis suggests that the organization of global protein interaction network is highly interconnected and hence interdependent, more like the continuous dense aggregations of stratus clouds than the segregated configuration of altocumulus clouds. If the network is configured in a stratus format, cross-talk between proteins is potentially a major source of noise. In turn, control of the activity of the most highly connected proteins may be vital. Indeed, we find that a fluctuation in steady-state levels of the most connected proteins is minimized.
Analysis of multi-validated protein interaction data reveals networks with greater interconnectivity than the more segregated structures seen in previously available data. To help visualize this, the authors draw comparisons between continuous stratus clouds and altocumulus clouds.
By integrating phenotypic and transcriptional profiling and mapping the data onto metabolic and regulatory networks, it was shown that arsenic probably channels sulfur into glutathione for detoxification, leads to indirect oxidative stress by depleting glutathione pools, and alters protein turnover via arsenation of sulfhydryl groups on proteins.
Arsenic is a nonmutagenic carcinogen affecting millions of people. The cellular impact of this metalloid in Saccharomyces cerevisiae was determined by profiling global gene expression and sensitivity phenotypes. These data were then mapped to a metabolic network composed of all known biochemical reactions in yeast, as well as the yeast network of 20,985 protein-protein/protein-DNA interactions.
While the expression data unveiled no significant nodes in the metabolic network, the regulatory network revealed several important nodes as centers of arsenic-induced activity. The highest-scoring proteins included Fhl1, Msn2, Msn4, Yap1, Cad1 (Yap2), Pre1, Hsf1 and Met31. Contrary to the gene-expression analyses, the phenotypic-profiling data mapped to the metabolic network. The two significant metabolic networks unveiled were shikimate, and serine, threonine and glutamate biosynthesis. We also carried out transcriptional profiling of specific deletion strains, confirming that the transcription factors Yap1, Arr1 (Yap8), and Rpn4 strongly mediate the cell's adaptation to arsenic-induced stress but that Cad1 has negligible impact.
By integrating phenotypic and transcriptional profiling and mapping the data onto the metabolic and regulatory networks, we have shown that arsenic is likely to channel sulfur into glutathione for detoxification, leads to indirect oxidative stress by depleting glutathione pools, and alters protein turnover via arsenation of sulfhydryl groups on proteins. Furthermore, we show that phenotypically sensitive pathways are upstream of differentially expressed ones, indicating that transcriptional and phenotypic profiling implicate distinct, but related, pathways.
Identification of regulatory molecules in signaling pathways is critical for understanding cellular behavior. Given the complexity of the transcriptional gene network, the relationship between molecular expression and phenotype is difficult to determine using reductionist experimental methods. Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. Integrating gene expression data with phenotypic data in transcriptional network models enables systematic identification of critical molecules in a biological network. We developed an approach based on fuzzy logic to model cell budding in Saccharomyces cerevisiae using time series expression microarray data of the cell cycle. Cell budding is a phenotype of viable cells undergoing division. Predicted interactions between gene expression and phenotype reflected known biological relationships. Dynamic simulation analysis reproduced the behavior of the yeast cell cycle and accurately identified genes and interactions which are essential for cell viability.
Patterns of network connection of members of multi-gene families were examined for two biological networks: a genetic network from yeast Saccharomyces cerevisiae and a protein-protein interaction network from Caenorhabditis elegans. In both networks, genes belonging to gene families represented by a single member in the genome (“singletons”) were disproportionately represented among the nodes having large numbers of connections. Of 68 single-member yeast families with 25 or more network connections, 28 (44.4%) were located in duplicated genomic segments believed to have originated from an ancient polyploidization event; thus, each of these 28 loci was thus presumably duplicated along with the genomic segment to which it belongs, but one of the two duplicates has subsequently been deleted. Nodes connected to major “hubs” with a large number of connections, tended to be relatively sparsely inter-connected among themselves. Furthermore, duplicated genes, even those arising from recent duplication, rarely shared many network connections, suggesting that network connections are remarkably labile over evolutionary time. These factors serve to explain well-known general properties of biological networks, including their scale-free and modular nature.
The objective was to evaluate the safety, reactogenicity and immunogenicity of the AMA-1-based blood-stage malaria vaccine FMP2.1/AS02A in adults exposed to seasonal malaria.
A phase 1 double blind randomized controlled dose escalation trial was conducted in Bandiagara, Mali, West Africa, a rural town with intense seasonal transmission of Plasmodium falciparum malaria. The malaria vaccine FMP2.1/AS02A is a recombinant protein (FMP2.1) based on apical membrane antigen-1 (AMA-1) from the 3D7 clone of P. falciparum, adjuvanted with AS02A. The comparator vaccine was a cell-culture rabies virus vaccine (RabAvert). Sixty healthy, malaria-experienced adults aged 18–55 y were recruited into 2 cohorts and randomized to receive either a half dose or full dose of the malaria vaccine (FMP2.1 25 µg/AS02A 0.25 mL or FMP2.1 50 µg/AS02A 0.5 mL) or rabies vaccine given in 3 doses at 0, 1 and 2 mo, and were followed for 1 y. Solicited symptoms were assessed for 7 d and unsolicited symptoms for 30 d after each vaccination. Serious adverse events were assessed throughout the study. Titers of anti-AMA-1 antibodies were measured by ELISA and P. falciparum growth inhibition assays were performed on sera collected at pre- and post-vaccination time points. Transient local pain and swelling were common and more frequent in both malaria vaccine dosage groups than in the comparator group. Anti-AMA-1 antibodies increased significantly in both malaria vaccine groups, peaking at nearly 5-fold and more than 6-fold higher than baseline in the half-dose and full-dose groups, respectively.
The FMP2.1/AS02A vaccine had a good safety profile, was well-tolerated, and was highly immunogenic in malaria-exposed adults. This malaria vaccine is being evaluated in Phase 1 and 2 trials in children at this site.
Motivation: Microarray-based gene expression data have been generated widely to study different biological processes and systems. Gene co-expression networks are often used to extract information about groups of genes that are ‘functionally’ related or co-regulated. However, the structural properties of such co-expression networks have not been rigorously studied and fully compared with known biological networks. In this article, we aim at investigating the structural properties of co-expression networks inferred for the species Saccharomyces Cerevisiae and comparing them with the topological properties of the known, well-established transcriptional network, MIPS physical network and protein–protein interaction (PPI) network of yeast.
Results: These topological comparisons indicate that co-expression networks are not distinctly related with either the PPI or the MIPS physical interaction networks, showing important structural differences between them. When focusing on a more literal comparison, vertex by vertex and edge by edge, the conclusion is the same: the fact that two genes exhibit a high gene expression correlation degree does not seem to obviously correlate with the existence of a physical binding between the proteins produced by these genes or the existence of a MIPS physical interaction between the genes. The comparison of the yeast regulatory network with inferred yeast co-expression networks would suggest, however, that they could somehow be related.
Conclusions: We conclude that the gene expression-based co-expression networks reflect more on the gene regulatory networks but less on the PPI or MIPS physical interaction networks.
Supplementary information: Supplementary data are available at Bioinformatics online.
Many complex random networks have been found to be scale-free. Existing literature on scale-free networks has rarely considered potential false positive and false negative links in the observed networks, especially in biological networks inferred from high-throughput experiments. Therefore, it is important to study the impact of these measurement errors on the topology of the observed networks.
This article addresses the impact of erroneous links on network topological inference and explores possible error mechanisms for scale-free networks with an emphasis on Saccharomyces cerevisiae protein interaction networks. We study this issue by both theoretical derivations and simulations. We show that the ignorance of erroneous links in network analysis may lead to biased estimates of the scale parameter and recommend robust estimators in such scenarios. Possible error mechanisms of yeast protein interaction networks are explored by comparisons between real data and simulated data.
Our studies show that, in the presence of erroneous links, the connectivity distribution of scale-free networks is still scale-free for the middle range connectivities, but can be greatly distorted for low and high connecitivities. It is more appropriate to use robust estimators such as the least trimmed mean squares estimator to estimate the scale parameter γ under such circumstances. Moreover, we show by simulation studies that the scale-free property is robust to some error mechanisms but untenable to others. The simulation results also suggest that different error mechanisms may be operating in the yeast protein interaction networks produced from different data sources. In the MIPS gold standard protein interaction data, there appears to be a high rate of false negative links, and the false negative and false positive rates are more or less constant across proteins with different connectivities. However, the error mechanism of yeast two-hybrid data may be very different, where the overall false negative rate is low and the false negative rates tend to be higher for links involving proteins with more interacting partners.
Endocytosis in the budding yeast Saccharomyces cerevisiae involves the ordered recruitment, activity and disassembly of nearly 60 proteins at distinct sites on the plasma membrane. Two-color live-cell fluorescence microscopy has proven to be invaluable for in vivo analysis of endocytic proteins: identifying new components, determining the order of protein arrival and dissociation, and revealing even very subtle mutant phenotypes. Yeast genetics and functional genomics facilitate identification of complex interaction networks between endocytic proteins and their regulators. Quantitive datasets produced by these analyses have made theoretical modeling possible. Here, we discuss recent findings on budding yeast endocytosis that have advanced our knowledge of how ~60 endocytic proteins are recruited, regulated by lipid and protein modifications, and disassembled with remarkable regularity.
The geno- and phenotypic diversity of commercial Saccharomyces cerevisiae wine yeast strains provides an opportunity to apply the system-wide approaches that are reasonably well established for laboratory strains to generate insight into the functioning of complex cellular networks in industrial environments. We have previously analyzed the transcriptomes of five industrial wine yeast strains at three time points during alcoholic fermentation. Here, we extend the comparative approach to include an isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic analysis of two of the previously analyzed wine yeast strains at the same three time points during fermentation in synthetic wine must. The data show that differences in the transcriptomes of the two strains at a given time point rather accurately reflect differences in the corresponding proteomes independently of the gene ontology (GO) category, providing strong support for the biological relevance of comparative transcriptomic data sets in yeast. In line with previous observations, the alignment proves to be less accurate when assessing intrastrain changes at different time points. In this case, differences between the transcriptome and proteome appear to be strongly dependent on the GO category of the corresponding genes. The data in particular suggest that metabolic enzymes and the corresponding genes appear to be strongly correlated over time and between strains, suggesting a strong transcriptional control of such enzymes. The data also allow the generation of hypotheses regarding the molecular origin of significant differences in phenotypic traits between the two strains.
The yeast Saccharomyces cerevisiae is an important microorganism for both industrial processes and scientific research. Consequently, there have been extensive efforts to characterize its cellular processes. In order to fully understand the relationship between yeast's genome and its physiology, the stockpiles of diverse biological data sets that describe its cellular components and phenotypic behavior must be integrated at the genome-scale. Genome-scale metabolic networks have been reconstructed for several microorganisms, including S. cerevisiae, and the properties of these networks have been successfully analyzed using a variety of constraint-based methods. Phenotypic phase plane analysis is a constraint-based method which provides a global view of how optimal growth rates are affected by changes in two environmental variables such as a carbon and an oxygen uptake rate. Some applications of phenotypic phase plane analysis include the study of optimal growth rates and of network capacity and function.
In this study, the Saccharomyces cerevisiae genome-scale metabolic network was used to formulate a phenotypic phase plane that displays the maximum allowable growth rate and distinct patterns of metabolic pathway utilization for all combinations of glucose and oxygen uptake rates. In silico predictions of growth rate and secretion rates and in vivo data for three separate growth conditions (aerobic glucose-limited, oxidative-fermentative, and microaerobic) were concordant.
Taken together, this study examines the function and capacity of yeast's metabolic machinery and shows that the phenotypic phase plane can be used to accurately predict metabolic phenotypes and to interpret experimental data in the context of a genome-scale model.
Attribution of biological robustness to the specific structural properties of a regulatory network is an important yet unsolved problem in systems biology. It is widely believed that the topological characteristics of a biological control network largely determine its dynamic behavior, yet the actual mechanism is still poorly understood. Here, we define a novel structural feature of biological networks, termed ‘regulation entropy’, to quantitatively assess the influence of network topology on the robustness of the systems. Using the cell-cycle control networks of the budding yeast (Saccharomyces cerevisiae) and the fission yeast (Schizosaccharomyces pombe) as examples, we first demonstrate the correlation of this quantity with the dynamic stability of biological control networks, and then we establish a significant association between this quantity and the structural stability of the networks. And we further substantiate the generality of this approach with a broad spectrum of biological and random networks. We conclude that the regulation entropy is an effective order parameter in evaluating the robustness of biological control networks. Our work suggests a novel connection between the topological feature and the dynamic property of biological regulatory networks.
Living organisms exert very complicated control on the functionality of their components. Such control systems can often operate in a surprisingly robust manner, in spite of constant perturbations from fluctuating internal conditions and a volatile external environment. What feature makes such control mechanisms robust? Is there a general way to achieve robustness? Here, we address these questions by investigating the wiring of interaction networks, which contains the most condensed information about the control mechanisms of biological systems. We suggest that one of the most important factors in the realization of biological robustness rests in the global coherency of the control strategy, i.e., the consistency of commands flowing through different routes in the network to the same destination. To implement this idea, we propose an order parameter termed ‘regulation entropy’ to quantitatively describe this control consistency of networks. We find that this order parameter correlates with the resistance of the system to external perturbations as well as internal fluctuations. Our results suggest that the self-consistency of the control strategy is important for the vitality and robustness of living organisms.
Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks.
Recent studies have shown that the degree distribution of the nodes is not an adequate statistic in many molecular networks. We sought to extend this statistic with 2nd and 3rd order degree correlations and developed a pseudo-likelihood approach to estimate the parameters. The approach was used to analyze the MIPS and BIOGRID yeast protein interaction networks, and two yeast coexpression networks. We showed that 2nd order degree correlation information gave better predictions of gene interactions in both protein interaction and gene coexpression networks. However, in the biologically important task of predicting functionally homogeneous modules, degree correlation information performs marginally better in the case of the MIPS and BIOGRID protein interaction networks, but worse in the case of gene coexpression networks.
Our use of dK models showed that incorporation of degree correlations could increase predictive power in some contexts, albeit sometimes marginally, but, in all contexts, the use of third-order degree correlations decreased accuracy. However, it is possible that other parameter estimation methods, such as maximum likelihood, will show the usefulness of incorporating 2nd and 3rd degree correlations in predicting functionally homogeneous modules.
In this study, we describe a versatile, flexible, and quick method to label different families of enveloped viruses with glycosylphosphatidylinositol-modified green fluorescent protein, termed fluorescence molecular painting (FMP). As an example for a potential application, we investigated virus attachment by means of flow cytometry to determine if viral binding behavior may be analyzed after FMP of enveloped viruses. Virus attachment was inhibited by using either dextran sulfate or by blocking attachment sites with virus pre-treatment. Results from the FMP–flow cytometry approach were verified by immunoblotting and enzyme-linked immunosorbent assay. Since the modification strategy is applicable to a broad range of proteins and viruses, variations of this method may be useful in a range of research and applied applications from bio-distribution studies to vaccine development and targeted infection for gene delivery.
Molecular painting; GPI-anchored protein; Surface modification; Lentivirus; Herpesvirus; HIV; Influenza; Fluorescence labeling; Viral attachment; Gene therapy
Delivery of proteins to the vacuole of the yeast Saccharomyces cerevisiae provides an excellent model system in which to study vacuole and lysosome biogenesis and membrane traffic. This organelle receives proteins from a number of different routes, including proteins sorted away from the secretory pathway at the Golgi apparatus and endocytic traffic arising from the plasma membrane. Genetic analysis has revealed at least 60 genes involved in vacuolar protein sorting, numerous components of a novel cytoplasm-to-vacuole transport pathway, and a large number of proteins required for autophagy. Cell biological and biochemical studies have provided important molecular insights into the various protein delivery pathways to the yeast vacuole. This review describes the various pathways to the vacuole and illustrates how they are related to one another in the vacuolar network of S. cerevisiae.
The antigen, falciparum malaria protein 1 (FMP1), represents the 42-kDa C-terminal fragment of merozoite surface protein-1 (MSP-1) of the 3D7 clone of P. falciparum. Formulated with AS02 (a proprietary Adjuvant System), it constitutes the FMP1/AS02 candidate malaria vaccine. We evaluated this vaccine's safety, immunogenicity, and efficacy in African children.
A randomised, double-blind, Phase IIb, comparator-controlled trial.The trial was conducted in 13 field stations of one mile radii within Kombewa Division, Nyanza Province, Western Kenya, an area of holoendemic transmission of P. falciparum. We enrolled 400 children aged 12–47 months in general good health.Children were randomised in a 1∶1 fashion to receive either FMP1/AS02 (50 µg) or Rabipur® rabies vaccine. Vaccinations were administered on a 0, 1, and 2 month schedule. The primary study endpoint was time to first clinical episode of P. falciparum malaria (temperature ≥37.5°C with asexual parasitaemia of ≥50,000 parasites/µL of blood) occurring between 14 days and six months after a third dose. Case detection was both active and passive. Safety and immunogenicity were evaluated for eight months after first immunisations; vaccine efficacy (VE) was measured over a six-month period following third vaccinations.
374 of 400 children received all three doses and completed six months of follow-up. FMP1/AS02 had a good safety profile and was well-tolerated but more reactogenic than the comparator. Geometric mean anti-MSP-142 antibody concentrations increased from1.3 µg/mL to 27.3 µg/mL in the FMP1/AS02 recipients, but were unchanged in controls. 97 children in the FMP1/AS02 group and 98 controls had a primary endpoint episode. Overall VE was 5.1% (95% CI: −26% to +28%; p-value = 0.7).
FMP1/AS02 is not a promising candidate for further development as a monovalent malaria vaccine. Future MSP-142 vaccine development should focus on other formulations and antigen constructs.
Localized network patterns are assumed to represent an optimal design principle in different biological networks. A widely used method for identifying functional components in biological networks is looking for network motifs – over-represented network patterns. A number of recent studies have undermined the claim that these over-represented patterns are indicative of optimal design principles and question whether localized network patterns are indeed of functional significance. This paper examines the functional significance of regulatory network patterns via their biological annotation and evolutionary conservation.
We enumerate all 3-node network patterns in the regulatory network of the yeast S. cerevisiae and examine the biological GO annotation and evolutionary conservation of their constituent genes. Specific 3-node patterns are found to be functionally enriched in different exogenous cellular conditions and thus may represent significant functional components. These functionally enriched patterns are composed mainly of recently evolved genes suggesting that there is no evolutionary pressure acting to preserve such functionally enriched patterns. No correlation is found between over-representation of network patterns and functional enrichment.
The findings of functional enrichment support the view that network patterns constitute an important design principle in regulatory networks. However, the wildly used method of over-representation for detecting motifs is not suitable for identifying functionally enriched patterns.
In order to determine if smoking, obesity, and insulin resistance mediated age at final menstrual period (FMP), we examined anti-Müllerian hormone (AMH), inhibin B, and follicle-stimulating hormone as biomarkers of changing follicle status and ovarian aging. We performed a longitudinal data analysis from a cohort of premenopausal women followed to their FMP. Our results found that smokers had an earlier age at FMP (p<0.003) and a more rapid decline in their AMH slope relative to age at FMP (p<0.002). Smokers had a lower baseline inhibin B level relative to age at the FMP than non-smokers (p=0.002). Increasing insulin resistance was associated with a shorter time to FMP (p<0.003) and associations of obesity and time to FMP were observed (p=0.004, in model with FSH). Change in ovarian biomarkers did not mediate the time to FMP. We found that smoking was associated with age at FMP and modified associations of AMH and inhibin B with age at FMP. Insulin resistance was associated with shorter time to FMP independent of the biomarkers. Interventions targeting smoking and insulin resistance could curtail the undue advancement of reproductive aging.
obesity; insulin resistance; smoking; anti-Müllerian hormone; inhibin B; menopause
Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations.
We report a significantly improved version (v. 2) of a probabilistic functional gene network  of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis.
YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.
Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models.
Saccharomyces cerevisiae is a petite-phenotype-positive (“petite-positive”) yeast, which can successfully grow in the absence of oxygen. On the other hand, Kluyveromyces lactis as well as many other yeasts are petite negative and cannot grow anaerobically. In this paper, we show that Saccharomyces kluyveri can grow under anaerobic conditions, but while it can generate respiration-deficient mutants, it cannot generate true petite mutants. From a phylogenetic point of view, S. kluyveri is apparently more closely related to S. cerevisiae than to K. lactis. These observations suggest that the progenitor of the modern Saccharomyces and Kluyveromyces yeasts, as well as other related genera, was a petite-negative and aerobic yeast. Upon separation of the K. lactis and S. kluyveri-S. cerevisiae lineages, the latter developed the ability to grow anaerobically. However, while the S. kluyveri lineage has remained petite negative, the lineage leading to the modern Saccharomyces sensu stricto and sensu lato yeasts has developed the petite-positive characteristic.
Several species of yeast, including the baker's yeast
Saccharomyces cerevisiae, underwent a genome duplication roughly 100 million years ago. We analyze genetic networks whose members were involved in this duplication. Many networks show detectable redundancy and strong asymmetry in their interactions. For networks of co-expressed genes, we find evidence for network partitioning whereby the paralogs appear to have formed two relatively independent subnetworks from the ancestral network. We simulate the degeneration of networks after duplication and find that a model wherein the rate of interaction loss depends on the “neighborliness” of the interacting genes produces networks with parameters similar to those seen in the real partitioned networks. We propose that the rationalization of network structure through the loss of pair-wise gene interactions after genome duplication provides a mechanism for the creation of semi-independent daughter networks through the division of ancestral functions between these daughter networks.
An analysis of how duplicated networks of genes (as a result of whole genome duplication in yeast) evolved shows that network partitioning occurred through loss of interactions, resulting in independent subnetworks.