Atomic Scale Fractal Dimensionality in Proteins

The soft condensed matter of biological organisms exhibits atomic motions whose properties depend strongly on temperature and hydration conditions. Due to the superposition of rapidly fluctuating alternative motions at both very low temperatures (quantum effects) and very high temperatures (classical Brownian motion regime), the dimension of an atomic ``path'' is in reality different from unity. In the intermediate temperature regime and under environmental conditions which sustain active biological functions, the fractal dimension of the sets upon which atoms reside is an open question. Measured values of the fractal dimension of the sets on which the Hydrogen atoms reside within the Azurin protein macromolecule are reported. The distribution of proton positions was measured employing thermal neutron elastic scattering from Azurin protein targets. As the temperature was raised from low to intermediate values, a previously known and biologically relevant dynamical transition was verified for the Azurin protein only under hydrated conditions. The measured fractal dimension of the geometrical sets on which protons reside in the biologically relevant temperature regime is given by $D=0.65 \pm 0.1$. The relationship between fractal dimensionality and biological function is qualitatively discussed.Comment: ReVTeX4 format with 5 *.eps figure

Pooled-sera hSBA titres predict individual seroprotection in infants and toddlers vaccinated with 4CMenB

AbstractThe Serum Bactericidal Antibody assay with human complement (hSBA) using individual immune sera is a surrogate of protection for meningococcal vaccines. Strain coverage of 4CMenB, a licensed vaccine against serogroup B meningococcal (MenB) disease, has been extensively assessed in hSBA using pooled sera, directly or through the Meningococcal Antigen Typing System (MATS). The extent to which pooled-sera hSBA titres reflect individual protection is not yet fully understood.We analysed more than 17000 individual hSBA titres from infants and toddlers vaccinated with 4CMenB, pooled-serum hSBA titres from subsets therein and MATS data from a 40 strain panel representative of invasive MenB disease in England and Wales.Individual hSBA titres segregated in two normal distributions, respectively from responding and non-responding subjects (fit_model-data: r=0.996, p-values <0.05). No individual subject showed abnormally high titres compared to the distributions. Also, when sera from the same subjects were tested individually and in pool, pooled-sera titre and average of individual titres from the same group were substantially indistinguishable (r=0.97, p-value <<0.001).We identified a robust mathematical relationship between the mean of individual hSBA titres and the proportion of subjects achieving a protective titre (seroprotection rate, r=0.95, p-value <<0.001). Using this relation, the seroprotection rate in 15 groups of vaccinees tested against 11 diverse meningococcal isolates was accurately predicted by the hSBA titre of the respective pooled sera (average prediction error 9%).Finally, strains defined covered by MATS had on average 77% predicted seroprotection rate (interquartile range, IQR: 66–100%) and 39% for non-covered strains (IQR: 19–46%).We conclude that seroprotection rates in infants and toddlers vaccinated with 4CMenB can be accurately predicted by pooled-serum hSBA, and that strain coverage defined by MATS is associated with high seroprotection rates.SummaryThe Serum Bactericidal Antibody assay (SBA) from individual sera is a surrogate of protection for meningococcal vaccines. We show that SBA performed on pooled sera predicts individual protection

Exploring the Limitations of Peripheral Blood Transcriptional Biomarkers in Predicting Influenza Vaccine Responsiveness

Systems biology has been recently applied to vaccinology to better understand immunological responses to the influenza vaccine. Particular attention has been paid to the identification of early signatures capable of predicting vaccine immunogenicity. Building fromprevious studies, we employed a recently established algorithm for signature-based clustering of expression profiles, SCUDO, to provide new insights into why blood-derived transcriptome biomarkers often fail to predict the seroresponse to the influenza virus vaccination. Specifically, preexisting immunity against one or more vaccine antigens, which was found to negatively affect the seroresponse, was identified as a confounding factor able to decouple early transcriptome fromlater antibody responses, resulting in the degradation of a biomarker predictive power. Finally, the broadly accepted definition of seroresponse to influenza virus vaccine, represented by the maximum response across the vaccine-targeted strains, was compared to a composite measure integrating the responses against all strains. This analysis revealed that compositemeasures provide amore accurate assessment of the seroresponse to multicomponent influenza vaccines

Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery

Reverse Vaccinology (RV) is a widely used approach to identify potential vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for bacterial vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of bacterial proteins characterized by negative experimental results

In-silico prediction and deep-DNA sequencing validation indicate phase variation in 115 Neisseria meningitidis genes

Parameters applied for SSR identification. (XLSX 32 kb

Bactericidal antibody against a representative epidemiological meningococcal serogroup B panel confirms that MATS underestimates 4CMenB vaccine strain coverage

AbstractBackground4CMenB (Bexsero), a vaccine developed against invasive meningococcal disease caused by capsular group B strains (MenB), was recently licensed for use by the European Medicines Agency. Assessment of 4CMenB strain coverage in specific epidemiologic settings is of primary importance to predict vaccination impact on the burden of disease. The Meningococcal Antigen Typing System (MATS) was developed to predict 4CMenB strain coverage, using serum bactericidal antibody assay with human complement (hSBA) data from a diverse panel of strains not representative of any specific epidemiology.ObjectiveTo experimentally validate the accuracy of MATS-based predictions against strains representative of a specific epidemiologic setting.Methods and resultsWe used a stratified sampling method to identify a representative sample from all MenB disease isolates collected from England and Wales in 2007–2008, tested the strains in the hSBA assay with pooled sera from infant and adolescent vaccinees, and compared these results with MATS. MATS predictions and hSBA results were significantly associated (P=0.022). MATS predicted coverage of 70% (95% CI, 55–85%) was largely confirmed by 88% killing in the hSBA (95% CI, 72–95%). MATS had 78% accuracy and 96% positive predictive value against hSBA.ConclusionMATS is a conservative predictor of strain coverage by the 4CMenB vaccine in infants and adolescents

Protein Homology Network Families Reveal Step-Wise Diversification of Type III and Type IV Secretion Systems

From the analysis of 251 prokaryotic genomes stored in public databases, the 761,260 deduced proteins were used to reconstruct a complete set of bacterial proteic families. Using the new Overlap algorithm, we have partitioned the Protein Homology Network (PHN), where the proteins are the nodes and the links represent homology relationships. The algorithm identifies the densely connected regions of the PHN that define the families of homologous proteins, here called PHN-Families, recognizing the phylogenetic relationships embedded in the network. By direct comparison with a manually curated dataset, we assessed that this classification algorithm generates data of quality similar to a human expert. Then, we explored the network to identify families involved in the assembly of Type III and Type IV secretion systems (T3SS and T4SS). We noticed that, beside a core of conserved functions (eight proteins for T3SS, seven for T4SS), a variable set of accessory components is always present (one to nine for T3SS, one to five for T4SS). Each member of the core corresponds to a single PHN-Family, while accessory proteins are distributed among different pure families. The PHN-Family classification suggests that T3SS and T4SS have been assembled through a step-wise, discontinuous process, by complementing the conserved core with subgroups of nonconserved proteins. Such genetic modules, independently recruited and probably tuned on specific effectors, contribute to the functional specialization of these organelles to different microenvironments

Interplay Between Virulence and Variability Factors as a Potential Driver of Invasive Meningococcal Disease

Neisseria meningitidis (Nm) is frequently found in the upper respiratory tract of the human population. Despite its prevalence as a commensal organism, Nm can occasionally invade the pharyngeal mucosal epithelium causing septicemia and life-threatening disease. A number of studies have tried to identify factors that are responsible for the onset of a virulent phenotype. Despite this however, we still miss clear causative elements. Several factors have been identified to be associated to an increased susceptibility to meningococcal disease in humans. None of them, however, could unambiguously discriminate healthy carrier from infected individuals. Similarly, comparative studies of virulent and apathogenic strains failed to identify virulence factors that could explain the emergence of the pathogenic phenotype. In line with this, a recent study of within host evolution found that Nm accumulates genomic changes during the asymptomatic carriage phase and that these are likely to contribute to the shift to a pathogenic phenotype. These results suggest that the presence of virulence factors in the meningococcal genome is not a sufficient condition for developing virulent traits, but is rather the ability to promote phenotypic variation, through the stochastic assortment of the repertoire of such factors, which could explain the occasional and unpredictable onset of IMD. Here, we present a series of argumentations supporting the hypothesis that invasive meningococcal disease comes as a result of the coexistence of bacterial virulence and variability factors in a plot that can be further complicated by additional latent factors, like host pre-existing immune status and genetic predisposition