Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors

We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining these sources of information, our approach results in a higher accuracy rate when compared to models that use each data source alone. Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information

Causal inference based on counterfactuals

BACKGROUND: The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. DISCUSSION: This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. SUMMARY: Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept

The consequences of reservoir host eradication on disease epidemiology in animal communities.

Non-native species have often been linked with introduction of novel pathogens that spill over into native communities, and the amplification of the prevalence of native parasites. In the case of introduced generalist pathogens, their disease epidemiology in the extant communities remains poorly understood. Here, Sphaerothecum destruens, a generalist fungal-like fish pathogen with bi-modal transmission (direct and environmental) was used to characterise the biological drivers responsible for disease emergence in temperate fish communities. A range of biotic factors relating to both the pathogen and the surrounding host communities were used in a novel susceptible-exposed-infectious-recovered (SEIR) model to test how these factors affected disease epidemiology. These included: (i) pathogen prevalence in an introduced reservoir host (Pseudorasbora parva); (ii) the impact of reservoir host eradication and its timing and (iii) the density of potential hosts in surrounding communities and their connectedness. These were modelled across 23 combinations and indicated that the spill-over of pathogen propagules via environmental transmission resulted in rapid establishment in adjacent fish communities (<1 year). Although disease dynamics were initially driven by environmental transmission in these communities, once sufficient numbers of native hosts were infected, the disease dynamics were driven by intra-species transmission. Subsequent eradication of the introduced host, irrespective of its timing (after one, two or three years), had limited impact on the long-term disease dynamics among local fish communities. These outputs reinforced the importance of rapid detection and eradication of non-native species, in particular when such species are identified as healthy reservoirs of a generalist pathogen

Sequence-Based Prediction of Type III Secreted Proteins

The type III secretion system (TTSS) is a key mechanism for host cell interaction used by a variety of bacterial pathogens and symbionts of plants and animals including humans. The TTSS represents a molecular syringe with which the bacteria deliver effector proteins directly into the host cell cytosol. Despite the importance of the TTSS for bacterial pathogenesis, recognition and targeting of type III secreted proteins has up until now been poorly understood. Several hypotheses are discussed, including an mRNA-based signal, a chaperon-mediated process, or an N-terminal signal peptide. In this study, we systematically analyzed the amino acid composition and secondary structure of N-termini of 100 experimentally verified effector proteins. Based on this, we developed a machine-learning approach for the prediction of TTSS effector proteins, taking into account N-terminal sequence features such as frequencies of amino acids, short peptides, or residues with certain physico-chemical properties. The resulting computational model revealed a strong type III secretion signal in the N-terminus that can be used to detect effectors with sensitivity of ∼71% and selectivity of ∼85%. This signal seems to be taxonomically universal and conserved among animal pathogens and plant symbionts, since we could successfully detect effector proteins if the respective group was excluded from training. The application of our prediction approach to 739 complete bacterial and archaeal genome sequences resulted in the identification of between 0% and 12% putative TTSS effector proteins. Comparison of effector proteins with orthologs that are not secreted by the TTSS showed no clear pattern of signal acquisition by fusion, suggesting convergent evolutionary processes shaping the type III secretion signal. The newly developed program EffectiveT3 (http://www.chlamydiaedb.org) is the first universal in silico prediction program for the identification of novel TTSS effectors. Our findings will facilitate further studies on and improve our understanding of type III secretion and its role in pathogen–host interactions

Evidence for the ‘Good Genes’ Model: Association of MHC Class II DRB Alleles with Ectoparasitism and Reproductive State in the Neotropical Lesser Bulldog Bat, Noctilio albiventris

The adaptive immune system has a major impact on parasite resistance and life history strategies. Immunological defence is costly both in terms of immediate activation and long-term maintenance. The ‘good genes’ model predicts that males with genotypes that promote a good disease resistance have the ability to allocate more resources to reproductive effort which favours the transmission of good alleles into future generations. Our study shows a correlation between immune gene constitution (Major Histocompatibility Complex, MHC class II DRB), ectoparasite loads (ticks and bat flies) and the reproductive state in a neotropical bat, Noctilio albiventris. Infestation rates with ectoparasites were linked to specific Noal-DRB alleles, differed among roosts, increased with body size and co-varied with reproductive state particularly in males. Non-reproductive adult males were more infested with ectoparasites than reproductively active males, and they had more often an allele (Noal-DRB*02) associated with a higher tick infestation than reproductively active males or subadults. We conclude that the individual immune gene constitution affects ectoparasite susceptibility, and contributes to fitness relevant trade-offs in male N. albiventris as suggested by the ‘good genes’ model

the prevention of chronic diseases through ehealth a practical overview

Disease prevention is an umbrella term embracing individual-based or population-based interventions aimed at preventing the manifestation of diseases (primary prevention), reducing the impact of a disease that has arisen (secondary prevention), or mitigating the impact of an ongoing illness (tertiary prevention). Digital health has the potential to improve prevention of chronic diseases. Its application ranges from effective mHealth weight-loss intervention to prevent or delay the onset of diabetes in overweight adults to the cost-effective intervention on the provision of mental-health care via mobile-based or Internet-based programs to reduce the incidence or the severity of anxiety. The present contribution focuses on the effectiveness of eHealth preventive interventions and on the role of digital health in improving health promotion and disease prevention. We also give a practical overview on how eHealth interventions have been effectively implemented, developed, and delivered for the primary, secondary, and tertiary prevention of chronic diseases

Molecular signatures for CCN1, p21 and p27 in progressive mantle cell lymphoma

Mantle cell lymphoma (MCL) is a comparatively rare non-Hodgkin’s lymphoma characterised by overexpression of cyclin D1.Many patients present with or progress to advanced stage disease within 3 years. MCL is considered an incurable disease withmedian survival between 3 and 4 years. We have investigated the role(s) of CCN1 (CYR61) and cell cycle regulators inprogressive MCL. We have used the human MCL cell lines REC1 G519 > JVM2 cells by RQ-PCR, depicting a decrease in CCN1expression with disease progression. Investigation of CCN1 isoform expression by western blotting showed that whilst expres-sion of full-length CCN1 was barely altered in the cell lines, expression of truncated forms (18–20 and 28–30 kDa) decreasedwith disease progression. We have then demonstrated that cyclin D1 and cyclin dependent kinase inhibitors (p21CIP1and p27KIP1)are also involved in disease progression. Cyclin D1 was highly expressed in REC1 cells (OD: 1.0), reduced to one fifth in G519cells (OD: 0.2) and not detected by western blotting in JVM2 cells. p27KIP1followed a similar profile of expression as cyclin D1.Conversely, p21CIP1was absent in the REC1 cells and showed increasing expression in G519 and JVM2 cells. Subcellularlocalization detected p21CIP1/p27KIP1primarily within the cytoplasm and absent from the nucleus, consistent with altered roles in treatment resistance. Dysregulation of the CCN1 truncated forms are associated with MCL progression. In conjunction withreduced expression of cyclin D1 and increased expression of p21, this molecular signature may depict aggressive disease andtreatment resistance