Coevolution of the hepatitis C virus polyprotein sites in patients on combined pegylated interferon and ribavirin therapy

Genotype-specific sensitivity of the hepatitis C virus (HCV) to interferon-ribavirin (IFN-RBV) combination therapy and reduced HCV response to IFN-RBV as infection progresses from acute to chronic infection suggest that HCV genetic factors and intrahost HCV evolution play important roles in therapy outcomes. HCV polyprotein sequences (n = 40) from 10 patients with unsustainable response (UR) (breakthrough and relapse) and 10 patients with no response (NR) following therapy were identified through the Virahep-C study. Bayesian networks (BNs) were constructed to relate interrelationships among HCV polymorphic sites to UR/NR outcomes. All models showed an extensive interdependence of HCV sites and strong connections (P ≤ 0.003) to therapy response. Although all HCV proteins contributed to the networks, the topological properties of sites differed among proteins. E2 and NS5A together contributed ～40% of all sites and ～62% of all links to the polyprotein BN. The NS5A BN and E2 BN predicted UR/NR outcomes with 85% and 97.5% accuracy, respectively, in 10-fold cross-validation experiments. The NS5A model constructed using physicochemical properties of only five sites was shown to predict the UR/NR outcomes with 83.3% accuracy for 6 UR and 12 NR cases of the HALT-C study. Thus, HCV adaptation to IFN-RBV is a complex trait encoded in the interrelationships among many sites along the entire HCV polyprotein. E2 and NS5A generate broad epistatic connectivity across the HCV polyprotein and essentially shape intrahost HCV evolution toward the IFN-RBV resistance. Both proteins can be used to accurately predict the outcomes of IFN-RBV therapy.     

A comparison of two sensitivity analysis techniques based on four bayesian models representing ecosystem services provision in the Argentine Pampas

Sensitivity analyses (SAs) identify how an output variable of a model is modified by changes in the input variables. These analyses are a good way for assessing the performance of probabilistic models, like Bayesian Networks (BN). However, there are several commonly used SAs in BN literature, and formal comparisons about their outcomes are scarce. We used four previously developed BNs which represent ecosystem services provision in Pampean agroecosystems (Argentina) in order to test two local sensitivity approaches widely used. These SAs were: 1) One-at-a-time, used in BNs but more commonly in linear modelling; and 2) Sensitivity to findings, specific to BN modelling. Results showed that both analyses provided an adequate overview of BN behaviour. Furthermore, analyses produced a similar influence ranking of input variables over each output variable. Even though their interchangeably application could be an alternative in our bayesian models, we believe that OAT is the suitable one to implement here because of its capacity to demonstrate the relation (positive or negative) between input and output variables. In summary, we provided insights about two sensitivity techniques in BNs based on a case study which may be useful for ecological modellers.     

Bayesian networks as a novel tool to enhance interpretability and predictive power of ecological models

In today's world, it is becoming increasingly important to have the tools to understand, and ultimately to predict, the response of ecosystems to disturbance. However, understanding such dynamics is not simple. Ecosystems are a complex network of species interactions, and therefore any change to a population of one species will have some degree of community level effect. In recent years, the use of Bayesian networks (BNs) has seen successful applications in molecular biology and ecology, where they were able to recover plausible links in the respective systems they were applied to. The recovered network also comes with a quantifiable metric of interaction strength between variables. While the latter is an invaluable piece of information in ecology, an unexplored application of BNs would be using them as a novel variable selection tool in the training of predictive models. To this end, we evaluate the potential usefulness of BNs in two aspects: (1) we apply BN inference on species abundance data from a rocky shore ecosystem, a system with well documented links, to test the ecological validity of the revealed network; and (2) we evaluate BNs as a novel variable selection method to guide the training of an artificial neural network (ANN). Here, we demonstrate that not only was this approach able to recover meaningful species interactions networks from ecological data, but it also served as a meaningful tool to inform the training of predictive models, where there was an improvement in predictive performance in models with BN variable selection. Combining these results, we demonstrate the potential of this novel application of BNs in enhancing the interpretability and predictive power of ecological models; this has general applicability beyond the studied system, to ecosystems where existing relationships between species and other functional components are unknown.     

Multi-class subcellular location prediction for bacterial proteins

Two algorithms, based onBayesian Networks (BNs), for bacterial subcellular location prediction, are explored in this paper: one predicts all locations for Gram+ bacteria and the other all locations for Gram- bacteria. Methods were evaluated using different numbers of residues (from the N-terminal 10 residues to the whole sequence) and residue representation (amino acid-composition, percentage amino acid-composition or normalised amino acid-composition). The accuracy of the best resulting BN was compared to PSORTB. The accuracy of this multi-location BN was roughly comparable to PSORTB; the difference in predictions is low, often less than 2%. The BN method thus represents both an important new avenue of methodological development for subcellular location prediction and a potentially value new tool of true utilitarian value for candidate subunit vaccine selection.     

Early signatures of bleeding and mortality in patients on left ventricular assist device support: novel methods for personalized risk-stratification

Background: Left ventricular assist devices (LVADs) provide support for patients with end-stage heart failure. The aims of this study were to determine whether baseline analysis and early trends in routine laboratory data, platelet activity, and thromboinflammatory biomarkers following LVAD implantation reveal trends that predict personalized risks of one-year gastrointestinal (GI) bleeding, stroke, pump thrombosis, drive-line infections and mortality in patients on LVAD support.

Methods: We performed an observational study at the University of Kentucky with 61 participants who underwent first-time LVAD implantation. Blood was collected at baseline and post-op days 0, 1, 3 and 6 as well as clinical follow-up. Demographics, clinical characteristics, one-year adverse events and routine laboratory data were collected from electronic medical records. Platelet function and plasma biomarkers were profiled.

Results: Evaluation of routine laboratory results revealed that sustained thrombocytopenia and increased mean platelet volume (MPV) were associated with development of GI bleeding and mortality. Platelet function at follow-up visit predicted one-year bleeding events. Thrombotic biomarker sCD40L strongly predicted one-year GI bleeding at baseline before implantation and within the first week following LVAD implant.

Conclusions: Early trends in routine bloodwork and platelet function may serve as novel signatures of patients at risk to experience adverse events.     

Growing Bayesian network models of gene networks from seed genes

MOTIVATION: For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do not scale to such high-dimensional databases. This means that the user has to decide in advance which genes are included in the learning process, typically no more than a few hundreds, and which genes are excluded from it. This is not a trivial decision. We propose an alternative approach to overcome this problem. RESULTS: We propose a new algorithm for learning BN models of gene networks from gene-expression data. Our algorithm receives a seed gene S and a positive integer R from the user, and returns a BN for the genes that depend on S such that less than R other genes mediate the dependency. Our algorithm grows the BN, which initially only contains S, by repeating the following step R + 1 times and, then, pruning some genes; find the parents and children of all the genes in the BN and add them to it. Intuitively, our algorithm provides the user with a window of radius R around S to look at the BN model of a gene network without having to exclude any gene in advance. We prove that our algorithm is correct under the faithfulness assumption. We evaluate our algorithm on simulated and biological data (Rosetta compendium) with satisfactory results.     

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks

MOTIVATION: An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. Various reverse engineering methods have been proposed in the literature, and it is important to understand their relative merits and shortcomings. In the present paper, we compare the accuracy of reconstructing gene regulatory networks with three different modelling and inference paradigms: (1) Relevance networks (RNs): pairwise association scores independent of the remaining network; (2) graphical Gaussian models (GGMs): undirected graphical models with constraint-based inference, and (3) Bayesian networks (BNs): directed graphical models with score-based inference. The evaluation is carried out on the Raf pathway, a cellular signalling network describing the interaction of 11 phosphorylated proteins and phospholipids in human immune system cells. We use both laboratory data from cytometry experiments as well as data simulated from the gold-standard network. We also compare passive observations with active interventions. RESULTS: On Gaussian observational data, BNs and GGMs were found to outperform RNs. The difference in performance was not significant for the non-linear simulated data and the cytoflow data, though. Also, we did not observe a significant difference between BNs and GGMs on observational data in general. However, for interventional data, BNs outperform GGMs and RNs, especially when taking the edge directions rather than just the skeletons of the graphs into account. This suggests that the higher computational costs of inference with BNs over GGMs and RNs are not justified when using only passive observations, but that active interventions in the form of gene knockouts and over-expressions are required to exploit the full potential of BNs. AVAILABILITY: Data, software and supplementary material are available from http://www.bioss.sari.ac.uk/staff/adriano/research.html     

Involvement of platelet-activating factor (PAF) in cerebral post-ischemic phase in Mongolian gerbils

Platelet-activating factor (PAF) is a potent mediator of anaphylaxis and shock. In addition, evidence for PAF participation in gastric, intestinal and heart post-ischemic phase has been recently demonstrated. Ginkgo biloba extracts improve cerebral metabolism and protect brain against hypoxic damage in various models of cerebral ischemia. Potent and specific antagonists of PAF have been found in Ginkgo biloba and termed Ginkgolides: BN 52020, BN 52021, BN 52022, BN 52024. We therefore undertook the investigation of the role of Ginkgolides in cerebral ischemia obtained by bilateral ligature of the common carotid for 10 min and 6 h of recirculation in male Mongolian adult gerbils. Given preventively (one week treatment 10 mg/kg/day orally) or at the time of clamping, BN 52021 and related Ginkgolides dose-dependently antagonize morbidity assessed by the stroke-index. Similarly the mitochondrial respiration evaluated by the respiratory control ratio is significantly improved. In both determinations, the range of activity: BN 52021 greater than, BN 52020 greater than BN 52022 greater than BN 52024 shows that the effect of Ginkgolides in cerebral ischemia are correlated with their PAF antagonistic properties. Given curatively, 1 h after declamping, BN 52021 is able to reverse the cerebral impairment trend. Kadsurenone and brotizolam, two other chemically unrelated PAF antagonists led to similar recovery. Therefore PAF appears to play an important role in the post-ischemic phase after bilateral carotid ligation in Mongolian gerbils.     

Application of Bayesian networks for inferring cause-effect relations from gene expression profiles of cancer versus normal cells

The paper is devoted to two questions: whether distinction of causes versus effects of neoplasia leaves a signature in the cancer versus normal gene expression profiles and whether roles of genes, "causes" or "effects", can be inferred from repeated measurements of gene expressions. We model joint probability distributions of logarithms of gene expressions with the use of Bayesian networks (BN). Fitting our models to real data confirms that our BN models have the ability to explain some aspects of observational evidence from DNA microarray experiments. Effects of neoplastic transformation are well seen among genes with the highest power to differentiate between normal and cancer cells. Likelihoods of BNs depend on the biological role of selected genes, defined by Gene Ontology. Also predictions of our BN models are coherent with the set of putative causes and effects constructed based on our data set of papillary thyroid cancer.     

Preventing LVAD implantation by early short-term mechanical support and prolonged inodilator therapy

Cardiogenic shock continues to be a life-threatening condition carrying a high mortality and morbidity, where the prognosis remains poor despite intensive modern treatment modalities. In recent years, mainly technical improvements have led to a more widespread use of short- and long-term mechanical circulatory support, such as veno-arterial extracorporeal membrane oxygenation (VA-ECMO) and left ventricular assist devices (LVADs). Currently, LVADs are indispensable as ‘bridge’ to cardiac recovery, heart transplantation (HTX), and/or as destination therapy Importantly, both LVADs and HTX put a vast burden on financial resources, besides significant short- and long-term risks of morbidity and mortality. These considerations underscore the importance of optimal timing and appropriate patient selection for LVAD therapy, avoiding as much as possible an unfortunate and costly clinical path. In this report, we present a series of three cases with acute refractory cardiogenic shock (‘crash and burn’, INTERMACS profile 1) successfully treated by ECMO and early optimal medical therapy preventing a certain path towards LVAD and/or HTX, for which they were initially referred. This conservative approach in INTERMACS profile one patients warrants very early introduction of adequate medical heart failure therapy under the umbrella of a combination of short-term mechanical circulatory and inotropic support by phosphodiesterase inhibitors. Therefore, this novel combined medical-mechanical approach could have important clinical implications for this extremely challenging patient category, as it may avoid an unnecessary and costly clinical path towards LVAD and/or heart transplantation.     

Evolving sensitivity balances Boolean Networks

We investigate the sensitivity of Boolean Networks (BNs) to mutations. We are interested in Boolean Networks as a model of Gene Regulatory Networks (GRNs). We adopt Ribeiro and Kauffman's Ergodic Set and use it to study the long term dynamics of a BN. We define the sensitivity of a BN to be the mean change in its Ergodic Set structure under all possible loss of interaction mutations. In silico experiments were used to selectively evolve BNs for sensitivity to losing interactions. We find that maximum sensitivity was often achievable and resulted in the BNs becoming topologically balanced, i.e. they evolve towards network structures in which they have a similar number of inhibitory and excitatory interactions. In terms of the dynamics, the dominant sensitivity strategy that evolved was to build BNs with Ergodic Sets dominated by a single long limit cycle which is easily destabilised by mutations. We discuss the relevance of our findings in the context of Stem Cell Differentiation and propose a relationship between pluripotent stem cells and our evolved sensitive networks.     

Various Hexoses and di-hexoses Differently Influence Growth,Morphology and Pigment Synthesis in Transformed Root Cultures of Red Beet (Beta vulgaris)

Red beet hairy root cultures, obtained after genetic transformation with Agrobacterium rhizogenes, are completely heterotrophic and synthesize betalaines (BNs). Upon subjecting the hairy roots to treatments containing different sugars (3% w/v) it was found that sucrose was rapidly utilized, followed by maltose, and a very limited use of glucose, but the other hexoses – fructose, lactose, xylose and galactose or glycerol totally suppressed both growth and BN synthesis. No habituation or adaptability to maltose or glucose occurred, evidenced by the lack of growth upon re-culture in respective medium. Glycerol, was not taken up alone, but was utilized to a considerable extent in the presence of low levels of sucrose for growth only but not BN synthesis. Red beet hairy root culture did not exogenously hydrolyse sucrose to hexoses, as there were only traces of reducing sugar present in the medium soon after inoculation, without an increase later, confirmed by HPLC. There was an increase in medium osmolarity in the presence of fructose indicating the exudation of certain compounds from the roots. Red beet hairy roots appear useful as a model system to study sugar metabolism/signalling due to their sensitivity to different sugars that may directly link to morphological changes and BN synthesis.     

The role of long-term mechanical circulatory support in patients with advanced heart failure

In patients with end-stage heart failure, advanced therapies such as heart transplantation and long-term mechanical circulatory support (MCS) with a left ventricular assist device (LVAD) have to be considered. LVADs can be implanted as a bridge to transplantation or as an alternative to heart transplantation: destination therapy. In the Netherlands, long-term LVAD therapy is gaining importance as a result of increased prevalence of heart failure together with a low number of heart transplantations due to shortage of donor hearts. As a result, the difference between bridge to transplantation and destination therapy is becoming more artificial since, at present, most patients initially implanted as bridge to transplantation end up receiving extended LVAD therapy. Following LVAD implantation, survival after 1, 2 and 3 years is 83%, 76% and 70%, respectively. Quality of life improves substantially despite important adverse events such as device-related infection, stroke, major bleeding and right heart failure. Early referral of potential candidates for long-term MCS is of utmost importance and positively influences outcome. In this review, an overview of the indications, contraindications, patient selection, clinical outcome and optimal time of referral for long-term MCS is given.

     

GRACE Score among Six Risk Scoring Systems (CADILLAC,PAMI, TIMI,Dynamic TIMI,Zwolle) Demonstrated the Best Predictive Value for Prediction of Long-Term Mortality in Patients with ST-Elevation Myocardial Infarction

Aim

To compare the prognostic accuracy of six scoring models for up to three-year mortality and rates of hospitalisation due to acute decompensated heart failure (ADHF) in STEMI patients.

Methods and Results

A total of 593 patients treated with primary PCI were evaluated. Prospective follow-up of patients was ≥3 years. Thirty-day, one-year, two-year, and three-year mortality rates were 4.0%, 7.3%, 8.9%, and 10.6%, respectively. Six risk scores—the TIMI score and derived dynamic TIMI, CADILLAC, PAMI, Zwolle, and GRACE—showed a high predictive accuracy for six- and 12-month mortality with area under the receiver operating characteristic curve (AUC) values of 0.73–0.85. The best predictive values for long-term mortality were obtained by GRACE. The next best-performing scores were CADILLAC, Zwolle, and Dynamic TIMI. All risk scores had a lower prediction accuracy for repeat hospitalisation due to ADHF, except Zwolle with the discriminatory capacity for hospitalisation up to two years (AUC, 0.80–0.83).

Conclusions

All tested models showed a high predictive value for the estimation of one-year mortality, but GRACE appears to be the most suitable for the prediction for a longer follow-up period. The tested models exhibited an ability to predict the risk of ADHF, especially the Zwolle model.     

Combination of Pre-Treatment DWI-Signal Intensity and S-1 Treatment: A Predictor of Survival in Patients with Locally Advanced Pancreatic Cancer Receiving Stereotactic Body Radiation Therapy and Sequential S-1

OBJECTIVE: To identify whether the combination of pre-treatment radiological and clinical factors can predict the overall survival (OS) in patients with locally advanced pancreatic cancer (LAPC) treated with stereotactic body radiation and sequential S-1 (a prodrug of 5-FU combined with two modulators) therapy with improved accuracy compared with that of established clinical and radiologic risk models. METHODS: Patients admitted with LAPC underwent diffusion weighted imaging (DWI) scan at 3.0-T (b = 600 s/mm²). The mean signal intensity (SI_b = 600) of region-of-interest (ROI) was measured. The Log-rank test was done for tumor location, biliary stent, S-1, and other treatments and the Cox regression analysis was done to identify independent prognostic factors for OS. Prediction error curves (PEC) were used to assess potential errors in prediction of survival. The accuracy of prediction was evaluated by Integrated Brier Score (IBS) and C index. RESULTS: 41 patients were included in this study. The median OS was 11.7 months (2.8-23.23 months). The 1-year OS was 46%. Multivariate analysis showed that pre-treatment SI_b = 600 value and administration of S-1 were independent predictors for OS. The performance of pre-treatment SI_b = 600 and S-1 treatment in combination was better than that of SI_b = 600 or S-1 treatment alone. CONCLUSION: The combination of pre-treatment SI_b = 600 and S-1 treatment could predict the OS in patients with LAPC undergoing SBRT and sequential S-1 therapy with improved accuracy compared with that of established clinical and radiologic risk models.     

The Predisposition,Infection, Response and Organ Failure (Piro) Sepsis Classification System: Results of Hospital Mortality Using a Novel Concept and Methodological Approach

Introduction

PIRO is a conceptual classification system in which a number of demographic, clinical, biological and laboratory variables are used to stratify patients with sepsis in categories with different outcomes, including mortality rates.

Objectives

To identify variables to be included in each component of PIRO aiming to improve the hospital mortality prediction.

Methods

Patients were selected from the Portuguese ICU-admitted community-acquired sepsis study (SACiUCI). Variables concerning the R and O component included repeated measurements along the first five days in ICU stay. The trends of these variables were summarized as the initial value at day 1 (D1) and the slope of the tendency during the five days, using a linear mixed model. Logistic regression models were built to assess the best set of covariates that predicted hospital mortality.

Results

A total of 891 patients (age 60±17 years, 64% men, 38% hospital mortality) were studied. Factors significantly associated with mortality for P component were gender, age, chronic liver failure, chronic renal failure and metastatic cancer; for I component were positive blood cultures, guideline concordant antibiotic therapy and health-care associated sepsis; for R component were C-reactive protein slope, D1 heart rate, heart rate slope, D1 neutrophils and neutrophils slope; for O component were D1 serum lactate, serum lactate slope, D1 SOFA and SOFA slope. The relative weight of each component of PIRO was calculated. The combination of these four results into a single-value predictor of hospital mortality presented an AUC-ROC 0.84 (IC_95%:0.81–0.87) and a test of goodness-of-fit (Hosmer and Lemeshow) of p = 0.368.

Conclusions

We identified specific variables associated with each of the four components of PIRO, including biomarkers and a dynamic view of the patient daily clinical course. This novel approach to PIRO concept and overall score can be a better predictor of mortality for patients with community-acquired sepsis admitted to ICUs.     

Maximum Number of Fixed Points in Regulatory Boolean Networks

Boolean networks (BNs) have been extensively used as mathematical models of genetic regulatory networks. The number of fixed points of a BN is a key feature of its dynamical behavior. Here, we study the maximum number of fixed points in a particular class of BNs called regulatory Boolean networks, where each interaction between the elements of the network is either an activation or an inhibition. We find relationships between the positive and negative cycles of the interaction graph and the number of fixed points of the network. As our main result, we exhibit an upper bound for the number of fixed points in terms of minimum cardinality of a set of vertices meeting all positive cycles of the network, which can be applied in the design of genetic regulatory networks.     

Generating Boolean networks with a prescribed attractor structure

MOTIVATION: Dynamical modeling of gene regulation via network models constitutes a key problem for genomics. The long-run characteristics of a dynamical system are critical and their determination is a primary aspect of system analysis. In the other direction, system synthesis involves constructing a network possessing a given set of properties. This constitutes the inverse problem. Generally, the inverse problem is ill-posed, meaning there will be many networks, or perhaps none, possessing the desired properties. Relative to long-run behavior, we may wish to construct networks possessing a desirable steady-state distribution. This paper addresses the long-run inverse problem pertaining to Boolean networks (BNs). RESULTS: The long-run behavior of a BN is characterized by its attractors. The rest of the state transition diagram is partitioned into level sets, the j-th level set being composed of all states that transition to one of the attractor states in exactly j transitions. We present two algorithms for the attractor inverse problem. The attractors are specified, and the sizes of the predictor sets and the number of levels are constrained. Algorithm complexity and performance are analyzed. The algorithmic solutions have immediate application. Under the assumption that sampling is from the steady state, a basic criterion for checking the validity of a designed network is that there should be concordance between the attractor states of the model and the data states. This criterion can be used to test a design algorithm: randomly select a set of states to be used as data states; generate a BN possessing the selected states as attractors, perhaps with some added requirements such as constraints on the number of predictors and the level structure; apply the design algorithm; and check the concordance between the attractor states of the designed network and the data states. AVAILABILITY: The software and supplementary material is available at http://gsp.tamu.edu/Publications/BNs/bn.htm     

Anorexia nervosa and bulimia nervosa.

No definitive therapy exists for anorexia nervosa (AN) or bulimia nervosa (BN). Nevertheless, biologic and psychologic research into these disorders has increased over the last decade. We examine the various drugs available for treatment. Advances in pharmacotherapy for AN have been modest and have reflected efforts either to stimulate hunger and weight gain or to control complications of the starvation process. Food remains the "drug" of choice. Antidepressants have been found to be beneficial in the treatment of BN. The meaning of this in the context of a relation between BN and mood disorders remains unclear, since coexistent depression does not predict a positive response to these drugs. Pharmacotherapy represents a single but important dimension of the management of patients with eating disorders. The optimal integration of drug therapy and psychotherapy and the identification of predictors of a positive response to drugs have yet to be addressed by clinical research.