Active immunization of hamsters against Clostridium difficile infection using surface-layer protein

Clostridium difficile is the leading cause of infectious antibiotic-associated diarrhoea, particularly among the elderly. Its surface-layer protein (SLP) was tested as a vaccine component in a series of immunization and challenge experiments with Golden Syrian hamsters, combined with different systemic and mucosal adjuvants. Some regimens were also tested in a nonchallenge BALB/c mouse model, enabling closer monitoring of the immune response. None of the regimens conferred complete protection in the hamster model, and antibody stimulation was variable within regimens, and generally modest or poor. Mice displayed stronger antibody responses to SLP compared with hamsters. Two hamsters of five given SLP with Ribi (monophosphoryl lipid A and synthetic trehalose dicorynomycolate) survived the challenge, as did two of three given SLP with Ribi and cholera toxin. This modest trend to protection is interpreted with caution, because the survivors had low anti-SLP serum antibody titres. The hamsters were an outbred line, and subject to more genetic variability than inbred animals; however, BALB/c mice also showed strongly variable antibody responses. There is a clear need for better adjuvants for single-component vaccines, particularly for mucosal delivery. The hamster challenge model may need to be modified to be useful in active immunization experiments with SLP.     

A network-based approach for resistance transmission in bacterial populations

Horizontal transfer of mobile genetic elements (conjugation) is an important mechanism whereby resistance is spread through bacterial populations. The aim of our work is to develop a mathematical model that quantitatively describes this process, and to use this model to optimize antimicrobial dosage regimens to minimize resistance development. The bacterial population is conceptualized as a compartmental mathematical model to describe changes in susceptible, resistant, and transconjugant bacteria over time. This model is combined with a compartmental pharmacokinetic model to explore the effect of different plasma drug concentration profiles. An agent-based simulation tool is used to account for resistance transfer occurring when two bacteria are adjacent or in close proximity. In addition, a non-linear programming optimal control problem is introduced to minimize bacterial populations as well as the drug dose. Simulation and optimization results suggest that the rapid death of susceptible individuals in the population is pivotal in minimizing the number of transconjugants in a population. This supports the use of potent antimicrobials that rapidly kill susceptible individuals and development of dosage regimens that maintain effective antimicrobial drug concentrations for as long as needed to kill off the susceptible population. Suggestions are made for experiments to test the hypotheses generated by these simulations.     

HIV drug resistance and optimization of antiviral treatment in resource-poor countries

HIV drug resistance has been associated with treatment failure in Western countries but the lessons learned can be useful in optimization of highly active antiretroviral treatment (HAART) in resource-poor settings. There is a need to improve access to HAART in such regions, but appropriate strategies must be rapidly implemented, such as adapted programs to facilitate adherence to therapy, rational use of genotypic drug resistance monitoring in specific situations, and use of alternative treatment regimens. The implications of HIV genetic diversity must also be considered in management of drug resistance.     

Understanding hereditary diseases using the dog and human as companion model systems

Animal models are requisite for genetic dissection of, and improved treatment regimens for, human hereditary diseases. While several animals have been used in academic and industrial research, the primary model for dissection of hereditary diseases has been the many strains of the laboratory mouse. However, given its greater (than the mouse) genetic similarity to the human, high number of naturally occurring hereditary diseases, unique population structure, and the availability of the complete genome sequence, the purebred dog has emerged as a powerful model for study of diseases. The major advantage the dog provides is that it is afflicted with approximately 450 hereditary diseases, about half of which have remarkable clinical similarities to corresponding diseases of the human. In addition, humankind has a strong desire to cure diseases of the dog so these two facts make the dog an ideal clinical and genetic model. This review highlights several of these shared hereditary diseases. Specifically, the canine models discussed herein have played important roles in identification of causative genes and/or have been utilized in novel therapeutic approaches of interest to the dog and human.     

Cellular origin of hepatocellular carcinomas

There are four levels of cells in the hepatic lineage which may respond to different carcinogenic regimens: (1) the mature hepatocyte, which responds to diethylnitrosamine (DEN) hepatocarcinogenesis. (2) The bile duct progenitor cells, which give rise to cholangiocellular carcinomas when the furan model is used or when hamsters infected with liver flukes (Clornorchis sinensis) are exposed to dimethylnitrosamine. (3) The ductular 'bipolar' progenitor cell which gives rise to hepatocellular carcinomas (HCC) in several N-2-acetylaminofluorene (N-2-AAF) based regimens, and (4) the periductular stem cell, which is the cell of origin of HCC induced by the choline deficiency models of hepatocarcinogenesis. Extrahepatic (bone marrow) origin of the periductular stem cells is supported by recent data showing that hepatocytes may express genetic markers of donor hematopoietic cells after bone marrow transplantation.     

Monitoring and managing genetic variation in group breeding populations without individual pedigrees

The genetic management of captive populations to conserve genetic variation is currently based on analyses of individual pedigrees to infer inbreeding and kinship coefficients and values of individuals as breeders. Such analyses require that individual pedigrees are known and individual pairing (mating) can be controlled. Many species in captivity, however, breed in groups due to various reasons, such as space constraints and fertility considerations for species living naturally in social groups, and thus have no pedigrees available for the traditional genetic analyses and management. In the absence of individual pedigree, such group breeding populations can still be genetically monitored, evaluated and managed by suitable population genetics models using population level information (such as census data). This article presents a simple genetic model of group breeding populations to demonstrate how to estimate the genetic variation maintained within and among populations and to optimise management based on these estimates. A numerical example is provided to illustrate the use of the proposed model. Some issues relevant to group breeding, such as the development and robustness evaluation of the population genetics model appropriate for a particular species under specific management and recording systems and the genetic monitoring with markers, are also briefly discussed.     

Integrating genetic data and population viability analyses for the identification of harbour seal (Phoca vitulina) populations and management units

Identification of populations and management units is an essential step in the study of natural systems. Still, there is limited consensus regarding how to define populations and management units, and whether genetic methods allow for inference at the relevant spatial and temporal scale. Here, we present a novel approach, integrating genetic, life history and demographic data to identify populations and management units in southern Scandinavian harbour seals. First, 15 microsatellite markers and model‐ and distance‐based genetic clustering methods were used to determine the population genetic structure in harbour seals. Second, we used harbour seal demographic and life history data to conduct population viability analyses (PVAs) in the vortex simulation model in order to determine whether the inferred genetic units could be classified as management units according to Lowe and Allendorf's (Molecular Ecology, 19, 2010, 3038) ‘population viability criterion’ for demographic independence. The genetic analyses revealed fine‐scale population structuring in southern Scandinavian harbour seals and pointed to the existence of several genetic units. The PVAs indicated that the census population size of each of these genetic units was sufficiently large for long‐term population viability, and hence that the units could be classified as demographically independent management units. Our study suggests that population genetic inference can offer the same degree of temporal and spatial resolution as ‘nongenetic’ methods and that the combined use of genetic data and PVAs constitutes a promising approach for delineating populations and management units.     

Genetic management of nonhuman primates

Genetic management is widely recognized as a critical component of the overall management of captive nonhuman primate colonies which produce animals for biomedical research. In this paper, we review the roles of conservation-oriented genetic management, research-oriented genetic management, genetic management at the level of taxomomic class, genetic management at the level of the population, and quantitative genetic analysis in comprehensive genetic management programs for nonhuman primate colonies. We conclude that genetic management is crucial for maintaining nonhuman primate populations suitable for genetic research on normal and disease-related phenotypes. In addition, for research programs that do not have specific genetic objectives, genetic management is essential to facilitate the selection of samples of well-matched unrelated animals for experimental purposes.     

Intracranial Stereotaxic Cannulation for Development of Orthotopic Glioblastoma Allograft in Sprague-Dawley Rats and Histoimmunopathological Characterization of the Brain Tumor

Glioblastoma is the most common brain tumor that causes significant mortality annually. Limitations of the current therapeutic regimens warrant development of new techniques and treatment strategies in orthotopic animal model for better management of this devastating brain cancer. There are only a few experimental orthotopic models of glioblastoma for pre-clinical testing. In the present investigation, we successfully implanted rat C6 cells via intracranial stereotaxic cannulation in adult Sprague-Dawley rats for development and histoimmunopathological characterization of an advanced orthotopic glioblastoma allograft model, which could be useful for investigating the course of glioblastoma development as well as for testing efficacy of new therapeutic agents. The orthotopic glioblastoma allograft was generated by intracerebral injection of rat C6 cells through a guide-cannula system and after 21 post-inoculation days the brain tumor was characterized by histoimmunopathological experiments. Histological staining and immunofluorescent labelings for TERT, VEGF, Bcl-2, survivin, XIAP, and GFAP revealed the distinct characteristics of glioblastoma in C6 allograft, which could be useful as a target for treatment with emerging new therapeutic agents. Our investigation indicated the successful development of intracranial cannulated orthotopic glioblastoma allograft in adult Sprague-Dawley rats, making it as a useful animal model of glioblastoma for pre-clinical evaluation of various therapeutic strategies for the management of glioblastoma. Special issue in honor of Naren Banik.     

Genetic variation and causes of genotype-environment interaction in the body size of blue tit (Parus caeruleus).

In several studies of natural populations of birds, the heritability of body size estimated by parent-offspring regression has been lower when offspring have developed in poor feeding regimens than when they developed in good feeding regimens. This has led to the suggestion that adaptation under poor regimens may be constrained by lack of genetic variation. We examined the influence of environmental conditions on expression of genetic variation in body size of nestling blue tits (Parus caeruleus) by raising full sibs in artificially reduced and enlarged broods, corresponding to good and poor feeding regimens, respectively. Individuals grown in the poor regimen attained smaller body size than their sibs grown in the good regimen. However, there was among-family variation in response to the treatments--i.e., genotype-environment interactions (GEIs). Partitioning the GEI variance into contributions attributable to (1) differences in the among-family genetic variance between the treatments and (2) imperfect correlation of genotypic values across treatments identified the latter as the main cause of the GEI. Parent-offspring regressions were not significantly different when offspring were reared in the good environment (h2 = 0.75) vs. when they were reared in the poor environment (h2 = 0.63). Thus, there was little evidence that genetic variance in body size was lower under the poor conditions than under the good conditions. These results do not support the view that the genetic potential for adaptation to poor feeding conditions is less than that for adaptation to good conditions, but they do suggest that different genotypes may be favored under the different conditions.     

Non-smooth plant disease models with economic thresholds

In order to control plant diseases and eventually maintain the number of infected plants below an economic threshold, a specific management strategy called the threshold policy is proposed, resulting in Filippov systems. These are a class of piecewise smooth systems of differential equations with a discontinuous right-hand side. The aim of this work is to investigate the global dynamic behavior including sliding dynamics of one Filippov plant disease model with cultural control strategy. We examine a Lotka–Volterra Filippov plant disease model with proportional planting rate, which is globally studied in terms of five types of equilibria. For one type of equilibrium, the global structure is discussed by the iterative equations for initial numbers of plants. For the other four types of equilibria, the bounded global attractor of each type is obtained by constructing appropriate Lyapunov functions. The ideas of constructing Lyapunov functions for Filippov systems, the methods of analyzing such systems and the main results presented here provide scientific support for completing control regimens on plant diseases in integrated disease management.     

A comparison of individual‐based genetic distance metrics for landscape genetics

A major aim of landscape genetics is to understand how landscapes resist gene flow and thereby influence population genetic structure. An empirical understanding of this process provides a wealth of information that can be used to guide conservation and management of species in fragmented landscapes and also to predict how landscape change may affect population viability. Statistical approaches to infer the true model among competing alternatives are based on the strength of the relationship between pairwise genetic distances and landscape distances among sampled individuals in a population. A variety of methods have been devised to quantify individual genetic distances, but no study has yet compared their relative performance when used for model selection in landscape genetics. In this study, we used population genetic simulations to assess the accuracy of 16 individual‐based genetic distance metrics under varying sample sizes and degree of population genetic structure. We found most metrics performed well when sample size and genetic structure was high. However, it was much more challenging to infer the true model when sample size and genetic structure was low. Under these conditions, we found genetic distance metrics based on principal components analysis were the most accurate (although several other metrics performed similarly), but only when they were derived from multiple principal components axes (the optimal number varied depending on the degree of population genetic structure). Our results provide guidance for which genetic distance metrics maximize model selection accuracy and thereby better inform conservation and management decisions based upon landscape genetic analysis.     

The impact of management practices and past demographic history on the genetic diversity of red deer (Cervus elaphus): an assessment of population and individual fitness

The influences of management practices and past demographic history on genetic diversity are of critical relevance to sustainable practices and the conservation of wildlife populations. The red deer (Cervus elaphus) is an interesting model species to address these questions because it has a wide geographical distribution and it has been intensively managed for humans in the last decades. In the present study, we have analyzed the impact of recent management practices on the genetic diversity of Iberian red deer populations and assessed the genetic variation effects on population and individual fitness‐related traits. Four populations subjected to distinct management systems were selected: Cabañeros (CB) and Doñana (DN), not hunted populations; Fraga/Caspe (FG/CP), open hunting area with very low or absent management; and PE, fenced private hunting estate founded 31 years ago through the introduction of deer of different origins. Ten microsatellites were amplified in a total of 172 individuals. Additionally, several fitness‐related traits such as the presence of tuberculosis compatible lesions (TBCL), spleen weight (SW), and body length (BL) were estimated. We found a marked genetic variation and differentiation among populations, suggesting a strong population structure. In the fenced population, the introduction of genetically distinct animals has led to high genetic variability (no evidence of inbreeding) despite intensive management. Lower levels of genetic diversity were observed in two historically isolated natural populations (DN and FG/CP). The past demographic history of Iberian populations appears to be more relevant than the current management policy in shaping the genetic variability of natural populations. Population genetic diversity may correlate with life‐history traits and disease susceptibility, which could compromise the conservation and management of these wildlife populations. Although no significant effects of individual genetic diversity (general and local effect hypotheses) were observed on TBCL, SW and BL, some single‐locus effects had almost significant trends for the TBCL and SW traits. © 2013 The Linnean Society of London, Biological Journal of the Linnean Society, 2014, 111 , 209–223.     

Evolution of resistance to anti-cancer therapy during general dosing schedules

Anti-cancer drugs targeted to specific oncogenic pathways have shown promising therapeutic results in the past few years; however, drug resistance remains an important obstacle for these therapies. Resistance to these drugs can emerge due to a variety of reasons including genetic or epigenetic changes which alter the binding site of the drug target, cellular metabolism or export mechanisms. Obtaining a better understanding of the evolution of resistant populations during therapy may enable the design of more effective therapeutic regimens which prevent or delay progression of disease due to resistance. In this paper, we use stochastic mathematical models to study the evolutionary dynamics of resistance under time-varying dosing schedules and pharmacokinetic effects. The populations of sensitive and resistant cells are modeled as multi-type non-homogeneous birth-death processes in which the drug concentration affects the birth and death rates of both the sensitive and resistant cell populations in continuous time. This flexible model allows us to consider the effects of generalized treatment strategies as well as detailed pharmacokinetic phenomena such as drug elimination and accumulation over multiple doses. We develop estimates for the probability of developing resistance and moments of the size of the resistant cell population. With these estimates, we optimize treatment schedules over a subspace of tolerated schedules to minimize the risk of disease progression due to resistance as well as locate ideal schedules for controlling the population size of resistant clones in situations where resistance is inevitable. Our methodology can be used to describe dynamics of resistance arising due to a single (epi)genetic alteration in any tumor type.     

Dispersal ecology of the endangered woodland lichen Lobaria pulmonaria in managed hemiboreal forest landscape

Changes in the forest management practices have strongly influenced the distribution of species inhabiting old-growth forests. The epiphytic woodland lichen Lobaria pulmonaria is frequently used as a model species to study the factors affecting the population biology of lichens. We sampled 252 L. pulmonaria individuals from 12 populations representing three woodland types differing in their ecological continuity and management intensity in Estonia. We used eight mycobiont-specific microsatellite loci to quantify genetic diversity among the populations. We calculated the Sørensen distance to estimate genetic dissimilarity among individuals within populations. We revealed that L. pulmonaria populations have significantly higher genetic diversity in old-growth forests than in managed forests and wooded meadows. We detected a significant woodland-type-specific pattern of genetic dissimilarity among neighbouring L. pulmonaria individuals, which suggests that in wooded meadows and managed forests dominating is vegetative reproduction. The vegetative dispersal distance between the host trees of L. pulmonaria was found to be only 15–30 m. Genetic dissimilarity among individuals was also dependent on tree species and trunk diameter. Lobaria pulmonaria populations in managed forests included less juveniles compared to old-growth forests and wooded meadows, indicating that forest management influences life stage structure within populations. We conclude that as intensive stand management reduces the genetic diversity of threatened species in woodland habitats, particular attention should be paid to the preservation of remnant populations in old-growth habitats. Within managed habitats, conservation management should target on maintenance of the stand’s structural diversity and availability of potential host trees.     

Month 2 Culture Status and Treatment Duration as Predictors of Tuberculosis Relapse Risk in a Meta-Regression Model

Background

New drugs and regimens with the potential to transform tuberculosis treatment are presently in early stage clinical trials.

Objective

The goal of the present study was to infer the required duration of these treatments.

Method

A meta-regression model was developed to predict relapse risk using treatment duration and month 2 sputum culture positive rate as predictors, based on published historical data from 24 studies describing 58 regimens in 7793 patients. Regimens in which rifampin was administered for the first 2 months but not subsequently were excluded. The model treated study as a random effect.

Results

The model predicted that new regimens of 4 or 5 months duration with rates of culture positivity after 2 months of 1% or 3%, would yield relapse rates of 4.0% or 4.1%, respectively. In both cases, the upper limit of the 2-sided 80% prediction interval for relapse for a hypothetical trial with 680 subjects per arm was <10%. Analysis using this model of published month 2 data for moxifloxacin-containing regimens indicated they would result in relapse rates similar to standard therapy only if administered for ≥5 months.

Conclusions

This model is proposed to inform the required duration of treatment of new TB regimens, potentially hastening their accelerated approval by several years.     

Optimal screening for genetic diseases

Screening for genetic diseases is performed in many regions and/or ethnic groups where there is a high prevalence of possibly malign genes. The propagation of such genes can be considered a dynamic externality. Given that many of these diseases are untreatable and give rise to truly tragic outcomes, they are a source of societal concern, and the screening process should perhaps be regulated. This paper incorporates a standard model of genetic propagation into an economic model of dynamic management to derive cost benefit rules for optimal screening. The highly non-linear nature of genetic dynamics gives rise to perhaps surprising results that include discontinuous controls and threshold effects. One insight is that any screening program that is in place for any amount of time should screen all individuals in a target population. The incorporation of genetic models may prove to be useful to several emerging fields in economics such as genoeconomics, neuroeconomics and paleoeconomics.     

Experimental endocarditis model of methicillin resistant Staphylococcus aureus (MRSA) in rat

Endovascular infections, including endocarditis, are life-threatening infectious syndromes. Staphylococcus aureus is the most common world-wide cause of such syndromes with unacceptably high morbidity and mortality even with appropriate antimicrobial agent treatments. The increase in infections due to methicillin-resistant S. aureus (MRSA), the high rates of vancomycin clinical treatment failures and growing problems of linezolid and daptomycin resistance have all further complicated the management of patients with such infections, and led to high healthcare costs. In addition, it should be emphasized that most recent studies with antibiotic treatment outcomes have been based in clinical settings, and thus might well be influenced by host factors varying from patient-to-patient. Therefore, a relevant animal model of endovascular infection in which host factors are similar from animal-to-animal is more crucial to investigate microbial pathogenesis, as well as the efficacy of novel antimicrobial agents. Endocarditis in rat is a well-established experimental animal model that closely approximates human native valve endocarditis. This model has been used to examine the role of particular staphylococcal virulence factors and the efficacy of antibiotic treatment regimens for staphylococcal endocarditis. In this report, we describe the experimental endocarditis model due to MRSA that could be used to investigate bacterial pathogenesis and response to antibiotic treatment.