Scott H. Harrison

Rapid evolution of silver nanoparticle resistance in Escherichia coli

The recent exponential increase in the use of engineered nanoparticles (eNPs) means both greater intentional and unintentional exposure of eNPs to microbes. Intentional use includes the use of eNPs as biocides. Unintentional exposure results from the fact that eNPs are included in a variety of commercial products (paints, sunscreens, cosmetics). Many of these eNPs are composed of heavy metals or metal oxides such as silver, gold, zinc, titanium dioxide, and zinc oxide. It is thought that since metallic/metallic oxide NPs impact so many aspects of bacterial physiology that it will difficult for bacteria to evolve resistance to them. This study utilized laboratory experimental evolution to evolve silver nanoparticle (AgNP) resistance in the bacterium Escherichia coli (K-12 MG1655), a bacterium that does not harbor any known silver resistance elements. After 225 generations of exposure to the AgNP environment, the treatment populations demonstrated greater fitness vs. control strains as measured by optical density (OD) and colony forming units (CFU) in the presence of varying concentrations of 10 nm citrate-coated silver nanoparticles (AgNP) or silver nitrate (AgNO3). Genomic analysis shows that changes associated with AgNP resistance were already accumulating within the treatment populations by generation 100, and by generation 200 three mutations had swept to high frequency in the AgNP resistance stocks. This study indicates that despite previous claims to the contrary bacteria can easily evolve resistance to AgNPs, and this occurs by relatively simple genomic changes. These results indicate that care should be taken with regards to the use of eNPs as biocides as well as with regards to unintentional exposure of microbial communities to eNPs in waste products.

Population-Genetic Inference from Pooled-Sequencing Data

Although pooled-population sequencing has become a widely used approach for estimating allele frequencies, most work has proceeded in the absence of a proper statistical framework. We introduce a self-sufficient, closed-form, maximum-likelihood estimator for allele frequencies that accounts for errors associated with sequencing, and a likelihood-ratio test statistic that provides a simple means for evaluating the null hypothesis of monomorphism. Unbiased estimates of allele frequencies (where N is the number of individuals sampled) appear to be unachievable, and near-certain identification of a polymorphism requires a minor-allele frequency . A framework is provided for testing for significant differences in allele frequencies between populations, taking into account sampling at the levels of individuals within populations and sequences within pooled samples. Analyses that fail to account for the two tiers of sampling suffer from very large false-positive rates and can become increasingly misleading with increasing depths of sequence coverage. The power to detect significant allele-frequency differences between two populations is very limited unless both the number of sampled individuals and depth of sequencing coverage exceed 100.

Mechanobiology of Antimicrobial Resistant Escherichia coli and Listeria innocua

A majority of antibiotic-resistant bacterial infections in the United States are associated with biofilms. Nanoscale biophysical measures are increasingly revealing that adhesive and viscoelastic properties of bacteria play essential roles across multiple stages of biofilm development. Atomic Force Microscopy (AFM) applied to strains with variation in antimicrobial resistance enables new opportunities for investigating the function of adhesive forces (stickiness) in biofilm formation. AFM force spectroscopy analysis of a field strain of Listeria innocua and the strain Escherichia coli K-12 MG1655 revealed differing adhesive forces between antimicrobial resistant and nonresistant strains. Significant increases in stickiness were found at the nanonewton level for strains of Listeria innocua and Escherichia coli in association with benzalkonium chloride and silver nanoparticle resistance respectively. This advancement in the usage of AFM provides for a fast and reliable avenue for analyzing antimicrobial resistant cells and the molecular dynamics of biofilm formation as a protective mechanism.

Stable Gene Regulatory Network Modeling From Steady-State Data

Gene regulatory networks represent an abstract mapping of gene regulations in living cells. They aim to capture dependencies among molecular entities such as transcription factors, proteins and metabolites. In most applications, the regulatory network structure is unknown, and has to be reverse engineered from experimental data consisting of expression levels of the genes usually measured as messenger RNA concentrations in microarray experiments. Steady-state gene expression data are obtained from measurements of the variations in expression activity following the application of small perturbations to equilibrium states in genetic perturbation experiments. In this paper, the least absolute shrinkage and selection operator-vector autoregressive (LASSO-VAR) originally proposed for the analysis of economic time series data is adapted to include a stability constraint for the recovery of a sparse and stable regulatory network that describes data obtained from noisy perturbation experiments. The approach is applied to real experimental data obtained for the SOS pathway in Escherichia coli and the cell cycle pathway for yeast Saccharomyces cerevisiae. Significant features of this method are the ability to recover networks without inputting prior knowledge of the network topology, and the ability to be efficiently applied to large scale networks due to the convex nature of the method.

EVALUATION OF HUSBANDRY AND MORTALITY IN LESSER HEDGEHOG TENRECS (ECHINOPS TELFAIRI)

Abstract Causes of morbidity and mortality for various species of tenrecs have not been widely published, aside from several reports of neoplasia, and these data are crucial for advancing objectives for preventive medicine, diagnosis, and treatment. A survey on husbandry, morbidity, and mortality of lesser hedgehog tenrecs (Echinops telfairi) in Association of Zoos and Aquariums (AZA) institutions was conducted. Out of 32 institutions, 20 responded with data for 98 living and 93 dead animals. The most common causes of mortality among the dead animals were neoplasia (24%), hepatic lipidosis (11%), septicemia (8.6%), pneumonia (8.6%), cardiomyopathy (7.5%), renal disease (6.5%), osteomyelitis (3.2%), and trauma (3.2%). There was no statistically significant correlation between sex and neoplasia. Data about educational usage were specifically provided by survey respondents for 50 of the tenrecs, with only 42% being excluded from educational programming. Tenrecs are common to many AZA institutions as both educational and exhibit animals, and this study provides a helpful reference for expected health problems and highlights the need for future investments into medical diagnosis and treatment for these animals.

Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network

BackgroundThe modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications.MethodsWe have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN.ResultsOur HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network sizes and levels of stochastic noise. We found our HRNN method to be superior in terms of accuracy for nonlinear data sets with higher amounts of noise.ConclusionsThe proposed method identifies time-delayed gene-gene interactions of GRNs. The topology-based advancement of our HRNN worked as expected by more effectively modeling nonlinear data sets. As a non-fully connected network, an added benefit to HRNN was how it helped to find the few genes which regulated the target gene over different time delays.

The Effects of Dietary Fat and Iron Interaction on Brain Regional Iron Contents and Stereotypical Behaviors in Male C57BL/6J Mice

Adequate brain iron levels are essential for enzyme activities, myelination, and neurotransmitter synthesis in the brain. Although systemic iron deficiency has been found in genetically or dietary-induced obese subjects, the effects of obesity-associated iron dysregulation in brain regions have not been examined. The objective of this study was to examine the effect of dietary fat and iron interaction on brain regional iron contents and regional-associated behavior patterns in a mouse model. Thirty C57BL/6J male weanling mice were randomly assigned to six dietary treatment groups (n = 5) with varying fat (control/high) and iron (control/high/low) contents. The stereotypical behaviors were measured during the 24th week. Blood, liver, and brain tissues were collected at the end of the 24th week. Brains were dissected into the hippocampus, midbrain, striatum, and thalamus regions. Iron contents and ferritin heavy chain (FtH) protein and mRNA expressions in these regions were measured. Correlations between stereotypical behaviors and brain regional iron contents were analyzed at the 5% significance level. Results showed that high-fat diet altered the stereotypical behaviors such as inactivity and total distance traveled (P < 0.05). The high-fat diet altered brain iron contents and FtH protein and mRNA expressions in a regional-specific manner: (1) high-fat diet significantly decreased the brain iron content in the striatum (P < 0.05), but not other regions, and (2) thalamus has a more distinct change in FtH mRNA expression compared with other regions. Furthermore, high-fat diet resulted in a significant decreased total distance traveled and a significant correlation between iron content and sleeping in midbrain (P < 0.05). Dietary iron also decreased brain iron content and FtH protein expression in a regionally specific manner. The effect of interaction between dietary fat and iron was observed in brain iron content and behaviors. All these findings will lay foundations to further explore the links among obesity, behaviors, and brain iron alteration.

HUMORAL RESPONSE TO CALICIVIRUS IN CAPTIVE TIGERS GIVEN A DUAL-STRAIN VACCINE

Abstract: The current feline vaccine with a single strain of calicivirus has been used for captive tigers, yet it may not protect against virulent systemic calicivirus infections. A cross-institutional study investigated the humoral response to a new dual-strain, killed-virus calicivirus vaccine for nine captive tigers. The subspecies of these tigers were Amur (Panthera tigris altaica), Bengal (Panthera tigris tigris), and Malayan (Panthera tigris jacksoni). Serum neutralization titers for virulent feline calicivirus strain FCV-DD1 were higher following dual-strain vaccine administration. There were no reports of adverse vaccine reactions. Dual-strain vaccination may afford broadened cross-protection against different calicivirus strains and is desirable to reduce the risk of virulent systemic calicivirus disease in tigers.

Application of circuit simulation method for differential modeling of TIM-2 iron uptake and metabolism in mouse kidney cells.

Circuit simulation is a powerful methodology to generate differential mathematical models. Due to its highly accurate modeling capability, circuit simulation can be used to investigate interactions between the parts and processes of a cellular system. Circuit simulation has become a core technology for the field of electrical engineering, but its application in biology has not yet been fully realized. As a case study for evaluating the more advanced features of a circuit simulation tool called Advanced Design System (ADS), we collected and modeled laboratory data for iron metabolism in mouse kidney cells for a H ferritin (HFt) receptor, T cell immunoglobulin and mucin domain-2 (TIM-2). The internal controlling parameters of TIM-2 associated iron metabolism were extracted and the ratios of iron movement among cellular compartments were quantified by ADS. The differential model processed by circuit simulation demonstrated a capability to identify variables and predict outcomes that could not be readily measured by in vitro experiments. For example, an initial rate of uptake of iron-loaded HFt (Fe-HFt) was 2.17 pmol per million cells. TIM-2 binding probability with Fe-HFt was 16.6%. An average of 8.5 min was required for the complex of TIM-2 and Fe-HFt to form an endosome. The endosome containing HFt lasted roughly 2 h. At the end of endocytosis, about 28% HFt remained intact and the rest was degraded. Iron released from degraded HFt was in the labile iron pool (LIP) and stimulated the generation of endogenous HFt for new storage. Both experimental data and the model showed that TIM-2 was not involved in the process of iron export. The extracted internal controlling parameters successfully captured the complexity of TIM-2 pathway and the use of circuit simulation-based modeling across a wider range of cellular systems is the next step for validating the significance and utility of this method.

Undiagnosed Kidney Injury in Uninsured and Underinsured Diabetic African American Men and Putative Role of Meprin Metalloproteases in Diabetic Nephropathy

Diabetes is the leading cause of chronic kidney disease. African Americans are disproportionately burdened by diabetic kidney disease (DKD) and end stage renal disease (ESRD). Disparities in DKD have genetic and socioeconomic components, yet its prevalence in African Americans is not adequately studied. The current study used multiple biomarkers of DKD to evaluate undiagnosed DKD in uninsured and underinsured African American men in Greensboro, North Carolina. Participants consisted of three groups: nondiabetic controls, diabetic patients without known kidney disease, and diabetic patients with diagnosed DKD. Our data reveal undiagnosed kidney injury in a significant proportion of the diabetic patients, based on levels of both plasma and urinary biomarkers of kidney injury, namely, urinary albumin to creatinine ratio, kidney injury molecule-1, cystatin C, and neutrophil gelatinase-associated lipocalin. We also found that the urinary levels of meprin A, meprin B, and two kidney meprin targets (nidogen-1 and monocytes chemoattractant protein-1) increased with severity of kidney injury, suggesting a potential role for meprin metalloproteases in the pathophysiology of DKD in this subpopulation. The study also demonstrates a need for more aggressive tests to assess kidney injury in uninsured diabetic patients to facilitate early diagnosis and targeted interventions that could slow progression to ESRD.