Genes for all metals—a bacterial view of the Periodic Table

Bacterial chromosomes have genes for transport proteins for inorganic nutrient cations and oxyanions, such as NH₄ ⁺, K⁺, Mg²⁺, Co²⁺, Fe³⁺, Mn²⁺, Zn²⁺ and other trace cations, PO₄ ^3-, SO₄ ^2- and less abundant oxyanions. Together these account for perhaps a few hundred genes in many bacteria. Bacterial plasmids encode resistance systems for toxic metal and metalloid ions including Ag⁺ AsO₂ ^-, AsO₄ ^3-, Cd²⁺, Co²⁺, CrO₄ ²⁻, Cu²⁺, Hg²⁺, Ni²⁺, Pb²⁺, TeO₃ ²⁻, TI⁺ and Zn²⁺. Most resistance systems function by energy-dependent efflux of toxic ions. A few involve enzymatic (mostly redox) transformations. Some of the efflux resistance systems are ATPases and others are chemiosmotic ion/proton exchangers. The Cd²⁺-resistance cation pump of Gram-positive bacteria is membrane P-type ATPase, which has been labeled with ³²P from [γ-³²P]ATP and drives ATP-dependent Cd²⁺ (and Zn²⁺) transport by membrane vesicles. The genes defective in the human hereditary diseases of copper metabolism, Menkes syndrome and Wilson’s disease, encode P-type ATPases that are similar to bacterial cadmium ATPases. The arsenic resistance system transports arsenite [As(III)], alternatively with the ArsB polypeptide functioning as a chemiosmotic efflux transporter or with two polypeptides, ArsB and ArsA, functioning as an ATPase. The third protein of the arsenic resistance system is an enzyme that reduces intracellular arsenate [As(V)] to arsenite [As(III)], the substrate of the efflux system. In Gram-negative cells, a three polypeptide complex functions as a chemiosmotic cation/protein exchanger to efflux Cd²⁺, Zn²⁺ and Co²⁺. This pump consists of an inner membrane (CzcA), an outer membrane (CzcC) and a membrane-spanning (CzcB) protein that function together. Received 08 August 1997/ Accepted in revised form 01 November 1997     

Mössbauer studies on oxygen binding in protoporphyrin IX iron(II) solutions in the presence of other ligands

Mössbauer parameters of frozen solutions of protoporphyrin IX iron(II) (containing either 2- methyl-piperidine or mercaptoethanol as the fifth iron ligand) that were exposed to oxygen before freezing are similar to those of oxyhaemoglobin. These results are discussed in relation to known porphyrin iron(II) chemistry.     

"PP2C7s", Genes Most Highly Elaborated in Photosynthetic Organisms,Reveal the Bacterial Origin and Stepwise Evolution of PPM/PP2C Protein Phosphatases

Mg⁺²/Mn⁺²-dependent type 2C protein phosphatases (PP2Cs) are ubiquitous in eukaryotes, mediating diverse cellular signaling processes through metal ion catalyzed dephosphorylation of target proteins. We have identified a distinct PP2C sequence class (“PP2C7s”) which is nearly universally distributed in Eukaryotes, and therefore apparently ancient. PP2C7s are by far most prominent and diverse in plants and green algae. Combining phylogenetic analysis, subcellular localization predictions, and a distillation of publically available gene expression data, we have traced the evolutionary trajectory of this gene family in photosynthetic eukaryotes, demonstrating two major sequence assemblages featuring a succession of increasingly derived sub-clades. These display predominant expression moving from an ancestral pattern in photosynthetic tissues toward non-photosynthetic, specialized and reproductive structures. Gene co-expression network composition strongly suggests a shifting pattern of PP2C7 gene functions, including possible regulation of starch metabolism for one homologue set in Arabidopsis and rice. Distinct plant PP2C7 sub-clades demonstrate novel amino terminal protein sequences upon motif analysis, consistent with a shifting pattern of regulation of protein function. More broadly, neither the major events in PP2C sequence evolution, nor the origin of the diversity of metal binding characteristics currently observed in different PP2C lineages, are clearly understood. Identification of the PP2C7 sequence clade has allowed us to provide a better understanding of both of these issues. Phylogenetic analysis and sequence comparisons using Hidden Markov Models strongly suggest that PP2Cs originated in Bacteria (Group II PP2C sequences), entered Eukaryotes through the ancestral mitochondrial endosymbiosis, elaborated in Eukaryotes, then re-entered Bacteria through an inter-domain gene transfer, ultimately producing bacterial Group I PP2C sequences. A key evolutionary event, occurring first in ancient Eukaryotes, was the acquisition of a conserved aspartate in classic Motif 5. This has been inherited subsequently by PP2C7s, eukaryotic PP2Cs and bacterial Group I PP2Cs, where it is crucial to the formation of a third metal binding pocket, and catalysis.     

An Agent-Based Model of Private Woodland Owner Management Behavior Using Social Interactions,Information Flow,and Peer-To-Peer Networks

Privately owned woodlands are an important source of timber and ecosystem services in North America and worldwide. Impacts of management on these ecosystems and timber supply from these woodlands are difficult to estimate because complex behavioral theory informs the owner’s management decisions. The decision-making environment consists of exogenous market factors, internal cognitive processes, and social interactions with fellow landowners, foresters, and other rural community members. This study seeks to understand how social interactions, information flow, and peer-to-peer networks influence timber harvesting behavior using an agent-based model. This theoretical model includes forested polygons in various states of ‘harvest readiness’ and three types of agents: forest landowners, foresters, and peer leaders (individuals trained in conservation who use peer-to-peer networking). Agent rules, interactions, and characteristics were parameterized with values from existing literature and an empirical survey of forest landowner attitudes, intentions, and demographics. The model demonstrates that as trust in foresters and peer leaders increases, the percentage of the forest that is harvested sustainably increases. Furthermore, peer leaders can serve to increase landowner trust in foresters. Model output and equations will inform forest policy and extension/outreach efforts. The model also serves as an important testing ground for new theories of landowner decision making and behavior.     

The Epidemiology of Hypertension in Uganda: Findings from the National Non-Communicable Diseases Risk Factor Survey

Background

Hypertension is an important contributor to global burden of disease and mortality, and is a growing public health problem in sub-Saharan Africa. However, most sub-Saharan African countries lack detailed countrywide data on hypertension and other non-communicable diseases (NCD) risk factors that would provide benchmark information for design of appropriate interventions. We analyzed blood pressure data from Uganda’s nationwide NCD risk factor survey conducted in 2014, to describe the prevalence and distribution of hypertension in the Ugandan population, and to identify the associated factors.

Methods

The NCD risk factor survey drew a countrywide sample stratified by the four regions of the country, and with separate estimates for rural and urban areas. The World Health Organization’s STEPs tool was used to collect data on demographic and behavioral characteristics, and physical and biochemical measurements. Prevalence rate ratios (PRR) using modified Poison regression modelling was used to identify factors associated with hypertension.

Results

Of the 3906 participants, 1033 were classified as hypertensive, giving an overall prevalence of 26.4%. Prevalence was highest in the central region at 28.5%, followed by the eastern region at 26.4%, western region at 26.3%, and northern region at 23.3%. Prevalence in urban areas was 28.9%, and 25.8% in rural areas. The differences between regions, and between rural-urban areas were not statistically significant. Only 7.7% of participants with hypertension were aware of their high blood pressure. The prevalence of pre-hypertension was also high at 36.9%. The only modifiable factor found to be associated with hypertension was higher body mass index (BMI). Compared to participants with BMI less than 25 kg/m², prevalence was significantly higher among participants with BMI between 25 to 29.9 kg/m² with an adjusted PRR = 1.46 [95% CI = 1.25–1.71], and even higher among obese participants (BMI ≥ 30 kg/m²) with an adjusted PRR = 1.60 [95% CI = 1.29–1.99]. The un-modifiable factor found to be associated with hypertension was older age with an adjusted PRR of 1.02 [95% CI = 1.02–1.03] per yearly increase in age.

Conclusions

The prevalence of hypertension in Uganda is high, with no significant differences in distribution by geographical location. Only 7.7% of persons with hypertension were aware of their hypertension, indicating a high burden of undiagnosed and un-controlled high blood pressure. Thus a big percentage of persons with hypertension are at high risk of hypertension-related cardiovascular NCDs.     

Designing Cell-Targeted Therapeutic Proteins Reveals the Interplay between Domain Connectivity and Cell Binding

The therapeutic efficacy of cytokines is often hampered by severe side effects due to their undesired binding to healthy cells. One strategy for overcoming this obstacle is to tether cytokines to antibodies or antibody fragments for targeted cell delivery. However, how to modulate the geometric configuration and relative binding affinity of the two domains for optimal activity remains an outstanding question. As a result, many antibody-cytokine complexes do not achieve the desired level of cell-targeted binding and activity. Here, we address these design issues by developing a computational model to simulate the dynamics and binding kinetics of natural and engineered fusion proteins such as antibody-cytokine complexes. To verify the model, we developed a modular system in which an antibody fragment and a cytokine are conjugated via a DNA linker that allows for programmable linker geometry and protein spatial configuration. By assembling and testing several anti-CD20 antibody fragment-interferon α complexes, we showed that varying the linker length and cytokine binding affinity controlled the magnitude of cell-targeted signaling activation in a manner that agreed with the model predictions, which were expressed as dose-signaling response curves. The simulation results also revealed that there is a range of cytokine binding affinities that would achieve optimal therapeutic efficacy. This rapid prototyping platform will facilitate the rational design of antibody-cytokine complexes for improved therapeutic outcomes.     

Estimating Haplotype Effects for Survival Data

Summary Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness‐of‐fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF‐receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm.     

The advantages of dense marker sets for linkage analysis with very large families

Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods.     

A comparison of background correction methods for two-colour microarrays

MOTIVATION: Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments. RESULTS: Using data where some independent truth in gene expression is known, eight different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates. Methods which stabilize the variances of the log-ratios along the intensity range perform the best. The normexp+offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp is applicable to most types of two-colour microarray data. AVAILABILITY: The background correction methods compared in this article are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Supplementary data are available from http://bioinf.wehi.edu.au/resources/webReferences.html.