Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs

Hepatitis C virus (HCV) chronically infects over 180 million people worldwide, with over 350,000 estimated deaths attributed yearly to HCV-related liver diseases. It disproportionally affects people who inject drugs (PWID). Currently there is no preventative vaccine and interventions feature long treatment durations with severe side-effects. Upcoming treatments will improve this situation, making possible large-scale treatment interventions. How these strategies should target HCV-infected PWID remains an important unanswered question. Previous models of HCV have lacked empirically grounded contact models of PWID. Here we report results on HCV transmission and treatment using simulated contact networks generated from an empirically grounded network model using recently developed statistical approaches in social network analysis. Our HCV transmission model is a detailed, stochastic, individual-based model including spontaneously clearing nodes. On transmission we investigate the role of number of contacts and injecting frequency on time to primary infection and the role of spontaneously clearing nodes on incidence rates. On treatment we investigate the effect of nine network-based treatment strategies on chronic prevalence and incidence rates of primary infection and re-infection. Both numbers of contacts and injecting frequency play key roles in reducing time to primary infection. The change from “less-” to “more-frequent” injector is roughly similar to having one additional network contact. Nodes that spontaneously clear their HCV infection have a local effect on infection risk and the total number of such nodes (but not their locations) has a network wide effect on the incidence of both primary and re-infection with HCV. Re-infection plays a large role in the effectiveness of treatment interventions. Strategies that choose PWID and treat all their contacts (analogous to ring vaccination) are most effective in reducing the incidence rates of re-infection and combined infection. A strategy targeting infected PWID with the most contacts (analogous to targeted vaccination) is the least effective.     

Phylodynamic Inference of Bacterial Outbreak Parameters Using Nanopore Sequencing

Nanopore sequencing and phylodynamic modeling have been used to reconstruct the transmission dynamics of viral epidemics, but their application to bacterial pathogens has remained challenging. Cost-effective bacterial genome sequencing and variant calling on nanopore platforms would greatly enhance surveillance and outbreak response in communities without access to sequencing infrastructure. Here, we adapt random forest models for single nucleotide polymorphism (SNP) polishing developed by Sanderson and colleagues (2020. High precision Neisseria gonorrhoeae variant and antimicrobial resistance calling from metagenomic nanopore sequencing. Genome Res. 30(9):1354–1363) to estimate divergence and effective reproduction numbers (R_e) of two methicillin-resistant Staphylococcus aureus (MRSA) outbreaks from remote communities in Far North Queensland and Papua New Guinea (PNG; n = 159). Successive barcoded panels of S. aureus isolates (2 × 12 per MinION) sequenced at low coverage (>5× to 10×) provided sufficient data to accurately infer genotypes with high recall when compared with Illumina references. Random forest models achieved high resolution on ST93 outbreak sequence types (>90% accuracy and precision) and enabled phylodynamic inference of epidemiological parameters using birth–death skyline models. Our method reproduced phylogenetic topology, origin of the outbreaks, and indications of epidemic growth (R_e > 1). Nextflow pipelines implement SNP polisher training, evaluation, and outbreak alignments, enabling reconstruction of within-lineage transmission dynamics for infection control of bacterial disease outbreaks on portable nanopore platforms. Our study shows that nanopore technology can be used for bacterial outbreak reconstruction at competitive costs, providing opportunities for infection control in hospitals and communities without access to sequencing infrastructure, such as in remote northern Australia and PNG.     

A Simulation Study Comparing Epidemic Dynamics on Exponential Random Graph and Edge-Triangle Configuration Type Contact Network Models

We compare two broad types of empirically grounded random network models in terms of their abilities to capture both network features and simulated Susceptible-Infected-Recovered (SIR) epidemic dynamics. The types of network models are exponential random graph models (ERGMs) and extensions of the configuration model. We use three kinds of empirical contact networks, chosen to provide both variety and realistic patterns of human contact: a highly clustered network, a bipartite network and a snowball sampled network of a “hidden population”. In the case of the snowball sampled network we present a novel method for fitting an edge-triangle model. In our results, ERGMs consistently capture clustering as well or better than configuration-type models, but the latter models better capture the node degree distribution. Despite the additional computational requirements to fit ERGMs to empirical networks, the use of ERGMs provides only a slight improvement in the ability of the models to recreate epidemic features of the empirical network in simulated SIR epidemics. Generally, SIR epidemic results from using configuration-type models fall between those from a random network model (i.e., an Erdős-Rényi model) and an ERGM. The addition of subgraphs of size four to edge-triangle type models does improve agreement with the empirical network for smaller densities in clustered networks. Additional subgraphs do not make a noticeable difference in our example, although we would expect the ability to model cliques to be helpful for contact networks exhibiting household structure.     

Variants in the interleukin-1 alpha and beta genes,and the risk for periodontal disease in dogs

Elevated levels of interleukin-1 (IL-1) have been shown to amplify the inflammatory response against periodontopathogenic bacteria. In humans, polymorphisms in the IL1A and IL1B genes are the most well-studied genetic polymorphisms associated with periodontal disease (PD). In contrast to human, there is a lack of knowledge on the genetic basis of canine PD. A case–control study was conducted in which a molecular analysis of dog IL1A and IL1B genes was performed. Of the eight genetic variants identified, seven in IL1A gene and one in IL1B gene, IL1A/1_g.388A >C and IL1A/1_g.521T >A showed statistically significant differences between groups (adjusted OR (95% CI): 0.15 (0.03–0.76), P= 0.022; 5.76 (1.03–32.1), P= 0.046, respectively). It suggests that in the studied population the IL1A/1_g.388C allele is associated with a decreased PD risk, whereas the IL1A/1_g.521A allele can confer an increased risk. Additionally, the IL1A/2_g.515G >T variation resulted in a change of amino acid, i.e. glycine to valine. In silico analysis suggests that this change can alter protein structure and function, predicting it to be deleterious or damaging. This work suggests that IL1 genetic variants may be important in PD susceptibility in canines.     

The promoter of filamentation (POF1) protein from <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis> is an ATPase involved in the protein quality control process

Background

The gene YCL047C, which has been renamed promoter of filamentation gene (POF1), has recently been described as a cell component involved in yeast filamentous growth. The objective of this work is to understand the molecular and biological function of this gene.

Results

Here, we report that the protein encoded by the POF1 gene, Pof1p, is an ATPase that may be part of the Saccharomyces cerevisiae protein quality control pathway. According to the results, Δpof1 cells showed increased sensitivity to hydrogen peroxide, tert-butyl hydroperoxide, heat shock and protein unfolding agents, such as dithiothreitol and tunicamycin. Besides, the overexpression of POF1 suppressed the sensitivity of Δpct1, a strain that lacks a gene that encodes a phosphocholine cytidylyltransferase, to heat shock. In vitro analysis showed, however, that the purified Pof1p enzyme had no cytidylyltransferase activity but does have ATPase activity, with catalytic efficiency comparable to other ATPases involved in endoplasmic reticulum-associated degradation of proteins (ERAD). Supporting these findings, co-immunoprecipitation experiments showed a physical interaction between Pof1p and Ubc7p (an ubiquitin conjugating enzyme) in vivo.

Conclusions

Taken together, the results strongly suggest that the biological function of Pof1p is related to the regulation of protein degradation.

     

Testing the quick meal hypothesis: The effect of pulp on hoarding and seed predation of Hymenaea courbaril by red‐rumped agoutis (Dasyprocta leporina)

Abstract: Red‐rumped agoutis (Dasyprocta leporina) are important seed dispersers/predators of Neotropical large‐seeded plants. Several species of seeds cached by agoutis have an edible reward, in contrast to temperate rodent‐dispersed diaspores. The quick meal hypothesis states that the presence of a reward such as edible pulp will enhance the efficiency of rodents as seed disperses by satiating the animal and, consequently, reducing seed predation and enhancing hoarding. In this study, this hypothesis was tested using as the reference system the pulp and seeds of Hymenaea courbaril. Seeds with and without pulp were offered to agoutis and the behaviour of each individual was recorded. Since the probability of predation and hoarding were complementary, we used the probability of predation. The proportion of agoutis that preyed on at least one seed was similar for seeds with (42.8% of individuals) and without (40.0% of individuals) pulp. In agoutis that preyed upon at least one seed, the probability that they killed a seed did not differ between seeds with (0.17 ± 0.03) and without (0.20 ± 0.08) pulp. Hence, these results do not support the ‘quick meal hypothesis’.     

Extrafloral nectaries as a deterrent mechanism against seed predators in the chemically protected weed Crotalaria pallida (Leguminosae)

Abstract In several plants, extrafloral nectaries (EFN) are located close to the reproductive structures, suggesting that ants may act as a defence against specialized seed predators that overcome chemical defences. Alternatively, ants may also deter herbivores in a generalized manner, thereby protecting the whole plant. In this work, we examined the relationship between the chemically protected weed Crotalaria pallida Ait. (Leguminosae) that bears EFN, its specialized seed predator, the larvae of the arctiid moth Utetheisa ornatrix L. (Arctiidae) and ants. We tested two hypotheses related to the type of deterrence caused by ants. The Seed Predator Deterrence Hypothesis predicts that ant deterrence is directed primarily towards herbivores that destroy seeds and other reproductive structures, without attacking herbivores on vegetative structures. The General Deterrence Hypothesis states that ants are general in their effects, equally deterring herbivores in vegetative and reproductive structures. Our results supported the predictions of the Seed Predator Deterrence Hypothesis, namely, that (i) ant activity on EFN was related to the vulnerability of reproductive structures to attack by U. ornatrix; (ii) ant patrolling was restricted almost entirely to racemes; (iii) ants removed termites used as baits more frequently on racemes than on leaves; and (iv) U. ornatrix larvae were often expulsed from the racemes. These results indicate that EFN can act as another deterrent mechanism in chemically protected plants by promoting the expulsion of specialist seed predators.     

Prediction of indirect interactions in proteins

Background  

Both direct and indirect interactions determine molecular recognition of ligands by proteins. Indirect interactions can be defined as effects on recognition controlled from distant sites in the proteins, e.g. by changes in protein conformation and mobility, whereas direct interactions occur in close proximity of the protein's amino acids and the ligand. Molecular recognition is traditionally studied using three-dimensional methods, but with such techniques it is difficult to predict the effects caused by mutational changes of amino acids located far away from the ligand-binding site. We recently developed an approach, proteochemometrics, to the study of molecular recognition that models the chemical effects involved in the recognition of ligands by proteins using statistical sampling and mathematical modelling.