Interactions between Kar2p and Its Nucleotide Exchange Factors Sil1p and Lhs1p Are Mechanistically Distinct

Kar2p, an essential Hsp70 chaperone in the endoplasmic reticulum of Saccharomyces cerevisiae, facilitates the transport and folding of nascent polypeptides within the endoplasmic reticulum lumen. The chaperone activity of Kar2p is regulated by its intrinsic ATPase activity that can be stimulated by two different nucleotide exchange factors, namely Sil1p and Lhs1p. Here, we demonstrate that the binding requirements for Lhs1p are complex, requiring both the nucleotide binding domain plus the linker domain of Kar2p. In contrast, the IIB domain of Kar2p is sufficient for binding of Sil1p, and point mutations within IIB specifically blocked Sil1p-dependent activation while remaining competent for activation by Lhs1p. Taken together, these results demonstrate that the interactions between Kar2p and its two nucleotide exchange factors can be functionally resolved and are thus mechanistically distinct.     

Vaginal microbiome-hormonal contraceptive interactions associate with the mucosal proteome and HIV acquisition

Alterations to the mucosal environment of the female genital tract, such as genital inflammation, have been associated with increased HIV acquisition in women. As the microbiome and hormonal contraceptives can affect vaginal mucosal immunity, we hypothesized these components may interact in the context of HIV susceptibility. Using previously published microbiome data from 685 women in the CAPRISA-004 trial, we compared relative risk of HIV acquisition in this cohort who were using injectable depot medroxyprogesterone acetate (DMPA), norethisterone enanthate (NET-EN), and combined oral contraceptives (COC). In women who were Lactobacillus-dominant, HIV acquisition was 3-fold higher in women using DMPA relative to women using NET-EN or COC (OR: 3.27; 95% CI: 1.24–11.24, P = 0.0305). This was not observed in non-Lactobacillus-dominant women (OR: 0.95, 95% CI: 0.44–2.15, P = 0.895) (interaction P = 0.0686). Higher serum MPA levels associated with increased molecular pathways of inflammation in the vaginal mucosal fluid of Lactobacillus-dominant women, but no differences were seen in non-Lactobacillus dominant women. This study provides data suggesting an interaction between the microbiome, hormonal contraceptives, and HIV susceptibility.     

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station (INBEST)

Due to rapid advances in genetic engineering, small rodents have become the preferred subjects in many disciplines of biomedical research. In studies of chronic CNS disorders, there is an increasing demand for murine models with high validity at the behavioral level. However, multiple pathogenic mechanisms and complex functional deficits often impose challenges to reliably measure and interpret behavior of chronically sick mice. Therefore, the assessment of peripheral pathology and a behavioral profile at several time points using a battery of tests are required. Video-tracking, behavioral spectroscopy, and remote acquisition of physiological measures are emerging technologies that allow for comprehensive, accurate, and unbiased behavioral analysis in a home-base-like setting. This report describes a refined phenotyping protocol, which includes a custom-made monitoring apparatus (Integrated Behavioral Station, INBEST) that focuses on prolonged measurements of basic functional outputs, such as spontaneous activity, food/water intake and motivated behavior in a relatively stress-free environment. Technical and conceptual improvements in INBEST design may further promote reproducibility and standardization of behavioral studies.     

Estimating prevalence and test accuracy in disease ecology: How Bayesian latent class analysis can boost or bias imperfect test results

1. Obtaining accurate estimates of disease prevalence is crucial for the monitoring and management of wildlife populations but can be difficult if different diagnostic tests yield conflicting results and if the accuracy of each diagnostic test is unknown. Bayesian latent class analysis (BLCA) modeling offers a potential solution, providing estimates of prevalence levels and diagnostic test accuracy under the realistic assumption that no diagnostic test is perfect.
2. In typical applications of this approach, the specificity of one test is fixed at or close to 100%, allowing the model to simultaneously estimate the sensitivity and specificity of all other tests, in addition to infection prevalence. In wildlife systems, a test with near‐perfect specificity is not always available, so we simulated data to investigate how decreasing this fixed specificity value affects the accuracy of model estimates.
3. We used simulations to explore how the trade‐off between diagnostic test specificity and sensitivity impacts prevalence estimates and found that directional biases depend on pathogen prevalence. Both the precision and accuracy of results depend on the sample size, the diagnostic tests used, and the true infection prevalence, so these factors should be considered when applying BLCA to estimate disease prevalence and diagnostic test accuracy in wildlife systems. A wildlife disease case study, focusing on leptospirosis in California sea lions, demonstrated the potential for Bayesian latent class methods to provide reliable estimates under real‐world conditions.
4. We delineate conditions under which BLCA improves upon the results from a single diagnostic across a range of prevalence levels and sample sizes, demonstrating when this method is preferable for disease ecologists working in a wide variety of pathogen systems.

     

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.     

Flight loss linked to faster molecular evolution in insects

The loss of flight ability has occurred thousands of times independently during insect evolution. Flight loss may be linked to higher molecular evolutionary rates because of reductions in effective population sizes (N_e) and relaxed selective constraints. Reduced dispersal ability increases population subdivision, may decrease geographical range size and increases (sub)population extinction risk, thus leading to an expected reduction in N_e. Additionally, flight loss in birds has been linked to higher molecular rates of energy-related genes, probably owing to relaxed selective constraints on energy metabolism. We tested for an association between insect flight loss and molecular rates through comparative analysis in 49 phylogenetically independent transitions spanning multiple taxa, including moths, flies, beetles, mayflies, stick insects, stoneflies, scorpionflies and caddisflies, using available nuclear and mitochondrial protein-coding DNA sequences. We estimated the rate of molecular evolution of flightless (FL) and related flight-capable lineages by ratios of non-synonymous-to-synonymous substitutions (dN/dS) and overall substitution rates (OSRs). Across multiple instances of flight loss, we show a significant pattern of higher dN/dS ratios and OSRs in FL lineages in mitochondrial but not nuclear genes. These patterns may be explained by relaxed selective constraints in FL ectotherms relating to energy metabolism, possibly in combination with reduced N_e.     

Community composition has greater impact on the functioning of marine phytoplankton communities than ocean acidification

Ecosystem functioning is simultaneously affected by changes in community composition and environmental change such as increasing atmospheric carbon dioxide (CO₂) and subsequent ocean acidification. However, it largely remains uncertain how the effects of these factors compare to each other. Addressing this question, we experimentally tested the hypothesis that initial community composition and elevated CO₂ are equally important to the regulation of phytoplankton biomass. We full‐factorially exposed three compositionally different marine phytoplankton communities to two different CO₂ levels and examined the effects and relative importance (ω²) of the two factors and their interaction on phytoplankton biomass at bloom peak. The results showed that initial community composition had a significantly greater impact than elevated CO₂ on phytoplankton biomass, which varied largely among communities. We suggest that the different initial ratios between cyanobacteria, diatoms, and dinoflagellates might be the key for the varying competitive and thus functional outcome among communities. Furthermore, the results showed that depending on initial community composition elevated CO₂ selected for larger sized diatoms, which led to increased total phytoplankton biomass. This study highlights the relevance of initial community composition, which strongly drives the functional outcome, when assessing impacts of climate change on ecosystem functioning. In particular, the increase in phytoplankton biomass driven by the gain of larger sized diatoms in response to elevated CO₂ potentially has strong implications for nutrient cycling and carbon export in future oceans.