Effects of Buprenorphine Treatment on Influenza Pathogenesis in the Ferret (Mustela putorius furo)

Ferrets are the gold-standard model for influenza A virus (IAV) research due to their natural susceptibility to human and zoonotic IAV, comparable respiratory anatomy and physiology to humans, and development of clinical signs similar to those seen in infected people. Because the presence and progression of clinical signs can be useful in infectious disease research, uncertainty in how analgesics alter research outcomes or compromise characteristics of disease progression have outweighed the concern regarding animal discomfort from these symptoms. Nonetheless, the principles of animal research require consideration of refinements for this important model for IAV research. Opioids offer a possible refinement option that would not directly affect the inflammatory cascade involved in IAV infection. Mirroring pathogenicity studies that use ferrets, 12 ferrets were inoculated intranasally with the A(H3N2) IAV A/Panama/2007/1999 and divided into 3 treatment groups (n = 4 each), of which 2 groups received buprenorphine treatments on different schedules and the third received a saline control. The duration and location of viral replication, lymphohematopoietic changes, and clinical signs were comparable across all groups at all time points. High quantities of infectious virus in nasal wash specimens were detected in ferrets from all groups through day 5 after inoculation, and peak viral titers from the upper respiratory tract did not differ between ferrets receiving buprenorphine treatments on either schedule. Compared with the saline group, ferrets receiving buprenorphine exhibited transient weight loss and pyrexia, but all groups ultimately achieved similar peaks in both of these measurements. Collectively, these findings support the continued evaluation of buprenorphine as a refinement for IAV-challenged ferrets.

Despite decades of international research and the availability of public health countermeasures, including vaccines and antivirals, influenza viruses remain a persistent threat to human and animal health.^26,35 Influenza A viruses (IAV) exhibit a diverse range of virulence, exist in several host reservoirs, and can show rapid rates of antigenic change.²⁶ As a result, IAV are associated with both seasonal epidemics and occasional pandemics in humans,³⁵ and animal infections with IAV have become key for understanding multifactorial traits that include pathogenicity, transmissibility, and vaccine efficacy. Due to their relatively small size, adaptability to the research setting, and similarities to human lung anatomy and physiology, ferrets provide an excellent model for respiratory diseases in humans and are a valuable small-animal model for such studies.^8,30 Data generated from ferrets are included in numerous risk-assessment rubrics evaluating the pandemic potential of novel and emerging influenza viruses, including those established by the Centers for Disease Control and Prevention and the World Health Organization.^14,51The study of influenza virus in ferrets dates back to the early 1930s, when this species was first found to be susceptible to influenza virus.⁴⁴ Ferrets are naturally susceptible to both human and zoonotic IAV.⁴⁷ After infection, ferrets present with clinical signs like those of humans; these signs are often not recapitulated in other species, such as mice and guinea pigs.^28,39,46 The severity and spectrum of clinical signs associated with influenza virus–inoculated ferrets can vary, depending on the virus strain, route and dose of inoculation, and various host parameters.⁵ Whereas influenza viruses with low virulence in ferrets may cause only acute pyrexia and mild to moderate weight loss, isolates with high virulence can cause severe, systemic illness with gastrointestinal and neurologic symptoms.⁴The 3Rs, replace, reduce, refine, encourage investigation of how research involving animals can be conducted in more humane ways.^2,13,37,41 Analgesia for symptoms of influenza in ferrets represents an opportunity for refinement, but this intervention could confound research assessing disease progression. NSAID and corticosteroids are often prescribed to treat the clinical signs associated with influenza in humans.⁴³ These interventions could alter the inflammatory cascade and subsequent pathophysiology of the disease, thus reducing the validity of studies designed to characterize and compare influenza viruses.^6,43 NSAID reportedly inhibit nuclear factor κB, a regulator of inflammatory processes that is involved in viral RNA synthesis.^25,27 In addition, NSAID have been found to increase survival rates in influenza virus-infected mice.⁵³ Therefore, the use of NSAID may be problematic in studies investigating the pathogenesis of influenza viruses.Buprenorphine, an opioid, is an established analgesic in ferrets that can be administered either intravascularly, intramuscularly, or subcutaneously at 0.01 to 0.05 mg/kg with an analgesic duration of 6 to 12 h.^{11,16,24,38,52} Historically buprenorphine has been described as a partial µ receptor agonist and κ and γ receptor antagonist,^22,29,40,48 but the drug recently was described to behave as a full µ agonist.³⁶ The ceiling effect of analgesia and the immunosuppressive effects reported with other opioids have not been documented to occur with buprenorphine.^15,36,42 However, the use of buprenorphine does have the possibility of adverse effects, including sedation, weight loss, constipation, and respiratory depression.^{10,15,16,22,23,34,42} Nonetheless, buprenorphine is a commonly prescribed analgesic for numerous small mammalian species used in research settings.^20,22,40Given that influenza is an ongoing threat to human and animal health and because no replacement is available for data gained with the ferret model, pain mitigation options for research conducted in this species must be addressed. To date, concerns about altering the course of the disease have precluded the evaluation of refinements options in IAV-infected ferrets. The goal of the current study was to assess the effects of buprenorphine treatments on the pathogenesis of a seasonal IAV in ferrets; this assessment was achieved by comparing virus-inoculated ferrets that were either sham-treated or that received buprenorphine according to 2 different dosing schedules. We hypothesized that buprenorphine treatments would not affect experimental readouts, including morbidity, viral shedding, lymphopenia, and seroconversion in convalescent serum; these parameters are commonly measured during IAV research. Study results indicate that buprenorphine did not uniformly or significantly modulate disease progression, peak viral titers in the upper respiratory tract, or clinical responses used to characterize viral pathogenicity in ferrets.     

Patient contrastive learning: A performant,expressive, and practical approach to electrocardiogram modeling

Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate the effect of small sample size, we introduce a pre-training approach, Patient Contrastive Learning of Representations (PCLR), which creates latent representations of electrocardiograms (ECGs) from a large number of unlabeled examples using contrastive learning. The resulting representations are expressive, performant, and practical across a wide spectrum of clinical tasks. We develop PCLR using a large health care system with over 3.2 million 12-lead ECGs and demonstrate that training linear models on PCLR representations achieves a 51% performance increase, on average, over six training set sizes and four tasks (sex classification, age regression, and the detection of left ventricular hypertrophy and atrial fibrillation), relative to training neural network models from scratch. We also compared PCLR to three other ECG pre-training approaches (supervised pre-training, unsupervised pre-training with an autoencoder, and pre-training using a contrastive multi ECG-segment approach), and show significant performance benefits in three out of four tasks. We found an average performance benefit of 47% over the other models and an average of a 9% performance benefit compared to best model for each task. We release PCLR to enable others to extract ECG representations at https://github.com/broadinstitute/ml4h/tree/master/model_zoo/PCLR.     

Demographic History and Natural Selection Shape Patterns of Deleterious Mutation Load and Barriers to Introgression across Populus Genome

Hybridization and resulting introgression are important processes shaping the tree of life and appear to be far more common than previously thought. However, how the genome evolution was shaped by various genetic and evolutionary forces after hybridization remains unresolved. Here we used whole-genome resequencing data of 227 individuals from multiple widespread Populus species to characterize their contemporary patterns of hybridization and to quantify genomic signatures of past introgression. We observe a high frequency of contemporary hybridization and confirm that multiple previously ambiguous species are in fact F₁ hybrids. Seven species were identified, which experienced different demographic histories that resulted in strikingly varied efficacy of selection and burdens of deleterious mutations. Frequent past introgression has been found to be a pervasive feature throughout the speciation of these Populus species. The retained introgressed regions, more generally, tend to contain reduced genetic load and to be located in regions of high recombination. We also find that in pairs of species with substantial differences in effective population size, introgressed regions are inferred to have undergone selective sweeps at greater than expected frequencies in the species with lower effective population size, suggesting that introgression likely have higher potential to provide beneficial variation for species with small populations. Our results, therefore, illustrate that demography and recombination have interplayed with both positive and negative selection in determining the genomic evolution after hybridization.     

MolMovDB: analysis and visualization of conformational change and structural flexibility

The Database of Macromolecular Movements (http://MolMovDB.org) is a collection of data and software pertaining to flexibility in protein and RNA structures. The database is organized into two parts. Firstly, a collection of 'morphs' of solved structures representing different states of a molecule provides quantitative data for flexibility and a number of graphical representations. Secondly, a classification of known motions according to type of conformational change (e.g. 'hinged domain' or 'allosteric') incorporates textual annotation and information from the literature relating to the motion, linking together many of the morphs. A variety of subsets of the morphs are being developed for use in statistical analyses. In particular, for each subset it is possible to derive distributions of various motional quantities (e.g. maximum rotation) that can be used to place a specific motion in context as being typical or atypical for a given population. Over the past year, the database has been greatly expanded and enhanced to incorporate new structures and to improve the quality of data. The 'morph server', which enables users of the database to add new morphs either from their own research or the PDB, has also been enhanced to handle nucleic acid structures and multi-chain complexes.     

Design and synthesis of a novel family of triazine-based inhibitors of sorbitol dehydrogenase with oral activity: 1-[4-[3R,5S-dimethyl-4-(4-methyl-[1,3,5]triazin-2-yl)-piperazin-1-yl]-[1,3,5]triazin-2-yl]-(R) ethanol

Two new templates, (R) 2-hydroxyethyl-pyridine and (R) 2-hydroxyethyl-triazine, were used to design novel sorbitol dehydrogenase inhibitors (SDIs). The design concept included spawning of these templates to function as effective ligands to the catalytic zinc within the enzyme through incorporation of optimally substituted piperazino-triazine side chains so as to accommodate the active site in the enzyme for efficient binding. This strategy resulted in orally active SDIs, which penetrate key tissues, for example, sciatic nerve of chronically diabetic rats. The latter template led to the design of the title inhibitor, 33, which normalized the elevated sciatic nerve fructose by 96% at an oral dose of 10mg/kg.     

Benefits and costs of mutualism: demographic consequences in a pollinating seed-consumer interaction

Interspecific interactions can affect population dynamics and the evolution of species traits by altering demographic rates such as reproduction and survival. The influence of mutualism on population processes is thought to depend on both the benefits and costs of the interaction. However, few studies have explicitly quantified both benefits and costs in terms of demographic rates; furthermore there has been little consideration as to how benefits and costs depend on the demographic effects of factors extrinsic to the interaction. I studied how benefits (pollination) and costs (larval fruit consumption) of pollinating seed-consumers (senita moths) affect the reproduction of senita cacti and how these effects may rely on extrinsic water limitation for reproduction. Fruit initiation was not limited by moth pollination, but survival of initiated fruit increased when moth eggs were removed from flowers. Watered cacti produced more flowers and initiated more fruit from hand-pollinated flowers than did unwatered cacti, but fruit initiation remained low despite excess pollen. Even though water, pollination and larvae each affected a component of cactus reproduction, when all of these factors were included in a factorial experiment, pollination and water determined rates of reproduction. Counter-intuitively, larval fruit consumption had a negligible effect on cactus reproduction. By quantifying both benefits and costs of mutualism in terms of demographic rates, this study demonstrates that benefits and costs can be differentially influential to population processes and that interpretation of their influences can depend on demographic effects of factors extrinsic to the interaction.     

Ecological and evolutionary conditions for fruit abortion to regulate pollinating seed-eaters and increase plant reproduction

Coevolved mutualisms, such as those between senita cacti, yuccas, and their respective obligate pollinators, benefit both species involved in the interaction. However, in these pollination mutualisms the pollinator's larvae impose a cost on plants through consumption of developing seeds and fruit. The effects of pollinators on benefits and costs are expected to vary with the abundance of pollinators, because large population sizes result in more eggs and larval seed-eaters. Here, we develop the hypothesis that fruit abortion, which is common in yucca, senita, and plants in general, could in some cases have the function of limiting pollinator abundance and, thereby, increasing fruit production. Using a general steady-state model of fruit production and pollinator dynamics, we demonstrate that plants involved in pollinating seed-eater mutualisms can increase their fecundity by randomly aborting fruit. We show that the ecological conditions under which fruit abortion can improve plants fecundity are not unusual. They are best met when the plant is long-lived, the population dynamics of the pollinator are much faster than those of the plant, the loss of one fruit via abortion kills a larva that would have the expectation of destroying more than one fruit through its future egg laying as an adult moth, and the effects of fruit abortion on pollinator abundance are spatially localized. We then use the approach of adaptive dynamics to find conditions under which a fruit abortion strategy based on regulating the pollinator population could feasibly evolve in this type of plant-pollinator interaction.     

Normal mode analysis of macromolecular motions in a database framework: developing mode concentration as a useful classifying statistic

We investigated protein motions using normal modes within a database framework, determining on a large sample the degree to which normal modes anticipate the direction of the observed motion and were useful for motions classification. As a starting point for our analysis, we identified a large number of examples of protein flexibility from a comprehensive set of structural alignments of the proteins in the PDB. Each example consisted of a pair of proteins that were considerably different in structure given their sequence similarity. On each pair, we performed geometric comparisons and adiabatic-mapping interpolations in a high-throughput pipeline, arriving at a final list of 3,814 putative motions and standardized statistics for each. We then computed the normal modes of each motion in this list, determining the linear combination of modes that best approximated the direction of the observed motion. We integrated our new motions and normal mode calculations in the Macromolecular Motions Database, through a new ranking interface at http://molmovdb.org. Based on the normal mode calculations and the interpolations, we identified a new statistic, mode concentration, related to the mathematical concept of information content, which describes the degree to which the direction of the observed motion can be summarized by a few modes. Using this statistic, we were able to determine the fraction of the 3,814 motions where one could anticipate the direction of the actual motion from only a few modes. We also investigated mode concentration in comparison to related statistics on combinations of normal modes and correlated it with quantities characterizing protein flexibility (e.g., maximum backbone displacement or number of mobile atoms). Finally, we evaluated the ability of mode concentration to automatically classify motions into a variety of simple categories (e.g., whether or not they are "fragment-like"), in comparison to motion statistics. This involved the application of decision trees and feature selection (particular machine-learning techniques) to training and testing sets derived from merging the "list" of motions with manually classified ones.     

Survival analysis using auxiliary variables via multiple imputation, with application to AIDS clinical trial data

We develop an approach, based on multiple imputation, to using auxiliary variables to recover information from censored observations in survival analysis. We apply the approach to data from an AIDS clinical trial comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable. To facilitate imputation, a joint model is developed for the data, which includes a hierarchical change-point model for CD4 counts and a time-dependent proportional hazards model for the time to AIDS. Markov chain Monte Carlo methods are used to multiply impute event times for censored cases. The augmented data are then analyzed and the results combined using standard multiple-imputation techniques. A comparison of our multiple-imputation approach to simply analyzing the observed data indicates that multiple imputation leads to a small change in the estimated effect of ZDV and smaller estimated standard errors. A sensitivity analysis suggests that the qualitative findings are reproducible under a variety of imputation models. A simulation study indicates that improved efficiency over standard analyses and partial corrections for dependent censoring can result. An issue that arises with our approach, however, is whether the analysis of primary interest and the imputation model are compatible.