Prior Virus Exposure Alters the Long-Term Landscape of Viral Replication during Feline Lentiviral Infection

We developed a feline model of lentiviral cross-species transmission using a puma lentivirus (PLV or FIVPco) which infects domestic cats but does not cause disease. Infection with PLV protects cats from CD4+ T-cell decline caused by subsequent infection with virulent feline immunodeficiency virus (FIV). Previous studies implicate innate immune and/or cellular restriction mechanisms for FIV disease attenuation in PLV-infected cats. In this study, we evaluated viral infection and cytokine mRNA transcription in 12 different tissue reservoirs approximately five months post infection. We quantitated tissue proviral load, viral mRNA load and relative transcription of IL-10, IL-12p40 and IFNγ from tissues of cats exposed to FIV, PLV or both viruses and analyzed these parameters using a multivariate statistical approach. The distribution and intensity of FIV infection and IFNγ transcription differed between single and co-infected cats, characterized by higher FIV proviral loads and IFNγ expression in co-infected cat tissues. Variability in FIV mRNA load and IFNγ was significantly more constrained in co-infected versus singly infected cat tissues. Single-infected:co-infected ratios of FIV mRNA load compared to FIV proviral load indicated that active viral transcription was apparently inhibited during co-infection. These results indicate that previous PLV infection increases activation of tissue innate immunity and constrains the ability of FIV to productively infect tissue reservoirs of infection for months, independent of FIV proviral load, supporting a model in which innate immunity and/or modulation of target cell susceptibility play a key role in PLV-induced protection from FIV disease

A high-throughput method for isolation of salicylic acid metabolic mutants

<p>Abstract</p> <p>Background</p> <p>Salicylic acid (SA) is a key defense signal molecule against biotrophic pathogens in plants. Quantification of SA levels in plants is critical for dissecting the SA-mediated immune response. Although HPLC and GC/MS are routinely used to determine SA concentrations, they are expensive and time-consuming. We recently described a rapid method for a bacterial biosensor <it>Acinetobacter </it>sp. ADPWH_<it>lux</it>-based SA quantification, which enables high-throughput analysis. In this study we describe an improved method for fast sample preparation, and present a high-throughput strategy for isolation of SA metabolic mutants.</p> <p>Results</p> <p>On the basis of the previously described biosensor-based method, we simplified the tissue collection and the SA extraction procedure. Leaf discs were collected and boiled in Luria-Bertani (LB), and then the released SA was measured with the biosensor. The time-consuming steps of weighing samples, grinding tissues and centrifugation were avoided. The direct boiling protocol detected similar differences in SA levels among pathogen-infected wild-type, <it>npr1 </it>(nonexpressor of pathogenesis-related genes), and <it>sid2 </it>(SA induction-deficient) plants as did the previously described biosensor-based method and an HPLC-based approach, demonstrating the efficacy of the protocol presented here. We adapted this protocol to a high-throughput format and identified six <it>npr1 </it>suppressors that accumulated lower levels of SA than <it>npr1 </it>upon pathogen infection. Two of the suppressors were found to be allelic to the previously identified <it>eds5 </it>mutant. The other four are more susceptible than <it>npr1 </it>to the bacterial pathogen <it>Pseudomonas syringae </it>pv. <it>maculicola </it>ES4326 and their identity merits further investigation.</p> <p>Conclusions</p> <p>The rapid SA extraction method by direct boiling of leaf discs further reduced the cost and time required for the biosensor <it>Acinetobacter </it>sp. ADPWH_<it>lux</it>-based SA estimation, and allowed the screening for <it>npr1 </it>suppressors that accumulated less SA than <it>npr1 </it>after pathogen infection in a high-throughput manner. The highly efficacious SA estimation protocol can be applied in genetic screen for SA metabolic mutants and characterization of enzymes involved in SA metabolism. The mutants isolated in this study may help identify new components in the SA-related signaling pathways.</p

Which Factors Are Associated with Monitoring Goal Progress?

Three studies examined how people assess their progress on personal goals (e.g., whether they compare their progress to the past and/or to a desired target state), along with factors that might influence the nature of progress monitoring (e.g., whether the goal involves attaining a positive outcome or avoiding a negative outcome). Study 1 involved semi-structured interviews with 40 participants, in which we examined how participants monitored their progress and whether this was related to: (a) their level of self-efficacy, (b) whether the goal was prevention focused, and (c) whether goal progress was represented in quantifiable terms. Studies 2 (N = 492) and 3 (N = 481) were conducted online and additionally examined whether how participants monitored their progress differed as a function of the domain of the goal (i.e., whether it was related to physical development/health, finances, work/study, or social relationships). The findings suggest that participants: (i) were less likely to monitor their progress toward goals that were related to avoiding negative outcomes, (ii) were less likely to monitor their progress toward goals related to finances, work, or study with reference to the past, than progress toward other goals (e.g., those relating to physical development and health), (iii) found it easier to monitor their progress toward goals that they felt confident of attaining, but harder to monitor their progress toward goals related to work or study. Finally, the more participants thought about their goal in quantifiable terms, the more likely they were to monitor their progress, and the easier they found monitoring their progress to be. Taken together, these studies begin to describe the nature of progress monitoring and the factors that influence this important self-regulatory process

When the going gets tough: the “Why” of goal striving matters

No prior research has examined how motivation for goal striving influences persistence in the face of increasing goal difficulty. This research examined the role of self-reported (Study 1) and primed (Study 2) autonomous and controlled motives in predicting objectively-assessed persistence during the pursuit of an increasingly difficult goal.\ud \ud In Study 1, 100 British athletes (64 males; Mage = 19.89 years, SDage = 2.43) pursued a goal of increasing difficulty on a cycle-ergometer. In Study 2, 90 British athletes (43 males; Mage = 19.63 years, SDage = 1.14) engaged in the same task, but their motivation was primed by asking them to observe a video of an actor describing her/his involvement in an unrelated study.\ud \ud In Study 1 self-reported autonomous goal motives predicted goal persistence via challenge appraisals and task-based coping. In contrast, controlled goal motives predicted threat appraisals and disengagement coping which, in turn, was a negative predictor of persistence. In Study 2 primed autonomous (compared to controlled) goal motives predicted greater persistence, positive affect, and future interest for task engagement.\ud \ud The findings underscore the importance of autonomous motivation for behavioral investment in the face of increased goal difficulty

Closing the gap on causal processes of infection risk from cross-sectional data:structural equation models to understand infection and co-infection

BACKGROUND: Epidemiological studies of disease exposure risk are frequently based on observational, cross-sectional data, and use statistical approaches as crucial tools for formalising causal processes and making predictions of exposure risks. However, an acknowledged limitation of traditional models is that the inferred relationships are correlational, cannot easily distinguish direct from indirect determinants of disease risk, and are often considerable simplifications of complex interrelationships. This may be particularly important when attempting to infer causality in patterns of co-infection through pathogen-facilitation. METHODS: We describe analyses of cross-sectional data using structural equation models (SEMs), a contemporary advancement on traditional regression approaches, based on our study system of feline gammaherpesvirus (FcaGHV1) in domestic cats. RESULTS: SEMs strongly supported a latent (host phenotype) variable associated with FcaGHV1 exposure and co-infection risk, suggesting these individuals are simply more likely to become infected with multiple pathogens. However, indications of pathogen-covariance (potential facilitation) were also variably detected: potentially among FcaGHV1, Bartonella spp and Mycoplasma spp. CONCLUSIONS: Our models suggest multiple exposures are primarily driven by host phenotypic traits, such as aggressive male phenotypes, and secondarily by pathogen-pathogen interactions. The results of this study demonstrate the application of SEMs to understanding epidemiological processes using observational data, and could be used more widely as a complementary tool to understand complex cross-sectional information in a wide variety of disciplines

Complete genome sequence of Streptomyces formicae KY5, the formicamycin producer

Here we report the complete genome of the new species Streptomyces formicae KY5 isolated from Tetraponera fungus growing ants. S. formicae was sequenced using the PacBio and 454 platforms to generate a single linear chromosome with terminal inverted repeats. Illumina MiSeq sequencing was used to correct base changes resulting from the high error rate associated with PacBio. The genome is 9.6 Mbps, has a GC content of 71.38% and contains 8162 protein coding sequences. Predictive analysis shows this strain encodes at least 45 gene clusters for the biosynthesis of secondary metabolites, including a type 2 polyketide synthase encoding cluster for the antibacterial formicamycins. Streptomyces formicae KY5 is a new, taxonomically distinct Streptomyces species and this complete genome sequence provides an important marker in the genus of Streptomyces

Spectral Dark Subtraction: A MODTRAN-Based Algorithm for Estimating Ground Reflectance without Atmospheric Information

Spectral Dark Subtraction (SDS) provides good ground reflectance estimates across a variety of atmospheric conditions with no knowledge of those conditions. The algorithm may be sensitive to errors from stray light, calibration, and excessive haze/water vapor. SDS seems to provide better estimates than traditional algorithms using on-site atmospheric measurements much of the time

Taking on a Community Solutions Process (Co-Solve) to the Pain and Opioid Epidemic: A Multi-disciplinary and Multi-institute Pain Panel and Community Response in Sacramento, California

America’s healthcare providers and patients are challenged by an overwhelming high prevalence of chronic pain and opioid misuse. Approximately 23.4 million adults suffer from daily pain and in 2014, nearly 61% of Americans who died from drug overdoses used an opioid analgesic. Unrecognized addiction, untreated psychiatric comorbidity, and lack of training/education for providers and patients are factors associated with chronic pain and opioid misuse. Communication strategies and structures are required to enhance collaboration between multidisciplinary providers and institutions. On September 28, 2017, an open panel discussion with pain specialists from three major academic and medical institutes in Sacramento, California initiated an integrative community solutions process to optimize pain education best practices and to protect public health. The attendees represented a wide range of healthcare disciplines. This commentary describes ideas derived from dialogue between community attendees and panelists, which considers both healthcare provider characteristics and patients’ cultural backgrounds. Providers of most disciplines underscored the need to share information and institute cross-disciplinary training on pain and behavioral health treatments. In conclusion, we outline an integrative community-based framework, namely the Community Solutions Process (Co-Solve), to help other communities to implement and derive their own action-oriented solutions unique to their population