Host diversity and behavior determine patterns of interspecies transmission and geographic diffusion of avian influenza A subtypes among North American wild reservoir species

Wild birds can carry avian influenza viruses (AIV), including those with pandemic or panzootic potential, long distances. Even though AIV has a broad host range, few studies account for host diversity when estimating AIV spread. We analyzed AIV genomic sequences from North American wild birds, including 303 newly sequenced isolates, to estimate interspecies and geographic viral transition patterns among multiple co-circulating subtypes. Our results show high transition rates within Anseriformes and Charadriiformes, but limited transitions between these orders. Patterns of transition between species were positively associated with breeding habitat range overlap, and negatively associated with host genetic distance. Distance between regions (negative correlation) and summer temperature at origin (positive correlation) were strong predictors of transition between locations. Taken together, this study demonstrates that host diversity and ecology can determine evolutionary processes that underlie AIV natural history and spread. Understanding these processes can provide important insights for effective control of AIV.     

The Giardia ventrolateral flange is a lamellar membrane protrusion that supports attachment

Attachment to the intestinal epithelium is critical to the lifestyle of the ubiquitous parasite Giardia lamblia. The ventrolateral flange is a sheet-like membrane protrusion at the interface between parasites and attached surfaces. This structure has been implicated in attachment, but its role has been poorly defined. Here, we identified a novel actin associated protein with putative WH2-like actin binding domains we named Flangin. Flangin complexes with Giardia actin (GlActin) and is enriched in the ventrolateral flange making it a valuable marker for studying the flanges’ role in Giardia biology. Live imaging revealed that the flange grows to around 1 μm in width after cytokinesis, then remains uniform in size during interphase, grows in mitosis, and is resorbed during cytokinesis. A flangin truncation mutant stabilizes the flange and blocks cytokinesis, indicating that flange disassembly is necessary for rapid myosin-independent cytokinesis in Giardia. Rho family GTPases are important regulators of membrane protrusions and GlRac, the sole Rho family GTPase in Giardia, was localized to the flange. Knockdown of Flangin, GlActin, and GlRac result in flange formation defects. This indicates a conserved role for GlRac and GlActin in forming membrane protrusions, despite the absence of canonical actin binding proteins that link Rho GTPase signaling to lamellipodia formation. Flangin-depleted parasites had reduced surface contact and when challenged with fluid shear force in flow chambers they had a reduced ability to remain attached, confirming a role for the flange in attachment. This secondary attachment mechanism complements the microtubule based adhesive ventral disc, a feature that may be particularly important during mitosis when the parental ventral disc disassembles in preparation for cytokinesis. This work supports the emerging view that Giardia’s unconventional actin cytoskeleton has an important role in supporting parasite attachment.     

Probing the ligand preferences of the three types of bacterial pantothenate kinase

Pantothenate kinase (PanK) catalyzes the transformation of pantothenate to 4′-phosphopantothenate, the first committed step in coenzyme A biosynthesis. While numerous pantothenate antimetabolites and PanK inhibitors have been reported for bacterial type I and type II PanKs, only a few weak inhibitors are known for bacterial type III PanK enzymes. Here, a series of pantothenate analogues were synthesized using convenient synthetic methodology. The compounds were exploited as small organic probes to compare the ligand preferences of the three different types of bacterial PanK. Overall, several new inhibitors and substrates were identified for each type of PanK.     

Gene expression changes reflect clinical response in a placebo-controlled randomized trial of abatacept in patients with diffuse cutaneous systemic sclerosis

IntroductionSystemic sclerosis is an autoimmune disease characterized by inflammation and fibrosis of the skin and internal organs. We sought to assess the clinical and molecular effects associated with response to intravenous abatacept in patients with diffuse cutaneous systemic.MethodsAdult diffuse cutaneous systemic sclerosis patients were randomized in a 2:1 double-blinded fashion to receive abatacept or placebo over 24 weeks. Primary outcomes were safety and the change in modified Rodnan Skin Score (mRSS) at week 24 compared with baseline. Improvers were defined as patients with a decrease in mRSS of ≥30 % post-treatment compared to baseline. Skin biopsies were obtained for differential gene expression and pathway enrichment analyses and intrinsic gene expression subset assignment.ResultsTen subjects were randomized to abatacept (n = 7) or placebo (n = 3). Disease duration from first non-Raynaud’s symptom was significantly longer (8.8 ± 3.8 years vs. 2.4 ± 1.6 years, p = 0.004) and median mRSS was higher (30 vs. 22, p = 0.05) in the placebo compared to abatacept group. Adverse events were similar in the two groups. Five out of seven patients (71 %) randomized to abatacept and one out of three patients (33 %) randomized to placebo experienced ≥30 % improvement in skin score. Subjects receiving abatacept showed a trend toward improvement in mRSS at week 24 (−8.6 ± 7.5, p = 0.0625) while those in the placebo group did not (−2.3 ± 15, p = 0.75). After adjusting for disease duration, mRSS significantly improved in the abatacept compared with the placebo group (abatacept vs. placebo mRSS decrease estimate −9.8, 95 % confidence interval −16.7 to −3.0, p = 0.0114). In the abatacept group, the patients in the inflammatory intrinsic subset showed a trend toward greater improvement in skin score at 24 weeks compared with the patients in the normal-like intrinsic subset (−13.5 ± 3.1 vs. −4.5 ± 6.4, p = 0.067). Abatacept resulted in decreased CD28 co-stimulatory gene expression in improvers consistent with its mechanism of action. Improvers mapped to the inflammatory intrinsic subset and showed decreased gene expression in inflammatory pathways, while non-improver and placebos showed stable or reverse gene expression over 24 weeks.ConclusionsClinical improvement following abatacept therapy was associated with modulation of inflammatory pathways in skin.

Trial registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00442611","term_id":"NCT00442611"}}NCT00442611. Registered 1 March 2007.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-015-0669-3) contains supplementary material, which is available to authorized users.     

NS-kNN: a modified <Emphasis Type="Italic">k</Emphasis>-nearest neighbors approach for imputing metabolomics data

Introduction

A common problem in metabolomics data analysis is the existence of a substantial number of missing values, which can complicate, bias, or even prevent certain downstream analyses. One of the most widely-used solutions to this problem is imputation of missing values using a k-nearest neighbors (kNN) algorithm to estimate missing metabolite abundances. kNN implicitly assumes that missing values are uniformly distributed at random in the dataset, but this is typically not true in metabolomics, where many values are missing because they are below the limit of detection of the analytical instrumentation.

Objectives

Here, we explore the impact of nonuniformly distributed missing values (missing not at random, or MNAR) on imputation performance. We present a new model for generating synthetic missing data and a new algorithm, No-Skip kNN (NS-kNN), that accounts for MNAR values to provide more accurate imputations.

Methods

We compare the imputation errors of the original kNN algorithm using two distance metrics, NS-kNN, and a recently developed algorithm KNN-TN, when applied to multiple experimental datasets with different types and levels of missing data.

Results

Our results show that NS-kNN typically outperforms kNN when at least 20–30% of missing values in a dataset are MNAR. NS-kNN also has lower imputation errors than KNN-TN on realistic datasets when at least 50% of missing values are MNAR.

Conclusion

Accounting for the nonuniform distribution of missing values in metabolomics data can significantly improve the results of imputation algorithms. The NS-kNN method imputes missing metabolomics data more accurately than existing kNN-based approaches when used on realistic datasets.

     

Inequality as a Powerful Predictor of Infant and Maternal Mortality around the World

Background

Maternal and infant mortality are highly devastating, yet, in many cases, preventable events for a community. The human development of a country is a strong predictor of maternal and infant mortality, reflecting the importance of socioeconomic factors in determinants of health. Previous research has shown that the Human Development Index (HDI) predicts infant mortality rate (IMR) and the maternal mortality ratio (MMR). Inequality has also been shown to be associated with worse health in certain populations. The main purpose of the present study was to determine the correlation and predictive power of the Inequality Adjusted Human Development Index (IHDI) as a measure of inequality with the Infant Mortality Rate (IMR), Maternal Mortality Rate (MMR), Early Neonatal Mortality Rate (ENMR), Late Neonatal Mortality Rate (LNMR), and the Post Neonatal Mortality Rate (PNMR).

Methods and Findings

Data for the present study were downloaded from two sources: infant and maternal mortality data were downloaded from the Global Burden of Disease 2013 Cause of Death Database and the Human Development Index (HDI) and Inequality-Adjusted Human Development Index (IHDI) data were downloaded from the United Nations Development Program (UNDP). Pearson correlation coefficients were estimated, following logarithmic transformations to the data, to examine the relationship between HDI and IHDI with MMR, IMR, ENMR, LNMR, and PNMR. Steiger’s Z test for the equality of two dependent correlations was utilized in order to determine whether the HDI or IHDI was more strongly associated with the outcome variables. Lastly, we constructed OLS regression models in order to determine the predictive power of the HDI and IHDI in terms of the MMR, IMR, ENMR, LNMR, and PNMR.Maternal and infant mortality were both strongly and negatively correlated with both HDI and IHDI; however, Steiger’s Z test for the equality of two dependent correlations revealed that IHDI was more strongly correlated than HDI with MMR (Z = 4.897, p < 0.001), IMR (Z = 2.524, p = 0.012), ENMR (Z = 2.936, p = 0.003), LNMR (Z = 2.272, p = 0.023), and PNMR (Z = 2.277, p = 0.023). Furthermore, side-by-side OLS regression models revealed that, when IHDI was used as the predictor variable instead of HDI, the R ² value was 0.053 higher for MMR, 0.025 higher for IMR, 0.038 higher for ENMR, 0.029 higher for LNMR, and 0.026 higher for PNMR.

Conclusions

Even when both the HDI and the IHDI correlate with the infant and maternal mortality rates, the IHDI is a better predictor for these two health indicators. Therefore, these results add more evidence that inequality is playing an important role in determining the health status of various populations in the world and more efforts should be put into programs to fight inequality.     

Rhodanine derivatives as novel inhibitors of PDE4

The discovery, synthesis and in vitro activity of a novel series of rhodanine based phosphodiesterase-4 (PDE4) inhibitors is described. Structure-activity relationship studies directed toward improving potency led to the development of submicromolar inhibitors 2n and 3i (IC(50)=0.89 & 0.74 microM). The replacement of rhodanine with structurally related heterocycles was also investigated and led to the synthesis of pseudothiohydantoin 7 (IC(50)=0.31 microM).