Infectivity of DWV Associated to Flower Pollen: Experimental Evidence of a Horizontal Transmission Route

Deformed wing virus (DWV) is a honeybee pathogen whose presence is generally associated with infestation of the colony by the mite Varroa destructor, leading to the onset of infections responsible for the collapse of the bee colony. DWV contaminates bee products such as royal jelly, bee-bread and honey stored within the infected hive. Outside the hive, DWV has been found in pollen loads collected directly from infected as well as uninfected forager bees. It has been shown that the introduction of virus-contaminated pollen into a DWV-free hive results in the production of virus-contaminated food, whose role in the development of infected bees from virus-free eggs has been experimentally demonstrated. The aim of this study was twofold: (i) to ascertain the presence of DWV on pollen collected directly from flowers visited by honeybees and then quantify the viral load and (ii) determine whether the virus associated with pollen is infective. The results of our investigation provide evidence that DWV is present on pollen sampled directly from visited flowers and that, following injection in individuals belonging to the pollinator species Apis mellifera, it is able to establish an active infection, as indicated by the presence of replicating virus in the head of the injected bees. We also provide the first indication that the pollinator species Osmia cornuta is susceptible to DWV infection.     

Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance,virologic testing,and mortality data: Four complementary approaches

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending our methods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.     

The helical content of the YscP molecular ruler determines the length of the Yersinia injectisome

The length of the Yersinia injectisome needle is determined by the protein YscP, which could act as a molecular ruler. The analysis of the correlation between the size of YscP and the needle length in seven wild-type strains of Yersinia enterocolitica reinforced this hypothesis but hinted that the secondary structure of YscP might influence needle length. Hence, 11 variants of YscP₅₁₅ were generated by multiple Pro or Gly substitutions. The needle length changed in inverse function of the helical content, indicating that not only the number of residues but also their structure controls length. Taking the secondary motifs into account, Pro/Gly-variants were subjected to in silico modelling to simulate the extension of YscP upon needle growth. The calculated lengths when the helical content is preserved correlated strikingly with the measured needle length, with a constant difference of ∼29 nm, which corresponds approximately to the size of the basal body. These data support the ruler model and show that the functional ruler has a helical structure.     

Complex Patterns of Chromosome 11 Aberrations in Myeloid Malignancies Target CBL,MLL, DDB1 and LMO2

Exome sequencing of primary tumors identifies complex somatic mutation patterns. Assignment of relevance of individual somatic mutations is difficult and poses the next challenge for interpretation of next generation sequencing data. Here we present an approach how exome sequencing in combination with SNP microarray data may identify targets of chromosomal aberrations in myeloid malignancies. The rationale of this approach is that hotspots of chromosomal aberrations might also harbor point mutations in the target genes of deletions, gains or uniparental disomies (UPDs). Chromosome 11 is a frequent target of lesions in myeloid malignancies. Therefore, we studied chromosome 11 in a total of 813 samples from 773 individual patients with different myeloid malignancies by SNP microarrays and complemented the data with exome sequencing in selected cases exhibiting chromosome 11 defects. We found gains, losses and UPDs of chromosome 11 in 52 of the 813 samples (6.4%). Chromosome 11q UPDs frequently associated with mutations of CBL. In one patient the 11qUPD amplified somatic mutations in both CBL and the DNA repair gene DDB1. A duplication within MLL exon 3 was detected in another patient with 11qUPD. We identified several common deleted regions (CDR) on chromosome 11. One of the CDRs associated with de novo acute myeloid leukemia (P=0.013). One patient with a deletion at the LMO2 locus harbored an additional point mutation on the other allele indicating that LMO2 might be a tumor suppressor frequently targeted by 11p deletions. Our chromosome-centered analysis indicates that chromosome 11 contains a number of tumor suppressor genes and that the role of this chromosome in myeloid malignancies is more complex than previously recognized.     

Anti-cyclic citrullinated peptide antibody titer predicts time to rheumatoid arthritis onset in patients with undifferentiated arthritis: results from a 2-year prospective study

Introduction

The diagnostic, predictive and prognostic role of anti-cyclic citrullinated peptide (CCP) antibodies in rheumatoid arthritis (RA) patients is widely accepted. Moreover, detection of these antibodies in subjects presenting with undifferentiated arthritis (UA) is associated with a significant risk to develop the disease. On the other hand, clinical and prognostic significance of evaluating anti-CCP levels in subjects with inflammatory arthritis at disease onset has not been fully clarified. The goal of this prospective study is to analyze the value and prognostic significance of anti-CCP titer quantification in UA subjects.

Methods

Serial anti-CCP assays were measured in 192 consecutive patients presenting with UA lasting less than 12 weeks. Clinical and serological data and arthritis outcome were evaluated every 6 months until two years of follow-up.

Results

Anti-CCP positivity, at both low and high titer, and arthritis of hand joints significantly predicted RA at two years, risk increasing in subjects with high anti-CCP titers at baseline. Moreover, time to RA diagnosis was shorter in patients with high anti-CCP2 titers at enrollment with respect to those with low antibody concentration.

Conclusions

Presence of anti-CCP antibodies, at both low and high concentration, is significantly associated with RA development in subjects with recent onset UA. However, time interval from the onset of the first symptoms to the fulfilment of the classification criteria appears to be directly related to the initial anti-CCP level.     

RosettaDDGPrediction for high-throughput mutational scans: From stability to binding

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction .