Vaccine Uptake in the Era of COVID-19: Associations between Willingness to Receive the Influenza and COVID-19 Vaccines

Background/purpose. Despite efforts to increase flu vaccine uptake, the rate of vaccination remains below target. Additionally, many Virginians remain unvaccinated or have not maintained a booster schedule in line with recommendations. The current study investigated willingness to receive the flu and COVID-19 vaccines during the COVID-19 pandemic, associations between the likelihood of receiving the two vaccines, and differences in uptake by racial group and insurance status. Methods. Participants were adult patients seen for appointments at an ambulatory clinic in Southeast, VA. Participants completed a questionnaire assessing vaccination history and future vaccination intentions. Results. Results revealed significant associations between having received the flu vaccine previously and receiving the current season vaccine as well as receiving the flu vaccine and intentions to receive the COVID-19 vaccine. No significant differences in flu vaccination were found between Black and White adults. Insured participants reported greater intentions to receive the flu vaccine compared to uninsured participants. The primary reason reported for vaccine hesitancy were safety concerns. Discussion. Study participants report a much higher flu vaccination rate compared to the national average. Participants who endorse the efficacy and safety of vaccines are likely to have greater intentions for future vaccine uptake. Conclusions. As new flu and COVID-19 variants emerge, the need to maintain protection via annual boosters will remain. Addressing the reasons behind vaccine hesitancy is essential to increasing vaccine coverage. The study adds to the growing literature on attitudes towards vaccination, the need for continued endorsement of vaccinations, and highlights opportunities to promote vaccination uptake for uninsured patients

Network-Informed Gene Ranking Tackles Genetic Heterogeneity in Exome-Sequencing Studies of Monogenic Disease.

Genetic heterogeneity presents a significant challenge for the identification of monogenic disease genes. Whole-exome sequencing generates a large number of candidate disease-causing variants and typical analyses rely on deleterious variants being observed in the same gene across several unrelated affected individuals. This is less likely to occur for genetically heterogeneous diseases, making more advanced analysis methods necessary. To address this need, we present HetRank, a flexible gene-ranking method that incorporates interaction network data. We first show that different genes underlying the same monogenic disease are frequently connected in protein interaction networks. This motivates the central premise of HetRank: those genes carrying potentially pathogenic variants and whose network neighbors do so in other affected individuals are strong candidates for follow-up study. By simulating 1,000 exome sequencing studies (20,000 exomes in total), we model varying degrees of genetic heterogeneity and show that HetRank consistently prioritizes more disease-causing genes than existing analysis methods. We also demonstrate a proof-of-principle application of the method to prioritize genes causing Adams-Oliver syndrome, a genetically heterogeneous rare disease. An implementation of HetRank in R is available via the Website http://sourceforge.net/p/hetrank/

Transcriptome map of mouse isochores

<p>Abstract</p> <p>Background</p> <p>The availability of fully sequenced genomes and the implementation of transcriptome technologies have increased the studies investigating the expression profiles for a variety of tissues, conditions, and species. In this study, using RNA-seq data for three distinct tissues (brain, liver, and muscle), we investigate how base composition affects mammalian gene expression, an issue of prime practical and evolutionary interest.</p> <p>Results</p> <p>We present the transcriptome map of the mouse isochores (DNA segments with a fairly homogeneous base composition) for the three different tissues and the effects of isochores' base composition on their expression activity. Our analyses also cover the relations between the genes' expression activity and their localization in the isochore families.</p> <p>Conclusions</p> <p>This study is the first where next-generation sequencing data are used to associate the effects of both genomic and genic compositional properties to their corresponding expression activity. Our findings confirm previous results, and further support the existence of a relationship between isochores and gene expression. This relationship corroborates that isochores are primarily a product of evolutionary adaptation rather than a simple by-product of neutral evolutionary processes.</p

Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation

Comparison of the aggregation of homologous β2-microglobulin variants reveals protein solubility as a key determinant of amyloid formation

The mouse and human β2-microglobulin protein orthologs are 70 % identical in sequence and share 88 % sequence similarity. These proteins are predicted by various algorithms to have similar aggregation and amyloid propensities. However, whilst human β2m (hβ2m) forms amyloid-like fibrils in denaturing conditions (e.g. pH 2.5) in the absence of NaCl, mouse β2m (mβ2m) requires the addition of 0.3 M NaCl to cause fibrillation. Here, the factors which give rise to this difference in amyloid propensity are investigated. We utilise structural and mutational analyses, fibril growth kinetics and solubility measurements under a range of pH and salt conditions, to determine why these two proteins have different amyloid propensities. The results show that, although other factors influence the fibril growth kinetics, a striking difference in the solubility of the proteins is the key determinant of the different amyloidogenicity of hβ2m and mβ2m. The relationship between protein solubility and lag time of amyloid formation is not captured by current aggregation or amyloid prediction algorithms, indicating a need to better understand the role of solubility on the lag time of amyloid formation. The results demonstrate the key contribution of protein solubility in determining amyloid propensity and lag time of amyloid formation, highlighting how small differences in protein sequence can have dramatic effects on amyloid formation