Availability of Receptors for Advanced Glycation End-Products (RAGE) Influences Differential Transcriptome Expression in Lungs from Mice Exposed to Chronic Secondhand Smoke (SHS).

The receptor for advanced glycation end-products (RAGE) has a central function in orchestrating inflammatory responses in multiple disease states including chronic obstructive pulmonary disease (COPD). RAGE is a transmembrane pattern recognition receptor with particular interest in lung disease due to its naturally abundant pulmonary expression. Our previous research demonstrated an inflammatory role for RAGE following acute exposure to secondhand smoke (SHS). However, chronic inflammatory mechanisms associated with RAGE remain ambiguous. In this study, we assessed transcriptional outcomes in mice exposed to chronic SHS in the context of RAGE expression. RAGE knockout (RKO) and wild-type (WT) mice were delivered nose-only SHS via an exposure system for six months and compared to control mice exposed to room air (RA). We specifically compared WT + RA, WT + SHS, RKO + RA, and RKO + SHS. Analysis of gene expression data from WT + RA vs. WT + SHS showed FEZ1, Slpi, and Msln as significant at the three-month time point; while RKO + SHS vs. WT + SHS identified cytochrome p450 1a1 and Slc26a4 as significant at multiple time points; and the RKO + SHS vs. WT + RA revealed Tmem151A as significant at the three-month time point as well as Gprc5a and Dynlt1b as significant at the three- and six-month time points. Notable gene clusters were functionally analyzed and discovered to be specific to cytoskeletal elements, inflammatory signaling, lipogenesis, and ciliogenesis. We found gene ontologies (GO) demonstrated significant biological pathways differentially impacted by the presence of RAGE. We also observed evidence that the PI3K-Akt and NF-κB signaling pathways were significantly enriched in DEGs across multiple comparisons. These data collectively identify several opportunities to further dissect RAGE signaling in the context of SHS exposure and foreshadow possible therapeutic modalities.

Secondary analysis of Staphylococcus aureus whole genomes reveals diverse antimicrobial resistance profiles.

Antimicrobial resistance (AMR) in microorganisms is an ongoing threat to human health across the globe. To better characterize the AMR profiles of six strains of Staphylococcus aureus , we performed a secondary analysis that consisted of the following steps: 1) download fastq files from the Sequence Read Archive, 2) perform a de novo genome assembly from the sequencing reads, 3) annotate the assembled contigs, 4) predict the presence of antimicrobial resistance genes. We predicted the presence of 75 unique genes that conferred resistance against 22 unique antimicrobial compounds.

Genomic Analyses of Major SARS-CoV-2 Variants Predicting Multiple Regions of Pathogenic and Transmissive Importance.

The rapid evolution of SARS-CoV-2 has fueled its global proliferation since its discovery in 2019, with several notable variants having been responsible for increases in cases of coronavirus disease 2019 (COVID-19). Analyses of codon bias and usage in these variants between phylogenetic clades or lineages may grant insights into the evolution of SARS-CoV-2 and identify target codons indicative of evolutionary or mutative trends that may prove useful in tracking or defending oneself against emerging strains. We processed a cohort of 120 SARS-CoV-2 genome sequences through a statistical and bioinformatic pipeline to identify codons presenting evidence of selective pressure as well as codon coevolution. We report the identification of two codon sites in the orf8 and N genes demonstrating such evidence with real-world impacts on pathogenicity and transmissivity.

Diversity and conservation of the genome architecture of phages infecting the Alphaproteobacteria.

IMPORTANCE: This study reports the results of the largest analysis of genome sequences from phages that infect the Alphaproteobacteria class of bacterial hosts. We analyzed over 100 whole genome sequences of phages to construct dotplots, categorize them into genetically distinct clusters, generate a bootstrapped phylogenetic tree, compute protein orthologs, and predict packaging strategies. We determined that the phage sequences primarily cluster by the bacterial host family, phage morphotype, and genome size. We expect that the findings reported in this seminal study will facilitate future analyses that will improve our knowledge of the phages that infect these hosts.

A Novel Application of Spinning Disk Technology to Collect Plasma from Whole Blood Prior to Quantifying Plasma RNA.

The spinning disk technology has previously been utilized to isolate bacterial components from blood in hours instead of days. We hypothesized that this platform could be applied as an alternative approach to isolating plasma RNA from a whole blood sample. We consequently tested the efficacy of the spinning disk technology to extract plasma from whole blood upstream of RNA isolation and analysis. To do so, we collected plasma using either the spinning disk or the typical two-spin centrifuge method. We found that the spinning disk method results in significantly more hemolysis during collection than the conventional two-spin centrifuge method. However, when plasma RNA recovered from both collection methods was quantified using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR), we found that the spinning disk method yielded a higher plasma RNA concentration than the two-spin centrifuge method. This suggests that the spinning disk may be an efficient alternative method to recover plasma RNA. Further work is needed to determine whether red blood cell RNA contamination is present in the plasma RNA extracted from spinning disk-processed plasma.

TK1 expression influences pathogenicity by cell cycle progression, cellular migration, and cellular survival in HCC 1806 breast cancer cells.

Breast cancer is the most common cancer diagnosis worldwide accounting for 1 out of every 8 cancer diagnoses. The elevated expression of Thymidine Kinase 1 (TK1) is associated with more aggressive tumor grades, including breast cancer. Recent studies indicate that TK1 may be involved in cancer pathogenesis; however, its direct involvement in breast cancer has not been identified. Here, we evaluate potential pathogenic effects of elevated TK1 expression by comparing HCC 1806 to HCC 1806 TK1-knockdown cancer cells (L133). Transcriptomic profiles of HCC 1806 and L133 cells showed cell cycle progression, apoptosis, and invasion as potential pathogenic pathways affected by TK1 expression. Subsequent in-vitro studies confirmed differences between HCC 1806 and L133 cells in cell cycle phase progression, cell survival, and cell migration. Expression comparison of several factors involved in these pathogenic pathways between HCC 1806 and L133 cells identified p21 and AKT3 transcripts were significantly affected by TK1 expression. Creation of a protein-protein interaction map of TK1 and the pathogenic factors we evaluated predict that the majority of factors evaluated either directly or indirectly interact with TK1. Our findings argue that TK1 elevation directly increases HCC 1806 cell pathogenicity and is likely occurring by p21- and AKT3-mediated mechanisms to promote cell cycle arrest, cellular migration, and cellular survival.

Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets.

Background: Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. Methods: We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. Results: As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.

Comparative sequence analysis elucidates the evolutionary patterns of Yersinia pestis in New Mexico over thirty-two years.

Background: Yersinia pestis, a Gram-negative bacterium, is the causative agent of plague. Y. pestis is a zoonotic pathogen that occasionally infects humans and became endemic in the western United States after spreading from California in 1899. Methods: To better understand evolutionary patterns in Y. pestis from the southwestern United States, we sequenced and analyzed 22 novel genomes from New Mexico. Analytical methods included, assembly, multiple sequences alignment, phylogenetic tree reconstruction, genotype-phenotype correlation, and selection pressure. Results: We identified four genes, including Yscp and locus tag YPO3944, which contained codons undergoing negative selection. We also observed 42 nucleotide sites displaying a statistically significant skew in the observed residue distribution based on the year of isolation. Overall, the three genes with the most statistically significant variations that associated with metadata for these isolates were sapA, fliC, and argD. Phylogenetic analyses point to a single introduction of Y. pestis into the United States with two subsequent, independent movements into New Mexico. Taken together, these analyses shed light on the evolutionary history of this pathogen in the southwestern US over a focused time range and confirm a single origin and introduction into North America.

Association between Religious Beliefs and HPV Vaccination Attitudes among College Students.

OBJECTIVE AND PARTICIPANTS: The authors sought an updated examination of attitudes toward Human Papillomavirus (HPV) catch-up vaccination among college students at a private religious university. METHODS: A total of 1557 college students completed a 62-question survey of religious and HPV vaccination attitudes during the fall of 2021. Students' willingness to receive catch-up HPV vaccination and willingness to vaccinate a future child against HPV were recorded. RESULTS: Of the 46.8% of students who reported being unvaccinated or unaware of vaccination status, ~26% reported being uninterested in receiving catch-up HPV vaccination; ~22% of all students surveyed reported being unwilling to vaccinate a future child against HPV. The strongest predictors of vaccine hesitancy included religious concerns about sexual abstinence and safety concerns. CONCLUSIONS: College health professionals can increase the rate of HPV vaccination among college students and subsequent future generations by addressing the safety and utility of the vaccine regardless of intentions for sexual abstinence prior to marriage. Additionally, rather than a uniform approach to all students who self-identify as Christian, an effort to identify and discuss the unique religiously influenced beliefs of individual students is recommended when discussing HPV vaccination.

Monolithic affinity columns in 3D printed microfluidics for chikungunya RNA detection.

Mosquito-borne pathogens plague much of the world, yet rapid and simple diagnosis is not available for many affected patients. Using a custom stereolithography 3D printer, we created microfluidic devices with affinity monoliths that could retain, noncovalently attach a fluorescent tag, and detect oligonucleotide and viral RNA. We optimized the fluorescent binding and sample load times using an oligonucleotide sequence from chikungunya virus (CHIKV). We also tested the specificity of CHIKV capture relative to genetically similar Sindbis virus. Moreover, viral RNA from both viruses was flowed through capture columns to study the efficiency and specificity of the column for viral CHIKV. We detected ~107 loaded viral genome copies, which was similar to levels in clinical samples during acute infection. These results show considerable promise for development of this platform into a rapid mosquito-borne viral pathogen detection system.

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes.

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

Comparison of Intracellular Transcriptional Response of NHBE Cells to Infection with SARS-CoV-2 Washington and New York Strains.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, China in December 2019 and caused a global pandemic resulting in millions of deaths and tens of millions of patients positive tests. While studies have shown a D614G mutation in the viral spike protein are more transmissible, the effects of this and other mutations on the host response, especially at the cellular level, are yet to be fully elucidated. In this experiment we infected normal human bronchial epithelial (NHBE) cells with the Washington (D614) strain or the New York (G614) strains of SARS-CoV-2. We generated RNA sequencing data at 6, 12, and 24 hours post-infection (hpi) to improve our understanding of how the intracellular host response differs between infections with these two strains. We analyzed these data with a bioinformatics pipeline that identifies differentially expressed genes (DEGs), enriched Gene Ontology (GO) terms and dysregulated signaling pathways. We detected over 2,000 DEGs, over 600 GO terms, and 29 affected pathways between the two infections. Many of these entities play a role in immune signaling and response. A comparison between strains and time points showed a higher similarity between matched time points than across different time points with the same strain in DEGs and affected pathways, but found more similarity between strains across different time points when looking at GO terms. A comparison of the affected pathways showed that the 24hpi samples of the New York strain were more similar to the 12hpi samples of the Washington strain, with a large number of pathways related to translation being inhibited in both strains. These results suggest that the various mutations contained in the genome of these two viral isolates may cause distinct effects on the host transcriptional response in infected host cells, especially relating to how quickly translation is dysregulated after infection. This comparison of the intracellular host response to infection with these two SARS-CoV-2 isolates suggest that some of the mechanisms associated with more severe disease from these viruses could include virus replication, metal ion usage, host translation shutoff, host transcript stability, and immune inhibition.

Comparative meta-analysis of host transcriptional response during Streptococcus pneumoniae carriage or infection.

Infection with Streptococcus pneumoniae causes over 150,000 cases of pneumonia annually in the United States alone. We performed a meta-analysis of publicly available RNA-sequencing data to compare the host-specific intracellular transcriptional responses that differ between infection and carriage with S. pneumoniae in humans and mice. We applied an automated preprocessing workflow to collections of publicly available fastq files that were categorized as either of two phenotypes-infection or carriage in humans and mice. We identified the differentially expressed genes and intracellular signaling pathways that changed in host cells during infection or carriage and whether these human phenotypes could be appropriately modeled at the molecular level in mice. Although we observed multiple differentially expressed genes shared among multiple comparisons, we found no overlapping significant signaling pathways between the mouse and human studies in either phenotype. Based on the samples included in this secondary analysis, we identified a minimal number of generalized transcriptional relationships between host infection and carriage phenotypes of S. pneumoniae that were consistently shared between the mouse and human hosts. Our findings suggest that additional controlled datasets in mouse and human carriage or infection models are needed to better understand the underlying mechanism(s) of invasion and pathogenesis. This knowledge could then contribute to the development of improved prophylactics and/or therapeutics against this pathogen.

Analyzing the impact of a real-life outbreak simulator on pandemic mitigation: An epidemiological modeling study.

An app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding epidemiological dynamics. The OO app enables experience-based learning about outbreaks, spreading a virtual pathogen via Bluetooth among participating smartphones. Deployed at many colleges and in other settings, OO collects anonymized spatiotemporal data, including the time and duration of the contacts among participants of the simulation. We report the distribution, timing, duration, and connectedness of student social contacts at two university deployments and uncover cryptic transmission pathways through individuals' second-degree contacts. We then construct epidemiological models based on the OO-generated contact networks to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we demonstrate that the granularity of OO data enables institutions to mitigate outbreaks by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.

Mutation in Hemagglutinin Antigenic Sites in Influenza A pH1N1 Viruses from 2015-2019 in the United States Mountain West, Europe, and the Northern Hemisphere.

H1N1 influenza A virus is a respiratory pathogen that undergoes antigenic shift and antigenic drift to improve viral fitness. Tracking the evolutionary trends of H1N1 aids with the current detection and the future response to new viral strains as they emerge. Here, we characterize antigenic drift events observed in the hemagglutinin (HA) sequence of the pandemic H1N1 lineage from 2015-2019. We observed the substitutions S200P, K147N, and P154S, together with other mutations in structural, functional, and/or epitope regions in 2015-2019 HA protein sequences from the Mountain West region of the United States, the larger United States, Europe, and other Northern Hemisphere countries. We reconstructed multiple phylogenetic trees to track the relationships and spread of these mutations and tested for evidence of selection pressure on HA. We found that the prevalence of amino acid substitutions at positions 147, 154, 159, 200, and 233 significantly changed throughout the studied geographical regions between 2015 and 2019. We also found evidence of coevolution among a subset of these amino acid substitutions. The results from this study could be relevant for future epidemiological tracking and vaccine prediction efforts. Similar analyses in the future could identify additional sequence changes that could affect the pathogenicity and/or infectivity of this virus in its human host.

Meta-Analysis of Two Human RNA-seq Datasets to Determine Periodontitis Diagnostic Biomarkers and Drug Target Candidates.

Periodontitis is a chronic inflammatory oral disease that affects approximately 42% of adults 30 years of age or older in the United States. In response to microbial dysbiosis within the periodontal pockets surrounding teeth, the host immune system generates an inflammatory environment in which soft tissue and alveolar bone destruction occur. The objective of this study was to identify diagnostic biomarkers and the mechanistic drivers of inflammation in periodontitis to identify drugs that may be repurposed to treat chronic inflammation. A meta-analysis comprised of two independent RNA-seq datasets was performed. RNA-seq analysis, signal pathway impact analysis, protein-protein interaction analysis, and drug target analysis were performed to identify the critical pathways and key players that initiate inflammation in periodontitis as well as to predict potential drug targets. Seventy-eight differentially expressed genes, 10 significantly impacted signaling pathways, and 10 hub proteins in periodontal gingival tissue were identified. The top 10 drugs that may be repurposed for treating periodontitis were then predicted from the gene expression and pathway data. The efficacy of these drugs in treating periodontitis has yet to be investigated. However, this analysis indicates that these drugs may serve as potential therapeutics to treat inflammation in gingival tissue affected by periodontitis.

Characterization of Staphylococcus aureus biofilms via crystal violet binding and biochemical composition assays of isolates from hospitals, raw meat, and biofilm-associated gene mutants.

Staphylococcus aureus (SA) is a gram-positive coccus and an opportunistic pathogen of humans. The ability of SA to form biofilms is an important virulence mechanism because biofilms are protected from host immune responses and antibiotic treatment. This study examines the relative biofilm strength of a variety of hospital and meat-associated strains of SA, using a crystal violet (CV) staining assay. Biofilms were treated with either DNase or proteinase K prior to CV staining, and compared to mock-treated results, to better understand the biochemical composition. Biofilm polysaccharide concentration was also measured using the phenol sulfuric-acid assay which was normalized to base biofilm strength. We found that hospital-associated isolates have biofilms that bind significantly more CV than for meat isolates and are significantly more protein and polysaccharide-based while meat isolates have significantly more DNA-based biofilms. This study also investigates the effects that biofilm-related genes have on biofilm formation and composition by analyzing specific transposon mutants of genes previously shown to play a role in biofilm development. agrA, atl, clfA, fnbA, purH, and sarA mutants produce significantly weaker biofilms (bind less CV) as compared to a wild-type control, whereas the acnA mutant produces a significantly stronger biofilm. Biofilms formed from these mutant strains were treated (or mock-treated) with DNase or proteinase K and tested with phenol and sulfuric acid to determine what role these genes play in biofilm composition. The acnA, clfA, fnbA, and purH mutants showed significant reduction in biofilm staining after either proteinase K or DNase treatment, agrA and sarA mutants showed significant biofilm reduction after only proteinase K treatment, and an atl mutant did not show significant biofilm reduction after either proteinase K or DNase treatment. These data suggest that biofilms that form without acnA, clfA, fnbA, and purH are DNA- and protein-based, that biofilms lacking agrA and sarA are mainly protein-based, and biofilms lacking atl are mainly polysaccharide-based. These results help to elucidate how these genes affect biofilm formation and demonstrate how mutating biofilm-related genes in SA can cause a change in biofilm composition.

CD5 Deficiency Alters Helper T Cell Metabolic Function and Shifts the Systemic Metabolome.

Metabolic function plays a key role in immune cell activation, destruction of foreign pathogens, and memory cell generation. As T cells are activated, their metabolic profile is significantly changed due to signaling cascades mediated by the T cell receptor (TCR) and co-receptors found on their surface. CD5 is a T cell co-receptor that regulates thymocyte selection and peripheral T cell activation. The removal of CD5 enhances T cell activation and proliferation, but how this is accomplished is not well understood. We examined how CD5 specifically affects CD4+ T cell metabolic function and systemic metabolome by analyzing serum and T cell metabolites from CD5WT and CD5KO mice. We found that CD5 removal depletes certain serum metabolites, and CD5KO T cells have higher levels of several metabolites. Transcriptomic analysis identified several upregulated metabolic genes in CD5KO T cells. Bioinformatic analysis identified glycolysis and the TCA cycle as metabolic pathways promoted by CD5 removal. Functional metabolic analysis demonstrated that CD5KO T cells have higher oxygen consumption rates (OCR) and higher extracellular acidification rates (ECAR). Together, these findings suggest that the loss of CD5 is linked to CD4+ T cell metabolism changes in metabolic gene expression and metabolite concentration.

Chikungunya virus time course infection of human macrophages reveals intracellular signaling pathways relevant to repurposed therapeutics.

Background: Chikungunya virus (CHIKV) is a mosquito-borne pathogen, within the Alphavirus genus of the Togaviridae family, that causes ~1.1 million human infections annually. CHIKV uses Aedes albopictus and Aedes aegypti mosquitoes as insect vectors. Human infections can develop arthralgia and myalgia, which results in debilitating pain for weeks, months, and even years after acute infection. No therapeutic treatments or vaccines currently exist for many alphaviruses, including CHIKV. Targeting the phagocytosis of CHIKV by macrophages after mosquito transmission plays an important role in early productive viral infection in humans, and could reduce viral replication and/or symptoms. Methods: To better characterize the transcriptional response of macrophages during early infection, we generated RNA-sequencing data from a CHIKV-infected human macrophage cell line at eight or 24 hours post-infection (hpi), together with mock-infected controls. We then calculated differential gene expression, enriched functional annotations, modulated intracellular signaling pathways, and predicted therapeutic drugs from these sequencing data. Results: We observed 234 pathways were significantly affected 24 hpi, resulting in six potential pharmaceutical treatments to modulate the affected pathways. A subset of significant pathways at 24 hpi includes AGE-RAGE, Fc epsilon RI, Chronic myeloid leukemia, Fc gamma R-mediated phagocytosis, and Ras signaling. We found that the MAPK1 and MAPK3 proteins are shared among this subset of pathways and that Telmisartan and Dasatinib are strong candidates for repurposed small molecule therapeutics that target human processes. The results of our analysis can be further characterized in the wet lab to contribute to the development of host-based prophylactics and therapeutics.

Impact of Local and Demographic Factors on Early COVID-19 Vaccine Hesitancy among Health Care Workers in New York City Public Hospitals.

Despite the development of several effective vaccines, SARS-CoV-2 continues to spread, causing serious illness among the unvaccinated. Healthcare professionals are trusted sources of information about vaccination, and therefore understanding the attitudes and beliefs of healthcare professionals regarding the vaccines is of utmost importance. We conducted a survey-based study to understand the factors affecting COVID-19 vaccine attitudes among health care professionals in NYC Health and Hospitals, at a time when the vaccine was new, and received 3759 responses. Machine learning and chi-square analyses were applied to determine the factors most predictive of vaccine hesitancy. Demographic factors, education, role at the hospital, perceptions of the pandemic itself, and location of work and residence were all found to significantly contribute to vaccine attitudes. Location of residence was examined for both borough and neighborhood, and was found to have a significant impact on vaccine receptivity. Interestingly, this borough-level data did not correspond to the number or severity of cases in the respective boroughs, indicating that local social or other influences likely have a substantial impact. Local and demographic factors should be strongly considered when preparing pro-vaccine messages or campaigns.