Biology subjects

Zand, M. S.

Publications and source records attributed to Zand, M. S..

7 recordsLinked to original sources

bioRxiv2024

Future Sequon Finder - A novel approach for predicting future N-linked glycosylation sequons on viral surface proteins

Influenza viruses are known to evade host immune responses by shielding vulnerable surface protein epitopes via N-linked glycosylation. A program titled Future Sequon Finder was developed to predict the locations in which glycan binding sites are most likely to emerge in future influenza hemagglutinin proteins. The predictive modeling approach considers how closely sites in currently circulating strains resemble glycosylation sequons genetically, the surface accessibility of those sites, and the site-specific mutation frequency of the amino acids within those sites that would need to change to generate a glycosylation sequon. The efficacy of this model is tested using historic human H1N1 and H3N2 influenza strains along with swine H1N1 strains. Through this analysis, it is revealed that glycosylation addition events in influenza hemagglutinin proteins are almost always the result of single nucleotide mutation events. It is also revealed that site-specific mutation frequency and surface accessibility are powerful predictors of which sites will become glycosylated in human influenza viruses when considered with the genetic composition of the sites in question. Having been designed to incorporate these factors, the program successfully predicted almost every historic sequon addition event and, in the case of the human strains, ranked them highly among falsely predicted sequons. After demonstrating the models power with historical data, the program is used to predict future HA glycosylation sequon locations based on currently circulating human influenza viruses.

microbiology↗

bioRxiv2022

An evaluation of RNA-seq differential analysis methods

RNA-seq is a high-throughput sequencing technology widely used for gene transcript discovery and quantification under different biological or biomedical conditions. A fundamental research question in most RNA-seq experiments is the identification of differentially expressed genes among experimental conditions or sample groups. Numerous statistical methods for RNA-seq differential analysis have been proposed since the emergence of the RNA-seq assay. To evaluate popular differential analysis methods used in the open source R and Bioconductor packages, we conducted multiple simulation studies to compare the performance of eight RNA-seq differential analysis methods used in RNA-seq data analysis (edgeR, DESeq, DESeq2, baySeq, EBSeq, NOISeq, SAMSeq, Voom). The comparisons were across different scenarios with either equal or unequal library sizes, different distribution assumptions and sample sizes. We measured performance using false discovery rate (FDR) control, power, and stability. No significant differences were observed for FDR control, power, or stability across methods, whether with equal or unequal library sizes. For RNA-seq count data with negative binomial distribution, when sample size is 3 in each group, EBSeq performed better than the other methods as indicated by FDR control, power, and stability. When sample sizes increase to 6 or 12 in each group, DESeq2 performed slightly better than other methods. All methods have improved performance when sample size increases to 12 in each group except DESeq. For RNA-seq count data with log-normal distribution, both DESeq and DESeq2 methods performed better than other methods in terms of FDR control, power, and stability across all sample sizes. Real RNA-seq experimental data were also used to compare the total number of discoveries and stability of discoveries for each method. For RNA-seq data analysis, the EBSeq method is recommended for studies with sample size as small as 3 in each group, and the DESeq2 method is recommended for sample size of 6 or higher in each group when the data follow the negative binomial distribution. Both DESeq and DESeq2 methods are recommended when the data follow the log-normal distribution.

bioinformatics↗

bioRxiv2021

Broadly-reactive IgG responses to heterologous H5 prime-boost influenza vaccination are shaped by antigenic relatedness to priming strains

Prime-boost vaccinations of humans with different H5 strains have generated broadly protective antibody levels. However, the effect of an individuals H5 exposure history on antibody responses to subsequent H5 vaccination is poorly understood. To investigate this, we analyzed the IgG response to H5 A/Indonesia/5/2005 (Ind05) vaccination in three cohorts: (1) a double primed group that received two H5 vaccinations: A/Vietnam/203/2004 (Vie04) 5 years ago and A/Hong Kong/156/1997 (HK97) 11 years ago, (2) a single primed group that received Vie04 5 years ago, and (3) an H5-naive group that received two doses of the Ind05 vaccine 28 days apart. Hemagglutinin (HA)-reactive IgG levels were estimated by multiplex assay against an HA panel that included 21 H5 strains and 9 other strains representing H1, H3, H7, and H9 subtypes. Relative HA antibody landscapes were generated to quantitatively analyze the magnitude and breadth of antibody binding after vaccination. We found that short-interval prime-boosting with the Ind05 in the naive group generated a low anti-H5 response. Both primed groups generated robust antibody responses reactive to a broad range of H5 strains after boosting with Ind05; IgG antibody levels persisted longer in subjects who had been double primed years ago. Notably, the IgG responses were strongest against the first priming H5 strain, that reflecting influenza virus immune imprinting. Finally, the broad anti-H5 IgG response was stronger against strains having a small antigenic distance to the initial priming strain. IMPORTANCEThe antigenic shift and draft of hemagglutinin (HA) in influenza viruses is accepted as one of the major reasons for immune evasion. The analysis of B cell immune responses to influenza infection and vaccination is complicated by the impact of exposure history and antibody cross-reaction between antigenically similar influenza strains. To assist in such analyses, the influenza "antibody landscape" method has been used to analyze and visualize the relationship of antibody mediated immunity to the antigenic distance between influenza strains. In this study, we describe a "relative antibody landscape" method, calculating the antigenic distance between the vaccine influenza strain and other H5 strains, and using this relative antigenic distance to plot with the anti-H5 IgG levels post-vaccination. This new method quantitatively estimates and visualizes the correlation between the humoral response to a particular influenza strain, and the antigenic distance to other strains. Our findings demonstrate the effect of H5 exposure history on H5 vaccine responses quantified by the relative antibody landscape method.

immunology↗

bioRxiv2020

Cross-reactive anti-hemagglutinin (HA) IgG responses are shaped by previous long-interval monovalent H5 vaccination and highly correlated with HA antigenic distance

Avian H5 influenza is an emerging influenza strain with the potential for human pandemic spread. One unresolved issue in pandemic vaccine preparedness is to what extent a vaccine recall response depends on the interval between the priming and boosting vaccinations. In this study, we analyzed the anti-H5 HA IgG responses to an H5 A/Indonesia/5/2005 boosting vaccination in three cohorts: (1) a short interval boosting cohort that received a prime and boost 28 days apart, (2) a long-interval boosting cohort that received an H5 A/Vietnam/203/2004 priming vaccination 5 years before boosting, and (3) a double long-boost cohort that received single doses of all three vaccines separated by 5-6 year intervals. Anti-HA IgG levels were measured using a multiple-plex assay against 21 H5 and 16 seasonal strains covering both influenza phylogenetic groups. We used the antigentic distance between the vaccine strain and each HA in the assay panel to de1ne the antibody response landscape. Both single and double long-interval boosting with the H5 variant vaccine elicited a broad antibody response to all H5 subtype strains, and double boosting resulted in sustained, vaccine-speci1c, anti-HA IgG levels over a six month period. Antibody-mediated immune responses were shaped by prior H5 exposure history, and the magnitude of both vaccine speci1c and cross-reactive anti-H5 HA IgG responses was highly correlated with the relative antigenic distance between the measured and the vaccine HAs. We conclude that the relative antibody landscape method can be used to quantify the phenomenon of antigenic imprinting on human influenza vaccine immune responses. IMPORTANCEA signi1cant obstacle to development of a universal influenza vaccine is understanding the relationship between multidimensional host humoral immunity, prior antigen exposure, and viral antigenicity. In this study, we used a multiplexed antibody assay to measure antibody cross-reactivities against antigenically similar H5 influenza virus strains. This work uses a novel method, relative antibody landscapes, to analyze the relationship between immune response and antigenic distance between the target H5 vaccine HA and other HAs in the assay after a boosting vaccination. This method improves analysis of immune responses by relating antigen exposure history to the influenza vaccination antibody response. This study also revealed that multiple vaccine boosting over several years can generate high levels of long-lasting cross-reactive antibodies against the priming H5 strains vaccine that subjects received, suggesting the HA imprinting mechanism(s) have a strong influence in the adult antibody response to H5 MIV vaccination.

immunology↗

bioRxiv2019

Cyclosporine A Directly Affects Human and Mouse B cell Migration in vitro by Disrupting a HIF-1α Dependent, O2 Sensing, Molecular Switch

BackgroundHypoxia is a potent molecular signal for cellular metabolism, mitochondrial function, and migration. Conditions of low oxygen tension trigger regulatory cascades mediated via the highly conserved HIF-1 post-translational modification system. In the adaptive immune response, B cells (Bc) are activated and differentiate under hypoxic conditions within lymph node germinal centers, and subsequently migrate to other compartments. During migration, they traverse through changing oxygen levels, ranging from 1-5% in the lymph node to 5-13% in the peripheral blood. Interestingly, the calcineurin inhibitor cyclosporine A is known to stimulate prolyl hydroxylase activity, resulting in HIF-1 destabilization and may alter Bc responses directly. Over 60% of patients taking calcineurin immunosuppressant medications have hypo-gammaglobulinemia and poor vaccine responses, putting them at high risk of infection with significantly increased morbidity and mortality.\n\nResultsWe demonstrate that O2 tension is a previously unrecognized Bc regulatory switch, altering CXCR4 chemokine receptor signaling in activated Bc through HIF-1 expression and controlling critical aspects of Bc migration. Our data demonstrate that calcineurin inhibition hinders this O2 regulatory switch in primary human Bc.\n\nConclusionThis previously unrecognized effect of calcineurin inhibition directly on human Bc has significant and direct clinical implications.

immunology↗

bioRxiv2019

Application of Volumetric Absorptive Micro Sampling to Measure Multidimensional Anti-Influenza Hemagglutinin IgG Antibodies by mPlex-Flu Assay

Recently, volumetric absorptive microsampling (VAMS) has been used for peripheral blood sampling and analyses in several fields. VAMS ensures accurate sampling by collecting a fixed blood volume (10 or 20 {micro}L) on a volumetric swab in blood spot format, and allows for long-term sample storage. The mPlex-Flu assay is a novel, multidimensional assay that measures the concentration of antibodies against multiple influenza virus hemagglutinins simultaneously strains with a small volume of serum (less than 5 {micro}L). Here we describe combining these two methods to measure multidimensional influenza antibody activity using a finger-stick and VAMS. In this study, we compared influenza antibody profiles measured from capillary blood obtained with a finger-stick, and venous whole blood collected by traditional phlebotomy from 20 subjects using the mPlex-Flu assay. We found that results with the two sampling methods were virtually identical across all influenza strains within the same subject (mean of R2 =0.9470), and that antibodies remained stable over three weeks when VAMS samples were stored at room temperature and transported using a variety of shipping methods. Additionally, VAMS sampling is an easy and highly reproducible process; when volunteers performed finger stick VAMS at home by themselves, the results of anti-HA antibody concentrations showed that they are highly consistent with sampling performed by study personnel on-site (R2 =0.9496). This novel approach provides advantages for clinical influenza vaccine studies, including ease of sampling, low cost, and high accuracy. We conclude that these methods could provide an accurate and low-cost means for monitoring the influenza virus antibody responses in large population studies.

immunology↗

bioRxiv2018

Inpatient mobility to predict hospital-onset Clostridium difficile: a network approach

With hospital-onset Clostridium difficile Infection (CDI) still a common occurrence in the U.S., this paper examines the relationship between unit-wide CDI susceptibility and inpatient mobility and creates a predictive measure of CDI called \"Contagion Centrality\". A mobility network was constructed using two years of patient electronic health record (EHR) data within a 739-bed hospital (Jan. 2013 - Dec. 2014; n=72,636 admissions). Network centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and compared to network centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. Closeness centrality was a statistically significant measure associated with unit susceptibility (p-value < 0.05), highlighting the importance of incoming patient mobility in CDI prevention at the unit-level. Contagion Centrality (CC) was calculated using incoming inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. This measure is statistically significant (p-value <0.05) with our outcome of hospital-onset CDI cases, and captures the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create an easily interpretable and informative clinical tool showing this relationship and risk of hospital-onset CDI in real-time. Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak, and thus provide clinicians and infection prevention staff with advanced warning and specific location data to concentrate prevention efforts.

epidemiology↗