Ventilation during COVID-19 in a school for students with intellectual and developmental disabilities (IDD).

BACKGROUND: This study examined the correlation of classroom ventilation (air exchanges per hour (ACH)) and exposure to CO2 ≥1,000 ppm with the incidence of SARS-CoV-2 over a 20-month period in a specialized school for students with intellectual and developmental disabilities (IDD). These students were at a higher risk of respiratory infection from SARS-CoV-2 due to challenges in tolerating mitigation measures (e.g. masking). One in-school measure proposed to help mitigate the risk of SARS-CoV-2 infection in schools is increased ventilation. METHODS: We established a community-engaged research partnership between the University of Rochester and the Mary Cariola Center school for students with IDD. Ambient CO2 levels were measured in 100 school rooms, and air changes per hour (ACH) were calculated. The number of SARS-CoV-2 cases for each room was collected over 20 months. RESULTS: 97% of rooms had an estimated ACH ≤4.0, with 7% having CO2 levels ≥2,000 ppm for up to 3 hours per school day. A statistically significant correlation was found between the time that a room had CO2 levels ≥1,000 ppm and SARS-CoV-2 PCR tests normalized to room occupancy, accounting for 43% of the variance. No statistically significant correlation was found for room ACH and per-room SARS-CoV-2 cases. Rooms with ventilation systems using MERV-13 filters had lower SARS-CoV-2-positive PCR counts. These findings led to ongoing efforts to upgrade the ventilation systems in this community-engaged research project. CONCLUSIONS: There was a statistically significant correlation between the total time of room CO2 concentrations ≥1,000 and SARS-CoV-2 cases in an IDD school. Merv-13 filters appear to decrease the incidence of SARS-CoV-2 infection. This research partnership identified areas for improving in-school ventilation.

Investigating Ethical Tradeoffs in Crisis Standards of Care through Simulation of Ventilator Allocation Protocols.

Introduction: Arguments over the appropriate Crisis Standards of Care (CSC) for public health emergencies often assume that there is a tradeoff between saving the most lives, saving the most life-years, and preventing racial disparities. However, these assumptions have rarely been explored empirically. To quantitatively characterize possible ethical tradeoffs, we aimed to simulate the implementation of five proposed CSC protocols for rationing ventilators in the context of the COVID-19 pandemic. Methods: A Monte Carlo simulation was used to estimate the number of lives saved and life-years saved by implementing clinical acuity-, comorbidity- and age-based CSC protocols under different shortage conditions. This model was populated with patient data from 3707 adult admissions requiring ventilator support in a New York hospital system between April 2020 and May 2021. To estimate lives and life-years saved by each protocol, we determined survival to discharge and estimated remaining life expectancy for each admission. Results: The simulation demonstrated stronger performance for age- and comorbidity-sensitive protocols. For a capacity of 1 bed per 2 patients, ranking by age bands saves approximately 28.7 lives and 3408 life-years per thousand patients, while ranking by Sequential Organ Failure Assessment (SOFA) bands saved the fewest lives (13.2) and life-years (416). For all protocols, we observed a positive correlation between lives saved and life-years saved. For all protocols except lottery and the banded SOFA, significant disparities in lives saved and life-years saved were noted between White non-Hispanic, Black non-Hispanic, and Hispanic sub-populations. Conclusion: While there is significant variance in the number of lives saved and life-years saved, we did not find a tradeoff between saving the most lives and saving the most life-years. Moreover, concerns about racial discrimination in triage protocols require thinking carefully about the tradeoff between enforcing equality of survival rates and maximizing the lives saved in each sub-population.

Ventilation during COVID-19 in a school for students with intellectual and developmental disabilities (IDD).

Background: This study examined the correlation of classroom ventilation (air exchanges per hour (ACH)) and exposure to CO2 ≥1,000 ppm with the incidence of SARS-CoV-2 over a 20-month period in a specialized school for students with intellectual and developmental disabilities (IDD). These students were at a higher risk of respiratory infection from SARS-CoV-2 due to challenges in tolerating mitigation measures (e.g. masking). One in-school measure proposed to help mitigate the risk of SARS-CoV-2 infection in schools is increased ventilation. Methods: We established a community-engaged research partnership between the University of Rochester and the Mary Cariola Center school for students with IDD. Ambient CO2 levels were measured in 100 school rooms, and air changes per hour (ACH) were calculated. The number of SARS-CoV-2 cases for each room was collected over 20 months. Results: 97% of rooms had an estimated ACH ≤4.0, with 7% having CO2 levels ≥2,000 ppm for up to 3 hours per school day. A statistically significant correlation was found between the time that a room had CO2 levels ≥1,000 ppm and SARS-CoV-2 PCR tests normalized to room occupancy, accounting for 43% of the variance. No statistically significant correlation was found for room ACH and per-room SARS-CoV-2 cases. Rooms with ventilation systems using MERV-13 filters had lower SARS-CoV-2-positive PCR counts. These findings led to ongoing efforts to upgrade the ventilation systems in this community-engaged research project. Conclusions: There was a statistically significant correlation between the total time of room CO2 concentrations ≥1,000 and SARS-CoV-2 cases in an IDD school. Merv-13 filters appear to decrease the incidence of SARS-CoV-2 infection. This research partnership identified areas for improving in-school ventilation.

Common Data Element Collection in Underserved School Communities: Challenges and Recommendations.

OBJECTIVES: To provide recommendations for future common data element (CDE) development and collection that increases community partnership, harmonizes data interpretation, and continues to reduce barriers of mistrust between researchers and underserved communities. METHODS: We conducted a cross-sectional qualitative and quantitative evaluation of mandatory CDE collection among Rapid Acceleration of Diagnostics-Underserved Populations Return to School project teams with various priority populations and geographic locations in the United States to: (1) compare racial and ethnic representativeness of participants completing CDE questions relative to participants enrolled in project-level testing initiatives and (2) identify the amount of missing CDE data by CDE domain. Additionally, we conducted analyses stratified by aim-level variables characterizing CDE collection strategies. RESULTS: There were 15 study aims reported across the 13 participating Return to School projects, of which 7 (47%) were structured so that CDEs were fully uncoupled from the testing initiative, 4 (27%) were fully coupled, and 4 (27%) were partially coupled. In 9 (60%) study aims, participant incentives were provided in the form of monetary compensation. Most project teams modified CDE questions (8/13; 62%) to fit their population. Across all 13 projects, there was minimal variation in the racial and ethnic distribution of CDE survey participants from those who participated in testing; however, fully uncoupling CDE questions from testing increased the proportion of Black and Hispanic individuals participating in both initiatives. CONCLUSIONS: Collaboration with underrepresented populations from the early study design process may improve interest and participation in CDE collection efforts.

Gene Expression Risk Scores for COVID-19 Illness Severity.

BACKGROUND: The correlates of coronavirus disease 2019 (COVID-19) illness severity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are incompletely understood. METHODS: We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2 infection clinically adjudicated as having mild, moderate, or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and nonsevere COVID-19. RESULTS: Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus nonsevere illness, we identified >4000 genes differentially expressed (false discovery rate < 0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T-cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated receiver operating characteristic-area under the curve [ROC-AUC] = 0.98), and need for intensive care in an independent cohort (ROC-AUC = 0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort. CONCLUSIONS: These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

A systems genomics approach uncovers molecular associates of RSV severity.

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.

Gene Expression Risk Scores for COVID-19 Illness Severity.

BACKGROUND: The correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood. METHODS: We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID. RESULTS: Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort. CONCLUSION: These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

Airway gene-expression classifiers for respiratory syncytial virus (RSV) disease severity in infants.

BACKGROUND: A substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness. METHOD: We defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2). RESULTS: NGSS1 is strongly correlated with the disease severity, demonstrating a naïve correlation (ρ) of ρ = 0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%. CONCLUSION: Airway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.

Temporal Dysbiosis of Infant Nasal Microbiota Relative to Respiratory Syncytial Virus Infection.

BACKGROUND: Respiratory syncytial virus (RSV) is a leading cause of infant respiratory disease. Infant airway microbiota has been associated with respiratory disease risk and severity. The extent to which interactions between RSV and microbiota occur in the airway, and their impact on respiratory disease susceptibility and severity, are unknown. METHODS: We carried out 16S rRNA microbiota profiling of infants in the first year of life from (1) a cross-sectional cohort of 89 RSV-infected infants sampled during illness and 102 matched healthy controls, and (2) a matched longitudinal cohort of 12 infants who developed RSV infection and 12 who did not, sampled before, during, and after infection. RESULTS: We identified 12 taxa significantly associated with RSV infection. All 12 taxa were differentially abundant during infection, with 8 associated with disease severity. Nasal microbiota composition was more discriminative of healthy vs infected than of disease severity. CONCLUSIONS: Our findings elucidate the chronology of nasal microbiota dysbiosis and suggest an altered developmental trajectory associated with RSV infection. Microbial temporal dynamics reveal indicators of disease risk, correlates of illness and severity, and impact of RSV infection on microbiota composition.

Airway Gene Expression Correlates of Respiratory Syncytial Virus Disease Severity and Microbiome Composition in Infants.

BACKGROUND: Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. The causes and correlates of severe illness in the majority of infants are poorly defined. METHODS: We recruited a cohort of RSV-infected infants and simultaneously assayed the molecular status of their airways and the presence of airway microbiota. We used rigorous statistical approaches to identify gene expression patterns associated with disease severity and microbiota composition, separately and in combination. RESULTS: We measured comprehensive airway gene expression patterns in 106 infants with primary RSV infection. We identified an airway gene expression signature of severe illness dominated by excessive chemokine expression. We also found an association between Haemophilus influenzae, disease severity, and airway lymphocyte accumulation. Exploring the time of onset of clinical symptoms revealed acute activation of interferon signaling following RSV infection in infants with mild or moderate illness, which was absent in subjects with severe illness. CONCLUSIONS: Our data reveal that airway gene expression patterns distinguish mild/moderate from severe illness. Furthermore, our data identify biomarkers that may be therapeutic targets or useful for measuring efficacy of intervention responses.

Eight practices for data management to enable team data science.

INTRODUCTION: In clinical and translational research, data science is often and fortuitously integrated with data collection. This contrasts to the typical position of data scientists in other settings, where they are isolated from data collectors. Because of this, effective use of data science techniques to resolve translational questions requires innovation in the organization and management of these data. METHODS: We propose an operational framework that respects this important difference in how research teams are organized. To maximize the accuracy and speed of the clinical and translational data science enterprise under this framework, we define a set of eight best practices for data management. RESULTS: In our own work at the University of Rochester, we have strived to utilize these practices in a customized version of the open source LabKey platform for integrated data management and collaboration. We have applied this platform to cohorts that longitudinally track multidomain data from over 3000 subjects. CONCLUSIONS: We argue that this has made analytical datasets more readily available and lowered the bar to interdisciplinary collaboration, enabling a team-based data science that is unique to the clinical and translational setting.

Aims, Study Design, and Enrollment Results From the Assessing Predictors of Infant Respiratory Syncytial Virus Effects and Severity Study.

BACKGROUND: The majority of infants hospitalized with primary respiratory syncytial virus (RSV) infection have no obvious risk factors for severe disease. OBJECTIVE: The aim of this study (Assessing Predictors of Infant RSV Effects and Severity, AsPIRES) was to identify factors associated with severe disease in full-term healthy infants younger than 10 months with primary RSV infection. METHODS: RSV infected infants were enrolled from 3 cohorts during consecutive winters from August 2012 to April 2016 in Rochester, New York. A birth cohort was prospectively enrolled and followed through their first winter for development of RSV infection. An outpatient supplemental cohort was enrolled in the emergency department or pediatric offices, and a hospital cohort was enrolled on admission with RSV infection. RSV was diagnosed by reverse transcriptase-polymerase chain reaction. Demographic and clinical data were recorded and samples collected for assays: buccal swab (cytomegalovirus polymerase chain reaction, PCR), nasal swab (RSV qualitative PCR, complete viral gene sequence, 16S ribosomal ribonucleic acid [RNA] amplicon microbiota analysis), nasal wash (chemokine and cytokine assays), nasal brush (nasal respiratory epithelial cell gene expression using RNA sequencing [RNAseq]), and 2 to 3 ml of heparinized blood (flow cytometry, RNAseq analysis of purified cluster of differentiation [CD]4+, CD8+, B cells and natural killer cells, and RSV-specific antibody). Cord blood (RSV-specific antibody) was also collected for the birth cohort. Univariate and multivariate logistic regression will be used for analysis of data using a continuous Global Respiratory Severity Score (GRSS) as the outcome variable. Novel statistical methods will be developed for integration of the large complex datasets. RESULTS: A total of 453 infants were enrolled into the 3 cohorts; 226 in the birth cohort, 60 in the supplemental cohort, and 78 in the hospital cohort. A total of 126 birth cohort infants remained in the study and were evaluated for 150 respiratory illnesses. Of the 60 RSV positive infants in the supplemental cohort, 42 completed the study, whereas all 78 of the RSV positive hospital cohort infants completed the study. A GRSS was calculated for each RSV-infected infant and is being used to analyze each of the complex datasets by correlation with disease severity in univariate and multivariate methods. CONCLUSIONS: The AsPIRES study will provide insights into the complex pathogenesis of RSV infection in healthy full-term infants with primary RSV infection. The analysis will allow assessment of multiple factors potentially influencing the severity of RSV infection including the level of RSV specific antibodies, the innate immune response of nasal epithelial cells, the adaptive response by various lymphocyte subsets, the resident airway microbiota, and viral factors. Results of this study will inform disease interventions such as vaccines and antiviral therapies.

Virus-Specific Antibody, Viral Load, and Disease Severity in Respiratory Syncytial Virus Infection.

Background: Maternally derived serum antibody and viral load are thought to influence disease severity in primary respiratory syncytial virus (RSV) infection. As part of the AsPIRES study of RSV pathogenesis, we correlated various serum antibody concentrations and viral load with disease severity. Methods: Serum neutralizing antibody titers and levels of immunoglobulin G (IgG) to RSV fusion protein (F), attachment proteins of RSV group A and B, the CX3C region of G, and nasal viral load were measured in 139 full-term previously healthy infants with primary RSV infection and correlated with illness severity. Results: Univariate analysis showed no relationship between measures of serum antibody and severity. However, a multivariate model adjusting for age at the time of infection found a significant 0.56 decrease in severity score for each 2-fold increase in antibody concentration to RSV F. The benefit of antibody was greatest in infants ≤ 2 months of age. Additionally, estimated antibody titer at birth was correlated with age at infection, suggesting that higher antibody titers delay infection. Viral load did not differ by illness severity. Conclusion: Our data support the concept of maternal immunization with an RSV vaccine during pregnancy as a strategy for reducing the burden of RSV infection in full-term healthy infants exposed to RSV during their first winter.

Association of Dynamic Changes in the CD4 T-Cell Transcriptome With Disease Severity During Primary Respiratory Syncytial Virus Infection in Young Infants.

Background: Nearly all children are infected with respiratory syncytial virus (RSV) within the first 2 years of life, with a minority developing severe disease (1%-3% hospitalized). We hypothesized that an assessment of the adaptive immune system, using CD4+ T-lymphocyte transcriptomics, would identify gene expression correlates of disease severity. Methods: Infants infected with RSV representing extremes of clinical severity were studied. Mild illness (n = 23) was defined as a respiratory rate (RR) < 55 and room air oxygen saturation (SaO2) ≥ 97%, and severe illness (n = 23) was defined as RR ≥ 65 and SaO2 ≤ 92%. RNA from fresh, sort-purified CD4+ T cells was assessed by RNA sequencing. Results: Gestational age, age at illness onset, exposure to environmental tobacco smoke, bacterial colonization, and breastfeeding were associated (adjusted P < .05) with disease severity. RNA sequencing analysis reliably measured approximately 60% of the genome. Severity of RSV illness had the greatest effect size upon CD4 T-cell gene expression. Pathway analysis identified correlates of severity, including JAK/STAT, prolactin, and interleukin 9 signaling. We also identified genes and pathways associated with timing of symptoms and RSV group (A/B). Conclusions: These data suggest fundamental changes in adaptive immune cell phenotypes may be associated with RSV clinical severity.

Transcriptomic Biomarkers to Discriminate Bacterial from Nonbacterial Infection in Adults Hospitalized with Respiratory Illness.

Lower respiratory tract infection (LRTI) commonly causes hospitalization in adults. Because bacterial diagnostic tests are not accurate, antibiotics are frequently prescribed. Peripheral blood gene expression to identify subjects with bacterial infection is a promising strategy. We evaluated whole blood profiling using RNASeq to discriminate infectious agents in adults with microbiologically defined LRTI. Hospitalized adults with LRTI symptoms were recruited. Clinical data and blood was collected, and comprehensive microbiologic testing performed. Gene expression was measured using RNASeq and qPCR. Genes discriminatory for bacterial infection were identified using the Bonferroni-corrected Wilcoxon test. Constrained logistic models to predict bacterial infection were fit using screened LASSO. We enrolled 94 subjects who were microbiologically classified; 53 as "non-bacterial" and 41 as "bacterial". RNAseq and qPCR confirmed significant differences in mean expression for 10 genes previously identified as discriminatory for bacterial LRTI. A novel dimension reduction strategy selected three pathways (lymphocyte, α-linoleic acid metabolism, IGF regulation) including eleven genes as optimal markers for discriminating bacterial infection (naïve AUC = 0.94; nested CV-AUC = 0.86). Using these genes, we constructed a classifier for bacterial LRTI with 90% (79% CV) sensitivity and 83% (76% CV) specificity. This novel, pathway-based gene set displays promise as a method to distinguish bacterial from nonbacterial LRTI.

Index case of acute myeloid leukemia in a family harboring a novel CEBPA germ line mutation.

The persistence of a CEBPA mutation at the time of complete remission warrants germ line analysis.Not all patients harboring germ line CEBPA mutations have a family history of AML.

Development of a Global Respiratory Severity Score for Respiratory Syncytial Virus Infection in Infants.

Background: Respiratory syncytial virus (RSV) infection in infants has recognizable clinical signs and symptoms. However, quantification of disease severity is difficult, and published scores remain problematic. Thus, as part of a RSV pathogenesis study, we developed a global respiratory severity score (GRSS) as a research tool for evaluating infants with primary RSV infection. Methods: Previously healthy infants <10 months of age with RSV infections representing the spectrum of disease severity were prospectively evaluated. Clinical signs and symptoms were collected at 3 time points from hospitalized infants and those seen in ambulatory settings. Data were also extracted from office, emergency department, and hospital records. An unbiased data-driven approach using factor analysis was used to develop a GRSS. Results: A total of 139 infants (84 hospitalized and 55 nonhospitalized) were enrolled. Using hospitalization status as the output variable, 9 clinical variables were identified and weighted to produce a composite GRSS. The GRSS had an area under the receiver operator curve of 0.961. Construct validity was demonstrated via a significant correlation with length of stay (r = 0.586, P < .0001). Conclusions: Using routine clinical variables, we developed a severity score for infants with RSV infection that should be useful as an end point for investigation of disease pathogenesis and as an outcome measure for therapeutic interventions.

The Healthy Infant Nasal Transcriptome: A Benchmark Study.

Responses by resident cells are likely to play a key role in determining the severity of respiratory disease. However, sampling of the airways poses a significant challenge, particularly in infants and children. Here, we report a reliable method for obtaining nasal epithelial cell RNA from infants for genome-wide transcriptomic analysis, and describe baseline expression characteristics in an asymptomatic cohort. Nasal epithelial cells were collected by brushing of the inferior turbinates, and gene expression was interrogated by RNA-seq analysis. Reliable recovery of RNA occurred in the absence of adverse events. We observed high expression of epithelial cell markers and similarity to the transcriptome for intrapulmonary airway epithelial cells. We identified genes displaying low and high expression variability, both inherently, and in response to environmental exposures. The greatest gene expression differences in this asymptomatic cohort were associated with the presence of known pathogenic viruses and/or bacteria. Robust bacteria-associated gene expression patterns were significantly associated with the presence of Moraxella. In summary, we have developed a reliable method for interrogating the infant airway transcriptome by sampling the nasal epithelium. Our data demonstrates both the fidelity and feasibility of our methodology, and describes normal gene expression and variation within a healthy infant cohort.

Plant MetGenMAP: an integrative analysis system for plant systems biology.

The information and resources generated from diverse "omics" technologies provide opportunities for producing novel biological knowledge. It is essential to integrate various kinds of biological information and large-scale omics data sets through systematic analysis in order to describe and understand complex biological phenomena. For this purpose, we have developed a Web-based system, Plant MetGenMAP, which can comprehensively integrate and analyze large-scale gene expression and metabolite profile data sets along with diverse biological information. Using this system, significantly altered biochemical pathways and biological processes under given conditions can be retrieved rapidly and efficiently, and transcriptional events and/or metabolic changes in a pathway can be easily visualized. In addition, the system provides a unique function that can identify candidate promoter motifs associated with the regulation of specific biochemical pathways. We demonstrate the functions and application of the system using data sets from Arabidopsis (Arabidopsis thaliana) and tomato (Solanum lycopersicum), respectively. The results obtained by Plant MetGenMAP can aid in a better understanding of the mechanisms that underlie interesting biological phenomena and provide novel insights into the biochemical changes associated with them at the gene and metabolite levels. Plant MetGenMAP is freely available at http://bioinfo.bti.cornell.edu/tool/MetGenMAP.