Search

Researchers in environmental justice contend that low-income communities and communities of color face greater impacts from environmental hazards. This is also of concern for policy makers. In this context, our paper has two principal objectives. First, we propose a method for creating an index capable of summarizing racial-ethnic and socioeconomic inequalities from the impact of cumulative environmental hazards. Second, we apply the index to Los Angeles County to illustrate the potential applications and complexities of its implementation. Individual environmental inequality indices are calculated based on unequal shares of environmental hazards for racial-ethnic groups and socioeconomic positions. The illustrated hazards include ambient concentrations of particulate matter, nitrogen dioxide, and estimates of cancer risk associated with modeled estimates for diesel particulate matter. The cumulative environmental hazard inequality index (CEHII) then combines individual environmental hazards, using either a multiplicative or an additive model. Significant but modest inequalities exist for both individual and cumulative environmental hazards in Los Angeles. The highest level of inequality among racial-ethnic and socioeconomic groups occurs when a multiplicative model is used to estimate cumulative hazard. The CEHII provides a generalized framework that incorporates environmental hazards and socioeconomic characteristics to assess inequalities in cumulative environmental risks.

This study examines links between racial residential segregation and estimated ambient air toxics exposures and their associated cancer risks using modeled concentration estimates from the U.S. Environmental Protection Agency's National Air Toxics Assessment. We combined pollutant concentration estimates with potencies to calculate cancer risks by census tract for 309 metropolitan areas in the United States. This information was combined with socioeconomic status (SES) measures from the 1990 Census. Estimated cancer risks associated with ambient air toxics were highest in tracts located in metropolitan areas that were highly segregated. Disparities between racial/ethnic groups were also wider in more segregated metropolitan areas. Multivariate modeling showed that, after controlling for tract-level SES measures, increasing segregation amplified the cancer risks associated with ambient air toxics for all racial groups combined [highly segregated areas: relative cancer risk (RCR) = 1.04; 95% confidence interval (CI), 1.01-107; extremely segregated areas: RCR = 1.32; 95% CI, 1.28-1.36]. This segregation effect was strongest for Hispanics (highly segregated areas: RCR = 1.09; 95% CI, 1.01-1.17; extremely segregated areas: RCR = 1.74; 95% CI, 1.61-1.88) and weaker among whites (highly segregated areas: RCR = 1.04; 95% CI, 1.01-1.08; extremely segregated areas: RCR = 1.28; 95% CI, 1.24-1.33), African Americans (highly segregated areas: RCR = 1.09; 95% CI, 0.98-1.21; extremely segregated areas: RCR = 1.38; 95% CI, 1.24-1.53), and Asians (highly segregated areas: RCR = 1.10; 95% CI, 0.97-1.24; extremely segregated areas: RCR = 1.32; 95% CI, 1.16-1.51). Results suggest that disparities associated with ambient air toxics are affected by segregation and that these exposures may have health significance for populations across racial lines.

Major histocompatibility complex class I-restricted CD8(+) cytotoxic T lymphocytes (CTL) are implicated in protective Th1 immunity to Mycobacterium tuberculosis infection. We report the identification of three novel HLA-A*0201-restricted CTL epitopes within mycobacterial superoxide dismutase (SodA), L-alanine dehydrogenase (AlaDH), and L-glutamine synthetase (GlnS) proteins.

Bioinformatics tools enable researchers to move rapidly from genome sequence to vaccine design. EpiMer and EpiMatrix are computer-driven pattern-matching algorithms that identify T cell epitopes. Conservatrix, BlastiMer, and Patent-Blast permit the analysis of protein sequences for highly conserved regions, for homology with other known proteins, and for homology with previously patented epitopes, respectively. Two applications of these tools to epitope-driven vaccine design are described in this review. Using Conservatrix and EpiMatrix, we analyzed more than 10000 HIV-1 sequences and identified peptides that were potentially immunostimulatory and highly conserved across HIV-1 clades. MHC binding assays and CTL assays have been carried out: 50 (69%) of the 72 candidate epitopes bound in assays with cell lines expressing the corresponding MHC molecule; 15 of the 24 B7 peptides (63%) stimulated gamma-interferon release in ELISpot assays. These results lend support to the bioinformatics approach to selecting novel, conserved, HIV-1 CTL epitopes. EpiMatrix was also applied to the entire 'proteome' derived from two Mycobacterium tuberculosis (Mtb) genomes. Using EpiMatrix, BlastiMer, and Patent-Blast, we narrowed the list of putative Mtb epitopes to be tested in vitro from 1600000 to 3000, a 99.8% reduction. The pace of vaccine design will accelerate when these and other bioinformatics tools are systematically applied to whole genomes and used in combination with in vitro methods for screening and confirming epitopes.

This preliminary study was undertaken to identify new human leucocyte antigens (HLA) ligands from human immunodeficiency virus type 1 (HIV-1) which are highly conserved across HIV-1 clades and which may serve to induce cross-reactive cytotoxic T lymphocytes (CTLs). EpiMatrix was used to predict putative ligands from HIV-1 for HLA-A2 and HLA-B27. Twenty-six peptides that were both likely to bind and also highly conserved across HIV-1 strains in the Los Alamos HIV sequence database were selected for binding assays using the T2 stabilization assay. Two peptides that were also highly likely to bind (for A2 and B27, as determined by EpiMatrix) and well conserved across HIV-1 strains, and had previously been described to bind in the published literature, were also selected to serve as positive controls for the assays. Ten new major histocompatibility complex (MHC) ligands were identified among the 26 study peptides. The control peptides bound, as expected. These data confirm that EpiMatrix can be used to screen HIV-1 protein sequences for highly conserved regions that are likely to bind to MHC and may prove to be highly conserved HIV-1 CTL epitopes.

We assessed the extent to which co-infection with HIV and Mycobacterium tuberculosis (Mtb) was diagnosed at several high risk clinical care sites from 1992 to 1994 to determine whether surveillance for co-infection was performed. Information on PPD skin testing, HIV status, and HIV risk exposures was extracted from records at HIV clinics in Rhode Island and a large database (HIV sites) and from records at the state TB clinic and the Rhode Island Health Department (TB sites). At the HIV sites, 34 of 1,408 HIV infected subjects were newly diagnosed with Mtb infection in the study period. At the TB sites, 16 of 1,389 subjects with newly diagnosed Mtb infection or disease were identified as HIV infected. Eighty per cent of the records reviewed for this study were incomplete. Hispanic subjects were at higher risk of being identified as co-infected at the HIV sites. At the TB sites, US-born subjects were at higher risk of being identified as co-infected Recommendations for high risk individuals include yearly tuberculosis skin testing. Adherence to these guidelines in selected high-risk clinical care sites in Rhode Island was substandard during the study period; the importance of Mtb screening was demonstrated in this study. Identification of groups that are at higher risk of having HIV and Mtb co-infection identified may enable health care providers to improve testing and prevention of tuberculosis at high risk clinical care settings.

EpiMatrix/HIV, a tool that is currently available on the World Wide Web, enables researchers to screen HIV proteins for potential MHC ligands. We have performed a comparison of EpiMatrix predictions to 158 published allotype-specific HLA-associated peptides (MHC ligands) derived from 133 proteins. The top 10 ranked EpiMatrix predictions for each of the 158 HLA allotype-protein pairs were selected for comparison with these published ligands. EpiMatrix correctly identified 134 of 158 published ligands (85%). The algorithm is now available for use by the HIV research community at the URL http:/(/)www.EpiMatrix.com/HIV.

Identification of promiscuous or multideterminant T cell epitopes is essential for HIV vaccine development, however, current methods for T cell epitope identification are both cost intensive and labor intensive. We have developed a computer-driven algorithm, named EpiMer, which searches protein amino acid sequences for putative MHC class I- and/or class II-restricted T cell epitopes. This algorithm identifies peptides that contain multiple MHC-binding motifs from protein sequences. To evaluate the predictive power of EpiMer, the amino acid sequences of the HIV-1 proteins nef, gp160, gag p55, and tat were searched for regions of MHC-binding motif clustering. We assessed the algorithm's predictive power by comparing the EpiMer-predicted peptide epitopes to T cell epitopes that have been published in the literature. The EpiMer method of T cell epitope identification was compared to the standard method of synthesizing short, overlapping peptides and testing them for immunogenicity (overlapping peptide method), and to an alternate algorithm that has been used to identify putative T cell epitopes from primary structure (AMPHI). For the four HIV-1 proteins analyzed, the in vitro testing of EpiMer peptides for immunogenicity would have required the synthesis of fewer total peptides than either AMPHI or the overlapping peptide method. The EpiMer algorithm proved to be more efficient and more sensitive per amino acid than both the overlapping peptide method and AMPHI. The EpiMer predictions for these four HIV proteins are described. Since EpiMer-predicted peptides have the potential to bind to multiple MHC alleles, they are strong candidates for inclusion in a synthetic HIV vaccine.