A 200-Antibody Microarray Biochip for Environmental Monitoring: Searching for Universal Microbial Biomarkers through Immunoprofiling

Environmental biomonitoring approaches require the measurement of either unequivocal biomarkers or specific biological profiles. Antibody microarrays constitute new tools for fast and reliable analysis of up to hundreds of biomarkers simultaneously. Herein we report 150 new polyclonal antibodies against microbial strains and environmental extracts, as well as the construction and validation of an antibody microarray (EMCHIP200, for “Environmental Monitoring Chip”) containing 200 different antibodies. Each antibody was tested against its antigen for its specificity and cross-reactivity by a sandwich microarray immunoassay. The limit of detection was 0.2 ng mL⁻¹ for some proteins and 10⁴−10⁵ cells mL⁻¹ for bacterial cells and spores. Partial biochemical characterization allowed identification of polymeric compounds (proteins and polysaccharides) as some of the targets recognized by the antibodies. We have successfully used the EMCHIP200 for the detection of biological polymers in samples from extreme environments around the world (e.g., a deep South African mine, Antarctica’s dry valleys, Yellowstone National Park, Iceland, and Rio Tinto surface and subsurface). Clustering analysis permitted us to associate similar immunoprofiles or patterns to samples from apparently very different environments, indicating that they indeed share similar universal biomarkers. Our EMCHIP200 constitutes a new generation of immunosensors for biomarker detection and profiling, for either environmental, industrial, biotechnological, or astrobiological applications

Deconvolution applied to sandwich microarray immunoassays from BF1a (a), BF1b (b), BF1c (c), BF2a (d), BF2b (e), BF2c (f), BF2d (g), BF2e (h) and BF2f (i) transect biofilm extracts.

<p>Black lines represent the experimental fluorescence intensities and red lines represent the deconvoluted signals. Antibodies are numbered according to the list shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114180#pone.0114180.s003" target="_blank">S2 Table</a>. Antibodies marked with asterisks represent spurious results (for details see ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114180#pone.0114180-Rivas2" target="_blank">[21]</a>).</p

Deciphering the Prokaryotic Community and Metabolisms in South African Deep-Mine Biofilms through Antibody Microarrays and Graph Theory

<div><p>In the South African deep mines, a variety of biofilms growing in mine corridor walls as water seeps from intersections or from fractures represents excellent proxies for deep-subsurface environments. However, they may be greatly affected by the oxygen inputs through the galleries of mining activities. As a consequence, the interaction between the anaerobic water coming out from the walls with the oxygen inputs creates new conditions that support rich microbial communities. The inherent difficulties for sampling these delicate habitats, together with transport and storage conditions may alter the community features and composition. Therefore, the development of in situ monitoring methods would be desirable for quick evaluation of the microbial community. In this work, we report the usefulness of an antibody-microarray (EMChip66) immunoassay for a quick check of the microbial diversity of biofilms located at 1.3 km below surface within the Beatrix deep gold mine (South Africa). In addition, a deconvolution method, previously described and used for environmental monitoring, based on graph theory and applied on antibody cross-reactivity was used to interpret the immunoassay results. The results were corroborated and further expanded by 16S rRNA gene sequencing analysis. Both culture-independent techniques coincided in detecting features related to aerobic sulfur-oxidizers, aerobic chemoorganotrophic <i>Alphaproteobacteria</i> and metanotrophic <i>Gammaproteobacteria</i>. 16S rRNA gene sequencing detected phylotypes related to nitrate-reducers and anaerobic sulfur-oxidizers, whereas the EMChip66 detected immunological features from methanogens and sulfate-reducers. The results reveal a diverse microbial community with syntrophic metabolisms both anaerobic (fermentation, methanogenesis, sulphate and nitrate reduction) and aerobic (methanotrophy, sulphur oxidation). The presence of oxygen-scavenging microbes might indicate that the system is modified by the artificial oxygen inputs from the mine galleries.</p></div

Metal content (ppm) of the transect biofilm samples analyzed by ICP-MS.

<p>Metal content (ppm) of the transect biofilm samples analyzed by ICP-MS.</p

Summary of the geochemical data from the fracture-associated water collected from BH1 and BH2 boreholes.

<p>Eh: oxidation-reduction potential; TDS: total dissolved solids; TOC: total organic carbon; DOC: dissolved organic carbon. NM: not measured.</p><p>Summary of the geochemical data from the fracture-associated water collected from BH1 and BH2 boreholes.</p

Syntrophic metabolisms in deep South African mine biofilms inferred from the complementary deconvolution method and the phylogenetic analysis.

<p>Metabolisms inferred from both methods are represented by solid circles (aerobic heterotrophs: white circles; aerobic S-oxidizers: yellow circles, and metanotrophs: orange circles), metabolisms inferred by deconvolution analysis by dotted circles (methanogens: purple circles and SRB: blue circles) and metabolisms inferred by 16S rRNA gene sequencing analysis are represented by horizontal lined circle (heterotrophic nitrate-reducers: green circle and anaerobic S-oxidizers: red circle).</p

Deconvolution applied to sandwich microarray immunoassays from BF1a (a), BF1b (b), BF1c (c), BF2a (d), BF2b (e), BF2c (f), BF2d (g), BF2e (h) and BF2f (i) transect biofilm extracts.

<p>Black lines represent the experimental fluorescence intensities and red lines represent the deconvoluted signals. Antibodies are numbered according to the list shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114180#pone.0114180.s003" target="_blank">S2 Table</a>. Antibodies marked with asterisks represent spurious results (for details see ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114180#pone.0114180-Rivas2" target="_blank">[21]</a>).</p

Mapping the positive immunodetections on the antibody graph <i>G</i> with 66 nodes and 125 links associated to our EMChip66 antibody microarray.

<p>Each node represents one antibody, and the links (arrows) represent cross-reactivity of weight <i>G_ij</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114180#pone.0114180-Rivas2" target="_blank">[21]</a>. Up to 18 colored nodes represent those antibody spots that rendered positive fluorescence in at least one biofilm extract. Self-loops are not shown for clarity. Prot_PfuFer and Prot_PfuDPS =  PfuFer and PfuDPS antibodies respectively.</p

Phylogenetic affiliation of the 16S rRNA gene sequences retrieved from BF1c (upper part) and BF2d (bottom part) transect samples biofilms.

<p>A maximum-likelihood (PHYLML) phylogenetic tree was chosen as a consensus tree, after reconstructing the phylogeny by using different algorithms, substitution models and filters. The trees show the relationship between representative 16S rRNA gene clone sequences from BF1c and BF2d (in bold) and related strains and environmental clones from different bacterial phyla. The number of sequences grouped into that specific OTU is indicated in parentheses. Positional filters were applied to discard high variable positions and a total number of 490 and 570 columns respectively, were finally compared. Taxonomic classification according to Silva104 database is also shown. The scale bars represent 7 and 6% nucleotide substitutions per sequence position respectively.</p

Clinical data of patients included in this study.

<p>Clinical data of patients included in this study.</p