CYANOCHIP: An Antibody Microarray for High-Taxonomical-Resolution Cyanobacterial Monitoring

Cyanobacteria are Gram-negative photosynthetic prokaryotes that are widespread on Earth. Eutrophication and global warming make some aquatic ecosystems behave as bioreactors that trigger rapid and massive cyanobacterial growth with remarkable economic and health consequences. Rapid and efficient early warning systems are required to support decisions by water body authorities. We have produced 17 specific antibodies to the most frequent cyanobacterial strains blooming in freshwater ecosystems, some of which are toxin producers. A sandwich-type antibody microarray immunoassay (CYANOCHIP) was developed for the simultaneous testing of any of the 17 strains, or other closely related strains, in field samples from different habitats (water, rocks, and sediments). We titrated and tested all of the antibodies in succession using a fluorescent sandwich microarray immunoassay. Although most showed high specificity, we applied a deconvolution method based on graph theory to disentangle the few existing cross-reactions. The CYANOCHIP sensitivity ranged from 10² to 10⁴ cells mL^–1, with most antibodies detecting approximately 10² cells mL^–1. We validated the system by testing multiple isolates and crude natural samples from freshwater reservoirs and rocks, both in the laboratory and by <i>in situ</i> testing in the field. The results demonstrated that CYANOCHIP is a valuable tool for the sensitive and reliable detection of cyanobacteria for early warning and research purposes

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

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

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

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

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

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

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

Sola-Valls_et_al._supplementary_table_1 – Supplemental material for Combined walking outcome measures identify clinically meaningful response to prolonged-release fampridine

<p>Supplemental material, Sola-Valls_et_al._supplementary_table_1 for Combined walking outcome measures identify clinically meaningful response to prolonged-release fampridine by Núria Sola-Valls, Yolanda Blanco, María Sepúlveda, Sara Llufriu, Elena H. Martínez-Lapiscina, Irati Zubizarreta, Irene Pulido-Valdeolivas, Carmen Montejo, Pablo Villoslada and Albert Saiz in Therapeutic Advances in Neurological Disorders</p