Cyanobacterial lipopolysaccharides and human health – a review

Cyanobacterial lipopolysaccharide/s (LPS) are frequently cited in the cyanobacteria literature as toxins responsible for a variety of heath effects in humans, from skin rashes to gastrointestinal, respiratory and allergic reactions. The attribution of toxic properties to cyanobacterial LPS dates from the 1970s, when it was thought that lipid A, the toxic moiety of LPS, was structurally and functionally conserved across all Gram-negative bacteria. However, more recent research has shown that this is not the case, and lipid A structures are now known to be very different, expressing properties ranging from LPS agonists, through weak endotoxicity to LPS antagonists. Although cyanobacterial LPS is widely cited as a putative toxin, most of the small number of formal research reports describe cyanobacterial LPS as weakly toxic compared to LPS from the Enterobacteriaceae. We systematically reviewed the literature on cyanobacterial LPS, and also examined the much lager body of literature relating to heterotrophic bacterial LPS and the atypical lipid A structures of some photosynthetic bacteria. While the literature on the biological activity of heterotrophic bacterial LPS is overwhelmingly large and therefore difficult to review for the purposes of exclusion, we were unable to find a convincing body of evidence to suggest that heterotrophic bacterial LPS, in the absence of other virulence factors, is responsible for acute gastrointestinal, dermatological or allergic reactions via natural exposure routes in humans. There is a danger that initial speculation about cyanobacterial LPS may evolve into orthodoxy without basis in research findings. No cyanobacterial lipid A structures have been described and published to date, so a recommendation is made that cyanobacteriologists should not continue to attribute such a diverse range of clinical symptoms to cyanobacterial LPS without research confirmation

CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair.

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes

Identification of new proteins involved in DNA damage responses.

A) A co-expression network for DNA damage was started with 34 known genes involved in DNA repair, and all co-expression partners that showed > 0.8 co-expression score and interaction with at least 5 of the known DNA repair genes. Specific functional classes are highlighted with different colors. Edge width corresponds to co-expression score, and node size represents number of connections to other genes in the network. B) Identification of novel genes involved in DNA damage responses. The indicated strains were grown overnight at 30°C in liquid YPD medium, and the 10-fold serially diluted cells were spotted onto YPD agar. For UV damage, the plates were immediately subjected to 200 μJ UV. For EMS, the cells were incubated in 100 μM EMS for 1 hr before serial dilution and plating. The plates were incubated at 30°C and imaged after 2 days.</p

Establishing concentrations for EMS treatment.

A) H99 wild type cells were incubated with the indicated concentrations of EMS for 1 hr before serial dilution and plating onto YPD. The plates were incubated at 30°C and imaged after 2 days. CFU/mL was calculated from the serial dilutions. (TIF)</p

CryptoCEN can recapitulate core biological processes in <i>C</i>. <i>neoformans</i>.

A) A co-expression network for capsule was generated by starting with genes known to be involved in capsule biosynthetic genes. All co-expressed partners with a score > 0.8 and at least 5 co-expression edges with known capsule genes were visualized in Cytoscape. Specific functional classes are highlighted with different colors. Edge width corresponds to degree of co-expression. B) Identification of genes involved in capsule. The indicated mutants were incubated in RPMI at 37°C with 5% CO2 for three days before staining with India ink and imaging using brightfield microscopy at 20X magnification. Increased or decreased capsule was determined by comparison with the wild type or cap64Δ control strains. C) A co-expression network for ergosterol biosynthesis was started with the known ergosterol biosynthetic genes and all co-expression partners that showed >0.8 co-expression score and interaction with >3 ergosterol biosynthetic genes. Specific functional classes are highlighted with different colors. Edge width corresponds to degree of co-expression. D) Identification of genes involved in fluconazole susceptibility. The indicated strains were grown overnight at 30°C in liquid YPD medium, and the serially diluted cells were spotted onto YPD agar with or without 4 μg/mL fluconazole. The plates were incubated at 30°C and imaged after 2 days.</p

Matched controls do not show enrichment for DNA repair.

A) Analysis of matched controls for phenotypes on DNA damaging agents. The indicated strains were grown overnight at 30°C in liquid YPD medium, and the 10-fold serially diluted cells were spotted onto YPD agar. For UV damage, the plates were immediately subjected to 200 μJ UV. For EMS, the cells were incubated in 5% EMS for 1 hr before serial dilution and plating. The plates were incubated at 30°C and imaged after 2 days. (TIF)</p

Saccharomyces complexes.

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.</div

orthogroups.

Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.</div

Evolutionary constraints inform co-expression analyses.

Distribution of co-expression scores for C. neoformans gene pairs across different types of evolutionary conservation. A) C. neoformans gene pairs with orthology to S. cerevisiae gene pairs that encode for proteins that are members of the same complex (13,950 pairs over 1,304 genes, coexp score mean: 0.80, IRQ50: [0.71, 0.90]), B) Significantly co-evolving gene pairs (140,592 pairs over 4,269 genes, coexp score mean: 0.50, interquartile-range at 50% (IRQ50): [0.40, 0.60]), and C) Paralogous gene pairs (1,056 pairs over 550 genes, coexp score mean: 0.58, IRQ50: [0.46, 0.67]). D) Scatter plot of the co-expression score by the geometric expression of the partners. E) The co-expressed partners at a 0.8 threshold for co-expression score of the duplicated genes Cdc42 and Cdc420 were compared and visualized in Cytoscape. Kinetochore proteins are highlighted in blue, septin proteins in purple, and unannotated or uncharacterized proteins are highlighted in yellow. Width of the lines indicates co-expression score. F) Tubulin is altered in the cdc420Δ and cdc42Δ mutant strains compared with the H99 wildtype. Tubulin was visualized by fusion of α-tubulin with GFP. Cells were incubated in liquid YPD at 30°C before imaging. Images taken at 40X magnification, scale = 5 microns.</p

Retrospective predictive accuracy of CryptoCEN.

A. A re-embedded gene by expression matrix, scanning the key embedding parameters umap_a and umap_b over the ranges [20, 30, 40, 50, 60] and [0.45, 0.5, 0.55], respectively, while keeping the remaining parameters fixed (prereduction of dimension to 500 using PCA, n_neighbors = 30, negative_sample_rate = 50, umap_repulsion_strength = 3, n_epochs = 2000). Rows are umap_a and the columns are umap_b. The points are clustered using leiden clustering using with a resolution parameter of 1e-3 and points are colored by the cluster index. B. UpSet plot of the retrospective prediction accuracy, as determined by the neighbor voting guilt-by-association (GBA) area under the ROC curve (AUROC). AUROCs values range between 0.5 for random predictor and 1 for a perfect predictor. As data sources are combined, the prediction accuracy increases. Each annotated GO term is colored by ontology biological process (BP), cellular component (CC), or molecular function (MF). C. Enrichment of co-expression in the gene by expression matrix UMAP. For each cluster, we selected inter and intra-cluster associations. We then computed the area under the receiver operator characteristic (AUROC) for the enrichment of the intra-cluster associations over the inter-cluster associations based on the co-expression score. Enrichment within each cluster is indicated by color. (TIF)</p