Sea Turtles in the Cancer Risk Landscape: A Global Meta-Analysis of Fibropapillomatosis Prevalence and Associated Risk Factors.

Several cancer risk factors (exposure to ultraviolet-B, pollution, toxins and pathogens) have been identified for wildlife, to form a "cancer risk landscape." However, information remains limited on how the spatiotemporal variability of these factors impacts the prevalence of cancer in wildlife. Here, we evaluated the cancer risk landscape at 49 foraging sites of the globally distributed green turtle (Chelonia mydas), a species affected by fibropapillomatosis, by integrating data from a global meta-analysis of 31 publications (1994-2019). Evaluated risk factors included ultraviolet light exposure, eutrophication, toxic phytoplanktonic blooms, sea surface temperature, and the presence of mechanical vectors (parasites and symbiotic species). Prevalence was highest in areas where nutrient concentrations facilitated the emergence of toxic phytoplankton blooms. In contrast, ultraviolet light exposure and the presence of parasitic and/or symbiotic species did not appear to impact disease prevalence. Our results indicate that, to counter outbreaks of fibropapillomatosis, management actions that reduce eutrophication in foraging areas should be implemented

Machine learning to detect marine animals in UAV imagery: effect of morphology, spacing, behaviour and habitat

Machine learning algorithms are being increasingly used to process large volumes of wildlife imagery data from unmanned aerial vehicles (UAVs); however, suitable algorithms to monitor multiple species are required to enhance efficiency. Here, we developed a machine learning algorithm using a low-cost computer. We trained a convolutional neural network and tested its performance in: (1) distinguishing focal organisms of three marine taxa (Australian fur seals, loggerhead sea turtles and Australasian gannets; body size ranges: 0.8–2.5 m, 0.6–1.0 m, and 0.8–0.9 m, respectively); and (2) simultaneously delineating the fine-scale movement trajectories of multiple sea turtles at a fish cleaning station. For all species, the algorithm performed best at detecting individuals of similar body length, displaying consistent behaviour or occupying uniform habitat (proportion of individuals detected, or recall of 0.94, 0.79 and 0.75 for gannets, seals and turtles, respectively). For gannets, performance was impacted by spacing (huddling pairs with offspring) and behaviour (resting vs. flying shapes, overall precision: 0.74). For seals, accuracy was impacted by morphology (sexual dimorphism and pups), spacing (huddling and creches) and habitat complexity (seal sized boulders) (overall precision: 0.27). For sea turtles, performance was impacted by habitat complexity, position in water column, spacing, behaviour (interacting individuals) and turbidity (overall precision: 0.24); body size variation had no impact. For sea turtle trajectories, locations were estimated with a relative positioning error of <50 cm. In conclusion, we demonstrate that, while the same machine learning algorithm can be used to survey multiple species, no single algorithm captures all components optimally within a given site. We recommend that, rather than attempting to fully automate detection of UAV imagery data, semi-automation is implemented (i.e. part automated and part manual, as commonly practised for photo-identification). Approaches to enhance the efficiency of manual detection are required in parallel to the development of effective implementation of machine learning algorithms

Functional analysis of yeast gene families involved in metabolism of vitamins B1 and B6

In order to clarify their physiological functions, we have undertaken a characterization of the three-membered gene families SNZ13 and SNO13. In media lacking vitamin B6, SNZ1 and SNO1 were both required for growth in certain conditions, but neither SNZ2, SNZ3, SNO2 nor SNO3 were required. Copies 2 and 3 of the gene products have, in spite of their extremely close sequence similarity, slightly different functions in the cell. We have also found that copies 2 and 3 are activated by the lack of thiamine and that the Snz proteins physically interact with the thiamine biosynthesis Thi5 protein family. Whereas copy 1 is required for conditions in which B6 is essential for growth, copies 2 and 3 seem more related with B1 biosynthesis during the exponential phase

Megasatellites: a peculiar class of giant minisatellites in genes involved in cell adhesion and pathogenicity in Candida glabrata

Minisatellites are DNA tandem repeats that are found in all sequenced genomes. In the yeast Saccharomyces cerevisiae, they are frequently encountered in genes encoding cell wall proteins. Minisatellites present in the completely sequenced genome of the pathogenic yeast Candida glabrata were similarly analyzed, and two new types of minisatellites were discovered: minisatellites that are composed of two different intermingled repeats (called compound minisatellites), and minisatellites containing unusually long repeated motifs (126–429 bp). These long repeat minisatellites may reach unusual length for such elements (up to 10 kb). Due to these peculiar properties, they have been named ‘megasatellites’. They are found essentially in genes involved in cell–cell adhesion, and could therefore be involved in the ability of this opportunistic pathogen to colonize the human host. In addition to megasatellites, found in large paralogous gene families, there are 93 minisatellites with simple shorter motifs, comparable to those found in S. cerevisiae. Most of the time, these minisatellites are not conserved between C. glabrata and S. cerevisiae, although their host genes are well conserved, raising the question of an active mechanism creating minisatellites de novo in hemiascomycetes

Systematic discovery of unannotated genes in 11 yeast species using a database of orthologous genomic segments

<p>Abstract</p> <p>Background</p> <p>In standard BLAST searches, no information other than the sequences of the query and the database entries is considered. However, in situations where two genes from different species have only borderline similarity in a BLAST search, the discovery that the genes are located within a region of conserved gene order (synteny) can provide additional evidence that they are orthologs. Thus, for interpreting borderline search results, it would be useful to know whether the syntenic context of a database hit is similar to that of the query. This principle has often been used in investigations of particular genes or genomic regions, but to our knowledge it has never been implemented systematically.</p> <p>Results</p> <p>We made use of the synteny information contained in the Yeast Gene Order Browser database for 11 yeast species to carry out a systematic search for protein-coding genes that were overlooked in the original annotations of one or more yeast genomes but which are syntenic with their orthologs. Such genes tend to have been overlooked because they are short, highly divergent, or contain introns. The key features of our software - called SearchDOGS - are that the database entries are classified into sets of genomic segments that are already known to be orthologous, and that very weak BLAST hits are retained for further analysis if their genomic location is similar to that of the query. Using SearchDOGS we identified 595 additional protein-coding genes among the 11 yeast species, including two new genes in <it>Saccharomyces cerevisiae</it>. We found additional genes for the mating pheromone a-factor in six species including <it>Kluyveromyces lactis</it>.</p> <p>Conclusions</p> <p>SearchDOGS has proven highly successful for identifying overlooked genes in the yeast genomes. We anticipate that our approach can be adapted for study of further groups of species, such as bacterial genomes. More generally, the concept of doing sequence similarity searches against databases to which external information has been added may prove useful in other settings.</p

Phenotypic Screens, Chemical Genomics, and Antimalarial Lead Discovery

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A Re-Annotation of the Saccharomyces Cerevisiae Genome

Discrepancies in gene and orphan number indicated by previous analyses suggest that S. cerevisiae would benefit from a consistent re-annotation. In this analysis three new genes are identified and 46 alterations to gene coordinates are described. 370 ORFs are defined as totally spurious ORFs which should be disregarded. At least a further 193 genes could be described as very hypothetical, based on a number of criteria. It was found that disparate genes with sequence overlaps over ten amino acids (especially at the N-terminus) are rare in both S. cerevisiae and Sz. pombe. A new S. cerevisiae gene number estimate with an upper limit of 5804 is proposed, but after the removal of very hypothetical genes and pseudogenes this is reduced to 5570. Although this is likely to be closer to the true upper limit, it is still predicted to be an overestimate of gene number. A complete list of revised gene coordinates is available from the Sanger Centre (S. cerevisiae reannotation: ftp://ftp/pub/yeast/SCreannotation)

Insertion of Horizontally Transferred Genes within Conserved Syntenic Regions of Yeast Genomes

Horizontal gene transfer has been occasionally mentioned in eukaryotic genomes, but such events appear much less numerous than in prokaryotes, where they play important functional and evolutionary roles. In yeasts, few independent cases have been described, some of which corresponding to major metabolic functions, but no systematic screening of horizontally transferred genes has been attempted so far. Taking advantage of the synteny conservation among five newly sequenced and annotated genomes of Saccharomycetaceae, we carried out a systematic search for HGT candidates amidst genes present in only one species within conserved synteny blocks. Out of 255 species-specific genes, we discovered 11 candidates for HGT, based on their similarity with bacterial proteins and on reconstructed phylogenies. This corresponds to a minimum of six transfer events because some horizontally acquired genes appear to rapidly duplicate in yeast genomes (e.g. YwqG genes in Kluyveromyces thermotolerans and serine recombinase genes of the IS607 family in Saccharomyces kluyveri). We show that the resulting copies are submitted to a strong functional selective pressure. The mechanisms of DNA transfer and integration are discussed, in relation with the generally small size of HGT candidates. Our results on a limited set of species expand by 50% the number of previously published HGT cases in hemiascomycetous yeasts, suggesting that this type of event is more frequent than usually thought. Our restrictive method does not exclude the possibility that additional HGT events exist. Actually, ancestral events common to several yeast species must have been overlooked, and the absence of homologs in present databases leaves open the question of the origin of the 244 remaining species-specific genes inserted within conserved synteny blocks

CYGD: the Comprehensive Yeast Genome Database

The Comprehensive Yeast Genome Database (CYGD) compiles a comprehensive data resource for information on the cellular functions of the yeast Saccharomyces cerevisiae and related species, chosen as the best understood model organism for eukaryotes. The database serves as a common resource generated by a European consortium, going beyond the provision of sequence information and functional annotations on individual genes and proteins. In addition, it provides information on the physical and functional interactions among proteins as well as other genetic elements. These cellular networks include metabolic and regulatory pathways, signal transduction and transport processes as well as co-regulated gene clusters. As more yeast genomes are published, their annotation becomes greatly facilitated using S.cerevisiae as a reference. CYGD provides a way of exploring related genomes with the aid of the S.cerevisiae genome as a backbone and SIMAP, the Similarity Matrix of Proteins. The comprehensive resource is available under http://mips.gsf.de/genre/proj/yeast/