A few diatom species produce toxins that affect human and animal health. Among these, members of the Pseudo-nitzschia genus were the first diatoms unambiguously identified as producer of domoic acid, a neurotoxin affecting molluscan shell-fish, birds, marine mammals, and humans. Evidence exists indicating the involvement of another diatom genus, Amphora, as a potential producer of domoic acid. We present a strategy for the detection of the diatom species Amphora coffeaeformis based on the development of species-specific oligonucleotide probes and their application in microarray hybridization experiments. This approach is based on the use of two marker genes highly conserved in all diatoms, but endowed with sufficient genetic divergence to discriminate diatoms at the species level. A region of approximately 450 bp of these previously unexplored marker genes, coding for elongation factor 1-a (eEF1-a) and silicic acid transporter (SIT), was used to design oligonucleotide probes that were tested for specificity in combination with the corresponding fluorescently labeled DNA targets. The results presented in this work suggest a possible use of this DNA chip technology for the selective detection of A. coffeaeformis in environmental settings where the presence of this potential toxin producer may represent a threat to human and animal health. In addition, the same basic approach can be adapted to a wider range of diatoms for the simultaneous detection of microorganisms used as biomarkers of different water quality levels.

Our understanding of the composition of diatom communities and their response to environmental changes is currently limited by laborious taxonomic identification procedures. Advances in molecular technologies are expected to contribute more efficient, robust and sensitive tools for the detection of these ecologically relevant microorganisms. There is a need to explore and test phylogenetic markers as an alternative to the use of rRNA genes, whose limited sequence divergence does not allow the accurate discrimination of diatoms at the species level. In this work, nine diatom species belonging to eight genera, isolated from epylithic environmental samples collected in central Italy, were chosen to implement a panel of diatoms covering the full range of ecological status of freshwaters. The procedure described in this work relies on the PCR amplification of specific regions in two conserved diatom genes, elongation factor 1-a (eEF1-a) and silicic acid transporter (SIT), as a first step to narrow down the complexity of the targets, followed by microarray hybridization experiments. Oligonucleotide probes with the potential to discriminate closely related species were designed taking into account the genetic polymorphisms found in target genes. These probes were tested, refined and validated on a small-scale prototype DNA chip. Overall, we obtained 17 highly specific probes targeting eEF1-a and SIT, along with 19 probes having lower discriminatory power recognizing at the same time two or three species. This basic array was validated in a laboratory setting and is ready for tests with crude environmental samples eventually to be scaled-up to include a larger panel of diatoms. Its possible use for the simultaneous detection of diatoms selected from the classes of water quality identified by the European Water Framework Directive is discussed.

Lassa virus (LASV) causes severe hemorrhagic fever across West Africa where the development of rapid, accurate diagnostics remains hindered by the extensive lineage-level genetic diversity and by the limited availability of high-level containment laboratories. We developed a LASV assay using the CRISPR-based Streamlined Highlighting of Infections to Navigate Epidemics (SHINE) platform to enable safe, field-deployable detection. To enable assay development without handling live virus, we established a plasmid-based system that generates LASV RNA controls spanning major viral lineages. Using these surrogate plasmids, we optimized the assay for 32 genetically distinct LASV isolates, demonstrating detection across major lineage subdivisions with lineage-dependent sensitivity. The assay maintained sensitivity in serum and whole blood, and was further evaluated using clinical samples in Nigeria. To reduce subjectivity in strip interpretation, we developed supervised machine-learning models for automated classification of lateral flow results. Together, this integrated diagnostic framework supports field-oriented detection of genetically diverse LASV and provides a broader approach for diagnostic development for high-consequence RNA viruses.