Viral infection of farmed fish and shellfish represents a major issue within the aquaculture industry. One potential control strategy involves RNA interference of viral gene expression through the oral delivery of specific double-stranded RNA (dsRNA). In previous work, we have shown that recombinant dsRNA can be produced in the chloroplast of the edible microalga Chlamydomonas reinhardtii and used to control disease in shrimp. Here, we report a significant improvement in antiviral dsRNA production and its use to protect shrimp against white spot syndrome virus (WSSV). A new strategy for dsRNA synthesis was developed that uses two convergent copies of the endogenous rrnS promoter to drive high-level transcription of both strands of the WSSV gene element in the chloroplast. Quantitative RT-PCR indicated that ~119 ng dsRNA was produced per liter of culture of the transgenic microalga. This represents an ~10-fold increase in dsRNA relative to our previous report. The engineered alga was assessed for its ability to prevent WSSV infection when fed to shrimp larvae prior to a challenge with the virus. The survival of shrimp given feed supplemented with dried alga containing the dsRNA was significantly enhanced (~69% survival) relative to a negative control (<10% survival). The findings suggest that this new dsRNA production platform could be employed as a low-cost, low-tech control method for aquaculture.

‘Marker-free’ strategies for creating transgenic microorganisms avoid the issue of potential transmission of antibiotic resistance genes to other microorganisms. An already-established strategy for engineering the chloroplast genome (=plastome) of the green microalga Chlamydomonas reinhardtii involves the restoration of photosynthetic function using a recipient strain carrying a plastome mutation in a key photosynthesis gene. Selection for transformant colonies is carried out on minimal media, such that only those cells in which the mutated gene has been replaced with a wild-type copy carried on the transgenic DNA are capable of phototrophic growth. However, this approach can suffer from issues of efficiency due to the slow growth of C. reinhardtii on minimal media and the slow die-back of the untransformed lawn of cells when using mutant strains with a limited photosensitivity phenotype. Furthermore, such phototrophic rescue has tended to rely on existing mutants that are not necessarily ideal for transformation and targeted transgene insertion: Mutants carrying point mutations can easily revert, and those with deletions that do not extend to the intended transgene insertion site can give rise to a sub-population of rescued lines that lack the transgene. In order to improve and accelerate the transformation pipeline for C. reinhardtii, we have created a novel recipient line, HNT6, carrying an engineered deletion in exon 3 of psaA, which encodes one of the core subunits of photosystem I (PSI). Such PSI mutants are highly light-sensitive allowing faster recovery of transformant colonies by selecting for light-tolerance on acetate-containing media, rather than phototrophic growth on minimal media. The deletion extends to a site upstream of psaA-3 that serves as a neutral locus for transgene insertion, thereby ensuring that all of the recovered colonies are transformants containing the transgene. We demonstrate the application of HNT6 using a luciferase reporter.

Algal biotechnology has received significant attention over the past two decades in fields ranging from biofuels to cosmeceuticals. However, the development of domesticated or genetically engineered microalgal strains for commercial applications depends on accurate and reliable growth data. To this end, several companies have developed lab-scale photobioreactors (PBRs) that enable precision control of conditions and automated growth recording. Whilst the transition from manual control of conditions and measurements to automated systems has allowed researchers to greatly improve the accuracy and scope of cultivation experiments, it has also presented novel challenges. The most pertinent of these being the analysis of the copious quantities of data produced. A standard PBR experiment can contain tens or even hundreds of thousands of data points, and often features outliers, noise, and a requirement for datasets to be calibrated with a standard curve or merged with replicates. Furthermore, complex analysis of multiple curves may be required in order to extract information such as the gradient or fit to a growth model. This can be laborious, time consuming and is not standardized between research groups. Proprietary software provided with most PBRs tends to lack these more advanced features and is typically unable to process data from other PBR manufacturers. To address these issues, we have developed the Algal Data Analyser (ADA), an open-source software platform providing the tools to rapidly plot and analyse microalgal data. ADA can simultaneously interpret datasets from three major PBR suppliers (Algenuity, Industrial Plankton, Photon Systems Instruments), and can also incorporate data from manual readings. Users can rapidly produce standardized, publication ready plots, and analyse multiple growth curves in parallel. Future iterations of ADA will include compatibility with datasets from other PBR suppliers as they become available, with the aim of making it a universal platform for all PBR data.

Solenoid proteins are elongated tandem repeat proteins with diverse biological functions, making them attractive targets for protein design. Advances in machine learning have transformed our understanding of sequence-structure relationships, enabling new approaches for de novo protein design. Here, we present an in silico evolution platform that couples a solenoid discriminator network with AlphaFold2 as an oracle within a genetic algorithm. Starting from random sequences, we design α-, β-, and αβ-solenoid backbones, generating structures that span natural and novel solenoid space. We experimentally characterise 41 solenoid designs, with α-solenoids consistently folding as intended, including one structurally validated design that closely matches the design model. All β-solenoids initially failed, reflecting the difficulty of designing β-strand majority proteins. By introducing terminal capping elements and refining designs based on earlier experimental screens, we generate two β-solenoids that have biophysical properties consistent with their designs. Our approach achieves fold-specific hallucination-based design without depending on explicit structural templates.