An Agrobacterium strain auxotrophic for methionine is useful for switchgrass transformation.

Auxotrophic strains of Agrobacterium tumefaciens can contribute to the development of more efficient transformation systems, especially for crops historically considered recalcitrant. Homologous recombination was used to derive methionine auxotrophs of two common A. tumefaciens strains, LBA4404 and EHA105. The EHA105 strains were more efficient for switchgrass transformation, while both the EHA105 and LBA4404 strains worked equally well for the rice control. Event quality, as measured by transgene copy number, was not affected by auxotrophy, but was higher for the LBA4404 strains than the EHA105 strains. Ultimately, the use of auxotrophs reduced bacterial overgrowth during co-cultivation and decreased the need for antibiotics.

Rapid Whole-Genome Identification of High Quality CRISPR Guide RNAs with the Crackling Method.

The design of CRISPR-Cas9 guide RNAs is not trivial and is a computationally demanding task. Design tools need to identify target sequences that will maximize the likelihood of obtaining the desired cut, while minimizing off-target risk. There is a need for a tool that can meet both objectives while remaining practical to use on large genomes. In this study, we present Crackling, a new method that is more suitable for meeting these objectives. We test its performance on 12 genomes and on data from validation studies. Crackling maximizes guide efficiency by combining multiple scoring approaches. On experimental data, the guides it selects are better than those selected by others. It also incorporates Inverted Signature Slice Lists (ISSL) for faster off-target scoring. ISSL provides a gain of an order of magnitude in speed compared with other popular tools, such as Cas-OFFinder, Crisflash, and FlashFry, while preserving the same level of accuracy. Overall, this makes Crackling a faster and better method to design guide RNAs at scale. Crackling is available at https://github.com/bmds-lab/Crackling under the Berkeley Software Distribution (BSD) 3-Clause license.

Metagenomic Geolocation Using Read Signatures.

We present a novel approach to the Metagenomic Geolocation Challenge based on random projection of the sample reads from each location. This approach explores the direct use of k-mer composition to characterise samples so that we can avoid the computationally demanding step of aligning reads to available microbial reference sequences. Each variable-length read is converted into a fixed-length, k-mer-based read signature. Read signatures are then clustered into location signatures which provide a more compact characterisation of the reads at each location. Classification is then treated as a problem in ranked retrieval of locations, where signature similarity is used as a measure of similarity in microbial composition. We evaluate our approach using the CAMDA 2020 Challenge dataset and obtain promising results based on nearest neighbour classification. The main findings of this study are that k-mer representations carry sufficient information to reveal the origin of many of the CAMDA 2020 Challenge metagenomic samples, and that this reference-free approach can be achieved with much less computation than methods that need reads to be assigned to operational taxonomic units-advantages which become clear through comparison to previously published work on the CAMDA 2019 Challenge data.

Rapid analysis of metagenomic data using signature-based clustering.

BACKGROUND: Sequencing highly-variable 16S regions is a common and often effective approach to the study of microbial communities, and next-generation sequencing (NGS) technologies provide abundant quantities of data for analysis. However, the speed of existing analysis pipelines may limit our ability to work with these quantities of data. Furthermore, the limited coverage of existing 16S databases may hamper our ability to characterise these communities, particularly in the context of complex or poorly studied environments. RESULTS: In this article we present the SigClust algorithm, a novel clustering method involving the transformation of sequence reads into binary signatures. When compared to other published methods, SigClust yields superior cluster coherence and separation of metagenomic read data, while operating within substantially reduced timeframes. We demonstrate its utility on published Illumina datasets and on a large collection of labelled wound reads sourced from patients in a wound clinic. The temporal analysis is based on tracking the dominant clusters of wound samples over time. The analysis can identify markers of both healing and non-healing wounds in response to treatment. Prominent clusters are found, corresponding to bacterial species known to be associated with unfavourable healing outcomes, including a number of strains of Staphylococcus aureus. CONCLUSIONS: SigClust identifies clusters rapidly and supports an improved understanding of the wound microbiome without reliance on a reference database. The results indicate a promising use for a SigClust-based pipeline in wound analysis and prediction, and a possible novel method for wound management and treatment.

Inhibition of local immune responses by the frog-killing fungus Batrachochytrium dendrobatidis.

Amphibians are suffering unprecedented global declines. A leading cause is the infectious disease chytridiomycosis caused by the chytrid fungus Batrachochytrium dendrobatidis. Chytridiomycosis is a skin disease which disrupts transport of essential ions leading to death. Soluble factors produced by B. dendrobatidis impair amphibian and mammalian lymphocytes in vitro, but previous studies have not shown the effects of these inhibitory factors in vivo. To demonstrate in vivo inhibition of immunity by B. dendrobatidis, a modified delayed-type-hypersensitivity (DTH) protocol was developed to induce innate and adaptive inflammatory swelling in the feet of Xenopus laevis by injection of killed bacteria or phytohemagglutinin (PHA). Compared to previous protocols for PHA injection in amphibians, this method induced up to 20-fold greater inflammatory swelling. Using this new protocol, we measured DTH responses induced by killed bacteria or PHA in the presence of B. dendrobatidis supernatants. Swelling induced by single injection of PHA or killed bacteria was not significantly affected by B. dendrobatidis supernatants. However, swelling caused by a secondary injection of PHA, was significantly reduced by B. dendrobatidis supernatants. As previously described in vitro, factors from B. dendrobatidis appear to inhibit lymphocyte-mediated inflammatory swelling but not swelling caused by an inducer of innate leukocytes. This suggests that B. dendrobatidis is capable of inhibiting lymphocytes in a localized response to prevent adaptive immune responses in the skin. The modified protocol used to induce inflammatory swelling in the present study may be more effective than previous methods to investigate amphibian immune competence, particularly in nonmodel species.