Synthetic Genomics (company)

ABSTRACT DNA sequencing of complex microbial communities allows for the classification and quantification of thousands of distinct organisms in diverse environmental niches. We present a three domain “Universal Amplicon” (UA) method to simultaneously amplify DNA from the ribosomal small subunit locus from bacteria, archaea, and eukaryotes (and their organelles) using a single pair of amplification primers. We demonstrate the amenability of the UA to multiplexed Illumina library preparation and MiSeq-based sequencing. We validate the UA by sequencing a commercially available microbial community of known quantitative composition and through direct comparison to a shotgun metagenomics dataset. Following validation, we apply the UA to a time-course study of the wetlands of the Northern Imperial Valley in California and show substantial and variable microbial life in the Salton Sea and nearby waters. We demonstrate that the microbial ecology of the Salton Sea varies on at least a monthly basis and is distinct from the surrounding area. Finally, we contribute an open-source Shiny app for real-time analysis of complex metagenomic communities, with application to this study and far beyond.

Production of high-energy lipids by microalgae may provide a sustainable, renewable energy source that can help tackle climate change. However, microalgae engineered to produce more lipids usually grow slowly, leading to reduced overall yields. Unfortunately, tools that enable the selection of cells based on growth while maintaining high biomass production, such as well-plates, water-in-oil droplet emulsions, and nanowell arrays do not provide production-relevant environments that cells experience in scaled-up cultures (e.g. bioreactors or outdoor cultivation farms). As a result, strains that are developed in the lab often do not exhibit the same beneficial phenotypic behavior when transferred to industrial production. Here we introduce PicoShells, picoliter-scale porous hydrogel compartments, that can enable >100,000 individual cells to be compartmentalized, cultured in production-relevant environments, and selected based on growth and biomass accumulation traits using standard flow cytometers. PicoShells consist of a hollow inner cavity where cells are encapsulated, and a porous outer shell that allows for continuous solution exchange with the external environment so that nutrients, cell-communication factors, and cytotoxic cellular byproducts can transport freely in and out of the inner cavity. PicoShells can also be placed directly into shaking flasks, bioreactors, or other production-relevant environments. We experimentally demonstrate that Chlorella sp. and Saccharomyces cerevisiae grow to significantly larger colony sizes in PicoShells than in water-in-oil droplet emulsions (P < 0.05). We have also demonstrated that PicoShells containing faster biomass accumulating Chlorella clonal colonies can be selected using a fluorescence-activated cell sorter and re-grown. Using the PicoShell process, we select a Chlorella population that accumulates biomass 8% faster than does an un-selected population after a single selection cycle.

For over a decade the term "Big data" has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, "Big data" no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as "data analytics" and "data science" have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises "Big Advances," significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set "Big data" analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.

Synthetic biology is a logical extension of what has been called recombinant DNA (rDNA) technology or genetic engineering since the 1970s. As rDNA technology has been the driver for the development of a thriving biotechnology industry today, starting with the commercialization of biosynthetic human insulin in the early 1980s, synthetic biology has the potential to take the industry to new heights in the coming years. Synthetic biology advances have been driven by dramatic cost reductions in DNA sequencing and DNA synthesis; by the development of sophisticated tools for genome editing, such as CRISPR/Cas9; and by advances in informatics, computational tools, and infrastructure to facilitate and scale analysis and design. Synthetic biology approaches have already been applied to the metabolic engineering of microorganisms for the production of industrially important chemicals and for the engineering of human cells to treat medical disorders. It also shows great promise to accelerate the discovery and development of novel secondary metabolites from microorganisms through traditional, engineered, and combinatorial biosynthesis. We anticipate that synthetic biology will continue to have broadening impacts on the biotechnology industry to address ongoing issues of human health, world food supply, renewable energy, and industrial chemicals and enzymes.

Computational assignment of function to proteins with no known homologs is still an unsolved problem. We have created a novel, function-based approach to protein annotation and discovery called D-SPACE (Deep Semantic Protein Annotation Classification and Exploration), comprised of a multi-task, multi-label deep neural network trained on over 70 million proteins. Distinct from homology and motif-based methods, D-SPACE encodes proteins in high-dimensional representations (embeddings), allowing the accurate assignment of over 180,000 labels for 13 distinct tasks. The embedding representation enables fast searches for functionally related proteins, including homologs undetectable by traditional approaches. D-SPACE annotates all 109 million proteins in UniProt in under 35 hours on a single computer and searches the entirety of these in seconds. D-SPACE further quantifies the relative functional effect of mutations, facilitating rapid in silico mutagenesis for protein engineering applications. D-SPACE incorporates protein annotation, search, and other exploratory efforts into a single cohesive model.