Use of a D-optimal design with constrains to quantify the effects of the mixture of sodium, potassium, calcium and magnesium chloride salts on the growth parameters of Saccharomyces cerevisiae

Strategic nutrient sourcing for biomanufacturing intensification

The successful design of economically viable bioprocesses can help to abate global dependence on petroleum, increase supply chain resilience, and add value to agriculture. Specifically, bioprocessing provides the opportunity to replace petrochemical production methods with biological methods and to develop novel bioproducts. Even though a vast range of chemicals can be biomanufactured, the constraints on economic viability, especially while competing with petrochemicals, are severe. There have been extensive gains in our ability to engineer microbes for improved production metrics and utilization of target carbon sources. The impact of growth medium composition on process cost and organism performance receives less attention in the literature than organism engineering efforts, with media optimization often being performed in proprietary settings. The widespread use of corn steep liquor as a nutrient source demonstrates the viability and importance of "waste" streams in biomanufacturing. There are other promising waste streams that can be used to increase the sustainability of biomanufacturing, such as the use of urea instead of fossil fuel-intensive ammonia and the use of struvite instead of contributing to the depletion of phosphate reserves. In this review, we discuss several process-specific optimizations of micronutrients that increased product titers by twofold or more. This practice of deliberate and thoughtful sourcing and adjustment of nutrients can substantially impact process metrics. Yet the mechanisms are rarely explored, making it difficult to generalize the results to other processes. In this review, we will discuss examples of nutrient sourcing and adjustment as a means of process improvement.

ONE-SENTENCE SUMMARY

The potential impact of nutrient adjustments on bioprocess performance, economics, and waste valorization is undervalued and largely undercharacterized.

The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies

BACKGROUND

Characterizing microbial communities via next-generation sequencing is subject to a number of pitfalls involving sample processing. The observed community composition can be a severe distortion of the quantities of bacteria actually present in the microbiome, hampering analysis and threatening the validity of conclusions from metagenomic studies. We introduce an experimental protocol using mock communities for quantifying and characterizing bias introduced in the sample processing pipeline. We used 80 bacterial mock communities comprised of prescribed proportions of cells from seven vaginally-relevant bacterial strains to assess the bias introduced in the sample processing pipeline. We created two additional sets of 80 mock communities by mixing prescribed quantities of DNA and PCR product to quantify the relative contribution to bias of (1) DNA extraction, (2) PCR amplification, and (3) sequencing and taxonomic classification for particular choices of protocols for each step. We developed models to predict the "true" composition of environmental samples based on the observed proportions, and applied them to a set of clinical vaginal samples from a single subject during four visits.

RESULTS

We observed that using different DNA extraction kits can produce dramatically different results but bias is introduced regardless of the choice of kit. We observed error rates from bias of over 85% in some samples, while technical variation was very low at less than 5% for most bacteria. The effects of DNA extraction and PCR amplification for our protocols were much larger than those due to sequencing and classification. The processing steps affected different bacteria in different ways, resulting in amplified and suppressed observed proportions of a community. When predictive models were applied to clinical samples from a subject, the predicted microbiome profiles were better reflections of the physiology and diagnosis of the subject at the visits than the observed community compositions.

CONCLUSIONS

Bias in 16S studies due to DNA extraction and PCR amplification will continue to require attention despite further advances in sequencing technology. Analysis of mock communities can help assess bias and facilitate the interpretation of results from environmental samples.