Biology subjects

Guest, J. S.

Publications and source records attributed to Guest, J. S..

3 recordsLinked to original sources

bioRxiv2026

Systematic comparisons between long-read and short-read based amplicon sequencing to characterize mixed microalgal communities.

The 18S rRNA gene has emerged as the primary molecular marker for amplicon-based characterization of microalgal communities, including in wastewater treatment systems, yet trade-offs between short- and long-read approaches remain poorly defined. We systematically compared V8-V9 short-read sequencing (Illumina MiSeq), full-length long-read sequencing with ss5ss3 primers (PacBio Sequel II), and computationally extracted V8-V9 regions from long-read data. Both in silico and in vitro analyses confirmed V8-V9 captured broader taxonomic coverage than ss5ss3, though partial reference sequences and taxonomic mis-annotations biased in silico assessments. Long-reads taxonomic advantage was database-dependent, constrained by SILVA databases genus-level curation but fully realized when paired with the species-level-curated and eukaryotes-focused PR{superscript 2} (Protist Ribosomal Reference) database. Long-read sequencing uniquely identified amplicon sequence variants (ASVs) assigned to key phosphorus assimilators (Scenedesmus obliquus, Desmodesmus sp., and Acutodesmus sp.) at species level during successful phosphorus removal in a full-scale microalgal cultivation system, while V8-V9 short-read sequencing revealed ASVs assigned to algal-predatory (Leptophryidae) and bacterivorous (Choanoflagellata and Rhogostoma-lineage) protists when performance declined, suggesting grazing pressure on the phosphorus-removing community. Although both approaches performed comparably for operational monitoring, these complementary strengths support short-read sequencing for routine community profiling and long-read sequencing for detailed functional investigations of Chlorophyta.

microbiology↗

bioRxiv2025

Encoding Cell Phenotype from Label-Free Imaging Flow Cytometry with Unsupervised Deep Learning

Phenotype characterization with single-cell resolution can enable deep and nuanced insights into microbiological systems. Currently, Flow Cytometry and Imaging Flow Cytometry (IFC) offer numerous advantages, but are marred by barriers to accessibility: (1) high instrument costs; (2) labor-intensive, technically demanding sample preparation; and (3) reliance on consumable reagents (i.e., fluorescent labels). To achieve phenotype characterization without these constraints, we evaluated the low-cost, low-input ARTiMiS IFC as a potential alternative instrument technology. To demonstrate this approach, we used intracellular lipid content in microalgae, an important phenotype for production of biofuels and high-value bioproducts, as the phenotype of interest. Variational Auto-Encoder (VAE) unsupervised deep learning methodology was implemented to encode phenotype variation from un-annotated training data. The VAE embeddings were compared with other label-free predictor modalities to evaluate the stability of VAE data encoding across replicates and its predictive power to estimate the target phenotype. The VAE embeddings were robust and consistent between culture batches, and yielded accurate, consistent predictions of the demonstration phenotype in a high-throughput, non-destructive, dye-free methodology. In this proof-of-concept study, we demonstrate that VAE-enabled ARTiMiS IFC may serve as a viable alternative for cell phenotype characterization while overcoming several of the key drawbacks of traditional high-fidelity techniques. SynopsisLabel-free Imaging Flow Cytometry data was processed by a Variational Auto-Encoder to accurately predict lipid content in microalgal cells.

microbiology↗

bioRxiv2023

An end-to-end pipeline for succinic acid production at an industrially relevant scale using Issatchenkia orientalis

As one of the top value-added chemicals, succinic acid has been the focus of numerous metabolic engineering campaigns since the 1990s. However, microbial production of succinic acid at an industrially relevant scale has been hindered by high downstream processing costs arising from neutral pH fermentation. Here we describe the metabolic engineering of Issatchenkia orientalis, a non-conventional yeast with superior tolerance to highly acidic conditions, for cost-effective succinic acid production. Through deletion of byproduct pathways, transport engineering, and expanding the substrate scope, the resulting strains could produce succinic acid at the highest titers in sugar-based media at low pH (pH 3) in fed-batch fermentations using bench-top reactors, i.e. 109.5 g/L in minimal medium and 104.6 g/L in sugarcane juice medium. We further performed batch fermentation in a pilot-scale fermenter with a scaling factor of 300x, achieving 63.1 g/L of succinic acid using sugarcane juice medium. A downstream processing comprising of two-stage vacuum distillation and crystallization enabled direct recovery of succinic acid, without further acidification of fermentation broth, with an overall yield of 64.0%. Finally, we simulated an end-to-end low-pH succinic acid production pipeline, and techno-economic analysis and life cycle assessment indicate our process is financially viable and can reduce life cycle greenhouse gas emissions by 34-90% relative to fossil-based production processes. We expect I. orientalis can serve as a general industrial platform for the production of a wide variety of organic acids.

synthetic biology↗