Krishnamoorthy, B. | Search bioRxiv

bioRxiv2026

Heterologous expression of lyngbyatoxin biosynthetic genes in Aspergillus oryzae reveals transcriptional barriers but enables LtxC-mediated biotransformation

Cyanobacterial natural products are a rich source of bioactive compounds, yet their heterologous production remains challenging. This study investigates the feasibility of expressing the lyngbyatoxin A (LTXA) biosynthetic gene cluster in a fungal host. The lyngbyatoxin biosynthetic genes (ltxA, ltxB, ltxC) were individually cloned and expressed in Aspergillus oryzae NSAR1 under the control of an inducible promoter. Metabolite production was assessed using LC- MS, and transcriptional analysis was performed by RT-PCR. Codon-optimized constructs and precursor feeding experiments were employed to evaluate pathway functionality. No production of LTXA or pathway intermediates was detected upon co-expression of ltxA-C despite confirmed transcription of ltxB and ltxC. RT-PCR analysis revealed truncation of the ltxA transcript, suggesting incompatibility with fungal transcriptional or splicing machinery. In contrast, expression of a codon-optimized ltxC enabled biotransformation of indolactam V to LTXA in A. oryzae, confirming functional expression of the prenyltransferase. These results highlight transcriptional limitations as a key barrier to heterologous expression of cyanobacterial NRPS pathways in fungal hosts, while demonstrating that downstream tailoring enzymes can remain functional. This work provides insights for future engineering of fungal platforms for cyanobacterial natural product biosynthesis.

synthetic biology↗

bioRxiv2017

Toward A Scalable Exploratory Framework for Complex High-Dimensional Phenomics Data

Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. The resulting high dimensional data represent a treasure trove of information for providing an in-depth understanding of how multiple factors interact and contribute to the overall growth and behavior of different genotypes. However, computational tools that can parse through such complex data and aid in extracting plausible hypotheses are currently lacking. In this paper, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits. We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data. We present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We test and evaluate Hyppo-X on two real-world plant (maize) data sets. Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. Notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.

bioinformatics↗