Biology subjects

Jayasundara, S.

Publications and source records attributed to Jayasundara, S..

6 recordsLinked to original sources

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↗

bioRxiv2026

Complete biosynthesis of penicillin G in Nicotiana benthamiana

Commercial penicillin production has relied on microbial fermentation for more than 80 years. Here, we engineered the plant, Nicotiana benthamiana, to produce penicillin G in its leaves by transient expression of up to seven fungal biosynthetic genes. Remarkably, all recombinant proteins localize to the analogous subcellular compartments without engineering signal peptide sequences or post-translational modification sites. Although non-ribosomal peptide synthetases occur widely in fungi and bacteria to produce a plethora of specialized metabolites, their evolutionary distribution does not extend to plants. Our results now open a new metabolic frontier for natural product synthesis, and offer possibilities to address global health concerns through an alternative biotechnology platform for fungal-derived pharmaceutical production.

synthetic biology↗

bioRxiv2026

Investigating the versatility of cytochalasan cytochrome P450 monooxygenases using combinatorial biosynthesis reveals stereochemical restrictions

BackgroundCytochalasans are a large family of fungal metabolites which inhibit actin polymerization and ultimately lead to a broad range of biological effects in different assays. Investigations into the biosynthesis of cytochalasans has revealed that the cytochrome P450 monooxygenase (P450s) tailoring enzymes possess a somewhat relaxed substrate-specificity and may accept structurally-related intermediates for oxidation, partly explaining the variety of structural variations observed in this family of molecules. In this study, we investigate a broad range of P450 enzymes via combinatorial biosynthesis to better understand their substrate scope and potential applications as biocatalysts. ResultsGenome mining enabled us to identify cryptic cytochalasan biosynthetic gene clusters (BGCs) in six different species of fungi, each with at least two P450 enzymes encoded. Comparative genomics identified a cryptic thioredoxin-like enzyme encoded in cytochalasan BGCs that co-occurs with the gene encoding a Baeyer-Villiger monooxygenase. Heterologous expression of seven P450s in Magnaporthe grisea mutant strains, lacking P450s required for pyrichalasin H biosynthesis, enabled functional characterization of three P450s, two of which were previously cryptic. The experimental results, combined with phylogenetic analysis of the P450 sequences, reveal subtle information regarding the structures of the associated cytochalasans and begins to explain why some P450s are inactive on the substrates available to them. ConclusionsThe P450 enzymes involved in cytochalasan biosynthesis are known to be site-selective in their native host but also possess intrinsic promiscuity due to being able to modify structurally-related analogues. By investigating a diverse set of P450s from characterized and cryptic BGCs, we were able to identify that the stereochemistry of functional groups around the cytochalasan backbone is more restrictive than the size of the macrocycle when introducing the P450 enzyme to non-native substrates.

biochemistry↗

bioRxiv2026

Joint Modeling of Transcriptomic and Morphological Phenotypes for Generative Molecular Design

BackgroundEffectively translating data on complex cellular responses from transcriptomic and morphological measurements into molecular design remains a significant computational challenge. Existing generative methods operate on single modalities and condition on post-treatment measurements without leveraging paired control-treatment dynamics to capture perturbation effects. ResultsWe present Pert2Mol, a framework for multi-modal phenotype-to-structure generation that integrates transcriptomic and morphological features from paired control-treatment experiments. Pert2Mol employs bidirectional cross-attention between control and treatment states to capture perturbation dynamics, conditioning a rectified flow transformer that generates molecular structures along straight-line trajectories. We introduce Student-Teacher Self-Representation (SERE) learning to stabilize training in high-dimensional multi-modal spaces. On the Ginkgo Data Platform (GDP) dataset, Pert2Mol achieves Frechet ChemNet Distance of 4.996 compared to 7.343 for diffusion baselines and 59.114 for transcriptomics-only methods, while maintaining perfect molecular validity and appropriate physicochemical property distributions. The model demonstrates 84.7% scaffold diversity and 12.4 times faster generation than diffusion approaches with deterministic sampling suitable for hypothesis-driven validation. ConclusionsPert2Mol establishes a new paradigm for linking high-content phenotypic screening data with computational hypothesis generation in drug discovery: joint multi-modal perturbation modeling enables more accurate and structurally diverse molecular design than single-modality or diffusion-based approaches. Code and pretrained models are available at https://github.com/wangmengbo/Pert2Mol.

bioinformatics↗

bioRxiv2025

Canonical microRNA loss drives tumor development implicating therapeutic efficacy of enoxacin in angiosarcoma

Angiosarcoma (AS) is a rare and aggressive tumor arising within the endothelium, characterized by a high metastatic rate and poor prognosis. Our prior work established that endothelial loss of Dicer1, a key enzyme in microRNA (miRNA) processing, drives AS formation in mice, indicating a tumor suppressive role for miRNAs in tumorigenesis. Here, we corroborated this hypothesis by generating a novel conditional knockout model targeting Dgcr8, a core component of the microprocessor complex required for pri-miRNA processing. Conditional deletion of Dgcr8 phenocopies Dicer1 loss, resulting in spontaneous AS formation and global loss of mature miRNAs. We further demonstrate that treatment with enoxacin (ENX), a repurposed antibiotic known to enhance miRNA processing, reduces viability, migration, and clonogenicity of AS cells. ENX increases the abundance of tumor-suppressive miRNAs and downregulates oncogenic pathways, including pathways related to cell cycle progression, angiogenesis, and cell migration. These results establish the essential role of miRNA biogenesis in suppressing AS and reveal a pharmacologically targetable vulnerability via ENX-mediated enhancement of miRNA expression in tumors.

cancer biology↗

bioRxiv2021

miRNAFinder: A Comprehensive Web Resource for Plant Pre-microRNA Classification

microRNAs (miRNAs) are known as one of the small non-coding RNA molecules that control the expression of genes at the RNA level, while some operate at the DNA level. They typically range from 20-24 nucleotides in length and can be found in the plant and animal kingdoms as well as in some viruses. Computational approaches have overcome the limitations of the experimental methods and have performed well in identifying miRNAs. Compared to mature miRNAs, precursor miRNAs (pre-miRNAs) are long and have a hairpin loop structure with structural features. Therefore, most in-silico tools are implemented for pre-miRNA identification. This study presents a multilayer perceptron (MLP) based classifier implemented using 180 features under sequential, structural, and thermodynamic feature categories for plant pre-miRNA identification. This classifier has a 92% accuracy, a 94% specificity, and a 90% sensitivity. We have further tested this model with other small non-coding RNA types and obtained 78% accuracy. Furthermore, we introduce a novel dataset to train and test machine learning models, addressing the overlapping data issue in the positive training and testing datasets presented in PlantMiRNAPred for the classification of real and pseudo-plant pre-miRNAs. The new dataset and the classifier that can be used with any plant species are deployed on a web server freely accessible at http://mirnafinder.shyaman.me/.

bioinformatics↗