Biology subjects

Sudarsan, S.

Publications and source records attributed to Sudarsan, S..

6 recordsLinked to original sources

bioRxiv2024

Interaction of Genetic Variants Activates Latent Metabolic Pathways in Yeast

Genetic interactions are fundamental to the architecture of complex traits, yet the molecular mechanisms by which variant combinations influence cellular pathways remain poorly understood. Here, we answer the question of whether interactions between genetic variants can activate unique pathways and if such pathways can be targeted to modulate phenotypic outcomes. The model organism Saccharomyces cerevisiae was used to dissect how two causal single-nucleotide polymorphisms (SNPs), MKT189G and TAO34477C, interact to modulate metabolic and phenotypic outcomes during sporulation. By integrating time-resolved transcriptomics, absolute proteomics, and targeted metabolomics in isogenic allele replacement yeast strains, we show that the combined presence of these SNPs uniquely activates the arginine biosynthesis pathway and suppresses ribosome biogenesis, reflecting a metabolic trade-off that enhances sporulation efficiency. Functional validation demonstrates that the arginine pathway is essential for mitochondrial activity and efficient sporulation only in the double-SNP background. Our findings reveal how genetic variant interactions can rewire core metabolic networks, providing a mechanistic framework for understanding polygenic trait regulation and the emergence of additive effects in complex traits.

systems biology↗

bioRxiv2024

Deciphering the transcriptional regulatory network of Yarrowia lipolytica using machine learning

The transcriptional regulatory network (TRN) in Yarrowia lipolytica coordinates its cellular processes, including the response to various stimuli. The TRN has been difficult to study due to its complex nature. In industrial-size fermenters, environments are often not homogenous, resulting in Yarrowia experiencing fluctuating conditions during a fermentation. Compared with homogenous laboratory conditions, these fluctuations result in altered cellular states and behaviors due to the action of the TRN. Here, a machine learning approach was deployed to modularize the transcriptome to enable meaningful description of its changing composition. To provide a sufficiently broad dataset, a wide range of relevant fermentation conditions (nutrient limitations, growth rates, pH values, oxygen availability and CO2 stresses) were run and samples obtained for RNA-Seq generation. We thus significantly increased the number of publicly available transcriptomic dataset on Y. lipolytica W29. In total, 23 independently modulated gene sets (termed iModulons) were identified of which 9 could be linked to corresponding regulons in S. cerevisiae. Strong responses were found in relation to oxygen limitation and elevated CO2 concentrations represented by (i) altered ribosomal protein synthesis, (ii) cell cycle disturbances, (iii) respiratory gene expression, and (iv) redox homeostasis. These results provide a fine-grained systems-level understanding of the Y. lipolytica TRN in response to industrially meaningful stresses, providing engineering targets to design more robust production strains. Moreover, this study provides a guide to perform similar work with poorly characterized single-cellular eukaryotic organisms. Highlights- A large screening setup significantly expands the public RNA-Seq library on Y. lipolytica. - This work provides a systems-level understanding of the TRN in response to industrial stresses. - iModulon analysis can help accelerate bioprocess development. - Results can guide similar work on poorly characterized single-cellular eukaryotic organisms.

systems biology↗

bioRxiv2024

Trade-off Between Resistance and Persistence in High Cell Density Escherichia Coli Cultures

Microbes experience high cell density in many environments that come with diverse resource limitations and stresses. However, high density physiology remains poorly understood. We utilized well-controlled culturing systems to grow wild-type and metabolically engineered Escherichia coli strains into high cell densities (50-80 g Cdry cell weight L-1) and determine the associated transcriptional dynamics. Knowledge-enriched machine-learning-based analytics reveal distinct stress-related gene expression patterns that are consistent with a fundamental trade-off between resistance and persistence. We suggest that this trade-off explains observed growth arrests in high-density cultures and that it results from the disruption of cellular homeostasis, due to reallocation of limited cellular resources from resistance functions towards maintenance requirements of engineered production pathways. This study deepens our understanding of high-density physiology and demonstrates its importance to fundamental biomanufacturing challenges.

microbiology↗

bioRxiv2023

The dominant role of post-transcriptional regulation in Yarrowia lipolytica to repeated O2 limitations

Rational scale-up strategies to accelerate bioprocess development, require sound knowledge of cellular behaviour under industrial conditions. In this study, the strictly aerobic yeast Yarrowia lipolytica is exposed to repeated oxygen limitations, approximated from a large-scale cultivation. A data-driven multi-omics strategy is deployed to elucidate its transcriptomic, proteomic, and metabolic response. Throughout a single perturbation, metabolite and protein levels showed dynamic profiles while they returned to steady state values when aerobic conditions were restored. After repeated oscillations, significant cellular rearrangements were found, with a special focus on central carbon metabolism, oxidative phosphorylation, lipid, and amino acid biosynthesis. Most notably, metabolite levels as well as the catabolic reduction charge are maintained at higher concentrations. Moreover, proteins involved in NADPH-consuming anabolic pathways showed an increased abundance, which is suggested to be compensated for through an increased pentose-phosphate pathway activity. Although dynamics were found on all three omics levels, the proteomic and metabolic changes were in most instances not supported by strong transcriptional changes. Thus, this work suggests that the response of Y. lipolytica to (repeated) oxygen oscillations is strongly regulated by post-transcriptional mechanisms. These findings provide novel insights into potential cellular regulation on an industrial scale, thereby facilitating a more efficient bioprocess development through mitigating any undesired behaviour. Key points- Dynamic response of Yarrowia lipolytica to industrial oxygen profiles. - New metabolic steady states are found after exposure to repeated oxygen oscillations. - A multi-omics strategy elucidates the importance of post-transcriptional mechanisms.

systems biology↗

bioRxiv2020

Regulatory control circuits for stabilizing long-term anabolic product formation in yeast

Engineering living cells for production of chemicals, enzymes and therapeutics can burden cells due to use of limited native co-factor availability and/or expression burdens, totalling a fitness deficit compared to parental cells encoded through long evolutionary trajectories to maximise fitness. Ultimately, this discrepancy puts a selective pressure against fitness-burdened engineered cells under prolonged bioprocesses, and potentially leads to complete eradication of high-performing engineered cells at the population level. Here we present the mutation landscapes of fitness-burdened yeast cells engineered for vanillin-{beta}-glucoside production. Next, we design synthetic control circuits based on transcriptome analysis and biosensors responsive to vanillin-{beta}-glucoside pathway intermediates in order to stabilize vanillin-{beta}-glucoside production over [~]55 generations in sequential passage experiments. Furthermore, using biosensors with two different modes of action we identify control circuits linking vanillin-{beta}-glucoside pathway flux to various essential cellular functions, and demonstrate control circuits robustness and 92% higher vanillin-{beta}-glucoside production, including 5-fold increase in total vanillin-{beta}-glucoside pathway metabolite accumulation, in a fed-batch fermentation compared to vanillin-{beta}-glucoside producing cells without control circuits.

synthetic biology↗

bioRxiv2017

An orthogonal and pH-tunable sensor-selector for muconic acid biosynthesis in yeast

Microbes offer enormous potential for production of industrially relevant chemicals and therapeutics, yet the rapid identification of high-producing microbes from large genetic libraries is a major bottleneck in modern cell factory development. Here, we develop and apply a synthetic selection system in Saccharomyces cerevisiae that couples the concentration of muconic acid, a plastic precursor, to cell fitness by using the prokaryotic transcriptional regulator BenM driving an antibiotic resistance gene. We show the sensor-selector does not affect production, and find that tuning pH of the cultivation medium limits the rise of non-producing cheaters. We apply the sensor-selector to selectively enrich for best-producing variants out of a large library of muconic acid production strains, and identify an isolate that produced more than 2 g/L muconic acid in a bioreactor. We expect that this sensor-selector can aid the development of other synthetic selection systems based on allosteric transcription factors.

synthetic biology↗