Biology subjects

Zavorskas, J.

Publications and source records attributed to Zavorskas, J..

3 recordsLinked to original sources

bioRxiv2025

Synthetic Generation of Dynamic Omics Data Demonstrates Aspergillus nidulans BrlA Paradoxical Wall Stress Response

We propose a method to generate additional dynamic omics trajectories which could support pathway analysis methods such as enrichment analysis, genetic programming, and machine learning. Using long short-term memory neural networks, we can effectively predict an organisms dynamic response to a stimulus based on an initial dataset with relatively few samples. We present both an in silico proof of principle, based on a model that simulates viral propagation, and an in vitro case study, tracking the dynamics of Aspergillus nidulans BrlA transcript in response to antifungal agent micafungin. Our silico experiment was conducted using a highly noisy dataset with only 25 replicates. This proof of principle shows that this method can operate on biological datasets, which often have high variance and few replicates. Our in silico validation achieved a maximum R2 value of approximately 0.95 on our highly noisy, stochastically simulated data. Our in vitro validation achieves an R2 of 0.71. As with any machine learning application, this method will work better with more data; however, both of our applications attain acceptable validation metrics with very few biological replicates. The in vitro experiments also revealed a novel paradoxical dose-response effect: transcriptional upregulation by Aspergillus nidulans BrlA is highest at an intermediate dose of 10 ng/mL and is reduced at both higher and lower concentrations of micafungin.

genomics↗

bioRxiv2024

Aspergillus nidulans Transcription Factor BrlA is Utilized in a Conidiation-Independent Response to Cell-Wall Stress

Under synchronized conidiation, over 2500 gene products show differential expression, including transcripts for both brlA and abaA, which increase steadily over time. In contrast, during wall-stress induced by the echinocandin micafungin, the brlA transcript is upregulated while the abaA transcript is not. In addition, when mpkA (last protein kinase in the cell wall integrity signaling pathway) is deleted, brlA expression is not upregulated in response to wall stress. Together, these data imply BrlA may play a role in a cellular stress-response which is independent of the canonical BrlA-mediated conidiation pathway. To test this hypothesis, we performed a genome-wide search and found 332 genes with a putative BrlA response element (BRE) in their promoter region. From this set, we identified 28 genes which were differentially expressed in response to wall-stress, but not during synchronized conidiation. This set included seven gene products whose homologues are involved in transmembrane transport and 14 likely to be involved in secondary metabolite biosynthesis. We selected six of these genes for further examination and find that they all show altered expression behavior in the brlA deletion strain. Together, these data support the idea that BrlA plays a role in various biological processes outside asexual development. ImportanceThe Aspergillus nidulans transcription factor BrlA is widely accepted as a master regulator of conidiation. Here, we show that in addition to this function BrlA appears to play a role in responding to cell-wall stress. We note that this has not been observed outside A. nidulans. Further, BrlA-mediated conidiation is highly conserved across Aspergillus species, so this new functionality is likely relevant in other Aspergilli. We identified several transmembrane transporters that have altered transcriptional responses to cell-wall stress in a brlA deletion mutant. Based on our observation, together with what is known about the brlA gene locus regulation, we identify brlA{beta} as the likely intermediary in function of brlA in the response to cell-wall stress.

cell biology↗

bioRxiv2024

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

Derivative profiling (DP) is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations. The results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines (MARS), significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organisms specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points. While we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

bioinformatics↗