Attractor Concepts to Evaluate the Transcriptome-wide Dynamics Guiding Anaerobic to Aerobic State Transition in Escherichia coli

Abstract

For any dynamical system, like living organisms, an attractor state is a set of variables or mechanisms that converge towards a stable system behavior despite a wide variety of initial conditions. Here, using multi-dimensional statistics, we investigate the global gene expression attractor mechanisms shaping anaerobic to aerobic state transition (AAT) of Escherichia coli in a bioreactor at early times. Out of 3,389 RNA-Seq expression changes over time, we identified 100 sharply changing genes that are key for guiding 1700 genes into the AAT attractor basin. Collectively, these genes were named as attractor genes constituting of 6 dynamic clusters. Apart from the expected anaerobic (glycolysis), aerobic (TCA cycle) and fermentation (succinate pathways) processes, sulphur metabolism, ribosome assembly and amino acid transport mechanisms together with 332 uncharacterised genes are also key for AAT. Overall, our work highlights the importance of multi-dimensional statistical analyses for revealing novel processes shaping AAT.

ScatLay: utilizing transcriptome-wide noise for identifying and visualizing differentially expressed genes

“…For studying Toll-like receptor induced immune response, the correlation metrics revealed that immune-related local genes were highly responsive while myriad global genes showed significantly less response 21,22 . In a similar way, for E. coli, we showed the subset of attractor genes, crucial for cell state transition, showed the most pronounced correlation metrics, while the rest of transcriptome lacked significance 23 . The PC metrics revealed that the attractor genes tracked almost identical trajectory compared with the transcriptome-wide response 23 .…”

“…In a similar way, for E. coli, we showed the subset of attractor genes, crucial for cell state transition, showed the most pronounced correlation metrics, while the rest of transcriptome lacked significance 23 . The PC metrics revealed that the attractor genes tracked almost identical trajectory compared with the transcriptome-wide response 23 . These data revealed that both correlation and PC metrics can be used to test the significance of Scatlay-derived DE genes.…”

“…Previously, we have used Pearson auto-correlation and principal component (PC) metrics to track the global, local and attractor gene expression responses of several cell types 13 , 21 – 23 . For studying Toll-like receptor induced immune response, the correlation metrics revealed that immune-related local genes were highly responsive while myriad global genes showed significantly less response 21 , 22 .…”

Combined Transcriptomic and Proteomic Profiling of E. coli under Microaerobic versus Aerobic Conditions: The Multifaceted Roles of Noncoding Small RNAs and Oxygen-Dependent Sensing in Global Gene Expression Control

“…Previous studies have revealed that during the switch from anaerobiosis to aerobiosis and vice versa, E. coli reprograms gene expression to adjust its cellular metabolism, energy production, and iron homeostasis. Recently, several studies have analyzed the impact of oxygen availability on genome-wide expression profiles, focusing on specific regulatory systems or changes associated with aerobic-anaerobic transitions in E. coli [6][7][8][9][10][11][12][13][14][15][16].…”

State-Transition Analysis of Time-Sequential microRNA Expression Predicts Development of Acute Myeloid Leukemia

“…Dimensionality reduction techniques, such as SVD used here and generalized SVD for multi-omic and pan-cancer studies, have proven useful because they represent the information of the entire system as linear combinations of orthogonal basis vectors [2][3][4] . Similar mathematical approaches including endogenous network theory have been used to identify steady states, or attractors, using configurations of gene regulatory networks [5][6][7][8][9] .…”