Biology subjects

Rychel, K.

Publications and source records attributed to Rychel, K..

15 recordsLinked to original sources

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

Laboratory evolution reveals transcriptional mechanisms underlying thermal adaptation of Escherichia coli

Adaptive laboratory evolution (ALE) is able to generate microbial strains which exhibit extreme phenotypes, revealing fundamental biological adaptation mechanisms. Here, we use ALE to evolve Escherichia coli strains that grow at temperatures as high as 45.3{degrees}C, a temperature lethal to wild type cells. The strains adopted a hypermutator phenotype and employed multiple systems-level adaptations that made global analysis of the DNA mutations difficult. Given the challenge at the genomic level, we were motivated to uncover high temperature tolerance adaptation mechanisms at the transcriptomic level. We employed independently modulated gene set (iModulon) analysis to reveal five transcriptional mechanisms underlying growth at high temperatures. These mechanisms were connected to acquired mutations, changes in transcriptome composition, sensory inputs, phenotypes, and protein structures. They are: (i) downregulation of general stress responses while upregulating the specific heat stress responses; (ii) upregulation of flagellar basal bodies without upregulating motility, and upregulation fimbriae; (iii) shift toward anaerobic metabolism, (iv) shift in regulation of iron uptake away from siderophore production, and (v) upregulation of yjfIJKL, a novel heat tolerance operon which we characterized using AlphaFold. iModulons associated with these five mechanisms explain nearly half of all variance in the gene expression in the adapted strains. These thermotolerance strategies reveal that optimal coordination of known stress responses and metabolism can be achieved with a small number of regulatory mutations, and may suggest a new role for large protein export systems. ALE with transcriptomic characterization is a productive approach for elucidating and interpreting adaptation to otherwise lethal stresses.

systems biology↗

bioRxiv2023

Data-Driven Strain Design Towards Mitigating Biomanufacturing Stresses

Microbial strains used in large-scale biomanufacturing of melatonin often experience stresses like reactive oxygen species (ROS), SOS response, and acid stress, which can reduce productivity. This study leveraged a data-driven workflow to identify mutations that could improve robustness to these stresses for an industrially important melatonin production strain. This work integrated more than 7000 E. coli adaptive laboratory evolution (ALE) mutations to statistically associate mutated genes to 2 ROS tolerance ALE conditions from 72 available conditions. oxyR, fur, iscR, and ygfZ were significantly associated and hypothesized to contribute to fitness in ROS stress. Across these genes, 259 total mutations were inspected and 10 were chosen for reintroduction based on mutation clustering and transcriptional signals as evidence of fitness impact. Strains engineered with mutations in oxyR, fur, iscR, and ygfZ exhibited increased tolerance to H2O2 and acid stress, and reduced SOS response suggesting improved genetic stability. Additionally, new evidence was generated towards understand the function of ygfZ, a gene of relatively uncertain function. This meta-analysis approach utilized interoperable multi-omics datasets to identify targeted mutations conferring industrially-relevant phenotypes, describing an approach for data-driven strain engineering to optimize microbial cell factories. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=43 SRC="FIGDIR/small/558093v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1c8b4f6org.highwire.dtl.DTLVardef@1e08eacorg.highwire.dtl.DTLVardef@1b6fcb7org.highwire.dtl.DTLVardef@89554f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Machine learning uncovers the Pseudomonas syringae transcriptome in microbial communities and during infection

The transcriptional regulatory network (TRN) of the phytopathogen Pseudomonas syringae pv. tomato DC3000 regulates its response to environmental stimuli, including interactions with hosts and neighboring bacteria. Despite the importance of transcriptional regulation during these agriculturally-significant interactions, a comprehensive understanding of the TRN of P. syringae is yet to be achieved. Here, we collected and decomposed a compendium of public RNA-seq data from P. syringae to obtain 45 independently modulated gene sets (iModulons) that quantitatively describe the TRN and its activity state across diverse conditions. Through iModulon analysis, we 1) untangle the complex interspecies interactions between P. syringae and other terrestrial bacteria in cocultures, 2) expand the current understanding of the Arabidopsis thaliana-P. syringae interaction, and 3) elucidate the AlgU-dependent regulation of flagellar gene expression. The modularized TRN yields a unique understanding of interaction-specific transcriptional regulation in P. syringae. ImportancePseudomonas syringae pv. tomato DC3000 is a model plant pathogen that infects tomatoes and Arabidopsis thaliana. The current understanding of global transcriptional regulation in the pathogen is limited. Here, we applied iModulon analysis to a compendium of RNA-seq data to unravel its transcriptional regulatory network. We characterize each co-regulated gene set, revealing the activity of major regulators across diverse conditions. We provide new insights on the transcriptional dynamics in interactions with the plant immune system and with other bacterial species, such as AlgU-dependent regulation of flagellar genes during plant infection and downregulation of siderophore production in the presence of a siderophore cheater. This study demonstrates the novel application of iModulons in studying temporal dynamics during host-pathogen and microbe-microbe interactions, and reveals specific insights of interest.

systems biology↗

bioRxiv2023

Proteome allocation is linked to transcriptional regulation through a modularized transcriptome

It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigated whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions could be modularized in the same way to reveal novel relationships between their compositions. We found that; 1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, 2) the modules in the proteome often represent combinations of modules from the transcriptome, 3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and 4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.

bioengineering↗

bioRxiv2022

Lab evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress tolerance mechanisms: 1) modification of transport, 2) activation of ROS stress responses, 3) use of ROS-sensitive iron regulation, 4) motility, 5) broad transcriptional reallocation toward growth, and 6) metabolic rewiring to decrease NADH production. This work thus reveals the genome-scale systems biology of ROS tolerance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/521246v1_ufig1.gif" ALT="Figure 1"> View larger version (83K): org.highwire.dtl.DTLVardef@187f455org.highwire.dtl.DTLVardef@ba0798org.highwire.dtl.DTLVardef@148b175org.highwire.dtl.DTLVardef@17a8f8c_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen and major cause of hospital acquired infections. The pathogenicity and virulence of P. aeruginosa is largely determined by its transcriptional regulatory network (TRN). We used 411 transcription profiles of P. aeruginosa from diverse growth conditions to construct a quantitative TRN by identifying independently modulated sets of genes (called iModulons) and their condition-specific activity levels. The current study focused on the use of iModulons to analyze pathogenicity and antibiotic resistance of P. aeruginosa. Our analysis revealed: 1) 116 iModulons, 81 of which show strong association with known regulators; 2) novel roles of two-component systems in regulating antibiotics efflux pumps; 3) substrate-efflux pump associations; 4) differential iModulon activity in response to beta-lactam antibiotics in bacteriological and physiological media; 5) differential activation of Cell Division iModulon resulting from exposure to different beta-lactam antibiotics; and 6) a role of the PprB iModulon in the stress-induced transition from planktonic to biofilm lifestyle. In light of these results, the construction of an iModulon-based TRN provides a transcriptional regulatory basis for key aspects of P. aeruginosa infection, such as antibiotic stress responses and biofilm formation. Taken together, our results offer a novel mechanistic understanding of P. aeruginosa pathogenicity. SignificanceLarge data sets and machine learning are impacting a growing number of areas of research in the life sciences. Once the compendia of bacterial transcriptomes reached a critical size, we could use source signal extraction algorithms to find lists of co-regulated genes (called iModulons) associated with a transcription factor (TF) to them. The gene composition of iModulons and their condition-dependent activity levels constitute a quantitative description of the composition of bacterial transcriptomes. This study shows how this approach can be used to reveal the responses of P. aeruginosa to antibiotics and thus yield a deep regulatory understanding of pathogenicity properties. This study motivates the execution of similar studies for the other ESKAPEEs to yield a broad understanding of the role of TRNs in antibiotic responses to these urgent threat bacterial pathogens.

systems biology↗

bioRxiv2022

Machine-learning from Pseudomonas putida transcriptomes reveals its transcriptional regulatory network

Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1,993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.

systems biology↗

bioRxiv2022

Pan-genomic analysis of transcriptional modules across Salmonella Typhimurium reveals the regulatory landscape of different strains

Salmonella enterica Typhimurium is a serious pathogen that is involved in human nontyphoidal infections. Tackling Typhimurium infections is difficult due to the species dynamic adaptation to its environment, which is dictated by a complex transcriptional regulatory network (TRN). While traditional biomolecular methods provide characterizations of specific regulators, it is laborious to construct the global TRN structure from this bottom-up approach. Here, we used a machine learning technique to understand the transcriptional signatures of S. enterica Typhimurium from the top down, as a whole and in individual strains. Furthermore, we conducted cross-strain comparison of 6 strains in serovar Typhimurium to investigate similarities and differences in their TRNs with pan-genomic analysis. By decomposing all the publicly available RNA-Seq data of Typhimurium with independent component analysis (ICA), we obtained over 400 independently modulated sets of genes, called iModulons. Through analysis of these iModulons, we 1) discover three transport iModulons linked to antibiotic resistance, 2) describe concerted responses to cationic antimicrobial peptides (CAMPs), 3) uncover evidence towards new regulons, and 4) identify two iModulons linked to bile responses in strain ST4/74. We extend this analysis across the pan-genome to show that strain-specific iModulons 5) reveal different genetic signatures in pathogenicity islands that explain phenotypes and 6) capture the activity of different phages in the studied strains. Using all high-quality publicly-available RNA-Seq data to date, we present a comprehensive, data-driven Typhimurium TRN. It is conceivable that with more high-quality datasets from more strains, the approach used in this study will continue to guide our investigation in understanding the pan-transcriptome of Typhimurium. Interactive dashboards for all gene modules in this project are available at https://imodulondb.org/ under the "Salmonella Typhimurium" page to enable browsing for interested researchers.

systems biology↗

bioRxiv2021

Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal thermoacidophile Sulfolobus acidocaldarius

Dynamic cellular responses to environmental constraints are coordinated by the transcriptional regulatory network (TRN), which modulates gene expression. This network controls most fundamental cellular responses, including metabolism, motility, and stress responses. Here, we apply independent component analysis, an unsupervised machine learning approach, to 95 high-quality Sulfolobus acidocaldarius RNA-seq datasets and extract 45 independently modulated gene sets, or iModulons. Together, these iModulons contain 755 genes (32% of the genes identified on the genome) and explain over 70% of the variance in the expression compendium. We show that 5 modules represent the effects of known transcriptional regulators, and hypothesize that most of the remaining modules represent the effects of uncharacterized regulators. Further analysis of these gene sets results in: (1) the prediction of a DNA export system composed of 5 uncharacterized genes, (2) expansion of the LysM regulon, and (3) evidence for an as-yet-undiscovered global regulon. Our approach allows for a mechanistic, systems-level elucidation of an extremophiles responses to biological perturbations, which could inform research on gene-regulator interactions and facilitate regulator discovery in S. acidocaldarius. We also provide the first global TRN for S. acidocaldarius. Collectively, these results provide a roadmap towards regulatory network discovery in archaea.

systems biology↗

bioRxiv2021

Machine Learning of Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

The transcriptional regulatory network (TRN) of Pseudomonas aeruginosa plays a critical role in coordinating numerous cellular processes. We extracted and quality controlled all publicly available RNA-sequencing datasets for P. aeruginosa to find 281 high-quality transcriptomes. We produced 83 new RNAseq data sets under critical conditions to generate a comprehensive compendium of 364 transcriptomes. We used this compendium to reconstruct the TRN of P. aeruginosa using independent component analysis (ICA). We identified 104 independently modulated sets of genes (called iModulons), among which 81 (78%) reflect the effects of known transcriptional regulators. We show that iModulons: 1) play an important role in defining the genomic boundaries of biosynthetic gene clusters (BGCs); 2) show increased expression of the BGCs and associated secretion systems in conditions that emulate cystic fibrosis (CF); 3) show the presence of a novel BGC named RiPP (bacteriocin producer) which might have a role in worsening CF outcomes; 4) exhibit the interplay of amino acid metabolism regulation and central metabolism across carbon sources, and 5) clustered according to their activity changes to define iron and sulfur stimulons. Finally, we compare the iModulons of P. aeruginosa with those of E. coli to observe conserved regulons across two gram negative species. This comprehensive TRN framework covers almost every aspect of the transcriptional regulatory machinery in P. aeruginosa, and thus could prove foundational for future research of its physiological functions.

bioinformatics↗

bioRxiv2021

Machine learning of all Mycobacterium Tuberculosis H37Rv RNA-seq data reveals complex interplay between metabolism, stress response, and infection

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, a systems level characterization and analysis of interactions among the controlling transcription factors remains to be achieved. Here, we applied an unsupervised machine learning method to modularize the M. tuberculosis transcriptome and describe the role of transcription factors (TFs) in the TRN. By applying Independent Component Analysis (ICA) to over 650 transcriptomic samples, we obtained 80 independently modulated gene sets known as "iModulons," many of which correspond to known regulons. These iModulons explain 61% of the variance in the organisms transcriptional response. We show that iModulons: 1) reveal the function of previously unknown regulons, 2) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and virulence events, and 3) identify intrinsic clusters of transcriptional regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation, and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.

systems biology↗

bioRxiv2021

Mining all publicly available expression data to compute dynamic microbial transcriptional regulatory networks

We are firmly in the era of biological big data. Millions of omics datasets are publicly accessible and can be employed to support scientific research or build a holistic view of an organism. Here, we introduce a workflow that converts all public gene expression data for a microbe into a dynamic representation of the organisms transcriptional regulatory network. This five-step process walks researchers through the mining, processing, curation, analysis, and characterization of all available expression data, using Bacillus subtilis as an example. The resulting reconstruction of the B. subtilis regulatory network can be leveraged to predict new regulons and analyze datasets in the context of all published data. The results are hosted at https://imodulondb.org/, and additional analyses can be performed using the PyModulon Python package. As the number of publicly available datasets increases, this pipeline will be applicable to a wide range of microbial pathogens and cell factories.

bioinformatics↗

bioRxiv2020

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (E. coli, S. aureus, and B. subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.

systems biology↗

bioRxiv2020

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcriptome and quantitatively describe regulatory activity under diverse conditions, creating an unbiased summary of gene expression. We obtain 83 independently modulated gene sets that explain most of the variance in expression, and demonstrate that 76% of them represent the effects of known regulators. The TRN structure and its condition-dependent activity uncover novel or recently discovered roles for at least 5 regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states, revealing a putative anaerobic metabolism role for SigG. As this TRN covers the majority of the transcriptome and concisely characterizes the global expression state, it could inform research on nearly every aspect of transcriptional regulation in B. subtilis.

systems biology↗