ORIGINAL RESEARCH article
Sec. Systems Microbiology
Volume 12 - 2021 | https://doi.org/10.3389/fmicb.2021.753521
Machine Learning Uncovers a Data-Driven Transcriptional Regulatory Network for the Crenarchaeal Thermoacidophile Sulfolobus acidocaldarius
- 1Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
- 2Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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 five 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 five 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 extremophile’s 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 toward regulatory network discovery in archaea.
Over the past few decades, scientists have been increasingly intrigued by the remarkable microorganisms that reside in extreme environments. The phenotypic diversity of these extremophiles is high, and due to the environments they inhabit, challenging to study. However, in recent years, advances in various molecular biology approaches – low-cost sequencing in particular – have enabled the exploration of the genotypic space these organisms inhabit.
Sulfolobus acidocaldarius is one such extremophilic archaeon; it is a thermoacidophile that resides in sulfur hot springs, which have an average pH of 2.4 and an average temperature of 83°C (Brock et al., 1972; Lewis et al., 2021). Such extremes in acidity and temperature necessitate specific adaptations to thrive in these habitats. Sulfolobus species are known to have an extraordinarily malleable yet chemiosmotic-resistant cell envelope, as well as exceptionally thermostable enzymes (Albers and Meyer, 2011). These characteristics make Sulfolobus valuable potential sources of biologics for future medical and biotechnological applications (e.g., production of carbocyclic nucleotides, halogen group removal, and esterification) (Littlechild, 2015; Quehenberger et al., 2017). S. acidocaldarius is also one of the best-studied archaea as it is one of the few genetically tractable archaeal model systems (Wagner et al., 2012). While much progress has been made using these approaches, especially with regards to RNA-seq studies that have found multiple regulators of gene expression (Reimann et al., 2012; Lassak et al., 2013; Song et al., 2013; Liu et al., 2016; Haurat et al., 2017; Lemmens et al., 2019; Wang et al., 2019), much of the TRN structure of S. acidocaldarius remains unknown. Here, we leverage machine learning to construct a global TRN for this extremophile.
Independent component analysis (ICA) (Jutten and Herault, 1991) is an unsupervised machine learning algorithm that is especially useful for blind source separation of mixed signals. This algorithm takes in observations of mixed signals and is able to deconvolute them back into their independent, unmixed forms with no additional inputs (Hyvärinen and Oja, 2000). This methodology has proven quite successful in deconvoluting observed gene expression profiles into linear combinations of statistically independent genetic modules as the blind signals (Teschendorff et al., 2007; Biton et al., 2014; Sastry et al., 2019; Sompairac et al., 2019; Poudel et al., 2020; Rychel et al., 2020). These independently modulated sets of genes (termed iModulons) contain anywhere from one to hundreds of genes, and are often controlled by a common regulatory source, such as a single transcription factor (TF) or a combination of regulatory elements acting in concert (Sastry et al., 2019). iModulons containing only one gene often form as a result of noise or genetic perturbation in the compendium (e.g., gene knockout) (Sastry et al., 2021a).
In contrast to regulons, which are bottom-up groupings of co-regulated genes derived from TF-to-DNA binding assays and other biomolecular methods, iModulons are co-expressed sets of genes which are derived from applying data analytics to large transcriptomic compendia. Thus, iModulons can be interpreted as data-driven analogs of regulons. Furthermore, the condition-dependent activity levels of each iModulon correspond to the activity of the underlying genetic signals and regulators. Despite the difference in approach (i.e., data analytics vs. direct molecular methods), iModulons recapitulate many known regulons from the literature, and have accurately predicted new genetic targets for regulators (Sastry et al., 2019; Poudel et al., 2020; Rychel et al., 2020) and elucidated gene functions (Rodionova et al., 2020, 2021). This top-down approach allows for an unbiased method for reconstructing the TRN of prokaryotic organisms of interest, including archaea. As iModulons enable us to learn the TRN structure from transcriptomes alone, they represent a valuable approach for quickly characterizing relatively under-studied TRNs, such as that of S. acidocaldarius.
Given the considerable insights provided by ICA, along with a proven track-record (Sastry et al., 2019; Poudel et al., 2020; Rychel et al., 2020), we applied it to a compendium of 95 publicly available RNA-seq expression profiles for S. acidocaldarius to deconvolute its TRN. This is the first application of ICA toward deconvolution of an archaeal TRN, and has led to the generation of the most complete, global TRN of S. acidocaldarius. Our deconvolution reveals 45 robust iModulons, which explain 72% of the variance in the RNA-seq compendium. Of these, five iModulons strongly resemble well-characterized regulons with identifiable regulators. This top-down, systems-level view of the TRN allows us to use our iModulons to propose informed hypotheses, backed with computational evidence of our claims. We also present various groupings of poorly characterized genes which warrant further study, given their importance in regulation of gene expression. Static graphical summaries of all S. acidocaldarius iModulons, including enriched gene members and iModulon activities, are presented in the Supplementary Material, with interactive summaries available on iModulonDB.org (Rychel et al., 2021).
Materials and Methods
Here, we demonstrate how we built the iModulon structure of Sulfolobus acidocaldarius from publicly available RNA-seq datasets (Figure 1A). The workflow followed here is based on the PyModulon workflow described by Sastry et al. (2021b). All code to reproduce this pipeline is available at: https://github.com/SBRG/modulome_saci and https://github.com/avsastry/modulome-workflow/. Since this process results in the totality of iModulons that can currently be computed for this organism, we have named the resulting database as the “S. acidocaldarius Modulome.”
Figure 1. Overview of compiled S. acidocaldarius RNA-seq compendium and its iModulon characterization. (A) Graphical representation of the PyModulon ICA workflow [adapted from Sastry et al. (2021b)]. (B) Number of samples that passed quality control. (C) Number of high-quality RNA-seq expression profiles for S. acidocaldarius in NCBI SRA over time. (D) Treemap of iModulons generated from the curated RNA-seq compendium. Sizes are representative of variance explained by iModulon (total 72%). Boldface and asterisk indicate that the corresponding iModulon recapitulates a known regulon.
Gathering and Processing RNA-Seq Data From NCBI SRA
Following the PyModulon workflow, we used a script that compiles the metadata for all publicly available RNA-seq data for a given organism in NCBI SRA1. As of August 2020, we identified 117 expression profiles labeled as Sulfolobus acidocaldarius RNA-seq data. The FASTQ files for these datasets were collected and processed as previously described (Sastry et al., 2021b).
Briefly, we downloaded raw FASTQ files using fasterq-dump2, and performed read trimming using Trim Galore3 with the default options, followed by FastQC4 on the trimmed reads. Next, reads were aligned to the genome using Bowtie (Langmead et al., 2009). The read direction was inferred using RSeQC (Wang et al., 2012) before generating read counts using featureCounts (Liao et al., 2014). Finally, all quality control metrics were compiled using MultiQC (Ewels et al., 2016) and the final expression compendium was reported in units of log-transformed Transcripts per Million (log-TPM).
To process the complete S. acidocaldarius RNA-seq compendium, we used Amazon Web Services (AWS) Batch to run the Nextflow pipeline (Di Tommaso et al., 2017)5.
A Jupyter notebook (IOS Press Ebooks, 2021) – Jupyter Notebooks – a publishing format for reproducible computational workflows) showcasing the quality control workflow can be found at https://github.com/SBRG/modulome_saci/blob/master/notebooks/1_expression_QC_SOP.ipynb. The final high-quality S. acidocaldarius compendium contained 95 RNA-seq datasets (Figures 1B,C). As part of the quality control procedure previously described (Sastry et al., 2021b), we performed manual curation of experimental metadata to identify which samples were biological replicates. We also examined the literature to identify each sample’s strain, media, additional treatments, environmental parameters/changes, and growth stage, if reported. During curation, we removed some non-traditional RNA-seq datasets, such as non-coding RNA-seq.
The resulting RNA-seq compendium consisted of 95 high-quality RNA-seq expression profiles of 6 groups of studies. These 6 groups are named Chrom (which studies differences between log and stationary phase), FadR (which studies differences between wild-type and FadR deletion mutants), GDGT (which studies differences at various combinations of temperature and log/stationary phase), NutLim (which studies nutrient limitation in a time-course), UV_irr (which studies differences between wild-type and tfb3 insertion mutants when both are exposed to UV irradiation in a time-course fashion), and YtrA (which studies differences between wild-type and ytrA overexpression mutants) (Lassak et al., 2013; Schult et al., 2018; Lemmens et al., 2019; Takemata et al., 2019; Wang et al., 2019). A full table of the final transcriptome data used for further analysis, including SRA and BioProject accession numbers (along with PMID numbers of corresponding literature) is provided as a metadata file (Supplementary File 1).
A draft TRN was also constructed at this stage. This process involved curating all reported regulatory interactions from S. acidocaldarius literature into a standardized table for further analysis downstream. Literature used in metadata annotation also served as material for developing a draft TRN (Supplementary File 2).
To obviate any batch effects resulting from collating different expression profile datasets, we selected a reference condition (consisting of at least two replicates) to normalize each project in the compendium. This ensured that nearly all independent components generated were due to biological variation rather than technical variation. After normalization, however, gene expression and iModulon activities can only be compared within a project to a reference condition, rather than across projects.
For all groups except for GDGT and Chrom, this reference baseline condition consisted of wild-type S. acidocaldarius cells grown at 75°C in log phase at pH of 3.5. This was chosen because it was listed as the control for many of the corresponding RNA-seq studies (as these samples were often compared to a corresponding deletion/insertion mutant). For the NutLim and UV_irr studies, the samples chosen were also specifically the ones measured at 0 min (just prior to the time-course studies starting) (Lassak et al., 2013; Schult et al., 2018). For the GDGT group, the baseline condition was chosen to be wild-type cells grown at 75°C in log phase at pH of 2.4. This change in pH was used as the baseline due to the fact that cells grown at 75°C in log phase at pH of 3.5 failed the initial quality control. Choosing cells at pH of 2.4 as a baseline also had the added benefit of allowing us to compare pH differences within this group, which varied from 1.6 to 3.5. Finally, the Chrom group’s baseline condition consisted of wild-type cells grown at 78°C in log phase at pH of 3.2. This was chosen as all samples from this group were grown at this specific temperature and pH.
Gene Naming and Annotation
The genome annotation derived from NCBI (accession number: NC_007181.1) contains many poorly characterized gene products, so the genome fasta was reannotated using the software tool prokka (Seemann, 2014). This resulted in prokka-specific locus tags and prokka-specific annotations. All of this data was then collated into a standardized gene annotation table using the following Jupyter notebooks6 ,7. We then manually parsed through the literature to find as many relevant gene names and functional annotations as possible, generating a curated gene annotation table for S. acidocaldarius (Supplementary File 3).
Independent Component Analysis
Following the PyModulon workflow, we implemented ICA using the optICA extension of the popular algorithm FastICA. The optICA script used can be found at https://github.com/avsastry/modulome-workflow/tree/main/4_optICA and produces two matrices. One matrix (the M matrix) contains the robust independent components (McConn et al., 2021), and the other (the A matrix) contains the corresponding activities. The product of the M and A matrices approximates the expression matrix (the X matrix), which is the curated RNA-seq compendium. Each independent component in the M matrix is filtered to find the genes with the largest absolute weightings, which ultimately generates iModulon gene sets.
Implementing this process resulted in 45 iModulons for the S. acidocaldarius Modulome that explained 72% of the expression variance in the compendium (Figure 1D). Although the fraction of variance explained by 45 iModulons is much lower than the fraction of variance explained by 45 principal components of the X matrix, explained variance has additional meaning in ICA compared to PCA. Principal components are a mathematical representation of the compendium and often lack biological interpretation. However, independent components (and the iModulons which derive from them) can be directly linked to transcriptional regulation (via characterization), revealing interpretable, and biologically relevant sources of expression variation in the compendium.
To facilitate iModulon characterization, we utilized the PyModulon Python package (Sastry et al., 2021b). As S. acidocaldarius has a poorly documented TRN, k-means clustering (with k = 3) was utilized to identify component-specific thresholds. This technique clustered genes into 3 groups based on the magnitude of each gene’s weighting. The cluster with the lowest average weighting was filtered out, and its bounds used as the cutoff in determining which genes were part of an iModulon. Each iModulon was then compared to the draft TRN table to find iModulons with significant overlap with known regulons. Next, KEGG and GO annotations were utilized to identify iModulons with significant overlap with known metabolic pathways. The remaining iModulons were functionally mapped by analyzing their activities and literature review. The scripts showing these characterizations can be found at: https://github.com/SBRG/modulome_saci/blob/master/notebooks/5_a_iModulon_characterization_GO_KEGG_setup.ipynb, https://github.com/SBRG/modulome_saci/blob/master/notebooks/5_b_iModulon_characterization_KEGG_enrichments.ipynb, and https://github.com/SBRG/modulome_saci/blob/master/notebooks/5_c_iModulon_characterization_remaining_iModulons.ipynb.
Generating iModulonDB Dashboards
iModulonDB dashboards were generated using the PyModulon package (Rychel et al., 2021; Sastry et al., 2021b); the pipeline for doing so can be found at https://pymodulon.readthedocs.io/en/latest/tutorials/creating_an_imodulondb_dashboard.html.
Independent Component Analysis Reveals the Structure of the Sulfolobus acidocaldarius Transcriptome
To prepare the data compendium, we first compiled all publicly available RNA-seq data from the NCBI Sequence Read Archive (Kodama et al., 2012). After processing the data and filtering through a quality control pipeline (see section “Materials and Methods,” Figure 1A), the final high-quality compendium consisted of 95 RNA-seq experiments, ranging a diverse set of conditions (see section “Methods,” in Supplementary File 1), including starvation (time-course), acid stress, and UV irradiation (time-course). Application of ICA to this compendium resulted in 45 robust iModulons, each of which constitutes a statistically independent signal of gene expression. Although these 45 iModulons (Figure 1D) only contain 755 genes (32% of the 2351 genes found on the genome), the iModulons can explain 72% of the observed variance in gene expression; genes which were not captured in any iModulons did not vary much in expression. Gene distribution in these iModulons follows a power law, with a few iModulons containing a large number of genes, while most contain fewer than 20 genes (Supplementary Figure 1).
In order to characterize the iModulons, we first built a scaffold TRN (Supplementary File 2) based on the existing literature for S. acidocaldarius, consisting of 346 gene-regulator interactions for 28 regulators (Koerdt et al., 2011; Reimann et al., 2012; Lassak et al., 2013; Märtens et al., 2013; Orell et al., 2013; Ouchi et al., 2013; Vassart et al., 2013; Anjum et al., 2015; Buetti-Dinh et al., 2016; Liu et al., 2016; Haurat et al., 2017; Li et al., 2017; Schult et al., 2018; Wagner et al., 2018; Lemmens et al., 2019; Wang et al., 2019; Baes et al., 2020; Stracke et al., 2020; Suzuki et al., 2020; van der Kolk et al., 2020). To our knowledge, this is the first comprehensive collection of gene-regulator interactions compiled for S. acidocaldarius. We used this scaffold to infer which regulon strongly overlapped with each iModulon (see section “Materials and Methods”). The iModulon-derived TRN of S. acidocaldarius consists of 957 gene-iModulon interactions, of which 65 are known gene-regulator interactions and the remaining 892 are new predictions. These newly predicted interactions provide a roadmap for regulon discovery as well as an opportunity for regulator-agnostic, data-driven discovery of the S. acidocaldarius TRN.
iModulons Reveal the Basis for the Variability in the Sulfolobus acidocaldarius Transcriptome
Unlike regulons, which require direct experimental evidence of TF binding sites, iModulons generate sets of co-regulated genes (even those with no known regulator) through extracting global patterns in the transcriptome. The resulting gene sets can be mapped onto known regulons or known cellular or biochemical processes. This curation has allowed for the functional characterization of 37 out of 45 iModulons, despite many gaps in this organism’s TRN. These characterized iModulons, which are clustered into five main groupings, account for approximately 48% of the explained variance in the compendium. The smallest grouping (accounting for genomic alterations to specific strains included in the compendium) explains < 1% of the variance, while the largest grouping (accounting for other functional aspects of cellular regulation) explains 22% of variance. Interestingly, stress-response iModulons and Vitamin B iModulons both explain a similar percentage of the variance in the compendium (6% and 4%, respectively), with miscellaneous metabolic iModulons explaining the remaining 15%.
Archaeal iModulons Recapitulate Known Regulons in Literature
Of the 45 robust iModulons generated by ICA, five recapitulate known regulons in literature: ArnRAB, FadR, YtrA, UV-tfb3, and LysM. An investigation into these iModulons validates that they represent the effects of transcriptional regulators in archaea, and provide additional biological insight into the TRN beyond the capabilities of currently known regulons. In the subsequent sections, we provide three specific examples.
The ArnRAB iModulon Recapitulates Core Archaellum Formation Genes
The ArnRAB iModulon consists of the core archaellum formation genes of the arl operon (Saci_1172 to Saci_1179, Figures 2A,B; Lassak et al., 2013). This operon forms archaeal-specific motility pili that are the primary drivers of cell movement, especially in starvation conditions (Figure 2C; Beeby et al., 2020). The arl operon is regulated by a complex interplay of TFs (Reimann et al., 2012; Lassak et al., 2013; Haurat et al., 2017; Ye et al., 2020). The activity of the iModulon also reflects observations from the literature; namely, upregulation over time in response to nutrient-limiting conditions (Figure 2D). The activity level of this iModulon is a useful measurement that likely combines the effects of several regulators: high activity could be the result of arl operon activation by ArnR/ArnR1, and low activity may be due to repression by ArnA/ArnB.
Figure 2. Overview of the ArnRAB iModulon. (A) Scatter plot of the gene weights for the ArnRAB iModulon. The X-axis represents the genomic location and the Y-axis represents the weight of each gene in the independent component. The dashed lines represent the threshold beyond which genes of the independent component are included in an iModulon. (B) Gene map of archaellum formation operon (Lassak et al., 2013). Known regulatory elements are also listed (Reimann et al., 2012; Lassak et al., 2013; Haurat et al., 2017; Ye et al., 2020). (C) Structural map showcasing individual gene products from the archaellum formation operon and how each gene product integrates to form these archaeal motility pili (Beeby et al., 2020; Tsai et al., 2020). Note that the S-layer, which typically consists of SlaA (the stalk) and SlaB (the cap) is not fully shown. The stalks in particular are not displayed to highlight the structural organization of the archaellum operon proteins. (D) Bar plot depicting the ArnRAB iModulon activity over time in nutrient-limiting conditions. Bars represent averages and points represent individual replicate samples. All activity levels are relative to a chosen control condition (0 min after nutrient limitation in this case).
The FadR iModulon Successfully Captures Its Corresponding Gene Cluster
The FadR iModulon represents another case study that demonstrates the ability of ICA to extract co-regulated gene clusters from archaeal RNA-seq compendia. This iModulon (Supplementary Figure 2) consists of 19 genes in the FadRSa gene cluster (Saci_1103 to Saci_1122) (Wang et al., 2019), excluding fadR itself (Saci_1107), as well as six additional genes not present in the regulon. However, the iModulon is missing five genes found in the regulon: fadR and Saci_1123 to Saci_1126. The absence of fadR in this iModulon is explained by the fact that fadR is contained in its own single-gene iModulon, which captures the FadR knockout condition (FadR-KO). The remaining four genes are regulated by FadRSa as it binds near Saci_1123. However, the confirmed FadR-binding region for this section of the gene cluster is known to be much weaker (Wang et al., 2019), which could result in a weaker signal that could not be detected in this dataset. It is worth noting, however, that Saci_1126 is just below the statistical threshold for enrichment in the FadR iModulon (0.079 vs. 0.08). There are also six new genes in the FadR iModulon that are not in the currently known regulon (Supplementary Figure 2). All six genes are functionally uncharacterized, with the exception of Saci_1992; this gene is a CRISPR-associated TF (Benninghoff et al., 2021). Five of these genes have negative gene weights, which means that their expression is anti-correlated with the expression of the FadRSa gene cluster. In this example, the differences between the regulon and iModulon capture differences in binding strength that have been identified previously, as well as propose new putative FadR-regulated genes.
The FadR iModulon is activated when FadRSa is knocked-out or during nutrient-limiting conditions (Supplementary Figure 2). This suggests derepression in these conditions, as FadRSa is known to repress its own gene cluster (Wang et al., 2019).
iModulons Suggest an Expanded Role for LysM
The LysM iModulon recapitulates canonical lysine biosynthesis in S. acidocaldarius. The iModulon consists of ten genes, of which four make up the LysM-regulated lysWXJK operon, which codes for enzymes in the LysW-mediated α-aminoadipate pathway (Zabriskie and Jackson, 2000; Ouchi et al., 2013; Suzuki et al., 2020; Figure 3A). Incidentally, the lysYZM operon, which also codes for two enzymes (LysY and LysZ) in the α-aminoadipate pathway, is absent from this iModulon. This absence suggests that some other regulatory elements may also influence the expression of lysYZM, and thus the entire LysM iModulon indirectly (Figure 3B).
Figure 3. Overview of the LysM regulon and iModulon. (A) Pathway map overviewing lysine biosynthesis in S. acidocaldarius (Ouchi et al., 2013; Suzuki et al., 2020). Genes in red are part of the LysM iModulon. The blue boxes specifically show the LysW-mediated pathway used in lysine and arginine biosynthesis which consists of lysYZMWXKJ (Zabriskie and Jackson, 2000; Ouchi et al., 2013; Suzuki et al., 2020). LysW attaches to the amino group of glutamate/α-aminoadipate until the substrate forms into ornithine/lysine, respectively (Ouchi et al., 2013). (B) Scatter plot of the gene weights for the LysM iModulon. Genes are colored by COG categories. Genes with an asterisk “*” are part of the known LysM regulon.
Additionally, this iModulon contains Saci_1304, which codes for homocitrate synthase (SaHCS) (Suzuki et al., 2020); this enzyme catalyzes the first step toward lysine biosynthesis in S. acidocaldarius (Ouchi et al., 2013; Suzuki et al., 2020). The LysM iModulon also contains Saci_0252 and Saci_0253, which encode for 3-isopropylmalate dehydratase, a part of the leucine biosynthesis pathway (Park et al., 2018; Quehenberger et al., 2019). These genes are also aconitase homologs (Gross et al., 1963; Calvo et al., 1964; Cole et al., 1973), and may also have catalytic activity in homocitrate to homoisocitrate conversion (Figure 3B).
Finally, the LysM iModulon consists of three poorly characterized genes: Saci_1028, Saci_2071, and Saci_2189. Saci_1028 putatively codes for an acetyl-ornithine aminotransferase family protein and Saci_2189 putatively codes for an APC family permease (potentially an aspartate-proton symporter), while Saci_2071 is completely uncharacterized, encoding a hypothetical protein. It should be noted that Saci_1028 and Saci_2071 are the only genes in this iModulon to have negative gene weights. This indicates that the expression of both of these genes are anti-correlated with the rest of the genes in the LysM iModulon; that is, these genes are upregulated when the rest of the iModulon is downregulated and vice versa. The presence of these genes in the LysM iModulon indicates that they are likely involved in lysine biosynthesis, and perhaps regulated by LysM or some other shared regulatory mechanism.
However, LysM, a TF which is highly conserved in Sulfolobus species, is known to bind with at least ten different amino acid effectors in the closely related species Saccharolobus solfataricus (previously Sulfolobus solfataricus) (Song et al., 2013, p. 3). This affinity with multiple amino-acid effectors could reasonably extend to the homologous LysM TF in S. acidocaldarius. This fact, combined with the proven ability of ICA to extract regulatory signals, leads us to hypothesize that all ten genes of this iModulon are regulated by LysM. Alternatively, both the lysYZM and lysWXKJ operons, alongside the remaining enriched genes of the LysM iModulon, could be regulated together by a common regulatory element(s). In either case, iModulons direct us toward six genes which warrant additional investigation, and may further elucidate amino acid biochemistry and its associated TRN in S. acidocaldarius.
iModulons Uncover Data-Driven Targets for Gene Function and Regulator Discovery
As seen with LysM, iModulons also function as data-driven aids for gene/regulator discovery. Rather than searching for regulators and their known binding locations along a genome, iModulons provide a set of co-regulated genes, and instead allow for a data-driven discovery of common regulatory elements. The following sections provide three such examples for the S. acidocaldarius TRN.
Uncharacterized Genes May Compose the UV-Induced DNA Export System
The UV-tfb3 iModulon contains genes that form the tfb3-dependent UV stress response (Figure 4A; Schult et al., 2018). Under UV irradiation, S. acidocaldarius cells aggregate, form specific adhesion pili, and exchange DNA with each other to repair their genomes (Götz et al., 2007; Schult et al., 2018). This process is performed in a tfb3-dependent manner, which is also reflected by this iModulon’s activities: steadily increasing after UV irradiation in wild-type cells, but almost completely absent in tfb3-disrupted mutants (Figure 4B). Two systems have been identified that help compose this response: the ups operon and the ced system. The ups operon (upsXEFAB) (Ajon et al., 2011; van Wolferen et al., 2013, 2015) consists of genes that code for the specific pili that aid S. acidocaldarius cells in adhering to each other post-UV irradiation. The ced system (cedA/A1/A2/B) (van Wolferen et al., 2016) consists of multiple transporters that import DNA. However, a crucial aspect of this response is still unidentified: the DNA export system. As the ced system only imports DNA, a corresponding DNA export system must exist for S. acidocaldarius cells to properly exchange DNA.
Figure 4. Overview of the tfb3-dependent UV-stress response iModulon (UV-tfb3). (A) Scatter plot of the UV-tfb3 iModulon gene weights. Genes are colored by COG categories. Genes in bold are part of the known tfb3-dependent response (Götz et al., 2007; Ajon et al., 2011; van Wolferen et al., 2013, 2015, 2016; Schult et al., 2018). Genes with an asterisk are proposed to make up the DNA export system. (B) UV-tfb3 iModulon activity plotted over time after UV-irradiation. The left three bars show the activities of the tfb3 insertion mutants (relative to wild-type cells before UV irradiation). The right three bars show the activities of the wild-type cells (relative to wild-type cells before UV irradiation). (C) A visual overlay of the tfb3-dependent UV-stress response (Ajon et al., 2011; van Wolferen et al., 2013, 2015, 2016).
The UV-tfb3 iModulon, in addition to the ups operon and the ced system, contains five uncharacterized genes, which we propose to constitute this undiscovered DNA export system (Figure 4C). InterPro scans of all the uncharacterized genes revealed that Saci_1270 and Saci_1302 contained multiple transmembrane, cytosolic, and non-cytosolic domains, providing further evidence that these genes encode enzymes that help export DNA (Supplementary File 4).
iModulons Provide Evidence of an Uncharacterized Global Regulator
ICA decomposition of our RNA-seq compendium (Figure 5A) revealed 45 iModulons, which we ranked by explained variance in Figure 1D. iModulons with high explained variance are nearly always regulated by global transcriptional regulators (Lamoureux et al., 2021). However, the iModulon that explained the largest variance in the S. acidocaldarius data was primarily enriched with uncharacterized or poorly characterized genes. This iModulon consists of 33 enriched genes: 11 characterized genes, 12 poorly characterized genes, and 10 completely uncharacterized genes (Figure 5B). Of the genes which are characterized, six genes encode acyl-CoA ligases, and of the genes which are poorly characterized, five are membrane-bound proteins. Beyond this information, however, little is known about the enriched genes of this iModulon.
Figure 5. Overview of the DARC iModulon. (A) Overview of the ICA decomposition process. The S. acidocaldarius Modulome (X matrix) which consists of 95 high-quality RNA-seq datasets is decomposed into the M and A matrices (see section “Materials and Methods”). The gene weights from a particular column of the M matrix are used to generate the gene weight scatter plot (B). The iModulon activities of the corresponding row of the A matrix are used to generate a corresponding boxplot (C). (B) Scatter plot of the gene weightings for the DARC iModulon. Genes are colored by COG categories. (C) Boxplot showcasing the DARC iModulon’s activities for various conditions. Dots represent the iModulon activity for individual sample values. “Cells at stationary phase” includes all samples in the RNA-seq compendium which were collected for cells in stationary phase. “Cells at log phase” includes all samples in the RNA-seq compendium which were collected for cells in log phase, except for cells which were exposed to any nutrient-limitation conditions; “Nutrient Limitation Conditions” represents those such samples.
The largest difference in this iModulon’s activity is between cells grown in log phase and cells grown in stationary phase or in nutrient-limiting conditions (Figure 5C). This iModulon contains two TFs: Saci_1223 (abfR2), a secondary biofilm regulator, and Saci_2103, a predicted MarR family TF. Taken together, we propose that this iModulon is related to the cell membrane, possibly in a growth-dependent manner, but further work is needed to fully elucidate the role of this iModulon in the overall TRN of S. acidocaldarius. As this iModulon represents a biological signal (extracted by ICA), but with no known regulators or clearly defined function, we name this iModulon a Discovered signal with Absent Regulatory Components, or DARC.
The XylR-SoxM iModulon May Be Governed by a Global Regulator and Could Contain an Undiscovered, Peptide-Induced Sugar Transporter
The XylR-SoxM iModulon consists of 47 enriched genes (Figure 6A), of which 10 are part of the XylR regulon (Wagner et al., 2018; van der Kolk et al., 2020), and 6 of the soxEFGHIM gene cluster (Park et al., 2018). This iModulon also contains Saci_2032, Saci_2033, and Saci_2034, which are all genes predicted to code for enzymes in glycerol uptake and metabolism. The remaining enriched genes are poorly characterized, and mostly encode either thiolases, thioredoxins, or hypothetical proteins. Of the ten genes shared between the XylR regulon and the XylR-SoxM iModulon (Figure 6B), eight are known to be downregulated in xylose-growth conditions: Saci_1147, Saci_1148, and Saci_2230 to Saci_2235 (Wagner et al., 2018). The remaining two, which are upregulated in xylose-growth conditions, are Saci_2122 (xylF) and Saci_1760. Saci_1760 codes for a glycosylated membrane protein which is only present during D-xylose/L-arabinose growth conditions, while Saci_2122 encodes a D-xylose/L-arabinose substrate-binding protein which works in concert with transport proteins XylG and XylH to import pentose sugars (Wagner et al., 2018; van der Kolk et al., 2020). However, xylG (which codes for the D-xylose/L-arabinose transmembrane domain transport protein XylG) and xylH (which codes for the cytosolic D-xylose/L-arabinose nucleotide-binding domain transport protein XylH) are not enriched in this iModulon.
Figure 6. Overview of the XylR-SoxM iModulon. (A) Scatter plot of the gene weightings for this iModulon. Genes are colored by COG categories. (B) Venn diagram comparing the XylR regulon, XylR-SoxM iModulon, and the SoxM gene cluster. (C) Scatterplot of xylR gene expression (X-axis) vs. XylR-SoxM iModulon activity (Y-axis). The correlation coefficient for this data is 0.91. (D) Boxplot of XylR-SoxM activities by condition. “Nutrient Limitation Conditions” refers to all samples in the RNA-seq compendium which were exposed to any nutrient-limitation conditions.
Taken together, this information suggests that pentose sugar uptake may have at least two forms of regulation. The first, which is captured by the XylR-SoxM iModulon, consists of the regulation of various genes, including Saci_2122, Saci_1760, and the entire SoxM gene cluster (which consists of an archaeal cytochrome system) (Komorowski et al., 2002). The second, which is not captured by the XylR-SoxM iModulon, should consist of the regulation of the remainder of the XylR regulon (xylG and xylH, etc.) and possibly also the genes involved in the aldolase-independent Weimberg pathway, responsible for converting D-xylose/L-arabinose into α-ketoglutarate (Wagner et al., 2018; van der Kolk et al., 2020). Having multiple regulatory substructures agrees with previous assertions which state that there may be additional layers of regulation in pentose sugar uptake and metabolism, beyond just the regulator XylR (Wagner et al., 2018; van der Kolk et al., 2020). Interestingly, while Saci_2116 (xylR) is not enriched in this iModulon, it is just below the statistical threshold (0.057 vs. 0.058) and its expression is highly correlated with the XylR-SoxM iModulon’s activity (Pearson-R = 0.91, Figure 6C). The XylR-SoxM iModulon also has positive activity in nutrient-limiting conditions and in stationary phase, with negative activity in log phase (Figure 6D), further suggesting that this iModulon contains genes related to growth and/or starvation. Altogether, this suggests two potential hypotheses: (1) xylR is a global regulator that has an expanded function related to growth, or (2) there is a separate global regulator that regulates all the genes in the XylR-SoxM iModulon (and potentially also xylR). To test these hypotheses, more RNA-seq data must be accumulated for S. acidocaldarius.
Additionally, the XylR-SoxM iModulon may include a gene which encodes an as-yet-undiscovered xylose transporter in S. acidocaldarius. While xylG and xylH both form known D-xylose transporters, there is evidence for the existence of another mechanism which can transport D-xylose into S. acidocaldarius in the presence of peptides (Wagner et al., 2018), but the gene that may encode such a transporter is currently unknown. There are three genes in the XylR-SoxM iModulon that encode putative transporters: Saci_0324, Saci_0675, and Saci_2095. In particular, Saci_2095 is predicted to encode an MFS transporter (likely a sugar transporter). Additionally, the EggNog archaeal cluster of orthologous genes (arCOG) mapper annotates this gene’s function as carbohydrate transport and metabolism. Furthermore, an InterPro scan was performed on this gene’s amino acid sequence and resulted in its classification as a sugar transporter (Supplementary File 5).
In summary, our investigation of the XylR-SoxM iModulon resulted in evidence suggesting regulation by a global regulator, as well as the potential identification of Saci_2095 as a peptide-induced D-xylose transporter.
Here, we collated all publicly available data to generate a high-quality RNA-seq compendium on S. acidocaldarius, and deconvoluted it using ICA. This deconvolution extracted 45 iModulons, whose overall activity can explain 72% of the variance in gene expression across the wide range of conditions contained in the compendium. 37 of these iModulons correspond to specific biological functions, with five corresponding to known regulators. We analyzed the enriched gene sets of these iModulons and presented findings that agree with previously existing knowledge. In addition, we generated data-driven hypotheses that could be experimentally tested in future investigations. We also showcase a previously unknown set of co-regulated genes which form an uncharacterized iModulon that explains 12% of the variance in the compendium. These results also generate the first comprehensive global TRN structure of S. acidocaldarius.
Through ICA, we identified well-studied regulons with high accuracy (such as the ArnRAB, FadR, and UV-tfb3 iModulons), in addition to discovering much more of the TRN landscape in S. acidocaldarius. ICA detected potential new regulatory targets for LysM, which may also further elucidate amino acid metabolism in S. acidocaldarius. Similarly, the added presence of five uncharacterized genes in the UV-tfb3 iModulon suggests more regulatory targets for tfb3, as they may form the currently undiscovered DNA export system for this organism. Completely new sections of TRN are also unearthed by ICA, as shown by the observation that the 33 co-regulated genes in the DARC iModulon account for 12% of the total variance in the compendium, by far the largest of any iModulon. Many of these genes are poorly understood, and warrant further investigation. The ability of iModulons to identify novel regulatory signals enables improved data-driven discovery over traditional differential gene expression studies.
Beyond the static plots provided in this manuscript, all generated iModulon data and interactive graphical summaries are available for interrogation online at iModulonDB.org (Rychel et al., 2021). Code for the analysis pipeline used is hosted on GitHub8. A file containing all iModulon tables with their respective gene members is also provided (Supplementary File 6).
As with many other machine learning methods, the results generated from ICA will improve as more high-quality data is used to generate the initial RNA-seq compendium. Already, many recently published RNA-seq studies for S. acidocaldarius exist that were not included in the compendium, as the data was unavailable at the onset of this project. Addition of such high-quality data, combined with transcriptome analysis under further unique conditions, will allow for a higher-resolution insight into the TRN. Larger, multifunction iModulons will likely separate into more biologically accurate modules. This division may also capture new regulons, some which may recapitulate existing knowledge of known regulators (e.g., BarR, Sa-Lrp, and AbfR1) and others which may reveal further insights. In principle, if transcriptomic data for every possible condition were to be obtained for this organism, ICA would generate a comprehensive, data-driven, and quantitatively irreducible TRN.
Data Availability Statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: imodulondb.org, https://github.com/SBRG/modulome_saci, and https://github.com/avsastry/modulome-workflow.
AS: conceptualization. SC and AS: data curation. SC: investigation and writing–original draft preparation. SC, AS, and KR: methodology. AS and BP: mentorship. All authors: software and writing–review and editing.
BP gratefully acknowledges the support of the Y.C. Fung Endowed Chair in Bioengineering at University of California, San Diego.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The authors would like to acknowledge Amitesh Anand, Sonja-Verena Albers, and Marleen van Wolferen for useful discussions.
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2021.753521/full#supplementary-material
- ^ https://github.com/SBRG/modulome_saci/tree/master/notebooks/0_metadata
- ^ https://github.com/ncbi/sra-tools/wiki/HowTo:-fasterq-dump
- ^ https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/
- ^ http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
- ^ https://github.com/avsastry/modulome-workflow/tree/main/2_process_data
- ^ https://github.com/SBRG/modulome_saci/blob/master/notebooks/3_a_gene_annotation-manual-curation-notebook.ipynb
- ^ https://github.com/SBRG/modulome_saci/tree/master/notebooks
- ^ https://github.com/SBRG/modulome_saci/
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Keywords: archaea, machine learning, independent component analysis (ICA), transcriptional regulatory network (TRN), systems biology, transcriptional regulation, thermophile, regulon discovery
Citation: Chauhan SM, Poudel S, Rychel K, Lamoureux C, Yoo R, Al Bulushi T, Yuan Y, Palsson BO and Sastry AV (2021) Machine Learning Uncovers a Data-Driven Transcriptional Regulatory Network for the Crenarchaeal Thermoacidophile Sulfolobus acidocaldarius. Front. Microbiol. 12:753521. doi: 10.3389/fmicb.2021.753521
Received: 05 August 2021; Accepted: 30 September 2021;
Published: 27 October 2021.
Edited by:Carmen Vargas, University of Seville, Spain
Reviewed by:Bettina Siebers, University of Duisburg-Essen, Germany
Ilya R. Akberdin, Biosoft.ru, Russia
Copyright © 2021 Chauhan, Poudel, Rychel, Lamoureux, Yoo, Al Bulushi, Yuan, Palsson and Sastry. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Bernhard O. Palsson, email@example.com