dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation of the modular architecture and regulatory mechanisms uncovered by the C. difficile EGRIN model We tested the accuracy of the EGRIN model to reconstruct previously characterized regulons and recapitulate key aspects of C. difficile biology. We performed functional enrichment analysis using an updated annotation of C. difficile genome  (Girinathan et al. , 2021). This analysis identified 93 of 297 modules (31%) with significant enrichment of functionally related genes in 45 pathways (hypergeometric test adjusted p value % 0. 05). Among these pathways, 14 were overrepresented in three or more modules (Figure  2A ), highlighting the capacity of the model to discover conditional partitioning of cellular processes. We also investigated whether the EGRIN model identified known regulatory interactions between TFs and their target genes.
We compiled regulons (i. e. , target genes) of 13 characterized TFs in C. difficile, representing a network of 1,349 TF-gene interactions (Table  S2 ). Notably, a total of 57 modules (19% of all high-quality modules) were significantly enriched with nine of these TF regulons (Figure  2B ). The EGRIN model recapitulated 514 of the 1,208 (42. 5%) previously characterized interactions, a value consistent with the EGRIN recall rate for M. tuberculosis (41%-49%)  (Peterson et al. , 2014). The poor recall of the remaining four regulons (141 regulatory interactions) could be due to underrepresentation of expression data from conditions in which these regulons are active. This analysis also uncovered combinatorial regulation of genes across 19 modules (i. e. , enriched with more than one TF regulon).
Consistent with the known hierarchical scheme for regulation of sporulation  (Saujet et al. , 2013) , Spo0A putatively influenced expression of 161 genes across at least eight modules in combination with sigma factors implicated in sporulation (e. g. , SigE). EGRIN also predicted CcpA contributions in six modules in combination with CodY, PrdR, and SigL, illustrating the complexity of modular transcriptional regulation in C. difficile. EGRIN detected gene coregulation within and across functionally related operons. For example, module #152, enriched with the SigD regulon, contains 16 genes among four operons including the flagellar operon flgG1G-fliMN-CD630_02720-htpG, and pyrBKDE, CD630_30270-CD630_30280-malY-CD630_30300, and CD630_32430-prdA (Figure  S2A ). Gene clustering in EGRIN is constrained by the de novo discovery of conserved GRE(s) within promoters to cluster genes that are co-regulated, and not just co-expressed.
C. difficile's EGRIN model uncovers regulatory networks for the pathogenicity locus We evaluated EGRIN capacity to recall known mechanisms of PaLoc regulation and provide additional information regarding complex regulatory effects on toxin production. The EGRIN model captured certain previously described effects of CodY on toxin gene expression (Figure  2E ), as shown in module #182, which is enriched with CodY targets including tcdA. In agreement with the EGRIN-predicted CodY regulation of PaLoc genes, genes encoding the toxin tcdA and its regulator tcdR were significantly overexpressed upon deletion of codY (Figure  2G ). Interestingly, tcdB (which is not part of module #182) was also upregulated in the codY mutant, suggesting that this effect might be an indirect consequence of disrupted CodY regulation of tcdR (Figure  2G ).
Lower affinity of CodY for the tcdA promoter has been proposed  (Dineen et al. , 2007). S2 ). (C) EGRIN identified the known DNA binding motif of CodY  (Dineen et al. , 2010). (legend continued on next page) However, the presence of the CodY motif in most members of module #182, including tcdA (purple font in Figure  2E ) suggests direct influence of CodY on tcdA. The EGRIN model also identified previously reported connections between sporulation and toxin production  (Underwood et al. , 2009). The tcdB toxin gene was assigned to module #397, which was significantly enriched with genes controlled by Spo0A, the master regulator of sporulation (Figure  2F ). Additional members of the PaLoc were assigned to other modules, supporting the presence of multiple condition-dependent promoters within the PaLoc (Table  S3 ).
Assignment of putative functions to genes in EGRIN modules Approximately 33% of gene features in the CD630 genome have unknown functions. The C. difficile EGRIN model provides a resource to define putative functions of uncharacterized genes per functional associations among co-regulated genes (i. e. , guilt-by-association)  (Wolfe et al. , 2005). We predicted functions for 48 uncharacterized genes by mining functional enrichment of modules under different experimental conditions (Table  S4 ), involving 13 functional categories such as ''sporulation'' and ''other sugar-family transporters'' (Figure  3A ). Ten genes were assigned sporulation-related functions based on their presence in the modules #206 and #251 (Figure  3B ). Module #206 includes 7 stage-III sporulation genes and 2 stage-IV sporulation genes (Figure  S3A ).
(D) EGRIN also identified the known DNA binding motif of SigL  (Soutourina et al. , 2020). (E) The EGRIN model recapitulated the previously reported influence of CodY on tcdA expression. The module #182 contains tcdA, it is enriched with the CodY regulon and contains a GRE (shown in C) similar to the experimentally determined CodY motif. (F) The EGRIN model captured the interaction between toxin expression and sporulation via module #397 that contains tcdB and is enriched with genes regulated by sporulation-related transcriptional regulators. (G) Expression profiles of tcdAB and tcdR (positive regulator of the PaLoc). Log 2 ratios were computed with respect to dataset-specific references (Table  S1 ) and grouped by condition blocks. Boxes cover the 25 th -75 th percentile range (median indicated by horizontal black line).
, 2019). With capacity to identify intestinal contributions to C. difficile responses, we leveraged the EGRIN model to analyze commensal modulation of the pathogen's virulence, using transcriptomic datasets from gnotobiotic mice monocolonized with the mouse-infective strain C. difficile ATCC43255 or co-colonized with C. difficile and the protective gut commensal species Paraclostridium bifermentans, or infection-worsening species Clostridium sardiniense  (Girinathan et al. , 2021). These datasets were not used in model construction. By mapping sets of differentially expressed genes into the EGRIN model we uncovered differential regulation of modules across 18 cellular processes and their associated TFs in the presence of P. bifermentans or C. sardiniense (Figures  4C-4F ). One Spo0A-enriched module (module #385) was upregulated by 24 h of infection in monocolonized mice (Figure  4C ).
Module #319 contains genes associated with electron transport via Rnf ferredoxin systems, and steps in glycolytic, butanoate, and succinate metabolism (Figure  S3E ). This module was consistently downregulated in P. bifermentans co-colonized mice (Figure  4E ), while it was upregulated in C. sardiniense cocolonized mice (Figure  4F ). These findings highlight activation of multiple co-regulated energy-generating pathways in hypervirulent states of C. difficile infection, which may contribute to virulence and the pathogen's responses to changing redox states from host inflammatory responses. Remarkably, the EGRIN model identified the NAD + /NADH sensing regulator Rex as a potential repressor of module #319 (Inferelator regression coefficient = À0. 075). Hence, the observed downregulation of module #319 in P.
We validated the completeness and accuracy of this model by confirming its ability to predict biomass production in three (B) Expression profile of module #158. Only one condition block (''in vivo versus in vitro'') was found in the 20% of highest fold-change. (C) EGRIN modules enriched with genes differentially expressed (absolute log 2 fold-change > 1 and adjusted p value < 0. 05) in C. difficile monocolonized mice at 24 versus 20 h of infection. x axis shows module IDs. Modules were annotated according to their functional enrichment and overlap with manually curated TF regulons (Table  S2 ). (D) Enriched EGRIN modules in C. sardiniense+C. difficile co-colonized mice versus C. difficile monocolonized mice at 24 h of infection. Due to space constraint, only abbreviations of functional terms not shown in other panels are displayed.
bifermentans co-colonized mice. This approach uncovered how CcpA and CodY act synergistically during in vivo adaptation of ATCC43255, especially in the presence of P. bifermentans. Thus, the model projection strategy is useful to study evolutionary conserved adaptive mechanisms across strains of C. difficile. However, this strategy will not be useful to study strain-specific phenotypes, which would require strain-specific network models. Comparative analysis of datasets and network models across strains will be ultimately needed to elucidate how genomic differences across strains manifest in distinct clinical phenotypes and outcomes. PRIME predicted conditionally essential metabolic genes and networks that promote C. difficile's growth in vivo (i. e. , in monocolonized mice). In simulating the consequences of single gene deletions, PRIME predicted that pathogen growth in monocolonized mouse would decrease by R 65% with individual knockouts of 10 genes involved in one-carbon cycling reactions, and nucleotide and central carbohydrate metabolism (Figures  5D  and 6A ; Table  S5 ).
The C. difficile web portal, a resource for the C. difficile community We have released the C. difficile web portal (http://networks. systemsbiology. net/cdiff-portal/) to provide a discovery and collaboration gateway for the C. difficile scientific community. The portal aims to accelerate the advancement of the science and understanding of C. difficile biology, and the relationships among gene regulation, metabolism, and virulence. Within the portal, users can access publicly available datasets (e. g. , transcriptional compendia), models, software, and supporting resources. The portal includes information on more than 4,000 C. difficile genes, 1,273 metabolic reactions, and 406 EGRIN modules. The EGRIN model can be interactively explored and queried with gene set enrichment analysis to elucidate regulatory networks that may be relevant for a user-specified group of genes.
Genes can be explored in the context of genome annotations, expression profiles, regulatory and metabolic membership, and other functional genomic information across databases including COG, Uniprot, and PATRIC  (Consortium, 2017; Galperin et al. , 2015; Wattam et al. , 2014). The portal provides access to detailed information on (1) genes, (2) predicted gene modules, and (3) metabolic reactions (Figure  S6A ). Each module page includes summary statistics for the module, context-specific differential activity patterns, GREs, TF regulatory influences, enrichment of biological functions and pathways, and information on each module member gene. The module pages are structured to facilitate the assessment of the quality and statistical significance of the modules and highlight functional connections, while allowing users to implement their own filters (e.
g. , regression coefficient and adjusted p value thresholds) (Figure  S6B ). The portal includes a table of metabolic reactions with details of each reaction, associated genes, metabolites, and sub-systems. Metabolites and subsystems are defined as taxonomic vocabularies that collect and group associated reactions to identify related metabolic processes. In addition, the portal provides access to algorithms, software, and data and will include information about animal models, strains, and other C. difficile-relevant community resources. As additional datasets are communicated, model predictions and tools will be successively updated to support systems-level analyses and assist in hypothesis generation in C. difficile biology and to enable tangible clinical interventions. , it also underscores the potential for further improving the model with new data and experimental validations.
The models will also be refined using new tools for genetically manipulating C. difficile strains, including ATCC43255  (Peltier et al. , 2020) , which will enable targeted validations of regulatory influences of TFs and GREs on critical aspects of its metabolism, growth, and virulence. The C. difficile EGRIN model enables a number of predictions relevant to in vivo disease. For example, PrdR, a regulator of the Stickland proline reductase (prdABDEF) and other genes, has long been hypothesized to have a role in PaLoc gene expression through as-yet unknown mechanisms. EGRIN identified combined PrdR and CodY effects on tcdA gene expression (suggested by the enrichment of module #182 with both PrdR and CodY regulons), providing a regulatory integration point and broader set of co-regulated genes to support further experiments.
Biclustering also identified interactions between Spo0A, another regulator hypothesized to modulate PaLoc expression, and tcdB expression in module #397. The identified modules, associated genes and regulators provide information to support further experimental investigation of combinatorial effects of these and other regulators identified in PaLoc geneassociated modules. The EGRIN model also predicted PrdR has an important role in vivo based on its systems-level effects on critical metabolic and regulatory networks supporting colonization, metabolism, and growth. These PrdR-mediated interactions involve multiple direct and indirect effects upon other modules (e. g. , module #48 and module #158) and aspects of the pathogen's metabolism and gene regulation. The current model did not identify all previously characterized regulators of PaLoc expression, including SigD regulation of TcdR, and effects of other more recently identified PaLoc regulators such as RstA and LexA, for which limited datasets exist from wild-type or mutant strains cultured under relevant nutritional and other environmental contexts.
Additional datasets with isogenic regulator mutants will likely improve predictions while further validating previously defined biologic effects. Nonetheless, as shown with our in vivo analyses, application of the EGRIN and PRIME models to new datasets offers key insights into causal mechanistic drivers of adaptive strategies of the pathogen. Notably, the CD630 model was trained on <10% of transcriptome information and <2% of ChIP-seq datasets used for constructing EGRIN models for other organisms. Yet, both models demonstrated high levels of accuracy in recapitulating previously characterized regulatory and metabolic phenomena associated with C. difficile growth in vitro and mouse infection and colonization. Thus, the predictive capabilities of EGRIN and PRIME serve as formative tools to uncover biological insights through hypothesis-driven design of experiments to characterize regulatory and metabolic mechanisms that are essential for infection and colonization by C.
difficile (e. g. , CD630_16930 and CD630_17820 are putative regulators of PRIME-predicted essential genes; Figure  6A ). This model-driven design of experiments will iteratively improve the coverage and accuracy of regulatory and metabolic mechanisms modeled by EGRIN and PRIME. We can also improve the models significantly by expanding the datasets with experiments that probe transcriptional and metabolic responses of the pathogen in infection-, host-, and treatment-relevant contexts that are poorly represented in the current compendium of transcriptomes. Some of the poorly represented conditions in the transcriptome compendium include in vivo responses of C. difficile to antibiotic treatment, as well as its responses to host-relevant conditions such as oxidative stress, acidic pH, and nutrient starvation  (Edwards et al.
, 2016b). Thus, EGRIN and PRIME will serve as a community-wide resource for model-driven experimentation that will iteratively advance systems-level understanding of adaptive strategies employed by C. difficile to infect and colonize the host. Leveraging additional Tn-seq and in vivo transcriptomic datasets, the expanded icdf836 model identified a broader set of amino acids, in addition to genes and anaerobe-specific pathways, needed to support colonization and growth expansion in vivo. Notably, predictions of in vivo gene essentially identified folate one-carbon cycling pathways including those connected with Wood-Ljundahl fixation of carbon dioxide to acetate  (Go ¨ssner et al. , 2008). Predictions of gene essentiality also identified multiple nucleotide synthesis and salvage pathway genes that were essential in vivo but not in vitro, including ones associated with xanthine transport and metabolism, an abundant nucleotide in gut secretions that originates from host sources  (Girinathan et al.
C. difficile transcriptional compendium To generate a transcriptional compendium for C. difficile, required for constructing an EGRIN model, a total of 151 publicly available transcriptomes of C. difficile 630 (identified by searching for the 'Clostridioides difficile 630' term) were downloaded from the NCBI Gene Expression Omnibus (GEO) repository  (Barrett et al. , 2013)  in March 2020. Downloaded transcriptomes were generated by 11 independent studies (Table  S1 )  (Berges et al. , 2018; Dineen et al. , 2010; Fimlaid et al. , 2013; Giordano et al. , 2018; Hastie et al. , 2018; Ho and Ellermeier, 2015; Hofmann et al. , 2018; Janoir et al. , 2013; Janvilisri et al. , 2010; Ng et al. , 2013; Pishdadian et al. , 2015). To integrate this data into a single dataset, we computed the log 2 fold-change of each transcriptome with respect to a (study-specific) control condition, as performed in the generation of other transcriptional compendia  (Moretto et al.
Construction of the EGRIN model The EGRIN model for C. difficile was constructed in two stages. First, we used cMonkey2  (Reiss et al. , 2015) , a biclustering algorithm, on the compiled compendium of 127 C. difficile transcriptomes to simultaneously detect co-regulated gene modules and the conditions where co-regulation occurs. cMonkey2 integrates functional annotation from the STRING database  (Szklarczyk et al. , 2016) , gene promoter sequences from the RSAT database  (Nguyen et al. , 2018) , and operon predictions from MicrobesOnline  (Dehal et al. , 2010)  when detecting gene modules. cMonkey2 was run using default parameters. Briefly, we used 2,000 iterations to optimize the co-regulated gene modules, each one with 3-70 genes. In each iteration, cMonkey2 refined the gene modules by evaluating and modifying (if necessary) condition and gene memberships.
cMonkey2 biclustering approach allowed genes and conditions to be assigned to a maximum of two and 204 different modules, respectively. De novo GRE search was performed using MEME v. 4. 12. 0  (Bailey et al. , 2015). Second, we used the Inferelator  (Arrieta-Ortiz et al. , 2015) , a network inference algorithm, to identify potential transcriptional regulators for the 406 gene modules generated by cMonkey2. The Inferelator uses a Bayesian Best Subset Regression to estimate the magnitude and sign (activation or repression) of potential interactions between TFs and gene modules based on TF transcriptional profiles and module eigengenes (i. e. , first principal component)  (Plaisier et al. , 2016). We bootstrapped the expression data (100 times) to avoid regression overfitting  (Arrieta-Ortiz et al.
DNA motif comparison The MEME suite  (Bailey et al. , 2015)  was used (with default parameters) to reconstruct the known DNA binding motifs of CodY and SigL based on the promoter sequence of their reported target genes  (Dineen et al. , 2010; Soutourina et al. , 2020). Reconstructed TF binding motifs were compared to the GREs associated with modules of interest using Tomtom with default parameters but without scoring of reverse complement sequences.
Analysis of in vivo data In vivo transcriptomic data from gnotobiotic mice mono-colonized with C. difficile ATCC43255 or co-colonized with P. bifermentans or C. sardiniense  (Girinathan et al. , 2021)  were analyzed as previously described using the updated reference genome of ATCC43255 to extract gene features for subsequent analysis with DESeq2  (Love et al. , 2014). For each relevant comparison (e. g. , C. difficile mono-colonized mice at 24-h vs 20-h of infection) we defined the set of differentially expressed genes (DEGs) as the genes with DESeq2 adjusted p-value < 0. 05 and absolute log 2 fold-change > 1. Up-and downregulated genes with orthologs in the CD630 strain were independently mapped into the EGRIN model (as explained below in the 'Module enrichment evaluation' section) to identify differentially expressed modules.
Analysis of transcriptomes not used for training To illustrate the capability of the EGRIN model to offer functional and mechanistic insights into transcriptional regulation from C. difficile transcriptomes that were not included in the compendium used for generating the EGRIN model, we mapped the sets of DEGs upon induced overexpression of SigB  (Boekhoud et al. , 2020)  and low glucose supplementation  (Hofmann et al. , 2021)  into the EGRIN model (Figure  S7 ). EGRIN modules enriched with DEGs were identified as explained above.
, 2017). For example, most anaerobes do not utilize the tricarboxylic citric acid (TCA) cycle, although some reactions, in reverse, support aspects of pyruvate, succinate and oxaloacete metabolism. In the icdf834 model, we changed subsystem pathway annotation of two reactions -i) acetyl-CoA:oxaloacetate C-acetyltransferase and ii) succinyl-CoA synthase from TCA cycle to pyruvate metabolism and butanoate fermentation respectively (Data S2). Similarly, we updated reactions originally assigned to gluconeogenesis and the pentose phosphate pathway. We evaluated the homology of metabolic genes between C. difficile 630 and ATCC43255 strain of C. difficile in order to use the icdf834 model for representing the in vivo infection state of ATCC43255 strain. The details of 766 genes that are predicted in this homology analysis is provided in Data S2.
We then extended the model by adding four genes (CD630_08700, CD630_08680, CD630_17090 and CD630_10810) and eight exchange reactions that are required for the growth of the pathogen in the in vivo micro-environment, based on KEGG annotations. We also expanded the proline reductase (PR) and glycine reductase (GR) systems by adding alanine, branched chain amino acids (valine, leucine and isoleucine) and aromatic amino acids (phenyl alanine, tyrosine and tryptophan) as donors in the Stickland metabolism  (Jackson et al. , 2006) , increasing the total number of reactions from 1,227 to 1,273 (Data S2). We named this expanded version of the model as ''icdf836''. Then, the transcriptome of C. difficile profiled from in vivo infections of specifically-colonized gnotobiotic mice  (Girinathan et al.
Transcriptional profiles of EGRIN modules To identify EGRIN modules that were responsive (i. e. , up-or down-regulated) to a particular perturbation (i. e. , condition block) included in the compiled transcriptional compendium (Table  S1 ), for each module we computed condition-wise median of the log 2 ratios of all genes in the module. Then, the set of conditions assigned (during biclustering) to a module was organized in ascending order based on the computed median values, and divided in quintiles. A hypergeometric test was used to identify condition blocks over-represented in the first and fifth quintiles, which correspond to the ones with the lowest and highest log 2 ratios, respectively. Hypergeometric test p-values % 0. 001 were considered significant. To filter out modules with inconsistent foldchanges, only modules enriched with condition blocks in their first (or fifth) quintile and average log 2 ratios < -1 (or average log 2 ratios > 1) were considered differentially active and included in Data S1.