Unravelling the Core and Accessory Genome Diversity of Enterobacteriaceae in Carbon Metabolism

Unravelling the Core and Accessory Genome Diversity of Enterobacteriaceae in Carbon Metabolism | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Unravelling the Core and Accessory Genome Diversity of Enterobacteriaceae in Carbon Metabolism View ORCID Profile Nicolas Näpflin , View ORCID Profile Christopher Schubert , View ORCID Profile Lukas Malfertheiner , View ORCID Profile Wolf-Dietrich Hardt , View ORCID Profile Christian von Mering doi: https://doi.org/10.1101/2025.02.12.637621 Nicolas Näpflin 1 Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nicolas Näpflin Christopher Schubert 2 Institute of Microbiology, D-BIOL, ETH Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher Schubert Lukas Malfertheiner 1 Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lukas Malfertheiner Wolf-Dietrich Hardt 2 Institute of Microbiology, D-BIOL, ETH Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wolf-Dietrich Hardt For correspondence: hardt{at}micro.biol.ethz.ch mering{at}mls.uzh.ch Christian von Mering 1 Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christian von Mering For correspondence: hardt{at}micro.biol.ethz.ch mering{at}mls.uzh.ch Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Enterobacteriaceae are commonly colonizing animal guts and impact health. While strain-specific metabolic features can promote gut colonization, we lack systematic knowledge regarding metabolic diversity and the core metabolism shared among Enterobacteriaceae . To address this gap, we have analyzed the pan-genome of nearly 20,000 genomes. We found that genes necessary for monosaccharide-fuelled mixed acid fermentation are part of the Enterobacteriaceae core genome, while most genes for anaerobic respiration and most carbohydrate utilization genes belong to the accessory genome. Understanding Enterobacteriaceae’s metabolic capacity helps clarify the distinction of nutrients consumed by all Enterobacteriaceae , and niche-defining nutrient sources, which are genus-, species- or strain-specific. This knowledge sheds light on bacterial nutrient exploitation during gut colonization in health and disease, aiding in the development of targeted interventions for microbiome research and infectious disease control. The theoretical framework described here can also be adapted to analyze core physiological characteristics of other microbiota taxa. Introduction The Enterobacteriaceae family consists of diverse Gram-negative bacteria, including both harmful pathogens and commensal members. This family plays a critical role in public health and veterinary medicine, as it is responsible for a large number of foodborne infections worldwide 1 – 4 . A defining trait of Enterobacteriaceae is their facultative anaerobic lifestyle, allowing them to thrive with or without oxygen. They play an important role in early neonatal life by consuming oxygen in the gut, thereby creating beneficial conditions for obligate anaerobe colonization 5 , 6 . On the other hand, Enterobacteriaceae can also play a prominent role in disease, e.g., in inflammatory bowel disease 7 , 8 or in diarrheal infections 4 . Extensive research has focused on the virulence factors of pathogenic Enterobacteriaceae within genera such as Escherichia , Salmonella , Citrobacter , and Shigella , which can cause disease in a wide range of mammals 9 – 12 . Growth in the animal gut lumen is important for commensal and pathogenic Enterobacteriaceae , alike. However, it is still not fully understood how Enterobacteriaceae strains can establish themselves in a gut which has already been colonized by another related strain. Rolf Freter’s concept of nutrient niches posits that two related bacterial strains can only co-exist if each occupies a preferred niche based on its superior use of a specific nutrient in a well-mixed environment 13 , 14 . This theory extends to spatial structuring within the gut, known as the Restaurant Hypothesis 15 – 18 . Nutrient competition among microbiota is a key factor in colonization resistance, where the gut microbiota helps protect against invading bacteria. The central principle is metabolic resource overlap, where microbes with overlapping metabolic needs compete for the same nutrients, effectively blocking access for others. This nutrient competition, which is shaped by the diversity of the microbial community, is particularly intense when the microbiota comprises members closely related to the invading pathogen 19 , 20 . However, our understanding of how widely metabolic characteristics are shared among the Enterobacteriaceae family remains limited. Pan-genome analysis assesses the entire genomic repertoire of a bacterial clade. It encompasses the core genome, which consists of genes present in most genomes, and the accessory genome, which includes lineage-specific genes and unique genes (singletons), which may often be acquired through horizontal gene transfer. The accessory genome is a key source of functional and genetic diversity 21 . Many bacterial species exhibit an open pan-genome, which tends to expand as more genomes are analyzed 22 , 23 . This underscores the need for comprehensive datasets to capture the full extent of genomic diversity. Advancements in high-throughput sequencing have led to an increase in available genomes 24 , 25 . Tools for pan-genome analysis vary in their approaches. For closely related species, whole-genome alignment strategies can be used 26 , while larger and more diverse pan-genome analyses rely on protein clustering and e-value cut-offs, bit score ratios, or sequence identity thresholds to define gene families 27 – 32 . Challenges such as contamination, fragmented assemblies, and especially computational complexity due to all-against-all comparisons can hinder the analysis of large datasets 33 , 34 . Tools like Roary and PIRATE address these issues by first clustering highly similar sequences to facilitate the analysis of thousands of genomes. Despite these advancements, a comprehensive analysis of the Enterobacteriaceae pan-genome is still lacking. Previous studies on bacterial pan-genomes have predominantly highlighted differences rather than commonalities, often concentrating on species-level distinctions. We utilized an innovative bioinformatics approach to categorize core and accessory genes across an extensive dataset of Enterobacteriaceae genomes. Our analysis covered sixteen Enterobacteriaceae genera, such as Salmonella , Escherichia , Klebsiella , Yersinia , Enterobacter , and Shigella . Furthermore, our approach aims to uncover similarities across Enterobacteriaceae , particularly in energy metabolism, while the dataset provided here can also be used to study other aspects of Enterobacteriaceae physiology. Additionally, future studies can build on this workflow to assess conserved features of the metabolism of other bacterial taxa, including pathogens and key microbiota members like Bacteroidota and Firmicutes . Results Bioinformatics workflow for analysing the pan-genome of Enterobacteriaceae We analyzed the Enterobacteriaceae pan-genome across 16 genera and 80 species, encompassing nearly 20,000 genomes ( Supplementary Table S1 ). A brief overview of the bioinformatic workflow is shown in Fig. 1a . High-quality genomes (completeness ≥ 95%, contamination ≤ 5%) were selected from progenomes3, and species represented by fewer than 10 genomes were excluded. Overrepresented species (mainly Escherichia coli , Klebsiella pneumoniae and Salmonella enterica ) were subsampled randomly ( Fig. 1b ). Orthologous gene families were identified over a range of amino acid identity thresholds from 50% to 95% (in increments of 5%, see Methods). The pan-genome includes nearly 95 million gene sequences clustered into 141,675 gene families. Gene ubiquity follows a universal U-shape curve 35 , with 1.2% (1,801) of gene families being part of the core genome (present in at least 95% of genomes) ( Fig. 1c ). We found that almost 60% of gene families are unique to specific genera, from which 48% are unique to a single genome (singletons), highlighting high intra-family genetic diversity ( Fig. 1d ). Gene families were annotated against the UniprotKB/Swiss-Prot database 36 , and categorized into three types: Resolved (i.e., 90% or more sequences with the same annotation), Ambiguous (mixed annotations), and Non-annotated ( Fig. 1a ). The more ubiquitous gene families tend to be well-annotated, while rare gene families (present in less than 5% of genomes) make up over 90% of the pan-genome and remain largely unannotated. Finally, we annotated all genes with Clusters of Orthologous Groups (COG) functional categories using eggnog-mapper ( Fig. 1e ) 37 . Translation, amino acid metabolism and cell wall/ membrane biogenesis represent the largest COG categories within the core genome. On the contrary, the “ function unknown ” category is by far the largest COG category within the accessory genome. Download figure Open in new tab Figure 1. Overview of the bioinformatics workflow. a The Enterobacteriaceae pan-genome currently includes 141’675 gene families. Gene families are grouped based on their annotation status: Resolved (>= 90% of the sequences contain the same annotation), ambiguous (< 90% of the sequences contain the same annotation and = 10% of the sequences are not annotated). b A total of 19’795 Enterobacteriaceae genomes across 16 genera were analyzed. c Gene ubiquity distribution (expressed as fraction of genomes containing a gene) following a U-shaped curve. d Distribution of gene families into core and accessory genome. The accessory genome is further split into genus specific and unspecific families. Almost 50% of genus specific gene families are unique to a single genome (singleton). e The functional categories of the accessory genome are dominated by gene families with unknown functions (68.2%). Most frequent function in the core gene families are Translation (9.8%), Amino acid metabolism and transport (9.7%) and cell wall/membrane/envelope biogenesis (7.7%). Phylogenetic analysis of the Enterobacteriaceae genomes The Enterobacteriaceae family, the largest within the Enterobacterales order, is closely related to Vibrionaceae and Pasteurellaceae . We performed phylogenetic analysis using 103 concatenated core proteins ( Fig. 2 , Supplementary Table S2 ). It supports the monophyly of many genera within Enterobacteriaceae 38 and aligns with the 16S rRNA-based phylogenetic tree from the All Species Living Tree project 39 . Historical classification grouped Enterobacteriaceae into core and peripheral members based on DNA-DNA hybridization in relation to Escherichia coli . Core members include Enterobacter , Klebsiella , Citrobacter , and Salmonella , which are more closely related to Escherichia coli in the core genome tree 40 . The close relationship among Escherichia , Citrobacter , and Salmonella has been confirmed by various single-gene and 16S rRNA-based studies 38 , 41 – 43 . In contrast, genera such as Plesiomonas , Yersinia , and Serratia appear more peripheral. Plesiomonas , initially thought to belong to Vibrionaceae based on 5S rRNA, is now placed within Enterobacteriaceae due to 16S rRNA and multilocus sequence typing, though its position remains debated 44 . The placement of Yersinia and Serratia within Enterobacteriaceae is less debatable and consistent with previous studies 40 , 45 . Notably, Shigella genomes are mixed within the Escherichia clade and do not form a distinct monophyletic group, supporting the view that Escherichia and Shigella should be considered a single species 46 – 48 . Overall, our core genome tree supports previous findings and clearly delineates the genera within Enterobacteriaceae . Download figure Open in new tab Figure 2. Phylogenetic reconstruction of the Enterobacteriaceae family based on 103 concatenated core genes (present in at least 99% of investigated genomes). The tree is midpoint rooted, and the outer ring represented taxonomic classification based on the NCBI taxonomy. Genomes not classified to genus level are labelled as ‘Other’. Peripheral members of Enterobacteriaceae ( Yersinia, Serratia and Plesiomonas) are shaded orange. The concatenated core genes used are listed in Supplementary Table S2. Enterobacteriaceae are adapted to abundant monosaccharides in nature Our previous phylogenetic analysis validates the broad genome selection for studying the Enterobacteriaceae pan-genome. To illustrate the benefit of this approach, we examined the metabolism of the Enterobacteriaceae family in depth. Nutrient exploitation is one of the key functions of colonization resistance provided by the microbiome 17 , 49 , 50 . Recent publications have highlighted the importance of Enterobacteriaceae - Enterobacteriaceae competition in effectively limiting or even preventing colonization 20 , 51 – 56 . This competition is often governed by the differential metabolic utilization of specific carbohydrates. For this reason, we examined the results of our pan-genome analysis to assess energy metabolism, starting with carbohydrate utilization ( Fig. 3 ). Download figure Open in new tab Figure 3. Core and accessory gene distribution of carbohydrate degradation pathways of Enterobacteriaceae . A core gene is present in at least 95 % of all analyzed Enterobacteriaceae genomes (green). A prevalence of less than 95% is defined as accessory (orange). Genes that were not found in the analysis but have experimental evidence are shown in grey. The top left subpanel indicates energy metabolism, with the respective part of interest marked in red. a Depiction of carbohydrates that are almost exclusively transported by the bacterial phosphotransferase system (PTS). Panels ( b, c ) show a mixture of pentoses, hexoses, disaccharides, glycosylamines, and amino sugars that are transported either through primary transport by an ATP-binding cassette (ABC) or via secondary transport through a proton-motive force (PMF)-driven system. d Depiction of hexuronate utilization driven by PMF transporters, and the triol glycerol. The different substrate groups are indicated above the metabolic pathways. The abbreviations of carbohydrates are defined in Supplementary Table S3. The main carbohydrate transport systems are the phosphotransferase system (PTS), ATP-binding cassette (ABC), and proton motive force (PMF)-driven transport systems ( Fig. 3 ). Carbohydrates transported by the PTS, such as D-glucose, are preferred by Enterobacteriaceae 57 . Genes required for degrading PTS sugars, such as D-glucose, D-mannose, D-fructose, N -acetylglucosamine, and D-glucosamine, are found within the core genome ( Fig. 3a , green ). The degradation of hexitols mainly belongs to the accessory genome ( Fig. 3a , orange ), except for D-mannitol (MtlA, MtlD) ( Fig. 3a ). Hexitols and other sugar alcohols present a metabolic challenge under anaerobic conditions of the animal gut, when no external electron acceptor is available, as they generate a reducing equivalent before entering glycolysis. The oxidation of a hydroxyl group (-OH) during sugar cyclization produces an additional reducing equivalent that cannot be disposed of through exclusively mixed acid fermentation. The only disaccharides associated with the core genome are trehalose and maltose, both composed of glucose molecules but differing in their glycosidic linkages: trehalose has a 1,1-linkage, while maltose features a 1,4-linkage ( Fig. 3a ). D-ribose is the only pentose whose transport and degradation belongs to the core genome. Unlike D-ribose, L-arabinose and D-xylose can be transported by two distinct systems: a low-affinity PMF-driven transporter and a high-affinity ABC transporter, both of which are associated with the accessory genome ( Fig. 3b ) 58 . The majority of cytosolic enzymes for degrading L-arabinose and D-xylose are found within the core genome. Thus, L-arabinose and D-xylose utilization is common among Enterobacteriaceae , though import occurs via diverse transporters ( Fig. 3b ). Notably, the D-glucose epimer D-galactose is associated with the core genome ( Fig. 3b ). Glycosylamines, which consist of a reducing sugar and an amino moiety, are produced during food processing through the Maillard reaction under heat, forming compounds such as fructose-asparagine and fructose-lysine ( Fig. 3c ). Amino sugars are sugar molecules in which a hydroxyl group is replaced with an amino group, such as D-glucosamine, D-mannosamine, and neuraminic acid. These sugars are also commonly found in their acetylated forms, such as N-acetylglucosamine. Only the pathway for D-glucosamine degradation is fully included in the core genome ( Fig. 3a, c ). Sugar acids, which are hexoses with a carboxyl group (-COOH) at the end of the chain, generally have their degradation genes included in the accessory genome ( Fig. 3d ). Reactive nitrogen species in the gut can oxidize hexoses during post-antibiotic stress, converting D-glucose to D-glucarate, for example 59 . Additionally, sugars like D-galacturonic acid, D-glucuronic acid, and L-iduronic acid naturally occur in various complex polysaccharides 60 . Enterobacteriaceae analyzed in this study, share a small number of carbohydrate uptake and degradation systems that are components of the core genome, which are focused on D-glucose, D-glucose epimers, D-glucose-containing disaccharides, and modified D-glucose molecules. This partly mirrors mammalian cells, which have evolved to preferentially utilize glucose 61 . The three glycolytic pathways are associated with the core genome Most carbohydrate utilization pathways in Enterobacteriaceae are linked to the accessory genome ( Fig. 3 ). However, the majority of pathways for phosphotransferase system (PTS)-transported carbohydrates - such as D-glucose, D-mannose, D-fructose, D-mannitol, N-acetylglucosamine, and trehalose - that route these carbohydrates into glycolysis are found in the core genome ( Fig. 3a ). The three glycolytic pathways - namely, glycolysis, the Entner-Doudoroff pathway, and the non-oxidative and oxidative pentose phosphate pathways (PPP)—as well as the tricarboxylic acid (TCA) cycle, are all part of the core genome ( Fig. 4a-e ). Glycolysis, a universal pathway present in both prokaryotes and eukaryotes, metabolizes glucose into two molecules of pyruvate while generating NADH and ATP ( Fig. 4a ) 62 . In contrast, the Entner-Doudoroff pathway in Enterobacteriaceae converts hexuronates, while the pentose phosphate pathway processes pentoses into glycolytic intermediates, enabling carbon flux into the TCA cycle ( Fig. 4b, c ). The oxidative pentose phosphate pathway links glycolysis and the Entner-Doudoroff pathway to the non-oxidative pentose phosphate pathway. This pathway provides NADPH for biosynthesis and contributes to the non-oxidative pentose phosphate pathway for biosynthetic precursors ( Fig. 4d ) 63 . The regulatory pathway that governs carbohydrate utilization is the PTS that primarily affects catabolite repression and phosphorylates incoming PTS-transported carbohydrates via a process known as group translocation 57 ( Fig. 4f ). In addition, the PTS impacts cyclic AMP-dependent catabolite repression, aligning bacterial proteome allocation with metabolic needs 64 . The PTS acts as the central regulatory circuit, allowing Enterobacteriaceae to optimize growth by adapting to varying carbon sources through a complex hierarchical logic, which has been studied in great detail for E. coli K12 65 . Download figure Open in new tab Figure 4. Core and accessory gene distribution of glycolytic pathways, TCA cycle, fermentation, and respiration of Enterobacteriaceae . Core genes are defined as present in at least 95 % of all analyzed Enterobacteriaceae genomes (green). A prevalence of less than 95% is defined as accessory (orange). Genes that were not found in the analysis but have experimental evidence are shown in black. The top left subpanel indicates energy metabolism, with the respective part of interest marked in red. The three glycolytic pathways— a glycolysis, b Entner-Doudoroff pathway, and c non-oxidative pentose phosphate pathway (PPP)—are shown, including d the oxidative PPP, which connects glycolysis to the non-oxidative PPP. The entry points for the various carbohydrate sources from Fig. 3 are indicated by grey boxes. The color-coding shows whether the utilization pathway is part of the core (green) or accessory (orange) genome of Enterobacteriaceae . e Tricarboxylic acid (TCA) cycle, including the carbon-saving glyoxylate shunt and the glutamine synthetase (GS) – glutamate – 2-oxoglutarate aminotransferase (GOGAT) pathway. f Depiction of the phosphotransferase system (PTS), which uses phosphoenolpyruvate (PEP) as a phosphoryl group donor to phosphorylate incoming PTS-transported sugars in a process known as group translocation. Furthermore, the EIIA Glc ( crr ) stimulation of adenylyl cyclase ( cyaA ), which largely controls catabolite repression via cAMP-CRP signaling, is indicated. The abbreviations of carbohydrates are defined in Supplementary Table S3 . Interestingly, some isozymes - enzymes that catalyze the same reaction—are linked to the accessory genome, while the primary enzyme is part of the core genome. For example, in E. coli , TktA (core) is the main enzyme in the non-oxidative pentose phosphate pathway, while TktB (accessory), an isozyme, has only minor activity 66 ( Fig. 4c ). Conversely, pykF and pykA , encoding pyruvate kinase I and II, respectively, are both associated with the core genome, likely due to their differing physical and chemical properties and kinetic behaviour. For example, PykF is allosterically activated by fructose 1,6-bisphosphate, while PykA is activated by AMP ( Fig. 4a ) 67 . Similarly, fumarases A, B, and C exhibit distinct properties: FumA is a dimeric iron-sulfur cluster-containing hydrolase expressed aerobically and anaerobically, FumB is expressed anaerobically, and FumC is an iron-independent fumarase induced during oxidative stress 68 , 69 . FumA and FumC are part of the core genome, while FumB is not ( Fig. 4e ). In conclusion, the three glycolytic pathways that provide NADH, NADPH, ATP, and important precursors for biosynthesis are part of the Enterobacteriaceae core genome. Isozymes without distinct enzymatic properties are associated to the accessory genome. In contrast to carbohydrate utilization, pathways that route nutrients into the three glycolytic pathways, central carbon metabolism is highly conserved in Enterobacteriaceae . Respiration of inorganic electron acceptors is associated to the accessory genome The three glycolytic pathways break down carbohydrates for anaplerosis, supplying important precursors for biosynthetic pathways and generating ATP and NADH for energy metabolism. Energy is conserved in the form of ATP, either by substrate level phosphorylation, or through oxidative phosphorylation. During oxidative phosphorylation, electrons are transferred through a series of redox reactions from a carbon-based electron donor, such as D-glucose, to an organic or inorganic electron acceptor, such as oxygen. Electrons are temporarily stored in the reduced forms of NAD⁺ and the quinones (reduced state: NADH and quinols). Respiratory dehydrogenases, such as nuo and ndh , oxidize NADH and reduce the quinone pool. In addition to NADH ( nuo and ndh ) and succinate ( sdh ) dehydrogenases, which are associated with the core genome, other respiratory dehydrogenases include those for glycerol 3-phosphate ( glpD ), D-lactate ( dld ), L-lactate ( lldD ), D-alanine ( dadA ), pyruvate ( poxB ), and NADPH ( mdaB ) ( Fig. 5a ). Quinols transfer electrons to a terminal reductase that reduces an electron acceptor ( Fig. 5b ). The aerobic ( cyo ) and the microaerobic ( cyd ) oxygen oxidoreductases, as well as the fumarate reductase ( frd ) are the only terminal reductases included in the core genome. The remaining terminal reductases, which utilize inorganic electron acceptors such as nitrate or tetrathionate (S₄O₆²⁻), are part of the accessory genome of Enterobacteriaceae ( Fig. 5b ). In brief, this encompasses the electron transport chain, coupled with the translocation of protons to establish a proton motive force, which is utilized, for instance, in ATP production via ATP synthase (oxidative phosphorylation) or in secondary transport 70 . Download figure Open in new tab Figure 5. Core and accessory gene distribution of respiratory dehydrogenases, terminal reductases, and mixed acid fermentation Enterobacteriaceae . A core gene is present in at least 95 % of all analyzed Enterobacteriaceae genomes (green). A prevalence of less than 95% is defined as accessory (orange). The top left subpanel indicates energy metabolism, with the respective part of interest marked in red. a Depiction of respiratory dehydrogenases that oxidize an electron donor (e.g., NADH, succinate, etc.) and transfer the electrons into the quinone pool, thereby reducing quinone (UQ and MQ) to quinol (UQH 2 and MQH 2 ). b Terminal reductases oxidize quinols and transfer the electrons to an electron acceptor, thereby balancing the redox. The reduced electron acceptor is typically an end product and is secreted. c The alternative pathway for redox balancing and ATP formation in the absence of an inorganic electron acceptor is mixed acid fermentation, which exclusively relies on carbon-based electron donors and acceptors for energy and redox balance. The abbreviations of sugars and complete list of individual genes in the respective complexes (e.g., hyb ) are defined in Supplementary Table S3 and S4 . Alternatively, Enterobacteriaceae can maintain redox balance and produce ATP during mixed acid fermentation ( Fig. 5c ). This process, a defining feature of facultative anaerobic Enterobacteriaceae , produces a range of acidic, reduced end products: succinate, acetate, formate, D-lactate, and the non-acidic ethanol. Acetate fermentation conserves energy in the form of ATP, while succinate, ethanol, and D-lactate fermentation regenerate NAD + and quinones. The acidic and non-acidic end products are exported from the cell, while formate can be used as an electron donor via a respiratory dehydrogenase ( fdn , fdo , and fdh-hyc ) ( Fig. 5a, c ). The pivotal enzyme in this process is pyruvate formate lyase (PflB), which catalyzes the non-oxidative cleavage of pyruvate into acetyl-CoA and formate. This step is more carbon- and redox-efficient compared to the aerobic counterpart, pyruvate dehydrogenase ( aceEF lpd ), which produces carbon dioxide and NADH. All genes involved in the key steps of mixed acid fermentation belong to the core genome ( Fig. 5c ). In conclusion, Enterobacteriaceae , which are predominantly facultative anaerobic bacteria, possess genes for both aerobic respiration and mixed acid fermentation within their core genome ( Fig. 5 ). The distribution of respiratory dehydrogenases and terminal reductases between the core and accessory genomes mirrors the distribution of carbohydrate utilization pathways ( Fig. 3 and 5 ). Both types of pathways serve as key interfaces within an ever-changing environment. How are the carbohydrate utilization pathways distributed between different strains? A key component of colonization resistance is nutrient exploitation. This feature is influenced by the complexity of the microbiota and further enhanced by close relatives of the incoming strain, since these will feature a particularly high metabolic resource overlap 20 , 51 . As a proxy for understanding the metabolic resource profiles of Enterobacteriaceae , we investigated the presence of different carbohydrate utilization pathways. On average, Enterobacteriaceae genomes feature 31 different carbohydrate utilization pathways. The number of detected carbohydrate utilization pathways varied from 3 in a Serratia symbiotica strain to 40 in an Escherichia coli strain ( Fig. 6a ). We observed a weak correlation between genome size and the size of the metabolic resource profile. For example, symbiotic Enterobacteriaceae species exhibit a limited metabolic resource profile, which correlates with their reduced genome size ( Fig. 6b , Supplementary Fig. S1 ). To explore the link between microbiota (specifically Enterobacteriaceae ) complexity and metabolic resource overlap, we assessed the number of strains minimally needed to achieve a high overlap in metabolic resources—defined as 90% of carbohydrate utilization pathways present. When selection was guided by carbohydrate utilization gene analysis ( Fig. 3 - 5 ), a median of just 2 out of 19795 strains sufficed, typically including at least one Klebsiella or Escherichia strain. In contrast, selecting genomes based on the distribution of Enterobacteriaceae in healthy human guts required 7 strains, while selection normalized by the number of genomes per genus required a median of 15 strains ( Fig. 6c ). Download figure Open in new tab Figure 6. Distribution of carbohydrate utilization pathways in Enterobacteriaceae . a Enterobacteriaceae genomes contain on average 31 carbohydrate utilization pathways (blue dashed line) out of the 49 investigated. b The number of carbohydrate utilization pathways is weakly correlated with genome size. Spearman’s correlation: 0.49, P-value < 2.2E-16 c Subsampling all investigated genomes while assuming equal probability for each genus, 15 randomly selected Enterobacteriaceae strains are on average sufficient to cover 90% of carbohydrate utilization pathways (random, green dashed line). Selecting genomes based on distribution of Enterobacteriaceae in a healthy human adult gut, 7 genomes are typically required (human adult gut like, blue dashed line). Constructing a minimal set of genomes to cover 90% of the pathways requires just two genomes on average (optimally selected, orange dashed line). d Percentage of genomes containing complete carbohydrate utilization pathways stratified by genera. Rows and columns are hierarchically clustered based on the presented data. Pathways that were not found in the analysis but have experimental evidence are shown in grey. Supplementary Table S5 provides the full list of genes analyzed for presence in each carbohydrate utilization pathway. Number of genomes per genera are as in Fig. 1b e Schematic overview of L-arabinose utilization using low-affinity proton-motive force (PMF)-driven transporter or high-affinity ATP-binding cassette (ABC) transporters. f Low-affinity and high-affinity transporters for L-arabinose uptake seem to be mostly mutually exclusive in Salmonella and Escherichia, whereas both systems are present in Citrobacter . To enhance visualization, we mapped the presence of carbohydrate utilization pathways across all 16 analyzed Enterobacteriaceae genera ( Fig. 6d ). Supplementary Table S5 details the complete list of enzymes included in each carbohydrate degradation pathway assessed. Transporters were generally excluded from this analysis due to their low substrate specificity. Notably, Klebsiella oxytoca strains exhibit, on average, the highest number of carbohydrate utilization pathways, which likely contributes to their context-dependent ability to be competitive against Salmonella spp. 20 , 53 . Our carbohydrate utilization gene analysis pipeline may help identify the most potent competitors for therapeutic approaches. Additionally, our analysis can shed light on why some Enterobacteriaceae encode a low-affinity L-arabinose importer, while others possess a high-affinity version. Salmonella strains encode an α-N-arabinofuranosidase, which liberates L-arabinose from arabinan polymers and is important in long-term infection experiments 71 . L-arabinose can be transported by a low-affinity proton motive force (PMF)-driven transporter and a high-affinity ATP binding cassette (ABC) transporter 58 ( Fig. 6e ). Our analysis has revealed that in certain instances, members of the Enterobacteriaceae family have evolved to specialize in using one of these transport systems and have consequently lost the other. This observation correlates with the presence of α-N-arabinofuranosidase, indicating that Enterobacteriaceae that have acquired this hydrolase are able to locally increase L-arabinose concentrations to an extent that would render a high-affinity transport system dispensable ( Fig. 6f ). In conclusion, our large-scale pan-genome analysis provides important insights into current questions in the microbiome field. A key goal is the active displacement of unwanted bacteria from the gut through the application of probiotic microbiota consortia 56 . Understanding metabolic resource overlap and how to rationally design these consortia to maximize this overlap is crucial for displacing problematic strains or using them as prebiotics to enhance colonization resistance against such strains. Discussion Due to the increasing availability of sequencing data, modern bioinformatic analysis has evolved from focusing on single model organisms to encompassing entire bacterial families. This shift allows for a comprehensive exploration of gene distribution, diversity, and genome plasticity. In our approach, we first analyzed a large number of enterobacterial genomes to construct the pan-genome, identifying genes present in over 95% of genomes as core genes, while those found in fewer than 95% are classified as accessory genes 72 , 73 . The Enterobacteriaceae family, although not highly abundant in the mammalian gut, plays a disproportionately large role in public health due to some pathogenic members 1 . Second, this study sheds light on the organization and function of the core and accessory genomes within the Enterobacteriaceae family. Notably, we identified a substantial accessory genome comprising non-annotated or hypothetical gene families, even within well-studied organisms like E. coli . Remarkably, nearly 50% of E. coli’s genome remains either unannotated or only annotated with predicted functions, with around 30% of protein functions still experimentally unstudied 74 , 75 . For less-researched Enterobacteriaceae members, this proportion could be even higher. Recent findings in E. coli highlight that unknown gene families significantly complicate the pan-genome structure through co-occurrence or avoidance 76 . We expect similar influences within the Enterobacteriaceae pan-genome, where over 70% of gene families are hypothetical. Additionally, the absence of certain pathway components in our dataset may stem from incomplete, inconsistent or missing annotations, as gene annotation continues to present a key challenge in the analysis of prokaryotic genomes and pan-genomes 31 , 77 . Other potential issues in our study may arise from the chosen algorithms for gene family clustering, including the merging of some gene families due to high sequence similarity, unresolved gene fusions, or incomplete fission events. Collectively, these challenges can obscure a comprehensive understanding of pan-genome dynamics. While our study primarily focuses on the pan-genome of Enterobacteriaceae at the family level, the extensive dataset also allows us to explore pan-genome structure at a finer taxonomic resolution. We constructed gene families at various amino acid sequence identity thresholds to accommodate variation in evolutionary rates among gene families 78 . For more detailed pan-genome analysis within specific Enterobacteriaceae species, stricter sequence identity thresholds may be required to account for the narrower taxonomic range. To define species-level pan-genomes fixed thresholds are commonly used in the literature 27 . Our dataset’s taxonomic classification relies on NCBI annotations, which we validated using 120 marker genes with GTDB-Tk. While genus-level classifications are generally consistent, we observed some discrepancies at the species level, particularly within the genus Yersinia , which may affect higher-level analyses. The current pan-genome analysis predominantly focuses on well-studied genera such as Escherichia , Klebsiella/Raoultella, and Salmonella reflecting current sequencing biases. For example over 95% of available Enterobacteriaceae genomes in ProGenomes3 originate from these genera 79 . As metagenomics and the availability of metagenome-assembled genomes (MAGs) continue to advance, we anticipate broadening this focus. This progress will enhance our understanding of gene presences and absences in a population context. Ideally, comparing genomes from various niches will allow us to define enterobacterial ecotypes - strains that use a similar ecological niche -and identify niche-specific genes 80 , providing insights into evolutionary advantages in specific ecological or genetic contexts. Our study utilized energy metabolism to exemplify how a pan-genome analysis at the family level of Enterobacteriaceae enhances our understanding of a key feature of the microbiome: nutrient exploitation. As facultative anaerobes, metabolic genes that enable Enterobacteriaceae to thrive in both aerobic and anaerobic environments are part of the core genome. This includes glycolysis, Entner-Doudoroff and the (non)-oxidative pentose phosphate pathway, as well as tricarboxylic acid (TCA) cycle and mixed acid fermentation ( Fig. 4 and 5c ). Noteworthy, most of the pathways required to utilize specific carbohydrates and most terminal reductases are found in the accessory genome, likely reflecting their high context-dependence, i.e. different environmental availability of the respective substrates in different niches. Recent publications have highlighted the availability of carbohydrates in the mammalian intestine, showing that carbohydrates whose degradation belong to the core genome are also abundant in the gut 81 , 82 . This observation largely reflects the sugar monomeric composition of diet, with D-glucose, D-fructose, D-galactose, and D-mannose being the most abundant 83 – 85 . Inorganic electron acceptors play a crucial role, particularly during enteric diseases or microbiota disturbances. However, the availability of these molecules is largely context dependent. In the gut, inflammation-induced oxidative stress is necessary to generate nitrate and tetrathionate, which favour the growth of Enterobacteriaceae 86 , 87 . At the same time, oxygen becomes available due to the host’s metabolic shift 88 – 90 . In contrast, fumarate respiration, a core metabolic pathway, is always used and contributes to Salmonella growth in the non-inflamed and inflamed gut because fumarate can be supplied through multiple sources 91 – 95 . This is essential because biosynthetic pathways, such as heme b and pyrimidine synthesis, rely on fumarate reductase and quinones as electron acceptors under anaerobic conditions. The importance of fumarate respiration is particularly evident in the genera Escherichia and Salmonella during all stages of infection 81 , 82 , 93 – 96 . Freter’s nutrient niche theory posits that a bacterial species needs to more efficiently utilize a nutrient niche than the competition 13 , 14 . This theory can translate into differences in bacterial metabolic capacity, such as the presence of genes that allow one species to use a carbohydrate inaccessible to others. Our analysis highlights the high variability in carbohydrate utilization systems among Enterobacteriaceae and provides a framework for understanding why different Enterobacteriaceae strains can often co-exist in the same intestine 97 . The sheer abundance of pathways per strain may also explain why Klebsiella strains are repeatedly discovered as excellent probiotics for limiting Salmonella colonization in certain contexts 20 , 53 . Our pan-genome analysis provides important groundwork for the future design of probiotic consortia aimed at efficient nutrient blocking or exploitation. By maximizing metabolic resource overlap, these consortia could effectively inhibit problematic bacteria that infiltrate a healthy, homeostatic gut microbiome. Interestingly, on average the highest number of carbohydrate utilization pathways were found in a Escherichia coli strain, with 40 pathways. It is important to note that defining metabolic capabilities based on bioinformatic analysis has limitations, as the presence of a gene does not necessarily imply the production of a functional protein, as previously demonstrated in Salmonella enterica serovars Typhi and Paratyphi A 98 . No strain possessed all 49 analyzed carbohydrate degradation pathways, raising questions about the cost-benefit balance that governs the number of carbohydrate utilization pathways within each Enterobacteriaceae species and why Klebsiella harbors so many. In conclusion, Freter’s nutrient niche theory can be differentiated into shared resources, accessible to various community members, and niche-defining nutrient sources, available to only one particular strain. The former nutrient sources, as evidenced by our pan-genome analysis, will likely include D-glucose, D-glucose epimers, D-glucose-containing disaccharides, and modified D-glucose molecules. In principle, these are nutrient sources that every Enterobacteriaceae strain can utilize. Niche-defining nutrient sources are exclusive, accessible by only one bacterial species but not by its competitors; this can provide the decisive advantage for successful colonization. Our carbohydrate-focussed genome analysis strategy may offer a rational approach to select strains for decolonization therapies or mitigating infection risks. Author contributions C.v.M, W.-D.H, C.S., L.M. and N.N. conceived and designed the study. N.N. performed the pan-genome analysis. C.S. applied the pan-genome analysis to energy metabolism. N.N. and C.S. wrote the manuscript with contributions from all authors. Funding This work has been funded by grants from the Swiss National Science Foundation (310030_192567, 10.001.588 and NCCR Microbiomes grant 51NF40_180575) to W.-D.H. and to C.v.M. C.S is supported by the German Research Foundation (SCHU 3606/1-1). This work has been further funded by a grant from the Swiss National Science Foundation (grant 310030_192569) attributed to N.N and C.v.M. Methods Selection of Enterobacteriaceae genomes From the progenomes v3 database 79 , 525’581 genomes classified as Enterobacteriaceae or Yersinia according to the NCBI taxonomy were downloaded. First, genomes were screened to select high quality genomes. In a first step, CheckM2 99 assembly statistics, kindly provided by Sebastian Schmidt, were used to remove genomes with completeness below 95% and contamination larger than 10%. Furthermore, highly fragmented genomes (more than 300 contigs) were discarded. Next, genomes with total contig sizes larger or smaller than 1.5 * IQR (inter quantile range) compared to genomes of the same species were removed. Additionally, genomes, with a coding density outside of the 0.5-1.5 range were not considered. Finally, all genomes of species containing less than 10 genomes in the filtered dataset were removed and overrepresented species (mainly Escherichia coli , Salmonella enterica and Klebsiella pneumoniae ) were randomly subset to maximally 3200 genomes to generate a Enterobacteriaceae genome collection consisting of totally 19’795 genomes. Unless otherwise stated, the NCBI taxonomic classification was used to assign taxonomy to each genome. The identifiers and taxonomic classification of all genomes are provided in Supplementary Table S1 . Pan-genome analysis All genomes were preprocessed with Prodigal v2.6.3 100 to obtain coding sequences (CDS). We generated a family wide pan-genome by clustering CDS into gene families using PIRATE v1.0.5 using standard parameters and an MCL inflation parameter of 3. Overall, 141’675 gene families were created. Ubiquity was calculated for each gene family and for each genus based on presence and absence. In short, for each genus the number of genomes containing a specific gene family was divided by the total number of genomes for the specific gene family. Taxonomic classification was based on the provided NCBI taxonomy. Gene families were labelled as core if they occurred in at least 95% of all investigated genomes (core genome). All remaining gene families were classified as the accessory genome. Accessory gene families, which were present only in genomes of the same genus were labelled as genus specific, while gene families present in at least 2 genera were labelled as genus unspecific. The core genomes compromised 1’801 gene families (1.3%) while the accessory genome consisted of 139’874 (98.7%) gene families. We annotated all gene families against the UniProtKB/Swiss-Prot database 36 using MMseqs2 v14.7e284 (--start-sens 1 --sens-steps 3 -s 7 –max-accept 10000). Hits with e-value > 1 e-5 were discarded, and the best hit was kept. Annotations were aggregated per gene family and if available, the two most abundant annotations were reported. Sequences which were not annotated were labelled as ‘unknown’. Based on the annotation, gene families were summarized into three annotation status brackets based on the inferred short gene name: Resolved (>= 90% of the sequences contain the same annotation), Ambiguous (= 10% of the sequences are not annotated). For each gene family a representative sequence was chosen. For annotated gene families, a random sequence from the most common annotation was selected. A random sequence was chosen for non-annotated gene families. A phylogenetic tree was constructed based on core gene families present in at least 99% of all genomes. Additionally, families with unresolved annotations or families containing non-single copy were removed. The full list is provided in Supplementary Table S2. The resulting 103 gene families were subsequently used in the phylogenetic analysis. Briefly, each gene family was individually aligned using muscle v5.1 101 and SNP-sites v2.5.1 102 was used to produce alignments of variant sites. The resulting alignments were concatenated, and a maximum-likelihood tree was constructed using RAxML-NG 103 with the general time-reversible model GTR+G model with a discrete GAMMA model of rate heterogeneity with 4 categories. ITOL v6.8.1 104 was used to midpoint root the tree and for tree visualizations. Peripheral members of Enterobacteriaceae are shaded orange and include Yersinia , Serratia and Plesiomonas 40 . Mapping core and accessory genome on metabolic pathways The E. coli K-12 database EcoCyc.org was utilized as a primary point of reference for metabolic pathways 105 , and EcoSal Reviews 70 , 106 , 107 . The pathways presented in these figures ( Fig. 3 - 5 ) are a curated selection of annotated and referenced research, primarily grounded on E. coli and Salmonella spp. research. A full list of abbreviations is available in Supplementary Table S3 Complexes, especially in Fig. 5 , are named by their operon rather than individual genes. A complete list of all genes within each complex is provided in Supplementary Table S4 . Investigating the distribution of carbohydrate utilization pathways We searched the pan-genome for gene families containing annotations presented in Figure 3 . A complete list can be found in Supplementary Table S5 . We consider a carbohydrate utilization pathway to be present only if each of its genes can be found within a specific genome. Due to the potential substrate ambiguity of transporters, we only considered intracellular genes, with exception to glucose utilization. Spearman correlation values of genome size against number of carbohydrate utilization pathways present were computed using the cor.test function of stats v4.1.3 in R. The core and accessory status of a carbohydrate utilization pathway is assessed based on the average presence of each gene in all genomes. A pathway is considered ‘core’ if the average is larger or equal to 95%, and considered ‘accessory’ otherwise. Genera and carbohydrate utilization pathways are ordered based on a hierarchical clustering performed using Euclidean distance and complete linkage as implemented in the ComplexHeatmap package in R 108 . To calculate the metabolic resource overlap of subsampled genomes, we consider the presence of any alternative pathways for a specific sugar molecule as sufficient to consider this resource as covered, except for L-ascorbat, fructoselysine, glycerol, sucrose and trehalose, resulting in a total of 49 metabolic resources ( Supplementary Table S5 ). We distinguish between three different subsampling methods: “optimally selected”, “random” and “human adult gut like”. For the “optimally selected” subsampling, we randomly selected an initial genome as a starting point. Subsequently, the next genomes are chosen to cover most of the missing resources until at least 90% are covered. In random subsampling, we randomly select a genome without replacement at each step until at least 90% of the metabolic resources are covered. To account for uneven distribution of genera in the dataset, each genus was assigned equal selection probability. To subsample according to a human adult gut distribution of Enterobacteriaceae , we obtained healthy human gut metagenomic samples from the Unified Human Gastrointestinal Genome (UHGG) v.1.0 catalogue 109 . To this end, we extracted healthy adult human samples (n = 5128) and read counts of non- Enterobacteriaceae species were removed. Additionally, genera not represented in our data set were excluded. Based on this filtered data set, read counts were aggregated by genera and average abundances were calculated by sample. Subsampling was implemented by first selecting a genus based on previously calculated average abundances, followed by randomly selecting a genome from the chosen genus. This process was repeated until at least 90% of the metabolic resources were covered. To visualize the presence of the high- and low-affinity L-arabinose transport systems and its correlation with the presence of an α-N-arabinofuranosidase, we randomly selected in total 100 Citrobacter , Escherichia and Salmonella genomes. Based on the retrieved annotation, we selected gene families annotated with araF , araG and araH for the high-affinity ABC transport system, araE and araF43 a for the low affinity PMF transporter, and araA , araB and araD for the intracellular pathway. Rare gene families and singletons containing any of the aforementioned annotations are not shown. Genomes and gene families are ordered based on a hierarchical clustering as described previously. Data availability A summary of each gene family created with PIRATE, along with all analyzed Enterobacteriaceae genomes and their associated gene family memberships in GFF3 format, are deposited in Zenodo at https://doi.org/10.5281/zenodo.14849847 (ref. 110 ). Supplemental Figures Figure S1. Symbiotic Enterobacteriaceae species exhibit a reduced metabolic resource profile. Symbiotic Serratia species exhibit a reduced metabolic resource profile, measured as number of carbohydrate utilization gene present, as well as a reduced genome size compared to non-symbiotic Serratia species. Acknowledgements We would like to acknowledge and thank the Hardt and von Mering labs, as well as the NCCR Microbiomes, for their helpful comments and discussions. Many thanks to Yassine Cherrak and Gottfried Unden for providing helpful comments. Footnotes https://doi.org/10.5281/zenodo.14849846 References 1. ↵ Janda , J.M. & Lopez , D.L. The family enterobacteriaceae , in Practical handbook of microbiology 353 – 362 ( CRC Press , 2021 ). 2. Kirk , M.D. et al. World Health Organization estimates of the global and regional disease burden of 22 foodborne bacterial, protozoal, and viral diseases, 2010: a data synthesis . PLoS medicine 12 , e1001921 ( 2015 ). OpenUrl CrossRef 3. EFSA The European Union One Health 2021 Zoonoses Report . EFSA Journal 20 , e07666 ( 2022 ). OpenUrl 4. ↵ Farmer , J. , Farmer , M. & Holmes , B . The Enterobacteriaceae: general characteristics . Topley & Wilson’s Microbiology and Microbial Infections 2 , 1317 – 1359 ( 2010 ). OpenUrl 5. ↵ Jimenez , E. et al. Is meconium from healthy newborns actually sterile? Res Microbiol 159 , 187 – 193 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 6. ↵ Matamoros , S. , Gras-Leguen , C. , Le Vacon , F. , Potel , G. & de La Cochetiere , M.F . Development of intestinal microbiota in infants and its impact on health . Trends Microbiol 21 , 167 – 173 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 7. ↵ Baldelli , V. , Scaldaferri , F. , Putignani , L. & Del Chierico , F . The Role of Enterobacteriaceae in Gut Microbiota Dysbiosis in Inflammatory Bowel Diseases . Microorganisms 9 ( 2021 ). 8. ↵ Lupp , C. et al. Host-mediated inflammation disrupts the intestinal microbiota and promotes the overgrowth of Enterobacteriaceae . Cell Host Microbe 2 , 119 – 129 ( 2007 ). OpenUrl CrossRef PubMed Web of Science 9. ↵ Kaper , J.B. , Nataro , J.P. & Mobley , H.L. Pathogenic Escherichia coli . Nat Rev Microbiol 2 , 123 – 140 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 10. Rogers , A.W.L. , Tsolis , R.M. & Baumler , A.J . Salmonella versus the Microbiome . Microbiol Mol Biol Rev 85 , doi: 10.1128/mmbr.00027-00019 ( 2021 ). OpenUrl CrossRef 11. Collins , J.W. et al. Citrobacter rodentium: infection, inflammation and the microbiota . Nature Reviews Microbiology 12 , 612 – 623 ( 2014 ). OpenUrl CrossRef PubMed 12. ↵ Ogawa , M. , Handa , Y. , Ashida , H. , Suzuki , M. & Sasakawa , C . The versatility of Shigella effectors . Nature Reviews Microbiology 6 , 11 – 16 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 13. ↵ Freter , R. , Brickner , H. , Fekete , J. , Vickerman , M.M. & Carey , K.E . Survival and implantation of Escherichia coli in the intestinal tract . Infection and immunity 39 , 686 – 703 ( 1983 ). OpenUrl Abstract / FREE Full Text 14. ↵ Freter , R. , Brickner , H. , Botney , M. , Cleven , D. & Aranki , A . Mechanisms that control bacterial populations in continuous-flow culture models of mouse large intestinal flora . Infection and immunity 39 , 676 – 685 ( 1983 ). OpenUrl Abstract / FREE Full Text 15. ↵ Leatham-Jensen , M.P. et al. The streptomycin-treated mouse intestine selects Escherichia coli envZ missense mutants that interact with dense and diverse intestinal microbiota . Infection and immunity 80 , 1716 – 1727 ( 2012 ). OpenUrl Abstract / FREE Full Text 16. Adediran , J. et al. An Escherichia coli Nissle 1917 missense mutant colonizes the streptomycin-treated mouse intestine better than the wild type but is not a better probiotic . Infection and immunity 82 , 670 – 682 ( 2014 ). OpenUrl Abstract / FREE Full Text 17. ↵ Doranga , S. , Krogfelt , K.A. , Cohen , P.S. & Conway , T . Nutrition of Escherichia coli within the intestinal microbiome . EcoSal Plus , eesp-0006-2023 ( 2024 ). 18. ↵ Pereira , F.C. & Berry , D . Microbial nutrient niches in the gut . Environmental microbiology 19 , 1366 – 1378 ( 2017 ). OpenUrl CrossRef PubMed 19. ↵ Mayfield , M.M. & Stouffer , D.B . Higher-order interactions capture unexplained complexity in diverse communities . Nat Ecol Evol 1 , 62 ( 2017 ). OpenUrl CrossRef PubMed 20. ↵ Spragge , F. et al. Microbiome diversity protects against pathogens by nutrient blocking . Science 382 , eadj3502 ( 2023 ). OpenUrl CrossRef PubMed 21. ↵ Medini , D. , Donati , C. , Tettelin , H. , Masignani , V. & Rappuoli , R. The microbial pan-genome . Curr Opin Genet Dev 15 , 589 – 594 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 22. ↵ Lapierre , P. & Gogarten , J.P . Estimating the size of the bacterial pan-genome . Trends Genet 25 , 107 – 110 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 23. ↵ Tettelin , H. , Riley , D. , Cattuto , C. & Medini , D . Comparative genomics: the bacterial pan-genome . Curr Opin Microbiol 11 , 472 – 477 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 24. ↵ Sayers , E.W. et al. GenBank 2023 update . Nucleic Acids Research 51 , D141 – D144 ( 2023 ). OpenUrl CrossRef PubMed 25. ↵ Achtman , M. , Zhou , Z.M. , Charlesworth , J. & Baxter , L . EnteroBase: hierarchical clustering of 100 000s of bacterial genomes into species/subspecies and populations . Philosophical Transactions of the Royal Society B-Biological Sciences 377 , 20210240 ( 2022 ). OpenUrl CrossRef PubMed 26. ↵ Vernikos , G. , Medini , D. , Riley , D.R. & Tettelin , H . Ten years of pan-genome analyses . Curr Opin Microbiol 23 , 148 – 154 ( 2015 ). OpenUrl CrossRef PubMed 27. ↵ Page , A.J. et al. Roary: rapid large-scale prokaryote pan genome analysis . Bioinformatics 31 , 3691 – 3693 ( 2015 ). OpenUrl CrossRef PubMed 28. Ding , W. , Baumdicker , F. & Neher , R.A. panX: pan-genome analysis and exploration . Nucleic Acids Res 46 , e5 ( 2018 ). OpenUrl CrossRef PubMed 29. Sahl , J.W. , Caporaso , J.G. , Rasko , D.A. & Keim , P . The large-scale blast score ratio (LS-BSR) pipeline: a method to rapidly compare genetic content between bacterial genomes . Peerj 2 , e332 ( 2014 ). OpenUrl CrossRef 30. Li , L. , Stoeckert , C.J. , Jr. & Roos , D.S . OrthoMCL: identification of ortholog groups for eukaryotic genomes . Genome Res 13 , 2178 – 2189 ( 2003 ). OpenUrl Abstract / FREE Full Text 31. ↵ Tonkin-Hill , G. et al. Producing polished prokaryotic pangenomes with the Panaroo pipeline . Genome Biol 21 , 180 ( 2020 ). OpenUrl CrossRef PubMed 32. ↵ Perrin , A. & Rocha , E.P.C . PanACoTA: a modular tool for massive microbial comparative genomics . NAR Genom Bioinform 3 , lqaa106 ( 2021 ). OpenUrl CrossRef 33. ↵ Inman , J.M. et al. Large-scale comparative analysis of microbial pan-genomes using PanOCT . Bioinformatics 35 , 1049 – 1050 ( 2019 ). OpenUrl CrossRef PubMed 34. ↵ Zhao , Y. et al. PGAP: pan-genomes analysis pipeline . Bioinformatics 28 , 416 – 418 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 35. ↵ Sela , I. , Wolf , Y.I. & Koonin , E.V . Assessment of assumptions underlying models of prokaryotic pangenome evolution . BMC Biol 19 , 27 ( 2021 ). OpenUrl CrossRef PubMed 36. ↵ Boutet , E. et al. UniProtKB/Swiss-Prot, the Manually Annotated Section of the UniProt KnowledgeBase: How to Use the Entry View . Plant Bioinformatics: Methods and Protocols , 2nd Edition 1374 , 23 – 54 ( 2016 ). OpenUrl CrossRef 37. ↵ Cantalapiedra , C.P. , Hernandez-Plaza , A. , Letunic , I. , Bork , P. & Huerta-Cepas , J . eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale . Mol Biol Evol 38 , 5825 – 5829 ( 2021 ). OpenUrl CrossRef PubMed 38. ↵ Rosenberg , E. , DeLong , E.F. , Lory , S. , Stackebrandt , E. & Thompson , F. ( Springer Berlin Heidelberg , 2014 ). 39. ↵ Ludwig , W. et al. Release LTP_12_2020, featuring a new ARB alignment and improved 16S rRNA tree for prokaryotic type strains . Systematic and Applied Microbiology 44 , 126218 ( 2021 ). OpenUrl CrossRef 40. ↵ Janda , J.M. & Abbott , S.L . The Changing Face of the Family Enterobacteriaceae (Order: “Enterobacterales”): New Members, Taxonomic Issues, Geographic Expansion, and New Diseases and Disease Syndromes . Clin Microbiol Rev 34 , doi: 10.1128/cmr.00174-00120 ( 2021 ). OpenUrl CrossRef 41. ↵ Dauga , C . Evolution of the gyrB gene and the molecular phylogeny of Enterobacteriaceae: a model molecule for molecular systematic studies . Int J Syst Evol Microbiol 52 , 531 – 547 ( 2002 ). OpenUrl CrossRef PubMed Web of Science 42. Nhung , P.H. et al. Phylogeny and species identification of the family based on sequences . Diagnostic Microbiology and Infectious Disease 58 , 153 – 161 ( 2007 ). OpenUrl CrossRef PubMed 43. ↵ Roggenkamp , A . Phylogenetic analysis of enteric species of the family Enterobacteriaceae using the oriC-locus . Syst Appl Microbiol 30 , 180 – 188 ( 2007 ). OpenUrl CrossRef PubMed 44. ↵ Janda , J.M. , Abbott , S.L. & McIver , C.J. Plesiomonas shigelloides Revisited . Clin Microbiol Rev 29 , 349 – 374 ( 2016 ). OpenUrl Abstract / FREE Full Text 45. ↵ Adeolu , M. , Alnajar , S. , Naushad , S. & Gupta , R.S . Genome-based phylogeny and taxonomy of the ‘Enterobacteriales’: proposal for Enterobacterales ord. nov. divided into the families Enterobacteriaceae, Erwiniaceae fam. nov., Pectobacteriaceae fam. nov., Yersiniaceae fam. nov., Hafniaceae fam. nov., Morganellaceae fam. nov., and Budviciaceae fam. nov . International Journal of Systematic and Evolutionary Microbiology 66 , 5575 – 5599 ( 2016 ). OpenUrl CrossRef PubMed 46. ↵ Brenner , D.J. , Fanning , G.R. , Miklos , G.V. & Steigerwalt , A.G . Polynucleotide Sequence Relatedness among Shigella Species . Int J Syst Bacteriol 23 , 1 – 7 ( 1973 ). OpenUrl CrossRef 47. Jain , C. , Rodriguez , R.L. , Phillippy , A.M. , Konstantinidis , K.T. & Aluru , S . High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries . Nat Commun 9 , 5114 ( 2018 ). OpenUrl CrossRef PubMed 48. ↵ Parks , D.H. , Chuvochina , M. , Reeves , P.R. , Beatson , S.A. & Hugenholtz , P . Reclassification of Shigella species as later heterotypic synonyms of Escherichia coli in the Genome Taxonomy Database . bioRxiv , 2021.2009. 2022.461432 ( 2021 ). 49. ↵ Herzog , M.K. et al. Mouse models for bacterial enteropathogen infections: insights into the role of colonization resistance . Gut Microbes 15 , 2172667 ( 2023 ). OpenUrl CrossRef PubMed 50. ↵ Woelfel , S. , Silva , M.S. & Stecher , B . Intestinal colonization resistance in the context of environmental, host, and microbial determinants . Cell Host Microbe 32 , 820 – 836 ( 2024 ). OpenUrl CrossRef PubMed 51. ↵ Eberl , C. et al. E. coli enhance colonization resistance against Salmonella Typhimurium by competing for galactitol, a context-dependent limiting carbon source . Cell Host Microbe 29 , 1680 – 1692 e1687 ( 2021 ). OpenUrl CrossRef PubMed 52. Oliveira , R.A. et al. Klebsiella michiganensis transmission enhances resistance to Enterobacteriaceae gut invasion by nutrition competition . Nat Microbiol 5 , 630 – 641 ( 2020 ). OpenUrl CrossRef PubMed 53. ↵ Osbelt , L. , et al. Klebsiella oxytoca inhibits Salmonella infection through multiple microbiota-context-dependent mechanisms . Nat Microbiol ( 2024 ). 54. Maltby , R. , Leatham-Jensen , M.P. , Gibson , T. , Cohen , P.S. & Conway , T . Nutritional basis for colonization resistance by human commensal Escherichia coli strains HS and Nissle 1917 against E. coli O157: H7 in the mouse intestine . PloS one 8 , e53957 ( 2013 ). OpenUrl CrossRef PubMed 55. Fabich , A.J. et al. Comparison of carbon nutrition for pathogenic and commensal Escherichia coli strains in the mouse intestine . Infect Immun 76 , 1143 – 1152 ( 2008 ). OpenUrl Abstract / FREE Full Text 56. ↵ Furuichi , M. et al. Commensal consortia decolonize Enterobacteriaceae via ecological control . Nature 633 , 878 – 886 ( 2024 ). OpenUrl CrossRef PubMed 57. ↵ Deutscher , J. et al. The Bacterial Phosphoenolpyruvate:Carbohydrate Phosphotransferase System: Regulation by Protein Phosphorylation and Phosphorylation-Dependent Protein-Protein Interactions . Microbiology and Molecular Biology Reviews 78 , 231 – 256 ( 2014 ). OpenUrl Abstract / FREE Full Text 58. ↵ Daruwalla , K.R. , Paxton , A.T. & Henderson , P.J. Energization of the transport systems for arabinose and comparison with galactose transport in Escherichia coli . Biochemical Journal 200 , 611 – 627 ( 1981 ). OpenUrl Abstract / FREE Full Text 59. ↵ Faber , F. et al. Host-mediated sugar oxidation promotes post-antibiotic pathogen expansion . Nature 534 , 697 – 699 ( 2016 ). OpenUrl CrossRef PubMed 60. ↵ Porter , N.T. & Martens , E.C . The Critical Roles of Polysaccharides in Gut Microbial Ecology and Physiology . Annual Review of Microbiology 71 , 349 – 369 ( 2017 ). OpenUrl CrossRef PubMed 61. ↵ Zhao , F.Q. & Keating , A.F . Functional properties and genomics of glucose transporters . Curr Genomics 8 , 113 – 128 ( 2007 ). OpenUrl CrossRef PubMed Web of Science 62. ↵ Romeo , T. & Snoep , J.L . Glycolysis and Flux Control . EcoSal Plus 1 ( 2005 ). 63. ↵ Zubay , G. ( Addison-Wesley Publishing Company, INC , 1983 ). 64. ↵ You , C. et al. Coordination of bacterial proteome with metabolism by cyclic AMP signalling . Nature 500 , 301 – 306 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 65. ↵ Okano , H. , Hermsen , R. , Kochanowski , K. & Hwa , T . Regulation underlying hierarchical and simultaneous utilization of carbon substrates by flux sensors in Escherichia coli . Nat Microbiol 5 , 206 – 215 ( 2020 ). OpenUrl PubMed 66. ↵ Iida , A. , Teshiba , S. & Mizobuchi , K . Identification and characterization of the tktB gene encoding a second transketolase in Escherichia coli K-12 . J Bacteriol 175 , 5375 – 5383 ( 1993 ). OpenUrl Abstract / FREE Full Text 67. ↵ Valentini , G. , Iadarola , P. , Somani , B.L. & Malcovati , M . Two forms of pyruvate kinase in Escherichia coli a comparison of chemical and molecular properties . Biochimica et Biophysica Acta (BBA)-Enzymology 570 , 248 – 258 ( 1979 ). OpenUrl CrossRef 68. ↵ Woods , S.A. , Schwartzbach , S.D. & Guest , J.R . Two biochemically distinct classes of fumarase in Escherichia coli . Biochimica et Biophysica Acta (BBA)-Protein Structure and Molecular Enzymology 954 , 14 – 26 ( 1988 ). OpenUrl CrossRef 69. ↵ Park , S.-J. & Gunsalus , R.P . Oxygen, iron, carbon, and superoxide control of the fumarase fumA and fumC genes of Escherichia coli: role of the arcA, fnr, and soxR gene products . Journal of bacteriology 177 , 6255 – 6262 ( 1995 ). OpenUrl Abstract / FREE Full Text 70. ↵ Unden , G. , Steinmetz , P.A. & Degreif-Dunnwald , P . The Aerobic and Anaerobic Respiratory Chain of Escherichia coli and Salmonella enterica: Enzymes and Energetics . EcoSal Plus 6 ( 2014 ). 71. ↵ Ruddle , S.J. , Massis , L.M. , Cutter , A.C. & Monack , D.M . Salmonella-liberated dietary L-arabinose promotes expansion in superspreaders . Cell Host & Microbe 31 , 405 – 417 .e405 ( 2023 ). OpenUrl CrossRef PubMed 72. ↵ Manzano-Morales , S. , Liu , Y. , González-Bodí , S. , Huerta-Cepas , J. & Iranzo , J . Comparison of gene clustering criteria reveals intrinsic uncertainty in pangenome analyses . Genome Biology 24 ( 2023 ). 73. ↵ Consortium , T.C.P.-G . Computational pan-genomics: status, promises and challenges . Brief Bioinform 19 , 118 – 135 ( 2018 ). OpenUrl CrossRef PubMed 74. ↵ Ghatak , S. , King , Z.A. , Sastry , A. & Palsson , B.O. The y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function . Nucleic Acids Research 47 , 2446 – 2454 ( 2019 ). OpenUrl CrossRef PubMed 75. ↵ Serres , M.H. et al. A functional update of the Escherichia coliK-12 genome . Genome Biology 2 , research0035.0031 ( 2001 ). 76. ↵ Hall , R.J. et al. Gene-gene relationships in an Escherichia coli accessory genome are linked to function and mobility . Microbial Genomics 7 ( 2021 ). 77. ↵ Richardson , E.J. & Watson , M . The automatic annotation of bacterial genomes . Briefings in bioinformatics 14 , 1 – 12 ( 2013 ). OpenUrl CrossRef PubMed 78. ↵ Denamur , E. & Matic , I . Evolution of mutation rates in bacteria . Molecular Microbiology 60 , 820 – 827 ( 2006 ). OpenUrl CrossRef PubMed Web of Science 79. ↵ Fullam , A. et al. proGenomes3: approaching one million accurately and consistently annotated high-quality prokaryotic genomes . Nucleic acids research 51 , D760 – D766 ( 2023 ). OpenUrl CrossRef PubMed 80. ↵ Cohan , F.M. Bacterial species and speciation . Systematic biology 50 , 513 – 524 ( 2001 ). OpenUrl CrossRef PubMed Web of Science 81. ↵ Schubert , C. et al. Monosaccharides Drive Salmonella Gut Colonization in a Context-Dependent Manner . bioRxiv , 2024.2008. 2006.606610 ( 2024 ). 82. ↵ Nguyen , B.D. et al. Salmonella Typhimurium screen identifies shifts in mixed acid fermentation 1 during gut colonization . Cell Host & Microbe , accepted ( 2024 ). 83. ↵ Englyst , K.N. , Liu , S. & Englyst , H.N . Nutritional characterization and measurement of dietary carbohydrates . Eur J Clin Nutr 61 Suppl 1 , S19 – 39 ( 2007 ). OpenUrl CrossRef PubMed Web of Science 84. Castillo , J.J. et al. The Development of the Davis Food Glycopedia-A Glycan Encyclopedia of Food . Nutrients 14 ( 2022 ). 85. ↵ Larke , J.A. et al. Dietary Intake of Monosaccharides from Foods is Associated with Characteristics of the Gut Microbiota and Gastrointestinal Inflammation in Healthy US Adults . J Nutr 153 , 106 – 119 ( 2023 ). OpenUrl CrossRef PubMed 86. ↵ Winter , S.E. et al. Gut inflammation provides a respiratory electron acceptor for Salmonella . Nature 467 , 426 – 429 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 87. ↵ Winter , S.E. et al. Host-derived nitrate boosts growth of E. coli in the inflamed gut . Science 339 , 708 – 711 ( 2013 ). OpenUrl Abstract / FREE Full Text 88. ↵ Rivera-Chavez , F. et al. Depletion of Butyrate-Producing Clostridia from the Gut Microbiota Drives an Aerobic Luminal Expansion of Salmonella . Cell Host Microbe 19 , 443 – 454 ( 2016 ). OpenUrl CrossRef PubMed 89. Rivera-Chavez , F. , Lopez , C.A. & Baumler , A.J . Oxygen as a driver of gut dysbiosis . Free Radic Biol Med 105 , 93 – 101 ( 2017 ). OpenUrl CrossRef PubMed 90. ↵ Chanin , R.B. et al. Epithelial-Derived Reactive Oxygen Species Enable AppBCX-Mediated Aerobic Respiration of Escherichia coli during Intestinal Inflammation . Cell Host Microbe 28 , 780 – 788 e785 ( 2020 ). OpenUrl CrossRef PubMed 91. ↵ Schubert , C. & Unden , G . C4-Dicarboxylates as Growth Substrates and Signaling Molecules for Commensal and Pathogenic Enteric Bacteria in Mammalian Intestine . J Bacteriol 204 , e0054521 ( 2022 ). OpenUrl CrossRef PubMed 92. Schubert , C. & Unden , G. Fumarate, a central electron acceptor for Enterobacteriaceae beyond fumarate respiration and energy conservation , in Advances in Microbial Physiology , Vol. 82 267 – 299 ( Elsevier , 2023 ). OpenUrl CrossRef PubMed 93. ↵ Nguyen , B.D. et al. Import of Aspartate and Malate by DcuABC Drives H(2)/Fumarate Respiration to Promote Initial Salmonella Gut-Lumen Colonization in Mice . Cell Host Microbe 27 , 922 – 936 e926 ( 2020 ). OpenUrl CrossRef PubMed 94. Yoo , W. et al. Salmonella Typhimurium expansion in the inflamed murine gut is dependent on aspartate derived from ROS-mediated microbiota lysis . Cell Host & Microbe ( 2024 ). 95. ↵ Rogers , A.W.L. et al. Salmonella re-engineers the intestinal environment to break colonization resistance in the presence of a compositionally intact microbiota . Cell Host Microbe ( 2024 ). 96. ↵ Jones , S.A. et al. Anaerobic Respiration of Escherichia coli in the Mouse Intestine . Infection and Immunity 79 , 4218 – 4226 ( 2011 ). OpenUrl Abstract / FREE Full Text 97. ↵ Hu , D. , Fuller , N.R. , Caterson , I.D. , Holmes , A.J. & Reeves , P.R . Single-gene long-read sequencing illuminates Escherichia coli strain dynamics in the human intestinal microbiome . Cell Rep 38 , 110239 ( 2022 ). OpenUrl CrossRef PubMed 98. ↵ Machado , L.F.M. & Galán , J.E . Loss of function of metabolic traits in typhoidal without apparent genome degradation . Mbio 15 , e00607 – 00624 ( 2024 ). OpenUrl PubMed 99. ↵ Parks , D.H. , Imelfort , M. , Skennerton , C.T. , Hugenholtz , P. & Tyson , G.W . CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes . Genome research 25 , 1043 – 1055 ( 2015 ). OpenUrl Abstract / FREE Full Text 100. ↵ Ye , J. , Pavlicek , A. , Lunney , E.A. , Rejto , P.A. & Teng , C.-H . Statistical method on nonrandom clustering with application to somatic mutations in cancer . BMC bioinformatics 11 , 1 – 9 ( 2010 ). OpenUrl CrossRef PubMed 101. ↵ Edgar , R.C . MUSCLE: multiple sequence alignment with high accuracy and high throughput . Nucleic acids research 32 , 1792 – 1797 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 102. ↵ Page , A.J. et al. SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments . Microbial genomics 2 ( 2016 ). 103. ↵ Kozlov , A.M. , Darriba , D. , Flouri , T. , Morel , B. & Stamatakis , A . RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference . Bioinformatics 35 , 4453 – 4455 ( 2019 ). OpenUrl CrossRef PubMed 104. ↵ Letunic , I. & Bork , P . Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation . Nucleic acids research 49 , W293 – W296 ( 2021 ). OpenUrl CrossRef PubMed 105. ↵ Karp , P.D. et al. The EcoCyc Database (2023) . EcoSal Plus , eesp00022023 ( 2023 ). 106. ↵ Mayer , C. & Boos , W. Hexose/Pentose and Hexitol/Pentitol Metabolism . EcoSal Plus 1 , doi: 10.1128/ecosalplus.1123.1124.1121 ( 2005 ). OpenUrl CrossRef 107. ↵ Unden , G. , Strecker , A. , Kleefeld , A. & Kim , O.B . C4-Dicarboxylate Utilization in Aerobic and Anaerobic Growth . EcoSal Plus 7 ( 2016 ). 108. ↵ Gu , Z. Complex heatmap visualization . Imeta 1 , e43 ( 2022 ). OpenUrl CrossRef 109. ↵ Almeida , A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome . Nat Biotechnol 39 , 105 – 114 ( 2021 ). OpenUrl CrossRef PubMed 110. ↵ Näpflin N. & Schubert C . Supporting data for “Unravelling the Core and Accessory Genome Dynamics of Enterobacteriaceae in Carbon Metabolism“ . Zenodo doi: 10.5281/zenodo.14849847 ( 2025 ). OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted February 14, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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