Genomic comparison of highly related pairs of E. coli and K. pneumoniae isolated from faeces and blood of the same neonatal patients hospitalized with fever in Dar es Salaam, Tanzania

Genomic comparison of highly related pairs of E. coli and K. pneumoniae isolated from faeces and blood of the same neonatal patients hospitalized with fever in Dar es Salaam, Tanzania | 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 Genomic comparison of highly related pairs of E. coli and K. pneumoniae isolated from faeces and blood of the same neonatal patients hospitalized with fever in Dar es Salaam, Tanzania View ORCID Profile Richard N. Goodman , View ORCID Profile Sabrina J. Moyo , Ilinca Memelis , View ORCID Profile Aakash Khanijau , View ORCID Profile Joel Manyahi , View ORCID Profile Upendo O. Kibwana , View ORCID Profile Said Aboud , Bjørn Blomberg , View ORCID Profile Nina Langeland , View ORCID Profile Adam P. Roberts doi: https://doi.org/10.1101/2025.04.28.650962 Richard N. Goodman 1 Department of Tropical Disease Biology, Liverpool School of Tropical Medicine , Pembroke Place, Liverpool, L3 5QA, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Richard N. Goodman Sabrina J. Moyo 1 Department of Tropical Disease Biology, Liverpool School of Tropical Medicine , Pembroke Place, Liverpool, L3 5QA, UK 2 Department of Clinical Science, University of Bergen , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sabrina J. Moyo Ilinca Memelis 1 Department of Tropical Disease Biology, Liverpool School of Tropical Medicine , Pembroke Place, Liverpool, L3 5QA, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aakash Khanijau 1 Department of Tropical Disease Biology, Liverpool School of Tropical Medicine , Pembroke Place, Liverpool, L3 5QA, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Aakash Khanijau Joel Manyahi 3 Department of Microbiology and Immunology, Muhimbili University of Health and Allied Sciences , Dar es Salaam, Tanzania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joel Manyahi Upendo O. Kibwana 3 Department of Microbiology and Immunology, Muhimbili University of Health and Allied Sciences , Dar es Salaam, Tanzania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Upendo O. Kibwana Said Aboud 3 Department of Microbiology and Immunology, Muhimbili University of Health and Allied Sciences , Dar es Salaam, Tanzania 4 National Institute of Medical Research (NIMR) , Dar es Salaam, Tanzania Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Said Aboud Bjørn Blomberg 2 Department of Clinical Science, University of Bergen , Bergen, Norway 5 Department of Medicine, Haukeland University Hospital , Bergen, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nina Langeland 2 Department of Clinical Science, University of Bergen , Bergen, Norway 5 Department of Medicine, Haukeland University Hospital , Bergen, Norway 6 Norwegian Institute of Public Health , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nina Langeland Adam P. Roberts 1 Department of Tropical Disease Biology, Liverpool School of Tropical Medicine , Pembroke Place, Liverpool, L3 5QA, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Adam P. Roberts For correspondence: Adam.Roberts{at}lstmed.ac.uk Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Blood stream infections (BSIs) are a major cause of hospitalisation and death for children under the age of five in sub-Saharan Africa with members of the Gram-negative bacteria Enterobacterales such as Klebsiella pneumoniae and Escherichia coli among the most common causative agents. These bacteria usually colonise the human gastrointestinal (GI) tract which has been identified as a reservoir for invasive infections into extra-intestinal environments such as the urinary tract and bloodstream. In this study we used comparative genomics to compare hybrid genome assemblies of blood and faecal isolates taken from the same patients (all neonates under 19 days old) to determine if the BSI associated bacterial isolates originated in their GI tract. We show that both E. coli and K. pneumoniae likely translocated from the GI tract to the blood in multiple cases of BSI. We also highlight key virulence genes and acquired mutations that are indicative of pathogenic strains capable of BSI. These findings expand our understanding of BSI pathogenesis and could help guide targeted interventions to prevent future BSI infections in neonates. Introduction Blood stream infections (BSI) are a major cause of sepsis, hospitalisation and death for children under the age of 5. Worldwide there was an estimated 2.9 million sepsis related deaths in 2017 with the highest burden in sub-Saharan Africa 1 . In this region neonates can be particularly susceptible to sepsis caused by invasive bacterial infections due to co-morbidities such as HIV infection, malnutrition, malaria and sickle-cell disease 2 – 4 . It is often difficult to distinguish between febrile illness in infants caused by bacteria and malaria or other infectious agents within low-middle-income countries (LMICs), making them hard to diagnose and treat 5 – 7 . Gram-negative bacteria, including the Enterobacterales Klebsiella pneumoniae and Escherichia coli , were shown to be the main causative agents of neonatal sepsis in a study of several LMICs in Africa and South Asia 8 . K. pneumoniae and E. coli both make up part of the human, commensal, intestinal microbiota, and colonisation of the neonatal gastrointestinal (GI) tract with such organisms occurs rapidly after birth, influenced by the maternal flora and peri-partum environment. In the context of neonatal sepsis, the colonised GI tract has been identified as a reservoir for BSI 9 . This can arise from direct bacterial translocation from the gastrointestinal (GI) tract to the blood across the gut epithelium, or via secondary colonisation and infection of other body sites such as the skin, respiratory or urinary tract 10 , 11 . Bacterial translocation through the intestinal mucosal barrier into the bloodstream can occur during gut dysbiosis 12 or increased intestinal permeability 13 . Premature neonates and those born with low birth weight are particularly vulnerable, as immature immune and gastrointestinal systems can lead to breach of the mucosal barrier 11 , 14 , 15 , and antibiotic exposure seen in healthcare environments such as the neonatal intensive care unit can contribute to gut dysbiosis 16 . Once the intestinal mucosal barrier has been traversed and bacteria are in the blood, this can lead to sepsis. To survive within two distinct environments as diverse as the intestine and blood, K. pneumoniae and E. coli isolates may already contain all the genes they need, or they may require adaptive mutations or the acquisition of genes via mobile genetic elements 17 . A previous study investigated the trends of bacteraemia amongst 2,226 children under 5 years of age who were hospitalised with fever in Dar es Salaam, Tanzania 7 . This study builds on that work, by directly comparing bacterial isolates taken from blood and faecal samples of individual neonates on the same day, to assess whether the faecal and blood isolates are likely related. We started with the hypothesis that E. coli and K. pneumoniae inhabiting the gut microbiome translocated to the blood before the onset of the BSI and associated symptoms. We used comparative genomics to determine differences between the paired blood and faecal isolates taken from the same patients. This enabled us to determine if they were highly related and if there were changes within the genome when the bacterial isolates translocated from the gut to the blood. Using a variety of bioinformatic methods, we show there are both E. coli and K. pneumoniae that likely translocated to the blood stream and highlight key virulence genes and acquired mutations that may be associated with this translocation. Studies from the USA and Taiwan have analysed faecal metagenomes of pre-term and very low birth weight neonates showing bacterial translocation in patients 11 , 12 , 18 . There have also been studies which used comparative genomics to compare E. coli and K. pneumoniae from the urinary tract and gut in patients in the USA 19 , 20 . There have been further studies exploring the virulence factors which contribute to bacterial translocation to extra-intestinal sites and subsequent infections in the urinary tract of patients in France 21 . However, to our knowledge, this is the first study that has directly compared hybrid assembled genomes of blood and faecal E. coli and K. pneumoniae isolates from the same patients at the genomic and molecular level and highlighted virulence genes and acquired mutations that may contribute to the gut-blood transition within individual patient isolates. This is also the first study to investigate bacterial translocation to extra-intestinal sites in a sub-Saharan Africa setting. The hybrid assemblies provide unique structural and contextual information about genes and replicons allowing an in-depth comparative genomics approach. These findings expand our understanding of BSI pathogenesis from Gram-negative organisms and the dynamic genetic landscape likely involved in the translocation from the gut to the blood. Results Typing of E. coli and K. pneumoniae isolated from blood and faeces In a previous study 2,226 blood-cultures were analysed from children below the age of 5 years hospitalised with fever in Dar es Salaam, Tanzania with 82% of participants under the age of two 7 . Sixty-two K. pneumoniae and 28 E. coli were isolated from the blood cultures of these patients, totalling 90 blood isolates. Thirteen of these blood isolates had a paired faecal isolate taken from the same patient 22 . These blood and faecal isolates were sequenced and hybrid assembled (see Supplementary Table 1) to determine relatedness and determine the likelihood of bacterial translocation from the gastrointestinal (GI) tract to the blood stream (see Table 1 ). Therefore, in total 26 isolates were assessed as 13 faecal (FC) and blood (BL) pairs; 4 pairs were confirmed as Escherichia coli ( n =8), 8 pairs of Klebsiella pneumoniae ( n =16), and 1 pair of consisting of Klebsiella pneumoniae and Klebsiella quasipneumoniae 23 (n=2). These were from patients under the age of 19 days with fevers lasting 1 – 3 days (Supplementary Table 2). When referencing the genomic data, the prefix FS refers to patient ID (e.g. FS2155), the prefix FSBL refers to the blood isolate (FSBL2155) and the prefix FSFC refers to the faecal isolate (FSFC2155). View this table: View inline View popup Download powerpoint Table 1: MLST profiles of the 13 paired blood and faecal isolates (n = 26). Multi-locus sequence typing (MLST) from Pasteur Institute, France for Klebsiella species ( https://bigsdb.pasteur.fr/klebsiella/ ) and Warwick University, UK for E. coli ( http://mlst.warwick.ac.uk/mlst/dbs/Ecoli ). Core genome MLST (cgMLST) from Pasteur Institute, France for Klebsiella species ( https://bigsdb.pasteur.fr/klebsiella/cgmlst-lincodes/ ). O-types, K-types and wzi allele for Klebsiella species were predicted with Kaptive 24 . For E. coli phylogroups were assigned using ClermonTyping 25 . The highly related pairs indicate shared MLST and, for Klebsiella spp., cgMLST. FS2155 shared the same cgMLST but not MLST so is not defined as a highly related pair. Firstly, the relatedness of the paired blood and faecal isolates was assessed using multi-locus sequence typing (MLST) and core genome MLST (cgMLST). Eight out of the thirteen pairs (4 E. coli and 4 K. pneumoniae ) shared the same MLST and nine shared the same cgMLST (4 E. coli and 5 K. pneumoniae ). The discrepancy occurred with patient FS2155, the faecal (FSFC2155) and blood (FSBL2155) MLST profiles were ST426 and ST3559, respectively, but the sublineage (323) and clonal group (ST429) from cgMLST were the same for both as well as the K-type, O-type and wzi capsular gene type (see Table 1 ). Of the four K. pneumoniae highly related pairs, all had the same within-pair K-type, O-type and wzi gene type. Of the four E. coli pairs all isolates, both from blood and faeces were of the phylogroup B2. The E. coli isolates from patient FS2071 belonged to ST131 and the E. coli isolates from patients FS1654, FS2246 and FS2258 belonged to ST1193. Determining genome similarity between paired isolates using Average Nucleotide identity (ANI) and core genome single nucleotide polymorphisms (SNPs) To further assess the relatedness of the paired blood and faecal, average nucleotide identities (ANI) and core genome SNP (SNPs) distances were determined for E. coli and Klebsiella spp. ( Figure 1 ) and between all isolates (Supplementary Figure 1). Of the 9 pairs sharing the same MLST, 8 had an ANI of 99.99% or above and < 23 SNPs, with FS2155 isolates having an ANI of 99.78 and 7 SNPs (see Figure 1 ). The ANI analysis also revealed close relatedness between isolates which were not part of the same pair. For example the FSBL2111 isolate shared an ANI of 100% and 0 SNPs with both FSFC2112 and FSBL2112 indicating they are all the same strain. FSBL2265 shares an ANI of 99.88% with FSBL2130 (0 SNPs) and 100% (0 SNPs) with FSFC2130 indicating they are the likely the same sequence type or possibly the same strain. FSBL1654 and FSBL2240 share an ANI of 100% with FSFC2258 (see Figure 1 ). Download figure Open in new tab Figure 1: Average nucleotide identities (ANI) and core genome single nucleotide polymorphisms (SNPs) across 4 E. coli and 9 Klebsiella spp. paired isolates. ANI was calculated using the BLAST alignment algorithm (ANIb). The core genome SNPs are displayed in each cell below the ANI %, this was calculated using snippy v4.6.3 and snp-dist using FSFC1386 as reference (see methods). Klebsiella spp. are displayed together with 17 Klebsiella pneumoniae and 1 (FSFC0558) Klebsiella quasipneumoniae highlighted by the lines of blue. Phylogenetics reveals the genomic relatedness across isolates Core genome trees were built based on SNP differences between the core genomes of closely related isolates (see Figure 2A ), all 8 pairs that share the same MLST and share an ANI above 99.99% were on the same branch with distances less than 0.00008 branch length between nodes. The isolates from FS2155 were also on the same branch. As with the ANI analysis, isolates from different patients were closely related, this included FSBL2111 which was on the same branch as FSBL2112 and FSFC2112, and FSBL2265 which was on the same branch as FSFC2130 and FSBL2130. Further to this, both blood and faecal isolates from FS1654, FS2240 and FS2258 were closely related on the tree. Download figure Open in new tab Figure 2: Core genome phylogenetic trees and admission timelines reveal relatedness across isolates. A) Core genome phylogenetic trees of K. pneumonia e and E. coli isolates. The phylogenetic trees are based on SNPs derived from the core genome alignment of K. pneumonia e and E. coli isolates. The Klebsiella pneumoniae subsp. pneumoniae HS11286 genome (GCF_000240185.1) was used as reference for Klebsiella pneumoniae isolates and the Escherichia coli str. K-12 substr. MG1655 genome (GCF_000005845.2) was used as reference for E. coli isolates. Branch lengths are representative of the numbers of SNPs between isolates. For the E. coli tree FS2071 was removed to clearly visualise the relationship between the other paired isolates (shown in black box). The coloured shapes indicate metadata such as species (green), sample type (brown/red) and hospital of origin (blue). MNH; Muhimbili National Hospital. B) Timelines from admission to discharge of neonatal patients in Temeke and Amana hospitals are represented by the start and end of the green lines in the timeline. MNH was excluded as only one patient was from this study site. The colours represent species consistent with the panel above. Isolates from patients FS2111 and FS2112 were taken from the same hospital (Amana) within 3 days of each other (see Figure 2B ) indicating possible nosocomial transmission. Isolates from patient FS2265 came from Temeke hospital and isolates from patient FS2130 came from Amana, so the similarity in sequence type is likely only incidental. Isolates from patients FS2258, FS1654 and FS2240 were all taken from Temeke hospital but in different months of the year so nosocomial transmission here would indicate an endemic strain within the hospital. Subsequently, the eight pairs (4 E. coli and 4 K. pneumoniae ) shown to be the same strain from MLST, ANI and SNP-based phylogeny (i.e. isolates from FS1386, FS1448, FS1654, FS2071, FS2112, FS2130, FS2240 and FS2258) will be referred to as highly related pairs. Together, it is likely each of these highly related pairs represent bacterial translocation of the same isolate from the GI tract to the blood stream. Comparison of AMR, virulence and metal resistance genes between highly related blood and faecal paired isolates We then looked to compare the antimicrobial resistance (AMR), virulence factor and metal resistance genes between the highly related pairs to determine whether any genes were acquired or lost in the translocation from gut to blood. The acquired AMR profiles did change in two highly related pairs (see Supplementary Table 3). The percentage identity to the database reference gene changed for sul2 and aph(3”)-lb in FS1448 isolates and there was a gain of dfrA17 on an integron in FSBL1654 compared to FSFC1654, the dfrA17 gene confers resistance to trimethoprim (part of co-trimoxazole) (Supplementary figure 2). There were no differences in the genes conferring resistance to amoxycillin, gentamicin and ceftriaxone, which were the main antibiotics used to treat the patients (see Supplementary Table 2). All 9 highly related pairs shared the same virulence factors when compared against the VFDB database and metal resistance genes when compared against the MEGARES database (see Supplementary Table 3). Comparative genomics of SNPs between highly related blood and faecal paired isolates We used breseq to run a genome comparison using the hybrid assembled faecal genome as reference and the long reads of the blood isolates as query for each of the highly related pairs which had been confirmed to be highly related. This identified SNPs (both synonymous and non-synonymous), small INDELs (<100bp) in the coding region and intergenic mutations which had been acquired in the blood isolate compared to the faecal isolate are shown in Figure 3 . The number of predicted mutations ranged from 1 to 516 across the nine pairs (Supplementary Table 4), with a median of 10, an average of 77.6 and standard deviation of 167. However, for some isolates large numbers of SNPs were concentrated within a single CDS, for example in isolates from FS2071 there were 97 SNPs between the faecal and blood isolates with 19 in a gene encoding a phage lysozyme, 5 in a gene encoding a phage endopeptidase, and 16 in a gene encoding a phage tail fibre protein. Download figure Open in new tab Figure 3: Predicted SNP mutations when comparing the blood genome against the faecal genome for a set of eight highly related paired isolates. All pairs compared with SNP mutations and INDELs <100bp categorised by gene class, gene context and eggNOG clusters of orthologous genes (COG). SNPs were identified with breseq. Heatmaps were visualised separately for synonymous SNPs, non-synonymous SNPs, INDELs <100bp within the coding region, and intergenic mutations (both SNPs and INDELs). For intergenic mutations the closest gene in bp to the SNP/INDEL is displayed. General trends across all the pairs can be seen in Figure 3 , SNPs were observed across pairs in genes involved in amino acid metabolism, DNA replication, metal transport/metabolism, sugar transport/metabolism and genes associated with mobile genetic elements and phages. Overall, most SNPs were found in genes related to prophages which contributed to a range of functions, but mostly within genes encoding phage endopeptidase, phage lysozyme and genes involved in the assembly of the phage tail. Synonymous SNPs were found in the genes involved in the synthesis of the amino acid glutamate in FS2240 and FS1654. Mutations in and around genes of the lac operon were found in FS2240 and FS2130 which included non-synonymous SNPs, INDELs and intergenic mutations. Synonymous SNPs and INDELs were found in the virulence gene vgrG in both FS1654 and FS2240. FS2155 is an outlier with 516 SNPs and therefore the SNPs are displayed separately in Supplementary Table 5 . Non-synonymous SNPs of note were found in genes involved in sugar metabolism/transport, including genes of the lac operon (e.g. beta-galactosidase) and those associated with galactose, glucose, lactose, sucrose and mannose. Genes encoding transcriptional regulators of the lac operon were also found to have non-synonymous SNPs. Ten non-synonymous SNPs and 3 intergenic mutations were found in the fec genes encoding for the iron dicitrate transport system, with 1 non-synonymous mutation found in the ferrichrome-iron receptor gene. One of the non-synonymous SNPs in fecA was an A16T mutation predicted to not be tolerated in the protein structure and therefore is likely to affect protein function. Together this shows a complex picture of SNPs in a varied set of genes and gene classes contributing to, or because of, translocation to the blood. Insertions, deletions and mobile genetic element movement between highly related paired isolates We investigated any discrepancies across the entire genome between the highly related pairs such as large insertions and deletions. The hybrid assemblies allowed us to compare contigs directly across pairs (see Supplementary Table 6) and a BLAST alignment of the blood and faecal isolates for each pair considered large insertions and deletions between and within contigs ( Figure 4 ). All the highly related pairs, except FS1654 and FS2140, have 1 chromosome and between 0 and 8 plasmids, however it is worth noting that the term “plasmid” here is used to refer to any extra-chromosomal contig, and not all extra-chromosomal contigs are likely to be plasmids, as some are <5kb and contain no identifiable rep gene. Generally, across the pairs there is a loss or gain of phage genes and mobile genetic elements when translocating from the GI tract to the blood (see Supplementary Figure 2). The blood and faecal genomes of FS2155 have the largest disparities of all paired samples in Figure 4 , with a 400kb disparity in chromosome size however this could be explained by the insertion of FC p1 into the chromosome (see Supplementary Figure 2). The connections in Figure 4 show that all 8 highly related pairs (from patients FS1386, FS1448, FS1654, FS2071, FS2112, FS2130, FS2240 and FS2258) have chromosomes which share greater than 99.99% sequence identity between blood and faecal isolates, providing further evidence that all these bloodstream isolates originated in the GI tract of their respective host. Download figure Open in new tab Figure 4: Whole genome comparison between the blood and faecal isolates for each highly related paired isolate. Connections shown are homologous regions which share greater than 99.9% sequence identity and are larger than 2kb for all pairs except FS1654 and FS2240 where no size limit was imposed (i.e. >0kb) due to the method of assembly. Each pair was aligned with BLASTn, blood isolate genome assemblies (red) were used as query sequence and faecal isolate genome assemblies (blue) were used as reference. Each genome is split into individual contigs, each with one chromosome and any extra-chromosomal contigs designated plasmids (even if a rep gene was absent). The exception is FS1654 and FS2240 where all contigs are numbered and not distinguished as chromosome or plasmid. FC, faecal isolates; BL, blood isolate; chr, chromosome; p, plasmid; c, contig. Hypermutator assay We investigated whether FS2155 was a hypermutator strain which could explain why there is a difference in the MLST, a lower ANI and large genomic changes between blood and faecal isolates. There was no clear difference between FS2155 and the other isolates in their ability to become resistant to rifampicin, indicating that the differences in the FS2155 isolates are not due to it being a hypermutator strain under the laboratory conditions under which it was tested. (Supplementary Table 7). Virulence, pathogenicity and invasiveness determinant genes across all isolates We screened all 26 isolates against the virulencefinder database to determine if there were certain genes which determined invasiveness/virulence in the translocation to the blood stream ( Figure 5A ). The E. coli and K. pneumoniae isolates can be easily distinguished by the absence or presence of certain genes. With the E. coli , all 8 isolates have similar genes which is expected as they are all part of phylogroup B2, the only difference is that FS2071 has 7 additional adhesion genes and lacks 2 genes, one involved in host defence evasion ( kpsT ) and another expressing a toxin ( vat ) which are found in the rest of the E. coli . With the Klebsiella spp., 61% of isolates (11/18) have the same set of virulence genes, these are all involved in iron-uptake including a set of ybt genes, irp and fyuA . The ybt genes, involved in the production of the yersiniabactin siderophore, are present in the paired isolates which likely translocated from the GI tract to the blood but not present in the unpaired isolates. This is also the case for the irp2 , encoding an iron regulatory protein, and fyuA , encoding the yersiniabactin receptor. This could indicate that these ybt , fyuA and irp2 genes are important for the translocation of K. pneumoniae into the blood. These genes are always found on the chromosome of these isolates and are co-located within the same biosynthetic gene cluster ( Figure 5C ). Of the rest of the Klebsiella isolates 22% (4/18) have only one virulence gene yagZ / ecpA , involved in adhesion, and 17% (3/18) had no detectable virulence gene from the virulence factor database. Download figure Open in new tab Figure 5: Virulence determinant genes in blood and faecal isolates across several datasets A) Paired and unpaired isolates from this study ( n = 26) were screened against the virulence factor database and the virulence genes present were visualised as a heatmap. Highly related pairs are in bold. The virulence genes were split into four classes. B) The percentage of the ybt , fyuA and ipA genes in two datasets, BSI and DASSIM, from Blantyre, Malawi containing 472 E. coli and 244 K. pneumoniae species complex isolates (KpSc). Out of the 472 E. coli 23 were BSI, 31 were DASSIM community, 334 were DASSIM sepsis and 84 were DASSIM inpatient. Out of the 244 KpSc 57 were BSI, 16 were DASSIM community, 138 were DASSIM sepsis and 33 were DASSIM inpatient. C) The genomic structure of the ybt biosynthetic gene cluster in the FSFC1448 genome. To further investigate the association of the ybt , fyuA and irp2 genes with BSI pathogenicity we analysed two datasets, BSI and DASSIM, containing 716 E. coli and K. pneumoniae species complex isolates (KpSc) from Blantyre, Malawi. The BSI dataset contained blood isolates from sepsis patients and the DASSIM dataset contained faecal samples from community patients, inpatients and sepsis patients. We determined the percentage of these isolates containing the ybt, fyuA and ipr2 genes ( Figure 5B ) to see if they were overrepresented in blood stream isolates compared to faecal isolates. Sixty-nine percent of E. coli isolates from BSI dataset had all ybt, fyuA and ipr2 genes, whereas in the DASSIM dataset, 58% were found in community patients, 59% found in inpatients and 59% found in sepsis patients. In this case the ybt, fyuA and ipr2 genes were overrepresented in the bloodstream infection isolates. With the K. pneumoniae the picture is different, with the genes present in 18% of BSI isolates, and from the DASSIM faecal isolates the genes are present in 18% of inpatients, 34% of sepsis patients and 38% of community patients. Discussion In this study we used comparative genomics with blood isolates and faecal samples taken from the same neonatal patients 7 , 22 to determine if the bacterial isolates translocated from the gut to the blood and what genomic changes, if any, accompanied this translocation event. Our methodology and study setting are novel in that we used in-depth comparisons of hybrid assembled genomes from the same patients in sub-Saharan Africa. Using MLST, ANI, and core genome phylogenetic trees we found that eight out of the thirteen pairs (4 E. coli and 4 K. pneumoniae ) shared the same MLST, had an ANI above 99.99% and had a very small difference in branch length on the phylogenetic tree. This high relatedness between the pairs indicates that the 4 E. coli and 4 K. pneumoniae isolates likely translocated from the GI tract to the blood prior to the onset of blood stream infection and fever, a phenomena which has been previously reported from NICUs in the USA 9 , 11 . The 4 E. coli highly-related pairs (from patients FS1654, FS2071, FS2240 and FS2258) were all of the phylogroup B2 ( Table 1 ) which has been associated with pathogenic clones, particularly extraintestinal pathogenic E. coli (ExPEC) 10 and has been indicative of urinary tract 21 , 26 and blood stream infection 27 . The 4 K. pneumoniae highly related pairs each shared the same within-pair K-type, O-type and wzi gene type, yet these differed between different pairs. The blood and faecal K. pneumoniae isolates from patient FS2155 shared the same cgMLST, K-type, O-type and wzi gene type but had a different MLST and a lower ANI of 99.78 between blood and faecal isolates. Further comparison of whole genome sequences ( Figure 4 ) also indicated it is unlikely to be the same strain as large parts of the chromosome did not share >99.9% sequence identity which would be expected if the strain was the same and in fact with the eight highly related pairs, the entire chromosome shares 99.9% homology. Seemingly, the young age of the patient (1 day old) and the short time of fever (see Supplementary Table 2) would not accommodate enough generations of K. pneumoniae to allow for large number for SNPs between the FS2155 blood and faecal isolates, however no isolates were found to have an elevated mutation rate based on the results of the rifampicin mutation assay (Supplementary Table 7). Alternatively, we may have just isolated different but related isolates from the faeces and the blood, or the selection procedure may have missed the blood isolate in the faecal sample when in fact it was present. The ANI comparisons and phylogenetic tree showed there was high relatedness between isolates from different patients in the same hospital such as FS2111 and FS2112 from Amana and FS2258, FS1654 and FS2240 from Temeke (see Figure 2 ) indicating potential nosocomial transmission between patients. Nosocomial transmission between patients within NICUs in the USA has been indicated prior to the onset of sepsis in another study 9 . It is difficult to apply ANI thresholds to isolate relatedness, however a recent study showed >95% indicate the same species, >99.5% indicate the same sequence type and >99.99% indicates the same strain 28 . Our methodology also included SNP distances (see Figure 1 ) which provides a clearer distinction between closely related isolates. In genomes containing millions of base pairs, percentages can provide misleading results, for example the E. coli K-12 strain has a reference genome with a size of 4,639,221bp, 400 SNPs would still give an ANI of 99.99% but they may not be clonal. Therefore, going forward we recommend the use of both ANI and SNP distances to determine isolate relatedness within a cohort of compared strains. The antibiotic resistance profiles did not differ between the highly related pairs. Previously it has been noted that the presence of ESBL– producing Gram-negative isolates in the faeces of hospitalized adults is associated with the subsequent onset of sepsis 9 and all our isolates that caused blood stream infections contained the common ESBL gene bla CTX-M-15 which confers resistance to 3 rd generation cephalosporins and is the most common ESBL gene found in E. coli isolates worldwide 29 . This is likely due to the similarity in phenotypic antibiotic resistance profiles being a pre-requisite for selection of isolates in this study. Comparative genomics showed there were common SNPs across patient paired isolates in amino acid metabolism, DNA replication, metal transport/metabolism, sugar transport/metabolism and genes associated with mobile genetic elements and phages. INDELs were found in genes of the lac operon in FS2240 and FS2130 ( Figure 3 ). INDEL mutations were also found in the lac operon and beta-galactosidase gene in FS2155 as well as genes involved in the metabolism of a wide set of sugars. E. coli can utilise a variety of sugars for growth in the gastrointestinal tract 19 and mutations in sugar metabolism/transport pathways have shown to have an effect on the colonisation of the enterohemorrhagic E. coli strain EDL933 in the intestine of mice 30 . Since all the patients involved in this study were aged <19 days, the main sugar source for gut bacteria would be lactose from breast or formula milk, this could explain the mutations in the lac operon as it would be redundant following a change of carbohydrate substrate. When comparing highly related pairs, a synonymous mutation was found in a copper resistance transcriptional regulator in FS1654, and in FS2155 ten non-synonymous mutations were found within the fec genes which encodes for the iron dicitrate transport system. This includes a A16T mutation in fecA, predicted to affect protein function. In a previous study a separate non-synonymous mutation was found in fecD when assessing E. coli moving from the gut to the urinary tract 17 . Most SNPs were found in genes related to phage/prophage ( Figure 3 ), this supports the whole genome comparative analysis in Figure 4 and Supplementary Figure 2 which show that differences in the genomes were mainly accounted for by movement of mobile elements, including phage. It could be the case that the movement from the nutrient rich environment of the gut to the nutrient sparse environment of the blood stream causes a stress response in the cell which in turn leads to widespread movement of mobile genetic elements (MGEs) and the activation of prophages and subsequent mobilisation of phages 31 , 32 . This activation of mobile genetic elements could be beneficial to bacteria, and the MGEs, allowing them to adapt to new environmental stresses by increasing genetic variability, as the movement of MGEs intracellularly can lead to gene knockouts or upregulation of mobilised genes when their genetic context changes 33 . Paradoxically the activation of prophage to a phage would likely be deleterious for the bacterial host and therefore there could be a selection bottleneck for mutated phage genes in isolates successfully translocating between the gut and blood. In this case, the inactivation of genes involved in the replication of phages, and subsequent lysis, would be advantageous to the bacterium 34 – 36 . This widespread movement of MGEs, including phage, could also enable the acquisition of beneficial traits from other bacteria, however, while there is a high abundance of plasmid transfer between bacteria in the gut 37 , there is unlikely to be high plasmid transfer in blood as there is a high amount of transience. During conjugation the cells must come into contact with one another and sustain the interaction long enough for genetic material to be exchanged 38 and previously it has been shown that conjugation occurs at a higher rate in fixed conditions such as biofilms rather than liquid culture 39 – 41 . We also looked to see if there were any genes in our blood isolates that were indicative of virulence, pathogenicity and invasiveness by screening all isolates against the virulence factor database ( Figure 5A ). All E. coli isolates had the same virulence genes except the ones from FS2071 which had 7 additional adhesion genes and lacked 2 genes when compared to the others. The Klebsiella spp. had more variation in virulence genes, with 61%, including all the highly related pairs, having 11 genes involved in iron-uptake, and the rest (39%) only having 1 or 0 virulence genes. The additional 11 genes were variants of ybt , irp and fyu genes which are all involved in iron uptake and metabolism. The ybt gene encodes for the yersiniabactin siderophore which, unlike the enterobactin siderophore, can escape the host innate immune system protein sideocalin, and is associated with pathogenicity in E. coli and K. pneumoniae 42 . This could indicate that the yersiniabactin siderophore is key for the translocation of K. pneumoniae into the blood since all the paired isolates had the yersiniabactin genes and the unpaired isolates did not contain these genes. These unpaired isolates unintentionally provide a background of faecal isolates that were not confirmed to translocate to the blood stream and therefore show the potential difference in genes between intestinal Klebsiella and extra-intestinal pathogenic Klebsiella . We tested this further by including two datasets from Blantyre, Malawi (BSI and DASSIM) to compare the presence of the ybt , iprA and fyuA genes in 716 blood and faecal isolates and see if the genes were overrepresented in the bloodstream isolates. While there was no clear distinction between blood and faecal Klebsiella isolates, in E. coli isolates there was a higher abundance of these genes in the BSI isolates when compared to faecal carriage isolates from DASSIM. Together, this indicates that important genes required for invasion into and / or persistence in the blood were already present in the gut isolates. This opens the possibility for diagnostic interventions, for example, certain patient groups, such as very low birthweight neonates, could have their stool screened by PCR for the presence of the ybt , fyuA and irp2 genes to determine if they are predisposed to bloodstream infections. This would require further investigation, however if successful this targeted intervention could contribute to the prevention of BSIs in neonates. Although this study expands our understanding of the transition of Enterobacterales from the gut to the bloodstream, it is not without limitations. We selected our isolates using single colony picks, therefore we may have missed some highly related paired isolates at the selection stage. Not all the originally paired isolates could be found and sequenced, therefore only 26 out of the original 32 isolates could be sequenced as pairs. By not sequencing these we may have lost information about genes or SNPs involved in bacterial translocation to the blood and we cannot predict how this may have biased the results. Further to this, while most of the genomes were assembled with the same hybrid assembly method (hybracter), due to a lack of short read sequences two were assembled with flye and due to poor long read quality two were assembled with unicycler. While this did not affect sequence typing, ANI, SNP analysis or screening for the absence of presence of genes it did have an impact on the whole genome comparison between the blood and faecal isolates ( Figure 4 ). To conclude, we have shown that 8 isolates (4 E. coli and 4 K. pneumoniae ) likely moved from the gut to the blood in neonatal patients with blood stream infections. The changes that marked this transition varied between the isolate pairs but were mainly found in genes associated with mobile genetic elements and phages. It is possible that most of the genes required for invasion into, or persistence in, the blood were already present in the gut isolates as a set of important iron uptake genes were shared across blood and faecal isolates within each species. This analysis also highlighted the potential importance of the yersiniabactin siderophore in BSI caused by both Klebsiella spp. and E. coli . These findings expand our understanding of BSI pathogenesis and provide potential genetic markers which could be utilised in targeted interventions to prevent future BSI infections in neonates. Methods Sample collection and bacterial isolation Enrolment of participants, collection of blood samples and bacterial isolation was carried out as described previously 7 . The faecal samples were collected as rectal swabs from each patient and bacteria was isolated as previously described 22 . The strains were sent to the Liverpool School of Tropical Medicine, UK and stored at −80°C until the DNA was extracted and sequenced. Whole genome sequencing Short read sequencing was carried out by MicrobesNG (Birmingham, UK). Briefly, the DNA was extracted from the blood and faecal bacterial strains and prepared into DNA libraries using the Nextera XT Library Prep Kit (Illumina, California, USA). The short-read sequencing was carried out using an Illumina machine (Hiseq X10 platform) according to the 250bp paired end protocol. For long read sequencing, DNA was extracted from blood isolates with the Fire Monkey Weight High molecular weight (HMW) genomic (gDNA) DNA Extraction Kit (Revolugen, UK) and the faecal isolates with the Wizard HMW DNA extraction kit (Promega, Wisconsin, USA). Library prep was carried out using the SQK-NBD114.24 Ligation Sequencing and Native Barcoding Kit according to the manufacturers protocol (Oxford Nanopore Technologies, Oxford, UK). Sequencing was carried out using a FLO-MIN114 (R10.4.1) flow cell (Oxford Nanopore Technologies, Oxford, UK) on a MinION Mk1B sequencer, running for 72 hr at a translocation speed of 400bp/s. Data acquisition used the MinKNOW software (v22.08.9). Hybrid genome assembly The raw data (fast5) was basecalled using guppy v6.4.2 with the super accuracy (sup) model specific to the flow cell (R10.4.1), motor protein (E8.2) and translocation speed (400bp/s). Read quality was assessed with Nanoplot v1.38.1 43 . The basecalled files were demultiplexed the guppy_barcoder from Guppy v6.3.8 with the SQK-446NBD114-24 kit. The reads were assembled into de novo genome assemblies using either hybracter v0.6.0 44 , unicycler v0.4.8 45 or flye v2.9.2 46 (see Supplementary Table 1). The majority were assembled with hybracter ( n = 22), FSFC1448 and FSBL1448 were assembled with flye as no short read match could be found and FSFC1654 and FSFC2240 were assembled with unicycler due to the short reads being better quality than the long reads. Hybracter is an automated pipeline for long-read first hybrid assembly. This workflow runs initial quality control, assembles the genome from long reads with flye 46 and plasmids with plassembler 47 , polishes with long reads using medaka ( https://github.com/nanoporetech/medaka ), polishes with short reads using polypolish 48 and pypolca ( https://github.com/gbouras13/pypolca ) and returns an output of assembly files and statistics. This workflow has been shown to be more accurate than the existing gold-standard hybrid assembly software 44 . Assemblies were assessed for quality with seqkit 49 (see Supplementary Table 1) and visualised with Bandage v0.8.1 50 . Genomic data analysis and phylogenetics The assembled genomes were annotated using RAST 51 and screened against the Resfinder 52 , virulence factor database (VFDB) 53 and MEGARes 54 databases using abricate v1.0.1 ( https://github.com/tseemann/abricate ). Average Nucleotide Identities (ANI) between genomes were determined using JSpeciesWS v4.1.1 55 and SNP distances were determined using snippy v4.6.3 and snp-dists v0.8.2 with the K. pneumoniae FSFC1386 isolate as reference. The heatmap showing both ANI and SNP distances between isolates was visualised with the pandas v 2.2.3, matplotlib v 3.9.4, seaborn v 0.13.2 and numpy v 2.2.0 packages in python v3.10.12. Sequence typing used multilocus sequence types (MLST) and core genome MLST (cgMLST) schemes as part of the Pathogenwatch platform 56 . The MLST database from Pasteur Institute, France was used for Klebsiella species ( https://bigsdb.pasteur.fr/klebsiella/ ) and from Warwick University, UK for E. coli ( http://mlst.warwick.ac.uk/mlst/dbs/Ecoli ). The cgMLST database from Pasteur Institute, France was used for Klebsiella species ( https://bigsdb.pasteur.fr/klebsiella/cgmlst-lincodes/ ). O- and K-types for Klebsiella species were predicted with Kaptive 24 along with wzi capsular gene type 57 . For E. coli phylogroups were assigned using ClermonTyping 25 which is an in silico typing method based on the quadruplex PCR of the arpA , chuA , yjaA and TspE4.C2 genes 58 . A core genome phylogenetic tree of 17 K. pneumoniae and 8 E. coli isolates was built ( n = 25), the K. quasipneumoniae isolate FSFC0558 was omitted from this analysis so that intra-species relationships could be analysed. The isolates were aligned to species-specific reference genomes using snippy v 4.3.6, with the Klebsiella pneumoniae subsp. pneumoniae HS11286 genome (GCF_000240185.1) used as reference for Klebsiella pneumoniae isolates and the Escherichia coli str. K-12 substr. MG1655 genome (GCF_000005845.2) used as reference for E. coli isolates. The core SNP alignments were then combined using snippy-core. To retain only vertically inherited SNPs gubbins v2.3.4 was used to remove any recombinant regions from the alignment. A SNP-only alignment was extracted using snp-sites v2.5.1 with the -c option to select for A,C,G and T only. This was passed to IQ-tree v 1.6.1 which was used to construct a maximum-likelihood phylogenetic tree. The model was determined using the ModelFinder function of IQ-tree (-m MF) which calculated TVM+F+ASC+R2 as the best model for K. pneumoniae alignments and TVMe+ASC for E. coli alignments. The model was run using ultrafast 1,000 bootstrap replicates (-bb 1000) and keeping identical data (-keep-ident). The phylogenetic tree and associated metadata was visualised using Interactive Tree Of Life (iTOL) v6 59 . The timelines were plotted from the admission metadata using dplyr v1.1.4 and ggplot v3.5.1. Breseq v0.38.3 60 was used to compare the RAST annotated genomes from the faecal isolates against the long reads from the blood isolates, with the predicted mutations and unassigned missing coverage (MC) analysed from the output. The genes of interest were classified into clusters of orthologous genes (COGs) 61 using eggNOG v5.0 62 , separated according to their mutations, and visualised with the pheatmap v1.0.12 package in R v4.3.1. Genes which were categorised as “Function unknown” within the COG framework were further investigated for putative functions leading to an additional classification termed “class” inferred from gene annotations and BLAST 63 searches against similar genes. We used SIFT 64 to determine the predicted effect of genetic mutations on their protein function. To determine the whole genome comparison between pairs the hybrid assembly from blood and faecal isolates were compared with BLASTn 63 with the faecal isolate as query compared against a database made from the blood isolate genome. The BLAST output was wrangled with dplyr v1.1.4 to visualise as a circos diagram with the “genomic initialize” function of the circlize v0.4.16 package in R v4.3.1. Connections plotted were homologous regions which shared greater than 99.9% sequence identity and were larger than 2kb except for FS1654 and FS1654 where connections were homologous regions with > 99.9% sequence identity with no threshold limit on basepair size. The 26 isolates from this study were screened against the virulence factor database (VFDB) 53 using abricate v1.0.1 ( https://github.com/tseemann/abricate ), the output was plotted as a heatmap with the pheatmap v1.0.12 package in R v4.3.1. Two datasets, BSI and DASSIM, were analysed from Blantyre, Malawi to determine percentage of iron uptake virulence genes ( ybt , fyuA and ipA ) in blood and faecal isolates from another country in sub-Saharan Africa. Raw sequence read data was downloaded from the European Nucleotide Archive from the following projects: PRJEB8265, PRJEB28522, PRJEB26677, and PRJEB36486 65 – 67 . The BSI dataset contains isolates from blood, cerebral spinal fluid (CSF) and rectal swabs, collected as part of routine BSI surveillance, only the blood isolates were used for this analysis 65 , 66 . The DASSIM dataset involved stool sampling and ESBL-E isolate selection from patients with sepsis 67 . The sequence reads were quality controlled, trimmed and assembled as previously described 68 . 716 isolates (472 E. coli and 244 Klebsiella spp.) were screened against the VFDB 53 using abricate v1.0.1. The data were separated by dataset and group (DASSIM_sepsis, DASSIM_inpatient, DASSIM_community, and BSI) and the percentage of occurrences were calculated by the number of gene occurrences divided by the total number in each group per species. Calculations were performed using the “summarize” function in dplyr v1.1.4 and the barplots were visualised with ggplot v3.5.1. The genomic structure of the ybt biosynthetic gene cluster from the RAST 51 annotated FSFC1448 genome was visualised using clinker v0.0.31 69 . Calculation of mutation frequency A rifampicin resistance assay was used to determine the mutation frequency for seven faecal isolates. The colonies were incubated at 37°C in LB broth for 18hrs, the cultures were normalised to an OD 600 of 0.1, then incubated at 37°C until the OD 600 reached 0.6-0.8. The cultures were centrifuged at 4,000 rpm to pellet the cells then resuspended in 1ml of PBS from this resuspended solution, 100ml was diluted in PBS to 10 -6 and plated on LB agar plates and 100ml was plated on LB agar containing rifampicin (20µg/ml). Plates were incubated at 37°C for 16hrs. CFU/ml was determined from colony counts of the total cell count plates. Mutation frequency was calculated by dividing the number of rifampicin-resistant colonies by the total number of viable cells plated (CFU/mL). Data availability Reads from all isolates sequenced as part of this study have been submitted to the Sequence Read Archive (SRA) of the National Centre for Biotechnology Information (NCBI) under the project ID PRJNA1254181. Supplementary Figures 1-2 and Supplementary Tables 1-7 are accessible in the supplementary material of this manuscript. Code availability All code to reproduce this analysis is publicly available through GitHub: https://rngoodman.github.io/blood-faecal-genomic-comparison . Contributions Conceptualization: R.N.G., S.J.M., B.B., N.L., A.P.R.; formal analysis: R.N.G., S.J.M., and A.P.R.; funding acquisition: A.P.R., N.L.; investigation: R.N.G., S.J.M., I.M., A.K., J.M., U.O.K., S.A., B.B., N.L. and A.P.R.; resources: S.J.M., J.M., U.O.K., S.A., B.B., N.L.; software: R.N.G.; methodology: R.N.G.; project administration: R.N.G., S.J.M., B.B., N.L., A.P.R.; supervision: S.J.M., B.B., N.L., A.P.R.; writing – original draft: R.N.G.; writing – review and editing: R.N.G., S.J.M, A.K., S.A., B.B., N.L., A.P.R. Ethics declarations The original sample collection was part of a study approved by the Senate Research and Publications Committee of Muhimbili University of Health and Allied Sciences, National Institute of Medical Research, Tanzania and the Regional Committee for Medical and Health Research Ethics (REK), Norway. Competing interests The authors declare no competing interests. Acknowledgements R.N.G and S.J.M. are supported by funding from the Medical Research Council (MRC), Biotechnology and Biological Sciences Research Council (BBSRC) and Natural Environmental Research Council (NERC), which are all Councils of UK Research and Innovation (Grant no. MR/W030578/1) and the Research Council of Norway (grant NFR333432) under the umbrella of the JPIAMR - Joint Programming Initiative on Antimicrobial Resistance via the STRESST project. Funder Information Declared UK Research and InnovationUK Research and Innovation, https://ror.org/001aqnf71 , MR/W030578/1 The Research Council of NorwayThe Research Council of Norway, , NFR333432 Footnotes https://github.com/rngoodman/blood-faecal-genomic-comparison References 1. ↵ Rudd , K. E. et al. Global, regional, and national sepsis incidence and mortality, 1990– 2017: analysis for the Global Burden of Disease Study . Lancet 395 , 200 – 211 ( 2020 ). OpenUrl CrossRef PubMed 2. ↵ Scott , J. A. G. et al. Relation between falciparum malaria and bacteraemia in Kenyan children: A population-based, case-control study and a longitudinal study . Lancet 378 , 1316 – 1323 ( 2011 ). OpenUrl CrossRef PubMed Web of Science 3. Berkley , J. A. et al. Bacteremia among Children Admitted to a Rural Hospital in Kenya . N. Engl. J. Med . 352 , 39 – 47 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 4. ↵ Gilchrist , J. J. et al. BIRC6 modifies risk of invasive bacterial infection in Kenyan children . Elife 2022 , 1 – 24 ( 2022 ). OpenUrl 5. ↵ Bejon , P. et al. Defining Childhood severe falciparum malaria for intervention studies . PLoS Med . 4 , 1333 – 1340 ( 2007 ). OpenUrl 6. Evans , J. A. et al. High mortality of infant bacteraemia clinically indistinguishable from severe malaria . QJM An Int. J. Med. 97 , 591 – 597 ( 2004 ). OpenUrl CrossRef 7. ↵ Moyo , S. J. et al. Bacteraemia, Malaria, and Case Fatality Among Children Hospitalized With Fever in Dar es Salaam, Tanzania . Front. Microbiol . 11 , 2118 ( 2020 ). OpenUrl CrossRef PubMed 8. ↵ Sands , K. et al. Characterization of antimicrobial-resistant Gram-negative bacteria that cause neonatal sepsis in seven low- and middle-income countries . Nat. Microbiol . 6 , 512 – 523 ( 2021 ). OpenUrl CrossRef PubMed 9. ↵ Carl , M. A. et al. Sepsis from the gut: The enteric habitat of bacteria that cause late-onset neonatal bloodstream infections . Clin. Infect. Dis . 58 , 1211 – 1218 ( 2014 ). OpenUrl CrossRef PubMed 10. ↵ Holmes , C. L. , Anderson , M. T. , Mobley , H. L. T. & Bachman , M. A . Pathogenesis of Gram-Negative Bacteremia . Clin. Microbiol. Rev . 34 , ( 2021 ). 11. ↵ Schwartz , D. J. et al. Gut pathogen colonization precedes bloodstream infection in the neonatal intensive care unit . Sci. Transl. Med . 15 , ( 2023 ). 12. ↵ Lee , C. C. et al. Gut Dysbiosis, Bacterial Colonization and Translocation, and Neonatal Sepsis in Very-Low-Birth-Weight Preterm Infants . Front. Microbiol . 12 , ( 2021 ). 13. ↵ Bischoff , S. C. et al. Intestinal permeability - a new target for disease prevention and therapy . BMC Gastroenterol . 14 , 1 – 25 ( 2014 ). OpenUrl CrossRef PubMed 14. ↵ Dong , Y. & Speer , C. P. Late-onset neonatal sepsis:Recent developments . Arch. Dis. Child. Fetal Neonatal Ed. 100 , F257 – F263 ( 2015 ). OpenUrl Abstract / FREE Full Text 15. ↵ Sherman , M. P . New Concepts of Microbial Translocation in the Neonatal Intestine: Mechanisms and Prevention . Clin. Perinatol . 37 , 565 – 579 ( 2010 ). OpenUrl CrossRef PubMed 16. ↵ Zaura , E. et al. Same Exposure but Two Radically Different Responses to Antibiotics : Resilience of the Salivary Microbiome versus Long-Term Microbial . Am. Soc. Microbiol . 6 , 1 – 11 ( 2015 ). OpenUrl 17. ↵ Nielsen , K. L. et al. Adaptation of Escherichia coli traversing from the faecal environment to the urinary tract . Int. J. Med. Microbiol . 306 , 595 – 603 ( 2016 ). OpenUrl CrossRef PubMed 18. ↵ Smith , A. et al. Concordance of gastrointestinal tract colonization and subsequent bloodstream infections with gram-negative bacilli in very low birth weight infants in the neonatal intensive care unit . Pediatr. Infect. Dis. J . 29 , 831 – 835 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 19. ↵ Chen , S. L. et al. Genomic Diversity and Fitness of E. coli Strains Recovered from the Intestinal and Urinary Tracts of Women with Recurrent Urinary Tract Infection . Sci. Transl. Med . 5 , 1252 – 1253 ( 2013 ). OpenUrl 20. ↵ Martin , R. M. et al. Molecular Epidemiology of Colonizing and Infecting Isolates of Klebsiella pneumoniae . mSphere 1 , 1 – 12 ( 2016 ). OpenUrl CrossRef 21. ↵ Bidet , P. et al. Comparative genomic analysis of ESBL-producing Escherichia coli from faecal carriage and febrile urinary tract infection in children: A prospective multicentre study . JAC-Antimicrobial Resist . 4 , 1 – 8 ( 2022 ). OpenUrl CrossRef 22. ↵ Kibwana , U. O. et al. Gastrointestinal colonization of extended-spectrum beta-lactamase-producing bacteria among children below five years of age hospitalized with fever in Dar es Salaam, Tanzania . J. Glob. Antimicrob. Resist . 30 , 107 – 114 ( 2022 ). OpenUrl CrossRef PubMed 23. ↵ Robins , D. et al. Genome Sequence of antibiotic resistant Klebsiella quasipneumoniae FSFC0558; a Novel Sequence Type (ST8212) . ( 2025 ) doi: 10.1099/acmi.0.000944.v2 . OpenUrl CrossRef 24. ↵ Lam , M. M. C. , Wick , R. R. , Judd , L. M. , Holt , K. E. & Wyres , K. L . Kaptive 2.0: updated capsule and lipopolysaccharide locus typing for the Klebsiella pneumoniae species complex. Microb . Genomics 8 , 1 – 12 ( 2022 ). OpenUrl 25. ↵ Beghain , J. , Bridier-Nahmias , A. , Nagard , H. Le , Denamur , E. & Clermont , O. ClermonTyping: An easy-to-use and accurate in silico method for Escherichia genus strain phylotyping. Microb . Genomics 4 , 1 – 8 ( 2018 ). OpenUrl 26. ↵ Nielsen , K. L. et al. Whole-genome comparison of urinary pathogenic Escherichia coli and faecal isolates of UTI patients and healthy controls . Int. J. Med. Microbiol . 307 , 497 – 507 ( 2017 ). OpenUrl CrossRef PubMed 27. ↵ Mora-Rillo , M. et al. Impact of virulence genes on sepsis severity and survival in Escherichia coli bacteremia . Virulence 6 , 93 – 100 ( 2015 ). OpenUrl CrossRef PubMed 28. ↵ Rodriguez-R , L. M. et al. An ANI gap within bacterial species that advances the definitions of intra-species units . MBio 15 , 1 – 14 ( 2024 ). OpenUrl CrossRef 29. ↵ Geurtsen , J. et al. Genomics and pathotypes of the many faces of Escherichia coli . FEMS Microbiol. Rev . 2022 , 1 – 30 ( 2022 ). OpenUrl 30. ↵ 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 31. ↵ Beaber , J. W. , Hochhut , B. & Waldor , M. K . SOS response promotes horizontal dissemination of antibiotic resistance genes . Nature 427 , 72 – 74 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 32. ↵ Hastings , P. , Rosenberg , S. & Slack , A. Antibiotic-induced lateral transfer of antibiotic resistance . Trends Microbiol . 12 , 401 – 404 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 33. ↵ Nielsen , T. K. , Browne , P. D. & Hansen , L. H . Antibiotic resistance genes are differentially mobilized according to resistance mechanism . Gigascience 11 , 1 – 17 ( 2022 ). OpenUrl CrossRef 34. ↵ Davies , E. V. , Winstanley , C. , Fothergill , J. L. & James , C. E . The role of temperate bacteriophages in bacterial infection . FEMS Microbiol. Lett . 363 , fnw015 ( 2016 ). OpenUrl CrossRef PubMed 35. Wagner , P. L. & Waldor , M. K . Bacteriophage Control of Bacterial Virulence . Infect. Immun . 70 , 3985 – 3993 ( 2002 ). OpenUrl FREE Full Text 36. ↵ Tinsley , C. R. , Bille , E. & Nassif , X . Bacteriophages and pathogenicity: more than just providing a toxin? Microbes Infect . 8 , 1365 – 1371 ( 2006 ). OpenUrl CrossRef PubMed 37. ↵ Bottery , M. J . Ecological dynamics of plasmid transfer and persistence in microbial communities . Curr. Opin. Microbiol . 68 , 102152 ( 2022 ). OpenUrl CrossRef PubMed 38. ↵ Thomas , C. M. & Nielsen , K. M . Mechanisms of, and barriers to, horizontal gene transfer between bacteria . Nat. Rev. Microbiol . 3 , 711 – 721 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 39. ↵ Roberts , A. P. , Mullany , P. & Wilson , M . Gene transfer in bacterial biofilms . Methods Enzymol . 336 , 60 – 65 ( 2001 ). OpenUrl CrossRef PubMed Web of Science 40. Alderliesten , J. B. et al. Effect of donor-recipient relatedness on the plasmid conjugation frequency: A meta-analysis . BMC Microbiol . 20 , 1 – 10 ( 2020 ). OpenUrl CrossRef PubMed 41. ↵ Hunter , P. R. , Wilkinson , D. C. , Catling , L. A. & Barker , G. C . Meta-analysis of experimental data concerning antimicrobial resistance gene transfer rates during conjugation . Appl. Environ. Microbiol . 74 , 6085 – 6090 ( 2008 ). OpenUrl Abstract / FREE Full Text 42. ↵ Kronstad , J. W. & Caza , M . Shared and distinct mechanisms of iron acquisition by bacterial and fungal pathogens of humans . Front. Cell. Infect. Microbiol . 4 , 1 – 23 ( 2013 ). OpenUrl 43. ↵ De Coster , W. , D’Hert , S. , Schultz , D. T. , Cruts , M. & Van Broeckhoven , C . NanoPack: Visualizing and processing long-read sequencing data . Bioinformatics 34 , 2666 – 2669 ( 2018 ). OpenUrl CrossRef PubMed 44. ↵ Bouras , G. et al. Hybracter: enabling scalable, automated, complete and accurate bacterial genome assemblies. Microb . genomics 10 , 1 – 15 ( 2024 ). OpenUrl 45. ↵ Wick , R. R. , Judd , L. M. , Gorrie , C. L. & Holt , K. E . Completing bacterial genome assemblies with multiplex MinION sequencing. Microb . Genomics 3 , 1 – 7 ( 2017 ). OpenUrl 46. ↵ Kolmogorov , M. , Yuan , J. , Lin , Y. & Pevzner , P. A . Assembly of long, error-prone reads using repeat graphs . Nat. Biotechnol . 37 , 540 – 546 ( 2019 ). OpenUrl CrossRef PubMed 47. ↵ Bouras , G. , Sheppard , A. E. , Mallawaarachchi , V. & Vreugde , S . Plassembler: an automated bacterial plasmid assembly tool . Bioinformatics 39 , 0 – 5 ( 2023 ). OpenUrl 48. ↵ Wick , R. R. & Holt , K. E . Polypolish: Short-read polishing of long-read bacterial genome assemblies . PLoS Comput. Biol . 18 , 1 – 13 ( 2022 ). OpenUrl CrossRef PubMed 49. ↵ Shen , W. , Le , S. , Li , Y. & Hu , F . SeqKit: A cross-platform and ultrafast toolkit for FASTA/Q file manipulation . PLoS One 11 , 1 – 10 ( 2016 ). OpenUrl CrossRef PubMed 50. ↵ Wick , R. R. , Schultz , M. B. , Zobel , J. & Holt , K. E . Bandage: interactive visualization of de novo genome assemblies: Fig. 1 . Bioinformatics 31 , 3350 – 3352 ( 2015 ). OpenUrl CrossRef PubMed 51. ↵ Aziz , R. K. et al. The RAST Server: Rapid Annotations using Subsystems Technology . BMC Genomics 9 , 75 ( 2008 ). OpenUrl CrossRef PubMed 52. ↵ Bortolaia , V. et al. ResFinder 4.0 for predictions of phenotypes from genotypes . J. Antimicrob. Chemother . 75 , 3491 – 3500 ( 2020 ). OpenUrl CrossRef PubMed 53. ↵ Chen , L. , Zheng , D. , Liu , B. , Yang , J. & Jin , Q. VFDB 2016: Hierarchical and refined dataset for big data analysis - 10 years on . Nucleic Acids Res . 44 , D694 – D697 ( 2016 ). OpenUrl CrossRef PubMed 54. ↵ Doster , E. et al. MEGARes 2.0: A database for classification of antimicrobial drug, biocide and metal resistance determinants in metagenomic sequence data . Nucleic Acids Res . 48 , D561 – D569 ( 2020 ). OpenUrl CrossRef PubMed 55. ↵ Richter , M. , Rosselló-Móra , R. , Oliver Glöckner , F. & Peplies , J . JSpeciesWS: A web server for prokaryotic species circumscription based on pairwise genome comparison . Bioinformatics 32 , 929 – 931 ( 2016 ). OpenUrl CrossRef PubMed 56. ↵ Argimón , S. et al. Rapid Genomic Characterization and Global Surveillance of Klebsiella Using Pathogenwatch . Clin. Infect. Dis . 73 , S325 – S335 ( 2021 ). OpenUrl CrossRef PubMed 57. ↵ Brisse , S. et al. Wzi gene sequencing, a rapid method for determination of capsulartype for klebsiella strains . J. Clin. Microbiol . 51 , 4073 – 4078 ( 2013 ). OpenUrl Abstract / FREE Full Text 58. ↵ Clermont , O. , Christenson , J. K. , Denamur , E. & Gordon , D. M . The Clermont Escherichia coli phylo-typing method revisited: Improvement of specificity and detection of new phylo-groups . Environ. Microbiol. Rep . 5 , 58 – 65 ( 2013 ). OpenUrl CrossRef PubMed 59. ↵ Letunic , I. & Bork , P . Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool . Nucleic Acids Res . 78 – 82 ( 2024 ) doi: 10.1093/nar/gkae268 . OpenUrl CrossRef PubMed 60. ↵ Sun , L. Shou , W. Deatherage , D. E. & Barrick , J. E. Identification of Mutations in Laboratory-Evolved Microbes from Next-Generation Sequencing Data Using breseq . in (eds. Sun , L. & Shou , W. ) vol. 1151 165 – 188 ( Springer New York , 2014 ). OpenUrl 61. ↵ Galperin , M. Y. , Makarova , K. S. , Wolf , Y. I. & Koonin , E. V . Expanded Microbial genome coverage and improved protein family annotation in the COG database . Nucleic Acids Res . 43 , D261 – D269 ( 2015 ). OpenUrl CrossRef PubMed 62. ↵ Huerta-Cepas , J. et al. EggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses . Nucleic Acids Res . 47 , D309 – D314 ( 2019 ). OpenUrl CrossRef PubMed 63. ↵ Camacho , C. et al. BLAST+: Architecture and applications . BMC Bioinformatics 10 , ( 2009 ). 64. ↵ Sim , N. L. et al. SIFT web server: Predicting effects of amino acid substitutions on proteins . Nucleic Acids Res . 40 , 452 – 457 ( 2012 ). OpenUrl CrossRef 65. ↵ Musicha , P. et al. Genomic landscape of extended-spectrum β-lactamase resistance in Escherichia coli from an urban African setting . J. Antimicrob. Chemother . 72 , 1602 – 1609 ( 2017 ). OpenUrl CrossRef PubMed 66. ↵ Musicha , P. et al. Genomic analysis of Klebsiella pneumoniae isolates from Malawi reveals acquisition of multiple ESBL determinants across diverse lineages . J. Antimicrob. Chemother . 74 , 1223 – 1232 ( 2019 ). OpenUrl CrossRef PubMed 67. ↵ Lewis , J. M. et al. Colonization dynamics of extended-spectrum beta-lactamase-producing Enterobacterales in the gut of Malawian adults . Nat. Microbiol . 7 , ( 2022 ). 68. ↵ Graf , F. E. et al. Molecular mechanisms of re-emerging chloramphenicol susceptibility in extended-spectrum beta-lactamase-producing Enterobacterales . Nat. Commun . 15 , 9019 ( 2024 ). OpenUrl CrossRef PubMed 69. ↵ Gilchrist , C. L. M. & Chooi , Y.-H. Clinker & clustermap.js : Automatic generation of gene cluster comparison figures . Bioinformatics 1 – 3 ( 2021 ) doi: 10.1093/bioinformatics/btab007 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted April 29, 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. You are going to email the following Genomic comparison of highly related pairs of E. coli and K. pneumoniae isolated from faeces and blood of the same neonatal patients hospitalized with fever in Dar es Salaam, Tanzania Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Genomic comparison of highly related pairs of E. coli and K. pneumoniae isolated from faeces and blood of the same neonatal patients hospitalized with fever in Dar es Salaam, Tanzania Richard N. Goodman , Sabrina J. Moyo , Ilinca Memelis , Aakash Khanijau , Joel Manyahi , Upendo O. Kibwana , Said Aboud , Bjørn Blomberg , Nina Langeland , Adam P. Roberts bioRxiv 2025.04.28.650962; doi: https://doi.org/10.1101/2025.04.28.650962 Share This Article: Copy Citation Tools Genomic comparison of highly related pairs of E. coli and K. pneumoniae isolated from faeces and blood of the same neonatal patients hospitalized with fever in Dar es Salaam, Tanzania Richard N. Goodman , Sabrina J. Moyo , Ilinca Memelis , Aakash Khanijau , Joel Manyahi , Upendo O. Kibwana , Said Aboud , Bjørn Blomberg , Nina Langeland , Adam P. Roberts bioRxiv 2025.04.28.650962; doi: https://doi.org/10.1101/2025.04.28.650962 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genomics Subject Areas All Articles Animal Behavior and Cognition (7629) Biochemistry (17660) Bioengineering (13881) Bioinformatics (41913) Biophysics (21436) Cancer Biology (18578) Cell Biology (25482) Clinical Trials (138) Developmental Biology (13372) Ecology (19889) Epidemiology (2067) Evolutionary Biology (24302) Genetics (15599) Genomics (22483) Immunology (17728) Microbiology (40365) Molecular Biology (17163) Neuroscience (88540) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4821) Physiology (7637) Plant Biology (15130) Scientific Communication and Education (2045) Synthetic Biology (4290) Systems Biology (9818) Zoology (2269)