Multidrug resistant pathogens respond differently to the presence of co-pathogen, commensal, probiotic and host cells

Multidrug resistant pathogens respond differently to the presence of co-pathogen, commensal,... www.nature.com/scientificreports OPEN Multidrug resistant pathogens respond differently to the presence of co-pathogen, commensal, Received: 11 January 2018 probiotic and host cells Accepted: 18 May 2018 Published: xx xx xxxx 1 1 1 1 1 Agnes P. Chan , Yongwook Choi , Lauren M. Brinkac , Radha Krishnakumar , Jessica DePew , 1 2 2,3 1 Maria Kim , Mary K. Hinkle , Emil P. Lesho & Derrick E. Fouts In light of the ongoing antimicrobial resistance crisis, there is a need to understand the role of co- pathogens, commensals, and the local microbiome in modulating virulence and antibiotic resistance. To identify possible interactions that influence the expression of virulence or survival mechanisms in both the multidrug-resistant organisms (MDROs) and human host cells, unique cohorts of clinical isolates were selected for whole genome sequencing with enhanced assembly and full annotation, pairwise co-culturing, and transcriptome profiling. The MDROs were co-cultured in pairwise combinations either with: (1) another MDRO, (2) skin commensals (Staphylococcus epidermidis and Corynebacterium jeikeium), (3) the common probiotic Lactobacillus reuteri, and (4) human fibroblasts. RNA-Seq analysis showed distinct regulation of virulence and antimicrobial resistance gene responses across different combinations of MDROs, commensals, and human cells. Co-culture assays demonstrated that microbial interactions can modulate gene responses of both the target and pathogen/commensal species, and that the responses are specific to the identity of the pathogen/commensal species. In summary, bacteria have mechanisms to distinguish between friends, foe and host cells. These results provide foundational data and insight into the possibility of manipulating the local microbiome when treating complicated polymicrobial wound, intra-abdominal, or respiratory infections. e em Th ergence of Multiple Drug Resistant Organisms (MDROs) that exhibit resistance to at least three classes of antibiotics , and the frequency of serious infections caused by MDROs combined with the lack of development and approval of new antibiotics has led the World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), and the Infectious Diseases Society of America (IDSA) to declare that antimicrobial 2–4 resistance poses an urgent threat to global public health . In addition to new antibiotics that are not inactivated by the resistance mechanisms described below , alternative and or adjunctive treatment strategies are critically 6,7 needed . Klebsiella pneumoniae, Acinetobacter baumannii, and Enterobacter species are some of the most challenging MDROs to treat, especially when they are isolated with other co-pathogens and/or commensals from polymicro- 8–10 bial respiratory, intra-abdominal and wound infections . These organisms have evolved and/or horizontally acquired a diverse arsenal of resistance mechanisms , including transmembrane transporters known as efflux 12 13,14 pumps, drug neutralizing enzymes such the carbapenemases and extended spectrum beta-lactamases , and 15 16 17 mutations in ribosomal RNA (rRNA) or topoisomerase genes to escape the target of the drug. Plasmids , 18 19 20 21 integrative conjugative elements (ICE) , integrons , resistance islands and even bacteriophage have all aided and abetted in disseminating antibiotic resistance genes between diverse bacterial species. Despite all we know regarding resistance mechanisms, it is not clear what role, if any, co-infecting pathogenic, resident microbiota, or J. Craig Venter Institute (JCVI), 9605 Medical Center Drive, Suite 150, Rockville, MD, 20850, United States. Multidrug-resistant organism Repository and Surveillance Network, Bacterial Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, 20910, United States. Present address: Infectious Diseases Unit, Rochester Regional Health, Rochester, NY, 14621, United States. Agnes P. Chan and Yongwook Choi contributed equally to this work. Correspondence and requests for materials should be addressed to A.P.C. (email: achan@jcvi.org) or D.E.F. (email: dfouts@jcvi.org) Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 1 www.nature.com/scientificreports/ the human host may have in influencing the expression of antibiotic resistance genes and virulence factors and ultimately pathogenicity of infectious species. Microbial communities consist of multiple species in commensal or symbiotic coexistence where cells can communicate via quorum sensing and influence interactions with each other. To detect these biological inter - actions, classical studies have used co-culture assays to monitor the effects of a pathogen/commensal species on the growth or survival of the target species. Such co-cultures have been implicated in the induction of genes and 22,23 cellular structures required for competitive viability . In a few cases, genome-wide transcriptomic changes have been measured and results from these studies suggest that microbial gene expression can be extensively 22,24–26 reprogrammed by other microbes in the same niche . Growth inhibition as a result of co-culture assays have been reported among human resident or pathogenic bacteria (such as P. aeruginosa against S. aureus , and Streptococcus sanguinis against Streptococcus mutans , and certain groups of soil bacteria and fungi (such 28 26 as Bacillus subtilis against Streptomyces coelicolor , Collimonas fungivorans against Aspergillus niger , and Pseudomonas fluorescens against Bacillus, Brevundimonas or Pedobacter . er Th e are also examples demonstrat- ing the influence of host factors on virulence strategies of the pathogen such as the induction of antibiotic resist- 29,30 31 ance in the presence of human host cells or sub-inhibitory concentrations of antibiotics . Co-culture studies are valuable in understanding co-infection with more than one type of MDRO . For example, in some studies, co-colonization with carbapenem-resistant Enterobacteriaceae (CRE) and Acinetobacter has been associated with a higher level of resistance to carbapenems . Co-infection can also manifest cross-kingdom effects by regulat- ing the survival of the host. Caenorhabditis elegans showed better survival when co-infected with both Candida albicans and A. baumannii instead of C. albicans alone . Thus, it appears that A . baumannii may attenuate the growth of a competing organism, thereby directly influencing the outcome of a co-infection and the survival of the nematode. Although there have been microarray and RNA-sequencing (RNA-Seq) based transcription profiles of bacte- 22,35–38 rial co-culture and host-pathogen interactions , there is a lack of information on how different nosocomial MDROs respond to each other and to the inhabitants of the human skin microbiome, and whether this inter- action might ae ff ct regulation of genes that ultimately inu fl ence the virulence capacity of the organisms. In this study, we conducted genome-wide RNA-Seq gene expression analysis to reveal the transcriptional regulation of individual MDROs when co-cultured with or confronted against another MDRO, selected probiotic and skin commensal organisms, or cultured human cells. The primary goal was to identify and characterize interactions among bacteria and host that influence the expression of virulence factors and antimicrobial resistance (AMR) in MDROs. Results and Discussion Whole Genome Sequencing of MDROs. MDROs used in this study were selected by collaborators at the Walter Reed Army Institute of Research (WRAIR) Multidrug-resistant Organism Repository and Surveillance Network (MRSN) as having one or more of the following characteristics: polytraumatic blast injuries, initial inju- ries in Afghanistan or Iraq, multi-drug resistant, isolated from traumatic wound surfaces or surveillance culture, and/or outbreak in the DOD health system from 2003–2015. Details of the multiple selection criteria that apply to each isolate are summarized in Supplementary Table S1. The antibiogram for the 3 selected MDROs was included as Supplementary Table S2. Bacteria considered human commensals and members of the human skin microbi- ome were chosen as confronting partners if they can be cultured aerobically at 37 °C and on media similar to the selected MDROs, and if their genome sequence was publicly available. Preference was also given to those isolates 39,40 41,42 known to inhabit skin . L. reuteri was chosen due to its growing use as a probiotic . Although strains of a given bacterial species are highly similar, their gene content can vary by as much as 35% . The differences among bacterial strains tend to be mobile elements (e.g., prophage, plasmids, integrated 44–48 elements) and genes encoding for O-antigen or capsular polysaccharides (CPS) . These “flexible” regions tend to encode genes involved in cell surface structures (i.e., O-antigen, CPS, teichoic acid, S-layer, flagella, pili, and porins) as well as genes for resource utilization, including antibiotic resistance genes. These regions that vary 49,50 between strains have been referred to as flexible genomic islands (fGIs) . To avoid missing biologically impor- tant genes within MDROs that may not be present in generic reference genomes, and to more precisely map mRNA transcripts generated during bacterial co-culture experiments, draft genome sequencing of these clinically relevant MDRO isolates was conducted. All three MDRO isolates were sequenced to a high depth of coverage (e.g., 79 to 115-fold) and improved using reads from other sequencing technologies, optical maps, and an in-house automated genome finishing tool, resulting in the genome finishing status of “Improved High-Quality Dra” (IH ft QD) (Table  1). Plasmid-related contigs were discovered in all three genomes: two in A. baumannii MRSN 7339, three in E. hormaechei MRSN 11489 and 23 in K. pneumoniae MRSN 1319. A list of the plasmid-related contigs can be found in Supplementary Table S3. In the K. pneumoniae and E. hormaechei isolates, plasmid-encoded genes were identified that belong to the Type IV secretion system, for example, the tra and virB gene clusters, likely involved in conjugation. For AMR genes, a total of 10 and 5 were annotated on plasmid-derived contigs in the K. pneumoniae and E. hormaechei iso- lates, respectively. The AMR genes encode for inhibitors against various drug class combinations of beta-lactams, aminoglycosides, quinolones, phenicols, sulfonamides, and/or trimethoprim. As for the A. baumannii isolate, interestingly, the AMR genes were not detected on the plasmid-derived contigs but instead 15 AMRs genes, against beta-lactams, aminoglycosides, quinolones, and trimethoprim, were identified on the chromosome. Plasmid-derived genes from the 3 MDROs can be found in Supplementary Tables S4–S6. Co-cultures of selected MDROs and human skin microbiome species. e s Th kin represents a com- plex microbial ecosystem, with interactions between microbial species, and between microbes and the host. Common skin inhabitants include bacteria, fungi, and viruses. Many of the microbes that colonize the skin are Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 2 www.nature.com/scientificreports/ Repository ID MRSN 1319 MRSN 7339 MRSN 11489 ATCC 55730 BEI HM-118 ATCC 43734 Organism K. pneumoniae A. baumannii E. hormaechei L. reuteri SD2112 S. epidermidis SK135 C. jeikeium Accession JSVB00000000 JPHV00000000 JTEP00000000 CP002844 ADEY00000000 ACYW00000000 Group MDRO MDRO MDRO Commensal Commensal Commensal Origin wound wound wound breast milk skin blood culture Sequencing Technology Illumina HiSeq. 2000 Illumina HiSeq. 2000 Illumina HiSeq. 2000 Roche 454 Roche 454 Roche 454 Finishing Status IHQD IHQD IHQD Finished HQD HQD Average Coverage 79.23× 115.46× 94.99× 63× 26× 35× #Contigs 52 34 28 5 33 93 Contig N50 392,725 246,634 332,009 na na na Average Contig Length (bp) 105,180 116,337 165,823 463,368 76,304 26,091 Max. Contig Length (bp) 765,691 847,614 652,496 2,264,399 751,210 161,413 Total Genome size (bp) 5,469,353 3,955,466 4,643,032 2,316,838 2,518,045 2,426,461 Total G + C% 57.31 39.09 55.30 39.04 32.24 61.63 #Proteins 5,430 3,787 4,513 2,300 2,395 2,224 #Plasmid contigs 23 2 3 4 nd nd References This study Chan et al., 2015 This study Spinler et al., 2014 BEI Resources Jackman et al., 1987 Table 1. Genomic features and metadata of bacterial genomes used in this study. Improved High-Quality Dra ft (IHQD); High-Quality Draft (HQD). na = Not available or applicable. nd = Not determined. MDRO species E. hormaechei K. pneumoniae A. baumannii Commensal Confronting bacteria Controls EH KP AB L. reuteri LR LR:LR EH:LR KP:LR AB:LR S. epidermidis SE SE:SE EH:SE KP:SE AB:SE C. jeikeium CJ CJ:CJ EH:CJ KP:CJ AB:CJ E. hormaechei EH na EH:EH KP:EH AB:EH K. pneumoniae KP na na KP:KP AB:KP A. baumannii AB na na na AB:AB Table 2. Bacteria-Bacteria Confrontation Assays. na = Not available or applicable. harmless and may play a beneficial role through colonization and protection against invasion by pathogens. The resident skin microbes may also offer an additional line of defense through priming the host immune system to counteract possible attacks by pathogens. The skin microflora consists of four major phyla: Actinobacteria (e.g., Corynebacterium, Propionibacterium, Micrococcus), Firmicutes (e.g., Staphylococcus), Bacteroidetes, and Proteobacteria . We selected A. baumannii, K. pneumoniae, E. hormaechei isolates from the WRAIR MRSN as the MDRO path- ogen species, S. epidermidis and Corynebacterium jeikeium as the representative skin commensal residents, and L. reuteri as a probiotic representative, focusing on the responses of the MDROs to each other and the selected pathogen/commensal species. The pairwise co-culture assays among MDROs and the commensals or probiotics are summarized in Table 2. e t Th otal number of trimmed mapped reads from each organism per pairwise co-culture assay is summarized in Supplementary Table S7. A total of 388 and 321 A. baumannii genes (out of 3,860 genes in the genome) were up or down-regulated when co-cultured with K. pneumoniae and E. hormaechei, respectively. As many as 909 and 212 K. pneumoniae genes (out of 5,548 genes in the genome) were up or down-regulated when co-cultured with A. baumannii and E. hormaechei, respectively. Compared to the other two MDROs, E. hormaechei gene expres- sion was only moderately ae ff cted when co-cultured with A. baumannii or K. pneumoniae, with 78 and 15 up or down-regulated genes out of 4,617 genes in the genome, respectively. A summary of differentially regulated gene counts obtained from co-cultures are summarized in Table 3. S. epidermidis (a skin resident) and L. reuteri (a common probiotic) stimulated the least gene expres- sion responses when co-cultured with the MDROs. It is possible that the growth of commensal organisms was impaired by the MDROs in co-culture since growth inhibition has been observed previously in bacte- 22,23 rial confrontations . To determine if the MDROs were inhibiting the growth of the commensal organism, phenylethyl-alcohol and Hektoen Enteric selective agars were used to quantitate colony forming units of S. epi- dermidis and E. hormaechei, respectively during co-culture. No growth inhibition was observed (Supplementary Fig. S1), suggesting that slower growth patterns and low biomass at the end of the co-culture was the cause for the low amount of RNA-Seq reads, at least for the E. hormaechei-S. epidermidis confrontation. The same analysis could not be performed with the other commensal bacteria in this study because of their specific media require- ments and the fact that other MDROs, A. baumannii and K. pneumoniae, both possess alcohol-dehydrogenase genes, which would have allowed growth on the selective phenylethyl-alcohol agar. Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 3 www.nature.com/scientificreports/ Confronting Bacteria #Annotated genes MDRO species in genome Regulation A. baumannii K. pneumoniae E. hormaechei C. jeikeium S. epidermidis L. reuteri Up na 200 171 191 9 0 A. baumannii 3,860 Down na 188 150 233 3 1 Up 466 na 60 46 18 5 K. pneumoniae 5,548 Down 443 na 152 118 94 9 Up 71 13 na 0 4 0 E. hormaechei 4,617 Down 7 2 na 0 0 1 Table 3. Differentially expressed gene in the MDROs E. hormaechei, K. pneumoniae, and A. baumannii when confronted with individual pathogenic/commensal bacteria. Differentially expressed genes are defined as genes with an FDR <0.05 from edgeR analysis. Annotated genes included protein-coding, tRNA, tmRNA, and ncRNA. na = Not available or applicable. Genes responsive in co-culture ranked by fold-change. We also examined extreme up/down gene responses of MDROs under co-culture conditions by requiring at least 4-fold up/down expression with FDR below 5%. For A. baumannii, a total of 25 and 19 genes were strongly up/down regulated respectively when co-cultured with E. hormaechei (Supplementary Fig. S2). A major fraction of the same gene set was similarly regulated when A. baumannii was co-cultured with another MDRO K. pneumoniae. Amongst the most strongly upregulated genes, by 11 to 12-fold, were alcohol dehydrogenase (T634_RS01410), aldehyde dehydrogenase (T634_RS01395), and lipoyl synthase (T634_RS06340). Two MFS transporters (T634_RS14350, T634_RS14800) and an outer membrane porin of the OprD family (T634_RS14805) were strongly downregulated by 5 to 10-fold. As for K. pneumoniae gene responses, 24 and 14 genes were up/down regulated respectively when the co-culture was performed with A. baumannii (Supplementary Fig. S3). The strongest K. pneumoniae gene response was found in genes involved in iron acquisition, including TonB-dependent receptor (T643_RS07920; 45-fold), iron ABC transporter permease (T643_RS07935; 39-fold), hemin ABC transporter substrate-binding protein (T643_RS07930; 36-fold), and hemin-degrading factor (T643_RS07925; 34-fold). When the co-culture partner used was E. hormaechei instead, a different gene response was observed which involved a strong downreg- ulation of the enterobactin gene clusters required for siderophore synthesis (discussed below). Finally, a total of 25 E. hormaechei genes responded to co-culturing with A. baumannii or K. pneumoniae. Most genes were strongly upregulated when co-cultured with A. baumannii with the exception of a hypothetical protein (T636_RS15175) which was down-regulated (Supplementary Fig. S4). The most strongly upregulated E. hormaechei genes were involved in iron transport including ligand-gated channel protein (T636_RS04035), hemin uptake protein (T636_RS04030), hemin ABC transporter substrate-binding protein (T636_RS04045) by 21 to 32-fold when co-cultured with A. baumannii, followed by the enterobactin and aerobactin siderophore synthesis gene clusters (also discussed below). Regulation of AMR genes during co-culturing. It is known that AMR is critically important to the sur- vival strategy of the MDROs used in this study and that expression of AMR determinants has a fitness cost asso- ciated with it, but it is unknown to what extent AMR gene (ARG) expression is influenced by other co-cultured microbes. To determine effects of MDRO bacterial challenge on the expression of ARGs, we first identified ARGs using the Resistance Gene Identifier (RGI) in strict mode against the Comprehensive Antibiotic Resistance 51–53 Database (CARD) . RNA-Seq analysis showed significant differential expression of ARGs when K . pneumo- niae and A. baumannii were co-cultured, with patterns of up- and down-regulation observed within each species (Fig. 1a,b). In A. baumannii, both the TEM-1 and ADC-2 beta-lactamase variants were upregulated when chal- lenged with K. pneumoniae along with RND-family efflux systems adeABC and adeIJK. Conversely, in K . pneu- moniae, lipid A modifying enzymes responsible for antimicrobial peptide resistance (arnA, pmrE, and pmrF), multi-drug transporters acrAB, and quinolone resistance protein parC, were downregulated when challenged with A. baumannii. Exceptions of ARGs being up-regulated in K. pneumoniae were pmrC and mdtG. There were few significant ARG expression changes observed in MDROs when co-cultured with commensals. For E . hor- maechei, no significant dier ff entially expressed ARG was observed when challenged with any of the other bac- teria – MDROs or commensals. One notable mention is the down regulation of the beta-lactamase OXA-69 (an OXA-51 subfamily carbapenemase ) in A. baumannii when co-cultured with the skin commensal C. jeikeium (Fig. 1a). This finding would be of considerable interest to the healthcare industry if the co-culturing of a skin commensal could result in an increase in susceptibility of A. baumannii to carbapenems through the lowering of the intrinsic OXA-51-like gene expression. This finding could also inform treatment decisions – for example by avoiding the concomitant use of vancomycin which kills commensals such as coagulase negative staphylococci and corynebacterial species. When confronted with HDF cells, transcriptome analysis of E. hormaechei showed significant changes in ARG expression (Fig. 1c). Most notable, the antibiotic efflux complexes marABC and bpeEF were upregulated when challenged with HDF cells, while the macB periplasmic ATPase component of the macAB cassette and the KPC-3 beta-lactamase variant were downregulated. In K. pneumoniae, the aminoglycoside APH(3″)-Ib and beta-lactam resistance enzyme CTX-M-15 were downregulated in the presence of HDF. No significant differentially expressed ARGs were observed in A. baumannii when co-cultured with HDF cells. Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 4 www.nature.com/scientificreports/ Figure 1. Antimicrobial resistance gene expression. Differentially regulated AMR genes in A. baumannii (AB) (a), K. pneumoniae (KP) (b) during bacterial co-culture experiments, and E. hormaechei (EH) in response to co-cultured HDF cells (c) are shown. (CJ) denotes C. jeikeium. Differentially regulated genes shown with edgeR false discovery rate (FDR): <0.05 (red circle) and <0.1 (yellow circle). Log -fold change values of gene expression was shown as red (up-regulated) and blue (down-regulated) bar graphs with reference to monocultures. Bar scale from −4 to 4. Averaged gene expression level across control and treatment experiments (log read counts per million, log CPM) was shown as green bar graphs. Bar scale from 0 to 15. 2 2 Differential expression of siderophore gene clusters. Siderophores are molecules essential for iron acquisition and the survival of bacteria, and are especially important for pathogens to compete for limiting free iron in an eukaryotic host environment. To determine how siderophore gene clusters are regulated during bacte- rial co-culture, we identified the corresponding siderophore gene clusters including acinetobactin in A. bauman- nii, enterobactin in K. pneumoniae, and both enterobactin-like and aerobactin in E. hormaechei. Genome-wide RNA-Seq expression and annotated virulence gene sets are shown in Supplementary Tables S4–S6. Across the 6 MDRO pairwise analysis performed, siderophore gene cluster expression was upregulated in almost all cases (Fig. 2). The only exception observed was when K . pneumoniae was co-cultured with E. hormaechei (i.e. KP vs. EH, Fig. 2a) in which case the K. pneumoniae enterobactin cluster was strongly downregulated by 8-fold (log fold change of 3) in the presence of E. hormaechei. Interestingly, although both K. pneumoniae and E. hormae- chei encode enterobactin, only K. pneumoniae downregulated the transcription of enterobactin in the presence of E. hormaechei but not vice versa. Conceivably, K. pneumoniae may have evolved iron utilization feedback mechanisms to largely shut down production of enterobactin in favor of utilizing the enterobactin produced by Enterobacter. In contrast, the expression of the A. baumannii siderophore acinetobactin was consistently upregu- lated regardless whether the co-cultured pathogen was K. pneumoniae or E. hormaechei. When the MDROs were individually co-cultured with the common skin resident C. jeikeium, a mild down-regulation of the K. pneumoniae and A. baumannii siderophore gene clusters was also observed (Fig. 2). The C. jeikeium-mediated reduction of siderophore production in competing co-cultured species could be a defense mechanism of commensals to outcompete invading pathogens in the human host. Differential expression of cell attachment components. Pili and fimbriae systems are important for cell attachment and physical interactions; therefore, we also performed extensive annotation of the three selected MDRO genomes to identify genes involved in surface attachment, including type I and type IV pili and fimbriae proteins. RNA-Seq analysis showed that one of the three annotated csu chaperone gene clusters in A. baumannii (type I pili formation) was upregulated when co-cultured with K. pneumoniae by 3-fold, but distinctly down- regulated almost 8-fold only when co-cultured with C. jeikeium (Fig. 3). This csu operon has been previously suggested to be involved in the adherence and formation of biofilm of A . baumannii on abiotic surfaces, but not human respiratory cells . In addition, our transcriptome analysis also showed that two of the annotated type IV pili gene clusters in the A. baumannii strain were upregulated specifically when co-cultured with C . jeikeium. Type IV pili play a role in natural transformation, twitching motility, and biofilm formation . Our results showed that Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 5 www.nature.com/scientificreports/ Figure 2. Siderophore gene cluster expression in (a) K. pneumoniae (enterobactin), (b) A. baumannii (acinetobactin), and (c) E. hormaechei (enterobactin-like and aerobactin) when co-cultured with individual pathogen/commensal species. K. pneumoniae (KP), A. baumannii (AB), E. hormaechei (EH), L. reuteri (LR), S. epidermidis (SE) and C. jeikeium (CJ). Differentially regulated genes shown with edgeR false discovery rate (FDR): <0.05 (red circle) and <0.1 (yellow circle). Log fold change values of gene expression was shown as red (up-regulated) and blue (down-regulated) bar graphs with reference to monocultures. Bar scale from −4 to 4. Averaged gene expression level across control and treatment experiments (log read counts per million, log CPM) was shown as green bar graphs. Bar scale from 0 to 15. C. jeikeium modifies type I and type IV pili expression of A . baumannii, which may be a mechanism to interfere with biofilm formation and surface attachment of the competitor species, in this case A. baumannii. Confirmation of gene expression by qPCR. RNA-Seq gene expression results were validated by per- forming qPCR for a select group of differentially expressed genes. Genes were chosen for analysis based on the ability to design unique primers and their reported fold change values. Biological replicates were tested and qPCR was performed on cDNA from reverse transcription of total RNA. For K. pneumoniae, 2 downregulated (acnB, T643_RS19850; gabT_2, T643_RS11485) and 3 upregulated (aminotransferase, T643_RS18930; cimH, T643_RS19470; PTS system fructose IIA component, T643_RS18905) genes were tested. For A. baumannii, 3 downregulated (papD, T634_RS02055; mmsA_1, T634_RS14320; EamA-like transporter, T634_RS10710) and 4 upregulated (pilN, T634_RS16475; NAD family protein, T634_RS01410; Hpt domain protein, T634_RS11200; Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 6 www.nature.com/scientificreports/ Figure 3. Regulation of A. baumannii type I and type IV pili gene cluster expression when co-cultured with individual pathogen/commensal species. K. pneumoniae (KP), A. baumannii (AB), E. hormaechei (EH), L. reuteri (LR), S. epidermidis (SE) and C. jeikeium (CJ). Differentially regulated genes shown with edgeR false discovery rate (FDR): <0.05 (red circle) and <0.1 (yellow circle). Log fold change values of gene expression was shown as red (up-regulated) and blue (down-regulated) bar graphs with reference to monocultures. Bar scale from −4 to 4. Averaged gene expression level across control and treatment experiments (log read counts per million, log CPM) was shown as green bar graphs. Bar scale from 0 to 15. hypothetical protein, T634_RS08500) genes were tested. Gene expression was normalized to gyrB (T634_ RS06810, T643_RS05975) and relative expression ratios were determined by comparing co-culture samples with monoculture samples. Relative expression ratios were converted to log FC and compared to the log FC 2 2 from RNA-Seq data. All genes tested showed the same direction of log FC in both RNA-Seq and qPCR analysis (Supplementary Fig. S5), confirming the trends observed in the RNA-Seq analysis. Bacterial responses to human fibroblast co-culture assays. To assess host interactions, each of the three selected MDROs for this study was also individually co-cultured with adult human fibroblast cells (HDF). Our transcriptome analysis using RNA-Seq showed that two gene clusters in K. pneumoniae were specif- ically down-regulated when co-cultured with HDF cells. The down-regulated K . pneumoniae gene clusters were involved in fimbriae production and capsule synthesis and showed a strong reduction of up to 3.8-fold (log fold change of −1.92) in both gene loci (Fig. 4). Down-regulation of bacterial surface antigen is likely a fitness strategy to cope with the constantly changing host environment. Evrard et al. showed that the capsule-deficient mutant of a K . pneumoniae clinical isolate could be more efficiently internalized by dendritic cells than its wild-type counterpart. It was suggested that the presence of a thick capsule at the surface of K. pneumoniae may interfere with binding and internalization by host cells. Similarly, in uropathogenic E. coli, King et al. reported that the capsule gene expression are spatially and temporally regulated during different phases of infection such that the unencapsulated cells in the population can preferentially adhere to and invade bladder epithelial cells, and only produce capsule once internalized for intracellular survival and spread. Our results further support these previous studies that expression of bacterial surface antigens are possibly regulated in response to interactions with host cells to evade host immune responses. Host responses to MDRO co-culture. With the same set of co-cultured assays in which MDROs were individually co-cultured with mammalian human fibroblast cells, we also measured human host gene responses. Analysis of HDF-derived human RNA-Seq reads revealed a set of 17 human genes that were significantly up-regulated across all three MDRO-fibroblast co-culture assays (Fig.  5). The up-regulated gene set included chemokines, cytokines, and transcription factors that were up-regulated from 2.36 (log fold change of 1.24) to 294-fold (log fold change of 8.2) across the three MDROs. Pathway and GO term enrichment analyses of the gene 59,60 set were performed using DAVID Bioinformatics Resources (https://david.ncifcrf.gov) . The top 5 enriched KEGG pathways and biological process GO terms were shown in Supplementary Table S8. The gene set was enriched in inflammatory and immune response functions. A total of 11 out of the 17 genes could be mapped to 61–63 the Tumor necrosis factor (TNF) signaling pathway in KEGG (Supplementary Fig. S6) . TNF is involved in the regulation of apoptosis, cell survival, and inflammation and immunity. Conclusion In summary, we performed pairwise co-culture experiments of three selected high-priority military MDROs individually with each other, commensal and probiotic microbes, and cultured human cells. We monitored tran- scriptome responses of both the target and co-cultured pathogen/commensal species using RNA-Seq. Our goal was to delineate gene responses associated with virulence, survival mechanisms, and AMR in the MDROs with partners likely found in combat wound infections and potential probiotic scenarios rather than monoculture conditions. Using this co-culturing approach, we observed differentially regulated gene responses associated with sidero- phore production, pili formation, and cell attachment. Our results suggested that a mixed microbial population may modulate the virulence and colonization of host cells. Future experiments are needed to determine the bio- logical signals involved in the regulation of these important survival and virulence mechanisms of MDROs for effective drug targeting. Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 7 www.nature.com/scientificreports/ Figure 4. Expression of K. pneumoniae (a) attachment-related and (b) capsule-related gene clusters in response to co-cultured HDF cells. Differentially regulated genes shown with edgeR false discovery rate (FDR): <0.05 (red circle) and <0.1 (yellow circle). Log fold change values of gene expression was shown as red (up-regulated) and blue (down-regulated) bar graphs with reference to monocultures. Bar scale from −4 to 4. Averaged gene expression level across control and treatment experiments (log read counts per million, log CPM) was shown 2 2 as green bar graphs. Bar scale from 0 to 15. Figure 5. Human host genes were upregulated when HDF cells were co-cultured with MDROs. Differentially regulated genes shown with edgeR false discovery rate (FDR): <0.05 (red circle). Log fold change values of human gene expression was shown as red (up-regulated) bar graphs with reference to monocultures of HDF cells. Bar scale from 0 to 9. Averaged gene expression level across control and treatment experiments (log read counts per million, log CPM) was shown as green bar graphs. Bar scale from 0 to 15. Our approach leveraged bacteria-bacteria and bacteria-host in vitro co-culture to observe die ff rential tran- scriptional regulation using RNA-Seq analysis. This study highlights the diverse stimuli an organism may encoun- ter in die ff rent environmental niches, and the complex gene regulation that takes place to achieve optimal survival and fitness. This type of analysis will be critical to understanding the interactions among the microbial pathogens, the host microbiome and the host environment, particularly in disease conditions. Our results provide founda- tional data and insight into the possibility of manipulating the local microbiome or avoiding ‘collateral-damage’ by minimizing exposure to overly broad-spectrum antibiotics or vancomycin, which kills skin commensals, when treating complicated polymicrobial wound, intra-abdominal, or respiratory infections. Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 8 www.nature.com/scientificreports/ Methods Genome Sequencing. The genomes of the MDRO isolates were sequenced at JCVI by Illumina HiSeq (2 × 100 bp). Briefly, paired-end libraries were constructed for each sequencing technology from randomly nebu- lized genomic DNA in the 300–800 bp size range following manufacturer recommendations. Sequence reads were generated with a target average read depth of ~60-fold coverage. Draft Genome Assembly. To integrate the JCVI Illumina HiSeq data with data generated through various sequencing platforms and OpGen optical restriction maps generated by WRAIR, an Automated Gap Closure pipeline that combined de novo assembly followed by reference-guided gap closure to resolve short and uncom- plicated gaps was implemented as described previously . Genome annotation. Contigs were annotated for protein- and RNA-encoding features using the JCVI auto- mated annotation pipeline essentially as described previously . Genes conferring drug resistance were identified using the RGI (Resistance Gene Identifier, Version 2) tool in strict mode against the Comprehensive Antibiotic 51–53 Resistance Database (CARD) . Bacterial strains, growth conditions and bacterial co-culture experiments. MDROs A. baumannii MRSN 7339, K. pneumoniae MRSN 1319, and E. hormaechei MRSN 11489 were selected by WRAIR MRSN based on having the highest degree of clinical importance as described earlier in the results section and identified by standard automated biochemical analysis as described previously . The selection criteria and antibiotic resistance prole o fi f the three MDROs against 20 antibiotics were shown in Supplementary Tables S1 and S2, respectively. WRAIR MRSN pro- vided purified genomic DNA and culture stocks of these MDROs. The Human Research Protection Office (HRPO) of the U.S. Army Medical Research and Material Command determined that this study was research not involving human subjects, as it did not “involve living individuals about whom an investigator conducting research obtains (1) data through intervention or interaction with the individual, or (2) identifiable private information, in accordance with 32 CFR 219.102(f ).” Likewise, the JCVI IRB determined that the use of MDROs from MRSN was exempt from IRB review because the material provided was pre-existing and de-identified. e co Th mmensal bacteria Lactobacillus reuteri SD2112 (ATCC 55730), Staphylococcus epidermidis SK135 (BEI HM-118), and C. jeikeium NCTC 11913 (ATCC 43734) were obtained from ATCC/BEI Resources, and therefore exempt from IRB review at JCVI since they are publicly available and de-identified from any human source. A . baumannii, K. pneumoniae, E. hormaechei, and S. epidermidis were grown in BHI broth (BD) to a concentration of 1 × 10 CFU/ml in log phase growth. C. jeikeium and L. reuteri were grown to the same target concentrations in BHI supplemented with 1% (vol/vol) Tween-80 (Fisher) and MRS broth (BD), respectively. For co-culture assays, equal amounts of cells from the cultures were combined and shaken at 37 °C for 5 min at 200 RPM. 1 × 10 CFU of each co-culture were plated onto a BHI agar plate in triplicates. Plates were incubated at 37 °C for 6 hours, at which point approximately 1 × 10 CFU from each triplicate plate were combined and treated with RNAprotect bacteria reagent (Qiagen) according to the manufacturer’s instructions. Bacterial cells were spun down and the RNAprotect bacterial reagent removed aer t ft reatment. Cell pellets were stored at − 80 °C until RNA extraction. Growth inhibition of S. epidermidis by E. hormaechei was analyzed by quantifying colony forming units (CFUs) 65,66 on phenylethyl alcohol and Hektoen Enteric selective agar media . The same experiment was performed using independent cultures on different days for biological replicates to control for reproducibility. Human dermal fibroblast growth conditions and co-culture experiments. Adult human dermal fibroblast (HDF) cells (Cat. no. C-013-5C) were obtained from Invitrogen and their experimental use in this study was found to be exempt from IRB review by the JCVI IRB because these cells are publicly available and de-identified. HDF cultures were set up to test multiplicity of infection (MOI) and incubation times for individual MDROs. The MOIs for all three MDROs with HDF cells was determined to be optimal at 100:1 (MDRO:HDF). e Th optimal duration of a bacterial-HDF co-culture assay was determined to be one hour. Prolonged co-culture times resulted in acidification of the cell culture media, which was avoided to eliminate interference with gene expression due to pH changes. Multiple control experiments to determine retention of bacteria to HDF cells were performed and bacteria were observed via light microscopy to be adherent to the HDF cells, even aer 3 r ft inses with PBS (data not shown). Cells (including MDRO bacteria and HDF cells) were isolated for RNA extraction from the co-culture assays aer r ft emov- ing excess culture media so only adherent/close proximity bacterial cells were harvested. Cells were then treated with RNAprotect Cell Reagent (Qiagen) to immediately stabilize RNA transcripts before further processing. RNA isolation. RNA was extracted using the RNeasy Mini Kit (Qiagen). The manufacturer’s protocol was followed with additional steps as described below. Enzymatic lysis was performed on cell pellets resuspended in TE with 15 mg/ml lysozyme (Fisher) and 3 mg/ml Proteinase K (NEB) and incubated for 10 min at room temper- ature with vortexing for 10 s every 2 min. Aer enzy ft matic lysis, mechanical lysis was performed by adding buffer RLT with 1% vol/vol 2-mercaptoethanol and transferring the lysate to a Lysis Matrix B bead beating tube (MP Bio). Samples were homogenized for 45 s at 6.5 m/s on a FastPrep120 Cell Disrupter system. On-column DNAse treatment (Qiagen) was used according to the manufacturer’s protocol. Isolated RNA was treated with TURBO DNase (Ambion) following the manufacturer’s protocol. RNA-Seq library preparation for Illumina sequencing. Enrichment for mRNA was done using the RiboZero rRNA Removal Kit for bacteria (Epicentre). Libraries were constructed from 10 ng of mRNA using the NEBNext Ultra Directional RNA Library Prep Kit (NEB) following the manufacturer’s protocol. Libraries were normalized and pooled for sequencing using qPCR (Kapa Biosystems Library Quantification Kit). The resulting pooled libraries were sequenced on the Illumina HiSeq platform (2 × 100 bp). Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 9 www.nature.com/scientificreports/ RNA-Seq data analysis. Illumina sequencing reads in FASTQ format were analyzed using CLC Genomics Workbench version 6.5 (http://www.clcbio.com) aer r ft emoving low quality reads (CLC quality score limit = 0.05, maximum of 2 ambiguities), reads less than 50 bp, and adaptors. To remove rRNA reads, a set of known 4,692 distinct rRNA sequences were collected from the SILVA database (http://www.arb-silva.de/) for all five spe - cies of bacteria used in the co-culture assays. All reads mapped to the known rRNA sequences were discarded. BWA was used for the mapping allowing up to three mismatches. Reference files for RNA-Seq analysis were prepared from Genbank files imported into CLC. For each pair of control and co-culture experiment, the expected genomes were selected and reads were mapped using the following settings: maximum number of mis- matches = 2, minimum length fraction = 0.8, minimum similarity fraction = 0.98, maximum number of hits for a read = 10, minimum and maximum distances for paired reads = 1,1000 and counting scheme of “include bro- ken pairs”. All reads mapped to each gene were used as raw read count to determine up or down-regulated genes using the Bioconductor package edgeR . Genes with at least 1 CPM in at least 2 samples were included for the differential expression analysis. Differentially expressed genes were determined using an exact test implemented for negative-binomially distributed counts with estimated genewise dispersion values using edgeR. e Th RNA-Seq gene expression for the three MDROs A. baumannii, K. pneumoniae, and E. hormaechei, together with curated virulence gene annotation and AMR annotation are shown in Supplementary Tables S4–S6. Reverse Transcription and qRT-PCR. Reverse transcription (RT) was performed to generate cDNA from two biological replicates to be used in qRT-PCR for the validation of RNA-Seq data. RNA concentrations were determined with an Agilent 2100 Bioanalyzer and 1.5 μg of total RNA was used in each reaction. RNA was incu- bated with 6 μg of random hexamers (Invitrogen) and 40 U of RNaseOUT (Invitrogen) in 18.5 μl at 70 °C for 10 min, followed by snap cooling on ice. The reaction was set up with 1 × First Strand Buffer, 10 mM DTT, 0.5 mM dNTPs (Invitrogen), and 400 U SuperScript III Reverse Transcriptase (Invitrogen) in 30 μl. The reaction was incubated in a 42 °C water bath for 16 h. The reaction was stopped and RNA was hydrolyzed with 0.1 M EDTA and 0.2 M NaOH at 65 °C for 12 min. Tris-HCl (pH 7.0) was added to neutralize the pH. Purification of cDNA was achieved by using QIAquick PCR Purification kit (Qiagen) following the manufacturer’s protocol and quantitated by fluorometric assays using SYBR Gold (Invitrogen). qRT-PCRs were performed with 10 ng of template cDNA in 10 μl triplicate reactions with SYBR Green Master Mix (Roche) in 384-well plates on the LightCycler480 qPCR instrument (Roche). Cycling conditions were 95 °C for 10 min followed by 50 cycles of 95 °C for 20 s, 58 °C for 30 s, and 72 °C for 15 s. A melting curve analysis was performed aer a ft ll cycles were completed to verify the Tm of amplified products. No-RT controls and qPCR con- trol reactions were performed as well. To determine the C value (crossing point-PCR-cycle or threshold cycle) for each reaction, the ‘Fit Point Method’ was performed in the LightCycler480 sowa ft re 1.5.0 (Roche Diagnostics). Efficiency for each primer was optimized by adjusting primer concentration and was determined for the reac- tion conditions. Efficiency and C values were used to calculate the gene expression with normalization to gyrB. Relative expression ratios were determined by comparing co-culture samples with monoculture samples using the method described previously . Primer sequences and concentrations used are provided in Supplementary Table S9. Data availability. Genome assembly and annotation for the three selected MDRO isolates are available under NCBI bioprojects: PRJNA223624, PRJNA223615 and PRJNA223617. 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Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009). 68. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for dier ff ential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). 69. Pfa, M. W ffl . A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29, e45 (2001). Acknowledgements The opinions and assertions herein are solely those of the authors and are not to be construed as official or representing those of the US Army or Department of Defense. This project was funded in whole or part with federal funds from the Department of Defense, Defense Medical Research and Development Program - Military Infectious Diseases Basic Research Award number W81XWH-12-2-0106 to D.E.F. and A.P.C. and from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services under award number U19AI110819. Author Contributions D.E.F. and A.P.C. conceived and designed the study, E.P.L. and M.K.H. selected and provided the military MDRO isolates, M.K. performed genome assembly, J.D. and R.K. conducted wet lab experiments, Y.C., A.P.C., L.M.B. and D.E.F. analyzed the results. A.P.C., Y.C., R.K., J.D., L.M.B., E.B.L. and D.E.F. wrote the manuscript. All authors reviewed the manuscript. Additional Information Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-26738-1. Competing Interests: The authors declare no competing interests. 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Multidrug resistant pathogens respond differently to the presence of co-pathogen, commensal, probiotic and host cells

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Abstract

www.nature.com/scientificreports OPEN Multidrug resistant pathogens respond differently to the presence of co-pathogen, commensal, Received: 11 January 2018 probiotic and host cells Accepted: 18 May 2018 Published: xx xx xxxx 1 1 1 1 1 Agnes P. Chan , Yongwook Choi , Lauren M. Brinkac , Radha Krishnakumar , Jessica DePew , 1 2 2,3 1 Maria Kim , Mary K. Hinkle , Emil P. Lesho & Derrick E. Fouts In light of the ongoing antimicrobial resistance crisis, there is a need to understand the role of co- pathogens, commensals, and the local microbiome in modulating virulence and antibiotic resistance. To identify possible interactions that influence the expression of virulence or survival mechanisms in both the multidrug-resistant organisms (MDROs) and human host cells, unique cohorts of clinical isolates were selected for whole genome sequencing with enhanced assembly and full annotation, pairwise co-culturing, and transcriptome profiling. The MDROs were co-cultured in pairwise combinations either with: (1) another MDRO, (2) skin commensals (Staphylococcus epidermidis and Corynebacterium jeikeium), (3) the common probiotic Lactobacillus reuteri, and (4) human fibroblasts. RNA-Seq analysis showed distinct regulation of virulence and antimicrobial resistance gene responses across different combinations of MDROs, commensals, and human cells. Co-culture assays demonstrated that microbial interactions can modulate gene responses of both the target and pathogen/commensal species, and that the responses are specific to the identity of the pathogen/commensal species. In summary, bacteria have mechanisms to distinguish between friends, foe and host cells. These results provide foundational data and insight into the possibility of manipulating the local microbiome when treating complicated polymicrobial wound, intra-abdominal, or respiratory infections. e em Th ergence of Multiple Drug Resistant Organisms (MDROs) that exhibit resistance to at least three classes of antibiotics , and the frequency of serious infections caused by MDROs combined with the lack of development and approval of new antibiotics has led the World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), and the Infectious Diseases Society of America (IDSA) to declare that antimicrobial 2–4 resistance poses an urgent threat to global public health . In addition to new antibiotics that are not inactivated by the resistance mechanisms described below , alternative and or adjunctive treatment strategies are critically 6,7 needed . Klebsiella pneumoniae, Acinetobacter baumannii, and Enterobacter species are some of the most challenging MDROs to treat, especially when they are isolated with other co-pathogens and/or commensals from polymicro- 8–10 bial respiratory, intra-abdominal and wound infections . These organisms have evolved and/or horizontally acquired a diverse arsenal of resistance mechanisms , including transmembrane transporters known as efflux 12 13,14 pumps, drug neutralizing enzymes such the carbapenemases and extended spectrum beta-lactamases , and 15 16 17 mutations in ribosomal RNA (rRNA) or topoisomerase genes to escape the target of the drug. Plasmids , 18 19 20 21 integrative conjugative elements (ICE) , integrons , resistance islands and even bacteriophage have all aided and abetted in disseminating antibiotic resistance genes between diverse bacterial species. Despite all we know regarding resistance mechanisms, it is not clear what role, if any, co-infecting pathogenic, resident microbiota, or J. Craig Venter Institute (JCVI), 9605 Medical Center Drive, Suite 150, Rockville, MD, 20850, United States. Multidrug-resistant organism Repository and Surveillance Network, Bacterial Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, 20910, United States. Present address: Infectious Diseases Unit, Rochester Regional Health, Rochester, NY, 14621, United States. Agnes P. Chan and Yongwook Choi contributed equally to this work. Correspondence and requests for materials should be addressed to A.P.C. (email: achan@jcvi.org) or D.E.F. (email: dfouts@jcvi.org) Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 1 www.nature.com/scientificreports/ the human host may have in influencing the expression of antibiotic resistance genes and virulence factors and ultimately pathogenicity of infectious species. Microbial communities consist of multiple species in commensal or symbiotic coexistence where cells can communicate via quorum sensing and influence interactions with each other. To detect these biological inter - actions, classical studies have used co-culture assays to monitor the effects of a pathogen/commensal species on the growth or survival of the target species. Such co-cultures have been implicated in the induction of genes and 22,23 cellular structures required for competitive viability . In a few cases, genome-wide transcriptomic changes have been measured and results from these studies suggest that microbial gene expression can be extensively 22,24–26 reprogrammed by other microbes in the same niche . Growth inhibition as a result of co-culture assays have been reported among human resident or pathogenic bacteria (such as P. aeruginosa against S. aureus , and Streptococcus sanguinis against Streptococcus mutans , and certain groups of soil bacteria and fungi (such 28 26 as Bacillus subtilis against Streptomyces coelicolor , Collimonas fungivorans against Aspergillus niger , and Pseudomonas fluorescens against Bacillus, Brevundimonas or Pedobacter . er Th e are also examples demonstrat- ing the influence of host factors on virulence strategies of the pathogen such as the induction of antibiotic resist- 29,30 31 ance in the presence of human host cells or sub-inhibitory concentrations of antibiotics . Co-culture studies are valuable in understanding co-infection with more than one type of MDRO . For example, in some studies, co-colonization with carbapenem-resistant Enterobacteriaceae (CRE) and Acinetobacter has been associated with a higher level of resistance to carbapenems . Co-infection can also manifest cross-kingdom effects by regulat- ing the survival of the host. Caenorhabditis elegans showed better survival when co-infected with both Candida albicans and A. baumannii instead of C. albicans alone . Thus, it appears that A . baumannii may attenuate the growth of a competing organism, thereby directly influencing the outcome of a co-infection and the survival of the nematode. Although there have been microarray and RNA-sequencing (RNA-Seq) based transcription profiles of bacte- 22,35–38 rial co-culture and host-pathogen interactions , there is a lack of information on how different nosocomial MDROs respond to each other and to the inhabitants of the human skin microbiome, and whether this inter- action might ae ff ct regulation of genes that ultimately inu fl ence the virulence capacity of the organisms. In this study, we conducted genome-wide RNA-Seq gene expression analysis to reveal the transcriptional regulation of individual MDROs when co-cultured with or confronted against another MDRO, selected probiotic and skin commensal organisms, or cultured human cells. The primary goal was to identify and characterize interactions among bacteria and host that influence the expression of virulence factors and antimicrobial resistance (AMR) in MDROs. Results and Discussion Whole Genome Sequencing of MDROs. MDROs used in this study were selected by collaborators at the Walter Reed Army Institute of Research (WRAIR) Multidrug-resistant Organism Repository and Surveillance Network (MRSN) as having one or more of the following characteristics: polytraumatic blast injuries, initial inju- ries in Afghanistan or Iraq, multi-drug resistant, isolated from traumatic wound surfaces or surveillance culture, and/or outbreak in the DOD health system from 2003–2015. Details of the multiple selection criteria that apply to each isolate are summarized in Supplementary Table S1. The antibiogram for the 3 selected MDROs was included as Supplementary Table S2. Bacteria considered human commensals and members of the human skin microbi- ome were chosen as confronting partners if they can be cultured aerobically at 37 °C and on media similar to the selected MDROs, and if their genome sequence was publicly available. Preference was also given to those isolates 39,40 41,42 known to inhabit skin . L. reuteri was chosen due to its growing use as a probiotic . Although strains of a given bacterial species are highly similar, their gene content can vary by as much as 35% . The differences among bacterial strains tend to be mobile elements (e.g., prophage, plasmids, integrated 44–48 elements) and genes encoding for O-antigen or capsular polysaccharides (CPS) . These “flexible” regions tend to encode genes involved in cell surface structures (i.e., O-antigen, CPS, teichoic acid, S-layer, flagella, pili, and porins) as well as genes for resource utilization, including antibiotic resistance genes. These regions that vary 49,50 between strains have been referred to as flexible genomic islands (fGIs) . To avoid missing biologically impor- tant genes within MDROs that may not be present in generic reference genomes, and to more precisely map mRNA transcripts generated during bacterial co-culture experiments, draft genome sequencing of these clinically relevant MDRO isolates was conducted. All three MDRO isolates were sequenced to a high depth of coverage (e.g., 79 to 115-fold) and improved using reads from other sequencing technologies, optical maps, and an in-house automated genome finishing tool, resulting in the genome finishing status of “Improved High-Quality Dra” (IH ft QD) (Table  1). Plasmid-related contigs were discovered in all three genomes: two in A. baumannii MRSN 7339, three in E. hormaechei MRSN 11489 and 23 in K. pneumoniae MRSN 1319. A list of the plasmid-related contigs can be found in Supplementary Table S3. In the K. pneumoniae and E. hormaechei isolates, plasmid-encoded genes were identified that belong to the Type IV secretion system, for example, the tra and virB gene clusters, likely involved in conjugation. For AMR genes, a total of 10 and 5 were annotated on plasmid-derived contigs in the K. pneumoniae and E. hormaechei iso- lates, respectively. The AMR genes encode for inhibitors against various drug class combinations of beta-lactams, aminoglycosides, quinolones, phenicols, sulfonamides, and/or trimethoprim. As for the A. baumannii isolate, interestingly, the AMR genes were not detected on the plasmid-derived contigs but instead 15 AMRs genes, against beta-lactams, aminoglycosides, quinolones, and trimethoprim, were identified on the chromosome. Plasmid-derived genes from the 3 MDROs can be found in Supplementary Tables S4–S6. Co-cultures of selected MDROs and human skin microbiome species. e s Th kin represents a com- plex microbial ecosystem, with interactions between microbial species, and between microbes and the host. Common skin inhabitants include bacteria, fungi, and viruses. Many of the microbes that colonize the skin are Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 2 www.nature.com/scientificreports/ Repository ID MRSN 1319 MRSN 7339 MRSN 11489 ATCC 55730 BEI HM-118 ATCC 43734 Organism K. pneumoniae A. baumannii E. hormaechei L. reuteri SD2112 S. epidermidis SK135 C. jeikeium Accession JSVB00000000 JPHV00000000 JTEP00000000 CP002844 ADEY00000000 ACYW00000000 Group MDRO MDRO MDRO Commensal Commensal Commensal Origin wound wound wound breast milk skin blood culture Sequencing Technology Illumina HiSeq. 2000 Illumina HiSeq. 2000 Illumina HiSeq. 2000 Roche 454 Roche 454 Roche 454 Finishing Status IHQD IHQD IHQD Finished HQD HQD Average Coverage 79.23× 115.46× 94.99× 63× 26× 35× #Contigs 52 34 28 5 33 93 Contig N50 392,725 246,634 332,009 na na na Average Contig Length (bp) 105,180 116,337 165,823 463,368 76,304 26,091 Max. Contig Length (bp) 765,691 847,614 652,496 2,264,399 751,210 161,413 Total Genome size (bp) 5,469,353 3,955,466 4,643,032 2,316,838 2,518,045 2,426,461 Total G + C% 57.31 39.09 55.30 39.04 32.24 61.63 #Proteins 5,430 3,787 4,513 2,300 2,395 2,224 #Plasmid contigs 23 2 3 4 nd nd References This study Chan et al., 2015 This study Spinler et al., 2014 BEI Resources Jackman et al., 1987 Table 1. Genomic features and metadata of bacterial genomes used in this study. Improved High-Quality Dra ft (IHQD); High-Quality Draft (HQD). na = Not available or applicable. nd = Not determined. MDRO species E. hormaechei K. pneumoniae A. baumannii Commensal Confronting bacteria Controls EH KP AB L. reuteri LR LR:LR EH:LR KP:LR AB:LR S. epidermidis SE SE:SE EH:SE KP:SE AB:SE C. jeikeium CJ CJ:CJ EH:CJ KP:CJ AB:CJ E. hormaechei EH na EH:EH KP:EH AB:EH K. pneumoniae KP na na KP:KP AB:KP A. baumannii AB na na na AB:AB Table 2. Bacteria-Bacteria Confrontation Assays. na = Not available or applicable. harmless and may play a beneficial role through colonization and protection against invasion by pathogens. The resident skin microbes may also offer an additional line of defense through priming the host immune system to counteract possible attacks by pathogens. The skin microflora consists of four major phyla: Actinobacteria (e.g., Corynebacterium, Propionibacterium, Micrococcus), Firmicutes (e.g., Staphylococcus), Bacteroidetes, and Proteobacteria . We selected A. baumannii, K. pneumoniae, E. hormaechei isolates from the WRAIR MRSN as the MDRO path- ogen species, S. epidermidis and Corynebacterium jeikeium as the representative skin commensal residents, and L. reuteri as a probiotic representative, focusing on the responses of the MDROs to each other and the selected pathogen/commensal species. The pairwise co-culture assays among MDROs and the commensals or probiotics are summarized in Table 2. e t Th otal number of trimmed mapped reads from each organism per pairwise co-culture assay is summarized in Supplementary Table S7. A total of 388 and 321 A. baumannii genes (out of 3,860 genes in the genome) were up or down-regulated when co-cultured with K. pneumoniae and E. hormaechei, respectively. As many as 909 and 212 K. pneumoniae genes (out of 5,548 genes in the genome) were up or down-regulated when co-cultured with A. baumannii and E. hormaechei, respectively. Compared to the other two MDROs, E. hormaechei gene expres- sion was only moderately ae ff cted when co-cultured with A. baumannii or K. pneumoniae, with 78 and 15 up or down-regulated genes out of 4,617 genes in the genome, respectively. A summary of differentially regulated gene counts obtained from co-cultures are summarized in Table 3. S. epidermidis (a skin resident) and L. reuteri (a common probiotic) stimulated the least gene expres- sion responses when co-cultured with the MDROs. It is possible that the growth of commensal organisms was impaired by the MDROs in co-culture since growth inhibition has been observed previously in bacte- 22,23 rial confrontations . To determine if the MDROs were inhibiting the growth of the commensal organism, phenylethyl-alcohol and Hektoen Enteric selective agars were used to quantitate colony forming units of S. epi- dermidis and E. hormaechei, respectively during co-culture. No growth inhibition was observed (Supplementary Fig. S1), suggesting that slower growth patterns and low biomass at the end of the co-culture was the cause for the low amount of RNA-Seq reads, at least for the E. hormaechei-S. epidermidis confrontation. The same analysis could not be performed with the other commensal bacteria in this study because of their specific media require- ments and the fact that other MDROs, A. baumannii and K. pneumoniae, both possess alcohol-dehydrogenase genes, which would have allowed growth on the selective phenylethyl-alcohol agar. Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 3 www.nature.com/scientificreports/ Confronting Bacteria #Annotated genes MDRO species in genome Regulation A. baumannii K. pneumoniae E. hormaechei C. jeikeium S. epidermidis L. reuteri Up na 200 171 191 9 0 A. baumannii 3,860 Down na 188 150 233 3 1 Up 466 na 60 46 18 5 K. pneumoniae 5,548 Down 443 na 152 118 94 9 Up 71 13 na 0 4 0 E. hormaechei 4,617 Down 7 2 na 0 0 1 Table 3. Differentially expressed gene in the MDROs E. hormaechei, K. pneumoniae, and A. baumannii when confronted with individual pathogenic/commensal bacteria. Differentially expressed genes are defined as genes with an FDR <0.05 from edgeR analysis. Annotated genes included protein-coding, tRNA, tmRNA, and ncRNA. na = Not available or applicable. Genes responsive in co-culture ranked by fold-change. We also examined extreme up/down gene responses of MDROs under co-culture conditions by requiring at least 4-fold up/down expression with FDR below 5%. For A. baumannii, a total of 25 and 19 genes were strongly up/down regulated respectively when co-cultured with E. hormaechei (Supplementary Fig. S2). A major fraction of the same gene set was similarly regulated when A. baumannii was co-cultured with another MDRO K. pneumoniae. Amongst the most strongly upregulated genes, by 11 to 12-fold, were alcohol dehydrogenase (T634_RS01410), aldehyde dehydrogenase (T634_RS01395), and lipoyl synthase (T634_RS06340). Two MFS transporters (T634_RS14350, T634_RS14800) and an outer membrane porin of the OprD family (T634_RS14805) were strongly downregulated by 5 to 10-fold. As for K. pneumoniae gene responses, 24 and 14 genes were up/down regulated respectively when the co-culture was performed with A. baumannii (Supplementary Fig. S3). The strongest K. pneumoniae gene response was found in genes involved in iron acquisition, including TonB-dependent receptor (T643_RS07920; 45-fold), iron ABC transporter permease (T643_RS07935; 39-fold), hemin ABC transporter substrate-binding protein (T643_RS07930; 36-fold), and hemin-degrading factor (T643_RS07925; 34-fold). When the co-culture partner used was E. hormaechei instead, a different gene response was observed which involved a strong downreg- ulation of the enterobactin gene clusters required for siderophore synthesis (discussed below). Finally, a total of 25 E. hormaechei genes responded to co-culturing with A. baumannii or K. pneumoniae. Most genes were strongly upregulated when co-cultured with A. baumannii with the exception of a hypothetical protein (T636_RS15175) which was down-regulated (Supplementary Fig. S4). The most strongly upregulated E. hormaechei genes were involved in iron transport including ligand-gated channel protein (T636_RS04035), hemin uptake protein (T636_RS04030), hemin ABC transporter substrate-binding protein (T636_RS04045) by 21 to 32-fold when co-cultured with A. baumannii, followed by the enterobactin and aerobactin siderophore synthesis gene clusters (also discussed below). Regulation of AMR genes during co-culturing. It is known that AMR is critically important to the sur- vival strategy of the MDROs used in this study and that expression of AMR determinants has a fitness cost asso- ciated with it, but it is unknown to what extent AMR gene (ARG) expression is influenced by other co-cultured microbes. To determine effects of MDRO bacterial challenge on the expression of ARGs, we first identified ARGs using the Resistance Gene Identifier (RGI) in strict mode against the Comprehensive Antibiotic Resistance 51–53 Database (CARD) . RNA-Seq analysis showed significant differential expression of ARGs when K . pneumo- niae and A. baumannii were co-cultured, with patterns of up- and down-regulation observed within each species (Fig. 1a,b). In A. baumannii, both the TEM-1 and ADC-2 beta-lactamase variants were upregulated when chal- lenged with K. pneumoniae along with RND-family efflux systems adeABC and adeIJK. Conversely, in K . pneu- moniae, lipid A modifying enzymes responsible for antimicrobial peptide resistance (arnA, pmrE, and pmrF), multi-drug transporters acrAB, and quinolone resistance protein parC, were downregulated when challenged with A. baumannii. Exceptions of ARGs being up-regulated in K. pneumoniae were pmrC and mdtG. There were few significant ARG expression changes observed in MDROs when co-cultured with commensals. For E . hor- maechei, no significant dier ff entially expressed ARG was observed when challenged with any of the other bac- teria – MDROs or commensals. One notable mention is the down regulation of the beta-lactamase OXA-69 (an OXA-51 subfamily carbapenemase ) in A. baumannii when co-cultured with the skin commensal C. jeikeium (Fig. 1a). This finding would be of considerable interest to the healthcare industry if the co-culturing of a skin commensal could result in an increase in susceptibility of A. baumannii to carbapenems through the lowering of the intrinsic OXA-51-like gene expression. This finding could also inform treatment decisions – for example by avoiding the concomitant use of vancomycin which kills commensals such as coagulase negative staphylococci and corynebacterial species. When confronted with HDF cells, transcriptome analysis of E. hormaechei showed significant changes in ARG expression (Fig. 1c). Most notable, the antibiotic efflux complexes marABC and bpeEF were upregulated when challenged with HDF cells, while the macB periplasmic ATPase component of the macAB cassette and the KPC-3 beta-lactamase variant were downregulated. In K. pneumoniae, the aminoglycoside APH(3″)-Ib and beta-lactam resistance enzyme CTX-M-15 were downregulated in the presence of HDF. No significant differentially expressed ARGs were observed in A. baumannii when co-cultured with HDF cells. Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 4 www.nature.com/scientificreports/ Figure 1. Antimicrobial resistance gene expression. Differentially regulated AMR genes in A. baumannii (AB) (a), K. pneumoniae (KP) (b) during bacterial co-culture experiments, and E. hormaechei (EH) in response to co-cultured HDF cells (c) are shown. (CJ) denotes C. jeikeium. Differentially regulated genes shown with edgeR false discovery rate (FDR): <0.05 (red circle) and <0.1 (yellow circle). Log -fold change values of gene expression was shown as red (up-regulated) and blue (down-regulated) bar graphs with reference to monocultures. Bar scale from −4 to 4. Averaged gene expression level across control and treatment experiments (log read counts per million, log CPM) was shown as green bar graphs. Bar scale from 0 to 15. 2 2 Differential expression of siderophore gene clusters. Siderophores are molecules essential for iron acquisition and the survival of bacteria, and are especially important for pathogens to compete for limiting free iron in an eukaryotic host environment. To determine how siderophore gene clusters are regulated during bacte- rial co-culture, we identified the corresponding siderophore gene clusters including acinetobactin in A. bauman- nii, enterobactin in K. pneumoniae, and both enterobactin-like and aerobactin in E. hormaechei. Genome-wide RNA-Seq expression and annotated virulence gene sets are shown in Supplementary Tables S4–S6. Across the 6 MDRO pairwise analysis performed, siderophore gene cluster expression was upregulated in almost all cases (Fig. 2). The only exception observed was when K . pneumoniae was co-cultured with E. hormaechei (i.e. KP vs. EH, Fig. 2a) in which case the K. pneumoniae enterobactin cluster was strongly downregulated by 8-fold (log fold change of 3) in the presence of E. hormaechei. Interestingly, although both K. pneumoniae and E. hormae- chei encode enterobactin, only K. pneumoniae downregulated the transcription of enterobactin in the presence of E. hormaechei but not vice versa. Conceivably, K. pneumoniae may have evolved iron utilization feedback mechanisms to largely shut down production of enterobactin in favor of utilizing the enterobactin produced by Enterobacter. In contrast, the expression of the A. baumannii siderophore acinetobactin was consistently upregu- lated regardless whether the co-cultured pathogen was K. pneumoniae or E. hormaechei. When the MDROs were individually co-cultured with the common skin resident C. jeikeium, a mild down-regulation of the K. pneumoniae and A. baumannii siderophore gene clusters was also observed (Fig. 2). The C. jeikeium-mediated reduction of siderophore production in competing co-cultured species could be a defense mechanism of commensals to outcompete invading pathogens in the human host. Differential expression of cell attachment components. Pili and fimbriae systems are important for cell attachment and physical interactions; therefore, we also performed extensive annotation of the three selected MDRO genomes to identify genes involved in surface attachment, including type I and type IV pili and fimbriae proteins. RNA-Seq analysis showed that one of the three annotated csu chaperone gene clusters in A. baumannii (type I pili formation) was upregulated when co-cultured with K. pneumoniae by 3-fold, but distinctly down- regulated almost 8-fold only when co-cultured with C. jeikeium (Fig. 3). This csu operon has been previously suggested to be involved in the adherence and formation of biofilm of A . baumannii on abiotic surfaces, but not human respiratory cells . In addition, our transcriptome analysis also showed that two of the annotated type IV pili gene clusters in the A. baumannii strain were upregulated specifically when co-cultured with C . jeikeium. Type IV pili play a role in natural transformation, twitching motility, and biofilm formation . Our results showed that Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 5 www.nature.com/scientificreports/ Figure 2. Siderophore gene cluster expression in (a) K. pneumoniae (enterobactin), (b) A. baumannii (acinetobactin), and (c) E. hormaechei (enterobactin-like and aerobactin) when co-cultured with individual pathogen/commensal species. K. pneumoniae (KP), A. baumannii (AB), E. hormaechei (EH), L. reuteri (LR), S. epidermidis (SE) and C. jeikeium (CJ). Differentially regulated genes shown with edgeR false discovery rate (FDR): <0.05 (red circle) and <0.1 (yellow circle). Log fold change values of gene expression was shown as red (up-regulated) and blue (down-regulated) bar graphs with reference to monocultures. Bar scale from −4 to 4. Averaged gene expression level across control and treatment experiments (log read counts per million, log CPM) was shown as green bar graphs. Bar scale from 0 to 15. C. jeikeium modifies type I and type IV pili expression of A . baumannii, which may be a mechanism to interfere with biofilm formation and surface attachment of the competitor species, in this case A. baumannii. Confirmation of gene expression by qPCR. RNA-Seq gene expression results were validated by per- forming qPCR for a select group of differentially expressed genes. Genes were chosen for analysis based on the ability to design unique primers and their reported fold change values. Biological replicates were tested and qPCR was performed on cDNA from reverse transcription of total RNA. For K. pneumoniae, 2 downregulated (acnB, T643_RS19850; gabT_2, T643_RS11485) and 3 upregulated (aminotransferase, T643_RS18930; cimH, T643_RS19470; PTS system fructose IIA component, T643_RS18905) genes were tested. For A. baumannii, 3 downregulated (papD, T634_RS02055; mmsA_1, T634_RS14320; EamA-like transporter, T634_RS10710) and 4 upregulated (pilN, T634_RS16475; NAD family protein, T634_RS01410; Hpt domain protein, T634_RS11200; Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 6 www.nature.com/scientificreports/ Figure 3. Regulation of A. baumannii type I and type IV pili gene cluster expression when co-cultured with individual pathogen/commensal species. K. pneumoniae (KP), A. baumannii (AB), E. hormaechei (EH), L. reuteri (LR), S. epidermidis (SE) and C. jeikeium (CJ). Differentially regulated genes shown with edgeR false discovery rate (FDR): <0.05 (red circle) and <0.1 (yellow circle). Log fold change values of gene expression was shown as red (up-regulated) and blue (down-regulated) bar graphs with reference to monocultures. Bar scale from −4 to 4. Averaged gene expression level across control and treatment experiments (log read counts per million, log CPM) was shown as green bar graphs. Bar scale from 0 to 15. hypothetical protein, T634_RS08500) genes were tested. Gene expression was normalized to gyrB (T634_ RS06810, T643_RS05975) and relative expression ratios were determined by comparing co-culture samples with monoculture samples. Relative expression ratios were converted to log FC and compared to the log FC 2 2 from RNA-Seq data. All genes tested showed the same direction of log FC in both RNA-Seq and qPCR analysis (Supplementary Fig. S5), confirming the trends observed in the RNA-Seq analysis. Bacterial responses to human fibroblast co-culture assays. To assess host interactions, each of the three selected MDROs for this study was also individually co-cultured with adult human fibroblast cells (HDF). Our transcriptome analysis using RNA-Seq showed that two gene clusters in K. pneumoniae were specif- ically down-regulated when co-cultured with HDF cells. The down-regulated K . pneumoniae gene clusters were involved in fimbriae production and capsule synthesis and showed a strong reduction of up to 3.8-fold (log fold change of −1.92) in both gene loci (Fig. 4). Down-regulation of bacterial surface antigen is likely a fitness strategy to cope with the constantly changing host environment. Evrard et al. showed that the capsule-deficient mutant of a K . pneumoniae clinical isolate could be more efficiently internalized by dendritic cells than its wild-type counterpart. It was suggested that the presence of a thick capsule at the surface of K. pneumoniae may interfere with binding and internalization by host cells. Similarly, in uropathogenic E. coli, King et al. reported that the capsule gene expression are spatially and temporally regulated during different phases of infection such that the unencapsulated cells in the population can preferentially adhere to and invade bladder epithelial cells, and only produce capsule once internalized for intracellular survival and spread. Our results further support these previous studies that expression of bacterial surface antigens are possibly regulated in response to interactions with host cells to evade host immune responses. Host responses to MDRO co-culture. With the same set of co-cultured assays in which MDROs were individually co-cultured with mammalian human fibroblast cells, we also measured human host gene responses. Analysis of HDF-derived human RNA-Seq reads revealed a set of 17 human genes that were significantly up-regulated across all three MDRO-fibroblast co-culture assays (Fig.  5). The up-regulated gene set included chemokines, cytokines, and transcription factors that were up-regulated from 2.36 (log fold change of 1.24) to 294-fold (log fold change of 8.2) across the three MDROs. Pathway and GO term enrichment analyses of the gene 59,60 set were performed using DAVID Bioinformatics Resources (https://david.ncifcrf.gov) . The top 5 enriched KEGG pathways and biological process GO terms were shown in Supplementary Table S8. The gene set was enriched in inflammatory and immune response functions. A total of 11 out of the 17 genes could be mapped to 61–63 the Tumor necrosis factor (TNF) signaling pathway in KEGG (Supplementary Fig. S6) . TNF is involved in the regulation of apoptosis, cell survival, and inflammation and immunity. Conclusion In summary, we performed pairwise co-culture experiments of three selected high-priority military MDROs individually with each other, commensal and probiotic microbes, and cultured human cells. We monitored tran- scriptome responses of both the target and co-cultured pathogen/commensal species using RNA-Seq. Our goal was to delineate gene responses associated with virulence, survival mechanisms, and AMR in the MDROs with partners likely found in combat wound infections and potential probiotic scenarios rather than monoculture conditions. Using this co-culturing approach, we observed differentially regulated gene responses associated with sidero- phore production, pili formation, and cell attachment. Our results suggested that a mixed microbial population may modulate the virulence and colonization of host cells. Future experiments are needed to determine the bio- logical signals involved in the regulation of these important survival and virulence mechanisms of MDROs for effective drug targeting. Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 7 www.nature.com/scientificreports/ Figure 4. Expression of K. pneumoniae (a) attachment-related and (b) capsule-related gene clusters in response to co-cultured HDF cells. Differentially regulated genes shown with edgeR false discovery rate (FDR): <0.05 (red circle) and <0.1 (yellow circle). Log fold change values of gene expression was shown as red (up-regulated) and blue (down-regulated) bar graphs with reference to monocultures. Bar scale from −4 to 4. Averaged gene expression level across control and treatment experiments (log read counts per million, log CPM) was shown 2 2 as green bar graphs. Bar scale from 0 to 15. Figure 5. Human host genes were upregulated when HDF cells were co-cultured with MDROs. Differentially regulated genes shown with edgeR false discovery rate (FDR): <0.05 (red circle). Log fold change values of human gene expression was shown as red (up-regulated) bar graphs with reference to monocultures of HDF cells. Bar scale from 0 to 9. Averaged gene expression level across control and treatment experiments (log read counts per million, log CPM) was shown as green bar graphs. Bar scale from 0 to 15. Our approach leveraged bacteria-bacteria and bacteria-host in vitro co-culture to observe die ff rential tran- scriptional regulation using RNA-Seq analysis. This study highlights the diverse stimuli an organism may encoun- ter in die ff rent environmental niches, and the complex gene regulation that takes place to achieve optimal survival and fitness. This type of analysis will be critical to understanding the interactions among the microbial pathogens, the host microbiome and the host environment, particularly in disease conditions. Our results provide founda- tional data and insight into the possibility of manipulating the local microbiome or avoiding ‘collateral-damage’ by minimizing exposure to overly broad-spectrum antibiotics or vancomycin, which kills skin commensals, when treating complicated polymicrobial wound, intra-abdominal, or respiratory infections. Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 8 www.nature.com/scientificreports/ Methods Genome Sequencing. The genomes of the MDRO isolates were sequenced at JCVI by Illumina HiSeq (2 × 100 bp). Briefly, paired-end libraries were constructed for each sequencing technology from randomly nebu- lized genomic DNA in the 300–800 bp size range following manufacturer recommendations. Sequence reads were generated with a target average read depth of ~60-fold coverage. Draft Genome Assembly. To integrate the JCVI Illumina HiSeq data with data generated through various sequencing platforms and OpGen optical restriction maps generated by WRAIR, an Automated Gap Closure pipeline that combined de novo assembly followed by reference-guided gap closure to resolve short and uncom- plicated gaps was implemented as described previously . Genome annotation. Contigs were annotated for protein- and RNA-encoding features using the JCVI auto- mated annotation pipeline essentially as described previously . Genes conferring drug resistance were identified using the RGI (Resistance Gene Identifier, Version 2) tool in strict mode against the Comprehensive Antibiotic 51–53 Resistance Database (CARD) . Bacterial strains, growth conditions and bacterial co-culture experiments. MDROs A. baumannii MRSN 7339, K. pneumoniae MRSN 1319, and E. hormaechei MRSN 11489 were selected by WRAIR MRSN based on having the highest degree of clinical importance as described earlier in the results section and identified by standard automated biochemical analysis as described previously . The selection criteria and antibiotic resistance prole o fi f the three MDROs against 20 antibiotics were shown in Supplementary Tables S1 and S2, respectively. WRAIR MRSN pro- vided purified genomic DNA and culture stocks of these MDROs. The Human Research Protection Office (HRPO) of the U.S. Army Medical Research and Material Command determined that this study was research not involving human subjects, as it did not “involve living individuals about whom an investigator conducting research obtains (1) data through intervention or interaction with the individual, or (2) identifiable private information, in accordance with 32 CFR 219.102(f ).” Likewise, the JCVI IRB determined that the use of MDROs from MRSN was exempt from IRB review because the material provided was pre-existing and de-identified. e co Th mmensal bacteria Lactobacillus reuteri SD2112 (ATCC 55730), Staphylococcus epidermidis SK135 (BEI HM-118), and C. jeikeium NCTC 11913 (ATCC 43734) were obtained from ATCC/BEI Resources, and therefore exempt from IRB review at JCVI since they are publicly available and de-identified from any human source. A . baumannii, K. pneumoniae, E. hormaechei, and S. epidermidis were grown in BHI broth (BD) to a concentration of 1 × 10 CFU/ml in log phase growth. C. jeikeium and L. reuteri were grown to the same target concentrations in BHI supplemented with 1% (vol/vol) Tween-80 (Fisher) and MRS broth (BD), respectively. For co-culture assays, equal amounts of cells from the cultures were combined and shaken at 37 °C for 5 min at 200 RPM. 1 × 10 CFU of each co-culture were plated onto a BHI agar plate in triplicates. Plates were incubated at 37 °C for 6 hours, at which point approximately 1 × 10 CFU from each triplicate plate were combined and treated with RNAprotect bacteria reagent (Qiagen) according to the manufacturer’s instructions. Bacterial cells were spun down and the RNAprotect bacterial reagent removed aer t ft reatment. Cell pellets were stored at − 80 °C until RNA extraction. Growth inhibition of S. epidermidis by E. hormaechei was analyzed by quantifying colony forming units (CFUs) 65,66 on phenylethyl alcohol and Hektoen Enteric selective agar media . The same experiment was performed using independent cultures on different days for biological replicates to control for reproducibility. Human dermal fibroblast growth conditions and co-culture experiments. Adult human dermal fibroblast (HDF) cells (Cat. no. C-013-5C) were obtained from Invitrogen and their experimental use in this study was found to be exempt from IRB review by the JCVI IRB because these cells are publicly available and de-identified. HDF cultures were set up to test multiplicity of infection (MOI) and incubation times for individual MDROs. The MOIs for all three MDROs with HDF cells was determined to be optimal at 100:1 (MDRO:HDF). e Th optimal duration of a bacterial-HDF co-culture assay was determined to be one hour. Prolonged co-culture times resulted in acidification of the cell culture media, which was avoided to eliminate interference with gene expression due to pH changes. Multiple control experiments to determine retention of bacteria to HDF cells were performed and bacteria were observed via light microscopy to be adherent to the HDF cells, even aer 3 r ft inses with PBS (data not shown). Cells (including MDRO bacteria and HDF cells) were isolated for RNA extraction from the co-culture assays aer r ft emov- ing excess culture media so only adherent/close proximity bacterial cells were harvested. Cells were then treated with RNAprotect Cell Reagent (Qiagen) to immediately stabilize RNA transcripts before further processing. RNA isolation. RNA was extracted using the RNeasy Mini Kit (Qiagen). The manufacturer’s protocol was followed with additional steps as described below. Enzymatic lysis was performed on cell pellets resuspended in TE with 15 mg/ml lysozyme (Fisher) and 3 mg/ml Proteinase K (NEB) and incubated for 10 min at room temper- ature with vortexing for 10 s every 2 min. Aer enzy ft matic lysis, mechanical lysis was performed by adding buffer RLT with 1% vol/vol 2-mercaptoethanol and transferring the lysate to a Lysis Matrix B bead beating tube (MP Bio). Samples were homogenized for 45 s at 6.5 m/s on a FastPrep120 Cell Disrupter system. On-column DNAse treatment (Qiagen) was used according to the manufacturer’s protocol. Isolated RNA was treated with TURBO DNase (Ambion) following the manufacturer’s protocol. RNA-Seq library preparation for Illumina sequencing. Enrichment for mRNA was done using the RiboZero rRNA Removal Kit for bacteria (Epicentre). Libraries were constructed from 10 ng of mRNA using the NEBNext Ultra Directional RNA Library Prep Kit (NEB) following the manufacturer’s protocol. Libraries were normalized and pooled for sequencing using qPCR (Kapa Biosystems Library Quantification Kit). The resulting pooled libraries were sequenced on the Illumina HiSeq platform (2 × 100 bp). Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 9 www.nature.com/scientificreports/ RNA-Seq data analysis. Illumina sequencing reads in FASTQ format were analyzed using CLC Genomics Workbench version 6.5 (http://www.clcbio.com) aer r ft emoving low quality reads (CLC quality score limit = 0.05, maximum of 2 ambiguities), reads less than 50 bp, and adaptors. To remove rRNA reads, a set of known 4,692 distinct rRNA sequences were collected from the SILVA database (http://www.arb-silva.de/) for all five spe - cies of bacteria used in the co-culture assays. All reads mapped to the known rRNA sequences were discarded. BWA was used for the mapping allowing up to three mismatches. Reference files for RNA-Seq analysis were prepared from Genbank files imported into CLC. For each pair of control and co-culture experiment, the expected genomes were selected and reads were mapped using the following settings: maximum number of mis- matches = 2, minimum length fraction = 0.8, minimum similarity fraction = 0.98, maximum number of hits for a read = 10, minimum and maximum distances for paired reads = 1,1000 and counting scheme of “include bro- ken pairs”. All reads mapped to each gene were used as raw read count to determine up or down-regulated genes using the Bioconductor package edgeR . Genes with at least 1 CPM in at least 2 samples were included for the differential expression analysis. Differentially expressed genes were determined using an exact test implemented for negative-binomially distributed counts with estimated genewise dispersion values using edgeR. e Th RNA-Seq gene expression for the three MDROs A. baumannii, K. pneumoniae, and E. hormaechei, together with curated virulence gene annotation and AMR annotation are shown in Supplementary Tables S4–S6. Reverse Transcription and qRT-PCR. Reverse transcription (RT) was performed to generate cDNA from two biological replicates to be used in qRT-PCR for the validation of RNA-Seq data. RNA concentrations were determined with an Agilent 2100 Bioanalyzer and 1.5 μg of total RNA was used in each reaction. RNA was incu- bated with 6 μg of random hexamers (Invitrogen) and 40 U of RNaseOUT (Invitrogen) in 18.5 μl at 70 °C for 10 min, followed by snap cooling on ice. The reaction was set up with 1 × First Strand Buffer, 10 mM DTT, 0.5 mM dNTPs (Invitrogen), and 400 U SuperScript III Reverse Transcriptase (Invitrogen) in 30 μl. The reaction was incubated in a 42 °C water bath for 16 h. The reaction was stopped and RNA was hydrolyzed with 0.1 M EDTA and 0.2 M NaOH at 65 °C for 12 min. Tris-HCl (pH 7.0) was added to neutralize the pH. Purification of cDNA was achieved by using QIAquick PCR Purification kit (Qiagen) following the manufacturer’s protocol and quantitated by fluorometric assays using SYBR Gold (Invitrogen). qRT-PCRs were performed with 10 ng of template cDNA in 10 μl triplicate reactions with SYBR Green Master Mix (Roche) in 384-well plates on the LightCycler480 qPCR instrument (Roche). Cycling conditions were 95 °C for 10 min followed by 50 cycles of 95 °C for 20 s, 58 °C for 30 s, and 72 °C for 15 s. A melting curve analysis was performed aer a ft ll cycles were completed to verify the Tm of amplified products. No-RT controls and qPCR con- trol reactions were performed as well. To determine the C value (crossing point-PCR-cycle or threshold cycle) for each reaction, the ‘Fit Point Method’ was performed in the LightCycler480 sowa ft re 1.5.0 (Roche Diagnostics). Efficiency for each primer was optimized by adjusting primer concentration and was determined for the reac- tion conditions. Efficiency and C values were used to calculate the gene expression with normalization to gyrB. Relative expression ratios were determined by comparing co-culture samples with monoculture samples using the method described previously . Primer sequences and concentrations used are provided in Supplementary Table S9. Data availability. Genome assembly and annotation for the three selected MDRO isolates are available under NCBI bioprojects: PRJNA223624, PRJNA223615 and PRJNA223617. RNA-Seq datasets for the co-culture assays are available at the NCBI SRA under bioprojects: PRJNA292770, PRJNA292776, PRJNA292777, PRJNA294773, PRJNA294780, PRJNA294781 and PRJNA314266. References 1. Magiorakos, A.-P. et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin. Microbiol. Infect. 18, 268–281 (2012). 2. Boucher, H. W. et al. Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. Clin. Infect. Dis. 48, 1–12 (2009). 3. Antibiotic Resistance Threats in the United States, 2013 | Antibiotic/Antimicrobial Resistance | CDC. Available at: https://www.cdc. gov/drugresistance/threat-report-2013/index.html. 4. WHO | Antimicrobial resistance: global report on surveillance 2014 (2016). 5. Spellberg, B. & Bonomo, R. A. 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Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009). 68. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for dier ff ential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). 69. Pfa, M. W ffl . A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29, e45 (2001). Acknowledgements The opinions and assertions herein are solely those of the authors and are not to be construed as official or representing those of the US Army or Department of Defense. This project was funded in whole or part with federal funds from the Department of Defense, Defense Medical Research and Development Program - Military Infectious Diseases Basic Research Award number W81XWH-12-2-0106 to D.E.F. and A.P.C. and from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services under award number U19AI110819. Author Contributions D.E.F. and A.P.C. conceived and designed the study, E.P.L. and M.K.H. selected and provided the military MDRO isolates, M.K. performed genome assembly, J.D. and R.K. conducted wet lab experiments, Y.C., A.P.C., L.M.B. and D.E.F. analyzed the results. A.P.C., Y.C., R.K., J.D., L.M.B., E.B.L. and D.E.F. wrote the manuscript. All authors reviewed the manuscript. Additional Information Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-26738-1. Competing Interests: The authors declare no competing interests. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2018 Scientific RePo R ts | (2018) 8:8656 | DOI:10.1038/s41598-018-26738-1 12

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