Haemaphysalis flava has a broad distribution that extends from East to South Asia. This tick is a reservoir of severe fever with thrombocytopenia syndrome virus (SFTSV) that causes severe hemorrhagic disease, with cases reported from China, Japan and South Korea. Recently, the distribution of H. flava in South Korea was found to overlap with the occurrence of SFTSV.Ticks are ectoparasites capable of directly damaging their hosts and transmitting vector-borne diseases. The ixodid tick H. flava female ticks using the Illumina HiSeq 4000 system, the Trinity de novo sequence assembler and annotation against public databases. The locally curated Protostome database (PANM-DB) was used to screen the putative adaptation-related transcripts classified to gene families, such as angiotensin-converting enzyme, aquaporin, adenylate cyclase, AMP-activated protein kinase, glutamate receptors, heat shock proteins, molecular chaperones, insulin receptor, mitogen-activated protein kinase and solute carrier family proteins. Also, the repeats and simple sequence repeats (SSRs) were screened from the unigenes using RepeatMasker (v4.0.6) and MISA (v1.0) software tools, followed by the designing of SSRs flanking primers using BatchPrimer 3 (v1.0) software.This study was undertaken to discover the molecular resources of H. flava and the related tick species.The transcriptome produced a total of 69,822 unigenes, of which 46,175 annotated to the homologous proteins in the PANM-DB. The unigenes were also mapped to the EuKaryotic Orthologous Groups (KOG), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) specializations. Promiscuous presence of protein kinase, zinc finger (C2H2-type), reverse transcriptase, and RNA recognition motif domains was observed in the unigenes. A total of 3480 SSRs were screened, of which 1907 and 1274 were found as tri- and dinucleotide repeats, respectively. A list of primer sequences flanking the SSR motifs was detailed for validation of polymorphism in H. flava female ticks will be useful for an enriched understanding of tick biology, its competency to act as a vector and the study of species diversity related to disease transmission.The reference transcriptome information on The online version contains supplementary material available at 10.1186/s13071-023-05923-w. Haemaphysalis flava belongs to the family (Ixodidae) of hard ticks. The ticks of this family are considered to be a public health concern due to their essential role in transmitting tick-borne pathogens ) consisted of a cylindrical dry ice container (10 × 30 cm [diameter × height]) containing approximately 2.5 kg ice for luring the ticks into the trap overnight. The ticks were found either on the surface of the trap or inside of it and collected using forceps into the tick collection tube (patent no. 10-0925882). Adult male and female ticks were separated and subsequently identified based on morphological keys using an optical microscope. Based on Yamaguti et al. [The whole body of ticks was ground with a pestle homogenizer using TRIzol® Reagent  in dry ice for total RNA extraction. Residual DNA (if any) was removed using RNase-free DNase I  and incubation at 37 °C for 30 min. The quality of the extracted RNA was assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) and Agilent Bioanalyzer 2100 system . Pooled RNA samples were prepared for constructing the complementary DNA (cDNA) library using the Illumina Stranded mRNA Prep Kit . Briefly, 2 µg of messenger RNA (mRNA) was fragmented randomly in fragmentation buffer using an RNA fragmentation kit  to obtain short mRNA fragments of 200 nucleotides. These short mRNA fragments were reverse-transcribed to second-strand cDNA using random primers, reverse transcriptase, RNase H and DNA polymerase I. After purification, terminal repair, A-tailing, ligation of sequencing adapters , size selection (200 ± 25 bp) and PCR amplification, the cDNA library was sequenced on the Illumina HiSeq 4000 platform to generate 125-bp paired-end (PE) reads. The sequencing was completed at GnC Bio-Company, Daejeon, Korea on 4 January 2019.http://code.google.com/p/cutadapt/). Subsequently, the reads were filtered for low-quality sequences using the Sickle program v1.33 (available at https://github.com/najoshi/sickle) [http://transdecoder.github.io). Clustering of the transcripts to unigenes (sequences that could not be extended on either ends) was performed using the TIGR Gene Indices Clustering Tool (TGICL) package [Raw data from Illumina sequencing (in FASTQ format) were filtered for adapter-only sequences using the Cutadapt program v4.2 (source code downloaded at /sickle) . The cle/sickle) , 38. The package .–5) against the locally curated Protostome database (PANM-DB) v3 [–5). For a comprehensive functional annotation of unigenes, we used KOG (EuKaryotic Orthologous Groups of proteins) at https://www.ncbi.nlm.nih.gov/COG/ [H. flava transcriptome.Functions of the assembled unigenes were annotated by performing Blastx  v3  and Swisgov/COG/ , GO (Gengov/COG/ . The PANhttp://www.girinst.org) was used to screen repeat elements from all unigenes of the H. flava transcriptome and categorize them under ‘short interspersed nuclear elements (SINEs),’ ‘long interspersed nuclear elements (LINEs),’ long terminal repeat elements (LTRs), and other ‘DNA elements.’ The PerlScript program MISA v1.0 was used for the detection of SSRs in the unigenes. Further, we designed a set of primers flanking the target SSR regions screened from the unigenes of SSRs in the H. flava transcriptome using the BatchPrimer 3 v1.0 program [RepeatMasker v4.0.6  and raw read sequences of 12,280,070,470 bases. A total of 93.45% bases with a mean length of 282.2 bp were retained after filtering the adapter-only sequences. In the next step the low-quality and ambiguous sequences were filtered, resulting in 79,329,970 sequences , which were designated as clean reads. Hence, a total of 97.55% raw read sequences (87.61% bases) were pre-processed as clean reads with a mean length, N50 length  and percent guanine-cytosine content (GC%) of 136 bp, 126 bp and 53.65%, respectively  with a mean length of 442 bp and an N50 length of 470 bp. Overall 26.57% and 3.94% of the contig sequences were ≥ 500 bp and ≥ 1000 bp, respectively, with the largest contig size being 8332 bp. The contigs were screened for candidate coding regions, and such transcript sequences constituted 27.24% of the contigs. Approximately 43.10% and 9.67% of the sequences were of lengths ≥ 500 bp and ≥ 1000 bp, respectively, with the retention of the largest sequence of 8332 bp. The clustered unigenes (sequences that cannot be extended at either end) constituted approximately 6.45% of the assembled contig sequences, with a mean length, N50 length and GC% of 792 bp, 947 bp and 53.09%, respectively. The statistical summary of the processing of clean reads is shown in Table H. flava female transcriptome assembly were functionally annotated by searching for homologs in the public databases, including the locally curated PANM-DB. The annotation statistics are shown in Table Amblyomma aureolatum, Rhipicephalus appendiculatus, Rhipicephalus pulchellus and I. ricinus  were assigned to GO terms. Significantly, 7245 sequences were classified under a single GO term, whereas 4720, 3623 and 2153 sequences were categorized to two, three and four GO terms, respectively Fig. . The maxH. flava unigenes based on the Blastx-based homology matching to the PANM-DB. These included the angiotensin-converting enzyme, aquaporins, adenylate cyclases, AMP-activated protein kinase, glutamate receptors, heat shock proteins , molecular chaperones (grp170/HSP70 superfamily), insulin receptor, mitogen-activated protein kinase, phospholipase and solute carrier family  and 67 elements (3979 bp), respectively.The repeat elements screened from all unigenes of the H. flava transcriptome. These SSRs were found in 2166 unigenes, with 826 unigenes containing > 1 SSR. Table A total of 3480 SSRs were screened from all unigenes of the Haemaphysalis spp., respectively, has possibly changed due to climate crisis, as suggested by empirical and theoretical studies [H. longicornis are prevalent in South Korea, and implicated as a major driver of SFTSV infection [H. flava  has been found to be distributed in all the study sites of South Korea, increasing the risks of SFTSV and TBEV infections [H. flava species collected from South Korea have not been sequenced to explore the resources related to vector competence and its distributional shifts. In the present study, we constructed the reference transcriptome of H. flava female ticks distributed in South Korea with de novo assembly of putative transcripts.Hard ticks (Acari: Ixodidae) are the most important vectors of human pathogens worldwide. They are also the focus of significant public health concern as they are competent vectors of agents causing diseases in humans, domesticated animals and wildlife. The distributional range of Ixodid hard ticks and  studies . The cli studies . This ha studies , 20. Ixonfection . Howeverfections . MoreoveH. flava female ticks in relation to adaptation. The PANM-DB has been consistently used to retrieve valuable transcripts related to adaptation in protostome species, including butterflies, moths, gastropods and other molluscs [H. flava transcriptome showed a match of 48.6% to the near-reported species, with Ixodes scapularis the most represented species [H. flava, 51.93% of unigenes were annotated to the databases, with maximum match to I. scapularis [H. flava retrieved 54,357 unigenes, of which 37.06% were matched to homologs in the Swiss-Prot database. The top-hit species in the annotation included R. pulchellus, I. scapularis and Amblyomma maculatum [Amblyomma spp., Rhipicephalus spp. and Ixodes spp. were largely represented in the present study’s PANM-DB database annotation. Many protein sequences are available for species under the genus name in NCBI, which relates to greater annotation hits. Further, the H. longicornis genome with 24,189 protein-coding genes have been made available [H. flava sequence queries. Moreover, the whole-genome characterization of ticks has been relatively slower due to a number of difficulties, such as the existence of abundant repeating sequences in the genome and contamination of host and pathogen transcripts [Annotation results showed that 68.32% of the transcripts had a homologous match to available sequences in public databases. For example, 66.13% of unigenes in our study had a match to homologous proteins in the PANM-DB, a local repository of Protostomes . The PANM-DB v3 was updated on 20 March 2019 with 11,612,243 protein sequences extracted from the NCBInr (protein) database and arranged in a multi-FASTA format . The annmolluscs –51. The  species . In the apularis . De novoaculatum . About 4vailable . This avnscripts .H. flava by annotating these against the KOG, GO and KEGG databases. KEGG analysis detected the presence of promiscuous carbohydrate, amino acid, lipid and energy metabolism pathways that represented the basic metabolic processes. The signal transduction pathway was the most prominent pathway among the non-metabolic processes. Among the KOG classifiers, the signal transduction mechanism category was promiscuous, with a high proportion of unigenes. Many signaling modules are channelized by membrane receptors, with intracellular kinases modulating the signals to activate gene transcription, thereby controlling adaptive processes. Hence, the over-representation of such an environmental information processing pathway module could be crucial to understanding the tick response to climate-sensitive variables. The differential expression of genes obtained for partially and fully engorged H. flava ovaries showed a similar representation of KEGG pathway enrichment analysis [H. flava salivary gland transcriptome data [Borrelia burgdorferi on the gut epithelium, which is essentially related to the transmission of the spirochete to the vertebrate host [I. scapularis genome data available through public databases [H. flava has significantly complemented transcriptome studies [H. flava was sufficient to screen the genes encoding secreted proteins that mediate hematophagy and blood ingestion [H. flava  has identified tick proteins classified to categories that include protease inhibitors, immunity-related proteins and an abundance of uncharacterized proteins predicted to regulate immune functions and anti-coagulation processes [The functional directions were ascribed to the de novo assembled unigenes of analysis . The GO ome data . Furtherate host , 55. Conatabases . For exaatabases , 58. Furatabases , 60. Pro studies , 61. Thengestion . Includengestion , 63. Furngestion . Analysirocesses . Similarrocesses .H. flava tick also provided rich insights into the adaptation-related transcripts screened from the PANM-DB-based annotations. Notably, HSPs and molecular chaperones were enriched in the transcriptome, supporting the involvement of HSPs in the physiological activities of ticks, especially blood-feeding [Dermacentor silvarum, HSP70—and not HSP90—was required for adaptation to cold stress, leading to an understanding of the acclimatization of overwintering ticks [Anaplasma phagocytophilum infection and questing behavior in I. scapularis [I. ricinus, I. scapularis and the triatomine bug Rhodnius prolixus, as is screened from this study. This receptor could be crucial for understanding the insulin signaling pathway involved in tick feeding, development and reproduction [H. flava transcriptome. Aquaporins in ticks (aquaporin 1 and aquaporin 2) are required during blood-feeding as they facilitate the excretion of excess water back into the host through the salivary glands, and a high-potential aquaporin 1 has been used as a candidate for anti-tick vaccine development [H. flava eggs for targeted interventions during embryogenesis. Most of these proteins are enzymes , proteinase inhibitors (including serpins), vitellogenins, immunity-related proteins (such as neutrophil elastase inhibitor) and HSPs (HSP70 and HSP83) [The whole transcriptome assembly of the female -feeding , 67. How-feeding . In the ng ticks . Furtheroduction . The infelopment , 73. Othd HSP83) .H. longicornis genome shows a high proportion of SSRs (54.72%), which support a genetic understanding of the species [H. flava, represented mainly by mononucleotide repeats . Further, these SSRs are long, with 10–12 iterations accounting for 24.21% and 20.75% of the total SSRs, respectively [H. flava, constituting 62.83% of SSRs [H. flava. We have therefore designed PCR primers flanking the highly iterated SSRs that could be utilized for such studies. Some of the earlier studies on the H. flava transcriptome did not present the SSR primers as a means to understand the polymorphic resources [I. ricinus, a total of 3,866 SNPs were utilized to calculate the heterozygosity of wild and laboratory tick populations [The discovery of polymorphic SSRs from the coding transcripts, realized by transcriptome sequencing, has revolutionized genetic diversity studies as these SSRs are considered to be more transferable than random genomic SSRs , 75. Esp species . SSRs haectively . Mononuc of SSRs . In the esources , 28. Theulations . We assuH. flava adult females collected in South Korea. The de novo assembled transcripts were annotated for functional directions, and many candidate transcripts associated with the adaptive physiology of the tick were scrutinized. The transcripts are mostly categorized into blood digestion, feeding and signaling cascades pertaining to host–pathogen interactions. Further, SSRs were screened on the functional transcripts, and primers were predicted in the SSR flanking regions to understand the ecology of the species. A few of the components discovered using this pipeline could be addressed as candidates for tick control.This is the first report on the transcriptome sequences of Additional file 1: Table S1. Summary of the pre-processing steps of H. flava transcriptome. Table S2. The adaptation-related candidate transcripts screened from H. flava unigenes based on PANM database annotations. Table S3. Primer sequences to validate polymorphic SSRs in the assembled unigenes of H. flava.


Pseudomonas aeruginosa and Staphylococcus aureus. When co-cultured, virulence factors secreted by P. aeruginosa reduce metabolism and growth in S. aureus. When grown in vitro, this allows P. aeruginosa to drive S. aureus toward extinction. However, when found in vivo, both species can co-exist. Previous work has noted that this may be due to altered gene expression or mutations. However, little is known about how the growth environment could influence the co-existence of both species. Using a combination of mathematical modeling and experimentation, we show that changes to bacterial growth and metabolism caused by differences in the growth environment can determine the final population composition. We found that changing the carbon source in growth media affects the ratio of ATP to growth rate for both species, a metric we call absolute growth. We found that as a growth environment increases the absolute growth for one species, that species will increasingly dominate the co-culture. This is due to interactions between growth, metabolism, and metabolism-altering virulence factors produced by P. aeruginosa. Finally, we show that the relationship between absolute growth and the final population composition can be perturbed by altering the spatial structure in the community. Our results demonstrate that differences in growth environment can account for conflicting observations regarding the co-existence of these bacterial species in the literature, provides support for the intermediate disturbance hypothesis, and may offer a novel mechanism to manipulate polymicrobial populations.Most bacteria exist and interact within polymicrobial communities. These interactions produce unique compounds, increase virulence and augment antibiotic resistance. One community associated with negative healthcare outcomes consists of  Staphylococcus aureus and Pseudomonas aeruginosa are so difficult to cure they may persist for years in humans and cause serious illness. But when these two types of bacteria are grown together in the laboratory, P. aeruginosa kills off all the S. aureus.Infections caused by multiple types of bacteria are tough to treat. For example, co-infections with Learning why these two types of bacteria can coexist in people but not in the laboratory may lead to new treatments to clear infections. It may also help scientists grow beneficial bacteria mixes that break down pollution or produce biofuels.S. aureus and P. aeruginosa coexist. In the experiments, they grew both types of bacteria in different environments with different food sources. They measured their growth and metabolism and how many bacteria of each species survived over time. Then, they used their data to develop a mathematical model and tested its predictions in the laboratory again. The type of bacteria that had more energy also grew faster and outcompeted the other species. Measuring the growth rate of the two species allowed the scientists to predict which one would win out and what the tipping point would be. Physically disrupting the mix of bacteria disrupted this relationship.Pajon and Fortoul et al. show that interactions between bacterial metabolism and growth rate determine whether P. aeruginosa and S. aureus that work by manipulating growth in the two species.These results may help explain what allows these bacteria to coexist in some settings but not others. It may enable scientists to develop new ways to treat infections with Bacterial growth and metabolism are known to drive antibacterial resistance. Studies in mice using drugs or other therapies to manipulate growth and metabolism may help scientists thwart these resistance mechanisms. The results may also help scientists design and grow beneficial multispecies bacteria communities. Microbes rarely exist in isolation. It is more common to find them as part of diverse polymicrobial communities comprised of multiple microbial species e.g. . InteracPseudomonas aeruginosa and Staphylococcus aureus. These bacteria co-colonize burns  to growth rate (/hr). Examining the combined influence of both growth and metabolism is important as together they may impact the co-existence of both bacteria. For example, consider a carbon source that provides high metabolism and slow growth to ariables . We founruginosa . ConversP. aeruginosa to S. aureus. Ratios >1 indicate that P. aeruginosa has a greater absolute growth, whereas ratios <1 indicate that S. aureus has a greater absolute growth. We observed a range of absolute growth ratios , P. aeruginosa  or neither . We initiated a co-culture of both bacterial species with equal initial densities and grew them for 24 hours without shaking at 37 °C. Final bacterial density in colony forming units CFU/mL was measured using selective plating; cetrimide plates were used to select for P. aeruginosa and mannitol salt agar plates were used to select for S. aureus. We then determined the final density ratio of P. aeruginosa to S. aureus; values greater than one would indicate dominance of P. aeruginosa in the growth environment. Conversely, values less than one would indicate dominance of S. aureus in the growth environment. We observed that differences in the absolute growth ratio determined the final population composition; as the ratio of absolute growth increased, thus increasingly favoring P. aeruginosa, the final ratio of P. aeruginosa to S. aureus significantly increased , relationship between the final density ratio of P. aeruginosa to S. aureus and the ratio of [ATP] , increasing the absolute growth ratio increased the dominance of P. aeruginosa in the community , pyoverdine and pyochelin (ΔpvdA/ΔpchE), and all three virulence factors (ΔpqsL/ΔpvdA/ΔpchE). When knockout strains lacking pqsL were co-cultured with S. aureus in the presence of glucose as the carbon source, there was a significant decrease  in the final density ratio as compared to co-culture with wildtype P. aeruginosa  by P. aeruginosa that reduce growth and metabolism, and the growth and death of both species independently  in the system; higher concentrations of virulence factors reduce the value of ε. This serves to reduce the entire growth term, thus reducing the rate at which S. aureus grows. Consistent with our experimental definition, absolute growth for each species can be determined by calculating ε/μ. Our model predicts that, over a wide range of value of μ and ε, increasing the ratio of absolute growth leads to an increase in the density of P. aeruginosa relative to S. aureus . Unlike other minimal media, such as MOPS or M9, AMM allows the growth of S. aureus  and there were significant differences amongst the final density ratios .To determine if the relationship between the absolute growth ratio and the final density ratio held true in additional growth medium, we co-cultured . aureus . For con. aureus  and ATP . aureus , only so. aureus . Importa 2=0.92, . This fiP. aeruginosa and S. aureus in synthetic cystic fibrosis medium (SCFM), which was developed to mimic the composition of sputum isolated from individuals with cystic fibrosis . We found significant differences between both species in terms of their growth rate  and we found significant differences amongst the final densities of S. aureus and P. aeruginosa . Similar to our work in TSB medium, we did not find significant relationships between the final density ratio and [ATP] or growth rate of either species when plotted independently or as ratios for both modified AMM medium and SCFM , if the initial fraction of S. aureus (sC = 0.9) is much greater than P. aeruginosa (pC = 0.1), the amount of virulence factors produced by P. aeruginosa is insufficient to reduce the growth of S. aureus  reaches an initial fraction that is sufficiently high to produce enough virulence factors to reduce the growth of S. aureus (sC = 0.5), P. aeruginosa dominates the population after 24 hr of growth (P. aeruginosa (pC = 0.9) is much greater than S. aureus (sC = 0.1) the growth of S. aureus is sufficiently hampered such that S. aureus does not grow appreciably and P. aeruginosa dominates the population , increasing the absolute growth ratio benefits the growth of P. aeruginosa. If the absolute growth ratio is sufficiently small (0.5), P. aeruginosa is unable to synthesize a high amount of virulence factors and grows slower than S. aureus. Slow growth of P. aeruginosa coupled with a low amount of virulence factors synthesized allows S. aureus to dominate the population (P. aeruginosa. At an intermediate absolute growth ratio (1), the amount of virulence factors produced is sufficient to reduce the growth rate of S. aureus while enhancing the growth rate of P. aeruginosa. Combined, this allows P. aeruginosa to dominate the population  decreased  sensed by S. aureus. High values of δ indicate a high degree of spatial overlap between the two populations; decreasing δ indicates reduced overlap, which diminishes the amount of virulence factors sensed by S. aureus. Experimentally, estimating δ would require the tracking of the positioning of multiple bacteria over both space and time whereupon a spatial overlap value could be extracted. Moreover, any movement of the plate for examination via microscopy would serve to disrupt the distribution of the cells, thus obscuring the results. Due to these challenges, we took a modeling approach to estimate trends in δ.To understand the relationship between the distribution of bacteria, differences in absolute growth, and their impact on population composition, we modified our series of ODEs to include a spatial overlap term, Methods) to estimate the extent of overlap of the two bacterial species. The simplified ABM consists of two populations of agents that are randomly placed in a world. The agents grow and die according to logistic growth. The agents also undergo a displacement event where the spatial positioning of the agents is perturbed at a given distance . Increasing the number of displacement events per hour increases the degree to which the spatial positions of the agents are perturbed each hour of the simulation. In the absence of any disturbance events, the populations of bacteria do not move, which represents a stationary condition. When the number of disturbance events is very high, the population becomes well-mixed, which approximates a continuously shaken culture. An intermediate value of disturbance events would represent a population whose spatial overlap is perturbed periodically. After every hour of in silico simulation time using the ABM, we measure the extent of spatial overlap of the population frequency of cohabited patches minus the expected frequency of cohabited patches (frequency of patches inhabited by P. aeruginosa agents x frequency of patches inhabited by S. aureus agents). We call this metric ‘cohabitation’. A negative cohabitation value means that the populations spatially overlap less than expected based on random chance; a positive cohabitation value means that the populations overlap more than expected. The cohabitation variable is calculated every hour, which is then averaged across the 24 hr in silico time period.We used an agent-based model , which represents a co-culture disturbed at an intermediate frequency , which continues to represent a co-culture disturbed at an intermediate frequency , which represents a co-culture that is undisturbed (stationary) or very frequently disturbed (continuous shaking) the amount of virulence factors sensed by S. aureus is high. This significantly reduces its overall growth rate, allowing P. aeruginosa to outcompete it. Accordingly, P. aeruginosa dominates the population after 24 hr under this condition. Importantly, under each of these simulated conditions the total amount of virulence factor synthesized is not altered ([vir]=1.77 μM); only the concentration sensed by S. aureus is altered owing to changes in δ.To understand these modeling predictions, we simulated temporal changes in cell density for both species independently and at three values of δ . When δ requency , S. aurerequency , the amoS. aureus and P. aeruginosa using the linear shaking function of a microplate reader. We previously demonstrated that using this function could alter the spatial distribution of bacteria , the final density of P. aeruginosa was significantly greater than that of S. aureus. We note that S. aureus was observed to dominate the population at a disturbance frequency of 6 /hr at lower initial dilutions of the population  and the ratio of growth rates (P. aeruginosa growth rate/S. aureus growth rate or the ratio of [ATP]) (P. aeruginosa [ATP]/S. aureus [ATP]).Next, we investigated how changing absolute growth through the use of different carbon sources might impact the ability of P. aeruginosa continues to have a predominant role in mediating the interaction between both bacterial species. When S. aureus was co-cultured with knockout strains of P. aeruginosa , growth with ΔpqsL/ΔpvdA/ΔpchE resulted in a significant decrease in the final density ratio when disturbed at 6 /hr  that impact growth and metabolism in S. aureus are regulated by quorum sensing. Quorum sensing involves the production and exchange of small diffusible molecules, called autoinducers  vs. 0.79 ([ATP] ratio)). Moreover, we did not find a significant relationship between the final ratio of both species and the ratio of [ATP] when grown in TSB medium in the continuously disturbed condition, and in either AMM medium or SCFM in the stationary condition  agar medium  were inoculated overnight in 3 mL of liquid LB medium and shaken (250 RPM and 37 °C) in 15 mL culture tubes . The following day, the cells were washed in fresh medium. For our assays, and as indicated in the text, we used tryptic soy broth (TSB) medium , Tryptone, Dipotassium Phosphate, and Sodium Chloride ), modified AMM medium , 30.3 mM ((NH4)2SO4) (Thermo Fisher Scientific), 2 mM CaCl2 , 10.3 mM KH2PO4 (Thermo Fisher Scientific), 0.2 mM FeSO4•7H2O , 0.4 mM MnSO4•4H2O (Alfa Aesar), 0.3 mM citric acid (Thermo Fisher Scientific), 100 mM Tris (Thermo Fisher Scientific), buffered to pH 7.4 and SCFM (prepared as previously described M medium  (162.6 m600]) for approximately 15 hr at 37 oC. OD600 values from cell-free medium were subtracted from all measurements prior to growth curve fitting. To help remove any artifacts of background and to ensure all data was initiated at the same starting point, we log-transformed and normalized to the initial minimum density. Together, this reduces the error when performing curve fitting. Growth rate was determined by fitting a logistic curve to the data using a custom MATLAB  code  plate reader, cell density was determined every 10 min  code . AverageoC for 3 hr whereupon the bacteria entered log phase growth; gentle shaking allowed ATP measurement to fit a wider range of conditions where the culture was disturbed (600) and converted to concentration using a standard curve prepared using purified ATP.Overnight cultures were washed once with fresh medium lacking a carbon source. Bacteria were then diluted 10-fold in fresh medium supplemented with a carbon source in a 6-well cell culture plate (Genesee Scientific). The plate was overlaid with two AeraSeal sealing membranes (Sigma-Aldrich). For TSB medium, the plates were shaken at 110 RPM pre-set to 37 isturbed . For AMMisturbed . All ATPP. aeruginosa and S. aureus were grown overnight and washed with fresh medium. Bacteria were then diluted to an OD600 of 0.1, which leads to an equal initial density between both strains  and continuously disturbed conditions, the plates were either placed in a stationary incubator at 37 oC or shaken at 110 RPM at 37 oC, respectively, for 24 hr. The plates that were periodically shaken were placed in a Victor X4 plate reader pre-set to 37 °C  at the indicated frequency. For experiments performed in an anaerobic environment, we overlaid the cultures in the cultures in the 96-well plate with 3 mL of mineral oil and we did not add AeraSeal membranes. After 24 hr of growth, 1 mL of culture was removed and washed with 1 x phosphate buffered saline (PBS) (Thermo Fisher Scientific). The culture was then sonicated in a water bath for 2 min to reduce clumping of S. aureus. We then performed a serial dilution and selective plating on mannitol salt agar  and cetrimide . For experiments performed in TSB or AMM medium we determined the number of colony forming units (CFU) grown on the selective agar medium following ~20 hr of incubation at 37 °C. For experiments performed in SCFM, colonies were counted after 24–48 hr of incubation at 37 °C. In this medium, cultures grown in some carbon sources  required additional incubation time so that colonies would grow on the MSA plates.Single colonies of  strains . The norP. aeruginosa to S. aureus to linearize the data; these values were used for linear regressions. To test if the ratios themselves differed within a dataset using a Mann-Whitney or Kruskal-Wallis, we used the raw ratio data that was not ln transformed. When performing a weighted least squares (WLS) regression  were performed using Microsoft Excel . Additional tests were performed in JMP Pro 16 . We took the natural logarithm of the ratio of gression , we firsgression  with an S. aureus and P. aeruginosa using three ordinary differential equations that capture the synthesis of virulence factors that affect metabolism in S. aureus d, our work provides additional support for the intermediate disturbance hypothesis using two clinically important bacterial pathogens.”“From an ecological perspective, our findings provide support for the intermediate disturbance hypothesis. This hypothesis suggests that the diversity of competing species is maximized at intermediate disturbance frequencies or intensities [1]. While the intermediate disturbance hypothesis has faced criticism [2], evidence of its influence on biodiversity has been observed multiple times [3], has helped to guide conservation programs [4], and can help explain the establishment of invasive species [5]. Herein, our results provide evidence that further supports the intermediate disturbance hypothesis; disturbance at intermediate frequencies facilitates the co-existence of both P. aeruginosa and S. aureus. In the introduction, we have expanded on the mechanisms by which both species interact and its impact on growth and metabolism. This section now reads:2) We have expanded the information provided on the interactions between P. aeruginosa, S. aureus upregulates the expression of virulence factors including staphylococcal protein A, α-hemolysis and Panton-Valentine leucocidin [7]. While S. aureus can initially aid in the establishment of the P. aeruginosa population [8], production of N-acetylglucosamine from S. aureus augments the virulence of P. aeruginosa by upregulating expression of multiple secreted virulence factors. Many of these are regulated by quorum sensing and include pyocyanin [9], pyoverdine [10], 2-Heptyl-4-hydroxyquinoline N-oxide [11], and 4-hydroxy-2-heptylquinoline N-oxide (HQNO)[12-15]. Some of these virulence factors reduce metabolism in S. aureus through multiple mechanisms. Pyocyanin induces the production of reactive oxygen species in S. aureus by reacting with menadione, which results in oxidative damage [16]. Pyocyanin has been found to interfere with electron transport, reducing respiration [17]. The siderophores pyoverdine and pyochelin drive S. aureus into fermentation [18]. While exposure to HQNO can initially promote biofilm formation in S. aureus [19], it eventually interferes with the activity of cytochrome b, which reduces ATP production [20]. N-3-oxo-dodecanoyl-L-homoserine lactone (3OC12HSL) produced by P. aeruginosa for the purposes of quorum sensing has been shown to reduce growth of S. aureus [21]. Overall, multiple virulence factors interfere with growth and metabolism in S. aureus, ultimately leading to facultative respiration and increased antibiotic tolerance [22]...”“…Their co-culture induces interspecies competition, which enhances antibiotic resistance and virulence. In the presence of In the Discussion section, and in response to a comment by reviewer 3 (below), we have also expanded on the interactions that facilitate co-existence of both species. Specifically, we write:P. aeruginosa and S. aureus, multiple previous studies have identified mechanisms of co-existence. Indeed, long term co-existence of both species can result in physiological changes that reduce their competitive interactions. Strains of P. aeruginosa isolated from patients that enter into a mucoid state show reduced production of siderophores, pyocyanin, rhamnolipids and HQNO, which facilitates the survival of S. aureus . These strains can also overproduce the polysaccharide alginate, which in itself is sufficient to decrease the production of these virulence factors. Moreover, exogenously supplied alginate can reduce the production of pyoverdine and expression from the PQS quorum sensing system, which is responsible for the production of HQNO [25]. Changes in the physiology of S. aureus can also facilitate co-existence. Strains of S. aureus isolated from patients with cystic fibrosis show multiple changes in the abundance of proteins including super oxide dismutase, the GroEL chaperone protein, and multiple surface associated proteins [26]. Interestingly, the majority of proteins that show changes in abundance in S. aureus are related to central metabolism, which is consistent with our findings demonstrating that metabolism can influence the co-existence of both species. While it is unclear as to how long-term co-culture would affect the ratio of absolute growth, our findings provide an additional mechanism that can determine the co-existence of these bacterial species.”“Given the clinical importance of co-infection with both Overall, we hope that all three Reviewers agree that we have satisfactorily addressed the essential revisions.Reviewer #1 (Recommendations for the authors):The analysis of virulence factors is somewhat less thorough given that their induction is often tied to many factors beyond nutrition, but it is remarkable how well fueling explains who wins and loses. Additional discussion of the wide range of inducers of virulence factors is worthwhile.To address this comment, we have included information of how quorum sensing also leads to the induction of virulence factor expression . We have made this modification in both the introduction and the Discussion sections.S. aureus can initially aid in the establishment of the P. aeruginosa population [8], production of N-acetylglucosamine from S. aureus augments the virulence of P. aeruginosa by upregulating expression of multiple secreted virulence factors. Many of these are regulated by quorum sensing and include pyocyanin [9], pyoverdine [10], 2-Heptyl-4-hydroxyquinoline N-oxide [11], and 4-hydroxy-2-heptylquinoline N-oxide (HQNO)[12-15].Introduction: “While P. aeruginosa  that impact growth and metabolism in S. aureus are regulated by quorum sensing. Quorum sensing involves the production and exchange of small diffusible molecules, called autoinducers [27]. Since each P. aeruginosa bacterium produces autoinducers, the concentration of autoinducer increases as a function of cell density. At a sufficiently high concentration, the autoinducer will activate expression of multiple virulence factors in P. aeruginosa, which begin to impact growth and metabolism in S. aureus. Given this density-dependent activation of virulence factors, it is likely that the tipping point in our systems is being determined by quorum sensing, although we did not explicitly investigate this hypothesis in this manuscript.”Discussion: “The absolute growth ratio also determines the tipping point of the co-cultured population. Importantly, many virulence factors produced by More explicit mention of the intermediate disturbance hypothesis and possible underlying effects of disturbance are worthwhile, to connect readers to this foundational concept in ecology.As noted in our response in the ‘essential revisions’ section, we have included mention of the intermediate disturbance hypothesis in the abstract and have discussed our findings in the scope of this hypothesis in the Discussion section.Reviewer #2 (Recommendations for the authors):I have some recommendations for additional required data/controls and comments:- line 282 – authors discuss 'tipping point' in their model, without the mention of quorum sensing which is likely what this is modeling. E.g a threshold cell density. This should be discussed in the context of the relevant data here and in the discussion. many of the virulence factors assessed here are also QS dependent. A way to test this experimentally may be to use a dual carbon source of casamino acids and those selected previously to control an initial density or tipping point .We would like to thank the reviewer for the suggestion for future work.We have also modified the Discussion section to acknowledge that quorum sensing is likely playing a role in the tipping point analysis as follows:P. aeruginosa  that impact growth and metabolism in S. aureus are regulated by quorum sensing. Quorum sensing involves the production and exchange of small diffusible molecules, called autoinducers [27]. Since each P. aeruginosa bacterium produces autoinducers, the concentration of autoinducer increases as a function of cell density. At a sufficiently high concentration, the autoinducer will activate expression of multiple virulence factors in P. aeruginosa, which begin to impact growth and metabolism in S. aureus. Given this density-dependent activation of virulence factors, it is likely that the tipping point in our systems is being determined by quorum sensing, although we did not explicitly investigate this hypothesis in this manuscript. ”“The absolute growth ratio also determines the tipping point of the co-cultured population. Importantly, many virulence factors produced by - Virulence mutants of Pa should be grown in each media condition to determine if there is a growth deficit to mutation compared to wt. If this is already in the text it was not clear and should be highlighted in the text as a control.This was something that we initially overlooked in our original manuscript. However, as noted in response to the ‘essential revisions’, we have indeed performed this analysis. We found that there was not a significant difference in growth rates between all three knockout strains and the wildtype strain. This is noted in the Results section and this new data is presented in Figure 2 —figure supplement 2.- Each of the virulence mutants should be complemented and the experiments performed again to show the phenotype is lost in co-culture with SA.P. aeruginosa allows S. aureus to grow to higher cell densities when in co-culture. Specifically, O’Toole et al. (2015) demonstrated that S. aureus killing is significantly reduced in co-culture with P. aeruginosa ΔpqsL compared to wildtype P. aeruginosa PA14[18]. This was also consistent when S. aureus was co-cultured with P. aeruginosa knockout strains ΔpvdA/ΔpchE and ΔpqsL /ΔpvdA/ΔpchE. These findings were also echoed by Barraza and Whiteley (2021) who demonstrated that P. aeruginosa strain ΔpqsL mutant lysed S. aureus to a lesser degree than wildtype P. aeruginosa under well-mixed conditions in co-culture [28]. Overall, these studies support the conclusion that P. aeruginosa is dependent upon the HQNO pathway and its major siderophores to suppress S. aureus growth in co-culture.We appreciate this suggestion by the reviewer. We chose not to perform these experiments as there was no difference in growth rate between the knockout strains and the wildtype strains. Moreover, multiple previous studies have demonstrated that the removal of these virulence factors from - Overall the paper is very well written and the figures are presented nicely. There are some spots where italics are needed for organism names etc.Thank you for your kind words and we apologize for this oversight. We have triple checked that all species names in the text are italicized. However, abbreviated genus names that serve as general identifiers in the figures remain unitalicized.Reviewer #3 (Recommendations for the authors):I have no additional major comments other than those described in the Public Review. If this study had been conducted within the SCFM2 media background via constituent substitution it could have been groundbreaking and immediately relevant to the field. Occurring, as it does, in TSB, it is subject to too many caveats to enhance or supplant existing work demonstrating situational contributions to co-culture.We hope that our use of SCFM, along with the additional modifications to our manuscript described here, has increased the relevance of our work in the field.References1. Connell JH. Diversity in tropical rain forests and coral reefs: high diversity of trees and corals is maintained only in a nonequilibrium state. Science. 1978;199(4335):1302-10.2. Fox JW. The intermediate disturbance hypothesis should be abandoned. Trends in ecology and evolution. 2013;28(2):86-92.3. Roxburgh SH, Shea K, Wilson JB. The intermediate disturbance hypothesis: patch dynamics and mechanisms of species coexistence. Ecology. 2004;85(2):359-71.4. Olff H, Ritchie ME. 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Background: Antibiograms are used to monitor antibiotic resistance trends and help guide empiric antibiotic treatment. Community pediatricians may not have access to or be comfortable using children’s hospital antibiograms. Creating and disseminating a statewide pediatric antibiogram can help inform antibiotic stewardship efforts.Objective: To develop a pediatric-specific antibiogram for the state of Georgia. Methods: Annual pediatric antibiograms for the 5 children’s hospitals in Georgia from 2014 through 2021 were collected. All sites complied with the Clinical and Laboratory Standards Institute guidelines for antibiomicrobic breakpoints and antibiogram development. Antibiogram data were combined, and the most common bacteria were selected to incorporate into the statewide antibiogram: Staphylococcus aureus, Streptococcus pneumoniae, Enterococcus faecalis, Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae complex, and Pseudomonas aeruginosa. Antibiogram data were reported as percentage susceptible and total number of isolates. Interhospital susceptibility differences were compared for methicillin-susceptible S. aureus (MSSA), methicillin-resistant S. aureus (MRSA), E. coli, and K. pneumoniae from 2018 through 2021. P < .05 was considered significant. The combined antibiogram data from 2014 through 2021 were used to show antibiotic susceptibility trends over time. Results: The 2021 antibiogram is shown in the Table. For MSSA and MRSA, clindamycin susceptibility was 80% and 85%, respectively. K. pneumoniae susceptibility to amoxicillin-clavulanate was 91%. For E. coli, using urine-specific breakpoints, susceptibility to cefazolin was 89%. A few statistically significant differences in antibiotic susceptibility were detected between hospitals, but most were unlikely to be clinically relevant . A notable exception was trimethoprim-sulfamethoxazole susceptibility for K. pneumoniae, which ranged from 74% to 98% in 2020 and from 74% to 86% in 2021. From 2014 to 2021, the percentage of MRSA decreased from 49% to 34%. Over the 8 years, susceptibility to ceftriaxone for E. coli ranged from 93% to 95% and from 90% to 95% for K. pneumoniae. Susceptibility to meropenem for E. coli and K. pneumoniae ranged from 99% to 100%. Conclusions: Antibiotic susceptibility for pediatric bacterial isolates in Georgia remained stable over time and supported the narrow-spectrum empiric antibiotic treatment recommended in national evidence-based guidelines for skin and soft-tissue infections, community-acquired pneumonia, and uncomplicated urinary tract infections. MRSA rates decreased over time and multidrug-resistant gram-negative bacilli were uncommon and remained stable.Disclosures: None


M. haemolytica and Fusobacterium are the crucial pathogens in the sheep pneumonia. In addition, the occurrence of sepsis in severe pneumonia sheep could contribute to the eventual mortality outcome. Taken together, the results of this study contribute to guiding preventive and therapeutic measures for pneumonia of different severities.Pneumonia, which represents a leading cause of mortality among sheep, presents a formidable peril to the sheep industry. In this study, we combine high-throughput 16SrRNA gene sequencing and bacterial culturing to meticulously examine the bacterial community present in lung samples obtained from healthy sheep, those with moderate pneumonia, and those suffering from severe pneumonia. The results demonstrate a significant decrease in pulmonary microbial diversity and alterations in taxonomic composition following the onset of pneumonia. Our results suggest that p < 0.05). The results showed that the abundance and diversity of lung bacteria in healthy sheep were significantly higher than those in pneumonia sheep (p = 0.139), while there was no significant difference between moderate and severe pneumonia. Furthermore, the composition of the lung microbiota community underwent significant alterations between different levels of pneumonia severity. The application of LEfSe analysis revealed a notable enrichment of Mannheimiae within the lungs of sheep afflicted with moderate pneumonia (p < 0.01), surpassing the levels observed in their healthy counterparts. Additionally, Fusobacterium emerged as the prevailing bacterial group within the lungs of sheep suffering from severe pneumonia. Integrating the results of bacterial isolation and identification, we conclusively determined that Mannheimia haemolytica was the primary pathogenic bacterium within the lungs of sheep afflicted with moderate pneumonia. Furthermore, the exacerbation of pneumonia may be attributed to the synergistic interplay between Fusobacterium spp. and other bacterial species. Our results provide new insights for guiding preventive and therapeutic measures for pneumonia of different severities in sheep.Hu sheep, a locally bred species in China known for its high productivity, is currently suffering from pneumonia. Here, we combine high-throughput 16SrRNA gene sequencing and bacterial culturing to examine the bacterial community in pneumonic Hu Sheep lungs ( Mannheimia haemolytica (M. haemolytica) and Pasteurella multocida (P. multocida) are generally recognized as the major causative pathogens of pneumonia in sheep [Mycoplasma ovipneumoniae (M. ovipneumoniae), originally identified within a sheep flock situated in Queensland, Australia in the year 1972 [Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pneumoniae) have also emerged as pathogenic bacteria associated with pneumonia in sheep [Sheep have long held a pivotal position as a vital economic animal worldwide. Nonetheless, pneumonia, which represents a leading cause of mortality among sheep, presents a formidable peril to the sheep industry ,2. Currein sheep ,4. Notabin sheep . Mycoplaear 1972 , is now ear 1972 ,9. Moreoin sheep . Recent in sheep . Mountinin sheep . Howeverin sheep . There hin sheep . Moreovein sheep ,16. HoweThe exploration of the human lung microbiome in the context of pulmonary diseases is undergoing continuous expansion, encompassing conditions such as lung cancer, lung adenocarcinoma, and emphysema ,18. SheeThe objective of this study was to characterize the viable bacterial community in lung samples from healthy sheep, moderate pneumonia sheep, and severe pneumonia sheep and to determine the main pathogens. We hope this research will provide insights into the prevention and treatment of pneumonia in Hu sheep.https://www.vetmed.wisc.edu/dms/fapm/fapmtools/8sheep/sheep_respiratory_scoring_chart.pdf/ (accessed on 15 April 2022). According to the health scoring criteria, a total of 18 Hu sheep (aged 4–5 months) were selected from the farm for the purpose of this experiment in April 2022. The sheep cohort comprised six individuals classified as healthy (group H), six individuals afflicted with moderate pneumonia but without any therapeutic intervention (group M), and an additional six individuals suffering from severe pneumonia (group S), also without therapy. The details about the sheep are listed in An outbreak of pneumonia recently unfolded at a significant Hu sheep farm situated in Yulin , Shaanxi province. Based on meticulous statistics compiled within this farm, pneumonia emerged as the primary cause of mortality among fattening sheep during March 2022. The main clinical presentation was respiratory distress, runny nose, wet cough, and hyperthermia. Eye, nasal, cough and respiratory score were classified referenced the Wisconsin sheep health scoring criteria Blood samples were obtained aseptically from 18 sheep via the jugular vein and anticoagulated with EDTA for bacterial culture purposes. Subsequently, all the sheep were euthanized in a humane manner, and their thoracic cavities were meticulously opened to expose the lungs. During this process, careful observation and documentation were conducted to assess the presence of lung–chest adhesions. A detailed description of the lung condition observed following slaughter in both clinically healthy and pneumonia-affected Hu sheep can be found in https://blast.ncbi.nlm.nih.gov/ (accessed on 23 April 2022)) algorithm for homologous sequence searches.The frozen lung tissues were meticulously homogenized on ice utilizing a specialized homogenizer, ensuring thorough and consistent tissue disruption. Both the blood and lung homogenate were streaked onto the brain heart infusion agar medium containing 5% sheep blood, followed by incubation at 37 °C in both aerobic and anaerobic conditions to monitor any bacterial growth. The dominant colonies were picked for purification culture. Identification was performed as previously described . The genFrozen lung tissues were homogenized on ice using a homogenizer and the DNA was extracted using Mabio DNB361B Bacterial DNA Extraction Mini Kit  according to the manufacturer’s instructions. The V4–V5 region of the 16S rRNA gene was amplified with barcode-indexed universal bacterial primers  following vmanufacturer’s recommendations, and index codes were added. The library quality was assessed on the Qubit@ 2.0 Fluorometer . Finally, the library was sequenced on an Illumina Nova6000 platform and 250 bp paired-end reads were generated .Sequencing libraries were generated using NEBNexthttps://github.com/OpenGene/fastp/ (accessed on 23 April 2022) was used to control the quality of the raw data using a sliding window (-W 4 -M 20). The primers were removed by using cutadapt software (https://github.com/marcelm/cutadapt/ (accessed on 25 May 2022) according to the primer information at the beginning and end of the sequence to obtain the paired-end clean reads. Paired-end clean reads were merged using usearch-fastq_mergepairs  according to the relationship of the overlap between the paired-end reads, when there was at least 16 bp overlap with the read generated from the opposite end of the same DNA fragment; the maximum mismatch allowed in the overlap region was 5 bp, and the spliced sequences were called raw tags. Fastp  was used to control the quality of the raw data using a sliding window (-W 4 -M 20) to obtain the paired-end clean tags. Sequence analysis was performed using Qiime2 software [Fastp . Based on the OTU information, rarefaction curves and alpha diversity indices, including observed OTUs, Chao1 richness, and Shannon index, were calculated using usearch-alpha_div . The similarity among the microbial communities in different samples was determined by principal coordinate analysis (PCoA) and non-metric multidimensional scaling analysis (NMDS) based on Bray-Curtis dissimilarity using the Vegan package.Bioinformatic analysis of the lung microbiota was carried out using the MAGICHAND Cloud platform (http://huttenhower.sph.harvard.edu/LEfSe/ (accessed on 27 May 2022) was determined to identify the significantly abundant taxa (phylum to genera) of bacteria among the different groups (LDA score = 3). Random forest analysis was performed based on the OTU_table, using the randomforest software package in R software to build prediction mode.The linear discriminant analysis (LDA) effect size (LEfSe) (M. haemolytica (38.89% (7/18)), three isolates of P. multocida (16.67% (3/18)), five isolates of Streptococcus lutetiensis (S. lutetiensis) (27.78% (5/18)), two isolates of E. coli (11.11% (2/18)), three isolates of Streptococcus pasteuaianus (S. pasteuaianus) (11.11% (2/18)), one isolate of P. aeruginasa (5.55% (1/18)), and one isolate of Streptococcus vicugnse (S. vicugnse) (5.55% (1/18)). We found that bacteria could be isolated in the lungs of both healthy (50.00% (3/6)) and diseased sheep (100% (12/12)). M. haemolytica was the dominant species isolated from the sheep with moderate pneumonia. However, a greater number of bacterial species and coinfections were found in lungs from severe pneumonia sheep. It is worth noting that five isolates were only recovered in blood from six sheep with severe pneumonia (83.33% (5/6)), including P. aeruginasa (60.00% (3/5)), Aeromonas veronii (A. veronii) (20.00% (1/5)), and Pseudomonas azotoformans (P. azotoformans) (20.00% (1/5)). The sepsis observed in severe pneumonia-afflicted sheep may ultimately contribute to their mortality.In total, lung tissue and blood samples from 18 sheep were used for bacterial isolation, and the outcomes of the separation are presented in After performing filtering, denoising, chimera checking, and singleton checking, we obtained a total of 1,193,756 pairs of 420 bp clean reads. A total of 3049 operational taxonomic units (OTUs) were obtained via clustering with a 97% identity threshold.From the richness presented in The Venn diagrams for shared and unique operational taxonomic unit distribution of OTUs and the quantity of OTUs in each sample are presented in p < 0.05), while no significant differences were observed in group M and group S.The relative abundance (Chao1) and diversity (Shannon) index values of bacterial communities for each group are compared in p < 0.05, for details see p < 0.05; for details, see To account for variations in the composition of lung microbial communities across different groups of samples, PCoA based on bray_curtis distances revealed that group H was distinct from group M and group S C , Bacteroidetes , Fusobacteria , Firmicutes , and Tenericutes  were predominant in the healthy, moderate, and severe pneumonia sheep , Fusobacteriaceae , Bactereoidaceae , Prevotellaceae1 , and Mycoplasmataceae  were the predominant bacteria in both groups , Fusobacterium , Bactereoides , Pasteurella , Prevotella_1 , and Mycoplasma  were the predominant bacteria in both groups , Fusobacterium (p < 0.01), Bacteroides (p < 0.01), Prevotella_7 (p < 0.05), Succiniclasticum (p < 0.05), Bibersteinia (p < 0.01), Pseudomonas (p < 0.05), Peptoniphilus (p < 0.05), Escherichia-Shigella (p < 0.01), and Mogibacterium (p < 0.05)—remained significantly different after applying one-way ANOVA , Prevotella_1 (p < 0.05), and Bacillus (p < 0.05). The results shown in Manheimia, Fusobacterium, and Bacteroides also had a high abundance in both the H and S groups. However, the abundance of Fusobacterium was significantly higher (p < 0.05) in the S group compared to the H group, while Pasterurella, Prevotella_1, Bacillus, Prevotella_7 was significantly higher (p < 0.05) in group H. It is widely believed that mycoplasma infection is more commonly associated with sheep and goat pneumonia; however, in this study, Mycoplasma exhibited an increasing tendency in both group M and group S, albeit with insignificant differences.In order to delve into the variations in the sheep lung microbiota among different severities of pneumonia, we performed a difference analysis of group H and group M, and group H and group S at the genus level using Student’s t test . As showPrevotellaceae, one of the major constituents in the healthy individual lower respiratory tract microbiota [Fusobacteriaceae, Aeromonadaceae, Peptococcus and Bacteroidaceae were highly abundant in sheep with severely symptomatic pneumonia. However, in the lungs from sheep with moderately symptomatic, features such as Pasteurellaceae, Mannheimia, Actinobacillus, Bibersteinia, and Escherichia were more abundant  effect size (LEfSe) to identify taxa exhibiting differential abundances between the two groups, enabling comparison of their relative contributions. LEfSe analysis demonstrated several features that were over-represented in sheep with asymptomatic, moderately symptomatic, and severely symptomatic pneumonia. As expected, crobiota ,25, wereabundant A,B.Fusobacterium, Mannheimia, Haemophilus, and Escherichia-Shigella were identified as the most discriminating predictors based on the mean decrease in the Gini criterion . Previous studies have revealed the specialized metabolic capability of Succiniclasticum to ferment succinate and convert it into propionate, a process that exerts advantageous effects on gut chemistry [M. haemolytica colonization in dairy calves [Succiniclasticum in sheep lung homeostasis and the potential of intranasal bacterial therapeutics warrants further investigation. D. Cid et al. [P. multocida isolation and pneumonic lesions was stronger than that of M. haemolytica, and the isolation of P. multocida resulted in more severe pneumonic lesions compared to M. haemolytica [P. multocida and the severity of pneumonia. Consistent with the trends observed in the 16SrRNA diversity sequencing results, the bacterial isolation outcomes also demonstrated a higher prevalence of M. haemolytica, particularly in the lungs of sheep with moderate pneumonia. Regrettably, there were no Fusobacterium spp that could be isolated from the lungs of all of the sheep. It is necessary to improve the isolation method of Fusobacterium. Finally, but most importantly, A. veronii, P. aeruginosa and P. azotoformans were frequently isolated from the bloodstream of sheep suffering from severe pneumonia, which could ultimately be one of the factors leading to their death.Robert et al.  showed themistry . Intranay calves ,43. Therd et al.  pointed s p < 0.0. PreviouFusobacterium spp. were obtained as a result. The next studies in our laboratory will address these issues.However, there are several limitations that should be acknowledged in the present study. Firstly, the sample size was too small. Secondly, this study did not include healthy sheep from alternative breeding environments for comparative analysis, despite previous research highlighting variations in lung microbiota across different feeding environments. Lastly, it is important to acknowledge the imperfections in the isolation method for strict anaerobic bacteria employed in this study; regrettably, no isolates of M. haemolytica and Fusobacterium were identified as the crucial pathogens in sheep pneumonia. In addition, P. aeruginosa, A. veronii, and P. azotoformans were isolated from blood samples in severe pneumonia sheep (83.3% (5/6)). Thus, the occurrence of sepsis in severe pneumonia sheep could contribute to the eventual mortality outcome. Veterinarians need to take sepsis into account when developing a treatment plan. This study contributes to the guidance of preventive and therapeutic measures for pneumonia of different severities.We combined high-throughput 16SrRNA gene sequencing and bacterial culturing to characterize the viable bacterial community in lung samples. The results demonstrated a significant decrease in pulmonary microbial diversity and alterations in taxonomic composition following the onset of pneumonia.


Cutaneotrichosporon cavernicola and C. spelunceum, whereas strain HIS471 remained unidentified. We analysed the genomes of these strains to elucidate their taxonomic relationship and genetic diversity.Since DNA information was first used in taxonomy, barcode sequences such as the internal transcribed spacer (ITS) region have greatly aided fungal identification; however, a barcode sequence alone is often insufficient. Thus, multi-gene- or whole-genome-based methods were developed. We previously isolated Basidiomycota yeasts classified in the Trichosporonales. Some strains were described as C. cavernicola despite sharing identical ITS barcode sequences and displaying an average nucleotide identity (ANI) above 93%. Also, the chromosome synteny between C. cavernicola and the related species, C. spelunceum, showed significant rearrangements, whereas the ITS sequence identity exceeds 98.6% and the ANI is approximately 82%. Our results indicate that the relative evolutionary rates of barcode sequences, whole-genome nucleotide sequences, and chromosome synteny in Cutaneotrichosporon significantly differ from those in the model yeast Saccharomyces.The long-read-based assembly resulted in chromosome-level draft genomes consisting of seven chromosomes and one mitochondrial genome. The genome of strain HIS471 has more than ten chromosome inversions or translocations compared to the type strain of Our results revealed that the relative evolutionary rates of nucleotide sequences and chromosome synteny are different among fungal clades, likely because different clades have diverse mutation/repair rates and distinct selection pressures on their genomic sequences and syntenic structures. Because diverse syntenic structures can be a barrier to meiotic recombination and may lead to speciation, the non-linear relationships between nucleotide and synteny diversification indicate that sequence-level distances at the barcode or whole-genome level are not sufficient for delineating species boundaries.The online version contains supplementary material available at 10.1186/s12864-023-09718-2. Fungal microorganisms are among the most ubiquitous life forms on Earth. Since fungi have fewer morphological traits than metazoans or land plants, molecular identification is widely used in fungal taxonomy. In fungal systematics, the internal transcribed spacer (ITS) sequence is the preferred barcode used for conventional molecular identification , 2; howeThe whole genome-based method is a more comprehensive way to identify species using molecular technology. DNA-DNA hybridization (DDH) is a traditional whole genome-based approach for species delineation of microorganisms, including fungi and bacteria , 7. In tYeasts are unicellular fungi that evolved convergently from filamentous fungi in the Ascomycota and Basidiomycota . CurrentCryptococcus spp. and Trichosporon spp. 19125.2) , S. para79055.1) , S. kudr92725.1) , S. euba98625.1) , S. uvar01205.1) , Cr. den091045.1 , registeoformans ), Cr. ga85945.1) , Cr. deu54075.1) , and Cr.20205.1) . Since t11372.1) . To calc91045.1) , as showBelow is the link to the electronic supplementary material.Fig. S1. Characteristics of Cutaneotrichosporon sp. HIS471. A, B; Microscopic images of strain HIS471. Cells were incubated on corn meal agar medium  at 20 °C for four days, and observed using a BX53 compound microscope with an UPlansXApo 40? objective lens . Bars; 20 m. C, D; Pairwise alignment of strain HIS019 (C. cavernicola type) and strain HIS471. C and D represent the ITS region and the D1/D2 region, respectively. The polymorphic sites are indicated by arrows. E, F; The possible RNA secondary structure of D1/D2 region of HIS019 (E) and HIS471 (F) predicted with the minimum free energy (MFE) method. Fig. S2. Self-synteny plots of C. cavernicola and Cryptococcus neoformans genomes. Self-synteny plots of C. cavernicola HIS019 and reference Cryptococcus neoformans H99 (GCA_011801205.1) genomes. The plot of the C. cavernicola genome shows no visible repeats, in contrast to the plot of the Cr. neoformans genome, which shows repetitive palindromes (which appear as “X” in the figure) corresponding to the centromeres in each chromosome. Fig. S3. Mauve alignment of Cutaneotrichosporon genomes. Chromosome synteny of Cutaneotrichosporon visualized with Mauve 2015-2-25. Each coloured block represents locally colinear blocks (LCBs). Fig. S4. Chromosome synteny of Saccharomyces. BLASTN-based chromosome synteny of the reference model yeast Saccharomyces. Line colour reflects the percentage of nucleotide identity in the alignment as shown in the legend. Fig. S5. Alignment of ITS sequences of Cutaneotrichosporon strains. Multiple alignment of ITS sequences of Cutaneotrichosporon strains. The ITS sequences were extracted from assembly genomes with the SeqKit amplicon. The polymorphic sites are indicated by arrows. Fig. S6. GBDP scores calculated by all three formulae of GGDC. The GBDP scores among Cutaneotrichosporon and among reference Saccharomyces and Cryptococcus calculated by using three formulae with the genome-to-genome distance calculator (GGDC). Blue boxes represent identical genomes and orange boxes represent the most distant interspecific comparison in the reference genomes. Fig. S7. Satellite syntenies in reference Saccharomyces caused by repetitive sequences. Chromosome synteny between reference S. cerevisiae and S. paradoxus. A; BLAST-based synteny visualization. Red arrowheads represent satellite syntenies caused by telomeric repeats. B; Synteny visualized with Mauve 2015-2-25 alignment. Red arrowheads represent LCBs from satellite syntenies of repetitive sequences.Table S1. Repeat sequence content of sequenced Cutaneotrichosporon and reference model yeastsTable S2. Predicted position of exons of secondary barcode genes by tBLASTn. The protein sequences of S. cerevisiae were used as queries. Split alignments (corresponding to introns) that are not common to all strains are shown in bold.Table S3. The positions of ITS sequences used for identity calculations in the genome assembly. If there are multiple identical sequences, up to five are shown. The sequence of Cr. amylolentus was used as deposited in GenBank (not extracted from assembly) because no ITS sequence was found in the assembly.Table S4. Sorted chromosomes of reference Cryptococcus genomes for the calculation of LCBsTable S5. Accession numbers for genome assembly


Escherichia (E.) coli is an essential tool to gain a better understanding of the potential impact of biocide selection pressure on antimicrobial resistance. We, therefore, determined the biocide and antimicrobial susceptibility of 216 extended-spectrum β-lactamase-producing (ESBL) and 177 non-ESBL E. coli isolated from swine feces, pork meat, voluntary donors and inpatients and evaluated associations between their susceptibilities. Minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of benzalkonium chloride, chlorhexidine digluconate (CHG), chlorocresol (PCMC), glutaraldehyde (GDA), isopropanol (IPA), octenidine dihydrochloride and sodium hypochlorite (NaOCl) showed unimodal distributions, indicating the absence of bacterial adaptation to biocides due to the acquisition of resistance mechanisms. Although MIC95 and MBC95 did not vary more than one doubling dilution step between isolates of porcine and human origin, significant differences in MIC and/or MBC distributions were identified for GDA, CHG, IPA, PCMC and NaOCl. Comparing non-ESBL and ESBL E. coli, significantly different MIC and/or MBC distributions were found for PCMC, CHG and GDA. Antimicrobial susceptibility testing revealed the highest frequency of resistant E. coli in the subpopulation isolated from inpatients. We observed significant but weakly positive correlations between biocide MICs and/or MBCs and antimicrobial MICs. In summary, our data indicate a rather moderate effect of biocide use on the susceptibility of E. coli to biocides and antimicrobials.Phenotypic susceptibility testing of  Biocides have been applied for decades as disinfectants, antiseptics and preservatives in healthcare settings and along the food production chain, and they play a major role in the prevention of zoonotic diseases ,2. HowevStaphylococcus (S.) aureus to both substances [Enterococcus faecium to isopropanol (IPA). In a recent outbreak investigation, we identified carbapenem-resistant Klebsiella (K.) pneumoniae isolates with a decreased susceptibility to CHG in an intensive care unit where patients were routinely washed with CHG to decrease the rate of catheter-related infections [S. aureus to CHG resulted in a reduced susceptibility to this antiseptic as well as to gentamicin (GEN), penicillin or tetracycline (TET) in several of the investigated strains. Similarly, contact of Escherichia (E.) coli, Listeria monocytogenes, Campylobacter coli and Salmonella enterica with increasing subinhibitory concentrations of the quaternary ammonium compound (QAC) didecyldimethylammonium chloride led to reduced biocide susceptibility and to bacterial antimicrobial resistance (AMR) [Epidemiological studies identified temporal associations between the application of biocidal substances and a decreasing susceptibility of bacteria to these substances of interest ,7. Hardybstances . Similarfections . As a mafections ). Dopceafections  showed tce (AMR) . AMR is ce (AMR) . AMR is ce (AMR) . Howeverce (AMR) ,15,16,17E. coli is a gram-negative indicator organism to monitor AMR trends [E. coli is an important nosocomial pathogen [qacE∆1, qacF, qacH and sugE(p) on extended-spectrum β-lactamase (ESBL) plasmids in E. coli [E. coli in higher biocide concentrations.R trends ,19,20. TR trends . PersistR trends . Furtherpathogen ,24,25. P E. coli ,27,28. TE. coli isolates from four different sources, namely primary production (swine feces), food (pork meat), voluntary donors and inpatients, to seven widely used biocides. The study population included 216 ESBL and 177 non-ESBL E. coli. According to their origin or ESBL carriage, we comparatively analyzed the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) data of E. coli to identify characteristics potentially specific to the different subpopulations. Furthermore, we determined their bacterial susceptibility to 15 antimicrobials and assessed associations between reduced biocide susceptibility and antimicrobial resistance.In our study, we investigated the susceptibility of 393 E. coli isolated from swine feces, pork meat, voluntary donors and inpatients , CHG, benzalkonium chloride (BAC), OCT, IPA, sodium hypochlorite (NaOCl) and chlorocresol (PCMC) were determined for 393 patients . Overallpatients  and frompatients  dilution95 and MBC95 values revealed minor variation. MIC95 and MBC95 values were the same  or differed by only one dilution step between the four E. coli subpopulations of different origin  as well as the MBC of GDA (p = 0.040) and CHG (p = 0.021).The initial comparative analysis of MICt origin . Despitesolation . Subsequsolation . The inpand PCMC . ESBL E.E. coli are summarized in The antimicrobial resistance profiles of 216 ESBL and 177 non-ESBL E. coli), and therefore, they were defined as multidrug-resistant (MDR).The proportion of isolates resistant to SME, TRI, CIP, NAL, AZI and GEN was higher in the subpopulation isolated from humans than those from swine feces and pork meat. Resistances against TET and CHL were evenly distributed among subgroups . IsolateE. coli population (sr) ranged from sr = 0.099 to sr = 0.280 in the total population and from sr = 0.201 to sr = 0.376 within the four origin-related subgroups. In addition, non-ESBL and ESBL E. coli isolates were tested separately, and the correlation coefficients ranged from sr = 0.138 to sr = 0.307.Significant positive correlations between high MIC and MBC values in biocides and increased MICs in antimicrobials were found in the total pulation  and withpulation . CorrelaE. coli population, significant positive correlation coefficients were highest between CHG MBC and NAL MIC (sr = 0.280), CHG MBC and CIP MIC (sr = 0.276), CHG MIC and GEN MIC (sr = 0.228), as well as CHG MIC and MBC and TET MIC . Significant positive correlation coefficients within subgroups were highest for BAC MIC and MBC and ceftazidime (CTZ) MIC in isolates from voluntary donors  and for CHG MIC and MBC and TET MIC in isolates from pork meat . The highest positive and significant correlation coefficient in the non-ESBL and ESBL E. coli subpopulations was determined for BAC MBC and CTZ MIC (sr = 0.307) in non-ESBL isolates. In ESBL isolates, the highest correlation coefficient was observed for CHG MBC and CIP MIC (sr = 0.259).In the total E. coli population (n = 393) including 55% (n = 216) ESBL E. coli originating from human (voluntary donors and inpatients) and swine (feces and pork meat) samples to better understand the potential impact of biocide usage on the spread of biocide and antimicrobial-resistant bacteria. We determined distributions of MICs and MBCs for biocidal substances frequently used in animal husbandry, along the food chain and/or in human healthcare settings.In our study, we investigated biocide and antimicrobial susceptibility in a large E. coli did not acquire biocide resistance. OCT and PCMC MIC95 values as well as BAC and NaOCl MBC95 values were the same for all subgroups. For all other substances tested, we observed slightly different MIC95 and/or MBC95 values in isolates from inpatients, voluntary donors, swine feces and pork meat. Overall, there was only one doubling dilution step difference in MIC95 and/or MBC95 values between subgroups. For example, twofold higher MIC95 values of CHG were determined for isolates from humans and swine feces (4 mg/L) compared to pork meat isolates (2 mg/L). A similar variability was described by Deus and colleagues, who compared MIC90 values of isolates from poultry (1 mg/L) and humans (2 mg/L) [90 of CHG at 16 mg/L was reported previously for E. coli isolates from hospital patients [95 and MBC95 values in our study population indicates rather low variability of biocide susceptibility in different settings independent of the underlying selection pressure. Nevertheless, statistical analysis revealed significant differences in MIC and/or MBC distributions between subpopulations. This might reflect diverse usage patterns of biocidal substances in these four settings. One limitation of our study is that we did not obtain information on the biocides applied or their frequency of use in the sampled areas. Therefore, it is impossible to draw conclusions regarding whether usage patterns may influence biocide susceptibility in E. coli. Nonetheless, according to our data, even high selection pressure due to frequent application of disinfectants, antiseptics and antimicrobials  seems to have a rather moderate impact on the development of reduced susceptibility to tested substances in E. coli. However, single isolates showed considerably high MIC and/or MBC values.MIC and MBC data showed unimodal distributions, indicating that the investigated (2 mg/L) . A highepatients . Indeed,patients . In contE. coli isolates from livestock [E. coli isolates are missing, impeding the assessment of biocide susceptibility data [90, MIC95 or MIC99.9 values of clinically relevant microorganisms including E. coli have been proposed [99.9 = 64 mg/L, MBC99.9 > 64 mg/L), BAC  and NaOCl  considering 368 E. coli isolates collected between 1998 and 2011 [E. coli isolated from various environments geographically and temporally widely dispersed indicates a lack of adaptation to tested substances in the recent past. However, comparing MIC and MBC values across studies remains a challenge because experimental standards for biocide susceptibility testing (BST) are still missing. Therefore, harmonized experiments and standardized methods are essential. Despite the recent development of quality control (QC) ranges for BST [The MIC ranges observed for BAC, CHG, PCMC, NaOCl and GDA in our study were consistent with those of previous studies that investigated ivestock ,30,31,32ivestock  and humaivestock ,27. Hithity data . Tentatiproposed ,15,27. Mand 2011 . The res for BST , standarE. coli. Susceptibility to BAC, IPA, NaOCl, PCMC and OCT was comparable for ESBL and non-ESBL E. coli, indicating that ESBL-carriage does not benefit survival during contact with these substances. Even though the MIC95 and MBC95 values of GDA, CHG and PCMC only differed in one doubling dilution step between both groups, statistically significant differences were observed in GDA and CHG MBC distributions as well as in PCMC MIC distributions. Although GDA and CHG MBCs of ESBL E. coli shifted towards higher values compared to the non-ESBL subpopulation, increased PCMC MICs were found in the non-ESBL subset. In our study, we did not observe higher MIC values for the quaternary ammonium compound BAC in ESBL E. coli, as previously described [To determine putative associations between an ESBL phenotype and reduced biocide susceptibility, we comparatively analyzed the MIC and MBC distributions of ESBL and non-ESBL escribed . The assE. coli of human origin (inpatients and voluntary donors) with reports from national and international surveillance systems, we considered clinical breakpoints [Interpretation of antimicrobial susceptibility testing data using ECOFFs is a powerful tool for the surveillance of antimicrobial resistance . Furtherakpoints  instead E. coli have been widely reported in livestock [E. coli to AMP, CIP and TRI has been frequently documented in the German antimicrobial resistance surveillance database (ARS) [E. coli isolates predominantly from swine feces. Interestingly, neither resistance to COL nor to TIG were reported within the German National Zoonoses Monitoring Program for E. coli of porcine origin in 2019 [E. coli have been isolated from German swine farms in the recent past [E. coli from swine has been previously reported from the United Kingdom, South Africa and Thailand [E. coli AMR data from swine feces and pork meat sampled in our study with reporting data from the German National Zoonoses Monitoring Program as well as with human data with resistance rates documented in ARS, the high number of ESBL E. coli included in our study group has to be considered (ranging from 44 to 74% in the four subpopulations). Accordingly, resistance rates were generally higher in the isolates included in our study.Overall, 64% of the tested isolates  were resistant to three or more classes of antimicrobials and were thereby classified as MDR. MDR ivestock ,38,39, fivestock  and nosoivestock ,43,44,45se (ARS) . In our se (ARS) . A low nse (ARS) . The Worse (ARS) ,49. We i in 2019 . Howeverent past . TIG resThailand ,53,54. WK. pneumoniae to COL and CHG [Acinetobacter baumannii to multiple antimicrobials and triclosan [Cross-resistance and co-resistance are discussed as potential drivers for biocide-induced emergence and the spread of antimicrobial resistance ,56,57,58 and CHG  and the riclosan . Co-resiriclosan ,61. Roedriclosan  describericlosan ,62,63. Iriclosan . Accordiriclosan ,65. BecaE. coli field isolates have adapted to tested biocidal substances in the recent past. Even though we found statistically significant differences for MIC and MBC distributions between subgroups, the low variability of phenotypic susceptibility data suggests a rather low impact of niche-specific selection pressure on the adaptation of E. coli to biocides. Whether the identified correlations between biocide and antimicrobial susceptibility are related to co- or cross-resistance needs to be elucidated in mechanistic analyses.In conclusion, our data lack the clear evidence that E. coli strains were investigated in our study, including 216 ESBL and 177 non-ESBL phenotypes. E. coli isolated from humans originated from the strain collection of the Institute of Infectious Diseases and Infection Control of Jena University Hospital. These isolates were obtained from previous research projects . Specifically, rectal swabs were collected from two groups: 96 voluntary donors in 2018 and 98 colonized hospital patients (inpatients) between 2014 and 2017. Isolates of porcine origin comprised 100 E. coli from the strain collection of the Institute of Microbiology and Epizootics isolated from swine fecal samples collected at 15 German swine farms in 2016 and 99 pork meat isolates from the National Reference Laboratory for Antimicrobial Resistance located at the German Federal Institute for Risk Assessment (BfR). E. coli from pork meat were obtained from the German National Zoonoses Monitoring Program in 2015 and 2017, following the requirements of commission implementing decision (CID) 2013/652/EU. The ESBL phenotypes of all isolates were determined in their respective diagnostic laboratories using either VITEK®-2  testing (VITEK®-2 software release 8.01) with the AST-GN69 card or using the ESBL confirmatory test according to the Clinical and Laboratory Standards Institute (CLSI) recommendations. Isolates were stored in glycerol stocks and were grown on Mueller–Hinton (MH) agar  overnight at 37 °C before use.In total, 393 E. coli to seven biocides frequently added to antiseptics and disinfectants applied in healthcare and food production was tested, namely GDA , CHG , BAC (Sigma Aldrich), OCT , IPA (Carl Roth), NaOCl  and PCMC (Sigma Aldrich). The concentrated chemical solutions were freshly diluted prior to the experiments with standardized hard water as defined in EN 1276. Serial twofold dilutions were prepared in 96-well plates within the following concentration ranges: 8192 to 64 mg/L of GDA, 64 to 0.5 mg/L of CHG, 256 to 2 mg/L of BAC, 32 to 0.25 mg/L of OCT, 262,144 to 2048 mg/L of IPA, 4096 to 64 mg/L of NaOCl and 8192 to 64 mg/L of PCMC.The susceptibility of 6 colony-forming units (CFU) per milliliter of double-concentrated Mueller–Hinton broth . In a 96-well microtiter plate , 50 μL of this bacterial solution was mixed with 50 μL of the twofold-concentrated biocide. The plates were incubated at 37 °C for 20 ± 2 h. Optical density (OD595 nm) was measured after 5 s of shaking using an iMark Microplate Absorbance Reader . Bacterial growth was compared to a negative control (microtiter plate containing biocide solution and MHB), and ΔOD595 nm = 0.08 was set as the cut-off. The MIC was defined as the lowest concentration of a biocide at which no growth was observed.The MICs and MBCs of all isolates to each biocide were determined via broth microdilution as previously reported . BrieflyE. coli ATCC 25922 was used as an internal quality control in both MIC and MBC tests and showed comparable results throughout the experiments. To evaluate the reproducibility of the MIC and MBC tests, both methods were performed in two biological replicates on separate days for a subset of 43 E. coli (>10% of the study population) and all biocides. MIC or MBC variations of one dilution step between the two experiments were accepted. The lower concentration was defined as MIC or MBC. In case of higher variations, we repeated the test for a third time, and the median was considered to be the final MIC or MBC (data not shown). The lowest biocide concentration inhibiting growth of 95% of the bacterial population was designated as MIC95, and the lowest lethal concentration killing 95% of the bacterial population was designated as MBC95.For MBC determination, 20 μL each from those wells without detectable bacterial growth and those with the lowest concentration of biocides at which bacterial growth was only just observed were added to 180 μL of Dey–Engley neutralizing broth solution (Sigma Aldrich) to quench biocidal effects. After a short incubation period (5 min), 25 μL were plated on MHA in duplicates and incubated at 37 °C for 24 h. Dey–Engley neutralizing broth solution was prepared according to the manufacturer’s recommendations. Neutralizer efficacy and toxicity were assessed prior to MIC and MBC testing according to the process of Roedel and colleagues . The MBCE. coli to antimicrobials was determined via broth microdilution following CLSI guidelines [The susceptibility of idelines  using thidelines  and the E. coli. Differences were considered significant at p-values < 0.05. Correlations between biocide susceptibility and antimicrobial resistance were evaluated by calculating Spearman’s rank coefficients (sr). Positive correlations were defined as weak (0.1 ≤ sr < 0.4), moderate (0.4 ≤ sr < 0.7) or strong (0.7 ≤ sr < 1) [p-values < 0.05. All statistical tests were carried out with SPSS .Biocide MIC and MBC data were tested for normal distribution using the Kolmogorov–Smirnov test. Comparison of MIC and MBC values between isolates of human and porcine origin (four subgroups) was performed using the Kruskal–Wallis test and the post hoc Dunn–Bonferroni test. The Mann–Whitney test was applied to evaluate differences between non-ESBL and ESBL  rs < 1) , and sig


Trichoderma spp. against the pathogen isolated from the seed. Fifty seed samples were collected from different farmers’ saved seeds of five major faba bean-producing varieties of the Ambo district and were tested by agar plate methods as recommended by the International Seed Testing Association (ISTA). A total of 7 fungal species belonging to 6 genera, viz. Fusarium oxysporum (Schlechlendahl), Fusarium solani (Mart.) Sacc, Aspergillus spp. Penicillium spp. Botrytis spp. Rhizoctonia solani (Kühn) and Alternaria spp. were isolated and identified. Among these, Fusarium spp., Aspergillus spp, and Penicillium spp. were the most predominant fungi in all seed samples. Seed-to-seedling transmission test results confirmed that F. oxysporum, F. solani and R. solani were major causal pathogens that caused root rot and damping-off disease in faba beans and were transmitted from seeds to seedlings. A higher germination rate was observed in Golja-GF2 (97%), and a lower germination rate was observed in Kure Gatira-KF8 (81%). A study on in vitro evaluation of plant extract and Trichoderma spp. against F. oxysporum, F. solani and R. solani revealed that plant extracts at 5%, 10% and 20% concentrations significantly inhibited the mycelial growth of all tested fungi. Inhibitory effects on the three tested fungi  were recorded on T. longibrachiatum (87.91%), T. atroviride (86.87%), Trichoderma virens (86.16%) and T. harzianum (85.45%). The inhibitory effect of the aqueous plant extracts on mycelial growth increased with an increase in concentration, and the hot water extracts showed higher effects compared to the cold water extract in all tested fungi. This study showed that the highest inhibitory effect of Allium sativum L. extracted at a 20% concentration against mycelial growth inhibition of the three test fungi  was 84.60%, 83.61% and 83.47%, respectively. However, Nicandra physalodes (L.) Gaertn.) extracts at the same concentration showed the lowest inhibitory effects on the three tested fungi .Fungal diseases are among the biotic factors limiting the production of faba bean in Ethiopia. The objective of this study was to isolate and identify seedborne mycoflora associated with faba bean seed samples, determine their effects on seed germination and disease transmission, and evaluate the antimicrobial activities of seven plant extracts and four