Borrelia burgdorferi, the Lyme disease spirochete, possesses genetically-encoded responses to doxycycline, but not to amoxicillin.

Some species of bacteria respond to antibiotic stresses by altering their transcription profiles, in order to produce proteins that provide protection against the antibiotic. Understanding these compensatory mechanisms allows for informed treatment strategies, and could lead to the development of improved therapeutics. To this end, studies were performed to determine whether Borrelia burgdorferi, the spirochetal agent of Lyme disease, also exhibits genetically-encoded responses to the commonly prescribed antibiotics doxycycline and amoxicillin. After culturing for 24 h in a sublethal concentration of doxycycline, there were significant increases in a substantial number of transcripts for proteins that are involved with translation. In contrast, incubation with a sublethal concentration of amoxicillin did not lead to significant changes in levels of any bacterial transcript. We conclude that B. burgdorferi has a mechanism(s) that detects translational inhibition by doxycycline, and increases production of mRNAs for proteins involved with translation machinery in an attempt to compensate for that stress.

Introduction

Lyme disease (Lyme borreliosis) is caused by infection by the spirochete Borrelia burgdorferi sensu lato (hereafter referred to as B. burgdorferi, for simplicity). Early manifestations include an expanding annular rash (erythema migrans) along with fever, body aches, and other “flu-like” symptoms. If untreated, more significant symptoms may be seen, including arthritis, meningitis, atrioventricular nodal block, or cardiac arrest [1-3]. This spirochete is sensitive to many types of antibiotics, and human Lyme disease is frequently treated with either doxycycline or amoxicillin [1, 2, 4–6]. Doxycycline inhibits bacterial translation, and amoxicillin inhibits assembly of cell wall peptidoglycan.

Some species of bacteria respond to the presence of antibiotics by modulating their gene and protein expression levels in efforts to overcome those stresses [7-12]. For examples, increasing production of efflux pumps or altering the relative expression levels of proteins involved with cell wall synthesis. Those observations raise the possibility that the Lyme disease spirochete may possess mechanisms that modify bacterial physiology in response to antibiotic therapies. Assessment of that possibility could inform prescribed antibiotics and dosages. Understanding these compensatory mechanisms allows for informed treatment strategies, and could lead to the development of new and/or improved therapeutics.

Exposing B. burgdorferi to sub-lethal levels of β-lactams may result in the spirochetes producing membrane protrusions or acquiring a spherical shape [13-18]. In other bacterial species, treatment with low levels of β-lactam antibiotics leads to weakening of the cell wall and cytoplasmic distortion due to osmotic influx of water [19-22]. However, there is a pervading hypothesis in the literature and among some physicians that β-lactam-induced “round bodies” are a genetically-encoded response by B. burgdorferi to avoid antibiotic killing [16–18, 23–34].

To address these points, we cultured B. burgdorferi in concentrations of doxycycline or amoxicillin that impaired, but did not completely prevent, bacterial replication. Bacteria were thus metabolically active, so changes could be interpreted as indicative of ongoing responses. To assess whether any physiological changes were due to genetically encoded processes, relative levels of mRNAs were compared for each condition.

Material and methods

Effects of antibiotic concentrations on replication of cultured B. burgdorferi

Strain B31-MI16, an infectious clone of B. burgdorferi type strain B31, was grown at 35°C to mid-exponential phase (3 x 107 bacteria/ml) in liquid BSK-II medium [35, 36]. Triplicate aliquots of the culture were diluted 1:100 into fresh BSK-II that contained either no antibiotic, or 0.1, 0.2, or 0.4 μg/ml doxycycline or amoxicillin (Sigma). Bacterial numbers in each culture were then counted using a Petroff-Hauser counting chamber and dark field microscopy, marking time point 0. All cultures were counted every 24 hours for the first four days and on the seventh day. Antibiotic susceptibility assays were performed twice.

Photomicrography

Aliquots of bacterial cultures were spread on glass slides, covered with coverslips, then visualized using dark field microscopy with a 40x objective lens. Images were recorded with a C-mounted Accu-scope Excelis HD camera using Captavision+ software. Bacterial lengths were determined by comparing their sizes against a reference stage micrometer, using Captavision+ software. To quantify B. burgdorferi with membrane distortions after incubation for 24 h in 0.2 μg/ml of amoxicillin, bacteria in randomly selected fields were photographed, then assessed manually for presence of membrane perturbations. Due to variations in numbers of bacteria per field, 109 control bacteria and 110 amoxicillin-treated bacteria were assessed.

Preparation of cultures for RNA sequencing

A mid-exponential phase (3 x 107 bacteria/ml) 35°C culture of B. burgdorferi clone B31-MI16 was used as 1:100 inoculum into 18 separate tubes of 20ml BSK-II broth. Six cultures were not given any antibiotic, 6 received doxycycline to a final concentration of 0.2 μg/ml, and 6 cultures received amoxicillin to a final concentration of 0.2 μg/ml. After 3 hours incubation at 35°C, 3 cultures of each condition were harvested by centrifugation for 15 min at 8200xG at 4°C, then frozen at -80°C. The remaining cultures were similarly harvested and frozen after 24 hours incubation at 35°C. Frozen B. burgdorferi were shipped on dry ice to ACGT Inc. (https://www.acgtinc.com) for RNA processing and sequencing.

RNA extraction and RNA sequencing (RNA-Seq)

Purification of RNA, preparation of libraries, and sequencing were performed by ACGT Inc. according to their standard protocols (https://www.acgtinc.com). Briefly, RNA was extracted from the bacterial pellets by using the Quick RNA-Microprep Kit (Zymo Research). RNA was evaluated with DeNovix and Nanodrop. An individual library was produced for each culture, using Zymo-Seq Ribofree Total RNA Library Kits (Zymo Research). Libraries were evaluated by Qubit and 2100 bioanalyzer to assess quality and quantity before sequencing. Sequencing was performed on Illumina NextSeq500 PE150. Runs were demultiplexed using bcl2fastq to obtain raw fastq files. Experimentally triplicated RNA-Seq produces robust data that do not require accompanying quantitative-reverse transcription PCR analyses [37].

Bioinformatics

Analysis of transcriptome sequencing (RNA-Seq) data were performed in house, essentially described previously [38-40]. Briefly, adapters were removed from the sequencing reads by Trimmomatic [41]. The reads were aligned and counted with a transcriptome reference compiled from the B. burgdorferi strain B31-MI genome (RefSeq numbers AE000783 to AE000794 and AE001575 to AE001584) by using Salmon v1.5.2 [42]. Reads were normalized and differential expression analysis was conducted using DEseq2 [43]. Genes were considered to have significantly different expression at Fold-Change ≥ 2, padj ≤ 0.05, basemean > 20.

Data generated from RNA sequencing analyses were visualized with R v.4.0.1 (https://www.R-project.org/) using ggplot2 (https://doi.org/10.1007/978-3-319-24277-4) for MA plots, pie charts, and bar graphs.

Raw RNA-Seq data have been deposited in the NCBI GEO sequence read archive database, and given accession number GSE197338.

Results and discussion

Study design overview

To determine appropriate sublethal concentrations of antibiotics, an infectious clone of B. burgdorferi type strain B31 was cultured in liquid BSK-II medium that included various concentrations of either doxycycline or amoxicillin. Numbers of bacteria were counted daily over a course of 7 days, with inclusion of all motile and immobile spirochetes. Counting the number of organisms enabled determination of the effects of antibiotic treatment on completion of cell division. Under these culture conditions, this strain was completely inhibited from replicating by 0.4 μg/ml amoxicillin, while the minimum inhibitory concentration of doxycycline was greater than 0.4 μg/ml (Fig 1). Consistent with our findings, prior studies determined that minimum inhibitory and minimum bactericidal concentrations of doxycycline were 0.25–4 and 4–16 μg/ml, respectively, for Lyme disease borreliae [44]. Reported minimum inhibitory and minimum bactericidal concentrations of amoxicillin were 0.015–0.25 and 0.25–0.5 μg/ml, respectively [44]. In our investigations, concentrations of 0.2 μg/ml doxycycline and amoxicillin were found to substantially inhibit, but not eliminate, B. burgdorferi duplication (Fig 1). Those concentrations were designated “sublethal”, and were subsequently tested for their effects on cell morphology and gene expression in B. burgdorferi.

Fig 1

Effects of antibiotics on B. burgdorferi replication rates.

(A) Doxycycline was added to freshly inoculated cultures at concentrations of 0.1 μg/mL, 0.2 μg/mL, and 0.4 μg/ml. (B) Amoxicillin was added to freshly inoculated cultures at concentrations 0.1 μg/mL, 0.2 μg/mL, and 0.4 μg/ml. Bacterial numbers were determined by microscopical examination with a Petroff-Hauser counting chamber after 1, 2, 3, 4 and 7 days of culture.

Cultures were then grown to mid-exponential phase (approximately 3 x 107 bacteria / ml), diluted 1:100 into aliquots of fresh media, then either no antibiotic, or 0.2 μg/ml of either doxycycline or amoxicillin were added. Cultures were incubated at 35°C for either 3 or 24 hours prior to phenotype analysis. Longer time points were not examined, due to the increased possibility that substantial numbers of bacteria would die and their decaying RNA obscure results. To assess the effects of antibiotics on total gene expression, we took an unbiased approach using RNA sequencing (RNA-Seq). Effects of the antibiotics on bacterial morphologies were assessed by darkfield microscopy.

Under the sublethal concentrations of antibiotics used in our studies, bacteria continued to move, elongate, and divide, indicating that the spirochetes were metabolically active (Fig 1 and discussion below). These conditions allowed us to differentiate biological responses to antibiotics from experimental artefacts from dead and/or dying bacteria. On the other hand, two previous transcriptomic analyses of B. burgdorferi cultivated in antibiotics used concentrations of 50 μg/ml doxycycline [45, 46] or 50 μg/ml amoxicillin [45] for 5 days before RNA analyses. Those levels are 12 to 100-times greater than the minimum bactericidal concentrations [44]. Neither of those studies examined the physiology of B. burgdorferi during incubation under those conditions [45, 46].

Doxycycline induced gene expression changes associated with protein translation

Exposure of B. burgdorferi to 0.2 μg/ml doxycycline led to an initial significant decrease in expression of 36 genes after three hours compared to control cells without antibiotics (Fold-Change ≥ 2, padj ≤ 0.05, basemean > 20), while no genes were significantly increased at this timepoint (Fig 2A; Table 1; S1 Table). Differentially expressed genes (DEGs) included those involved in protein translation, DNA replication/repair, cell motility, and carbohydrate metabolism, however only a small number of genes (≤ 7) from each pathway were affected (Fig 2B and 2C). Due to the low number of differentially expressed genes and the diversity of predicted functions, it is possible that these differences reflect nonspecific mRNA turnover differences in the presence of doxycycline. There are no obvious benefits to reducing levels of those transcripts.

Fig 2

Doxycycline induced gene expression changes associated with protein translation.

(A) Fold change versus expression strength for all detectable genes after 3 or 24 hours doxycycline treatment compared to untreated controls. Red (increased) and blue (decreased) dots represent genes with significantly different levels in treated vs. control bacteria (α = 0.05, log2(fold-change) > 1). Yellow dots represent significantly different expression (α = 0.05) without meeting our fold-change cutoff for differential expression (“sigNC”). Gray dots represent genes that were not significantly different between treatment and control bacteria (“NS”). Numbers of significantly upregulated (up) and downregulated (down) genes are shown as proportions of all detectable genes. (B) Clusters of Orthologous Genes (COG) pathways displayed as proportion of all detectable genes (“Total”) compared to differentially expressed genes after 3h or 24h of doxycycline treatment [47]. (C) Stacked bar graph showing the number of increased (red) and decreased (blue) genes in each COG pathway at 3h and 24h timepoints. Percentage of genes in each pathway that were differentially expressed is stated within each bar. Note: Unclassified and general function prediction not shown.

Table 1

Differentially expressed genes in doxycycline treated B. burgdorferi versus untreated controls.

locus	Description	COG pathway	log 2 (fold change) a
			3h	24h
BB_0691	elongation factor G (fusA)	Translation ribosomal structure and biogenesis	-1.01	1.52
BB_0786	50S ribosomal protein L25/general stress protein Ctc	Translation ribosomal structure and biogenesis	-1.04	1.30
BB_0479	50S ribosomal protein L4 (rplD)	Translation ribosomal structure and biogenesis	-1.08	1.40
BB_0690	neutrophil activating protein A (napA)	Replication recombination and repair	-1.08	2.02
BB_0055	triosephosphate isomerase (tpiA)	Carbohydrate transport and metabolism	-1.08	1.38
BB_0328	family 5 extracellular solute-binding protein	Unclassified	-1.10	NS
BB_0428	hypothetical protein	Unclassified	-1.10	1.12
BB_0330	peptide ABC transporter substrate-binding protein	Unclassified	-1.11	NS
BB_J09	outer surface protein D (ospD)	Unclassified	-1.12	NS
BB_0383	basic membrane protein A (bmpA)	Cell motility	-1.13	NS
BB_0603	integral outer membrane protein p66 (p66)	Unclassified	-1.13	NS
BB_0715	cell division protein FtsA (ftsA)	Unclassified	-1.14	NS
BB_0651	protein translocase subunit YajC	Cell motility	-1.14	1.57
BB_0034	outer membrane protein P13	Unclassified	-1.14	1.77
BB_0387	30S ribosomal protein S12 (rpsL)	Translation ribosomal structure and biogenesis	-1.15	1.11
BB_A15	outer surface protein A (ospA)	Unclassified	-1.18	1.33
BB_0337	enolase (eno)	Carbohydrate transport and metabolism	-1.19	NS
BB_0650	hypothetical protein	Unclassified	-1.20	1.58
BB_A16	outer surface protein B (ospB)	Unclassified	-1.20	1.41
BB_0293	flagellar basal body rod protein FlgC (flgC)	Cell motility	-1.21	1.17
BB_0396	50S ribosomal protein L33 (rpmG)	Translation ribosomal structure and biogenesis	-1.26	1.91
BB_0090	V-type ATP synthase subunit K	Unclassified	-1.29	1.06
BB_0385	basic membrane protein D (bmpD)	Cell motility	-1.36	NS
BB_A74	outer membrane porin OMS28 (osm28)	Cell motility	-1.40	NS
BB_0147	flagellin (flaB)	Cell motility	-1.41	1.22
BB_r05	rna13 gene (16S)	Unclassified	-1.41	NS
BB_0054	protein-export membrane protein SecG (secG)	Cell motility	-1.43	1.26
BB_r02	rna8 gene (23S rrlA)	Unclassified	-1.44	1.18
BB_0243	glycerol-3-phosphate dehydrogenase	Unclassified	-1.52	NS
BB_0240	glycerol uptake facilitator	Carbohydrate transport and metabolism	-1.57	NS
BB_0386	30S ribosomal protein S7 (rpsG)	Translation ribosomal structure and biogenesis	-1.58	NS
BB_0465	hypothetical protein	Unclassified	-1.64	1.91
BB_r01	rna7 gene = BB r01	Unclassified	-1.81	1.38
BB_r04	rna10 gene (23S rrlB)	Unclassified	-1.97	1.40
BB_0631	hypothetical protein	Unclassified	-2.03	1.76
BB_0241	glycerol kinase (glpK)	Unclassified	-2.29	NS
rnaseP	rnaseP	Unclassified	NS	2.42
BB_0188	50S ribosomal protein L20 (rplT)	Translation ribosomal structure and biogenesis	NS	2.37
BB_P40	hypothetical protein	Unclassified	NS	2.26
BB_0649	chaperonin GroEL (groL)	Posttranslational modification protein turnover chaperones	NS	2.25
bsrW	bsrW	Unclassified	NS	2.25
BB_B29	PTS system transporter subunit IIBC	Carbohydrate transport and metabolism	NS	2.17
BB_0614	hypothetical protein	Unclassified	NS	2.14
BB_0741	chaperonin GroS (groS)	Posttranslational modification protein turnover chaperones	NS	2.05
BB_0501	30S ribosomal protein S11 (rpsK)	Translation ribosomal structure and biogenesis	NS	1.88
BB_0780	50S ribosomal protein L27 (rpmA)	Translation ribosomal structure and biogenesis	NS	1.82
BB_0445	fructose-bisphosphate aldolase (fbaA)	Carbohydrate transport and metabolism	NS	1.82
BB_0393	50S ribosomal protein L11 (rplK)	Translation ribosomal structure and biogenesis	NS	1.79
BB_A62	6.6 kDa lipoprotein (lp6.6)	Unclassified	NS	1.78
BB_0503	50S ribosomal protein L17 (rplQ)	Translation ribosomal structure and biogenesis	NS	1.73
BB_0405	hypothetical protein	Unclassified	NS	1.73
BB_0778	50S ribosomal protein L21 (rplU)	Translation ribosomal structure and biogenesis	NS	1.72
BB_0482	30S ribosomal protein S19 (rpsS)	Translation ribosomal structure and biogenesis	NS	1.71
BB_0776	hypothetical protein	Unclassified	NS	1.69
BB_0559	PTS system glucose-specific transporter subunit IIA	Carbohydrate transport and metabolism	NS	1.61
BB_O27	protein BdrN (bdrN)	Unclassified	NS	1.58
BB_0238	hypothetical protein	General function prediction only	NS	1.58
BB_0057	glyceraldehyde 3-phosphate dehydrogenase (gap)	Carbohydrate transport and metabolism	NS	1.57
BB_0489	50S ribosomal protein L24 (rplX)	Translation ribosomal structure and biogenesis	NS	1.55
BB_0113	30S ribosomal protein S18 (rpsR)	Translation ribosomal structure and biogenesis	NS	1.55
BB_0781	GTPase Obg	General function prediction only	NS	1.53
BB_0779	hypothetical protein	Translation ribosomal structure and biogenesis	NS	1.52
BB_0189	50S ribosomal protein L35 (rpmI)	Translation ribosomal structure and biogenesis	NS	1.52
BB_0488	50S ribosomal protein L14 (rplN)	Translation ribosomal structure and biogenesis	NS	1.52
BB_0802	ribosome-binding factor A (rbfA)	Translation ribosomal structure and biogenesis	NS	1.51
BB_0392	50S ribosomal protein L1 (rplA)	Translation ribosomal structure and biogenesis	NS	1.50
BB_0502	DNA-directed RNA polymerase subunit alpha (rpoA)	Transcription	NS	1.50
BB_0114	single-stranded DNA-binding protein	Replication recombination and repair	NS	1.50
BB_0366	aminopeptidase	Unclassified	NS	1.49
BB_0115	30S ribosomal protein S6	Translation ribosomal structure and biogenesis	NS	1.49
BB_0805	polyribonucleotide nucleotidyltransferase	Translation ribosomal structure and biogenesis	NS	1.47
BB_0485	50S ribosomal protein L16 (rplP)	Translation ribosomal structure and biogenesis	NS	1.46
BB_0695	30S ribosomal protein S16 (rpsP)	Translation ribosomal structure and biogenesis	NS	1.46
BB_0500	30S ribosomal protein S13 (rpsM)	Translation ribosomal structure and biogenesis	NS	1.45
BB_0390	50S ribosomal protein L7/L12 (rplL)	Translation ribosomal structure and biogenesis	NS	1.45
BB_0504	ribonuclease Y	General function prediction only	NS	1.44
BB_0476	elongation factor Tu (tuf)	Translation ribosomal structure and biogenesis	NS	1.44
BB_0348	pyruvate kinase (pyk)	Carbohydrate transport and metabolism	NS	1.43
BB_0699	50S ribosomal protein L19 (rplS)	Translation ribosomal structure and biogenesis	NS	1.43
BB_0558	phosphoenolpyruvate-protein phosphatase (ptsP)	Carbohydrate transport and metabolism	NS	1.40
BB_0128	cytidylate kinase (cmk)	Nucleotide transport and metabolism	NS	1.38
BB_0785	septation protein SpoVG (spoVG)	Cell wall membrane biogenesis	NS	1.38
BB_0478	50S ribosomal protein L3 (rplC)	Translation ribosomal structure and biogenesis	NS	1.38
BB_0493	50S ribosomal protein L6	Translation ribosomal structure and biogenesis	NS	1.37
BB_0069	aminopeptidase II	Unclassified	NS	1.37
BB_0394	transcription termination/antitermination factor (nusG)	Transcription	NS	1.35
BB_0283	flagellar hook protein FlgE (flgE)	Cell motility	NS	1.34
BB_0494	50S ribosomal protein L18 (rplR)	Translation ribosomal structure and biogenesis	NS	1.34
BB_0495	30S ribosomal protein S5 (rpsE)	Translation ribosomal structure and biogenesis	NS	1.34
BB_0229	50S ribosomal protein L31 type B (rpmE)	Translation ribosomal structure and biogenesis	NS	1.33
BB_0269	ATP-binding protein	Unclassified	NS	1.33
BB_0112	50S ribosomal protein L9 (rplI)	Translation ribosomal structure and biogenesis	NS	1.32
BB_0683	3-hydroxy-3-methylglutaryl-CoA synthase	Lipid transport and metabolism	NS	1.32
BB_0087	L-lactate dehydrogenase	Unclassified	NS	1.30
BB_0477	30S ribosomal protein S10 (rpsJ)	Translation ribosomal structure and biogenesis	NS	1.30
BB_0047	hypothetical protein	Unclassified	NS	1.30
BB_0355	transcription factor	Transcription	NS	1.30
BB_0277	flagellar motor switch protein FliN (fliN)	Cell motility	NS	1.29
BB_0481	50S ribosomal protein L2 (rplB)	Translation ribosomal structure and biogenesis	NS	1.29
BB_0658	23-bisphosphoglycerate-dependent phosphoglycerate mutase	Carbohydrate transport and metabolism	NS	1.29
BB_0570	chemotaxis response regulator	Signal transduction mechanisms	NS	1.28
BB_0436	DNA gyrase subunit B (gyrB)	Replication recombination and repair	NS	1.28
BB_0483	50S ribosomal protein L22 (rplV)	Translation ribosomal structure and biogenesis	NS	1.27
BB_0056	phosphoglycerate kinase (pgk)	Carbohydrate transport and metabolism	NS	1.26
BB_0841	arginine deiminase (arcA)	Unclassified	NS	1.26
BB_0539	hypothetical protein	General function prediction only	NS	1.26
BB_0694	signal recognition particle protein (ffh)	Cell motility	NS	1.26
BB_0557	phosphocarrier protein HPr	Carbohydrate transport and metabolism	NS	1.24
BB_0777	adenine phosphoribosyltransferase (apt)	Nucleotide transport and metabolism	NS	1.24
BB_0127	30S ribosomal protein S1	Translation ribosomal structure and biogenesis	NS	1.24
BB_0122	elongation factor Ts (tsf)	Translation ribosomal structure and biogenesis	NS	1.22
BB_0789	ATP-dependent zinc metalloprotease FtsH	Posttranslational modification protein turnover chaperones	NS	1.21
BB_0492	30S ribosomal protein S8 (rpsH)	Translation ribosomal structure and biogenesis	NS	1.21
BB_0704	acyl carrier protein (acpP)	Lipid transport and metabolism	NS	1.21
BB_0104	periplasmic serine protease DO	Posttranslational modification protein turnover chaperones	NS	1.21
BB_0426	nucleoside 2-deoxyribosyltransferase superfamily protein	Function unknown	NS	1.20
BB_0027	hypothetical protein	Unclassified	NS	1.20
BB_0123	30S ribosomal protein S2 (rpsB)	Translation ribosomal structure and biogenesis	NS	1.20
BB_B19	outer surface protein C (ospC)	Unclassified	NS	1.18
BB_0727	phosphofructokinase	Carbohydrate transport and metabolism	NS	1.18
BB_0697	ribosome maturation factor RimM (rimM)	Translation ribosomal structure and biogenesis	NS	1.18
BB_B22	guanine/xanthine permease	General function prediction only	NS	1.17
BB_0061	thioredoxin (trx)	Posttranslational modification protein turnover chaperones	NS	1.17
BB_0338	30S ribosomal protein S9 (rpsI)	Translation ribosomal structure and biogenesis	NS	1.17
BB_0499	50S ribosomal protein L36 (rpmJ)	Translation ribosomal structure and biogenesis	NS	1.16
BB_0121	ribosome recycling factor (frr)	Translation ribosomal structure and biogenesis	NS	1.16
BB_0744	p83/100 antigen (p83/100)	Unclassified	NS	1.16
BB_0172	von Willebrand factor type A domain-containing protein	Function unknown	NS	1.16
BB_0615	30S ribosomal protein S4 (rpsD)	Translation ribosomal structure and biogenesis	NS	1.15
BB_0391	50S ribosomal protein L10	Translation ribosomal structure and biogenesis	NS	1.14
BB_0190	translation initiation factor IF-3 (infC)	Translation ribosomal structure and biogenesis	NS	1.14
BB_0059	CBS domain-containing protein	General function prediction only	NS	1.14
BB_0698	tRNA (guanine-N(1)-)-methyltransferase (trmD)	Translation ribosomal structure and biogenesis	NS	1.13
BB_0435	DNA gyrase subunit A (gyrA)	Replication recombination and repair	NS	1.13
BB_0735	rare lipoprotein A	Cell wall membrane biogenesis	NS	1.12
BB_0120	isoprenyl transferase (uppS)	Lipid transport and metabolism	NS	1.12
BB_0518	chaperone protein DnaK (dnaK)	Posttranslational modification protein turnover chaperones	NS	1.12
BB_0684	isopentenyl-diphosphate delta-isomerase (fni)	Unclassified	NS	1.12
BB_0487	30S ribosomal protein S17 (rpsQ)	Translation ribosomal structure and biogenesis	NS	1.11
BB_0231	hypothetical protein	Function unknown	NS	1.10
BB_0020	diphosphate—fructose-6-phosphate 1-phosphotransferase	Carbohydrate transport and metabolism	NS	1.10
BB_0339	50S ribosomal protein L13 (rplM)	Translation ribosomal structure and biogenesis	NS	1.09
BB_0144	glycine/betaine ABC transporter substrate-binding protein	Unclassified	NS	1.09
BB_0588	MTA/SAH nucleosidase	Nucleotide transport and metabolism	NS	1.08
BB_0125	hypothetical protein	Unclassified	NS	1.07
BB_0230	transcription termination factor Rho (rho)	Transcription	NS	1.06
BB_0497	50S ribosomal protein L15 (rplO)	Translation ribosomal structure and biogenesis	NS	1.06
BB_0611	ATP-dependent Clp protease proteolytic subunit (clpP)	Cell motility	NS	1.06
BB_0842	ornithine carbamoyltransferase (argF)	Unclassified	NS	1.05
BB_0171	hypothetical protein	General function prediction only	NS	1.04
BB_0533	protein PhnP	General function prediction only	NS	1.04
BB_0491	30S ribosomal protein S14 (rpsN)	Translation ribosomal structure and biogenesis	NS	1.03
BB_0652	protein translocase subunit SecD (secD)	Cell motility	NS	1.01
BB_0281	motility protein A (motA)	Cell motility	NS	1.01
BB_0484	30S ribosomal protein S3 (rpsC)	Translation ribosomal structure and biogenesis	NS	1.01
BB_0070	hypothetical protein	Function unknown	NS	1.00
BB_0067	peptidase	Unclassified	NS	1.00
BB_0460	lipoprotein	Unclassified	NS	-1.01
BB_J46	hypothetical protein	Replication recombination and repair	NS	-1.02
BB_M37	protein BppC (bppC)	Unclassified	NS	-1.04
BB_0546	hypothetical protein	Cell wall membrane biogenesis	NS	-1.06
BB_M14	hypothetical protein	Unclassified	NS	-1.10
BB_F14	hypothetical protein	Unclassified	NS	-1.17
BB_L41	hypothetical protein	Unclassified	NS	-1.40
tmRNA	tmRNA	Unclassified	NS	-3.57

Fold changes are expressed as log2. NS = not a significant change

a”NS” denotes not significantly different (α = 0.05; log2(fold change)>1)

After 24 hours of doxycycline treatment, microscopical examination showed that B. burgdorferi were motile and were, therefore, metabolically active. RNA-Seq analyses at that time point revealed that 151 genes were differentially expressed (143 upregulated, 8 downregulated) compared to control cells (Fig 2A, Table 1, and S1 Table). Notably, a plurality of differentially expressed genes (53/151 DEGs; 35%) are involved in protein synthesis, all of which were upregulated in the treatment group (Fig 2B and 2C). These genes account for nearly half (47%) of all genes annotated as belonging to the translation, ribosomal structure, and biogenesis pathway (Fig 2C) [47]. These gene expression changes indicate that B. burgdorferi possesses a genetically-encoded mechanism(s) that attempts to overcome ribosome impairment, which is focused on enhanced production of mRNAs for components of translation.

The most common mechanism of bacterial resistance to tetracyclines is through efflux pumps that export the antibiotic from the cell [48]. While B. burgdorferi naturally encodes an efflux pump, BesCAB [49], levels of besCAB mRNA were not affected by presence of doxycycline (Table 1 and S1 Table). B. burgdorferi does not encode homologues of any known enzyme that could modify doxycycline [50, 51], so that possible mechanism is unlikely to affect survival in the presence of the antibiotic.

Notably, expression of napA increased after 24h exposure to doxycycline (Table 1). This transcript encodes a periplasmic protein (also called BicA) that can bind copper and manganese, and associates with cell wall peptidoglycan [52-54]. The predicted sequence of NapA is similar to the Dps proteins of other bacterial species, which are involved with protecting DNA from stresses [55], although borrelia NapA lacks the Dps sequences that are involved with DNA-binding [52]. NapA derives its name from eutrophil ttracting rotein , and has been demonstrated to enhance immune responses [52, 54, 56–58]. It remains to be seen whether differential expressionof NapA occurs during doxycycline treatment in the context of mammalian infection.

As noted above, two other research groups have published results of RNA-Seq analyses of B. burgdorferi that were incubated for 5 days in 50 μg/ml doxycycline [45, 46]. The doxycycline concentration used in those studies was many times greater than what we and others found to inhibit B. burgdorferi replication in culture [44]. Although Feng et al. [45] described B. burgdorferi that had been incubated in 50 μg/ml doxycycline as “persisters”, those researchers did not assess the viability of the bacteria that were used for RNA-Seq analysis. We also point out that the accepted definition of bacterial persistence cannot be applied to bacteriostatic antibiotics such as doxycycline, since the nature of those antibiotics does not directly kill bacteria [59]. The high dosages used by Feng at al. and Caskey et al. may explain why there is very little overlap between their results, despite both using essentially the same culture conditions [45, 46]. Feng et al. reported significant (> 2-fold) increases of 35 transcripts and decreases of 33 transcripts, encoding a broad range of functions [45]. In contrast, Caskey et al. [46] noted increases of 20 transcripts, while 40 transcripts were found to be downregulated. Many of the downregulated transcripts were of various plasmid encoded outer surface proteins. Of the 20 upregulated transcripts reported by Caskey et al., all but one came from the Lyme spirochete’s resident cp32 prophages [60]. This is unlike the broad-ranging transcript groups reported to be upregulated by Feng et al. It is not clear whether the Caskey et al. results can be interpreted to imply anything about the native prophage’s responses to doxycycline stress, since the vast majority of prophage genes were not affected. The increased transcripts encode portal proteins of four different cp32 bacteriophages, and three different Erp lipoproteins that localize to the bacteria’s outer surface, are not predicted to be components of the bacteriophage particle, and do not possess functions relevant to survival in doxycycline [60-64].

Caskey et al. found that some bacteria had survived incubation for 5 days in 50μg/ml doxycycline, and resumed growth when subcultured in fresh medium without antibiotic or injected into mice [46]. That result is consistent with our observations of continued bacterial motility when exposed to 0.2 μg/ml doxycycline. As with other bacterial species, tetracyclines are bacteriostatic to B. burgdorferi, rather than overtly bactericidal [65].

Amoxicillin resulted in morphological changes, but not changes in gene expression

Amoxicillin is a β-lactam, which inhibits cell wall production. In contrast to doxycycline, exposure for 3 or 24 hours to 0.2 μg/ml amoxicillin did not result in significant changes to any transcript, even without a fold-change cutoff for differential expression designation (Fig 3 and S1 Table). The previous study by Feng et al. [45] reported that 5 days incubation in 50 μg/ml amoxicillin resulted in their detection of significant increases in 41 mRNAs of a range of functions, but none of which encode proteins involved with cell wall or membrane synthesis or remodeling. As noted above, Feng et al. did not assess bacterial viability before their RNA-Seq analyses.

Fig 3

Amoxicillin did not induce gene expression changes.

Fold change versus expression strength for all detectable genes after 3 or 24 hours amoxicillin treatment compared to untreated controls. No genes were significantly different between treatment and control groups (α = 0.05), as indicated by gray dots (“NS”).

The absence of cell wall-directed responses to amoxicillin suggests that B. burgdorferi may lack a mechanism to assess cell wall integrity. While many bacterial species recycle peptidoglycan components as they grow in size, B. burgdorferi lacks such an ability, and instead sheds remnants of cell wall remodeling into the environment [66]. Together, these suggest that B. burgdorferi transports peptidoglycan components into the periplasm to build its cell wall as it grows in length, while “assuming” that the cell wall is being assembled correctly.

Examination under the microscope revealed that amoxicillin-treated B. burgdorferi displayed evidence of membrane swelling (Fig 4). After 24 h in the antibiotic, microscopical examination of randomly selected bacteria showed membrane distensions in 49/110 (44.6%) of amoxicillin-treated spirochetes, as compared to 6/109 (5.5%) of control B. burgdorferi. Those bacteria were comparable in shape to the so-called “round bodies” or “cysts” that have previously been described upon treatment of cultured B. burgdorferi with sublethal concentrations of β-lactams [13, 14, 16, 18, 33]. However, our transcriptomic analyses indicate that the amoxicillin-induced morphological changes were not genetically encoded. Instead, the observed membrane swellings were probably results of water diffusing into the cytoplasm and expanding the inner membrane that was no longer constrained by an intact cell wall. Similar osmotically-induced spheroplasts can be generated in other bacterial species through β-lactam induced weakening of their cell walls [19-22]. Although β-lactam derived spheroplasts of B. burgdorferi are evidently not biologically relevant to these bacteria in nature, such experimentally-derived structures can be useful for investigations of membrane functions [21, 22].

Fig 4

Photomicrographs of representative B. burgdorferi from (A) control, or (B, C, and D) amoxicillin-treated cultures after 24h incubation.

All fields are shown at the same relative magnification. Imaged with a 40x objective lens and darkfield illumination.

Conclusions

In nature, the Lyme disease spirochete exists only within vertebrates or ticks. In those environments, it is unlikely that B. burgdorferi would routinely encounter molds that produce β-lactam antibiotics and thus would not have been under pressure to evolve escape strategies. The evident inability of B. burgdorferi to respond to amoxicillin’s inhibition of cell wall synthesis supports that hypothesis. Our data also suggest that B. burgdorferi does not naturally encounter other conditions that block peptidoglycan synthesis, and thus has not evolved mechanisms to respond to such a stress.

In contrast, B. burgdorferi evidently possesses a mechanism(s) that detects the impairment of translation due to doxycycline, and attempts to overcome that inhibition by increasing expression of genes involved with translation. Tetracyclines are synthesized in nature by actinomycete bacteria, which are predominantly soil microbes and are therefore unlikely to be encountered by B. burgdorferi in nature [67]. It remains to be seen whether other methods that inhibit translation yield similar effects. Nonetheless, the response of B. burgdorferi raises questions about where these spirochetes encounter translational impairment in their natural tick-vertebrate infectious cycle. One possibility is in the midgut of an unfed tick, where B. burgdorferi is starved for amino acids; accumulation of mRNAs for producing translation-associated proteins might allow rapid production of those proteins when the tick begins feeding on nutrient-rich blood. Further studies of Lyme disease spirochete physiology during its infectious cycle can help solve this question.

Taken together, our studies found that B. burgdorferi demonstrates distinct responses to different antibiotics. While it may be that B. burgdorferi within vertebrate tissues activate regulatory pathways that are not observed in culture, and thereby adapt to tolerate antibiotics, we also note that there is no direct evidence to support such hypothetical mechanisms. Importantly, neither our studies or those of Feng at al. or Caskey et al. [45, 46] directly addressed the efficacy of doxycycline or amoxycillin for treatment of Lyme disease in humans, as those treatments have been determined empirically. Rather, these insights shed light on the feedback mechanisms to environmental stresses by B. burgdorferi, and could lead to the development of novel therapeutic treatments for this important pathogen.

All results for doxycycline after 3h and 24h, and all results for amoxicillin after 3h and 24h.

(XLSX)

Click here for additional data file.

7 Jun 2022

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Reviewer #1: This report by Saylor, et al. describes the gene expression changes associated with culture of Borrelia burgdorferi in sublethal doses of doxycycline versus amoxicillin. The results are indeed novel and serve to help explain antibiotic tolerance to doxycycline. This reviewer has no specific issues with the science, yet there are numerous areas that need improvement with regard to presentation and interpretation of the data.

Introduction: paragraph 4, lines 71-79 need revision. First, the sentence lines 71-73 should include references 36, 37, 38, 40, 41 and 42 and should read “..have also reported persistence of intact spirochetes or their components by tick acquisition and molecular detection.” Line 74-76 only cites a critical interpretation of the results, suggesting that antibiotic treatment is sufficient and that the persisting organisms are not viable. Also, it is antibiotic regimens, not regiments. Finally, the lines 78-79 are erroneous in that these 2 papers were not considered:

Caskey JR, Hasenkampf NR, Martin DS, Chouljenko VN, Subramanian R, Cheslock MA, Embers ME. The Functional and Molecular Effects of Doxycycline Treatment on Borrelia burgdorferi Phenotype. Front Microbiol. 2019 Apr 18;10:690. doi: 10.3389/fmicb.2019.00690. PMID: 31057493; PMCID: PMC6482230.

Feng J., Shi W., Zhang S., Zhang Y. (2015). Persister mechanisms in Borrelia burgdorferi: implications for improved intervention. Emerg. Microbes Infect. 4:e51. 10.1038/emi.2015.51

While the Caskey paper is cited later in the manuscript, it was published well before this was submitted.

An important aspect of the reported study (versus the 2 others) is that the antibiotic treatment concentration is much lower that the reported MIC in vitro, it does reflect a concentration that can be expected to be achieved in the serum of human patients. This can be added to the discussion, where references on antibiotic tolerance by B. burgdorferi and other bacteria is warranted (and absent).

Materials/Methods:

-the section on preparation of cultures (lines 102-110) only lists amoxicillin twice instead of doxycycline.

For the RNA extraction, bioanalyzer results: please describe what “adequacy” means in terms of the data obtained. Also, the methods list 3 cultures (biological replicates) used for each time point. Were each of these analyzed individually with transx sequencing, or were there technical replicates as well? Please add more detail on how the replicates were processed and determined to be significant using the stated software. When revising this section, consider whether or not someone could replicate the study with the level of detail provided.

Results:

-line 150 states a “1:00” dilution

-line 216 should read “Microscopic examination..”

-Figures 2 and 4 should be presented side-by-side and labeled by drug treatment.

-Figure 3 has no panel labels

Discussion:

Two major aspects were lacking in the discussion: (1) a comparison to previous published results in terms of study design, common findings, different findings and interpretation. The Feng, et al. paper should be included here; and (2) an assessment of how these findings relate to patient treatment, antibiotic tolerance and treatment failure with doxycycline. The caveat that host adaptation (by the spirochete) is lacking should be included as a study limitation as well.

Reviewer #2: The author worked on the antibiotic response by Borrelia burgdorferi and observed the transcriptional changes after 3 h and 24 h post-treatment with sub-lethal concentrations of doxycycline and amoxicillin. The project design indicates the author's lack of in-depth knowledge about the antibiotic response. I did not find any novelty in this study; the paper is poorly written, and the explanation of their finding is quite confusing. Though this work has potential and is on a medically relevant topic, overall, the authors have not met the criteria to publish this work. The work does not distinguish itself from a previously published work in 2019. Thus, as written, it does not appear to be new work. The 2019 article (DOI: 10.3389/fmicb.2019.00690) has far more experimental data, contains a model organism (mice), and conclusions are well supported.

Most importantly, this manuscript does not contradict the 2019 findings. Furthermore, the authors' data analysis and assertions lack the expected scientific justification and rigor. Therefore, I have no choice but to recommend the rejection of this manuscript.

Strength:

Lyme disease is a relevant topic, and transcriptional analysis of antibiotic-treated and untreated populations is a reasonable approach, though I do have concerns about the concentrations of antibiotics used.

I appreciate the presence of Table 1 in the manuscript. Too often in the literature are the number of genes listed but not the actual names (Locus), or they are hard to find (bared) in the supplemental material.

Limitation/weakness:

Major concerns:

Line 30-32. The major conclusion is that sublethal concentration of doxycycline leads to increased levels of proteins involved in translation. How is this a new finding?

Line 136-137. Why were sublethal concentrations of antibiotics used for this work? The authors made it clear that they were studying this bacterium because it causes Lyme disease, and they want to understand how it survives antibiotics. Lyme disease is treated with typically Lethal dosages of antibiotics. Therefore, it only seems logical to isolate RNA from antibiotic persisters as others have previously done (see DOI: 10.1038/s41598-021-85509-7).

Lines 201-202: I am concerned if we can consider this work new and relevant. Another group already used doxycycline and published their data. Ref 69 used 50 ug/ml of doxycycline at a higher antibiotic concentration than the authors used here, 2 ug/ml. Furthermore, based on 10.2147/IDR.S19201, doxycycline MBC is 25 ug/ml. Thus, Ref 69 work is more relevant to antibiotic concentrations that kill this bacterium and more medically relevant. In addition, the data from Ref 69 is available. The authors could have compared their findings to this work. If they found something different, that would be interesting. For this work to be relevant, the authors would need to do mutational studies (knockdown, knockout, overexpression, or point mutation studies) of the genes they identified. Then test how well the bacteria survive the antibiotics.

I am also confused about the statement in lines 201-202, "While our studies were in progress, another research group published RNA-Seq results of B. burgdorferi that had been cultured for 5 days in 50 μg/ml doxycycline [69]." Ref 69 was published in 2019, which is about 3 years ago. They also did this work with mice and thus had the addition of a model organism to support their results. Based on this information and without comparing the author's work to Ref 69, it is my opinion that the paper's finding relies solely on the results for Amoxicillin. The authors state that "amoxicillin did not lead to significant changes in levels of any bacterial transcript."

Line 32-34: The authors state that the "amoxicillin did not lead to significant changes in levels of any bacterial transcript." I question this finding. If the amoxicillin concertation is at the MIC or above, one would expect a change in the transcriptome. Otherwise, how are the cells surviving? The authors should have described how they survived, for example, a protein level response. All other reported antibiotic-challenge studies have seen a change in gene expression. For this to be believable, the authors would need to test different levels of amoxicillin concentrations. Alternatively and the best course of action, would be to use lethal amoxicillin concentrations like others have done.

Line 247-248: "However, our transcriptomic analyses indicate that the amoxicillin-induced morphological changes were not genetically encode." This is a bold statement that I did not find substantial evidence from this work to be supported. Here is why:

1. RNA-seq analysis generally relies on using a 2-fold-cutoff as the authors did here. However, it is possible that a 1.5 gene change can have a significant effect on cell physiology? The authors must titrate antibiotic concentrations and isolate RNA from them to make this claim with reasonable support. Alternatively, they could test at lethal (MBC) concentrations.

2. A heterogenous population will have high noise levels and high variations (e.g. the standard deviations or SEM would be large). Bacterial antibiotic persister populations and tolerant populations are heterogeneous. Some cells survive, and others die. This has been tested for nearly 80 years; see Biggers 1944 (https://doi.org/10.1016/S0140-6736(00)74210-3). These variations mean that differences may be hidden. One method to get around these variations is to eliminate non-persister cells by using lethal (MBC) concentrations of the antibiotic, as others have done.

This study lacks a clear goal and significance. The explanation of their finding is vague, the author has a considerable lack of knowledge in literature, and more importantly, there is no novelty in this study.

Bacteria evade antibiotic treatment by entering into a metabolically repressed state, which is known as persistence (https://doi.org/10.1038/s41579-019-0196-3). The persistence mechanisms are widely studied and found in almost all bacterial species including the minimal cell (Mycoplasma mycoides JCVI-Syn3B) (doi: 10.1016/j.isci.2021.102391). There are also several studies was done about the persister formation of Borrelia burgdorferi strain (doi: 10.1186/s13071-019-3495-7 ). Here, the author studied antibiotic response on Borrelia burgdorferi, but they did not mention anything about the persistence, which seems to be quite unusual. The author should mention antibiotic persistence, and explain whether their study shows any light to reveal the mechanism of antibiotic tolerance or persistence, which is one of the crucial reasons behind antibiotic resistance.

Since the author did not observe any transcriptional changes in bactericidal concentration, then how are lines 56-60 relevant to this study?

Lines 78-79 contradict lines 201-202. In lines 78-79, the author said no study has been done yet at molecular level to observe genetically encoded responses during antibiotic treatment of this strain. In line 201-202, they mentioned another study had been done to understand transcriptional responses at bactericidal concentration. Please clarify.

As written, the introduction is not useful and verbose. The significance of the work beyond the connection to Lyme disease is not clear. The significance (the REASON) for the study should be the main focus of the introduction. I highly suggest the author remove the first paragraph describing Lyme disease symptoms because it is unnecessary. Instead, the authors should focus more on the bigger picture of their study, like why the study of antibiotic response is important.

The author checked the MIC value by adding antibiotic treatment and counting the cells/mL under a microscope using a Petroff Hausser hemocytometer. However, the method is not entirely accurate since the author will count both live and dead cells at the same time, and there will be cells a different planes of the hemocytometer, so there is a high chance of miscalculation. The author did not validate their counts using CFU/ml. Although the generally accepted way of doing MIC and MBC tests is using optical density assay, CFU/mL agar plate assays, strip assays, or disk assay using Kirby-Bauer method. Thus, I am confused why the author did the MIC test in this way. Moreover, they did not show the MBC value, which is important to understand the level of antibiotics used. Therefore, the authors need to explain the reason for this unusual experimental design.

In Fig. 1, the author showed that cell growth was inhibited with 0.2 and 0.4 ug/mL of an antibiotic (doxycycline and amoxicillin). However, the growth inhibition was visible at 2 days, not at 24 h. In 24 h, the doxycycline showed a little bit of difference. However, without statistics (e.g. standard deviation, SEM, etc.), the reader does not know if they overlap. Based on Fig. 1, there is no difference with or without amoxicillin. Again, no statistics to support this claim. Instead of seeing very little inhibition or no inhibition at all at 24 h of antibiotic treatment, the author should/could have used other time points to isolate RNA. Why did they use 3 h and 24 h of antibiotic-treated cells? Is there any good explanation for that? Why did the author not go more extended time point? The author should address these questions in the discussion.

The author also mentioned that they did not see any significant changes in gene expression levels at 24 h with amoxicillin treatment. It seems most plausible the author did not see any significant change because there is no significant growth inhibition at that time point (24 h) with antibiotic treatment. The author should address this concern in the manuscript. I also believe the author should isolate RNA from longer antibiotic-treated cells where the inhibition is quite visible and statically significant with the control. Also, the author should add p-values for each time point in Fig. 1.

If the authors are unwilling to do RNA-seq for a longer time point with amoxicillin, I suggest the author remove this part from the paper. Because their experimental design has a huge flaw, and their explanation does not make any sense. I am adding here another reference (https://doi.org/10.1038/s41598-021-85509-7). They studied the transcriptional response on Ampicillin (beta-lactam antibiotic) treated E. coli cells and observed several genes are upregulated or downregulated at 3 h and 6 h of antibiotic treated cells. Adding the amoxicillin results and their hypothesis weakens the overall manuscript, so I highly suggest removing this part.

10. Fig. 3: At what time point during antibiotic-treated cells were considered for this microscopic analysis? Also, why do the authors show only one or two cells in one image rather than showing multiple cells? Do all the cells follow the same pattern during long-term Amoxicillin treatment? There is also a morphological difference in Fig. 3. D, E, and F. Why so? Could you hypothesize or find a reference for why? Please clarify.

Minor concerns:

Fig. 3 does not have any numbering on top of the figure. You must add letters (A, B, C…) called out in the legend to the figure.

Lines 112-120: Was rRNA removed from the samples? Though it is not stated in the Materials and Methods, I am assuming so because this is a typical step to save on cost and expression analysis time. If so, it should be stated and how.

The author should mention how many reads of each sample they used to analyze the RNA-seq data.

Line 244: Fig. 3A should be removed since the author only referred to amoxicillin-treated cultures. So, it should be Fig. 3D-F.

4. Line 107: Authors should mention the speed, time and temperature used for centrifugation to isolate the cells. If the author used 4˚C to centrifuge the cells, does this temperature change affects the transcriptional response?

Author Contributions is missing? Who did what on this manuscript?

Grammar and such needs major improvement. Here are some examples:

Line 27: Remove the comma from "profiles, in order"

Line 150: No spaces between bacteria/ml

Table 1: Capitalize Locus and Log2

Line 164: A space is needed between Fig. and 2

Fig.: Fig. is an abbreviation for figure, and there should be a period after Fig.

Add reference for line 47 and lines 50-55

**********

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18 Jul 2022

Dear Dr. Wooten,

Our thanks to you and the two reviewers for your evaluation of our research and manuscript. We have extensively revised the manuscript in response to the reviewers’ concerns. In particular, Reviewer 2 appears to have overlooked several aspects of the initial manuscript, and we hope that the revisions make our points more clear.

Additionally, we have removed the Introduction section’s mention of apparent failures of antibiotics to clear B. burgdorferi infections from animals and human patients. Both reviewers interpreted our study as having direct impact on those apparent treatment failures, which we did not intend to imply. In response to their comments, we do address this issue at the end of the revised Conclusions section.

We addressed the reviewers’ comments as follows (our responses indicated by ***).

Reviewer 1:

This report by Saylor, et al. describes the gene expression changes associated with culture of Borrelia burgdorferi in sublethal doses of doxycycline versus amoxicillin. The results are indeed novel and serve to help explain antibiotic tolerance to doxycycline. This reviewer has no specific issues with the science, yet there are numerous areas that need improvement with regard to presentation and interpretation of the data.

*** Thank you

Introduction: paragraph 4, lines 71-79 need revision. First, the sentence lines 71-73 should include references 36, 37, 38, 40, 41 and 42 and should read “..have also reported persistence of intact spirochetes or their components by tick acquisition and molecular detection.” Line 74-76 only cites a critical interpretation of the results, suggesting that antibiotic treatment is sufficient and that the persisting organisms are not viable.

*** We have rearranged the order of the manuscript, and incorporated these suggestions into what is now the final paragraph of the manuscript (lines 289 - 298)

Also, it is antibiotic regimens, not regiments.

*** The typographical error has been fixed.

Finally, the lines 78-79 are erroneous in that these 2 papers were not considered: Caskey JR, Hasenkampf NR, Martin DS, Chouljenko VN, Subramanian R, Cheslock MA, Embers ME. The Functional and Molecular Effects of Doxycycline Treatment on Borrelia burgdorferi Phenotype. Front Microbiol. 2019 Apr 18;10:690. doi: 10.3389/fmicb.2019.00690. PMID: 31057493; PMCID: PMC6482230. Feng J., Shi W., Zhang S., Zhang Y. (2015). Persister mechanisms in Borrelia burgdorferi: implications for improved intervention. Emerg. Microbes Infect. 4:e51. 10.1038/emi.2015.51

*** Thank you for your suggestions. Both Caskey et al. and Feng et al. are discussed, with contrasts and comparisons to each other and to our results.

While the Caskey paper is cited later in the manuscript, it was published well before this was submitted.

*** This was a holdover from earlier versions of the manuscript. These studies were begun several years ago, as a Master student’s work. We have removed the confusing phrase.

An important aspect of the reported study (versus the 2 others) is that the antibiotic treatment concentration is much lower that the reported MIC in vitro, it does reflect a concentration that can be expected to be achieved in the serum of human patients. This can be added to the discussion, where references on antibiotic tolerance by B. burgdorferi and other bacteria is warranted (and absent).

*** The purpose of our study was to determine whether antibiotic stresses to B. burgdorferi result in transcriptional changes. This can only be addressed with living bacteria. Antibiotic concentrations above the MIC will kill substantial numbers of bacteria, and dead bacteria cannot respond to the stress. Moreover, RNA of dead bacteria is subject to degradation, which can complicate interpretation of results.

Materials/Methods:

-the section on preparation of cultures (lines 102-110) only lists amoxicillin twice instead of doxycycline.

*** Typographical error has been fixed.

For the RNA extraction, bioanalyzer results: please describe what “adequacy” means in terms of the data obtained.

*** We have re-written the method, and clarified that RNA extraction and library production was performed by ACGT Inc., according to their standard methods.

Also, the methods list 3 cultures (biological replicates) used for each time point. Were each of these analyzed individually with transx sequencing, or were there technical replicates as well?

*** Each culture was analyzed individually, without technical replicates.

Please add more detail on how the replicates were processed and determined to be significant using the stated software. When revising this section, consider whether or not someone could replicate the study with the level of detail provided.

*** The methods are described herein, and in our previous publications, such that they can be readily replicated.

Results:

line 150 states a “1:00” dilution

*** Typo fixed.

-line 216 should read “Microscopic examination..”

Either “microscopical” or “microscopic” are acceptable English. We prefer “microscopical”.

-Figures 2 and 4 should be presented side-by-side and labeled by drug treatment.

*** Due to rearrangement, the previous Figure 4 is now Figure 3, and former Figure 3 is now Figure 4. The data in Figures 2 and (now) 3 report data from two distinct studies, and should remain as separate figures.

-Figure 3 has no panel labels

*** Oops! The wrong version of this figure was originally submitted. This has been remedied.

Discussion:

Two major aspects were lacking in the discussion: (1) a comparison to previous published results in terms of study design, common findings, different findings and interpretation. The Feng, et al. paper should be included here;

*** Thank you for calling the Feng et al. paper to our attention. The initial manuscript compared our results with those of Caskey et al. We have now expanded to include comparisons and contrasts between our results and those of both Feng and Caskey. Notably, both Feng and Caskey incubated B. burgdorferi in 50 �  g/ml doxycycline for 5 days, which is well above the MIC, and did not address whether or not their studied bacteria were metabolically active. In our studies, both antibiotics were used at sublethal concentrations, and both growth curves and microscopical analyses showed that the bacteria were metabolically active. We also note that the results of Feng and Caskey were distinct from each other, even though both used the same methods.

(2) an assessment of how these findings relate to patient treatment, antibiotic tolerance and treatment failure with doxycycline.

*** We have addressed these points in lines 289-298.

The caveat that host adaptation (by the spirochete) is lacking should be included as a study limitation as well.

*** We now bring this up in lines 290-293.

Reviewer 2:

The author worked on the antibiotic response by Borrelia burgdorferi and observed the transcriptional changes after 3 h and 24 h post-treatment with sub-lethal concentrations of doxycycline and amoxicillin. The project design indicates the author's lack of in-depth knowledge about the antibiotic response.

*** This statement, and other below, suggest that reviewer was confused about the goals of our study. We hope that the revised manuscript is more clear.

I did not find any novelty in this study; the paper is poorly written, and the explanation of their finding is quite confusing.

*** These studies have never been performed before, to the best of our knowledge. We hope that the revised manuscript is easier to understand.

Though this work has potential and is on a medically relevant topic, overall, the authors have not met the criteria to publish this work. The work does not distinguish itself from a previously published work in 2019. Thus, as written, it does not appear to be new work.

*** The initial manuscript compared and contrasted with that 2019 work (Caskey et al.). There are substantial differences in the methods. Chiefly among these, Caskey et al. incubated B. burgdorferi for 5 days in 50 �  g/ml doxycycline, which is well above the MIC for that antibiotic. In contrast, we incubated B. burgdorferi in 0.2 �  g/ml doxycycline, which is below the MIC, and thus allowed the bacteria to remain metabolically active and, potentially, adjust transcription levels.

The 2019 article (DOI: 10.3389/fmicb.2019.00690) has far more experimental data, contains a model organism (mice), and conclusions are well supported.

*** The only mouse work of Caskey et al. involved them injecting bacteria (which had been cultured in 50 �  g/ml doxycycline for 5 days) into mice. The result showed that some bacteria were not killed by that level of doxycycline. We do not debate that point.

Most importantly, this manuscript does not contradict the 2019 findings.

*** In the initial manuscript, and again in the revised version, we discussed differences in our results and those of Caskey et al. Primarily, Caskey et al. reported increases of 20 transcripts, all but one of which came from the Lyme spirochete’s resident cp32 prophages. The vast majority of prophage genes were not affected. The increased transcripts encode portal proteins of 4 different cp32 bacteriophages, and 3 different Erp lipoproteins. In contrast, we observed differential expression of 151 genes, with 143 upregulated and 8 downregulated. 53/151 differentially expressed genes are involved in protein synthesis, and account for nearly half (47%) of all genes annotated as belonging to the translation, ribosomal structure, and biogenesis pathway.

Furthermore, the authors' data analysis and assertions lack the expected scientific justification and rigor. Therefore, I have no choice but to recommend the rejection of this manuscript.

*** The reviewer did not support these assertions, so we cannot address their complaints.

Strength:

Lyme disease is a relevant topic, and transcriptional analysis of antibiotic-treated and untreated populations is a reasonable approach, though I do have concerns about the concentrations of antibiotics used.

*** The concentrations of antibiotics were appropriate for the questions at hand. We also note that reviewer 1 stated that the studied antibiotic concentrations are medically relevant.

I appreciate the presence of Table 1 in the manuscript. Too often in the literature are the number of genes listed but not the actual names (Locus), or they are hard to find (bared) in the supplemental material.

*** Thank you. We also find it frustrating when reports do not lay out RNA-Seq data in ways that are difficult for readers to follow.

Limitation/weakness:

Major concerns:

Line 30-32. The major conclusion is that sublethal concentration of doxycycline leads to increased levels of proteins involved in translation. How is this a new finding?

*** We are not aware of any publication that addresses this. The reviewer did not support their assertion that our findings are not new.

Line 136-137. Why were sublethal concentrations of antibiotics used for this work?

*** Dead bacteria do not actively transcribe mRNAs. We wanted to understand how B. burgdorferi respond to antibiotic stresses, which requires use of sublethal concentrations of antibiotics.

The authors made it clear that they were studying this bacterium because it causes Lyme disease, and they want to understand how it survives antibiotics.

*** We hope that the revised manuscript makes it more clear that we were addressing how B. burgdorferi responds to antibiotic stresses, not how “it survives antibiotics”. Those are separate questions.

Lyme disease is treated with typically Lethal dosages of antibiotics. Therefore, it only seems logical to isolate RNA from antibiotic persisters as others have previously done (see DOI: 10.1038/s41598-021-85509-7).

*** The referenced paper is about E. coli in amoxicillin. Our studies indicated that B. burgdorferi does not undergo transcriptional alternations in response to amoxicillin stress. Use of higher doses would have killed the B. burgdorferi and, as noted above, dead bacteria do not produce mRNAs.

Lines 201-202: I am concerned if we can consider this work new and relevant.

*** As noted above, there are substantial differences between our studies and anything else that has been published before.

Another group already used doxycycline and published their data. Ref 69 used 50 ug/ml of doxycycline at a higher antibiotic concentration than the authors used here, 2 ug/ml. Furthermore, based on 10.2147/IDR.S19201, doxycycline MBC is 25 ug/ml. Thus, Ref 69 work is more relevant to antibiotic concentrations that kill this bacterium and more medically relevant.

*** Use of 50 �  g/ml doxycycline would mean that the vast majority of bacteria are dead. We wanted to know how live bacteria respond to antibiotic stress, and so we used antibiotic concentrations that were stressful but not lethal. Microscopical examination revealed that bacteria in our studies were metabolically active at the times of harvest.

In addition, the data from Ref 69 is available. The authors could have compared their findings to this work. If they found something different, that would be interesting.

*** We discussed those differences in the initial manuscript, and include that discussion in the revised manuscript on lines 213-234 and 293-296.

For this work to be relevant, the authors would need to do mutational studies (knockdown, knockout, overexpression, or point mutation studies) of the genes they identified. Then test how well the bacteria survive the antibiotics.

*** We were not asking whether B. burgdorferi survive in antibiotics, but whether they possess genetically encoded responses to amoxicillin and doxycycline. Mutations of proteins that are involved with translation processes would not provide any insights.

*** We gently remind the reviewer that neither Caskey et al. nor Feng et al. performed any analyses of mutant B. burgdorferi.

I am also confused about the statement in lines 201-202, "While our studies were in progress, another research group published RNA-Seq results of B. burgdorferi that had been cultured for 5 days in 50 μg/ml doxycycline [69]." Ref 69 was published in 2019, which is about 3 years ago.

*** This was a holdover from earlier versions of the manuscript. These studies were begun several years ago, as a Master student’s work. We have removed the confusing phrase.

They also did this work with mice and thus had the addition of a model organism to support their results.

*** The only use of mice in Caskey et al. was to help show that B. burgdorferi were not all dead after 5 days in 50 ug/ml doxycycline.

Based on this information and without comparing the author's work to Ref 69, it is my opinion that the paper's finding relies solely on the results for Amoxicillin.

*** For the reasons discussed above, we reject this conclusion.

Line 32-34: The authors state that the "amoxicillin did not lead to significant changes in levels of any bacterial transcript." I question this finding. If the amoxicillin concertation is at the MIC or above, one would expect a change in the transcriptome.

*** The concentration of amoxicillin that we used caused stress to the bacteria, as evidenced by the reduced replication rate and formation of spheroplasts. The reviewer appears to be assuming that a change in transcript levels should occur, even though we found that there were no significant changes.

Otherwise, how are the cells surviving?

*** The bacteria survived because the level of amoxicillin was below the threshold for killing.

The authors should have described how they survived, for example, a protein level response.

*** Our dose was below the MIC.

All other reported antibiotic-challenge studies have seen a change in gene expression. For this to be believable, the authors would need to test different levels of amoxicillin concentrations.

*** Our growth curve analyses indicated that B. burgdorferi were severely reduced in replication rate, but did continue to replicate at 0.2 ug/ml amoxicillin. Microscopical analyses indicated that they were metabolically active. Use of lower antibiotic concentrations would be less stressful, and we consider it unlikely that lower stress would provoke a greater response. Use of higher antibiotic concentrations would have killed the bacteria, and dead bacteria do not yield useful RNA-Seq data.

Alternatively and the best course of action, would be to use lethal amoxicillin concentrations like others have done.

*** Dead bacteria do not actively transcribe RNA, so RNA-Seq analyses reveal only the transcripts that were produced before antibiotic was administered, with a complication of RNA degradation in the dead cells.

Line 247-248: "However, our transcriptomic analyses indicate that the amoxicillin-induced morphological changes were not genetically encode." This is a bold statement that I did not find substantial evidence from this work to be supported.

*** The data show otherwise.

Here is why: 1. RNA-seq analysis generally relies on using a 2-fold-cutoff as the authors did here. However, it is possible that a 1.5 gene change can have a significant effect on cell physiology?

*** We agree with the reviewer that fold-change cutoffs are arbitrary and can lead to misleading conclusions. To this end, we displayed our data so that the reader can see the potential contribution of those genes with statistically significant expression differences that don't make the somewhat arbitrary 2-fold cutoff. MA plots in Figs 2-3 distinguish those as yellow dots (denoted "significant no change (sigNC)"), whereas red/blue dots are those statistically significant genes that did meet our cutoff to be considered differentially expressed. As such, figure 3 shows that amoxicillin did not induce any statistically significant changes, regardless of the fold-change cutoff at any timepoint. The text has been modified to reflect this.

The authors must titrate antibiotic concentrations and isolate RNA from them to make this claim with reasonable support. Alternatively, they could test at lethal (MBC) concentrations.

*** These were addressed above.

2. A heterogenous population will have high noise levels and high variations (e.g. the standard deviations or SEM would be large).

*** This is good reason to use a stringent cutoff for data analysis.

Bacterial antibiotic persister populations and tolerant populations are heterogeneous. Some cells survive, and others die. This has been tested for nearly 80 years; see Biggers 1944 (https://doi.org/10.1016/S0140-6736(00)74210-3).

*** The cited paper dealt with penicillin treatment of staphylococcal infections. Staphylococcus spp. are distinct from Borrelia spp.

These variations mean that differences may be hidden. One method to get around these variations is to eliminate non-persister cells by using lethal (MBC) concentrations of the antibiotic, as others have done.

*** We addressed this above.

This study lacks a clear goal and significance.

*** We disagree. We hope that the revised manuscript clears up the reviewer’s apparent confusion.

The explanation of their finding is vague, the author has a considerable lack of knowledge in literature, and more importantly, there is no novelty in this study.

*** We disagree, as discussed above.

Bacteria evade antibiotic treatment by entering into a metabolically repressed state, which is known as persistence (https://doi.org/10.1038/s41579-019-0196-3).

*** We have included this reference (Balaban et al) in our revised manuscript. Among other things, the authors of that publication explicitly state that bacteria that do not die in bacteriostatic antibiotics (such as doxycycline) are not to be called “persisters”. The reason being that bacteria can survive bacteriostatic antibiotics without undergoing any biological changes.

The persistence mechanisms are widely studied and found in almost all bacterial species including the minimal cell (Mycoplasma mycoides JCVI-Syn3B) (doi: 10.1016/j.isci.2021.102391). There are also several studies was done about the persister formation of Borrelia burgdorferi strain (doi: 10.1186/s13071-019-3495-7 ). Here, the author studied antibiotic response on Borrelia burgdorferi, but they did not mention anything about the persistence, which seems to be quite unusual. The author should mention antibiotic persistence, and explain whether their study shows any light to reveal the mechanism of antibiotic tolerance or persistence, which is one of the crucial reasons behind antibiotic resistance.

*** Our studies revealed that exposure of B. burgdorferi to amoxicillin stress did not result in any significant transcriptional changes. This suggests that B. burgdorferi does not possess a mechanism to adapt to amoxicillin, and argues against the hypothesis that B. burgdorferi can produce true “persister” cells against amoxicillin.

*** Survival in doxycycline does not indicate production of “persister” bacteria. Indeed, Balaban et al. argue that discussion of persistence is not appropriate for tetracyclines and other bacteriostatic antibiotics.

*** The revised manuscript includes discussion of these points on lines 208-213.

Since the author did not observe any transcriptional changes in bactericidal concentration, then how are lines 56-60 relevant to this study?

*** We recognize that those lines were tangential to the report, and have removed them from the revised manuscript.

Lines 78-79 contradict lines 201-202. In lines 78-79, the author said no study has been done yet at molecular level to observe genetically encoded responses during antibiotic treatment of this strain. In line 201-202, they mentioned another study had been done to understand transcriptional responses at bactericidal concentration. Please clarify.

*** We have removed the sentence.

As written, the introduction is not useful and verbose. The significance of the work beyond the connection to Lyme disease is not clear. The significance (the REASON) for the study should be the main focus of the introduction. I highly suggest the author remove the first paragraph describing Lyme disease symptoms because it is unnecessary. Instead, the authors should focus more on the bigger picture of their study, like why the study of antibiotic response is important.

*** This is an opinion on writing style. We disagree with the reviewer’s opinion.

The author checked the MIC value by adding antibiotic treatment and counting the cells/mL under a microscope using a Petroff Hausser hemocytometer. However, the method is not entirely accurate since the author will count both live and dead cells at the same time, and there will be cells a different planes of the hemocytometer, so there is a high chance of miscalculation.

*** Dr. Zückert and I each have over 30 years of experience working with B. burgdorferi. The other authors also have numerous years of experience in this field. We know how to count B. burgdorferi with a hemocytometer.

The author did not validate their counts using CFU/ml.

*** Efficiency of plating for B. burgdorferi is generally less than 100%. Counting by microscopy with a hemocytometer is more accurate and is widely used in this field.

Although the generally accepted way of doing MIC and MBC tests is using optical density assay, CFU/mL agar plate assays, strip assays, or disk assay using Kirby-Bauer method. Thus, I am confused why the author did the MIC test in this way.

*** B. burgdorferi do not form lawns on the surfaces of agar plates, which precludes use of diffusion assays.

Moreover, they did not show the MBC value, which is important to understand the level of antibiotics used. Therefore, the authors need to explain the reason for this unusual experimental design.

*** MBC data have previously been determined. Our results were consistent with those data. Contrary to the reviewer’s opinion, there is nothing unusual about examining responses of bacteria to sublethal antibiotic stresses.

In Fig. 1, the author showed that cell growth was inhibited with 0.2 and 0.4 ug/mL of an antibiotic (doxycycline and amoxicillin). However, the growth inhibition was visible at 2 days, not at 24 h. In 24 h, the doxycycline showed a little bit of difference. However, without statistics (e.g. standard deviation, SEM, etc.), the reader does not know if they overlap. Based on Fig. 1, there is no difference with or without amoxicillin. Again, no statistics to support this claim.

*** We sought to examine stressed, but not dead, bacteria. The suggested statistical analyses are irrelevant.

Instead of seeing very little inhibition or no inhibition at all at 24 h of antibiotic treatment, the author should/could have used other time points to isolate RNA. Why did they use 3 h and 24 h of antibiotic-treated cells? Is there any good explanation for that? Why did the author not go more extended time point? The author should address these questions in the discussion.

*** This is addressed in lines 151-153 of the revised manuscript.

The author also mentioned that they did not see any significant changes in gene expression levels at 24 h with amoxicillin treatment. It seems most plausible the author did not see any significant change because there is no significant growth inhibition at that time point (24 h) with antibiotic treatment. The author should address this concern in the manuscript.

*** We noted blebbing and other signs of stress as a result of 24 h treatment with amoxicillin.

I also believe the author should isolate RNA from longer antibiotic-treated cells where the inhibition is quite visible and statically significant with the control.

*** Discussed above.

Also, the author should add p-values for each time point in Fig. 1.

*** Discussed above.

If the authors are unwilling to do RNA-seq for a longer time point with amoxicillin, I suggest the author remove this part from the paper.

*** Discussed above.

Because their experimental design has a huge flaw, and their explanation does not make any sense. I am adding here another reference (https://doi.org/10.1038/s41598-021-85509-7). They studied the transcriptional response on Ampicillin (beta-lactam antibiotic) treated E. coli cells and observed several genes are upregulated or downregulated at 3 h and 6 h of antibiotic treated cells.

*** E. coli are not the same bacteria as B. burgdorferi. One cannot infer responses of B. burgdorferi by examining another species.

Adding the amoxicillin results and their hypothesis weakens the overall manuscript, so I highly suggest removing this part.

*** Discussed above.

10. Fig. 3: At what time point during antibiotic-treated cells were considered for this microscopic analysis?

*** The legend states 24 hours.

Also, why do the authors show only one or two cells in one image rather than showing multiple cells?

*** Bacteria were spread out, so 40x images generally included only a single bacterium. Lower magnifications make it difficult to clearly see bacteria.

*** We have revisited the study, and now morphological analyses of ca. 100 bacteria after 24 h in amoxicillin in lines 252-266.

*** To avoid distraction, we have omitted the effects of doxycycline on B. burgdorferi cell lengths. The figure (now Fig. 4) has been revised to show only a control bacterium and 3 representative amoxicillin-treated bacteria.

Do all the cells follow the same pattern during long-term Amoxicillin treatment? There is also a morphological difference in Fig. 3. D, E, and F. Why so? Could you hypothesize or find a reference for why? Please clarify.

*** These are probably spheroplasts. We cite several previously published papers of B. burgdorferi that were incubated in sublethal concentrations of �  -lactam antibiotics which showed similar morphologies.

Minor concerns:

Fig. 3 does not have any numbering on top of the figure. You must add letters (A, B, C…) called out in the legend to the figure.

*** Fixed.

Lines 112-120: Was rRNA removed from the samples? Though it is not stated in the Materials and Methods, I am assuming so because this is a typical step to save on cost and expression analysis time. If so, it should be stated and how.

*** Line 110 of the materials and methods section states that Zymo-Seq Ribofree Total RNA Library Kits were used.

The author should mention how many reads of each sample they used to analyze the RNA-seq data.

*** Those data are in the Supplemental Tables.

Line 244: Fig. 3A should be removed since the author only referred to amoxicillin-treated cultures. So, it should be Fig. 3D-F.

*** Panel 3A shows contrast between untreated and treated bacteria.

4. Line 107: Authors should mention the speed, time and temperature used for centrifugation to isolate the cells. If the author used 4˚C to centrifuge the cells, does this temperature change affects the transcriptional response?

*** Centrifugation conditions have been added to line 100. Chilling bacteria inhibits activities of enzymes that could degrade RNA. Our earlier studies of B. burgdorferi found that even 8 hours of changed temperature did not result in alterations (Stevenson et al., 1995, Infect. Immun. 63: 4535-4539, PMID: 7591099).

Author Contributions is missing? Who did what on this manuscript?

*** The journal does not appear to have a place for that.

Grammar and such needs major improvement. Here are some examples: Line 27: Remove the comma from "profiles, in order" Line 150: No spaces between bacteria/ml Table 1: Capitalize Locus and Log2 Line 164: A space is needed between Fig. and 2 Fig.: Fig. is an abbreviation for figure, and there should be a period after Fig. Add reference for line 47 and lines 50-55

*** Thank you.

Submitted filename: Bb Abx Responses.doc

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23 Aug 2022

Borrelia burgdorferi, the Lyme disease spirochete, possesses genetically-encoded responses to doxycycline, but not to amoxicillin

PONE-D-22-10059R1

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Reviewers' comments:

22 Sep 2022

PONE-D-22-10059R1

Borrelia burgdorferi, the Lyme disease spirochete, possesses genetically-encoded responses to doxycycline, but not to amoxicillin

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