Abstract

   The tetracycline antibiotics are widely used in biomedical research as
   mediators of inducible gene expression systems. Despite many known
   effects of tetracyclines on mammalian cells–including inhibition of the
   mitochondrial ribosome–there have been few reports on potential
   off-target effects at concentrations commonly used in inducible
   systems. Here, we report that in human cell lines, commonly used
   concentrations of doxycycline change gene expression patterns and
   concomitantly shift metabolism towards a more glycolytic phenotype,
   evidenced by increased lactate secretion and reduced oxygen
   consumption. We also show that these concentrations are sufficient to
   slow proliferation. These findings suggest that researchers using
   doxycycline in inducible expression systems should design appropriate
   controls to account for potential confounding effects of the drug on
   cellular metabolism.

Introduction

   The tetracycline family is a class of broad-spectrum antibiotics that
   have been used clinically since the mid-twentieth century. Since then,
   they have found application beyond their anti-microbial activity in
   both the clinic and biomedical research [37][1]–[38][3]. They are
   widely used in the latter context as mediators of inducible gene
   expression systems, but often with little discussion of or control for
   potential off-target effects they may have on mammalian cells. Because
   the tetracyclines have been shown to inhibit matrix metalloproteinases,
   retard proliferation, induce apoptosis, and impair mitochondrial
   function in various experimental settings, we were interested to
   determine whether these drugs can alter cellular metabolism at
   concentrations commonly used in inducible systems [39][4]–[40][12].

   The canonical prokaryotic target of the tetracyclines is the bacterial
   ribosome, the inhibition of which blocks bacterial protein synthesis
   [41][1]. But there is significant evidence that tetracyclines can
   impair mitochondrial function in eukaryotic cells by inhibiting
   translation at the mitochondrial ribosome, an observation that has been
   explained by the origin of these organelles as endosymbiotic bacteria
   [42][8], [43][12]–[44][15]. Despite a reportedly weak interaction
   between the antibiotics and the mitochondrial ribosome, at high
   concentrations they have been shown to impair synthesis of proteins
   encoded in the mitochondrial genome–many of which are involved in
   oxidative metabolism–and promote a shift towards glycolysis [45][4].

   In this study, we expanded upon these findings to determine potential
   confounding effects of the tetracyclines–particularly doxycycline
   (Dox), the predominantly used compound–at concentrations commonly
   employed in inducible gene expression systems: 100 ng/mL - 5 µg/mL. We
   found that these concentrations of drug can significantly alter the
   metabolic profile of the cell, as well as reduce the proliferative
   rate, though the effect size depends upon the particular cell line
   used. These data strongly suggest that researchers using Dox-inducible
   systems should carefully optimize experiments to minimize potentially
   confounding effects of the drug, and design additional controls as
   needed.

Results

Doxycycline Induces Metabolic Gene Expression Changes in Human Cells

   To look in an unbiased way at the effects of Dox on cells in culture,
   we performed gene expression analysis on MCF12A cells–an untransformed
   breast epithelial line–treated with the drug at 1 µg/mL or with a
   vehicle control. Metabolic pathway enrichment analysis (using Gene Set
   Enrichment Analysis (GSEA)) revealed several pathways, including
   oxidative phosphorylation and glycolysis, to be significantly enriched
   in the Dox-treated cells ([46]Figure 1A; for enrichment plots, see
   [47]Figure S1). Many of the constituent genes in these pathways show a
   robust change in expression in response to treatment ([48]Figure 1B;
   for annotated gene sets, see [49]Figure S2), including key enzymes in
   glycolysis and its major carbon shunts ([50]Figure 1C). These results
   demonstrate that Dox, at a concentration commonly used in inducible
   systems, can alter the metabolic gene expression profile of cells.

Figure 1. Doxycycline alters the metabolic gene expression profile of MCF12A
cells.

   [51]Figure 1
   [52]Open in a new tab

   Treatment of MCF12A cells with Dox at a concentration of 1 µg/mL shows
   widespread changes in expression of metabolic genes. A) GSEA reveals
   the most significantly altered metabolic pathways, ranked by normalized
   enrichment score (NES), in Dox treatment compared to vehicle. KEGG
   pathway entries are denoted in parentheses where appropriate. Pathways
   without KEGG entries–All Metabolic Genes and Glycolysis-Gluconeogenesis
   & Pentose Phosphate–are artificial combinations of other pathways with
   redundant genes collapsed. All Metabolic Pathways includes all
   non-redundant genes from every KEGG pathway analyzed. B) This heat map
   highlights changes in the constituent genes of the oxidative
   phosphorylation and glycolysis/gluconeogenesis/pentose phosphate
   pathways upon treatment. Annotated genes include those encoding
   regulatory enzymes in glycolysis (phosphofructokinase (PFK), hexokinase
   (HK), pyruvate kinase (PK), shown in blue) and in gluconeogenesis
   (glucose-6-phosphatase (G6PC) and fructose-1,6-bisphosphatase (FBP),
   shown in orange). (C) Altered expression of regulatory enzymes in
   glycolysis and its proximal carbon shunts are shown schematically, with
   red indicating upregulation and green indicating downregulation.

Doxycycline Increases Glycolytic Metabolism in Multiple Human Cell Lines

   Because treatment with Dox alters expression of genes involved in
   glycolysis and oxidative phosphorylation, we tested whether Dox
   treatment causes corresponding functional metabolic changes in MCF12A
   cells. As shown in [53]Figure 2, glucose consumption ([54]Figure 2A)
   and lactate production rates ([55]Figure 2B) are elevated in MCF12A
   cells after 96 hours of treatment with 1 µg/mL Dox. The Dox analogs
   tetracycline (Tet) and minocycline (Mino) were also tested at 1 µg/mL.
   Both drugs induced increased lactate production rates ([56]Figure 2B),
   and Mino concomitantly caused increased glucose uptake–though this
   latter phenotype is not observed with Tet ([57]Figure 2A). Significant
   changes were also observed at 100 ng/mL with Mino and Tet, but the
   effect size is considerably smaller.

Figure 2. Tetracycline antibiotics affect glucose metabolism and oxygen
consumption in a dose-dependent fashion.

   [58]Figure 2
   [59]Open in a new tab

   Effects of the tetracyclines on glycolytic flux were determined by
   measuring changes in A) glucose consumption and B) lactate production
   rates of MCF12A cells when treated with Dox, Mino, or Tet at 100 ng/mL
   or 1 µg/mL. A dose-response relationship was established between Dox
   and C) lactate production rate in MCF12A cells, D) lactate production
   rate in 293T cells, E) oxygen consumption rate in MCF12A cells, and F)
   oxygen consumption rate in 293T cells by treating with a range of drug
   concentrations. Error bars represent standard deviation from
   experimental triplicate measurements for all assays, except for oxygen
   consumption dose-response in 293T cells, which was performed with 8
   replicates. * denotes a p-value ≤0.05, ** a p-value ≤0.01 from a
   two-tailed Student’s t-test.

   To determine whether the effect of Dox on cellular metabolism is
   dose-dependent, we measured lactate production rates ([60]Figures 2C
   and 2D) and oxygen consumption rates ([61]Figures 2E and 2F) of MCF12A
   and 293T cells over a range of Dox doses commonly used experimentally
   (100 ng/mL–5 µg/mL). We found that the amount of Dox used correlates
   with the shift towards a glycolytic phenotype, as shown by increased
   lactate production and decreased oxygen consumption. Higher doses of
   Dox (10 µg/mL) lead to cytotoxic effects (data not shown). In this
   range of concentrations, there appears to be a clear relationship
   between the Dox dose and the magnitude shift towards glycolysis and
   away from oxidative metabolism in MCF12A and 293T cells. These results
   suggest that the tetracycline family of antibiotics can promote
   glycolytic metabolism in human cell lines.

   To assess whether the Dox-mediated metabolic changes are cell
   type-specific or broadly generalizable to human cell lines, we repeated
   the metabolic assays on 9 different human cell lines–both transformed
   and untransformed–and observed enhanced glycolysis in a majority of
   them (6/9, for both glucose consumption and lactate production rates)
   ([62]Figures 3A and 3B). There is considerable evidence suggesting that
   the tetracyclines interact with the mitochondrial ribosome and inhibit
   translation of transcripts from the mitochondrial genome. Accordingly,
   we measured basal oxygen consumption rates in the cell line panel after
   96 hours of Dox treatment ([63]Figure 3C) [64][14]. In all cell lines
   but 293T, oxygen consumption was significantly impaired at 1 µg/mL;
   three lines showed significant defects at 100 ng/mL. The magnitude of
   the decrease varied among the cell lines, with H157 cells experiencing
   a 70% reduction in oxygen consumption, despite having unaffected
   glycolytic flux. To understand the temporality of this phenotype, we
   measured oxygen consumption after treating MCF12A cells with 1 µg/mL
   Dox for 24, 48, and 72 hours, which revealed a significant reduction in
   the oxygen consumption rate post 48 hours of treatment ([65]Figure 3D).
   Together, these data demonstrate that Dox concentrations commonly used
   in genetic systems can substantially reprogram metabolism in human cell
   lines, and that the phenotype can vary significantly among cell lines.

Figure 3. Doxycycline can alter both glycolytic and oxidative metabolism in a
heterogeneous panel of human cell lines.

   [66]Figure 3
   [67]Open in a new tab

   Findings were extended to a panel of human cell lines, which were
   treated with Dox for 96 hours at 100 ng/mL, 1 µg/mL, or with vehicle
   control and then assayed for A) glucose consumption rate, B) lactate
   production rate, and C) basal oxygen consumption rate. D) To assess the
   temporality of this phenotype, MCF12A cells were treated with Dox at 1
   µg/mL at shorter time points–24, 48, and 72 hours–and oxygen
   consumption rate was measured. Error bars represent standard deviation
   from experimental triplicate measurements in 3A and 3B; from 6 (100
   ng/mL) or 7 (control, 1 µg/mL) replicates in 3C; and from 5 replicates
   in 3D. Oxygen consumption measurements for 293T cells from [68]Figure
   2F were repurposed for 3C. * denotes a p-value ≤0.05, ** a p-value
   ≤0.01 from a two-tailed Student’s t-test.

Doxycycline Reduces Proliferation of Multiple Human Cell Lines

   Previous studies have shown that Dox can impair cell proliferation in
   vitro [69][8]. Measuring cell number over a 96-hour time course, we
   found that at 1 µg/mL–but not 100 ng/mL–Dox treatment significantly
   reduces proliferation in most of the cell lines in our panel (7/9);
   only one cell line, the LNCaP prostate cancer line, experienced a
   proliferative defect at the lower concentration. Here, we show both
   representative growth curves for MCF12A and 293T cells ([70]Figures 4A
   and 4B) and normalized values for the entire panel ([71]Figure 4C).
   Interestingly, not all cell lines that experience substantial
   Dox-induced metabolic shifts have simultaneously impaired proliferative
   capacity–most clearly seen in MCF10A cells ([72]Figures 3A–3C;
   [73]Figure 4C)–suggesting potentially independent or decoupled
   mechanisms of action.

Figure 4. Doxycycline reduces proliferation of multiple human cell lines.

   [74]Figure 4
   [75]Open in a new tab

   Cell numbers were measured over 96 hours of Dox treatment. Shown here
   are representative growth curves for A) MCF12A and B) 293T cells, as
   well as C) the full panel of cell lines with cell counts after 96 hours
   of treatment normalized to vehicle control. To interrogate the reason
   for the proliferation defect, PI-based cell cycle analysis was
   performed on D) MCF12A and E) 293T cells, and Annexin V/PI viability
   analysis was performed on F) MCF12A and G) 293T cells. Both
   proliferation and flow cytometry-based assays were performed in
   triplicate, with error bars representing standard deviation. * denotes
   a p-value ≤0.05, ** a p-value ≤0.01 from a two-tailed Student’s t-test.

   To determine what might cause reduced proliferation, cell cycle
   analysis was performed on MCF12A and 293T cells, both of which
   experience metabolic and proliferative changes with Dox treatment.
   Consistent with a previous report of tetracycline-induced G[1] arrest,
   propidium iodide (PI) staining showed modest enrichment of cells in
   this phase of the cell cycle in MCF12A cells ([76]Figure 4D), but not
   in 293T cells ([77]Figure 4E) [78][5]. Because apoptosis has also been
   described in cells treated with tetracyclines, Annexin V/PI co-staining
   was performed on the same cells ([79]Figures 4F and 4G) [80][5],
   [81][8]. At the concentrations used here, Dox significantly increases
   apoptotic cell death in 293T cells ([82]Figure 4G), but not in MCF12A
   cells ([83]Figure 4F). Since the Dox-induced effects on cell cycle and
   apoptosis vary between the cell lines tested, the mechanistic link
   between Dox treatment and the observed modestly impaired proliferation
   remains elusive.

Discussion

   We show here that Dox, as well as other members of the tetracycline
   family, can cause substantial changes in cellular metabolism and impair
   proliferative capacity of human cell lines. Importantly, these
   phenotypes are induced at concentrations commonly used in inducible
   gene expression systems, which indicates a strong possibility of
   confounding effects in these experiments. Our findings are surprising
   given the presumed inactivity of these drugs in eukaryotic cells, and
   particularly relevant because metabolism and proliferation are
   fundamental properties of the cell–inextricable from many other
   phenotypes. While inducible gene expression systems are a powerful tool
   for probing gene function, it is advisable that researchers carefully
   optimize experiments in order to minimize the concentration of Dox and
   the concomitant confounding phenotype. Additional controls can also be
   designed so that the effects of the mediating drug are disentangled
   from that of the gene of interest.

   Because we did not investigate the mechanism of action, we cannot fully
   explain how Dox causes the observed phenotypes. But the evidence of
   interactions between the drug and the mitochondria are compelling, and
   may contribute to our observations. Since subunits of electron
   transport chain complexes are encoded in the mitochondrial genome, a
   potential stoichiometric imbalance caused by decreased translation of
   these genes could lead to reduced capacity for oxidative metabolism and
   a compensatory shift towards glycolysis [84][12], [85][14], [86][15].
   While further investigation is needed to refine our understanding of
   the effects of the drug on human cells, these results highlight
   important confounding phenotypes of Dox treatment and underscore the
   importance of rigorous experimental design when using this compound in
   inducible expression systems.

Materials and Methods

Cell Culture

   U87 astrocytoma cells were a gift from Dr. Paul Mischel (UCSD), lung
   cancer cell lines H157 and H1437 from Dr. Steven Dubinett (UCLA), and
   LNCaP prostate cancer and HeLa cervical cancer cell lines from Dr.
   Steven Bensinger (UCLA), and all other cell lines from Dr. Frank
   McCormick (UCSF); all cell lines have been well characterized
   [87][16]–[88][20]. MCF12A and MCF10A cells were cultured in DMEM/F12
   (1∶1) containing 5% horse serum, 1% penicillin/streptomycin, 10 µg/mL
   insulin (Invitrogen), 0.5 µg/mL hydrocortisone (Sigma), 20 ng/mL EGF
   (Peprotech), and 10 µg/mL cholera toxin (Sigma). HeLa and U87 cells
   were cultured in DMEM containing 10% FBS and 1%
   penicillin/streptomycin. H1437, H157, and LNCaP cells were cultured in
   RPMI containing 10% FBS and 1% penicillin/streptomycin. SUM159PT cells
   were cultured in F12 containing 5% FBS, 1% penicllin/streptomycin, 5
   µg/mL insulin (Invitrogen), 1 µg/mL hydrocortisone (Sigma), and 0.1%
   gentamicin (Sigma). Doxycycline, tetracycline, and minocycline (Sigma)
   were dissolved in water. Media containing the drug was refreshed every
   48 hours, as appropriate.

Metabolic Pathway Enrichment Analysis

   RNA extraction was performed using RNeasy (Qiagen) according to
   manufacturer’s guidelines. Samples were analyzed using the Affymetrix
   Genechip U133 Plus 2 array by the UCLA Clinical Microarray Core
   Laboratory. Resulting data are available through NCBI’s Gene Expression
   Omnibus (GEO Series accession number [89]GSE45029). To identify
   metabolic pathways associated with Dox treatment, we used the GSEA
   algorithm and metabolic pathway annotation defined by the Kyoto
   Encyclopedia of Genes and Genomes (KEGG) (release #58). We collapsed
   gene expression probes based on enzyme activity (Enzyme Commission [EC]
   numbers) rather than on gene identity to avoid unequal representation
   of equivalent enzymatic function within pathways, thus emphasizing
   potential flux through the network. The metric used for gene ranking
   was the signal to noise ratio (SNR) between the Dox treatment and
   control. The probesets were collapsed by average SNR for GSEA, and by
   maximum absolute SNR for each enzyme in the heat maps. Probeset
   annotation was based on UniGene build #230 and UniGene identifiers were
   mapped to each EC using the gene names provided by KEGG.

Media Metabolite Measurement

   Media was collected from culture plates and analyzed for glucose and
   lactate concentrations using the Bioanalyzer 4 (Nova Biomedical). Cells
   were treated with Dox or vehicle control for 96 hours. Fresh media was
   added to plates between 20 and 24 hours before sample collection.
   Values were normalized to both cell number and time interval. Some
   statistical variability was observed among experiments, particularly
   with respect to glucose, because the effect size measured at these
   concentrations of Dox is small compared to the error of the assay.
   Lactate is a more consistently sensitive readout of glycolytic
   activity.

Oxygen Consumption Measurement

   Basal oxygen consumption rates were measured using the XF24
   Extracellular Flux Analyzer (Seahorse Bioscience). Cells were treated
   with Dox or vehicle control for 96 hours, unless otherwise indicated.
   18 hours prior to assay, cells were seeded at 50,000–80,000 cells per
   well (optimized for each cell line) in 24-well proprietary assay plates
   with between 3 and 8 replicates per treatment group. Oxygen consumption
   values were normalized to total protein using BCA Protein Assay Kit
   (Pierce). Because plating density around the oxygen sensors affects
   readings, there is considerable well-to-well variability. Because cell
   morphology can alter the distribution of cells in the well, different
   cell lines are measured with varying sensitivity.

Proliferation Assay

   Cells were seeded in 6 well plates at 50,000 cells per well and treated
   with Dox (100 ng/mL, 1 µg/mL, or vehicle control). Cells were counted
   24, 72, and 96 hours after plating using a particle counter (Beckman
   Coulter).

Cell Cycle Analysis

   Cell cycle analysis was performed using propidium iodide (PI) staining.
   In brief, cells were trypsinized and resuspended in hypotonic DNA
   staining buffer (3.5 mM sodium citrate, 0.1 g/L propidium iodide, 0.3%
   Triton X-100, 20 mg/L Ribonuclease A) at a concentration of 10^6
   cells/mL and incubated for 20 minutes at room temperature in the dark.
   Analysis was performed using the BD FACSDiva software on an LSR II flow
   cytometer (BD Biosciences). PI fluorescence was recorded in the PE
   channel. For cell cycle analysis, flow cytometry data was imported and
   analyzed using the ModFit LT software.

Viability Analysis

   Cell viability was measured using the FITC Annexin V Apoptosis
   Detection Kit II (BD Pharmingen) according to the manufacturer’s
   protocol. Cells were incubated with Annexin V and PI for 15 min and
   then analyzed by flow cytometry as previously described. Annexin V
   fluorescence was recorded on the FITC channel and PI on the PE channel.
   Data was analyzed using FlowJo. Cells in early apoptosis were indicated
   by Annexin V positive, PI negative staining (lower right quadrant)
   while cells in late apoptosis were marked by Annexin V/PI double
   positive staining (upper right quadrant).

Supporting Information

   Figure S1

   Metabolic gene sets are enriched upon treatment with Dox. Enrichment
   plots for the top ranked KEGG-defined pathways in MCF12A cells treated
   with Dox at 1 µg/mL, compared to vehicle control.

   (TIF)
   [90]Click here for additional data file.^ (1.8MB, tif)
   Figure S2

   Dox treatment alters expression of genes involved in major central
   carbon metabolism pathways. Heat maps highlight changes in expression
   of genes in A) the Glycolysis-Gluconeogenesis & Pentose Phosphate
   Pathway (artificial combination of KEGG pathways) and in B)
   KEGG-defined Oxidative Phosphorylation after Dox treatment at 1 µg/mL
   in MCF12A cells, compared to vehicle control. Entries are ranked by SNR
   and are collapsed by enzyme function; the constituent gene with the
   maximum absolute SNR is shown in the heat map. Regulated enzymes in
   glycolysis are shown in blue and those in gluconeogenesis are shown in
   orange.

   (PDF)
   [91]Click here for additional data file.^ (111.1KB, pdf)

Funding Statement

   H.R. Christofk is a Damon Runyon–Rachleff Innovation Awardee supported
   (in part) by the Damon Runyon Cancer Research Foundation, the Searle
   Scholars Program, the National Institutes of Health Director's New
   Innovator Award (DP2 OD008454-01), and the Caltech/UCLA Nanosystems
   Biology Cancer Center (NCI U54 CA151819). The funders had no role in
   study design, data collection and analysis, decision to publish, or
   preparation of the manuscript.

References