Acinetobacter baumannii is a prevalent nosocomial pathogen with a high incidence of multidrug resistance. Treatment of infections with colistin can result in emergence of colistin-resistant strains. This occurs via modifications of the phosphate moieties of lipopolysaccharide-derived lipid A, which are readily identified by mass spectrometry (MS). In this article, we describe colistin susceptibility determinations by lipid MS of A. baumannii and our recent study in which we correlate MS results with traditional antimicrobial susceptibility testing of clinical isolates.

by Dr Lisa M. Leung, Dr Robert A. Myers, Dr Yohei Doi and Prof. Robert K. Ernst

Background
Colistin resistance in Gram-negative pathogens
Multidrug-resistant, Gram-negative bacterial pathogens continue to pose serious threats to public health. Carbapenem-resistant Enterobacteriaceae (CRE), Pseudomonas aeruginosa (CRPA), and Acinetobacter baumannii (CRAB) are given the highest global priority among drug-resistant organisms by organizations, such as the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) [1]. Carbapenem-resistant infections can be treated with colistin, a last resort antibiotic of the polymyxin class, leading to an increase in colistin resistance and resulting in devastating consequences as it is one of the last remaining effective antimicrobials [2]. Furthermore, discovery of a plasmid-mediated colistin resistance gene, mcr, has intensified this urgency given the potential for rapid and widespread dissemination of colistin-resistant bacteria across the globe [3, 4]. Therefore, the WHO and CDC have prioritized development of novel diagnostics and therapeutics to address the global threat of pathogens, such as multidrug-resistant A. baumannii [5].

A novel diagnostic approach is proposed
In elucidating the mechanism of colistin resistance, researchers analysed microbial glycolipids by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). These findings contributed to determination of the resistance mechanism in A. baumannii, via addition of phosphoethanolamine onto the terminal phosphate moieties of the lipopolysaccharide (LPS)-derived lipid A (LA), decreasing the electronegativity of the membrane and, subsequently, the binding affinity of colistin [6]. These modifications create unique features on the resultant mass spectra of colistin-resistant strains that can be used as a diagnostic marker. Our group has published proof-of-concept studies utilizing this platform in the identification of the ESKAPE pathogens [7], as well as elucidation of colistin susceptibility in organisms such as Klebsiella pneumoniae [8], E. coli, and P. aeruginosa [9]. Protein-based microbial identification using MALDI-TOF MS is a simple and effective means of identifying causative agents although it still faces challenges, such as identification of closely related organisms (Candida or Shigella subspecies), antimicrobial susceptibility determination, or identification of organisms in polymicrobial or biologically relevant samples (urine, blood or wound effluent) [10]. Therefore, we offered this novel platform as an alternative and complementary approach to strengthen the overall diagnostic power of MALDI-TOF MS and continue to demonstrate its capability in our latest study detecting colistin resistance in A. baumannii [11].

Methods and results
Overview of clinical data
In this study, we prospectively collected A. baumannii complex clinical isolates from a hospital system in Pennsylvania between 2014 and 2016, a total of 451 isolates from 284 patients. Among the 284 unique isolates from each patient, 73.6% (209 isolates) were determined to be A. baumannii, 18.7% (53 isolates) Acinetobacter pittii, 3.5% (10 isolates) Acinetobacter nosocomialis, and 1.8% (5 isolates) Acinetobacter calcoaceticus. The remaining <1% were identified as the following Acinetobacter genospecies that do not belong to the A. baumannii complex: Acinetobacter radioresistens (2 isolates), Acinetobacter guillouliae (1 isolate), and Acinetobacter junii (1 isolate). Three isolates (0.7%) could not be reliably identified. All isolates were evaluated for colistin resistance using standard minimum inhibitory concentration (MIC) testing by both agar dilution and broth microdilution in accordance with the clinical breakpoint provided by the EUCAST [12]. Of the 451 clinical isolates, 394 isolates from 249 patients were found to be susceptible to colistin (≤2 µg/mL), and a total of 39 isolates (8.6%) from 20 patients were identified as resistant (>2 µg/mL).

The colistin-resistant A. baumannii mass spectrum
All strains were cultured overnight and subjected to a hot ammonium isobutyrate reaction to extract cellular lipids. Extracts were analysed by MALDI-TOF in negative ion mode using a Bruker microflex LRF MALDI-TOF mass spectrometer operated in reflectron mode and using norharmane as a matrix. Ions most often observed in the mass spectra were m/z 1404, 1728, and 1910; these have been previously characterized [6], with m/z 1910 representing the full bis-phosphorylated, hepta-acylated lipid A structure (Fig. 1). Resistant isolates were defined by the presence of an ion at m/z 2033, representing the addition of a phosphoethanolamine moiety to one of the phosphate moieties of the m/z 1910 structure (∆m/z=123) (Fig. 1). Determination of resistance was made by observing this ion in acquired mass spectra for each sample above a signal-to-noise ratio of 3. Of the 451 clinical isolates, 397 were determined to be susceptible to colistin (i.e. lacking an ion at m/z 2033), whereas 54 (12.0%) showed the presence of the m/z 2033 and were classified as resistant.

Differentiation of the A. baumannii complex
Differences were observed between spectra collected from the A. baumannii complex isolates, A. baumannii, A. pittii, and A. nosocomialis (Fig. 2). In general, an ion at m/z 1882 displayed higher signal intensity in A. pittii and A. nosocomialis isolates, about 80% relative intensity to the base peak at m/z 1910 compared to about 10% for A. baumannii, which may indicate differences in relative abundances of specific LPS structures. This ion most likely results from an exchange of a shorter chain fatty acyl group (C₂H₄, ∆m/z=28) from one of the acyl chains of the base structure at m/z 1910, although this structure is inferred and further analyses would need to be conducted for positive structural determinations. In addition, A. pittii and A. nosocomialis isolates showed prominent novel ions at m/z 1866 and 1894, indicating differences in hydroxylation events (∆m/z=16) from ions at m/z 1882 and 1910, respectively, potentially representing the addition of a hydroxyl moiety to one of the attached fatty acyls of lipid A. Among the 39 colistin-resistant isolates, only one was identified as non-baumannii (A. nosocomialis). This means that non-baumannii isolates occur at a lower incidence among resistant isolates (3.1%), as compared to their incidence among Acinetobacter isolates in general (19.7%) indicating a higher resistance rate of A. baumannii versus non-baumannii complex isolates in this study.

MIC versus MS
Discordant results between MIC and MS findings were resolved by multiple-replicate retesting to confirm susceptibility profiles, and final determinations were compared. Of the 451 total isolates used in our study, 394 isolates from 249 patients were determined to be susceptible by both MIC and MS and 39 isolates from 20 patients were determined to be resistant, giving a specificity of 94.0% and a sensitivity of 92.9%. Three isolates were determined to be resistant by MIC yet susceptible by MS and 15 isolates were found to be resistant by MS but susceptible by MIC. When considering only the first isolates isolated from the 284 patients in our study, sensitivity and specificity values change slightly – to 83.3% and 97.4%, respectively. Thirty-nine isolates were subjected to multiple-replicate retesting based on discordant results between agar dilution and broth microdilution methods, MIC and MS results, or both. Of the 33 isolates that underwent MIC retesting, 26 (or 89.7%) gave different susceptibility profiles, 25 went from resistant to susceptible and one was classified as indeterminate. Of the 26 isolates that underwent MS retesting, only three (11.5%) saw a change in their susceptibility profiles; two went from resistant to susceptible and one from susceptible to resistant. Although there was a high association between susceptibility determinations by MIC and MS overall, the positive predictive value was calculated as 72.2% (negative predictive value=99.2%). This is largely owing to the 15 isolates where resistance-associated ions were observed in the mass spectra, but which were determined susceptible by MIC. Chromosomally-mediated colistin resistance in Acinetobacter species is due to overexpression of LPS-modifying genes; therefore, modification of LPS will vary over time. It is presently unclear whether this ‘resistant’ profile is a valid determination of resistance or whether this isolate would present as a resistant infection in a clinical scenario.

Conclusion
A. baumannii, a prevalent, Gram-negative coccobacillus pathogen, poses a significant challenge to clinicians due to the incidence of hospital-acquired and drug-resistant infections. Close monitoring of this pathogen and other A. baumannii complex organisms is considered of critical importance to public health organizations. Here, we surveyed 451 Acinetobacter isolates prospectively collected from patients at a major Pennsylvania health system over a 3-year period. We determined colistin resistance by MIC testing, as well as by MALDI-TOF MS. As in previous studies of colistin-resistant K. pneumoniae, P. aeruginosa, and A. baumannii [6, 8, 13], the data showed a strong association between resistant MIC determinations and the observation of higher m/z ions by MS consistent with modification to LA and previously demonstrated to confer resistance. A. nosocomialis, A. pittii, and A. calcoaceticus, along with A. baumannii are collectively identified as the A. baumannii complex organisms. In our prospective study, we found that A. baumannii isolates were the predominant species within the A. baumannii complex, yet represented a smaller proportion (73.6%) than what has previously been observed [14]. We also demonstrated that a lipid MS profile offers another diagnostic tool for differentiation and accurate surveillance of these pathogens. Furthermore, the finding of resistance ions among a resistant A. nosocomialis isolate demonstrates that A. baumannii complex organisms likely achieve colistin resistance via the same LPS-modifying mechanism (Fig. 2). Overall, we conclude that glycolipid MS profiling can effectively detect colistin resistance in A. baumannii and has the potential to direct antimicrobial stewardship in the clinic, further validating our recently introduced diagnostic platform [7].
References
1. Antibiotic resistance threats in the United States, 2013; p114. Centers for Disease Control and Prevention 2013 (https://www.cdc.gov/drugresistance/pdf/ar-threats-2013-508.pdf)
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3. Liu YY, Wang Y, Walsh TR, Yi LX, Zhang R, Spencer J, et al. Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: a microbiological and molecular biological study. Lancet Infect Dis 2016; 16(2): 161–168.
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11. Leung LM, McElheny CL, Gardner FM, Chandler CE, Bowler SL, Mettus RT, et al. A prospective study of Acinetobacter baumannii complex isolates and colistin susceptibility monitoring by mass spectrometry of microbial membrane glycolipids. J Clin Microbiol 2019; 57(3): pii: e01100-18.
12. Recommendations for MIC determination of colistin (polymyxin E ); as recommended by the joint CLSI-EUCAST Polymyxin Breakpoints Working Group. EUCAST 2016 (http://www.bioconnections.co.uk/files/merlin/Recommendations_for_MIC_determination_of_colistin_March_2016.pdf).
13. Miller AK, Brannon MK, Stevens L, Johansen HK, Selgrade SE, Miller SI, et al. PhoQ mutations promote lipid a modification and polymyxin resistance of Pseudomonas aeruginosa found in colistin-treated cystic fibrosis patients. Antimicrob Agents Chemother 2011; 55(12): 5761–579.
14. Queenan AM, Pillar CM, Deane J, Sahm DF, Lynch AS, Flamm RK, et al. Multidrug resistance among Acinetobacter spp. in the USA and activity profile of key agents: results from CAPITAL Surveillance 2010. Diagn Microbiol Infect Dis 2012; 73(3): 267–270

The authors
Lisa M. Leung1,2 PhD, Robert A. Myers3 PhD, Yohei Doi4 MD, and Robert K. Ernst*2 PhD
1Divisions of Molecular Biology and Microbiology, Maryland Department of Health Laboratories Administration, Baltimore, MD, USA
2Department of Microbial Pathogenesis, University of Maryland School of Dentistry, Baltimore, MD, USA
3Maryland Department of Health Laboratories Administration, Baltimore, MD, USA
4Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

*Corresponding author
E-mail: rkernst@umaryland.edu

Quantitative specific drug analysis by tandem mass spectrometry allows a wide range of drugs to be analysed in either urine or oral fluid to confirmation standards. The repertoire of drugs is based on drugs of abuse implicated in drug-related deaths in Scotland and currently includes 27 specific drugs and metabolites.

by Dr Paul Cawood and Joanne McCauley

Background
Drugs of abuse have traditionally been identified by immunoassay screening methods. Some of these are relatively non-specific and require second-line confirmatory tests, traditionally by gas chromatography–mass spectrometry (GC-MS). As drugs are not volatile this requires derivatization to render the drugs volatile. Tandem mass spectrometry (TMS) has the advantage that samples can be analysed directly without derivatization.

Drug-related deaths in Scotland are the highest in Europe and are increasing steeply [1, 2], even though the number of substance misusers has not changed recently. Most deaths are due to accidental overdosing with opiates, which causes death from heart or respiratory failure. The steep increase is the result of poly-drug use, with gabapentin/pregabalin and street benzodiazepines (such as etizolam and alprazolam) implicated in a large number of these deaths. Identification of many of these drugs is not possible by traditional immunoassay screening methods even with GC-MS confirmation. However, it is possible to identify many of these drugs by TMS.

Specific quantitative drug analysis by TMS
Urine and oral fluid drugs of abuse method
A rapid method for the analysis of drugs of abuse in urine has been reported previously [3]. This method has been modified for the analysis of drugs implicated in drug-related deaths in Scotland [2]. One transition per drug can increase the risk of false-positive results [4]; hence,   each drug has two transitions and a closely matched deuterated internal standard in order to avoid these issues. Calibrators and quality control samples are made from Ceriliant certified standards. The standard set comprises morphine, codeine, 6-monoacetyl morphine (6-MAM), dihydrocodeine (DHC), oxycodone, gabapentin, pregabalin, methadone, EDDP (2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine, methadone metabolite), buprenorphine, norbuprenorphine, tramadol, amphetamine, 3,4-methylenedioxymethamphetamine (MDMA, or ecstasy) , methamphetamine, cocaine, benzoyl ecgonine (BEC), diazepam, nordiazepam, temazepam, oxazepam, 7-amino-clonazepam, nitrazepam, alprazolam, diclazepam, delorazepam and etizolam. Stock standard solution is made by adding 100 µg of each standard to a 20 ml volumetric flask, resulting in 5 000 µg/L. Calibrators are prepared at: 5, 10, 20, 30, 100, 300 and 1000 µg/L with quality controls at 10, 20, 50, 100, 300 and 400 µg/L in 3 % human serum albumin. The albumin prevents non-specific binding to the container.

Spot urine samples are collected in universal containers and oral fluid is collected into a Sarstedt salivette cortisol collection device (without preservative).

50 µL of calibrator, quality control, patient urine or oral fluid has 20 µL of zinc sulphate (0.1 mol/L) and 150 µL internal standard mixture (containing 17 deuterated internal standards – 1 µg/100 mL methanol) added. The sample is mixed and centrifuged. 75 µL of supernatant is removed and added to 300 µL of water. A volume of 20 µL is injected.

TMS analysis
Samples are analysed on a Waters Xevo tandem mass spectrometer using a Waters Acquity ultra high performance liquid chromatography HSS C18 1.8 µm, 100 mm column at 50 °C. The sample is eluted using a multi-step gradient of water (1 % formic acid 2 mM ammonium acetate) and acetonitrile (1 % formic acid), starting at 98 % water/2 % acetonitrile to 63 % / 37 % at 3.4 min then to 5 % / 95 % at 4.5 min, reverting to 98 %/2 % at 5.2 min (Fig. 1).

Drugs are identified using the quantitative ion transition having the same peak shape as the qualitative ion transition; retention times need to match the corresponding deuterated internal standard and the quantifying ion to qualifying ion ratio matches that of the calibrators (Fig. 2). Drugs are reported as positive when above the corresponding threshold level. Threshold levels are broadly based on Driving Under the Influence of Drugs (DRUID) or European Workplace Drug Testing Society (EWDTS) confirmation test levels for both urine and oral fluid (Table 1).

We analyse 4 000 urine and 17 000 oral fluid samples each year. These are predominantly from drug problem users (Fig. 3).

Drugs of abuse in urine
TMS has the advantage of greatly reducing false-positive results seen with immunoassay methods and negating the need for second-line confirmatory tests. However, the use of urine as a sample medium still has a number of disadvantages: it is susceptible to adulteration or spiking with drugs; sample collection is not witnessed; urine drug concentrations vary depending on hydration status. This can affect whether a drug is reported as positive or negative relative to threshold levels. Additionally, some drugs are excreted relatively unchanged in urine, whereas other drugs are highly metabolized and conjugated, in which case unchanged parent drug levels can be low. In order to keep the sample preparation simple it was decided not to hydrolyse drugs in urine but to measure predominantly parent drugs, including metabolites only where necessary. This required threshold levels to be adjusted to give comparable positivity to immunoassay methods (Table 1).

Drugs of abuse in oral fluid
Oral fluid overcomes many of the disadvantages of urine: sample collection can be witnessed; samples cannot be adulterated or spiked; and threshold levels are not affected by hydration status. Since we have offered an oral fluid service most clinicians have switched from urine to oral fluid testing. Parent drugs predominate in oral fluid, with metabolite levels being generally absent or uninformative, with the exception of BEC and nordiazepam. Drugs are predominantly weak bases and diffuse from serum (pH 7.4) into oral fluid (pH 4.0–6.0). As such, some drugs are then unable to diffuse back out again. This can result in oral fluid drug levels being higher in oral fluid than in blood. Levels can remain positive for longer in oral fluid than in blood or urine, giving a longer duration of detectability for some drugs (Table 1) [5].

Opiates
Heroin contains diacetyl morphine and acetyl codeine. Both of these are rapidly metabolized into 6-MAM and codeine respectively. Both 6-MAM and codeine further metabolize to morphine. Morphine is the major excretory product of heroin in urine and is detectable in urine up to 72 h after heroin has been taken [6]. Finding 6-MAM confirms heroin has been taken. Finding codeine in the absence of 6-MAM is also compatible with codeine consumption. 6-MAM is the major heroin component in oral fluid and this always indicates heroin use. Morphine and codeine levels are generally lower than 6-MAM in oral fluid. Finding morphine in oral fluid, in the absence of 6-MAM or codeine usually indicates a pure morphine preparation has been taken. Long detection times for 6-MAM in oral fluid have been reported in a Norwegian study which analysed daily blood, urine and oral fluid samples in 20 heroin overdose cases. They reported that 6-MAM can remain positive in oral fluid for 5 days or more after heroin had been taken. In one case, the heroin test was positive 8 days after exposure [7]. Dihydrocodeine, tramadol and oxycodone can be readily identified in both urine and oral fluid.

Cocaine
Cocaine is rapidly metabolized into BEC. BEC is better than cocaine as a urine marker of cocaine use, and can be detected for 48–72 h after cocaine use [6]. However, cocaine predominates in oral fluid at much higher levels than BEC. Cocaine can remain positive in oral fluid for up to 5 days after cocaine has been taken.

Methadone/buprenorphine
Methadone and buprenorphine are prescribed for the treatment of opioid dependence and are metabolized into EDDP and norbuprenorphine, respectively. EDDP/methadone and norbuprenorphine/buprenorphine concentrations are measured in urine. Usually EDDP levels are significantly higher than methadone. Norbuprenorphine levels are usually much higher than buprenorphine. Finding methadone/buprenorphine levels greater than EDDP/norbuprenorphine indicates the sample has been spiked. Parent methadone and buprenorphine appear in oral fluid whereas EDDP and norbuprenorphine do not. Buprenorphine is administered sublingually and levels in oral fluid are very high in samples collected immediately after administration. To avoid this, oral fluid samples should not be collected within 1 h of the buprenorphine dose. Buprenorphine half-life varies from 2 to 24 h [8] and oral fluid can be negative for buprenorphine if the sample is collected the next day after a low dose.

Amphetamines
Amphetamine, MDMA and methamphetamine are excreted relative unchanged in urine. Hence, parent drugs are analysed in both urine and oral fluid.

Gabapentinoids
Gabapentin and pregabalin are predominantly excreted unchanged in urine so the parent drug is readily detected in both urine and oral fluid. A survey of substance misusers in Lothian in 2012 indicated that gabapentin was taken to potentiate the high obtained from methadone and to increase the level of intoxication [9]. 92 % of sample positive for gabapentinoids are also positive for methadone or buprenorphine confirming that these drugs are taken to boost the intoxicating effects of opiate and opioids.

Benzodiazepines
These drugs are highly metabolized and conjugated with only a small amount of parent drug excreted unchanged in urine. As such threshold levels are much lower than immunoassay screening methods. Diazepam metabolizes into nordiazepam and temazepam, both of which metabolize into oxazepam. Nordiazepam is also a metabolite of chlordiazepoxide. Finding diazepam, nordiazepam, temazepam and/or oxazepam is consistent with diazepam. Finding nordiazepam in the absence of diazepam is also consistent with chlordiazepoxide. Nordiazepam has a longer half-life than both diazepam and chlordiazepoxide and remains positive for longer than either parent drug. Detecting temazepam only, nitrazepam only or oxazepam only is consistent with those drugs being taken. These patterns persist in both urine and oral fluid, although threshold levels are lower in oral fluid compared to urine (Table 1).

Street benzodiazepines
Following the 2016 drug-related deaths Scotland report [1] we introduced testing for etizolam, delorazepam, diclazepam and alprazolam into the standard set. These drugs are generally not available by prescription in the UK. Alprazolam and etizolam are short acting, whereas delorazepam and diclazepam are long acting. Alprazolam is six times more potent than diazepam [10].

Conclusion and future developments
Gabapentinoid use is widespread and is almost always used to potentiate methadone and other opiates or opioids. There is an increasing trend for more potent street benzodiazepines. This poly-drug use has a detrimental effect on judgement and behaviour leading to inadvertent overdosing. Poly-drug use is the main reason for the increase in drug-related deaths in Scotland in recent years [2]. Identifying the main drugs implicated in these deaths is only possible by TMS. In the future, additional drugs can be considered for inclusion, such as phenazepam (30 deaths in 2017); flubromazepam (9); fentanyl (15); mirtazapine (59); amitriptyline (36); sertraline (12); fluoxetine (12); olanzapine (9); quetiapine (11) and zopiclone (29). There is evidence that these are being abused by substance misuse clients and these are all implicated in significant numbers of drug-related deaths in Scotland [11].

References
1. Drug-related deaths in Scotland in 2016. A National Statistics report for Scotland. National Records of Scotland 2017 (https://www.nrscotland.gov.uk/files//statistics/drug-related-deaths/drd2016/drug-related-deaths-16-pub.pdf).
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9. Vindenes V, Enger A, Nordal K, Johansen U, Christophersen AS, Øiestad EL. Very long detection times after high and repeated intake of heroin and methadone, measured in oral fluid. Forensic Sci 2014; 20(2): 34–41.
10. Aden GC, Thein SG Jr. Alprazolam compared to diazepam and placebo in the treatment of anxiety. J Clin Psychiatry 1980; 41(7): 245–248.
11. Barnsdale L, Gounari X, Graham L. The National Drug-Related Deaths Database (Scotland) Report. Analysis of deaths occurring in 2015 and 2016. Information Services Division, NHS National Services Scotland 2018 (https://www.isdscotland.org/Health-Topics/Drugs-and-Alcohol-Misuse/Publications/2018-06-12/2018-06-12-NDRDD-Report.pdf).

The authors
Paul Cawood* PhD
Joanne McCauley BSc
Department of Clinical Biochemistry, Royal Infirmary of Edinburgh, Edinburgh, UK

*Corresponding author
E-mail: Paul.cawood@nhs.net