Current Methods for Bacterial Infection Clinical Diagnosis: Comparison
Please note this is a comparison between Version 1 by Rafi Ahmad and Version 2 by Camila Xu.

Antimicrobial resistance (AMR), defined as the ability of microorganisms to withstand antimicrobial treatment, is responsible for millions of deaths annually. The rapid spread of AMR across continents warrants systematic changes in healthcare routines and protocols. One of the fundamental issues with AMR spread is the lack of rapid diagnostic tools for pathogen identification and AMR detection. Resistance profile identification often depends on pathogen culturing and thus may last up to several days. This contributes to the misuse of antibiotics for viral infection, the use of inappropriate antibiotics, the overuse of broad-spectrum antibiotics, or delayed infection treatment.

  • antimicrobial resistance
  • rapid diagnostics
  • whole genome sequencing
  • infection diagnostics

1. Introduction

Antibiotics are natural or synthetic chemical compounds that inhibit bacterial growth and are the first-line drugs against bacterial infection. Bacteria have rapidly developed and spread antibiotic resistance across different bacterial classes in response to unnecessary antibiotic use, flaws in proper diagnostic tools, poor hygiene conditions, and suboptimal healthcare practices. During resistance development, antibiotics gradually become less effective, and bacteria can adapt and continue to grow in their presence [1].
Antibiotic resistance arises mainly via four different mechanisms: reducing efflux transport, target modification, limiting drug uptake, and enzyme-catalyzed inactivation [2][3][2,3]. Efflux pumps are a large family of protein pumps in bacteria that emit antibiotics from inside the cell to the outside [4]. Bacteria also develop resistance toward a specific class of antibiotics upon a series of DNA mutations or producing specific enzymes, resulting in modification of the targets of that class of antibiotic [5]. Alternatively, some proteins can bind to antibiotics or their targets, reducing antibiotic uptake [6]. Bacteria also inactivate antibiotics by producing enzymes that recognize and destroy antibiotics’ structure [7]. It has been shown that bacteria develop resistance via post-translational mechanisms as well [8]. These resistance mechanisms can be categorized as intrinsic or expected resistance (found across all strains/bacteria) or acquired (first appearing only in a few strains and then spreading to microorganisms of distant taxonomical relatedness). Therefore, acquired resistance poses a greater risk to human and animal health.
Antibiotic resistance, although having existed before antibiotics were discovered [9], is strongly driven via antibiotic use in human and veterinary medicine. Since the 1960s, antibiotic resistance has dramatically increased, fast becoming a global public health concern [10][11][10,11]. A recent meta-analysis of resistant bacteria burden on human health and well-being revealed that in 2019 alone, a striking 1.27 M deaths were caused directly by antibiotic-resistant bacteria (ARBs), and 4.95 M deaths were associated with ARBs. This number has surpassed the human immunodeficiency virus (HIV) and malaria [11]. Given the elevated use of antibiotics in 2020 due to the coronavirus disease (COVID-19) pandemic and low AMR resilience in many countries, this number has potentially increased in recent years.

2. Current Methods for the Clinical Diagnosis of Bacterial Infection

2.1. Pathogen Identification

2.1.1. Culture-Dependent Techniques

In these methods, culturable bacteria are enriched using a non-specific or selective medium and then characterized based on their morphology and metabolic traits. [12][25] Biochemical tests are based on qualitative biochemical reactions, e.g., visible changes in media because of bacterial metabolic activity. In colony morphology, microorganisms are identified based on the morphological properties of a specific strain [12][25]. The conventional approaches benefit from simplicity, reproducibility, and low cost. However, one should remember that the enrichment of microorganisms is a labor-intensive and time-consuming procedure, and colonies often require 1–3 days and sometimes longer to grow [13][26]. Besides that, correct pathogen identification based on culture often requires vast expertise, which might lead to inaccurate diagnosis [14][27]. To minimize such inaccuracy in biochemical methods, analytical profile index (API) tests [15][28] and automated systems such as VITEK (bioMerieux, Inc., Marcy-l’Étoile, France) have been developed [16][29]. The principle of immunological techniques is based on the interaction between diagnostic antibodies and certain antigenic elements of bacteria. Among different immunological assays, the enzyme-linked immunosorbent assay (ELISA) is the most frequently used technique in microbiology [17][30]. The ELISA technique became popular for its ability to identify multiple pathogens simultaneously, high-throughput capacity, low cost, and the opportunity to gauge the invading pathogens. Nonetheless, immunological techniques suffer from their dependency on the number of antibodies against pathogens epitopes (in the case of indirect ELISA). Furthermore, as some antigens are shared between bacterial species, the antigen–antibody binding is not specific. Therefore, immunological techniques lack selectivity and sensitivity.

2.1.2. Mass Spectroscopy

Several mass spectroscopy (MS) techniques have been developed in recent years. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-ToF MS) is the most common MS technique to identify bacterial species and even strains [18][31]. In this technique, the prepared bacteria are exposed to short laser pulses, and consequently, their proteins become ionized. The ions travel through a vacuum tube and are registered by the sensor, thus creating a spectrum. The resulting spectrum is then compared with a database of known bacteria spectra. A more in-depth methodology of MALDI-ToF is described by Singhal et al. [19][32]. Although microbial identification using MALDI-ToF MS is fast and accurate, the protein spectra for closely related species, e.g., Escherichia coli and Shigella, are highly similar [20][33], which hampers correct identification. Furthermore, initial and maintenance costs are the major concerns [19][32]. It has been estimated that a MALDI-ToF MS system requires EUR 200,000 per year for maintenance, limiting its usage to large hospitals capable of covering these costs [21][34]. Furthermore, the limited application of the MALDI-ToF MS system to the early detection of antibiotic resistance [22][35] makes this technique less attractive for the AMR field. However, a recent study demonstrated improved prediction of antibiotic resistance based on MALDI-ToF data by implementing a machine learning algorithm [23][36].

2.1.3. Nucleic-Acid-Based Techniques

Nucleic-acid-based techniques, such as the polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), and transcription-mediated amplification (TMA), are used in microbial diagnostic laboratories. The main feature of such techniques is their ability to amplify specific regions of the pathogen genome (conserved genes or 16 S ribosomal RNA genes), commonly used as taxonomic markers [24][25][37,38]. Although nucleic-acid-based techniques are highly sensitive and accurate, bacterial infections are, in some cases, caused by multiple bacteria, which might cause species–species or strain–strain interactions in this type of assay [26][39]. The PCR also requires primer design, which limits flexibility as one needs to have prior knowledge of the bacterial species present in the clinical samples. Generally, PCR techniques are culture-independent; however, a low-grade infection may require pathogen enrichment before analysis [27][40]. To overcome such limitations, some syndromic panels (taking benefits from the multiplexed real-time PCR) have recently been developed and certified for diagnostics, which allows users to quickly identify the most common pathogens [28][29][41,42]. The main disadvantages of these panels are the limited number of pathogens and AMR resistance profiles covered by the currently available kits [28][29][41,42].

2.2. Antibiotic Susceptibility Profiling

2.2.1. Phenotypic Techniques

The broth dilution test is one of the initial methods in antibiotic resistance screening. Here, a series of antibiotic dilutions within a liquid culture medium are prepared, and the bacteria of interest are inoculated. Then, the tubes are incubated overnight at 37 °C, and bacterial growth is detected. The lowest concentration of antibiotics that inhibit growth is considered the minimal inhibitory concentration (MIC) [30][43]. The method is highly reproducible, and the MIC value is highly informative. However, users might face time limitations and a lack of flexibility regarding antibiotic choice [31][44]. With the antimicrobial gradient method, bacteria are cultured on an agar medium and co-incubated with a few plastic test strips, each containing a gradient concentration of a specific antibiotic. The point where the lower segment of the ellipse-shaped growth inhibition zone touches the test strip would be considered the minimum inhibitory concentration (MIC) value [32][45]. The method is simple and easy to run and requires no specific expertise; however, like the broth dilution test, this method is limited to the number of antibiotics and could be costly if one tends to test multiple antibiotics [33][34][46,47]. Disc diffusion tests have been commonly used for years in many diagnostic laboratories. The single concentration of antibiotics impregnated into small round paper is placed on the agar surface with cultured bacteria. After 16–24 h incubation at 37 °C, the zone diameter with no bacterial growth close to each antibiotic disc is measured and compared with the reference values [30][43]. This method is simple and cheap to run, while to date, no automated system has been developed for fast screening of the no-growth zone on the plates. The main drawback of phenotypic methods is that they take up to a few days due to culturing. However, the new culture-based technology, Accelerate PhenoSystem (APS, Accelerate Diagnostics, Inc., Tucson, AZ, USA), allows pathogen ID and MIC values to be obtained within 2 and 7 h, respectively. The system uses the fluorescence in situ hybridization (FISH) technique for identifying common pathogens in bloodstream infections and morphokinetic cellular analysis for AST [30][32][35][43,45,48]. Another recent technology that allows same-day pathogen ID and AST is based on acoustic-enhanced flow cytometry [36][49]. When used for infection identification in peritoneal-dialysis-associated peritonitis, this technology provided an infection confirmation within 1 h and AST within 3–6 h after sample reception.

2.2.2. Molecular Techniques

The PCR and the qPCR (in some cases) are the most valuable techniques for quantifying and detecting resistance genes in bacteria in clinical laboratories [37][50]. New PCR variants such as digital droplet PCR (ddPCR) offered even higher sensitivity than qPCR for SARS-CoV-2 detection [38][51]. Another study suggested a hybrid combination of the PCR and the isothermal PCR as a highly sensitive method for SARS-CoV-2 diagnostics [39][52]. The advantage of the qPCR over the PCR is the quantitative measurement of the targeted gene(s). The main weakness of these methods is that one needs to decide about the targeted gene(s) in advance. Therefore, both assays have a limited capacity to detect unexpected/unanticipated genes. Furthermore, the variants of resistance genes might be difficult to detect. Another critical point in the field of antibiotic resistance is the discrepancy between the nucleic acid amplification test and AST results. Culture-dependent AST results are considered the gold standard for resistance detection. The Clinical and Laboratory Standards Institute (CLSI) has provided specific guidelines for resolving such disagreements in Gram-positive organisms and ESBL and carbapenemase in Gram-negative organisms [40][41][53,54].
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