Throughout human history, infections have remained the primary cause of diseases. The discovery, introduction, and subsequent widespread use of antibiotics aimed at eliminating the problems of infections. However, several bacterial species of animal and human origin have been able to develop several mechanisms to be resistant to antimicrobial agents.

Many threatening pathogens are now resistant to multiple classes of antimicrobial agents. A study tested 500 Streptomyces strains isolated from soil against 21 antibiotics of different class found that all the strains were multidrug resistant to at least 7 of the tested antibiotics (D’Costa et al., 2006). These multidrug-resistant (MDR) organisms cause infections that may compromise effective therapy and limit treatment options.

Economic and Mortality Impacts of Antimicrobial Resistance

Morbidity and mortality are significant consequences of antimicrobial resistance in patients. Resistant organisms tend to multiply the chances of serious health issues and double the risks of death. Across the globe, the current number of people that lose their lives due to drug-resistant infections is approximately 700,000, with 2 million people affected in the United States each year and about 23,000 deaths occurring. This number is roughly similar to that of the European Union, with an annual mortality rate of 25,000.

Official reports estimate about 10 million deaths globally by 2050 if effective and strong actions are not taken against antimicrobial resistance (AMR). The cost of AMR is also extremely high worldwide, although it is different for each country. Recent researches indicate the possibility of antimicrobial resistance elevating the poverty rate and seriously affecting low-income countries. The gap between developing and developed countries tends to become more pronounced, leading to a substantial increase in inequity.

Mechanisms of Antimicrobial Resistance

Biochemical and genetic basis of resistance are the major mechanisms of AMR encountered in clinical practice and these mechanisms fall into four main categories: (1) inactivation of the antimicrobial molecule; (2) target modification; (3) active drug efflux; (4) drug uptake limitation. Table 1 summarizes the mechanisms of antimicrobial resistance against different antimicrobial agents.

• Inactivation of the Antimicrobial Molecule

Microorganisms now produce enzymes that can inactivate drug molecules by destroying the molecule or adding certain chemical moieties to render the antimicrobial unable to interact with its target site. Scientists have described various modifying enzymes that catalyze different biochemical reactions, such as (i) acetylation, (ii) phosphorylation, and (iii) adenylation. Regardless of the catalyzed reaction, the overall effect is related to steric hindrance, decreasing the drug’s avidity for its target site, with the overall effect being higher bacterial MIC. For example, β-lactam resistance works on the mechanism of destroying the β-lactam molecules by the action of β-lactamases. They are enzymes that destroy amide bonds of the β-lactam ring to render the antimicrobial ineffective. Studies have identified over 1000 β-lactamases, with several others likely to be reported as bacterial evolution continues.

• Target Modification

This is another major mechanism of resistance involving the modification of the antimicrobial target site to prevent the proper binding of the antimicrobial molecule. The target sites have crucial cellular functions in antimicrobial action. However, some mutational changes can occur on the target site to reduce inhibition susceptibility while retaining their cellular functions. In certain cases, modifying target structures to cause resistance will require other cellular changes to compensate for the alteration. A typical example is the acquisition of penicillin-binding transpeptidase (PBP2a) in methicillin-resistant Staphylococcus aureus.

• Active Drug Efflux

Some complex mechanisms can rapidly extrude toxic compounds out of the cell. Microorganisms can produce these machinery (efflux pumps) to result in antimicrobial resistance. These efflux pumps can affect every class of antibiotics because they all must be present intracellularly to exert their effects. Many efflux systems are capable of extruding a wide spectrum of unrelated molecules (multidrug transporters) to contribute immensely to multidrug resistance. There are different families of efflux pumps:

1. major facilitator superfamily (MFS)
2. small multidrug resistance family (SMR)
3. resistance-nodulation cell division family (RND)
4. ATP-binding cassette family (ABC)
5. multidrug and toxic compound extrusion family (MATE)

• Drug Uptake Limitation

Bacteria have natural differences in their abilities to limit drug uptake. The composition of the outer membranes of some organisms (like gram-negative bacteria) slows down the penetration of antimicrobial agents and their transport across the water-filled channels of the membrane. Mycobacteria also have outer memberance with high lipid content making it difficult for hydrophilic drugs to penetrate them. Furthermore, organisms lacking cell wall (e.g., Mycoplasma) are intrinsically resistant to those agents that target cell walls. Formation of biofilms in pathogenic organisms will protect the microorganism for attacks by host’s immune system. Biofilms also provide adequate protection against antimicrobials agents.

Table 1: A summary of antimicrobial resistance mechanisms against different classes of antimicrobial agents.

Origins of Antimicrobial Resistance

Microorganisms have remarkable genetic plasticity, allowing them to wade off varying environmental threats such as the presence of antimicrobial agents. Analysis of isolated microbial DNA from the dental plaque of human remains showed gene sequences similar to those conferring resistance to aminoglycosides, tetracycline, β-lactams, bacitracin, and macrolides in clinical strains (Warriner et al., 2014). Some organisms share similar ecology with antimicrobial-producing species, allowing them to develop mechanisms to withstand harmful antimicrobials.

• Mutational Resistance

In this case, the microbial cells of susceptible populations develop mutations in genes affecting drug activity, leading to cell survival, even when antimicrobial molecules are present.

• Spontaneous mutations

These are mutation events that occur randomly due to replication errors or incorrect repairs of damaged DNA strands in actively dividing cells. They are often referred to as growth-dependent mutations, and they present a vital mechanism for generating antimicrobial resistance. Spontaneous mutations are also nucleotide point mutations that occur at the same permissive growth time and can produce a resistance phenotype. For example, Escherichia coli has quinolone resistance due to changes in at least seven different gyrA gene positions but only three in the parC gene. 

• Hypermutations

Some organisms now have elevated mutation rates (hyper mutators or hypermutable strains) among laboratory and natural populations. Studies have found that clinical and natural bacterial isolates now have a higher frequency of mutators than expected. This suggests that mutators may confer selective advantages in some situations in nature.

The most acceptable ‘hypermutable state’ model currently available states that some bacterial populations enter transient states of a high rate of mutation during prolonged non-lethal antibiotic pressure. Upon useful mutation to relieve the pressure, the cell grows and reproduces while remaining in the hypermutable state. While the trigger of hypermutation is still unclear, scientists believe that it is regulated by special inductively mutator DNA polymerases (pol) IV.

• Adaptive mutagenesis

Recent experimental data further shows that mutations can also occur in slowly dividing or non-dividing cells with some relation to the selective pressure introduced. These adaptive mutations only arise when there is non-lethal selective pressure present, favoring them. This feature differentiates them from spontaneous mutations and may be the major source of resistant mutants under natural conditions. From analysis, it is found that stress-enhanced mutagenesis is regulated. The major factors include stress-responsive, error-prone DNA polymerase IV (dinB) and V (umuCD), transiently increasing mutation rate.

• Horizontal Gene Transfer (HGT)

Another major driver of bacterial evolution is the acquisition of foreign materials through horizontal gene transfer. This mechanism is also responsible for developing antimicrobial resistance. Clinically relevant bacterial sharing a similar environment with natural antimicrobial agents have intrinsic genes for developing resistance. The simplest HGT type is transformation. However, some bacterial species can naturally incorporate naked DNA for the development of resistance.

Conjugation involves using mobile genetic elements (MGEs) to share genetic information, with the most important ones being transposing and plasmids. Another well-characterized mechanism is the direct chromosome-to-chromosome transfer. Finally, integrons represent some of the most efficient antimicrobial resistance mechanisms. They help to add newer genes to the bacterial chromosome with important machinery for expression.

Tackling Antimicrobial Resistance with Metagenomic Next Generation Sequencing (mNGS)

As routine microbiological screening includes resistant variants identification, the potential patient diagnosis waiting times should be as short as possible, especially for serious infections such as sepsis. However, in light of the overall AMR threat, not only the character of the resistance should be taken into account during pathogen ID in medical practice, but also the molecular mechanisms determining the occurrence and evolution of antimicrobial traits through genome sequencing.

mNGS technologies (metagenomic Next Generation Sequencing) have become an essential tool for unbiased, culture-independent diagnosis as well as drug and diagnostic tests development by enabling the rapid identification and surveillance of resistance mechanisms. Including a complete genomic sequence provided by the metagenomics advancements represent the highest practicable level of structural detail on the individuating traits of an organism or population. It can be used to provide more reliable microbial identification, definitive phylogenetic relationships, and a comprehensive catalog of traits relevant for epidemiological investigations. This is having a major impact on outbreak investigations and the diagnosis and treatment of infectious diseases, as well as the practice of microbiology and epidemiology. However, the major bottleneck keeping mNGS from mass adoption in clinical microbiology, is human DNA contamination with patient’s genetic information depletion being the main cost in rapid pathogen ID and a timely treatment.

Devin® is a novel blood fractionation filter device that can be applied to efficiently deplete human cellular DNA background and enrich microorganisms in the whole blood samples in less than 5 minutes. With ten most frequent microorganisms causing bacteremia and fungemia in adults belonging to the Enterobacteriaceae, Staphylococcaceae and Streptococcaceae, families, whose AMR strains are listed by the CDC as either concerning, serious or urgent threats , the PaRTI-Seq® test, a metagenomic sequencing workflow built upon Devin® can identify these potential pathogens from whole blood samples within 24 hours with a sensitivity of 10² genome copies per milliliter. Increasing the scope of available genomic screening for the presence of antimicrobial resistance factors using PaRTI-Seq® and Devin® technology could not only speed up the diagnostic process, especially during BSI but also improve the efficiency of the AMR screening process, increasing existing antimicrobial databases with crucial data sharing and exchange. Devin® and PaRTI-Seq® have the potential for saving the sequencing cost significantly and constitute a faster method for AMR ID, especially from whole blood samples.

References

1. D’Costa, V. M., McGrann, K. M., Hughes, D. W., and Wright, G. D. Sampling the antibiotic resistome. Science. 2006;311,374–377.
2. Jim O’Neill. Tackling drug-resistant infections globally: final report and recommendations the review on antimicrobial resistance chaired. [Online] 2016. Available from: https://amr-review.org/sites/default/files/160518_Final paper_with cover.pdf. 
3. Founou RC, Founou LL, Essack SY. Clinical and economic impact of antibiotic resistance in developing countries: a systematic review and meta-analysis. PLoS One. [Online]. 2017;12:e0189621. Available from: https//doi.org/10.1371/journal.pone.0189621
4. Shrestha P, Cooper BS, Coast J, et al. Enumerating the economic cost of antimicrobial resistance per antibiotic consumed to inform the evaluation of interventions affecting their use. Antimicrob Resist Infect Control. [Online]. 2018;7(1):98. Available from: doi:10.1186/s13756-018-0384-3
5. World Bank. Drug-resistant infections a threat to our economic future. [Online] 2017. Available from: https://worldbank.org. 
6. Martinez JL. General principles of antibiotic resistance in bacteria. Drug Discov Today. 2014;11:33–39.
7. Cox G, Wright GD. Intrinsic antibiotic resistance: mechanisms, origins, challenges and solutions. Int J Med Microbiol. 2013;303:287–292.
8. Davies J. Where have all the antibiotics gone? Can J Infect Dis Med Microbiol. 2006;17:287–90.
9. Wilson DN. Ribosome-targeting antibiotics and mechanisms of bacterial resistance. Nat Rev Microbiol. 2014;12:35–48.
10. G. Spratt. Resistance to antibiotics mediated by target alterations. Science. 1994;264:388–393.
11. Bush K. The ABCD’s of β-lactamase nomenclature. [Internet]. 2013;19:549–559. Available from: https://doi.org/10.1007/s10156-013-0640-7
12. Bush K, Jacoby GA. Updated functional classification of β-lactamases. Antimicrob Agents Chemother. [Internet]. 2010;54:969–976. Available from: https://doi.org/10.1128/AAC.01009-09
13. Warinner, C., Rodrigues, J. F., Vyas, R., Trachsel, C., Shved, N., Grossmann, J., et al. Pathogens and host immunity in the ancient human oral cavity. Nat. Genet. 2014;46:336–344.
14. J. Piddock, R. Wise. Induction of the SOS response in Escherichia coli by 4-quinolone antimicrobial agents. FEMS Microbiol. Lett. 1987;41:289–294.
15. Coculescu BI. Antimicrobial resistance induced by genetic changes. J Med Life. 2009;2:114–123.
16. I. Aminov, R.I. Mackie. Evolution and ecology of antibiotic resistance genes. FEMS Microbiol. Lett. 2007;271:147– 161.
17. Angers A., Petrillo M., Patak A., Querci M., Van den Eede G. The role and implementation of next-generation sequencing technologies in the coordinated action lan against antimicobial resistance. Joint Research Center. 2017;4-7.
18. Schmidt, K., Mwaigwisya, S., Crossman, L.C., Doumith, M., Munroe, D., Pires, C., Khan, A.M., Woodford, N., Saunders, N.J., Wain, J., et al. Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J. Antimicrob. Chemother. 2017;72:104–114.
19. Köser et al. (2014). Whole-genome sequencing to control antimicrobial resistance. Trends Genet. 30(9):401–7.
20. Han et al. (2019). mNGS in clinical microbiology laboratories: on the road to maturity. Crit Rev Microbiol 45(5-6):668-85.
21. Balloux et al. (2018), From Theory to Practice: Translating WGS into the Clinic. Trends in Microbiology 26(2).
22. Chen et al. (2021). Novel Human Cell Depletion Method For Rapid Pathogen ID by NGS. Labroots Microbiol. Week 10.13140/RG.2.2.23888.64007.
23. CDC (2019). Antibiotic Resistance Threats in the US. Atlanta, GA: U.S. HHS Dept, cdc.gov/drugresistance/biggest-threats.html.

Infective endocarditis is inflammation of endocardium that usually affects heart valves. Mortality rate of the disease is up to  30% in 1^st year ^[1] for many malignant cancers. The most common risk factors for infective endocarditis include previous heart damage, recent heart surgery or poor dental hygiene. According to report of National Organization for Rare Diseases, people over the age of 50 with prosthetic heart valves or cardiac pacemakers are more prone to develop endocarditis. Healthcare contact is major source of infection.

Bacteria account for most cases while fungi are the least common cause of endocarditis. Damaged heart valves and inner lining of the heart is the main risk factor for infective endocarditis because it leaves the tissue susceptible to bacteria overgrowth. Valvular vegetation which is caused by the clumps of bacteria and cells on the heart valves is the classic lesion of infective endocarditis. Untreated endocarditis can cause abscesses beneath valves, heart failure and even death. New regurgitation murmurs may also occur as a result of valvular disorders such as mitral regurgitation, aortic regurgitation or tricuspid regurgitation.  Therefore, early diagnosis is the key to improve clinical outcomes and survival odds. Antibiotics are essential for treatment and are most effective if the disease is caught early. Population at risk and microbiology of endocarditis has changed with advances in healthcare and emergence of antibiotic resistance (Bernard Iung. Presse Med. 2019 May) ^[3].

Microbiology of endocarditis

The five most common pathogens causing infective endocarditis are Staphylococcus aureus,  Streptococcus viridans,  Staphylococcus epidermidis, Enterococcal endocarditis and Streptococcus bovis. Staphylococcus aureus is the most predominant causative microorganism of endocarditis and mostly attacks tricuspid valve in IV drugs abusers. It is high virulence organism and can also infect intact heart valves. Infection is acute and severe. Staphylococcus aureus prosthetic valve endocarditis (PVE) was found to be one of the most morbid bacterial infections with mortality rate ranging from 40% to 80% (Alicia Galar et al. Clin Microbiol Rev. 2019)^[4] .Streptococcus viridans are second most common cause of endocarditis (Vladimír Krčméry et al. 2019) ^[6]   and presents part of mouth flora, mostly causing endocarditis in people with damaged heart valves (e.g. mitral valve prolapse) after dental procedures. It synthesizes dextran to adhere to platelets and fibrin found in damaged valves resulting in formation of vegetations. Staphylococcus epidermidis is a low virulence, coagulase-negative staphylococcus which found in normal skin flora. Treatment for antibiotic resistant staphylococcus epidermidis is known to be challenging in the clinics. Enterococcal endocarditis is caused by Lancefield group D gram-positive cocci and it is commonly found in older men with genitourinary tract infection (D W Megran. Clin Infect Dis. 1992 Jul) ^[7]. Streptococcus bovis is also a Lancefield group D gram-positive coccus and part of normal gut flora. All subtypes, especially Streptococcus gallolyticus are associated with colon cancer.

Table 1. Microbiology of infective endocarditis. Table shows incidence of various causative microorganisms detected in a long-term multicentre study ^[5].

Culture-negative endocarditis

To identify the causative pathogens for infective endocarditis, in most cases blood culture  is used. However, studies show that 2-7% of endocarditis cases come culture-negative, especially in patients with antibiotics therapy prior to blood collection (Sheibani, H., Salari, M., Azmoodeh, E. et al, 2020) ^[8].  Culture-negative endocarditis is highly morbid and mortal infection accounts for up to 35% of infective endocarditis (S Subedi et al.2017) ^[9-10]. Bartonella and coxiella are two of the intracellular organisms most commonly associated with blood culture-negative endocarditis. In addition, HACEK organisms, Tropheryma whipplei and some fungi are commonly found in culture-negative endocarditis patients.

Fast detection of infection is crucial to prevent neurological manifestations of the disease. Next-generation sequencing can prove useful for fast detection and timely treatment of the infection. Even though clinical utility is not wide spread today, application of the diagnostic technique may greatly reduce the chances of misdiagnosis in future. Next-generation sequencing is widely used outside of clinical microbiology laboratories to find the etiology of infectious diseases.

Sishi Cai et al (2021) ^[11] conducted a research to assess the value of metagenomic next-generation sequencing in diagnosis of infective endocarditis. Total of 49 endocarditis patients were enrolled of which 28 were culture positive and 21 were culture negative. Next-generation sequencing was applied to study the positive detection rate among culture-negative endocarditis patients. Resected valves of 8 patients with non-infective valvular diseases were collected and used as negative control group. Results revealed 100% positive pathogen detection rate by mNGS among culture-negative endocarditis patients. These suggest that mNGS is a valuable tool for diagnosing infective endocarditis, especially culture-negative endocarditis and it can be utilized to guide post-surgical antibiotic treatment.

Devin® filter and Parti-Seq^® platform are amongst the newest and most advanced methods developed for the early detection of pathogenic microorganisms from human blood. Devin^® Filter filters up to 95% of the patient’s nucleated cells from the blood sample, allowing a passage more than 99% of bacteria and viruses. This filtration process is essential as human genetic information (DNA) in the blood samples that need to be analyzed is a critical holdback in rapidly identifying pathogenic microorganisms. Further, pathogen identification is performed with NGS-based Pathogen Real-Time Identification by Sequencing (PaRTI-Seq^®) developed by Micronbrane for rapid pathogen identification within less than 24 hours with a sensitivity of 10² genome copy / ml^[12].

References

1. Cahill, T. J., Baddour, L. M., Habib, G., Hoen, B., Salaun, E., Pettersson, G. B., Schäfers, H. J., & Prendergast, B. D. (2017). Challenges in Infective Endocarditis. Journal of the American College of Cardiology, 69(3), 325–344. https://doi.org/10.1016/j.jacc.2016.10.066
2. Wang, A., Gaca, J. G., & Chu, V. H. (2018). Management Considerations in Infective Endocarditis: A Review. JAMA, 320(1), 72–83. https://doi.org/10.1001/jama.2018.7596
3. Iung B. (2019). Endocardite infectieuse. Épidémiologie, physiopathologie et anatomopathologie [Infective endocarditis. Epidemiology, pathophysiology and histopathology]. Presse medicale (Paris, France: 1983), 48(5), 513–521. https://doi.org/10.1016/j.lpm.2019.04.009
4. Galar, A., Weil, A. A., Dudzinski, D. M., Muñoz, P., & Siedner, M. J. (2019). Methicillin-Resistant Staphylococcus aureus Prosthetic Valve Endocarditis: Pathophysiology, Epidemiology, Clinical Presentation, Diagnosis, and Management. Clinical microbiology reviews, 32(2), e00041-18. https://doi.org/10.1128/CMR.00041-18
5. Murdoch, D. R., Corey, G. R., Hoen, B., Miró, J. M., Fowler, V. G., Jr, Bayer, A. S., Karchmer, A. W., Olaison, L., Pappas, P. A., Moreillon, P., Chambers, S. T., Chu, V. H., Falcó, V., Holland, D. J., Jones, P., Klein, J. L., Raymond, N. J., Read, K. M., Tripodi, M. F., Utili, R., … International Collaboration on Endocarditis-Prospective Cohort Study (ICE-PCS) Investigators (2009). Clinical presentation, etiology, and outcome of infective endocarditis in the 21st century: the International Collaboration on Endocarditis-Prospective Cohort Study. Archives of internal medicine, 169(5), 463–473. https://doi.org/10.1001/archinternmed.2008.603
6. Krčméry, V., Hricak, V., Fischer, V., Mrazova, M., Brnova, J., Hulman, M., Outrata, R., Bauer, F., Kalavsky, E., Babela, R., Mikolasova, G., Spanik, S., Karvaj, M., & Marks, P. (2019). Etiology, Risk Factors and Outcome of 1003 Cases of Infective Endocarditis from a 33-year National Survey in the Slovak Republic: An increasing proportion of elderly patients. Neuro endocrinology letters, 39(8), 544–549.
7. Megran D. W. (1992). Enterococcal endocarditis. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 15(1), 63–71. https://doi.org/10.1093/clinids/15.1.63
8. Sheibani, H., Salari, M., Azmoodeh, et al. Culture-negative endocarditis with neurologic presentations and dramatic response to heparin: a case report. BMC Infect Dis 20, 476 (2020). https://doi.org/10.1186/s12879-020-05206-0
9. Subedi, S., Jennings, Z., & Chen, S. C. (2017). Laboratory Approach to the Diagnosis of Culture-Negative Infective Endocarditis. Heart, lung & circulation, 26(8), 763–771. https://doi.org/10.1016/j.hlc.2017.02.009
10. Tattevin, P., Watt, G., Revest, M., Arvieux, C., & Fournier, P. E. (2015). Update on blood culture-negative endocarditis. Medecine et maladies infectieuses, 45(1-2), 1–8. https://doi.org/10.1016/j.medmal.2014.11.003
11. Cai, S., Yang, Y., Pan, J., Miao, Q., Jin, W., Ma, Y., Zhou, C., Gao, X., Wang, C., & Hu, B. (2021). The clinical value of valve metagenomic next-generation sequencing when applied to the etiological diagnosis of infective endocarditis. Annals of translational medicine, 9(19), 1490. https://doi.org/10.21037/atm-21-2488.
12. Micronbrane (2021, November 17). [White Paper] Needle in the Haystack: How to Remove Human Background When You Want to Detect Microorganisms. https://micronbrane.com/white-paper-needle-in-the-haystack-how-to-remove-human-background-when-you-want-to-detect-microorganisms/

What Is Cell-Free DNA?

Human bodily fluids have been traditionally considered to be a completely sterile environment, such as blood with its morphotic (cell) elements suspended in surrounding plasma (water-based dispersant, rich in organic and inorganic compounds) as a supposed whole, the homeostatic composition of this liquid tissue. Until recent advancements in the field of biofluid microbiology, any confirmed presence of non-host elements, especially of bacteria, were treated as abnormal physiological states^{[1] [2]}. Indeed, blood infectious diseases leading to sepsis are life-threatening emergencies, being the cause of one in five deaths worldwide. However, these conditions stem mainly from viable pathogen infections development due to weakened immune systems’ response of vulnerable populations (e.g. immunocapacitated, newborn, pregnant, elderly, or low-income setting individuals), not necessarily from colonization or contamination, which are one of the baseline strategies for the survival of naturally occurring blood microbiota (Fig. 1.)^{[3] [4] [5] [6]}.

Figure 1. Blood vessel cross-section graphic visualization with plasma-suspended red/white cells and platelets as the main tissue elements (MAG – magnification of plasma biocomponents). Besides the confirmation of blood bacteriome, virome, and fungome presence in both healthy and diseased individuals, other components have been proven to circulate in the bloodstream: live host cells, including cancerous ones, as well as undigested cell organelles and macromolecules, such as genetic information in form of genomic DNA (gDNA) parts, released to extracellular space from disintegrating biological agents, referred to as circulating cell-free DNA (cfDNA)^{4 [7] [8]}.

Cell-free DNA is therefore an endo- or exogenous fragment of coding or non-coding deoxyribonucleic acid with an average size smaller than 300 bp, remaining in the biofluids after apoptosis, necrosis, or another breakdown pathway of cells or capsids^{[5] [9] [10]}. Discovered in human blood samples as early as 1948, cfDNA has gained track only in recent decades as nucleic acid isolation and amplification techniques became more sensitive and efficient in detecting the nanogram per milliliter concentration of these biomolecules^{[11] [26]}.

Why Is cfDNA Used for Diagnosis of Infections?

The makeup of human blood cfDNA profile is mostly composed of the genetic material coming from the organism’s own cells, constituting up to 99,99% of all free circulating deoxyribonucleic acids fragments, with as low as 0.00025% of the total cfDNA coming from the host’s malignant or fetal cells if present^{[9] [12]}. The development of targeted molecular methods, including digital PCR (dPCR) and beads, emulsions, amplification, and magnetics (BEAMing), allowed for the detection of tumor cfDNA present in frequencies of 1% to 0.001% of the total cfDNA in vascular circulation. Based on these advancements, cfDNA sequencing has emerged as a novel laboratory technique for rapid and noninvasive diagnosis in cancer and prenatal screening as well as organ transplantation^{9 [13] [14]}. The remaining part of extracellular DNA originates either from pathogenic or non-pathogenic components of microbiota such as bacteria (0.08%-4.85%), viruses (0.00%-0.16%), fungi (0.00%-0.01%) or protozoa. Collectively, the so-called microbial cell-free DNA (mcfDNA), have been proposed as an infectious disease biomarker for clinical diagnosis on the same principles as implemented in gynecology, oncology, or transplantology practice ^{[5] [11]}.

Many DNA sequences, including cfDNA fragments, are species-specific and with the development of next-generation sequencing (NGS) technology, these molecules have been described as a promising, non-invasive tool for the early detection of several human diseases^{[15] [20]}, including sepsis for which the latest research have reported significantly elevated fractions of cfDNA from retrotransposable elements in blood^[16]. Indeed, the use of cfDNA for the detection of infectious Epstein-Barr virus has been reported since the 20^th century and more recently for the diagnosis of invasive fungal infection. Some more specific examples of infectious agents reported to be detected using cfDNA sequencing include Plasmodium, Trypanosoma, Leishmania, Schistosoma, Leptospira, and HIV^[9]. Microbial cfDNA can be also detected in biofluids of patients undergoing intensive antimicrobial treatments^{[9] [17]}.

How cfDNA Is Used For Diagnosis of Infections?

mcfDNA-based next-generation sequencing (mcfDNA sequencing) is an emerging hypothesis-free test that detects mcfDNA shed into noninvasive samples (e.g. peripheral venous blood, urine) from sites of infection, increasingly promoted as a faster diagnostic method with higher sensitivity than standard blood cultures (BC)^{[5] [10]}.

As of today, there is only one commercially available, however not FDA-approved test^{[17] [18]} for cfDNA-based medical disease diagnosis, developed by Karius, Inc. (USA) in 2018^[19]. The Karius^® Test (KT) is an assay utilizing metagenomic NGS (mNGS) for microbial cfDNA detection in patient blood samples during infections diagnosis^[20], based on a clinical-grade database limited to around 1,200 from more than 1,400 species of all known human pathogens^{[21] [22]}. Following a set of custom-designed methods, including specific artificial intelligence algorithms, KT allows for efficient isolation, purification, sequencing, and taxonomic origin determination of identifiable, blood-circulating extracellular mcfDNA fragments, derived from non-viable microbes present in either healthy, diseased, or vulnerable individuals^{[23] [24]}. Since its commercial introduction, the mcfDNA sequencing potential of KT has been also employed as a possible aid in the hard-to-detect and latent infections such as tuberculosis or some forms of sepsis, however with a varied success rate and the significance of Mycobacterium tuberculosis cfDNA detection in patients with pulmonary tuberculosis still remaining unclear^{[5] [9] [25]}.

What Are cfDNA Limitations and Possible Alternatives?

With continued advancements in molecular microbiology techniques, a wide number of sequencing-based diagnostics tests for an array of pathogens are emerging, but these tests often require invasive biopsy samples taken from infected tissue, which is not advised during some medical conditions or emergencies, therefore prolonging and hindering the determination of a proper diagnosis. Based on the fact that cfDNA from specific tissues or sites circulates freely around the bloodstream, mcfDNA has been proposed as an alternative method of noninvasive blood biopsies for rapid infectious diseases diagnosis ongoing throughout the body. Nevertheless, recent research indicates the distribution of circulating cfDNA coverage over the respective reference genome is highly non-uniform increasing the risk of missing relevant fragments, especially given the small volume of biofluid analyzed, low amounts of mcfDNA in relation to interfering human cfDNA, even smaller if taken into account overall cfDNA concentration in the blood varies significantly, ranging between 0–5 and >1000 ng per ml in patients with cancer and between 0 and 100 ng per ml in healthy subjects ^{[18] [21] [24] [26]}.

The overall value of each infectious agent’s detection method for clinical practice is determined by a wide spectrum of features including their accuracy, safety, technical, or manufacturing parameters. From the patient’s healthcare point of view, the diagnosis speed and effectiveness of subsequent therapy are influenced to the highest degree by the test’s specificity, sensitivity, turnaround time, costs, and the degree with which pathogen characteristics are determined (e.g. species, strains, and specific traits). The summary of these frequently addressed tests’ features for clinical application indicates the mcfDNA mNGS technique indeed has improved sensitivity as compared with most widely used BC, yet its specificity is decreased in relation to both BC and gDNA sequencing of whole blood pathogen samples (Tabl. 1.). As with every DNA sequencing technology, both gDNA and mcfDNA rapid diagnostics are less available and more expensive (with the former still cheaper than the latter) than pathogen ID based on microbial culturing, but at the same time constitute much faster and more sensitive methods. Although KT could diagnose opportunistic pathogens otherwise missed by standard microbiological testing, it also yields polymicrobial detections and organisms of uncertain clinical significance and actionability. Recent empirical and theoretical research points out the KT detection method is not uniform and the standardization process is lacking, with the main obstacles of mcfDNA use in rapid diagnostics of infectious diseases (e.g. low abundance, high rate of non-microbial impurities, or incorrect sequence attribution) overcomed on a basis of custom solutions. A significant drawback of the mcfDNA mNGS is definitely seen in the inability to determine specific strains and traits of detected species, as most of the genetic information needed for such ID is fragmented and the diversity of genes encoding the clinically relevant features such as antimicrobial resistance, is defined in hundreds of sequences among single species. The quantification of viable microbiome presence and unconstricted range of reference genomes during pathogen gDNA sequencing allowing for the NGS-based identification of pathogenic and non-pathogenic microbes in whole blood samples with a sensitivity of 102 genome copies per ml, is unavailable while using mcfDNA for diagnostic purposes, resulting in the knowledge gaps about the nature of detected microbiota components (e.g. infection, colonization, contamination could not be distinguished) ^{[10] [12] [14] [15] [21] [27] [28] [29] [30] [31] [32]}.

Table 1. Comparison of different methods in clinical microbiology for diagnosis of human infectious diseases from blood.

All in all, the mcfDNA sequencing could in the future become a valuable resource for clinical microbiology practice, especially useful during difficult or extraordinary cases, however as an additional and not the only or definitive source of medical diagnoses during infectious diseases. KT results should be examined with caution, by physicians with solid ID expertise, familiarity with the technology and result interpretation, preferably as a supplemental verification of BC or whole blood gDNA sequencing interpretations^{[12] [29]}.

References:

^[1]Castillo et al. (2019). The Healthy Human Blood Microbiome: Fact or Fiction? Front. Cell Infect. Microbiol. 9: 148.

^[2]Stinson et al. (2019). The Not-so-Sterile Womb: Evidence That the Human Fetus Is Exposed to Bacteria Prior to Birth. Front. Microbiol. 10: 1124.

^[3]WHO (2020). Global report on the epidemiology and burden of sepsis: current evidence, identifying gaps and future directions. Geneva.

^[4]D’Aquila et al. (2021). Microbiome in Blood Samples From the General Population in the MARK-AGE Project. Front. Microbiol. 12: 707151.

^[5]Han et al. (2020). Liquid biopsy for infectious diseases: a focus on microbial cell-free DNA sequencing. Theranostics 10(12): 5501–5513.

^[6]Viscoli (2016). Bloodstream Infections: The peak of the iceberg. Virulence 7(3): 248–251.

^[7]Rejniak (2016). Circulating Tumor Cells: When a Solid Tumor Meets a Fluid Microenvironment. Adv Exp Med Biol. 936: 93–106.

^[8]Dache et al. (2020). Blood contains circulating cell-free respiratory competent mitochondria.The FASEB Journal 34: 3616–3630.

^[9]Fernández-Carballo et al. (2019). The Dev. of cfDNA-Based Diagnostic Test for Infectious Diseases. J. Clin. Microbiol. 57(4): e01234-18.

^[10]Chen et al. (2020). Rapid diagnosis and comprehensive bacteria profiling of sepsis based on cell-free DNA. J. Transl. Med. 18: 5.

^[11]Boguszewska-Byczkiewicz et al. (2020). A comparison of four commercial kits used for isolating circulating cfDNA. Med. Res. J. 5(2): 92–99.

^[12]Sun et al. (2019). Circulating Cell-Free DNA. Liquid Biopsy, Ilze Strumfa and Janis Gardovskis, IntechOpen.

^[13]Bredno et al. (2021). Clinical correlates of circulating cell-free DNA tumor fraction. PLoS ONE 16(8): e0256436.

^[14]Camargo et al. (2020). NGS of mcfDNA for Rapid Noninvasive Diagnosis of Infect. Diseases in Immunocompr. Hosts. F1000Res. 8: 1194.

^[15]Wang et al. (2021). Plasma Microbial cfDNA Sequencing Technol. for the Diagnosis of Sepsis in the ICU. Front. Mol. Biosci. 8: 659390.

^[16]Grabuschnig et al. (2020). Circul. cfDNA is pred. composed of retrotransposable elem. and non-telom. satellite DNA. J. Biotechnol. 313:48-56.

^[17]Karius, Inc. (2021). Diagnosing Infections During the COVID-19 Pandemic. https://kariusdx.com/covid-19/.

^[18]Pew Charitable Trusts (2021). Diagnostic Tests Not Reviewed by FDA Present Growing Risks to Patients. Fact Sheet, Oct 2021: 1-6.

^[19]Karius, Inc. (2018a). Fighting infectious disease with cfDNA. Nature, Medtech Dealmakers Advertisement Feature: M13.

^[20]Morales (2021). The Next Big Thing? NGS of Microbial Cell-Free DNA Using the Karius Test. Clin. Microbiol. 43(9): 69-79.

^[21]Blauwkamp et al. (2019). Analytical and clinical validation of a microbial cfDNA seq. test for infectious disease. Nat. Microbiol. 4(4): 663-674.

^[22]Woolhouse & Gowtage-Sequeria (2005). Host Range and Emerging and Reemerging Pathogens. Emerg. Infect. Dis. 11(12): 1842–1847.

^[23]Karius, Inc. (2018b). Multi-pathogen detection from a single blood draw. Nature, Biopharma Dealmakers Advertisement Feature: B26.

^[24]O’Grady (2019). A powerful, non-invasive test to rule out infection. Nat. Microbiol. 4: 554-555.

^[25]Pan et al. (2021). M.tuberculosis-derived circulating cfDNA in PTB patients and persons with latent TB infection. PLoS ONE 16(6): e0253879.

^[26]Kustanovich et al. (2019). Life and death of circulating cell-free DNA. Cancer Biol. Ther. 20(8): 1057–1067.

^[27]Hung (2021). Needle in the Haystack: How to Remove Human Background to Detect Microorganisms. Micronbrane Medical, White Paper: 1-10.

^[28]bioMérieux (2018). BLOOD CULTURE: A key investigation for diagnosis of bloodstream infection. biomerieux-usa.com.

^[29]Benamu et al. (2021). Plasma Microbial cfDNA NGS in the Diagnosis and Management of Febrile Neutropenia. Clin Infect Dis., Oxford Acad.

^[30]Han et al. (2019). mNGS in clinical microbiology laboratories: on the road to maturity. Crit Rev Microbiol 45(5-6):668-85.

^[31]Chen et al. (2021). Novel Human Cell Depletion Method For Rap. Pathog. ID by NGS. Labroots Microbiol. Week 10.13140/RG.2.2.23888.64007.

^[32]Baekkeskov et al. (2020). AMR as a Global Health Crisis. Stern (Ed.). Oxford Encyclopedia of Crisis Analysis (1), Oxford University Press.

Antimicrobial Resistance = Global Public Health Concern

It has been nearly two years since the global healthcare resources have been diverted into the prolonged fight against the SARS-CoV2 pandemic^[1], often being forced to put other medical issues and emergencies on hold^{[2] [3] [4]}. Aside from the undeniable threat of the COVID-19 crisis, there are other urgent challenges to the health of the general population, such as the accelerating development of resistance to antimicrobials, seen among many harmful pathogens. Collectively referred to as the antimicrobial resistance (AMR) traits, the development of defense mechanisms to drugs previously effective against the infectious agent, is being increasingly reported in different species and strains of pathogenic bacteria, viruses, fungi, and protozoan parasites. Preventing successful therapy outcomes of treatable infectious diseases in patients, AMR pathogen (superbug) infections have been responsible for more than 700,000 deaths in 2019, with current worst-case scenarios predicting an annual total loss of 10 million lives as of 2050^{[5] [6] [7]}.

With the current use of antibiotics alone estimated at 100,000-200,000 tons per year^[1], massive and uncontrolled consumption results in a subsequent release of a wide range of substances with antimicrobial properties into the environment. Antibiotics, antivirals, antifungals, antiparasitics, analgesics, and anti-inflammatory drugs are among the most commonly detected classes of pharmaceuticals in the anthropogenic pollution makeup of water, soil, air, and living tissues^{[2] [3] [4]}.

The Importance of AMR Genes Identification in Medical Microbiology

As ​​medical microbiology concerns the nature, distribution, and activities of microbes and how they impact health and wellbeing, most particularly as agents of infection^[1], it allows for the discovery of effective ways of infectious disease treatments. Besides the illness etiology, the resistance of pathogen and its variants have to be determined to assure susceptibility to drugs of choice for particular infections^[2]. Antimicrobial susceptibility is an appropriate test whenever a specimen is collected from a suspected infection site. In the face of active infection, this information, along with the Gram stain and culture, allows selecting an appropriate antimicrobial agent to treat an infection^[3].

The latest research indicates AMR is a growing threat i.a. to neonates in low- and middle-income countries, such as India^[4]. Gram-negative (GN) Klebsiella spp., Citrobacter spp. and Escherichia spp. with Gram-positive (GP) Staphylococcus spp. are the most commonly identified etiologic agents of bloodstream infections (BSI^[5], e.g. Figures 1. and 2.), including in newborns¹⁶. Compared with neonates without BSI, all-cause mortality is higher among ones with early and late-onset BSI, with a substantial percentage of AMR strains causing the infections, including GN carbapenem-resistant bacteria, where a critical gap remains in research and development, in particular for antibacterial targeting^{7 16}.

Bloodstream infection example. Figure 1. Colorized SEM of methicillin-resistant Staphylococcus aureus (MRSA) bacteria on the surface of a wound dressing^[1]. Figure 2. The hypothesized life cycle of methicillin-resistant Staphylococcus aureus isolates that cause persistent bacteremia in the context of endovascular infection^[2]. GP bacteria acquire resistance to beta-lactam antibiotics through the production of a protein called PBP2a, which is able to avoid the inhibitory effects of the antibiotics. This is the mechanism by which MRSA is able to persist despite treatment with multiple beta-lactam antibiotics^[3].

Antibiotic resistance is thus not a future risk, but one of another epidemic threats that, without appropriate action could, like COVID-19, turn into a pandemic costing humanity millions of lives and dollars. We can no longer “sit tight and assess”^[1] the situation, we must be proactive to get off course towards a post-antibiotic era, for which the invention of rapid diagnostic methods is essential^{[2] [3] [4]}. The importance of AMR Genes Identification (ID) is evidenced by the Global Antimicrobial Resistance and Use Surveillance System – GLASS – which has been conceived by the WHO to incorporate data from infections of AMR in humans, the use of antimicrobial medicines, AMR in the food chain and in the environment, filling knowledge gaps and to inform strategies at all levels⁷, helping in the development of more effective therapies such us the clinical use of bacteriophages and new drugs^[5].

State of the Art Pathogen Detection Methods: Possibilities & Limitations

As routine microbiological screening includes resistant variants identification, the potential patient diagnosis waiting times should be as short as possible, especially for serious infections such as sepsis. However, in light of the overall AMR threat, not only the character of the resistance should be taken into account during pathogen ID in medical practice, but also the molecular mechanisms determining the occurrence and evolution of antimicrobial traits through genome sequencing^{13 [1]}. Therefore, a need for fast but highly sensitive and efficient methods emerges while looking for improvements to current State of the Art Pathogen ID Methods^{14 [2]}.

• Conventional Blood Cultures

Traditional phenotypic ID methods of bacteria and fungi from blood cultures (i.e. inoculation of a patient’s blood sample on special microbiological media) and determining antimicrobial susceptibility (i.e. growing isolated bacteria on media with certain antimicrobials) last at least 24 hours due to their requirement for microbial growth rates, delaying optimal therapy and negatively impacting patient outcomes. In general, the detection and identification is a process taking two days for most organisms or even longer for fastidious organisms. Moreover, blood cultures do not provide the information about the nature of the resistance e.g. specific genes or plasmids determining observed culture resistant growth, however in some molecular research methods the amount of input pathogen material for testing is relatively high, so that the growth of bacterial cultures must also be taken into account^{13 14 19 26 [1] [2]}.

• Immunological Assays

Immunological Assays look at the pathogen components (antigens) by antibodies raised against that pathogen specific antigens. Current immuno-methods include antigen binding, latex or coagglutination products for specific identification, Lateral Flow Immunoassays with phages and direct biochemical tests, providing more rapid ID of bacteria or fungi, and in some cases antimicrobial resistance markers, from positive blood cultures, as well as directly from whole blood. Sensitivities, specificities, and predictive values for the ​​Streptococcaceae products have generally been high, but for Staphylococcaceae, the sensitivities in particular have been low. Immunological testing can be also applied directly towards specific nucleic acids, providing an opportunity for more specific strain ID^{14 32}.

• Nucleic Acid Detection Schemes

To investigate this multitude of mechanisms without relying on phenotypic observations, there is a large repertoire of molecular diagnostics technologies already made their way to clinical routine, as e.g. polymerase chain reaction (PCR) and real time (RT) PCR-based detection or whole genome sequencing (WGS). The currently most popular used tests are based on RT-PCRs due to their relatively good sensitivity, specificity and speed. Using highly conserved ribosomal RNA (rRNA) genes as targets usually allows a higher sensitivity since multiple copies of this genes are present in the genome. The application as well as the variability of PCR are multitudinous. The most important in clinical diagnostics are the conventional and the real-time PCR, which allows to observe the amplification live using unspecifically intercalating dyes or specific DNA seq which give rise to a fluorescence signal only after hybridizing to the amplicon. However, these tests can only identify a low number of targets because of the limited availability of differentiable fluorescence dyes. In addition, with an increasing degree of multiplexing, the sensitivity and specificity of PCRs is reduced due to unintended amplification products and primer dimer formation^{26 28 [3] [4]}.

Strategies for Improvement of AMR ID

Both phenotypic and molecular-based (PCR-derived) methods are considered Conventional AMR Diagnostic Techniques, with Immunological Assays seemingly fitting as Microfluidic Technologies. Genome Sequencing and Metagenomics are Non-Conventional Methods. These newly introduced second and third generation sequencing approaches have paved the way for single genome sequencing, as well as for the characterization of complex microbial communities and the identification of antibiotic resistance determinants. Whole metagenome sequencing (WMS) and analysis of genetic material in patient samples allows for the identification of AMR genes directly from clinical specimens without the need for prior isolation or identification of specific pathogen^[1].

mNGS technologies (metagenomic Next Generation Sequencing) have become an essential tool for unbiased, culture-independent diagnosis as well as drug and diagnostic tests development by enabling the rapid identification and surveillance of resistance mechanisms. Including a complete genomic sequence provided by the metagenomics advancements represent the highest practicable level of structural detail on the individuating traits of an organism or population. It can be used to provide more reliable microbial identification, definitive phylogenetic relationships, and a comprehensive catalog of traits relevant for epidemiological investigations. This is having a major impact on outbreak investigations and the diagnosis and treatment of infectious diseases, as well as the practice of microbiology and epidemiology^{[2] [3] [4]}. However, the major bottleneck keeping mNGS from mass adoption in clinical microbiology, is human DNA contamination with patient’s genetic information depletion being the main cost in rapid pathogen ID and a timely treatment^{35 [5] [6]} .

Devin^® is a novel blood fractionation filter device that can be applied to efficiently deplete human cellular DNA background and enrich microorganisms in the whole blood samples in less than 5 minutes³⁷. With ten most frequent microorganisms causing bacteremia and fungemia in adults belonging to the Enterobacteriaceae, Staphylococcaceae and ​​Streptococcaceae, families¹⁴, whose AMR strains are listed by the CDC as either concerning, serious or urgent threats^[7], the PaRTI-Seq^® test, a metagenomic sequencing workflow built upon Devin^® can identify these potential pathogens from whole blood samples within 24 hours with a sensitivity of 102 genome copies per milliliter. Increasing the scope of available genomic screening for the presence of antimicrobial resistance factors using PaRTI-Seq^® and Devin^® technology could not only speed up the diagnostic process, especially during BSI but also improve the efficiency of the AMR screening process, increasing existing antimicrobial databases with crucial data sharing and exchange^{4 37}. Devin^® and PaRTI-Seq^® have the potential for saving the sequencing cost significantly and constitute a faster method for AMR ID, especially from whole blood samples³⁷.

Medicine and medical technology have evolved rapidly in the past decades. Infectious diseases that were once impossible to confirm can now be diagnosed and cured efficiently. Unfortunately, in the face of advanced treatment, microorganism pathogenicity and virulence grew stronger to survive. Severe infections still take medical professionals into a race against time to accurately diagnose pathogenic microorganisms and treat patients with the right antimicrobial drug.

The fight between the pathogenic microorganisms and the host at risk of being infected

The pathogenicity of an infectious agent, either bacterial, viral, fungal, or parasitic, refers to its capability to cause disease. On the other hand, virulence is described as an ability of a pathogen to infect the host. Virulence factors are the molecules that help the microorganism enter a host, deceive the defense mechanisms of the infected organism, and cause illness. These molecules are often synthesized by the pathogens and encoded in their genome, but may also be acquired from the environment via transmissible genetic elements.

Virulence factors of pathogenic bacteria include adherence and invasion factors which help to colonize and enter host cells. Protective capsules shelter pathogenic bacteria from being tagged (opsonization) as invaders by specific proteins (opsonins), ingested, and eliminated (phagocytosis process) by the host’s immune system. Other factors include endotoxins and exotoxins responsible for severe clinical signs such as fever, inflammation, alterations of blood pressure, and shock.

The virulence factors of viruses are encoded in their genetic information. The basic structure of a virus contains one type of nucleic acid, either RNA or DNA, and a protective protein coat. The protein coating protects the virus from the destructive enzymes of the infected host (nucleases), helps it attach to the host cell, and suppresses the type I interferon (IFN) response, an inhibitor of viral replication and activator of the immune response.

Virulence factors such as toxins and toxin-producing genes, species- and strain-selective genes can be used as targets associated with a specific pathogen, helping establish a quicker, accurate diagnosis.

The susceptibility or the likelihood of infection is dependent on the integrity of the host’s immune system. Once the pathogenic microorganism enters the organism, complex mechanisms involving a series of proteins (interleukins) and mediators of inflammation (eicosanoids and complement system) and white blood cells fight against the invading pathogen. When the pathogenic bacteria breach the immune system and cause infection, early diagnosis is critical in preventing the aggravation of clinical signs ¹.

The limitation of current techniques for the detection of pathogenic microorganism

Current traditional methods for pathogen detection include polymerase chain reaction (PCR), immunology-based techniques, and culture and colony counting.

PCR amplifies the genetic material (nucleic acid) or the information-carrying molecules within a microorganism or virus. Since the first development of PCR tests, the used methods have developed and improved in terms of quality, efficiency, cost savings, techniques, increase in sensitivity and specificity, required time before getting the results, and detection of more samples simultaneously. Current methods vary and include real-time PCR, multiplex PCR, and reverse transcriptase PCR (RT-PCR). PCR can also be coupled with other diagnostic techniques like acoustic wave sensor (SAW) or evanescent wave biosensors.

Unfortunately, there are a series of limitations in PCR assays. One crucial factor might be that PCR tests don’t discriminate between dead or alive pathogens, as the genetic material is still present. Other factors are that PCR testing is expensive as the protocol requires specific primers complementary to the targeted genome, special enzymes (Taq polymerase enzyme), and viral or bacterial-specific targeted primers. PCR test is highly sensitive, and sample contamination can lead to misleading results ^{2, 3}.

Fig. 1 Schematic representation of a PCR cycle².

Immunology-based methods include immunomagnetic separation (IMS), which is a method that only captures and extracts the suspected pathogen. The IMS needs to be combined with other optical methodologies for detection. Another essential immunology-based method is the enzyme-linked immunosorbent assay (ELISA). This procedure detects and measures the antigen or antibodies for a specific pathogen in a sample. ELISA method is an expensive procedure requiring specific antibodies developed on cell cultures. Even further, these antibodies are unstable proteins that need refrigerated transport and storage. ELISA test may fail to detect low antibodies titer in the early phases of a disease or in immunosuppressed individuals. Also, it does not differentiate active forms of illness from recovering patients who are no longer sick but still produce antibodies ⁴.

The culturing and plating method, although it is the oldest method, it is still the gold standard for bacterial detection. Unfortunately, it might be the method with the most limitations. The culturing and plating process is time-consuming, and in some cases, it can take four to nine days to rule out an infection and up to 16 days for confirmation of a specific bacteria (Campylobacter). Even more, the results of up to 70% of cultured samples are negative. In many patients, specifically those in critical conditions who require immediate, specific treatment, the wait is incompatible with life ^{5, 6, 7}.

What does the future hold for early diagnosis?

Biosensors are among the newest methods developed for detecting pathogens, especially those that take a long time to culture and diagnose with the previous methods. Nanomaterials combined with the biological systems overcome most limitations of current traditional methods used to diagnose bacterial infections. Biosensors allow the measurement of chemical, biological, physiological, or biochemical analytes and biological processes.

The structure of a biosensor integrates a molecular identification or recognition component (bio-receptor), a signal generator or transducer, and a reading device or amplifier. In the field of microorganisms, bio-receptors that can be used are for antigens, nucleic acids, or antibodies. Biosensors recognize specific biological analytes and convert them into measurable electrochemical, optical, acoustic, or electronic signals. Types of biosensors include Surface Plasmon Resonance (SPR), genosensors, immunosensors, micromechanical sensors, or phage-based biosensors ⁶. Research on biosensors is still developing, and in the future, diagnosis methods will probably include much more complex and rapid possibilities for diagnosis and early treatment ^{5,8, 9}.

Fig. 2 Components and measurement formats associated with electrochemical biosensors for pathogen detection ¹⁰. 

Studies of virulence factors are important for better understanding microbial pathogenesis and host defense mechanisms. It also provides key information for the vaccine and drug development in preventing and treating infectious diseases. Metagenomic sequencing-based approaches offer a better way to decode the virulence factors and their contributions to overall pathogenesis by having access to the full gene repertoire of a strain.

Devin® filter and Parti-Seq®platform are amongst the newest and most advanced methods developed for the early detection of pathogenic microorganisms. This combined protocol allows the accurate identification of a pathogenic microorganism within 24 hours from the sample’s submission. Devin®Filter, as the name states, filters up to 95% of the patient’s nucleated cells from the blood sample, allowing a high passage of bacteria and viruses in just five minutes. This filtration process is essential as human genetic information (DNA) in the blood samples that need to be analyzed is a critical holdback in rapidly identifying pathogenic microorganisms. Further, pathogen identification is performed with NGS-based Pathogen Real-Time Identification by Sequencing (PaRTI-Seq®) developed by Micronbrane for rapid pathogen identification within less than 24 hours with a sensitivity of 10²  genome copies /mL  ¹¹.

Fig. 3 Overview of contemporary depletion techniques ¹¹.

While the traditional methods used for the current diagnosis of bacterial or viral infections might give accurate results, they have significant limitations that are not favoring a critically ill patient. New highly specific diagnostic methods are currently used worldwide, helping patients overcome severe infections.