Where Sepsis and Antimicrobial Resistance Countermeasures Converge

Antibiotic Resistance: Do We Need Only Cutting-Edge Methods, or Can New Visions Such as One Health Be More Useful for Learning from Nature?

Antibiotic resistance is an increasing global problem for public health, and focusing on biofilms has provided further insights into resistance evolution in bacteria. Resistance is innate in many bacterial species, and many antibiotics are derived from natural molecules of soil microorganisms. Is it possible that nature can help control AMR diffusion? In this review, an analysis of resistance mechanisms is summarized, and an excursus of the different approaches to challenging resistance spread based on natural processes is presented as "lessons from Nature". On the "host side", immunotherapy strategies for bacterial infections have a long history before antibiotics, but continuous new inputs through biotechnology advances are enlarging their applications, efficacy, and safety. Antimicrobial peptides and monoclonal antibodies are considered for controlling antibiotic resistance. Understanding the biology of natural predators is providing new, effective, and safe ways to combat resistant bacteria. As natural enemies, bacteriophages were used to treat severe infections before the discovery of antibiotics, marginalized during the antibiotic era, and revitalized upon the diffusion of multi-resistance. Finally, sociopolitical aspects such as education, global action, and climate change are also considered as important tools for tackling antibiotic resistance from the One Health perspective.

Speaking of sepsis: semantics, syntax, and slang

Medical language is in a constant state of evolution. Its grammar and vocabulary are not fixed by rigid rules. The interdisciplinary field of sepsis has become a meeting point for new insights arising from advances in systems biology, epidemiology, mechanistic understandings of disease process and antimicrobial interventions. This convergence has gained from our recent experience of SARS-CoV-2 infection and COVID-19 and possibilities inferred from emerging information technology. Biomedical descriptors have diverged along disciplinary lines creating an unfortunate disconnect between clinical and laboratory-based terminology. The resulting confusion between clinically determined sepsis and laboratory verified bloodstream infection raises practical questions that affect daily operational processes in the ward, clinic and laboratory. There is an urgent need to understand how the clinical sepsis pathway and corresponding clinical laboratory workflow can be better aligned as a single coherent entity. There is also an implicit need to understand how this process should produce actionable information in a timely and orderly manner, and identify residual obselete terminology that has crept into common usage. A widely accepted sepsis epistemology, ontology and heuristic will help us improve our clinical management of sepsis.

Transmission of gram-negative antibiotic-resistant bacteria following differing exposure to antibiotic-resistance reservoirs in a rural community: a modelling study for bloodstream infections

Exposure to community reservoirs of gram-negative antibiotic-resistant bacteria (GN-ARB) genes poses substantial health risks to individuals, complicating potential infections. Transmission networks and population dynamics remain unclear, particularly in resource-poor communities. We use a dynamic compartment model to assess GN-ARB transmission quantitatively, including the susceptible, colonised, infected, and removed populations at the community-hospital interface. We used two side streams to distinguish between individuals at high- and low-risk exposure to community ARB reservoirs. The model was calibrated using data from a cross-sectional cohort study (N = 357) in Chile and supplemented by existing literature. Most individuals acquired ARB from the community reservoirs (98%) rather than the hospital. High exposure to GN-ARB reservoirs was associated with 17% and 16% greater prevalence for GN-ARB carriage in the hospital and community settings, respectively. The higher exposure has led to 16% more infections and attributed mortality. Our results highlight the need for early-stage identification and testing capability of bloodstream infections caused by GN-ARB through a faster response at the community level, where most GN-ARB are likely to be acquired. Increasing treatment rates for individuals colonised or infected by GN-ARB and controlling the exposure to antibiotic consumption and GN-ARB reservoirs, is crucial to curve GN-ABR transmission.

Plasma cfDNA predictors of established bacteraemic infection

Introduction. Increased plasma cell-free DNA (cfDNA) has been reported for various diseases in which cell death and tissue/organ damage contribute to pathogenesis, including sepsis.

Gap Statement. While several studies report a rise in plasma cfDNA in bacteraemia and sepsis, the main source of cfDNA has not been identified.

Aim. In this study, we wanted to determine which of nuclear, mitochondrial or bacterial cfDNA is the major contributor to raised plasma cfDNA in hospital subjects with bloodstream infections and could therefore serve as a predictor of bacteraemic disease severity.

Methodology. The total plasma concentration of double-stranded cfDNA was determined using a fluorometric assay. The presence of bacterial DNA was identified by PCR and DNA sequencing. The copy numbers of human genes, nuclear β globin and mitochondrial MTATP8, were determined by droplet digital PCR. The presence, size and concentration of apoptotic DNA from human cells were established using lab-on-a-chip technology.

Results. We observed a significant difference in total plasma cfDNA from a median of 75 ng ml⁻¹ in hospitalised subjects without bacteraemia to a median of 370 ng ml⁻¹ (P=0.0003) in bacteraemic subjects. The copy numbers of nuclear DNA in bacteraemic also differed between a median of 1.6 copies µl⁻¹ and 7.3 copies µl⁻¹ (P=0.0004), respectively. In contrast, increased mitochondrial cfDNA was not specific for bacteraemic subjects, as shown by median values of 58 copies µl⁻¹ in bacteraemic subjects, 55 copies µl⁻¹ in other hospitalised subjects and 5.4 copies µl⁻¹ in healthy controls. Apoptotic nucleosomal cfDNA was detected only in a subpopulation of bacteraemic subjects with documented comorbidities, consistent with elevated plasma C-reactive protein (CRP) levels in these subjects. No bacterial cfDNA was reliably detected by PCR in plasma of bacteraemic subjects over the course of infection with several bacterial pathogens.

Conclusions. Our data revealed distinctive plasma cfDNA signatures in different groups of hospital subjects. The total cfDNA was significantly increased in hospital subjects with laboratory-confirmed bloodstream infections comprising nuclear and apoptotic, but not mitochondrial or bacterial cfDNAs. The apoptotic cfDNA, potentially derived from blood cells, predicted established bacteraemia. These findings deserve further investigation in different hospital settings, where cfDNA measurement could provide simple and quantifiable parameters for monitoring a disease progression.

CD11b Deficiency Exacerbates Methicillin-Resistant Staphylococcus aureus-Induced Sepsis by Upregulating Inflammatory Responses of Macrophages

Macrophages are important for the first line of defense against microbial pathogens. Integrin CD11b, which is encoded by Itgam, is expressed on the surface of macrophages and has been implicated in adhesion, migration, and cell-mediated cytotoxicity. However, the functional impact of CD11b on the inflammatory responses of macrophages upon microbial infection remains unclear. Here, we show that CD11b deficiency resulted in increased susceptibility to sepsis induced by methicillin-resistant Staphylococcus aureus (MRSA) infection by enhancing the pro-inflammatory activities of macrophages. Upon infection with MRSA, the mortality of Itgam knockout mice was significantly higher than that of control mice, which is associated with increased production of TNF-α and IL-6. In response to MRSA, both bone marrow-derived macrophages and peritoneal macrophages lacking CD11b produced elevated amounts of pro-inflammatory cytokines and nitric oxide. Moreover, CD11b deficiency upregulated IL-4-induced expression of anti-inflammatory mediators such as IL-10 and arginase-1, and an immunomodulatory function of macrophages to restrain T cell activation. Biochemical and confocal microscopy data revealed that CD11b deficiency augmented the activation of NF-κB signaling and phosphorylation of Akt, which promotes the functional activation of macrophages with pro-inflammatory and immunoregulatory phenotypes, respectively. Overall, our experimental evidence suggests that CD11b is a critical modulator of macrophages in response to microbial infection.

Same-day antimicrobial susceptibility test using acoustic-enhanced flow cytometry visualized with supervised machine learning

Purpose. Antimicrobial susceptibility is slow to determine, taking several days to fully impact treatment. This proof-of-concept study assessed the feasibility of using machine-learning techniques for analysis of data produced by the flow cytometer-assisted antimicrobial susceptibility test (FAST) method we developed.Methods. We used machine learning to assess the effect of antimicrobial agents on bacteria, comparing FAST results with broth microdilution (BMD) antimicrobial susceptibility tests (ASTs). We used Escherichia coli (1), Klebsiella pneumoniae (1) and Staphylococcus aureus (2) strains to develop the machine-learning algorithm, an expanded panel including these plus E. coli (2), K. pneumoniae (3), Proteus mirabilis (1), Pseudomonas aeruginosa (1), S. aureus (2) and Enterococcus faecalis (1), tested against FAST and BMD (Sensititre, Oxoid), then two representative isolates directly from blood cultures.Results. Our data machines defined an antibiotic-unexposed population (AUP) of bacteria, classified the FAST result by antimicrobial concentration range, and determined a concentration-dependent antimicrobial effect (CDE) to establish a predicted inhibitory concentration (PIC). Reference strains of E. coli, K. pneumoniae and S. aureus tested with different antimicrobial agents demonstrated concordance between BMD results and machine-learning analysis (CA, categoric agreement of 91 %; EA, essential agreement of 100 %). CA was achieved in 35 (83 %) and EA in 28 (67 %) by machine learning on first pass in a challenge panel of 27 Gram-negative and 15 Gram-positive ASTs. Same-day AST results were obtained from clinical E. coli (1) and S. aureus (1) isolates.Conclusions. The combination of machine learning with the FAST method generated same-day AST results and has the potential to aid early antimicrobial treatment decisions, stewardship and detection of resistance.

Rapid antimicrobial susceptibility tests for sepsis; the road ahead

Current methods for antimicrobial susceptibility testing (AST) are too slow to affect initial treatment decisions in the early stages of sepsis, when the prescriber is most concerned to select effective therapy immediately, rather than finding out what will not work 1 or 2 days later. There is a clear need for much faster differentiation between viral and bacterial infection, and AST, linked to earlier aetiological diagnosis, without sacrificing either the accuracy of quantitative AST or the low cost of qualitative AST. Truly rapid AST methods are eagerly awaited, and there are several candidate technologies that aim to improve the targeting of our limited stock of effective antimicrobial agents. However, none of these technologies are approaching the point of care and nor can they be described as truly culture-independent diagnostic tests. Rapid chemical and genomic methods of resistance detection are not yet reliable predictors of antimicrobial susceptibility and often rely on prior bacterial isolation. In order to resolve the trade-off between diagnostic confidence and therapeutic efficacy in increasingly antimicrobial-resistant sepsis, we propose a series of three linked decision milestones: initial clinical assessment (e.g. qSOFA score) within 10 min, initial laboratory tests and presumptive antimicrobial therapy within 1 h, and definitive AST with corresponding antimicrobial amendment within an 8 h window (i.e. the same working day). Truly rapid AST methods therefore must be integrated into the clinical laboratory workflow to ensure maximum impact on clinical outcomes of sepsis, and diagnostic and antimicrobial stewardship. The requisite series of development stages come with a substantial regulatory burden that hinders the translation of innovation into practice. The regulatory hurdles for the adoption of rapid AST technology emphasize technical accuracy, but progress will also rely on the effect rapid AST has on prescribing behaviour by physicians managing the care of patients with sepsis. Early adopters in well-equipped teaching centres in close proximity to large clinical laboratories are likely to be early beneficiaries of rapid AST, while simplified and lower-cost technology is needed to support poorly resourced hospitals in developing countries, with their higher burden of AMR. If we really want the clinical laboratory to deliver a specific, same-day diagnosis underpinned by definitive AST results, we are going to have to advocate more effectively for the clinical benefits of bacterial detection and susceptibility testing at critical decision points in the sepsis management pathway.