Genomic sequencing has revolutionized microbial typing methods and transformed high-throughput methods in reference, clinical, and research laboratories. The detection of antimicrobial-resistant (AMR) determinants using genomic methods can provide valuable information on the emergence of resistance. Here we describe an approach to detecting AMR determinants using an open access and freely available platform which does not require bioinformatic expertise.

Whole genome sequencing of Mycobacterium tuberculosis complex (MTBC) isolates has been shown to provide accurate predictions for resistance and susceptibility for many first- and second-line anti-tuberculosis drugs. However, bioinformatic pipelines and mutation catalogs to predict antimicrobial resistances in MTBC isolates are often customized and detailed protocols are difficult to access. Here, we provide a step-by-step workflow for the processing and interpretation of short-read sequencing data and give an overview of available analysis pipelines.

Antibiotic resistance is a global challenge likely to cost trillions of dollars in excess costs in the health system and more importantly, millions of lives every year. A major driver of resistance is the absence of susceptibility testing at the time a healthcare worker needs to prescribe an antimicrobial. The effect is that many prescriptions are unintentionally wasted and expose mutable organisms to antibiotics increasing the risk of resistance emerging. Often simplistic solutions are applied to this growing issue, such as a naïve drive to increase the speed of drug susceptibility testing. This puts a spotlight on a technological solution and there is a multiplicity of such candidate DST tests in development. Yet, if we do not define the necessary information and the speed at which it needs to be available in the clinical decision-making progress as well as the necessary integration into clinical pathways, then little progress will be made. In this chapter, we place the technological challenge in a clinical and systems context. Further, we will review the landscape of some promising technologies that are emerging and attempt to place them in the clinic where they will have to succeed.

Going back in time through a phylogenetic tree makes it possible to evaluate ancestral genomes and assess their potential to acquire key polymorphisms of interest over evolutionary time. Knowledge of this kind may allow for the emergence of key traits to be predicted and pre-empted from currently circulating strains in the future. Here, we present a novel genome-wide survival analysis and use the emergence of drug resistance in Mycobacterium tuberculosis as an example to demonstrate the potential and utility of the technique.

To model complex systems, individual-based models (IBMs), sometimes called \"agent-based models\" (ABMs), describe a simplification of the system through an adequate representation of the elements. IBMs simulate the actions and interaction of discrete individuals/agents within a system in order to discover the pattern of behavior that comes from these interactions. Examples of individuals/agents in biological systems are individual immune cells and bacteria that act independently with their own unique attributes defined by behavioral rules. In IBMs, each of these agents resides in a spatial environment and interactions are guided by predefined rules. These rules are often simple and can be easily implemented. It is expected that following the interaction guided by these rules we will have a better understanding of agent-agent interaction as well as agent-environment interaction. Stochasticity described by probability distributions must be accounted for. Events that seldom occur such as the accumulation of rare mutations can be easily modeled.Thus, IBMs are able to track the behavior of each individual/agent within the model while also obtaining information on the results of their collective behaviors. The influence of impact of one agent with another can be captured, thus allowing a full representation of both direct and indirect causation on the aggregate results. This means that important new insights can be gained and hypotheses tested.

Mathematical models have been used to study the spread of infectious diseases from person to person. More recently studies are developing within-host modeling which provides an understanding of how pathogens-bacteria, fungi, parasites, or viruses-develop, spread, and evolve inside a single individual and their interaction with the host\'s immune system.Such models have the potential to provide a more detailed and complete description of the pathogenesis of diseases within-host and identify other influencing factors that may not be detected otherwise. Mathematical models can be used to aid understanding of the global antibiotic resistance (ABR) crisis and identify new ways of combating this threat.ABR occurs when bacteria respond to random or selective pressures and adapt to new environments through the acquisition of new genetic traits. This is usually through the acquisition of a piece of DNA from other bacteria, a process called horizontal gene transfer (HGT), the modification of a piece of DNA within a bacterium, or through. Bacteria have evolved mechanisms that enable them to respond to environmental threats by mutation, and horizontal gene transfer (HGT): conjugation; transduction; and transformation. A frequent mechanism of HGT responsible for spreading antibiotic resistance on the global scale is conjugation, as it allows the direct transfer of mobile genetic elements (MGEs). Although there are several MGEs, the most important MGEs which promote the development and rapid spread of antimicrobial resistance genes in bacterial populations are plasmids and transposons. Each of the resistance-spread-mechanisms mentioned above can be modeled allowing us to understand the process better and to define strategies to reduce resistance.

The use of animal models is still widespread in science but there is a movement away from this manner of experimentation. One option approved by the FDA for human-like studies is the hollow fiber bioreactor (HFS). HFSs are highly controllable, self-contained systems that allow for the modeling of individual tissues and disease phenotypes. Oxygen, drug concentration & half-life, and immune cell invasion are all scalable to human and veterinary conditions using a HFS. There are drawbacks to the systems including cost and contamination so the use of these systems must be carefully managed.With these limitations in mind, the scope of the technology is great. Antimicrobial susceptibility testing (AST) is possible with greater accuracy and clinical validity than classical in vitro techniques making minimal inhibitory concentration (MIC) data generated on the bench more translatable to the clinic.In this chapter, we will outline the background of the HFS and some typical uses.

Mycobacterium tuberculosis is the main causative agent of tuberculosis (TB)-an ancient yet widespread global infectious disease to which 1.6 million people lost their lives in 2021. Antimicrobial resistance (AMR) has been an ongoing crisis for decades; 4.95 million deaths were associated with antibiotic resistance in 2019. While AMR is a multi-faceted problem, drug discovery is an urgent part of the solution and is at the forefront of modern research.The landscape of drug discovery for TB has undoubtedly been transformed by the development of high-throughput gene-silencing techniques that enable interrogation of every gene in the genome, and their relative contribution to fitness, virulence, and AMR. A recent advance in this area is CRISPR interference (CRISPRi). The application of this technique to antimicrobial susceptibility testing (AST) is the subject of ongoing research in basic science.CRISPRi technology can be used in conjunction with the high-throughput SPOT-culture growth inhibition assay (HT-SPOTi) to rapidly evaluate and assess gene essentiality including non-essential, conditionally essential (by using appropriate culture conditions), and essential genes. In addition, the HT-SPOTi method can develop drug susceptibility and drug resistance profiles.This technology is further useful for drug discovery groups who have designed target-based inhibitors rationally and wish to validate the primary mechanisms of their novel compounds\' antibiotic action against the proposed target.

Patients with chronic limb-threatening ischaemia (CLTI) are at risk of foot infections, which is associated with an increase in amputation rates. The use of antibiotics may lead to a higher incidence of antimicrobial resistance (AMR) in subsequent episodes of ischaemic foot infections (IFI). This retrospective single-centre cohort study included 130 patients with IFI undergoing endovascular revascularisation. Staphylococcus aureus and Pseudomonas aeruginosa were the two most common pathogens, accounting for 20.5% and 10.8% of cases, respectively. The prevalence of antimicrobial resistance (AMR) and multi-drug resistance did not significantly increase between episodes (10.2% vs. 13.4%, p = 0.42). In 59% of subsequent episodes, the identified pathogens were unrelated to the previous episode. However, the partial concordance of identified pathogens significantly increased to 66.7% when S. aureus was identified (p = 0.027). Subsequent episodes of IFI in the same patient are likely to differ in causative pathogens. However, in the case of S. aureus, the risk of reinfection, particularly with S. aureus, is increased. Multi-drug resistance does not appear to change between IFI episodes. Therefore, recommendations for empirical antimicrobial therapy should be based on local pathogen and resistance statistics without the need to broaden the spectrum of antibiotics in subsequent episodes.

UNASSIGNED: The Eastern Mediterranean Regional Office (EMRO) region accounts for almost 8% of all global Mycobacterium tuberculosis (TB) cases, with TB incidence rates ranging from 1 per 100,000 per year in the United Arab Emirates (UAE) to 204 per 100,000 in Djibouti. The national surveillance data from the Middle East and North Africa (MENA) region on the epidemiology and antimicrobial resistance trends of TB, including MDR-TB remains scarce.
UNASSIGNED: A retrospective 12-year analysis of N = 8,086 non-duplicate diagnostic Mycobacterium tuberculosis complex (MTB complex) isolates from the UAE was conducted. Data were generated through routine patient care during the 2010-2021 years, collected by trained personnel and reported by participating surveillance sites to the UAE National Antimicrobial Resistance (AMR) Surveillance program. Data analysis was conducted with WHONET, a windows-based microbiology laboratory database management software developed by the World Health Organization Collaborating Center for Surveillance of Antimicrobial Resistance, Boston, United States (https://whonet.org/).
UNASSIGNED: A total of 8,086 MTB-complex isolates were analyzed. MTB-complex was primarily isolated from respiratory samples (sputum 80.1%, broncho-alveolar lavage 4.6%, pleural fluid 4.1%). Inpatients accounted for 63.2%, including 1.3% from ICU. Nationality was known for 84.3% of patients, including 3.8% Emiratis. Of UAE non-nationals, 80.5% were from 110 countries, most of which were Asian countries. India accounted for 20.8%, Pakistan 13.6%, Philippines 12.7%, and Bangladesh 7.8%. Rifampicin-resistant MTB-complex isolates (RR-TB) were found in 2.8% of the isolates, resistance to isoniazid, streptomycin, pyrazinamide, and ethambutol, was 8.9, 6.9, 3.4 and 0.4%, respectively. A slightly increasing trend of resistance among MTB-complex was observed for rifampicin from 2.5% (2010) to 2.8% (2021).
UNASSIGNED: Infections due to MTB-complex are relatively uncommon in the United Arab Emirates compared to other countries in the MENA region. Most TB patients in the UAE are of Asian origin, mainly from countries with a high prevalence of TB. Resistance to first line anti-tuberculous drugs is generally low, however increasing trends for MDR-TB mainly rifampicin linked resistance is a major concern. MDR-TB was not associated with a higher mortality, admission to ICU, or increased length of hospitalization as compared to non-MDR-TB.