How Genome Sequencing Will Help Control Bacterial Infections Flashcards
What are the principles of routine culture-based diagnosis
- Patient samples: collect from actual site of infection
- Specific media for culture: inhibit growth of unwanted organisms and distinguish bacteria by biochemical traits
- Species identification: based on gram stain, colony morphology and biochemical testing
- Antimicrobial susceptibility testing (AST): disc diffusion assay, MIC determination
- Molecular typing (tracking/surveillance)
What would the hypothetical diagnostic workflow be if WGS was used
1) Isolate the pathogen via culture
2) Bacterial cells are lysed to release the DNA and then the DNA is purified and quantified, prepped for sequencing.
- high-throughput sequencing (usually illumina) is used to read millions of short fragments of DNA
3) There is computational analysis: raw data is obtained
4) Compare the sequence to huge curated databases: NCBI, ResFinder, Enterobase
5) Sequence is Analysed to determine specific bacterial traits
How raw data analysed using computational analysis
Bioinformatics is used to for quality control, Genome assembly, mapping to a known reference genome, and identifying mutations, resistance genes and virulence factors
How can the output of WGS be used to determine a wide range of clinically and epidemiologically relevant traits
Traits include:
- Species ID
- AMR
- Virulence factors
- Serotypes
- Plasmids and mobile elements
- Phylogenetics - tracks transmission chains in hospitals
Why is WGS useful for epidemiological investigations in clinical microbiology
WGS provides the highest possible discriminatory power for comparing bacterial species.
It enables fine-scale resolution to distinguish between closely related strains.
This makes it ideal for investigating hospital outbreaks, determining clonal spread, and tracing transmission pathways.
How does WGS contribute to detecting AMR and pathogenic traits
- WGS can detect the presence of Amr genes and pathogenicity-associated genes directly from the bacterial genome
- Enables targeted NGS, focusing on relevant genomic features
- This allows clinicians to anticipate drug resistance and virulence potential without needing culture based tests
How can WGS combined with machine learning be used to predict antibiotic susceptibility?
Data from WGS and the antibiotic resistome (collection of all resistance genes) can be analyzed using machine learning algorithms.
These models can predict phenotypic antibiotic susceptibility with accuracy comparable to traditional culture-based approaches.
Enables faster, data-driven decisions in antimicrobial therapy.
What is metagenomic next-generation sequencing (mNGS) and how is it used in diagnostics?
mNGS is a culture-free technique that sequences all DNA in a clinical or environmental sample.
It allows for the identification of multiple pathogens, including viruses, bacteria, fungi, and parasites.
Useful in complex polymicrobial infections where traditional methods may miss key organisms.
Why is mNGS especially useful for detecting rare or fastidious pathogens?
mNGS is hypothesis-free — it does not require prior knowledge of what pathogen is present.
Can identify rare, unexpected, or novel microbes that are hard or impossible to culture.
Enables earlier detection of fastidious organisms, improving patient outcomes in critical cases.
What is S. Aureus
- Gram-positive, opportunistic pathogen
- Huge health and economic burden
- Multidrug resistant
- Colonises the human nasopharynx (30%)
and more recently been shown to be a coloniser of the intestine (20-30%) - Colonization is a risk factor for disease
What was the main aim of the WGS-based study involving S. aureus
To develop a genotypic prediction method for antibiotic resistance by:
Sequencing 501 unrelated S. aureus isolates using whole genome sequencing (WGS).
Using BLASTn to search for known resistance determinants (genes and mutations) against 12 antibiotics.
Comparing genotypic predictions with phenotypic results from routine culture-based AST (e.g., antibiotic gradient diffusion).
How well did the genotypic prediction match the phenotypic results in the study
439 out of 501 isolates (87%) showed complete concordance between genotype (WGS-predicted) and phenotype (AST-tested).
Discrepancies were further investigated and addressed to optimize prediction accuracy.
What caused the major discrepancy between genotype and phenotype in penicillin resistance
Some isolates were phenotypically resistant to penicillin but lacked the blaZ gene in initial WGS analysis.
Upon inspection, blaZ was found on small/low-coverage contigs that were missed due to algorithm thresholds.
Inclusion of these contigs improved genotype-phenotype concordance.
How was the WGS-based prediction tool validated after optimisation?
A second set of 491 unrelated S. aureus isolates was tested.
This “validation set” showed high prediction accuracy when tested with the improved algorithm.
Demonstrated reproducibility and reliability of WGS for antimicrobial resistance (AMR) detection.
What are the key limitations of using WGS for antimicrobial resistance (AMR) prediction?
Unidentified resistance mechanisms, especially those in regulatory regions, may not be detected.
Novel variants can’t be recognized if they are not present in the reference database.
This is a challenge for in silico resistotyping, which relies on existing knowledge for accurate predictions.
What is M. tuberculosis
- Acid-fast bacilli (rod-shaped and can be identified using the acid-fast stain).
- Protective cell envelope – core contains peptidoglycan, arabinogalactan and mycolic acid layers – waxy outer membrane
- Transmission – aerosol, droplets which can infect the lungs
- Approximately 2 billion people worldwide are infected with M. tb
What is the epidemiology of M. tuberculosis
People with weakened immune systems are particularly at risk,
particularly patients living with AIDS.
Active TB = 75% are pulmonary
Following inhalation M. tb infects the alveolar macrophages – can
develop into a granuloma – nodule composed of lung tissue, recruited immune cells and M. tb
What are the MDR-TB treatments
- 6-12 month course, multiple drug regimen is very effective (90% cure rate if taken fully)
What are the serious concerns with the new TB cases
- There is resistance to all antibiotics
- Extensively drug-resistant tuberculosis strains that are resistant to first, second and third line drugs
What are the main causes of spread of resistant strains of M. tb
- Weak healthcare systems.
- Poor antibiotic stewardship
- Incorrect treatment amplifying resistance
- Poor education surrounding transmission and treatment
What are traditional M. tb diagnosis techniques - Microbiological methods
1) TB colonies grow on Lowenstein-Jensen media at 37 degrees for 2 weeks
2) Acid Fast bacilli staining - used on patient sample to detect TB
3) Mycobacterial growth incubator tube culture system monitors O2 in suspected TB clinical sample:
- no M. tb means theres a lot of oxygen and therefore no fluorescence
- presence of M. tb uses up the oxygen and therefore fluorescence increases and the tube glows under UV light
What are the limitations to using traditional diagnostic techniques for M. tb - Microbiological techniques
- Time consuming growth of colonies
- Negative smear does not indicate absence of M. tb: it just means that a higher bacterial load is required in the sample
What are traditional M. tb diagnosis techniques - Cellular methods
These are blood tests used to detect latent or active TB infection
1) Blood sample is taken from patient
2) The blood is mixed with M. tb-specific antigens
3) If the person has been infected with M. tb, memory T cells recognise the antigens and release IFN-gamma
4) The amount of IFN-gamma released is measured using ELISA
What are limitations with using traditional M. tb diagnosis techniques - Cellular methods
- Cost
- False positive in CG vaccinated patients
- Cross reactivity with non-tuberculosis mycobacteria
- Cannot differentiate between active or latent TB.