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.
What does the molecular detection of M. tb entail
- Xpert MTB/RIF assays as the initial test for TB
- It is automated, integrated, cartridge-based real-time PCR assay - detects M. tb and identifies rifampicin resistance directly from sputum through presence of rpoB gene
- This is then probed with molecular beacons (special DNA probes that light up) to detect presence of M. tb DNA and rpoB mutations
Why is rpoB mutations detected in molecular diagnostics for M. tb
This gene codes for the RNA polymerase beta subunit, a key target of rifampicin.
Mutations in a specific region of rpoB are strongly associated with rifampicin resistance.
What are the advantages to using an Xpert MTB/RIF assay
- Results in around 2 hours form a sample input to result
- High sensitivity and specificity even in HIV co-infected individuals
- Minimal lab infrastructure needed
What are the limitation to using an Xpert MTB/RIF assay
Primarily detects rifampicin resistance, so additional tests may be needed to confirm MDR-TB or other drug resistances.
More expensive than microscopy or culture.
What is the significance of WGS for M. tb AMR detection
1) Major time-saver for diagnosis and treatment decisions
2) High-resolution insight into drug resistance
3) Uncovers complex infections
4) Supports public health surveillance
How is WGS a major time-saver for diagnosis and treatment decisions
Traditional diagnosis of M. tb, including drug susceptibility testing (AST), takes 1–2 months due to the slow growth rate of the bacterium.
WGS dramatically shortens this time — results can be available in a few days after culture is positive (e.g., from a MGIT culture).
Faster diagnosis = faster, more targeted treatment → crucial for preventing disease progression and transmission.
how does WGS serve as a high-resolution insight into drug resistance
WGS identifies the exact mutations in the genome that confer resistance to antibiotics (e.g., mutations in rpoB, katG, gyrA, etc.).
This is especially important for XDR-TB (resistant to at least isoniazid, rifampicin, a fluoroquinolone, and a second-line injectable).
How does WGS uncover complex infections
WGS revealed that the patient had two distantly related Beijing strains — this can’t be detected with standard tests.
This level of strain typing helps in outbreak investigations, tracking transmission chains, and understanding pathogen evolution.
How does WGS support public health surveillance
Ultimate molecular resolution = highly accurate identification and typing of strains.
Useful in global and local surveillance programs for TB control.
Helps health systems detect emerging resistance patterns or hypervirulent strains.
What is the disadvantage of WGS
Not all genetic resistance mechanisms are known.
Some phenotypic resistance occurs without a known genetic mutation - So phenotypic AST is still needed to confirm susceptibility, especially for new drugs or unexplained resistance.
MIC ambiguity
Low genome coverage areas
Dependence on known mutations
How is MIC ambiguity a disadvantage in WGS
When MIC is near the therapeutic threshold, even WGS may not reliably say whether a strain is resistant or susceptible — this supports the earlier point about WGS needing AST confirmation.
How is low genome coverage areas a disadvantage in WGS
These regions may hide important SNPs (mutations) that WGS can’t confidently call — again aligning with the earlier case where blaZ was missed due to short contigs.
How can dependence on known mutations limit WGS
WGS can only detect resistance if we already know the relevant genetic mutations — so it can’t identify novel or rare resistance mechanisms. This echoes the earlier limitation: “a query-based method cannot recognize novel variants.”
What is an example of how WGS can transform outbreak detection and infection control
The Neonatal MRSA Outbreak Investigation
Why were MRSA outbreaks in hospitals a serious threat
Infants are vulnerable to infection
Before WGS, MRSA strains from the same general genetic lineage were hard to tell apart using traditional typing methods - which made it difficult to know if patients were part of the same outbreak or not
what did the MRSA Neonatal study entail
They used Whole Genome Sequencing (WGS) to analyze:
7 isolates from infants linked to the outbreak.
7 additional MRSA isolates from the same hospital that were not obviously linked to the outbreak
They mapped the genome sequences of 10 of these isolates to a reference MRSA strain
Then they performed phylogenetic analysis to look at how similar the isolates were.
What were the key findings in the MRSA outbreak study
Two genetically distinct groups were found:
1) Group 1 - Outbreak group
- Contained all 7 outbreak-associated isolates that were all similar to each other, differing by very few SNPs, suggesting person-to-person transmission within the NICU
2) Group 2 - Non-outbreak group
- These differed by at least 136 SNPs which means its too genetically distant to be part of the same transmission chain
What were the conclusions of the MRSA outbreak using WGS
WGS confirmed the outbreak cluster by showing the NICU-associated isolates were nearly identical.
It ruled out transmission for the other 3 MRSA isolates, which traditional methods may not have been able to do.
What can WGS do in real time
Quickly identify the source of infection
Guide infection control, such as decolonisation strategies
Prevent unnecessary quarantines or treatments
Lead to faster containment and fewer infections
What are the future implications of WGS
WGS could be a routine part of hospital surveillance.
Offers better patient care, reduced hospital stays, and lower healthcare costs.
Helps distinguish between colonization and actual outbreaks — which traditional diagnostics often struggle with.
What is Escherichia coli
Gram-negative, versatile bacterium with high degree of genome plasticity (amenable to natural and random genetic alteration)
Important member of the normal intestinal microflora of humans and other mammals
Variants can be facultative or obligate pathogens
Major health and economic burden and AMR
How many pathogenic variants are there of E. coli
Six
pathotypes cause enteric disease (diarrhoea or dysentery) and extra-intestinal diseases (UTIs and meningitis)
What was the E. coli - EHEC O104:H4 outbreak
A large severe outbreak occurred in Germany in May-June 2011 where more than 4,000 people developed bloody diarrhoea and 850 people developed Hemolytic Uremic Syndrome - a life threatening kidney complication.
What was the E. coli outbreak strain
EHEC O104:H4 - a rare hybrid strain of E. coli
It showed traits of enteroaggregative E. coli (EAEC) known for mild, persistent diarrhoea
It also had gene for the Shiga toxin which causes the Hemolytic Uremic Syndrome (HUS)
Carried ESBLs genes
What did WGS reveal about this specific strain
Sequenced the outbreak strain and compare it to other E. coli and Shigella strains
Analysed the core genomes to build a phylogenetic tree showing evolutionary relationships
What were the key findings in the WGS study about the E. coli outbreak
The outbreak strain belonged to the EAEC lineage but recently acquired:
- A shiga toxin phage: the genetic element that produces Shiga toxin
- Antibiotic resistance plasmids such as TEM-1 and CTX-M
What was the conclusion of the WGS on the E. coli outbreak
The strain was not a typical EHEC, nor a regular EAEC - it was a new hybrid:
Enteroaggregative Hemorrhagic E. coli (EAHEC)
It combined the adhesive properties of EAEC (making it “sticky”) with the toxin production of EHEC (making it dangerous), and added antibiotic resistance - leading to a highly virulent, hard-to-treat strain.
How did WGS help in this E. coli outbreak
WGS provided the resolution needed to:
- Trace the origin and evolution of the outbreak strain.
- Discover that the strain acquired Shiga toxin genes recently.
- Clarify outbreak sources
- Identify new pathotypes
- Guide public health interventions
- And improve preparedness for emerging hybrid pathogens
What was the study done on S. aureus
Focus: Within host evolution
Case: Single patient colonised with methicillin-susceptible S. aureus
Outcome: Transition from nasal colonisation to fata bacteraemia
What mutation caused this S. aureus transformation
8 mutations were identified between the colonising strains and the strain that caused fatal bacteraemia.
They were protein truncating mutations (premature stop codons) in regulatory genes like rsp which increased intracellular persistence
Why is WGS useful in studying within-host evolution of bacteria like S. aureus
WGS can detect subtle mutations over time, helping trace the genetic changes that transform a commensal strain into a lethal pathogen - information often missed by traditional methods
What is the clinical significance of the WGS-based finding
It demonstrates that even with minimal genetic variation within a host can result in a major outcomes, highlighting the importance WGS in diagnostics, infection prediction, and personalised medicine
