Category Archives: Future of Microbiology

“Will Nanopore sequencing be the next big disruptor in clinical microbiology laboratories?”

Will the next big “disruption” in clinical microbiology be Nanopore sequencing technology? I believe this is entirely possible, but there is still work to do…

The last big “disruption” to take place in clinical microbiology laboratories was the introduction of MALDI-TOF for organism identification. From proof of concept to commercial introduction of this technology took a couple of decades. Major disruptions actually take a lot of fine tuning and polishing…

For a platform to be successful in a routine diagnostic microbiology laboratory it needs to have several key characteristics. It needs to be fast, it needs to be cost-effective (compared with existing methodology), it needs to be scalable, and it needs to perform well (good sensitivity and specificity). MALDI-TOF has achieved each of these key goals. That’s why it has been adopted, almost universally in clinical microbiology laboratories. Lots of other innovative technologies come close, but don’t quite get there…

So what about Nanopore sequencing?

Championed by Oxford Nanotech , nanopore sequencing is a unique, scalable technology that enables direct, real-time analysis of long DNA or RNA fragments. It works by monitoring changes to an electrical current as nucleic acids are passed through a protein “nanopore”. The different bases give a specific change in ionic current. The resulting signal can thus be decoded to provide the specific DNA or RNA sequence. Nanopore sequencing enables direct, real-time long-read analysis of DNA or RNA fragments.

Nanopore sequencing has several potential applications in the clinical microbiology laboratory, such as:

  • Organism detection directly from clinical samples, either by 16S/18S rRNA or by a metagenomic approach
  • Detection of genotypic resistance determinants
  • Detection of genotypic virulence determinants
  • Typing of microorganisms for infection control or public health reasons

How does Nanopore sequencing weigh up on each of the key features required to break into a routine diagnostic microbiology laboratory.

  • Speed: The technology allows both base reading and bioinformatic analysis of the sequences to be performed in real-time. Depending on what is being sequenced, it is possible to get useful information from sequencing in a matter of minutes. Potentially the sequencing process can be “stopped” when the necessary information has been obtained, saving on both time and flow cell.
  • Cost-effectiveness: Compared to other sequencing platforms, the start up costs are relatively low. For just a few thousand dollars, it is possible to get hold of a MinION, a few flow cells, and start “sequencing”. However other costs to consider include the bioinformatic software, and hardware to assess DNA/RNA quality and quantity. The flow cells containing the nanopores are still expensive at the moment, but the cost is decreasing. Flow cells can be washed and re-used to a certain extent, which will reduce costs. In addition, a smaller & cheaper flow cell called a “flongle” has just been released.


    “Flongle”
  • Scalability: The ability to “barcode” the nucleic acid extracts going into the flow cell allows the processing of multiple samples simultaneously. Platforms which include multiple flow cells such as the GridION and PromethION can thus process literally hundreds and thousands of samples in the one day. The recently released LamPORE testifies to this.
  • Good performance: There is a lot of validation work currently going on for Nanopore sequencing for various clinical applications, both microbiological and non-microbiological. A lot of the bioinformatic pipelines that would facilitate commercialisation of Nanopore sequencing are still in development. This will take time. Metagenomic approaches to organism identification from clinical samples using Nanopore sequencing are potentially very attractive. The issues of filtering pathogenic DNA out from the human DNA are currently being addressed.

The exciting thing about this technology is that it seems to be improving very quickly. One of the main issues historically with nanopore sequencing was the fidelity/accuracy of the base calling. However recent improvements in the nanopore design and the reading software have improved this dramatically.

I suspect over the next few years, routine clinical microbiology laboratories, like my own, will start looking closely at this technology to see whether it is ready for implementation in diagnostic clinical microbiology. I suspect it will have an initial role in sterile site samples and resistance genotyping, but may well extend to more routine samples in due course.

I think it is just a matter of time…

Michael

I am keen to hear from clinical microbiologists who have Nanopore sequencing in their laboratory, so I can learn from their experiences!

Challenging the Dogma of Empirical Antibiotics

A lot of antibiotic treatment of infectious diseases is still “empirical” in nature. “Empirical” generally means “based on experience”, so to administer an empirical antibiotic means to give the antibiotic that is most likely to work based on previous experience with that infection.

Antibiotic treatment has traditionally been empirical because waiting for culture and susceptibility results was simply far too slow. Only a generation ago the average turnaround time for a microbiology culture test was 3 days and counting….

A lot of antibiotic treatment is still empirical in nature. If you go to your GP with a sore throat or a urinary tract infection, chances are high you will receive an empirical antibiotic. If you attend your local Sexual Health clinic with symptoms of gonorrhoea, you will likely be getting some empirical ceftriaxone long before the diagnosis is established. If you go to your local hospital with pneumonia, you will likely get a macrolide antibiotic (to cover atypical pathogens) as well as a beta-lactam.

However microbiology labs are improving all the time. We now have the potential in many areas to make empirical treatment redundant for certain infections. Check out the following examples:

  • Rapid antigen and PCR tests are now available for the laboratory diagnosis of sore throat, with a turnaround time of minutes to a few hours.
  • Bacteriology automation, such as Kiestra TLA, can reduce turnaround times for urine cultures to less than a day, and a day and a half for wound swabs. I suspect a lot of patients with straightforward UTIs and wound infections can wait that long for a result without outcomes being adversely affected.
  • Rapid PCR tests for atypical respiratory pathogens (Legionella spp., Mycoplasma pneumoniae, Chlamydia pneumoniae) can mean that macrolide coverage for community acquired pneumonia can be stopped early, or not even started.
  • Ultra-fast PCR tests for influenza can prevent any antibiotics being prescribed in patients who present with Upper Respiratory Tract Infection (URTI).
  • PCR tests with genotypic antimicrobial susceptibility information (e.g. Neisseria gonorrhoeae, Mycoplasma genitalium) can avoid the use of empirical antibiotics and selection pressure for antibiotic resistance.

Costing silos, resistance to change, and a lack of vision are amongst the main reasons that it takes so long for treatment protocols to move to “directed therapy” wherever possible.

There are still areas where empirical antibiotics will continue to be necessary. The acutely septic patient presenting to hospital is one such example. However large swathes of infections, particularly in the community setting are still managed by empirical antibiotic therapy. This is the way it always has been. However that doesn’t mean it is the way it always should be…

The next big revolution in clinical microbiology labs should be to challenge the dogma of empirical antibiotic treatment. This would be a huge step forward towards counteracting the development of antibiotic resistance.

Michael

“The Annual Ritual”

A lot of diagnostic clinical microbiology laboratories create an annual antibiogram at the start of each year in order to inform laboratory users of local susceptibility rates for common microbe/antibiotic combinations. Here is a link to the one for my own laboratory.

It is a time honoured tradition, a ritual of sorts… There would be uproar from the clinicians if we didn’t produce it.

And yet such antibiograms are fundamentally flawed…

They are overly simplistic because resistance rates can vary markedly in different patient cohorts and different sample types.

Take the following examples (based on my local data searches):

  • Antibiotic resistance rates for urinary isolates differ markedly according to age and sex. Urinary isolates from young women have much lower resistance rates to uropathogens than old men, with the difference being up to 25% depending on what microbe/antibiotic is being tested. This has very obvious implications for empirical antibiotic choices for UTI in different population cohorts.
  • Staphylococcus aureus resistance rates to mupirocin are much higher in young people with recurrent skin infections than in the (elderly) cohort about to go elective  joint replacement.
  • MRSA rate as a percentage of total Staphylococcus aureus isolates is significantly higher in superficial wound swabs than it is in blood cultures.

These are just a few examples of many, but the common theme here is that different exposure rates to particular antibiotics in different population cohorts lead to different resistance rates.

So I suspect the days are numbered of static antibiograms shown in table form on an A4 sheet of paper.

So last year!

I see the future being an electronic interactive antibiogram, possibly in the form of a smartphone “app”. The clinician enters a few important variables, such as patient age, sex, sampling site, and community/hospital patient, along with the microbe isolated. The app then calculates a more accurate antibiogram based on the particular cohort that this patient falls into.

This is the future, I am sure of it.

The only downside to such an approach is by splitting the total susceptibility data available into different cohorts, the sample size for analysis goes down, which can then lead to bigger margins of error in the results for less common microbe/antimicrobial combinations. This however could be addressed in the app by adding a disclaimer to resistance rates calculated from small sample numbers.

And maybe an interactive electronic antibiogram is in existence already, in an ultra-progressive laboratory somewhere… If so, please let me know!

I had better get started on creating that app!

Michael