How is this technology transforming the diagnosis of neurological diseases?

Metagenomic sequencing is a powerful tool that has revolutionized the way neurological infections are studied. Instead of relying on traditional microbial cultures, metagenomic sequencing allows for the detection of a wide range of microorganisms quickly and accurately, which can be crucial in the diagnosis and treatment of certain infections, such as neurological infections.

Traditional methods vs. metagenomic sequencing in neurological infections

There are various methods for identifying bacteria such as polymerase chain reaction (PCR) (such as single-strand conformation polymorphisms or terminal restriction fragment length polymorphisms) or DNA sequencing of targeted PCR products. But sequencing of 16S rRNA genes is the primary method for conducting a community census because fingerprinting methods do not adequately measure low-abundance organisms.

When using 16S rRNA gene sequencing to compare individuals, it is not necessary to know which organisms are present, only whether the 16S rRNA gene sequence spectra are similar and the degree of difference between samples. A loss of sensitivity for organism identification can be tolerated, and NGS allows for cost-effective deep sampling of large cohorts, which is necessary to reach statistically significant conclusions.

Neurological infections: accurate and rapid diagnosis

Neurological infections are a diverse group of diseases that affect the central and peripheral nervous system. They can be caused by bacteria, viruses, fungi, parasites or other microorganisms, and can have serious consequences if not diagnosed and treated appropriately and early. Metagenomic sequencing has proven to be an invaluable tool in identifying the causative agents of these infections, as it can identify microorganisms that may be difficult to detect using traditional methods.

This is based on the sequencing of DNA or RNA from clinical samples, such as blood, cerebrospinal fluid or brain tissue, to identify the microorganisms present in them. This technique can even detect microorganisms that are present in very low quantities in the sample, making it especially useful in the diagnosis of neurological infections of unknown or unusual etiology.

In addition to identifying the causative agents of neurological infections, metagenomic sequencing can also provide information on the antimicrobial resistance of the identified microorganisms. This is crucial to guide the treatment of these infections and avoid the prescription of ineffective antimicrobials, which may contribute to the emergence of antimicrobial resistance.

Antimicrobial resistance: a challenge in public health

Antibiotic-resistant bacteria represent one of the major challenges to public health worldwide. Today, bacteria resistant to all available antibiotics are found in hospital environments. The number of deaths caused by antibiotic resistance in Europe is estimated at 23,000 per year and worldwide at 700,000. 

A figure that has been increasing in an alarming and continuous manner. An epidemiological estimate based on global trends regarding bacterial resistance is that by 2050, infections by resistant pathogens will be the leading cause of death, above cancer or heart disease, and will cause more than 10 million deaths annually worldwide.

Identification of co-infections in immunocompromised patients

That is why it is so important to use tests that can provide us with specific and sensitive information about the organisms causing infections, in order to initiate effective antimicrobial therapies and avoid antimicrobial resistance, and therefore the creation of superbacteria.

Another important aspect of metagenomic sequencing in neurological infections is its ability to identify co-infections, i.e. the presence of more than one microorganism in the same sample. This is especially relevant in immunocompromised patients, where co-infections are more common and can complicate the diagnosis and treatment of neurological infections.

Sensitivity and specificity are two fundamental concepts in the evaluation of any diagnostic test, including metagenomic sequencing. Sensitivity refers to the ability of a test to correctly detect people who have the disease, while specificity refers to the ability of the test to correctly rule out people who do not have the disease. In the case of metagenomic sequencing, these parameters are crucial to assess its usefulness in detecting microorganisms in clinical samples.

The sensitivity of metagenomic sequencing refers to its ability to correctly identify microorganisms present in a clinical sample. In other words, it refers to the probability that metagenomic sequencing will detect a microorganism when it is actually present in the sample. High sensitivity is crucial in the context of neurological infections, as accurate detection of the causative agents is critical for proper diagnosis and treatment.

The specificity of metagenomic sequencing, on the other hand, refers to its ability to correctly identify the absence of a microorganism in a sample. In other words, it refers to the probability that metagenomic sequencing will not detect a microorganism when it is actually not present in the sample. High specificity is important to avoid false positives and ensure that metagenomic sequencing results are reliable and accurate.

The sensitivity and specificity of metagenomic sequencing can vary depending on several factors, including the type of clinical sample, the quality of the sequencing data, the detection threshold used, and the presence of contaminants in the sample. In general, the sensitivity of metagenomic sequencing has been shown to be high, meaning that it has the ability to detect a wide range of microorganisms in clinical samples, even in low sample quantities. However, specificity can be more variable and may be influenced by the presence of contaminants or the quality of the sequencing data.

Several studies have evaluated the sensitivity and specificity of metagenomic sequencing in the context of neurological infections. For example, a recent study found that the sensitivity of metagenomic sequencing in identifying infectious agents in cerebrospinal fluid samples was 85%, suggesting that the technique is highly sensitive in this context. However, the specificity was 75%, indicating that there were a significant number of false positives in the study.

Analysis of samples with low amounts of DNA: a challenge overcome

Another study evaluated the sensitivity and specificity of metagenomic sequencing in identifying microorganisms in brain tissue samples from patients with encephalitis. The results showed a sensitivity of 90% and a specificity of 80%, suggesting that the technique is highly sensitive but slightly less specific in this context.

Overall, the sensitivity and specificity of metagenomic sequencing in neurological infections may vary depending on several factors, such as the type of sample, the quality of the sequencing data, and the presence of contaminants. Despite these limitations, metagenomic sequencing remains a powerful tool in the diagnosis of these infections, as it has the ability to detect a wide range of microorganisms rapidly and accurately. 

However, it is important to interpret metagenomic sequencing results with caution and in conjunction with other clinical data to ensure accurate diagnosis and appropriate treatment of neurological infections.

One of the important factors is the powerful tool for the analysis of samples with low amounts of genetic material, which makes it especially useful in the study of clinical and environmental samples where DNA availability is limited. In the context of neurological infections, where clinical samples such as cerebrospinal fluid (CSF) may be scarce, the ability of metagenomic sequencing to work with limited amounts of DNA is crucial for the detection of causative microorganisms.

There are several approaches and strategies that can be used for metagenomic sequencing of samples with low amounts of genetic material. Some of these approaches include:

1. DNA amplification: DNA amplification is a common strategy to increase the amount of genetic material available for sequencing. Amplification can be performed using polymerase chain reaction (PCR) or other amplification techniques, allowing for increased DNA quantity from an initially scarce sample. However, it is important to note that amplification can introduce bias and contamination, so it is crucial to perform quality control and minimize the possibility of non-specific amplification.
2. Use of specialized kits and protocols: There are kits and protocols specifically designed for the extraction and amplification of DNA from samples with low amounts of genetic material. These kits usually include reagents and protocols optimized to maximize extraction and amplification performance, which can be especially useful for clinical samples with limited availability.
3. Sequencing optimization: Some metagenomic sequencing platforms allow optimization for working with limited amounts of DNA. This may include adjustments to library preparation protocols, the concentration of DNA required for sequencing, and the use of next-generation sequencing techniques that are more sensitive to limited amounts of DNA.

It is important to note that working with samples with low amounts of genetic material presents unique challenges, such as the risk of contamination and the possibility of introducing bias into the results. Therefore, it is crucial to perform rigorous quality controls and take into account the limitations associated with sequencing samples with low amounts of DNA.

Despite these challenges, metagenomic sequencing remains an ideal tool for the analysis of samples with low amounts of genetic material. With the continuous evolution of sequencing technologies and the optimization of sample preparation protocols, metagenomic sequencing is expected to continue to play a crucial role in the detection and characterization of microorganisms in samples with low amounts of DNA.

It is a revolution in the field of neurological infections by allowing the rapid and accurate identification of the causal agents of these diseases. Its ability to detect a wide range of microorganisms, including those present in very low quantities, makes it an invaluable tool in the diagnosis and treatment of these infections. Furthermore, its ability to identify antimicrobial resistance and co-infections makes it even more relevant in the management of neurological infections. Without a doubt, metagenomic sequencing will continue to play a crucial role in the research and treatment of these diseases in the future.

The challenge of contamination in metagenomic sequencing

The biggest challenge is sample contamination, which is a major challenge in metagenomic sequencing, as the sensitivity of the technique can lead to the detection of microorganisms present in the sample processing environment instead of the microorganisms of interest. Contamination can come from various sources, such as the laboratory environment, reagents used in sample processing, laboratory instruments, personnel, and genetic material present on the sequencing equipment.

Contamination can be especially problematic in samples with low amounts of genetic material, as signals from contaminants may be more prominent compared to signals from the microorganisms of interest. This can lead to misinterpretation of metagenomic sequencing results and the generation of incorrect data.

To minimize the risk of contamination in metagenomic sequencing, several strategies and control measures can be implemented, such as:

1. Contamination controls: It is important to include contamination controls at every stage of the metagenomic sequencing process, from DNA extraction to library preparation and sequencing. Negative controls, consisting of water samples or other reagents processed in the same manner as the samples of interest but without genetic material, can help identify and control contamination.
2. Clean Laboratory Practices: Maintaining a clean, contaminant-free laboratory environment is critical. This includes using laminar flow cabinets, regularly cleaning laboratory equipment, using sterile pipettes, and implementing good laboratory practices to minimize cross-contamination.
3. Validation of results: It is important to validate the results of metagenomic sequencing using complementary techniques, such as PCR targeting specific microorganisms, microbiological cultures or serological tests. This can help confirm the presence of microorganisms identified by sequencing and rule out possible contaminants.
4. Data analysis: When analyzing metagenomic sequencing data, it is important to consider the possibility of contamination and carefully assess the quality of the results. Identifying contamination patterns and excluding unwanted reads can help improve the accuracy of the results.

But despite all these factors, control strategies and measures can be implemented to minimize their impact and ensure the reliability of the results. By following good laboratory practices, performing contamination controls and validating the results, it is possible to reduce the risk of contamination and obtain accurate and reliable data in metagenomic sequencing studies.

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Article written by Enevia Health Advisor and Collaborator: Dr. Julianny Albarran 

Medical surgeon, general medicine with more than 5 years of experience in the field.

Direct links to the consultation with Drs. Julianny Albarrán and Sara Setti:

https://eneviacare.com/tienda/consulta-medica-dra-julianny-albarran-enevia-care/

https://eneviacare.com/tienda/consulta-especializada-dra-sara-setti/

Bibliography

A systematic review and meta-analysis of the diagnostic accuracy of metagenomic next-generation sequencing for diagnosing tuberculous meningitis – PubMed (nih.gov)

234_metagenmica_de_rna_ribosom_zj2sl.pdf (colmayor.edu.co)

Genomic approaches to studying the human microbiota – PMC (nih.gov)

Antimicrobial resistance alarm: current situation and challenges (scielo.edu.uy)

Use of massive sequencing technologies for the diagnosis and epidemiology of infectious diseases | Infectious Diseases and Clinical Microbiology (elsevier.es)