Clinical microbiology in detection and identification of emerging microbial pathogens: past, present and future

Wang, Hui; Zhang, Wenhong; Tang, Yi‐Wei

doi:10.1080/22221751.2022.2125345

Cited by 13 publications

(10 citation statements)

References 107 publications

(134 reference statements)

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“…Detecting pathogenic organisms is crucial for clinical diagnostics, public health initiatives, and understanding environmental microbiomes (Ko et al ., 2022; Wang et al ., 2022; Yang et al ., 2020). Traditional methods in metagenomics for pathogen detection rely on conserved gene families that are commonly shared among strains in the same species, such as 16S rRNA for bacteria and 18S rRNA for protozoa and fungi.…”

Section: Applications Of Metagenomics Knowledge Graphmentioning

confidence: 99%

MetagenomicKG: a knowledge graph for metagenomic applications

Ma,

Liu,

Koslicki

2024

Preprint

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Motivation: The sheer volume and variety of genomic content within microbial communities makes metagenomics a field rich in biomedical knowledge. To traverse these complex communities and their vast unknowns, metagenomic studies often depend on distinct reference databases, such as the Genome Taxonomy Database (GTDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), for various analytical purposes. These databases are crucial for genetic and functional annotation of microbial communities. Nevertheless, the inconsistent nomenclature or identifiers of these databases present challenges for effective integration, representation, and utilization. Knowledge graphs (KGs) offer an appropriate solution by organizing biological entities and their interrelations into a cohesive network. The graph structure not only facilitates the unveiling of hidden patterns but also enriches our biological understanding with deeper insights. Despite KGs having shown potential in various biomedical fields, their application in metagenomics remains underexplored. Results: We present MetagenomicKG, a novel knowledge graph specifically tailored for metagenomic analysis. MetagenomicKG integrates taxonomic, functional, and pathogenesis-related information from widely used databases, and further links these with established biomedical knowledge graphs to expand biological connections. Through several use cases, we demonstrate its utility in enabling hypothesis generation regarding the relationships between microbes and diseases, generating sample-specific graph embeddings, and providing robust pathogen prediction.

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Section: Applications Of Metagenomics Knowledge Graphmentioning

confidence: 99%

MetagenomicKG: a knowledge graph for metagenomic applications

Ma,

Liu,

Koslicki

2024

Preprint

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“…This necessitates a multifaceted strategy and prompt treatment, as it was discovered that the majority of fatalities were due to the presence of more than one infection [ 7 , 8 , 9 , 10 ]. The most common bacterial infections, according to recent articles, systematic reviews and meta-analyses, are Klebsiella pneumoniae , Streptococcus pneumoniae , Staphylococcus aureus , Mycoplasma pneumoniae , Chlamydophila pneumoniae , Legionella pneumophila , and Acinetobacter baumannii [ 11 , 12 , 13 , 14 ]. These pathogens may be community-acquired, contracted outside of a hospital setting and diagnosed before or 48 h after admission, which indicates that the individual was infected prior or simultaneously to the SARS-CoV-2 infection.…”

Section: Introductionmentioning

confidence: 99%

The Assessment of Multiplex PCR in Identifying Bacterial Infections in Patients Hospitalized with SARS-CoV-2 Infection: A Systematic Review

et al. 2023

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Bacterial infection can occur in patients hospitalized with SARS-CoV-2 in various conditions, resulting in poorer outcomes, such as a higher death rate. This current systematic review was conducted in order to assess the efficiency of multiplex PCR in detecting bacterial infections in hospitalized COVID-19 patients, as well as to analyze the most common bacterial pathogens and other factors that interfere with this diagnosis. The research was conducted using four electronic databases (PubMed, Taylor&Francis, Web of Science, and Wiley Online Library). Out of 290 studies, nine were included in the systematic review. The results supported the use of multiplex PCR in detecting bacteria, considering its high sensitivity and specificity rates. The most common bacterial pathogens found were Klebsiella pneumoniae, Staphylococcus aureus, Pseudomonas aeruginosa, Streptococcus pneumoniae, and Haemophilus influenzae. The median age at admission was 61.5 years, and the majority of patients were men (70.3%), out of a total of 1553 patients. The proportion of ICU admission was very high, with a pooled proportion of 52.6% over the analyzed studies, and an average duration of hospitalization of 13 days. The mortality rate was proportionally high, as was the rate of ICU admission, with a pooled mortality of 24.9%. It was discovered that 65.2% of all patients used antibiotics before admission, with or without medical prescription. Antibiotic treatment should be considered consciously, considering the high risks of developing antibiotic resistance.

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“…Many methods are used for the clinical detection of microbial infections. This includes standard approaches like phenotype-based speciation, microscopy-based techniques, immunoassays, and advanced techniques like respiratory panels [10,11]. Although commonly employed in microbial detection, these techniques are not as efficient, especially in a pandemic situation that requires quick detection of a wide range of microbes in a large number of samples.…”

Section: Introductionmentioning

confidence: 99%

Metaproteomic analysis of nasopharyngeal swab samples to identify microbial peptides and potential co-infection status in COVID-19 patients

Bihani

Gupta

Mehta

et al. 2023

Preprint

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Respiratory infections disrupt the microbiota in the upper respiratory tract (URT), putting patients at a risk for subsequent infections. During the pandemic, cases of COVID-19 were aggravated by secondary infections because of impaired immunity and medical interventions, which was clearly evident in the second wave of COVID-19 in India. The potential dangers and clinical difficulties of bacterial and fungal secondary infections in COVID-19 patients necessitate microbial exploration of the URT. In this regard, mass spectrometry (MS)-based proteome data of nasopharyngeal swab samples from COVID-19 patients was used to investigate the metaproteome. The MS datasets were searched against a comprehensive protein sequence database of common URT pathogens using multiple search platforms (MaxQuant, MSFragger, and Search GUI/PeptideShaker). The detected microbial peptides were verified using PepQuery, which analyses peptide-spectrum pairs to give statistical output for determining confident microbial peptides. Finally, a protein sequence database was generated using the list of verified microbial peptides for identification and quantitation of microbial peptides and proteins, respectively. The taxonomic analysis of the detected peptides revealed several opportunistic pathogens like Streptococcus pneumoniae, Rhizopus microsporus, Clavispora lusitaniae, and Syncephalastrum racemosum among others. Using parallel reaction monitoring (PRM), we validated a few identified microbial peptides in clinical samples. The analysis also revealed proteins belonging to species like Pseudomonas fluorescens, Enterobacter, and Clostridium to be up-regulated in severe COVID-19 samples. Thus, MS can serve as a powerful tool for untargeted detection of a wide range of microorganisms. Metaproteomic analysis in COVID-19 patients for early identification and characterisation of co-infecting microorganisms can significantly impact the diagnosis and treatment of patients.

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Clinical microbiology in detection and identification of emerging microbial pathogens: past, present and future

Cited by 13 publications

References 107 publications

MetagenomicKG: a knowledge graph for metagenomic applications

MetagenomicKG: a knowledge graph for metagenomic applications

The Assessment of Multiplex PCR in Identifying Bacterial Infections in Patients Hospitalized with SARS-CoV-2 Infection: A Systematic Review

Metaproteomic analysis of nasopharyngeal swab samples to identify microbial peptides and potential co-infection status in COVID-19 patients

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