The study, published online in the journal Microbiology Spectrum on August 21, 2026, was led by Professor Hiroki Takahashi of the Medical Mycology Research Center at Chiba University, with co-authors Dr. Isato Yoshioka, Dr. Momotaka Uchida, Professor Akira Watanabe, and Dr. Takashi Yaguchi. Their approach combines selective depletion of human DNA, whole-genome amplification, and real-time nanopore sequencing into a single streamlined pipeline designed to work on samples collected while the blood culture is still incubating. The result is species-level identification of fungal pathogens with remarkably high accuracy, achieved well before conventional systems would alert clinicians that anything is growing at all.

The clinical stakes are considerable. Candida species are responsible for the majority of invasive fungal infections worldwide and rank among the leading causes of hospital-acquired bloodstream infections overall. Because different fungal species respond differently to antifungal drugs—some are susceptible to first-line agents such as fluconazole, while others, like certain strains of Nakaseomyces glabratus, show reduced susceptibility—doctors strive to identify the specific organism before committing to a treatment regimen. In the absence of rapid identification, clinicians face an unenviable choice: start broad-spectrum antifungal therapy that may be unnecessary or suboptimal, or delay targeted treatment while the diagnostic clock ticks.

Conventional diagnosis compounds this dilemma. Blood cultures require incubation until microbial growth reaches levels that automated detection systems can register, a process that alone can take one to several days depending on the organism and the burden of infection. Only after the culture turns positive do laboratory staff perform identification tests to determine which species is present. During this entire window, the patient’s infection continues to progress. The Chiba University team set out to attack precisely this bottleneck by interrogating the sample before the culture system declares it positive.

The workflow unfolds in three main steps. First, the researchers selectively break down human cells in the blood culture sample and then degrade liberated human DNA using an enzyme called benzonase, which leaves fungal and bacterial DNA intact. This host-DNA depletion step is critical: in blood, human genetic material vastly outnumbers microbial DNA, and without removing it, fungal sequences would be buried under an avalanche of irrelevant signal. By stripping away the host background, the method dramatically increases the proportion of pathogen DNA available for analysis.

Next comes amplification. Because even a depleted sample contains only small quantities of pathogen DNA, the team employs PCR-based whole-genome amplification to make many copies of DNA fragments from across the genomes present in the sample. This random amplification strategy does not target specific genes; instead, it generates enough genetic material from whatever genomes are present to support sequencing. That choice matters, because it means the method is not limited to a preselected panel of species—it can in principle reveal anything in the sample, including organisms nobody was actively looking for.

The amplified DNA is then loaded onto a portable nanopore sequencing device. Nanopore sequencing works by threading individual DNA molecules through nanoscale pores and reading their sequence as they pass through, and unlike platforms that require a completed run before results can be assessed, it generates data in real time as the run progresses. The resulting sequences are matched against a custom-built reference database containing genetic information from a wide range of microorganisms, including the fungal and bacterial pathogens most commonly associated with bloodstream infections. This combination of real-time sequencing and a curated database is what allows the workflow to deliver an answer within hours rather than days.

In tests using 48 clinical blood culture samples representing eight fungal species, the workflow achieved species-level identification within approximately seven hours. The method successfully identified a range of clinically important pathogens, including Candida albicans, Nakaseomyces glabratus, Candida parapsilosis, Candida tropicalis, and Cryptococcus neoformans. Perhaps most strikingly, it detected mixed infections in some samples—involving either two fungal species or both fungi and bacteria—a scenario that conventional culture-based workflows often handle poorly, since one organism can outgrow and mask another during incubation.

Because the workflow captures the full genome of the pathogen rather than a single marker gene, it offers a bonus beyond speed: the ability to detect genetic variants in genes associated with drug resistance. That capability could eventually inform not just which species is infecting a patient, but which drugs that particular strain is likely to respond to, bringing precision medicine to an area of infectious disease where treatment decisions have long been made under uncertainty. “Our method may enable clinicians to initiate appropriate antifungal treatment earlier, potentially improving outcomes for patients with life-threatening fungal bloodstream infections,” Professor Takahashi noted. His motivation, he explained, grew from a background in sequence analysis and genomics and a desire to apply modern genomic technologies to the clinical challenge of fungal infections.

The researchers are careful to note that hurdles remain before the approach reaches routine clinical use. Further work is needed to determine the optimal timing for collecting samples during the incubation period, and detection still falters when heavy bacterial growth masks fungal signals in mixed infections. Additional validation will be required to confirm performance across broader patient populations and diverse pathogen profiles. Even so, the study demonstrates a credible path toward rapid diagnostic procedures for life-threatening fungal infections—procedures that could shorten the dangerous gap between infection and effective therapy, reduce the indiscriminate use of broad-spectrum antifungals, and ultimately improve survival for the hospitalized patients most at risk.

Subject of Research: Rapid nanopore whole-genome sequencing for species-level diagnosis of fungal bloodstream infections

Article Title: Faster diagnosis for fungal bloodstream infections

Article References: Faster diagnosis for fungal bloodstream infections. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: fungal bloodstream infections, Candida, nanopore sequencing, blood culture, host DNA depletion, whole-genome amplification, antifungal resistance, rapid diagnostics, Chiba University, Microbiology Spectrum, clinical microbiology, drug resistance genes