Rationale & Objective: Conventional culture can be insensitive for the detection of rare infections and for the detection of common infections in the setting of recent antibiotic usage. Patients receiving peritoneal dialysis (PD) with suspected peritonitis have a significant proportion of negative conventional cultures. This study examines the utility of metagenomic sequencing of peritoneal effluent cell-free DNA (cfDNA) for evaluating the peritoneal effluent in PD patients with and without peritonitis. Study Design: Prospective cohort study. Setting & Participants: We prospectively characterized cfDNA in 68 peritoneal effluent samples obtained from 33 patients receiving PD at a single center from September 2016 to July 2018. Outcomes: Peritoneal effluent, microbial, and human cfDNA characteristics were evaluated in culture-confirmed peritonitis and culture-negative peritonitis. Analytical Approach: Descriptive statistics were analyzed and microbial cfDNA was detected in culture-confirmed peritonitis and culture-negative peritonitis. Results: Metagenomic sequencing of cfDNA was able to detect and identify bacterial, viral, and eukaryotic pathogens in the peritoneal effluent from PD patients with culture-confirmed peritonitis, as well as patients with recent antibiotic usage and in cases of culture-negative peritonitis. Limitations: Parallel cultures were not obtained in all the peritoneal effluent specimens. Conclusions: Metagenomic cfDNA sequencing of the peritoneal effluent can identify pathogens in PD patients with peritonitis, including culture-negative peritonitis.

Cell-free DNA (cfDNA) sequencing has been helpful in diagnosing infectious disease. It allows a pathologist to unbiasly search for all pathogens in a sample instead of culturing for specific ones. However, the presence of contaminant DNA and misidentification of microbes are possible, leading to false-positive diagnoses when using this highly sensitive diagnostic method. A recent study describes a new bioinformatics platform called Low Biomass Background Correction, or LBBC, which searches for and helps remove contaminant DNA from cfDNA samples. In a urinary tract infection screen, LBBC reduced the false positive rate while minimally affecting the true positive rate. And among pregnant women, it allowed researchers to generate a new cfDNA dataset for amniotic fluids. That could help clinicians identify intra-amniotic infection more easily while supporting the view that amniotic fluid is sterile during normal pregnancy. Although it’s important to remember that LBBC may still filter out pathogens of interest, this new tool could make cfDNA sequencing more reliable in detecting pathogens in biological samples.

Background: Cell-free DNA (cfDNA) in blood, urine and other biofluids provides a unique window into human health. A proportion of cfDNA is derived from bacteria and viruses, creating opportunities for the diagnosis of infection via metagenomic sequencing. The total biomass of microbial-derived cfDNA in clinical isolates is low, which makes metagenomic cfDNA sequencing susceptible to contamination and alignment noise. Results: Here, we report Low Biomass Background Correction (LBBC), a bioinformatics noise filtering tool informed by the uniformity of the coverage of microbial genomes and the batch variation in the absolute abundance of microbial cfDNA. We demonstrate that LBBC leads to a dramatic reduction in false positive rate while minimally affecting the true positive rate for a cfDNA test to screen for urinary tract infection. We next performed high throughput sequencing of cfDNA in amniotic fluid collected from term uncomplicated pregnancies or those complicated with clinical chorioamnionitis with and without intra-amniotic infection. Conclusions: The data provide unique insight into the properties of fetal and maternal cfDNA in amniotic fluid, demonstrate the utility of cfDNA to screen for intra-amniotic infection, support the view that the amniotic fluid is sterile during normal pregnancy, and reveal cases of intra-amniotic inflammation without infection at term.

We describe droplet-assisted RNA targeting by single-cell sequencing (DART-seq), a versatile technology that enables multiplexed amplicon sequencing and transcriptome profiling in single cells. We applied DART-seq to simultaneously characterize the non-A-tailed transcripts of a segmented dsRNA virus and the transcriptome of the infected cell. In addition, we used DART-seq to simultaneously determine the natively paired, variable region heavy and light chain amplicons and the transcriptome of B lymphocytes.

Infections of the urinary tract are the most common form of infection in the human population. Here, we tested the utility of urinary cell-free DNA (cfDNA) to comprehensively monitor host and pathogen dynamics in the scope of bacterial and viral urinary tract infections. We assayed cfDNA isolated from 141 urine samples obtained from a cohort of 82 kidney transplant recipients by next-generation sequencing. We find that urinary cfDNA simultaneously informs about the composition of the bacterial and viral components of the microbiome, antimicrobial susceptibility, bacterial growth dynamics, kidney allograft injury, and the host response to infection. These different layers of information are accessible from a single assay and individually agree with corresponding clinical tests based on quantitative PCR, conventional bacterial culture, and urinalysis. In addition, cfDNA reveals the frequent occurrence of pathologies that remain undiagnosed in conventional diagnostic workups. Our work identifies urinary cfDNA as a highly versatile tool to monitor infections of the urinary tract.

Urinary tract infections are one of the most common infections in humans. Here we tested the utility of urinary cell-free DNA (cfDNA) to comprehensively monitor host and pathogen dynamics in bacterial and viral urinary tract infections. We isolated cfDNA from 141 urine samples from a cohort of 82 kidney transplant recipients and performed next-generation sequencing. We found that urinary cfDNA is highly informative about bacterial and viral composition of the microbiome, antimicrobial susceptibility, bacterial growth dynamics, kidney allograft injury, and host response to infection. These different layers of information are accessible from a single assay and individually agree with corresponding clinical tests based on quantitative PCR, conventional bacterial culture, and urinalysis. In addition, cfDNA reveals the frequent occurrence of pathologies that remain undiagnosed with conventional diagnostic protocols. Our work identifies urinary cfDNA as a highly versatile analyte to monitor infections of the urinary tract.