Primary infection or reactivation of latent human cytomegalovirus (HCMV) or herpes simplex viruses (HSV) 1 or 2 during pregnancy can transmit the virus in utero or during natural childbirth to the fetus. The majority of these infections are asymptomatic at birth but may present later with potentially lethal disseminated infection or meningitis (HSV), or long-term neurodevelopmental sequelae including sensorineural hearing loss or neurodevelopmental impairments (HCMV). Unfortunately, early signs and symptoms of disseminated viral infections may be misdiagnosed as bacterial sepsis. Therefore, immediate testing for viral etiologies may not be ordered or even considered by skilled clinicians. In asymptomatic HCMV infections, early detection is necessary to monitor for and treat future neurologic sequelae. In acutely ill-appearing infants, specific detection of viruses against other disease-causing agents is vital to inform correct patient management, including early administration of the correct antimicrobial(s). An ideal test should be rapid, inexpensive, require low sample volumes, and demonstrate efficacy in multiple tissue matrices to aid in timely clinical decision-making for neonatal infections. This work discusses the development of a rapid probe-free qPCR assay for HSV and HCMV that enables early and specific detection of these viruses in neonates. The assays probe free chemistry would allow easier extension to a broad-based multiplexed pathogenic panel as compared to assays utilizing sequence-specific probes or nested PCR.

MOTIVATION: The need to rapidly screen complex samples for a wide range of nucleic acid targets, like infectious diseases, remains unmet. Digital High-Resolution Melt (dHRM) is an emerging technology with potential to meet this need by accomplishing broad-based, rapid nucleic acid sequence identification. Here, we set out to develop a computational framework for estimating the resolving power of dHRM technology for defined sequence profiling tasks. By deriving noise models from experimentally generated dHRM datasets and applying these to in silico predicted melt curves, we enable the production of synthetic dHRM datasets that faithfully recapitulate real-world variations arising from sample and machine variables. We then use these datasets to identify the most challenging melt curve classification tasks likely to arise for a given application and test the performance of benchmark classifiers. RESULTS: This toolbox enables the in silico design and testing of broad-based dHRM screening assays and the selection of optimal classifiers. For an example application of screening common human bacterial pathogens, we show that human pathogens having the most similar sequences and melt curves are still reliably identifiable in the presence of experimental noise. Further, we find that ensemble methods outperform whole series classifiers for this task and are in some cases able to resolve melt curves with single-nucleotide resolution. AVAILABILITY AND IMPLEMENTATION: Data and code available on https://github.com/lenlan/dHRM-noise-modeling. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.

Group B Streptococcus (GBS) emerged as a leading cause of invasive infectious disease in neonates in the 1970s, but has recently been identified as an escalating public health threat in non-pregnant adults, particularly those of advanced aged or underlying medical conditions. GBS infection can rapidly develop into life-threatening disease despite prompt administration of effective antibiotics and initiation of state-of-the-art intensive care protocols and technologies due to deleterious bacterial virulence factors, such as the GBS pore-forming toxin β-hemolysin/cytolysin (β-H/C). β-H/C is known to have noxious effects on a wide range of host cells and tissues, including lung epithelial cell injury, blood brain barrier weakening, and immune cell apoptosis. Neonatal and adult survivors of GBS infection are at a high risk for substantial long-term health issues and neurologic disabilities due to perturbations in organ systems caused by bacterial- and host- mediated inflammatory stressors. Previously engineered anti-virulence inhibitors, such as monoclonal antibodies and small molecular inhibitors, generally require customized design for each different pathogenic toxin and do not target deleterious host pro-inflammatory responses that may cause organ injury, septic shock, or death. By simply wrapping donor red blood cells (RBCs) around polymeric cores, we have created biomimetic "nanosponges." Because nanoparticles retain the same repertoire of cell membrane receptors as their host cell, they offer non-specific and all-purpose toxin decoy strategies with a broad ability to sequester and neutralize various bacterial toxins and host pro-inflammatory chemokines and cytokines to attenuate the course of infectious disease. This proof-of-concept study successfully demonstrated that intervention with nanosponges reduced the hemolytic activity of live GBS and stabilized β-H/C in a dose-dependent manner. Nanosponge treatment also decreased lung epithelial and macrophage cell death following exposure to live GBS bacteria and stabilized β-H/C, improved neutrophil killing of GBS, and diminished GBS-induced macrophage IL-1β production. Our results, therefore, suggest biomimetic nanosponges provide a titratable detoxification therapy that may provide a first-in-class treatment option for GBS infection by sequestering and inhibiting β-H/C activity.

In clinical diagnostics and pathogen detection, profiling of complex samples for low-level genotypes represents a significant challenge. Advances in speed, sensitivity, and extent of multiplexing of molecular pathogen detection assays are needed to improve patient care. We report the development of an integrated platform enabling the identification of bacterial pathogen DNA sequences in complex samples in less than four hours. The system incorporates a microfluidic chip and instrumentation to accomplish universal PCR amplification, High Resolution Melting (HRM), and machine learning within 20,000 picoliter scale reactions, simultaneously. Clinically relevant concentrations of bacterial DNA molecules are separated by digitization across 20,000 reactions and amplified with universal primers targeting the bacterial 16S gene. Amplification is followed by HRM sequence fingerprinting in all reactions, simultaneously. The resulting bacteria-specific melt curves are identified by Support Vector Machine learning, and individual pathogen loads are quantified. The platform reduces reaction volumes by 99.995% and achieves a greater than 200-fold increase in dynamic range of detection compared to traditional PCR HRM approaches. Type I and II error rates are reduced by 99% and 100% respectively, compared to intercalating dye-based digital PCR (dPCR) methods. This technology could impact a number of quantitative profiling applications, especially infectious disease diagnostics.

Applying digital PCR (dPCR) technology to challenging clinical and industrial detection tasks has become more prevalent because of its capability for absolute quantification and rare target detection. However, practices learned from quantitative PCR (qPCR) that promote assay robustness and wide-ranging utility are not readily applied in dPCR. These include internal amplification controls to account for false-negative reactions and amplicon high-resolution melt (HRM) analysis to distinguish true positives from false positives. Incorporation of internal amplification controls in dPCR is challenging because of the limited fluorescence channels available on most machines, and the application of HRM analysis is hindered by the separation of heating and imaging functions on most dPCR systems. We use a custom digital HRM platform to assess the utility of HRM-based approaches for mitigation of false positives and false negatives in dPCR. We show that detection of an exogenous internal control using dHRM analysis reduces the inclusion of false-negative partitions, changing the calculated DNA concentration up to 52%. The integration of dHRM analysis enables classification of partitions that would otherwise be considered ambiguous "rain," which accounts for up to ∼3% and ∼10% of partitions in intercalating dye and hydrolysis probe dPCR, respectively. We focused on developing an internal control method that would be compatible with broad-based microbial detection in dPCR-dHRM. Our approach can be applied to a number of DNA detection methods including microbial profiling and may advance the utility of dPCR in clinical applications where accurate quantification is imperative.