Novel DNA Markers for Identification of Actinobacillus pleuropneumoniae

Species-specific markers are crucial for infectious disease diagnostics. Mutations within a marker sequence can lead to false-negative results, inappropriate treatment, and economic loss.

A Pre-Processing Pipeline to Quantify, Visualize and Reduce Technical Variation in Protein Microarray Studies

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies, and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.1Impact StatementProtein microarrays are in wide use in cancer research, infectious disease diagnostics and biomarker identification. To inform research and practice in these and other fields, technical variation must be corrected using normalization and pre-processing. Current protein microarray studies use a variety of normalization methods, many of which were developed for DNA microarrays, and therefore are based on assumptions and data that are not ideal for protein microarrays. To address this issue, we develop, evaluate, and implement a pre-processing pipeline that corrects for technical variation in protein microarrays. We show that pre-processing and normalization directly impact the validity of downstream analysis, and protein-specific approaches are essential.

Antiviral metabolite 3'-Deoxy-3',4'-didehydro-cytidine is detectable in serum and identifies acute viral infections including COVID-19

There is a critical need for improved infectious disease diagnostics to enable rapid case identification in a viral pandemic and support targeted antimicrobial prescribing. Here we use high-resolution liquid chromatography coupled with mass spectrometry to compare the admission serum metabolome of patients attending hospital with a range of viral infections, including SARS-CoV-2, to those with bacterial infections, non-infected inflammatory conditions and healthy controls. We demonstrate for the first time that 3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), is detectable in serum. ddhC acts as an accurate biomarker for viral infections, generating an area under the receiver operating characteristic curve of 0.954 (95% confidence interval 0.923-0.986) when comparing viral to non-viral cases. Gene expression of viperin, the enzyme responsible for ddhCTP synthesis, is highly correlated with ddhC, providing a biological mechanism for its increase during viral infection. These findings underline a key future diagnostic role of ddhC in the context of pandemic preparedness and antimicrobial stewardship.

Metabolomics in the Diagnosis and Prognosis of COVID-19

Coronavirus disease 2019 (COVID-19) pandemic triggered an unprecedented global effort in developing rapid and inexpensive diagnostic and prognostic tools. Since the genome of SARS-CoV-2 was uncovered, detection of viral RNA by RT-qPCR has played the most significant role in preventing the spread of the virus through early detection and tracing of suspected COVID-19 cases and through screening of at-risk population. However, a large number of alternative test methods based on SARS-CoV-2 RNA or proteins or host factors associated with SARS-CoV-2 infection have been developed and evaluated. The application of metabolomics in infectious disease diagnostics is an evolving area of science that was boosted by the urgency of COVID-19 pandemic. Metabolomics approaches that rely on the analysis of volatile organic compounds exhaled by COVID-19 patients hold promise for applications in a large-scale screening of population in point-of-care (POC) setting. On the other hand, successful application of mass-spectrometry to detect specific spectral signatures associated with COVID-19 in nasopharyngeal swab specimens may significantly save the cost and turnaround time of COVID-19 testing in the diagnostic microbiology and virology laboratories. Active research is also ongoing on the discovery of potential metabolomics-based prognostic markers for the disease that can be applied to serum or plasma specimens. Several metabolic pathways related to amino acid, lipid and energy metabolism were found to be affected by severe disease with COVID-19. In particular, tryptophan metabolism via the kynurenine pathway were persistently dysregulated in several independent studies, suggesting the roles of several metabolites of this pathway such as tryptophan, kynurenine and 3-hydroxykynurenine as potential prognostic markers of the disease. However, standardization of the test methods and large-scale clinical validation are necessary before these tests can be applied in a clinical setting. With rapidly expanding data on the metabolic profiles of COVID-19 patients with varying degrees of severity, it is likely that metabolomics will play an important role in near future in predicting the outcome of the disease with a greater degree of certainty.

Time-domain signal averaging to improve microparticles detection and enumeration accuracy in a microfluidic impedance cytometer

Microfluidic impedance cytometry is a powerful system to measure micro and nano-sized particles and is routinely used in point-of-care settings disease diagnostics and other biomedical applications. However, small objects near a sensor’s detection limit are plagued with relatively significant background noise and are difficult to identify for every case. While many data processing techniques can be utilized to reduce noise and improve signal quality, frequently they are still inadequate to push sensor detection limits. Here, we report the first demonstration of a novel signal averaging algorithm effective in noise reduction of microfluidic impedance cytometry data, improving enumeration accuracy and reducing detection limits. Our device uses a 22 μm tall microchannel and gold coplanar microelectrodes that generates an electric field, recording bipolar pulses from polystyrene microparticles flowing through the channel. In addition to outlining a modified moving signal averaging technique theoretically and with a model dataset, we also performed a compendium of characterization experiments including variations in flow rate, input voltage, and particle size. Multi-variate metrics from each experiment are compared including signal amplitude, pulse width, background noise, and signal-to-noise ratio (SNR). Incorporating our technique resulted in improved SNR and counting accuracy across all experiments conducted, and the limit of detection improved from 5 μm to 1 μm particles without modifying microchannel dimensions. Succeeding this, we envision implementing our modified moving average technique to develop next generation microfluidic impedance cytometry devices with an expanded dynamic range and improved enumeration accuracy. This can be exceedingly useful for many biomedical applications, such as infectious disease diagnostics where devices may enumerate larger-scale immune cells alongside sub-micron bacterium in the same sample.

Metagenomic sequencing as a pathogen-agnostic clinical diagnostic tool for infectious diseases: a systematic review and meta-analysis of diagnostic test accuracy studies

Background: Metagenomic sequencing is frequently claimed to have the potential to revolutionise microbiology through rapid species identification and antimicrobial resistance (AMR) prediction. We assess progress towards this. Methods: We perform a systematic review and meta-analysis of all published literature on culture-independent metagenomic sequencing for pathogen-agnostic infectious disease diagnostics to August 12, 2020. Methodologic bias and applicability were assessed using QUADAS-2. (PROSPERO CRD42020163777) Results: A total of 2023 clinical samples from 13/21 eligible diagnostic test accuracy studies were included in the meta-analysis. Reference standards were culture, molecular testing, clinical decision or a composite measure. Sensitivity and specificity in the most widely investigated sample types were 90%(78-96%) and 86%(45-98%) for blood, 75%(95%CI, 54-89%) and 96%(72-100%) for CSF, and 84%(79-88%) and 67%(38-87%) for orthopaedic samples respectively. We identified limited use of controls, especially negative controls which were used in only 62%(13/21) studies. AMR prediction and comparison to phenotypic results was undertaken in four studies: categorical agreement was 88%(80%-97%), very major and major error rates were 24%(8-40%) and 5%(0-12%) respectively. Better human DNA depletion methods are required: a median 91%(IQR 82-98%)[range 76-98%] of sequences were classified as human. The median(IQR)[range] time from sample to result was 29(24-94)[4-144] hours. The reported consumables cost per sample ranged from $130-$685. Conclusions: There is scope for improving the quality of reporting in clinical metagenomic studies. Although our results are limited by the heterogeneity displayed, our results reflect a promising outlook for clinical metagenomics. Methodological improvements, and convergence around protocols and best practises may improve performance in future.