Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections

Background A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet approximately two thirds of urine samples typically yield negative culture results. By reducing the number of query samples to be cultured and enabling diagnostic services to concentrate on those in which there are true microbial infections, a significant improvement in efficiency of the service is possible. Methodology Screening process for urine samples prior to culture was modelled in a single clinical microbiology laboratory covering three hospitals and community services across Bristol and Bath, UK. Retrospective analysis of all urine microscopy, culture, and sensitivity reports over one year was used to compare two methods of classification: a heuristic model using a combination of white blood cell count and bacterial count, and a machine learning approach testing three algorithms (Random Forest, Neural Network, Extreme Gradient Boosting) whilst factoring in independent variables including demographics, historical urine culture results, and clinical details provided with the specimen. Results A total of 212,554 urine reports were analysed. Initial findings demonstrated the potential for using machine learning algorithms, which outperformed the heuristic model in terms of relative workload reduction achieved at a classification sensitivity > 95%. Upon further analysis of classification sensitivity of subpopulations, we concluded that samples from pregnant patients and children (age 11 or younger) require independent evaluation. First the removal of pregnant patients and children from the classification process was investigated but this diminished the workload reduction achieved. The optimal solution was found to be three Extreme Gradient Boosting algorithms, trained independently for the classification of pregnant patients, children, and then all other patients. When combined, this system granted a relative workload reduction of 41% and a sensitivity of 95% for each of the stratified patient groups. Conclusion Based on the considerable time and cost savings achieved, without compromising the diagnostic performance, the heuristic model was successfully implemented in routine clinical practice in the diagnostic laboratory at Severn Pathology, Bristol. Our work shows the potential application of supervised machine learning models in improving service efficiency at a time when demand often surpasses resources of public healthcare providers

Presence of ventriculoperitoneal and lumbar shunts stimulate long lasting non-inflammatory changes in the cerebrospinal fluid distinct from the response to bacterial infection

Ventriculoperitoneal (VP) shunts are effective at relieving hydrocephalus but are prone to malfunction. There are two hypotheses as to how shunts may malfunction independently of mechanical failure or blockage by debris from initial placement. The first is that the presence of a foreign object results in cells migrating into and colonising the shunt. The second is that the shunts contain either small numbers of live bacteria or residual bacterial products from manufacture or handling, triggering an inflammatory response that attracts cells to the site which go on to cause malfunctions. The presence of bacteria can be difficult to definitively rule in or out, given that they are capable of forming biofilms which poses challenges for isolation and microbiological culture. In this study, we measured 91 soluble immunological molecules and 91 soluble neurological molecules in CSF of patients with VP shunts and compared them to both patients without shunts and those with bacterial infection to determine whether there is an ongoing inflammatory response to shunting. We find that shunts elicit a soluble signature of neural wound healing and cell migration proteins that is distinct from the inflammatory signature of patients with neurological infection. This appears to represent a long-term response, persisting for at least 5 years in one patient

GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data

Motivation Clustering is an unsupervised method for identifying structure in unlabelled data. In the context of cytometry, it is typically used to categorise cells into subpopulations of similar phenotypes. However, clustering is greatly dependent on hyperparameters and the data to which it is applied as each algorithm makes different assumptions and generates a different ‘view’ of the dataset. As such, the choice of clustering algorithm can significantly influence results, and there is often not one preferred method but different insights to be obtained from different methods. To overcome these limitations, consensus approaches are needed that directly address the effect of competing algorithms. To the best of our knowledge, consensus clustering algorithms designed specifically for the analysis of cytometry data are lacking. Results We present a novel ensemble clustering methodology based on geometric median clustering with weighted voting (GeoWaVe). Compared to graph ensemble clustering methods that have gained popularity in scRNA-seq analysis, GeoWaVe performed favourably on different sets of high-dimensional mass and flow cytometry data. Our findings provide proof of concept for the power of consensus methods to make the analysis, visualisation and interpretation of cytometry data more robust and reproducible. The wide availability of ensemble clustering methods is likely to have a profound impact on our understanding of cellular responses, clinical conditions, and therapeutic and diagnostic options

CytoPy: An autonomous cytometry analysis framework

Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is open source and available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/

Toll-like receptor linked cytokine profiles in cerebrospinal fluid discriminate neurological infection from sterile inflammation

Rapid determination of an infective aetiology causing neurological inflammation in the cerebrospinal fluid (CSF) can be challenging in clinical practice. Post-surgical nosocomial infection is difficult to diagnose accurately, as it occurs on a background of altered CSF composition due to the underlying pathologies and surgical procedures involved. There is additional diagnostic difficulty after external ventricular drain or ventriculoperitoneal shunt surgery, as infection is often caused by pathogens growing as biofilms, which may fail to elicit a significant inflammatory response and are challenging to identify by microbiological culture. Despite much research effort, a single sensitive and specific CSF biomarker has yet to be defined which reliably distinguishes infective from non-infective inflammation. As a result, many patients with suspected infection are treated empirically with broad-spectrum antibiotics in the absence of definitive diagnostic criteria. To begin to address these issues, we examined CSF taken at the point of clinical equipoise to diagnose CSF infection in 14 consecutive neurosurgical patients showing signs of inflammatory complications. Using the guidelines of the Infectious Diseases Society of America, 6 cases were subsequently characterised as infected, and 8 as sterile inflammation. 24 contemporaneous patients with idiopathic intracranial hypertension or normal pressure hydrocephalus were included as non-inflamed controls. We measured 182 immune and neurological biomarkers in each sample and used pathway analysis to elucidate the biological underpinnings of any biomarker changes. Increased levels of the inflammatory cytokine interleukin (IL)-6 and IL-6 related mediators such as oncostatin M were excellent indicators of inflammation. However, IL-6 levels alone could not distinguish between bacterially infected and uninfected patients. Within the patient cohort with neurological inflammation, a pattern of raised IL-17, IL-12p40/p70 and IL-23 levels delineated nosocomial bacteriological infection from background neuroinflammation. Pathway analysis showed that the observed immune signatures could be explained through a common generic inflammatory response marked by IL-6 in both nosocomial and non-infectious inflammation, overlaid with a Toll-like receptor associated and bacterial peptidoglycan-triggered IL-17 pathway response that occurred exclusively during infection. This is the first demonstration of a pathway dependent CSF biomarker differentiation distinguishing nosocomial infection from background neuroinflammation. It is especially relevant to the commonly encountered pathologies in clinical practice, such as subarachnoid haemorrhage and post cranial neurosurgery. While requiring confirmation in a larger cohort, the current data indicate the potential utility of CSF biomarker strategies to identify differential initiation of a common downstream IL-6 pathway to diagnose nosocomial infection in this challenging clinical cohort

Control of neutrophil influx during peritonitis by transcriptional cross‐regulation of chemokine CXCL1 by IL‐17 and IFN‐γ

Neutrophil infiltration is a hallmark of peritoneal inflammation, but mechanisms regulating neutrophil recruitment in patients with peritoneal dialysis (PD)-related peritonitis are not fully defined. We examined 104 samples of PD effluent collected during acute peritonitis for correspondence between a broad range of soluble parameters and neutrophil counts. We observed an association between peritoneal IL-17 and neutrophil levels. This relationship was evident in effluent samples with low but not high IFN-γ levels, suggesting a differential effect of IFN-γ concentration on neutrophil infiltration. Surprisingly, there was no association of neutrophil numbers with the level of CXCL1, a key IL-17-induced neutrophil chemoattractant. We investigated therefore the production of CXCL1 by human peritoneal mesothelial cells (HPMCs) under in vitro conditions mimicking clinical peritonitis. Stimulation of HPMCs with IL-17 increased CXCL1 production through induction of transcription factor SP1 and activation of the SP1-binding region of the CXCL1 promoter. These effects were amplified by TNFα. In contrast, IFN-γ dose-dependently suppressed IL-17-induced SP1 activation and CXCL1 production through a transcriptional mechanism involving STAT1. The SP1-mediated induction of CXCL1 was also observed in HPMCs exposed to PD effluent collected during peritonitis and containing IL-17 and TNFα, but not IFN-γ. Supplementation of the effluent with IFN-γ led to a dose-dependent activation of STAT1 and a resultant inhibition of SP1-induced CXCL1 expression. Transmesothelial migration of neutrophils in vitro increased upon stimulation of HPMCs with IL-17 and was reduced by IFN-γ. In addition, HPMCs were capable of binding CXCL1 at their apical cell surface. These observations indicate that changes in relative peritoneal concentrations of IL-17 and IFN-γ can differently engage SP1–STAT1, impacting on mesothelial cell transcription of CXCL1, whose release and binding to HPMC surface may determine optimal neutrophil recruitment and retention during peritonitis

Age-dependent maintenance of motor control and corticostriatal innervation by death receptor 3

Death receptor 3 is a proinflammatory member of the immunomodulatory tumor necrosis factor receptor superfamily, which has been implicated in several inflammatory diseases such as arthritis and inflammatory bowel disease. Intriguingly however, constitutive DR3 expression has been detected in the brains of mice, rats, and humans, although its neurological function remains unknown. By mapping the normal brain expression pattern of DR3, we found that DR3 is expressed specifically by cells of the neuron lineage in a developmentally regulated and region-specific pattern. Behavioral studies on DR3-deficient (DR3(ko)) mice showed that constitutive neuronal DR3 expression was required for stable motor control function in the aging adult. DR3(ko) mice progressively developed behavioral defects characterized by altered gait, dyskinesia, and hyperactivity, which were associated with elevated dopamine and lower serotonin levels in the striatum. Importantly, retrograde tracing showed that absence of DR3 expression led to the loss of corticostriatal innervation without significant neuronal loss in aged DR3(ko) mice. These studies indicate that DR3 plays a key nonredundant role in the retention of normal motor control function during aging in mice and implicate DR3 in progressive neurological disease

Altered gut microbiota activate and expand insulin B15-23-Reactive CD8+ T-Cells

Insulin is a major autoantigen in type 1 diabetes, targeted by both CD8 and CD4 T-cells. We studied an insulin-reactive T-cell receptor (TCR) alpha-chain transgenic non-obese diabetic (NOD) mouse on a TCRCα and proinsulin2 (PI2)-deficient background, designated as A22Cα-/-PI2-/-NOD mice. These mice develop a low incidence of autoimmune diabetes. To test the role of gut microbiota on diabetes development in this model system, we treated the A22Cα-/-PI2-/-NOD mice with enrofloxacin, a broad-spectrum antibiotic. The treatment led to male mice developing accelerated diabetes. We found that enrofloxacin increased the frequency of the insulin-reactive CD8+ T-cells and activated the cells in the Peyer’s patches (PP) and pancreatic lymph nodes (PLNs), together with induction of immunological effects on the antigen-presenting cell populations. The composition of gut microbiota differed between the enrofloxacin-treated and untreated mice and also between the enrofloxacin-treated mice that developed diabetes, compared with those that remained normoglycemic. Our results provide evidence that the composition of the gut microbiota is important for determining the expansion and activation of insulin-reactive CD8+ T-cells

Two novel human cytomegalovirus NK cell evasion functions target MICA for lysosomal degradation

NKG2D plays a major role in controlling immune responses through the regulation of natural killer (NK) cells, αβ and γδ T-cell function. This activating receptor recognizes eight distinct ligands (the MHC Class I polypeptide-related sequences (MIC) A andB, and UL16-binding proteins (ULBP)1–6) induced by cellular stress to promote recognition cells perturbed by malignant transformation or microbial infection. Studies into human cytomegalovirus (HCMV) have aided both the identification and characterization of NKG2D ligands (NKG2DLs). HCMV immediate early (IE) gene up regulates NKGDLs, and we now describe the differential activation of ULBP2 and MICA/B by IE1 and IE2 respectively. Despite activation by IE functions, HCMV effectively suppressed cell surface expression of NKGDLs through both the early and late phases of infection. The immune evasion functions UL16, UL142, and microRNA(miR)-UL112 are known to target NKG2DLs. While infection with a UL16 deletion mutant caused the expected increase in MICB and ULBP2 cell surface expression, deletion of UL142 did not have a similar impact on its target, MICA. We therefore performed a systematic screen of the viral genome to search of addition functions that targeted MICA. US18 and US20 were identified as novel NK cell evasion functions capable of acting independently to promote MICA degradation by lysosomal degradation. The most dramatic effect on MICA expression was achieved when US18 and US20 acted in concert. US18 and US20 are the first members of the US12 gene family to have been assigned a function. The US12 family has 10 members encoded sequentially through US12–US21; a genetic arrangement, which is suggestive of an ‘accordion’ expansion of an ancestral gene in response to a selective pressure. This expansion must have be an ancient event as the whole family is conserved across simian cytomegaloviruses from old world monkeys. The evolutionary benefit bestowed by the combinatorial effect of US18 and US20 on MICA may have contributed to sustaining the US12 gene family