Extracellular vesicles in mycobacteria: new findings in biogenesis, host-pathogen interactions, and diagnostics.

Since the discovery of extracellular vesicles (EVs) in mycobacterial species 15 years back, we have learned that this phenomenon is conserved in the Mycobacterium genus and has critical roles in bacterial physiology and host-pathogen interactions. Mycobacterium tuberculosis (Mtb), the tuberculosis (TB) causative agent, produces EVs both in vitro and in vivo including a diverse set of biomolecules with demonstrated immunomodulatory effects. Moreover, Mtb EVs (MEVs) have been shown to possess vaccine properties and carry biomarkers with diagnostic capacity. Although information on MEV biogenesis relative to other bacterial species is scarce, recent studies have shed light on how MEVs originate and are released to the extracellular space. In this minireview, we discuss past and new information about the vesiculogenesis phenomenon in Mtb, including biogenesis, MEV cargo, aspects in the context of host-pathogen interactions, and applications that could help to develop effective tools to tackle the disease.

Mandating indoor air quality for public buildings.

If some countries lead by example, standards may increasingly become normalized.

Respiratory infectious burden in a cohort of antibody deficiency patients treated with immunoglobulin replacement therapy: The impact of lung pathology and gastroesophageal reflux disease.

Background: Antibody deficiencies result from reduced immunoglobulin levels and function, increasing susceptibility to, primarily, bacterial infection. Primary antibody deficiencies comprise intrinsic defects in B-cell physiology, often due to inherited errors. Hematological malignancies or B-cell suppressive therapy are major causes of secondary antibody deficiency. Although immunoglobulin replacement therapy (IGRT) reduces infectious burden in antibody deficiency patients, respiratory tract infections remain a significant health burden. We hypothesize that lung pathology and gastroesophageal reflux disease (GORD) increase the risk of pneumonia in antibody deficiency patients, as in the general population. Objective: For our cohort of patients with primary antibody deficiency and secondary antibody deficiency, we reviewed their respiratory infectious burden and the impact of lung pathologies and GORD. Methods: The medical records of 231 patients on IGRT at a tertiary referral center, from October 26, 2014, to February 19, 2021, were reviewed to determine microbial isolates from sputum samples and prevalence of common lung pathologies and GORD. Results: Haemophilus and Pseudomonas species represent a large infectious burden, being identified in 30.2% and 21.4% of sputum samples demonstrating growth, respectively; filamentous fungal and mycobacterial infections were rare. Diagnosed lung pathology increased the proportion of patients with Pseudomonas, Klebsiella, Stenotrophomonas, and Candida species isolated in their sputum, and diagnosed GORD increased the proportion with Enterobacter and Candida species isolated. Conclusions: Bacterial respiratory infectious burden remains in primary antibody deficiency and secondary antibody deficiency despite IGRT. Lung pathologies encourage growth of species less susceptible to IGRT, so specialist respiratory medicine input and additional treatments such as inhaled antibiotics are indicated to optimize respiratory outcomes.

Impact of transient acquired hypermutability on the inter- and intra-species competitiveness of Pseudomonas aeruginosa.

Once acquired, hypermutation is unrelenting, and in the long-term, leads to impaired fitness due to its cumulative impact on the genome. This raises the question of why hypermutators arise so frequently in microbial ecosystems. In this work, we explore this problem by examining how the transient acquisition of hypermutability affects inter- and intra-species competitiveness, and the response to environmental insults such as antibiotic challenge. We do this by engineering Pseudomonas aeruginosa to allow the expression of an important mismatch repair gene, mutS, to be experimentally controlled over a wide dynamic range. We show that high levels of mutS expression induce genomic stasis (hypomutation), whereas lower levels of induction lead to progressively higher rates of mutation. Whole-genome sequence analyses confirmed that the mutational spectrum of the inducible hypermutator is similar to the distinctive profile associated with mutS mutants obtained from the airways of people with cystic fibrosis (CF). The acquisition of hypermutability conferred a distinct temporal fitness advantage over the wild-type P. aeruginosa progenitor strain, in both the presence and the absence of an antibiotic selection pressure. However, over a similar time-scale, acquisition of hypermutability had little impact on the population dynamics of P. aeruginosa when grown in the presence of a competing species (Staphylococcus aureus). These data indicate that in the short term, acquired hypermutability primarily confers a competitive intra-species fitness advantage.

Coronavirus Disease 2019 and Airborne Transmission: Science Rejected, Lives Lost. Can Society Do Better?

This is an account that should be heard of an important struggle: the struggle of a large group of experts who came together at the beginning of the COVID-19 pandemic to warn the world about the risk of airborne transmission and the consequences of ignoring it. We alerted the World Health Organization about the potential significance of the airborne transmission of SARS-CoV-2 and the urgent need to control it, but our concerns were dismissed. Here we describe how this happened and the consequences. We hope that by reporting this story we can raise awareness of the importance of interdisciplinary collaboration and the need to be open to new evidence, and to prevent it from happening again. Acknowledgement of an issue, and the emergence of new evidence related to it, is the first necessary step towards finding effective mitigation solutions.

External validity of machine learning-based prognostic scores for cystic fibrosis: A retrospective study using the UK and Canadian registries.

Precise and timely referral for lung transplantation is critical for the survival of cystic fibrosis patients with terminal illness. While machine learning (ML) models have been shown to achieve significant improvement in prognostic accuracy over current referral guidelines, the external validity of these models and their resulting referral policies has not been fully investigated. Here, we studied the external validity of machine learning-based prognostic models using annual follow-up data from the UK and Canadian Cystic Fibrosis Registries. Using a state-of-the-art automated ML framework, we derived a model for predicting poor clinical outcomes in patients enrolled in the UK registry, and conducted external validation of the derived model using the Canadian Cystic Fibrosis Registry. In particular, we studied the effect of (1) natural variations in patient characteristics across populations and (2) differences in clinical practice on the external validity of ML-based prognostic scores. Overall, decrease in prognostic accuracy on the external validation set (AUCROC: 0.88, 95% CI 0.88-0.88) was observed compared to the internal validation accuracy (AUCROC: 0.91, 95% CI 0.90-0.92). Based on our ML model, analysis on feature contributions and risk strata revealed that, while external validation of ML models exhibited high precision on average, both factors (1) and (2) can undermine the external validity of ML models in patient subgroups with moderate risk for poor outcomes. A significant boost in prognostic power (F1 score) from 0.33 (95% CI 0.31-0.35) to 0.45 (95% CI 0.45-0.45) was observed in external validation when variations in these subgroups were accounted in our model. Our study highlighted the significance of external validation of ML models for cystic fibrosis prognostication. The uncovered insights on key risk factors and patient subgroups can be used to guide the cross-population adaptation of ML-based models and inspire new research on applying transfer learning methods for fine-tuning ML models to cope with regional variations in clinical care.

Evaluation of a novel ELISA test using synthetic mycolic acid antigens for serodiagnosis of non-tuberculous mycobacterial (NTM) infections.

The diagnosis of non-tuberculous mycobacteria (NTM) is a particular challenge in people with cystic fibrosis. Current standard diagnostic approaches rely on serial sputum culture, which is resource demanding, dependent on patient expectoration and may be compromised by excessive decontamination, conventional bacterial overgrowth and masking by concomitant oral and nebulised antibiotics. An alternative rapid, reliable and inexpensive diagnostic method is therefore urgently needed. Serum of patients with Mycobacterium abscessus infection and chronic suppurative lung disease without NTM infection was tested against an array of novel synthetic mycolic acids, identical or similar to natural components of mycobacterial cell walls, and glycopeptidolipid (GPL)-core antigen, which has previously been investigated in Mycobacterium avium pulmonary infection. Diagnostic accuracy of individual antigens and combination of various antigens were calculated. An ELISA using individual trehalose dimycolates and GPL-core antigen was able to effectively distinguish serum from infected and non-infected individuals with a specificity of 88% and a sensitivity of up to 88%, which increased to 88% sensitivity and 93% specificity by combining several antigens in the test. These results suggest synthetic mycolic acid antigens, used individually or in combination with GPL-core antigen could be successfully used to distinguish patients with M. abscessus infection from disease controls.

Case studies to illustrate good practice in the management of non-tuberculous mycobacterial pulmonary disease.

Pulmonary disease caused by non-tuberculous mycobacteria (NTM-PD) can be a complex condition for health care providers to manage, and delayed diagnosis and treatment failure are common. Here we present three case studies that illustrate key challenges in the diagnosis and treatment of NTM-PD, and provide guidance on these issues. In addition, we make recommendations on how the overall management of NTM-PD may be improved, through strategies such as physician education to recognise NTM-PD, and the development of multidisciplinary teams and patient-support groups.

Human NLRP1 is a sensor of pathogenic coronavirus 3CL proteases in lung epithelial cells.

Inflammation observed in SARS-CoV-2-infected patients suggests that inflammasomes, proinflammatory intracellular complexes, regulate various steps of infection. Lung epithelial cells express inflammasome-forming sensors and constitute the primary entry door of SARS-CoV-2. Here, we describe that the NLRP1 inflammasome detects SARS-CoV-2 infection in human lung epithelial cells. Specifically, human NLRP1 is cleaved at the Q333 site by multiple coronavirus 3CL proteases, which triggers inflammasome assembly and cell death and limits the production of infectious viral particles. Analysis of NLRP1-associated pathways unveils that 3CL proteases also inactivate the pyroptosis executioner Gasdermin D (GSDMD). Subsequently, caspase-3 and GSDME promote alternative cell pyroptosis. Finally, analysis of pyroptosis markers in plasma from COVID-19 patients with characterized severe pneumonia due to autoantibodies against, or inborn errors of, type I interferons (IFNs) highlights GSDME/caspase-3 as potential markers of disease severity. Overall, our findings identify NLRP1 as a sensor of SARS-CoV-2 infection in lung epithelia.

Sounds of COVID-19: exploring realistic performance of audio-based digital testing.

To identify Coronavirus disease (COVID-19) cases efficiently, affordably, and at scale, recent work has shown how audio (including cough, breathing and voice) based approaches can be used for testing. However, there is a lack of exploration of how biases and methodological decisions impact these tools' performance in practice. In this paper, we explore the realistic performance of audio-based digital testing of COVID-19. To investigate this, we collected a large crowdsourced respiratory audio dataset through a mobile app, alongside symptoms and COVID-19 test results. Within the collected dataset, we selected 5240 samples from 2478 English-speaking participants and split them into participant-independent sets for model development and validation. In addition to controlling the language, we also balanced demographics for model training to avoid potential acoustic bias. We used these audio samples to construct an audio-based COVID-19 prediction model. The unbiased model took features extracted from breathing, coughs and voice signals as predictors and yielded an AUC-ROC of 0.71 (95% CI: 0.65-0.77). We further explored several scenarios with different types of unbalanced data distributions to demonstrate how biases and participant splits affect the performance. With these different, but less appropriate, evaluation strategies, the performance could be overestimated, reaching an AUC up to 0.90 (95% CI: 0.85-0.95) in some circumstances. We found that an unrealistic experimental setting can result in misleading, sometimes over-optimistic, performance. Instead, we reported complete and reliable results on crowd-sourced data, which would allow medical professionals and policy makers to accurately assess the value of this technology and facilitate its deployment.

A multicentre analysis of Clostridium difficile in persons with Cystic Fibrosis demonstrates that carriage may be transient and highly variable with respect to strain and level.

PURPOSE: Clostridium difficile has been reported to occur in the gastrointestinal tract of 50% of Cystic Fibrosis (CF) subjects, however, clinical C. difficile infection (CDI) is a rare occurrence in this cohort despite the presence of toxigenic and hypervirulent ribotypes. Here, we present the first longitudinal, multicentre analysis of C. difficile prevalence among adult CF subjects. METHODOLOGY: Faecal samples were collected from adults with CF (selected based on confirmed Pseudomonas aeruginosa pulmonary colonisation) from Ireland, UK and Belgium as part of the CFMATTERS clinical research trial (grant No. 603038) and from non-CF controls. Faecal samples were collected on enrolment, at three monthly intervals, during pulmonary exacerbation and three months post exacerbation. C. difficile was isolated from faecal samples by ethanol shocking followed by culturing on cycloserine cefoxitin egg yolk agar. Isolates were characterised in terms of ribotype, toxin type and antibiotic susceptibility to antibiotics routinely used in the treatment of CDI (metronidazole and vancomycin) and those implicated in induction of CDI (ciprofloxacin and moxifloxacin). RESULTS: Prevalence of C. difficile among CF subjects in the three sites was similar ranging from 47% to 50% at baseline, while the healthy control cohort had a carriage rate of 7.1%. Including subjects who were positive for C. difficile at any time point there was a higher carriage rate of 71.4%, 66.7% and 63.2% in Ireland, UK, and Belgium, respectively. Ribotyping of 80 isolates from 45 CF persons, over multiple time points revealed 23 distinct ribotypes with two ribotypes (046 and 078) shared by all centres. The proportion of toxigenic isolates varied across the sites, ranging from 66.7% in Ireland to 52.9% in Belgium and 100% in the UK. Antibiotic susceptibility rates to vancomycin, metronidazole, ciprofloxacin and moxifloxacin was 100%, 97.5%, 1.3% and 63.8%, respectively. CONCLUSIONS: This study demonstrates the highest carriage rate of C. difficile to date in a CF cohort. Longitudinal data show that C. difficile can be a transient inhabitant of the CF gut, changing both in terms of strain and excretion rates.

How artificial intelligence and machine learning can help healthcare systems respond to COVID-19.

The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide personalized patient management plans, inform policy, and expedite clinical trials. In this paper, we introduce five of the most important challenges in responding to COVID-19 and show how each of them can be addressed by recent developments in machine learning (ML) and artificial intelligence (AI). We argue that the integration of these techniques into local, national, and international healthcare systems will save lives, and propose specific methods by which implementation can happen swiftly and efficiently. We offer to extend these resources and knowledge to assist policymakers seeking to implement these techniques.

How can airborne transmission of COVID-19 indoors be minimised?

During the rapid rise in COVID-19 illnesses and deaths globally, and notwithstanding recommended precautions, questions are voiced about routes of transmission for this pandemic disease. Inhaling small airborne droplets is probable as a third route of infection, in addition to more widely recognized transmission via larger respiratory droplets and direct contact with infected people or contaminated surfaces. While uncertainties remain regarding the relative contributions of the different transmission pathways, we argue that existing evidence is sufficiently strong to warrant engineering controls targeting airborne transmission as part of an overall strategy to limit infection risk indoors. Appropriate building engineering controls include sufficient and effective ventilation, possibly enhanced by particle filtration and air disinfection, avoiding air recirculation and avoiding overcrowding. Often, such measures can be easily implemented and without much cost, but if only they are recognised as significant in contributing to infection control goals. We believe that the use of engineering controls in public buildings, including hospitals, shops, offices, schools, kindergartens, libraries, restaurants, cruise ships, elevators, conference rooms or public transport, in parallel with effective application of other controls (including isolation and quarantine, social distancing and hand hygiene), would be an additional important measure globally to reduce the likelihood of transmission and thereby protect healthcare workers, patients and the general public.

Which microbial factors really are important in Pseudomonas aeruginosa infections?

Over the last two decades, tens of millions of dollars have been invested in understanding virulence in the human pathogen, Pseudomonas aeruginosa. However, the top 'hits' obtained in a recent TnSeq analysis aimed at identifying those genes that are conditionally essential for infection did not include most of the known virulence factors identified in these earlier studies. Instead, it seems that P. aeruginosa faces metabolic challenges in vivo, and unless it can overcome these, it fails to thrive and is cleared from the host. In this review, we look at the kinds of metabolic pathways that the pathogen seems to find essential, and comment on how this knowledge might be therapeutically exploited.

Calcium channels in Fc receptor signaling.

The calcium ion (Ca(2+)) is the main common second messenger involved in signaling transduction subsequent to immunoreceptor activation. Its rapid intracellular elevation induces multiple cellular responses, such as secretion, proliferation, mobility, and gene transcription. Intracellular levels of Ca(2+) need to reach a specific threshold to efficiently transduce the signal to activate transcription factors through the recruitment of Ca(2+)-binding molecules. However, since Ca(2+) cannot be metabolized, its intracellular concentration is tightly regulated to avoid the induction of programmed cell death. This highly controlled regulation of Ca(2+) homeostasis has recently been clarified by the uncovering of new ion channels. The regulation of these channels allows the role of Ca(2+) in Fc receptor transduction pathways to be more precisely defined.

The novel inhibitory receptor G6B is expressed on the surface of platelets and attenuates platelet function in vitro.

The G6B cell-surface receptor, which contains a single Ig-like domain, has been shown to bind to SHP-1 and SHP-2 after phosphorylation of 2 immunoreceptor tyrosine-based inhibitory motifs (ITIMs) in its cytoplasmic tail, classifying this protein as a new member of the family of inhibitory receptors. In this study, we demonstrate by real-time polymerase chain reaction (PCR) and Western-blot analysis that G6B is expressed on platelets. Cross-linking of G6B with polyclonal antisera has a significant inhibitory effect on platelet aggregation and activation by agonists such as ADP and collagen-related peptide (CRP). This inhibitory function of G6B appears to operate in a calcium-independent manner. Our results suggest that G6B represents a novel inhibitory receptor found on the surface of platelets and that it could be an antithrombotic drug target.

Cutting edge: TREM-like transcript-1, a platelet immunoreceptor tyrosine-based inhibition motif encoding costimulatory immunoreceptor that enhances, rather than inhibits, calcium signaling via SHP-2.

To date, immunoreceptor tyrosine-based inhibition motifs (ITIMs) have been shown to mediate inhibitory properties. We report a novel triggering receptor expressed on myeloid cells (TREM) family member, TREM-like transcript-1 (TLT1), which differs from the activating members because its cytoplasmic tail contains two ITIMs at Y245 and Y281. A TLT1 splice variant (TLT1sp) encodes a different cytoplasmic tail lacking ITIMs. Both isoforms are expressed in resting platelet alpha-granules, which are up-regulated to the cell surface following activation. TLT1 recruited Src homology 2 domain-containing tyrosine phosphatase (SHP)-2 to the "classical" ITIM (Y281) but not the "nonclassical" ITIM (Y245). In contrast to previously characterized ITIM receptors, TLT1 enhanced, rather than inhibited, FcepsilonRI-mediated calcium signaling in rat basophilic leukemia cells, a property dependent on the SHP-2 recruiting classical Y281 ITIM. Therefore, TLT1 represents a new costimulatory ITIM immunoreceptor and is the second ITIM-bearing receptor to be identified in platelets after platelet endothelial cell adhesion molecule-1.