Spatiotemporal Virus Surveillance for Severe Acute Respiratory Infections in Resource-limited Settings: How Deep Need We Go?

Metagenomic next-generation sequencing to characterize potential etiologies of non-malarial fever in a cohort living in a high malaria burden area of Uganda

Causes of non-malarial fevers in sub-Saharan Africa remain understudied. We hypothesized that metagenomic next-generation sequencing (mNGS), which allows for broad genomic-level detection of infectious agents in a biological sample, can systematically identify potential causes of non-malarial fevers. The 212 participants in this study were of all ages and were enrolled in a longitudinal malaria cohort in eastern Uganda. Between December 2020 and August 2021, respiratory swabs and plasma samples were collected at 313 study visits where participants presented with fever and were negative for malaria by microscopy. Samples were analyzed using CZ ID, a web-based platform for microbial detection in mNGS data. Overall, viral pathogens were detected at 123 of 313 visits (39%). SARS-CoV-2 was detected at 11 visits, from which full viral genomes were recovered from nine. Other prevalent viruses included Influenza A (14 visits), RSV (12 visits), and three of the four strains of seasonal coronaviruses (6 visits). Notably, 11 influenza cases occurred between May and July 2021, coinciding with when the Delta variant of SARS-CoV-2 was circulating in this population. The primary limitation of this study is that we were unable to estimate the contribution of bacterial microbes to non-malarial fevers, due to the difficulty of distinguishing bacterial microbes that were pathogenic from those that were commensal or contaminants. These results revealed the co-circulation of multiple viral pathogens likely associated with fever in the cohort during this time period. This study illustrates the utility of mNGS in elucidating the multiple potential causes of non-malarial febrile illness. A better understanding of the pathogen landscape in different settings and age groups could aid in informing diagnostics, case management, and public health surveillance systems.

Epidemiology of Severe Acute Respiratory Syndrome Coronavirus 2 Emergence Amidst Community-Acquired Respiratory Viruses

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in China as the cause of coronavirus disease 2019 in December 2019 and reached Europe by late January 2020, when community-acquired respiratory viruses (CARVs) are at their annual peak. We validated the World Health Organization (WHO)-recommended SARS-CoV-2 assay and analyzed the epidemiology of SARS-CoV-2 and CARVs.

METHODS

Nasopharyngeal/oropharyngeal swabs (NOPS) from 7663 patients were prospectively tested by the Basel S-gene and WHO-based E-gene (Roche) assays in parallel using the Basel N-gene assay for confirmation. CARVs were prospectively tested in 2394 NOPS by multiplex nucleic acid testing, including 1816 (75%) simultaneously for SARS-CoV-2.

RESULTS

The Basel S-gene and Roche E-gene assays were concordant in 7475 cases (97.5%) including 825 (11%) SARS-CoV-2 positives. In 188 (2.5%) discordant cases, SARS-CoV-2 loads were significantly lower than in concordant positive ones and confirmed in 105 (1.4%). Adults were more frequently SARS-CoV-2 positive, whereas children tested more frequently CARV positive. CARV coinfections with SARS-CoV-2 occurred in 1.8%. SARS-CoV-2 replaced CARVs within 3 weeks, reaching 48% of all detected respiratory viruses followed by rhinovirus/enterovirus (13%), influenza virus (12%), coronavirus (9%), respiratory syncytial virus (6%), and metapneumovirus (6%).

CONCLUSIONS

Winter CARVs were dominant during the early SARS-CoV-2 pandemic, impacting infection control and treatment decisions, but were rapidly replaced, suggesting competitive infection. We hypothesize that preexisting immune memory and innate immune interference contribute to the different SARS-CoV-2 epidemiology among adults and children.

BK Polyomavirus Evades Innate Immune Sensing by Disrupting the Mitochondrial Network and Promotes Mitophagy

Immune escape contributes to viral persistence, yet little is known about human polyomaviruses. BK-polyomavirus (BKPyV) asymptomatically infects 90% of humans but causes premature allograft failure in kidney transplant patients. Despite virus-specific T cells and neutralizing antibodies, BKPyV persists in kidneys and evades immune control as evidenced by urinary shedding in immunocompetent individuals. Here, we report that BKPyV disrupts the mitochondrial network and membrane potential when expressing the 66aa-long agnoprotein during late replication. Agnoprotein is necessary and sufficient, using its amino-terminal and central domain for mitochondrial targeting and network disruption, respectively. Agnoprotein impairs nuclear IRF3-translocation, interferon-beta expression, and promotes p62/SQSTM1-mitophagy. Agnoprotein-mutant viruses unable to disrupt mitochondria show reduced replication and increased interferon-beta expression but can be rescued by type-I interferon blockade, TBK1-inhibition, or CoCl₂-treatment. Mitochondrial fragmentation and p62/SQSTM1-autophagy occur in allograft biopsies of kidney transplant patients with BKPyV nephropathy. JCPyV and SV40 infection similarly disrupt mitochondrial networks, indicating a conserved mechanism facilitating polyomavirus persistence and post-transplant disease.

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BK polyomavirus agnoprotein disrupts mitochondrial membrane potential and network

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Agnoprotein impairs nucleus IRF3 translocation and interferon-β expression

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Agnoprotein facilitates innate immune evasion during the late viral replication phase

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Damaged mitochondria are targeted for p62/SQSTM1 autophagy

Community-Acquired Respiratory Viruses in Transplant Patients: Diversity, Impact, Unmet Clinical Needs

Patients undergoing solid-organ transplantation (SOT) or allogeneic hematopoietic cell transplantation (HCT) are at increased risk for infectious complications. Community-acquired respiratory viruses (CARVs) pose a particular challenge due to the frequent exposure pre-, peri-, and posttransplantation. Although influenza A and B viruses have a top priority regarding prevention and treatment, recent molecular diagnostic tests detecting an array of other CARVs in real time have dramatically expanded our knowledge about the epidemiology, diversity, and impact of CARV infections in the general population and in allogeneic HCT and SOT patients. These data have demonstrated that non-influenza CARVs independently contribute to morbidity and mortality of transplant patients. However, effective vaccination and antiviral treatment is only emerging for non-influenza CARVs, placing emphasis on infection control and supportive measures. Here, we review the current knowledge about CARVs in SOT and allogeneic HCT patients to better define the magnitude of this unmet clinical need and to discuss some of the lessons learned from human influenza virus, respiratory syncytial virus, parainfluenzavirus, rhinovirus, coronavirus, adenovirus, and bocavirus regarding diagnosis, prevention, and treatment.

Comparing the Cobas Liat Influenza A/B and respiratory syncytial virus assay with multiplex nucleic acid testing

Influenza virus and respiratory syncytial virus (RSV) detection with short turn‐around‐time (TAT) is pivotal for rapid decisions regarding treatment and infection control. However, negative rapid testing results may come from poor assay sensitivity or from influenza‐like illnesses caused by other community‐acquired respiratory viruses (CARVs). We prospectively compared the performance of Cobas Liat Influenza A/B and RSV assay (LIAT) with our routine multiplexNAT‐1 (xTAG Respiratory Pathogen Panel; Luminex) and multiplexNAT‐2 (ePlex‐RPP; GenMark Diagnostics) using 194 consecutive nasopharyngeal swabs from patients with influenza‐like illness during winter 2017/2018. Discordant results were reanalyzed by specific in‐house quantitative nucleic acid amplification testing (NAT). LIAT was positive for influenza virus‐A, ‐B, and RSV in 18 (9.3%), 13 (6.7%), and 55 (28.4%) samples, and negative in 108 samples. Other CARVs were detected by multiplexNAT in 66 (34.0%) samples. Concordant results for influenza and RSV were seen in 190 (97.9%), discordant results in 4 (2.1%), which showed low‐level RSV (<40 000 copies/mL). Sensitivity and specificity of LIAT for influenza‐A, ‐B, and RSV were 100%, 100% and 100%, and 100%, 99.5% and 100%, respectively. The average TAT of LIAT was 20 minutes compared to 6 hours and 2 hours for the multiplexNAT‐1 and ‐2, respectively. Thus, LIAT demonstrated excellent sensitivity and specificity for influenza and RSV, which together with the simple sample processing and short TAT renders this assay suitable for near‐patient testing.

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Cobas Liat is highly sensitive and specific for Influenza and RSV in 86/194 children

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Other CARVs were detected by multiplex NAT in 66/196 children

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Simple processing and TAT of 20 min suitable for near‐patient testing

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LIAT assay shows excellent sensitivity and specificity for influenza and RSV

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Simple sample processing and short turn‐around‐time of 20 min render the assay suitable for near‐patient testing

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Barcode reading and direct transfer of results into the laboratory information system

Spatial infectious disease epidemiology: on the cusp

Infectious diseases continue to pose a significant public health burden despite the great progress achieved in their prevention and control over the last few decades. Our ability to disentangle the factors and mechanisms driving their propagation in space and time has dramatically advanced in recent years. The current era is rich in mathematical and computational tools and detailed geospatial information, including sociodemographic, geographic, and environmental data, which are essential to elucidate key drivers of infectious disease transmission from epidemiological and genetic data. Indeed, this paradigm shift was driven by dramatic advances in complex systems approaches along with substantial improvements in data availability and computational power. The burgeoning output of infectious disease spatial modeling suggests that we are close to a fully integrated approach for early epidemic detection and intervention. This special collection in BMC Medicine aims to bring together a broad range of quantitative investigations that improve our understanding of the spatiotemporal transmission dynamics of infectious diseases in order to mitigate their impact on the human population.