Diagnosis of Multisystem Inflammatory Syndrome in Children by a Whole-Blood Transcriptional Signature

BACKGROUND

To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections.

METHODS

Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39).

RESULTS

In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV.

CONCLUSIONS

MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.

2351. Systemic Influenza Infection Impairs Whole Blood Control of Mycobacteria in a Human Challenge Study

Background

Of the millions of people infected with Mycobacterium tuberculosis (Mtb) each year, only a small proportion will develop TB disease, with most immunologically containing or clearing Mtb. Factors influencing TB progression risk are incompletely understood. Co-infections, including influenza, have been proposed as a risk factor for TB progression via disruption of anti-Mtb immune responses. We employed a human influenza challenge study to investigate the effect of systemic influenza infection on host mycobacterial control.

Methods

A whole blood (WB) mycobacterial growth inhibition assay was utilised to compare mycobacterial growth and anti-mycobacterial immune responses before and after influenza infection. Thirty adults were per-nasally inoculated with H3N2 influenza virus (Day [D] 0). Influenza PCR assay of D4 nasal swab confirmed infection. WB, collected pre- (D0, “pre-influenza”) and post-inoculation (D6, “post-influenza”), was infected with Mycobacterium bovis Bacille Calmette Guerin (BCG)-lux, incubated for 72 hours (h) and WB mycobacterial growth (growth ratio [GR]) measured. In parallel, BCG-lux-infected and uninfected blood aliquots were incubated for 0, 6, 24 and 72 h and measurements of cytokines (Meso Scale) and gene expression (RNA-Sequencing) undertaken. Comparisons between pre- and post-influenza infection blood samples were made (Fig. 1).

Blood was aliquoted into triplicate for each timepoint (0 hours [h], 72 h), diluted 1:1 with Gibco Roswell Park Memorial Institute 1640 Medium (RPMI), inoculated with a fixed quantity of BCG-lux and incubated at 37°C in a rocker-incubator. Previous experiments had confirmed and quantified the correlation between BCG-lux luminescence (measured in Relative Light Units [RLU] by a luminometer) and number of mycobacterial colony forming units. At 0 h and 72 h, the luminescence of each triplicate sample was measured in duplicate. The growth ratio (GR) was calculated by division of the median 72 h luminescence value by the median 0 h luminescence value. In parallel, BCG-lux inoculum or sterile PBS were added to additional whole blood aliquots (to give infected and uninfected aliquots), which were incubated at 37°C in a rocker-incubator. At timepoints 0 h, 6 h, 24 h, and 72 h, aliquots were removed from the incubator and centrifuged. Supernatants were frozen and stored for future cytokine analyses (quantification using Meso Scale Discovery [MSD] U-plex). Cell pellets were stabilised and stored for future RNA-sequencing and transcriptomic analysis. Figure created with BioRender.com.

Results

In 22 influenza PCR-positive (+) subjects, median GR was significantly higher in post- (1.69) vs pre-influenza samples (1.03, p=0.0016) with no significant difference in the PCR-negative subjects (Fig. 2). Significant differences in BCG-lux-stimulated cytokine production were observed between post- and pre-influenza samples in PCR+ subjects (Fig. 3). Transcriptomic analysis identified significantly differentially expressed genes in the post- vs pre-influenza samples and differences between the groups over time, mapping to pathways related to TB susceptibility.

Complete BCG-lux growth ratio (GR) data were available for 28/30 participants. Comparisons of median GRs pre- and post-influenza infection for (A) 22 participants who were influenza PCR-positive on day 4 and (B) 6 participants who were influenza PCR-negative on day 4. Significance (paired analysis): ** p≤0.01; ns p>0.05. (C) Change in GR from pre-influenza baseline was calculated for each individual (GR-Post divided by GR-Pre, expressed as a percentage) and compared between the influenza PCR-positive and PCR-negative participants. Significance: ** p≤0.01.

Cytokine concentrations were measured in duplicate in supernatants from blood incubated with BCG-lux for 0 hours (h), 6 h, 24 h and 72 h, using Meso Scale Discovery U-plex. Results are available for a subset of 15 participants who had influenza infection confirmed by influenza PCR-positive assay on day 4 nasal swab. Comparisons of median concentrations between pre- and post-influenza samples and individual participants’ paired samples are shown for (A, B) Interleukin-1β (IL-1β) at 24 h; (C, D) Tumour necrosis factor-α (TNF-α) at 6 h; (E, F) Interleukin-10 (IL-10) at 0 h; (G, H) IL-10 at 24 h. Significance (paired analysis): **** p≤0.0001; ** p≤0.01.

Conclusion

Systemic influenza infection reduces whole blood control of mycobacteria through modulation of anti-mycobacterial immune responses. Cytokine differences may contribute. These novel findings suggest that influenza may be a risk factor for TB disease and influenza vaccine could have a non-specific effect on TB immunity.

Disclosures

Christopher W. Woods, MD MPH, Predigen, Inc: Co-founder Christopher Chiu, BMBCh PhD, GlaxoSmithKline: Grant/Research Support|Merck: Grant/Research Support|Reithera: Advisor/Consultant.

RAPD PCR detects co-colonisation of multiple group B streptococcus genotypes: A practical molecular technique for screening multiple colonies

Group B Streptococcus (GBS) is a leading cause of neonatal meningitis, pneumonia, and sepsis. The biggest contributing factor of neonatal infections is due to vertical transmission from maternal colonisation of GBS in the genitourinary tract. Multiple serotype colonisation is often not investigated in epidemiological studies, but it is an important consideration for serotype-based vaccine development and implementation to ensure less abundant serotypes are not under-represented. In this study, we show that RAPD PCR is a quick tool useful in screening the presence of genetically different strains using multiple colony picks from a single patient swab. We observed a maximum of five different GBS strains colonising a single patient at a specific time.

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Co-colonisation of GBS strains is present in pregnant women and infants.

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GBS serotype acquisition and loss is a common occurrence during colonisation.

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RAPD PCR can be used as a screening tool to identify genetically distinct strains.

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Assay may be useful to complement serotyping before sending GBS isolates for further characterisations.

In Search of Lost Springs: A Protocol for Locating Active and Inactive Springs

Groundwater springs are significant landscape features for humans and the biota that occupies their habitat. Many springs become inactive where groundwater exploitation by humans has lowered the water table or artesian pressure. In order to assess this impact, it is important to identify and locate active, and with more difficulty, inactive springs. Using a variety of archival, environmental and field-based data, this study presents a protocol for the determination of the location and status of springs across the Great Artesian Basin of Australia. This protocol underpins a database of springs, which is not only important for the assessment of spring ecosystems, but also contributes to understand groundwater extraction impacts and hydrogeological processes. The database indicates that 30.0% of discharge (artesian) springs in the Great Artesian Basin are entirely inactive and another 11.8% are partially inactive. For the outcrop (gravity) springs of the Basin, only 1.9% are entirely inactive and 7.4% partially inactive, and for the outcrop springs in the Tertiary sandstone overlying the Basin 30.9% are inactive and 18.2% are partially inactive.

Revision of the Sheehan total knee arthroplasty

The use of the Sheehan knee prosthesis extended from 1971 to 2002. It incorporated a semi-constrained hinge with intra-medullary stems cemented into the femur and tibia. While some authors have reported excellent short-term results, others have reported revision rates of up to 31% at 5--10 years. The aim of this study was to review the senior author's experience in revising these arthroplasties. We review 54 Sheehan total knee replacements and discuss the difficulties encountered during first and subsequent revisions and the often-complex reconstruction techniques used to overcome these.

Orbital cellulitis and sinusitis caused by group B beta streptococcus in a 3-year-old child

This paper reports a case in which an odontogenic infection extended into a maxillary sinus in an otherwise well 3-year-old child. The resulting sinusitis and orbital cellulitis were caused by an organism which does not usually cause disease in this age group, group B β-hemolytic streptococcus.