An AI-guided invariant signature places MIS-C with Kawasaki disease in a continuum of host immune responses

A significant surge in cases of multisystem inflammatory syndrome in children (MIS-C, also called Pediatric Inflammatory Multisystem Syndrome - PIMS) has been observed amidst the COVID-19 pandemic. MIS-C shares many clinical features with Kawasaki disease (KD), although clinical course and outcomes are divergent. We analyzed whole blood RNA sequences, serum cytokines, and formalin fixed heart tissues from these patients using a computational toolbox of two gene signatures, i.e., the 166-gene viral pandemic (ViP) signature, and its 20-gene severe (s)ViP subset that were developed in the context of SARS-CoV-2 infection and a 13-transcript signature previously demonstrated to be diagnostic for KD. Our analyses revealed that KD and MIS-C are on the same continuum of the host immune response as COVID-19 but diverge with two different cardiac phenotypes. The ViP signatures helped unravel the nature of the host immune response (IL15-centric) in MIS-C and KD, reveal unique targetable cytokine pathways in MIS-C, place MIS-C farther along in the spectrum in severity compared to KD and pinpoint key clinical (reduced cardiac function) and laboratory (thrombocytopenia and eosinopenia) parameters that can be useful to monitor severity.

Identification of a Minimal 3-Transcript Signature to Differentiate Viral from Bacterial Infection from Best Genome-Wide Host RNA Biomarkers: A Multi-Cohort Analysis

The fight against the spread of antibiotic resistance is one of the most important challenges facing health systems worldwide. Given the limitations of current diagnostic methods, the development of fast and accurate tests for the diagnosis of viral and bacterial infections would improve patient management and treatment, as well as contribute to reducing antibiotic misuse in clinical settings. In this scenario, analysis of host transcriptomics constitutes a promising target to develop new diagnostic tests based on the host-specific response to infections. We carried out a multi-cohort meta-analysis of blood transcriptomic data available in public databases, including 11 different studies and 1209 samples from virus- (n = 695) and bacteria- (n = 514) infected patients. We applied a Parallel Regularized Regression Model Search (PReMS) on a set of previously reported genes that distinguished viral from bacterial infection to find a minimum gene expression bio-signature. This strategy allowed us to detect three genes, namely BAFT, ISG15 and DNMT1, that clearly differentiate groups of infection with high accuracy (training set: area under the curve (AUC) 0.86 (sensitivity: 0.81; specificity: 0.87); testing set: AUC 0.87 (sensitivity: 0.82; specificity: 0.86)). BAFT and ISG15 are involved in processes related to immune response, while DNMT1 is related to the preservation of methylation patterns, and its expression is modulated by pathogen infections. We successfully tested this three-transcript signature in the 11 independent studies, demonstrating its high performance under different scenarios. The main advantage of this three-gene signature is the low number of genes needed to differentiate both groups of patient categories.

Identification of Reduced Host Transcriptomic Signatures for Tuberculosis Disease and Digital PCR-Based Validation and Quantification

Recently, host whole blood gene expression signatures have been identified for diagnosis of tuberculosis (TB). Absolute quantification of the concentrations of signature transcripts in blood have not been reported, but would facilitate diagnostic test development. To identify minimal transcript signatures, we applied a transcript selection procedure to microarray data from African adults comprising 536 patients with TB, other diseases (OD) and latent TB (LTBI), divided into training and test sets. Signatures were further investigated using reverse transcriptase (RT)—digital PCR (dPCR). A four-transcript signature (GBP6, TMCC1, PRDM1, and ARG1) measured using RT-dPCR distinguished TB patients from those with OD (area under the curve (AUC) 93.8% (CI95% 82.2–100%). A three-transcript signature (FCGR1A, ZNF296, and C1QB) differentiated TB from LTBI (AUC 97.3%, CI95%: 93.3–100%), regardless of HIV. These signatures have been validated across platforms and across samples offering strong, quantitative support for their use as diagnostic biomarkers for TB.

Host Transcriptomic Response Following Administration of Rotavirus Vaccine in Infants’ Mimics Wild Type Infection

BackgroundRotavirus (RV) is an enteric pathogen that has devastating impact on childhood morbidity and mortality worldwide. The immunologic mechanism underlying the protection achieved after RV vaccination is not yet fully understood.MethodsWe compared the transcriptome of children affected by community-acquired RV infection and children immunized with a live attenuated RV vaccine (RotaTeq®).ResultsRV vaccination mimics the wild type infection causing similar changes in children’s transcriptome, including transcripts associated with cell cycle, diarrhea, nausea, vomiting, intussusception, and abnormal morphology of midgut. A machine learning approach allowed to detect a combination of nine-transcripts that differentiates vaccinated from convalescent-naturally infected children (AUC: 90%; 95%CI: 70–100) and distinguishes between acute-infected and healthy control children (in both cases, AUC: 100%; 95%CI: 100–100). We identified a miRNA hsa-mir-149 that seems to play a role in the host defense against viral pathogens and may have an antiviral role.DiscussionOur findings might shed further light in the understanding of RV infection, its functional link to intussusception causes, as well as guide development of antiviral treatments and safer and more effective vaccines. The nine-transcript signature may constitute a marker of vaccine protection and helps to differentiate vaccinated from naturally infected or susceptible children.

Long-term epilepsy-associated tumors: transcriptional signatures reflect clinical course

Long-term epilepsy-associated tumors (LEATs) represent mostly benign brain tumors associated with drug-resistant epilepsy. The aim of the study was to investigate the specific transcriptional signatures of those tumors and characterize their underlying oncogenic drivers. A cluster analysis of 65 transcriptome profiles from three independent datasets resulted in four distinct transcriptional subgroups. The first subgroup revealed transcriptional activation of STAT3 and TGF-signaling pathways and contained predominantly dysembryoplastic neuroepithelial tumors (DNTs). The second subgroup was characterized by alterations in the MAPK-pathway and up-stream cascades including FGFR and EGFR-mediated signaling. This tumor cluster exclusively contained neoplasms with somatic BRAFV600E mutations and abundance of gangliogliomas (GGs) with a significantly higher recurrence rate (42%). This finding was validated by examining recurrent tumors from the local database exhibiting BRAFV600E in 90% of the cases. The third cluster included younger patients with neuropathologically diagnosed GGs and abundance of the NOTCH- and mTOR-signaling pathways. The transcript signature of the fourth cluster (including both DNTs and GGs) was related to impaired neural function. Our analysis suggests distinct oncological pathomechanisms in long-term epilepsy-associated tumors. Transcriptional activation of MAPK-pathway and BRAFV600E mutation are associated with an increased risk for tumor recurrence and malignant progression, therefore the treatment of these tumors should integrate both epileptological and oncological aspects.

“Salicylic Acid Mutant Collection” as a Tool to Explore the Role of Salicylic Acid in Regulation of Plant Growth under a Changing Environment

The phytohormone salicylic acid (SA) has a crucial role in plant physiology. Its role is best described in the context of plant response to pathogen attack. During infection, SA is rapidly accumulated throughout the green tissues and is important for both local and systemic defences. However, some genetic/metabolic variations can also result in SA overaccumulation in plants, even in basal conditions. To date, more than forty Arabidopsis thaliana mutants have been described as having enhanced endogenous SA levels or constitutively activated SA signalling pathways. In this study, we established a collection of mutants containing different SA levels due to diverse genetic modifications and distinct gene functions. We chose prototypic SA-overaccumulators (SA-OAs), such as bon1-1, but also “non-typical” ones such as exo70b1-1; the selection of OA is accompanied by their crosses with SA-deficient lines. Here, we extensively studied the plant development and SA level/signalling under various growth conditions in soil and in vitro, and showed a strong negative correlation between rosette size, SA content and PR1/ICS1 transcript signature. SA-OAs (namely cpr5, acd6, bon1-1, fah1/fah2 and pi4kβ1β2) had bigger rosettes under high light conditions, whereas WT plants did not. Our data provide new insights clarifying a link between SA and plant behaviour under environmental stresses. The presented SA mutant collection is thus a suitable tool to shed light on the mechanisms underlying trade-offs between growth and defence in plants.