Shared IgG Infection Signatures vs. Hemorrhage-Restricted IgA Clusters in Human Dengue: A Phenotype of Differential Class-Switch via TGFβ1

The dengue-specific immune response and antibody identification with machine learning

Dengue virus poses a serious threat to global health and there is no specific therapeutic for it. Broadly neutralizing antibodies recognizing all serotypes may be an effective treatment. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) and bioinformatic analysis enable in-depth understanding of the B-cell immune response. Here, we investigate the dengue antibody response with these technologies and apply machine learning to identify rare and underrepresented broadly neutralizing antibody sequences. Dengue immunization elicited the following signatures on the antibody repertoire: (i) an increase of CDR3 and germline gene diversity; (ii) a change in the antibody repertoire architecture by eliciting power-law network distributions and CDR3 enrichment in polar amino acids; (iii) an increase in the expression of JNK/Fos transcription factors and ribosomal proteins. Furthermore, we demonstrate the applicability of computational methods and machine learning to AIRR-seq datasets for neutralizing antibody candidate sequence identification. Antibody expression and functional assays have validated the obtained results.

Immunoglobulin M seroneutralization for improved confirmation of Japanese encephalitis virus infection in a flavivirus-endemic area

BACKGROUND

The mainstay of diagnostic confirmation of acute Japanese encephalitis (JE) involves detection of anti-JE virus (JEV) immunoglobulin M (IgM) by enzyme-linked immunosorbent assay (ELISA). Limitations in the specificity of this test are increasingly apparent with the introduction of JEV vaccinations and the endemicity of other cross-reactive flaviviruses. Virus neutralization testing (VNT) is considered the gold standard, but it is challenging to implement and interpret. We performed a pilot study to assess IgG depletion prior to VNT for detection of anti-JEV IgM neutralizing antibodies (IgM-VNT) as compared with standard VNT.

METHODS

We evaluated IgM-VNT in paired sera from anti-JEV IgM ELISA-positive patients (JE n=35) and negative controls of healthy flavivirus-naïve (n=10) as well as confirmed dengue (n=12) and Zika virus (n=4) patient sera. IgM-VNT was subsequently performed on single sera from additional JE patients (n=76).

RESULTS

Anti-JEV IgG was detectable in admission serum of 58% of JE patients. The positive, negative and overall percentage agreement of IgM-VNT as compared with standard VNT was 100%. A total of 12/14 (86%) patient samples were unclassified by VNT and, with sufficient sample available for IgG depletion and IgG ELISA confirming depletion, were classified by IgM-VNT. IgM-VNT enabled JE case classification in 72/76 (95%) patients for whom only a single sample was available.

CONCLUSIONS

The novel approach has been readily adapted for high-throughput testing of single patient samples and it holds promise for incorporation into algorithms for use in reference centres.

Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences

Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design.

Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics

Dengue virus (DENV) poses a serious threat to global health as the causative agent of dengue fever. The virus is endemic in more than 128 countries resulting in approximately 390 million infection cases each year. Currently, there is no approved therapeutic for treatment nor a fully efficacious vaccine. The development of therapeutics is confounded and hampered by the complexity of the immune response to DENV, in particular to sequential infection with different DENV serotypes (DENV1-5). Researchers have shown that the DENV envelope (E) antigen is primarily responsible for the interaction and subsequent invasion of host cells for all serotypes and can elicit neutralizing antibodies in humans. The advent of high-throughput sequencing and the rapid advancements in computational analysis of complex data, has provided tools for the deconvolution of the DENV immune response. Several types of complex statistical analyses, machine learning models and complex visualizations can be applied to begin answering questions about the B- and T-cell immune responses to multiple infections, antibody-dependent enhancement, identification of novel therapeutics and advance vaccine research.

A need to raise the bar - A systematic review of temporal trends in diagnostics for Japanese encephalitis virus infection, and perspectives for future research

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Japanese encephalitis virus (JEV) remains a leading cause of neurological infection in Asia.

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A systematic review identified 20,212 published human cases of laboratory-confirmed JEV infections from 205 studies.

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15,167 (75%) of cases were confirmed with the lowest confidence diagnostic test, i.e., level 3 or 4, or level 4.

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Only 109 (53%) of the studies reported contemporaneous testing for dengue-specific antibodies.

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A fundamental pre-requisite for the control of JE is lacking — that of a simple and specific diagnostic procedure that can be adapted for point-of-care tests and readily used throughout JE endemic regions of the world.

Objective

Japanese encephalitis virus infection (JE) remains a leading cause of neurological disease in Asia, mainly involving individuals living in remote areas with limited access to treatment centers and diagnostic facilities. Laboratory confirmation is fundamental for the justification and implementation of vaccination programs. We reviewed the literature on historical developments and current diagnostic capability worldwide, to identify knowledge gaps and instill urgency to address them.

Methods

Searches were performed in Web of Science and PubMed using the term 'Japanese encephalitis' up to 13th October 2019. Studies reporting laboratory-confirmed symptomatic JE cases in humans were included, and data on details of diagnostic tests were extracted. A JE case was classified according to confirmatory levels (Fischer et al., 2008; Campbell et al., 2011; Pearce et al., 2018; Heffelfinger et al., 2017), where level 1 represented the highest level of confidence.

Findings

20,212 published JE cases were identified from 205 studies. 15,167 (75%) of these positive cases were confirmed with the lowest-confidence diagnostic tests (level 3 or 4, or level 4). Only 109 (53%) of the studies reported contemporaneous testing for dengue-specific antibodies.

Conclusion

A fundamental pre-requisite for the control of JEV is lacking — that of a simple and specific diagnostic procedure that can be adapted for point-of-care tests and readily used throughout JE-endemic regions of the world.

Regulation of TGF-β1 on PI3KC3 and its role in hypertension-induced vascular injuries

The aim of the present study was to investigate the expression and role of transforming growth factor (TGF)-β1/phosphatidylinositol 3-kinase catalytic subunit type 3 (PI3KC3) in the peripheral blood in patients with hypertension. A total of 28 patients with primary hypertension and 20 healthy control subjects were included. Peripheral blood samples were collected. The mRNA and protein expression levels were detected by reverse transcription-quantitative polymerase chain reaction and western blot analysis, respectively. Cell counting kit-8 assay, Transwell chamber assay and flow cytometry were performed to detect the cell proliferation, migration ability and cellular apoptosis, respectively. Laser scanning confocal microscopy was used to detect the intracellular autophagosomes. The expression of TGF-β1 was significantly elevated, whereas the expression of PI3KC3 was significantly downregulated in the patients with hypertension compared with controls. There was negative correlation between the TGF-β1 and PI3KC3 expression. Following treatment with TGF-β1, the protein expression of PI3KC3 was significantly decreased in human umbilical vein endothelial cells (HUVECs), and the autophagic activity was significantly decreased. Furthermore, following the treatment of TGF-β1 the proliferation of HUVECs was significantly reduced in the HUVECs, the hypoxia-induced apoptosis rates were significantly elevated and the number of penetrating cells were significantly declined (indicating declined migration ability). However, the overexpression of PI3KC3 significantly ameliorated the proliferation, migration ability and hypoxia tolerance of TGF-β1-treated HUVECs. In conclusion, the present results indicated that TGF-β1 expression was elevated in the peripheral blood in hypertensive patients and negatively correlated with the PI3KC3 expression; and that TGF-β1 regulates the PI3KC3 signaling pathway to inhibit the autophagic activity of vascular endothelial cells, and regulate the cell proliferation, migration and anti-apoptosis ability, thus aggregating the endothelial cell injuries in hypertension. The results of the current study revealed a novel mechanism of TGF-β1 in the regulation of endothelial cell injury in hypertension, which may provide a potential target for disease therapy.