The antigenic landscape of human influenza N2 neuraminidases from 2009 until 2017.

Human H3N2 influenza viruses are subject to rapid antigenic evolution which translates into frequent updates of the composition of seasonal influenza vaccines. Despite these updates, the effectiveness of influenza vaccines against H3N2-associated disease is suboptimal. Seasonal influenza vaccines primarily induce hemagglutinin-specific antibody responses. However, antibodies directed against influenza neuraminidase (NA) also contribute to protection. Here, we analysed the antigenic diversity of a panel of N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. The antigenic breadth of these NAs was determined based on the NA inhibition (NAI) of a broad panel of ferret and mouse immune sera that were raised by infection and recombinant N2 NA immunisation. This assessment allowed us to distinguish at least four antigenic groups in the N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. Computational analysis further revealed that the amino acid residues in N2 NA that have a major impact on susceptibility to NAI by immune sera are in proximity of the catalytic site. Finally, a machine learning method was developed that allowed to accurately predict the impact of mutations that are present in our N2 NA panel on NAI. These findings have important implications for the renewed interest to develop improved influenza vaccines based on the inclusion of a protective NA antigen formulation.

Two proteins, the hemagglutinin and the neuraminidase, protrude from the surface of the influenza virus. Their detection by the immune system allows the host organism to mount defences against the viral threat. The virus evolves in response to this pressure, which manifests as changes in the appearance of its hemagglutinin and neuraminidase. This process, known as antigenic drift, leads to the proteins evading detection. It is also why flu vaccines require frequent updates, as they rely on 'training' the immune system to recognise the most important strains in circulation ­ primarily by exposing it to appropriate versions of hemagglutinin. While the antigenic drift of hemagglutinin has been extensively studied, much less is known about how the neuraminidase accumulates mutations, and how these affect the immune response. To investigate this question, Catani et al. selected 43 genetically distant neuraminidases from human viral samples isolated between 2009 and 2017. Statistical analyses were applied to define their relatedness, revealing that a group of closely related neuraminidases predominated from 2009 to 2015, before they were being taken over by a second group. A third group, which was identified in viruses isolated in 2013, was remarkably close to the neuraminidase of strains that circulated in the late 1990s. The fourth and final group of neuraminidases was derived from influenza viruses that normally circulate in pigs but can also occasionally infect humans. Next, Catani et al. examined the immune response that these 43 neuraminidases could elicit in mice, as well as in ferrets ­ the animal most traditionally used in influenza research. This allowed them to pinpoint which changes in the neuraminidase sequences were important to escape recognition by the host. Data obtained from the two model species were comparable, suggesting that these experiments could be conducted on mice going forward, which are easier to work with than ferrets. Finally, Catani et al. used machine learning to build a computational model that could predict how strongly the immune system would respond to a specific neuraminidase variant. These findings could help guide the development of new vaccines that include neuraminidases tailored to best prime and train the immune system against a larger variety of strains. This may aid the development of 'supra-seasonal' vaccines that protect against a broad range of influenza viruses, reducing the need for yearly updates.

SNPeffect 5.0: large-scale structural phenotyping of protein coding variants extracted from next-generation sequencing data using AlphaFold models.

BACKGROUND: Next-generation sequencing technologies yield large numbers of genetic alterations, of which a subset are missense variants that alter an amino acid in the protein product. These variants can have a potentially destabilizing effect leading to an increased risk of misfolding and aggregation. Multiple software tools exist to predict the effect of single-nucleotide variants on proteins, however, a pipeline integrating these tools while starting from an NGS data output list of variants is lacking. RESULTS: The previous version SNPeffect 4.0 (De Baets in Nucleic Acids Res 40(D1):D935-D939, 2011) provided an online database containing pre-calculated variant effects and low-throughput custom variant analysis. Here, we built an automated and parallelized pipeline that analyzes the impact of missense variants on the aggregation propensity and structural stability of proteins starting from the Variant Call Format as input. The pipeline incorporates the AlphaFold Protein Structure Database to achieve high coverage for structural stability analyses using the FoldX force field. The effect on aggregation-propensity is analyzed using the established predictors TANGO and WALTZ. The pipeline focuses solely on the human proteome and can be used to analyze proteome stability/damage in a given sample based on sequencing results. CONCLUSION: We provide a bioinformatics pipeline that allows structural phenotyping from sequencing data using established stability and aggregation predictors including FoldX, TANGO, and WALTZ; and structural proteome coverage provided by the AlphaFold database. The pipeline and installation guide are freely available for academic users on https://github.com/vibbits/snpeffect and requires a computer cluster.

Identification and preliminary characterization of a putative C-type lectin receptor-like protein in the T. cruzi tomato lectin endocytic-enriched proteome.

Trypanosoma cruzi, the etiological agent of the Chagas' disease in Latin America undergoes a complex life cycle involving two hosts, a mammalian host and a reduviid insect vector (triatomine). In the insect midgut the parasite multiplies as epimastigote forms, which rely on endocytosis for their energy requirement. We recently showed that posttranslational modification of endocytic N-glycoproteins by tomato lectin (TL) binding-N-glycans is crucial for receptor-mediated endocytosis (RME) in epimastigote forms. In an attempt to characterize the endocytic proteome we used a TL affinity chromatography, which significantly enriched glycoproteins of the trypanosomal endocytic pathway. In addition to various lysosomal hydrolases, we found an endosomal C-type lectin-like protein, which displays some structural and topological characteristics of the mammalian lectin receptor superfamily. This lectin encoding a large transmembrane protein of around 375kDa contained three putative extracellular N-terminal C-type lectin domains (CTLD) and located inside the flagellar pocket (FP)/cytostome and endosomal compartments of the insect stage of the parasite and on the surface of the plasma membrane of intracellular amastigote parasites. Noteworthy, this endogenous lectin displayed similar sugar-binding specificity to that of TL and therefore could be important in either the N-glycan mediated endocytosis or parasite adhesion to host cells. We postulated that during the evolution of trypanosomatids, genes encoding lectin harboring 3 CTDLs represent an old acquisition present in free-living, monoxenic and heteroxenic trypanosomatids, which would have been secondarily lost in extracellular parasites from the T. brucei clade.

In vitro metacyclogenesis of Trypanosoma cruzi induced by starvation correlates with a transient adenylyl cyclase stimulation as well as with a constitutive upregulation of adenylyl cyclase expression.

The Trypanosoma cruzi adenylyl cyclase (AC) multigene family encodes different isoforms (around 15) sharing a variable large N-terminal domain, which is extracellular and receptor-like, followed by a transmembrane helix and a conserved C-terminal catalytic domain. It was proposed that these key enzymes in the cAMP signalling pathway allow the parasite to sense its changing extracellular milieu in order to rapidly adapt to its new environment, which is generally achieved through a differentiation process. One of the critical differentiation events the parasitic protozoan T. cruzi undergoes during its life cycle, known as metacyclogenesis, occurs in the digestive tract of the insect and corresponds to the differentiation from noninfective epimastigotes to infective metacyclic trypomastigote forms. By in vitro monitoring the activity of AC during metacyclogenesis, we showed that both the activity of AC and the intracellular cAMP content follow a similar pattern of transient stimulation in a two-step process, with a first activation peak occurring during the first hours of nutritional stress and a second peak between 6 and 48 h, corresponding to the cellular adhesion. During this differentiation process, a general mechanism of upregulation of AC expression of both mRNA and protein is triggered and in particular for a major subclass of these enzymes that are present in various gene copies commonly associated to the THT gene clusters. Although the scattered genome distribution of these gene copies is rather unusual in trypanosomatids and seems to be a recent acquisition in the evolution of the T. cruzi clade, their encoded product redistributed on the flagellum of the parasite upon differentiation could be important to sense the extracellular milieu.

RNA sequencing identifies dysregulation of the human pancreatic islet transcriptome by the saturated fatty acid palmitate.

Pancreatic ß-cell dysfunction and death are central in the pathogenesis of type 2 diabetes (T2D). Saturated fatty acids cause ß-cell failure and contribute to diabetes development in genetically predisposed individuals. Here we used RNA sequencing to map transcripts expressed in five palmitate-treated human islet preparations, observing 1,325 modified genes. Palmitate induced fatty acid metabolism and endoplasmic reticulum (ER) stress. Functional studies identified novel mediators of adaptive ER stress signaling. Palmitate modified genes regulating ubiquitin and proteasome function, autophagy, and apoptosis. Inhibition of autophagic flux and lysosome function contributed to lipotoxicity. Palmitate inhibited transcription factors controlling ß-cell phenotype, including PAX4 and GATA6. Fifty-nine T2D candidate genes were expressed in human islets, and 11 were modified by palmitate. Palmitate modified expression of 17 splicing factors and shifted alternative splicing of 3,525 transcripts. Ingenuity Pathway Analysis of modified transcripts and genes confirmed that top changed functions related to cell death. Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis of transcription factor binding sites in palmitate-modified transcripts revealed a role for PAX4, GATA, and the ER stress response regulators XBP1 and ATF6. This human islet transcriptome study identified novel mechanisms of palmitate-induced ß-cell dysfunction and death. The data point to cross talk between metabolic stress and candidate genes at the ß-cell level.

The human pancreatic islet transcriptome: expression of candidate genes for type 1 diabetes and the impact of pro-inflammatory cytokines.

Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic beta cells are killed by infiltrating immune cells and by cytokines released by these cells. Signaling events occurring in the pancreatic beta cells are decisive for their survival or death in diabetes. We have used RNA sequencing (RNA-seq) to identify transcripts, including splice variants, expressed in human islets of Langerhans under control conditions or following exposure to the pro-inflammatory cytokines interleukin-1ß (IL-1ß) and interferon-γ (IFN-γ). Based on this unique dataset, we examined whether putative candidate genes for T1D, previously identified by GWAS, are expressed in human islets. A total of 29,776 transcripts were identified as expressed in human islets. Expression of around 20% of these transcripts was modified by pro-inflammatory cytokines, including apoptosis- and inflammation-related genes. Chemokines were among the transcripts most modified by cytokines, a finding confirmed at the protein level by ELISA. Interestingly, 35% of the genes expressed in human islets undergo alternative splicing as annotated in RefSeq, and cytokines caused substantial changes in spliced transcripts. Nova1, previously considered a brain-specific regulator of mRNA splicing, is expressed in islets and its knockdown modified splicing. 25/41 of the candidate genes for T1D are expressed in islets, and cytokines modified expression of several of these transcripts. The present study doubles the number of known genes expressed in human islets and shows that cytokines modify alternative splicing in human islet cells. Importantly, it indicates that more than half of the known T1D candidate genes are expressed in human islets. This, and the production of a large number of chemokines and cytokines by cytokine-exposed islets, reinforces the concept of a dialog between pancreatic islets and the immune system in T1D. This dialog is modulated by candidate genes for the disease at both the immune system and beta cell level.

methBLAST and methPrimerDB: web-tools for PCR based methylation analysis.

BACKGROUND: DNA methylation plays an important role in development and tumorigenesis by epigenetic modification and silencing of critical genes. The development of PCR-based methylation assays on bisulphite modified DNA heralded a breakthrough in speed and sensitivity for gene methylation analysis. Despite this technological advancement, these approaches require a cumbersome gene by gene primer design and experimental validation. Bisulphite DNA modification results in sequence alterations (all unmethylated cytosines are converted into uracils) and a general sequence complexity reduction as cytosines become underrepresented. Consequently, standard BLAST sequence homology searches cannot be applied to search for specific methylation primers. RESULTS: To address this problem we developed methBLAST, a sequence similarity search program, based on the original BLAST algorithm but querying in silico bisulphite modified genome sequences to evaluate oligonucleotide sequence similarities. Apart from the primer specificity analysis tool, we have also developed a public database termed methPrimerDB for the storage and retrieval of validated PCR based methylation assays. The web interface allows free public access to perform methBLAST searches or database queries and to submit user based information. Database records can be searched by gene symbol, nucleotide sequence, analytical method used, Entrez Gene or methPrimerDB identifier, and submitter's name. Each record contains a link to Entrez Gene and PubMed to retrieve additional information on the gene, its genomic context and the article in which the methylation assay was described. To assure and maintain data integrity and accuracy, the database is linked to other reference databases. Currently, the database contains primer records for the most popular PCR-based methylation analysis methods to study human, mouse and rat epigenetic modifications. methPrimerDB and methBLAST are available at http://medgen.ugent.be/methprimerdb and http://medgen.ugent.be/methblast. CONCLUSION: We have developed two integrated and freely available web-tools for PCR based methylation analysis. methBLAST allows in silico assessment of primer specificity in PCR based methylation assays that can be stored in the methPrimerDB database, which provides a search portal for validated methylation assays.