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.

Systematic review and meta-analysis of genome-wide pooled CRISPR screens to identify host factors involved in influenza A virus infection.

The host-virus interactome is increasingly recognized as an important research field to discover new therapeutic targets to treat influenza. Multiple pooled genome-wide CRISPR-Cas screens have been reported to identify new pro- and antiviral host factors of the influenza A virus. However, at present, a comprehensive summary of the results is lacking. We performed a systematic review of all reported CRISPR studies in this field in combination with a meta-analysis using the algorithm of meta-analysis by information content (MAIC). Two ranked gene lists were generated based on evidence in 15 proviral and 4 antiviral screens. Enriched pathways in the proviral MAIC results were compared to those of a prior array-based RNA interference (RNAi) meta-analysis. The top 50 proviral MAIC list contained genes whose role requires further elucidation, such as the endosomal ion channel TPCN1 and the kinase WEE1. Moreover, MAIC indicated that ALYREF, a component of the transcription export complex, has antiviral properties, whereas former knockdown experiments attributed a proviral role to this host factor. CRISPR-Cas-pooled screens displayed a bias toward early-replication events, whereas the prior RNAi meta-analysis covered early and late-stage events. RNAi screens led to the identification of a larger fraction of essential genes than CRISPR screens. In summary, the MAIC algorithm points toward the importance of several less well-known pathways in host-influenza virus interactions that merit further investigation. The results from this meta-analysis of CRISPR screens in influenza A virus infection may help guide future research efforts to develop host-directed anti-influenza drugs. IMPORTANCE: Viruses rely on host factors for their replication, whereas the host cell has evolved virus restriction factors. These factors represent potential targets for host-oriented antiviral therapies. Multiple pooled genome-wide CRISPR-Cas screens have been reported to identify pro- and antiviral host factors in the context of influenza virus infection. We performed a comprehensive analysis of the outcome of these screens based on the publicly available gene lists, using the recently developed algorithm meta-analysis by information content (MAIC). MAIC allows the systematic integration of ranked and unranked gene lists into a final ranked gene list. This approach highlighted poorly characterized host factors and pathways with evidence from multiple screens, such as the vesicle docking and lipid metabolism pathways, which merit further exploration.

ARACINs, Brassicaceae-specific peptides exhibiting antifungal activities against necrotrophic pathogens in Arabidopsis.

Plants have developed a variety of mechanisms to cope with abiotic and biotic stresses. In a previous subcellular localization study of hydrogen peroxide-responsive proteins, two peptides with an unknown function (designated ARACIN1 and ARACIN2) have been identified. These peptides are structurally very similar but are transcriptionally differentially regulated during abiotic stresses during Botrytis cinerea infection or after benzothiadiazole and methyl jasmonate treatments. In Arabidopsis (Arabidopsis thaliana), these paralogous genes are positioned in tandem within a cluster of pathogen defense-related genes. Both ARACINs are small, cationic, and hydrophobic peptides, known characteristics for antimicrobial peptides. Their genes are expressed in peripheral cell layers prone to pathogen entry and are lineage specific to the Brassicaceae family. In vitro bioassays demonstrated that both ARACIN peptides have a direct antifungal effect against the agronomically and economically important necrotrophic fungi B. cinerea, Alternaria brassicicola, Fusarium graminearum, and Sclerotinia sclerotiorum and yeast (Saccharomyces cerevisiae). In addition, transgenic Arabidopsis plants that ectopically express ARACIN1 are protected better against infections with both B. cinerea and A. brassicicola. Therefore, we can conclude that both ARACINs act as antimicrobial peptides.

Mining the genome of Arabidopsis thaliana as a basis for the identification of novel bioactive peptides involved in oxidative stress tolerance.

Although evidence has accumulated on the role of plant peptides in the response to external conditions, the number of peptide-encoding genes in the genome is still underestimated. Using tiling arrays, we identified 176 unannotated transcriptionally active regions (TARs) in Arabidopsis thaliana that were induced upon oxidative stress generated by the herbicide paraquat (PQ). These 176 TARs could be translated into 575 putative oxidative stress-induced peptides (OSIPs). A high-throughput functional assay was used in the eukaryotic model organism Saccharomyces cerevisiae allowing us to test for bioactive peptides that increase oxidative stress tolerance. In this way, we identified three OSIPs that, upon overexpression in yeast, resulted in a significant rise in tolerance to hydrogen peroxide (H2O2). For one of these peptides, the decapeptide OSIP108, exogenous application to H2O2-treated yeast also resulted in significantly increased survival. OSIP108 is contained within a pseudogene and is induced in A. thaliana leaves by both the reactive oxygen species-inducer PQ and the necrotrophic fungal pathogen Botrytis cinerea. Moreover, infiltration and overexpression of OSIP108 in A. thaliana leaves resulted in increased tolerance to treatment with PQ. In conclusion, the identification and characterization of OSIP108 confirms the validity of our high-throughput approach, based on tiling array analysis in A. thaliana and functional screening in yeast, to identify bioactive peptides.

RNAseq-based transcriptome analysis of Lactuca sativa infected by the fungal necrotroph Botrytis cinerea.

The fungal pathogen Botrytis cinerea establishes a necrotrophic interaction with its host plants, including lettuce (Lactuca sativa), causing it to wilt, collapse and eventually dry up and die, which results in serious economic losses. Global expression profiling using RNAseq and the newly sequenced lettuce genome identified a complex network of genes involved in the lettuce-B. cinerea interaction. The observed high number of differentially expressed genes allowed us to classify them according to the biological pathways in which they are implicated, generating a holistic picture. Most pronounced were the induction of the phenylpropanoid pathway and terpenoid biosynthesis, whereas photosynthesis was globally down-regulated at 48 h post-inoculation. Large-scale comparison with data available on the interaction of B. cinerea with the model plant Arabidopsis thaliana revealed both general and species-specific responses to infection with this pathogen. Surprisingly, expression analysis of selected genes could not detect significant systemic transcriptional alterations in lettuce leaves distant from the inoculation site. Additionally, we assessed the response of these lettuce genes to a biotrophic pathogen, Bremia lactucae, revealing that similar pathways are induced during compatible interactions of lettuce with necrotrophic and biotrophic pathogens.

Genome-Wide Characterization of ISR Induced in Arabidopsis thaliana by Trichoderma hamatum T382 Against Botrytis cinerea Infection.

In this study, the molecular basis of the induced systemic resistance (ISR) in Arabidopsis thaliana by the biocontrol fungus Trichoderma hamatum T382 against the phytopathogen Botrytis cinerea B05-10 was unraveled by microarray analysis both before (ISR-prime) and after (ISR-boost) additional pathogen inoculation. The observed high numbers of differentially expressed genes allowed us to classify them according to the biological pathways in which they are involved. By focusing on pathways instead of genes, a holistic picture of the mechanisms underlying ISR emerged. In general, a close resemblance is observed between ISR-prime and systemic acquired resistance, the systemic defense response that is triggered in plants upon pathogen infection leading to increased resistance toward secondary infections. Treatment with T. hamatum T382 primes the plant (ISR-prime), resulting in an accelerated activation of the defense response against B. cinerea during ISR-boost and a subsequent moderation of the B. cinerea induced defense response. Microarray results were validated for representative genes by qRT-PCR. The involvement of various defense-related pathways was confirmed by phenotypic analysis of mutants affected in these pathways, thereby proving the validity of our approach. Combined with additional anthocyanin analysis data these results all point to the involvement of the phenylpropanoid pathway in T. hamatum T382-induced ISR.

Arabidopsis thaliana plant defensin AtPDF1.1 is involved in the plant response to biotic stress.

*Previously, it was shown that the Arabidopsis thaliana plant defensins AtPDF1.1 (At1g75830) and AtPDF1.2a (At5g44420) exert in vitro antimicrobial properties and that their corresponding genes are expressed in seeds and induced in leaves upon pathogen attack, respectively. *In this study, the expression profile of both AtPDF1.1 and AtPDF1.2a is analysed in wild-type plants upon different stress-related treatments and the effect of modulation of their expression in transgenic plants is examined in both host and nonhost resistance. *AtPDF1.1, which was originally considered to be seed-specific, is demonstrated to be locally induced in leaves upon fungal attack and exhibits an expression profile distinct from that of AtPDF1.2a, a gene frequently used as marker for the ethylene/jasmonate-mediated signaling pathway. Transgenic plants with modulated AtPDF1.1 or AtPDF1.2a gene expression show no altered phenotype upon Botrytis cinerea inoculation. However, constitutive overexpression of AtPDF1.1 in A. thaliana leads to a reduction in symptoms caused by the nonhost Cercospora beticola causing non-spreading spots on A. thaliana leaves. *These results indicate that AtPDF1.1 and AtPDF1.2a clearly differ regarding their expression profile and functionality in planta. It emphasizes the additional level of complexity and fine-tuning within the highly redundant plant defensin genes in A. thaliana.

Mitochondrial dysfunction leads to reduced chronological lifespan and increased apoptosis in yeast.

We previously isolated a Saccharomyces cerevisiae mutant (HsTnII), which displays 40% reduced chronological lifespan as compared to the wild type (WT). In this study, we found HsTnII cultures to be characterized by fragmented and dysfunctional mitochondria, and by increased initiation of apoptosis during chronological aging as compared to WT. Expression of genes encoding subunits of mitochondrial electron transport chain and ATP synthase is significantly downregulated in HsTnII, and as a consequence, HsTnII is not able to respire ethanol. All these data confirm the importance of functional mitochondria and respiration in determining yeast chronological lifespan and apoptosis.

Plant pathogenesis-related (PR) proteins: a focus on PR peptides.

The novel classes of plant pathogenesis-related (PR) proteins identified during the last decade also include novel peptide families. This review specifically focuses on these pathogenesis-related peptides, including proteinase inhibitors (PR-6 family), plant defensins (PR-12 family), thionins (PR-13 family) and lipid transfer proteins (PR-14 family). For each family of PR peptides, the general features concerning occurrence, expression and possible functions of their members are described. Next, more specifically the occurrence of each PR peptide family in the model plant Arabidopsis thaliana is discussed. Single-gene studies performed on particular gene members of a PR peptide family are reported. In addition, expression data of yet undescribed gene members of that particular PR peptide family are presented by consultation of publicly available micro-array databases. Finally an update is provided on the potential role of these PR peptides in A. thaliana, with a focus on their possible involvement in plant defense.

PKA and Sch9 control a molecular switch important for the proper adaptation to nutrient availability.

In the yeast Saccharomyces cerevisiae, PKA and Sch9 exert similar physiological roles in response to nutrient availability. However, their functional redundancy complicates to distinguish properly the target genes for both kinases. In this article, we analysed different phenotypic read-outs. The data unequivocally showed that both kinases act through separate signalling cascades. In addition, genome-wide expression analysis under conditions and with strains in which either PKA and/or Sch9 signalling was specifically affected, demonstrated that both kinases synergistically or oppositely regulate given gene targets. Unlike PKA, which negatively regulates stress-responsive element (STRE)- and post-diauxic shift (PDS)-driven gene expression, Sch9 appears to exert additional positive control on the Rim15-effector Gis1 to regulate PDS-driven gene expression. The data presented are consistent with a cyclic AMP (cAMP)-gating phenomenon recognized in higher eukaryotes consisting of a main gatekeeper, the protein kinase PKA, switching on or off the activities and signals transmitted through primary pathways such as, in case of yeast, the Sch9-controlled signalling route. This mechanism allows fine-tuning various nutritional responses in yeast cells, allowing them to adapt metabolism and growth appropriately.

TXTGate: profiling gene groups with text-based information.

We implemented a framework called TXTGate that combines literature indices of selected public biological resources in a flexible text-mining system designed towards the analysis of groups of genes. By means of tailored vocabularies, term- as well as gene-centric views are offered on selected textual fields and MEDLINE abstracts used in LocusLink and the Saccharomyces Genome Database. Subclustering and links to external resources allow for in-depth analysis of the resulting term profiles.

Annotation of the pRhico plasmid of Azospirillum brasilense reveals its role in determining the outer surface composition.

The plant growth-promoting soil bacterium Azospirillum brasilense enhances growth of economically important crops, such as wheat, corn and rice. In order to improve plant growth, a close bacterial association with the plant roots is needed. Genes encoded on a 90-MDa plasmid, denoted pRhico plasmid, present in A. brasilense Sp7, play an important role in plant root interaction. Sequencing, annotation and in silico analysis of this 90-MDa plasmid revealed the presence of a large collection of genes encoding enzymes involved in surface polysaccharide biosynthesis. Analysis of the 90-MDa plasmid genome provided evidence for its essential role in the viability of the bacterial cell.

Microarray screening for target genes of the proto-oncogene PLAG1.

PLAG1 is a proto-oncogene whose ectopic expression can trigger the development of pleomorphic adenomas of the salivary glands and of lipoblastomas. As PLAG1 is a transcription factor, able to activate transcription through the binding to the consensus sequence GRGGC(N)(6-8)GGG, its ectopic expression presumably results in the deregulation of target genes, leading to uncontrolled cell proliferation. The identification of PLAG1 target genes is therefore a crucial step in understanding the molecular mechanisms involved in PLAG1-induced tumorigenesis. To this end, we analysed the changes in gene expression caused by the conditional induction of PLAG1 expression in fetal kidney 293 cell lines. Using oligonucleotide microarray analyses of about 12 000 genes, we consistently identified 47 genes induced and 12 genes repressed by PLAG1. One of the largest classes identified as upregulated PLAG1 targets consists of growth factors such as the insulin-like growth factor II and the cytokine-like factor 1. The in silico search for PLAG1 consensus sequences in the promoter of the upregulated genes reveals that a large proportion of them harbor several copies of the PLAG1-binding motif, suggesting that they represent direct PLAG1 targets. Our approach was complemented by the comparison of the expression profiles of pleomorphic adenomas induced by PLAG1 versus normal salivary glands. Concordance between these two sets of experiments pinpointed 12 genes that were significantly and consistently upregulated in pleomorphic adenomas and in PLAG1-expressing cells, identifying them as putative PLAG1 targets in these tumors.

INCLUSive: A web portal and service registry for microarray and regulatory sequence analysis.

INCLUSive is a suite of algorithms and tools for the analysis of gene expression data and the discovery of cis-regulatory sequence elements. The tools allow normalization, filtering and clustering of microarray data, functional scoring of gene clusters, sequence retrieval, and detection of known and unknown regulatory elements using probabilistic sequence models and Gibbs sampling. All tools are available via different web pages and as web services. The web pages are connected and integrated to reflect a methodology and facilitate complex analysis using different tools. The web services can be invoked using standard SOAP messaging. Example clients are available for download to invoke the services from a remote computer or to be integrated with other applications. All services are catalogued and described in a web service registry. The INCLUSive web portal is available for academic purposes at http://www.esat.kuleuven.ac.be/inclusive.

Adaptive quality-based clustering of gene expression profiles.

MOTIVATION: Microarray experiments generate a considerable amount of data, which analyzed properly help us gain a huge amount of biologically relevant information about the global cellular behaviour. Clustering (grouping genes with similar expression profiles) is one of the first steps in data analysis of high-throughput expression measurements. A number of clustering algorithms have proved useful to make sense of such data. These classical algorithms, though useful, suffer from several drawbacks (e.g. they require the predefinition of arbitrary parameters like the number of clusters; they force every gene into a cluster despite a low correlation with other cluster members). In the following we describe a novel adaptive quality-based clustering algorithm that tackles some of these drawbacks. RESULTS: We propose a heuristic iterative two-step algorithm: First, we find in the high-dimensional representation of the data a sphere where the "density" of expression profiles is locally maximal (based on a preliminary estimate of the radius of the cluster-quality-based approach). In a second step, we derive an optimal radius of the cluster (adaptive approach) so that only the significantly coexpressed genes are included in the cluster. This estimation is achieved by fitting a model to the data using an EM-algorithm. By inferring the radius from the data itself, the biologist is freed from finding an optimal value for this radius by trial-and-error. The computational complexity of this method is approximately linear in the number of gene expression profiles in the data set. Finally, our method is successfully validated using existing data sets. AVAILABILITY: http://www.esat.kuleuven.ac.be/~thijs/Work/Clustering.html

INCLUSive: integrated clustering, upstream sequence retrieval and motif sampling.

INCLUSive allows automatic multistep analysis of microarray data (clustering and motif finding). The clustering algorithm (adaptive quality-based clustering) groups together genes with highly similar expression profiles. The upstream sequences of the genes belonging to a cluster are automatically retrieved from GenBank and can be fed directly into Motif Sampler, a Gibbs sampling algorithm that retrieves statistically over-represented motifs in sets of sequences, in this case upstream regions of co-expressed genes.