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Anisimova, M.

Publications and source records attributed to Anisimova, M..

At least 19 recordsLinked to original sources

Interpreting Short Tandem Repeat Variations with Plant Genomic Foundation Models in Olive

Short tandem repeats (STRs) are highly mutable, multiallelic variants with distinctive mutation dynamics and have been associated with phenotypic variation across diverse species. However, genome-wide STR variation remains poorly characterized in olive, and the large number of naturally occurring alleles creates a challenge for variant interpretation. Genomic language models offer a potential way to assess how STR length changes alter learned sequence representations, but their responses to natural multiallelic STR series have not been systematically evaluated. Here, we identified 117,119 polymorphic STRs across 212 Iranian and Mediterranean olive accessions. STRs recovered the major SNP-defined ancestry axes while resolving additional geographically structured subcomponents. We then quantified STR-induced embedding perturbation using three plant genomic language models: AgroNT, PlantCAD2, and GPN. Across all three models, embedding distance generally increased with repeat-length difference from the reference allele, and 7,417 loci were embedding-sensitive in all three models. These signals were robust to fixed-length input construction and exceeded those of matched nonrepetitive indels at most tested loci. Integration with oil-composition associations prioritized four exploratory loci supported by all three models. Overall, we provide a genome-wide olive STR resource and show that plant genomic language models capture reproducible responses to natural STR length variation. Combining cross-model embedding evidence with phenotype association provides a potential framework for prioritizing candidate STRs for further investigation.

bioinformatics↗

High-resolution population structure inference using genome-wide short tandem repeat variations

Short tandem repeats (STRs) are a major source of genetic variation, yet their potential for genome-wide population structure inference remains underexplored. Here we present a multi-modal framework for STR-based population inference, integrating unsupervised clustering, supervised population assignment, and a novel admixture inference model, Directional Non-negative Matrix Factorization (dNMF). Applying this framework to thousands of genomes from multiple global cohorts, we first demonstrate that genome-wide STR variations provide substantially finer resolution of human population structure than single-nucleotide polymorphisms (SNPs), particularly at regional levels. The dNMF model estimates ancestry coefficients under the hypothesis that true ancestral populations are consistently encoded in the bidirectional mutation dynamics of STRs. Population structures inferred by dNMF are fine-grained, reproducible across datasets, and robust to technical artifacts. Motif-specific analyses further reveal directionbiased mutational tendencies and show that distinct STR motif classes encode complementary layers of population structure at different evolutionary scales. These results establish STRs as powerful and biologically interpretable markers for population structure inference, offering a mutation-aware perspective that complements traditional SNP-based frameworks and refines understanding of human demographic history.

bioinformatics↗

Ultrastructural analysis of synapses after induction of spike-timing-dependent plasticity

Repeated sequential activation of connected neurons causes lasting changes in synaptic strength, a process known as spike-timing-dependent plasticity (STDP). Recently, sequential spike patterns have been induced without electrodes, using two spectrally separated channelrhodopsins. However, due to the difficulty of labeling and localizing the few connecting synapses between the stimulated pre and postsynaptic neurons ([~]1-5 per neuron pair), ultrastructural analysis after STDP has not been reported. Here, we optogenetically induce STDP at CA3-CA1 hippocampal synapses and identify stimulated boutons and spines in CA1 using transmission electron microscopy (TEM). Presynaptic CA3 neurons express vesicle-targeted horseradish peroxidase, cre recombinase and cre-dependent ChrimsonR, a red light activatable channelrhodopsin. Postsynaptic neurons express violet light activatable CheRiff and dAPEX2, an enhanced ascorbate peroxidase. In transmission electron microscopy, presynaptic boutons and postsynaptic spines are readily identifiable with well-preserved ultrastructural features. Our labeling strategy allows ultrastructural analysis of optogenetically manipulated neurons and their synapses.

neuroscience↗

Negative epistasis limits current codon optimization approaches

Demand for high-yield protein production in biotechnological applications is driving efforts to maximize heterologous protein expression in scalable microorganisms such as E. coli. While codon optimization techniques employed by contemporary sequence providers promise high-expression products, expression levels are often unsatisfactory. Whether the causes for this performance unreliability are due to fundamental constraints on the predictability of protein yields, or whether they stem from differences in theoretical approaches, is unknown. Here, we performed a comparative analysis to address this question. We assessed the performance of twelve different optimization approaches at enhancing expression of a sequence encoding a cinnamyl alcohol dehydrogenase. Six approaches stemmed from commercial providers and six from freely available sources. Through analysis of their elongation time profiles and multidimensional scaling we assessed which algorithms follow a unique optimization approach. We found that codon-optimized sequences are, on average, capable to raise protein expression levels with respect to the nonoptimised source sequence. However, variation in the protein expression levels was large. Simple, non-proprietary optimization techniques were capable of achieving protein expression levels that fall within the top expression range amongst candidate sequences. Lastly, we found that negative epistasis influences a sequences protein expression level. Since therefore the protein expression landscape arising from synonymous sequence space must exhibit a non-negligible degree of ruggedness, standard approaches will be limited in their capacity to predict protein expression levels of sequences.

molecular biology↗

Multicancer analyses of short tandem repeat variations reveal shared gene regulatory mechanisms

BackgroundShort tandem repeats (STRs) have been reported to influence gene expression across various human tissues. While STR variations are enriched in colorectal (CRC), stomach (STAD) and endometrial (UCEC) cancers, particularly in microsatellite instable (MSI) tumors, their functional effects and regulatory mechanisms on gene expression remain poorly understood across these cancer types. ResultsHere, we leverage whole-exome sequencing and gene expression data to identify STRs for which repeat lengths are associated with the expression of nearby genes (eSTRs) in CRC, STAD and UCEC tumors. Our analyses reveal that tumor STR profiles effectively capture both MSI phenotype and population structure. While most eSTRs are cancer-specific, shared eSTRs across multiple cancers exhibit consistent effects on gene expression. Notably, coding-region eSTRs identified in all three cancer types show positive correlations with nearby gene expression. We further validate the functional effects of eSTRs by demonstrating associations between somatic eSTR mutations and gene expression changes during the transition from normal to tumor tissues, suggesting their potential roles in tumorigenesis. Combined with DNA methylation data, we perform the first quantitative analysis of the interplay between STR variations and DNA methylation in tumors. We identify eSTRs where repeat lengths are associated with methylation levels of nearby CpG sites (meSTRs) and show that over 70% of eSTRs are significantly linked to local DNA methylation. Importantly, the effects of meSTRs on DNA methylation remain consistent across cancer types. ConclusionsOverall, our findings enhance the understanding of how functional STR variations influence gene expression and DNA methylation. Our study highlights shared regulatory mechanisms of STRs across multiple cancers, offering a foundation for future research into their broader implications in tumor biology.

bioinformatics↗

Genotyping Short Tandem Repeats Across Copy Number Alterations, Aneuploidies, and Polyploid Organisms

Short tandem repeats (STRs) are a rich source of genetic variation, but are difficult to genotype. While specialized repeat variant callers exist, they typically assume a euploid human genome. This means recent findings regarding phenotypic effects of STR variants in human health and disease cannot be readily extended to polyploid organisms or cancer, which is characterised by copy number alterations (CNAs). Here we present ConSTRain, a novel STR variant caller that explicitly accounts for the copy number of loci in its genotyping approach. We benchmark ConSTRain using a euploid human 100X whole genome sequencing sample where it calls STR allele lengths for over 1.7 x 106 loci in under 20 minutes with an accuracy of 98.28%. Subsequently, we show that ConSTRain resolves complex STR genotypes in an artificial trisomy 21 sample and a polyploid Dwarf Cavendish banana harbouring a large duplication. Finally, we analyse a microsatellite instable colorectal cancer tumoroid, where ConSTRain tackles CNAs and whole-genome duplications. ConSTRain is the first STR variant caller that allows for the investigation of repeats affected by CNAs, aneuploidies, and polyploid genomes. This unlocks the investigation of STRs across a wide range of contexts and organisms where they previously could not be easily studied.

bioinformatics↗

D-Serine inhibits non-ionotropic NMDA receptor signaling

NMDA-type glutamate receptors (NMDARs) are widely recognized as master regulators of synaptic plasticity, most notably for driving long-term changes in synapse size and strength that support learning. NMDARs are unique among neurotransmitter receptors in that they require binding of both neurotransmitter (glutamate) and co-agonist (e.g. O_SCPLOWDC_SCPLOW-serine) to open the receptor channel, which leads to the influx of calcium ions that drive synaptic plasticity. Over the past decade, evidence has accumulated that NMDARs also support synaptic plasticity via ion flux-independent (non-ionotropic) signaling upon the binding of glutamate in the absence of co-agonist, although conflicting results have led to significant controversy. Here, we hypothesized that a major source of contradictory results can be attributed to variable occupancy of the co-agonist binding site under different experimental conditions. To test this hypothesis, we manipulated co-agonist availability in acute hippocampal slices from mice of both sexes. We found that enzymatic scavenging of endogenous co-agonists enhanced the magnitude of LTD induced by non-ionotropic NMDAR signaling in the presence of the NMDAR pore blocker, MK801. Conversely, a saturating concentration of O_SCPLOWDC_SCPLOW-serine completely inhibited both LTD and spine shrinkage induced by glutamate binding in the presence of MK801. Using a FRET-based assay in cultured neurons, we further found that O_SCPLOWDC_SCPLOW-serine completely blocked NMDA-induced conformational movements of the GluN1 cytoplasmic domains in the presence of MK801. Our results support a model in which O_SCPLOWDC_SCPLOW-serine inhibits ion flux-independent NMDAR signaling and plasticity, and thus O_SCPLOWDC_SCPLOW-serine availability could serve to modulate NMDAR signaling even when the NMDAR is blocked by magnesium. Significance StatementNMDARs are glutamate-gated cation channels that are key regulators of neurodevelopment and synaptic plasticity and unique in their requirement for binding of a co-agonist (e.g. O_SCPLOWDC_SCPLOW-serine) in order for the channel to open. NMDARs have been found to drive synaptic plasticity via non-ionotropic (ion flux-independent) signaling upon the binding of glutamate in the absence of co-agonist, though conflicting results have led to controversy. Here, we found that O_SCPLOWDC_SCPLOW-serine inhibits non-ionotropic NMDAR-mediated LTD and LTD-associated spine shrinkage. Thus, a major source of the contradictory findings might be attributed to experimental variability in O_SCPLOWDC_SCPLOW-serine availability. In addition, the developmental regulation of O_SCPLOWDC_SCPLOW-serine levels suggests a role for non-ionotropic NMDAR plasticity during critical periods of plasticity.

neuroscience↗

Please Mind the Gap: Indel-Aware Parsimony for Fast and Accurate Ancestral Sequence Reconstruction and Multiple Sequence Alignment including Long Indels

Despite having important biological implications, insertion and deletion (indel) events are often disregarded or mishandled during phylogenetic inference. In multiple sequence alignment, indels are represented as gaps and are estimated without considering the distinct evolutionary history of insertions and deletions. Consequently, indels are usually excluded from subsequent inference steps, such as ancestral sequence reconstruction and phylogenetic tree search. Here, we introduce indel-aware parsimony (indelMaP), a novel way to treat gaps under the parsimony criterion by considering insertions and deletions as separate evolutionary events and accounting for long indels. By identifying the precise location of an evolutionary event on the tree, we can separate overlapping indel events and use affine gap penalties for long indel modelling. Our indel-aware approach harnesses the phylogenetic signal from indels, including them into all inference stages. Validation and comparison to state-of-the-art inference tools on simulated data show that indelMaP is most suitable for densely sampled datasets with closely to moderately related sequences, where it can reach alignment quality comparable to probabilistic methods and accurately infer ancestral sequences, including indel patterns. Due to its remarkable speed, our method is well-suited for epidemiological datasets, eliminating the need for downsampling and enabling the exploitation of the additional information provided by dense taxonomic sampling. Moreover, indelMaP offers new insights into the indel patterns of biologically significant sequences and advances our understanding of genetic variability by considering gaps as crucial evolutionary signals rather than mere artefacts.

genomics↗

Single-character insertion-deletion model preserves long indels in ancestral sequence reconstruction

Insertions and deletions (indels) play a significant role in genome evolution across species. Realistic modelling of indel evolution is challenging and is still an open research question. Several attempts have been made to explicitly model multi-character (long) indels, such as TKF92, by relaxing the site independence assumption and introducing fragments. However, these methods are computationally expensive On the other hand, the Poisson Indel Process (PIP) assumes site independence but allows one to infer single-character indels on the phylogenetic tree, distinguishing insertions from deletions. PIPs marginal likelihood computation has linear time complexity, enabling ancestral sequence reconstruction (ASR) with indels in linear time. Recently, we developed ARPIP, an ASR method using PIP, capable of inferring indel events with explicit evolutionary interpretations Here, we investigate the effect of the single-character indel assumption on reconstructed ancestral sequences on mammalian protein orthologs and on simulated data. We show that ARPIPs ancestral estimates preserve the gap length distribution observed in the input alignment. In mammalian proteins the lengths of inserted segments appear to be substantially longer compared to deleted segments. Further, we confirm the well-established deletion bias observed in real data To date, ARPIP is the only ancestral reconstruction method that explicitly models insertion and deletion events over time. Given a good quality input alignment, it can capture ancestral long indel events on the phylogeny

evolutionary biology↗

Short tandem repeat mutations regulate gene expression in colorectal cancer

Short tandem repeat (STR) mutations are prevalent in colorectal cancer (CRC), especially in tumours with the microsatellite instability (MSI) phenotype. While STR length variations are known to regulate gene expression under physiological conditions, the functional impact of STR mutations in CRC remains unclear. Here, we integrate STR mutation data with clinical information and gene expression levels to study the gene regulatory effects of STR mutations in CRC. We confirm that STR mutability in CRC highly depends on the MSI status, repeat unit size, and repeat length. Furthermore, we present a set of 1244 putative expression STRs (eSTRs) for which the STR length is associated with gene expression levels in CRC tumours. The length of 73 eSTRs is associated with expression levels of cancer-related genes, nine of which are CRC-specific genes. We show that linear models describing eSTR-gene expression relationships allow for predictions of gene expression changes in response to eSTR mutations. Moreover, we found an increased mutability of eSTRs in MSI tumours. Our evidence of gene regulatory roles for eSTRs in CRC highlights a mostly overlooked way through which tumours may modulate their phenotypes. The increased mutability of eSTRs in MSI tumours may be an early indication that eSTR mutations can confer a selective advantage to tumours. Future extensions of our findings into larger cohorts could uncover new STR-based targets in the treatment of cancer.

cancer biology↗

Neuronal FOS reports synchronized activity of presynaptic neurons

The immediate early gene FOS is frequently used as a marker for highly active neurons. Implicit in this use is the assumption that there is a correlation between neuronal spiking and FOS expression. Here we use optogenetic stimulation of hippocampal neurons to investigate the relation between spike frequency and FOS expression, and report several surprising observations. First, FOS expression is cell-type specific, spiking CA2 pyramidal neurons rarely express FOS. Second, FOS has a U-shaped dependence on frequency: Spiking at 0.1 Hz is more effective than high frequency spiking (50 Hz) while intermediate frequencies do not induce FOS. Third, the pathway from spiking to FOS is not cell-autonomous. Instead, transmitter release and metabotropic glutamate receptor (mGluR) activation are required and, at 0.1 Hz, FOS is induced independently of CREB/calcineurin/MEK pathways. We propose that FOS does not primarily encode a neurons own spike frequency but indicates repeated participation in highly synchronized activity, e.g. sharp wave ripples.

neuroscience↗

Characterization of a Novel Hyper-Variable Variable Number Tandem Repeat in the Dopamine Transporter Gene (SLC6A3)

The dopamine transporter gene, SLC6A3, has received substantial attention in genetic association studies of various phenotypes. Although some variable number tandem repeats (VNTRs) present in SLC6A3 have been tested in genetic association studies, results have not been consistent. VNTRs in SLC6A3 that have not been examined genetically were characterized. Tandem Repeat Annotation Library (TRAL) was used to characterize the VNTRs of 64 unrelated long-read haplotype-phased SLC6A3 sequences. Sequence similarity of each repeat unit of the five VNTRs is reported, along with the correlations of SNP-SNP, SNP-VNTR and VNTR-VNTR alleles across the gene. One of these VNTRs is a novel hyper-VNTR (hyVNTR) in intron 8 of SLC6A3, which contains a range of 3.4-133.4 repeat copies and has a consensus sequence length of 38bp, with 82% G+C content. The 38-base repeat was predicted to form G-quadruplexes in silico and was confirmed by circular dichroism spectroscopy. Additionally, this hyVNTR contains multiple putative binding sites for PRDM9, which, in combination with low levels of linkage disequilibrium around the hyVNTR, suggests it might be a recombination hotspot. Summary BlurbThis VNTR has a heterozygosity value of 0.93, forms G-tetrads, and is in low linkage disequilibrium with surrounding sequence, making it a new site for genetic analysis.

genomics↗

Ontology-Aware Biomedical Relation Extraction

MotivationAutomatically extracting relationships from biomedical texts among multiple sorts of entities is an essential task in biomedical natural language processing with numerous applications, such as drug development or repurposing, precision medicine, and other biomedical tasks requiring knowledge discovery. Current Relation Extraction (RE) systems mostly use one set of features, either as text, or more recently, as graph structures. The state-of-the-art systems often use resource-intensive hence slow algorithms and largely work for a particular type of relationship. However, a simple yet agile system that learns from different sets of features has the advantage of adaptability over different relationship types without an extra burden required for system re-design. ResultsWe model RE as a classification task and propose a new multi-channel deep neural network designed to process textual and graph structures in separate input channels. We extend a Recurrent Neural Network (RNN) with a Convolutional Neural Network (CNN) to process three sets of features, namely, tokens, types, and graphs. We demonstrate that entity type and ontology graph structure provide better representations than simple token-based representations for RE. We also experiment with various sources of knowledge, including data resources in the Unified Medical Language System (UMLS) to test our hypothesis. Extensive experiments on four well-studied biomedical benchmarks with different relationship types show that our system outperforms earlier ones. Thus, our system has state-of-the-art performance and allows processing millions of full-text scientific articles in a few days on one typical machine.

bioinformatics↗

Joint Alignment and Tree Inference

Multiple sequence alignment and phylogenetic tree inference are connected problems that are often solved as independent steps in the inference process. Several attempts at doing simultaneous inference have been made, however currently the available methods are greatly limited by their computational complexity and can only handle small datasets. In this manuscript we introduce a combinatorial optimisation approach that will allow us to resolve the circularity of the problem and efficiently infer both alignments and trees under maximum likelihood.

bioinformatics↗

Long vs short-term synaptic learning rules after optogenetic spike-timing-dependent plasticity

Spike-timing-dependent plasticity (STDP) is a candidate mechanism for information storage in the brain, but the whole-cell recordings required for the experimental induction of STDP are typically limited to one hour. This mismatch of time scales is a long-standing weakness in synaptic theories of memory. Here we use spectrally separated optogenetic stimulation to fire precisely timed action potentials (spikes) in CA3 and CA1 pyramidal cells. Twenty minutes after optogenetic induction of STDP (oSTDP), we observed timing-dependent depression (tLTD) and timing-dependent potentiation (tLTP), depending on the sequence of spiking. As oSTDP does not require electrodes, we could also assess the strength of these paired connections three days later. At this late time point, late tLTP was observed for both causal (CA3 before CA1) and anti-causal (CA1 before CA3) timing, but not for asynchronous activity patterns ({Delta}t = 50 ms). Blocking activity after induction of oSTDP prevented stable potentiation. Our results confirm that neurons wire together if they fire together, but suggest that synaptic depression after anti-causal activation (tLTD) is a transient phenomenon. HighlightsO_LIOptogenetic induction of spike-timing-dependent plasticity at Schaffer collateral synapses C_LIO_LICausal pairing induces potentiation whereas anti-causal pairing induces depression during patch-clamp recordings. C_LIO_LIThree days after optogenetic induction, the consequence of STDP is potentiation (tLTP) irrespective of spiking order. C_LIO_LILate tLTP requires ongoing activity in the days following oSTDP. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/863365v4_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@1937ad5org.highwire.dtl.DTLVardef@16566f8org.highwire.dtl.DTLVardef@4b4a73org.highwire.dtl.DTLVardef@104fc97_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

Enabling Semantic Queries Across Federated Bioinformatics Databases

MotivationData integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases.\n\nResultsWe introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: 1) Bgee, a gene expression relational database; 2) OMA, a Hierarchical Data Format 5 (HDF5) orthology data store, and 3) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialised RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface.\n\nProject URLhttp://biosoda.expasy.org, https://github.com/biosoda/bioquery

bioinformatics↗

Accounting for programmed ribosomal frameshifting in the computation of codon usage bias indices

Experimental evidence shows that synonymous mutations can have important consequences on genetic fitness. Many organisms display codon usage bias (CUB), where synonymous codons that are translated into the same amino acid appear with distinct frequency. CUB is thought to arise from selection for translational efficiency and accuracy, termed the translational efficiency hypothesis (TEH). Indeed, CUB indices correlate with protein expression levels, which is widely interpreted as evidence for translational selection. However, these tests neglect -1 programmed ribosomal frameshifting (-1 PRF), an important translational disruption effect found across all organisms of the tree of life. Genes that contain -1 PRF signals should cost more to express than genes without. Thus, CUB indices that do not consider -1 PRF may overestimate genes true adaptation to translational efficiency and accuracy constraints. Here, we first investigate whether -1 PRF signals do indeed carry such translational cost. We then propose two corrections for CUB indices for genes containing -1 PRF signals. We retest the TEH under these corrections. We find that the correlation between corrected CUB index and protein expression remains intact for most levels of uniform -1 PRF efficiencies, and tends to increase when these efficiencies decline with protein expression. We conclude that the TEH is strengthened and that -1 PRF events constitute a promising and useful tool to examine the relationships between CUB and selection for translation efficiency and accuracy.

genetics↗

Cross-reactive immunity drives global oscillation and opposed alternation patterns of seasonal influenza A viruses

Several human pathogens exhibit distinct patterns of seasonality and circulate as pairs of discrete strains. For instance, the activity of the two co-circulating influenza A virus subtypes oscillates and peaks during winter seasons of the worlds temperate climate zones. These periods of increased activity are usually caused by a single dominant subtype. Alternation of dominant strains in successive influenza seasons makes epidemic forecasting a major challenge. From the start of the 2009 influenza pandemic we enrolled influenza A virus infected patients (n = 2,980) in a global prospective clinical study. Complete hemagglutinin (HA) sequences were obtained from 1,078 A/H1N1 and 1,033 A/H3N2 viruses and were linked to patient data. We then used phylodynamics to construct high resolution spatio-temporal phylogenetic HA trees and estimated global influenza A effective reproductive numbers (R) over time (2009-2013). We demonstrate that R, a parameter to define host immunity, oscillates around R = 1 with a clear opposed alternation pattern between phases of the A/H1N1 and A/H3N2 subtypes. Moreover, we find a similar alternation pattern for the number of global virus migration events between the sampled geographical locations. Both observations suggest a between-strain competition for susceptible hosts on a global level. Extrinsic factors that affect person-to-person transmission are a major driver of influenza seasonality, which forces influenza epidemics to coincide with winter seasons. The data presented here indicate that also cross-reactive host immunity is a key intrinsic driver of global influenza seasonality, which determines the outcome of competition between influenza A virus strains at the onset of each epidemic season.\n\nSignificance statementAnnual influenza epidemics coincide with winter seasons in many parts of the world. Environmental factors, such as air humidity variation or temperature change, are commonly believed to drive these seasonality patterns. Interestingly, three out of the four latest pandemics (1918, 1968 and 2009) did not spread in winter initially, but during summer. This questions to what extent other factors could also impact virus spread among humans. We demonstrate that cross-reactive host immunity is a key factor. It drives the well-known seasonal patterns of virus activity oscillation and alternation of the dominant influenza virus subtype in successive seasons. Furthermore, this factor may also explain the efficient spread of pandemic viruses during summer when cross-reactive host immunity is relatively low.

epidemiology↗