A Canine Distemper Outbreak in Urban Red Foxes (Vulpes vulpes), in Brussels, Belgium, 2020.

Canine distemper has been observed infrequently in Belgian wildlife, mainly stone martens (Martes foina) and red foxes (Vulpes vulpes). This report describes an outbreak in the Brussels urban red fox population, characterized by its high density. The identified virus matched those within a cluster of viruses found previously in red foxes in Germany. Different canine distemper virus (CDV) strains, found in Belgian wild carnivores, share relationships with viruses found farther east. This and other reports indicate an endemic distribution of CDV in wild carnivores in Europe whereby the complex interplay of population density, group immunity, and infection of metapopulations determines the pattern of spatiotemporally alternating outbreaks.

Improving replicability in single-cell RNA-Seq cell type discovery with Dune.

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

Trajectory inference across multiple conditions with condiments.

In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments, a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cell's conditions along the trajectory's path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

Polar lake microbiomes have distinct evolutionary histories.

Toward the poles, life on land is increasingly dominated by microorganisms, yet the evolutionary origin of polar microbiomes remains poorly understood. Here, we use metabarcoding of Arctic, sub-Antarctic, and Antarctic lacustrine benthic microbial communities to test the hypothesis that high-latitude microbiomes are recruited from a globally dispersing species pool through environmental selection. We demonstrate that taxonomic overlap between the regions is limited within most phyla, even at higher-order taxonomic levels, with unique deep-branching phylogenetic clades being present in each region. We show that local and regional taxon richness and net diversification rate of regionally restricted taxa differ substantially between polar regions in both microeukaryotic and bacterial biota. This suggests that long-term evolutionary divergence resulting from low interhemispheric dispersal and diversification in isolation has been a prominent process shaping present-day polar lake microbiomes. Our findings illuminate the distinctive biogeography of polar lake ecosystems and underscore that conservation efforts should include their unique microbiota.

A Guide to Trajectory Inference and RNA Velocity.

Technological developments have led to an explosion of high-throughput single-cell data, which are revealing unprecedented perspectives on cell identity. Recently, significant attention has focused on investigating, from single-cell RNA-sequencing (scRNA-seq) data, cellular dynamic processes, such as cell differentiation, cell cycle and cell (de)activation. In particular, trajectory inference methods, by ordering cells along a trajectory, allow estimating a differentiation tree of cells. While trajectory inference tools typically work with gene expression levels, common scRNA-seq protocols allow the identification and quantification of unspliced pre-mRNAs and mature spliced mRNAs for each gene. By exploiting the abundance of unspliced and spliced mRNA, one can infer the RNA velocity of individual cells, i.e., the time derivative of the gene expression state of cells. Whereas traditional trajectory inference methods reconstruct cellular dynamics given a population of cells of varying maturity, RNA velocity relies on a dynamical model describing splicing dynamics. Here, we initially discuss conceptual and theoretical aspects of both approaches, then illustrate how they can be combined together, and finally present an example use case on real data.

Differential detection workflows for multi-sample single-cell RNA-seq data.

In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.

Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects.

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of epigenetic regulation of gene expression by assessing chromatin configuration for an entire genome. Despite its popularity, there have been limited studies investigating the analytical challenges related to ATAC-seq data, with most studies leveraging tools developed for bulk transcriptome sequencing. Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins and evaluate it together with 11 different normalization procedures on 8 public ATAC-seq datasets. Accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, improving accuracy and interpretability. Through case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.

Strain-specific transcriptional responses overshadow salinity effects in a marine diatom sampled along the Baltic Sea salinity cline.

The salinity gradient separating marine and freshwater environments represents a major ecological divide for microbiota, yet the mechanisms by which marine microbes have adapted to and ultimately diversified in freshwater environments are poorly understood. Here, we take advantage of a natural evolutionary experiment: the colonization of the brackish Baltic Sea by the ancestrally marine diatom Skeletonema marinoi. To understand how diatoms respond to low salinity, we characterized transcriptomic responses of acclimated S. marinoi grown in a common garden. Our experiment included eight strains from source populations spanning the Baltic Sea salinity cline. Gene expression analysis revealed that low salinities induced changes in the cellular metabolism of S. marinoi, including upregulation of photosynthesis and storage compound biosynthesis, increased nutrient demand, and a complex response to oxidative stress. However, the strain effect overshadowed the salinity effect, as strains differed significantly in their response, both regarding the strength and the strategy (direction of gene expression) of their response. The high degree of intraspecific variation in gene expression observed here highlights an important but often overlooked source of biological variation associated with how diatoms respond to environmental change.

Prevalence of Fox Tapeworm in Invasive Muskrats in Flanders (North Belgium).

One way in which invasive alien species affect their environment is by acting as pathogen hosts. Pathogens limited by the availability of the native host species can profit from the presence of additional hosts. The muskrat (Ondatra zibethicus) is known to act as an intermediate host for the fox tapeworm (Echinococcus multilocularis). From 2009 to 2017, 15,402 muskrats caught in Flanders and across the border with Wallonia and France were collected and dissected with the aim of understanding the prevalence of this parasite in muskrats. Visual examination of the livers revealed 202 infected animals (1.31%). Out of the 9421 animals caught in Flanders, we found 82 individuals (0.87%) infected with E. multilocularis. No increase in prevalence was observed during this study. All of the infected animals in Flanders were found in municipalities along the Walloon border. We did not observe a northward spread of E. multilocularis infection from Wallonia to Flanders. We hypothesise that the low prevalence is the result of the reduced availability of intermediate hosts and the successful control programme which is keeping muskrat densities in the centre of the region at low levels and is preventing influx from other areas. Our results illustrate that muskrats are good sentinels for E. multilocularis and regular screening can gain valuable insight into the spread of this zoonosis.

A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

Altering the Sex Pheromone Cyclo(l-Pro-l-Pro) of the Diatom Seminavis robusta towards a Chemical Probe.

As a major group of algae, diatoms are responsible for a substantial part of the primary production on the planet. Pennate diatoms have a predominantly benthic lifestyle and are the most species-rich diatom group, with members of the raphid clades being motile and generally having heterothallic sexual reproduction. It was recently shown that the model species Seminavis robusta uses multiple sexual cues during mating, including cyclo(l-Pro-l-Pro) as an attraction pheromone. Elaboration of the pheromone-detection system is a key aspect in elucidating pennate diatom life-cycle regulation that could yield novel fundamental insights into diatom speciation. This study reports the synthesis and bio-evaluation of seven novel pheromone analogs containing small structural alterations to the cyclo(l-Pro-l-Pro) pheromone. Toxicity, attraction, and interference assays were applied to assess their potential activity as a pheromone. Most of our analogs show a moderate-to-good bioactivity and low-to-no phytotoxicity. The pheromone activity of azide- and diazirine-containing analogs was unaffected and induced a similar mating behavior as the natural pheromone. These results demonstrate that the introduction of confined structural modifications can be used to develop a chemical probe based on the diazirine- and/or azide-containing analogs to study the pheromone-detection system of S. robusta.

satuRn: Scalable analysis of differential transcript usage for bulk and single-cell RNA-sequencing applications.

Alternative splicing produces multiple functional transcripts from a single gene. Dysregulation of splicing is known to be associated with disease and as a hallmark of cancer. Existing tools for differential transcript usage (DTU) analysis either lack in performance, cannot account for complex experimental designs or do not scale to massive single-cell transcriptome sequencing (scRNA-seq) datasets. We introduce satuRn, a fast and flexible quasi-binomial generalized linear modelling framework that is on par with the best performing DTU methods from the bulk RNA-seq realm, while providing good false discovery rate control, addressing complex experimental designs, and scaling to scRNA-seq applications.

Mating type specific transcriptomic response to sex inducing pheromone in the pennate diatom Seminavis robusta.

Sexual reproduction is a fundamental phase in the life cycle of most diatoms. Despite its role as a source of genetic variation, it is rarely reported in natural circumstances and its molecular foundations remain largely unknown. Here, we integrate independent transcriptomic datasets to prioritize genes responding to sex inducing pheromones (SIPs) in the pennate diatom Seminavis robusta. We observe marked gene expression changes associated with SIP treatment in both mating types, including an inhibition of S phase progression, chloroplast division, mitosis, and cell wall formation. Meanwhile, meiotic genes are upregulated in response to SIP, including a sexually induced diatom specific cyclin. Our data further suggest an important role for reactive oxygen species, energy metabolism, and cGMP signaling during the early stages of sexual reproduction. In addition, we identify several genes with a mating type specific response to SIP, and link their expression pattern with physiological specialization, such as the production of the attraction pheromone diproline in mating type - (MT-) and mate-searching behavior in mating type + (MT+). Combined, our results provide a model for early sexual reproduction in pennate diatoms and significantly expand the suite of target genes to detect sexual reproduction events in natural diatom populations.

Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia.

Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) - the causal agent in COVID-19 - affects olfaction directly, by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing demonstrated that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing revealed that ACE2 is expressed in support cells, stem cells, and perivascular cells, rather than in neurons. Immunostaining confirmed these results and revealed pervasive expression of ACE2 protein in dorsally-located olfactory epithelial sustentacular cells and olfactory bulb pericytes in the mouse. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients.

Distinctive Growth and Transcriptional Changes of the Diatom Seminavis robusta in Response to Quorum Sensing Related Compounds.

In aquatic habitats, diatoms are frequently found in association with Proteobacteria, many members of which employ cell-to-cell communication via N-acyl homoserine lactones (AHLs). It has been suggested that diatoms could distinguish between beneficial and algicidal bacteria in their surroundings by sensing AHLs. Although some microalgae can interfere with AHL signaling, e.g., by releasing AHL mimics or degrading them, molecular responses to AHLs in microalgae are still unclear. Therefore, we tested the effects of short-chained AHLs, i.e., N-hexanoyl homoserine lactone (C6-HSL), N-3-hydroxyhexanoyl homoserine lactone (OH-C6-HSL), and N-3-oxohexanoyl homoserine lactone (oxo-C6-HSL) and long-chained AHLs, i.e., N-tetradecanoyl homoserine lactone (C14-HSL), N-3-hydroxytetradecanoyl homoserine lactone (OH-C14-HSL), and N-3-oxotetradecanoyl homoserine lactone (oxo-C14-HSL), on growth of the benthic diatom Seminavis robusta. All tested short-chained AHLs did not affect diatom growth, while long-chained AHLs promoted (C14-HSL) or inhibited (OH-C14-HSL and oxo-C14-HSL) growth. To investigate the physiological effects of these long-chained AHLs in more detail, an RNA-seq experiment was performed during which S. robusta was treated with the growth-promoting C14-HSL and the growth-inhibiting oxo-C14-HSL. One tetramic acid was also tested (TA14), a structural rearrangement product of oxo-C14-HSL, which also induced growth inhibition in S. robusta. After 3 days of treatment, analysis revealed that 3,410 genes were differentially expressed in response to at least one of the compounds. In the treatment with the growth-promoting C14-HSL many genes involved in intracellular signaling were upregulated. On the other hand, exposure to growth-inhibiting oxo-C14-HSL and TA14 triggered a switch in lipid metabolism towards increased fatty acid degradation. In addition, oxo-C14-HSL led to downregulation of cell cycle genes, which is in agreement with the stagnation of cell growth in this treatment. Combined, our results indicate that bacterial signaling molecules with high structural similarity induce contrasting physiological responses in S. robusta.

Trajectory-based differential expression analysis for single-cell sequencing data.

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.

A database of threat statuses and life-history traits of Red List species in Flanders (northern Belgium).

BACKGROUND: Red Lists estimate the extinction risk of species at global or regional levels and are important instruments in conservation policies. Global Red List assessments are readily available via the IUCN website (https://www.iucnredlist.org) and are regularly updated by (taxonomic) experts. Regional Red Lists, however, are not always easy to find and often use local criteria to assess the local extinction risk of species. NEW INFORMATION: Here, we publish a database with the outcome of 38 Red List assessments in Flanders (northern Belgium) between 1994 and 2018. In total, the database contains 6,224 records of 5,039 unique taxa pertaining to 24 different taxonomic groups. Using a quality control procedure, we evaluated the criteria used, the number of records, the temporal and spatial distribution of the data and the up-to-dateness of the Red Lists. This way, nineteen Red Lists were approved as being of sufficient high quality (i.e. validated) and nineteen others were not. Once validated, Red Lists are approved by the regional Minister of Environment and published in the Belgian Official Gazette acquiring legal status. For the validated Red Lists, we additionally compiled (life-history) traits that are applicable to a wide variety of species groups (taxonomic kingdom, environment, biotope, nutrient level, dispersal capacity, lifespan and cuddliness). The publication of this dataset allows comparison of Red List statuses with other European regions and countries and permits analyses about how certain (life-history) traits can explain the Red List status of species. The dataset will be regularly updated by adding new Red List (re)assessments and/or additional (life-history) traits.

Role of mustelids in the life-cycle of ixodid ticks and transmission cycles of four tick-borne pathogens.

BACKGROUND: Elucidating which wildlife species significantly contribute to the maintenance of Ixodes ricinus populations and the enzootic cycles of the pathogens they transmit is imperative in understanding the driving forces behind the emergence of tick-borne diseases. Here, we aimed to quantify the relative contribution of four mustelid species in the life-cycles of I. ricinus and Borrelia burgdorferi (sensu lato) in forested areas and to investigate their role in the transmission of other tick-borne pathogens. Road-killed badgers, pine martens, stone martens and polecats were collected in Belgium and the Netherlands. Their organs and feeding ticks were tested for the presence of tick-borne pathogens. RESULTS: Ixodes hexagonus and I. ricinus were found on half of the screened animals (n = 637). Pine martens had the highest I. ricinus burden, whereas polecats had the highest I. hexagonus burden. We detected DNA from B. burgdorferi (s.l.) and Anaplasma phagocytophilum in organs of all four mustelid species (n = 789), and Neoehrlichia mikurensis DNA was detected in all species, except badgers. DNA from B. miyamotoi was not detected in any of the investigated mustelids. From the 15 larvae of I. ricinus feeding on pine martens (n = 44), only one was positive for B. miyamotoi DNA, and all tested negative for B. burgdorferi (s.l.), N. mikurensis and A. phagocytophilum. The two feeding larvae from the investigated polecats (n = 364) and stone martens (n = 39) were negative for all four pathogens. The infection rate of N. mikurensis was higher in feeding nymphs collected from mustelids compared to questing nymphs, but not for B. burgdorferi (s.l.), B. miyamotoi or A. phagocytophilum. CONCLUSIONS: Although all stages of I. ricinus can be found on badgers, polecats, pine and stone martens, their relative contribution to the life-cycle of I. ricinus in forested areas is less than 1%. Consequently, the relative contribution of mustelids to the enzootic cycles of I. ricinus-borne pathogens is negligible, despite the presence of these pathogens in organs and feeding ticks. Interestingly, all four mustelid species carried all stages of I. hexagonus, potentially maintaining enzootic cycles of this tick species apart from the cycle involving hedgehogs as main host species.

Neurogenomic Profiling Reveals Distinct Gene Expression Profiles Between Brain Parts That Are Consistent in Ophthalmotilapia Cichlids.

The detection of external and internal cues alters gene expression in the brain which in turn may affect neural networks that underly behavioral responses. Previous studies have shown that gene expression profiles differ between major brain regions within individuals and between species with different morphologies, cognitive abilities and/or behaviors. A detailed description of gene expression in all macroanatomical brain regions and in species with similar morphologies and behaviors is however lacking. Here, we dissected the brain of two cichlid species into six macroanatomical regions. Ophthalmotilapia nasuta and O. ventralis have similar morphology and behavior and occasionally hybridize in the wild. We use 3' mRNA sequencing and a stage-wise statistical testing procedure to identify differential gene expression between females that were kept in a social setting with other females. Our results show that gene expression differs substantially between all six brain parts within species: out of 11,577 assessed genes, 8,748 are differentially expressed (DE) in at least one brain part compared to the average expression of the other brain parts. At most 16% of these DE genes have |log2FC| significantly higher than two. Functional differences between brain parts were consistent between species. The majority (61-79%) of genes that are DE in a particular brain part were shared between both species. Only 32 genes show significant differences in fold change across brain parts between species. These genes are mainly linked to transport, transmembrane transport, transcription (and its regulation) and signal transduction. Moreover, statistical equivalence testing reveals that within each comparison, on average 89% of the genes show an equivalent fold change between both species. The pronounced differences in gene expression between brain parts and the conserved patterns between closely related species with similar morphologies and behavior suggest that unraveling the interactions between genes and behavior will benefit from neurogenomic profiling of distinct brain regions.

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications.

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.