Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphological and genomic characterisation of the hybrid schistosome infecting humans in Europe reveals a complex admixture between Schistosoma haematobium and Schistosoma bovis parasites

Schistosomes cause schistosomiasis, the world’s second most important parasitic disease after malaria. A peculiar feature of schistosomes is their ability to produce viable and fertile hybrids. Originally only present in the tropics, schistosomiasis is now also endemic in Europe. Based on two genetic markers the European species had been identified as a hybrid between the ruminant-infective Schistosoma bovis and the human-infective Schistosoma haematobium.Here we describe for the first time the genomic composition of the European schistosome hybrid (77% of S. haematobium and 23% of S. bovis origins), its morphometric parameters and its compatibility with the European vector snail and intermediate host Compatibility is a key parameter for the parasites life cycle progression. We also show that egg morphology (a classical diagnostic parameter) does not allow for differential diagnosis while genetic tests do so. Additionally, we performed genome assembly improvement and annotation of S. bovis, the parental species for which no satisfactory genome assembly was available.For the first time since the discovery of hybrid schistosomes, these results reveal at the whole genomic level a complex admixture of parental genomes highlighting (i) the high permeability of schistosomes to other species’ alleles, and (ii) the importance of hybrid formation for pushing species boundaries not only conceptionally but also geographically.
Efferent Synaptic Transmission at the Vestibular Type II Hair Cell Synapse

In the vestibular peripheral organs, type I and type II hair cells (HCs) transmit incoming signals via glutamatergic quantal transmission onto afferent nerve fibers. Additionally, type I HCs transmit via ‘non-quantal’ transmission to calyx afferent fibers, by accumulation of glutamate and potassium in the synaptic cleft. Vestibular efferent inputs originating in the brainstem contact type II HCs and vestibular afferents. Here, we aimed at characterizing the synaptic efferent inputs to type II HCs using electrical and optogenetic stimulation of efferent fibers combined with in vitro whole-cell patch clamp recording from type II HCs in the rodent vestibular crista. Properties of efferent synaptic currents in type II HCs were similar to those found in cochlear hair cells and mediated by activation of α9/α10 nicotinic acetylcholine receptors (AChRs) and SK potassium channels. While efferents showed a low probability of release at low frequencies of stimulation, repetitive stimulation resulted in facilitation and increased probability of release. Notably, the membrane potential of type II HCs measured during optogenetic stimulation of efferents showed a strong hyperpolarization even in response to single pulses and was further enhanced by repetitive stimulation. Such efferent-mediated inhibition of type II HCs can provide a mechanism to adjust the contribution of signals from type I and type II HCs to vestibular nerve fibers. As a result, the relative input of type I hair cells to vestibular afferents will be strengthened, emphasizing the phasic properties of the incoming signal that are transmitted via fast non-quantal transmission. New and Noteworthy Type II vestibular hair cells (HCs) receive inputs from efferent fibers originating in the brainstem. We used in vitro optogenetic and electrical stimulation of efferent fibers to study their synaptic inputs to type II HCs. Efferent inputs inhibited type II HCs, similar to cochlear efferent effects. We propose that efferent inputs adjust the contribution of signals from type I and type II HCs that report different components of the incoming signal to vestibular nerve fibers.
Copulas and their potential for ecology

All branches of ecology study relationships among and between environmental and biological variables. However, standard approaches to studying such relationships, based on correlation and regression, provide only a small slice of the complex information contained in the relationships. Other statistical approaches exist that provide a complete description of relationships between variables, based on the concept of the copula; they are applied in finance, neuroscience and other fields, but rarely in ecology. We here explore the concepts that underpin copulas and examine the potential for those concepts to improve our understanding of ecology. We find that informative copula structure in dependencies between variables is common across all the environmental, species-trait, phenological, population, community, and ecosystem functioning datasets we considered. Many datasets exhibited asymmetric tail associations, whereby two variables were more strongly related in their left compared to right tails, or vice versa. We describe mechanisms by which observed copula structure and asymmetric tail associations can arise in ecological data, including a Moran-like effect whereby dependence structures between environmental variables are inherited by ecological variables; and asymmetric or nonlinear influences of environments on ecological variables, such as under Liebig’s law of the minimum. We also describe consequences of copula structure for ecological phenomena, including impacts on extinction risk, Taylor’s law, and the stability through time of ecosystem services. By documenting the importance of a complete description of dependence between variables, advancing conceptual frameworks, and demonstrating a powerful approach, we aim to encourage widespread use of copulas in ecology, which we believe can benefit the discipline.
VTA dopamine neurons multiplex external with internal representations of goal-directed action

Dopamine (DA) released from VTA neurons in response to external cues or rewards may represent a learning signal. Here we introduce a spatial task where a reward-predicting cue instructs mice to navigate to a specific location. Simultaneous in vivo single-unit recordings revealed that DA neurons multiplexed an internal representation of the animal’s goal-directed actions (locomotion, licking, distance) with phasic responses to cue and reward. Neuronal activity discriminated between rewarded and failed trials, generating an error signal even in the absence of external cues. Following a contingency change, mice readily learned to move to a different location, which became impossible, if the internal error signal was jammed by optogenetic stimulation. We conclude that a multiplexed internal representation of the task modulates VTA DA neuron activity, engaging a learning process that leads to the behavioral adaptation of goal directed action.
Flux, toxicity and protein expression costs shape genetic interaction in a metabolic pathway

Our ability to predict the impact of mutations on traits relevant for disease and evolution remains severely limited by the dependence of their effects on the genetic background and environment. Even when molecular interactions between genes are known, it is unclear how these translate to organism-level interactions between alleles. We therefore characterized the interplay of genetic and environmental dependencies in determining fitness by quantifying ~4,000 fitness interactions between expression variants of two metabolic genes, in different environments. We detect a remarkable variety of environment-dependent interactions, and demonstrate they can be quantitatively explained by a mechanistic model accounting for catabolic flux, metabolite toxicity and expression costs. Complex fitness interactions between mutations can therefore be predicted simply from their simultaneous impact on a few connected molecular phenotypes.
Tape lures swell bycatch on a Mediterranean island harbouring illegal bird trapping

Mediterranean islands are critical for migrating birds, providing shelter and sustenance for millions of individuals each year. Humans have long exploited bird migration through hunting and illegal trapping. On the island of Cyprus, trapping birds during their migratory peak is considered a local tradition, but has long been against the law. Illegal bird trapping is a lucrative business, however, with trappers using tape lures that broadcast species’ vocalizations because it is expected to increase numbers of target species. Yet, by how much the use of song playback increases capture rates remains underappreciated. In particular, it is not known whether song playback of target species affects bycatch rates. Here, we show with the use of playback experiments that song playback is highly effective in luring birds towards trapping sites. We found that playback increases six to eight times the number of individuals of target species captured, but also significantly increases bycatch. Our findings thus show that in contrast to popular belief that tape lures are a selective trapping method, they also lead to increased captures of non-target species, which can include species of conservation concern.
Differential Impacts of Repeated Sampling on Odor Representations by Genetically-Defined Mitral and Tufted Cell Subpopulations in the Mouse Olfactory Bulb

Sniffing, the active control of breathing beyond passive respiration, is used by mammals to modulate olfactory sampling. Sniffing allows animals to make odor-guided decisions within ∼200 ms, but animals routinely engage in bouts of high-frequency sniffing spanning several seconds; the impact of such repeated odorant sampling on odor representations remains unclear. We investigated this question in the mouse olfactory bulb (OB), where mitral and tufted cells (MTCs) form parallel output streams of odor information processing. To test the impact of repeated odorant sampling on MTC responses, we used two-photon imaging in anesthetized male and female mice to record activation of MTCs while precisely varying inhalation frequency. A combination of genetic targeting and viral expression of GCaMP6 reporters allowed us to access mitral cell (MC) and superficial tufted cell (sTC) subpopulations separately. We found that repeated odorant sampling differentially affected responses in MCs and sTCs, with MCs showing more diversity than sTCs over the same time period. Impacts of repeated sampling among MCs included both increases and decreases in excitation, as well as changes in response polarity. Response patterns across simultaneously-imaged MCs reformatted over time, with representations of different odorants becoming more distinct. Individual MCs responded differentially to changes in inhalation frequency, whereas sTC responses were more uniform over time and across frequency. Our results support the idea that MCs and TCs comprise functionally distinct pathways for odor information processing, and suggest that the reformatting of MC odor representations by high-frequency sniffing may serve to enhance the discrimination of similar odors. SIGNIFICANCE STATEMENT Repeated sampling of odorants during high-frequency respiration (sniffing) is a hallmark of active odorant sampling by mammals; however, the adaptive function of this behavior remains unclear. We found distinct effects of repeated sampling on odor representations carried by the two main output channels from the mouse olfactory bulb (OB), mitral and tufted cells (MTCs). Mitral cells (MCs) showed more diverse changes in response patterns over time as compared with tufted cells (TCs), leading to odorant representations that were more distinct after repeated sampling. These results support the idea that MTCs contribute different aspects to encoding odor information, and they indicate that MCs (but not TCs) may play a primary role in the modulation of olfactory processing by sampling behavior.
Preprocessing choices affect RNA velocity results for droplet scRNA-seq data

Experimental single-cell approaches are becoming widely used for many purposes, including investigation of the dynamic behaviour of developing biological systems. Consequently, a large number of computational methods for extracting dynamic information from such data have been developed. One example is RNA velocity analysis, in which spliced and unspliced RNA abundances are jointly modeled in order to infer a ‘direction of change’ and thereby a future state for each cell in the gene expression space. Naturally, the accuracy and interpretability of the inferred RNA velocities depend crucially on the correctness of the estimated abundances. Here, we systematically compare four widely used quantification tools, in total yielding twelve different quantification approaches, in terms of their estimates of spliced and unspliced RNA abundances in four experimental droplet scRNA-seq data sets. We show that there are substantial differences between the quantifications obtained from different tools, and identify typical genes for which such discrepancies are observed. We further show that these abundance differences propagate to the downstream analysis, and can have a large effect on estimated velocities as well as the biological interpretation. Our results highlight that abundance quantification is a crucial aspect of the RNA velocity analysis workflow, and that both the definition of the genomic features of interest and the quantification algorithm itself require careful consideration.
Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes

Background This work is aimed at improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis by generating a multi-omic disease signature. Methods/results We combined classic plasma biochemistry and plasma biomarkers with the transcriptional and epigenetic characterisation of cell types involved in thrombosis, obtained from two extreme phenotype groups (morbidly obese and lipodystrophy) and lean individuals to identify the molecular mechanisms at play, highlighting patterns of abnormal activation in innate immune phagocytic cells. Our analyses showed that extreme phenotype groups could be distinguished from lean individuals, and from each other, across all data layers. The characterisation of the same obese group, 6 months after bariatric surgery, revealed the loss of the abnormal activation of innate immune cells previously observed. However, rather than reverting to the gene expression landscape of lean individuals, this occurred via the establishment of novel gene expression landscapes. NETosis and its control mechanisms emerge amongst the pathways that show an improvement after surgical intervention. Conclusions We showed that the morbidly obese and lipodystrophy groups, despite some differences, shared a common cardiometabolic syndrome signature. We also showed that this could be used to discriminate, amongst the normal population, those individuals with a higher likelihood of presenting with the disease, even when not displaying the classic features.
The positive evidence bias in perceptual confidence is not post-decisional

Confidence in a perceptual decision is a subjective estimate of the accuracy of one’s choice. As such, confidence is thought to be an important computation for a variety of cognitive and perceptual processes, and it features heavily in theorizing about conscious access to perceptual states. Recent experiments have revealed a “positive evidence bias” (PEB) in the computations underlying confidence reports. A PEB occurs when confidence, unlike objective choice, over-weights the evidence for the chosen option, relative to evidence against the chosen option. Accordingly, in a perceptual task, appropriate stimulus conditions can be arranged that produce selective changes in confidence reports but no changes in accuracy. Although the PEB is generally assumed to reflect the observer’s perceptual and/or decision processes, post-decisional accounts have not been ruled out. We therefore asked whether the PEB persisted under novel conditions that eliminated two possible post-decisional accounts: 1) post-decision evidence accumulation that contributes to a confidence report solicited after the perceptual choice, and 2) a memory bias that emerges in the delay between the stimulus offset and the confidence report. We found that even when the stimulus remained on the screen until observers responded, and when observers reported their choice and confidence simultaneously, the PEB still emerged. Signal detection-based modeling also showed that the PEB was not associated with changes to metacognitive efficiency, but rather to confidence criteria. We conclude that once-plausible post-decisional accounts of the PEB do not explain the bias, bolstering the idea that it is perceptual or decisional in nature.
parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants

Several prediction problems in Computational Biology and Genomic Medicine are characterized by both big data as well as a high imbalance between examples to be learned, whereby positive examples can represent a tiny minority with respect to negative examples. For instance, deleterious or pathogenic variants are overwhelmed by the sea of neutral variants in the non-coding regions of the genome: as a consequence the prediction of deleterious variants is a very challenging highly imbalanced classification problem, and classical prediction tools fail to detect the rare pathogenic examples among the huge amount of neutral variants or undergo severe restrictions in managing big genomic data. To overcome these limitations we propose parSMURF, a method that adopts a hyper-ensemble approach and oversampling and undersampling techniques to deal with imbalanced data, and parallel computational techniques to both manage big genomic data and significantly speed-up the computation. The synergy between Bayesian optimization techniques and the parallel nature of parSMURF enables efficient and user-friendly automatic tuning of the hyper-parameters of the algorithm, and allows specific learning problems in Genomic Medicine to be easily fit. Moreover, by using MPI parallel and machine learning ensemble techniques, parSMURF can manage big data by partitioning them across the nodes of a High Performance Computing cluster. Results with synthetic data and with single nucleotide variants associated with Mendelian diseases and with GWAS hits in the non-coding regions of the human genome, involving millions of examples, show that parSMURF achieves state-of-the-art results and a speed-up of 80× with respect to the sequential version. In conclusion parSMURF is a parallel machine learning tool that can be trained to learn different genomic problems, and its multiple levels of parallelization and its high scalability allow us to efficiently fit problems characterized by big and imbalanced genomic data. Availability and Implementation The C++ OpenMP multi-core version tailored to a single workstation and the C++ MPI/OpenMP hybrid multi-core and multi-node parSMURF version tailored to a High Performance Computing cluster are both available from github: https://github.com/AnacletoLAB/parSMURF
Primary cilium-dependent cAMP/PKA signaling at the centrosome regulates neuronal migration

The primary cilium regulates neuronal migration via cyclic AMP production activating protein kinase A at the centrosome. The primary cilium (PC) is a small centrosome-assembled organelle, protruding from the surface of most eukaryotic cells. It plays a key role in cell migration, but the underlying mechanisms are unknown. Here, we show that the PC regulates neuronal migration via cyclic adenosine 3’-5’ monosphosphate (cAMP) production activating centrosomal protein kinase A (PKA). Biosensor live imaging revealed a periodic cAMP hotspot at the centrosome of embryonic, postnatal, and adult migrating neurons. Genetic ablation of the PC, or knockdown of ciliary adenylate cyclase 3, caused hotspot disappearance and migratory defects, with defective centrosome dynamics and altered nucleokinesis. Delocalization of PKA from the centrosome phenocopied the migratory defects. Our results show that the PC and centrosome form a single cAMP signaling unit dynamically regulating migration, further highlighting the centrosome as a signaling hub.
The Persistence Potential of Mobile Genetic Elements

Mobile genetic elements (MGEs), such as plasmids, phages, and transposons, play a critical role in mediating the transfer and maintenance of diverse traits and functions in microbial communities. This role depends on the ability of MGEs to persist. For a community consisting of multiple populations transferring multiple MGEs, however, the conditions underlying the persistence of these MGEs are poorly understood. Computationally, this difficulty arises from the combinatorial explosion associated with describing the gene flow in a complex community using the conventional modeling framework. Here, we describe an MGE-centric framework that makes it computationally feasible to analyze such transfer dynamics. Using this framework, we derive the persistence potential: a general, heuristic metric that predicts the persistence and abundance of any MGEs. We validate the metric with engineered microbial consortia transferring mobilizable plasmids and quantitative data available in the literature. Our modeling framework and the resulting metric have implications for developing a quantitative understanding of natural microbial communities and guiding the engineering of microbial consortia.
Fungal chromatin mapping identifies BasR, as the regulatory node of bacteria-induced fungal secondary metabolism

The eukaryotic epigenetic machinery is targeted by bacteria to reprogram the response of eukaryotes during their interaction with microorganisms. In line, we discovered that the bacterium Streptomyces rapamycinicus triggered increased chromatin acetylation and thus activation of the silent secondary metabolism ors gene cluster leading to the production of orsellinic acid in the fungus Aspergillus nidulans. Using this model we aim at understanding molecular mechanisms of communication between bacteria and eukaryotic microorganisms based on bacteria-triggered chromatin modification. By genome-wide ChIP-seq analysis of acetylated histone H3 (H3K9ac, H3K14ac) we uncovered the unique chromatin landscape in A. nidulans upon co-cultivation with S. rapamycinicus. Genome-wide acetylation of H3K9 correlated with increased gene expression, whereas H3K14 appears to function in transcriptional initiation by providing a docking side for regulatory proteins. In total, histones belonging to six secondary metabolism gene clusters showed higher acetylation during co-cultivation including the ors, aspercryptin, cichorine, sterigmatocystin, anthrone and 2,4-dihydroxy-3-methyl-6-(2-oxopropyl)benzaldehyde gene cluster with the emericellamide cluster being the only one with reduced acetylation and expression. Differentially acetylated histones were also detected in genes involved in amino acid and nitrogen metabolism, signaling, and genes encoding transcription factors. In conjunction with LC-MS/MS and MALDI-MS imaging, molecular analyses revealed the cross-pathway control and Myb-like transcription factor BasR as regulatory nodes for transduction of the bacterial signal in the fungus. The presence of basR in other fungal species allowed forecasting the inducibility of ors-like gene clusters by S. rapamycinicus in these fungi, and thus their effective interaction with activation of otherwise silent gene clusters.