Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource sharing leads to the emergence of division of labour

Division of labour occurs in a broad range of organisms. Yet, how division of labour can emerge in the absence of pre-existing interindividual differences is poorly understood. Using a simple but realistic model, we show that in a group of initially identical individuals, division of labour emerges spontaneously if returning foragers share part of their resources with other group members. In the absence of resource sharing, individuals follow an activity schedule of alternating between foraging and other tasks. If non-foraging individuals are fed by other individuals, their alternating activity schedule becomes interrupted, leading to task specialisation and the emergence of division of labour. Furthermore, nutritional differences between individuals reinforce division of labour. Such differences can be caused by increased metabolic rates during foraging or by dominance interactions during resource sharing. Our model proposes a plausible mechanism for the self-organised emergence of division of labour in animal groups of initially identical individuals. This mechanism could also play a role for the emergence of division of labour during the major evolutionary transitions to eusociality and multicellularity.
Context-dependent sensory modulation underlies Bayesian vocal sequence perception

Vocal communication in both songbirds and humans relies on categorical perception of smoothly varying acoustic spaces. Vocal perception can be biased by expectation and context, but the mechanisms of this bias are not well understood. We developed a behavioral task in which songbirds, European starlings, are trained to to classify smoothly varying song syllables in the context of predictive syllable sequences. We find that syllable-sequence predictability biases perceptual categorization following a Bayesian model of probabilistic information integration. We then recorded from populations of neurons in the auditory forebrain while birds actively categorized song syllables, observing large proportions of neurons that track the smoothly varying natural feature space of syllable categories. We observe that predictive information in the syllable sequences dynamically modulates sensory neural representations. These results support a Bayesian model of perception where predictive information acts to dynamically reallocate sensory neural resources, sharpening acuity (i.e. the likelihood) in high-probability regions of stimulus space. One-Sentence Summary Predictive information in vocal sequences biases Bayesian categorical perception through rapid sensory reorganization. Graphical Abstract
Integrative chromatin domain annotation through graph embedding of Hi-C data

Motivation The organization of the genome into domains plays a central role in gene expression and other cellular activities. Researchers identify genomic domains mainly through two views: 1D functional assays such as ChIP-seq, and chromatin conformation assays such as Hi-C. Fully understanding domains requires integrative modeling that combines these two views. However, the predominant form of integrative modeling uses segmentation and genome annotation (SAGA) along with the rigid assumption that loci in contact are more likely to share the same domain type, which is not necessarily true for epigenomic domain types and genome-wide chromatin interactions. Results Here, we present an integrative approach that annotates domains using both 1D functional genomic signals and Hi-C measurements of genome-wide 3D interactions without the use of a pairwise prior. We do so by using a graph embedding to learn structural features corresponding to each genomic region, then inputting learned structural features along with functional genomic signals to a SAGA algorithm. We show that our domain types recapitulate well-known subcompartments with an additional granularity that distinguishes a combination of the spatial and functional states of the genomic regions. In particular, we identified a division of the previously-identified A2 subcompartment such that the divided domain types have significantly varying expression levels. Availability https://github.com/nedashokraneh/IChDA Contact maxwl@sfu.ca Supplementary information
WNT5A-RHOA axis is a new vulnerability in small-cell lung cancer

WNT signaling presents an attractive target for cancer therapy due to its widespread oncogenic role. However, the molecular players involved in WNT signaling and the impact of their perturbation remain unknown for numerous recalcitrant cancers including small-cell lung cancer (SCLC). Here we show that beta-catenin, a master mediator of canonical WNT signaling, is not required for SCLC development in genetically engineered mouse models (GEMMs) and its transcriptional program is largely silenced during tumor development. Instead, inactivation of p130 in SCLC cells induces expression of WNT5A, a ligand for beta-catenin-independent WNT pathways. WNT5A is both sufficient and required for SCLC development and cell proliferation and selectively induces Rhoa transcription and activates RHOA protein to drive SCLC. Rhoa knockout suppresses SCLC development in vivo, and chemical perturbation of RHOA selectively inhibits SCLC cell proliferation. These findings suggest a novel requirement for the WNT5A-RHOA axis in SCLC that is distinct from other noncanonical WNT pathways. This vulnerability of p130-WNT5A-RHOA pathway provides critical insight into the development of novel therapeutic strategies for the recalcitrant cancer, as well as the stratification of patients who may benefit from them. This study also sheds new light on the heterogeneity of WNT signaling and the molecular determinants of its cell-type specificity.
Stepwise origin and evolution of a transcriptional activator and repressor system integrating nutrient signaling in plants

Plants possess a unique transcriptional regulatory system in which two related MYB-related transcription factors (TFs) coordinate gene expression according to phosphate (Pi) and nitrogen (N) availability. The Phosphorus Starvation Response (PSR) type TFs are transcriptional activators integrating the cellular Pi sensing machinery and gene regulation majorly under Pi starvation. The Hypersensitivity To Low Pi-Elicited Primary Root Shortening (HRS) type TFs are transcriptional repressors integrating the Pi and N availability signals through different feedback loops. They are highly connected through multiple signaling loops to finetune the transcriptional responses according to nutrient availability. Molecular functions of these TFs are fairly uncovered in model systems; however, how plants evolved this activator-repressor system is currently unknown. In this study, using sensitive evolutionary analysis, we identified a stepwise origin of the PSR-HRS regulatory system in plants. The PSR TFs were originated before the split of Prasinodermophyta and Chlorophyta. The HRS TFs were originated later in the Streptrophycean algae. We also identified the asymmetric expansion of this TF repertoire in land plants majorly shaped by genome duplication and triplication events. The phylogenetic reconstruction coupled with motif analysis revealed that the origin of the specific accessory motifs is a major contributing factor in the functional divergence which led to the evolution of different sub-families preceding the angiosperm radiation. The spatiotemporal gene expression analysis in different developmental stages and nutrient availability conditions in angiosperms identified a critical role of expression divergence in shaping the functions of these TF families which is essential for adaptive plasticity of plants.
Systems Biology Approach of Understanding Insulin Resistance: Linkage between Type 2 Diabetes & Alzheimer’s Disease

Insulin resistance (IR) is a physiological condition in which cells in the body become resistant to insulin. It is a known risk factor associated to type 2 diabetes (T2D). Recently, the idea that IR plays an important role in the progression of Alzheimer’s disease (AD) has been gaining a lot of attention. Comparing the components of the insulin signalling pathway in relation to T2D and AD, there seems to be a lot of commonality. However, on what role IR plays in linking T2D and AD remains unknown. Through systems biology approach, we extended an existing mathematical model (i.e. ODE based) to study and understand the role IR plays in linking T2D and AD. The simulations, together with the experimental data collected from the literature, show that the common components in T2D and AD express the same dynamical behaviors. This result provides the bases for further modelling of the insulin signaling pathway in determining the link between T2D and AD.
Genomic Underpinnings of Population Persistence in Isle Royale Moose

Island ecosystems provide models to assess the impacts of isolation on population persistence. However, most studies of persistence have focused on a single species, without comparisons to other organisms they interact with in the ecosystem. The simple predator-prey system of moose and gray wolves on Isle Royale provides allows a direct contrast of genetic variation in a prey species with their natural predator. Wolves on Isle Royale exhibited signs of severe inbreeding depression, which nearly drove the population to extinction in 2019. In the relative absence of wolves, the moose population has thrived and exhibits no obvious signs of inbreeding depression despite being isolated for ∼120 years and having low genetic diversity. Here, we examine the genomic underpinnings of population persistence in the Isle Royale moose population. We document high levels of inbreeding in the population, roughly as high as the wolf population at the time of its decline. However, inbreeding in the moose population manifests in the form of intermediate-length runs of homozygosity indicative of gradual inbreeding, contrasting with the severe recent inbreeding observed in the wolf population. Using simulations, we demonstrate that this more gradual inbreeding in the moose population has resulted in an estimated 50% purging of the inbreeding load, helping to explain the continued persistence of the population. However, we also document notable increases in genetic load, which could eventually threaten population viability over the long term. Finally, we document low diversity in mainland North American moose populations due to a severe founder event occurring near the end of the Holocene. Overall, our results demonstrate a complex relationship between inbreeding, genetic diversity, and population viability that highlights the importance of maintaining isolated populations at moderate size to avert extinction from genetic factors. Significance statement Isolated wildlife populations face a high risk of extinction due in part to the deleterious consequences of inbreeding. Whether purifying natural selection can overcome these negative impacts by “purging” harmful recessive mutations is a topic of active debate. We characterized the extent of purging in an isolated moose population. Our results demonstrate signatures of gradual inbreeding in the population, ideal circumstances to facilitate purging. Using simulations, we demonstrate substantial potential for purging in the population, though we also show that fitness is reduced by small population size and inbreeding. Our findings provide insight into the mechanisms enabling persistence in isolated populations, with implications for conserving the growing number of isolated populations worldwide.
Dcifer: an IBD-based method to calculate genetic distance between polyclonal infections

An essential step toward reconstructing pathogen transmission and answering epidemiologically relevant questions from genomic data is obtaining pairwise genetic distance between infections. For recombining organisms such as malaria parasites, relatedness measures quantifying recent shared ancestry would provide a meaningful distance, suggesting methods based on identity by descent (IBD). While the concept of relatedness and consequently an IBD approach is fairly straightforward for individual parasites, the distance between polyclonal infections, which are prevalent in malaria, presents specific challenges and awaits a general solution that could be applied to infections of any clonality and accommodate multiallelic (e.g. microsatellite or microhaplotype) and biallelic (SNP) data. Filling this methodological gap, we present Dcifer (Distance for complex infections: fast estimation of relatedness), a method for calculating genetic distance between polyclonal infections, which is designed for unphased data, explicitly accounts for population allele frequencies and complexity of infection, and provides reliable inference. Dcifer’s IBD-based framework allows us to define model parameters that represent interhost relatedness and to propose corresponding estimators with attractive statistical properties. By using combinatorics to account for unobserved phased haplotypes, Dcifer is able to quickly process large datasets and estimate pairwise relatedness along with measures of uncertainty. We show that Dcifer delivers accurate and interpretable results and detects related infections with statistical power that is 2-4 times greater than that of approaches based on identity by state. Applications to real data indicate that relatedness structure aligns with geographic locations. Dcifer is implemented in a comprehensive publicly available software package.
Estimation of cochlear frequency selectivity using a convolution model of forward-masked compound action potentials

Frequency selectivity is a fundamental property of the peripheral auditory system; however, the invasiveness of auditory nerve (AN) experiments limits its study in the human ear. Compound action potentials (CAPs) associated with forward-masking have been suggested as an alternative means to assess cochlear frequency selectivity. Previous methods relied on an empirical comparison of AN and CAP tuning curves in animal models, arguably not taking full advantage of the information contained in forward-masked CAPs. In this work, we seek to provide a direct estimate of the quality factor characterizing AN frequency tuning using many forward-masked CAP responses. The method is based on a convolution model of the CAP that takes into account the masking of AN populations induced by notched-noise maskers with various notch widths and attenuations. The model produces masking patterns that, once convolved by a unitary response, predict forward-masked CAP waveforms. Model parameters, including those characterizing frequency selectivity, are fine-tuned by minimizing waveform prediction errors across the different masking conditions, yielding robust estimates. The method was applied to click-evoked CAPs at the round window of anesthetized chinchillas. The estimated quality factor Q10 as a function of center frequency is shown to closely match the average quality factor obtained from AN-fiber tuning curves, without the need for an empirical correction factor. Beyond the estimation of frequency selectivity, the proposed model proves to be accurate in predicting forward-masked CAP responses, and therefore could be extended to study more complex aspects of cochlear signal processing using a similar experimental approach.
Structure of the dengue virus RNA promoter

Dengue virus, a single-stranded positive sense RNA virus, is the most prevalent mosquito-borne pathogen in the world. Like all RNA viruses, it uses conserved structural elements within its genome to control essential replicative steps. A 70 nucleotides stem-loop RNA structure (called SLA) found at the 5’-end of the genome of all flaviviruses, functions as the promoter for viral replication. This highly conserved structure interacts with the viral polymerase NS5 to initiate RNA synthesis. Here we report the NMR structure of a monomeric SLA from Dengue virus serotype 1, assembled to high-resolution from independently folded structural elements. The DENV1 SLA has an L-shape structure, where the top and side helices are coaxially-stacked and the bottom helix is roughly perpendicular to them. Because the sequence is highly conserved among different flavivirus genomes, it is likely that the three-dimensional fold and local structure of SLA are also conserved among flaviviruses and required for efficient replication. This work provides structural insight into the Dengue promoter and provides the foundation for the discovery of new antiviral drugs that target this essential replicative step.
MetaPhage: an Automated Pipeline for Analyzing, Annotating, and Classifying Bacteriophages in Metagenomics Sequencing Data

Bacteriophages (viruses that infect bacteria) are the most abundant biological entities on earth and are increasingly studied as members of the resident microbiota community in many environments, from oceans to soils and the human gut. Their identification is of great importance to better understand complex bacterial dynamics and microbial ecosystem function. ABSTRACT Phages are the most abundant biological entities on the planet, and they play an important role in controlling density, diversity, and network interactions among bacterial communities through predation and gene transfer. To date, a variety of bacteriophage identification tools have been developed that differ in the phage mining strategies used, input files requested, and results produced. However, new users attempting bacteriophage analysis can struggle to select the best methods and interpret the variety of results produced. Here, we present MetaPhage, a comprehensive reads-to-report pipeline that streamlines the use of multiple phage miners and generates an exhaustive report. The report both summarizes and visualizes the key findings and enables further exploration of key results via interactive filterable tables. The pipeline is implemented in Nextflow, a widely adopted workflow manager that enables an optimized parallelization of tasks in different locations, from local server to the cloud; this ensures reproducible results from containerized packages. MetaPhage is designed to enable scalability and reproducibility; also, it can be easily expanded to include new miners and methods as they are developed in this continuously growing field. MetaPhage is freely available under a GPL-3.0 license at https://github.com/MattiaPandolfoVR/MetaPhage. IMPORTANCE Bacteriophages (viruses that infect bacteria) are the most abundant biological entities on earth and are increasingly studied as members of the resident microbiota community in many environments, from oceans to soils and the human gut. Their identification is of great importance to better understand complex bacterial dynamics and microbial ecosystem function. A variety of metagenome bacteriophage identification tools have been developed that differ in the phage mining strategies used, input files requested, and results produced. To facilitate the management and the execution of such a complex workflow, we developed MetaPhage (MP), a comprehensive reads-to-report pipeline that streamlines the use of multiple phage miners and generates an exhaustive report. The pipeline is implemented in Nextflow, a widely adopted workflow manager that enables an optimized parallelization of tasks. MetaPhage is designed to enable scalability and reproducibility and offers an installation-free, dependency-free, and conflict-free workflow execution.
First clinical validation of whole genome screening on standard trophectoderm biopsies of preimplantation embryos

Whole Genome Sequencing (WGS) is used in healthcare and in the clinic, with the notable exception of preimplantation genetic testing (PGT). In PGT, only a few cells are available for sequencing, requiring DNA amplification which reduces data quality, sequence fidelity and sharply limits subsequent clinical impact. Here we demonstrate the first clinical validation of WGS on embryo biopsies using our lab development protocol, opening the door to broad use of WGS in fertility. We find that amplified DNA with comparable sensitivity and specificity to genomic DNA when performing whole genome sequencing assays. DNA amplification on cell lines and donated human embryos had an amplification success rate of >99.9% and 98.2% respectively and accuracy on both was >99.9% on aneuploidy status. GIAB samples (Genome in the Bottle reference NA12878) showed that our amplified DNA was broadly comparable to genomic DNA (99.99% accuracy, 99.99% specificity, 98.0% sensitivity and 98.1% precision). Using our assay, we were able to call variants, detect mitochondrial heteroplasmy, perform high precision screening without access to parental genomes, detect compound heterozygous variants, and score microdups/dels and uniparental disomies (to reduce risk of diseases such as DiGeorge syndrome and Prader-Willi syndrome). Our clinical study suggests that the full spectrum of traditional clinical genome bioinformatics, so far reserved to large samples, can now be performed on embryos before implantation.
Improving the validity of neuroimaging decoding tests of invariant and configural neural representation

Many research questions in sensory neuroscience involve determining whether the neural representation of a stimulus property is invariant or specific to a particular stimulus context (e.g., Is object representation invariant to translation? Is the representation of a face feature specific to the context of other face features? ). Between these two extremes, representations may also be context-tolerant or context-sensitive. Most neuroimaging studies have used operational tests in which a target property is inferred from a significant test against the null hypothesis of the opposite property. For example, the popular cross-classification test concludes that representations are invariant or tolerant when the null hypothesis of specificity is rejected. A recently developed neurocomputational theory provides two insights regarding such tests. First, tests against the null of context-specificity, and for the alternative of context-invariance, are prone to false positives due to the way in which the underlying neural representations are transformed into indirect measurements in neuroimaging studies. Second, jointly performing tests against the nulls of invariance and specificity allows one to reach more precise and valid conclusions about the underlying representations. Here, we provide empirical and computational evidence supporting both of these theoretical insights. In our empirical study, we use encoding of orientation and spatial position in primary visual cortex as a case study, as previous research has established that these properties are encoded in a context-sensitive way. Using fMRI decoding, we show that the cross-classification test produces false-positive conclusions of invariance, but that more valid conclusions can be reached by jointly performing tests against the null of invariance. The results of two simulations further support both of these conclusions. We conclude that more valid inferences about invariance or specificity of neural representations can be reached by jointly testing against both hypotheses, and using neurocomputational theory to guide the interpretation of results. Author Summary Many research questions in sensory neuroscience involve determining whether the representation of a stimulus property is invariant or specific to a change in stimulus context (e.g., translation-invariant object representation; configural representation of face features). Between these two extremes, representations may also be context-tolerant or context-sensitive. Most neuroimaging research has studied invariance using operational tests, among which the most widely used in recent years is cross-classification. We provide evidence from a functional MRI study, simulations, and theoretical results supporting two insights regarding such tests: (1) tests that seek to provide evidence for invariance (like cross-classification) have an inflated false positive rate, but (2) using complementary tests that seek evidence for context-specificity leads to more valid conclusions.