Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic variations (eQTLs) in muscle transcriptome and mitochondrial genes, and trans-eQTL molecular pathways in feed efficiency from Danish breeding pigs

Feed efficiency (FE) is a key trait in pig production, as it has both high economic and environmental impact. FE is a challenging phenotype to study, as it is complex and affected by many factors, such as metabolism, growth and activity level. Furthermore, testing for FE is expensive, as it requires costly equipment to measure feed intake of individual animals, making FE biomarkers valuable. Therefore, there has been a desire to find single nucleotide polymorphisms (SNPs) as biomarkers, to assist with improved selection and improve our biological understanding of FE. We have done a cis- and trans-eQTL (expressed quantitative trait loci) analysis, in a population of Danbred Durocs (N=11) and Danbred Landrace (N=27) using both a linear and Anova model. We used bootstrapping and enrichment analysis to validate and analyze our detected eQTLs. We identified 15 eQTLs with FDR < 0.01, affecting several genes found in previous studies of commercial pig breeds. Examples include IFI6, PRPF39, TMEM222, CSRNP1,PARK7 and MFF. The bootstrapping results showed statistically significant enrichment of eQTLs with p-value < 0.01 (p-value < 2.2×0-16) in both cis and trans-eQTLs. Based on this, enrichment analysis of top trans-eQTLs revealed high enrichment for gene categories and gene ontologies associated with genomic context and expression regulation. This includes transcription factors (p-value=1.0×10-13), DNA-binding (GO:0003677, p-value=8.9×10-14), DNA-binding transcription factor activity (GO:0003700,) nucleus gene (GO:0005634, p-value<2.2×10-16), positive regulation of expression (GO:0010628), negative regulation of expression (GO:0010629, p-value<2.2×10-16). These results would be useful for future genome assisted breeding of pigs to improve FE, and in the improved understanding of the functional mechanism of trans-eQTLs.
Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions

Abstract For the first time in history, on March 17, 2020, the European Union closed all its external borders in an attempt to contain the spreading of the coronavirus 2019, COVID-19. Throughout two past months, governments around the world have implemented massive travel restrictions and border control to mitigate the outbreak of this global pandemic. However, the precise effects of travel restrictions on the outbreak dynamics of COVID-19 remain unknown. Here we combine a global network mobility model with a local epidemiology model to simulate and predict the outbreak dynamics and outbreak control of COVID-19 across Europe. We correlate our mobility model to passenger air travel statistics and calibrate our epidemiology model using the number of reported COVID-19 cases for each country. Our simulations show that mobility networks of air travel can predict the emerging global diffusion pattern of a pandemic at the early stages of the outbreak. Our results suggest that an unconstrained mobility would have significantly accelerated the spreading of COVID-19, especially in Central Europe, Spain, and France. Ultimately, our network epidemiology model can inform political decision making and help identify exit strategies from current travel restrictions and total lockdown.
Socio-ecological network structures from process graphs

We propose a process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species. Originally developed as a process engineering tool for designing industrial plants, the P-graph framework has key advantages over conventional ecological network analysis (ENA) techniques. A P-graph is a bipartite graph consisting of two types of nodes, which we propose to represent components of an ecosystem. Compartments within ecosystems (e.g., organism species) are represented by one class of nodes, while the roles or functions that they play relative to other compartments are represented by a second class of nodes. This bipartite graph representation enables a powerful, unambiguous representation of relationships among ecosystem compartments, which can come in tangible (e.g., mass flow in predation) or intangible form (e.g., symbiosis). For example, within a P-graph, the distinct roles of bees as pollinators for some plants and as prey for some animals can be explicitly represented, which would not otherwise be possible using conventional ENA. After a discussion of the mapping of ecosystems into P-graph, we also discuss how this framework can be used to guide understanding of complex networks that exist in nature. Two component algorithms of P-graph, namely maximal structure generation (MSG) and solution structure generation (SSG), are shown to be particularly useful for ENA. This method can be used to determine the (a) effects of loss of specific ecosystem compartments due to extinction, (b) potential efficacy of ecosystem reconstruction efforts, and (c) maximum sustainable exploitation of human ecosystem services by humans. We illustrate the use of P-graph for the analysis of ecosystem compartment loss using a small-scale stylized case study, and further propose a new criticality index that can be easily derived from SSG results. Author summary In this study, we propose the novel application of the process graph (P-graph) methodology to the analysis of ecological networks. P-graph was originally developed for engineering design problems; in our work, we show how its five axioms and two algorithms – maximal structure generation (MSG) and solution structure generation (SSG) can be adapted to the problem of understanding complex interactions in ecosystems. The methodology allows multiple types of interactions among ecosystem components to be handled simultaneously based on representation as a bipartite graph. Complete network structures can be deduced from knowledge of local interactions of components using MSG. Finally, all structurally feasible networks of viable ecosystems can be identified with SSG. We illustrate the features of the P-graph methodology with a stylized illustrative example.
Omics-Based Interaction Framework – a systems model to reveal molecular drivers of synergy

Bioactive molecule library screening strategies may empirically identify effective combination therapies. However, without a systems theory to interrogate synergistic responses, the molecular mechanisms underlying favorable drug-drug interactions remain unclear, precluding rational design of combination therapies. Here, we introduce Omics-Based Interaction Framework (OBIF) to reveal molecular drivers of synergy through integration of statistical and biological interactions in supra-additive biological responses. OBIF performs full factorial analysis of feature expression data from single vs. dual factor exposures to identify molecular clusters that reveal synergy-mediating pathways, functions and regulators. As a practical demonstration, OBIF analyzed a therapeutic dyad of immunostimulatory small molecules that induces synergistic protection against influenza A pneumonia. OBIF analysis of transcriptomic and proteomic data identified biologically relevant, unanticipated cooperation between RelA and cJun that we subsequently confirmed to be required for the synergistic antiviral protection. To demonstrate generalizability, OBIF was applied to data from a diverse array of Omics platforms and experimental conditions, successfully identifying the molecular clusters driving their synergistic responses. Hence, OBIF is a phenotype-driven systems model that supports multiplatform exploration of synergy mechanisms.
Auditory features modelling reveals sound envelope representation in striate cortex

The striate cortex is no longer considered exclusively visual in its function. Proofs that its activity is modulated by acoustic inputs have accrued. By employing category-based and feature modeling approaches, here we characterized V1 activity (in absence of retinal input) during the processing of natural and synthetically derived sounds. First, we showed that distinct sound categories could be dissociated by the analysis of V1 multivoxel response patterns. Hence, we assessed whether a hallmark of sound neural representations is mapped in V1. In each sound category, we modeled sound envelopes and assessed whether these were represented at the single-voxel level in the striate cortex and, as a control, in the temporal cortex. The hierarchical organization of sound categories allowed to exert control over dimensions that could spuriously lead to sound envelope V1 mapping. Variations of sound amplitude over time were successfully decoded in V1 regardless of the category class. Results confirm that the human striate cortex receives acoustic category-based input and demonstrate that V1 is a genuine locus of sound envelope representation.
Inducible Depletion of Calpain-2 Mitigates Abdominal Aortic Aneurysm in Mice

BACKGROUND Cytoskeletal structural proteins maintain cell structural integrity by bridging extracellular matrix (ECM) with contractile filaments. During AAA development, (i) aortic medial degeneration is associated with loss of smooth muscle cell (SMC) integrity, and (ii) fibrogenic mesenchymal cells (FMSCs) mediates ECM remodeling. Calpains cleave cytoskeletal proteins that maintain cell structural integrity. Pharmacological inhibition of calpains exert beneficial effects on Angiotensin II (AngII)-induced AAAs in low density receptor deficient (LDLR-/-) mice. OBJECTIVES To evaluate the functional contribution of FMSCs-derived calpain-2 on (i) cytoskeletal structural protein and ECM alterations, and (ii) AAA progression. METHODS Calpain-2 protein, and cytoskeletal protein (e.g. filamin or talin) fragmentation in human and mice AAA tissues were assessed by immunohistochemical and western blot analyses. LDLR-/- mice that were either inducible-whole body or FMSC-specific calpain-2 deficient were fed a fat-enriched diet and infused with AngII for 4 weeks. The association of cytoskeletal protein to ECM was evaluated using aortic SMCs, in vitro. In addition, the effect of calpain-2 deficiency on the stability of established AAA was examined. RESULTS Calpain-2 protein, and filamin/talin fragmentation are significantly elevated in AAAs. Ubiquitous or FMSC-specific depletion of calpain-2 suppressed AngII-induced AAAs, filamin/talin fragmentation and promoted ECM protein, collagen. Calpain-2 silencing in SMCs reduced AngII-induced filamin/talin fragmentation. In addition, silencing of filamin or talin in SMCs significantly reduced collagen protein. Furthermore, calpain-2 deficiency suppressed established AAA rupture. CONCLUSION Calpain-2 activation promotes cytoskeletal structural protein fragmentation and ECM degradation of experimental AAA aortas. Treatment with calpain-2 specific inhibitor may facilitate the clinical management of AAA.
Comprehensive transcriptome data of normal and Ascosphaera apis-infected western honeybee larval guts

Apis mellifera ligustica is a well-known subspecies of western honeybee, Apis mellifera. Ascosphaera apis is a common fungal pathogen of honeybee larvae, resulting in Chalkbrood disease. In this article, deep sequencing of un-treated 4-, 5-, and 6-day-old larval guts (AmCK1, AmCK2, AmCK3) and A. apis-treated 4-, 5- and 6-day-old larval guts (AmT1, AmT2, AmT3) of Apis mellifera ligustica were conducted using Illumina HiSeq™ 4000 platform. In total, 85811046, 81962296, 85636572, 79267686, 82889882, and 100211796 raw reads were respectively yielded from above-mentioned six groups. The result of sequencing satuation analysis suggested that the sequencing depth in this work was enough to detect nearly all expressed genes. After quality control, 85739414, 81896402, 85573798, 79202304, 82828926, and 100128692 clean reads were obtained. Additionally, the GC content of each group was above 45.26%. Furthermore, 47035852, 65612676, 71803878, 62560904, 65018360, and 56278272 clean reads were mapped to the Apis mellifera genome, including 41221479, 61172916, 66724233, 57531335, 60245732, and 52638986 unique mapped clean reads, and 2078427, 986825, 915375, 1082925, 1097130, and 716436 multiple mapped clean reads. In addition, exons were the most abundant regions mapped by clean reads, follow by intergenic regions and introns. The strand-specific cDNA library-based RNA sequencing data documented here will faciliate study on molecualr mechanisms underlying host immune response and host-pathogen interaction during chalkbrood disease, and benefit understanding of non-coding RNA-mediated cross-kingdom regulation between A. m. ligustica larvae and A. apis. Value of the Data This data reported here could be used to explore circRNAs, lncRNAs, mRNAs and their competing endogenous RNA networks involved in response of western honeybee larvae to Ascosphaera apis infection. The current data contributes to better understanding mechanisms regulating host-pathogen interaction during chalkbrood disease. Our data can provide novel insights into understanding non-coding RNA-mediated cross-kingdom regulation between Apis mellifera ligustica and A. apis.
Optogenetic stimulation of the basolateral amygdala-medial entorhinal cortex pathway after spatial training has sex-specific effects on downstream activity-regulated cytoskeletal-associated protein expression

Previous work from our laboratory suggests that projections from the basolateral amygdala (BLA) to the medial entorhinal cortex (mEC) are a critical pathway by which the BLA modulates the consolidation of spatial learning. Posttraining optogenetic stimulation of this pathway enhances retention of spatial memories. Evidence also indicates that intra-BLA administration of memory-enhancing drugs increases protein levels of activity-regulated cytoskeletal-associated protein (ARC) in the dorsal hippocampus (DH) and that blocking ARC in the DH impairs spatial memory consolidation. Yet, whether optical manipulations of the BLA-mEC pathway after spatial training also alter ARC in the DH is unknown. To address this question, male and female Sprague-Dawley rats received optogenetic stimulation of the BLA-mEC pathway immediately after spatial training using a Barnes maze and, 45 min later, were sacrificed for ARC analysis. Initial experiments found that spatial training increased ARC levels in the DH of rats above those observed in control rats and rats that underwent a cued-response version of the task. Optogenetic stimulation of the BLA-mEC pathway following spatial training, using parameters effective at enhancing spatial memory consolidation, enhanced ARC protein levels in the DH of male rats without affecting ARC levels in the dorsolateral striatum (DLS) or somatosensory cortex. In contrast, similar optical stimulation decreased ARC protein levels in the DLS of female rats without altering ARC in the DH or somatosensory cortex. Together, the present findings suggest a mechanism by which BLA-mEC stimulation enhances spatial memory consolidation in rats and reveals a possible sex-difference in this mechanism.
The activity of human enhancers is modulated by the splicing of their associated lncRNAs

Pervasive enhancer transcription is at the origin of more than half of all long noncoding RNAs in humans. Transcription of enhancer-associated long noncoding RNAs (elncRNA) contribute to their cognate enhancer activity and gene expression regulation in cis. Recently, splicing of elncRNAs was shown to be associated with elevated enhancer activity. However, whether splicing of elncRNA transcripts is a mere consequence of accessibility at highly active enhancers or if elncRNA splicing directly impacts enhancer function, remains unanswered. We analysed genetically driven changes in elncRNA expression, in humans, to address this outstanding question. We showed that splicing related motifs within multi-exonic elncRNAs evolved under selective constraints during human evolution, suggesting the processing of these transcripts is unlikely to have resulted from transcription across spurious splice sites. Using a genome-wide and unbiased approach, we used nucleotide variants as independent genetic factors to directly assess the causal relationship that underpin elncRNA splicing and their cognate enhancer activity. We found that the splicing of most elncRNAs is associated with changes in chromatin signatures at cognate enhancers and target mRNA expression. We conclude that efficient and conserved processing of enhancer-associated elncRNAs contributes to enhancer activity.
Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets

Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive. Consequently, QSAR models predicting the activity on a target have gained importance in drug discovery or in the de-risking of chemicals. However, a restricted number of targets are well characterized and hold enough data to build such in silico models. A good alternative to individual target evaluations is to use integrative evaluations such as transcriptomics obtained from compound-induced gene expression measurements derived from cell cultures. The advantage of these particular experiments is to capture the consequences of the interaction of compounds on many possible molecular targets and biological pathways, without having any constraints concerning the chemical space. In this work, we assessed the value of a large public dataset of compound-induced transcriptomic data, to predict compound activity on a selection of 69 molecular targets. We compared such descriptors with other QSAR descriptors, namely the Morgan fingerprints (similar to extended-connectivity fingerprints). Depending on the target, active compounds could show similar signatures in one or multiple cell lines, whether these active compounds shared similar or different chemical structures. Random forest models using gene expression signatures were able to perform similarly or better than counterpart models built with Morgan fingerprints for 25% of the target prediction tasks. These performances occurred mostly using signatures produced in cell lines showing similar signatures for active compounds toward the considered target. We show that compound-induced transcriptomic data could represent a great opportunity for target prediction, allowing to overcome the chemical space limitation of QSAR models.
SRF is a non-histone methylation target of KDM2B and SET7 in the regulation of myogenesis

Demethylation of histone lysines, one of the most important modifications in transcriptional regulation, is associated with various physiological states. KDM2B is a histone H3K4, H3K36, and H3K79 demethylase associated with the repression of transcription. Here, we present a novel mechanism by which KDM2B demethylates serum response factor (SRF) K165 to negatively regulate muscle differentiation, which is counteracted by histone methyltransferase SET7. We show that KDM2B inhibited skeletal muscle differentiation by inhibiting the transcription of SRF-dependent genes. Both KDM2B and SET7 regulated the balance of SRF K165 methylation. SRF K165 methylation was required for the transcriptional activation of SRF and for the promoter occupancy of SRF-dependent genes. SET7 inhibitors blocked muscle cell differentiation. Taken together, these data indicate that SRF is a non-histone target of KDM2B and that the methylation balance of SRF maintained by KDM2B and SET7 plays an important role in muscle cell differentiation.
multicrispr: gRNA design for prime editing and parallel targeting of thousands of targets

A bioinformatics R package for a fast and generic guideRNA design, optimized for large-scale perturbation assays. Targeting the coding genome to introduce nucleotide deletions/insertions via the CRISPR/Cas9 technology has become a standard procedure. It has quickly spawned a multitude of methods such as prime editing, APEX proximity labeling, or homology directed repair, for which supporting bioinformatics tools are, however, lagging behind. New CRISPR/Cas9 applications often require specific gRNA design functionality, and a generic tool is critically missing. Here, we introduce multicrispr, an R/bioconductor tool, intended to design individual gRNAs and complex gRNA libraries. The package is easy to use; detects, scores, and filters gRNAs on both efficiency and specificity; visualizes and aggregates results per target or CRISPR/Cas9 sequence; and finally returns both genomic ranges and sequences of gRNAs. To be generic, multicrispr defines and implements a genomic arithmetic framework as a basis for facile adaptation to techniques recently introduced such as prime editing or yet to arise. Its performance and design concepts such as target set–specific filtering render multicrispr a tool of choice when dealing with screening-like approaches.
Selfies as Duplex Non-verbal Communication: Human—Media Interaction, Human—Human Interaction, Case Study, and Research Manifesto

Using conceptual tools from semiotics, proxemics, and sensorimotor neuroscience, we propose a duplex model for understanding selfies as non-verbal communication involving an interplay between two layers of interaction: human—media (semiotically primary) and human—human (secondary). We suggest that this approach has promise as a tool for understanding this newborn form of human social behavior and its social, psychological, and neural underpinnings. To support our claim, we do several things. We offer a definition of selfies and outline our model. We review the existing literature on selfies as non-verbal communication to show that there is evidence bearing on our theoretical framework. We present a case study documenting how a combination of image analysis and kinematic measurement can be used to compare taker—smartphone interactions during selfie-taking with image features that play a role in the virtual interaction between the selfie-taker and his or her viewers. Our results support the feasibility of our approach and reveal a sex-related effect on the composition of selfies matching a related difference in the kinematic markers that describe the taker—smartphone interaction. Finally, we discuss outstanding questions in understanding selfies as duplex non-verbal communication and conclude by inviting further research on this topic.