Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanosensitive Ion Channel Piezo1 Regulates Myocyte Fusion during Skeletal Myogenesis

Mechanical stimuli such as stretch and resistance training are essential to regulate growth and function of skeletal muscle. However, the molecular mechanisms involved in sensing mechanical stress remain unclear. Here, the purpose of this study was to investigate the role of the mechanosensitive ion channel Piezo1 during myogenic progression. Muscle satellite cell-derived myoblasts and myotubes were modified with stretch, siRNA knockdown and agonist-induced activation of Piezo1. Direct manipulation of Piezo1 modulates terminal myogenic progression. Piezo1 knockdown suppressed myoblast fusion during myotube formation and maturation. This was accompanied by downregulation of the fusogenic protein Myomaker. Piezo1 knockdown also lowered Ca2+ influx in response to stretch. Conversely Piezo1 activation stimulated fusion and increased Ca2+ influx in response to stretch. These evidences indicate that Piezo1 is essential for myotube formation and maturation, which may have implications for msucular dystrophy prevention through its role as a mechanosensitive Ca2+ channel.
Computational design of genes encoding completely overlapping protein domains: Influence of genetic code and taxonomic rank

Overlapping genes (OLGs) with long protein-coding overlapping sequences are often excluded by genome annotation programs, with the exception of virus genomes. A recent study used a novel algorithm to construct OLGs from arbitrary protein domain pairs and concluded that virus genes are best suited for creating OLGs, a result which fitted with common assumptions. However, improving sequence evaluation using Hidden Markov Models shows that the previous result is an artifact originating from dataset-database biases. When parameters for OLG design and evaluation are optimized we find that 94.5% of the constructed OLG pairs score at least as highly as naturally occurring sequences, while 9.6% of the artificial OLGs cannot be distinguished from typical sequences in their protein family. Constructed OLG sequences are also indistinguishable from natural sequences in terms of amino acid identity and secondary structure, while the minimum nucleotide change required for overprinting an overlapping sequence can be as low as 1.8% of the sequence. Separate analysis of datasets containing only sequences from either archaea, bacteria, eukaryotes or viruses showed that, surprisingly, virus genes are much less suitable for designing OLGs than bacterial or eukaryotic genes. An important factor influencing OLG design is the structure of the standard genetic code. Success rates in different reading frames strongly correlate with their code-determined respective amino acid constraints. There is a tendency indicating that the structure of the standard genetic code could be optimized in its ability to create OLGs while conserving mutational robustness. The findings reported here add to the growing evidence that OLGs should no longer be excluded in prokaryotic genome annotations. Determining the factors facilitating the computational design of artificial overlapping genes may improve our understanding of the origin of these remarkable genetic constructs and may also open up exciting possibilities for synthetic biology.
Five new taxa of Piptadenia (Leguminosae: Mimosoideae) from Brazil

Two new species, Piptadenia santosii and P. irwinii, a new subspecies, P. obliqua subsp. brasiliensis and two new varieties, P. irwinii var. unijuga and P. killipii var. cacaophila are described from Bahia, Brazil. All but variety cacaophila are illustrated. Three of the taxa are closely related to plants growing thousands of kilometres from the Bahian localities and, as such, present fascinating phytogeographical disjunctions.
Microbial map of the world’s vineyards: Applying the concept of microbial terroir on a global scale

The specific microbial biodiversity linked to a particular vineyard location is reported to be a crucial aspect, in conjunction with edaphic, climatic and human factors, in the concept of wine terroir. These biogeographical patterns are known as microbial terroirs. This study applied an HTS amplicon library approach in order to conduct a global survey of vineyards’ soil microbial communities. In all, soil samples from 200 vineyards on four continents were analysed in an attempt to establish the basis for the development of a vineyard soil microbiome map to represent microbial wine terroirs on a global scale. This study established links between vineyard locations and microbial biodiversity on different scales: between continents and countries, and between different wine regions within the same country. Geography had a strong effect on the composition of microbial communities on a global scale, which was also maintained on a country scale. Furthermore, a predictive model was developed, based on random forest analyses, to discriminate between microbial patterns in order to identify the geographical source of the samples with reasonable precision. Finally this study is the first to describe the microbial community of new and northern wine-producing regions, such as Denmark, that could be of great interest for viticulture adaptation in a context of climate change.
DEELIG: A Deep Learning-based approach to predict protein-ligand binding affinity

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and has wide protein applications. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. In order to perform such analyses, it requires intense computational power and it becomes impossible to cover the entire chemical space of small molecules. It has been aided by a shift towards using Machine Learning-based methodologies that aids in binding prediction using regression. Recent developments using deep learning has enabled us to make sense of massive amounts of complex datasets. Herein, the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated Convolutional Neural Networks that find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies towards a diverse set of ligands. The models were trained and validated using a detailed methodology for feature extraction. We have also tested DEELIG on protein complexes relevant to the current public health scenario. Our approach to network construction and training on protein-ligand dataset prepared in-house has provided significantly better results than previously existing methods in the field.
Discovery of a New Neisseria gonorrhoeae Type IV Pilus Assembly Factor, TfpC

Most bacterial species express one or more extracellular organelles called pili/fimbriae that are required for many properties of each bacterial cell. The Neisseria gonorrhoeae type IV pilus is a major virulence and colonization factor for the sexually transmitted infection gonorrhea. We have discovered a new protein of Neisseria gonorrhoeae called TfpC that is required to maintain type IV pili on the bacterial cell surface. There are similar proteins found in other members of the Neisseria genus and many other bacterial species important for human health. ABSTRACT Neisseria gonorrhoeae relies on type IV pili (T4p) to promote colonization of their human host and to cause the sexually transmitted infection gonorrhea. This organelle cycles through a process of extension and retraction back into the bacterial cell. Through a genetic screen, we identified the NGO0783 locus of N. gonorrhoeae strain FA1090 as containing a gene encoding a protein required to stabilize the type IV pilus in its extended, nonretracted conformation. We have named the gene tfpC and the protein TfpC. Deletion of tfpC produces a nonpiliated colony morphology, and immuno-transmission electron microscopy confirms that the pili are lost in the ΔtfpC mutant, although there is some pilin detected near the bacterial cell surface. A copy of the tfpC gene expressed from a lac promoter restores pilus expression and related phenotypes. A ΔtfpC mutant shows reduced levels of pilin protein, but complementation with a tfpC gene restored pilin to normal levels. Bioinformatic searches show that there are orthologues in numerous bacterial species, but not all type IV pilin-expressing bacteria contain orthologous genes. Coevolution and nuclear magnetic resonance (NMR) analysis indicates that TfpC contains an N-terminal transmembrane helix, a substantial extended/unstructured region, and a highly charged C-terminal coiled-coil domain. IMPORTANCE Most bacterial species express one or more extracellular organelles called pili/fimbriae that are required for many properties of each bacterial cell. The Neisseria gonorrhoeae type IV pilus is a major virulence and colonization factor for the sexually transmitted infection gonorrhea. We have discovered a new protein of Neisseria gonorrhoeae called TfpC that is required to maintain type IV pili on the bacterial cell surface. There are similar proteins found in other members of the Neisseria genus and many other bacterial species important for human health.
Evenness-richness scatter plots: a graphical, intuitive approach to alpha diversity analysis

Shannon’s diversity index is a popular alpha diversity metric because it estimates both richness and evenness in a single equation. However, since its value is dependent of both those parameters, there is theoretically an infinite number of richness / evenness value combinations translating into the same index score. By decoupling both components measured by Shannon’s index, two communities having identical indices can be differentiated by mapping richness and evenness coordinates on a scatter plot. In such graphs, confidence ellipses would allow testing significant differences between groups of samples. Multivariate statistical tests such as PERMANOVA can be performed on distance matrices calculated from richness and evenness coordinates and detect statistically significant differences that would have remained unforeseen otherwise.
Genomics of an avian neo-sex chromosome reveals the evolutionary dynamics of recombination suppression and sex-linked genes

How the avian sex chromosomes first evolved from autosomes remains elusive as 100 million years (Myr) of divergence and degeneration obscure their evolutionary history. Sylvioidea songbirds is an emerging model for understanding avian sex chromosome evolution because a unique chromosome fusion event ∼24 Myr ago has formed enlarged “neo-sex chromosomes” consisting of an added (new) and an ancestral (old) part. Here, we report the female genome (ZW) of one Sylvioidea species, the great reed warbler (Acrocephalus arundinaceus). We confirm that the added region has been translocated to both Z and W, and show that the added-W part has been heavily reorganised within itself and with the ancestral-W. Next, we show that recombination between Z and W continued after the fusion event, and that recombination suppression took ∼10 Myr to be completed and arose locally and non-linearly along the sex chromosomes. This pattern is inconsistent with that of large inversions and instead suggests gradual and mosaic recombination suppression. We find that the added-W mirrors the ancestral-W in terms of repeat accumulation, loss of genetic variation, and gene degeneration. Lastly, we show that genes being maintained on W are slowly evolving and dosage sensitive, and that highly conserved genes across broad taxonomic groups, regardless of sex-linkage, evolve slower on both Z and W. This study reveals complex expansion of recombination suppression along avian sex chromosomes, and that the evolutionary trajectory of sex-linked genes is highly predictable and governed partly by sex-linkage per se, partly by their functional properties.
Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Although combination antiretoviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Here, we present a methodology for the comparison of mutational pathways in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational pathways from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models on a large number of resistance mutations and develop a statistical test to assess differences in the inferred mutational pathways between two groups. We apply this method to the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional data set of South African individuals living with HIV-1 subtype C, as well as a genotype data set of subtype B infections derived from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. Our results also show that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Furthermore, the maximum likelihood mutational networks for subtypes B and C share only 7 edges (Jaccard distance 0.802) and imply many different evolutionary pathways. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational pathways between any two groups. Author summary There is a disparity in the distribution of infections by HIV-1 subtype in the world. Subtype B is predominant in America, Western Europe and Australia, and most therapeutic strategies are based on research and clinical studies on this subtype. However, non-B subtypes represent the majority of global HIV-1 infections; e.g., subtype C alone accounts for nearly half of all HIV-1 infections. We present a statistical framework enabling the comparison of patterns of accumulating mutations in different HIV-1 subtypes. Specifically, we study lopinavir resistance pathways in HIV-1 subtypes B versus C, but the methodology can be generally applied to compare the temporal ordering of genetic events in different subgroups.
The Probabilistic Description Logic BALC

Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. Due to their first-order semantics, these languages (in their classical form) are not suitable for representing and handling uncertainty. A probabilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension \BALC of the propositionally closed DL \ALC. We present a tableau-based procedure for deciding consistency, and adapt it to solve other probabilistic, contextual, and general inferences in this logic. We also show that all these problems remain \ExpTime-complete, the same as reasoning in the underlying classical \ALC.
Why future friends matter: impact of expectancy of relational growth on online gamer loyalty

Relational cohesion theory posits that relational cohesion helps build relationships among communication partners, implying that users would expect a growth in relations or making more friends in the future. However, little is known about expectancy of a better future state of relations, i.e. expectancy of relational growth, and its impact on users' continued engagement in online communication. Our study extends relational cohesion theory to explain how expectancy of relational growth impacts online gamer loyalty. ,We test the framework using structural equation modeling to analyze a large sample of 1,429 responses from online gamers. ,We find that expectancy of relational growth is positively related to norm compliance and relational cohesion, which are further related to network convergence and interdependence, fostering online gamer loyalty. ,The findings contribute to relational cohesion theory by identifying novel sources of relational cohesion, i.e. expectancy of relational growth and norm compliance. Hence, game providers should create gamers' expectancy of growth in online relationships. Such expectancy could motivate continued gaming communication, even when gamers are not satisfied with the current state of online relationships. Moreover, we propose the new concept of expectancy of relational growth, which should have a strong impact on online communication in various areas.
Population dynamics of GC-changing mutations in humans and great apes

Background The nucleotide composition of the genome is a balance between origin and fixation rates of different mutations. For example, it is well-known that transitions occur more frequently than transversions, particularly at CpG sites. Differences in fixation rates of mutation types are less explored. Specifically, recombination-associated GC-biased gene conversion (gBGC) may differentially impact GC-changing mutations, due to differences in their genomic distributions and efficiency of mismatch repair mechanisms. Given that recombination evolves rapidly across species, we explore gBGC of different mutation types across human populations and among great ape species. Results We report a stronger correlation between GC frequency and recombination for transitions than for transversions. Notably, CpG transitions are most strongly affected by gBGC. We show that the strength of gBGC differs for transitions and transversions but that its overall strength is positively correlated with effective population sizes of human populations and great ape species, with some notable exceptions, such as a stronger effect of gBGC on transversions in populations of European descent. We study the dependence of gBGC dynamics on flanking nucleotides and show that some mutation types evolve in opposition to the gBGC expectation, likely due to hypermutability of specific nucleotide contexts. Conclusions Differences in GC-biased gene conversion are evident between different mutation types, and dependent on sex-specific recombination, population size and flanking nucleotide context. Our results therefore highlight the importance of different gBGC dynamics experienced by GC-changing mutations and their impact on nucleotide composition evolution.
Transposon- and genome dynamics in the fungal genus Neurospora: insights from nearly gapless genome assemblies

Background A large portion of nuclear DNA is composed of transposable element (TE) sequences, whose transposition is controlled by diverse host defense strategies in order to maintain genomic integrity. One such strategy is the fungal-specific Repeat-Induced Point (RIP) mutation that hyper-mutates repetitive DNA sequences. While RIP is found across Fungi, it has been shown to vary in efficiency. To date, detailed information on the TE landscapes and associated RIP patterns exist only in a few species belonging to highly divergent lineages. Result We investigated 18 nearly gapless genome assemblies of ten Neurospora species, which diverged from a common ancestor about 7 MYA, to determine genome-wide TE distribution and their associated RIP patterns. We showed that the TE contents between 8.7-18.9% covary with genome sizes that range between 37.8-43.9 Mb. Degraded copies of Long Terminal Repeat (LTR) retrotransposons were abundant among the identified TEs, and these are distributed across the genome at varying frequencies. In all investigated genomes, TE sequences had signs of numerous C-to-T substitutions, suggesting that RIP occurred in all species. RIP signatures in all genomes correlated with TE-dense regions. Conclusions Essentially gapless genome assemblies allowed us to identify TEs in Neurospora genomes, and reveal that TEs contribute to genome size variation in this group. Our study suggests that TEs and RIP are highly correlated in Neurospora, and hence, the pattern of interaction is conserved over the investigated evolutionary timescale. We show that RIP signatures can be used to facilitate the identification of TE-rich region in the genome.
Semantics-Preserving Graph Propagation for Zero-Shot Object Detection

Most existing object detection models are restricted to detecting objects from previously seen categories, an approach that tends to become infeasible for rare or novel concepts. Accordingly, in this paper, we explore object detection in the context of zero-shot learning, i.e., Zero-Shot Object Detection (ZSD), to concurrently recognize and localize objects from novel concepts. Existing ZSD algorithms are typically based on a strict mapping-transfer strategy that suffers from a significant visual-semantic gap. To bridge the gap, we propose a novel Semantics-Preserving Graph Propagation model for ZSD based on Graph Convolutional Networks (GCN). More specifically, we develop a graph construction module to flexibly build category graphs by leveraging diverse correlations between category nodes; this is followed by two semantics-preserving graph propagation modules that enhance both category and region representations. Benefiting from the multi-step graph propagation process, both the semantic description and structural knowledge exhibited in prior category graphs can be effectively leveraged to boost the generalization capability of the learned projection function. Experiments on existing seen/unseen splits of three popular object detection datasets demonstrate that the proposed approach performs favorably against state-of-the-art ZSD methods.