Miracle-dz/UniFAR-FTUs huggingface dataset:
N-glycosylation of CD24 mediates cell motility but inhibits cell proliferation in colorectal cancer

CD24 confers features of stemness and enhances cell motility in colorectal cancer. It contains numerous O-glycosylation sites but just two putative N-glycosylation sites. The study aimed to mutate N-glycosylation as it thought to be important in mediating many glycoproteins’ function. Site-directed mutagenesis performed to mutate N-glycosylation sites by converting asparagine residues to glutamine. Each site was mutated individually (CD24N36Q and CD24N52Q) and in combination (CD24N[36,52]Q) and were overexpressed in HCT116 and SW480 cell lines. The effect of the mutant CD24 on cell viability, cell motility and induction of downstream targets was compared to wild-type CD24 and empty vector control (EVC). Overexpression of the individually mutated clones increased cell motility compared to EVC but was significantly less than that induced by CD24WT. Overexpression of the double mutant clone resulted in near-complete abrogation of cell motility induction. Unexpectedly, the expression of mutant clones (single and double) increased cell viability. Analysis of downstream targets and F-actin staining demonstrated reduced induction of known CD24 targets by mutant vectors and reduced epithelial-mesenchymal transition. In conclusion, each N-glycosylation site on CD24 contributes partially to induce motility by CD24, and these are additive. Loss of the N-glycosylation results in an unexpected gain of proliferative function. Highlights CD24 is a heavily glycosylated molecule that contains two N-glycosylation sites, and these contribute to the activation of downstream targets and induction of cell motility Each N-glycosylation site contributes partially, and their effects appear to be additive The N-glycosylation sites may exert an inhibitory effect on cell proliferation Much of the CD24 activity is mediated through post-transcriptional effects
Ecological basis and genetic architecture of crypsis polymorphism in the desert clicker grasshopper (Ligurotettix coquilletti)

Color polymorphic species can offer exceptional insight into the ecology and genetics of adaptation. Although the genetic architecture of animal coloration is diverse, many color polymorphisms are associated with large structural variants and maintained by biotic interactions. Grasshoppers are exceptionally polymorphic in both color and karyotype, making them excellent models for understanding the ecological drivers and genetic underpinnings of color variation. Banded and uniform morphs of the desert clicker grasshopper (Ligurotettix coquilletti) are found across the western deserts of North America. To address the hypothesis that predation maintains local color polymorphism and shapes regional crypsis variation, we surveyed morph frequencies and tested for covariation with two predation environments. Morphs coexisted at intermediate frequencies at most sites, consistent with local balancing selection. Morph frequencies covaried with the appearance of desert substrate – an environment used only by females – indicating that ground-foraging predators are major agents of selection on crypsis. We next addressed the hypothesized link between morph variation and genome structure. To do so, we designed an approach for detecting inversions and indels using only RADseq data. The banded morph was perfectly correlated with a large putative indel. Remarkably, indel dominance differed among populations, a rare example of dominance evolution in nature.
Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for prediction within populations. However, it usually cannot capture the complex effects due to combination of alleles in haplotypes. Therefore, accuracy across populations has usually been low. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE, RNA expression of genes by haplotype), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism, so it would be more transferrable across different tissues and populations. We showed that HARE estimates captured one-third of the variation in gene expression and were more transferable across diverse tissues than the measured transcript expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels – a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel) – for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues whereas accuracy using HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations. Author summary The increasing availability of genomic data has been widely used in the prediction of many traits. However, genome wide prediction has been mostly carried out within populations and without explicit modeling of RNA or protein expression. In this study, we explored the prediction of field traits within and across populations using estimated RNA expression attributable to only the DNA sequence around a gene. We showed that the estimated RNA expression was more transferable than overall measured RNA expression. We improved prediction of field traits up to 15% using estimated gene expression as compared to observed expression or gene sequence alone. Overall, these findings indicate that structural and functional information in the gene sequence are highly transferable.
Evidence for the equilibrium between monomers and dimers of the death domain of the p75 neurotrophin receptor

The p75 neurotrophin receptor (p75NTR) is an important mediator of synaptic depression and neuronal cell death, and its expression increases upon nerve injury and in neurodegenerative diseases. However, the molecular mechanisms leading to the activation of this receptor are still a matter of debate. The oligomerization properties of the death domain (DD) of p75NTR are critical for our understanding of the activation mechanisms of the receptor. In this paper, we present additional evidence supporting the existence of an equilibrium between monomeric and dimeric forms of the p75NTR DD in solution and in the absence of any other protein. Dynamic light scattering (DLS) measurements of native, untagged human p75NTR DD at room temperature yielded Rh=2.11 for this domain in 20mM phosphate buffer, corresponding to a molecular weight (MW) of approximately 19kDa, much closer to the theoretical MW of the homodimer (i.e. 21kDa) than the monomer. MWs deduced from the Rh of different control proteins used as standards were all congruent with their theoretical MWs. In addition, size-exclusion FPLC profiles of un-tagged human p75NTR DD in both HEPES and phosphate buffers revealed elution volumes corresponding to a MW of about 15kDa, which is intermediate between monomer and dimer, and indicative of dynamic monomer/dimer interconversion during the run. Together with our previous NMR studies, as well as biophysical data for other investigators, these results support the notion that the DD of p75NTR exists in equilibrium between monomers and dimers in solution, a notion that is in agreement with the oligomerization properties of all members of the DD superfamily.
Preserved motor memory in Parkinson's disease

Patients with Parkinson’s disease, who lose the dopaminergic projections to the striatum, are impaired in certain aspects of motor learning. Recent evidence suggests that, in addition to its role in motor performance, the striatum plays a key role in the memory of motor learning. Whether Parkinson’s patients have impaired motor memory and whether motor memory is modulated by dopamine at the time of initial learning is unknown. To address these questions, we measured memory of a learned motor sequence in Parkinson’s patients who were either On or Off their dopaminergic medications. We compared them to a group of older and younger controls. Contrary to our predictions, motor memory was not impaired in patients compared to older controls, and was not influenced by dopamine state at the time of initial learning. To probe post-learning consolidation processes, we also tested whether learning a new sequence shortly after learning the initial sequence would interfere with later memory. We found that, in contrast to younger adults, neither older adults nor patients were susceptible to this interference. These findings suggest that motor memory is preserved in Parkinson’s patients and raise the possibility that motor memory in patients is supported by compensatory non-dopamine sensitive mechanisms. Furthermore, given the similar performance characteristics observed in the patients and older adults and the absence of an effect of dopamine, these results raise the possibility that aging and Parkinson’s disease affect motor memory in similar ways.
Covariance-based information processing in reservoir computing systems

In biological neuronal networks, information representation and processing are achieved through plasticity learning rules that have been empirically characterized as sensitive to second and higher-order statistics in spike trains. However, most models in both computational neuroscience and machine learning aim to convert diverse statistical properties in inputs into first-order statistics in outputs, like in modern deep learning networks. In the context of classification, such schemes have merit for inputs like static images, but they are not well suited to capture the temporal structure in time series. In contrast, the recently developed covariance perceptron uses second-order statistics by mapping input covariances to output covariances in a consistent fashion. Here, we explore the applicability of covariance-based perceptron readouts in reservoir computing networks to classify synthetic multivariate time series structured at different statistical orders (first and second). We show that the second-order framework outperforms or matches the classical mean paradigm in terms of accuracy. Our results highlight a nontrivial relationship between input and reservoir properties in generating the output reservoir activity, which suggests an important role for recurrent connectivity in transforming information representations in biologically inspired architectures. Finally, we solve a speech recognition task for the classification of spoken digits to further demonstrate the potential of covariance-based decoding for real data.
yEvo: Experimental evolution in high school classrooms selects for novel mutations and epistatic interactions that impact clotrimazole resistance in S. cerevisiae

Antifungal resistance in pathogenic fungi is a growing global health concern. Non-pathogenic laboratory strains of Saccharomyces cerevisiae are a useful model for studying mechanisms of antifungal resistance that are relevant to understanding the same processes in pathogenic fungi. We developed a series of lab modules in which high school students used experimental evolution to study antifungal resistance by isolating azole-resistant S. cerevisiae and examining the genetic basis of resistance. All 99 sequenced clones from these experiments possessed mutations previously shown to impact azole resistance, demonstrating the efficacy of our protocols. We additionally found recurrent mutations in an mRNA degradation pathway and an uncharacterized mitochondrial protein (Csf1) that have possible mechanistic connections to azole resistance. The scale of replication in this high school-led initiative allowed us to identify epistatic interactions, as evidenced by pairs of mutations that occur in the same clone more frequently than expected by chance (positive epistasis) or less frequently (negative epistasis). We validated one of these pairs, a negative epistatic interaction between gain-of-function mutations in the multidrug resistance transcription factors Pdr1 and Pdr3. This high school-university collaboration can serve as a model for involving members of the broader public in the scientific process to make meaningful discoveries in biomedical research.
Morphine exposure bidirectionally alters c-Fos expression in a sex-, age-, and brain region-specific manner during adolescence

Drug and alcohol use during adolescence is common, and data in both humans and preclinical animal models clearly indicate drug exposure during adolescence increases the risk of substance use and other mental health disorders later in life. Adolescence is a period of social, emotional, and cognitive development, and is characterized by increased exploratory behavior, risk-taking, and peer-centered social interactions. These are thought to be behavioral manifestations of developmental plasticity in ‘reward’ regions of the brain. Human data indicate that adolescence is not a unitary developmental period, but rather different neural and behavioral sequelae can be observed in early vs. late adolescence. However, most studies with rodent models examine a single adolescent age compared to a mature adult age, and often only in males. Herein, we sought to determine whether the acute response to the opioid morphine would also differ across adolescence, and by sex. By quantifying c-Fos positive cells, a proxy for neural activity, at different stages during adolescence (pre-, early, mid-, and late adolescence) and in multiple reward regions (prefrontal cortex, nucleus accumbens, caudate/putamen), we determined that acute morphine can either reduce or increase c-Fos expression dependent on adolescent age, sex, and brain region. These data suggest that heterogeneity in the consequences of adolescent opioid exposure may be due to the interaction between age- and sex-specific developmental profiles of reward processing in individual brain regions. In future studies, it will be important to add age within adolescence as an independent variable to fully capture the consequences of healthy or abnormal reward-related neural development.
Optimized Sample Preparation for Glass Fragments by Scanning Electron Microscopy–Energy Dispersive X-ray Spectrometry

ABSTRACT Quantitative analysis by scanning electron microscopy–energy dispersive X-ray spectrometer (SEM–EDX) requires a flat and smooth sample surface. To fulfill these requirements, an embedding procedure is generally used for sample preparation. This approach is impractical for small glass fragments, which are frequently the subject of forensic examination. The authors report the use of optical microscopy for selecting glass fragments that are smooth and flat as possible and directly placing them on a scanning electron microscopy stub. The results using two SEM–EDXs were compared for embedded and nonembedded glass standards. No significant differences in accuracy, precision, reproducibility, and false answer rates were observed using likelihood ratio models suggesting that the reported method of sample preparation is suitable for forensic analysis.
Reconstructing co-dependent cellular crosstalk in lung adenocarcinoma using REMI

Cellular crosstalk in tissue microenvironments is fundamental to normal and pathological biological processes. Global assessment of cell-cell interactions (CCI) is not yet technically feasible, but computational efforts to reconstruct these interactions have been proposed. Current computational approaches that identify CCI often make the simplifying assumption that pairwise interactions are independent of one another, which can lead to reduced accuracy. We present REMI (REgularized Microenvironment Interactome), a graph-based algorithm that predicts ligand-receptor (LR) interactions by accounting for LR dependencies on high-dimensional, small sample size datasets. We apply REMI to reconstruct the human lung adenocarcinoma (LUAD) interactome from a bulk flow-sorted RNA-seq dataset, then leverage single-cell transcriptomics data to increase its resolution and identify LR prognostic signatures. We experimentally confirmed colocalization of CTGF:LRP6 as an interaction predicted to be associated with LUAD progression. Our work presents a novel way to reconstruct interactomes and a new approach to identify clinically-relevant cell-cell interactions.
Data fusion for abundance estimation: community science augments systematically collected removal-in-time distance sampling data

Ecologists use a variety of systematically and opportunistically sampled count data to estimate bird abundance, and integrating or fusing different datasets has emerged as a critical challenge in recent years. While previous work provides data integration methodology for occupancy (presence/absence) estimation, methods for abundance estimation that account for imperfect detection and disparate survey protocols remains an active area of research. Here we show how to integrate systematically collected removal-in-time distance sampling data from the Integrated Monitoring in Bird Conservation Regions (IMBCR) program with North American Breeding Bird Survey (BBS) point counts and eBird community science observations. Using the Grasshopper Sparrow (Ammodramus savannarum) in the Great Plains of the United States as a focal species, we demonstrate that BBS and eBird data improve predictive performance for IMBCR count data, providing more spatially refined and precise estimates of abundance at regional scales. Data fusion increased predictive performance even despite relatively weak spatial correlations among data sets. The methodology developed here provides a principled way to fuse data when estimating abundance with distance sampling, that accounts for imperfect detection and variable effort.
Genomic consequences of domestication of the Siamese fighting fish

Siamese fighting fish, commonly known as betta, are among the world’s most popular and morphologically diverse pet fish, but the genetic processes leading to their domestication and phenotypic diversification are largely unknown. We assembled de novo the genome of a wild Betta splendens and whole-genome sequenced multiple individuals across five species within the B. splendens species complex, including wild populations and domesticated ornamental betta. Given our estimate of the mutation rate from pedigrees, our analyses suggest that betta were domesticated at least 1,000 years ago, centuries earlier than previously thought. Ornamental betta individuals have variable contributions from other Betta species and have also introgressed into wild populations of those species. We identify dmrt1 as the main sex determination gene in ornamental betta but not in wild B. splendens, and find evidence for recent directional selection at the X-allele of the locus. Furthermore, we find genes with signatures of recent, strong selection that have large effects on color in specific parts of the body, or the shape of individual fins, and are almost all unlinked. Our results demonstrate how simple genetic architectures paired with anatomical modularity can lead to vast phenotypic diversity generated during animal domestication, and set the stage for using betta as a modern system for evolutionary genetics. One-Sentence Summary Genomic analyses reveal betta fish were domesticated more than 1,000 years ago and the genes that changed in the process.
Multimodal brain features at preschool age and the relationship with pre-reading measures one year later: an exploratory study

Pre-reading language skills develop rapidly in early childhood and are related to brain structure and function in young children prior to formal education. However, the early neurobiological development that support these skills is not well understood and has not been assessed longitudinally using multiple imaging approaches. Here we acquired anatomical, diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) from 35 children at 3.5 years of age. Children were assessed for pre-reading abilities using the NEPSY-II subtests one year later (4.5 years). We applied a data-driven linked independent component analysis to explore the shared co-variation of grey and white matter measures. Two sources of structural variation at 3.5 years of age demonstrated a weak relationship with Speeded Naming scores at 4.5 years of age. The first imaging component involved volumetric variability in reading-related cortical regions alongside microstructural features of the superior longitudinal fasciculus. The second component was dominated by cortical volumetric variations within the cerebellum and visual association area. In a subset of children with rs-fMRI data, we evaluated the inter-network functional connectivity of the left-lateralized fronto-parietal language (FPL) network and its relationship with pre-reading measures. Higher functional connectivity between the FPL functional network and the default mode and visual networks at 3.5 years predicted better Phonological Processing scores at 4.5 years. Together, these results suggest that the integration of functional networks, as well as the co-development of white and grey matter brain structures in early childhood, may support the emergence of pre-reading measures in preschool children.
Enhancing the dark side: Asymmetric gain of cone photoreceptors underpins discrimination of visual scenes based on their skewness

Psychophysical data indicates humans can discriminate visual scenes based on their skewness – the ratio of dark and bright patches within a visual scene. It was also shown that on a phenomenological level this skew discrimination is described by the so-called Blackshot mechanism, which accentuates strong negative contrasts within a scene. Here we demonstrate that the Blackshot mechanism is implemented by the asymmetric gain of the cone phototransduction cascade, which is higher for strong negative contrasts than for strong positive contrasts. We recorded from goldfish cone photoreceptors and found that the asymmetry in the phototransduction gain leads to higher amplitude of the responses to negatively than to positively skewed light stimuli. This asymmetry in the amplitude was present in the photocurrent, voltage response and cone synaptic output. These results highlights the importance of the early photoreceptor non-linearity for the perception. Additionally, we found that stimulus skewness leads to a subtle change in photoreceptor kinetics. For negatively skewed stimuli, the cone’s impulse response functions peak later than for positively skewed stimulus. However, stimulus skewness does not affect the cone’s overall integration time.