Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure and dynamics of a cold-active esterase reveals water entropy and active site accessibility as the likely drivers for cold-adaptation

Cold-active esterases hold great potential for undertaking useful biotransformations at low temperatures. Here, we determined the structure of a cold active family IV esterase (EstN7) cloned from Bacillus cohnii strain N1. EstN7 is a dimer with a classical α/β hydrolase fold. It has an acidic surface that is thought to play a role in cold-adaption by retaining solvation under changed water solvent entropy at lower temperatures. The conformation of the functionally important cap region is significantly different to EstN7’s closest relatives, forming a bridge-like structure with reduced helical content providing greater access to the active site through more than one access tunnel. However, dynamics do not appear to play a major role in cold adaption. Molecular dynamics at different temperatures, rigidity analysis, normal mode analysis and geometric simulations of motion confirm the flexibility of the cap region but suggest that the rest of the protein is largely rigid. Comparison of B-factors with the closest related mesophilic and thermophilic esterases suggests the EstN7 cap region is proportionally less flexible. Rigidity analysis indicates the distribution of hydrophobic tethers is appropriate to colder conditions, where the hydrophobic effect is weaker than in mesophilic conditions due to reduced water entropy. Thus, it is likely that increased substrate accessibility and tolerance to changes in water entropy are the main drivers of EstN7’s cold adaptation rather than changes in dynamics.
Competitive coordination of the dual roles of the Hedgehog co-receptor in homophilic adhesion and signal reception

Hedgehog (Hh) signaling patterns embryonic tissues and contributes to homeostasis in adults. In Drosophila, Hh transport and signaling are thought to occur along a specialized class of actin-rich filopodia, termed cytonemes. Here, we report that Interference hedgehog (Ihog) not only forms a Hh receptor complex with Patched to mediate intracellular signaling, but Ihog also engages in trans-homophilic binding leading to cytoneme stabilization in a manner independent of its role as the Hh receptor. Both functions of Ihog (trans-homophilic binding for cytoneme stabilization and Hh binding for ligand sensing) involve a region of the first fibronectin repeat of the extracellular domain. Thus, the Ihog-Ihog interaction and the Hh-Ihog interaction cannot occur simultaneously for a single Ihog molecule. By combining experimental data and mathematical modeling, we determined that Hh-Ihog heterophilic interaction dominates and Hh can disrupt and displace Ihog molecules involved in trans-homophilic binding. Consequently, we proposed that the weaker Ihog-Ihog trans interaction promotes and stabilizes direct membrane contacts along cytonemes and that, as the cytoneme encounters secreted Hh ligands, the ligands trigger release of Ihog from trans Ihog-Ihog complex enabling transport or internalization of the Hh ligand-Ihog-Patched -receptor complex. Thus, the seemingly incompatible functions of Ihog in homophilic adhesion and ligand binding cooperate to assist Hh transport and reception along the cytonemes. One-sentence summary Apparently incompatible functions of Ihog in homophilic adhesion and ligand binding cooperate in Hh transport and reception.
Dynamic Predictive Coding with Hypernetworks

The original predictive coding model of Rao & Ballard [1] focused on spatial prediction to explain spatial receptive fields and contextual effects in the visual cortex. Here, we introduce a new dynamic predictive coding model that achieves spatiotemporal prediction of complex natural image sequences using time-varying transition matrices. We overcome the limitations of static linear transition models (as in, e.g., Kalman filters) using a hypernetwork to adjust the transition matrix dynamically for every time step, allowing the model to predict using a time-varying mixture of possible transition dynamics. We developed a single level model with recurrent modulation of transition weights by a hypernetwork and a two-level hierarchical model with top-down modulation based on a hypernetwork. At each time step, the model predicts the next input and estimates a sparse neural code by minimizing prediction error. When exposed to natural movies, the model learned localized, oriented spatial filters as well as both separable and inseparable (direction-selective) space-time receptive fields at the first level, similar to those found in the primary visual cortex (V1). Longer timescale responses and stability at the second level also emerged naturally from minimizing prediction errors for the first level dynamics. Our results suggest that the multiscale temporal response properties of cortical neurons could be the result of the cortex learning a hierarchical generative model of the visual world with higher order areas predicting the transition dynamics of lower order areas.
A Ratio-Dependent Model of Replicator-Genetic Parasite Coevolution Demonstrates Instability of the Parasite-Free State

Nearly all organisms on earth are hosts to diverse genetic parasites including viruses and various types of mobile genetic elements. The emergence and persistence of genetic parasites was hypothesized to be an intrinsic feature of biological evolution. Here we examine this proposition by analysis of a ratio-dependent Lotka-Volterra type model of replicator(host)-parasite coevolution where the evolutionary outcome depends on the ratio of the host and parasite numbers. In a large, unbounded domain of the space of the model parameters, which include the replicator carrying capacity, the damage inflicted by the parasite, the replicative advantage of the parasites, and its mortality rate, the parasite-free equilibrium takes the form of a saddle and accordingly is unstable. Therefore, the evolutionary outcome is either the stable coexistence of the replicator and the parasite or extinction of both. Thus, the results of ratio-dependent model analysis are compatible with the hypothesis that genetic parasites are inherent to life.
A cannabinoid receptor agonist shows anti-inflammatory and survival properties in human SARS-CoV-2-infected iPSC-derived cardiomyocytes

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which can infect several organs, especially impacting respiratory capacity. Among the extrapulmonary manifestations of COVID-19 is myocardial injury, which is associated with a high risk of mortality. Myocardial injury, caused directly or indirectly by SARS-CoV-2 infection, can be triggered by inflammatory processes that provoke damage to the heart tissue. Since one of the hallmarks of severe COVID-19 is the “cytokine storm”, strategies to control inflammation caused by SARS-CoV-2 infection have been considered. Cannabinoids are known to have anti-inflammatory properties by negatively modulating the release of pro-inflammatory cytokines. Herein, we investigated the effects of the cannabinoid agonist WIN 55,212-2 (WIN) in human iPSC-derived cardiomyocytes (hiPSC-CMs) infected with SARS-CoV-2. WIN did not modify angiotensin-converting enzyme II protein levels, nor reduced viral infection and replication in hiPSC-CMs. However, WIN reduced the levels of interleukins 6, 8, 18 and tumor necrosis factor-alpha (TNF-α) released by infected cells, and attenuated cytotoxic damage measured by the release of lactate dehydrogenase (LDH). Our findings suggest that WIN shows anti-inflammatory properties in human cardiomyocytes infected with SARS-Cov-2. These results suggest that the potential of cannabinoids as a complementary therapeutic tool for reducing inflammation in COVID-19 patients should be further explored.
Ribosomes in RNA Granules Are Stalled on mRNA Sequences That Are Consensus Sites for FMRP Association

Local translation in neurons is partly mediated by the reactivation of stalled polysomes. Stalled polysomes may be enriched within the granule fraction, defined as the pellet of sucrose gradients used to separate polysomes from monosomes. The mechanism of how elongating ribosomes are reversibly stalled and unstalled on mRNAs is still unclear. In the present study, we characterize the ribosomes in the granule fraction using immunoblotting, cryogenic electron microscopy (cryo-EM), and ribosome profiling. We find that this fraction, isolated from 5-d-old rat brains of both sexes, is enriched in proteins implicated in stalled polysome function, such as the fragile X mental retardation protein (FMRP) and Up-frameshift mutation 1 homologue. Cryo-EM analysis of ribosomes in this fraction indicates they are stalled, mainly in the hybrid state. Ribosome profiling of this fraction reveals (1) an enrichment for footprint reads of mRNAs that interact with FMRPs and are associated with stalled polysomes, (2) an abundance of footprint reads derived from mRNAs of cytoskeletal proteins implicated in neuronal development, and (3) increased ribosome occupancy on mRNAs encoding RNA binding proteins. Compared with those usually found in ribosome profiling studies, the footprint reads were longer and were mapped to reproducible peaks in the mRNAs. These peaks were enriched in motifs previously associated with mRNAs cross-linked to FMRP in vivo, independently linking the ribosomes in the granule fraction to the ribosomes associated with FMRP in the cell. The data supports a model in which specific sequences in mRNAs act to stall ribosomes during translation elongation in neurons. SIGNIFICANCE STATEMENT Neurons send mRNAs to synapses in RNA granules, where they are not translated until an appropriate stimulus is given. Here, we characterize a granule fraction obtained from sucrose gradients and show that polysomes in this fraction are stalled on consensus sequences in a specific state of translational arrest with extended ribosome-protected fragments. This finding greatly increases our understanding of how neurons use specialized mechanisms to regulate translation and suggests that many studies on neuronal translation may need to be re-evaluated to include the large fraction of neuronal polysomes found in the pellet of sucrose gradients used to isolate polysomes.
DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs

Motivation The cooperativity of transcription factors (TFs) is a widespread phenomenon in the gene regulation system. However, the interaction patterns between TF binding motifs remain elusive. The recent high-throughput assays, CAP-SELEX, have identified over 600 composite DNA sites (i.e. heterodimeric motifs) bound by cooperative TF pairs. However, there are over 25,000 inferentially effective heterodimeric TFs in human cell. It is not practically feasible to validate all heterodimeric motifs due to cost and labour. Therefore, it is highly demanding to develop a fast and accurate computational tool for heterodimeric motif synthesis. Results We introduce DeepMotifSyn, a deep-learning-based tool for synthesizing heterodimeric motifs from monomeric motif pairs. Specifically, DeepMotifSyn is composed of heterodimeric motif generator and evaluator. The generator is a U-Net-based neural network that can synthesize heterodimeric motifs from aligned motif pairs. The evaluator is a machine-learning-based model that can score the generated heterodimeric motif candidates based on the motif sequence features. Systematic evaluations on CAP-SELEX data illustrates that DeepMotif-Syn significantly outperforms the current state-of-the-art predictors. In addition, DeepMotifSyn can synthesize multiple heterodimeric motifs with different orientation and spacing settings. Such a feature can address the shortcomings of previous models. We believe Deep-MotifSyn is a more practical and reliable model than current predictors on heterodimeric motif synthesis. Availability and implementation The software is freely available at https://github.com/JasonLinjc/deepMotifSyn.
Unraveling the role of MADS transcription factor complexes in apple tree dormancy

Background The effect of global warming on dormancy and flowering patterns of crop trees threatens world-wide fruit production and food security. In Rosaceous tree species, it is believed that a group of genes encoding MADS transcription factors (TFs) controls temperature-mediated dormancy cycle. These genes are similar to SHORT VEGETATIVE PHASE (SVP) from Arabidopsis thaliana and referred as DORMANCY-ASSOCIATED MADS-BOX (DAM) genes. Results By making use of apple tree (Malus x domestica) as a model for Rosaceous species, we have investigated the function of MADS TFs during the dormancy cycle. We found that MdDAM and other dormancy related MADS TFs form multimeric complexes with MdSVPa, and that MdSVPa is essential for the transcriptional complex activity. Then, for the first time in non-model plant species, we performed sequential DNA Affinity Purification sequencing (seq-DAP-seq) to define the genome-wide binding sites of these MADS TF complexes. Target genes associated with the binding sites were identified by combining seq-DAP-seq data with transcriptomics datasets obtained by the inducible glucocorticoid receptor expression system, and reanalyzing preexisting data related to dormancy cycle in apple trees. Conclusion We have determined a gene regulatory network formed by MdSVPa-containing complexes that regulate the dormancy cycle in apple trees in response to environmental cues. Key genes identified with our genomic approach and the elucidated regulatory relationships provide leads for breeding fruit trees better adapted to changing climate conditions. Moreover, we provide novel molecular evidence on the evolutionary functional segregation between DAM and SVP proteins in the Rosaceae family.
Cellular, physiological, and behavioral validation of a CRFR1:Cre -tdTomato transgenic rat for use in basic neuroscience research

Corticotropin-releasing factor type-1 receptors (CRFR1) are important for mediating the endocrine stress response, modulating synaptic transmission in the central nervous system, and are involved in mediating behaviors that include stress reactivity, anxiety, fear, pain, motivation, and addiction. Understanding the precise role of specific CRFR1 neuronal populations and circuits/networks in CRFR1-relevant behavior is limited by a lack of genetic access to CRFR1-expressing cells in rats. Here, we describe the generation and validation of a transgenic CRFR1:Cre-tdTomato rat line on a Wistar background. Within the central amygdala (CeA) of male and female CRFR1:Cre-tdTomato rats, we show that Crfr1 and Cre mRNA expression are highly colocalized and that CRFR1:Cre-tdTomato cells are largely confined to the medial subdivision of the CeA, consistent with CRF expression patterns in outbred animals. Using tdTomato fluorescent protein as a reporter, we measured membrane properties, inhibitory synaptic transmission, and CRF sensitivity in CeA CRFR1-expressing cells and found that these properties were similar to those previously reported in CRFR1:Cre mice, and that CeA CRFR1 neurons were excited by exogenous CRF application. We also show that stimulatory Gq-coupled DREADD receptors can be targeted to CeA CRFR1 cells via Cre-dependent expression and that these cells can be activated by clozapine-n-oxide (CNO) in vitro and in vivo. Finally, we report that DREADD-mediated activation of CeA CRFR1 cells increases anxiety-like behavior and increases nocifensive responses. Our results demonstrate the utility of this novel CRFR1:Cre-tdTomato transgenic rat line for studying the anatomy, physiology, and behavioral function of select CRFR1-expressing cell populations and circuits under normal conditions and in rat models of human disease.
Dynamic Electrode-to-Image (DETI) mapping reveals the human brain’s spatiotemporal code of visual information

A chief goal of systems neuroscience is to understand how the brain encodes information in our visual environments. Understanding that neural code is crucial to explaining how visual content is transformed via subsequent semantic representations to enable intelligent behavior. Although the visual code is not static, this reality is often obscured in voxel-wise encoding models of BOLD signals due to fMRI’s poor temporal resolution. We leveraged the high temporal resolution of EEG to develop an encoding technique based in state-space theory. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. This technique offers a spatiotemporal visualization of the evolution of the neural code of visual information thought impossible to obtain from EEG and promises to provide insight into how visual meaning is developed through dynamic feedforward and recurrent processes.
SARS-CoV-2 causes brain inflammation and induces Lewy body formation in macaques

SARS-CoV-2 may cause acute respiratory disease, but the infection can also initiate neurological symptoms. Here we show that SARS-CoV-2 infection causes brain inflammation in the macaque model. An increased metabolic activity in the pituitary gland of two macaques was observed by longitudinal positron emission tomography-computed tomography (PET-CT). Post-mortem analysis demonstrated infiltration of T-cells and activated microglia in the brain, and viral RNA was detected in brain tissues from one animal. We observed Lewy bodies in brains of all rhesus macaques. These data emphasize the virus’ capability to induce neuropathology in this nonhuman primate model for SARS-CoV-2 infection. As in humans, Lewy body formation is an indication for the development of Parkinson’s disease, this data represents a warning for potential long-term neurological effects after SARS-CoV-2 infection. Teaser SARS-CoV-2 causes brain inflammation and Lewy bodies, a hallmark for Parkinson, after an asymptomatic infection in macaques.
Dietary Vitamin B12 reduces amyloid-β proteotoxicity by alleviating oxidative stress and mitochondrial dysfunction

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder with no effective treatment. Diet, as a modifiable risk factor for AD, could potentially be targeted to slow disease onset and progression. However, complexity of the human diet and indirect effects of the microbiome make it challenging to identify protective nutrients. Multiple factors contribute to AD pathogenesis including amyloid beta (Aβ) deposition, mitochondrial dysfunction, and oxidative stress. Here we used Caenorhabditis elegans to define the impact of diet on Aβ proteotoxicity. We discovered that dietary vitamin B12 alleviated mitochondrial fragmentation, bioenergetic defects, and oxidative stress, delaying Aβ-induced paralysis without affecting Aβ accumulation. Vitamin B12 had this protective effect by acting as a cofactor for methionine synthase rather than as an antioxidant. Vitamin supplementation of B12 deficient adult Aβ animals was beneficial, demonstrating potential for vitamin B12 as a therapy to target pathogenic features of AD triggered by both aging and proteotoxic stress.
A neural signature of regularity in sound is reduced in older adults

Sensitivity to repetitions in sound amplitude and frequency is crucial for sound perception. As with other aspects of sound processing, sensitivity to such patterns may change with age, and may help explain some age-related changes in hearing such as segregating speech from background sound. We recorded magnetoencephalography to characterize differences in the processing of sound patterns between younger and older adults. We presented tone sequences that either contained a pattern (made of a repeated set of tones) or did not contain a pattern. We show that auditory cortex in older, compared to younger, adults is hyperresponsive to sound onsets, but that sustained neural activity in auditory cortex, indexing the processing of a sound pattern, is reduced. Hence, the sensitivity of neural populations in auditory cortex fundamentally differs between younger and older individuals, overresponding to sound onsets, while underresponding to patterns in sounds. This may help to explain some age-related changes in hearing such as increased sensitivity to distracting sounds and difficulties tracking speech in the presence of other sound.
Analysing variations in pelage patterning among European Felis silvestris silvestris populations

The pelage pattern of wildcats from six regions within the distribution range of the species is analysed to test the hypothesis that clear differences between different regions exist. In total, 98 furs were used, but with different distribution from the Eifel (Germany), Harz Mountains (Germany), Caucasus, Western Spain, Switzerland/France and Greece. Specimens were at least seven months old, sex was not considered. The characteristics used were adapted from the literature and included typical wildcat features like the tail and stripes on the neck. Polychoric correlations, as a binary version of Spearman’s rank correlation coefficient, were used as a measure of correlation between pelage characteristics. Pairwise tetrachoric correlations were computed with R 3.1. 2. , cluster analyses were conducted with SPSS 14. No clear relationship between the pelage characteristics and the geographic distribution of the six studied wildcat populations were found, the hypothesis needs to be rejected. It is however suggested, that coat patterning of Caucasian wildcats to some degree differ from other European wildcat. The results, however, do not clearly corroborate this hypothesis as Caucasian wildcats were not grouped into closed clusters in all the cluster analyses. To a lesser extent, pelage characteristic differences may also exist between wildcats from Western Spain as well as South-Western Greece and other European regions, but the samples are too small to draw conclusions. To test these refined hypotheses, more specimens from the Caucasus, Western Spain and South-Western Greece (but also Switzerland/North-Eastern France) need to be collected and compared according to our protocol.
ERICA: Emulated Retinal Image CApture - A tool for testing, training and validating retinal image processing methods

High resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo. However, a major challenge is the development of robust and automated methods for processing and analysing these images. We present ERICA (Emulated Retinal Image CApture), a simulation tool that generates realistic synthetic images of the human cone mosaic, mimicking images that would be captured by an AOSLO, with specified image quality and with corresponding ground truth data. The simulation includes a self-organising mosaic of photoreceptors, the eye movements an observer might make during image capture, and data capture through a real system incorporating diffraction, residual optical aberrations and noise. The retinal photoreceptor mosaics generated by ERICA have a similar packing geometry to human retina, as determined by expert labelling of AOSLO images of real eyes. In the current implementation ERICA outputs convincingly realistic en face images of the cone photoreceptor mosaic but extensions to other imaging modalities and structures are also discussed. These images and associated ground-truth data can be used to develop, test and validate image processing and analysis algorithms or to train and validate machine learning approaches. The use of synthetic images has the advantage that neither access to an imaging system, nor to human participants is necessary for development.