Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence for a bacterial Lands cycle phospholipase A: Structural and mechanistic insights into membrane phospholipid remodeling

Cells steadily adapt their membrane glycerophospholipid (GPL) composition to changing environmental and developmental conditions. While the regulation of membrane homeostasis via GPL synthesis in bacteria has been studied in detail, the mechanisms underlying the controlled degradation of endogenous GPLs remain unknown. Thus far, the function of intracellular phospholipases A (PLAs) in GPL remodeling (Lands cycle) in bacteria is not clearly established. Here, we identified the first cytoplasmic membrane-bound phospholipase A1 (PlaF) from Pseudomonas aeruginosa involved in the Lands cycle. PlaF is an important virulence factor, as the P. aeruginosa ΔplaF mutant showed strongly attenuated virulence in Galleria mellonella and macrophages. We present a 2.0-Å-resolution crystal structure of PlaF, the first structure that reveals homodimerization of a single-pass transmembrane (TM) full-length protein. PlaF dimerization, mediated solely through the intermolecular interactions of TM and juxtamembrane regions, inhibits its activity. A dimerization site and the catalytic sites are linked by an intricate ligand-mediated interaction network which likely explains the product (fatty acid) feedback inhibition observed with the purified PlaF protein. We used molecular dynamics simulations and configurational free energy computations to suggest a model of PlaF activation through a coupled monomerization and tilting of the monomer in the membrane, which constrains the active site cavity into contact with the GPL substrates. Thus, these data show the importance of the GPL remodeling pathway for virulence and pave the way for the development of a novel therapeutic class of antibiotics targeting PlaF-mediated membrane GPL remodeling. Synopsis Membrane homeostasis can be regulated by phospholipase-controlled deacylation of endogenous glycerophospholipids (GPLs) followed by reacylation of products, known as the Lands cycle in eukaryotes. Here we show that the human pathogen Pseudomonas aeruginosa uses intracellular phospholipase A1 (PlaF) to modulate membrane GPL composition, which is the first example in bacteria. This newly identified PLA1 indirectly regulates the bacterial virulence properties by hydrolyzing a specific set of membrane GPLs. The crystal structure of full-length PlaF dimers bound to natural ligands, MD simulations, and biochemical approaches provide insights into the molecular mechanism of dimerization-mediated inactivation of this single-pass transmembrane PLA1. Our findings shed light on a mechanism by which bacterial intracellular PLAs might regulate membrane homeostasis what showcases these enzymes as a promising target for a new class of antibiotics.
The Caenorhabditis elegans TDRD5/7-like protein, LOTR-1, interacts with the helicase ZNFX-1 to balance epigenetic signals in the germline

LOTUS and Tudor domain containing proteins have critical roles in the germline. Proteins that contain these domains, such as Tejas/Tapas in Drosophila, help localize Vasa to the germ granules and facilitate piRNA-mediated transposon silencing. The homologous proteins in mammals, TDRD5 and TDRD7, are required during spermiogenesis. Until now, proteins containing both LOTUS and Tudor domains in Caenorhabditis elegans have remained elusive. Here we describe LOTR-1 (D1081.7), which derives its name from its LOTUS and Tudor domains. Interestingly, LOTR-1 docks next to P granules to colocalize with the broadly conserved Z-granule helicase, ZNFX-1. LOTR-1’s Z-granule association requires its Tudor domain, but both LOTUS and Tudor deletions affect brood size when coupled with a knockdown of the Vasa homolog glh-1. In addition to interacting with the germ-granule components WAGO-1, PRG-1 and DEPS-1, we identified a Tudor-dependent association with ZNFX-1. Like znfx-1 mutants, lotr-1 mutants lose small RNAs from the 3’ ends of WAGO and Mutator targets, reminiscent of the loss of piRNAs from the 3’ ends of piRNA precursor transcripts in mouse Tdrd5 mutants. Our work suggests that LOTR-1 acts in a conserved mechanism that brings small RNA generating mechanisms towards the 3’ ends of small RNA templates or precursors.
Genetics of tibia bone properties of crossbred commercial laying hens in different housing systems

Osteoporosis and bone fractures are a severe problem for the welfare of laying hens, with genetics and environment, such as housing system, each making substantial contributions to bone strength. In this work, we performed genetic analyses of bone strength, bone mineral density and bone composition, as well as body weight, in 860 commercial crossbred laying hens from two different companies, kept in either furnished cages or floor pens. We compared bone traits between housing systems and crossbreds, and performed a genome-wide association study of bone properties and body weight. As expected, the two housing systems produced a large difference in bone strength, with layers housed in floor pens having stronger bones. These differences were accompanied by differences in bone geometry, mineralisation and chemical composition. Genome-scans either combining or independently analysing the two housing systems revealed no genome-wide significant loci for bone breaking strength. We detected three loci for body weight that were shared between the housing systems on chromosomes 4, 6 and 27 (either genome-wide significant or suggestive when the housing systems were analysed individually) and these coincide with associations for bone length. In summary, we found substantial differences in bone strength, content and composition between hens kept in floor pens and furnished cages that could be attributed to greater physical activity in pen housing. We found little evidence for large-effect loci for bone strength in commercial crossbred hens, consistent with a highly polygenic architecture for bone strength in the production environment. The lack of consistent genetic associations between housing systems in combination with the differences in bone phenotypes support gene-by-environment interactions with housing system.
Developmental plasticity in visual cortex is necessary for normal visuomotor integration and visuomotor skill learning

The experience of coupling between motor output and visual feedback is necessary for the development of visuomotor skills and shapes visuomotor integration in visual cortex. Whether these experience-dependent changes involve plasticity in visual cortex remains unclear. Here, we probed the role of NMDA receptor-dependent plasticity in mouse primary visual cortex (V1) during visuomotor development. Using a conditional knockout of NMDA receptors and a photoactivatable inhibitor of CaMKII, we locally perturbed plasticity in V1 during first visual experience, recorded neuronal activity in V1, and tested the mice in a visuomotor task. We found that perturbing plasticity before, but not after, first visuomotor experience reduces responses to unpredictable stimuli, diminishes the suppression of predictable feedback in V1, and impairs visuomotor skill learning later in life. Our results demonstrate that plasticity in the local V1 circuit during early life is critical for shaping visuomotor integration. *** Dear reader, please note this manuscript is formatted in a standard submission format, and all statistical information is provided in Table S1. ***
The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

Motor Imagery (MI) is a mental process by which an individual rehearses body movements without actually performing physical actions. Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with this mental process and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extract changes in sensorimotor rhythms (SMR). However, in recent years, Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals. In this study, EEG signals from 54 subjects who performed a MI task of left-or right-hand grasp was employed to compare the performance of two MI-BCI classifiers; a ML approach vs. a DL approach. In the ML approach, Common Spatial Patterns (CSP) was used for feature extraction and then Linear Discriminant Analysis (LDA) model was employed for binary classification of the MI task. In the DL approach, a Convolutional Neural Network (CNN) model was constructed on the raw EEG signals. The mean classification accuracies achieved by the CNN and CSP+LDA models were 69.42% and 52.56%, respectively. Further analysis showed that the DL approach improved the classification accuracy for all subjects within the range of 2.37 to 28.28% and that the improvement was significantly stronger for low performers. Our findings show promise for employment of DL models in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.
Essential role for Ggct in erythrocyte antioxidant defense

GGCT encodes γ-glutamyl cyclotransferase enzyme activity, and its expression is up-regulated in various human cancers. γ-glutamyl cyclotransferase enzyme activity was originally purified from human red blood cells (RBCs), however physiological function of GGCT in RBCs is still not clear. Here we reported that Ggct deletion in mouse leads to splenomegaly and progressive anemia phenotypes, due to elevated oxidative damage and shortened life span of Ggct−/− RBCs. Ggct−/− RBCs have increased reactive oxygen species (ROS), and are more sensitive to H2O2 induced damage compared to control RBCs. Glutathione (GSH) and GSH synthesis precursor L-cysteine are decreased in Ggct−/− RBCs. Our study suggests a critical function of Ggct in RBC redox balance and life span maintenance through regulating GSH metabolism.
Localization of planteose hydrolysis during seed germination of Orobanche minor

Root parasitic weeds of the Orobanchaceae, such as witchweeds (Striga spp.) and broomrapes (Orobanche and Phelipanche spp.), cause serious losses in agriculture worldwide. No practical method to control these parasitic weeds has been developed to date. Understanding the characteristic physiological processes in the life cycles of root parasitic weeds is particularly important to identify specific targets for growth modulators. In our previous study, planteose metabolism was revealed to be activated soon after the perception of strigolactones in germinating seeds of O. minor. Nojirimycin inhibited planteose metabolism and impeded seed germination of O. minor, indicating that planteose metabolism is a possible target for root parasitic weed control. In the present study, we investigated the distribution of planteose in dry seeds of O. minor by matrix-assisted laser desorption/ionization–mass spectrometry imaging. Planteose was detected in tissues surrounding—but not within—the embryo, supporting its suggested role as a storage carbohydrate. Biochemical assays and molecular characterization of an α-galactosidase family member, OmAGAL2, indicated the enzyme is involved in planteose hydrolysis in the apoplast around the embryo after the perception of strigolactones to provide the embryo with essential hexoses for germination. These results indicated that OmAGAL2 is a potential molecular target for root parasitic weed control. Highlight Planteose accumulated in tissues surrounding the embryo in Orobanche minor dry seeds and was indicated to be hydrolyzed in the apoplast around the embryo by α-galactosidase during germination.
ROPIUS0: A deep learning-based protocol for protein structure prediction and model selection and its performance in CASP14

Protein structure prediction has recently been revolutionized when AlphaFold2 [1] predicted protein structures with near-experimental accuracy in the latest CASP14 season of critical assessment of methods of protein structure prediction (CASP). Among numerous implications, this breakthrough has led to a rapidly growing number of high-quality structural models [2]. We present a protocol ROPIUS0 for protein structure prediction and model selection and discuss its benefits in the new era of structure prediction. At the core of the ROPIUS0 protocol is the deep learning module developed for the selection of protein structural models. It is shown that the direct use of predicted inter-residue distances may be sufficient to discriminate between correct and incorrect protein folds, considering only a small fraction of predicted distances. We extensively tested the protocol: In the latest CASP14 prediction season, a ROPIUS0 variant based on model selection ranked 13th in the category of tertiary structure prediction. Its performance is on par with top-performing automated prediction servers when tested on the CASP13 dataset, and it performs similarly on a CAMEO dataset. The results suggest ways to improve searching for structurally similar and homologous proteins without considerably increasing speed. Our new open-source threading tool based on comparing a subset of inter-residue distances demonstrates the effectiveness and application of the deep learning module of the ROPIUS0 protocol.
Multi-omics-based label-free metabolic flux inference reveals obesity-associated dysregulatory mechanisms in liver glucose metabolism

Glucose homeostasis is maintained by modulation of metabolic flux. Enzymes and metabolites regulate the involved metabolic pathways. Dysregulation of glucose homeostasis is a pathological event in obesity. Analyzing metabolic pathways and the mechanisms contributing to obesity-associated dysregulation in vivo is challenging. Here, we introduce OMELET: Omics-Based Metabolic Flux Estimation without Labeling for Extended Trans-omic Analysis. OMELET uses metabolomic, proteomic, and transcriptomic data to identify changes in metabolic flux, and to quantify contributions of metabolites, enzymes, and transcripts to the changes in metabolic flux. By evaluating the livers of fasting ob/ob mice, we found that increased metabolic flux through gluconeogenesis resulted primarily from increased transcripts, whereas that through the pyruvate cycle resulted from both increased transcripts and changes in substrates of metabolic enzymes. With OMELET, we identified mechanisms underlying the obesity-associated dysregulation of metabolic flux in liver. Highlights We created OMELET to infer metabolic flux and its regulation from multi-omic data. Gluconeogenic and pyruvate cycle fluxes increased in fasting ob/ob mice. Transcripts increases mediated the increase in gluconeogenic fluxes in ob/ob mice. Increases in transcripts and substrates enhanced pyruvate cycle flux in ob/ob mice.
Genome-Scale Metabolic Model Accurately Predicts Fermentation of Glucose by Chromochloris zofingiensis

Algae have the potential to be sources of renewable fuels and chemicals. One particular strain, Chromochloris zofingiensis, is of interest due to the co-production of triacylglycerols (TAGs) and astaxanthin, a valuable nutraceutical. To aid in future engineering efforts, we have developed the first genome-scale metabolic model on C. zofingiensis, iChr1915. This model includes 1915 genes, 3413 metabolic reactions and 2652 metabolites. We performed detailed biomass composition analysis for three growth conditions: autotrophic, mixotrophic and heterotrophic and used this data to develop biomass formation equations for each growth condition. The completed model was then used to predict flux distributions for each growth condition; interestingly, for heterotrophic growth, the model predicts the excretion of fermentation products due to overflow metabolism. We confirmed this experimentally via metabolomics of spent medium and fermentation product assays. An in silico gene essentiality analysis was performed on this model, as well as a flux variability analysis to test the production capabilities of this organism. In this work, we present the first genome scale metabolic model of C. zofingiensis and demonstrate its use predicting metabolic activity in different growth conditions, setting up a foundation for future metabolic engineering studies in this organism.
A novel bivalent DNA vaccine encoding both spike protein receptor-binding domain and nucleocapsid protein of SARS-CoV-2 to elicit T cell and neutralising antibody responses that cross react with variants

The efficacy of vaccines targeting SARS-CoV-2 is becoming apparent now that the mRNA and adenovirus vector vaccines that have been approved for emergency use are showing promise. However, the longevity of the protective immune response and its efficacy against emerging variants remains to be determined. To improve longevity and future protection against variants, we have designed a DNA vaccine encoding both the SARS-CoV-2 spike (S) protein receptor-binding domain (RBD) and its nucleocapsid (N) protein, the latter of which is highly conserved amongst beta coronaviruses. The vaccine elicits strong pro-inflammatory CD4 Th1 and CD8 T-cell responses to both proteins, with these responses being significantly enhanced by fusing the nucleocapsid sequence to a modified Fc domain. We have shown that the vaccine also stimulates high titre antibody responses to RBD which efficiently neutralise in both a pseudotype and live virus neutralisation assay and show cross reactivity with S proteins from the emerging variants Alpha (B.1.1.7) and Beta (B.1.351). This DNA platform can be easily adapted to target variant RBD and N proteins and we show that a vaccine variant encoding the B.1. 351 RBD sequence stimulates cross-reactive humoral and T-cell immunity. These data support the translation of this DNA vaccine platform into the clinic, thereby offering a particular advantage for targeting emerging SARS-CoV-2 variants.
Deep learning assessment of cultural ecosystem services from social media images

Crowdsourced social media data has become popular in the assessment of cultural ecosystem services (CES). Advances in deep learning show great potential for the timely assessment of CES at large scales. Here, we describe a procedure for automating the assessment of image elements pertaining to CES from social media. We focus on a binary (natural, human) and a multiclass (posing, species, nature, landscape, human activities, human structures) classification of those elements using two Convolutional Neural Networks (CNNs; VGG16 and ResNet152) with the weights from two large datasets - Places365 and ImageNet -, and our own dataset. We train those CNNs over Flickr and Wikiloc images from the Peneda-Gerês region (Portugal) and evaluate their transferability to wider areas, using Sierra Nevada (Spain) as test. CNNs trained for Peneda-Gerês performed well, with results for the binary classification (F1-score > 80%) exceeding those for the multiclass classification (> 60%). CNNs pre-trained with Places365 and ImageNet data performed significantly better than with our data. Model performance decreased when transferred to Sierra Nevada, but their performances were satisfactory (> 60%). The combination of manual annotations, freely available CNNs and pre-trained local datasets thereby show great relevance to support automated CES assessments from social media.
Deamidation of the human eye lens protein γS-crystallin accelerates oxidative aging

Cataract disease, a clouding of the eye lens due to precipitation of lens proteins, affects millions of people every year worldwide. The proteins that comprise the lens, the crystallins, show extensive post-translational modifications (PTMs) in aged and cataractous lenses, most commonly deamidation and oxidation. Although surface-exposed glutamines and asparagines show the highest rates of deamidation, multiple modifications can accumulate over time in these long-lived proteins, even for buried residues. Both deamidation and oxidation have been shown to promote crystallin aggregation in vitro; however, it is not clear precisely how these modified crystallins contribute to insolubilization. Here, we report six novel crystal structures of a major human lens protein, γS-crystallin (γS): one of the wild-type in a monomeric state, and five of deamidated γS variants, ranging from three to nine deamidation sites, after varying degrees of sample aging. Consistent with previous work that focused on single-to triple-site deamidation, the deamidation mutations do not appear to drastically change the fold of γS; however, increasing deamidation leads to accelerated oxidation and disulfide bond formation. Successive addition of deamidated sites progressively destabilized protein structure as evaluated by differential scanning fluorimetry. Light scattering showed the deamidated variants display an increased propensity for aggregation compared to the wild-type protein. The results suggest the deamidated variants are useful as models for accelerated aging; the structural changes observed over time provide support for redox activity of γS-crystallin in the human lens. Highlights Novel structures of cataract-associated variants of human eye lens protein γS-crystallin reported Increasing deamidation of γS-crystallin decreases stability and affects aggregation propensity Overall fold of γS-crystallin maintained among deamidated and disulfide-bonded variants Deamidated γS variants form disulfide bonds more rapidly than wild-type γS Potential functional advantage of disulfide bonding in the CXCXC motif proposed
Multi-dynamic modelling reveals strongly time-varying resting fMRI correlations

The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability Statement of Significance MAGE is multi-dynamic in that it models temporal fluctuations in FC independently from fluctuations in the mean of the activity. MAGE reveals stronger changes in FC over time than single-dynamic approaches, such as sliding window correlations. Multi-dynamic modelling provides an explanation and a solution as to why resting fMRI FC has previously looked so stable. MAGE models fMRI data as a set of reoccurring brain states, and importantly, these states do not have to be binary and mutually exclusive (e.g., multiple states can be active at one time-point). MAGE estimated time-varying FC is a better predictor of behavioural variability in the resting-state fMRI data than established methods.