Miracle-dz/UniFAR-FTUs huggingface dataset:
The G protein-Coupled Metabotropic Glutamate Receptor 1 controls neuronal macroautophagy

Autophagy is an evolutionarily conserved, highly regulated catabolic process critical to neuronal homeostasis, function and survival throughout organismal lifespan. However, the external factors and signals that control autophagy in neurons are still poorly understood. Here we report that the G protein-coupled metabotropic glutamate receptor 1 (mGlu1) contributes to control basal autophagy in the brain. Autophagy is upregulated in the brain of adult mGlu1 knockout mice and genetic deletion or pharmacological inhibition of native mGlu1 receptors enhances autophagy flux in neurons. The evolutionarily conserved adaptor protein FEZ1, identified by a genome-wide screen as mGlu1 receptor interacting partner, was found to participate in the regulation of neuronal autophagy and to be required for repression of autophagy flux by the mGlu1 receptor. Furthermore, FEZ1 appears to enable association of mGlu1 with Ulk1, a core component of the autophagy pathway. Thus, we propose that the mGlu1 receptor contributes to restrain constitutive autophagy in neurons.
Generalized neural decoders for transfer learning across participants and recording modalities

Objective. Advances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants. Approach. We introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (a) a Hilbert transform that computes spectral power at data-driven frequencies and (b) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant. Main results. HTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet’s trained weights and demonstrate its ability to extract physiologically-relevant features. Significance. By generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.
A plausible mechanism for longitudinal lock-in of cortical microtubule array after light-induced reorientation

The light-induced reorientation of the cortical microtubule array in dark-grown A. thaliana hypocotyl cells is a striking example of the dynamical plasticity of the microtubule cytoskeleton. A consensus model, based on katanin-mediated severing at microtubule crossovers, has been developed that successfully describes the onset of the observed switch between a transverse and longitudinal array orientation. However, we currently lack an understanding of of why the newly populated longitudinal array direction remains stable for longer times, when the initial trigger for the reorientation has died out, and re-equilibration effects would tend to drive the system back to a mixed orientation state. Using both simulations and analytical calculations, we show that the assumption of a small orientation-dependent shift in microtubule dynamics is sufficient to explain the long term lock-in of the longitudinal array orientation. Furthermore, we show that the natural alternative hypothesis that there is a selective advantage in severing longitudinal microtubules, is neither necessary nor sufficient to achieve cortical array reorientation, but is able to accelerate this process significantly.
Downstream of gasdermin D cleavage, a Ragulator-Rag-mTORC1 pathway promotes pore formation and pyroptosis

The process of pyroptosis is mediated by inflammasomes and a downstream effector known as gasdermin D (GSDMD). Upon cleavage by inflammasome-associated caspases, the N-terminal domain of GSDMD forms membrane pores that promote cytolysis. Numerous proteins are recognized to promote GSDMD cleavage, but none are known to be required for pore formation after GSDMD cleavage. Herein, we report a forward genetic screen that was designed to identify regulators of pyroptosis that act downstream of GSDMD cleavage. This screen identified several components of the Ragulator-Rag complex, which is known for its metabolic control of mTOR. Mechanistic studies revealed that Ragulator-Rag is not necessary for GSDMD localization to the plasma membrane, but is necessary for pore formation and mitochondrial inactivation. Downstream of Ragulator-Rag is mTORC1, which we found to promote pyroptosis in response to diverse natural stimuli, including infection. GSDMD therefore requires a Ragulator-Rag-mTORC1 pathway in order to form pores and execute pyroptosis.
Combining functional and structural genomics to track antibiotic resistance genes in mobile elements in clinical bacterial strains

The rise of multi-antibiotics resistant bacteria represents an emergent threat to human health. Here, we investigate antibiotic resistance mechanisms in bacteria of several species isolated from an intensive care unit in Brazil. We used whole-genome analysis to identify antibiotic resistance genes (ARGs) and plasmids in 35 strains of Gram-negative and Gram-positive bacteria, including the first genomic description of Morganella morganii and Ralstonia mannitolilytica clinical isolates from South America. We identify a high abundance of beta-lactamase genes in highly resistant organisms, including seven extended-spectrum β-lactamases shared between organisms from different species. Additionally, we identify several ARGs-carrying plasmids indicating the potential for fast transmission of resistance mechanism between bacterial strains, comprising a novel IncFII plasmid recently introduced in Brazil from Asia. Through comparative genomic analysis, we demonstrate that some pathogens identified here are very distantly related to other bacteria isolated worldwide, demonstrating the potential existence of endemic bacterial pathogens in Brazil. Also, we uncovered at least two couples of (near)-identical plasmids exhibiting multi-drug resistance, suggesting that plasmids were transmitted between bacteria of the same or different species in the hospital studied. Finally, since many highly resistant strains carry several different ARGs, we used functional genomics to investigate which of them were indeed functional. In this sense, for three bacterial strains (Escherichia coli, Klebsiella pneumoniae, and M. morganii), we identify six beta-lactamase genes out of 15 predicted in silico as the main responsible for the resistance mechanisms observed, corroborating the existence of redundant resistance mechanisms in these organisms. Importance Big data and large-scale sequencing projects have revolutionized the field, achieving a greater understanding of ARGs identification and spreading at global level. However, given that microbiota and associated ARGs may fluctuate across geographic zones, hospital-associated infections within clinical units still remain underexplored in Brazil – the largest country in South America; 210 million inhabitants – and neighboring countries. This work highlighted the identification of several ARGs shared between species co-occurring simultaneously into a Brazilian hospital, some of them associated with large plasmids, mostly endowed with transposable elements. Also, genomic features of clinically underrepresented pathogens such M. morganii and B. cepacia were revealed. Taken together, our results demonstrate how structural and functional genomics can help to identify emerging mechanisms of shared antibiotic resistance in bacteria from clinical environments. Systematic studies as the one presented here should help to prevent outbreaks of novel multidrug resistance bacteria in healthcare facilities.
Combining ability and heterosis studyin maize inbreds throughout diallel mating design

Combining ability effects were estimated for grain yield and some other important agronomic traits of maize in a 7×7 diallel analysis excluding reciprocals. The variances for general combining ability (GCA) were found significant for yield, days to pollen shedding, days to silking and ear height while it was found non-significant for plant height and number of kernels/ear. Non-significant general combining ability (GCA) variance for plant height and number of kernels/ear indicates that these two traits were predominantly controlled by non- additive type of gene action. Specific combining ability (SCA) was significant for all the characters except yield and days to silking. Non-significant specific combining ability (SCA) variance for yield and days to silking suggests that these two traits were predominantly controlled by additive type of gene action. Both GCA and SCA variances were found significant only in days to pollen shedding and ear height indicated the presence of additive as well as non additive gene effects for controlling the traits. However, relative magnitude of these variances indicated that additive gene effects were more prominent for all the characters studied except days to silking. Parent BIL95 was the best general combiner for both high yield and number of kernels/ear and parent BML4 for dwarf plant type. Two crosses (BML4× BML36 and BIL114× BIL31) exhibited significant and positive SCA effects for grain yield involved low × average and average × average general combining parents. The range of heterosis expressed by different crosses for grain yield and days to silking was from -65.83 to 21.26 percent and -17.85 to 8.22 percent, respectively. The better performing three crosses (BIL114×BIL31, BIL138×BIL95 and BIL31×BIL95) can be utilized for developing high yielding hybrid varieties as well as for exploiting hybrid vigour.Bangladesh J. Agril. Res. 43(4): 599-609, December 2018
Evidence for adaptive evolution in the receptor-binding domain of seasonal coronaviruses OC43 and 229e

Seasonal coronaviruses (OC43, 229E, NL63 and HKU1) are endemic to the human population, regularly infecting and reinfecting humans while typically causing asymptomatic to mild respiratory infections. It is not known to what extent reinfection by these viruses is due to waning immune memory or antigenic drift of the viruses. Here, we address the influence of antigenic drift on immune evasion of seasonal coronaviruses. We provide evidence that at least two of these viruses, OC43 and 229E, are undergoing adaptive evolution in regions of the viral spike protein that are exposed to human humoral immunity. This suggests that reinfection may be due, in part, to positively-selected genetic changes in these viruses that enable them to escape recognition by the immune system. It is possible that, as with seasonal influenza, these adaptive changes in antigenic regions of the virus would necessitate continual reformulation of a vaccine made against them.
Dynamic connectivity analyses of intracranial EEG during recognition memory reveal various large-scale functional networks

Recognition memory is the ability to recognize previously encountered events, objects, or people. It is characterized by its robustness and rapidness. Even this relatively simple ability requires the coordinated activity of a surprisingly large number of brain regions. These spatially distributed, but functionally linked regions are interconnected into large-scale networks. Understanding memory requires an examination of the involvement of these networks and the interactions between different regions while memory processes unfold. However, little is known about the dynamical organization of large-scale networks during the early phases of recognition memory. We recorded intracranial EEG, which affords high temporal and spatial resolution, while epileptic subjects performed a visual recognition memory task. We analyzed dynamic functional and effective connectivity as well as network properties. Various networks were identified, each with its specific characteristics regarding information flow (feedforward or feedback), dynamics, topology, and stability. The first network mainly involved the right visual ventral stream and bilateral frontal regions. It was characterized by early predominant feedforward activity, modular topology, and high stability. It was followed by the involvement of a second network, mainly in the left hemisphere, but notably also involving the right hippocampus, characterized by later feedback activity, integrated topology, and lower stability. The transition between networks was associated with a change in network topology. Overall, these results confirm that several large-scale brain networks, each with specific properties and temporal manifestation, are involved during recognition memory. Ultimately, understanding how the brain dynamically faces rapid changes in cognitive demand is vital to our comprehension of the neural basis of cognition. Various dynamic large-scale networks support recognition memory. The first is mostly feedforward and involves the right hemisphere and the bilateral frontal lobes. The second is mostly feedback and includes left MTL regions and the right hippocampus. Changes in network topology accompany the switch between the networks.
Inflexible Updating of the Self-Other Divide During a Social Context in Autism; Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

Autism spectrum disorder (ASD) is a heterogenous disorder predominantly characterized by social and communicative differences, but increasingly recognized to also alter (multi)sensory function. To face the heterogeneity and ubiquity of ASD, researchers have proposed models of statistical inference operating at the level of ‘computations’. Here, we attempt to bridge both across domains – from social to sensory – and levels of description – from behavioral computations to neural ensemble activity to a biologically-plausible artificial neural network – in furthering our understanding of autism. We do so by mapping visuo-tactile peri-personal space (PPS), and examining its electroencephalography (EEG) correlates, in individuals with ASD and neurotypical individuals during both a social and non-social context given that (i) the sensory coding of PPS is well understood, (ii) this space is thought to distinguish between self and other, and (iii) PPS is known to remap during social interactions. In contrast to their neurotypical counterparts, psychophysical and EEG evidence suggested that PPS does not remap in ASD during a social context. To account for this observation, we then employed a neural network model of PPS and demonstrate that PPS remapping may be driven by changes in neural gain operating at the level of multisensory neurons. Critically, under the anomalous excitation-inhibition (E/I) regime of ASD, this gain modulation does not result in PPS resizing. Overall, our findings are in line with recent statistical inference accounts suggesting diminished flexibility in ASD, and further these accounts by demonstrating within an example relevant for social cognition that such inflexibility may be due to E/I imbalances.
Mitochondrial copper and phosphate transporter specificity was defined early in the evolution of eukaryotes

Mitochondrial carrier family (MCF/SLC25) proteins are selective transporters that maintain the mitochondrial metabolome. Here we combine computational, biochemical and phenotypic approaches to understand substrate selectivity of SLC25A3. In mammals, SLC25A3 transports both copper and phosphate, yet in Saccharomyces cerevisiae the transport of these substrates is partitioned across two paralogs: PIC2, which transports copper, and MIR1, which transports phosphate. To understand whether the ancestral state of this transporter was a single promiscuous transporter that duplicated and gained selectivity, we explored the evolutionary relationships of PIC2 and MIR1 orthologs across the eukaryotic tree of life. Phylogenetic analyses reveal that PIC2-like and MIR1-like orthologs are present in all major eukaryotic supergroups, indicating that the gene duplication that created these paralogs occurred early in eukaryotic evolution. Frequent lineage-specific gene duplications and losses suggest that substrate specificity may be evolutionarily labile. To link this phylogenetic signal to protein function and resolve the residues involved in substrate selection, we used structural modelling and site-directed mutagenesis to identify PIC2 residues involved in copper and phosphate transport activities. Based on these analyses, we generated a Leu175Ala variant of mouse SLC25A3 that retains the ability to transport copper, but not phosphate, and rescues the cytochrome c oxidase defect in SLC25A3 knockout cells. Taken together, this work uses an evolutionary framework to uncover amino acids involved in substrate recognition by MCF proteins responsible for copper and phosphate transport.
The evolution of social learning as phenotypic cue integration

Most analyses of the origins of cultural evolution focus on when and where social learning prevails over individual learning, overlooking the fact that there are other developmental inputs that influence phenotypic fit to the selective environment. This raises the question how the presence of other cue ‘channels’ affects the scope for social learning. Here, we present a model that considers the simultaneous evolution of (i) multiple forms of social learning (involving vertical or horizontal learning based on either prestige or conformity biases) within the broader context of other evolving inputs on phenotype determination, including (ii) heritable epigenetic factors, (iii) individual learning, (iv) environmental and cascading maternal effects, (v) conservative bet-hedging and (vi) genetic cues. In fluctuating environments that are autocorrelated (and hence predictable), we find that social learning from members of the same generation (horizontal social learning) explains the large majority of phenotypic variation, whereas other cues are much less important. Moreover, social learning based on prestige biases typically prevails in positively autocorrelated environments, whereas conformity biases prevail in negatively autocorrelated environments. Only when environments are unpredictable or horizontal social learning is characterised by an intrinsically low information content, other cues such as conservative bet-hedging or vertical prestige biases prevail.
TAD cliques predict key features of chromatin organization

Background Mechanisms underlying genome 3D organization and domain formation in the mammalian nucleus are not completely understood. Multiple processes such as transcriptional compartmentalization, DNA loop extrusion and interactions with the nuclear lamina dynamically act on chromatin at multiple levels. Here, we explore long-range interaction patterns between topologically associated domains (TADs) in several cell types. Results We find that TAD long-range interactions are connected to many key features of chromatin organization, including open and closed compartments, compaction and loop extrusion processes. Domains that form large TAD cliques tend to be repressive across cell types, when comparing gene expression, LINE/SINE repeat content and chromatin subcompartments. Further, TADs in large cliques are larger in genomic size, less dense and depleted of convergent CTCF motifs, in contrast to smaller and denser TADs formed by a loop extrusion process. Conclusions Our results shed light on the organizational principles that govern repressive and active domains in the human genome.
In vivo optical metabolic imaging of long-chain fatty acid uptake in orthotopic models of triple negative breast cancer

Targeting a tumor’s metabolic dependencies is a clinically actionable therapeutic approach, but identifying subtypes of tumors likely to respond remains difficult. The use of lipids as a nutrient source is of particular importance, especially in breast cancer. Imaging techniques offer the opportunity to quantify nutrient use in preclinical models to aid in the development of new drugs to restrict uptake or utilization of these nutrients. We describe a fast and dynamic approach to image fatty acid uptake in vivo, a tool relevant to study tumor metabolic reprogramming or for studying the effectiveness of drugs targeting lipid metabolism spanning beyond breast cancer and optical imaging alone. Specifically, we developed a quantitative optical approach to spatially and longitudinally map the kinetics of long-chain fatty acid uptake in in vivo murine models of breast cancer using a fluorescently labeled palmitate molecule, Bodipy FL c16. We chose intra-vital microscopy of mammary tumor windows to validate our approach in two orthotopic breast cancer models: a MYC-overexpressing transgenic triple-negative breast cancer (TNBC) model and a murine model of the 4T1 family. Following injection, Bodipy FL c16 fluorescence increased and reached its maximum after approximately 30 minutes, with the signal remaining stable during the 30-80-minute post-injection period. We used the fluorescence at 60 minutes (Bodipy60), the mid-point in the plateau region, as a summary parameter to quantify Bodipy FL c16 fluorescence in subsequent experiments. Using our imaging platform, we observed a two- to four-fold decrease in fatty acid uptake in response to the downregulation of the MYC oncogene consistent with findings from in vitro metabolic assays. In contrast, our imaging studies report an increase in fatty acid uptake with tumor aggressiveness (6NR, 4T07, and 4T1), and uptake was significantly decreased after treatment with a fatty acid transport inhibitor, perphenazine, in both normal mammary pads and in the most aggressive 4T1 tumor model. Our approach fills an important gap between in vitro assays, which provide rich metabolic information but at static time points, and imaging approaches that can visualize metabolism in whole organs, but which suffer from poor resolution.