Biology subjects

Pereira, F.

Publications and source records attributed to Pereira, F..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Distinct brain-wide presynaptic networks underlie the functional identity of individual cortical neurons

Neuronal connections provide the scaffolding for neuronal function. Revealing the connectivity of functionally identified individual neurons is necessary to understand how activity patterns emerge and support behavior. Yet, the brain-wide presynaptic wiring rules that lay the foundation for the functional selectivity of individual neurons remain largely unexplored. Cortical neurons, even in primary sensory cortex, are heterogeneous in their selectivity, not only to sensory stimuli but also to multiple aspects of behavior. Here, to investigate presynaptic connectivity rules underlying the selectivity of pyramidal neurons to behavioral state1-12 in primary somatosensory cortex (S1), we used two-photon calcium imaging, neuropharmacology, single-cell based monosynaptic input tracing, and optogenetics. We show that behavioral state-dependent neuronal activity patterns are stable over time. These are minimally affected by neuromodulatory inputs and are instead driven by glutamatergic inputs. Analysis of brain-wide presynaptic networks of individual neurons with distinct behavioral state-dependent activity profiles revealed characteristic patterns of anatomical input. While both behavioral state-related and unrelated neurons had a similar pattern of local inputs within S1, their long-range glutamatergic inputs differed. Individual cortical neurons, irrespective of their functional properties, received converging inputs from the main S1-projecting areas. Yet, neurons that tracked behavioral state received a smaller proportion of motor cortical inputs and a larger proportion of thalamic inputs. Optogenetic suppression of thalamic inputs reduced behavioral state-dependent activity in S1, but this activity was not externally driven. Our results revealed distinct long-range glutamatergic inputs as a substrate for preconfigured network dynamics associated with behavioral state.

neuroscience↗

bioRxiv2023

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches. We present a model comprising eight somatosensory cortex subregions, 4.2 million morphological and electrically-detailed neurons, and 13.2 billion local and mid-range synapses. In silico tools enabled reproduction and extension of complex laboratory experiments under a single parameterization, providing strong validation. The model reproduced millisecond-precise stimulus-responses, stimulus-encoding under targeted optogenetic activation, and selective propagation of stimulus-evoked activity to downstream areas. The models direct correspondence with biology generated predictions about how multiscale organization shapes activity; for example, how cortical activity is shaped by high-dimensional connectivity motifs in local and mid-range connectivity, and spatial targeting rules by inhibitory subpopulations. The latter was facilitated using a rewired connectome which included specific targeting rules observed for different inhibitory neuron types in electron microscopy. The model also predicted the role of inhibitory interneuron types and different layers in stimulus encoding. Simulation tools and a large subvolume of the model are made available to enable further community-driven improvement, validation and investigation.

neuroscience↗

bioRxiv2023

Manifold Learning for fMRI time-varying FC

Whole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolve over time in meaningful ways at temporal scales going from years (e.g., development) to seconds (e.g., within-scan time-varying FC (tvFC)). Yet, our ability to explore tvFC is severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers seek to generate low dimensional representations (e.g., 2D and 3D scatter plots) expected to retain its most informative aspects (e.g., relationships to behavior, disease progression). Limited prior empirical work suggests that manifold learning techniques (MLTs)--namely those seeking to infer a low dimensional non-linear surface (i.e., the manifold) where most of the data lies--are good candidates for accomplishing this task. Here we explore this possibility in detail. First, we discuss why one should expect tvFC data to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (i.e., minimum number of latent dimensions; ID) of tvFC data manifolds. Third, we describe the inner workings of three state-of-the-art MLTs: Laplacian Eigenmaps (LE), T-distributed Stochastic Neighbor Embedding (T-SNE), and Uniform Manifold Approximation and Projection (UMAP). For each method, we empirically evaluate its ability to generate neuro-biologically meaningful representations of tvFC data, as well as their robustness against hyper-parameter selection. Our results show that tvFC data has an ID that ranges between 4 and 26, and that ID varies significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed: UMAP and T-SNE can capture these two levels of detail concurrently, but LE could only capture one at a time. We observed substantial variability in embedding quality across MLTs, and within-MLT as a function of hyper-parameter selection. To help alleviate this issue, we provide heuristics that can inform future studies. Finally, we also demonstrate the importance of feature normalization when combining data across subjects and the role that temporal autocorrelation plays in the application of MLTs to tvFC data. Overall, we conclude that while MLTs can be useful to generate summary views of labeled tvFC data, their application to unlabeled data such as resting-state remains challenging.

neuroscience↗

bioRxiv2022

Modeling and Simulation of Rat Non-Barrel Somatosensory Cortex. Part I: Modeling Anatomy

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, interregional connectivity. We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. The model consists of 4.2 million morphologically detailed neurons, placed in a digital brain atlas. They are connected by 14.2 billion synapses, comprising local, mid-range and extrinsic connectivity. We delineated the limits of determining connectivity from neuron morphology and placement, finding that it reproduces targeting by Sst+ neurons, but requires additional specificity to reproduce targeting by PV+ and VIP+ interneurons. Globally, connectivity was characterized by local clusters tied together through hub neurons in layer 5, demonstrating how local and interegional connectivity are complicit, inseparable networks. The model is suitable for simulation-based studies, and a 211,712 neuron subvolume is made openly available to the community.

neuroscience↗

bioRxiv2022

Loss of Bcl6 transcriptionally alters classical dendritic cells and diminishes T follicular helper cell-inducing cDC2 in mouse spleen

Conventional dendritic cells (cDC) play key roles in immune induction, but what drives their heterogeneity and functional specialization is still ill-defined. Here we show that cDC-specific deletion of the transcriptional repressor Bcl6 in mice alters the phenotype and transcriptome of cDC1s and cDC2s, while their lineage identity is preserved. Bcl6-deficient cDC1s are diminished in the periphery but maintain their ability to cross-present antigen to CD8+ T cells, confirming general maintenance of this subset. Surprisingly, the absence of Bcl6 in cDCs causes a complete loss of Notch2-dependent cDC2s in the spleen and intestinal lamina propria. DC-targeted Bcl6-deficient mice induced fewer T follicular helper cells despite a profound impact on T follicular regulatory cells in response to immunization and mounted diminished Th17 immunity to Citrobacter rodentium in the colon. Our findings establish Bcl6 as an essential transcription factor for subsets of cDC and add to our understanding of the transcriptional landscape underlying cDC heterogeneity.

immunology↗

bioRxiv2022

ER translocation of suboptimal targeting sequences depends on Sec61β/Sbh1 and its phosphorylation

The endoplasmic reticulum (ER) protein translocation channel subunit Sec61{beta}/Sbh1 is non-essential, but contains multiple phosphorylation sites suggesting a regulatory role in ER protein import. We show here that mutating two N-terminal, proline-flanked, phosphorylation sites in the Sbh1 cytosolic domain phenocopies the temperature-sensitivity of a yeast strain lacking SBH1/SBH2, and results in reduced translocation into the ER of an Sbh1-dependent substrate, Gls1. In a microscopic screen we show that about 12% of GFP-tagged secretory proteins depend on Sbh1 for translocation. Sbh1-dependent proteins have targeting sequences with less pronounced hydrophobicity and often no or an inverse charge bias. A subset of these proteins was dependent on N-terminal phosphorylation of Sbh1 and on the phospho-S/T-specific proline isomerase Ess1 (PIN1 in mammals) for ER import. We conclude that Sbh1 promotes ER translocation of substrates with suboptimal targeting sequences and that its activity is regulated by a conformational change induced by N-terminal phosphorylation.

biochemistry↗

bioRxiv2022

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

AO_SCPLOWBSTRACTC_SCPLOWThe need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURONs ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURONs reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

neuroscience↗

bioRxiv2021

Evidence for a long-r ange RNA-RNA interaction between ORF8 and the downstream region of the Spike polybasic insertion of SARS-CoV-2

SARS-CoV-2 has affected people worldwide as the causative agent of COVID-19. The virus is related to the highly lethal SARS-CoV responsible for the 2002-2003 SARS outbreak in Asia. Research is ongoing to understand why both viruses have different spreading capacities and mortality rates. Like other beta coronaviruses, RNA-RNA interactions occur between different parts of the viral genomic RNA, resulting in discontinuous transcription and production of various sub-genomic RNAs. These sub-genomic RNAs are then translated into other viral proteins. In this work, we performed a comparative analysis for novel long-range RNA-RNA interactions that may involve the Spike region. Comparing predictions between reference sequences of SARS-CoV-1 and SARS-CoV-2 revealed several predictions amongst which a thermodynamically stable long-range RNA-RNA interaction between (23660-23703 Spike) and (28025-28060 ORF8) unique to SARS-CoV-2 was observed. Using data gathered worldwide, sequence variation patterns observed in the population support the in-silico RNA-RNA base-pairing predictions within these regions, suggesting further evidence for the interaction. The predicted interactions can potentially be related to the regulation of sub-genomic RNA production rates in SARS-CoV-2 and their subsequent accessibility to the host transcriptome.

bioinformatics↗

bioRxiv2021

The learning curve of murine subretinal injection among clinically trained ophthalmic surgeons

PO_SCPLOWURPOSEC_SCPLOWSubretinal injection (SRI) in mice is widely used in retinal research, yet the learning curve (LC) of this surgically challenging technique is unknown. MO_SCPLOWETHODSC_SCPLOWTo evaluate the LC for SRI in a murine model, we analyzed training data from 3 clinically trained ophthalmic surgeons from 2018 to 2020. Successful SRI was defined as either the absence of retinal pigment epithelium (RPE) degeneration after phosphate buffer saline injection and the presence of RPE degeneration after Alu RNA injection. Multivariable survival-time regression models were used to evaluate the association between surgeon experience and success rate, with adjustment for injection agents, and to calculate an approximate case number to achieve a 95% success rate. A Cumulative Sum (CUSUM) analysis was performed and plotted individually to monitor each surgeons simultaneous performance. RO_SCPLOWESULTSC_SCPLOWDespite prior microsurgery experience, the combined average success rate of the first 50 cases in mice was only 27%. The predicted SRI success rate did not reach a plateau above 95% until approximately 364 prior cases. Using the 364-training case as a "cutoff" point, the predicted probability of success before and after the 364th case was 65.38% and 99.32%, respectively (P < 0.0001). CUSUM analysis showed an initial upward slope and then remained within the decision intervals with an acceptable success rate set at 95% in the late stage. CO_SCPLOWONCLUSIONSC_SCPLOWThis study demonstrates the complexity and substantial LC for successful SRI in mice with high confidence. A systematic training system could improve the reliability and reproducibility of SRI-related experiments and improve the interpretation of experimental results using this technique. Translational RelevanceOur prediction model and monitor system allow objective quantification of technical proficiency in the field of subretinal drug delivery and gene therapy for the first time.

neuroscience↗

bioRxiv2021

Co-translational assembly counteracts promiscuous interactions

During the co-translational assembly of protein complexes, a fully synthesized subunit engages with the nascent chain of a newly synthesized interaction partner. Such events are thought to contribute to productive assembly, but their exact physiological relevance remains underexplored. Here, we examined structural motifs contained in nucleoporins for their potential to facilitate co-translational assembly. We experimentally tested candidate structural motifs and identified several previously unknown co-translational interactions. We demonstrate by selective ribosome profiling that domain invasion motifs of beta-propellers, coiled-coils, and short linear motifs act as co-translational assembly domains. Such motifs are often contained in proteins that are members of multiple complexes (moonlighters) and engage with closely related paralogs. Surprisingly, moonlighters and paralogs assembled co-translationally in only one but not all of the relevant assembly pathways. Our results highlight the regulatory complexity of assembly pathways.

molecular biology↗

bioRxiv2021

Validating the Representational Space of DeepReinforcement Learning Models of Behavior withNeural Data

Deep Reinforcement Learning (Deep RL) agents have in recent years emerged as successful models of animal behavior in a variety of complex learning tasks, as exemplified by Song et al. [2017]. As agents are typically trained to mimic an animal subject, the emphasis in past studies on behavior as a means of evaluating the fitness of models to experimental data is only natural. But the true power of Deep RL agents lies in their ability to learn neural computations and codes that generate a particular behavior--factors that are also of great relevance and interest to computational neuroscience. On that basis, we believe that model evaluation should include an examination of neural representations and validation against neural recordings from animal subjects. In this paper, we introduce a procedure to test hypotheses about the relationship between internal representations of Deep RL agents and those in animal neural recordings. Taking a sequential learning task as a running example, we apply our method and show that the geometry of representations learnt by artificial agents is similar to that of the biological subjects, and that such similarities are driven by shared information in some latent space. Our method is applicable to any Deep RL agent that learns a Markov Decision Process, and as such enables researchers to assess the suitability of more advanced Deep Learning modules, or map hierarchies of representations to different parts of a circuit in the brain, and help shed light on their function. To demonstrate that point, we conduct an ablation study to deduce that, in the sequential task under consideration, temporal information plays a key role in molding a correct representation of the task.

neuroscience↗

bioRxiv2021

Predictive evolution of metabolic phenotypes using model-designed selection niches

Traits lacking fitness benefit cannot be directly selected for under Darwinian evolution. Thus, features such as metabolite secretion are currently inaccessible to adaptive laboratory evolution. Here, we utilize environment-dependency of trait correlations to enable Darwinian selection of fitness-neutral or costly traits. We use metabolic models to design selection niches and to identify surrogate traits that are genetically correlated with cell fitness in the selection niche but coupled to the desired trait in the target niche. Adaptive evolution in the selection niche and subsequent return to the target niche is thereby predicted to enhance the desired trait. We experimentally validate the theory by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds in wine fermentation. Genomic, transcriptomic, and proteomic changes in the evolved strains confirmed the predicted flux re-routing to aroma biosynthesis. The use of model-designed selection niches facilitates the predictive evolution of fitness-costly traits for ecological and biotechnological applications.

systems biology↗

bioRxiv2021

Identification of Fluoxetine as a direct NLRP3 inhibitor to treat atrophic macular degeneration

The atrophic form of age-related macular degeneration (dry AMD) affects nearly 200 million people worldwide. There is no FDA-approved therapy for this disease, which is the leading cause of irreversible blindness among people over 50 years of age. Vision loss in dry AMD results from degeneration of the retinal pigmented epithelium (RPE). RPE cell death is driven in part by accumulation of Alu RNAs, which are noncoding transcripts of a human retrotransposon. Alu RNA induces RPE degeneration by activating the NLRP3-ASC inflammasome. We report that fluoxetine, an FDA-approved drug for treating clinical depression, binds NLRP3 in silico, in vitro, and in vivo, and that it inhibits activation of the NLRP3-ASC inflammasome in RPE cells and macrophages, two critical cell types in dry AMD. We also demonstrate that fluoxetine, unlike several other anti-depressant drugs, reduces Alu RNA-induced RPE degeneration in mice. Finally, by analyzing two health insurance databases comprising more than 100 million Americans, we report a reduced hazard of developing dry AMD among patients with depression who were treated with fluoxetine. Collectively, these studies triangulate to link fluoxetine as a potential drug repurposing candidate for a major unmet medical need that causes blindness in millions of people in the United States and across the world. Significance StatementDry age-related macular degeneration (AMD) affects the vision of millions of people worldwide. There is currently no FDA-approved treatment for dry AMD. The inflammasome components NLRP3 and ASC have been implicated in the pathogenesis of dry AMD. We report that fluoxetine, which is approved for the treatment of clinical depression, directly binds the NLRP3 protein and prevents the assembly and activation of the NLRP3-ASC inflammasome. As a result, it also blocks the degeneration of retinal pigmented epithelium (RPE) cells in an animal model of dry AMD. Furthermore, we demonstrate through an analysis of health insurance databases that use of this FDA-approved anti-depressant drug is associated with reduced incidence of dry AMD. These studies identify that fluoxetine is a potential repurposing candidate for AMD, a prevalent cause of blindness.

immunology↗

bioRxiv2020

Operation of a TCA cycle subnetwork in the mammalian nucleus

Nucleic acid and histone modifications critically depend on central metabolism for substrates and co-factors. Although a few enzymes related to the formation of these required metabolites have been reported in the nucleus, the corresponding metabolic pathways are considered to function elsewhere in the cell. Here we show that a substantial part of the mitochondrial tricarboxylic acid (TCA) cycle, the biosynthetic hub of epigenetic modification factors, is operational also in the nucleus. Using 13C-tracer analysis, we identified activity of glutamine-to-fumarate, citrate-to-succinate, and glutamine-to-aspartate routes in the nuclei of HeLa cells. Proximity labeling mass-spectrometry revealed a spatial vicinity of the involved enzymes with core nuclear proteins, supporting their nuclear location. We further show nuclear localization of aconitase 2 and 2-oxoglutarate dehydrogenase in mouse embryonic stem cells. Together, our results demonstrate operation of an extended metabolic pathway in the nucleus warranting a revision of the canonical view on metabolic compartmentalization and gene expression regulation.

systems biology↗

bioRxiv2020

Insufficient Eye Tracking Data Leads to Errors in Evaluating Typical and Atypical Fixation Preferences

Eye tracking provides insights into social processing and its deficits in disorders such as autism spectrum disorder (ASD), especially in conjunction with dynamic, naturalistic stimuli. However, reliance on manual stimuli segmentation severely limits scalability. We assessed how the amount of available data impacts individual reliability of fixation preference for different facial features, and the effect of this reliability on between-group differences. We trained an artificial neural network to segment 22 Hollywood movie clips (7410 frames). We then analyzed fixation preferences in typically developing participants and participants with ASD as we incrementally introduced movie data for analysis. Although fixations were initially variable, results stabilized as more data was added. Additionally, while those with ASD displayed significantly fewer face-centered fixations (p<.001), they did not differ in eye or mouth fixations. Our results highlight the validity of treating fixation preferences as a stable individual trait, and the risk of misinterpretation with insufficient data.

neuroscience↗

bioRxiv2020

CRISPRi screens reveal genes modulating yeast growth in lignocellulose hydrolysate

BackgroundBakers yeast is a widely used eukaryotic cell factory, producing a diverse range of compounds including biofuels and fine chemicals. The use of lignocellulose as feedstock offers the opportunity to run these processes in an environmentally sustainable way. However, the required hydrolysis pretreatment of lignocellulosic material releases toxic compounds that hamper yeast growth and consequently productivity. ResultsHere, we employ CRISPR interference in S. cerevisiae to identify genes modulating fermentative growth in plant hydrolysate and in presence of lignocellulosic toxins. We find that at least one third of hydrolysate-associated gene functions are explained by effects of known toxic compounds, such as the decreased growth of YAP1 or HAA1, or increased growth of DOT6 knock-down strains in hydrolysate. ConclusionOur study confirms previously known genetic elements and uncovers new targets towards designing more robust yeast strains for the utilization of lignocellulose hydrolysate as sustainable feedstock, and, more broadly, paves the way for applying CRISPRi screens to improve industrial fermentation processes.

microbiology↗

bioRxiv2020

Recognition memory via repetition suppression in mouse hippocampal dorsal CA2 pyramidal neurons expressing the vasopressin 1b receptor

Recognition memory, often compromised in psychiatric disorders, is a major component of declarative memory, which permits the realization that an event, object or social subject has been previously encountered. The CA2 region of the dorsal hippocampus (dCA2) is involved in social memory and responds to novel objects, in time and space. However, it remains unclear how these neurons encode either social or inanimate object recognition. Here, we show that in dCA2, encoding of social recognition memory entails suppression of pyramidal neurons activity leading to a sparse representation of the familiar conspecific. We discuss the neural coding scheme by which dCA2 pyramidal neurons contribute to social memory.

neuroscience↗

bioRxiv2020

CoV2ID: Detection and Therapeutics Oligo Database for SARS-CoV-2

The ability to detect the SARS-CoV-2 in a widespread epidemic is crucial for screening of carriers and for the success of quarantine efforts. Methods based on real-time reverse transcription polymerase chain reaction (RT-qPCR) and sequencing are being used for virus detection and characterization. However, RNA viruses are known for their high genetic diversity which poses a challenge for the design of efficient nucleic acid-based assays. The first SARS-CoV-2 genomic sequences already showed novel mutations, which may affect the efficiency of available screening tests leading to false-negative diagnosis or inefficient therapeutics. Here we describe the CoV2ID (http://covid.portugene.com/), a free database built to facilitate the evaluation of molecular methods for detection of SARS-CoV-2 and treatment of COVID-19. The database evaluates the available oligonucleotide sequences (PCR primers, RT-qPCR probes, etc.) considering the genetic diversity of the virus. Updated sequences alignments are used to constantly verify the theoretical efficiency of available testing methods. Detailed information on available detection protocols are also available to help laboratories implementing SARS-CoV-2 testing.

bioinformatics↗