Biology subjects

Gutierrez, G. J.

Publications and source records attributed to Gutierrez, G. J..

10 recordsLinked to original sources

bioRxiv2026

Multivalent ubiquitin recognition regulates multiple stages of Tau self-assembly

Liquid-liquid phase separation (LLPS) has emerged as an important intermediate in the pathological self-assembly of Tau, yet the molecular mechanisms that regulate this process remain incompletely understood. Although ubiquitin is a prominent component of pathological inclusions in tauopathies and other neurodegenerative diseases, its presence has largely been linked to targeting aberrant proteins for proteasomal or autophagic clearance. Here, we identify Tau as a ubiquitin-binding protein and demonstrate that multivalent, non-covalent ubiquitin recognition regulates multiple stages of Tau self-assembly. Biochemical, biophysical, structural, and computational analyses identified four ubiquitin-binding elements within the Tau microtubule-binding repeat region that engage the canonical hydrophobic patch of ubiquitin, providing a structural basis for multivalent recognition of mono- and poly-ubiquitin species. Functionally, mono- and poly-ubiquitin suppress Tau condensate formation while enhancing the liquid-like properties of condensates that persist. In contrast, mono-ubiquitin selectively enhances aggregation of the disease-linked Tau-P301L variant under conditions without detectable LLPS, demonstrating that ubiquitin exerts distinct effects depending on the molecular context and stage of Tau self-assembly. Together, these findings establish non-covalent ubiquitin recognition as a previously unrecognized mechanism regulating Tau phase behavior and aggregation. More broadly, they expand the role of ubiquitin beyond protein quality control, with ubiquitin architecture providing an additional layer of regulation over Tau condensate formation, dynamics, maturation, and aggregation.

biophysics↗

bioRxiv2026

Inter-hemispheric connections modulate splitting in a computational model of the bilateral SCN

The phenomenon of splitting was originally observed in hamsters which, after prolonged exposure to constant light, exhibit two rest/wake cycles within a subjective day. Splitting is a consequence of the left and right suprachiasmatic nuclei (SCN) falling out of synchrony. While it is known that split activity is characterized by an antiphase relationship between the left and right SCN and between the core and shell within each hemisphere, the role of the commissural projections that connect the right and left SCN is not known. In the present study, we investigate the impact of the inter-hemispheric connections on the split and unsplit dynamics of a computational model of the bilateral SCN. Our model has 4 nodes corresponding to each right and left core and shell. We simulated our bilateral model under different lighting conditions and measured its period and the phase relationships among the 4 nodes. To further characterize the dynamics of the system, we performed a bifurcation analysis. We found that the bilateral model automatically splits unless entrained by bright light/dark cycles, or unless it has excitatory inter-hemispheric connections. This suggests that excitatory cross-connections may be important for freerunning behavior. We found that constant light of varying intensities transitions the model between split and unsplit activity only in very limited conditions, but the strength and polarity of the contralateral connections play a much greater role in this dynamical transition. These findings suggest that splitting may involve plasticity of the inter-hemispheric connections of the SCN.

neuroscience↗

bioRxiv2025

Rod-cone signal interference impairs mesopic motion discriminability in a model circuit

Under mesopic conditions, such as dawn or dusk, signals from both rod and cone photoreceptors contribute to perception. These parallel inputs are combined within the retina before being sent to subsequent visual areas. The integration of these kinetically-distinct parallel signals poses unique challenges for human vision. Though previous behavioral studies have found that dim lighting conditions specifically impair motion perception in human subjects, the origin of this dependence is unclear. In the present study, we create a model circuit that predicts ganglion cell responses to moving stimuli by incorporating electrophysiologically-derived circuit components into a Hassenstein-Reichardt correlator, a classical motion-detection model. The model circuit demonstrates that interactions between rod- and cone-derived signals negatively impact the encoding of a moving objects direction under mesopic conditions. Furthermore, we found that the model circuit could enhance its motion discriminability if it was only sensitive to the cone-activating components of the stimuli. We conclude that rod-cone signal interference occurring at the lowest level of vision has an impact on motion direction discrimination, a higher-level task with relevance for behavior.

neuroscience↗

bioRxiv2024

The Oviposition Inhibitory Neuron is a potential hub of multi-circuit integration in the Drosophila brain

Understanding how neural circuits integrate sensory and state information to support context-dependent behavior is a central challenge in neuroscience. Oviposition is a complex process during which a fruit fly integrates context and sensory information to choose an optimal location to lay her eggs. The circuit that controls oviposition is known, but how the oviposition circuit integrates multiple sensory modalities and internal states is not. Using the Hemibrain connectome, we identified the Oviposition Inhibitory Neuron (oviIN) as a key hub in the oviposition circuit and analyzed its inputs to uncover potential parallel pathways that may be responsible for computations related to sensory integration and decision-making. We applied a network analysis to the subconnectome of inputs to the oviIN to identify clusters of interconnected neurons - many of which are uncharacterized cell types. Our findings indicate that the inputs to oviIN form multiple parallel pathways through the unstructured neuropils of the Superior Protocerebrum, a region implicated in context-dependent processing. Significance StatementThe recent advent of the Drosophila connectome enables researchers to probe the connectivity of uncharacterized cell types in the parts of the fruit fly brain that are responsible for cognitive-level computations. Our study analyzed the connectivity of the oviposition circuit which controls a complex behavior that depends on sensory and context integration and decision-making computations. Using graph theoretic and computational methods, we found that the sole inhibitory neuron in the circuit is a hub that integrates information from multiple clusters of uncharacterized neurons with potentially novel functions. Our work presents a new and timely perspective by demonstrating how new targets for study can be identified from the vast trove of uncharacterized neurons and cell types in the connectome.

neuroscience↗

bioRxiv2024

Protein kinase Sut1 from the Crenarchaeon Sulfolobus acidocaldarius displays tyrosine phosphorylation activity

Protein phosphorylation is a key cellular signaling mechanism that exists in all life forms. Unlike Bacteria and Eukarya, in which protein phosphorylation has thoroughly been studied, post-translational modification by means of phosphorylation have only been limitedly explored in Archaea. A previous study of the phosphoproteome of the model Crenarchaeon Sulfolobus acidocaldarius revealed a widespread occurrence of protein phosphorylation, especially on tyrosine residues. Moreover, several (putative) transcription factors, including AbfR1 and FadR, were previously shown to be phosphorylated on tyrosine residues, a phenomenon that is directly linked to a phosphorylation-mediated regulation of these transcription factors. Despite this potentially important role for tyrosine phosphorylation in S. acidocaldarius, to our knowledge, a kinase capable of directly phosphorylating tyrosine residues has not yet been identified in this organism, neither in Archaea as a whole. Here, we identify and characterize a protein kinase in S. acidocaldarius, Sut1, which displays tyrosine phosphorylation activity in vitro and represents a novel protein kinase family that is widespread in different archaeal organisms.

microbiology↗

bioRxiv2022

The EGFR-STYK1-FGF1 axis sustains functional drug tolerance to EGFR inhibitors in EGFR-mutant non-small cell lung cancer

Non-small cell lung cancer (NSCLC) patients harboring activating mutations in epidermal growth factor receptor (EGFR) are sensitive to therapy with EGFR tyrosine kinase inhibitors (TKI). Despite remarkable clinical responses using EGFR TKI, surviving drug tolerant cells serve as a reservoir from which drug resistant tumors may emerge. This study addresses the need for improved efficacy of EGFR TKI by identifying targets involved in functional drug tolerance against them. To this aim, a high-throughput siRNA kinome screen was performed using two EGFR TKI-sensitive EGFR-mutant NSCLC cell lines in the presence/absence of the second-generation EGFR TKI afatinib. From the screen, Serine/Threonine/Tyrosine Kinase 1 (STYK1) was identified as a target that when downregulated potentiates the effects of EGFR inhibition in vitro. We found that chemical inhibition of EGFR combined with the siRNA-mediated knockdown of STYK1 led to a significant decrease in cancer cell viability and anchorage-independent cell growth. Further, we show that STYK1 selectively interacts with mutant EGFR and that the interaction is disrupted upon EGFR inhibition. Finally, we identified fibroblast growth factor 1 (FGF1) as a downstream effector of STYK1 in NSCLC cells. Accordingly, downregulation of STYK1 counteracted the afatinib-induced upregulation of FGF1. Altogether, we unveil STYK1 as a valuable target to repress the pool of surviving drug tolerant cells arising upon EGFR inhibition. Co-targeting of EGFR and STYK1 could lead to a better overall outcome for NSCLC patients.

cancer biology↗

bioRxiv2022

Connectomic Analysis of the Drosophila Lateral Neuron Clock Cells Reveals the Synaptic Basis of Functional Pacemaker Classes.

The circadian clock orchestrates daily changes in physiology and behavior to ensure internal temporal order and optimal timing across the day. In animals, a central brain clock coordinates circadian rhythms throughout the body and is characterized by a remarkable robustness that depends on synaptic connections between constituent neurons. The clock neuron network of Drosophila, which shares network motifs with clock networks in the mammalian brain yet is built of many fewer neurons, offers a powerful model for understanding the network properties of circadian timekeeping. Here we report an assessment of synaptic connectivity within a clock network, focusing on the critical lateral neuron (LN) clock neuron classes. Our results reveal that previously identified anatomical and functional subclasses of LNs represent distinct connectomic types. Moreover, we identify a small number of clock cell subtypes representing highly synaptically coupled nodes within the clock neuron network. This suggests that neurons lacking molecular timekeeping likely play integral roles within the circadian timekeeping network. To our knowledge, this represents the first comprehensive connectomic analysis of a circadian neuronal network.

neuroscience↗

bioRxiv2020

Immuno-informatics approach for multi-epitope vaccine designing against SARS-CoV-2

The novel Corona Virus Disease 2019 (COVID-19) pandemic has set the fatality rates ablaze across the world. So, to combat this disease, we have designed a multi-epitope vaccine from various proteins of Severe Acute Respiratory Syndrome Corona virus 2 (SARS-CoV-2) with an immuno-informatics approach, validated in silico to be stable, non-allergic and antigenic. Cytotoxic T-cell, helper T-cell, and B-cell epitopes were computationally predicted from six conserved protein sequences among four viral strains isolated across the world. The T-cell epitopes, overlapping with the B-cell epitopes, were included in the vaccine construct to assure the humoral and cell-mediated immune response. The beta-subunit of cholera toxin was added as an adjuvant at the N-terminal of the construct to increase immunogenicity. Interferon-gamma inducing epitopes were even predicted in the vaccine. Molecular docking and binding energetics studies revealed strong interactions of the vaccine with immune-stimulatory toll-like receptors (TLR) -2, 3, 4. Molecular dynamics simulation of the vaccine ensured in vivo stability in the biological system. The immune simulation of vaccine evinced elevated immune response. The efficient translation of the vaccine in an expression vector was assured utilizing in silico cloning approach. Certainly, such a vaccine construct could reliably be effective against COVID-19.

bioinformatics↗

bioRxiv2019

Nonlinear convergence preserves information

Neural circuits are structured with layers of converging and diverging connectivity, and selectivity-inducing nonlinearities at neurons and synapses. These components have the potential to hamper an accurate encoding of the circuit inputs. Past computational studies have optimized the nonlinearities of single neurons, or connection weights in networks, to maximize encoded information, but have not grappled with the simultaneous impact of convergent circuit structure and nonlinear response functions for efficient coding. Our approach is to compare model circuits with different combinations of convergence, divergence, and nonlinear neurons to discover how interactions between these components affect coding efficiency. We find that a convergent circuit with divergent parallel pathways can encode more information with nonlinear subunits than with linear subunits, despite the compressive loss induced by the convergence and the nonlinearities when considered individually. These results show that the combination of selective nonlinearities and a convergent architecture - both elements that reduce information when acting separately - can promote efficient coding. Significance StatementComputation in neural circuits relies on a common set of motifs, including divergence of common inputs to parallel pathways, convergence of multiple inputs to a single neuron, and nonlinearities that select some signals over others. Convergence and circuit nonlinearities, considered individually, can lead to a loss of information about inputs. Past work has detailed how optimized nonlinearities and circuit weights can maximize information, but here, we show that incorporating non-invertible nonlinearities into a circuit with divergence and convergence, can enhance encoded information despite the suboptimality of these components individually. This study extends a broad literature on efficient coding to convergent circuits. Our results suggest that neural circuits may preserve more information using suboptimal components than one might expect.

neuroscience↗

bioRxiv2017

E/I balance enables adaptation to coordinate a neural population for a stable sensory representation

Adaptation is a key component of efficient coding in sensory neurons. However, it remains unclear how neurons can provide a stable representation of external stimuli given their history-dependent responses. Here we show that a stable representation is maintained if efficiency is optimized by a population of neurons rather than by neurons individually. We show that spike-frequency adaptation and E/I balanced recurrent connectivity emerge as solutions to a global cost-accuracy tradeoff. The network will re-distribute sensory responses from highly excitable neurons to less excitable neurons as the cost of neural activity increases. This does not change the representation at the population level, despite causing dynamic changes in individual neurons. By applying this framework to an orientation coding network, we reconcile neural and behavioral findings. Our approach underscores the common mechanisms behind the diversity of neural adaptation and its role in producing a reliable representation of the stimulus while minimizing metabolic cost.

neuroscience↗