Biology subjects

Liu, D.

Publications and source records attributed to Liu, D..

At least 217 records · Page 12Linked to original sources

bioRxiv2024

Noncaloric monosaccharides induce excessive sprouting angiogenesis in zebrafish via foxo1a-marcksl1a signal

Artificially sweetened beverages containing noncaloric monosaccharides were suggested as healthier alternatives to sugar-sweetened beverages. Nevertheless, the potential detrimental effects of these noncaloric monosaccharides on blood vessel function remain inadequately understood. We have established a zebrafish model that exhibits significant excessive angiogenesis induced by high glucose, resembling the hyperangiogenic characteristics observed in proliferative diabetic retinopathy (PDR). Utilizing this model, we observed that glucose and noncaloric monosaccharides could induce excessive formation of blood vessels, especially intersegmental vessels (ISVs). The excessively branched vessels were observed to be formed by ectopic activation of quiescent endothelial cells (ECs) into tip cells. Single-cell transcriptomic sequencing analysis of the endothelial cells in the embryos exposed to high glucose revealed an augmented ratio of capillary ECs, proliferating ECs, and a series of upregulated proangiogenic genes. Further analysis and experiments validated that reduced foxo1a mediated the excessive angiogenesis induced by monosaccharides via up-regulating the expression of marcksl1a. This study has provided new evidence showing the negative effects of noncaloric monosaccharides on the vascular system and the underlying mechanisms.

pharmacology and toxicology↗

bioRxiv2024

Genome streamlining to improve performance of a fast-growing cyanobacterium Synechococcus elongatus UTEX 2973

Cyanobacteria are photosynthetic organisms that have garnered significant recognition as potential hosts for sustainable bioproduction. However, their complex regulatory networks pose significant challenges to major metabolic engineering efforts, thereby limiting their feasibility as production hosts. Genome streamlining has been demonstrated to be a successful approach for improving productivity and fitness in heterotrophs but is yet to be explored to its full potential in phototrophs. Here we present the systematic reduction of the genome of the cyanobacterium exhibiting the fastest exponential growth, Synechococcus elongatus UTEX 2973. This work, the first of its kind in a photoautotroph, involved an iterative process using state-of-the-art genome-editing technology guided by experimental analysis and computational tools. CRISPR/Cas3 enabled large, progressive deletions of predicted dispensable regions and aided in the identification of essential genes. The large deletions were combined to obtain a strain with 55 kb genome reduction. The strains with streamlined genome showed improvement in growth (up to 23%) and productivity (by 22.7%) as compared to the WT. This streamlining strategy not only has the potential to develop cyanobacterial strains with improved growth and productivity traits but can also facilitate a better understanding of their genome to phenome relationships. ImportanceGenome streamlining is an evolutionary strategy used by natural living systems to dispense unnecessary genes from their genome as a mechanism to adapt and evolve. While this strategy has been successfully borrowed to develop synthetic heterotrophic microbial systems with desired phenotype, it has not been extensively explored in photoautotrophs. Genome streamlining strategy incorporates both computational predictions to identify the dispensable regions and experimental validation using genome editing tool and in this study we have employed a modified strategy with the goal to minimize the genome size to an extent that allows optimal cellular fitness under specified conditions. Our strategy has explored a novel genome-editing tool in photoautotrophs which, unlike other existing tools, enables large, spontaneous optimal deletions from the genome. Our findings demonstrate the effectiveness of this modified strategy in obtaining strains with streamlined genome, exhibiting improved fitness and productivity.

synthetic biology↗

bioRxiv2024

Unraveling wheat endosperm development: epigenetic regulation and novel regulators for enhanced yield and quality

Starch content and seed storage protein (SSP) composition are critical factors influencing wheat grain yield and quality. To uncover the molecular mechanisms governing their biosynthesis, we conducted transcriptome and epigenome profiling across key endosperm developmental stages, revealing that chromatin accessibility, H3K27ac, and H3K27me3 collectively regulate SSP and starch genes with varying impact. Population transcriptome and phenotype analyses highlighted the crucial role of accessible promoter regions as a genetic variation resource, influencing grain yield and quality in a core collection of wheat accessions. By integrating time-serial RNA-seq and ATAC-seq data, we constructed a hierarchical transcriptional regulatory network (TRN) governing starch and SSP biosynthesis, identifying 42 high-confidence novel candidates. These candidates exhibited overlap with genetic regions associated with grain size and quality traits, and their functional significance was validated through expression-phenotype association analysis among wheat accessions and TILLING mutants. In-depth functional analysis of wheat abscisic acid insensitive 3-A1 (TaABI3-A1) with genome editing knock-out lines demonstrated its role in promoting SSP accumulation while repressing starch biosynthesis through transcriptional regulation. An elite haplotype of TaABI3-A1 with higher grain weight was identified during the breeding process in China, and its superior trait was associated with altered TaABI3-A1 expression levels. Additionally, we identified the potential upstream regulator, wheat GAGA-binding transcription factor 1 (TaGBP1), influencing TaABI3-A1 expression. Our study provides novel and high-confidence regulators, presenting an effective strategy for understanding the regulation of SSP and starch biosynthesis and contributing to breeding enhancement.

plant biology↗

bioRxiv2023

Liver Protects Cytoarchitecture, Neuron Viability and Electrocortical Activity in Post-Cardiac Arrest Brain Injury

BACKGROUNDBrain injury is the major reason for patient deaths in victims who survive after cardiac arrest. Clinical studies have shown that the presence of hypoxic hepatitis and pre-cardiac arrest liver disease is associated with increased mortality and inferior neurological recovery. However, how the liver might impact the pathogenesis of post-cardiac arrest is still unknown. METHODSAn in vivo global cerebral ischemia model was established to assess how simultaneous liver ischemia affected the recovery of brain ischemic injury. In addition, an ex vivo brain normothermic machine perfusion (NMP) model was established to evaluate how addition of a functioning liver might impact the circulation, cytoarchitecture, neuron viability and electrocortical activity of the reperfused brain post-cardiac arrest. RESULTSIn the in vivo model, we observed a larger infarct area in the frontal lobe, elevated tissue injury scores in the CA1 region, as well as increased intravascular immune cell adhesion in the reperfused brains with hepatic ischemia, compared to those without simultaneous hepatic ischemia. The results of the ex vivo model demonstrated that the addition of a functioning liver to the brain NMP circuit significantly reduced post-cardiac arrest brain injury, increased neuronal viability and improved electrocortical activity. Furthermore, we observed significant alterations in gene expressions and metabolites in the presence or absence of hepatic ischemia. CONCLUSIONSOur research highlights the crucial role of the liver in the pathogenesis of post-cardiac arrest brain injury. These findings shed lights on a cardio-pulmonary-hepatic brain resuscitation strategy for patients with cardiac arrest.

neuroscience↗

bioRxiv2023

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition

Designing protein with specified structure and function involves a key component named sequence design, which can provide valuable insights into understanding the life systems as well for the diagnosis and therapy of diseases. Although deep learning methods have made great progress in protein sequence design, most of these studies focus on network structure optimization, while ignoring protein-specific physicochemical features. Inspired by the successful application of structure templates and pre-trained models in the field of protein structure prediction, we explored whether the representation of structural sequence profile can be used for protein sequence design. In this work, we proposed SPDesign, a method for protein sequence design based on structural sequence profile using ultrafast shape recognition. Given an input back-bone structure, SPDesign utilizes ultrafast shape recognition vectors to accelerate the search for similar protein structures (aka, structural analogs) in our in-house PAcluster80 structure database, and then extracts the sequence profile from the analogs through structure alignment. Combined with structural pre-trained knowledge and geometric features, they are further feed into an enhanced graph neural network to predict the sequence. Experimental results show that SPDesign significantly outperforms the state-of-the-art methods, such as ProteinMPNN, Pifold and LM-Design, leading to 21.89%, 15.54% and 11.4% accuracy gains in sequence recovery rate on CATH 4.2 benchmark, respectively. Encouraging results also have been achieved on the TS50 and TS500 benchmarks, with performance reaching 68.64% and 71.63%. Furthermore, detailed analysis conducted by the PDBench tool suggest that SPDesign performs well in subdivided structures such as buried residues and solenoid. More interestingly, we found that SPDesign can well reconstruct the sequences of some proteins that have similar structures but different sequences. Finally, the structural modeling verification experiment bears out that the sequences designed by our method can fold into the native structures more accurately.

bioinformatics↗

bioRxiv2023

Inhibition of KLK8 promotes pulmonary endothelial repair by restoring VE-cadherin/Akt/FOXM1 pathway

The tissue kallikrein-related peptidases (KLKs) are secreted serine proteases deeply involved in angiogenesis. However, whether KLKs are involved in the regulation of endothelial regeneration during sepsis remains unknown. By comparing the mRNA levels of 15 KLKs, we found that KLK8 was the highest induced KLK member in lung tissues or primary isolated mouse lung vascular endothelial cells (MLVECs) exposed to lipopolysaccharide (LPS). Adenovirus-mediated overexpression of KLK8 caused endothelial hyperpermeability both in vitro and in vivo. Inhibition of KLK8, by either gene knockout or KLK8 neutralizing antibodies, alleviated sepsis-induced endothelial hyperpermeability, acute lung injury and mortality. Mechanistically, transcription profiling of KLK8-overexpressed endothelial cells revealed a central role of forkhead box M1 (FOXM1) downregulation in mediating the pro-injury and anti-proliferation effects of KLK8. KLK8 cleaved VE-cadherin and consequently suppressed FOXM1 expression by inactivation of the VE-cadherin/Akt pathway. KLK8 deficiency or blockade rescued VE-cadherin/Akt/FOXM1 pathway, thus promoting endothelium regeneration. This study reveals a critical role for KLK8-induced inactivation of VE-cadherin/Akt/FOXM1 pathway in mediating the impairment of endothelial regeneration and the consequent lung vascular leakiness in response to sepsis. HighlightsO_LIUpregulated KLK8 mediates lung endothelial barrier dysfunction during sepsis C_LIO_LIKLK8 inactivates VE-cadherin/Akt/FOXM1, thus impairing endothelium regeneration C_LIO_LIKLK8 deficiency or blockade rescues VE-cadherin/Akt/FOXM1 signaling pathway C_LIO_LIKLK8 deficiency or blockade promotes endothelium regeneration during sepsis C_LIO_LIKLK8 deficiency or blockade attenuates sepsis-induced acute lung injury and mortality C_LI

physiology↗

bioRxiv2023

Application of slow-controlled release fertilizer coordinates the carbon flow in carbon-nitrogen metabolism to effect rice quality

Slow-controlled release fertilizers are experiencing a popularity in rice cultivation due to their effectiveness in yield and quality with low environmental costs. However, the underlying mechanism by which these fertilizers regulate grain quality remains inadequately understood. This study investigated the effects of five fertilizer management practices on rice quality in a two-year field experiment: CK, conventional fertilization, and four applications of slow-controlled release fertilizer (UF, urea formaldehyde; SCU, sulfur-coated urea; PCU, polymer-coated urea; BBF, controlled-release bulk blending fertilizer). In 2020 and 2021, the yields of UF and SCU groups showed significant decreases when compared to conventional fertilization, accompanied by a decline in nutritional quality. Additionally, PCU group exhibited poorer cooking and eating qualities. However, BBF group achieved increases in both yield (10.8 t hm-2 and 11.0 t hm-2) and grain quality reaching the level of CK group. The sufficient nitrogen supply in both the PCU and BBF groups during the grain-filling stage led to a greater capacity for the accumulation of proteins and amino acids in the PCU group compared to starch accumulation. Intriguingly, BBF group showed better carbon-nitrogen metabolism than that of PCU group. The optimal nitrogen supply present in BBF group suitable boosted the synthesis of amino acids involved in the glycolysis/ tricarboxylic acid cycle, thereby effectively coordinating carbon-nitrogen metabolism. The application of the new slow-controlled release fertilizer, BBF, is advantageous in regulating the carbon flow in the carbon-nitrogen metabolism to enhance rice quality.

plant biology↗

bioRxiv2023

Artificial intelligence-assisted stimulated Raman molecular cytology enables accurate and rapid intraoperative assessment of the surgical margin in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly mortal cancer whose only potentially curative treatment is surgical resection. Intraoperative assessment of its surgical margins is vital for patient survival. Frozen-section biopsy is routinely performed for this purpose. However, its high dependence on pathologists experience frequently poses diagnostic discrepancies. The essential invasiveness of PDAC also causes sampling errors. This study developed an intelligent molecular cytology approach with improved diagnostic objectivity and broader sampling coverage. Our method, Multi-Instance Cytology with Learned Raman Embedding (MICLEAR), is characterized by compositional information provided by label-free Raman imaging. First, 4085 cells were brushed off from the pancreases of 41 patients and imaged using stimulated Raman scattering microscopy. Then, a contrastive learning-based cell embedding model was developed to compress each cells morphological and compositional information into a compact cell vector. Finally, a multi-instance learning-based diagnosis model using cell vectors was employed to predict the likelihood of a patients margin being positive. MICLEAR achieved 80% sensitivity, 94.1% specificity, and an AUC of 0.86 on 27 patients for validation, comprising 10 with positive margins and 17 with negative ones, within approximately 8 minutes per patient. It may hold promises for more efficient and accurate intraoperative assessment of PDAC surgical margins.

biophysics↗

bioRxiv2023

Glycodiversification of gentamicins through in vivo glycosyltransferase swapping enabled the creation of novel hybrid aminoglycoside antibiotics with potent activity and low ototoxicity

Aminoglycosides (AGs) are a class of potent antibiotics with a broad spectrum of activity. However, their use is limited by safety concerns associated with nephrotoxicity and ototoxicity, as well as drug resistance. To address these issues, semi-synthetic approaches for modifying natural AGs have successfully generated new generations of AGs, however, with limited types of modification due to significant challenges in synthesis. This study explores a novel approach that harness the bacterial biosynthetic machinery of gentamicins and kanamycins to create hybrid AGs, installing extensive natural modifications from gentamicins onto kanamycins. This was achieved by glycodiversification of gentamicins via swapping the glycosyltransferase (GT) in their producer with the GT from kanamycins biosynthetic pathway and resulted in the creation of a series of novel AGs with combined structural features of two, therefore referred to as genkamicins (GKs). The manipulation of the hybrid metabolic pathway enabled the target accumulation of different GK species and the successful isolation and characterization of six GK components. These compounds display retained antimicrobial activity against a panel of World Health Organization (WHO) critical priority pathogens, and GK-C2a, in particular, demonstrates low ototoxicity compared to clinical drugs in zebrafish embryos. This study provides a new strategy for diversifying the structure of AGs and a potential avenue for developing less toxic AG drugs to combat infectious diseases.

synthetic biology↗

bioRxiv2023

SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data

BackgroundCancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. ResultsThis study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data. ConclusionsSpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis. Availability of code and materialshttps://github.com/SemenovLab/SpatialCells.

bioinformatics↗

bioRxiv2023

Rapid nongenomic estrogen signaling controls alcohol drinking behavior

Ovarian-derived estrogen is a key modulator of numerous physiological processes via genomic and nongenomic mechanisms, including signaling non-canonically at membrane-associated estrogen receptors in the brain to rapidly regulate neuronal function. However, the mechanisms mediating estrogen regulation of behaviors such as alcohol consumption remain unclear. Early alcohol drinking confers greater risk for alcohol use disorder in women than men, and binge alcohol drinking is correlated with high circulating estrogen levels, but a causal role for estrogen signaling in driving alcohol drinking in gonadally-intact animals has not been established. We found that female mice displayed greater binge alcohol drinking and reduced avoidance behavior when circulating estrogen was high during the proestrus phase of the estrous cycle than when it was low, contributing to sex differences in these behaviors. The pro-drinking, but not anxiolytic, effect of high endogenous estrogen state occurred via rapid estrogen signaling at membrane-associated estrogen receptor alpha in the bed nucleus of the stria terminalis, which promoted synaptic excitation of corticotropin-releasing factor neurons and facilitated their activity during alcohol drinking behavior. This study is the first to demonstrate a rapid, nongenomic signaling mechanism for ovarian-derived estrogen signaling in the brain controlling behavior in gonadally intact females, and it establishes a causal role for estrogen in an intact hormonal context for driving alcohol consumption that contributes to known sex differences in this behavior.

neuroscience↗

bioRxiv2023

Morphine-context associative memory and locomotor sensitization in mice are modulated by sex and context in a dose-dependent manner

Sex differences in opioid use, development of opioid used disorder, and relapse behaviors indicate potential variations in opioid effects between men and women. The locomotor and interoceptive effects of opioids play essential roles in opioid addiction, and uncovering the neural mechanisms underlying these effects remain crucial for developing effective treatments. In this study, we examined the dose-dependent effects of morphine on locomotor sensitization and the strength and stability of morphine-context associations in the conditioned place preference (CPP) paradigm in male and female mice, as well as the relationships between these measures. We observed that while CPP is similar between sexes, the locomotor effects of repeated morphine administration and withdrawal differentially contributed to the strength and stability of morphine-context associations. Specifically, females exhibited higher morphine-induced hyperlocomotion than males regardless of the context in which morphine was experienced. Greater locomotor sensitization to morphine in females than males emerged in a dose-dependent manner only when there was sufficient context information for CPP to be established. Additionally, the relationships between the locomotor effects of morphine and the strength and stability of CPP were different in males and females. In females, positive acute and sensitizing locomotor effects of morphine were correlated with a higher CPP score, while the opposite direction of this relationship was found in males. These results suggest that different aspects of the subjective experience of morphine intoxication and withdrawal are important for morphine abuse-related behaviors and highlight the importance of sex-specific responses in the context of opioid addiction.

neuroscience↗

bioRxiv2023

A Generalized Supervised Contrastive Learning Framework for Integrative Multi-omics Prediction Models

Recent technological advances have highlighted the significant impact of the human microbiome and metabolites on physiological conditions. Integrating microbiome and metabolite data has shown promise in predictive capabilities. We developed a new supervised contrastive learning framework, MB-SupCon-cont, that (1) proposes a general contrastive learning framework for continuous outcomes and (2) improves prediction accuracy over models using single omics data. Simulation studies confirmed the improved performance of MB-SupCon-cont, and applied scenarios in type 2 diabetes and high-fat diet studies also showed improved prediction performance. Overall, MB-SupCon-cont is a versatile research tool for multi-omics prediction models.

bioinformatics↗

bioRxiv2023

Decoding Heterogenous Single-cell Perturbation Responses

Understanding diverse responses of individual cells to the same perturbation is central to many biological and biomedical problems. Current methods, however, do not precisely quantify the strength of perturbation responses and, more importantly, reveal new biological insights from heterogeneity in responses. Here we introduce the perturbation-response score (PS), based on constrained quadratic optimization, to quantify diverse perturbation responses at a single-cell level. Applied to single-cell transcriptomes of large-scale genetic perturbation datasets (e.g., Perturb-seq), PS outperforms existing methods for quantifying partial gene perturbation responses. In addition, PS presents two major advances. First, PS enables large-scale, single-cell-resolution dosage analysis of perturbation, without the need to titrate perturbation strength. By analyzing the dose-response patterns of over 2,000 essential genes in Perturb-seq, we identify two distinct patterns, depending on whether a moderate reduction in their expression induces strong downstream expression alterations. Second, PS identifies intrinsic and extrinsic biological determinants of perturbation responses. We demonstrate the application of PS in contexts such as T cell stimulation, latent HIV-1 expression, and pancreatic cell differentiation. Notably, PS unveiled a previously unrecognized, cell-type-specific role of coiled-coil domain containing 6 (CCDC6) in guiding liver and pancreatic lineage decisions, where CCDC6 knockouts drive the endoderm cell differentiation towards liver lineage, rather than pancreatic lineage. The PS approach provides an innovative method for dose-to-function analysis and will enable new biological discoveries from single-cell perturbation datasets. One sentence summaryWe present a method to quantify diverse perturbation responses and discover novel biological insights in single-cell perturbation datasets.

bioinformatics↗

bioRxiv2023

Listeria adhesion protein orchestrates caveolae-mediated apical junctional remodeling of epithelial barrier for L. monocytogenes translocation

The cellular junctional architecture remodeling by LAP-Hsp60 interaction for L. monocytogenes (Lm) passage through the epithelial barrier is incompletely understood. Here, using the gerbil model, permissive to internalin (Inl) A/B-mediated pathways like in humans, we demonstrate that Lm crosses the intestinal villi at 48 h post-infection. In contrast, the single isogenic (lap[boxh] or {Delta}inlA) or double (lap[boxh]{Delta}inlA) mutant strains show significant defects. LAP promotes Lm translocation via endocytosis of cell-cell junctional complex in enterocytes that do not display luminal E-cadherin. In comparison, InlA-mediated transcytosis occurs in enterocytes displaying apical E-cadherin during cell extrusion and mucus expulsion from goblet cells. LAP hijacks caveolar endocytosis to traffic integral junctional proteins to the early and recycling endosomes. Pharmacological inhibition in a cell line and genetic knock-out of caveolin-1 in mice prevents LAP-induced intestinal permeability, junctional endocytosis, and Lm translocation. Furthermore, LAP-Hsp60-dependent tight junction remodeling is also necessary for InlA access to E-cadherin for Lm intestinal barrier crossing in InlA-permissive hosts.

microbiology↗

bioRxiv2023

Accurate identification of cancer cells in complex pre-clinical models using deep-learning: a transfection free approach.

3D co-cultures are key tools for in vitro biomedical research as they recapitulate more closely the in vivo environment, while allowing control of the density and type of cells included in the analysis, as well as the experimental conditions in which they are maintained. More widespread application of these models is hampered however by the limited technologies available for their analysis. The separation of the contribution of the different cell types, in particular, is a fundamental challenge. In this work, we present ORACLE, a deep neural network trained to distinguish between ovarian cancer and healthy cells based on the shape of their nucleus. The extensive validation that we have conducted includes multiple cell lines and patient derived cultures to characterise the effect of all the major potential confounding factors. High accuracy and reliability were maintained throughout the analysis demonstrating ORACLE effectiveness with this detection and classification task. ORACLE is freely available (https://github.com/MarilisaCortesi/ORACLE/tree/main) and can be used to recognise both ovarian cancer cell lines and primary patient-derived cells. This feature sets ORACLE apart from currently available analysis methods and opens the possibility of analysing in vitro co-cultures comprised solely of patient-derived cells.

bioengineering↗

bioRxiv2023

The chromatin accessibility dynamics during cell fate specifications in zebrafish early embryogenesis

Chromatin accessibility plays a critical role in the regulation of cell fate decisions. Although gene expression changes have been extensively profiled at the single-cell level during early embryogenesis, the dynamics of chromatin accessibility at cis-regulatory elements remain poorly studied. Here, we used a plate-based single-cell ATAC-seq method to profile the chromatin accessibility dynamics of over 10, 000 nuclei from zebrafish embryos. We investigated several important time points immediately after zygotic genome activation (ZGA), covering key developmental stages up to dome. The results revealed key chromatin signatures in the first cell fate specifications when cells start to differentiate into enveloping layer (EVL) and yolk syncytial layer (YSL) cells. Finally, we uncovered many potential cell-type specific enhancers and transcription factor motifs that are important for the cell fate specifications.

genomics↗

bioRxiv2023

Bayesian nonparametric (non-)renewal processes foranalyzing neural spike train variability

Neural spiking activity is generally variable, non-stationary, and exhibits complex dependencies on covariates, such as sensory input or behavior. These dependencies have been proposed to be signatures of specific computations, and so characterizing them with quantitative rigor is critical for understanding neural computations. Approaches based on point processes provide a principled statistical framework for modeling neural spiking activity. However, currently, they only allow the instantaneous mean, but not the instantaneous variability, of responses to depend on covariates. To resolve this limitation, we propose a scalable Bayesian approach generalizing modulated renewal processes using sparse variational Gaussian processes. We leverage pathwise conditioning for computing nonparametric priors over conditional interspike interval distributions and rely on automatic relevance determination to detect lagging interspike interval dependencies beyond renewal order. After systematically validating our method on synthetic data, we apply it to two foundational datasets of animal navigation: head direction cells in freely moving mice and hippocampal place cells in rats running along a linear track. Our model exhibits competitive or better predictive power compared to state-of-the-art baselines, and outperforms them in terms of capturing interspike interval statistics. These results confirm the importance of modeling covariate-dependent spiking variability, and further analyses of our fitted models reveal rich patterns of variability modulation beyond the temporal resolution of flexible count-based approaches.

neuroscience↗