bluuebunny/crossref_metadata_2025_split huggingface dataset:
Behavioral decomposition reveals rich encoding structure employed across neocortex
Authors: Mimica Bartul , Tombaz Tuçe , Battistin Claudia , Fuglstad Jingyi Guo , Dunn Benjamin A. , Whitlock Jonathan R. 
Published: February 2022

AbstractThe Cortical population code is pervaded by activity patterns evoked by movement, but how such signals relate to the natural actions of unrestrained animals remains largely unknown, particularly in sensory areas. To address this we compared high-density neural recordings across four cortical regions (visual, auditory, somatosensory, motor) in relation to sensory modulation, posture, movement, and ethograms of freely foraging rats. Momentary actions, such as rearing or turning, were represented ubiquitously and could be decoded from all sampled structures. However, more elementary and continuous features, such as pose and movement, followed region-specific organization, with neurons in visual and auditory cortices preferentially encoding mutually distinct head-orienting features in world-referenced coordinates, and somatosensory and motor cortices principally encoding the trunk and head in egocentric coordinates. The tuning properties of synaptically coupled cells also exhibited connection patterns suggestive of different uses of behavioral information within and between regions. Together, our results speak for detailed, multi-level encoding of animal behavior subserving its dynamic employment in local cortical computations.
Abundance and expression ofhgcABgenes and mercury availability jointly explain methylmercury formation in stratified brackish waters
Authors: Capo Eric , Feng Caiyan , Bravo Andrea G. , Bertilsson Stefan , Soerensen Anne L. , Pinhassi Jarone , Buck Moritz , Karlsson Camilla , Hawkes Jeffrey , Björn Erik 
Published: February 2022

AbstractNeurotoxic methylmercury (MeHg) is formed by microbial methylation of inorganic divalent Hg (HgII) and constitutes severe environmental and human health risks. The methylation is enabled byhgcAandhgcBgenes, but it is not known if the associated molecular-level processes are rate-limiting or enable accurate prediction of MeHg formation in nature. In this study, we investigated the relationships betweenhgcAgenes and MeHg across redox stratified water columns in the brackish Baltic Sea. We found that the abundance ofhgcAgenes and transcripts combined with the concentration of dissolved HgII-sulfide species were strong predictors of both HgIImethylation rate and MeHg concentration, implying their roles as principal joint drivers of MeHg formation in these systems. In establishing relationships betweenhgcAgenes and MeHg, we advance the fundamental understanding of mechanistic principles governing MeHg formation in nature and enable refined predictions of MeHg levels in coastal seas in response to the accelerating spread of oxygen deficient zones.
Network States Classification based on Local Field Potential Recordings in the Awake Mouse Neocortex
Authors: Zerlaut Yann , Zucca Stefano , Fellin Tommaso , Panzeri Stefano 
Published: February 2022

SummaryRecent studies using intracellular recordings in awake behaving mice revealed that cortical network states, defined based on membrane potential features, modulate sensory responses and perceptual outcomes. Single cell intracellular recordings are difficult to achieve and have low yield compared to extracellular recordings of population signals, such as local field potentials (LFPs). However, it is currently unclear how to identify these behaviorally-relevant network states from the LFP. We used simultaneous LFP and intracellular recordings in the somatosensory cortex of awake mice to design and calibrate a model-based analysis method, the Network State Index (NSI), that enables network state classification from the LFP. We used the NSI to analyze the relationship between single-cell (intracellular) and population (LFP) signals over different network states of wakefulness. We found that graded levels of population signal faithfully predicted the levels of single cell depolarization in non-rhythmic regimes whereas, in delta ([2-4 Hz]) oscillatory regimes, the graded levels of rhythmicity in the LFP mapped into a stereotypical oscillatory pattern of membrane potential. Finally, we showed that the variability of network states, beyond the occurrence of slow oscillatory activity, critically shaped the average correlations between single cell and population signals. NSI-based characterization provides a ready-to-use tool to understand from LFP recordings how the modulation of local network dynamics shapes the flexibility of sensory processing during behavior.Significance statementSensation during behaviour is strongly modulated by the animal’s internal state. Such context-dependent modulation of sensory processing is believed to largely stem from top-down control of network states in sensory cortices, with different network states being associated with distinct computational properties of the circuit. So far, a detailed characterization of network states in the awake cortex has mostly been achieved through single-cell intracellular recordings, which however cannot be easily recorded. Here, we developed a new method to classify network states from the easily accessible extracellular LFP recordings of population activity. Given the widespread use of LFPs, our work provides a critical methodology to greatly expand our understanding of the mechanisms underlying state-dependent computations in neocortex.
Express detection and discrimination of visual objects by primate superior colliculus neurons
Authors: Bogadhi Amarender R. , Hafed Ziad M. 
Published: February 2022

AbstractPrimate superior colliculus (SC) neurons exhibit rich visual feature tuning properties and are implicated in a subcortical network hypothesized to mediate fast threat and/or conspecific detection. However, the mechanisms through which generalized object detection may be mediated by SC neurons remain unclear. Here we explored whether, and how quickly, SC neurons detect and discriminate real-life object stimuli. We presented experimentally-controlled gray-scale images of seven different object categories within the response fields (RF’s) of SC neurons, and we also presented a variety of luminance- and spectral-matched image controls. We found that all of our functionally-identified SC neuron types preferentially detected real-life objects even in their very first stimulus-evoked visual bursts (starting within approximately 50 ms from image onset). Intriguingly, even visually-responsive motor-related neurons exhibited such robust early object detection, and they also preferentially discriminated between object categories in their initial visual bursts. We further identified spatial frequency information in visual images as a critical source for early object detection and discrimination by SC neurons. Our results demonstrate rapid and robust SC processing of visual objects, and they underline how the visual processing capabilities of the primate SC support perception and action.
Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth
Authors: Gustafsson Johan , Roshanzamir Fariba , Hagnestål Anders , Patel Sagar M. , Daudu Oseeyi I. , Becker Donald F. , Robinson Jonathan L. , Nielsen Jens 
Published: February 2022

AbstractThe tumor microenvironment is comprised of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell typesin vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells’ preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme constrained models for accurate prediction of metabolism in cells and tumor microenvironments.
Principled clustering of functional signals reveals gradients in processing both within the anterior hippocampus and across its long axis
Authors: Thorp John N. , Gasser Camille , Blessing Esther , Davachi Lila 
Published: February 2022

AbstractA particularly elusive puzzle concerning the hippocampus is how the structural differences along its long, anteroposterior axis might beget meaningful functional differences, particularly in terms of the granularity of information processing. One measure posits to quantify this granularity by calculating the average statistical independence of the BOLD signal across neighboring voxels, or inter-voxel similarity (IVS), and has shown the anterior hippocampus to process coarser-grained information than that in the posterior hippocampus. This model of the hippocampus, however, conflicts with a number of task-oriented findings, many of which have varied in their fMRI acquisition parameters and hippocampal parcellation methods. In order to reconcile these findings, we measured IVS across two separate resting-state fMRI acquisitions and compared the results across many of the most widely used parcellation methods in a large young-adult sample (Acquisition 1, N = 253; Acquisition 2, N = 183). Finding conflicting results across acquisitions and parcellations, we reasoned that a principled, data-driven approach to hippocampal parcellation is necessary. To this end, we implemented a group masked independent components analysis (mICA) to identify functional subunits of the hippocampus, most notably separating the anterior hippocampus into separate anterior-medial, anterior-lateral, and posteroanterior-lateral components. Measuring IVS across these components revealed a decrease in IVS along the medial-lateral axis of the anterior hippocampus but an increase from anterior to posterior. We conclude that representational granularity may not change linearly or unidirectionally across the hippocampus, and that moving the study of the hippocampus towards reproducibility requires grounding it in a functionally informed approach.Significance StatementProcessing information along hierarchical scales of granularity is critical for many of the feats of cognition considered most human. Recently, the changes in structure, cortical connectivity, and apparent functional properties across parcels of the hippocampal long axis have been hypothesized to underlie this hierarchical gradient in information processing. We show here, however, that the choice of parcellation method itself drastically affects the perceived granularity across the hippocampus, and that a principled, functionally informed approach to parcellation reveals gradients both within the anterior hippocampus and in non-linear form across the long axis. These results point to the issue of parcellation as a critical one in the study of the hippocampus and reorient interpretation of existing results.
Unbiased stereological estimates of dopaminergic and GABAergic neurons in the A10, A9, and A8 subregions in the young male Macaque
Authors: Kelly Emily A. , Contreras Jancy , Duan Annie , Vassell Rochelle , Fudge Julie L. 
Published: February 2022

ABSTRACTThe ventral midbrain is the primary source of dopamine- (DA) expressing neurons in most species. GABA-ergic and glutamatergic cell populations are intermixed among DA-expressing cells and purported to regulate both local and long-range dopamine neuron activity. Most work has been conducted in rodent models, however due to evolutionary expansion of the ventral midbrain in primates, the increased size and complexity of DA subpopulations warrants further investigation. Here, we quantified the number of DA neurons, and their GABA-ergic complement in classic DA cell groups A10 (midline ventral tegmental area nuclei [VTA] and parabrachial pigmented nucleus [PBP]), A9 (substantia nigra, pars compacta [SNc]) and A8 (retrorubral field [RRF]) in the macaque. Because the PBP is a disproportionately expanded feature of the A10 group, and has unique connectional features in monkeys, we analyzed A10 data by dividing it into ‘classic’ midline nuclei and the PBP. Unbiased stereology revealed total putative DA neuron counts to be 210,238 +/− 17,127 (A10 = 110,319 +/− 9,649, A9= 87,399 +/−7,751 and A8=12,520 +/− 827). Putative GABAergic neurons were fewer overall, and evenly dispersed across the DA subpopulations (GAD67= 71,215 +/− 5,663; A10=16,836 +/− 2,743; A9=24,855 +/− 3,144 and A8=12,633 +/− 3,557). Calculating the GAD67/TH ratio for each subregion revealed differential balances of these two cell types across the DA subregions. The A8 subregion had the highest complement of GAD67-positive neurons compared to TH-positive neurons (1:1), suggesting a potentially high capacity for GABAergic inhibition of DA output in this region.HIGHLIGHTSThe A10 subregion expands laterally and caudally in nonhuman primatesThe A10, A9, and A8 comprise 52%, 42% and 6% of DA neurons, respectivelyGABAergic neurons are more evenly dispersed across subregionsThe A8 subpopulation has the highest ratio of GABA: DA neurons
Mutagenesis at non-B DNA motifs in the human genome: a course correction
Authors: McGinty RJ , Sunyaev SR 
Published: February 2022

AbstractNon-B DNA structures formed by repetitive sequence motifs are known instigators of mutagenesis in experimental systems. Analyzing this phenomenon computationally in the human genome requires careful disentangling of intrinsic confounding factors, including overlapping and interrupted motifs, and recurrent sequencing errors. Accounting for these factors eliminates all signals of repeat-induced mutagenesis that extend beyond the motif boundary, and eliminates or dramatically shrinks the magnitude of mutagenesis within some motifs, contradicting previous reports. Mutagenesis not attributable to artifacts revealed several biological mechanisms. Polymerase slippage generates frequent indels within every variety of short tandem repeat motif, implicating slipped-strand structures. Interruption-correcting SNVs within STRs distinctly implicate error-prone Polκ. Secondary-structure formation promotes SNVs within palindromic repeats, as well as duplications within direct repeats. G-quadruplex motifs cause recurrent sequencing errors, while mutagenesis at Z-DNAs is conspicuously absent.
The cortical N1 response to balance perturbation is associated with balance and cognitive function in different ways between older adults with and without Parkinson’s disease
Authors: Payne Aiden M. , McKay J. Lucas , Ting Lena H. 
Published: February 2022

ABSTRACTMechanisms underlying associations between balance and cognitive impairments in older adults with and without Parkinson’s disease are poorly understood. Balance disturbances evoke a cortical N1 response that is associated with both balance and cognitive abilities in unimpaired populations. We hypothesized that the N1 response reflects neural mechanisms that are shared between balance and cognitive function, and would therefore be associated with both balance and cognitive impairments in Parkinson’s disease. Although N1 responses did not differ at the group level, they showed different associations with balance and cognitive function in the Parkinson’s disease vs. control groups. In the control group, higher N1 amplitudes were correlated with lower cognitive set shifting ability and lower balance confidence. However, in Parkinson’s disease, narrower N1 widths (i.e., shorter durations) were associated with greater parkinsonian motor symptom severity, lower balance ability and confidence, lower mobility, and lower overall cognitive function. Despite different relationships across populations, the present results suggest the N1 response reflects neural processes related to both balance and cognitive function. A better understanding of neural mechanisms linking balance and cognitive function could provide insight into associations between balance and cognitive decline in aging populations.
RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks
Authors: Seal Souvik , Li Qunhua , Basner Elle Butler , Saba Laura M. , Kechris Katerina 
Published: February 2022

AbstractInferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When gene-expression data from multiple conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical lasso (FGL) model for estimating multiple co-expression networks. However, the current implementation of CFGL is prohibitively slow even for a moderate number of genes and can only be used for a maximum of three conditions. In this paper, we propose a fast alternative of CFGL known as rapid condition adaptive fused graphical lasso (RCFGL). In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation and making it easily generalizable for more than three conditions. We also present a novel screening rule to determine if the full network estimation problem can be broken down into estimation of smaller disjoint sub-networks, thereby reducing the complexity further. We demonstrate the computational advantage and superior performance of our method compared to two non-condition adaptive methods, FGL and FMGL, and one condition adaptive method, CFGL in several simulation scenarios. We use RCFGL to jointly estimate the gene co-expression networks of different brain regions (conditions) using a cohort of heterogeneous stock rats. We also provide an accommodatingCandPythonbased package that implements RCFGL.Author summaryInferring gene co-expression networks can be useful for understanding pathway activity and gene regulation. While jointly estimating co-expression networks of multiple conditions, taking into account condition specificity such as information about an edge being present only in a specific condition, substantially increases the power. In this paper, a computationally rapid condition adaptive method for jointly estimating gene co-expression networks of multiple conditions is proposed. The novelty of the method is demonstrated through a broad range of simulation studies and a real data analysis with multiple brain regions from a genetically diverse cohort of rats.
Keap1 moderates the transcription of virus induced genes through G9a-GLP and NFкB p50 recruitment, and H3K9me2 deposition
Authors: Burns Veronica Elizabeth , Kerppola Tom Klaus 
Published: February 2022

AbstractCells must moderate transcription that is induced by virus infection to mitigate deleterious consequences of inflammation. We investigated the mechanisms whereby Keap1 moderates the transcription of genes that are induced by Sendai virus infection in mouse embryo fibroblasts (MEFs). Virus infection induced Keap1 to bindIfnb1, TnfandIl6, and reduced Keap1 binding atCdkn1aandCcng1. Keap1 was required for G9a and GLP to bind and to deposit H3K9me2 at these genes upon virus infection. Keap1 moderated the transcription of genes that were induced by virus infection in concert with G9a, GLP, and NFкB p50 recruitment. G9a-GLP lysine methyltransferase activity was required for Keap1 to moderate the transcription of virus induced genes. G9a-GLP inhibitors enhanced the transcription of virus induced genes, and they augmented Keap1 and NFкB p50 recruitment, in parallel with the inhibition of H3K9me2 deposition. The interdependent effects of Keap1 and G9a-GLP on transcription and on the recruitment of each other constitute a feedback circuit that moderates the transcription of virus induced genes. G9a-GLP inhibitors augmented Keap1 binding to different genes in virus infected and in uninfected MEFs, whereas they inhibited H3K9me2 deposition that was induced by virus infection selectively. G9a-GLP inhibitors stabilized Keap1 retention in permeabilized MEFs and augmented Keap1 binding to specific genes in parallel. Keap1 was required for NFкB p50 recruitment, and for the augmentation of NFкB binding by G9a-GLP inhibitors. Keap1 and the electrophile tBHQ attenuated virus induced gene transcription through independent mechanisms, and they regulated the recruitment of different NFкB subunits.ImportanceExcess and maladaptive immune responses to virus infections are a major contributing factor to the morbidity and mortality of COVID-19 and other diseases. Conversely, inadequate immune responses to vaccines and pathogens by individuals with suppressed immune function expose them to infections. Currently available drugs that enable therapeutic management of immune responses have low specificity and can blunt beneficial immune functions. The molecular mechanisms that moderate the transcription of genes that are induced by virus infection are incompletely understood. Characterization of the mechanisms whereby Keap1, G9a-GLP and NFкB p50 moderate virus induced gene transcription in mouse embryo fibroblasts represents the first step toward the identification of new targets for therapeutic agents that can modulate immune responsiveness.