Biology subjects

Keller, D.

Publications and source records attributed to Keller, D..

26 records · Page 2Linked to original sources

bioRxiv2021

Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons

Knowledge of the cell-type-specific composition of the brain is useful in order to understand the role of each cell type as part of the network. Here, we estimated the composition of the whole cortex in terms of well characterised morphological and electrophysiological inhibitory neuron types (me-types). We derived probabilistic me-type densities from an existing atlas of molecularly defined cell-type densities in the mouse cortex. We used a well-established me-type classification from rat somatosensory cortex to populate the cortex. These me-types were well characterized morphologically and electrophysiologically but they lacked molecular marker identity labels. To extrapolate this missing information, we employed an additional dataset from the Allen Institute for Brain Science containing molecular identity as well as morphological and electrophysiological data for mouse cortical neurons. We first built a latent space based on a number of comparable morphological and electrical features common to both data sources. We then identified 13 morpho-electrical clusters that merged neurons from both datasets while being molecularly homogeneous. The resulting clusters best mirror the molecular identity classification solely using available morpho-electrical features. Finally, we stochastically assigned a molecular identity to a me-type neuron based on the latent space cluster it was assigned to. The resulting mapping was used to derive inhibitory me-types densities in the cortex. Author SummaryThe computational abilities of the brain arise from its organisation principles at the cellular level. One of these principles is the neuronal type composition over different regions. Since computational functions of neurons are best described by their morphological and electrophysiological properties, it is logical to use morpho-electrically defined cell types to describe brain composition. However, characterizing morpho-electrical properties of cells involve low-throughput techniques not very well suited to scan the whole brain. Thanks to recent progress on transcriptomic and immuno-staining techniques we are now able to get a more accurate snapshot of the mouse brain composition for molecularly defined cell types.How to link molecularly defined cell types with morpho-electrical cell types remains an open question. Several studies have explored this problem providing valuable three-modal datasets combining electrical, morphological and molecular properties of cortical neurons. The long-term goal of the Blue Brain Project (BBP) is to accurately model the mouses whole brain, which requires detailed biophysical models of neurons. Instead of going through the time-consuming process of producing detailed models from the three-modal datasets, we explored a time-saving method. We mapped the already available detailed morpho-electrical models from the BBP rat dataset to cells from a three-modal mouse dataset. We thus assigned a molecular identity to the neuron models allowing us to populate the whole mouse cortex with detailed neuron models.

neuroscience↗

bioRxiv2021

Atlas of Inhibitory Neurons in the Mouse Brain

The mouse brain contains a rich diversity of inhibitory neuron types that have been characterized by their patterns of gene expression. However, it is still unclear how these cell types are distributed across the mouse brain. We developed a computational method to estimate the densities of different inhibitory neuron types across the mouse brain. Our method allows the unbiased integration of diverse and disparate datasets into one framework to predict inhibitory neuron densities for uncharted brain regions. We constrained our estimates based on previously computed brain-wide neuron densities, gene expression data from in situ hybridization image stacks together with a wide range of values reported in the literature. Using constrained optimization, we derived coherent estimates of cell densities for the different inhibitory neuron types. We estimate that 20.3% of all neurons in the mouse brain are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. We find that our density estimations improve as more literature values are integrated. Our pipeline is extensible, allowing new cell types or data to be integrated as they become available. The data, algorithms, software, and results of our pipeline are publicly available and update the Blue Brain Cell Atlas. This work therefore leverages the research community to collectively converge on the numbers of each cell type in each brain region. Author summaryObtaining a global understanding of the cellular composition of the brain is a very complex task, not only because of the great variability that exists between reports of similar counts but also because of the numerous brain regions and cell types that make up the brain. Previously, we presented a model of a cell atlas, which provided an estimate of the densities of neurons, glia and their subtypes for each region in the mouse brain. Here, we describe an extension of this model to include more inhibitory neuron types. We collected estimates of inhibitory neuron counts from literature and built a framework to combine them into a consistent cell atlas. Using brain slice images, we also estimated inhibitory neuron density in regions where no literature data are available. We estimated that in the mouse brain 20.3% of all neurons are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. Our approach can be further extended to any other cell type and provides a resource to build tissue-level models of the rodent brain.

neuroscience↗

bioRxiv2021

Architecture of the Neuro-Glia-Vascular System

Astrocytes connect the vasculature to neurons and mediate the supply of nutrients and biochemicals. They also remove metabolites from the neurons and extracellular environment. They are involved in a growing number of physiological and pathophysiological processes. Understanding the biophysical, physiological, and molecular interactions in this neuro-glia-vascular ensemble (NGV) and how they support brain function is severely restricted by the lack of detailed cytoarchitecture. To address this problem, we used data from multiple sources to create a data-driven digital reconstruction of the NGV at micrometer anatomical resolution. We reconstructed 0.2 mm3 of rat somatosensory cortical tissue with approximately 16000 morphologically detailed neurons, its microvasculature, and approximately 2500 morphologically detailed protoplasmic astrocytes. The consistency of the reconstruction with a wide array of experimental measurements allows novel predictions of the numbers and locations of astrocytes and astrocytic processes that support different types of neurons. This allows anatomical reconstruction of the spatial microdomains of astrocytes and their overlapping regions. The number and locations of end-feet connecting each astrocyte to the vasculature can be determined as well as the extent to which they cover the microvasculature. The structural analysis of the NGV circuit showed that astrocytic shape and numbers are constrained by vasculatures spatial occupancy and their functional role to form NGV connections. The digital reconstruction of the NGV is a resource that will enable a better understanding of the anatomical principles and geometric constraints which govern how astrocytes support brain function. Table of contentsO_ST_ABSMain pointsC_ST_ABSO_LIThe Blue Brain Project digitally reconstructs a part of neocortical Neuro-Glia-Vascular organization C_LIO_LIInterdependencies and topological methods allow dense in silico reconstruction from sparse experimental data C_LIO_LIThe polarized role of protoplasmic astrocytes constrains their shapes and numbers C_LI Table of contents image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/427241v3_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@1260843org.highwire.dtl.DTLVardef@1e88718org.highwire.dtl.DTLVardef@fd66c5org.highwire.dtl.DTLVardef@6628a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Neuroscience incorporates knowledge from a range of scales, from molecular dynamics to neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB(R) scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model. Information Sharing StatementBoth the source code and documentation of the Subcellular Workflow are available at https://github.com/jpgsantos/Subcellular_Workflow and licensed under GNU General Public License v3.0. The model is stored in the SBtab format (Lubitz et al. 2016). Model reduction, parameter estimation and global sensitivity analysis tools are written in MATLAB(R) (RRID:SCR_001622) and require the SimBiology(R) toolbox. Conversion script to VFGEN (Weckesser 2008), MOD and SBML (RRID:SCR_007422) is written in R (RRID:SCR_001905). Conversion to SBML requires the use of libSBML (RRID:SCR_014134). Validations are run in COPASI (RRID:SCR_014260; Hoops et al. 2006), NEURON (RRID:SCR_005393; Hines and Carnevale 1997) and with the subcellular simulation setup application (RRID:SCR_018790; available at https://subcellular.humanbrainproject.eu/model/simulations) that uses a spatial solver provided by STEPS (RRID:SCR_008742; Hepburn et al. 2012) and network-free solver NFsim (available at http://michaelsneddon.net/nfsim/). The medium spiny neuron model (Lindroos et al. 2018) used in NEURON simulations is available in ModelDB database (RRID:SCR_007271) with access code 237653. The FindSim use case model is available in https://github.com/BhallaLab/FindSim (Viswan et al. 2018).

systems biology↗

bioRxiv2020

The ApiAP2 factor PfAP2-HC is an integral component of heterochromatin in the malaria parasite Plasmodium falciparum

Malaria parasites undergo a highly complex life cycle in the human host and the mosquito vector. The ApiAP2 family of sequence-specific DNA-binding proteins plays a dominant role in parasite development and life cycle progression. Of the ApiAP2 factors studied to date, most act as transcription factors regulating stage-specific gene expression. Here, we characterised a new ApiAP2 factor in Plasmodium falciparum (PF3D7_1456000) that we termed PfAP2-HC. Via detailed investigation of several single or double genetically engineered parasite lines, we demonstrate that PfAP2-HC specifically binds to heterochromatin throughout the genome. Intriguingly, PfAP2-HC does not bind DNA in vivo and recruitment of PfAP2-HC to heterochromatin is independent of its DNA-binding domain but strictly dependent on heterochromatin protein 1. Furthermore, our results suggest that PfAP2-HC functions neither in the regulation of gene expression nor in heterochromatin formation or maintenance. In summary, our findings reveal that PfAP2-HC constitutes a core component of heterochromatin in malaria parasites. They furthermore identify unexpected properties of ApiAP2 factors and suggest substantial functional divergence among the members of this important family of regulatory proteins.

microbiology↗

bioRxiv2020

Patient-derived xenografts and organoids model therapy response in prostate cancer

Therapy resistance and metastatic processes in prostate cancer (PCa) remain undefined, due to lack of experimental models that mimic different disease stages. We describe a novel androgen-dependent PCa patient-derived xenograft (PDX) model from treatment-naive, soft tissue metastasis (PNPCa). RNA and whole-exome sequencing of the PDX tissue and organoids confirmed transcriptomic and genomic similarity to primary tumor. PNPCa harbours BRCA2 and CHD1 somatic mutations, shows an SPOP/FOXA1-like transcriptomic signature and microsatellite instability, which occurs in 3% of advanced PCa and has never been modelled in vivo. Comparison of the treatment-naive PNPCa with additional metastatic PDXs (BM18, LAPC9), in a medium-throughput organoid screen of FDA-approved compounds, revealed differential drug sensitivities. Multikinase inhibitors (ponatinib, sunitinib, sorafenib) were broadly effective on all PDX- and patient-derived organoids from advanced cases with acquired resistance to standard-of-care compounds. This proof-of-principle study may provide a preclinical tool to screen drug responses to standard-of-care and newly identified, repurposed compounds.

cancer biology↗

bioRxiv2019

Four QTL underlie resistance to a microsporidian parasite that may drive genome evolution in its Daphnia host

Despite its pivotal role in evolutionary and ecological processes the genetic architecture underlying host-parasite interactions remains understudied. Here we use a quantitative trait loci approach to identify regions in the Daphnia magna genome that provide resistance against its microsporidium parasite Ordospora colligata. The probability that Daphnia became infected was affected by a single locus and an interaction between two additional loci. A fourth locus influenced the number of spores that grew within the host. Comparing our findings to previously published genetic work on Daphnia magna revealed that two of these loci may be the same as detected for another microsporidium parasite, suggesting a general immune response to this group of pathogens. More importantly, this comparison revealed that two regions previously identified to be under selection coincided with parasite resistance loci, highlighting the pivotal role parasites may play in shaping the host genome.

genetics↗

bioRxiv2017

Primary cilia mediate diverse kinase inhibitor resistance mechanisms in cancer

Primary cilia are microtubule-based organelles that detect mechanical and chemical stimuli. Although cilia house a number of oncogenic molecules (including Smoothened, KRAS, EGFR, and PDGFR), their precise role in cancer remains unclear. We have interrogated the role of cilia in acquired and de novo resistance to a variety of kinase inhibitors, and found that in several examples, resistant cells are distinctly characterized by an increase in the number and/or length of cilia with altered structural features. Changes in cilia length seem to be linked to the lack of recruitment of Kif7 and IFT81 to cilia tips, and result in enhanced hedgehog pathway activation. Notably, Kif7 knockdown is sufficient to confer drug resistance in drug sensitive cells. Conversely, targeting of cilia length or integrity through genetic and pharmacological approaches overcomes kinase inhibitor resistance. The identification of a broad mechanism of pathway-unbiased drug resistance, represents a major advancement in oncology, and helps define a specific and important role for cilia in human cancer.

cell biology↗