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Tavakoli, N.

Publications and source records attributed to Tavakoli, N..

4 recordsLinked to original sources

Peripheral B cell populations tune spontaneous neuronal activity in the uninjured hippocampus after stroke

B cells infiltrate the contralesional hippocampus following stroke, but whether lymphocytes modulate post-stroke plasticity and neuronal network function remains unknown. To identify immune cell mechanism(s) supporting remote plasticity, we examined the impact of B cell depletion on synaptic and neuronal activity in the hippocampal circuit following stroke. Basal synaptic transmission in the contralesional dentate gyrus (DG) following a stroke in adult male mice was decreased with B cell depletion. Expanding our studies to encompass the CA1 and DG regions of the hippocampal circuit in male and female mice of different ages, we utilized synapsin-Cre/GCaMP6s mice to visualize spontaneous calcium activity during a 3-week B cell depletion with and without prior stroke. Systemic B cell depletion in the absence of injury altered neuronal activity in the DG, suggesting a novel neuromodulatory role for circulating immune cells. Stroke increased Ca2+ transient amplitudes in the contralesional DG and CA1, with B cell depletion again reducing DG amplitudes while increasing the frequency of Ca2+ transients. Robust linear regression revealed significant main effects and higher-order interactions (depletionxsexxagexinjury), including increased Ca2+ transient amplitudes in older post-stroke mice lowered by systemic B cell depletion, though overall the DG appears more sensitive to modulation versus CA1. These results suggest that circulating B cells can tune hippocampal network activity dependent on age, sex, and the presence of brain injury. The selective vulnerability of the DG to depletion-age-injury interactions opens an avenue for future studies on region-specific neuroimmune crosstalk during post-stroke cognitive recovery. Presubmission inquiry for NeuronWe uncover crucial insights on the capacity of circulating B cells to directly modulate hippocampal network activity, showing that B cells are not just passive players, but active neuromodulators whose effects are dependent on sex, age, and stroke injury status. In fact, B cells are central players to functional recovery whose evolving role shifts over time, from acutely beneficial and neurotrophic to chronically maladaptive, depending on timing, context, and responding B cell subset. Our study demonstrates a mechanistic link between systemic immune modulation and neuronal calcium activity. This integrative perspective aligns with Neurons mission to publish studies that link cellular processes to systems-level functions. These novel findings also add to a more unified model of neuro-immune interactions that highlights how immunotherapies could be harnessed to improve neuronal function during stroke and aging, with several FDA-approved immunotherapeutics available to modulate systemic adaptive immune responses.

neuroscience↗

Cancer-associated fibroblasts drive metabolic heterogeneity in KRAS-mutant colorectal cancer cells

KRAS-mutant colorectal cancer (CRC) is characterized by metabolic reprogramming that can lead to tumor progression and drug resistance. The tumor microenvironment (TME) plays a pivotal role in modulating these metabolic adaptations. In particular, cancer-associated fibroblasts (CAFs), which make up a large portion of the TME, have been shown to strongly contribute to metabolic reprogramming in CRC. This study applies flux sampling, a computational method that explores the full range of feasible metabolic states, combined with representation learning and hierarchical clustering, to a computational model of central carbon metabolism to understand how CAFs influence metabolic adaptations of KRAS-mutant CRC cells following targeted enzyme knockdowns. Focusing on twelve key enzymes involved in glycolysis and the pentose phosphate pathway, knockdowns were simulated under both normal CRC media and CAF-conditioned media (CCM) conditions. Analysis revealed that CCM induces greater metabolic heterogeneity, with knockdown models exhibiting more variable and distinct metabolic states compared to those cultured in normal CRC media. While some enzyme knockdowns produced similar metabolic states, this overlap was less frequent in CCM, indicating that CAF-derived factors diversify the metabolic responses of CRC cells to enzyme perturbations. Pathway-level flux analysis demonstrated media-specific shifts in central carbon metabolism pathways. Importantly, the predicted biomass flux showed that enzyme knockdowns reduced growth across both conditions, but models in the CCM condition indicated CAFs could offer a protective effect against metabolic perturbation. Overall, this study reveals that CCM significantly influences the metabolic state and adaptability of KRAS-mutant CRC cells to enzyme perturbations, emphasizing the importance of including TME components in metabolic modeling and therapeutic development. These findings provide valuable insights into the metabolic adaptability of CRC and suggest that targeting tumor-CAF metabolic interactions may improve treatment strategies. Graphical Abstract Overview of computational workflowModels of interest represent simulated enzyme knockdowns in central carbon metabolism. Flux sampling searches the entire metabolic solution space and results in a distribution of flux values for each reaction within each model. Samples can be organized by knockdown and condition into matrices for input into representation learning. Representation learning is applied to sampling data to identify shared and independent metabolic states. Metabolic states indicate a heterogeneous response to enzyme knockdowns. Overlap of dark and light blue flux distributions, sampling clusters, and metabolic responses exemplify a shared metabolic state separate from to the gray unperturbed state. This workflow provides a low-dimensional representation of metabolic state that captures both the pathway- and reaction-level differences that describe each simulated knockdown. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/679631v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@cb6226org.highwire.dtl.DTLVardef@98d94eorg.highwire.dtl.DTLVardef@e2b20aorg.highwire.dtl.DTLVardef@116cfcd_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer

Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. This approach reveals the network-wide effects of metabolic perturbations. Our results highlighted hexokinase (HK) as a crucial target, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF-conditioned media exhibited increased sensitivity to HK inhibition, confirming the model predictions. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk.

systems biology↗

A variant selection framework for genome graphs

MotivationVariation graph representations are projected to either replace or supplement conventional single genome references due to their ability to capture population genetic diversity and reduce reference bias. Vast catalogues of genetic variants for many species now exist, and it is natural to ask which among these are crucial to circumvent reference bias during read mapping. ResultsIn this work, we propose a novel mathematical framework for variant selection, by casting it in terms of minimizing variation graph size subject to preserving paths of length with at most{delta} differences. This framework leads to a rich set of problems based on the types of variants (SNPs, indels), and whether the goal is to minimize the number of positions at which variants are listed or to minimize the total number of variants listed. We classify the computational complexity of these problems and provide efficient algorithms along with their software implementation when feasible. We empirically evaluate the magnitude of graph reduction achieved in human chromosome variation graphs using multiple and{delta} parameter values corresponding to short and long-read resequencing characteristics. When our algorithm is run with parameter settings amenable to long-read mapping ( = 10 kbp,{delta} = 1000), 99.99% SNPs and 73% indel structural variants can be safely excluded from human chromosome 1 variation graph. The graph size reduction can benefit downstream pan-genome analysis. Implementationhttps://github.com/at-cg/VF Contactchirag@iisc.ac.in, neda.tavakoli@gatech.edu, aluru@cc.gatech.edu

bioinformatics↗