Biology subjects

Mitchell, J. M.

Publications and source records attributed to Mitchell, J. M..

14 recordsLinked to original sources

bioRxiv2024

Long-term effects of psilocybin on dynamic and effectivity connectivity of fronto-striatal-thalamic circuits

Psilocybin has been shown to induce fast and sustained improvements in mental well-being across various populations, yet its long-term mechanisms of action are not fully understood. Initial evidence suggests that longitudinal functional and structural brain changes implicate fronto-striatal-thalamic (FST) circuitry, a broad system involved in goal-directed behavior and motivational states. Here, we apply empirical methods and computational modeling to resting-state fMRI data from a within-subject longitudinal psilocybin trial in psychedelic-naive healthy volunteers. We first show increases in FST dynamic activity four weeks after a full dose of psilocybin. We then proceed to mechanistically account for these increased dynamics, by showing that reduced structural constraints underlie increased FST dynamic activity post psilocybin. Further, we show that these reduced structural constraints come along with increased bottom-up and reduced top-down modulation of FST circuits. While cortical reductions in top-down modulation are linked to regional 5-HT2A receptor availability, increased information outflow via subcortical and limbic regions relate to local D2 receptor availability. Together, these findings show that increased FST flexibility weeks after psilocybin administration is linked to serotonergic-mediated decreases in top-down information flow and dopaminergic-mediated increases in bottom-up information flow. This long-term functional re-organization of FST circuits may represent a common mechanism underling the potential clinical efficacy of psilocybin across various neuropsychiatric disorders including substance abuse, major depression, and anorexia. Significance StatementFronto-striatal-thalamic systems, which underlie motivation and reward, go through profound functional and structural changes following psilocybin administration. We leveraged longitudinal fMRI data from a within-subject psilocybin trial in psychedelic-naive healthy participants to show that psilocybin increases fronto-striatal-thalamic dynamic activity as well as flexibility four weeks after dosing. Computational modeling revealed that this increased flexibility is mechanistically caused by reduced structural constraints on functional dynamics. Further long-term changes included increased bottom-up and reduced top-down information flow mediated by the serotonergic and dopaminergic systems. This long-term functional re-organization of fronto-striatal-thalamic circuits may reflect a common mechanism underlying clinical symptoms improvements across diagnostic groups, such as increased openness, improved well-being, and reductions in anhedonia, apathy, and substance craving.

neuroscience↗

bioRxiv2024

In situ crystalline structure of the Eosinophil major basic protein-1

Eosinophils are white blood cells that participate in innate immune responses and have an essential role in the pathogenesis of inflammatory and neoplastic disorders. Upon activation, eosinophils release cytotoxic proteins such as major basic protein-1 (MBP-1) from cytoplasmic secretory granules (SGr) wherein MBP-1 is stored as nanocrystals. How the MBP-1 nanocrystalline core is formed, stabilized, and subsequently mobilized remains unknown. Here, we report the in-situ structure of crystalline MBP-1 within SGrs of human eosinophils. The structure reveals a mechanism for intragranular crystal packing and stabilization of MBP-1 via a structurally conserved loop region that is associated with calcium-dependent carbohydrate binding in other C-type lectin (CTL) proteins. Single-cell and single-SGr profiling correlating real-space three-dimensional information from cellular montage cryo-electron tomography (cryo-ET) and microcrystal electron diffraction (MicroED) data obtained from non-activated and IL33-activated eosinophils revealed activation-dependent crystal expansion and extrusion of expanded crystals from SGr. These results suggest that MBP-1 crystals play a dynamic role in the release of SGr contents. Collectively, this research demonstrates the importance of in-situ macromolecular structure determination.

biophysics↗

bioRxiv2023

TFAP2 paralogs regulate midfacial development in part through a conserved ALX genetic pathway.

Cranial neural crest development is governed by positional gene regulatory networks (GRNs). Fine-tuning of the GRN components underly facial shape variation, yet how those in the midface are connected and activated remain poorly understood. Here, we show that concerted inactivation of Tfap2a and Tfap2b in the murine neural crest even during the late migratory phase results in a midfacial cleft and skeletal abnormalities. Bulk and single-cell RNA-seq profiling reveal that loss of both Tfap2 members dysregulated numerous midface GRN components involved in midface fusion, patterning, and differentiation. Notably, Alx1/3/4 (Alx) transcript levels are reduced, while ChIP-seq analyses suggest TFAP2 directly and positively regulates Alx gene expression. TFAP2 and ALX co-expression in midfacial neural crest cells of both mouse and zebrafish further implies conservation of this regulatory axis across vertebrates. Consistent with this notion, tfap2a mutant zebrafish present abnormal alx3 expression patterns, and the two genes display a genetic interaction in this species. Together, these data demonstrate a critical role for TFAP2 in regulating vertebrate midfacial development in part through ALX transcription factor gene expression.

developmental biology↗

bioRxiv2023

Physical Activity Delays Obesity-Associated Pancreatic Ductal Adenocarcinoma in Mice and Decreases Inflammation

BACKGROUND & AIMSObesity is a risk factor for pancreatic ductal adenocarcinoma (PDAC), a deadly disease with limited preventive strategies. Lifestyle interventions to decrease obesity might prevent obesity-associated PDAC. Here, we examined whether decreasing obesity by increased physical activity (PA) and/or dietary changes would decrease inflammation in humans and prevent PDAC in mice. METHODSCirculating inflammatory-associated cytokines of overweight and obese subjects before and after a PA intervention were compared. PDAC pre-clinical models were exposed to PA and/or dietary interventions after obesity-associated cancer initiation. Body composition, tumor progression, growth, fibrosis, inflammation, and transcriptomic changes in the adipose tissue were evaluated. RESULTSPA decreased the levels of systemic inflammatory cytokines in overweight and obese subjects. PDAC mice on a diet-induced obesity (DIO) and PA intervention, had delayed weight gain, decreased systemic inflammation, lower grade pancreatic intraepithelial neoplasia lesions, reduced PDAC incidence, and increased anti-inflammatory signals in the adipose tissue compared to controls. PA had additional cancer prevention benefits when combined with a non-obesogenic diet after DIO. However, weight loss through PA alone or combined with a dietary intervention did not prevent tumor growth in an orthotopic PDAC model. Adipose-specific targeting of interleukin (IL)-15, an anti-inflammatory cytokine induced by PA in the adipose tissue, slowed PDAC growth. CONCLUSIONSPA alone or combined with diet-induced weight loss delayed the progression of PDAC and reduced systemic and adipose inflammatory signals. Therefore, obesity management via dietary interventions and/or PA, or modulating weight loss related pathways could prevent obesity-associated PDAC in high-risk obese individuals. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/521203v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@961f1org.highwire.dtl.DTLVardef@1fd4da7org.highwire.dtl.DTLVardef@f5247aorg.highwire.dtl.DTLVardef@2de0bf_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2022

Molecular anatomy of eosinophil activation by IL5 and IL33

IL5 and IL33 are major activating cytokines that cause circulating eosinophils to polarize, adhere, and release their granule contents. We correlated microscopic features of purified human blood eosinophils stimulated for 10 min with IL5 or IL33 with phosphoproteomic changes determined by multiplexed isobaric labeling. IL5 caused phosphorylation of sites implicated in JAK/STAT signaling and localization of pYSTAT3 to nuclear speckles whereas IL33 caused phosphorylation of sites implicated in NF{kappa}B signaling and localization of RELA to nuclear speckles. Phosphosites commonly impacted by IL5 and IL33 were involved in networks associated with cytoskeletal organization and eosinophil adhesion and migration. Many differentially regulated phosphosites were in a diverse set of large proteins--RAB44, a "large RAB" associated with crystalloid granules; NHSL2 and VIM that change localization along with the nucleus during polarization; TNFAIP3 vital for control of NF{kappa}B signaling, and SRRM2 and PML that localize, respectively, to nuclear speckles and PML bodies. Gene expression analysis demonstrated differential effects of IL5 and IL33 on IL18, CCL5, CSF1, and TNFSF14. Thus, common effects of IL5 and IL33 on the eosinophil phosphoproteome are important for positioning in tissues, degranulation, and initiation of new protein synthesis whereas specific effects on protein synthesis contribute to phenotypic heterogeneity. KEY POINTSIL33 and IL5 impact common pathways of eosinophil cytoskeletal reorganization, adhesion and migration. Each lobe of the human eosinophil nucleus has a specific anatomy poised for new onset of cytokine-specific transcription and splicing.

cell biology↗

bioRxiv2022

Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments

We introduce a novel, scan-centric method for characterizing peaks from direct injection multi-scan Fourier transform mass spectra of complex samples that utilizes frequency values derived directly from the spacing of raw m/z points in spectral scans. Our peak characterization method utilizes intensity independent noise removal and normalization of scan-level data to provide a much better fit of relative intensity to natural abundance probabilities for low abundance isotopologues that are not present in all of the acquired scans. Moreover, our method calculates both peak- and scan-specific statistics incorporated within a series of quality control steps that are designed to robustly derive peak centers, intensities and intensity ratios with their scan-level variances. These cross-scan characterized peaks are suitable for use in our previously published peak assignment methodology, Small Molecule Isotope Resolved Formula Enumeration (SMIRFE).

bioinformatics↗

bioRxiv2022

Efficient generation of lower induced Motor Neurons by coupling Ngn2 expression with developmental cues

Human pluripotent stem cells (hPSCs) are a powerful tool for disease modelling and drug discovery, especially when access to primary tissue is limited, such as in the brain. Current neuronal differentiation approaches use either small molecules for directed differentiation or transcription-factor-mediated programming. In this study we coupled the overexpression of the neuralising transcription factor Neurogenin2 (Ngn2) with small molecule patterning to differentiate hPSCs into lower induced Motor Neurons (liMoNes). We showed that this approach induced activation of the motor neuron (MN) specific transcription factor Hb9/MNX1, using anHb9::GFP-reporter line, with up to 95% of cells becoming Hb9::GFP+. These cells acquired and maintained expression of canonical early and mature MN markers. Molecular and functional profiling revealed that liMoNes resembled bona fide hPSC-derived MN differentiated by conventional small molecule patterning. liMoNes exhibited spontaneous electrical activity, expressed synaptic markers and formed contacts with muscle cells in vitro. Pooled, multiplex single-cell RNA sequencing on 50 cell lines revealed multiple anatomically distinct MN subtypes of cervical and brachial, limb-innervating MNs in reproducible quantities. We conclude that combining small molecule patterning with Ngn2 can facilitate the high-yield, robust and reproducible production of multiple disease-relevant MN subtypes, which is fundamental in the path to propel forward our knowledge of motoneuron biology and its disruption in disease.

neuroscience↗

bioRxiv2021

Natural variation in gene expression and Zika virus susceptibility revealed by villages of neural progenitor cells

Variation in the human genome contributes to abundant diversity in human traits and vulnerabilities, but the underlying molecular and cellular mechanisms are not yet known, and will need scalable approaches to accelerate their recognition. Here, we advanced and applied an experimental platform that analyzes genetic, molecular, and phenotypic heterogeneity across cells from very many human donors cultured in a single, shared in vitro environment, with algorithms (Dropulation and Census-seq) for assigning phenotypes to individual donors. We used natural genetic variation and synthetic (CRISPR-Cas9) genetic perturbations to analyze the vulnerability of neural progenitor cells to infection with Zika virus. These analyses identified a common variant in the antiviral IFITM3 gene that regulated IFITM3 expression and explained most inter-individual variation in NPCs susceptibility to Zika virus infectivity. These and other approaches could provide scalable ways to recognize the impact of genes and genetic variation on cellular phenotypes. HIGHLIGHTSO_LIMeasuring cellular phenotypes in iPSCs and hPSC-derived NPCs from many donors C_LIO_LIEffects of donor sex, cell source, genetic and other variables on hPSC RNA expression C_LIO_LINatural genetic variation and synthetic perturbation screens both identify IFITM3 in NPC susceptibility to Zika virus C_LIO_LIA common genetic variant in IFITM3 explains most inter-individual variation in NPC susceptibility to Zika virus C_LI

genomics↗

bioRxiv2020

Mapping genetic effects on cellular phenotypes with "cell villages"

Tens of thousands of genetic variants shape human phenotypes, mostly by unknown cellular mechanisms. Here we describe Census-seq, a way to measure cellular phenotypes in cells from many people simultaneously. Analogous to pooled CRISPR screens but for natural variation, Census-seq associates cellular phenotypes to donors genotypes by quantifying the presence of each donors DNA in cell "villages" before and after sorting or selection for cellular traits of interest. Census-seq enables population-scale cell-biological phenotyping with low cost and high internal control. We demonstrate Census-seq through investigation of genetic effects on the SMN protein whose deficiency underlies spinal muscular atrophy (SMA). Census-seq quantified and mapped effects of many common alleles on SMN protein levels and response to SMN-targeted therapeutics, including a common, cryptic non-responder allele. We provide tools enabling population-scale cell experiments and explain how Census-seq can be used to map genetic effects on diverse cell phenotypes. O_FIG O_LINKSMALLFIG WIDTH=185 HEIGHT=200 SRC="FIGDIR/small/174383v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@169bf4borg.highwire.dtl.DTLVardef@18dd9c0org.highwire.dtl.DTLVardef@1d30ca5org.highwire.dtl.DTLVardef@e35bf0_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LICensus-seq reveals how inherited genetic variation affects cell phenotypes C_LIO_LIGenetic analysis of cellular traits in cell villages of >100 donors C_LIO_LICharacterizing human alleles that shape SMN protein expression and drug responses C_LIO_LIDevelopment of protocols and software to enable cellular population genetics C_LI

genetics↗

bioRxiv2020

Atom Identifiers Generated by a Graph Coloring Method Enable Compound Harmonization Across Metabolic Databases

Metabolic flux analysis requires both a reliable metabolic model and metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2019

Deriving Accurate Lipid Classification based on Molecular Formula

IntroductionAlthough Fourier-transform mass spectrometry has substantially improved our ability to detect lipids and other metabolites; the untargeted and accurate assignment of detected metabolites remains an unsolved problem in metabolomics. New assignment methods such as our SMIRFE algorithm can assign elemental molecular formula to observed spectral features in an untargeted manner without orthogonal information from tandem MS or chromatography. However, for many lipidomics applications, it is necessary to know at least the lipid category or class that is associated with a detected spectral feature in order to derive biochemical interpretation.\n\nObjectivesOur goal is to develop a method for robustly classifying elemental molecular formula assignments into lipid categories for application to SMIRFE-generated assignments.\n\nResultsUsing machine learning, we developed a method that can predict lipid category and class from SMIRFE molecular formula assignments. Our methods achieve high accuracy (>90%) and precision (>83%) for all eight of the lipid categories in the LIPIDMAPS database. Model performance was evaluated using sets of theoretical, data-derived, and artifactual molecular formulas. Our models were generalizable, applicable to real-world datasets, and very discriminating with most molecular formulas classified to the \"not lipid\" category. Lipid categories with the highest classification propensities were glycerophospholipids and sphingolipids, matching the highest category prevalence in LIPIDMAPS.\n\nConclusionsOur methods enable the lipid classification of untargeted molecular formula assignments generated by SMIRFE without orthogonal information, facilitating biochemical interpretation of highly untargeted lipidomics experiments. However, this lipid classification appears insufficient for validating single-spectrum assignments, but could be useful in cross-spectrum assignment validation.

systems biology↗

bioRxiv2018

Genome-wide screens in accelerated human stem cell-derived neural progenitor cells identify Zika virus host factors and drivers of proliferation

Neural progenitor cells (NPCs) are essential to brain development and their dysfunction is linked to several disorders, including autism, Zika Virus Congenital Syndrome, and cancer. Understanding of these conditions has been improved by advancements with stem cell-derived NPC models. However, current differentiation methods require many days or weeks to generate NPCs and show variability in efficacy among cell lines. Here, we describe human Stem cell-derived NGN2-accelerated Progenitor cells (SNaPs), which are produced in only 48 hours. SNaPs express canonical forebrain NPC protein markers, are proliferative, multipotent, and like other human NPCs, are susceptible to Zika-mediated death. We further demonstrate SNaPs are valuable for large-scale investigations of genetic and environmental influencers of neurodevelopment by deploying them for genome-wide CRISPR-Cas9 screens. Our studies expand knowledge of NPCs by identifying known and novel Zika host factors, as well as new regulators of NPC proliferation validated by re-identification of the autism spectrum gene PTEN.

neuroscience↗

bioRxiv2018

Inferring metabolite interactomes via molecular structure informed Bayesian graphical model selection with an application to coronary artery disease

IntroductionWhile the generation of reference genomes facilitates the elucidation of gene-phenome associations, reference models of the metabolome that are specific to organism, sample type (e.g. plasma, serum, urine, cell-culture), and state (including disease), remain uncommon. In studying heart disease in humans, a reference model describing the relationships between metabolites in plasma has not been determined but would have great utility as a reference for comparing acute disease states such as myocardial infarction.\n\nMaterials and MethodsWe present a methodology for deriving probabilistic models that describe the partial correlation structure of metabolite distributions (\"interactomes\") from metabolomics data. As determining partial correlation structures requires estimating p*(p-1)/2 parameters for p metabolites, the dimension of the search space for parameter values is immense. Consequently, we have developed a Bayesian methodology for the penalized estimation of model parameters in which the magnitude of penalization is drawn from probability distributions with hyperparameters linked to molecular structure similarity. In our work, structural similarity was determined as the Tanimoto coefficient of algorithmically-generated \"atom colors\" that capture the local structure around each atom within each structure. A Gibbs sampler (a Markov chain Monte Carlo technique) was implemented for simulating the posterior distribution of model parameters. We have made software for implementing this methodology publicly available via the R package BayesianGLasso.\n\nResults / ConclusionsFirst, we demonstrate robust performance of our methodology (sensitivity, specificity, and measures of accuracy) for recovering the true underlying partial correlation structure over simulated datasets (with simulated metabolite abundances and simulated known structural similarity). We then present an interactome model for stable heart disease inferred from non-targeted mass spectrometry data via this methodology. Inspection of the local graph topology about cholate reveals probabilistic interactions with other primary bile acids, secondary bile acids, and many steroid hormones sharing the same precursors.

systems biology↗

bioRxiv2017

High Peak Density Artifacts in Fourier Transform Mass Spectra and their Effects on Data Analysis

Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolites. Observed spectral features (peaks) that are not isotopologues do not directly correspond to known compounds and cannot be placed into existing metabolic networks. Spectral artifacts account for many of these unidentified peaks, and misassignments made to these artifact peaks can create large interpretative errors. Without accurate identification of artifactual features and correct assignment of real features, discerning their roles within living systems is effectively impossible.\n\nWe have observed three types of artifacts unique to FT-MS that often result in regions of abnormally high peak density (HPD), which we collectively refer to as HPD artifacts: i) fuzzy sites representing small regions of m/z space with a fuzzy appearance due to the extremely high number of peaks present; ii) ringing due to a very intense peak producing side bands of decreasing intensity that are symmetrically distributed around the main peak; and iii) partial ringing where only a subset of the side bands are observed for an intense peak. Fuzzy sites and partial ringing appear to be novel artifacts previously unreported in the literature and we hypothesize that all three artifact types derive from Fourier transformation-based issues. In some spectra, these artifacts account for roughly a third of the peaks present in the given spectrum. We have developed a set of tools to detect these artifacts and approaches to mitigate their effects on downstream analyses.

systems biology↗