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Results for “Pathology”

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bioRxiv2020

Diverse Changes in Microglia Morphology and Axonal Pathology Over One Year after Mild Traumatic Brain Injury in Pigs

Over 2.8 million people experience mild traumatic brain injury (TBI) in the United States each year, which may lead to long-term neurological dysfunction. The mechanical forces that occur due to TBI propagate through the brain to produce diffuse axonal injury (DAI) and trigger secondary neuroinflammatory cascades. The cascades may persist from acute to chronic time points after injury, altering the homeostasis of the brain. However, the relationship between the hallmark axonal pathology of diffuse TBI and potential changes in glial cell activation or morphology have not been established in a clinically relevant large animal model at chronic time points. In this study, we assessed tissue from pigs subjected to rapid head rotation in the coronal plane to generate mild TBI. Neuropathological assessments for axonal pathology, microglial morphological changes, and astrocyte reactivity were conducted in specimens out to 1 year post injury. We detected an increase in overall amyloid precursor protein pathology, as well as periventricular white matter and fimbria/fornix pathology after a single mild TBI. We did not detect changes in corpus callosum integrity or astrocyte reactivity. However, detailed microglial skeletal analysis revealed changes in morphology, most notably increases in the number of microglial branches, junctions, and endpoints. These subtle changes were most evident in periventricular white matter and certain hippocampal subfields, and were observed out to 1 year post injury in some cases. These ongoing morphological alterations suggest persistent change in neuroimmune homeostasis. Additional studies are needed to characterize the underlying molecular and neurophysiological alterations, as well as potential contributions to neurological deficits.

pathology↗

bioRxiv2021

Deep learning identifies pathological abnormalities predictive of graft loss in kidney transplant biopsies

BackgroundInterstitial fibrosis, tubular atrophy, and inflammation are major contributors to renal allograft failure. Here we seek an objective, quantitative pathological assessment of these lesions to improve predictive utility. MethodsWe constructed a deep-learning-based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte (MNL) infiltrates from Periodic acid-Schiff (PAS) stained slides of transplant biopsies (training: n=60, testing: n=33) that quantified pathological lesions specific for interstitium, tubules and MNL infiltration. The pipeline was applied to 789 whole slide images (WSI) from baseline (n=478, pre-implantation) and 12-month post-transplant (n=311) protocol biopsies in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss. ResultsOur model accurately recognized kidney tissue compartments and MNLs. The digital features significantly correlated with Banff scores, but were more sensitive to subtle pathological changes below the thresholds in Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of 1-year graft loss (p=2.8e-05), while a Composite Damage Score (CDS) in 12-month post-transplant protocol biopsies predicted later graft loss (p=7.3e-05). ITAS and CDS outperformed Banff scores or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITAS or CDS also demonstrated significantly higher incidence of eGFR decline and subsequent graft damage. ConclusionsThis deep-learning approach accurately detected and quantified pathological lesions from baseline or post-transplant biopsies, and demonstrated superior ability for prediction of posttransplant graft loss with potential application as a prevention, risk stratification or monitoring tool.

pathology↗

bioRxiv2024

Highly Repeatable Tissue Proteomics for Kidney Transplant Pathology: Technical and Biological Validation of Protein Analysis using LC-MS/MS

Accurate pathological assessment of tissue samples is key for diagnosis and optimal treatment decisions. Traditional pathology techniques suffer from subjectivity resulting in inter-observer variability, and limitations in identifying subtle molecular changes. Omics approaches provide both molecular evidence and unbiased classification, which increases the quality and reliability of final tissue assessment. Here, we focus on mass spectrometry (MS)-based proteomics as a method to reveal biopsy tissue differences. For MS data to be useful, molecular information collected from formalin fixed paraffin embedding (FFPE) biopsy tissues needs to be consistent and quantitatively accurate and contain sufficient clinically relevant molecular information. Therefore, we developed an MS-based workflow and assessed the analytical repeatability on 36 kidney biopsies, ultimately analysing molecular differences and similarities of over 5000 proteins per biopsy. Additional 301 transplant biopsies were analysed to understand other physical parameters including effects of tissue size, standing time in autosampler, and the effect on clinical validation. MS data were acquired using Data-Independent Acquisition (DIA) which provides gigabytes of data per sample in the form of high proteome (and genome) representation, at exquisitely high quantitative accuracy. The FFPE-based method optimised here provides a coefficient of variation below 20%, analysing more than 5000 proteins per sample in parallel. We also observed that tissue thickness does affect the outcome of the data quality: 5 m sections show more variation in the same sample than 10 m sections. Notably, our data reveals an excellent agreement for the relative abundance of known protein biomarkers with kidney transplantation lesion scores used in clinical pathological diagnostics. The findings presented here demonstrate the ease, speed, and robustness of the MS-based method, where a wealth of molecular data from minute tissue sections can be used to assist and expand pathology, and possibly reduce the inter-observer variability.

pathology↗

bioRxiv2024

Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning algorithms into the laboratory information system

Digital pathology (DP) has revolutionized cancer diagnostics, allowing the development of deep-learning (DL) models supporting pathologists in their daily work and contributing to the improvement of patient care. However, the clinical adoption of such models remains challenging. Here we describe a proof-of-concept framework that, leveraging open-source DP software and Health Level 7 (HL7) standards, allows the integration of DL models in the clinical workflow. Development and testing of the workflow were carried out in a fully digitized Italian pathology department. A Python-based server-client architecture was implemented to interconnect the anatomic pathology laboratory information system (AP-LIS) with an external artificial intelligence decision support system (AI-DSS) containing 16 pre-trained DL models through HL7 messaging. Open-source toolboxes for DL model deployment, including WSInfer and WSInfer-MIL, were used to run DL model inference. Visualization of model predictions as colored heatmaps was performed in QuPath. As soon as a new slide is scanned, DL model inference is automatically run on the basis of the slides tissue type and staining. In addition, pathologists can initiate the analysis on-demand by selecting a specific DL model from the virtual slides tray. In both cases the AP-LIS transmits an HL7 message to the AI-DSS, which processes the message, runs DL model inference, and creates the appropriate type of colored heatmap on the basis of the employed classification model. The AI-DSS transmits model inference results to the AP-LIS, where pathologists can visualize the output in QuPath and/or directly from the virtual slides tray. The developed framework supports multiple DL toolboxes and it is thus suitable for a broad range of applications. In addition, this integration workflow is a key step to enable the future widespread adoption of DL models in pathology diagnostics.

pathology↗

bioRxiv2025

Deep-learning triage of 3D pathology datasets for comprehensive and efficient pathologist assessments

Standard-of-care slide-based 2D histopathology severely undersamples spatially heterogeneous tissue specimens, with each thin 2D section representing <1% of the entire tissue volume (in the case of a biopsy). Recent advances in non-destructive 3D pathology, such as open-top light-sheet microscopy (OTLS), enable comprehensive high-resolution imaging of large clinical specimens. While fully automated computational analyses of such 3D pathology datasets are being explored, a potential low-risk route for accelerated clinical adoption would be to continue to rely upon pathologists to provide final diagnoses. Since manual review of these massive and complex 3D datasets is infeasible for routine clinical practice, we present CARP3D, a deep learning triage framework that identifies high-risk 2D cross sections within large 3D pathology datasets to enable time-efficient pathologist evaluation. CARP3D assigns risk scores to all 2D levels within a tissue volume by leveraging context from a subset of neighboring depth levels, outperforming models in which predictions are based on isolated 2D levels. In two use cases - risk stratification based on prostate cancer biopsies and screening for dysplasia/cancer in endoscopic biopsies of Barretts esophagus - AI-triaged 3D pathology, enabled by CARP3D, demonstrates the potential to improve the detection of high-risk diseases in comparison to slide-based 2D histopathology while optimizing pathologist workloads.

pathology↗

bioRxiv2025

Histopathology and spatial transcriptomics jointly map myofiber-specific pathological programs in mTORC1-driven myopathy

Skeletal muscle is a structurally organized and functionally diverse organ composed of heterogeneous myofiber types and supporting non-myocyte populations that act in concert to generate force, regulate metabolism, and maintain systemic homeostasis. Myopathies occur in many different diseases, but the mechanisms that drive these muscle pathologies are still largely unknown, partly because conventional approaches cannot link histopathological features to molecular states at single-fiber resolution. To address this challenge, we brought histopathology and spatial transcriptomics together by applying high-resolution Seq-Scope technology to a rodent model of mTORC1 hyperactivation, which produces diverse pathological alterations within individual myofibers. Cross-sections from extensor digitorum longus (EDL) and soleus (SOL), two muscles with distinct fiber-type compositions, were profiled to determine how transcriptome changes are linked to histopathological outcomes. Our analyses reveal that mTORC1 hyperactivation elicits distinct, fiber-type-dependent pathological programs. Type I and IIa fibers, abundant in SOL but scarce in EDL, were largely resistant to mTORC1-induced pathology, exhibiting only minimal morphological alterations and no fiber type-specific responses beyond those commonly observed throughout the tissue. In contrast, type IIx fibers, shared between both muscles, diverged into opposing fates: in SOL, they underwent abnormal enlargement driven by sustained growth signaling, cytoskeletal remodeling, and impaired proteostasis with defective autophagy; whereas in EDL, they developed basophilia characterized by lipid-supported respiration fueling excessive ribonucleotide synthesis and RNA accumulation. Within the same muscle, type IIb fibers displayed striking heterogeneity with discrete transcriptional states encompassing canonical stress responses, oxidative metabolic activation, and developmental reprogramming. In parallel, non-myocytic populations, including activated macrophages and fibroblasts, accumulated preferentially in SOL, forming a fibrotic microenvironment supporting inflammation, tissue remodeling and hypertrophy. Taken together, these findings reveal that sustained mTORC1 signaling disrupts muscle homeostasis through distinct metabolic and structural routes, directly linking histopathological phenotypes to their molecular states at single-fiber resolution.

pathology↗

bioRxiv2026

Dissecting and directing pathology foundation models

Foundation models (FMs) are central to digital pathology, encoding histology images into dense embeddings for facilitating diagnostic classification, molecular alteration prediction, and clinical outcome modeling. However, the opacity of these embeddings renders FM-based systems "black boxes," limiting their trustworthiness for clinical translation and utility for scientific discovery. Here, we introduce PICASSO (Pathology Image Concept Atlas built via SparSe dictiOnary learning), a framework that makes pathology FMs interpretable and controllable. PICASSO decomposes FM embeddings into human-interpretable visual concepts using a sparse autoencoder. It is trained on more than 120 million tissue patches across 32 cancer types, producing the first pan-cancer atlas of histomorphological concepts. We demonstrate that PICASSO enables diverse downstream applications of FM embeddings by exposing interpretable structure within learned representations and supporting concept-level intervention. It enables auditing of clinical model behavior by revealing the morphological features driving predictions. Beyond transparency and validation, PICASSO enables the discovery of new biological insights; for example, it identified hobnailing epithelial morphology as a previously unrecognized biomarker of EGFR mutations in lung adenocarcinoma. By linking PICASSO-derived concepts with spatial transcriptomics, we uncover associations between morphological patterns and gene expression programs. Furthermore, PICASSO allows suppression of concepts associated with technical artifacts, thereby reducing model reliance on spurious signals. Finally, PICASSO enables controlled manipulation of learned concepts to generate counterfactual embeddings for exploratory therapeutic analysis, such as modulating tumour-infiltrating lymphocyte density to assess impacts on predict survival outcomes. Together, PICASSO provides a principled framework for transforming pathology FMs into platforms for mechanistic insight and discovery.

pathology↗

bioRxiv2026

Microvascular pathology of proteotoxic endothelial signature characterizes Progressive Supranuclear Palsy

Cerebrovascular pathology is increasingly implicated in neurodegenerative diseases, yet its pathomechanistic contribution remains poorly defined. Building on prior evidence of dysregulated iron and oxygen homeostasis in early-affected brain regions of progressive supranuclear palsy (PSP), we hypothesized that brain microvascular alterations may play an etiological role in select neurodegenerative proteinopathies. First, we conducted a systematic neuropathological evaluation of 178 brains from the University Health Network Neurodegenerative Brain Collection, including Alzheimers disease-related neuropathologic change (ADNC; n=30), Lewy body disease with high or intermediate ADNC (n=38) and low ADNC (n=16), multiple system atrophy (MSA; n=14), PSP (n=39), frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP; n=10), and controls (n=31). Arteriolosclerosis, microinfarction, and calcification were assessed in the basal ganglia and frontal cortex. Iron burden was correlated by quantification of Perls staining in MSA and PSP, where vessel pathology was most severe. Single-nucleus RNA-sequencing (snRNA-seq) of frontal cortex tissue from control (n=5) and PSP (n=8) cases with varying arteriolosclerosis severity was performed to characterize the vascular transcriptome, with validation against an independent snRNA-seq evaluation of PSP (n= 11), Picks disease (n=9), AD (n=10), and control (n=10) brains. Histological analysis revealed disease-specific involvement of microvascular pathology in neurodegenerative diseases, identifying PSP to demonstrate most prominent and widespread vessel wall thickening across regions examined. Regression analysis using demographic, APOE and MAPT genetic risk status, and neuropathological features of cases corroborated the distinct association with PSP pathology. Elevated iron load in early affected regions of MSA and PSP brains correlated with greater vessel wall thickening, suggesting a possible pathomechanistic relationship between the two disease physiologies. snRNA-seq analysis of vascular transcriptome identified robust upregulation of heat shock proteins and hypoxia-related genes in PSP endothelial cells and pericytes across both datasets. Importantly, we found the proteotoxic signature to be strongly associated with higher vessel scores in PSP cases, linking microvascular morphology to endothelial dysfunction. Our comprehensive neuropathological evaluation coupled with correlative snRNA-seq analysis establish PSP-specific arteriolar thickening associated with endothelial proteotoxic state as a candidate pathogenic mechanism. The cerebral arteriolar unit represents a compelling therapeutic target for disease modification in PSP.

pathology↗

bioRxiv2026

Pathology foundation-model embeddings reveal a morphological landscape of breast lesions beyond diagnostic categories

Background: Despite rapid advances in computational pathology, including foundation-model embeddings, most approaches remain classifier-based. Breast lesion pathology is an area of unmet clinicopathological need and a compelling setting for morphology-based modeling: diagnostic categories occupy overlapping morphological spectra and reflect heterogeneous pathways of tumor development, thereby posing substantial challenges for pathological diagnosis and subsequent clinical decision-making. However, approaches to delineate morphological continua across diagnostic categories and tumor development remain understudied. Here, we leveraged morphology-based foundation-model embeddings to define a morphologic state space across breast lesion categories and dissect how these embeddings could reflect a pathobiological continuum or sequence associated with tumor development. Methods: To model breast lesion categories in continuous morphology space, we established a leakage-controlled, patient-aware workflow using the frozen foundation model Prov-GigaPath and the BReAst Carcinoma Subtyping (BRACS) dataset, a pathologist-annotated hematoxylin-and-eosin (H&E) breast lesion dataset spanning normal tissue, benign proliferative lesions, atypical lesions, ductal carcinoma in situ (DCIS) and invasive carcinoma (IC). Using the BRACS training split, we defined a train-derived principal component analysis (PCA) morphology space. We then performed two complementary analyses: normal-rooted k-nearest-neighbor (kNN)-Dijkstra graph-distance scoring to derive a trajectory-like morphology score without imposing the full ordinal diagnostic sequence; pairwise Energy distance and squared maximum mean discrepancy with a radial-basis-function kernel (RBF-MMD2) to quantify order-free distributional separation among diagnostic categories. Validation and test data were projected into the train-defined reference space to assess reproducibility. Results: Prov-GigaPath embeddings defined a train-derived morphology space with category-associated organization. Without imposing the full diagnostic sequence, normal-rooted kNN-Dijkstra graph distances recovered a trajectory-like morphology arrangement from normal/benign to malignant lesions. Order-free Energy distance and RBF-MMD2 further supported graded separation among lesion categories. Validation and test datasets preserved the pathological organization. Furthermore, morphological embeddings dissected DCIS heterogeneity associated with concurrent invasive carcinoma, without requiring additional molecular information. Conclusions: Leveraging morphology-based foundation-model embeddings, we defined a morphologic state space across breast lesion categories, allowing nuanced positioning of breast lesions along a pathobiological continuum associated with tumor development. Our approaches pave the way for extracting biologically and clinically relevant information from routine histopathology to guide molecular testing, individualized risk assessment, and cost-efficient clinicopathological decision-making.

pathology↗

bioRxiv2016

Conformational dynamics of Tau in the cell quantified by an intramolecular FRET biosensor in physiological and pathological context

Impaired interactions of Tau protein with microtubules (MT) and Tau misfolding play a key role in Alzheimer disease (AD) and other neurodegenerative diseases collectively named Tauopathies. However, little is known about the molecular conformational changes that underlie Tau misfolding and aggregation in pathological conditions, due to the difficulty of studying structural aspects of this intrinsically unfolded protein, particularly in the context of living cells.\n\nHere we developed a new Conformational-Sensitive Tau sensor (CST), based on human Tau full length protein, to investigate the changes in 3D conformation and aggregation state of Tau upon modulation of its interactions with MTs in living cells, in physiological and pathological conditions. After showing that the CST fully preserves functional Tau activities in living cells, we demonstrated that MT-bound Tau displays a loop-like conformation, while soluble Tau assumes a relaxed conformation.\n\nThe imaging readout based on CST allowed to discover new conformational properties of full length Tau in living cells, when challenged with Alzheimer-relevant seeds from different sources, and to learn about different ways to induce the self-aggregation of full length Tau in cells. Furthermore, it allowed to investigate the contribution to the pathology of point mutations known to alter Tau/MTs interaction.\n\nSIGNIFICANCEThe microtubule-associated protein Tau regulates the stability of microtubules (MT) and its alteration and misfolding plays an important role in Alzheimer disease (AD) and in several other neurodegenerative diseases collectively named Tauopathies. However, little is known about the molecular mechanism that modulate the conformational changes of Tau in pathological conditions. Here, we developed a conformational sensitive Tau biosensor providing insights into the 3D conformation of Tau in living cells. This new tool showed that MT-bound Tau displays a loop-like conformation, while soluble Tau assume a relaxed conformation, which might be more prone to aggregation. Furthermore the CST was used to develop an assay to screen molecules and mutations involved in Tau self-aggregation in living cells. Remarkably, the imaging tool developed allowed to demonstrate that A{beta} oligomers induced Tau aggregation in living cells.

Neuroscience↗

bioRxiv2020

Induction Of Chronic Stress Reveals An Interplay Of Stress Granules And TDP-43 Pathological Aggregates In Human ALS Fibroblasts And iPSC-Neurons

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are fatal neurodegenerative diseases characterized by the presence of neuropathological aggregates of phosphorylated TDP-43 (P-TDP-43). The RNA-binding protein TDP-43 is also a component of stress granules (SG), cytoplasmic foci forming to arrest translation under sub-lethal stress conditions. Although commonly considered as distinct structures, a link between SG and pathological TDP-43 inclusions may occur despite evidence that TDP-43 pathology directly arises from SG is still under debate. Primary fibroblasts and iPSC-derived neurons (iPSC-N) from ALS patients carrying mutations in TARDBP (n=3) and C9ORF72 (n=3) genes and from healthy controls (n=3) were exposed to oxidative stress by sodium arsenite. SG formation and cell response to stress was evaluated and quantified by immunofluorescence and electron microscopy analyses. We found that, not only an acute, but also a chronic oxidative insult, better mimicking a persistent condition of stress as in neurodegeneration, is able to induce SG formation in primary fibroblasts and iPSC-N. Importantly, only upon chronic stress, we observed TDP-43 recruitment into SG and the formation of distinct P-TDP-43 aggregates, very similar to the abnormal inclusions observed in ALS/FTD autoptic brains. Moreover, in fibroblasts, cell response to stress was different in control compared with mutant ALS cells, probably due to their different vulnerability. A quantitative analysis revealed also differences in terms of number of SG-forming cells and SG size, suggesting a different composition of foci in acute and chronic stress. In condition of prolonged stress, SG and P-TDP-43 aggregate formation was concomitant with p62 increase and autophagy dysregulation in both ALS fibroblasts and iPSC-N, as confirmed by immunofluorescence and ultrastructural analyses. We found that exposure to a chronic oxidative insult promotes the formation of both SG and P-TDP-43 aggregates in patient-derived cells, reinforcing the idea that SG fail to properly disassemble, interfering with the protein quality control system. Moreover, we obtained a disease cell model recapitulating ALS/FTD P-TDP-43 aggregates, which represents an invaluable bioassay to study TDP-43 pathology and develop therapeutic strategies aimed at disaggregating or preventing the formation of pathological inclusions.

neuroscience↗

bioRxiv2020

Poxvirus-encoded TNF receptor homolog dampens inflammation and protects the host from uncontrolled lung pathology and death during respiratory infection

Ectromelia virus (ECTV) causes mousepox, a surrogate mouse model for smallpox caused by variola virus in humans. Both viruses encode tumor necrosis factor receptor (TNFR) homologs termed cytokine response modifier (Crm) proteins, containing a TNF-binding domain and a chemokine-binding domain termed smallpox virus-encoded chemokine receptor (SECRET) domain. Infection of ECTV-resistant C57BL/6 mice with an ECTV CrmD deletion mutant resulted in uniform mortality due to excessive TNF secretion and dysregulated inflammatory cytokine production but viral load was not affected. CrmD dampened lung pathology, leukocyte recruitment and inflammatory cytokines including TNF, IL-6, IL-10 and IFN-{gamma}. Blockade of IL-6, IL-10R or TNF function with monoclonal antibodies reduced lung pathology and provided 60-100% protection from an otherwise lethal infection. IFN-{gamma} caused lung pathology only when both the TNF-binding and SECRET domains were deleted but it was neither necessary nor sufficient to cause pathology when only the CrmD SECRET domain was expressed by virus.

immunology↗

bioRxiv2020

Murine Radiation-Induced Stomach Pathology from Whole Thoracic Irradiation

PurposeRadiation-induced lung injury is a common side effect in the treatment of lung and breast cancers. There is a large focus in the field on leveraging mouse models of radiation-induced lung injury to find novel treatments for the condition. While attempting to irradiate mouse lungs for purposes of creating a radiation-induced pulmonary fibrosis model, noticeable declines in health were observed at much earlier time points than recorded for lung pathology. This was later attributed to stomach pathology observed in CT images and ex vivo dissection. MethodsFor this study, we used longitudinal microCT to characterize male C57Bl/6 mice irradiated with a single dose of 20 Gy to the whole thoracic area delivered by an X-Rad cabinet irradiator. CT was performed with respiratory gating at 2 to 4 week timepoints to construct a timeline of pathology leading up to fibrosis and quantify severity of fibrosis afterwards. However, a mouse imaged at the 10 week timepoint showed evidence of stomach distention. These mice were sacrificed and their stomachs removed. Histology was performed on the stomachs using H&E staining. ResultsOn the CT images, we observed a large, spherical volume of hypointense signal, caudal to the lungs (Figure 1). This correlated with a distended stomach caused by constipation and gas build-up within the stomach. Statistical analysis showed 81% of mice (n=105) died prematurely after irradiation and before significant development of pulmonary fibrosis. Mice sacrificed and dissected showed unpassed bolus as contents of the stomach, and histology showed cell necrosis of the stomach walls. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/980987v1_fig1.gif" ALT="Figure 1"> View larger version (84K): org.highwire.dtl.DTLVardef@12de14borg.highwire.dtl.DTLVardef@122180corg.highwire.dtl.DTLVardef@158bd6forg.highwire.dtl.DTLVardef@fcdcbf_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Median Survival Times for male and female C57Bl/6 mice receiving varying Whole Thoracic Irradiaiton doses. C_FIG ConclusionThe histology indicated an inability for food to be digested and moved into the small intestine. This lead to a blockage and ensuing stomach distention. Given the severity of the pathologys consequences, it lead to the mouses imminent mortality inhibiting the efficacy of the study. Future studies need to consider careful placement of shields or any beam contouring devices to ensure protection of the stomach given its higher radiosensitivity in contrast to the lungs.

cancer biology↗

bioRxiv2020

Ahr-bacterial diet interaction modulates aging and associated pathologies in C. elegans

Genetic and environmental factors concurrently shape the aging process. The nematode Caenorhabditis elegans has been instrumental in the past 30 years to identify most genes and interventions nowadays known to modulate aging (Lopez-Otin et al. 2013, Tigges et al. 2014, Dato et al. 2017). The aryl-hydrocarbon-receptor (AhR) was discovered as a dioxin-binding transcription factor involved in the metabolism of different environmental toxicants and xenobiotics (Poland et al. 1976). Since then, the variety of pathophysiological processes regulated by the AhR has rapidly grown, ranging from the immune response to cell death and metabolic pathways, and we disclosed it promotes aging phenotypes across species (Eckers et al. 2016, Esser et al. 2018). Interestingly, many AhR modulators may impact on aging and age-associated pathologies, but, whether their effects are actually AhR-dependent has never been explored. Here, we show for the first time that lack of C. elegans AHR-1 affects health and lifespan in a context-dependent manner. Using known mammalian AhR modulators we found that, similar to mammals, ahr-1 has protective effects against environmental insults and identified a new role for AhR-bacterial diet interaction in animal lifespan, stress resistance, and age-associated pathologies. We narrowed down the dietary factor involved in the observed AhR-dependent features to a bacterially-extruded metabolite likely involved in tryptophan metabolism. This is the first study clearly establishing C. elegans as a good model organism to investigate evolutionarily conserved functions of AhR-modulators and -regulated processes, indicating it can be exploited to contribute to the discovery of novel information about AhR in mammals. Significance StatementThe roundworm C. elegans has been instrumental for the identification of many of the genetic and environmental factors known to concurrently shape the aging process. The aryl-hydrocarbon-receptor (AhR) was originally discovered as a dioxin-binding transcription factor involved in the metabolism of environmental toxicants and, in the past, we found it promotes aging phenotypes across species. Many AhR modulators may impact on aging and age-associated pathologies, but, whether their effects are actually AhR-dependent has never been explored. We show here for the first time that C. elegans AhR has protective effects against environmental insults such as UVB radiations and the xenobiotic BaP and identified a new critical role for AhR-bacterial diet interaction in animal lifespan, stress resistance, and age-associated pathologies.

physiology↗

bioRxiv2020

Adult-born granule cells support pathological microcircuits in the chronically epileptic dentate gyrus

Temporal lobe epilepsy (TLE) is characterized by recurrent seizures driven by synchronous neuronal activity. The dentate gyrus (DG) region of the hippocampal formation is highly reorganized in chronic TLE; in particular, pathological remodeling of the "dentate gate" is thought to open up pathological conduction pathways for synchronous discharges and seizures in the mesial temporal lobe. However, this pathophysiological framework lacks a mechanistic explanation of how macroscale synchronous dynamics emerge from alterations of the DG at the microcircuit level. In particular, the relative contribution of developmentally defined subpopulations of adult-born (abGCs) and mature (mGCs) granule cells to epileptiform network events remains unknown. To address this question, we optically recorded activity dynamics of identified populations of abGCs and mGCs during interictal epileptiform discharges (IEDs) in mice with chronic TLE. We find that disjoint subsets of IEDs differentially recruit abGC and mGC populations. We used these observations to develop a neural topic modeling framework, under which we find that the epileptic DG network organizes into disjoint, cell-type specific pathological ensembles, a subset of which are recruited by each IED. We found that statistics of this ensemble structure are highly conserved across animals, with abGCs disproportionately driving network activity in the epileptic DG during IEDs. Our results provide the first in vivo characterization of activity dynamics of identified GC subpopulations in the epileptic DG, the first microcircuit-level correlates of IEDs in vivo, and reveal a specific contribution of abGCs to interictal epileptic events. HighlightsO_LIWe relate electrographic signatures of epilepsy to microcircuit dynamics at single-cell resolution C_LIO_LIThe chronically epileptic dentate gyrus granule cell network is organized in lineage-specific pathological ensembles C_LIO_LIA novel generative model framework for ensemble recruitment relates local field potential signatures to microcircuit activation during interictal epileptiform discharges C_LIO_LIAdult-born granule cell-dominated ensembles are disproportionately represented among the inferred ensembles C_LIO_LIThe most active ensemble during an interictal epileptiform discharge can be decoded directly from the local field potential spectrum C_LIO_LIThis Latent Ensemble Recruitment model of cell recruitment by interictal events is the first application of Bayesian topic modeling to in vivo two-photon calcium imaging data C_LI

neuroscience↗

bioRxiv2020

Tau forms oligomeric complexes on microtubules that are distinct from pathological oligomers in disease

Tau is a microtubule-associated protein, which promotes neuronal microtubule assembly and stability. Accumulation of tau into insoluble aggregates known as neurofibrillary tangles (NFTs) is a pathological hallmark of several neurodegenerative diseases. The current hypothesis is that small, soluble oligomeric tau species preceding NFT formation cause toxicity. However, thus far visualizing the spatial distribution of tau monomers and oligomers inside cells under physiological or pathological conditions has not been possible. Here, using single molecule localization microscopy (SMLM), we show that, in vivo, tau forms small oligomers on microtubules under physiological conditions. These physiological oligomers are distinct from those found in cells exhibiting tau aggregation and could be pre-cursors of aggregated tau in pathology. Further, using an unsupervised shape classification algorithm that we developed, we show that different tau phosphorylation states are associated with distinct tau aggregate species. Our work elucidates taus nanoscale composition under physiological and pathological conditions in vivo.

cell biology↗

bioRxiv2020

Increased processing of SINE B2 non coding RNAs unveils a novel type of transcriptome de-regulation underlying amyloid beta neuro-pathology

More than 97% of the mammalian genome is non-protein coding, and repetitive elements account for more than 50% of noncoding space. However, the functional importance of many non-coding RNAs generated by these elements and their connection with pathologic processes remains elusive. We have previously shown that B2 RNAs, a class of non-coding RNAs that belong to the B2 family of SINE repeats, mediate the transcriptional activation of stress response genes (SRGs) upon application of a stimulus. Notably, B2 RNAs bind RNA Polymerase II (RNA Pol II) and suppress SRG transcription during pro-stimulation state. Upon application of a stimulus, B2 RNAs are processed into fragments and degraded, which in turn releases RNA Pol II from suppression and upregulates SRGs. Here, we demonstrate a novel role for B2 RNAs in transcriptome response to amyloid beta toxicity and pathology in the mouse hippocampus. In healthy hippocampi, activation of SRGs is followed by a transient upregulation of pro-apoptotic factors, such as p53 and miRNA-34c, which target SRGs creating a negative feedback loop that facilitates transition to the pro-stimulation state. Using an integrative RNA genomics approach, we show that in mouse hippocampi of an amyloid precursor protein knock-in mouse model and in an in vitro cell culture model of amyloid beta toxicity, this regulatory loop is dysfunctional due to increased levels of B2 RNA processing, constitutively elevated SRG expression and high p53 levels. Evidence indicates that Hsf1, a master regulator of stress response, mediates B2 RNA processing in cells, and is upregulated during amyloid toxicity accelerating the processing of SINE RNAs and SRG hyper-activation. Our study reveals that in mouse, SINE RNAs constitute a novel pathway deregulated in amyloid beta pathology, with potential implications for similar cases in the human brain, such as Alzheimers disease (AD). This data attributes a role to SINE RNA processing in a pathological process as well as a new function to Hsf1 that is independent of its transcription factor activity.

genomics↗

bioRxiv2020

Imaging stable isotope labelling kinetics (iSILK) for following spatial Aβ plaque aggregation dynamics in evolving Alzheimers disease pathology

For our understanding of Alzheimers disease (AD) pathophysiology, it is of critical importance to determine how key pathological factors, specifically amyloid {beta} (A{beta}) plaque formation, are interconnected and implicated in neurodegeneration, disease progression and the development of clinical symptoms. Exactly how A{beta} plaque formation is initiated and how the ongoing plaque deposition proceeds is not well understood. This is partly because we can only examine details of the molecular pathology after death in humans, and in mice, we can only examine a particular point in time without any longitudinal information on the fate of individually formed deposits. Herein, we used metabolic labelling of proteins with stable isotopes, together with multimodal imaging mass spectrometry (IMS) for imaging stable isotope labelling kinetics (iSILK) in the APPNL-G-F knock-in mouse model of AD. The aim was to monitor the earliest seeds of A{beta} deposition through ongoing plaque development and track the deposition of A{beta} that is produced later in relation to already deposited plaques. This allowed us to visualize A{beta} peptide aggregation dynamics within individual deposits across different brain regions. We identified the cortex as a primary site of deposition in precipitating plaque pathology. Further, our data show that structural plaque heterogeneity is associated with differential peptide deposition. Specifically, A{beta}1-42 is forming an initial core seed followed by radial outgrowth and late secretion and deposition of A{beta}1-38. Together these data prove the potential of iSILK for probing amyloid protein secretion, processing and aggregation dynamics in AD pathology.

neuroscience↗