Biology subjects

Schmidt, J.

Publications and source records attributed to Schmidt, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Autism gene variants disrupt enteric neuron migration and cause gastrointestinal dysmotility

The comorbidity of autism spectrum disorders and severe gastrointestinal symptoms is well-established, yet the molecular underpinnings remain unknown. The identification of high-confidence large-effect autism risk genes offers the opportunity to identify convergent, underlying biology by studying these genes in the context of the gastrointestinal system. Here we show that the expression of these genes is enriched in human prenatal gut neurons as well as their migratory progenitors, suggesting that the development and/or function of these neurons may be disrupted by autism-associated pathogenic variants, leading to gastrointestinal dysfunction. Here we document the prevalence of gastrointestinal issues in patients with large-effect variants in sixteen of these genes, highlighting dysmotility, consistent with potential enteric neuron dysfunction. Using the high-throughput diploid frog Xenopus tropicalis, we individually target five of these genes (SYNGAP1, CHD8, SCN2A, CHD2, and DYRK1A) and observe disrupted enteric neuronal progenitor migration for each. More extensive analysis of DYRK1A reveals that perturbation causes gut dysmotility in vivo, which can be ameliorated by treatment with a selective serotonin reuptake inhibitor (escitalopram) or a serotonin receptor 6 agonist, identified by in vivo drug screening. This work suggests that atypical development of enteric neurons contributes to the gastrointestinal distress commonly seen in individuals with autism and that increasing serotonin signaling may be a productive therapeutic avenue.

neuroscience↗

bioRxiv2024

Time-Domain Diffuse Optical Tomography for Precision Neuroscience

AbstractRecent years have witnessed a rise in research utilizing neuroimaging for precision neuromedicine, but clinical translation has been hindered by scalability and cost. Time Domain functional Near Infrared Spectroscopy (TD-fNIRS), the gold standard of optical neuroimaging techniques, offers a unique opportunity in this domain since it provides superior depth sensitivity and enables resolution of absolute properties unlike its continuous wave counterparts. However, current TD systems have limited commercial availability, slow sampling rates, and sparse head coverage. Our team has overcome the technical challenges involved in developing a whole-head time-domain diffuse optical tomography (TD-DOT) system. Here, we present the system characterization results using standardized protocols and compare them to the state-of-the-art. Furthermore, we showcase the system performance in retrieving cortical activation maps during standard hemodynamic, sensory, and motor tasks. A combination of the system performance, signal quality, and ease-of-use can enable future studies aimed at investigating TD-DOT clinical applications.

bioengineering↗

bioRxiv2024

Developmental origins and evolution of pallial cell types and structures in birds

The advanced cognitive abilities of birds rival those of mammals and have been attributed to evolutionary innovations in the pallium. However, a comprehensive cellular characterization of this brain region in birds has been lacking. We scrutinized the structures, cell types and evolutionary origins of the avian pallium based on single-cell and spatial transcriptomics atlases for the adult and developing chicken, and comparisons to corresponding data from mammals and non-avian reptiles. We found that the avian pallium shares most inhibitory neuron types with other amniotes. While excitatory neuron repertoires in the (medial) hippocampal formation show high conservation, they substantially diverged in other pallial regions during avian evolution, defining novel structures like the avian-specific (dorsal) hyperpallium, whose neuronal gene expression identities partly converge during late development with those of the (ventral) nidopallium. Our work also unveils the evolutionary relationships of pallial structures across amniotes, like the previously unknown homology between avian (lateral) mesopallial and mammalian deep layer cortical neurons. One-Sentence SummaryAn avian neural cell type atlas illuminates the developmental origins and evolution of the amniote pallium.

evolutionary biology↗

bioRxiv2024

Nanoparticle-mediated delivery of placental gene therapy via uterine artery catheterization in a pregnant rhesus macaque

Nanoparticles offer promise as a mechanism to non-invasively deliver targeted placental therapeutics. Our previous studies utilizing intraplacental administration demonstrate efficient nanoparticle uptake into placental trophoblast cells and overexpression of human IGF1 (hIGF1). Nanoparticle-mediated placental overexpression of hIGF1 in small animal models of placental insufficiency and fetal growth restriction improved nutrient transport and restored fetal growth. The objective of this pilot study was to extend these studies to the pregnant nonhuman primate and develop a method for local delivery of nanoparticles to the placenta via maternal blood flow from the uterine artery. Nanoparticles containing hIGF1 plasmid driven by the placenta-specific PLAC1 promoter were delivered to a mid-gestation pregnant rhesus macaque via a catheterization approach that is clinically used for uterine artery embolization. Maternal-fetal interface, fetal and maternal tissues were collected four days post-treatment to evaluate the efficacy of hIGF1 treatment in the placenta. The uterine artery catheterization procedure and nanoparticle treatment was well tolerated by the dam and fetus through the four-day study period following catheterization. Nanoparticles were taken up by the placenta from maternal blood as plasmid-specific hIGF1 expression was detected in multiple regions of the placenta via in situ hybridization and qPCR. The uterine artery catheterization approach enabled successful delivery of nanoparticles to maternal circulation in close proximity to the placenta with no concerns to maternal or fetal health in this short-term feasibility study. In the future, this delivery approach can be used for preclinical evaluation of the long-term safety and efficacy of nanoparticle-mediated placental therapies in a rhesus macaque model. HighlightsNovel method to deliver therapeutics to maternal-fetal interface Delivery of nanoparticles to the placenta via maternal catheterization

physiology↗

bioRxiv2024

Machine learning driven image segmentation and shape clustering of algal microscopic images obtained from various water types

Algae and cyanobacteria are microorganisms found in almost all fresh and marine waters, where they can pose environmental and public health risks when they grow excessively and produce blooms. Accurate identification and quantification of these microorganisms are vital for ecological research, water quality monitoring, and public health safety. However, traditional methods of manually counting and morphologically identifying these microorganisms are time-consuming and prone to human error. Application of the machine learning-driven Fast Segment Anything Model (FastSAM), an image segmentation model, automates and potentially enhances the accuracy and efficiency of cell identification and enumeration from microscopic images. We assessed FastSAM for algal cell image segmentation, and three clustering evaluation metrics. Segmentation of microscopic images of algal and cyanobacterial cells in water and treated wastewater samples using the Convolutional Neural Network based FastSAM algorithm demonstrated benefits and challenges of this machine learning-driven image processing. Notably, the pre-trained algorithm segmented entire elements in all microscopic images used in this study. Depending on the shape, 50-100% similarity was observed between machine-based segmentation and manual validation of all segmented elements, with 100% of single cells being correctly segmented by FastSAM. The performance of clustering metrics varied between 57-94% with the Spectral Angle Mapper achieving the most accurate performance, 84-94%, compared to the manually chosen clustering benchmarks. Cyanobacterial and algal communities are biologically diverse and have ecological significance. The application of image clustering techniques in studying their cell shapes marks an important advancement in microbial ecology and environmental monitoring. As technology progresses, these methods will become increasingly utilised to decipher the complex roles that algae and cyanobacteria play in our ecosystems supporting mitigation and public health protection measures.

microbiology↗

bioRxiv2024

Targeting protein-ligand neosurfaces using a generalizable deep learning approach

Molecular recognition events between proteins drive biological processes in living systems. However, higher levels of mechanistic regulation have emerged, where protein-protein interactions are conditioned to small molecules. Here, we present a computational strategy for the design of proteins that target neosurfaces, i.e. surfaces arising from protein-ligand complexes. To do so, we leveraged a deep learning approach based on learned molecular surface representations and experimentally validated binders against three drug-bound protein complexes. Remarkably, surface fingerprints trained only on proteins can be applied to neosurfaces emerging from small molecules, serving as a powerful demonstration of generalizability that is uncommon in deep learning approaches. The designed chemically-induced protein interactions hold the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells.

biochemistry↗

bioRxiv2024

Neuronal ARHGAP8 controls synapse structure and AMPA receptor-mediated synaptic transmission

The aberrant formation and function of neuronal synapses are recognized as major phenotypes in many cases of neurodevelopmental (NDDs) and -psychiatric disorders (NPDs). A growing body of research has identified an expanding number of susceptibility genes encoding proteins with synaptic function. Here, we present the first brain-focused characterization of a potential new susceptibility gene, ARHAGP8, which encodes a Rho GTPase activating protein (RhoGAP). Accumulating evidence suggests that ARHGAP8 plays a pivotal role in the pathogenesis of NPDs/NDDs. We provide the first evidence for ARHGAP8 as a novel player at excitatory synapses, with its synaptic localisation linked to the presence of the developmentally important NMDA receptor subunit GluN2B. By increasing ARHGAP8 levels in hippocampal neurons to mimic the copy number variant found in a subset of patients, we observed reductions in dendritic complexity and spine volume, accompanied by a significant decrease in synaptic AMPA receptor-mediated transmission. These results suggest that ARHGAP8 plays a role in shaping the morphology and function of excitatory synapses, and prompt further investigation of ARHGAP8 as a candidate gene in NDDs/NPDs.

neuroscience↗

bioRxiv2024

Gene regulatory network landscape of Group 3/4 medulloblastoma

Cellular heterogeneity in Group 3 and Group 4 medulloblastomas is a major driver of therapeutic intractability. Here, we describe transcription factor-driven mechanisms underlying cellular diversity in these tumors using a comprehensive single-nucleus multi-omics atlas. Our analysis reveals that rather than fixed entities, these tumors exist along a continuum of cell states defined by four molecular identity axes. We show that perturbing transcription factor activity drives cellular plasticity, a key contributor to tumor heterogeneity. Strikingly, modulation of the lineage determinant PAX6 redirects tumor cells along both Group 3- and Group 4-like differentiation trajectories, modeling a bi-lineage medulloblastoma, and reduces aggressiveness of MYC-driven tumor models. Together, our findings reveal how oncogenic signals co-opt cerebellar unipolar brush cell programs to rewire tumor cell states and identify cellular plasticity as a potential targetable vulnerability in medulloblastoma.

cancer biology↗

bioRxiv2023

DNA Transposons favour de novo transcript emergence through enrichment of transcription factor binding motifs

De novo genes emerge from non-coding regions of genomes via succession of mutations. Among others, such mutations activate transcription and create a new open reading frame (ORF). Although the mechanisms underlying ORFs emergence are well documented, relatively little is known about the mechanisms enabling new transcription events. Yet, in many species a continuum between absent and very prominent transcription has been reported for essentially all regions of the genome. In this study we searched for de novo transcripts by using newly assembled genomes and transcriptomes of seven inbred lines of Drosophila melanogaster, originating from six European and one African population. This setup allowed us to detect line specific de novo transcripts, and compare them to their homologous non-transcribed regions in other lines, as well as genic and intergenic control sequences. We studied the association with transposable elements and the enrichment of transcription factor motifs upstream of de novo emerged transcripts and compared them with regulatory elements. We found that de novo transcripts overlap with TEs more often than expected by chance. The emergence of new transcripts correlates with high CpG islands and regions of TEs activity. Moreover, upstream regions of de novo transcripts are highly enriched with regulatory motifs. Such motifs abound in new transcripts overlapping with TEs, particularly DNA TEs, and are more conserved upstream de novo transcripts than upstream their non-transcribed homologs. Overall, our study demonstrates that TEs insertion is important for transcript emergence, partly by introducing new regulatory motifs from DNA TE families.

genomics↗

bioRxiv2023

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells

T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T-cell activation is elicited by the binding of the T-cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its and {beta} chains. Here, we collected and curated a dataset of 17,715 {beta}TCRs interacting with dozens of class I and class II epitopes. We used this curated data to develop MixTCRpred, a deep learning TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual T cells demonstrates that MixTCRpred can identify chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.

bioinformatics↗

bioRxiv2023

Tumor-reactive clonotype dynamics underlying clinical response to TIL therapy in melanoma

The profiles, specificity and dynamics of tumor-specific clonotypes that are associated with clinical response to adoptive cell therapy (ACT) using tumor-infiltrating lymphocytes (TILs) remain unclear. Using single-cell RNA/TCR-sequencing, we tracked TIL clonotypes from baseline tumors to ACT products and post-ACT blood and tumor samples in melanoma patients treated with TIL-ACT. Patients with clinical responses had baseline tumors enriched in tumor-reactive TILs, which were more effectively mobilized upon in vitro expansion, yielding products with higher numbers of tumor-specific CD8+ cells, which also preferentially infiltrated tumors post-ACT. Conversely, lack of clinical responses was associated with tumors devoid of tumor-reactive resident clonotypes, and with cell products mostly composed of blood-borne clonotypes mainly persisting in blood but not in tumors post-ACT. Upon expansion, tumor-specific TILs lost the specific signatures of states originally exhibited in tumors, including exhaustion, and in responders acquired an intermediate exhausted effector state after tumor engraftment, revealing important functional cell reinvigoration.

cancer biology↗

bioRxiv2023

Dimerisation of European robin cryptochrome 4a

Homo-dimer formation is important for the function of many proteins. Although dimeric forms of cryptochromes (Cry) have been found by crystallography and were recently observed in vitro for European robin Cry4a, little is known about the dimerisation of avian cryptochromes and the role it could play in the mechanism of magnetic sensing in migratory birds. Here we present a combined experimental and computational investigation of the dimerisation of robin Cry4a resulting from covalent and non-covalent interactions. Experimental studies using native mass spectrometry, mass spectrometric analysis of disulphide bonds, chemical cross-linking and photometric measurements show that disulphide-linked dimers are routinely formed, the most likely cysteines being C317 and C412. Computational modelling and molecular dynamics simulations were used to generate and assess a number of possible dimer structures. The relevance of these findings to the proposed role of Cry4a in avian magnetoreception is discussed.

biochemistry↗

bioRxiv2023

Production of 130 diterpenoids by combinatorial biosynthesis in yeast

Diterpenoids form a diverse group of natural products, many of which are or could become pharmaceuticals or industrial chemicals. However, low concentration, presence in complex mixtures and challenging synthesis often limit their exploitation. The modular character of diterpene biosynthesis and the substrate flexibility of the enzymes involved make combinatorial biosynthesis a promising approach. Here, we report on the assembly in yeast of 130 diterpenoids by pairwise combinations of ten diterpene synthases producing (+)-copalyl diphosphate-derived backbones and four cytochrome P450 enzymes (CYPs); 80 of these diterpenoids have not yet been reported. The CYPs accepted the majority of substrates they were given but remained regioselective. Our results bode well for the systematic exploration of diterpenoid chemical space using combinatorial assembly in yeast.

synthetic biology↗

bioRxiv2023

Physical contacts between sparse biofilms promote plasmid transfer and generate functional novelty

The horizontal transfer of plasmids is an important driver of microbial evolution, such as conferring antibiotic resistance (AR) to new genotypes. In biofilms, the abundance of cell-cell contacts promotes the frequent transfer of plasmids and their associated genes. In this study, we expand our knowledge about AR-encoding plasmids by investigating their transfer between discrete biofilms as the biofilms grow and physically collide with each other. Using an experimental system consisting of two fluorescently labelled Pseudomonas stutzeri strains and an Escherichia coli strain, we show that biofilm collisions promote plasmid transfer along the collision boundaries. The extent of plasmid transfer depends on the plasmid loss probability, the plasmid transfer probability, and the relative growth rates of plasmid-free and plasmid-carrying cells. We further show that the proliferation of plasmids after biofilm collision depends on the spatial positionings of plasmid-carrying cells along the collision boundary, thus establishing a link between the large-scale spatial distribution of discrete biofilms and the small-scale spatial arrangement of cells within individual biofilms. Our study reveals that plasmid transfer during biofilm collisions is determined by spatial factors operating at different organizational levels and length scales, expanding our understanding of the fate of plasmid-encoded traits in microbial communities.

microbiology↗

bioRxiv2022

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes

CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on MHC-II molecules. The high polymorphism of MHC-II genes represents an important hurdle towards accurate prediction and identification of CD4+ T-cell epitopes in different individuals and different species. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across human, mouse, cattle and chicken. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse binding mode in MHC-II ligands. We then developed a machine learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T-cell epitopes, and enabled us to discover and characterize several viral and bacterial epitopes following the aforementioned reverse binding mode.

immunology↗

bioRxiv2022

Predictions of immunogenicity reveal potent SARS-CoV-2 CD8+ T-cell epitopes

The recognition of pathogen or cancer-specific epitopes by CD8+ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I Human Leukocyte Antigen (HLA-I) molecules and recognized by the T-Cell Receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data with new algorithmic developments to improve predictions of both antigen presentation and TCR recognition. Applying our tool to SARS-CoV-2 proteins enabled us to uncover several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8+ T cells against one of these epitopes and cross-reactivity with the homologous SARS-CoV-1 peptide.

bioinformatics↗

bioRxiv2022

Reconstructing the ancestral vertebrate brain using a lamprey neural cell type atlas

The vertebrate brain emerged more than [~]500 million years ago in common evolutionary ancestors. To systematically trace its cellular and molecular origins, we established a spatially resolved cell type atlas of the entire brain of the sea lamprey - a jawless species whose phylogenetic position affords the reconstruction of ancestral vertebrate traits - based on extensive single-cell RNA-seq and in situ sequencing data. Comparisons of this atlas to neural data from the mouse and other jawed vertebrates unveiled various shared features that enabled the reconstruction of the core cell type composition, tissue structures, and gene expression programs of the ancestral brain. However, our analyses also revealed key tissues and cell types that arose later in evolution. For example, the ancestral vertebrate brain was likely devoid of cerebellar cell types and oligodendrocytes (myelinating cells); our data suggest that the latter emerged from astrocyte-like evolutionary precursors on the jawed vertebrate lineage. Our work illuminates the cellular and molecular architecture of the ancestral vertebrate brain and provides a foundation for exploring its diversification during evolution.

evolutionary biology↗

bioRxiv2022

Large-scale systematic feasibility study on the pan-cancer predictability of multi-omic biomarkers from whole slide images with deep learning

We assessed the pan-cancer predictability of multi-omic biomarkers from haematoxylin and eosin (H&E)-stained whole slide images (WSI) using deep learning (DL) throughout a systematic study. A total of 13,443 DL models predicting 4,481 multi-omic biomarkers across 32 cancer types were trained and validated. The investigated biomarkers included a broad range of genetic, transcriptomic, proteomic, and metabolic alterations, as well as established markers relevant for prognosis, molecular subtypes and clinical outcomes. Overall, we found that DL can predict multi-omic biomarkers directly from routine histology images across solid cancer types, with 50% of the models performing at an area under the curve (AUC) of more than 0.633 (with 25% of the models having an AUC larger than 0.711). A wide range of biomarkers were detectable from routine histology images across all investigated cancer types, with a mean AUC of at least 0.62 in almost all malignancies. Strikingly, we observed that biomarker predictability was mostly consistent and not dependent on sample size and class ratio, suggesting a degree of true predictability inherent in histomorphology. Together, the results of our study show the potential of DL to predict a multitude of biomarkers across the omics spectrum using only routine slides. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients.

cancer biology↗