Biology subjects

Robertson, S.

Publications and source records attributed to Robertson, S..

14 recordsLinked to original sources

bioRxiv2025

Acinetobacter enrichment shapes composition and function of the bacterial microbiota of field-grown tomato plants

Tomato is a staple crop and an excellent model to study host-microbiota interactions in the plant food chain. In this study we describe a lab-in-the-field approach to investigate the microbiota of field-grown tomato plants. High-throughput amplicon sequencing revealed a three-microhabitat partition, phyllosphere, rhizosphere and root interior, differentiating host-associated communities from the environmental microbiota. An individual bacterium, classified as Acinetobacter sp., emerged as a dominant member of the microbiota at the plant-soil continuum. To gain insights into the functional significance of this enrichment, we subjected rhizosphere specimens to shotgun metagenomics. Similar to the amplicon sequencing survey, a microhabitat effect defined by a set of rhizosphere-enriched functions was identified. Mobilisation of mineral nutrients, as well as adaptation to salinity and polymicrobial communities, including antimicrobial resistance genes (ARGs), emerged as a functional requirement sustaining metagenomic diversification. A metagenome-assembled genome (MAG) representative of Acinetobacter calcoaceticus was retrieved and metagenomic reads associated to this species identified a functional specialisation for plant-growth promotion traits (PGPTs), such as phosphate solubilization, siderophores production and reactive oxygen species detoxification, which were similarly represented in across tomato genotype-independent fashion. Our results revealed that the enrichment of a beneficial bacterium capable of alleviating plants abiotic stresses appears decoupled from ARGs facilitating microbiota persistence at the root-soil interface. IMPORTANCETomatoes are at centre-stage in global food security due to their high nutritional value, widespread cultivation, and versatility. Tomatoes provide essential vitamins and minerals, contribute to diverse diets, and support farmer livelihoods, making them a cornerstone of sustainable food systems. Beyond direct dietary benefits, the intricate relationship between tomatoes, their associated microbiota, and ARG is increasingly recognised. Tomato plants host diverse microbial communities in association with their organs, which influence plant health and productivity. Crop management impacts on composition and function of these communities, contributing to the prevalence of ARG in the soil and on the plants themselves. These genes can potentially transfer to human pathogens, posing a food safety and public health risk. Understanding these complex interactions is critical for developing sustainable agricultural practices capable of mitigating the impact of climatic modifications and the global threat of antimicrobial resistance.

plant biology↗

bioRxiv2025

Parallel adaptation to geothermally-warmed habitats due to common structural variation and functional developmental pathways

Climate change is causing rapid increases in temperature which drives genomic changes tied to adaptation. However, predicting the outcomes of climate change presents challenges as the anticipated conditions have yet to be experienced by natural populations. Modelling and lab experiments suggest that natural populations will experience shifts in life history, physiology, phenology, and ecology, but the underlying genomic mechanisms involved are unknown. However, some contemporary natural populations experience habitat warming through geothermal activity and can provide valuable insights into evolutionary responses. Geothermally warmed habitats should impose strong selection on ectotherms compared to ambient habitats as they increase metabolic demands, alter developmental processes, and offer novel ecological conditions. We leveraged Icelandic threespine sticklebacks (Gasterosteus aculeatus) from populations that have adaptively diverged along a geothermal/ambient habitat axis. We obtained 173,485 single nucleotide polymorphisms (SNPs) across four independent instances of population divergence using whole genome sequencing. While the majority of genomic differentiation between geothermal/ambient ecotypes was non-parallel, the MAPK signalling pathway appeared across all ecotype pairs. We also identified a putative inversion located on chromosome XXI which appears to drive parallel genomic differentiation between geothermal and ambient ecotypes. Candidate genes within the putative inversion correspond to metabolic adaptations, including regulation of appetite and fat content. Appetite level showed strong heritable divergence between ecotypes, while the rate of weight loss during starvation and fat levels differed between ecotypes. Overall, both polygenic adaptation and parallel structural variation appeared to be key genomic mechanisms for adaptation to geothermally warmed environments. While allelic divergence was largely unique across populations, it resulted in similar functional phenotypic outcomes. Thus, structural and allelic variation both operate to facilitate adaptation to warming environments. Therefore, while management from a genomic perspective will play a role in mitigating the effects of climate change, this study suggests that consideration of functional molecular pathways will be key to conservation but with precise changes being difficult to predict due to the highly polygenic nature of thermal adaptation.

evolutionary biology↗

bioRxiv2024

FLEXIBLE, PRODUCTION-SCALE, HUMAN WHOLE GENOME SEQUENCING ON A BENCHTOP SEQUENCER

Human whole-genome sequencing (hWGS) provides comprehensive genomic information that can potentially help guide research in disease prevention and treatment and ultimately improve human health. Recent advancements in sequencing technology have improved sequencing quality and further reduced sequencing costs on bench-top sized instruments, making whole-genome sequencing an accessible technology for broader use. Here, we demonstrate the feasibility of a large whole genome sequencing project using a benchtop sequencer in a small laboratory setting, on a scale previously reserved for production-scale factory-sized machines. In this project, 807 samples were prepared and sequenced across 313 flow cells, with high sequencing quality at a median %Q30 of 96.6% and a median %Q40 of 89.31%. To screen library quality and maximize sample yield, we utilized 48-plex sample pre-pool QC runs to provide >1x coverage per sample prior to sample pooling and full-depth sequencing. With this strategy, we consistently achieved >30x human whole genome sequencing of three-plex sample trios with standard settings or up to 4 samples per run with a high-throughput run setting. Additionally, this low-pass data provided valuable sample-level insights, allowing for detection of chromosome copy number variations (CNV) prior to full-depth sequencing. With three instruments running concurrently, >2,800 30x human whole genomes could be sequenced per year. To further demonstrate additional flexibility present in the platform, we also explored two different use cases 1) large insert sizes (1kb+) library to achieve superior genome coverage; 2) a proof of concept for rapid WGS sequencing to minimize sample to answer turnaround time for time-critical sequencing applications. Sequencing of a 2x100 >30x human WGS can be achieved in <12 hours and subsequent generation of fastq, bam and vcf in <1 additional hour. This study provides a cost-effective and flexible real-world demonstration of achieving both high quality hWGS sequencing and instrument flexibility without the need for complex batching schemes or factory-sized sequencers.

molecular biology↗

bioRxiv2024

A novel induced pluripotent stem cell model of schwann cell differentiation reveals NF2-related gene regulatory networks of the extracellular matrix

Schwann cells are vital to development and maintenance of the peripheral nervous system and their dysfunction has been implicated in a range of neurological and neoplastic disorders, including NF2-related schwannomatosis. We developed a novel human induced pluripotent stem cell (hiPSC) model to study Schwann cell differentiation in health and disease. We performed transcriptomic, immunofluorescence, and morphological analysis of hiPSC derived Schwann cell precursors (SPCs) and terminally differentiated Schwann cells (SCs) representing distinct stages of development. To validate our findings, we performed integrated, cross-species analyses across multiple external datasets at bulk and single cell resolution. Our hiPSC model of Schwann cell development shared overlapping gene expression signatures with human amniotic mesenchymal stem cell (hAMSCs) derived SCs and in vivo mouse models, but also revealed unique features that may reflect species-specific aspects of Schwann cell biology. Moreover, we identified gene co-expression modules that are dynamically regulated during hiPSC to SC differentiation associated with ear and neural development, cell fate determination, the NF2 gene, and extracellular matrix (ECM) organization. By cross-referencing results between multiple datasets, we identified new genes potentially associated with NF2 expression. Our hiPSC model further provides a tractable platform for studying Schwann cell development in the context of human disease.

bioinformatics↗

bioRxiv2024

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet {beta}-cells are the sole source of endogenous insulin and our understanding of {beta}-cell dysfunction and death in type 2 diabetes (T2D) is incomplete. Single-cell RNA-seq data supports heterogeneity as an important factor in {beta}-cell function and survival. However, it is difficult to identify which {beta}-cell phenotypes are critical for T2D etiology and progression. Our goal was to prioritize specific disease-related {beta}-cell subpopulations to better understand T2D pathogenesis and identify relevant genes for targeted therapeutics. To address this, we applied a deep transfer learning tool, DEGAS, which maps disease associations onto single-cell RNA-seq data from bulk expression data. Independent runs of DEGAS using T2D or obesity status identified distinct {beta}-cell subpopulations. A singular cluster of T2D-associated {beta}-cells was identified; however, {beta}-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic or T2D donors. The obesity-associated non-diabetic cells were enriched for translation and unfolded protein response genes compared to T2D cells. We selected CDKN1C and DLK1 for validation by immunostaining in human pancreas sections from healthy and T2D donors. Both CDKN1C and DLK1 were heterogeneously expressed among {beta}-cells. CDKN1C was increased in {beta}-cells from T2D donors, in agreement with the DEGAS predictions, while DLK1 appeared depleted from T2D islets of some donors. In conclusion, DEGAS has the potential to advance our holistic understanding of the {beta}-cell transcriptomic phenotypes, including features that distinguish {beta}-cells in obese non-diabetic or lean T2D states. Future work will expand this approach to additional human islet omics datasets to reveal the complex multicellular interactions driving T2D.

bioinformatics↗

bioRxiv2023

ScopeViewer: A Browser-Based Solution for Visualizing Spatial Transcriptomics Data

MotivationSpatial transcriptomics (ST) enables a high-resolution interrogation of molecular characteristics within specific spatial contexts and tissue morphology. Despite its potential, visualization of ST data is a challenging task due to the complexities in handling, sharing and visualizing large image datasets together with molecular information. ResultsWe introduce ScopeViewer, a browser-based software designed to overcome these challenges. ScopeViewer offers the following functionalities: (1) It visualizes large image data and associated annotations at various zoom levels, allowing for intricate exploration of the data; (2) It enables dual interactive viewing of the original images along with their annotations, providing a comprehensive understanding of the context; (3) It displays spatial molecular features with optimized bandwidth, ensuring a smooth user experience; and (4) It bolsters data security by circumventing data transfers. AvailabilityScopeViewer is available at: https://datacommons.swmed.edu/scopeviewer ContactXiaowei.Zhan@UTSouthwestern.edu, Guanghua.Xiao@UTSouthwestern.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2023

Quantifying the relationship between cell proliferation and morphology during development of the face

Morphogenesis requires highly coordinated, complex interactions between cellular processes: proliferation, migration, and apoptosis, along with physical tissue interactions. How these cellular and tissue dynamics drive morphogenesis remains elusive. Three dimensional (3D) microscopic imaging poses great promise, and generates elegant images. However, generating even moderate through-put quantified images is challenging for many reasons. As a result, the association between morphogenesis and cellular processes in 3D developing tissues has not been fully explored. To address this critical gap, we have developed an imaging and image analysis pipeline to enable 3D quantification of cellular dynamics along with 3D morphology for the same individual embryo. Specifically, we focus on how 3D distribution of proliferation relates to morphogenesis during mouse facial development. Our method involves imaging with light-sheet microscopy, automated segmentation of cells and tissues using machine learning-based tools, and quantification of external morphology via geometric morphometrics. Applying this framework, we show that changes in proliferation are tightly correlated to changes in morphology over the course of facial morphogenesis. These analyses illustrate the potential of this pipeline to investigate mechanistic relationships between cellular dynamics and morphogenesis during embryonic development.

developmental biology↗

bioRxiv2023

Ensembles for improved detection of invasive breast cancer in histological images

Accurate detection of invasive breast cancer (IC) can provide decision support to pathologists as well as improve downstream computational analyses, where detection of IC is a first step. Tissue containing IC is characterized by the presence of specific morphological features, which can be learned by convolutional neural networks (CNN). Here, we compare the use of a single CNN model versus an ensemble of several base models with the same CNN architecture, and we evaluate prediction performance as well as variability across ensemble based model predictions. Two in-house datasets comprising 587 WSI are used to train an ensemble of ten InceptionV3 models whose consensus is used to determine the presence of IC. A novel visualization strategy was developed to communicate ensemble agreement spatially. Performance was evaluated in an internal test set with 118 WSIs, and in an additional external dataset (TCGA breast cancer) with 157 WSI. We observed that the ensemble-based strategy outperformed the single CNN-model alternative with respect to accuracy on tile level in 89% of all WSIs in the test set. The overall accuracy was 0.92 (DICE coefficient, 0.90) for the ensemble model, and 0.85 (DICE coefficient, 0.83) for the single CNN alternative in the internal test set. For TCGA the ensemble outperformed the single CNN in 96.8% of the WSI, with an accuracy of 0.87 (DICE coefficient 0.89), the single model provides an accuracy of 0.75 (DICE coefficient 0.78) The results suggest that an ensemble-based modeling strategy for breast cancer invasive cancer detection consistently outperforms the conventional single model alternative. Furthermore, visualization of the ensemble agreement and confusion areas provide direct visual interpretation of the results. High performing cancer detection can provide decision support in the routine pathology setting as well as facilitate downstream computational analyses.

bioinformatics↗

bioRxiv2022

Identification of CD317-Positive Pro-inflammatory Immune Stromal Cells in Human Mesenchymal Stromal Cell Preparations

Mesenchymal stromal cell (MSC) heterogeneity clouds biological understanding and hampers their clinical development. In MSC cultures most commonly used in research and therapy, we have identified an MSC subtype characterised by CD317 expression (CD317pos (29.77{+/-}3.00% of the total MSC population), comprising CD317dim (28.10{+/-}4.60%) and CD317bright (1.67{+/-}0.58%) MSCs) and a constitutive interferon signature linked to human disease. We demonstrate that CD317pos MSCs induced cutaneous tissue damage when applied a skin explant model of inflammation, whereas CD317neg MSCs had no effect. Only CD317neg MSCs were able to suppress proliferative cycles of activated human T cells in vitro, whilst CD317pos MSCs increased polarisation towards pro-inflammatory Th1 cells and CD317neg cell lines did not. Using an in vivo peritonitis model, we found that CD317neg and CD317pos MSCs suppressed leukocyte recruitment but only CD317neg MSCs suppressed macrophage numbers. Using MSC-loaded scaffolds implanted subcutaneously in immunocompromised mice we were able to observe tissue generation and blood vessel formation with CD317neg MSC lines, but not CD317pos MSC lines. Our evidence is consistent with the identification of an immune stromal cell, which is likely to contribute to specific physiological and pathological functions and influence clinical outcome of therapeutic MSCs.

cell biology↗

bioRxiv2021

Genetically diverse mouse models of SARS-CoV-2 infection model clinical variation and cytokine responses in COVID-19

Inflammation in response to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection drives severity of coronavirus disease 2019 (COVID-19) and is influenced by host genetics. To understand mechanisms of inflammation, animal models that reflect genetic diversity and clinical outcomes observed in humans are needed. We report a mouse panel comprising the genetically diverse Collaborative Cross (CC) founder strains crossed to human ACE2 transgenic mice (K18-hACE2) that confers susceptibility to SARS-CoV-2. Infection of CC x K18- hACE2 resulted in a spectrum of survival, viral replication kinetics, and immune profiles. Importantly, in contrast to the K18-hACE2 model, early type I interferon (IFN-I) and regulated proinflammatory responses were required for control of SARS-CoV-2 replication in PWK x K18-hACE2 mice that were highly resistant to disease. Thus, virus dynamics and inflammation observed in COVID-19 can be modeled in diverse mouse strains that provide a genetically tractable platform for understanding anti-coronavirus immunity. One Sentence SummaryGenetically diverse mice model a spectrum of clinically relevant innate immune responses to SARS-CoV-2 infection.

microbiology↗

bioRxiv2021

A pigtailed macaque model for Kyasanur Forest disease virus and Alkhurma hemorrhagic disease virus

Kyasanur Forest disease virus (KFDV) and the closely related Alkhurma hemorrhagic disease virus (AHFV) are emerging flaviviruses that cause severe viral hemorrhagic fevers in humans. Increasing geographical expansion and case numbers, particularly of KFDV in southwest India, class these viruses as a public health threat. Viral pathogenesis is not well understood and additional vaccines and antivirals are needed to effectively counter the impact of these viruses. However, current animal models for KFDV do not accurately reproduce viral tissue tropism or clinical outcomes observed in humans. Here, we show pigtailed macaques (Macaca nemestrina) infected with KFDV or AHFV develop viremia that peaks 2 to 4 days following inoculation. Over the course of infection, animals developed lymphocytopenia, thrombocytopenia, and elevated liver enzymes. Infected animals exhibited hallmark signs of human disease characterized by a flushed appearance, piloerection, dehydration, loss of appetite, weakness, and hemorrhagic signs such as epistaxis. Virus was commonly present in the gastrointestinal tract, consistent with human disease caused by KFDV and AHFV where gastrointestinal symptoms (hemorrhage, vomiting, diarrhea) are common. This work characterizes a nonhuman primate model for KFDV and AHFV that closely resembles human disease for further utilization in understanding host immunity and development of antiviral countermeasures. Author SummaryKyasanur Forest disease virus (KFDV) and Alkhurma hemorrhagic disease virus (AHFV) are tick-borne flaviviruses that cause viral hemorrhagic fevers in India and the Arabian Peninsula, respectively. Bonnet macaques and black-faced langurs are susceptible to KFDV infection, but these animals do not experience hemorrhagic signs as seen in human cases with KFDV. This work characterizes for the first time experimental infection of KFDV and AHFV in pigtailed macaques (PTMs). Infected PTMs can develop moderate to severe disease that mirrors many aspects of human disease, including some hemorrhagic signs. Together these data describe the PTM model for KFDV and AHFV as a valuable tool for future work to study viral pathogenesis and for assessing the efficacy of vaccines and antivirals.

microbiology↗

bioRxiv2021

Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images using Convolutional Neural Networks

Background and ObjectiveA variety of genetic mutations are known to affect cell proliferation and apoptosis during organism development, leading to structural birth defects such as facial clefting. Yet, the mechanisms how these alterations influence the development of the face remain unclear. Cell proliferation and its relation to shape variation can be studied in high detail using Light-Sheet Microscopy (LSM) imaging across a range of developmental time points. However, the large number of LSM images captured at cellular resolution precludes manual analysis. Thus, the aim of this work was to develop and evaluate automatic methods to segment tissues and proliferating cells in these images in an accurate and efficient way. MethodsWe developed, trained, and evaluated convolutional neural networks (CNNs) for segmenting tissues, cells, and specifically proliferating cells in LSM datasets. We compared the automatically extracted tissue and cell annotations to corresponding manual segmentations for three specific applications: (i) tissue segmentation (neural ectoderm and mesenchyme) in nuclear-stained LSM images, (ii) cell segmentation in nuclear-stained LSM images, and (iii) segmentation of proliferating cells in Phospho-Histone H3 (PHH3)-stained LSM images. ResultsThe automatic CNN-based tissue segmentation method achieved a macro-average F-score of 0.84 compared to a macro-average F-score of 0.89 comparing corresponding manual segmentations from two observers. The automatic cell segmentation method in nuclear-stained LSM images achieved an F-score of 0.57, while comparing the manual segmentations resulted in an F-score of 0.39. Finally, the automatic segmentation method of proliferating cells in the PHH3-stained LSM datasets achieved an F-score of 0.56 for the automated method, while comparing the manual segmentations resulted in an F-score of 0.45. ConclusionsThe proposed automatic CNN-based framework for tissue and cell segmentation leads to results comparable to the inter-observer agreement, accelerating the LSM image analysis. The trained CNN models can also be applied for shape or morphological analysis of embryos, and more generally in other areas of cell biology.

bioinformatics↗

bioRxiv2021

All-or-none disconnection of pyramidal inputs onto parvalbumin-positive interneurons gates ocular dominance plasticity

Disinhibition is an obligatory initial step in the remodeling of cortical circuits by sensory experience. Our investigation on disinhibitory mechanisms in the classical model of ocular dominance plasticity uncovered an unexpected novel form of experience-dependent circuit plasticity. In layer 2/3 of mouse visual cortex monocular deprivation triggers a complete, "all-or-none", elimination of connections from pyramidal cells onto nearby parvalbumin-positive interneurons (PyrPV). This circuit plasticity is unique as it is transient, local and discrete. It lasts only one day, and it does not manifest as widespread changes in synaptic strength, rather, only about half of local connections are lost and the remaining ones are not affected in strength. Mechanistically, the deprivation-induced loss of PyrPV is contingent on a reduction of the protein neuropentraxin2 (NPTX2). Functionally, the loss of PyrPV is absolutely necessary for ODP. We surmise, therefore, that this "all-or-none" loss of local PyrPV circuitry gates experience-dependent cortical plasticity.

neuroscience↗

bioRxiv2019

TRIM5α restricts flavivirus replication by targeting the viral protease for proteasomal degradation.

Tripartite motif-containing protein 5 (TRIM5) functions as a cellular antiviral restriction factor with exquisite specificity towards the capsid lattices of retroviruses. The relative avidity of TRIM5 binding to retrovirus capsids directly impacts primate species susceptibility to infection, but the antiviral role of TRIM5 is thought limited to retroviruses. In contrast to this current understanding, here we show that both human and rhesus TRIM5 possess potent antiviral function against specific flaviviruses through interaction with the viral protease (NS2B/3) to inhibit virus replication. Importantly, TRIM5 was essential for the antiviral function of IFN-I against sensitive flaviviruses in human cells. However, TRIM5 was ineffective against mosquito-borne flaviviruses (yellow fever, dengue, and Zika viruses) that establish transmission cycles in humans following emergence from non-human primates. Thus, TRIM5 is revealed to possess remarkable plasticity in recognition of diverse virus families, with potential to influence human susceptibility to emerging flaviviruses of global concern.

microbiology↗