Biology subjects

Middleton, M.

Publications and source records attributed to Middleton, M..

7 recordsLinked to original sources

bioRxiv2023

Single-cell immune multi-omics and repertoire analyses in pancreatic ductal adenocarcinoma reveal differential immunosuppressive mechanisms within different tumour microenvironments

Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. Understanding the multiple mechanisms by which the tumour evades immune control, and how these mechanisms may be disrupted is critical to developing targeted immunotherapies. Previous studies have shown that higher lymphocyte infiltration is associated with better survival, and here we investigated what mediates these differences. We performed a comprehensive analysis of PDAC-associated immune cells using single cell multi-omics coupled with re-analysis of public PDAC scRNA-seq datasets. We introduce novel single-cell and repertoire analyses that have uncoupled diverse roles and contributions of various immune cell populations within different tumour microenvironments (TMEs). They revealed clear distinctions in the clonal characteristics among different patient groups, provided valuable insights into the mechanisms of immune cell migration and tissue adaptation underlying these disparities. These results point to differential CD4 polarisation of intra-tumoural T cells, differential B cell differentiation, GC reactions, antigen presentation pathways, and distinct cell-cell communication between the myeloid-enriched and adaptive-enriched groups. Overall, we identified two major distinct themes for future immune intervention within PDAC patients between those with higher adaptive versus myeloid immune cell infiltration.

immunology↗

bioRxiv2023

Triploid Pacific oysters exhibit stress response dysregulation and elevated mortality following marine heatwaves

Polyploidy has been shown to negatively impact environmental stress tolerance, resulting in increased susceptibility to extreme climate events such as marine heatwaves (MHWs). In this study, we used the response of the Pacific oyster Crassostrea gigas to MHWs as a model system to identify key ploidy-specific differences in the physiological and transcriptomic response of oysters to environmental stress. In this study, adult diploid (2n) and triploid (3n) oysters were exposed to elevated seawater temperature (single stressor; 30{degrees}C), elevated temperature followed by acute desiccation stress (multiple stressor; 30{degrees}C + emersion at an aerial temperature of 44{degrees}C for 4h), or a control (17{degrees}C) within a hatchery setting. Oyster mortality rate was elevated within stress treatments with respect to the control and was significantly higher in triploids than diploids following multiple stress exposure (36.4% vs. 14.8%). Triploids within the multiple stressor treatment exhibited signs of energetic limitation, including metabolic depression, a significant reduction in ctenidium Na+/K+ ATPase activity, and the dysregulated expression of key genes associated with heat tolerance, the inhibition of apoptosis, and mitochondrial function. Functional enrichment analysis of ploidy-specific gene sets identified that biological processes associated with metabolism, stress tolerance, and immune function were overrepresented within triploids across stress treatments. Our results demonstrate that triploidy impacts the transcriptional regulation of key metabolic processes that underly the environmental stress response of Pacific oysters, resulting in downstream shifts in physiological tolerance limits that may be detrimental to survival. The impact of chromosome set manipulation on the climate resilience of marine populations has important implications for the adaptability of marine populations and domestic food security within future climate scenarios, especially as triploidy induction becomes an increasingly popular tool to elicit reproductive control across a wide range of marine organisms used within marine aquaculture.

genomics↗

bioRxiv2021

Predictive Modeling of Pseudomonas syringae Virulence on Bean using Gradient Boosted Decision Trees

Pseudomonas syringae is a genetically diverse bacterial species complex responsible for numerous agronomically important crop diseases. Individual P. syringae isolates are typically given pathovar designations based on their host of isolation and the associated disease symptoms, and these pathovar designations are often assumed to reflect host specificity although this assumption has rarely been rigorously tested. Here we developed a rapid seed infection assay to measure the virulence of 121 diverse P. syringae isolates on common bean (Phaseolus vulgaris). This collection includes P. syringae phylogroup 2 (PG2) bean isolates (pathovar syringae) that cause bacterial spot disease and P. syringae phylogroup 3 (PG3) bean isolates (pathovar phaseolicola) that cause the more serious halo blight disease. We found that bean isolates in general were significantly more virulent on bean than non-bean isolates and observed no significant virulence difference between the PG2 and PG3 bean isolates. However, when we compared virulence within PGs we found that PG3 bean isolates were significantly more virulent than PG3 non-bean isolates, while there was no significant difference in virulence between PG2 bean and non-bean isolates. These results indicate that PG3 strains have a higher level of host specificity than PG2 strains. We then employed machine learning to investigate if we could use genomic data to predict virulence on bean. We used gradient boosted decision trees to model the virulence using whole genome kmers, type III secreted effector kmers, and the presence/absence of type III effectors and phytotoxins. Our model performed best using whole genome data and was able to predict virulence with high accuracy (mean absolute error = 0.05). Finally, we functionally validated the model by predicting virulence for 16 strains and found that 15 (94%) had virulence levels within the bounds of estimated predictions. This study demonstrates the power of machine learning for predicting host specific adaptation and strengthens the hypothesis that P. syringae PG2 strains have evolved a different lifestyle than other P. syringae strains. AUTHOR SUMMARYPseudomonas syringae is a genetically diverse Gammaproteobacterial species complex responsible for numerous agronomically important crop diseases. Strains in the P. syringae species complex are frequently categorized into pathovars depending on pathogenic characteristics such as host of isolation and disease symptoms. Common bean pathogens from P. syringae are known to cause two major diseases: the halo blight disease, which is characterized by large necrotic lesions surrounded by a chlorotic zone or halo of yellow tissue; and the bacterial spot disease, which is characterized by brown leaf spots. While halo blight can cause serious crop losses, bacterial spot disease is generally of minor agronomic concern. The application of statistical genetic and machine learning approaches to genomic data has greatly increased our power to identify genes underlying traits of interest, such as host specificity. Machine learning models can be used to predict outcomes from new samples or to identify the genetic feature(s) that carry the most importance when predicting a particular phenotype. Here, we implemented a rapid method for screening a proxy of virulence for P. syringae isolates on common bean, and used this screen to assess virulence of P. syringae strains on bean. We found that halo blight pathogens display a stronger degree of host specificity compared to brown spot pathogens, and that genomic kmers and virulence factors can be used to predict the virulence of P. syringae isolates on bean using machine learning models.

genomics↗

bioRxiv2021

ParticleChromo3D: A Particle Swarm Optimization Algorithm for Chromosome and Genome 3D Structure Prediction from Hi-C Data

The three-dimensional (3D) structure of chromatin has a massive effect on its function. Because of this, it is desirable to have an understanding of the 3D structural organization of chromatin. To gain greater insight into the spatial organization of chromosomes and genomes and the functions they perform, chromosome conformation capture techniques, particularly Hi-C, have been developed. The Hi-C technology is widely used and well-known because of its ability to profile interactions for all read pairs in an entire genome. The advent of Hi-C has greatly expanded our understanding of the 3D genome, genome folding, gene regulation and has enabled the development of many 3D chromosome structure reconstruction methods. Here, we propose a novel approach for 3D chromosome and genome structure reconstruction from Hi-C data using Particle Swarm Optimization approach called ParticleChromo3D. This algorithm begins with a grouping of candidate solution locations for each chromosome bin, according to the particle swarm algorithm, and then iterates its position towards a global best candidate solution. While moving towards the optimal global solution, each candidate solution or particle uses its own local best information and a randomizer to choose its path. Using several metrics to validate our results, we show that ParticleChromo3D produces a robust and rigorous representation of the 3D structure for input Hi-C data. We evaluated our algorithm on simulated and real Hi-C data in this work. Our results show that ParticleChromo3D is more accurate than most of the existing algorithms for 3D structure reconstruction. Our results also show that constructed ParticleChromo3D structures are very consistent, hence indicating that it will always arrive at the global solution at every iteration. The source code for ParticleChromo3D, the simulated and real Hi-C datasets, and the models generated for these datasets are available here: https://github.com/OluwadareLab/ParticleChromo3D

bioinformatics↗

bioRxiv2020

Cell state diversity promotes metastasis through heterotypic cluster formation in melanoma

In melanoma, transcriptional profiling has revealed multiple co-existing cell states, including proliferative versus invasive sub-populations that have been posited to represent a "go or grow" tradeoff. Both of these populations are maintained in tumors, but how they physically interact to promote metastasis is unknown. We demonstrate that these subpopulations form spatially structured heterotypic clusters that cooperate in the seeding of metastasis. We unexpectedly found that INV cells were tightly adherent to each other, and formed clusters with a rim of PRO cells. Intravital imaging demonstrated cooperation between these populations, in which the INV cells facilitated the spread of less metastatic PRO cells. We identified the TFAP2 neural crest transcription factor as a master regulator of both clustering and the PRO/INV states. Our data suggest a framework for the co-existence of these two divergent cell populations, in which differing cell states form heterotypic clusters that promote metastasis via cell-cell cooperation.

cancer biology↗

bioRxiv2018

Single cell RNA-seq reveals profound transcriptional similarity between Barretts esophagus and esophageal glands

Barretts esophagus is a precursor of esophageal adenocarcinoma. In this common condition, squamous epithelium in the esophagus is replaced by columnar epithelium in response to acid reflux. Barretts esophagus is highly heterogeneous and its relationships to normal tissues are unclear. We investigated the cellular complexity of Barretts esophagus and the upper gastrointestinal tract using RNA-sequencing of 2895 single cells from multiple biopsies from four patients with Barretts esophagus and two patients without esophageal pathology. We found that uncharacterised cell populations in Barretts esophagus, marked by LEFTY1 and OLFM4, exhibit a profound transcriptional overlap with a subset of esophageal cells, but not with gastric or duodenal cells. Additionally, SPINK4 and ITLN1 mark cells that precede morphologically identifiable goblet cells in colon and Barretts esophagus, potentially aiding the identification of metaplasia. Our findings reveal striking transcriptional relationships between normal tissue populations and cells in a premalignant condition, with implications for clinical practice.

genetics↗

bioRxiv2017

Immuno-phenotypes of Pancreatic Ductal Adenocarcinoma: Metaanalysis of transcriptional subtypes

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas and has one of the highest mortality rates of any cancer type with a 5-year survival rate of < 5% and median overall survival of typically six months from diagnosis. Recent transcriptional studies of PDAC have provided several competing stratifications of the disease. However, the development of therapeutic strategies will depend on a unique and coherent classification of PDAC. Here, we use an integrative meta-analysis of four different PDAC gene expression studies to derive the consensus PDAC classification. Despite the fact that immunotherapies have yet to have an impact in treatment of PDAC, the gene expression signatures that stratify PDAC across studies are immunologic. We define these as \"adaptive\", \"innate\" and \"immune-exclusion\" immunologic signatures, which are prognostic across independent cohorts. An appreciation of the immune composition of PDAC with prognostic significance is an opportunity to understand distinct immune escape mechanisms in development of the disease and design novel immune-oncology therapeutic strategies to overcome current barriers.

cancer biology↗