Biology subjects

Morton, J.

Publications and source records attributed to Morton, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Pharmacological arginine deprivation renders pancreatic cancer cells susceptible to senolysis and inhibition of the integrated stress response

Pancreatic ductal adenocarcinoma (PDAC) is almost inevitably fatal and largely resistant to current chemotherapy regimens. Senolysis, i. e. the selective elimination of senescent cells, and the targeting of cancer specific cellular metabolism are two novel treatment approaches that have proven to be effective for a variety of cancer types in pre-clinical studies. We now demonstrate that pharmacological depletion of arginine with pegylated recombinant human arginase 1 (PEG-rhARG1) induced senescence and activated the integrated stress response (IRS) in PDAC cells. Cells treated in such a manner were strikingly susceptible towards senolysis with ABT-263 (navitoclax) and also sensitive towards inhibition of the IRS. These results demonstrate a novel mechanism for an induced sensitivity of PDAC cells that could be exploited for cancer therapy.

cancer biology↗

bioRxiv2026

Nanoparticle encapsulation enhances spatial distribution of Panobinostat to treat metastatic medulloblastoma via the intrathecal route

Medulloblastoma (MB) is an aggressive central nervous system (CNS) malignancy that primarily affects children and frequently exhibits metastasis to the leptomeninges of the brain and spinal cord. We developed a {beta}-Cyclodextrin-poly({beta}-Amino Ester) nanoparticle system to deliver the histone deactylase inhibitor (HDACi) Panobinostat to MB by the intrathecal route. Various imaging methods were utilized to study nanoparticle and payload fate following infusion into the cerebrospinal fluid (CSF) of mice via cisterna magna or lumbar access points. Nanoparticles dramatically improved penetration of hydrophobic small molecules into distal regions of the spinal cord. Panobinostat-loaded nanoparticles were effective at treating patient-derived MB, activating pharmacodynamic targets, slowing growth of the primary tumor, decreasing incidence of metastasis at the time of death, and ultimately prolonging survival. These studies provide insight into the mechanisms mediating transport of colloids and therapeutic molecules in the subarachnoid space and highlight new approaches for treating metastatic disease in the CNS.

bioengineering↗

bioRxiv2026

Efficient and scalable Python implementation of ANCOM-BC for omics differential abundance testing

ANCOM-BC (Analysis of Compositions of Microbiomes with Bias Correction) is a critical algorithm for differential abundance analysis. Designed for handling compositional data, it features the ability to estimate the unknown sampling fractions and correct the bias induced by their differences. While the original R package is robust for standard dataset sizes, the increasing magnitude of modern multi-omics studies presents computational challenges, often resulting in runtime bottleneck for high-dimensional data. Here, we present a native Python implementation of ANCOM-BC in the scikit-bio package that significantly accelerates the workflow, enabling its application to large-scale datasets. By leveraging the vectorization with NumPy and numerical optimization in SciPy, we enhanced the efficiency without compromising the statistical accuracy of the method. The benchmarking results revealed a dramatic increase in performance in both simulated and real datasets. We demonstrated that our Python implementation generates test results that match the original R package, while achieving a 100-fold acceleration in runtime and an 87% reduction in memory consumption. This new implementation allows for the analysis of datasets that are significantly larger than previously possible, and enables applications to alternative omics data types and complex statistical models that vary greatly by the number of samples, features, and covariates.

bioinformatics↗

bioRxiv2025

Comparative Analysis of Primary and Liver Fibroblasts Reveals MET as a Potent Target in Pancreatic Cancer Metastasis

The tumor microenvironment drives many malignant features of pancreatic ductal adenocarcinoma (PDAC). The fibroblasts within pancreatic tumors promote tissue remodeling, immune suppression, and resistance to therapy. However, the interactions between stromal populations and pancreatic cancer cells are less understood in the liver, the most frequent site of PDAC metastasis. To address this, we employ single cell transcriptomics to compare primary pancreatic vs. liver PDAC lesions. Here, we identify the expression of hepatocyte growth factor (HGF) in fibroblasts and its receptor MET in cancer cells are both markedly increased in the PDAC liver niche. Using functional assays, we validate that mitogenic MET signaling is activated in PDAC cells by liver-derived fibroblasts. Importantly, the inhibition of MET signaling leads to reduced tumor growth in immune competent mouse models. Collectively, our data demonstrates that liver stromal-epithelial crosstalk networks engage in signaling pathways distinct from primary pancreatic tumors, highlighting opportunities to develop new treatments for metastatic disease.

cancer biology↗

bioRxiv2025

Variational Autoencoders for Metabolomics: Data Imputation, Deconfounding, and Correlation Discovery

This protocol describes a computational approach for constructing correlation-based molecular networks from untargeted metabolomics data using MetVAE, a variational autoencoder-based framework. Complementing spectral similarity networks, it captures functional relationships re-flected in cross-sample correlations. The workflow imports metabolomics features and sample metadata, adjusts for compositionality, missingness, confounding, and high-dimensionality, esti-mates sparse metabolite correlations, and exports GraphML files for network visualization. In a hepatocellular carcinoma mouse model, it links lipid classes in high-fat-diet animals, suggesting an endogenous "auto-brewery" route to lipotoxic metabolites.

bioinformatics↗

bioRxiv2024

Meta-analysis of the human gut microbiome uncovers shared and distinct microbial signatures between diseases

Microbiome studies have revealed gut microbiotas potential impact on complex diseases. However, many studies often focus on one disease per cohort. We developed a meta-analysis workflow for gut microbiome profiles and analyzed shotgun metagenomic data covering 11 diseases. Using interpretable machine learning and differential abundance analysis, our findings reinforce the generalization of binary classifiers for Crohns disease (CD) and colorectal cancer (CRC) to hold-out cohorts and highlight the key microbes driving these classifications. We identified high microbial similarity in disease pairs like CD vs ulcerative colitis (UC), CD vs CRC, Parkinsons disease vs type 2 diabetes (T2D), and schizophrenia vs T2D. We also found strong inverse correlations in Alzheimers disease vs CD and UC. These findings detected by our pipeline provide valuable insights into these diseases. IMPORTANCEAssessing disease similarity is an essential initial step preceding disease-based approach for drug repositioning. Our study provides a modest first step in underscoring the potential of integrating microbiome insights into the disease similarity assessment. Recent microbiome research has predominantly focused on analyzing individual disease to understand its unique characteristics, which by design excludes comorbidities individuals. We analyzed shotgun metagenomic data from existing studies and identified previously unknown similarities between diseases. Our research represents a pioneering effort that utilize both interpretable machine learning and differential abundance analysis to assess microbial similarity between diseases.

microbiology↗

bioRxiv2023

Tissue-resident NK cells support survival in pancreatic cancer through promotion of cDC1-CD8T activity

The immunosuppressive microenvironment in pancreatic ductal adenocarcinoma (PDAC) prevents tumor control and strategies to restore anti-cancer immunity (i.e. by increasing CD8 T cell activity) have had limited success. Here we demonstrate how inducing localized physical damage using ionizing radiation (IR) unmasks the benefit of immunotherapy by increasing tissue-resident NK (trNK) cells that support CD8 T activity. Our data confirms that targeting mouse orthotopic PDAC tumors with IR together with CCR5 inhibition and PD1 blockade reduces E-cadherin positive tumor cells by recruiting a hypoactive NKG2D-ve NK population, phenotypically reminiscent of trNK cells, that supports CD8 T cell involvement. We show an equivalent population in human PDAC cohorts that represents immunomodulatory trNK cells that could similarly support CD8 T cell levels in a cDC1-dependent manner. Importantly, a trNK signature associates with survival in PDAC and solid malignancies revealing a potential beneficial role for trNK in improving adaptive anti-tumor responses and supporting CCR5i/PD1 and IR-induced damage as a novel therapeutic approach.

cancer biology↗

bioRxiv2022

TM-Vec: template modeling vectors for fast homology detection and alignment

Exploiting sequence-structure-function relationships in molecular biology and computational modeling relies on detecting proteins with high sequence similarities. However, the most commonly used sequence alignment-based methods, such as BLAST, frequently fail on proteins with low sequence similarity to previously annotated proteins. We developed a deep learning method, TM-Vec, that uses sequence alignments to learn structural features that can then be used to search for structure-structure similarities in large sequence databases. We train TM-Vec to accurately predict TM-scores as a metric of structural similarity for pairs of structures directly from sequence pairs without the need for intermediate computation or solution of structures. For remote homologs (sequence similarity [≤] 10%) that are highly structurally similar (TM-score ? 0.6), we predict TM-scores within 0.026 of their value computed by TM-align. TM-Vec outperforms traditional sequence alignment methods and performs similar to structure-based alignment methods. TM-Vec was trained on the CATH and SwissModel structural databases and it has been tested on carefully curated structure-structure alignment databases that were designed specifically to test very remote homology detection methods. It scales sub-linearly for search against large protein databases and is well suited for discovering remotely homologous proteins.

bioinformatics↗

bioRxiv2021

Scalable estimation of microbial co-occurrence networks with Variational Autoencoders

Estimating microbe-microbe interactions is critical for understanding the ecological laws governing microbial communities. Rapidly decreasing sequencing costs have promised new opportunities to estimate microbe-microbe interactions across thousands of uncultured, unknown microbes. However, typical microbiome datasets are very high dimensional and accurate estimation of microbial correlations requires tens of thousands of samples, exceeding the computational capabilities of existing methodologies. Furthermore, the vast majority of microbiome studies collect compositional metagenomics data which enforces a negative bias when computing microbe-microbe correlations. The Multinomial Logistic Normal (MLN) distribution has been shown to be effective at inferring microbe-microbe correlations, however scalable Bayesian inference of these distributions has remained elusive. Here, we show that carefully constructed Variational Autoencoders (VAEs) augmented with the Isometric Log-ratio (ILR) transform can estimate low-rank MLN distributions thousands of times faster than existing methods. These VAEs can be trained on tens of thousands of samples, enabling co-occurrence inference across tens of thousands of microbes without regularization. The latent embedding distances computed from these VAEs are competitive with existing beta-diversity methods across a variety of mouse and human microbiome classification and regression tasks, with notable improvements on longitudinal studies.

bioinformatics↗

bioRxiv2021

Short term treatment with a cocktail of rapamycin, acarbose and phenylbutyrate slows aging in mice

Pharmaceutical intervention of aging requires targeting multiple pathways, thus there is rationale to test combinations of drugs each targeting different but overlapping processes. In order to determine if combining drugs previously shown to improve lifespan would have greater impact than any individuyal drug, a diet containing rapamycin at 14 ppm, acarbose at 1000 ppm, and phenylbutyrate at 1000 ppm was fed to 20-month-old C57BL/6 and HET3 4-way cross mice of both sexes for three months. Mice fed the cocktail diet showed a strain and gender-dependent phenotype consistent with healthy aging including decreased body fat and blood glucose, improved cognition, and increased grip strength and walking ability compared to mice fed individual drug or control diets. A cocktail diet containing [1/2] dosing of each compound was overall less effective than the full dose. The composite age-related lesion score of heart, lungs, liver and kidney was decreased in mice fed the cocktail diet compared to mice fed individual drug or control diets suggesting an interactive advantage of the three drugs. Senescence and inflammatory cytokine levels in kidneys from mice fed the cocktail diet were lower than in kidneys from mice fed control diet, and consistent with low expression levels in kidneys from young untreated mice, suggesting the cocktail diet delayed aging partly by senolytic and anti-inflammatory effects.

systems biology↗

bioRxiv2021

Hexavalent Sperm-Binding IgG Antibody Released from Self-Dissolving Vaginal Film Enables Potent, On-Demand Non-Hormonal Female Contraception

Non-hormonal products for on-demand contraception are a global health technology gap, motivating us to pursue the use of sperm-binding monoclonal antibodies as a strategy to enable safe, effective, desirable, on-demand contraception. Here, using cGMP-compliant Nicotiana-expression system, we produce an ultra-potent sperm-binding IgG antibody possessing 6 Fab arms per molecule that bind a well-established contraceptive antigen target, CD52g. We term this hexavalent antibody "Fab-IgG-Fab" (FIF) to reflect its molecular orientation. The Nicotiana-produced FIF exhibits at least 10-fold greater sperm agglutination potency and kinetics than the parent IgG, while preserving Fc-mediated trapping of individual spermatozoa in mucus. We formulate the Nicotiana-produced FIF into a polyvinyl alcohol-based water-soluble contraceptive film, and evaluate its potency in reducing progressively motile sperm in the sheep vagina. Two minutes after vaginal instillation of human semen, no progressively motile sperm are recovered from the vaginas of sheep receiving FIF-Film. In contrast, high numbers of progressively motile sperm are recovered from sheep receiving a placebo film control. Our work supports the potential of highly multivalent contraceptive antibodies to provide safe, effective, on-demand non-hormonal contraception.

bioengineering↗

bioRxiv2020

Protein Structural Alignments From Sequence

Computing sequence similarity is a fundamental task in biology, with alignment forming the basis for the annotation of genes and genomes and providing the core data structures for evolutionary analysis. Standard approaches are a mainstay of modern molecular biology and rely on variations of edit distance to obtain explicit alignments between pairs of biological sequences. However, sequence alignment algorithms struggle with remote homology tasks and cannot identify similarities between many pairs of proteins with similar structures and likely homology. Recent work suggests that using machine learning language models can improve remote homology detection. To this end, we introduce DeepBLAST, that obtains explicit alignments from residue embeddings learned from a protein language model integrated into an end-to-end differentiable alignment framework. This approach can be accelerated on the GPU architectures and outperforms conventional sequence alignment techniques in terms of both speed and accuracy when identifying structurally similar proteins.

bioinformatics↗

bioRxiv2020

EMPress enables tree-guided, interactive, and exploratory analyses of multi-omic datasets

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive tool for visualizing trees in the context of microbiome, metabolome, etc. community data scalable beyond modern large datasets like the Earth Microbiome Project. EMPress provides novel functionality--including ordination integration and animations--alongside many standard tree visualization features, and thus simplifies exploratory analyses of many forms of omic data.

bioinformatics↗

bioRxiv2020

BRD4-mediated repression of p53 is a target for combination therapy in AML

Acute Myeloid Leukemia (AML) is a typically-lethal molecularly heterogeneous disease, with few broad-spectrum therapeutic targets. Unusually, most AML retain wild-type TP53, encoding the pro-apoptotic tumor suppressor p53. MDM2 inhibitors (MDM2i), which activate wild-type p53, and BET inhibitors (BETi), targeting the BET-family co-activator BRD4, both show encouraging pre-clinical activity, but limited clinical activity as single agents. Here, we report synergistic toxicity of combined MDM2i and BETi towards AML cell lines, primary human blasts and mouse models, resulting from BETis ability to evict an unexpected repressive form of BRD4 from p53 target genes, and hence potentiate MDM2i-induced p53 activation. These results indicate that wild-type TP53 and a transcriptional repressor function of BRD4 together represent a potential broad-spectrum synthetic therapeutic vulnerability for AML.

cell biology↗

bioRxiv2020

The primary donor of far-red Photosystem II: ChlD1 or PD2?

Far-red light (FRL) Photosystem II (PSII) isolated from Chroococcidiopsis thermalis is studied using parallel analyses of low-temperature absorption, circular dichroism (CD) and magnetic circular dichroism (MCD) spectroscopies in conjunction with fluorescence measurements. This extends earlier studies (Nurnberg et al 2018 Science 360 (2018) 1210-1213). We confirm that the chlorophyll absorbing at 726 nm is the primary electron donor. At 1.8 K efficient photochemistry occurs when exciting at 726 nm and shorter wavelengths; but not at wavelengths longer than 726 nm. The 726 nm absorption peak exhibits a 21 {+/-} 4 cm-1 electrochromic shift due to formation of the semiquinone anion, QA*-. Modelling indicates that no other FRL pigment is located among the 6 central reaction center chlorins: PD1, PD2 ChlD1, ChlD2, PheoD1 and PheoD2. Two of these chlorins, ChlD1 and PD2, are located at a distance and orientation relative to QA*- so as to account for the observed electrochromic shift. Previously, ChlD1 was taken as the most likely candidate for the primary donor based on spectroscopy, sequence analysis and mechanistic arguments. Here, a more detailed comparison of the spectroscopic data with exciton modelling of the electrochromic pattern indicates that PD2 is at least as likely as ChlD1 to be responsible for the 726 nm absorption. The correspondence in sign and magnitude of the CD observed at 726 nm with that predicted from modelling favors PD2 as the primary donor. The pros and cons of PD2 vs ChlD1 as the location of the FRL-primary donor are discussed. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/021097v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@159ea46org.highwire.dtl.DTLVardef@23c04forg.highwire.dtl.DTLVardef@126c235org.highwire.dtl.DTLVardef@1982d58_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPrimary Donor confirmed at 726 nm C_LIO_LIDetermination of far-red chl pigment Qy excitation positions, widths, CD and MCD amplitudes C_LIO_LIQuantification of electrochromic shifts and QA*- photoconversion yield C_LIO_LIElectrochromic shift consistent with primary donor at either ChlD1 or PD2 C_LIO_LIThe CD amplitude favors the primary donor at PD2 C_LI

biophysics↗

bioRxiv2020

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS.

Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a "balance score" that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.

bioinformatics↗

bioRxiv2019

Revisiting microbe-metabolite interactions: doing better than random

I.Recently, Quinn and Erb et al [1] made the case that when used correctly, correlation and proportionality can outperform MMvec when identifying microbe-metabolite interactions. We revisit this comparison and show that the proposed correlation and proportionality are outperformed by MMvec on real data due to their inability to deal with sparsity commonly observed in microbiome and metabolome datasets.

bioinformatics↗

bioRxiv2019

Red Sea SAR11 and Prochlorococcus Single-cell Genomes Reflect Globally Distributed Pangenomes

Evidence suggests many marine bacteria are cosmopolitan, with widespread but sparse strains poised to seed abundant populations upon conducive growth conditions. However, studies supporting this "microbial seed bank" hypothesis have analyzed taxonomic marker genes rather than whole genomes/metagenomes, leaving open the possibility that disparate ocean regions harbor endemic gene content. The Red Sea is isolated geographically from the rest of the ocean and has a combination of high irradiance, high temperature, and high salinity that is unique among the ocean; we therefore asked whether it harbors endemic gene content. We sequenced and assembled single-cell genomes of 21 SAR11 (subclades Ia, Ib, Id, II) and 5 Prochlorococcus (ecotype HLII) cells from the Red Sea and combined them with globally-sourced reference genomes to cluster genes into ortholog groups (OGs). Ordination of OG composition could distinguish clades, including phylogenetically cryptic Prochlorococcus ecotypes LLII and LLIII. Compared with reference genomes, 1% of Prochlorococcus and 17% of SAR11 OGs were unique to the Red Sea genomes (RS-OGs). Most (83%) RS-OGs had no annotated function, but 65% of RS-OGs were expressed in diel Red Sea metatranscriptomes, suggesting they could be functional. Searching Tara Oceans metagenomes, RS-OGs were as likely to be found as non-RS-OGs; nevertheless, Red Sea and other warm samples could be distinguished from cooler samples using the relative abundances of OGs. The results suggest that the prevalence of OGs in these surface ocean bacteria is largely cosmopolitan, with differences in population metagenomes manifested by differences in relative abundance rather than complete presence-absence of OGs.

microbiology↗