Biology subjects

Dillon, M.

Publications and source records attributed to Dillon, M..

11 recordsLinked to original sources

bioRxiv2026

Mannosylated nanoparticle immunogens enhance the circumsporozoite protein-specific B cell response and improve protection against sporozoite challenge

Underprocessed oligomannose glycans on protein nanoparticle immunogens engage the innate immune system through mannose-binding lectin and complement, enhancing immunogen trafficking and B cell responses. However, the extent to which oligomannose glycans directly improve protective immunity has remained unclear. Here we generate a series of CSP-bearing I53-50 nanoparticle malaria vaccine candidates with defined numbers and types of engineered N-linked glycans and systematically evaluate their immunogenicity and protective efficacy. Oligomannose display enhanced early plasmablast and germinal center B cell responses, leading to increased CSP-specific memory B cells, long-lived plasma cells, and durable serum antibody titers. Furthermore, nanoparticles bearing oligomannose glycans conferred the strongest protection against sporozoite challenge. By comparing immunogens with defined glycoforms, we attribute improved immune responses and protection specifically to oligomannose rather than complex or truncated glycans. These results will help guide the development of general strategies for glycan engineering aimed at enhancing the protective efficacy of nanoparticle vaccines.

immunology↗

bioRxiv2025

CDR Conformation Aware Antibody Sequence Design with ConformAb

Antibody lead optimization methods seek to enhance a lead candidates therapeutic properties through targeted sequence mutation. However, the mutations introduced during this process can inadvertently induce structural changes that disrupt binding, particularly by altering CDR loop conformations which destabilize CDR-target binding interactions. To address this, we present ConformAb, a guided discrete diffusion model for designing antibody sequences that explicitly conform to the CDR canonical conformation of the lead. With seed sequences as the starting point, we show that our method is capable of generating 3- 5x better binders that conform to the same CDR backbone structure in a one-shot design setup, and in some cases, binders with better affinity than seed repertoire picks without any target-specific data included in training. Across the targets tested, ConformAb demonstrated binding rates ranging from 15 -60% in wet lab experiments, a result obtained using fewer than 100 designs for each target. ConformAb offers a unique one-shot approach for antibody lead optimization in data-scarce scenarios where experimental/repertoire data cannot be leveraged for model training.

biophysics↗

bioRxiv2025

HRDPath: An Explainable Multi-Model Deep Learning Architecture for Predicting Homologous Recombination Deficiency from Histopathology Images

Homologous recombination deficiency (HRD) is a critical biomarker for guiding treatment decisions in high-grade serous tubo-ovarian carcinoma (HGSOC), a cancer with few reliable biomarkers. However, existing genomic-based tests for HRD are variable, expensive, and time-consuming. To this end, we developed HRDPath, a novel patient-level deep learning architecture that combines the strengths of two complementary models with a multi-task design, to predict genomically derived HRD status from whole slide images in HGSOC. HRDPath was comprehensively validated across three datasets and benchmarked against leading deep learning models. It achieved an AUC of 0.846, surpassing previously reported H&E-based HRD prediction results for HGSOC images by 0.09, and for the first time, reporting a specificity of 0.938, where accuracy significantly increased when multiple slides per patient were used. Our proposed patient-level approach and interpretability pipeline enhance model trustworthiness and reveal important clinical and biological insights into HRD-positive cancers, highlighting the associated morphological and pathological changes at the cellular and tissue levels. HRDPath is a potentially accessible and scalable digital biomarker that could improve ovarian cancer diagnosis and therapy selection.

bioinformatics↗

bioRxiv2025

Protective immunity against malaria by a nanoparticle CIS43-based junctional vaccine alone or in combination with R21

Repetitive display of the major repeats of the Plasmodium falciparum circumsporozoite protein (PfCSP) is the basis for two WHO-recommended vaccines: RTS,S/AS01 and R21/Matrix-M. Recently, however, the CIS43 monoclonal antibody that preferentially targets the junctional region of PfCSP has been shown to be highly protective in humans, highlighting its junctional epitope as a key vaccine target. Here, we develop a vaccine based on the tandem repeats of the junctional epitope displayed on a self-assembling nanoparticle, and compare this CIS43-based junctional vaccine alone or in combination with the benchmark R21 vaccine, using both B cell analysis and monoclonal antibody isolation to define targeting of the immune response. Comparable reduction in liver burden was observed following vaccination with junctional and R21 vaccines at a dose of 1 g. At a dose of 0.25 g, a modest reduction of malaria-liver burden with the junctional vaccine was observed compared to R21. Further, combining junctional and R21 vaccines induced modestly enhanced protection compared to either vaccine alone. While the R21 vaccine elicited antibodies primarily against the major repeats, the junctional vaccine elicited antibodies against both junctional and major repeat regions. In vivo-B cell analysis and isolation of monoclonal antibodies confirmed differences in vaccine-induced antibody specificities. Altogether, these data suggest the nanoparticle-formatted tandem-repeated CIS43-junctional vaccine to be a promising approach to broaden immunity against malaria, either as a standalone intervention or in combination with R21. HIGHLIGHTSO_LIDeveloped a self-assembling nanoparticle-displayed junctional vaccine of PfCSP based on tandem repeats of the epitope preferentially targeted by the highly protective CIS43 antibody C_LIO_LIThe CIS43-based junctional vaccine at low doses significantly reduced liver burden following malaria challenge in mice C_LIO_LIFollowing either low or high doses of the junctional vaccine in naive mice, adoptively transferred B cells expressing the CIS43 inferred germline sequence yielded a high frequency of germinal center and ASC responses C_LIO_LIThe CIS43-based junctional vaccine elicits antibodies against junctional and major repeat regions whereas the R21 vaccine elicits responses primarily against the major repeat region C_LIO_LIAt low dose, the CIS43-based junctional vaccine given together with the R21 vaccine showed modestly improved control of liver burden compared to either vaccine alone C_LI

immunology↗

bioRxiv2022

In Silico Improvement of Highly Protective Anti-Malarial Antibodies

Antibody CIS43 binds Plasmodium falciparum circumsporozoite protein (PfCSP) and protects against malaria, as recently demonstrated clinically. To improve the efficacy of CIS43, we developed an in silico pipeline to optimize the interaction energy of CIS43 to its junctional epitope (peptide 21: PfCSP residues 101-115). Starting from two improved CIS43 variants, recently elicited from a CIS43-germline knock-in mice, single and double amino acid substitutions in the peptide 21-proximal heavy (VH) and light (VL) variable regions were introduced. CIS43-variants, selected on the basis of improved in silico interface and stability energies, showed increased affinity to peptide 21 and superior malaria-protective efficacy. The best designed variant, antibody P3-43, was significantly more protective than its template antibody m43.151, with greater liver-burden protection than the current best-in-class (antibody iGL-CIS43.D3). Crystal structures of improved antibodies revealed atomic-level interactions explaining gains in binding affinity. The reported pipeline provides a powerful in silico approach to improve antibody functionality.

immunology↗

bioRxiv2021

The light chain of the L9 antibody is critical for binding circumsporozoite protein minor repeats and preventing malaria

L9 is a potent human monoclonal antibody (mAb) that preferentially binds two adjacent NVDP minor repeats and cross-reacts with NANP major repeats of the Plasmodium falciparum circumsporozoite protein (PfCSP) on malaria-infective sporozoites. Understanding this mAbs ontogeny and mechanisms of binding PfCSP to neutralize sporozoites will facilitate vaccine development. Here, we isolated mAbs clonally related to L9 and showed that this B-cell lineage has baseline NVDP affinity and evolves to acquire NANP reactivity. Pairing the L9 kappa light chain (L9{kappa}) with clonally-related heavy chains resulted in chimeric mAbs that cross-linked two NVDP, cross-reacted with NANP, and more potently neutralized sporozoites compared to their original light chain. Structural analyses revealed that chimeric mAbs bound the minor repeat motif in a type-1 {beta}-turn seen in other repeat-specific antibodies. These data highlight the importance of L9{kappa} in binding NVDP on PfCSP to neutralize SPZ and suggest that PfCSP-based immunogens might be improved by presenting [≥]2 NVDP.

immunology↗

bioRxiv2021

Expression of soluble methane monooxygenase in Escherichia coli enables methane conversion

Natural gas and biogas provide an opportunity to harness methane as an industrial feedstock. Bioconversion is a promising alternative to chemical catalysis, which requires extreme operating conditions and exhibits poor specificities. Though methanotrophs natively utilize methane, efforts have been focused on engineering platform organisms like Escherichia coli for synthetic methanotrophy. Here, a synthetic E. coli methanotroph was developed by engineering functional expression of the Methylococcus capsulatus soluble methane monooxygenase in vivo via expression of its cognate GroESL chaperone. Additional overexpression of E. coli GroESL further improved activity. Incorporation of an acetone formation pathway then enabled the conversion of methane to acetone in vivo, as validated via 13C tracing. This work provides the first reported demonstration of methane bioconversion to liquid chemicals in a synthetic methanotroph.

bioengineering↗

bioRxiv2019

Energetic and Environmental Constraints on the Community Structure of Benthic Microbial Mats in Lake Fryxell, Antarctica

Ecological communities are commonly thought to be controlled by the dynamics of energy flow through environments. Two of the most important energetic constraints on all communities are photosynthetically active radiation (PAR) and oxygen concentration ([O2]). Microbial mats growing on the bottom of Lake Fryxell, Antarctica, span environmental gradients in PAR and [O2], which we used to test the extent to which each controls community structure. Metagenomic analyses showed variation in the diversity and relative abundances of Archaea, Bacteria, and Eukaryotes across three [O2] and PAR conditions. Where [O2] saturated the mats or was absent from the overlying water, PAR structured the community. Where [O2] varied within mats, microbial communities changed across covarying PAR and [O2] gradients. Diversity negatively correlated with [O2] and PAR through mat layers in each habitat suggesting that, on the millimeter-scale, communities are structured by the optimization of energy use. In contrast, [O2] positively correlated with diversity and affected the distribution of dominant populations across the three habitats, suggesting that meter-scale diversity is structured by energy availability. The benthic microbial communities in Lake Fryxell are thus structured by energy flow in a scale-dependent manner.

microbiology↗

bioRxiv2018

q2-sample-classifier: machine-learning tools for microbiome classification and regression

Microbiome studies often aim to predict outcomes or differentiate samples based on their microbial compositions, tasks that can be efficiently performed by supervised learning methods. Here we present a benchmark comparison of supervised learning classifiers and regressors implemented in scikit-learn, a Python-based machine-learning library. We additionally present q2-sample-classifier, a plugin for the QIIME 2 microbiome bioinformatics framework, that facilitates application of the scikit-learn classifiers to microbiome data. Random forest, extra trees, and gradient boosting models demonstrate the highest performance for both supervised classification and regression of microbiome data. Automated feature selection and hyperparameter tuning enhance performance of most methods but may not be necessary under all circumstances. The q2-sample-classifier plugin makes these methods more accessible and interpretable to a broad audience of microbiologists, clinicians, and others who wish to utilize supervised learning methods for predicting sample characteristics based on microbiome composition. The q2-sample-classifier source code is available at https://github.com/qiime2/q2-sample-classifier. It is released under a BSD-3-Clause license, and is freely available including for commercial use.

bioinformatics↗

bioRxiv2017

q2-longitudinal: a QIIME 2 plugin for longitudinal and paired-sample analyses of microbiome data

Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre/post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform (https://qiime2.org). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including paired differences and distances, linear mixed effects models, microbial interdependence test, first differencing, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open source software released under a BSD-3-Clause license and is freely available, including for commercial use.

bioinformatics↗

bioRxiv2016

The genome-wide transcriptional response to varying RpoS levels in Escherichia coli K-12

The alternative sigma factor RpoS is a central regulator of a many stress responses in Escherichia coli. The level of functional RpoS differs depending on the stress. The effect of these differing concentrations of RpoS on global transcriptional responses remains unclear. We investigated the effect of RpoS concentration on the transcriptome during stationary phase in rich media. We show that 23% of genes in the E. coli genome are regulated by RpoS level, and we identify many RpoS-transcribed genes and promoters. We observe three distinct classes of response to RpoS by genes in the regulon: genes whose expression changes linearly with increasing RpoS level, genes whose expression changes dramatically with the production of only a little RpoS (\"sensitive\" genes), and genes whose expression changes very little with the production of a little RpoS (\"insensitive\"). We show that sequences outside the core promoter region determine whether a RpoS-regulated gene in sensitive or insensitive. Moreover, we show that sensitive and insensitive genes are enriched for specific functional classes, and that the sensitivity of a gene to RpoS corresponds to the timing of induction as cells enter stationary phase. Thus, promoter sensitivity to RpoS is a mechanism to coordinate specific cellular processes with growth phase, and may also contribute to the diversity of stress responses directed by RpoS.\n\nImportanceThe sigma factor RpoS is a global regulator that controls the response to many stresses in Escherichia coli. Different stresses result in different levels of RpoS production, but the consequences of this variation are unknown. We describe how changing the level of RpoS does not influence all RpoS-regulated genes equally. The cause of this variation is likely the action of transcription factors that bind the promoters of the genes. We show that the sensitivity of a gene to RpoS levels explains the timing of expression as cells enter stationary phase, and that genes with different RpoS sensitivities are enriched for specific functional groups. Thus, promoter sensitivity to RpoS is a mechanism to coordinate specific cellular processes in response to stresses.

microbiology↗