Biology subjects

Connolly, J.

Publications and source records attributed to Connolly, J..

7 recordsLinked to original sources

bioRxiv2026

Central Glucagon-Like Peptide-1 Agonism Ameliorates Colonic Inflammation in a Murine Model of Inflammatory Bowel Disease

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have revolutionized the treatment of obesity and type 2 diabetes (T2D). These drugs also have anti-inflammatory effects that have recently been extended to the treatment of inflammatory bowel disease (IBD). Whether the mechanism underlying this benefit involves a direct action on the intestine or instead is mediated via GLP-1Rs in the brain, however, is unknown. Based on evidence implicating the brain as the target for the anti-obesity and anti-inflammatory effects of GLP-1RAs, we sought to determine if central GLP-1R signaling is sufficient to explain its intestinal anti-inflammatory effects. To this end, we compared the efficacy of peripheral vs central administration of the GLP-1RA, semaglutide (SEMA), in diet-induced obese (DIO) mice with dextran sodium sulfate (DSS)-induced colitis. We report that compared to vehicle, both colitis severity and histopathological injury following DSS administration are reduced by peripheral administration of SEMA. We also report that this benefit is largely recapitulated by intracerebroventricular administration of SEMA at a low dose that, when administered peripherally, is below that needed to affect food intake and body weight. We conclude that in a mouse model of colitis, activation of GLP-1Rs in the brain is sufficient to explain the intestinal anti-inflammatory effect conferred by GLP-1R agonism.

neuroscience↗

bioRxiv2026

MetagenomicsBench: A Verifiable Benchmark for Agentic Metagenomics Analysis

Metagenomics has transformed our ability to characterize microbial communities without relying on cultivation, but translating complex microbiome datasets into reliable biological conclusions remains a major analytical challenge. Although AI agents have improved substantially at software engineering and general data analysis, it remains unclear whether they can reliably analyze and interpret real-world metagenomic data. We introduce MetagenomicsBench, a benchmark of 100 verifiable evaluations derived from published datasets spanning community structure, host-microbiome associations, microbial function, longitudinal dynamics, and microbiome interventions. Each evaluation provides experimental data and a deterministic grader that evaluates recovery of a key analytical or biological result. Across 10,200 trajectories from 34 model-harness configurations, the strongest configuration achieved a 60.0% pass rate. Performance varied across functional domains, data modalities, and biological systems, while greater cost, token usage, and tool use did not consistently correspond to higher accuracy. Trajectory analysis showed that agents often produced technically plausible analyses while making mistakes in problem interpretation, statistical reasoning, and biological interpretation. MetagenomicsBench provides a framework for measuring progress toward AI agents that can reliably make and validate the analytical and biological decisions required for metagenomic research.

microbiology↗

bioRxiv2026

Systemic iron sequestration restricts the intratumoral CD8+ T cell landscape in pancreas cancer

Systemic iron sequestration occurs frequently in cancer due to inflammation-driven expression of the iron-regulatory hormone hepcidin. The impact of systemic iron availability on tumoral immunity is unclear. Here, we show that elevated serum hepcidin is associated with reduced survival and decreased intratumoral CD8+ T cells in patients with pancreas cancer. While hepcidin is not induced in murine tumor models, administration of a hepcidin mimetic phenocopies the T-cell-depleted tumor microenvironment seen in patients. Mechanistically, chronic antigen-driven mitochondrial dysfunction disrupts iron metabolism and selectively depletes high avidity CD8+ T cells during iron restriction. These findings establish a direct link between hepcidin-mediated iron sequestration and tumoral immunity and nominate systemic iron dysregulation as a therapeutic target to enhance anti-tumoral CD8+ T cell responses.

immunology↗

bioRxiv2026

Deep learning representations of human Immune Health for precision immunology

The human immune system is composed of [~]30-50 distinct cell types, each of which can exist in different states of activation or differentiation. Indeed, the mammalian immune system has evolved to sense and respond to infections, cancers, injuries, and changes in tissue or host homeostasis (1). Moreover, an increasingly large fraction of approved drugs target the immune system directly, and/or cause immune changes (2-4). A key feature of the immune system is to store some of this information, for example as innate or adaptive immune memory (5). In addition, rewiring of immune network architecture induced by disease, environmental exposures, drug treatments, and/or chronological age allows the immune system to store information in the pattern of connections and activity across populations of immune cells. This ensemble information storage, in addition to changes to individual cells, functions as a major way the immune system encodes aspects of immune history and future potential. Genetic information can identify inherited risk alleles, but cannot capture the continual remodeling of the immune system shaped by exposures, infection, inflammation, therapy, and aging (6, 7). To define and use such ensemble immunotypes, we developed a self-supervised deep learning framework that transforms high-dimensional immune profiles into representations of immune health. MAESTRO (MAsked Encoding Set TRansformer with self-distillatiOn) encodes a set of cells from an individual into an embedding that captures immune cell population-level organization. Pretrained on 1,792 peripheral blood samples comprising over 418 million immune cells across 13 clinical diagnoses, MAESTRO learns immune fingerprints that are stable within individuals yet diverse across populations, states of health, disease, and treatment, providing a quantitative basis for comparing immune states across individuals and over time. These fingerprints capture immune architecture beyond coarse cell type proportions, enabling patient-efficient clinical prediction using simple task specific models. MAESTRO model embeddings retain a temporal dimension of immune history and potential, reflecting signatures of past exposures and baseline features that predict future immune responses. Finally, we demonstrate a translational precision immunotherapy application by testing this approach in metastatic Pancreatic Ductal Adenocarcinoma (PDAC), where pretreatment immune landscape circuitry maps enable patient stratification and therapeutic response prediction. Overall, we developed a large, attention-based model that captures deep network architecture of immune states through self- supervised representations of immune cytometry data as a reusable foundation for precision immunology, converting immune complexity into clinically actionable embeddings for diagnosis, monitoring, and therapy selection.

immunology↗

bioRxiv2023

Reverse Engineering DNA Origami Nanostructure Designs from Raw Scaffold and Staple Sequence Lists

Designs for scaffolded DNA origami nanostructures are commonly and minimally published as the list of DNA staple and scaffold sequences required. In nearly all cases, high-level editable design files (e.g. caDNAno) which generated the low-level sequences are not made available. This de facto raw sequence exchange format allows published origami designs to be re-attempted in the laboratory by other groups, but effectively stops designs from being significantly modified or re-purposed for new future applications. To make the raw sequence exchange format more accessible to further design and engineering, in this work we propose the first algorithmic solution to the inverse problem of converting staple/scaffold sequences back to a guide schematic resembling the original origami schematic. The guide schematic can be used to aid the manual re-input of an origami into a CAD tool like caDNAno, hence recovering a high-level editable design file. Creation of a guide schematic can also be used to double check that a list of staple strand sequences does not have errors and indeed does assemble into a desired origami nanostructure prior to costly laboratory experimentation. We tested our reverse algorithm on 36 diverse origami designs from the literature and found that 29 origamis (81%) had a good quality guide schematic recovered from raw sequences. Our software is made available at https://revnano.readthedocs.io.

bioinformatics↗

bioRxiv2019

The potential for polyphosphate metabolism in Archaea and anaerobic polyphosphate formation in Methanosarcina mazei.

Inorganic polyphosphate (polyP) is ubiquitous across all forms of life, but the study of its metabolism has been mainly confined to bacteria and yeasts. Few reports detail the presence and accumulation of polyP in Archaea, and little information is available on its functions and regulation. Here, we report that homologs of bacterial polyP metabolism proteins are present across the major taxa in the Archaea, suggesting that archaeal populations may have a greater contribution to global phosphorus cycling than has previously been recognised. We also demonstrate that polyP accumulation can be induced under strictly anaerobic conditions, in response to changes in phosphate (Pi) availability, i.e. Pi starvation, followed by incubation in Pi replete media (overplus), in cells of the methanogenic archaeon Methanosarcina mazei. Pi-starved M. mazei cells increased transcript abundance of the PHO-regulated alkaline phosphatase (phoA) gene and of the high-affinity phosphate transport (pstSCAB-phoU) operon: no increase in polyphosphate kinase 1 (ppk1) transcript abundance was observed. Subsequent incubation of Pi-starved M. mazei cells under Pi replete conditions, led to a 237% increase in intracellular polyphosphate content and a >5.7-fold increase in ppk1 gene transcripts. Ppk1 expression in M. mazei thus appears not to be under classical PHO regulon control.

microbiology↗

bioRxiv2018

MethylSight: Taking a wider view of lysine methylation through computer-aided discovery to provide insight into the human methyl-lysine proteome

Protein Lys methylation plays a critical role in numerous cellular processes, yet it has been challenging to identify Lys methylation in a systematic manner. We present here an approach combining in silico prediction with targeted mass spectrometry (MS) to identify Lys methylation (Kme) sites at the proteome level. We have developed MethylSight, a program that predicts Kme events solely on physicochemical and biochemical properties of putative methylation sites, which can then be validated by targeted MS. Using this approach, we have identified 70 new histone Kme marks with a 90% validation rate. H2BK43me2, which undergoes dynamic changes during stem cell differentiation, is found to be a substrate of KDM5b. Furthermore, MethylSight predicts ~50,000 Kme sites in non-histone proteins with high confidence, suggesting that Lys methylation is a prevalent post-translational modification. Our work provides a useful resource for systematic exploration of the role of Lys methylation in human health and disease.

biochemistry↗