Biology subjects

Blackburn, T.

Publications and source records attributed to Blackburn, T..

5 recordsLinked to original sources

bioRxiv2026

Bispecific antibody-drug conjugates targeting EGFR and LGR5 exert potent antitumor activity in colorectal cancer models

Colorectal cancer (CRC) remains a significant contributor to cancer-associated deaths worldwide, indicating the need for new therapeutic targets and modalities. Antibody-drug conjugates (ADCs) have demonstrated remarkable potential for the treatment of various cancer types, although their efficacy as monotherapies is often limited by insufficient targeting of tumor heterogeneity, dose-limiting toxicities, and drug resistance. Accordingly, multi-targeting therapeutic strategies, such as bispecific ADCs (bsADCs), which simultaneously target two cancer-associated antigens or non-overlapping epitopes on the same antigen, may prove more effective at overcoming resistance and eliminating tumors compared to monospecific ADCs. In this work, we describe the development of EGFR:LGR5 bispecific antibodies (bsAbs) and bsADCs. EGFR:LGR5 bsAbs were shown to internalize to the lysosome to a greater extent than EGFR- and LGR5-targeting monoclonal antibodies (mAbs) and drive EGFR lysosomal degradation in an LGR5-mediated fashion. However, EGFR:LGR5 bsAbs exerted suboptimal cytotoxicity in CRC cell lines. We therefore engineered an EGFR:LGR5 bsADC that demonstrated 100- to 1000-fold enhanced efficacy over a previously developed LGR5-targeting monospecific ADC (8E11-CPT2) with an identical linker-payload in CRC cell lines of various genetic backgrounds and EGFR and LGR5 expression levels. EGFR:LGR5 bsADC potency was strongly correlated with cell line sensitivity to the CPT2 payload. EGFR:LGR5 bsADC induced tumor regression in select RASMUT CRC xenograft models and demonstrated superior antitumor activity and prolonged survival benefit in all evaluated models versus EGFR mAb cetuximab (CTX), bsAb, and 8E11-CPT2. These findings strongly support the further development of EGFR and LGR5 dual-targeting approaches for CRC and other EGFR- and LGR5-expressing malignancies. One Sentence SummaryEGFR:LGR5 bsADCs exert robust antitumor activity and outperform EGFR:LGR5 bsAb and LGR5 monospecific ADC in RASWT and RASMUT colorectal cancer models.

Cancer Biology↗

bioRxiv2025

Ancient metagenomics reveals subglacial microbiomes driven by oxygen availability

Beneath Earths glaciers and ice sheets lies an aquatic realm where ice, water, rock, and microbial life interact, driving chemical reactions that can collectively influence the global carbon cycle, polar oceans, and climate. Efforts to describe subglacial microbiomes have been limited by the challenge of cleanly drilling through hundreds of meters of ice, such that only a few sites have ever been directly sampled. Here we use ancient metagenomics to present the first spatiotemporal characterization of subglacial bacteria and archaea. We extracted DNA from 25 subglacial precipitate samples, sedimentary accumulations of minerals that form in subglacial waters prior to exposure on the surface. The precipitates studied here formed between 16,000 and 570,000 years ago beneath the Antarctic and Laurentide Ice Sheets. We show that postmortem DNA damage patterns can reliably distinguish between ancient subglacial and modern surface taxa, and that this approach can enable reconstruction of subglacial microbiomes across poles and ice ages. Our analysis suggests that subglacial microbiomes are dominated by chemolithoautotrophs, ultra-small microbes, and taxa closely related to those found in deep subsurface or extreme cold and hypersaline environments. These microbiomes split into two distinct clusters distinguished by oxygen availability and redox conditions, irrespective of geography or age. Geochemical measurements of subglacial redox state, measured either indirectly via precipitate calcite Fe and Mn concentrations or directly via water reduction potential, reproduce these same two clusters exactly. Our findings describe how subglacial water redox states are held in balance by microbes, hydrology, and oxygen input from fresh subglacial meltwater, that we interpret to be controlled by the ice sheet response to past climate variations.

genomics↗

bioRxiv2022

A diverse network of pericoerulear neurons control arousal states

As the primary source of norepinephrine (NE) in the brain, the locus coeruleus (LC) regulates arousal, avoidance and stress responses1,2. However, how local neuromodulatory inputs control LC function remains unresolved. Here we identify a population of transcriptionally, spatially and functionally diverse GABAergic neurons in the LC dendritic field that receive distant inputs and modulate modes of LC firing to control global arousal levels and arousal-related processing and behaviors. We define peri-LC anatomy using viral tracing and combine single-cell RNA sequencing with spatial transcriptomics to molecularly define both LC-NE and peri-LC cell types. We identify several neuronal cell types which underlie peri-LC functional diversity using a series of complementary neural circuit approaches in behaving mice. Our findings indicate that LC and peri-LC neurons are transcriptionally, functionally, and anatomically heterogenous neuronal populations which modulate arousal and avoidance states. Defining the molecular, cellular, and functional diversity of the LC and peri-LC provides a road map for understanding the neurobiological basis of arousal, motivation and neuropsychiatric disorders.

neuroscience↗

bioRxiv2020

Antisense oligonucleotide therapy for SCN2A gain-of-function epilepsy

The clinical spectrum associated with SCN2A de novo mutations (DNMs) continues to expand and includes autism spectrum disorder with or without seizures, in addition to early and late seizure onset developmental and epileptic encephalopathies (DEEs). Recent biophysical studies on SCN2A variants suggest that the majority of early seizure onset DEE DNMs cause gain of function. Gain of function in SCN2A, the principal sodium channel of excitatory pyramidal neurons, would result in heightened neuronal activity and is likely to underlie the pathology seen in early seizure onset DEE patients. Supratherapeutic dosing of the non-selective sodium channel blocker phenytoin, is effective in controlling seizures in these patients but does not impact neurodevelopment, raising the idea that more profound and specific reduction in SCN2A function could significantly improve clinical outcome. To test the potential therapeutic benefit of reducing SCN2A in early seizure onset DEE we centrally administered an antisense oligonucleotide (ASO) targeting mouse Scn2a (Scn2a ASO) to a mouse model of human SCN2A early seizure onset DEE. Mice were genetically engineered to harbour the human equivalent SCN2A p.R1882Q mutation (Q/+), one of the most recurrent mutations in early seizure onset DEE. Q/+ mice presented with spontaneous seizures at postnatal day (P) 1 and did not survive beyond P30. Intracerebroventricular Scn2a ASO administration into Q/+ mice between P1-2 (that reduced Scn2a mRNA levels by 50%) significantly extended lifespan and markedly reduced spontaneous seizures occurrence. Across a range of cognitive and motor behavioural tests, Scn2a ASO treated Q/+ mice were largely indistinguishable from wildtype (+/+) mice. Further improvements in survival and behaviour were seen by adjustment of dosing regimens during development. Scn2a ASO efficacy was also evident at the cellular level. Whole cell patch clamp recording showed that Scn2a ASO administration reversed changes in neuronal excitability in layer 2/3 pyramidal neurons of Q/+ mice to levels seen in +/+ mice. Safety was assessed in +/+ mice and showed a developmental stage dependent tolerability and a favourable therapeutic index. This study suggests that a human SCN2A gapmer ASO could profoundly and safely impact early seizure onset DEE patients and heralds a new era of precision therapy in neurodevelopmental disorders.

neuroscience↗

bioRxiv2017

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data

1. Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often non-randomly distributed and geographically restricted. Although available SDM methods address some of these problems, the errors could be more directly and accurately modelled using a spatially-explicit approach. Software to implement spatial autocorrelation terms into SDMs are now widely available, but whether such approaches for inferring SDMs are an improvement over existing methodologies is unknown.\n\n2. Here, within a simulated environment using 1000 generated species ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and Boosted Regression Trees, BRT) to a spatially-explicit Bayesian SDM method (Integrated Laplace Approximation, INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested whether any recommended methodological settings for all methods were further impacted by spatially non-random patterns in these data.\n\n3. Spatially-explicit INLA was the most consistently accurate method, being most or equal most accurate in 5 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly, but when sampling points were randomly spread BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, spatial-INLA had a 4%-8% better in AUC score. Alternatively, when sampling points were restricted to a small section of the true range, all methods were on average 10-12% less accurate, with higher variation among the methods. None of the recommended settings for the different methods were found to be sensitive to clumping or restriction of data, except the complexity of the INLA spatial term.\n\n4. INLA-based modelling approaches can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit INLA-based SDMs may be the better choice when modelling the spatial distribution of target species.

ecology↗