Biology subjects

Marsh, E.

Publications and source records attributed to Marsh, E..

4 recordsLinked to original sources

bioRxiv2025

Genetic variation of human G6PD impacts Red Blood Cell transfusion efficacy

Glucose-6-phosphate dehydrogenase (G6PD) deficiency, the most common human enzymopathy, affects 6% of the global population, yet its impact on blood storage and transfusion efficacy remains undefined. We integrated genome-metabolome-proteome analyses of 13,091 blood donors (362 G6PD SNPs), validated in a recalled cohort (n=643), linked donor-recipient databases, humanized mouse models (canonical, African A- [V68M+N126D], Mediterranean [S188F]), and a prospective sickle cell disease study. Common G6PD variants reduced protein abundance, reprogrammed redox metabolism, and increased storage hemolysis. In mice, G6PD-deficient RBCs showed lower post-transfusion recovery, higher oxidative stress, and impaired renal oxygenation. Clinically, recipients of G6PD-deficient units exhibited smaller hemoglobin increments and reduced RBC L{superscript 1}Cr-survival (-8% at 24 h; -12% at 4 weeks). Structural studies revealed kinetic fragility for A- and thermodynamic fragility for Med-, linking genotype to protein instability and transfusion outcome. These findings identify donor G6PD genotype as a determinant of transfusion efficacy, supporting genotype-aware inventory-management strategies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/689741v1_ufig1.gif" ALT="Figure 1"> View larger version (76K): org.highwire.dtl.DTLVardef@1897489org.highwire.dtl.DTLVardef@1420587org.highwire.dtl.DTLVardef@178e8ddorg.highwire.dtl.DTLVardef@1003b44_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Single-oocyte transcriptional profile of early-stage human oocytes reveals differentially expressed genes in the primordial and transitioning stages.

The critical initial step in human oocyte maturation - the transition of ovarian follicles from dormancy to activation - remains poorly understood. Here we performed RNA sequencing on single oocytes isolated from early-stage follicles from nine healthy reproductive-age donors. Data for 133 high-quality oocytes formed two connected clusters, C1 and C2, with 5,449 significantly differentially expressed genes. Using recently reported gene lists for early-stage follicles we found that C1 oocytes likely came from earlier, dormant primordial follicles, while C2 oocytes match later-stage primordial or transitioning follicles. We sought to validate two DE genes, UHRF1 for C1 and CCN2 for C2, by their RNA-FISH in situ pattern in morphologically classified follicles, but did not observe statistically significant differences between follicle stages. This apparent discrepancy between follicles stage determined by its morphology and oocytes transcriptional state, if replicated in additional studies, may indicate a lack of closely coupled synchrony between follicle morphology and oocytes functional state.

cell biology↗

bioRxiv2023

In vitro validated antibody design against multiple therapeutic antigens using generative inverse folding

Deep learning approaches have demonstrated the ability to design protein sequences given backbone structures [1, 2, 3, 4, 5]. While these approaches have been applied in silico to designing antibody complementarity-determining regions (CDRs), they have yet to be validated in vitro for designing antibody binders, which is the true measure of success for antibody design. Here we describe IgDesign, a deep learning method for antibody CDR design, and demonstrate its robustness with successful binder design for 8 therapeutic antigens. The model is tasked with designing heavy chain CDR3 (HCDR3) or all three heavy chain CDRs (HCDR123) using native backbone structures of antibody-antigen complexes, along with the antigen and antibody framework (FWR) sequences as context. For each of the 8 antigens, we design 100 HCDR3s and 100 HCDR123s, scaffold them into the native antibodys variable region, and screen them for binding against the antigen using surface plasmon resonance (SPR). As a baseline, we screen 100 HCDR3s taken from the models training set and paired with the native HCDR1 and HCDR2. We observe that both HCDR3 design and HCDR123 design outperform this HCDR3-only baseline. IgDesign is the first experimentally validated antibody inverse folding model. It can design antibody binders to multiple therapeutic antigens with high success rates and, in some cases, improved affinities over clinically validated reference antibodies. Antibody inverse folding has applications to both de novo antibody design and lead optimization, making IgDesign a valuable tool for accelerating drug development and enabling therapeutic design. The data generated in this study serve as a useful benchmark of diverse antibody-antigen interactions. We use this data to benchmark self-consistency RMSD (scRMSD), using ABodyBuilder2 [6], ABodyBuilder3 [7], and ESMFold [8], as a metric for assessing binding. We open source the code for IgDesign and the SPR datasets. Code and datasets can be found at https://github.com/AbSciBio/igdesign.

synthetic biology↗

bioRxiv2018

Internal noise in contrast discrimination propagates forwards from early visual cortex

Human contrast discrimination performance is limited by transduction nonlinearities and variability of the neural representation (noise). Whereas the nonlinearities have been well-characterised, there is less agreement about the specifics of internal noise. Psychophysical models assume that it impacts late in sensory processing, whereas neuroimaging and intracranial electrophysiology studies suggest that the noise is much earlier. We investigated whether perceptually-relevant internal noise arises in early visual areas or later decision making areas. We recorded EEG and MEG during a two-interval-forced-choice contrast discrimination task and used multivariate pattern analysis to decode target/non-target and selected/non-selected intervals from evoked responses. We found that perceptual decisions could be decoded from both EEG and MEG signals, even when the stimuli in both intervals were physically identical. Above-chance decision classification started <100ms after stimulus onset, suggesting that neural noise affects sensory signals early in the visual pathway. Classification accuracy increased over time, peaking at >500ms. Applying multivariate analysis to separate anatomically-defined brain regions in MEG source space, we found that occipital regions were informative early on but then information spreads forwards across parietal and frontal regions. This is consistent with neural noise affecting sensory processing at multiple stages of perceptual decision making. We suggest how early sensory noise might be resolved with Birdsalls linearisation, in which a dominant noise source obscures subsequent nonlinearities, to allow the visual system to preserve the wide dynamic range of early areas whilst still benefitting from contrast-invariance at later stages. A preprint of this work is available at: http://dx.doi.org/10.1101/364612

neuroscience↗