Biology subjects

Bennett, N.

Publications and source records attributed to Bennett, N..

8 recordsLinked to original sources

bioRxiv2026

Metabolomic, lipidomic, and N-glycomic analyses of a human cell model of Krabbe disease reveal treatable deficits in glycosylation and serine-ceramide metabolism

Krabbe disease is a rare autosomal recessive lysosomal disease caused by deficiency of galactocerebrosidase (GALC), leading to accumulation of galactosylceramide and formation of the toxic metabolite galactosylsphingosine (psychosine). While psychosine accumulation is well-established as a primary pathogenic mechanism, the broader metabolic consequences of GALC deficiency remain incompletely understood. In this study, we used stable isotope tracing to comprehensively characterize metabolic perturbations in a human oligodendrocellular Krabbe disease model. This approach revealed elevated de novo ceramide synthesis in GALC knock-out cells, characterized by increased incorporation of glucose-derived serine into ceramide biosynthetic pathways. This enhanced ceramide production was amenable to pharmacological intervention by tezacaftor, an inhibitor of sphingolipid {Delta}4-desaturate (DEGS); tezacaftor administration also normalized psychosine levels, raising the possibility of its use as substrate reduction therapy. Additionally, we identified significant disruption of UDP-hexose metabolism, manifesting as an overabundance of truncated and hypogalactosylated glycans. These findings suggest impaired protein glycosylation as a previously unrecognized pathogenic mechanism in Krabbe disease. Our findings reveal novel metabolic dysregulation in Krabbe disease extending beyond established psychosine toxicity. The identification of enhanced de novo ceramide synthesis presents a new therapeutic target, while the discovery of galactose-deficient glycosylation defects supports galactose supplementation as a potential therapeutic intervention. These metabolic insights provide new mechanistic understanding and therapeutic opportunities for this devastating neurodegenerative disorder.

systems biology↗

bioRxiv2022

Cryo-electron microscopy of the f1 filamentous phage reveals a new paradigm in viral infection and assembly

Phages are viruses that infect bacteria and dominate every ecosystem on our planet. As well as impacting microbial ecology, physiology and evolution, phages are exploited as tools in molecular biology and biotechnology. This is particularly true for the Ff (f1, fd or M13) phages, which represent a widely distributed group of filamentous viruses. Over nearly five decades, Ff has seen an extraordinary range of applications, including in phage display and nanotechnology. However, the complete structure of the phage capsid and consequently the mechanisms of infection and assembly remain largely mysterious. Using cryo-electron microscopy and a highly efficient system for production of short Ff-derived nanorods, we have determined the first structure of a filamentous virus, including the filament tips. Structure combined with mutagenesis was employed to identify domains of the phage that are important in bacterial attack and for release of new phage progeny. These data allow new models to be proposed for the phage lifecycle and will undoubtedly enable the development of novel biotechnological applications.

microbiology↗

bioRxiv2022

Improving de novo Protein Binder Design with Deep Learning

We explore the improvement of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.

bioengineering↗

bioRxiv2022

Robust deep learning based protein sequence design using ProteinMPNN

While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins. One-sentence summaryA deep learning based protein sequence design method is described that is widely applicable to current design challenges and shows outstanding performance in both in silico and experimental tests.

biophysics↗

bioRxiv2021

In vivo CRISPRi screen reveals the differential requirement for mitochondrial respiratory chain function between in vivo and in vitro tumor growth

The Warburg effect, aerobic glycolysis, is a hallmark feature of cancer cells grown in culture. However, the relative roles of glycolysis and respiratory metabolism in supporting in vivo tumor growth and significant processes such as tumor dissemination and metastases remain poorly understood, particularly on a systems level. Using a CRISPRi mini-library enriched for mitochondrial ribosomal protein and respiratory chain genes in multiple human lung cancer cell lines we analyzed in vivo metabolic requirements in xenograft tumors grown in distinct anatomic contexts. While knockdown of mitochondrial ribosomal protein and respiratory chain genes (mito-respiratory genes) has little impact on growth in vitro, tumor cells depend heavily on these genes when grown in vivo as either flank or primary orthotopic lung tumor xenografts. In contrast, respiratory function is comparatively dispensable for metastatic tumor growth. RNA-Seq and metabolomics analysis of tumor cells expressing individual sgRNAs against mito-respiratory genes indicate overexpression of glycolytic genes and increased sensitivity of glycolytic inhibition compared to control when grown in vitro, but when grown in vivo as primary tumors these cells downregulate glycolytic mechanisms. These studies demonstrate that discrete perturbations of mitochondrial metabolism impact in vivo tumor growth in a context-specific manner and provides systems-level evidence that respiratory function modulates tumor growth in vivo, suggesting that ATP limits growth and metastatic potential.

cancer biology↗

bioRxiv2021

Robust de novo design of protein binding proteins from target structural information alone

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains an outstanding challenge. We describe a general solution to this problem which starts with a broad exploration of the very large space of possible binding modes and interactions, and then intensifies the search in the most promising regions. We demonstrate its very broad applicability by de novo design of binding proteins to 12 diverse protein targets with very different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of four of the binder-target complexes, and all four are very close to the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein-protein interactions, and should guide improvement of both. Our approach now enables targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.

synthetic biology↗

bioRxiv2020

Amplification of potential thermogenetic mechanisms in cetacean brains

To elucidate causality underlying the evolution of large brains in cetaceans, we examined the brains of 16 cetartiodactyl species for evidence of non-shivering thermogenesis. In comparison to the artiodactyl brain, the cetacean brain exhibits an expanded expression of uncoupling protein 1 (UCP1, UCPs being mitochondrial inner membrane proteins that dissipate the proton gradient to generate heat) in cortical neurons, localization of UCP4 within a substantial proportion of glia throughout the brain, and an increased density of noradrenergic axonal boutons (noradrenaline functioning to control concentrations of and activate UCPs). Thus, cetacean brains possess multiple characteristics indicative of intensified thermogenetic functionality that can be related to their current and historical obligatory aquatic niche. These findings necessitate reassessment of our concepts regarding the reasons for large brain evolution and associated functional capacities in cetaceans.

neuroscience↗

bioRxiv2020

Ivermectin converts cold tumors hot and synergies with immune checkpoint blockade for treatment of breast cancer

We show that treatment with the FDA-approved anti-parasitic drug ivermectin induces immunogenic cancer cell death (ICD) and robust T cell infiltration into breast tumors. As an allosteric modulator of the ATP/P2x4/P2x7 axis which operates in both cancer and immune cells, ivermectin also selectively targets immunosuppressive populations including myeloid cells and Tregs, resulting in enhanced Teff/Tregs ratio. While neither agent alone showed efficacy in vivo, combination therapy with ivermectin and checkpoint inhibitor anti-PD1 antibody achieved synergy in limiting tumor growth (p=0.03) and promoted complete responses (p<0.01), also leading to immunity against contralateral re-challenge with demonstrated anti-tumor immune responses. Going beyond primary tumors, this combination achieved significant reduction in relapse after neoadjuvant (p=0.03) and adjuvant treatment (p<0.001), and potential cures in metastatic disease (p<0.001). Statistical modeling confirmed bona fide synergistic activity in both the adjuvant (p=0.007) and metastatic settings (p<0.001). Ivermectin has dual immunomodulatory and ICD-inducing effects in breast cancer, converting cold tumors hot, thus represents a rational mechanistic partner with checkpoint blockade.

immunology↗