Biology subjects

Lewis, N.

Publications and source records attributed to Lewis, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Mutant KRAS dosage contributes to heterogeneity in lung cancer therapeutic response

Oncogenic KRAS mutations promote tumorigenesis by constitutive activation of multiple, well-characterised signalling pathways. However, there is significant heterogeneity across mutant KRAS tumours in terms of mutation present, mutant allele abundance and downstream signalling strength. It is unclear whether these variations can impact responses to specific therapies. Here, we demonstrate that [~]20% of lung adenocarcinomas (LUAD) show an increase in mutant KRAS dosage (KRASmutant allele fraction > KRASwild-type). Furthermore, we show that KRAS mutant dosage can directly influence clinical outcome and therapeutic susceptibilities in lung cancer. Our findings show that mutant KRAS copy gains specifically affect platinum lung cancer response, promoting resistance to this standard-of-care therapy. Importantly, increases in KRAS mutant dosage are also associated with an increased vulnerability to pS6K inhibition, due to the unique metabolic rewiring of these cells. Together, we show that mutant KRAS dosage contributes to the phenotypic heterogeneity of mutant KRAS NSCLC and that assessment of mutant KRAS content or signalling strength can help optimise treatments strategies for these patients.

cancer biology↗

bioRxiv2026

Discovery, characterisation and optimisation of bicyclic peptide inhibitors that disarm Staphylococcus aureus a-hemolysin

-Hemolysin (Ahly) is a major Staphylococcus aureus virulence determinant implicated in tissue injury and immune dysregulation; antibody inhibitors have reached clinical trials but alternatives with improved ease of manufacture and tissue penetration are desirable. Here we demonstrate that phage-derived bicyclic peptides can serve as compact, chemically tractable Ahly neutralisers. Using TATB-scaffolded M13 phage libraries we identified WNP-motif containing bicyclic binders, with a lead hit of Peptide 14 (KD = 1792nM) and progressed the lead by iterative affinity maturation to Peptide 20 (KD = 609 nM) and by incorporation of strategically chosen non-canonical amino acids to yield Peptide 88 (KD = 96 nM). A 2.2 [A] co-crystal structure with AhlyH35A locates the binding footprint on the rim domain and explains the critical role of the WNP motif in target engagement. Functional assays show that the Peptide 88 blocks Ahly mediated hemolysis, inhibits Ahly driven ADAM10 activation, and elucidate its inhibitory mechanism of preventing Ahly binding to human A549 epithelial cells. Peptide 88 protects A549 cells from recombinant toxin and attenuates cytotoxicity in S. aureus co-culture experiments, whilst showing no toxicity to A549 cells. Bicyclic peptides thus represent a new and promising anti-virulence modality: small, synthetically accessible molecules that mimic antibody recognition, with therapeutic potential against S. aureus infections.

molecular biology↗

bioRxiv2026

Emergence and antigenic characterisation of influenza A(H3N2) viruses with hemagglutinin substitutions N158K and K189R during the 2024/25 influenza season

BackgroundSeasonal human influenza viruses can escape from antibody-mediated neutralization when amino acid changes occur in the hemagglutinin protein. Routine surveillance identified circulation of an A(H3N2) virus variant in the Netherlands with amino acid substitutions at hemagglutinin positions 158 and 189. These amino acid positions were previously responsible for antigenic change of influenza A(H3N2) viruses and potentially lead to escape of this variant from vaccine-mediated immunity. AimTo characterize the emergence and antigenic properties of N158K and K189R double substitution virus variants. MethodsWe analyzed the geographical and temporal dynamics of the double-substitution variant using a phylogeographic approach and used hemagglutination inhibition assays and antigenic cartography methods to map its antigenic properties. ResultsA(H3N2) viruses carrying K189R were first detected in Guatemala in June 2024, before subsequently gaining the N158K substitution, which was intially detected in Colombia in November 2024, followed by detection in the Netherlands in December 2024. However, detections within Europe remained almost entirely confined to the Netherlands. The proportion of viruses carrying the N158K and K189R substitutions increased to 16% - 24% per collection week of sequenced Dutch viruses during the peak of the epidemic of the 2024-2025 respiratory season. Antigenic characterization of viruses with N158K and K189R substitutions indicated that these are antigenically distinct from the A(H3N2) components of 2025-2026 Northern Hemisphere vaccines, showing 8-192-fold reduction in hemagglutination inhibition titers with antisera against the vaccine strain compared to antisera against the homologous virus. ConclusionsInfluenza A(H3N2) viruses with N158K and K189R escaped recognition by antibodies raised against the 2024-2025 and 2025/2026 Northern Hemipshere vaccine strains in hemagglutination inhibition assays. These variants circulated widely in the Netherlands during the 2024-2025 influenza season, raising concerns about reduced vaccine-mediated protection if such variants would spread more broadly during 2025-2026 Northern Hemipshere season.

microbiology↗

bioRxiv2025

NEST-Scoring, a Novel miRNA Target Gene Profile Network Analysis of Placenta-Derived Extracellular Vesicles in a Transgenic Rat Model of Preeclampsia

Preeclampsia is a multisystem hypertensive disorder of pregnancy and a leading cause of maternal morbidity and mortality. Despite its increasing incidence and the debilitating nature of its cerebrovascular complications, the underlying mechanisms remain incompletely understood. The goals of this study were to 1) determine whether middle cerebral artery (MCA) hemodynamics are altered in late gestation (LG) or two months postpartum in transgenic rats with preeclampsia compared to normal pregnant Sprague-Dawley (SD) rats, and 2) evaluate the microRNA (miRNA) profiles of circulating placental-derived extracellular vesicles (EVPD) using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway framework using NEST-Scoring [Node, Edge, System, Topology], a novel target gene profile network analysis. Our data showed elevated blood pressure in transgenic preeclampsia rats at both LG and two months post-partum (PP-2M) compared to normal pregnant SD rats in the setting of hypertension. The heart rate was also significantly higher in preeclampsia rats at LG. No differences were observed in left MCA (LMCA) velocities between strains or time points. However, LMCA resistance and pulsatility indexes were lower at PP-2M in transgenic preeclampsia rats compared to SD rats. Exposure to preeclampsia differentially altered the miRNA profiles of circulating EVPD in transgenic preeclampsia rats at both LG and PP-2M. The identified miRNA targets were associated with key vascular and cellular regulatory pathways, suggesting a mechanistic link between placental dysfunction and postpartum vascular alterations in this preeclampsia model. Overall, this study suggests that the TGA-PE rat model could be a valuable platform to study the mechanistic link between preeclampsia and cerebrovascular disease.

bioinformatics↗

bioRxiv2025

Integrative modeling of seasonal influenza evolution via AI-powered antigenic cartography

Seasonal influenza viruses evade host immunity through rapid antigenic evolution. Antigenicity is assessed by serological assays and typically visualized as antigenic maps, which represent antigenic differences among virus strains. However, conventional maps cannot directly infer the antigenicity of unexamined variants from their genotypes. Here, we present PLANT, a protein language model that projects influenza A/H3N2 viruses onto an antigenic map using HA protein sequences. Using PLANT-based cartography, we show that (i) H3N2 antigenic evolution accelerates during periods of disrupted global circulation, (ii) antigenic novelty accounts for a substantial portion of viral fitness advantage, and (iii) vaccine strains are often antigenically distant from circulating viruses. We further propose a PLANT-based framework for selecting vaccine strains with improved antigenic match than the WHO-recommended strains. This study provides a statistical foundation for integrated modeling of viral genotype, antigenicity, and fitness, offering quantitative insights into seasonal influenza virus evolution and supporting rational vaccine design.

microbiology↗

bioRxiv2025

A lectin receptor-like kinase controls self-pollen recognition in Phlox (Polemoniaceae)

Self-incompatibility (SI) describes a widespread collection of genetic mechanisms in flowering plants used to specifically recognize and reject self-pollen. These mechanisms are fundamental to plant sexual reproduction and offer valuable insight into the molecular basis of cell-cell communication and self-recognition more broadly. Here, we leverage an independent evolution of SI in the lineage containing Phlox (Polemoniaceae) to characterize a novel gene causing self-pollen recognition which we name Phlox drummondii Pistil Identity Receptor Kinase (PdPIRK). Recognition of self-pollen associates with a single genomic region containing the Phlox S-locus. We generate predictions regarding how S-loci must function and evolve to identify a single candidate gene within this S-associated region. This gene, PdPIRK, is highly and specifically expressed in the pistil and has exceptionally high polymorphism maintained by negative frequency dependent selection, two hallmarks of self-pollen recognition genes. Functional validation with gene silencing confirms that PdPIRK is necessary for self-incompatibility, and we further demonstrate allele specific activity, confirming its role in self-pollen recognition per se. PdPIRK encodes a G-type lectin receptor-like kinase, which is a member of the same gene family as SRK, the gene controlling self-pollen recognition in the distantly related Brassicaceae. Our findings suggest the presence of genetic constraints or paths of least resistance governing how S-loci evolve and add to our understanding of the diverse molecular mechanisms through which organisms achieve self-recognition.

plant biology↗

bioRxiv2025

An information theory framework for capturing multi-connectivity via spatial network encoding reveals reduced population count (Hamming weight) localized to auditory, visual, and motor networks

1.The human brain operates as a complex system where functional networks evolve and interact across spatially distributed regions. In traditional neuroimaging analyses, functional connectivity (FC), based on pairwise correlations or statistical dependencies of temporal fluctuations in the BOLD signal, has been a primary method for exploring interactions between brain regions and decoding brain function. However, traditional FC methods often overlook the intricate, multi-way interplay among brain elements that emerge from the brains densely interconnected nature. To overcome these limitations, we introduce a novel voxel-centric framework that captures the multi-way interactions between voxels and networks identified via high-model order independent component analysis. This framework posits that individual voxels serve as critical mediators of multi-network communication, reflecting the brains complex functional architecture. By encoding voxel contributions from brain networks into binary representations and quantifying the population count at each voxel via Hamming weights, the proposed method prioritizes high-contribution voxels that facilitate inter-network interactions. This approach provides new insights into the brains functional organization, revealing previously unrecognized patterns of voxel-to-network entanglement. Specifically, in the context of schizophrenia, our method enables the identification of spatial patterns that may underpin the cognitive and perceptual disturbances characteristic of the disorder. This enhanced understanding could improve diagnostic precision and help tailor interventions that target specific dysfunctional networks, offering a pathway to more effective treatments and better patient outcomes in schizophrenia.

neuroscience↗

bioRxiv2025

Topologically Optimized Intrinsic Brain Networks

The estimation of brain networks is instrumental in quantifying and evaluating brain function. Nevertheless, achieving precise estimations of subject-level networks has proven to be a formidable task. In response to this challenge, researchers have developed group-inference frameworks that leverage robust group-level estimations as a common reference point to infer corresponding subject-level networks. Generally, existing approaches either leverage the common reference as a strict, voxel-wise spatial constraint (i.e., strong constraints at the voxel level) or impose no constraints. Here, we propose a targeted approach that harnesses the topological information of group-level networks to encode a high-level representation of spatial properties to be used as constraints, which we refer to as Topologically Optimized Intrinsic Brain Networks (TOIBN). Consequently, our method inherits the significant advantages of constraint-based approaches, such as enhancing estimation efficacy in noisy data or small sample sizes. On the other hand, our method provides a softer constraint than voxel-wise penalties, which can result in the loss of individual variation, increased susceptibility to model biases, and potentially missing important subject-specific information. Our analyses show that the subject maps from our method are less noisy and true to the group networks while promoting subject variability that can be lost from strict constraints. We also find that the topological properties resulting from the TOIBN maps are more expressive of differences between individuals with schizophrenia and controls in the default mode, subcortical, and visual networks.

neuroscience↗

bioRxiv2024

Dispersal evolution can only rescue a limited set of species from climate change

Anthropogenic climate change threatens biodiversity on Earth. In response, species can adapt evolutionarily to changing environments or shift their ranges via dispersal. However, dispersal itself can evolve on ecological timescales. We explore theoretically how dispersal evolution modulates the response of metacommunities to climate change. We find that this response depends on the environmental conditions prior to climate change. Variable environments harbour few, dispersive species that are likely to survive climate change by shifting their ranges with their evolved dispersal abilities. Stable environments house many, less dispersive species. Their survival during climate change is less likely, as they can evolve robust, low dispersal traits that prevent range shifts. We identify a limited set of scenarios in which contemporary dispersal evolution can rescue species from climate change, highlighting the importance of species evolutionary histories and evolutionary rates, as determined by their genotype-phenotype maps, for their responses to rapid environmental change.

ecology↗

bioRxiv2024

scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity by providing gene expression data at single-cell resolution, uncovering insights into rare cell populations, cell-cell interactions, and gene regulation. Foundation models pretrained on large-scale scRNA-seq datasets have shown great promise in analyzing such data, but existing approaches are often limited to modeling a small subset of highly expressed genes and lack the integration of external genespecific knowledge. To address these limitations, we present sc-Long, a billion-parameter foundation model pretrained on 48 million cells. sc-Long performs self-attention across the entire set of 28,000 genes in the human genome. This enables the model to capture long-range dependencies between all genes, including lowly expressed ones, which often play critical roles in cellular processes but are typically excluded by existing foundation models. Additionally, sc-Long integrates gene knowledge from the Gene Ontology using a graph convolutional network, enriching its contextual understanding of gene functions and relationships. In extensive evaluations, sc-Long surpasses both stateof-the-art scRNA-seq foundation models and task-specific models across diverse tasks, including predicting transcriptional responses to genetic and chemical perturbations, forecasting cancer drug responses, and inferring gene regulatory networks.

bioinformatics↗

bioRxiv2024

Mitochondrial metabolism is a key determinant of chemotherapy sensitivity in Colorectal Cancer

Therapy resistance is attributed to over 80% of cancer deaths per year emphasizing the urgent need to overcome this challenge for improved patient outcomes. Despite its widespread use in colorectal cancer (CRC) treatment, resistance to 5-fluorouracil (5FU) remains poorly understood. Here, we investigate the transcriptional responses of CRC cells to 5FU treatment, revealing significant metabolic reprogramming towards heightened mitochondrial activity. Utilizing CRC models, we demonstrate sustained enhancement of mitochondrial biogenesis and function following 5FU treatment, leading to resistance in both in vitro and in vivo settings. Furthermore, we show that targeting mitochondrial metabolism, specifically by inhibiting Complex I (CI), sensitizes CRC cells to 5FU, resulting in delayed tumour growth and prolonged survival in preclinical models. Additionally, our analysis of patient data suggests that oxidative metabolism signatures may predict responses to 5FU-based chemotherapy. These findings shed light on mechanisms underlying 5FU resistance and propose a rational strategy for combination therapy in CRC, emphasizing the potential clinical benefit of targeting mitochondrial metabolism to overcome resistance and enhance patient outcomes.

cancer biology↗

bioRxiv2024

Birds affected by a 2021 avian mortality event are strongly associated with supplemental feeding and ground foraging behaviors

In 2021, news outlets and state natural resources agencies reported a large number of avian deaths across several states in the eastern and midwestern USA. This event fomented a rapid and robust response from animal health experts from across the country. Given the clustered pattern of disease and death, an infectious etiology was rigorously investigated. No single causative pathogen was identified, leaving the cause and thus epidemiology of the mortality event unex-plained. In this study, we attempted to hone in on potential causes or contributors to this event by constructing a dataset on affected birds life history, phylogeny, and ecology. After a preliminary analysis of these features, we developed a statistical pipeline to test two hypotheses regarding features of birds associated with the mortality event: (1) that a significant proportion of affected birds in the total sample are members of the Cornell Feederwatch list (i.e., birds that consume supplemental feed, and their predators), and that (2) ground-feeding species would be significantly represented in the sample. While logistic regression models support the plausibility of the two hypotheses, they are statistically indistinguishable. We discuss the implications of these findings, propose future work, and highlight the importance of ecological and behavioral expertise in understanding epidemiological phenomena.

ecology↗

bioRxiv2024

Protein structure, a genetic encoding for glycosylation

Unlike DNA, RNA, and protein biosynthesis, dogma describes glycosylation as primarily determined by intrinsic cellular limitations, such as glycosyltransferase expression and precursor availability. However, this cannot explain the commonly-observed differences between glycans on the same protein. By examining site-specific glycosylation on diverse human proteins, we detected associations between protein structure and glycan structure, broadly generalizable to human-expressed glycoproteins. Through structural analysis of site-specific glycosylation data, we found protein-sequence and structural features consistently correlated with specific glycan features. To quantify these relationships, we present a new amino acid substitution matrix describing "glycoimpact", i.e., the association of primary protein structure and glycosylation. High-glycoimpact amino acids co-evolve with glycosites, and glycoimpact is high when estimates of amino acid conservation and variant pathogenicity diverge. We report thousands of disease variants near glycosites with high-glycoimpact, including several with known links to aberrant glycosylation (e.g., Oculocutaneous Albinism, Jakob-Creutzfeldt disease, Gerstmann-Straussler-Scheinker, and Gauchers Disease). Finally, glycoimpact quantification is validated by studying oligomannose-complex glycan ratios on HIV ENV, differential sialylation on IgG3 Fc, differential glycosylation on SARS-CoV-2 Spike, and fucose-modulated function of a tuberculosis monoclonal antibody. Finally, to test the causality of protein-glycan associations, we created 5 glycoimpact-designed novel Rituximab variants, 4 of which substantially changed glycoprofiles as predicted. In all, we report that site-specific glycan biosynthesis is influenced by underlying protein structure, enabling glycan structure prediction and genetic sequence-guided glycoengineering.

genetics↗

bioRxiv2024

Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network

Glycosylation is described as a non-templated biosynthesis. Yet, the template-free premise is antithetical to the observation that different N-glycans are consistently placed at specific sites. It has been proposed that glycosite-proximal protein structures could constrain glycosylation and explain the observed microheterogeneity. Using site-specific glycosylation data, we trained a hybrid neural network to parse glycosites (recurrent neural network) and match them to feasible N-glycosylation events (graph neural network). From glycosite-flanking sequences, the algorithm predicts most human N-glycosylation events documented in the GlyConnect database and proposed structures corresponding to observed monosaccharide composition of the glycans at these sites. The algorithm also recapitulated glycosylation in Enhanced Aromatic Sequons, SARS-CoV-2 spike, and IgG3 variants, thus demonstrating the ability of the algorithm to predict both glycan structure and abundance. Thus, protein structure constrains glycosylation, and the neural network enables predictive in silico glycosylation of uncharacterized or novel protein sequences and genetic variants.

bioinformatics↗

bioRxiv2024

Mismatch between pollen and pistil size causes asymmetric mechanical reproductive isolation across Phlox species

In flowering plants, pollen-pistil interactions can serve as an important barrier to reproduction between species. As the last barrier to reproduction before fertilization, interactions between these reproductive organs are both complex and important for determining a suitable mate. Here, we test whether differences in style length generate a post-mating prezygotic mechanical barrier between five species of perennial Phlox wildflowers with geographically overlapping distributions. We perform controlled pairwise reciprocal crosses between three species with long styles and two species with short styles to assess crossing success (seed set). We find that heterospecific seed set is broadly reduced compared to conspecific cross success and reveal a striking asymmetry in heterospecific crosses between species with different style lengths. To determine the mechanism underlying this asymmetric reproductive isolating barrier we assess pollen tube growth in vitro and in vivo. We demonstrate that pollen tubes of short-styled species do not grow long enough to reach the ovaries of long-styled species. We find that short-styled species also have smaller pollen and that both within and between species pollen diameter is highly correlated with pollen tube length. Our results support the hypothesis that the small pollen of short-styled species lacks resources to grow pollen tubes long enough to access the ovaries of the long-styled species, resulting in an asymmetrical, mechanical barrier to reproduction. Such mechanisms, combined with additional pollen-pistil incompatibilities, may be particularly important for closely related species in geographic proximity that share pollinators.

evolutionary biology↗

bioRxiv2024

Discovery and chemical optimisation of a Potent, Bi-cyclic (Bicycle(R)) Antimicrobial Inhibitor of Escherichia coli PBP3

Penicillin binding proteins (PBPs) are well validated antimicrobial targets, but the prevalence of {beta}-lactamase driven resistance and, more rarely, target-based mutations, necessitates new classes of PBP-targeting drugs. Here we describe the discovery and optimisation of novel, bicyclic peptide (Bicycle(R)) inhibitors of E. coli PBP3 (EcPBP3) using a proprietary phage display platform, and their conjugation to linear antimicrobial peptides to confer outer membrane permeation. These molecules exhibited high-affinity binding to E. coli PBP3 and a viable spectrum of killing activity against clinically relevant species of the Enterobacterales. X-ray crystallography was used to explore the mode of binding to PBP3, enabling increased target affinity and improvement of in vitro stability. These compounds bind to the transpeptidase active site cleft of PBP3 and represent a novel non-{beta}-lactam chemical class of high affinity, non-covalent penicillin binding protein inhibitors.

biochemistry↗

bioRxiv2024

Time-varying Spatial Propagation of Brain Networks in fMRI data

Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel-wise network-specific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated to blood oxygenation level-dependent (BOLD) signal for 6-8 seconds, whereas the visual network propagates much quicker. We also show that summaries of network specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between schizophrenia patients and controls within four large-scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia.

neuroscience↗

bioRxiv2023

Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic Risk

BackgroundRecent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects. MethodsWe adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data. ResultsOur findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks. ConclusionsOur method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.

neuroscience↗