Search bioRxiv⌕ Search

Biology subjects

Lewis, N.

Publications and source records attributed to Lewis, N..

At least 37 records · Page 2Linked to original sources
bioRxiv2023

A Boltzmann model predicts glycan structures from lectin binding

Glycans are complex oligosaccharides involved in many diseases and biological processes. Unfortunately, current methods for determining glycan composition and structure (glycan sequencing) are laborious and require a high level of expertise. Here, we assess the feasibility of sequencing glycans based on their lectin binding fingerprints. By training a Boltzmann model on lectin binding data, we predict the approximate structures of 88 {+/-} 7% of N-glycans and 87 {+/-} 13% of O-glycans in our test set. We show that our model generalizes well to the pharmaceutically relevant case of Chinese Hamster Ovary (CHO) cell glycans. We also analyze the motif specificity of a wide array of lectins and identify the most and least predictive lectins and glycan features. These results could help streamline glycoprotein research and be of use to anyone using lectins for glycobiology.

bioinformatics↗
bioRxiv2023

Synchrony of Bird Migration with Avian Influenza Global Spread; Implications for Vulnerable Bird Orders

Highly pathogenic avian influenza virus (HPAIV) A H5 clade 2.3.4.4 has caused worldwide outbreaks in domestic poultry, occasional spillover to humans, and increasing deaths of diverse species of wild birds since 2014. Wild bird migration is currently acknowledged as an important ecological process contributing to the global dispersal of HPAIV H5. However, it is unclear how seasonal bird migration facilitates global virus dispersal, and which avian species are exposed to HPAI H5 clade 2.3.4.4 viruses and where. To shed light on ongoing global outbreaks, we sought to explore these questions through phylodynamic analyses based on empirical data of bird movement tracking and virus genome sequences. First, based on viral phylogeography and bird migration networks, we demonstrate that seasonal bird migration can explain salient features of the global dispersal of clade 2.3.4.4. Second, we detect synchrony between the seasonality of bird annual cycle phases and virus lineage movements. We reveal the differing vulnerable bird orders at geographical origins and destinations of HPAIV H5 lineage movements. Notably, we highlight the potential importance of relatively under-discussed Suliformes and Ciconiiformes, in addition to Anseriformes and Charadriiformes, in virus lineage movements. Our study provides a phylodynamic framework that links the bird movement ecology and genomic epidemiology of avian influenza; it highlights the importance of integrating bird behaviour and life history in avian influenza studies.

ecology↗
bioRxiv2023

Inferring secretory and metabolic pathway activity from omic data with secCellFie

Understanding protein secretion has considerable importance in the biotechnology industry and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathways activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer protein secretion capabilities from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can be used to predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

systems biology↗
bioRxiv2022

Fine temporal brain network structure modularizes and localizes differently in men and women: Insights from a novel explainability framework

AO_SCPLOWBSTRACTC_SCPLOWDeep learning has become an effective tool for classifying biological sex based on functional magnetic resonance imaging (fMRI), but research on what features within the brain are most relevant to this classification is still lacking. Model interpretability has become a powerful way to understand "black box" deep-learning models and select features within the input data that are most relevant to the correct classification. However, very little work has been done employing these methods to understand the relationship between the temporal dimension of functional imaging signals and classification of biological sex, nor has there been attention paid to rectifying problems and limitations associated with feature explanation models, e.g. underspecification and instability. We provide a methodology to limit the impact of underspecification on the stability of the measured feature importance, and then, using intrinsic connectivity networks (ICNs) from fMRI data, we provide a deep exploration of sex differences among functional brain networks. We report numerous conclusions, including activity differences in the visual and cognitive domains, as well as major connectivity differences.

neuroscience↗
bioRxiv2022

Antigenic characterization and pandemic risk assessment of North American H1 influenza A viruses circulating in swine

The first pandemic of the 21st century was caused by an H1N1 influenza A virus (IAV) introduced from pigs into humans, highlighting the importance of swine as reservoirs for pandemic viruses. Two major lineages of swine H1 circulate in North America: the 1A classical swine lineage (including the 2009 pandemic H1N1) and 1B human seasonal-like lineage. Here, we investigated the evolution of these H1 IAV lineages in North American swine and their potential pandemic risk. We assessed the antigenic distance between the HA of representative swine H1 and human seasonal vaccine strains (1978-2015) in hemagglutination inhibition (HI) assays using a panel of monovalent anti-sera raised in pigs. Antigenic cross-reactivity varied by strain but was associated with genetic distance. Generally, swine 1A lineage viruses that seeded the 2009 H1 pandemic were antigenically most similar to H1 pandemic vaccine strains, with the exception of viruses in the genetic clade 1A.1.1.3 that had a two-amino acid deletion mutation near the receptor-binding site, dramatically reducing antibody recognition. The swine 1B lineage strains, which arose from previously circulating (pre-2009 pandemic) human seasonal viruses, were more antigenically similar to pre-2009 human seasonal H1 vaccine viruses than post-2009 strains. Human population immunity was measured by cross-reactivity in HI assays to representative swine H1 strains. There was a broad range of titers against each swine strain that was not associated with age, sex, or location. However, there was almost no cross-reactivity in human sera to the 1A.1.1.3 and 1B.2.1 genetic clades of swine viruses, and the 1A.1.1.3 and 1B.2.1 clades were also the most antigenically distant from all human vaccine strains. Our data demonstrate that antigenic distances of representative swine strains from human vaccine strains represent a rational assessment of swine IAV for zoonotic risk research and pandemic preparedness prioritization. ImportanceHuman H1 influenza A viruses (IAV) spread to pigs in North America, resulting in sustained circulation of two major groups of H1 viruses in swine. We quantified the genetic diversity of H1 in swine and measured antigenic phenotypes. We demonstrated that swine H1 lineages were significantly different from human vaccine strains and this antigenic dissimilarity increased over time as the viruses evolved in swine. Pandemic preparedness vaccine strains for human vaccines also demonstrated a loss in similarity with contemporary swine strains. Human sera revealed a range of responses to swine IAV, including two groups of viruses with little to no immunity. Surveillance and risk assessment of IAV diversity in pig populations are essential to detect strains with reduced immunity in humans, providing critical information for pandemic preparedness.

microbiology↗
bioRxiv2022

Comparative study between radiofrequency- and muscimol-induced inhibition of cultured networks of cortical neuron

Previous studies have shown that spontaneously active cultured networks of cortical neuron grown planar microelectrode arrays are sensitive to radiofrequency (RF) fields and exhibit an inhibitory response more pronounced as the exposure time and power increase. To better understand the mechanism behind the observed effects, we aimed at identifying similarities and differences between the inhibitory effect of RF fields (continuous wave, 1800 MHz) to the {gamma}-aminobutyric acid type A (GABAA) receptor agonist muscimol (MU). Inhibition of the network bursting activity in response to RF exposure became apparent at an SAR level of 28.6 W/kg and co-occurred with an elevation of the culture medium temperature of ~1 {degrees}C. Exposure to RF fields preferentially inhibits bursting over spiking activity and exerts fewer constraints on neural network bursting synchrony, differentiating it from a pharmacological inhibition with MU. Network rebound excitation, a phenomenon relying on the intrinsic properties of cortical neurons, was observed following the removal of tonic hyperpolarization after washout of MU but not in response to cessation of RF exposure. This implies that hyperpolarization is not the main driving force mediating the inhibitory effects of RF fields. At the level of single neurons, network inhibition induced by MU and RF fields occurred with reduced action potential (AP) half-width. As changes in AP waveform strongly influence efficacy of synaptic transmission, the narrowing effect on AP seen under RF exposure might contribute to reducing network bursting activity. By pointing only to a partial overlap between the inhibitory hallmarks of these two forms of inhibition, our data suggest that the inhibitory mechanisms of the action of RF fields differ from the ones mediated by the activation of GABAA receptors.

neuroscience↗
bioRxiv2021

Harnessing secretory pathway differences between HEK293 and CHO to rescue production of difficult to express proteins

Biologics represent the fastest growing group of therapeutics, but many advanced recombinant protein moieties remain difficult to produce. Here, we identify bottlenecks limiting expression of recombinant human proteins through a systems biology analysis of the transcriptomes of CHO and HEK293 during recombinant overexpression. Surprisingly, one third of the challenging human proteins displayed improved secretion upon host cell swapping from CHO to HEK293. While most components of the secretory machinery showed comparable expression levels in both expression hosts, genes with significant expression variation were identified. Among these, ATF4, SRP9, JUN, PDIA3 and HSPA8 were validated as productivity boosters in CHO. Further, more heavily glycosylated products benefitted more from the elevated activities of the N- and O-glycosyltransferases found in HEK293. Collectively, our results demonstrate the utilization of HEK293 for expression rescue of human proteins and suggest a methodology for identification of secretory pathway components improving recombinant protein yield in HEK293 and CHO.

systems biology↗
bioRxiv2019

A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production

Chinese hamster ovary (CHO) cells are the main workhorse in the biopharmaceutical industry for the production of recombinant proteins, such as monoclonal antibodies. To date, a variety of metabolic engineering approaches have been used to improve the productivity of CHO cells. While genetic manipulations are potentially laborious in mammalian cells, rational design of CHO cell culture medium or efficient fed-batch strategies are more popular approaches for bioprocess optimization. In this study, a genome-scale metabolic network model of CHO cells was used to design feeding strategies for CHO cells to improve monoclonal antibody (mAb) production. A number of metabolites, including threonine and arachidonate, were suggested by the model to be added into cell culture medium. The designed composition has been experimentally validated, and then optimized, using design of experiment methods. About a two-fold increase in the total mAb expression has been observed using this strategy. Our approach can be used in similar bioprocess optimization problems, in order to suggest new ways of increasing production in different cell factories.

bioengineering↗
bioRxiv2019

Multiplex secretome engineering enhances recombinant protein production and purity

Host cell proteins (HCPs) are process-related impurities generated during biotherapeutic protein production. HCPs can be problematic if they pose a significant metabolic demand, degrade product quality, or contaminate the final product. Here, we present an effort to create a \"clean\" Chinese hamster ovary (CHO) cell by disrupting multiple genes to eliminate HCPs. Using a model of CHO cell protein secretion, we predicted the elimination of unnecessary HCPs could have a non-negligible impact on protein production. We analyzed the total HCP content of 6-protein, 11-protein, and 14-protein knockout clones and characterized their growth in shake flasks and bioreactors. These cell lines exhibited a substantial reduction in total HCP content (40%-70%). We also observed higher productivity and improved growth characteristics, in specific clones. With the reduced HCP content, protein A and ion exchange chromatography more efficiently purified a monoclonal antibody (mAb) produced in these cells during a three-step purification process. Thus, substantial improvements can be made in protein titer and purity through large-scale HCP deletion, providing an avenue to increased quality and affordability of high-value biopharmaceuticals.

bioengineering↗
bioRxiv2019

Application of extracellular flux analysis for determining mitochondrial function in mammalian oocytes and early embryos

1.BackgroundMitochondria provide the major source of ATP for mammalian oocyte maturation and early embryo development. Oxygen Consumption Rate (OCR) is an established measure of mitochondrial function. OCR by mammalian oocytes and embryos has generally been restricted to overall uptake and detailed understanding of the components of OCR dedicated to specific molecular events remains lacking.\n\nResultsHere, extracellular flux analysis (EFA) was applied to small groups of bovine, equine, mouse and human oocytes and bovine early embryos to measure OCR. Using EFA, we report the changes in mitochondrial activity during the processes of oocyte maturation, fertilization, and pre-implantation development to blastocyst stage in response to physiological demands in mammalian embryos. Crucially, we describe the real time partitioning of overall OCR to spare capacity, proton leak, non-mitochondrial and coupled respiration - showing that while there are alterations in activity over the course of development to respond to physiological demand, the overall efficiency is unchanged.\n\nConclusionEFA is shown to be able to measure mitochondrial function in small groups of mammalian oocytes and embryos in a manner which is robust, rapid and easy to use. EFA is non-invasive and allows real-time determination of the impact of compounds on OCR, facilitating an assessment of the parameters of mitochondrial activity. This provides proof-of-concept for EFA as an accessible system with which to study oocyte and embryo metabolism.

developmental biology↗
bioRxiv2019

StanDep: capturing transcriptomic variability improves context-specific metabolic models

Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB toolbox, is available at https://github.com/LewisLabUCSD/StanDep

systems biology↗
bioRxiv2019

Static and Dynamic Functional Connectivity Analysis of Cerebrovascular Reactivity: An fMRI Study

The human brain, as a finely-tuned system, needs a constant flow of oxygen to function properly. To accomplish this, the cerebrovascular system ensures a steady stream of oxygenation to brain cells. One tool that the cerebrovascular system uses is cerebrovascular reactivity (CVR), which is the systems ability to react to vasoactive stimuli. Understanding CVR can provide unique information about cerebrovascular diseases and general brain function. CVR can be evaluated by scanning subjects with blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) while they periodically inhale room air and CO2-enriched gas, a powerful and widely-used vasodilator. Our goal is to understand the effect of vasodilation on individual intrinsic connectivity networks (ICNs), as well as how functional network connectivity (FNC) adapts to the same vasodilation. To achieve this goal, we first developed an innovative metric to measure the effect of CVR on ICNs, which contrasts to the commonly used voxel-wise CVR. Furthermore, for the first time, we studied static (sFNC) and dynamic (dFNC) FNC in the context of CVR. Our results show that network connectivity is generally weaker during vascular dilation, and these results are more pronounced in dFNC analysis. dFNC analysis reveals that participants did not return to the pre-CO2 inhalation state, suggesting that the one-minute period of room-air inhalation is not enough for the CO2 effect to fully dissipate in humans. Overall, we see new relationships between CVR and ICNs, as well as how FNC adapts to vascular system changes.

neuroscience↗
bioRxiv2019

Decentralized Distribution-sampled Classification Models with Application to Brain Imaging

0.1 backgroundIn this age of big data, large data stores allow researchers to compose robust models that are accurate and informative. In many cases, the data are stored in separate locations requiring data transfer between local sites, which can cause various practical hurdles, such as privacy concerns or heavy network load. This is especially true for medical imaging data, which can be constrained due to the health insurance portability and accountability act (HIPAA). Medical imaging datasets can also contain many thousands or millions of features, requiring heavy network load.\n\n0.2 New MethodOur research expands upon current decentralized classification research by implementing a new singleshot method for both neural networks and support vector machines. Our approach is to estimate the statistical distribution of the data at each local site and pass this information to the other local sites where each site resamples from the individual distributions and trains a model on both locally available data and the resampled data.\n\n0.3 ResultsWe show applications of our approach to handwritten digit classification as well as to multi-subject classification of brain imaging data collected from patients with schizophrenia and healthy controls. Overall, the results showed comparable classification accuracy to the centralized model with lower network load than multishot methods.\n\n0.4 Comparison with Existing MethodsMany decentralized classifiers are multishot, requiring heavy network traffic. Our model attempts to alleviate this load while preserving prediction accuracy.\n\n0.5 ConclusionsWe show that our proposed approach performs comparably to a centralized approach while minimizing network traffic compared to multishot methods.\n\n0.6 HighlightsO_LIA novel yet simple approach to decentralized classification\nC_LIO_LIReduces total network load compared to current multishot algorithms\nC_LIO_LIMaintains a prediction accuracy comparable to the centralized approach\nC_LI

bioinformatics↗
bioRxiv2019

A blood-based prognostic biomarker in inflammatory bowel disease

ObjectiveWe have previously described a prognostic transcriptional signature in CD8 T cells that separates inflammatory bowel disease (IBD) patients into 2 phenotypically-distinct subgroups, which we termed IBD1 and IBD2. Here we sought to develop a blood-based prognostic test that could identify these patient subgroups without the need for cell separation, and thus be suitable for routine clinical use in Crohns disease (CD) and ulcerative colitis (UC). DesignPatients with active IBD were recruited before treatment. Transcriptomic analyses were performed on purified CD8 T cells and/or whole blood. Detailed phenotype information was collected prospectively. IBD1/IBD2 patient subgroups were identified by consensus clustering of CD8 T cell transcriptomes. In a training cohort, statistical (machine) learning was used to identify groups of genes ("classifiers") whose differential expression in whole blood re-created the IBD1/IBD2 patient subgroups. Genes from the best classifiers were qPCR-optimised, and further statistical learning was applied to the qPCR dataset to identify the optimal classifier, which was locked-down for further testing. Independent validation was sought in separate cohorts of CD (n=66) and UC patients (n=57). ResultsIn both independent validation cohorts, a 17-gene qPCR-based classifier stratified patients into two distinct subgroups. Irrespective of the underlying diagnosis, IBDhi patients (analogous to the poor prognosis IBD1 subgroup) experienced significantly more aggressive disease than IBDlo patients (analogous to IBD2), with earlier need for treatment escalation and more escalations over time. ConclusionThis is the first validated prognostic biomarker that can predict prognosis in newly-diagnosed IBD patients, and represents a step towards personalised therapy. What is already known about this subject?The course of CD and UC varies considerably between patients, but reliable prognostic markers are not available in clinical practice. This hinders disease management because treatment approaches that would be optimal for patients with indolent disease - characterised by infrequent flare-ups that can be readily controlled by first-line therapy - will inevitably undertreat those with progressive disease. Conversely, strategies that would appropriately control frequently-relapsing, progressive disease will expose patients with more quiescent disease to the risks and side effects of unnecessary treatment. We have previously described a CD8 T cell gene expression signature that corresponds to differences in T cell exhaustion, is detectable during active untreated disease (including at diagnosis), and predicts disease course in both UC and CD. However, the need for cell separation and microarray-based gene expression analysis would make this difficult to translate to clinical practice. What are the new findings?We have developed, optimised, and independently validated a whole blood qPCR-based classifier - designed to identify the IBD1 and IBD2 patient subgroups - that can reliably predict prognosis in patients with CD or UC from diagnosis without the need for cell separation. We also present a detailed phenotypic update on the disease course experienced by patients in either the IBD1/IBDhi or IBD2/IBDlo subgroups, incorporating both expanded patient cohorts and substantially longer follow-up. This affords new insights into the spectrum of therapies that are differentially required in these patient subgroups, and reinforces their association with disease prognosis. How might it impact on clinical practice in the foreseeable future?The qPCR-based classifier has performance characteristics that compare favourably to prognostic biomarkers currently in use in oncology, and should be sufficient to guide therapy from diagnosis in patients with CD or UC. This represents an important step towards personalised therapy in inflammatory bowel disease.

genomics↗
bioRxiv2018

Proteogenomic annotation of the Chinese hamster reveals extensive novel translation events and endogenous retroviral elements

A high quality genome annotation greatly facilitates successful cell line engineering. Standard draft genome annotation pipelines are based largely on de novo gene prediction, homology, and RNA-Seq data. However, draft annotations can suffer from incorrectly predictions of translated sequence, incorrect splice isoforms and missing genes. Here we generated a draft annotation for the newly assembled Chinese hamster genome and used RNA-Seq, proteomics, and Ribo-Seq to experimentally annotate the genome. We identified 4,333 new proteins compared to the hamster RefSeq protein annotation and 2,503 novel translational events (e.g., alternative splices, mutations, novel splices). Finally, we used this pipeline to identify the source of translated retroviruses contaminating recombinant products from Chinese hamster ovary (CHO) cell lines, including 131 type-C retroviruses, thus enabling future efforts to eliminate retroviruses by reducing the costs incurred with retroviral particle clearance. In summary, the improved annotation provides a more accurate platform for guiding CHO cell line engineering, including facilitating the interpretation of omics data, defining of cellular pathways, and engineering of complex phenotypes.

bioinformatics↗
bioRxiv2018

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns are changing in fundamental and recurring ways through time. Most previous work has assumed fixed spatial nodes/networks, ignoring the possibility that spatial nodes or networks may vary in time, particularly at the level of the voxel. Here, we introduce an approach allowing for a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variation in spatiotemporal coupling at the voxel level within each network. We identify a novel set of spatially dynamic features which can be obtained and evaluated under different conditions. Results reveal transient spatially fluid interactions between intra- and inter-network relationships in which brain networks transiently merge and then separate again, emphasizing the dynamic interplay between segregation and integration. We also show that brain networks exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies which assumed static networks. Moreover, we show for the first time that anticorrelative connections to the default mode network, are transient as opposed to constant across the entire scan. Preliminary assessments of the approach using a multi-site dataset collected from 160 healthy subjects and 149 patients with schizophrenia (SZ) revealed the ability of the approach to obtain new information and nuanced alterations of brain networks that remain undetected during static analysis. For example, patients with SZ display transient decreases in voxel-wise network coupling including within visual and auditory networks that are not detectable in a spatially static analysis. Our approach also enabled calculation of a novel parameter, the intra-domain coupling variability which was higher within patients with SZ. The significant association between spatiotemporal uniformity and attention/vigilance cognitive domain highlights the cognitive relevance of the spatial chronnectome. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks that are transient at the voxel level and identification of mechanisms for within and between-subject spatial variability to study functional brain homeostasis.

neuroscience↗
bioRxiv2018

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology.

systems biology↗