Biology subjects

Lewis, N. E.

Publications and source records attributed to Lewis, N. E..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Restoration of deficient DNA Repair Genes Mitigates Genome Instability and Increases Productivity of Chinese Hamster Ovary Cells

Chinese Hamster Ovary (CHO) cells are the primary host used for manufacturing of therapeutic proteins. However, production instability of high-titer cell lines is a major problem and is associated with genome instability, as chromosomal aberrations reduce transgene copy number and decrease protein titer. We analyzed whole-genome sequencing data from 11 CHO cell lines and found deleterious single-nucleotide polymorphisms (SNPs) in DNA repair genes. Comparison with other mammalian cells confirmed DNA repair is compromised in CHO. Restoration of key DNA repair genes by SNP reversal or expression of intact cDNAs improved DNA repair and genome stability. Moreover, the restoration of LIG4 and XRCC6 in a CHO cell line expressing secreted alkaline phosphatase mitigated transgene copy loss and improved protein titer retention. These results show for the first time that correction of key DNA repair genes yields considerable improvements in stability and protein expression in CHO, and provide new opportunities for cell line development and a more efficient and sustainable production of therapeutic proteins.

cell biology↗

bioRxiv2020

Inferring the spatial code of cell-cell interactions and communication across a whole animal body

Cell-cell interactions shape cellular function and ultimately organismal phenotype. However, the code embedded in the molecular interactions driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans body. Leveraging a 3D atlas of C. elegans cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.

bioinformatics↗

bioRxiv2020

A Chinese hamster transcription start site atlas that enables targeted editing of CHO cells

Chinese hamster ovary (CHO) cells, with their human-compatible glycosylation and high protein titers, are the most widely used cells for producing biopharmaceuticals. Engineering gene expression in CHO is key to improving drug quality and affordability. However, engineering gene expression or activating silent genes requires accurate annotation of the underlying regulatory elements and transcription start sites (TSSs). Unfortunately, most TSSs in the Chinese hamster genome were computationally predicted and are frequently inaccurate. Here, we revised TSS annotations for 15,308 Chinese hamster genes and 4,478 non-coding RNAs based on experimental data from CHO-K1 cells and 10 hamster tissues. The experimental realignment and discovery of TSSs now expose previously hidden motifs, such as the TATA box. We further demonstrate, by targeting the glycosyltransferase gene Mgat3, how accurate annotations readily facilitate activating silent genes by CRISPRa to obtain more human-like glycosylation. Together, we envision our annotation and data will provide a rich resource for the CHO community, improve genome engineering efforts and aid comparative and evolutionary studies.

genomics↗

bioRxiv2020

Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration

AO_SCPLOWBSTRACTC_SCPLOWHuman Milk Oligosaccharides (HMOs) are abundant carbohydrates fundamental to infant health and development. Although these oligosaccharides were discovered more than half a century ago, their biosynthesis in the mammary gland remains largely uncharacterized. Here, we used a systems biology framework that integrated glycan and RNA expression data to construct an HMO biosynthetic network and predict glycosyltransferases involved. To accomplish this, we constructed models describing the most likely pathways for the synthesis of the oligosaccharides accounting for >95% of the HMO content in human milk. Through our models, we propose candidate genes for elongation, branching, fucosylation, and sialylation of HMOs. We further explored selected enzyme activities through kinetic assay and their co-regulation through transcription factor analysis. These results provide the molecular basis of HMO biosynthesis necessary to guide progress in HMO research and application with the ultimate goal of understanding and improving infant health and development. SO_SCPLOWIGNIFICANCEC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWSTATEMENTC_SCPLOWWith the HMO biosynthesis network resolved, we can begin to connect genotypes with milk types and thereby connect clinical infant, child and even adult outcomes to specific HMOs and HMO modifications. Knowledge of these pathways can simplify the work of synthetic reproduction of these HMOs providing a roadmap for improving infant, child, and overall human health with the specific application of a newly limitless source of nutraceuticals for infants and people of all ages.

systems biology↗

bioRxiv2020

A genome-scale metabolic network model synergizes with statistical learning to predict amino acid concentrations in Chinese Hamster Ovary cell cultures

The control of nutrient availability is critical to large-scale manufacturing of biotherapeutics. However, the quantification of proteinogenic amino acids is time-consuming and thus is difficult to implement for real-time in situ bioprocess control. Genome-scale metabolic models describe the metabolic conversion from media nutrients to proliferation and recombinant protein production, and therefore are a promising platform for in silico monitoring and prediction of amino acid concentrations. This potential has not been realized due to unresolved challenges: (1) the models assume an optimal and highly efficient metabolism, and therefore tend to underestimate amino acid consumption, and (2) the models assume a steady state, and therefore have a short forecast range. We address these challenges by integrating machine learning with the metabolic models. Through this we demonstrate accurate and time-course dependent prediction of individual amino acid concentration in culture medium throughout the production process. Thus, these models can be deployed to control nutrient feeding to avoid premature nutrient depletion or provide early predictions of failed bioreactor runs.

systems biology↗

bioRxiv2020

Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity

The human microbiota has a close relationship with human disease and it remodels components of the glycocalyx including heparan sulfate (HS). Studies of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spike protein receptor binding domain suggest that infection requires binding to HS and angiotensin converting enzyme 2 (ACE2) in a codependent manner. Here, we show that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex. Common human-associated commensal bacteria whose genomes encode HS-modifying enzymes were identified. The prevalence of these bacteria and the expression of key microbial glycosidases in bronchoalveolar lavage fluid (BALF) was lower in adult COVID-19 patients than in healthy controls. The presence of HS-modifying bacteria decreased with age in two large survey datasets, FINRISK 2002 and American Gut, revealing one possible mechanism for the observed increase in COVID-19 susceptibility with age. In vitro, bacterial glycosidases from unpurified culture media supernatants fully blocked SARS-CoV-2 spike binding to human H1299 protein lung adenocarcinoma cells. HS-modifying bacteria in human microbial communities may regulate viral adhesion, and loss of these commensals could predispose individuals to infection. Understanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/238444v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@14ff1ecorg.highwire.dtl.DTLVardef@193d84corg.highwire.dtl.DTLVardef@15d6f9eorg.highwire.dtl.DTLVardef@14b16c6_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract. Diagram of hypothesis for bacterial mediation of SARS-CoV-2 infection through heparan sulfate (HS).It is well known that host microbes groom the mucosa where they reside. Recent investigations have shown that HS, a major component of mucosal layers, is necessary for SARS-CoV-2 infection. In this study we examine the impact of microbial modification of HS on viral attachment. C_FIG

microbiology↗

bioRxiv2020

Atypical genomic patterning of the cerebral cortex in autism with poor early language outcome

Cortical regional identities develop through anterior-posterior (A-P) and dorsal-ventral (D-V) prenatal genomic patterning gradients. Here we find that A-P and D-V genomic patterning of cortical surface area (SA) and thickness (CT) is intact in typically developing and autistic toddlers with good language outcome, but is absent in autistic toddlers with poor early language outcome. Genes driving this effect are prominent in midgestational A-P and D-V gene expression gradients and prenatal cell types driving SA and CT variation (e.g., progenitor cells versus excitatory neurons). These genes are also important for vocal learning, human-specific evolution, and prenatal co-expression networks enriched for high-penetrance autism risk genes. Autism with poor early language outcome may be linked to atypical genomic cortical patterning starting in prenatal periods and which impacts later development of regional functional specialization and circuit formation. One Sentence SummaryGenomic patterning of the cortex is atypical in autistic toddlers with poor early language outcome.

neuroscience↗

bioRxiv2020

Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation

A hallmark of amyloid disorders, such as Alzheimers disease, is aggregation of secreted proteins. However, it is largely unclear how the hundreds of secretory pathway proteins contribute to amyloid formation. We developed a systems biology framework that integrates expression data with protein-protein interaction networks to successfully estimate a tissues fitness for producing specific secreted proteins. Using this framework, we analyzed the fitness of the secretory pathway of various brain regions and cell types for synthesizing the Alzheimers disease-associated amyloid-precursor protein (APP). While none of the key amyloidogenic pathway components were differentially expressed in AD brain, we found the deposition of A{beta} is associated with repressed expression of the secretory pathway components proximal to APP. Concurrently, we detected systemic up-regulation of the secretory pathway components proximal to {beta}- and {gamma}-secretases in AD brains. Our analyses suggest that perturbations from 3 high confidence AD risk genes cascade through the secretory machinery support network for APP and into the endocytosis pathway. Thus, we present a model where amyloidogenesis is associated with dysregulation of dozens of secretory pathway components supporting APP, which could yield novel therapeutic targets for the treatment of AD.

neuroscience↗

bioRxiv2020

Systematic evaluation of parameterization for genome-scale metabolic models of cultured mammalian cells

Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells - including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.

systems biology↗

bioRxiv2020

Virus-Receptor Interactions of Glycosylated SARS-CoV-2 Spike and Human ACE2 Receptor

The current COVID-19 pandemic is caused by the SARS-CoV-2 betacoronavirus, which utilizes its highly glycosylated trimeric Spike protein to bind to the cell surface receptor ACE2 glycoprotein and facilitate host cell entry. We utilized glycomics-informed glycoproteomics to characterize site-specific microheterogeneity of glycosylation for a recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. We combined this information with bioinformatic analyses of natural variants and with existing 3D-structures of both glycoproteins to generate molecular dynamics simulations of each glycoprotein alone and interacting with one another. Our results highlight roles for glycans in sterically masking polypeptide epitopes and directly modulating Spike-ACE2 interactions. Furthermore, our results illustrate the impact of viral evolution and divergence on Spike glycosylation, as well as the influence of natural variants on ACE2 receptor glycosylation that, taken together, can facilitate immunogen design to achieve antibody neutralization and inform therapeutic strategies to inhibit viral infection.

biochemistry↗

bioRxiv2020

A consensus-based and readable extension of Linear Code for Reaction Rules (LiCoRR)

Systems glycobiology aims to provide models and analysis tools that account for the biosynthesis, regulation, and interactions with glycoconjugates. To facilitate these methods, there is a need for a clear glycan representation accessible to both computers and humans. Linear Code, a linearized and readily parsable glycan structure representation, is such a language. For this reason, Linear Code was adapted to represent reaction rules, but the syntax has drifted from its original description to accommodate new and originally unforeseen challenges. Here, we delineate the consensuses and inconsistencies that have arisen through this adaptation. We recommend options for a consensus-based extension of Linear Code that can be used for reaction rule specification going forward. Through this extension and specification of Linear Code to reaction rules, we aim to minimize inconsistent symbology thereby making glycan database queries easier. With a clear guide for generating reaction rule descriptions, glycan synthesis models will be more interoperable and reproducible thereby moving glycoinformatics closer to compliance with FAIR standards. Reaction rule-extended Linear Code is an unambiguous representation for describing glycosylation reactions in both literature and code.

systems biology↗

bioRxiv2020

Autism genetics perturb prenatal neurodevelopment through a hierarchy of broadly-expressed and brain-specific genes

Numerous genes are associated with autism spectrum disorder (ASD); however, it remains unclear how most ASD risk genes influence neurodevelopment and result in similar traits. Recent genetic models of complex traits suggest non-tissue-specific genes converge on core disease genes; so we analyzed ASD genetics in this context. We found ASD risk genes partition cleanly into broadly-expressed and brain-specific genes. The two groups show sequential roles during neurodevelopment with broadly-expressed genes modulating chromatin remodeling, proliferation, and cell fate, while brain-specific risk genes are involved in neural maturation and synapse functioning. Broadly-expressed risk genes converge onto brain-specific risk genes and core neurodevelopmental genes through regulatory networks including PI3K/AKT, RAS/ERK, and WNT/{beta}-catenin signaling pathways. Broadly-expressed and brain-specific risk genes show unique properties, wherein the broadly-expressed risk gene network is expressed prenatally and conserved in non-neuronal cells like microglia. However, the brain-specific gene network expression is limited to excitatory and inhibitory neurons, spanning prenatal to adulthood. Furthermore, the two groups are linked differently to comorbidities associated with ASD. Collectively, we describe here the organization of the genetic architecture of ASD as a hierarchy of broadly-expressed and brain-specific genes that disrupt successive stages of core neurodevelopmental processes.

systems biology↗

bioRxiv2020

Using targeted genome integration for virus-free genome-wide mammalian CRISPR screen

Pooled CRISPR screens have been widely applied in mammalian cells to discover genes regulating various phenotypes of interest. In such screens, CRISPR components are generally delivered with a lentivirus. However, lentiviral CRISPR screens are limited by unpredictable genome insertion, the requirement of biosafety level II lab facilities and personnel trained to work with viruses. Here we established a virus-free (VF) genome-wide CRISPR screening platform for Chinese hamster ovary (CHO) cells with 74,617 gRNAs targeting 18,353 genes. Each gRNA expression cassette in the library is precisely integrated into a genomic landing pad thus reducing the clonal variation. Using this VF CRISPR screening platform, 338 genes are identified as essential for CHO cell growth and 76 genes were found to be involved in the unfolded protein response (UPR) induced endoplasmic reticulum (ER) stress. Extensive validation of the candidate genes further demonstrated the robustness of this novel non-viral CRISPR screen method.

genomics↗

bioRxiv2020

A metabolic CRISPR-Cas9 screen in Chinese hamster ovary cells identifies glutamine-sensitive genes

Over the past decades, optimization of media formulation and feeding strategies have fueled a many-fold improvement in CHO-based biopharmaceutical production. While Design of Experiments (DOE) and media screens have led to many advances, genome editing offers another avenue for enhancing cell metabolism and bioproduction. However the complexity of metabolism, involving thousands of genes, makes it unclear which engineering strategies will result in desired traits. Here we developed a comprehensive pooled CRISPR screen for CHO cell metabolism, including [~]16,000 gRNAs against [~]2500 metabolic enzymes and regulators. We demonstrated the value of this screen by identifying a glutamine response network in CHO cells. Glutamine is particularly important since it is often substantially over-fed to drive increased TCA cycle flux but can lead to accumulation of toxic ammonia. Within the glutamine-response network, the deletion of a novel and poorly characterized lipase, Abhd11, was found to substantially increase growth in glutamine-free media by altering the regulation of the TCA cycle. Thus, the screen provides an invaluable targeted platform to comprehensively study genes involved in any metabolic trait.

bioengineering↗

bioRxiv2020

In situ detection of protein interactions for recombinant therapeutic enzymes

Despite their therapeutic potential, many protein drugs remain inaccessible to patients since they are difficult to secrete. Each recombinant protein has unique physicochemical properties and requires different machinery for proper folding, assembly, and post-translational modifications (PTMs). Here we aimed to identify the machinery supporting recombinant protein secretion by measuring the protein-protein interaction (PPI) networks of four different recombinant proteins (SERPINA1, SERPINC1, SERPING1 and SeAP) with various PTMs and structural motifs using the proximity-dependent biotin identification (BioID) method. We identified PPIs associated with specific features of the secreted proteins using a Bayesian statistical model, and found proteins involved in protein folding, disulfide bond formation and N-glycosylation were positively correlated with the corresponding features of the four model proteins. Among others, oxidative folding enzymes showed the strongest association with disulfide bond formation, supporting their critical roles in proper folding and maintaining the ER stability. Knockdown of disulfide-isomerase PDIA4, a measured interactor with significance for SERPINC1 but not SERPINA1, led to the decreased secretion of SERPINC1, which relies on its extensive disulfide bonds, compared to SERPINA1, which has no disulfide bonds. Proximity-dependent labeling successfully identified the transient interactions supporting synthesis of secreted recombinant proteins and refined our understanding of key molecular mechanisms of the secretory pathway during recombinant protein production.

synthetic biology↗

bioRxiv2020

What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data

Large-scale omics experiments have become standard in biological studies, leading to a deluge of data. However, researchers still face the challenge of connecting changes in the omics data to changes in cell functions, due to the complex interdependencies between genes, proteins and metabolites. Here we present a novel framework that begins to overcome this problem by allowing users to infer how metabolic functions change, based on omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. We then used genome-scale metabolic networks to define gene modules responsible for each specific metabolic task. We further developed a framework to overlay omics data on these modules to predict pathway usage for each metabolic task. The proposed approach allows one to directly predict how changes in omics experiments change cell or tissue function. We further demonstrated how this new approach can be used to leverage the metabolic functions of biological entities from the single cell to their organization in tissues and organs using multiple transcriptomic datasets (human and mouse). Finally, we created a web-based CellFie module that has been integrated into the list of tools available in GenePattern (www.genepattern.org) to enable adoption of the approach.

systems biology↗

bioRxiv2020

Multiple freeze-thaw cycles lead to a loss of consistency in poly(A)-enriched RNA sequencing

RNA-Seq is ubiquitous, but depending on the study, sub-optimal sample handling may be required, resulting in repeated freeze-thaw cycles. However, little is known about how each cycle impacts downstream analyses, due to a lack of study and known limitations in common RNA quality metrics, e.g., RIN, at quantifying RNA degradation following repeated freeze-thaws. Here we quantify the impact of repeated freeze-thaw on the reliability of downstream RNA-Seq analysis. To do so, we developed a method to estimate the relative noise between technical replicates independently of RIN. Using this approach we inferred the effect of both RIN and the number of freeze-thaw cycles on sample noise. We find that RIN is unable to fully account for the change in sample noise due to freeze-thaw cycles. Additionally, freeze-thaw is detrimental to sample quality and differential expression (DE) reproducibility, approaching zero after three cycles for poly(A)-enriched samples, wherein the inherent 3 bias in read coverage is more exacerbated by freeze-thaw cycles, while ribosome-depleted samples are less affected by freeze-thaws. The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/020792v2_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@c2c756org.highwire.dtl.DTLVardef@1ad1c03org.highwire.dtl.DTLVardef@a4136org.highwire.dtl.DTLVardef@13f403f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

CHOmics: a web-based tool for multi-omics data analysis and interactive visualization in CHO cell lines

Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. High-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an interactive visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multilayer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and interactively. The tool was also designed with the flexibility to allow other omics data input and thereby enabling multi-omics comparison and visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, visualization and management with expectations to promote the broader use of omics in CHO cell research. The open-source tool is freely available at http://www.chomics.org.

bioinformatics↗