Biology subjects

Johnson, W.

Publications and source records attributed to Johnson, W..

10 recordsLinked to original sources

bioRxiv2023

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of the production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity but cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic characterization approach combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to reprogramming features associated with high-producer clones and metabolic bottlenecks limiting product production in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

systems biology↗

bioRxiv2023

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

systems biology↗

bioRxiv2023

Investigating robust associations between functional connectivity based on graph theory and general intelligence

Previous research investigating relations between general intelligence and graph-theoretical properties of the brains intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency in any sample, but significant positive associations with global clustering coefficient and small-world propensity in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for nodal efficiency and only led to significant predictions between two data sets for local clustering. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.

neuroscience↗

bioRxiv2023

Sirtuin 2 inhibition modulates chromatin landscapes genome- wide to induce senescence in ATRX-deficient malignant glioma

Inactivating mutations in ATRX characterize large subgroups of malignant gliomas in adults and children. ATRX deficiency in glioma induces widespread chromatin remodeling, driving transcriptional shifts and oncogenic phenotypes. Effective strategies to therapeutically target these broad epigenomic sequelae remain undeveloped. We utilized integrated mulit-omics and the Broad Institute Connectivity Map (CMAP) to identify drug candidates that could potentially revert ATRX-deficient transcriptional changes. We then employed disease-relevant experimental models to evaluate functional phenotypes, coupling these studies with epigenomic profiling to elucidate molecular mechanim(s). CMAP analysis and transcriptional/epigenomic profiling implicated the Class III HDAC Sirtuin2 (Sirt2) as a central mediator of ATRX-deficient cellular phenotypes and a driver of unfavorable prognosis in ATRX-deficient glioma. Sirt2 inhibitors reverted Atrx-deficient transcriptional signatures in murine neuroprogenitor cells (mNPCs) and impaired cell migration in Atrx/ATRX-deficient mNPCs and human glioma stem cells (GSCs). While effects on cellular proliferation in these contexts were more modest, markers of senescence significantly increased, suggesting that Sirt2 inhibition promotes terminal differentiation in ATRX-deficient glioma. These phenotypic effects were accompanied by genome-wide shifts in enhancer-associated H3K27ac and H4K16ac marks, with the latter in particular demonstrating compelling transcriptional links to Sirt2-dependent phenotypic reversals. Motif analysis of these data identified the transcription factor KLF16 as a mediator of phenotype reversal in Atrx-deficient cells upon Sirt2 inhibition. Finally, Sirt2 inhibition impaired growth and increased senescence in ATRX-deficient GSCs in vivo. Our findings indicate that Sirt2 inhibition selectively targets ATRX-deficient gliomas through global chromatin remodeling, while demonstrating more broadly a viable approach to combat complex epigenetic rewiring in cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/523324v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1d1c7cborg.highwire.dtl.DTLVardef@19421fcorg.highwire.dtl.DTLVardef@433aaaorg.highwire.dtl.DTLVardef@1c07f72_HPS_FORMAT_FIGEXP M_FIG C_FIG One Sentence SummaryOur study demonstrates that SIRT2 inhibition promotes senescence in ATRX-deficient glioma model systems through global epigenomic remodeling, impacting key downstream transcriptional profiles.

cancer biology↗

bioRxiv2022

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data

Genome-scale metabolic models comprehensively describe an organisms metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organisms phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

systems biology↗

bioRxiv2022

Robust associations between white matter microstructure and general intelligence

Early research on the neural correlates of human intelligence was almost exclusively focused on gray matter properties. The advent of diffusion-weighted imaging led to an exponential growth of white matter brain imaging studies. However, this line of research has yielded mixed observations, especially about the relations between general intelligence and white matter microstructure. We used a multi-center approach to identify white matter regions that show replicable structure-function associations, employing data from four independent samples comprising over 2000 healthy participants. We used tract-based spatial statistics to examine associations between g factor scores and white matter microstructure and identified 188 voxels which exhibited positive associations between g factor scores and fractional anisotropy in all four data sets. Replicable voxels formed three clusters: one located around the forceps minor, crossing with extensions of the anterior thalamic radiation, the cingulum-cingulate gyrus, and the inferior fronto-occipital fasciculus in the left hemisphere, one located around the left-hemispheric superior longitudinal fasciculus, and one located around the left-hemispheric cingulum-cingulate gyrus, crossing with extensions of the anterior thalamic radiation and the inferior fronto-occipital fasciculus. Our results indicate that individual differences in general intelligence are robustly associated with white matter organization in specific fiber bundles.

neuroscience↗

bioRxiv2022

Immune-responsive biodegradable scaffolds for enhancing neutrophil regeneration

Neutrophils are essential effector cells for mediating rapid host defense and their insufficiency arising from therapy-induced side-effects, termed neutropenia, can lead to immunodeficiency-associated complications. In autologous hematopoietic stem cell transplantation (HSCT), neutropenia is a complication that limits therapeutic efficacy. Here, we report the development and in vivo evaluation of an injectable, biodegradable hyaluronic acid (HA)-based scaffold, termed HA cryogel, with myeloid responsive degradation behavior. In mouse models of immune deficiency, we show that the infiltration of functional myeloid-lineage cells, specifically neutrophils, is essential to mediate HA cryogel degradation. Post-HSCT neutropenia in recipient mice delayed degradation of HA cryogels by up to 3 weeks. We harnessed the neutrophil-responsive degradation to sustain the release of granulocyte colony stimulating factor (G-CSF) from HA cryogels. Sustained release of G-CSF from HA cryogels enhanced post-HSCT neutrophil recovery, comparable to pegylated G-CSF, which, in turn, accelerated cryogel degradation. HA cryogels are a potential approach for enhancing neutrophils and concurrently assessing immune recovery in neutropenic hosts.

bioengineering↗

bioRxiv2021

A Genome-Phenome Association study in native microbiomes identifies a mechanism for cytosine modification in DNA and RNA

Shotgun metagenomic sequencing is a powerful approach to study microbiomes in an unbiased manner and of increasing relevance for identifying novel enzymatic functions. However, the potential of metagenomics to relate from microbiome composition to function has thus far been underutilized. Here, we introduce the Metagenomics Genome-Phenome Association (MetaGPA) study framework, which allows to link genetic information in metagenomes with a dedicated functional phenotype. We applied MetaGPA to identify enzymes associated with cytosine modifications in environmental samples. From the 2365 genes that met our significance criteria, we confirm known pathways for cytosine modifications and proposed novel cytosine-modifying mechanisms. Specifically, we characterized and identified a novel nucleic acid modifying enzyme, 5-hydroxymethylcytosine carbamoyltransferase, that catalyzes the formation of a previously unknown cytosine modification, 5-carbamoyloxymethylcytosine, in DNA and RNA. Our work introduces MetaGPA as a novel and versatile tool for advancing functional metagenomics.

genomics↗

bioRxiv2019

Lower limb chronic edema management program: Perspectives of disengaged patients on challenges, enablers and barriers to program attendance and adherence

BackgroundChronic edema (CO) is a progressive, physically disfiguring and currently incurable condition. A multifaceted program has been recommended to manage the swelling. However, there is little evidence investigating patients perspectives following the program, particularly for those who have poor adherence or are disengaged.\n\nAimTo investigate the perceived challenges faced by disengaged participants with lower limb CO by identifying their enablers and barriers to participating in a Physiotherapy CO program.\n\nMethodAn exploratory qualitative approach was used. A purposive sampling strategy was adopted to recruit participants. Those with more than three months swelling and who had low adherence or attendance (disengaged) to the CO program were invited to participate. Semi-structured interviews with six participants from a CO clinic in a tertiary hospital were conducted. Data was thematically analyzed and findings in terms of enablers and barriers were subsequently reflected in the light of a theoretical framework.\n\nResultsAll six participants were morbidly obese (BMI 47 {+/-} 4 kg/m2) with multiple chronic comorbidities. Enablers and barriers detected included physical, psychological and social factors that interplay to present multidimensional challenges that influence the participants adjustment to managing their CO. For the disengaged participants in this study, their under-managed lower limb CO was a progression towards being housebound and having a gradually increasing level of disability.\n\nConclusionChronic edema, if left unmanaged, can have a profound debilitating impact on the individual permanently. Understanding the challenges faced by patients with lower limb CO has implications for developing a more patient-centered multidisciplinary approach to clinical practice and suggests the need for further research.

scientific communication and education↗

bioRxiv2019

Determinants of the population health distribution, or why are risk factor-body mass index associations larger at the upper end of the BMI distribution?

Most epidemiological studies examine how risk factors relate to average difference in outcomes (linear regression) or odds a binary outcome (logistic regression); they do not explicitly examine whether risk factors are associated differentially across the distribution of the health outcome investigated. This paper documents a phenomenon found repeatedly in the minority of epidemiological studies which do this (via quantile regression) -associations between a range of established risk factors and body mass index (BMI) are progressively stronger in the upper ends of the BMI distribution. In this paper, we document this finding and provide illustrative evidence of it in a single dataset (the 1958 British birth cohort study). Associations of low childhood socioeconomic position, high maternal weight, low childhood general cognition and adult physical inactivity with higher BMI are larger at the upper end of the BMI distribution, on both absolute and relative scales. For example, effect estimates for socioeconomic position and childhood cognition were around three times larger at the 90th compared with 10th quantile, while effect estimates for physical inactivity were increasingly larger from the 50th-90th quantiles, yet null at lower quantiles. We provide potential explanations for these findings and discuss possible research and policy implications. We conclude by stating that tools such as quantile regression may be useful to better understand how risk factors relate to the distribution of health -particularly so in obesity research given conventional reliance on cut-points -yet for other outcomes in addition given the continuous nature of population health.

epidemiology↗