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The functions of proteins are usually determined by domains, and the sequential order in which domains are connected to make up a protein chain is known as the domain architecture. Here, we constructed evolutionary networks of protein domain architectures in species from three major life lineages (bacteria, fungi, and metazoans) by connecting any two architectures between which an evolutionary event could be inferred by a model that assumes maximum parsimony. We found that proteins with domain architectures with a higher level of evolvability, indicated by a greater number of connections in the evolutionary network, are present in a wider range of species. However, these proteins tend to be less essential to the organism, are duplicated more often during evolution, have more isoforms, and, intriguingly, tend to be associated with functional categories important for organismal adaptation. These results reveal the presence, in many genomes, of genes coding for a core set of nonessential proteins that have a highly evolvable domain architecture and thus a repertoire of genetic materials accessible for organismal adaptation. [ABSTRACT FROM AUTHOR]

Background A major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to systems dynamics. As biological processes are often complex in nature, it is desirable to address this issue using a systematic approach. Here, we propose a simple methodology that first performs an enrichment test to find patterns in the values of globally profiled kinetic parameters with which a model can produce the required system dynamics; this is then followed by a statistical test to elucidate the association between individual parameters and different parts of the system's dynamics. Results We demonstrate our methodology on a prototype biological system of perfect adaptation dynamics, namely the chemotaxis model for Escherichia coli. Our results agreed well with those derived from experimental data and theoretical studies in the literature. Using this model system, we showed that there are motifs in kinetic parameters and that these motifs are governed by constraints of the specified system dynamics. Conclusions A systematic approach based on enrichment statistical tests has been developed to elucidate the relationships between model parameters and the roles they play in affecting system dynamics of a prototype biological network. The proposed approach is generally applicable and therefore can find wide use in systems biology modeling research. [ABSTRACT FROM AUTHOR]

High-throughput transcriptomic experiments have made it possible to classify genes that are ubiquitously expressed as housekeeping (HK) genes and those expressed only in selective tissues as tissue-specific (TS) genes. Although partitioning a transcriptome into HK and TS genes is conceptually problematic owing to the lack of precise definitions and gene expression profile criteria for the two, information whether a gene is an HK or a TS gene can provide an initial clue to its cellular and/or functional role. Consequently, the development of new and novel HK (TS) classification methods has been a topic of considerable interest in post-genomics research. Here, we report such a development. Our method, called HKera, differs from the others by utilizing a novel property of HK genes that we have previously uncovered, namely that the ranking order of their expression levels, as opposed to the expression levels themselves, tends to be preserved from one tissue to another. Evaluated against multiple benchmark sets of human HK genes, including one recently derived from second generation sequencing data, HKera was shown to perform significantly better than five other classifiers that use different methodologies. An enrichment analysis of pathway and gene ontology annotations showed that HKera-predicted HK and TS genes have distinct functional roles and, together, cover most of the ontology categories. These results show that HKera is a good transcriptome partitioner that can be used to search for, and obtain useful expression and functional information for, novel HK (TS) genes. [ABSTRACT FROM AUTHOR]

Background: An increasing number of genetic components are available in several depositories of such components to facilitate synthetic biology research, but picking out those that will allow a designed circuit to achieve the specified function still requires multiple cycles of testing. Here, we addressed this problem by developing a computational pipeline to mathematically simulate a gene circuit for a comprehensive range and combination of the kinetic parameters of the biological components that constitute the gene circuit. Results: We showed that, using a well-studied transcriptional repression cascade as an example, the sets of kinetic parameters that could produce the specified system dynamics of the gene circuit formed clusters of recurrent combinations, referred to as kinetic motifs, which appear to be associated with both the specific topology and specified dynamics of the circuit. Furthermore, the use of the resulting “handbook” of performance-ranked kinetic motifs in finding suitable circuit components was illustrated in two application scenarios. Conclusions: These results show that the computational pipeline developed here can provide a rational-based guide to aid in the design and improvement of synthetic gene circuits. [ABSTRACT FROM AUTHOR]

Background: Pathologic tissue remodeling with scarring and tissue rigidity has been demonstrated in inflammatory, autoimmune, and allergic diseases. Eosinophilic esophagitis (EoE) is an allergic disease that is diagnosed and managed by repeated biopsy procurement, allowing an understanding of tissue fibroblast dysfunction. While EoE associated tissue remodeling causes clinical dysphagia, food impactions and esophageal rigidity and strictures, molecular mechanisms driving these complications remain under investigation., Objective: We hypothesized that chronic EoE inflammation induces pathogenic fibroblasts with dysfunctional tissue regeneration and motility., Methods: We used single cell RNA sequence (scRNA-Seq), fluorescence activated cell sorting analysis, and fibroblast differentiation and migration assays to decipher the induced and retained pathogenic dysfunctions in EoE versus healthy esophageal fibroblasts., Results: Differentiation assays demonstrated that active EoE fibroblasts retain regenerative programs for rigid cells such as chondrocytes (p<0.05) but lose healthy fibroblast capacity for soft cells such as adipocytes (p<0.01) which was reflected in biopsy immunostaining (p<0.01). EoE, but not healthy, fibroblasts have pro-inflammatory and pro-rigidity transcriptional programs on scRNA-Seq. In vivo, regenerative fibroblasts reside in perivascular regions and near the epithelial junction and, during EoE, have significantly increased migration (p<0.01). Flow analysis and functional assays demonstrated that regenerative EoE fibroblasts have decreased surface CD73 expression and activity (both p<0.05) compared to healthy, indicating aberrant ATP handling. EoE fibroblast dysfunctions were induced in healthy fibroblasts by reducing CD73 activity and rescued in EoE using adenosine repletion., Conclusion: A normalization of perturbed extracellular ATP handling and CD73 could improve pathogenic fibroblast dysfunction and tissue regeneration in type 2 inflammatory diseases., (Copyright © 2024. Published by Elsevier Inc.)

The gastrointestinal system is a hollow organ affected by fibrostenotic diseases that cause volumetric compromise of the lumen via smooth muscle hypertrophy and fibrosis. Many of the driving mechanisms remain unclear. Yes-associated protein-1 (YAP) is a critical mechanosensory transcriptional regulator that mediates cell hypertrophy in response to elevated extracellular rigidity. In the type 2 inflammatory disorder, eosinophilic esophagitis (EoE), phospholamban (PLN) can induce smooth muscle cell hypertrophy. We used EoE as a disease model for understanding a mechanistic pathway in which PLN and YAP interact in response to rigid extracellular substrate to induce smooth muscle cell hypertrophy. PLN-induced YAP nuclear sequestration in a feed-forward loop caused increased cell size in response to a rigid substrate. This mechanism of rigidity sensing may have previously unappreciated clinical implications for PLN-expressing hollow systems such as the esophagus and heart., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl 4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data., (Copyright © 2024 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Variations in N-glycosylation, which is crucial to glycoprotein functions, impact many diseases and the safety and efficacy of biotherapeutic drugs. Here, we present a protocol for using GlycoMME (Glycosylation Markov Model Evaluator) to study N-glycosylation biosynthesis from glycomics data. We describe steps for annotating glycomics data and quantifying perturbations to N-glycan biosynthesis with interpretable models. We then detail procedures to predict the impact of mutations in disease or potential glycoengineering strategies in drug development. For complete details on the use and execution of this protocol, please refer to Liang et al. (2020). 1 ., Competing Interests: Declaration of interests The authors have no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

GlyCompareCT is a portable command-line tool to facilitate downstream glycomic data analyses, by addressing data inherent sparsity and non-independence. Inputting glycan abundances, users can run GlyCompareCT with one line of code to obtain the abundances of a minimal substructure set, named glycomotif, thereby quantifying hidden biosynthetic relationships between measured glycans. Optional parameters tuning and annotation are supported for personal preference. For complete details on the use and execution of this protocol, please refer to Bao et al. (2021). 1 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Chinese hamster ovary (CHO) cells are extensively used for the production of glycoprotein therapeutics proteins, for which N-linked glycans are a critical quality attribute due to their influence on activity and immunogenicity. Manipulation of protein glycosylation is commonly achieved through cell or process engineering, which are often guided by mathematical models. However, each study considers a unique glycosylation reaction network that is tailored around the cell line and product at hand. Herein, we use 200 glycan datasets for both recombinantly produced and native proteins from different CHO cell lines to reconstruct a comprehensive reaction network, CHOGlycoNET, based on the individual minimal reaction networks describing each dataset. CHOGlycoNET is used to investigate the distribution of mannosidase and glycosyltransferase enzymes in the Golgi apparatus and identify key network reactions using machine learning and dimensionality reduction techniques. CHOGlycoNET can be used for accelerating glycomodel development and predicting the effect of glycoengineering strategies. Finally, CHOGlycoNET is wrapped in a SBML file to be used as a standalone model or in combination with CHO cell genome scale models., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Glycans are complex, yet ubiquitous across biological systems. They are involved in diverse essential organismal functions. Aberrant glycosylation may lead to disease development, such as cancer, autoimmune diseases, and inflammatory diseases. Glycans, both normal and aberrant, are synthesized using extensive glycosylation machinery, and understanding this machinery can provide invaluable insights for diagnosis, prognosis, and treatment of various diseases. Increasing amounts of glycomics data are being generated thanks to advances in glycoanalytics technologies, but to maximize the value of such data, innovations are needed for analyzing and interpreting large-scale glycomics data. Artificial intelligence (AI) provides a powerful analysis toolbox in many scientific fields, and here we review state-of-the-art AI approaches on glycosylation analysis. We further discuss how models can be analyzed to gain mechanistic insights into glycosylation machinery and how the machinery shapes glycans under different scenarios. Finally, we propose how to leverage the gained knowledge for developing predictive AI-based models of glycosylation. Thus, guiding future research of AI-based glycosylation model development will provide valuable insights into glycosylation and glycan machinery., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2022. Published by Elsevier Inc.)

Humans frequently encounter Staphylococcus aureus (SA) throughout life. Animal studies have yielded SA candidate vaccines, yet all human SA vaccine trials have failed. One notable vaccine "failure" targeted IsdB, critical for host iron acquisition. We explored a fundamental difference between humans and laboratory animals-natural SA exposure. Recapitulating the failed phase III IsdB vaccine trial, mice previously infected with SA do not mount protective antibody responses to vaccination, unlike naive animals. Non-protective antibodies exhibit increased α2,3 sialylation that blunts opsonophagocytosis and preferentially targets a non-protective IsdB domain. IsdB vaccination of SA-infected mice recalls non-neutralizing humoral responses, further reducing vaccine efficacy through direct antibody competition. IsdB vaccine interference was overcome by immunization against the IsdB heme-binding domain. Purified human IsdB-specific antibodies also blunt IsdB passive immunization, and additional SA vaccines are susceptible to SA pre-exposure. Thus, failed anti-SA immunization trials could be explained by non-protective imprint from prior host-SA interaction., Competing Interests: Declaration of interests C.-M.T. and G.Y.L. have filed for a patent application for the use of IsdB NEAT2 as a vaccine., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Amyloid disorders such as Alzheimer's disease (AD) involve the aggregation of secreted proteins. However, it is largely unclear how secretory-pathway proteins contribute to amyloid formation. We developed a systems biology framework integrating expression data with protein-protein interaction networks to estimate a tissue's fitness for producing specific secreted proteins and analyzed the fitness of the secretory pathway of various brain regions and cell types for synthesizing the AD-associated amyloid precursor protein (APP). While key amyloidogenic pathway components were not differentially expressed in AD brains, we found Aβ deposition correlates with systemic down- and upregulation of the secretory-pathway components proximal to APP and amyloidogenic secretases, respectively, in AD. Our analyses suggest that perturbations from three AD risk loci cascade through the APP secretory-support network and into the endocytosis pathway, connecting amyloidogenesis to dysregulation of secretory-pathway components supporting APP and suggesting novel therapeutic targets for AD. A record of this paper's transparent peer review process is included in the supplemental information., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Eosinophilic esophagitis (EoE) is a chronic T H 2 disorder complicated by tissue fibrosis and loss of esophageal luminal patency. The fibrostenotic esophagus does not respond well to therapy, but profibrotic therapeutic targets are largely unclear., Objective: Our aim was to utilize proteomics and primary cells as a novel approach to determine relevant profibrotic factors., Methods: We utilized primary esophageal EoE and normal fibroblasts, their derivative extracellular matrixes (ECMs), an approach of fibroblast culture on autologous versus nonautologous ECM, and proteomics to elucidate EoE ECM proteins that dysregulate cellular function., Results: We cultured esophageal fibroblasts from normal esophagi and esophagi from patients with severe EoE on autologous versus nonautologous ECM. The EoE ECM proteome shifted normal esophageal fibroblast protein expression. Proteomic analysis demonstrated that thrombospondin-1 is detected only in the EoE ECM, is central in the EoE ECM protein-protein interactome, is found at significantly elevated levels in biopsy specimens from patients with active EoE, and induces fibroblast collagen I production., Conclusion: Fibroblasts from patients with EoE secrete a unique ECM proteome that reflects their in vivo state and induces collagen I and α-smooth muscle actin protein expression from normal fibroblasts. Thrombospondin-1 is a previously unappreciated profibrotic molecule in EoE., (Copyright © 2021 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie)., Competing Interests: DECLARATION OF INTERESTS The authors declare no competing interests.

Background & Aims: Eosinophilic esophagitis (EoE) is an antigen-mediated eosinophilic disease of the esophagus that involves fibroblast activation and progression to fibrostenosis. Cytokines produced by T-helper type 2 cells and transforming growth factor beta 1 (TGFβ1) contribute to the development of EoE, but other cytokines involved in pathogenesis are unknown. We investigate the effects of tumor necrosis factor superfamily member 14 (TNFSF14, also called LIGHT) on fibroblasts in EoE., Methods: We analyzed publicly available esophageal CD3 + T-cell single-cell sequencing data for expression of LIGHT. Esophageal tissues were obtained from pediatric patients with EoE or control individuals and analyzed by immunostaining. Human primary esophageal fibroblasts were isolated from esophageal biopsy samples of healthy donors or patients with active EoE. Fibroblasts were cultured; incubated with TGFβ1 and/or LIGHT; and analyzed by RNA sequencing, flow cytometry, immunoblots, immunofluorescence, or reverse transcription polymerase chain reaction. Eosinophils were purified from peripheral blood of healthy donors, incubated with interleukin 5, cocultured with fibroblasts, and analyzed by immunohistochemistry., Results: LIGHT was up-regulated in the esophageal tissues from patients with EoE, compared with control individuals, and expressed by several T-cell populations, including T-helper type 2 cells. TNF receptor superfamily member 14 (TNFRSF14, also called HVEM) and lymphotoxin beta receptor are receptors for LIGHT that were expressed by fibroblasts from healthy donors or patients with active EoE. Stimulation of esophageal fibroblasts with LIGHT induced inflammatory gene transcription, whereas stimulation with TGFβ1 induced transcription of genes associated with a myofibroblast phenotype. Stimulation of fibroblasts with TGFβ1 increased expression of HVEM; subsequent stimulation with LIGHT resulted in their differentiation into cells that express markers of myofibroblasts and inflammatory chemokines and cytokines. Eosinophils tethered to esophageal fibroblasts after LIGHT stimulation via intercellular adhesion molecule-1., Conclusions: T cells in esophageal tissues from patients with EoE express increased levels of LIGHT compared with control individuals, which induces differentiation of fibroblasts into cells with inflammatory characteristics. TGFβ1 increases fibroblast expression of HVEM, a receptor for LIGHT. LIGHT mediates interactions between esophageal fibroblasts and eosinophils via ICAM1. This pathway might be targeted for the treatment of EoE., (Copyright © 2020 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Glycosylated biopharmaceuticals are important in the global pharmaceutical market. Despite the importance of their glycan structures, our limited knowledge of the glycosylation machinery still hinders controllability of this critical quality attribute. To facilitate discovery of glycosyltransferase specificity and predict glycoengineering efforts, here we extend the approach to model N-linked protein glycosylation as a Markov process. Our model leverages putative glycosyltransferase (GT) specificity to define the biosynthetic pathways for all measured glycans, and the Markov chain modelling is used to learn glycosyltransferase isoform activities and predict glycosylation following glycosyltransferase knock-in/knockout. We apply our methodology to four different glycoengineered therapeutics (i.e., Rituximab, erythropoietin, Enbrel, and alpha-1 antitrypsin) produced in CHO cells. Our model accurately predicted N-linked glycosylation following glycoengineering and further quantified the impact of glycosyltransferase mutations on reactions catalyzed by other glycosyltransferases. By applying these learned GT-GT interaction rules identified from single glycosyltransferase mutants, our model further predicts the outcome of multi-gene glycosyltransferase mutations on the diverse biotherapeutics. Thus, this modeling approach enables rational glycoengineering and the elucidation of relationships between glycosyltransferases, thereby facilitating biopharmaceutical research and aiding the broader study of glycosylation to elucidate the genetic basis of complex changes in glycosylation.

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. Here, we present Linear Code for Reaction Rules (LiCoRR), version 1.0, an unambiguous representation for describing glycosylation reactions in both literature and code., (Copyright © 2020, Kellman et al.; licensee Beilstein-Institut.)