Biology subjects

Glass, K.

Publications and source records attributed to Glass, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Newly identified COPD GWAS protein interactors reveal a potential disease network module

Background: Chronic obstructive pulmonary disease (COPD) is a complex, heterogeneous disease influenced by genetic and environmental factors. However, the interactions between COPD risk genes and their collective role in COPD susceptibility remain largely elusive. We hypothesize that the protein-protein interaction (PPI) network of genes associated with COPD risk can provide molecular insight into the mechanisms of COPD pathogenesis. Methods: We used affinity purification mass spectrometry (AP-MS) to detect protein-protein interactions of six well-established COPD GWAS gene products (AGER, FAM13A, FBXO38, HHIP, IREB2, MFAP2) in two relevant lung cell lines (IMR90, 16HBE). These genes are located in significant COPD GWAS regions, and their physiological role in COPD has been previously confirmed by functional studies. We analyzed the impact of the newly identified interactions in contextual, cell type-specific PPI networks built from the combination of publicly available PPI data (HUBRIS) and cell type-specific gene expression data. Results: Using AP-MS, we identified 482 unique interactors across 701 newly identified protein interactions with the six GWAS gene products in at least one of the cell lines (AGER: 122, FAM13A: 83, FBXO38: 71, HHIP: 108, IREB2: 8, MFAP2: 309). Subsequent network analysis incorporating these new PPIs with public PPI databases revealed: (1) the new interactions significantly reduce the network distance between known COPD GWAS gene products; (2) 5.2% of the newly identified interactors are differentially abundant between COPD cases and controls in lung proteomic data from the Lung Tissue Research Consortium (LTRC); and (3) 41 (14.3%) of the newly identified interactors were directly connected to more than one of the six COPD GWAS genes. We identified a tightly connected core network of COPD GWAS gene products and protein biomarkers with intersecting signals; this network consists of 8 proteins, including 5 of the 6 GWAS gene products (AGER, FAM13A, HHIP, FBXO38, and MFAP2) as well as CAVIN1, TGM2, and HSPA1A. Conclusions: By combining AP-MS experimental data, multiple types of "omics" data, and network analysis, we have constructed a disease network module for COPD. This module can be a foundation for understanding the collective influence of COPD GWAS gene products in disease pathogenesis.

systems biology↗

bioRxiv2026

A role for CREB5 in skin wound healing

Compared with skin wounds, oral mucosal wounds heal more quickly, with minimal scarring, faster re-epithelialization, and reduced inflammation. One differentiating factor may be the differential transcription factor-associated gene networks involved in tissue regeneration. One such transcription factor, BATF3, was recently shown by us to promote wound-healing responses in vitro and in vivo. Our prior analyses also suggest that CREB5 is a differentially regulated transcription factor in oral wounds and may be involved in early wound-healing gene expression programs. CREB5 expression was induced in immortalized skin keratinocytes (HaCaT) to examine its effect on in vitro wound healing relative to immortalized gingival keratinocytes (TIGK). CREB5 overexpression let to differential expression of predicted downstream genes and improved skin keratinocyte migration in vitro. This work suggests that examining transcription factors and gene networks that regulate wound-healing responses in the oral mucosa may lead to the discovery of novel targets to improve skin wound healing.

cell biology↗

bioRxiv2026

Identification of smoking-enabled blood miRNA regulatory networks

Cigarette smoking induces complex signaling disruptions that contribute to diseases such as COPD and lung cancer, yet the molecular mechanisms underlying these effects remain incompletely understood. To address this gap, we analyzed peripheral blood from 3190 COPDGene participants using LIONESS and PUMA and constructed miRNA-mRNA regulatory networks associated with smoking status. Comparing networks for active versus former smokers uncovered a striking shift in regulatory architecture: active smokers exhibited elevated miRNA targeting of the mitochondrial complex I protein NDUFA12. This finding was validated in lung tissue expression data from the Lung Genomics Research Consortium (LGRC), where we observed that ever-smokers showed consistent dysregulation of Ndufa12-targeting miRNAs compared to never-smokers. This allowed us to identify a set of smoking-defined circulating and tissue-associated miRNAs. To investigate the specific cellular compartment, we analyzed cell-type deconvoluted expression data from COPDGene blood and LTRC (Lung Tissue Research Consortium) lung tissue, as well as lung transcriptomics data from cigarette smoke-exposed mice, and identified the monocyte/macrophage compartment as a principal site of NDUFA12/Ndufa12 expression. Human THP-1 macrophages treated with cigarette smoke extract demonstrated selective inhibition of NDUFA12 by network-defined miRNAs. These distinct, NDUFA12-targeting, smoking-associated miRNA signatures, revealed through network analysis, describe new smoking-mitochondrial interactions that may serve as novel targets for therapeutic intervention.

Molecular Biology↗

bioRxiv2026

Challenges and Opportunities in Single-Sample Network Modeling

1Analysis of biological networks can provide unprecedented insights into the mechanisms underlying disease. Although many methods have been developed to estimate biological networks, these approaches typically use multiple experimental samples to estimate a single aggregate network, which fails to capture population-level heterogeneity. Recently, several methods have been developed that overcome this limitation by inferring networks for individual samples, i.e. single-sample networks. However, each approach for inferring single-sample networks has been formulated differently, making it challenging to compare them. To address this issue, we re-cast the mathematics of several single-sample network methods using common variables. We then systematically explore the parameters, caveats, and underlying assumptions made by each method and examine how these differences impact single-sample network prediction. Our analyses point to a critical trade-off that occurs when trying to simultaneously predict network edges that are both shared across samples as well as edges that are specific to a given sample. For example, the mathematics of both SWEET and BONOBO includes a scale factor that drives the weights of edges in the predicted single-sample networks toward a background network. The result is that, although networks predicted by these methods tend to have the highest accuracy, this often comes at the cost of very low specificity, an important caveat since the primary goal of sample-specific network modeling is to obtain networks that are specific to each input sample. In contrast, SSN estimates the most specific but least accurate networks, while LIONESS straddles these domains, with an accuracy almost as high as SWEET and BONOBO and a specificity almost as high as SSN. Overall, our analyses highlight some of the broader challenges in this emerging field. However, they also point to important methodological synergies, providing an opportunity to create a common framework that can be used to improve single-sample network inference.

systems biology↗

bioRxiv2025

Sex differences in gene regulation and its impact on cancer incidence

Sex differences in the incidence rates of many cancer types are well-documented. While differences in lifestyle, environmental exposures, and hormone levels can influence disease risk and incidence, there is limited understanding of how cancer driver genes are differentially regulated between males and females in normal tissues, and how these differences may contribute to the observed sex differences in cancer epidemiology. We studied 8,279 gene regulatory networks across 29 non-cancerous tissues in the GTEx dataset, referred here as normal tissues. Using network centrality measures, we compared the networks between males and females, focusing on interactions between 644 transcription factors and 476 cancer genes (COSMIC Cancer Gene Census). Cancer genes were differentially targeted by transcription factors in males and females across normal tissues. We found an overrepresentation of sex-biased cancer genes on the X chromosome, particularly among genes escaping X chromosome inactivation, with higher regulatory targeting of tumor suppressor genes in females compared to males. Key signaling pathways, including WNT, NOTCH, and p53, showed differential transcriptional targeting by sex. We observed higher targeting of cancer-related pathways in females for tissues that have higher tumor incidence in females (breast, lung, and thyroid) and higher targeting in males for tissues with increased tumor incidence in males (stomach, colon, and liver). Sex-biased transcription factors that were consistently observed across multiple tissues are enriched for sex hormone response elements in their promoters. Our findings showed that normal tissues have sex-biased regulation of genes implicated in tumorigenesis, helping to explain the molecular basis of sex differences observed in cancer incidence rates and in identifying targets for cancer prevention in a sex-aware manner.

systems biology↗

bioRxiv2025

miRNA-mRNA network analysis identifies PAX5 as a potential regulator of adaptive immune response in COPD

Micro-ribonucleic acids (miRNAs) are key post-transcriptional regulators of the immune system and may play a role in Chronic Obstructive Pulmonary Disease (COPD). In this paper, we constructed subject-specific miRNA-mRNA regulatory networks using bulk and deconvoluted whole blood RNA-sequencing, whole blood miRNA-sequencing, and B-cell receptor-sequencing data from up to 570 miRNAs, 11,859 mRNAs, and 3,190 participants in the COPDGene study. Analysis of whole blood networks revealed two subnetworks of miRNA-mRNA interactions significantly (FDR<0.05) associated with changes in FEV1/FVC. We found that miRNAs (and mRNAs) in the network-identified groups had distinct expression patterns, with miRNAs (and mRNAs) in one group having overall higher expression in COPD (decreasing FEV1/FVC) and miRNAs (and mRNAs) in the other group having overall higher expression in controls (increasing FEV1/FVC). In addition, miRNAs (and mRNAs) within the same group were positively correlated, while those in different groups were negatively correlated, indicating distinct functional roles for these miRNAs (and mRNAs) as a function of increased COPD severity. Network analysis also identified PAX5, a transcription factor master regulator of B-cell development, as the main mRNA network hub. Using ChIP-seq data in lymphoblastoid cells, we identified a PAX5 binding site overlapping with a COPD genome-wide association signal in the promoter region of ADAM19. We also found a loss of co-expression between PAX5 and ADAM19 in COPD subjects. Furthermore, in B-cell deconvoluted data, PAX5 was differentially co-expressed with genes associated with B-cell activation and differentiation, revealing a possible mechanism for the regulation of the immune response in COPD. Finally, in B-cell receptor sequencing data, PAX5 and the identified mRNA subnetworks were negatively associated (FDR<0.05) with immunoglobulin class switching, and positively associated with IgM and IgD counts. In conclusion, PAX5 is a known regulator of B-cell identity. B cells are recognized as key players in chronic inflammation and immune dysregulation in COPD. Our work suggests that PAX5 plays a mediating role both in ADAM19 regulation and in miRNA regulation of early B cells in COPD.

systems biology↗

bioRxiv2025

Identifying Sex Differences in Lung Adenocarcinoma Using Multi-Omics Integrative Protein Signaling Networks

Lung adenocarcinoma (LUAD) exhibits differences between the sexes in incidence, prognosis, and therapy, suggesting underexplored molecular mechanisms. We conducted an integrative multi-omics analysis using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets to contrast transcriptomes and proteomes between sexes. We used TIGER to analyze TCGA-LUAD expression data and found sex-biased activity of transcription factors (TFs); we used PTM-SEA with CPTAC-LUAD proteomics data and found sex-biased kinase activity. We combined these to construct a kinase-TF signaling network and discovered druggable pathways linked to cancer-related processes. We also found significant sex biases in clinically relevant TFs and kinases, including NR3C1, AR, and AURKA. Using the PRISM drug screening database, we identified potential sex-specific drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors. Our findings emphasize the importance of considering sex and using multi-omics network methods to discover personalized cancer therapies.

cancer biology↗

bioRxiv2024

HHIP's Dynamic Role in Epithelial Wound Healing Reveals a Potential Mechanism of COPD Susceptibility

A genetic variant near HHIP has been consistently identified as associated with increased risk for Chronic Obstructive Pulmonary Disease (COPD), the third leading cause of death worldwide. However HHIPs role in COPD pathogenesis remains elusive. Canonically, HHIP is a negative regulator of the hedgehog pathway and downstream GLI1 and GLI2 activation. The hedgehog pathway plays an important role in wound healing, specifically in activating transcription factors that drive the epithelial mesenchymal transition (EMT), which in its intermediate state (partial EMT) is necessary for the collective movement of cells closing the wound. Herein, we propose a mechanism to explain HHIPs role in faulty epithelial wound healing, which could contribute to the development of emphysema, a key feature of COPD. Using two different Boolean models compiled from the literature, we show dysfunctional HHIP results in a lack of negative feedback on GLI, triggering a full EMT, where cells become mesenchymal and do not properly close the wound. We validate these Boolean models with experimental evidence gathered from published scientific literature. We also experimentally test if low HHIP expression is associated with EMT at the edge of wounds by using a scratch assay in a human lung epithelial cell line. Finally, we show evidence supporting our hypothesis in bulk and single cell RNA-Seq data from different COPD cohorts. Overall, our analyses suggest that aberrant wound healing due to dysfunctional HHIP, combined with chronic epithelial damage through cigarette smoke exposure, may be a primary cause of COPD-associated emphysema. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/611545v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1396884org.highwire.dtl.DTLVardef@1fcb9eborg.highwire.dtl.DTLVardef@111f451org.highwire.dtl.DTLVardef@1bf2ee9_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2024

HHIP protein interactions in lung cells provide insight into COPD pathogenesis

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. The primary causes of COPD are environmental, including cigarette smoking; however, genetic susceptibility also contributes to COPD risk. Genome-Wide Association Studies (GWASes) have revealed more than 80 genetic loci associated with COPD, leading to the identification of multiple COPD GWAS genes. However, the biological relationships between the identified COPD susceptibility genes are largely unknown. Genes associated with a complex disease are often in close network proximity, i.e. their protein products often interact directly with each other and/or similar proteins. In this study, we use affinity purification mass spectrometry (AP-MS) to identify protein interactions with HHIP, a well-established COPD GWAS gene which is part of the sonic hedgehog pathway, in two disease-relevant lung cell lines (IMR90 and 16HBE). To better understand the network neighborhood of HHIP, its proximity to the protein products of other COPD GWAS genes, and its functional role in COPD pathogenesis, we create HUBRIS, a protein-protein interaction network compiled from 8 publicly available databases. We identified both common and cell type-specific protein-protein interactors of HHIP. We find that our newly identified interactions shorten the network distance between HHIP and the protein products of several COPD GWAS genes, including DSP, MFAP2, TET2, and FBLN5. These new shorter paths include proteins that are encoded by genes involved in extracellular matrix and tissue organization. We found and validated interactions to proteins that provide new insights into COPD pathobiology, including CAVIN1 (IMR90) and TP53 (16HBE). The newly discovered HHIP interactions with CAVIN1 and TP53 implicate HHIP in response to oxidative stress.

genetics↗

bioRxiv2023

Unlocking Mitochondrial Dysfunction-Associated Senescence (MiDAS) with NAD+ -- a Boolean Model of Mitochondrial Dynamics and Cell Cycle Control

The steady accumulation of senescent cells with aging creates tissue environments that aid cancer evolution. Aging cell states are highly heterogeneous. Deep senescent cells rely on healthy mitochondria to fuel a strong proinflammatory secretome, including cytokines, growth and transforming signals. Yet, the physiological triggers of senescence such as the reactive oxygen species (ROS) can also trigger mitochondrial dysfunction, and sufficient energy deficit to alter their secretome and cause chronic oxidative stress - a state termed Mitochondrial Dysfunction-Associated Senescence (MiDAS). Here, we offer a mechanistic hypothesis for the molecular processes leading to MiDAS, along with testable predictions. To do this we have built a Boolean regulatory network model that qualitatively captures key aspects of mitochondrial dynamics during cell cycle progression (hyper-fusion at the G1/S boundary, fission in mitosis), apoptosis (fission and dysfunction) and glucose starvation (reversible hyper-fusion), as well as MiDAS in response to SIRT3 knockdown or oxidative stress. Our model reaffirms the protective role of NAD+ and external pyruvate. We offer testable predictions about the growth factor- and glucose-dependence of MiDAS and its reversibility at different stages of reactive oxygen species (ROS)-induced senescence. Our model provides mechanistic insights into the distinct stages of DNA-damage induced senescence, the relationship between senescence and epithelial-to-mesenchymal transition in cancer and offers a foundation for building multiscale models of tissue aging. HighlightsO_LIBoolean regulatory network model reproduces mitochondrial dynamics during cell cycle progression, apoptosis, and glucose starvation. C_LIO_LIModel offers a mechanistic explanation for the positive feedback loop that locks in Mitochondrial Dysfunction-Associated Senescence (MiDAS), involving autophagy-resistant, hyperfused, dysfunctional mitochondria. C_LIO_LIModel reproduces ROS-mediated mitochondrial dysfunction and suggests that MiDAS is part of the early phase of damage-induced senescence. C_LIO_LIModel predicts that cancer-driving mutations that bypass the G1/S checkpoint generally increase the incidence of MiDAS, except for p53 loss. C_LI

systems biology↗

bioRxiv2023

Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

Kinetic models of enzymes have a long history of use for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of bottom up parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard macroscopic kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and microscopic rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. The code and case studies are provided in the MASSef package built on top of the MASS Toolbox in Mathematica. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates. Author SummaryDetailed kinetic models of metabolism offer the promise of enabling new predictions of metabolic behavior and prospective design of metabolic function. However, parameterizing such models remains a substantial challenge. Historically, the kinetics of many enzymes have been measured using in vitro assays, but integrating this data into consistent large-scale models and filling gaps in available data has been a primary difficulty. Here, we provide an algorithmic approach to parameterize enzyme kinetic models using diverse enzyme kinetic data. The approach reconciles inconsistent data and addresses the issue of gaps in available data implicitly through sampling alternative parameter sets. We provide a number of case studies demonstrating the approach on different enzymes. This work empowers the use of the large amount of historical and machine learning-estimated enzyme data and will aid in the construction of biochemically-accurate models of metabolism.

systems biology↗

bioRxiv2023

Human extracellular sulfatases use a dual mechanism for regulation of growth factor interactions with heparan sulfate proteoglycans

Membrane-associated heparan sulfate (HS) proteoglycans (PGs) contribute to the regulation of extracellular cellular signaling cues, such as growth factors (GFs) and chemokines, essential for normal organismal functions and implicated in various pathophysiologies. PGs accomplish this by presenting high affinity binding sites for GFs and their receptors through highly sulfated regions of their HS polysaccharide chains. The composition of HS, and thus GF-binding specificity, are determined during biosynthetic assembly prior to installation at the cell surface. Two extracellular 6-O-endosulfatase enzymes (Sulf-1 and Sulf-2) can uniquely further edit mature HS and alter its interactions with GFs by removing specific sulfation motifs from their recognition sequence on HS. Despite being implicated as signaling regulators during development and in disease, the Sulfs have resisted structural characterization, and their substrate specificity and effects on GF interactions with HS are still poorly defined. Using a panel of PG-mimetics comprising compositionally-defined bioengineered recombinant HS (rHS) substrates in combination with GF binding and enzyme activity assays, we have discovered that Sulfs control GF-HS interactions through a combination of catalytic processing and competitive blocking of high affinity GF-binding sites, providing a new conceptual framework for understanding the functional impact of these enzymes in biological context. Although the contributions from each mechanism are both Sulf- and GF-dependent, the PG-mimetic platform allows for rapid analysis of these complex relationships. Significance StatementCells rely on extracellular signals such as growth factors (GFs) to mediate critical biological functions. Membrane-associated proteins bearing negatively charged heparan sulfate (HS) sugar chains engage with GFs and present them to their receptors, which regulates their activity. Two extracellular sulfatase (Sulf) enzymes can edit HS and alter GF interactions and activity, although the precise mechanisms remain unclear. By using chemically defined HS-mimetics as probes, we have discovered that Sulfs can modulate HS by means of catalytic alterations and competitive blocking of GF-binding sites. These unique dual activities distinguish Sulfs from other enzymes and provide clues to their roles in development and disease.

biochemistry↗

bioRxiv2023

Bayesian Optimized sample-specific Networks Obtained ByOmics data (BONOBO)

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring co-expression networks is a critical element of GRN inference as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate co-expression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. To address these concerns, we introduce BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data), a scalable Bayesian model for deriving individual sample-specific co-expression networks by recognizing variations in molecular interactions across individuals. For every sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific co-expression matrix constructed from all other samples in the data. Combining the sample-specific gene expression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices, thus making the method extremely scalable. We demonstrate the utility of BONOBO in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other sample-specific co-expression network inference methods and provides insight into individual differences in the drivers of biological processes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=191 SRC="FIGDIR/small/567119v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@f97de5org.highwire.dtl.DTLVardef@1b864a9org.highwire.dtl.DTLVardef@d8a384org.highwire.dtl.DTLVardef@d73091_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Repeated Plasmodium vivax blood stage infection provides sterile protection against homologous challenge in non-human primates

The malaria parasite Plasmodium vivax remains a major global public health challenge, causing major morbidity across tropical and subtropical regions. Several candidate vaccines are in preclinical and clinical trials, however no vaccine against P. vivax malaria is approved for use in humans. Here we assessed whether P. vivax strain-transcendent immunity can be achieved by repeated infection in Aotus monkeys. For this purpose, we repeatedly infected six animals with blood stages of the P. vivax Salvador 1 (SAL-1) strain until sterile immune, and then challenged with the AMRU-1 strain. Sterile immunity was achieved in 4/4 Aotus monkeys after two homologous infections with the SAL-1 strain, while partial protection against a heterologous AMRU-1 challenge (i.e., delay to infection and reduction in peak parasitemia compared to control) was achieved in 3/3 monkeys. IgG levels based on P. vivax lysate ELISA and protein microarray increased with repeated infections and correlated with the level of homologous protection. Analysis of parasite transcriptional profiles across inoculation levels provided no evidence of major antigenic switching upon homologous or heterologous challenge. In contrast, we observed significant transcriptional differences in the P. vivax core gene repertoire between SAL-1 and AMRU-1. Together with the strain-specific genetic diversity between SAL-1 and AMRU-1 these data suggest that the partial protection upon heterologous challenge is due to molecular differences between strains (at genome and transcriptome level) rather than immune evasion by antigenic switching. Our study demonstrates that sterile immunity against P. vivax can be achieved by repeated homologous blood stage infection in Aotus monkeys, thus providing a benchmark to test the efficacy of candidate blood stage P. vivax malaria vaccines. Author summaryPlasmodium vivax is the most widespread human malaria parasite. Elimination efforts are complicated due to the peculiar biology of P. vivax including dormant liver forms, cryptic reservoirs in bone marrow and spleen and a large asymptomatic infectious reservoir in affected populations. Currently there is no vaccine against malaria caused by P. vivax. Here we induce sterile immunity by repeated P. vivax infection with the SAL-1 strain in non-human primates. In contrast, heterologous challenge with the AMRU-1 strain only provided partial protection. Antibody levels against a crude antigen and a protein microarray correlated with the level of homologous protection. Parasite transcriptional profiles across inoculation levels failed to show major antigenic switching across SAL-1 infections or upon heterologous challenge, instead suggesting other mechanisms of immune evasion. Our study demonstrates that sterile immunity against P. vivax can be achieved by repeated blood stage infection in Aotus monkeys, thus providing a benchmark to test the efficacy of candidate blood stage P. vivax malaria vaccines.

microbiology↗

bioRxiv2022

Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions

The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we predict crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of prediction performance, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk for each prediction. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.

systems biology↗

bioRxiv2022

Transcriptional changes in human palate and skin healing

Most human tissue injuries lead to the formation of a fibrous scar and result in the loss of functional tissue. One adult tissue that exhibits a more regenerative response to injury with minimal scarring is the oral mucosa. We generated a microarray gene expression dataset to examine the response to injury from human palate and skin excisional biopsies spanning the first seven days after wounding. Differential expression analyses were performed in each tissue to identify genes overexpressed or underexpressed over time compared to baseline unwounded tissue. To attribute biological processes of interest to these gene expression changes, gene set enrichment analysis was used to identify core gene sets that are enriched over the time-course of the wound healing process with respect to unwounded tissue. This analysis identified gene sets uniquely enriched in either palate or skin wounds and gene sets that are enriched in both tissues in at least one time point after injury. Finally, a cell-deconvolution analysis was performed to better understand the cell type distribution in these tissues and how it changes over the time course of wound healing. This work provides a source of human wound gene expression data that includes two tissue types with distinct regenerative and scarring phenotypes.

cell biology↗

bioRxiv2022

The Network Zoo: a multilingual package for the inference and analysis of biological networks

Inference and analysis of cellular biological networks requires software tools that integrate multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is an open-source software suite to model biological networks, including context-specific gene regulatory networks and multi-omics partial correlation networks, to conduct differential analyses, estimate community structure, and model the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages (R, Python, MATLAB, and C) and between methods to allow a better integration of these tools into analytical pipelines. To demonstrate the value of this integrated toolkit, we analyzed the multi-omic data from the Cancer Cell Line Encyclopedia (CCLE) by inferring gene regulatory networks for each cancer cell line and associating network features with other phenotypic attributes such as drug sensitivity. This allowed us to identify transcription factors that play a critical role in both drug resistance and cancer development in melanoma. We also used netZoo to build a pan-cancer, multi-tiered CCLE map and used it to identify known metabolic hallmarks of cancer and to estimate novel context-specific elements that mediate post-transcriptional regulation. Because the netZoo tools are open-source and there is a growing community of both users and developers, we built an ecosystem to support community contributions, share use cases, and visualize networks online. As additional data types become available and our suite of methods grows, we will expand "the zoo" to incorporate an increasingly sophisticated collection of tools for network inference and analysis.

genomics↗

bioRxiv2022

Using methylation data to improve transcription factor binding prediction

Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) is commonly used to estimate transcription factor (TF) binding "motif" locations. However, the PWM-based scores assigned to these inferred locations are associated with only modest accuracy when benchmarked against in vivo TF binding. One reason for this limited performance may be because PWMs do not incorporate information regarding the epigenetic context necessary for TF binding. To investigate this, we developed a framework to score inferred TF binding locations using CpG methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that, for most TFs, binding is better predicted using methylation-based scoring compared to standard PWM scores. In addition, our analysis shows that, while most TFs do not bind to methylated promoter regions, there are several exceptions to this rule, indicating that the role of methylation in TF binding may be cell-type and context specific. Finally, we also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context specific TF binding using methylation data, a crucial initial step for understanding the impact of methylation on gene regulatory processes.

bioinformatics↗