Vertical Transmission of SARS-CoV-2-Specific Antibodies and Cytokine Profiles in Pregnancy.

Despite intensive characterization of immune responses after COVID-19 infection and vaccination, research examining protective correlates of vertical transmission in pregnancy are limited. Herein, we profiled humoral and cellular characteristics in pregnant women infected or vaccinated at different trimesters and in their corresponding newborns. We noted a significant correlation between spike S1-specific IgG antibody and its RBD-ACE2 blocking activity (receptor-binding domain-human angiotensin-converting enzyme 2) in maternal and cord plasma (P < .001, R > 0.90). Blocking activity of spike S1-specific IgG was significantly higher in pregnant women infected during the third trimester than the first and second trimesters. Elevated levels of 28 cytokines/chemokines, mainly proinflammatory, were noted in maternal plasma with infection at delivery, while cord plasma with maternal infection 2 weeks before delivery exhibited the emergence of anti-inflammatory cytokines. Our data support vertical transmission of protective SARS-CoV-2-specific antibodies. This vertical antibody transmission and the presence of anti-inflammatory cytokines in cord blood may offset adverse outcomes of inflammation in exposed newborns.

Regulostat Inferelator: a novel network biology platform to uncover molecular devices that predetermine cellular response phenotypes.

With the emergence of genome editing technologies and synthetic biology, it is now possible to engineer genetic circuits driving a cell's phenotypic response to a stressor. However, capturing a continuous response, rather than simply a binary 'on' or 'off' response, remains a bioengineering challenge. No tools currently exist to identify gene candidates responsible for predetermining and fine-tuning cell response phenotypes. To address this gap, we devised a novel Regulostat Inferelator (RSI) algorithm to decipher intrinsic molecular devices or networks that predetermine cellular phenotypic responses. The RSI algorithm is designed to extract gene expression patterns from basal transcriptomic data in order to identify 'regulostat' constituent gene pairs, which exhibit rheostat-like mode-of-cooperation capable of fine-tuning cellular response. Our proof-of-concept study provides computational evidence for the existence of regulostats and that these networks predetermine cellular response prior to exposure to a stressor or drug. In addition, our work, for the first time, provides evidence of context-specific, drug-regulostat interactions in predetermining drug response phenotypes in cancer cells. Given RSI-inferred regulostat networks offer insights for prioritizing gene candidates capable of rendering a resistant phenotype sensitive to a given drug, we envision that this tool will be of great value in bioengineering and medicine.

NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets.

Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.

Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which "big data" and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.

Molecular insights into 4-nitrophenol-induced hepatotoxicity in zebrafish: transcriptomic, histological and targeted gene expression analyses.

BACKGROUND: 4-Nitrophenol (4-NP) is a prioritized environmental pollutant and its toxicity has been investigated using zebrafish, advocated as an alternative toxicological model. However, molecular information of 4-NP induced hepatotoxicity is still limited. This study aimed to obtain molecular insights into 4-NP-induced hepatotoxicity using zebrafish as a model. METHODS: Adult male zebrafish were exposed to 4-NP for 8, 24, 48 and 96h. Livers were sampled for microarray experiment, qRT-PCR and various histological analyses. RESULTS: Transcriptomic analysis revealed that genes associated with oxidative phosphorylation and electron transport chain were significantly up-regulated throughout early and late stages of 4-NP exposure due to oxidative phosphorylation uncoupling by 4-NP. This in turn induced oxidative stress damage and up-regulated pathways associated with tumor suppressors Rb and p53, cell cycle, DNA damage, proteasome degradation and apoptosis. Pathways associated with cell adhesion and morphology were deregulated. Carbohydrate and lipid metabolisms were down-regulated while methionine and aromatic amino acid metabolisms as well as NFKB pathway associated with chronic liver conditions were up-regulated. Up-regulation of NFKB, NFAT and interleukin pathways suggested hepatitis. Histological analyses with specific staining methods and qRT-PCR analysis of selected genes corroborated with the transcriptomic analysis suggesting 4-NP induced liver injury. CONCLUSION: Our findings allowed us to propose a plausible model and provide a broader understanding of the molecular events leading to 4-NP induced acute hepatotoxicity for future studies involving other nitrophenol derivatives. GENERAL SIGNIFICANCE: This is the first transcriptomic report on 4-NP induced hepatotoxicity.

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities.

With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning organs in the body. An imminent question is how interorgan crosstalk contributes to the etiology of chronic diseases. We conceived the locked-state model (LoSM), which illustrates how interorgan communication can give rise to body-wide memory-like properties that 'lock' healthy or pathological conditions. Next, we propose cutting-edge systems biology and artificial intelligence strategies to decipher chronic multiorgan locked states. Finally, we discuss the clinical implications of the LoSM and assess the power of systems-based therapies to dismantle pathological multiorgan locked states while improving treatments for chronic diseases.

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology.

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.

Liver X receptor agonist T0901317 induced liver perturbation in zebrafish: histological, gene set enrichment and expression analyses.

BACKGROUND: Liver X receptor (LXR), a ligand-activated transcription factor, regulates important biological processes. It has been associated with pathology and proposed as a therapeutic target. The zebrafish is a new vertebrate model for disease modeling, drug and toxicity screening and will be interesting to test for its potential for LXR-related studies. METHODS: Adult male fish were exposed to LXR agonist T0901317 at 20, 200 and 2000nM for 96h and the livers were sampled for histological, microarray and qRT-PCR analyses. RESULTS: Histological analysis suggests dose-dependent perturbation of carbohydrate and lipid metabolisms by T0901317 in the liver, which lead to hepatocyte swelling and cell death. Microarray data revealed several conserved effects of T0901317 with mammalian models, including up-regulation of LXR-targeted genes, modulation of biological pathways associated with proteasome, cell death, extracellular matrix and adhesions, maturity onset diabetes of the young and lipid beta oxidation. Interestingly, this study identified the complement and coagulation systems as down-regulated by T0901317 for the first time, potentially via transcriptional repression by LXR activation. qRT-PCR validated the expression of 16 representative genes, confirming activation of LXR signaling and down-regulation of these biological pathways by T0901317 which could be linked to the anti-thrombogenic, anti-atherogenic and anti-inflammatory actions, as well as metabolic disruptions via LXR activation. CONCLUSION AND GENERAL SIGNIFICANCE: Our study underscores the potential of using zebrafish model coupled with transcriptomic analysis to capture pharmacological and toxicological or pathological events induced by LXR modulators.

Manifold epigenetics: A conceptual model that guides engineering strategies to improve whole-body regenerative health.

Despite the promising advances in regenerative medicine, there is a critical need for improved therapies. For example, delaying aging and improving healthspan is an imminent societal challenge. Our ability to identify biological cues as well as communications between cells and organs are keys to enhance regenerative health and improve patient care. Epigenetics represents one of the major biological mechanisms involving in tissue regeneration, and therefore can be viewed as a systemic (body-wide) control. However, how epigenetic regulations concertedly lead to the development of biological memories at the whole-body level remains unclear. Here, we review the evolving definitions of epigenetics and identify missing links. We then propose our Manifold Epigenetic Model (MEMo) as a conceptual framework to explain how epigenetic memory arises and discuss what strategies can be applied to manipulate the body-wide memory. In summary we provide a conceptual roadmap for the development of new engineering approaches to improve regenerative health.

SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes.

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.

Manifold medicine: A schema that expands treatment dimensionality.

Drug discovery currently focuses on identifying new druggable targets and drug repurposing. Here, we illustrate a third domain of drug discovery: the dimensionality of treatment regimens. We formulate a new schema called 'Manifold Medicine', in which disease states are described by vectorial positions on several body-wide axes. Thus, pathological states are represented by multidimensional 'vectors' that traverse the body-wide axes. We then delineate the manifold nature of drug action to provide a strategy for designing manifold drug cocktails by design using state-of-the-art biomedical and technological innovations. Manifold Medicine offers a roadmap for translating knowledge gained from next-generation technologies into individualized clinical practice.

Uncovering Pharmacological Opportunities for Cancer Stem Cells-A Systems Biology View.

Cancer stem cells (CSCs) represent a small fraction of the total cancer cell population, yet they are thought to drive disease propagation, therapy resistance and relapse. Like healthy stem cells, CSCs possess the ability to self-renew and differentiate. These stemness phenotypes of CSCs rely on multiple molecular cues, including signaling pathways (for example, WNT, Notch and Hedgehog), cell surface molecules that interact with cellular niche components, and microenvironmental interactions with immune cells. Despite the importance of understanding CSC biology, our knowledge of how neighboring immune and tumor cell populations collectively shape CSC stemness is incomplete. Here, we provide a systems biology perspective on the crucial roles of cellular population identification and dissection of cell regulatory states. By reviewing state-of-the-art single-cell technologies, we show how innovative systems-based analysis enables a deeper understanding of the stemness of the tumor niche and the influence of intratumoral cancer cell and immune cell compositions. We also summarize strategies for refining CSC systems biology, and the potential role of this approach in the development of improved anticancer treatments. Because CSCs are amenable to cellular transitions, we envision how systems pharmacology can become a major engine for discovery of novel targets and drug candidates that can modulate state transitions for tumor cell reprogramming. Our aim is to provide deeper insights into cancer stemness from a systems perspective. We believe this approach has great potential to guide the development of more effective personalized cancer therapies that can prevent CSC-mediated relapse.

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer.

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction. Here, we devise an innovative weight engineering approach and ANN platform called artificial neural network encoder (ANNE) using an autoencoder and apply it to a breast cancer dataset to extract knowledge learned by the autoencoder model that explains clinical traits. Intriguingly, the extracted biological knowledge in the form of gene-gene associations from ANNE shows immune-related components such as chemokines, carbonic anhydrase, and iron metabolism that modulate immune-related processes and the tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological "knowledge" learned by an ANN model is indeed encoded as weights throughout its neuronal connections, and it is possible to extract learned knowledge via a novel weight engineering approach to uncover important biological insights.

Modular network mechanism of CCN1-associated resistance to HSV-1-derived oncolytic immunovirotherapies for glioblastomas.

Glioblastomas (GBMs) are the most common and lethal primary brain malignancy in adults. Oncolytic virus (OV) immunotherapies selectively kill GBM cells in a manner that elicits antitumor immunity. Cellular communication network factor 1 (CCN1), a protein found in most GBM microenvironments, expression predicts resistance to OVs, particularly herpes simplex virus type 1 (HSV-1). This study aims to understand how extracellular CCN1 alters the GBM intracellular state to confer OV resistance. Protein-protein interaction network information flow analyses of LN229 human GBM transcriptomes identified 39 novel nodes and 12 binary edges dominating flow in CCN1high cells versus controls. Virus response programs, notably against HSV-1, and cytokine-mediated signaling pathways are highly enriched. Our results suggest that CCN1high states exploit IDH1 and TP53, and increase dependency on RPL6, HUWE1, and COPS5. To validate, we reproduce our findings in 65 other GBM cell line (CCLE) and 174 clinical GBM patient sample (TCGA) datasets. We conclude through our generalized network modeling and system level analysis that CCN1 signals via several innate immune pathways in GBM to inhibit HSV-1 OVs before transduction. Interventions disrupting this network may overcome immunovirotherapy resistance.

Machine Learning and Systems Biology Approaches to Characterize Dosage-Based Gene Dependencies in Cancer Cells.

Mapping of cancer survivability factors allows for the identification of novel biological insights for drug targeting. Using genomic editing techniques, gene dependencies can be extracted in a high-throughput and quantitative manner. Dependencies have been predicted using machine learning techniques on -omics data, but the biological consequences of dependency predictor pairs has not been explored. In this work we devised a framework to explore gene dependency using an ensemble of machine learning methods, and our learned models captured meaningful biological information beyond just gene dependency prediction. We show that dosage-based dependent predictors (DDPs) primarily belonged to transcriptional regulation ontologies. We also found that anti-sense RNAs and long- noncoding RNA transcripts display DDPs. Network analyses revealed that SOX10, HLA-J, and ZEB2 act as a triad of network hubs in the dependent-predictor network. Collectively, we demonstrate the powerful combination of machine learning and systems biology approach can illuminate new insights in understanding gene dependency and guide novel targeting avenues.

The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches.

Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.

GAS7 Deficiency Promotes Metastasis in MYCN-Driven Neuroblastoma.

One of the greatest barriers to curative treatment of neuroblastoma is its frequent metastatic outgrowth prior to diagnosis, especially in cases driven by amplification of the MYCN oncogene. However, only a limited number of regulatory proteins that contribute to this complex MYCN-mediated process have been elucidated. Here we show that the growth arrest-specific 7 (GAS7) gene, located at chromosome band 17p13.1, is preferentially deleted in high-risk MYCN-driven neuroblastoma. GAS7 expression was also suppressed in MYCN-amplified neuroblastoma lacking 17p deletion. GAS7 deficiency led to accelerated metastasis in both zebrafish and mammalian models of neuroblastoma with overexpression or amplification of MYCN. Analysis of expression profiles and the ultrastructure of zebrafish neuroblastoma tumors with MYCN overexpression identified that GAS7 deficiency led to (i) downregulation of genes involved in cell-cell interaction, (ii) loss of contact among tumor cells as critical determinants of accelerated metastasis, and (iii) increased levels of MYCN protein. These results provide the first genetic evidence that GAS7 depletion is a critical early step in the cascade of events culminating in neuroblastoma metastasis in the context of MYCN overexpression. SIGNIFICANCE: Heterozygous deletion or MYCN-mediated repression of GAS7 in neuroblastoma releases an important brake on tumor cell dispersion and migration to distant sites, providing a novel mechanism underlying tumor metastasis in MYCN-driven neuroblastoma.See related commentary by Menard, p. 2815.

Mercury-induced hepatotoxicity in zebrafish: in vivo mechanistic insights from transcriptome analysis, phenotype anchoring and targeted gene expression validation.

BACKGROUND: Mercury is a prominent environmental contaminant that causes detrimental effects to human health. Although the liver has been known to be a main target organ, there is limited information on in vivo molecular mechanism of mercury-induced toxicity in the liver. By using transcriptome analysis, phenotypic anchoring and validation of targeted gene expression in zebrafish, mercury-induced hepatotoxicity was investigated and a number of perturbed cellular processes were identified and compared with those captured in the in vitro human cell line studies. RESULTS: Hepato-transcriptome analysis of mercury-exposed zebrafish revealed that the earliest deregulated genes were associated with electron transport chain, mitochondrial fatty acid beta-oxidation, nuclear receptor signaling and apoptotic pathway, followed by complement system and proteasome pathway, and thereafter DNA damage, hypoxia, Wnt signaling, fatty acid synthesis, gluconeogenesis, cell cycle and motility. Comparative meta-analysis of microarray data between zebrafish liver and human HepG2 cells exposed to mercury identified some common toxicological effects of mercury-induced hepatotoxicity in both models. Histological analyses of liver from mercury-exposed fish revealed morphological changes of liver parenchyma, decreased nucleated cell count, increased lipid vesicles, glycogen and apoptotic bodies, thus providing phenotypic evidence for anchoring of the transcriptome analysis. Validation of targeted gene expression confirmed deregulated gene-pathways from enrichment analysis. Some of these genes responding to low concentrations of mercury may serve as toxicogenomic-based markers for detection and health risk assessment of environmental mercury contaminations. CONCLUSION: Mercury-induced hepatotoxicity was triggered by oxidative stresses, intrinsic apoptotic pathway, deregulation of nuclear receptor and kinase activities including Gsk3 that deregulates Wnt signaling pathway, gluconeogenesis, and adipogenesis, leading to mitochondrial dysfunction, endocrine disruption and metabolic disorders. This study provides important mechanistic insights into mercury-induced liver toxicity in a whole-animal physiology context, which will help in understanding the syndromes caused by mercury poisoning. The molecular conservation of mercury-induced hepatotoxicity between zebrafish and human cell line reveals the feasibility of using zebrafish to model molecular toxicity in human for toxicant risk assessments.

Simulation of crosstalk between small GTPase RhoA and EGFR-ERK signaling pathway via MEKK1.

MOTIVATION: Small GTPase RhoA regulates cell-cycle progression via several mechanisms. Apart from its actions via ROCK, RhoA has recently been found to activate a scaffold protein MEKK1 known to promote ERK activation. We examined whether RhoA can substantially affect ERK activity via this MEKK1-mediated crosstalk between RhoA and EGFR-ERK pathway. By extending the published EGFR-ERK simulation models represented by ordinary differential equations, we developed a simulation model that includes this crosstalk, which was validated with a number of experimental findings and published simulation results. RESULTS: Our simulation suggested that, via this crosstalk, RhoA elevation substantially prolonged duration of ERK activation at both normal and reduced Ras levels. Our model suggests ERK may be activated in the absence of Ras. When Ras is overexpressed, RhoA elevation significantly prolongs duration of ERK activation but reduces the amount of active ERK partly due to competitive binding between ERK and RhoA to MEKK1. Our results indicated possible roles of RhoA in affecting ERK activities via MEKK1-mediated crosstalk, which seems to be supported by indications from several experimental studies that may also implicate the collective regulation of cell fate and progression of cancer and other diseases.

Simulation of the regulation of EGFR endocytosis and EGFR-ERK signaling by endophilin-mediated RhoA-EGFR crosstalk.

Deregulations of EGFR endocytosis in EGFR-ERK signaling are known to cause cancers and developmental disorders. Mutations that impaired c-Cbl-EGFR association delay EGFR endocytosis and produce higher mitogenic signals in lung cancer. ROCK, an effector of small GTPase RhoA was shown to negatively regulate EGFR endocytosis via endophilin A1. A mathematical model was developed to study how RhoA and ROCK regulate EGFR endocytosis. Our study suggested that over-expressing RhoA as well as ROCK prolonged ERK activation partly by reducing EGFR endocytosis. Overall, our study hypothesized an alternative role of RhoA in tumorigenesis in addition to its regulation of cytoskeleton and cell motility.