Maternal SARS-CoV-2 impacts fetal placental macrophage programs and placenta-derived microglial models of neurodevelopment.

BACKGROUND: The SARS-CoV-2 virus activates maternal and placental immune responses. Such activation in the setting of other infections during pregnancy is known to impact fetal brain development. The effects of maternal immune activation on neurodevelopment are mediated at least in part by fetal brain microglia. However, microglia are inaccessible for direct analysis, and there are no validated non-invasive surrogate models to evaluate in utero microglial priming and function. We have previously demonstrated shared transcriptional programs between microglia and Hofbauer cells (HBCs, or fetal placental macrophages) in mouse models. METHODS AND RESULTS: We assessed the impact of maternal SARS-CoV-2 on HBCs isolated from 24 term placentas (N = 10 SARS-CoV-2 positive cases, 14 negative controls). Using single-cell RNA-sequencing, we demonstrated that HBC subpopulations exhibit distinct cellular programs, with specific subpopulations differentially impacted by SARS-CoV-2. Assessment of differentially expressed genes implied impaired phagocytosis, a key function of both HBCs and microglia, in some subclusters. Leveraging previously validated models of microglial synaptic pruning, we showed that HBCs isolated from placentas of SARS-CoV-2 positive pregnancies can be transdifferentiated into microglia-like cells (HBC-iMGs), with impaired synaptic pruning behavior compared to HBC models from negative controls. CONCLUSION: These findings suggest that HBCs isolated at birth can be used to create personalized cellular models of offspring microglial programming.

Assessment of network module identification across complex diseases.

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

hnRNP L is essential for myogenic differentiation and modulates myotonic dystrophy pathologies.

INTRODUCTION: RNA-binding proteins (RBPs) play an important role in skeletal muscle development and disease by regulating RNA splicing. In myotonic dystrophy type 1 (DM1), the RBP MBNL1 (muscleblind-like) is sequestered by toxic CUG repeats, leading to missplicing of MBNL1 targets. Mounting evidence from the literature has implicated other factors in the pathogenesis of DM1. Herein we sought to evaluate the functional role of the splicing factor hnRNP L in normal and DM1 muscle cells. METHODS: Co-immunoprecipitation assays using hnRNPL and MBNL1 expression constructs and splicing profiling in normal and DM1 muscle cell lines were performed. Zebrafish morpholinos targeting hnrpl and hnrnpl2 were injected into one-cell zebrafish for developmental and muscle analysis. In human myoblasts downregulation of hnRNP L was achieved with shRNAi. Ascochlorin administration to DM1 myoblasts was performed and expression of the CUG repeats, DM1 splicing biomarkers, and hnRNP L expression levels were evaluated. RESULTS: Using DM1 patient myoblast cell lines we observed the formation of abnormal hnRNP L nuclear foci within and outside the expanded CUG repeats, suggesting a role for this factor in DM1 pathology. We showed that the antiviral and antitumorigenic isoprenoid compound ascochlorin increased MBNL1 and hnRNP L expression levels. Drug treatment of DM1 muscle cells with ascochlorin partially rescued missplicing of established early biomarkers of DM1 and improved the defective myotube formation displayed by DM1 muscle cells. DISCUSSION: Together, these studies revealed that hnRNP L can modulate DM1 pathologies and is a potential therapeutic target.

A history of opioid exposure in females increases the risk of metabolic disorders in their future male offspring.

Worldwide consumption of opioids remains at historic levels. Preclinical studies report intergenerational effects on the endogenous opioid system of future progeny following preconception morphine exposure. Given the role of endogenous opioids in energy homeostasis, such effects could impact metabolism in the next generation. Thus, we examined diet-induced modifications in F1 male progeny of morphine-exposed female rats (MORF1). When fed a high fat-sugar diet (FSD) for 6 weeks, MORF1 males display features of emerging metabolic syndrome; they consume more food, gain more weight, and develop fasting-induced hyperglycemia and hyperinsulinemia. In the hypothalamus, proteins involved in energy homeostasis are modified and RNA sequencing revealed down-regulation of genes associated with neuronal plasticity, coupled with up-regulation of genes associated with immune, inflammatory, and metabolic processes that are specific to FSD-maintained MORF1 males. Thus, limited preconception morphine exposure in female rats increases the risk of metabolic syndrome/type 2 diabetes in the next generation.

Amniotic fluid transcriptomics reflects novel disease mechanisms in fetuses with myelomeningocele.

BACKGROUND: Cell-free RNA in amniotic fluid supernatant reflects developmental changes in gene expression in the living fetus, which includes genes that are specific to the central nervous system. Although it has been previously shown that central nervous system-specific transcripts are present in amniotic fluid supernatant, it is not known whether changes in the amniotic fluid supernatant transcriptome reflect the specific pathophysiologic condition of fetal central nervous system disorders. In myelomeningocele, there is open communication between the central nervous system and amniotic fluid. OBJECTIVES: The purpose of this study was to identify molecular pathophysiologic changes and novel disease mechanisms that are specific to myelomeningocele by the analysis of amniotic fluid supernatant cell-free RNA in fetuses with open myelomeningocele. STUDY DESIGN: Amniotic fluid supernatant was collected from 10 pregnant women at the time of the open myelomeningocele repair in the second trimester (24.5±1.0 weeks); 10 archived amniotic fluid supernatant from sex and gestational age-matched euploid fetuses without myelomeningocele were used as controls (20.9±0.9 weeks). Differentially regulated gene expression patterns were analyzed with the use of human genome expression arrays. RESULTS: Fetuses with myelomeningocele had 284 differentially regulated genes (176 up- and 108 down-regulated) in amniotic fluid supernatant. Known genes that were associated with myelomeningocele (PRICKLE2, GLI3, RAB23, HES1, FOLR1) and novel dysregulated genes were identified in association with neurodevelopment and neuronal regeneration (up-regulated, GAP43 and ZEB1) or axonal growth and guidance (down-regulated, ACAP1). Pathway analysis demonstrated a significant contribution of inflammation to disease and a broad influence of Wnt signaling pathways (Wnt1, Wnt5A, ITPR1). CONCLUSION: Transcriptomic analyses of living fetuses with myelomeningocele with the use of amniotic fluid supernatant cell-free RNA demonstrated differential regulation of specific genes and molecular pathways relevant to this central nervous system disorder, which resulted in a new understanding of pathophysiologic changes. The data also suggested the importance of pathways that involve secondary disease, such as inflammation, in myelomeningocele. These newly identified pathways may lead to hypotheses that can test novel therapeutic targets as adjuncts to fetal surgical repair.

The fetal brain transcriptome and neonatal behavioral phenotype in the Ts1Cje mouse model of Down syndrome.

Human fetuses with Down syndrome demonstrate abnormal brain growth and reduced neurogenesis. Despite the prenatal onset of the phenotype, most therapeutic trials have been conducted in adults. Here, we present evidence for fetal brain molecular and neonatal behavioral alterations in the Ts1Cje mouse model of Down syndrome. Embryonic day 15.5 brain hemisphere RNA from Ts1Cje embryos (n = 5) and wild type littermates (n = 5) was processed and hybridized to mouse gene 1.0 ST arrays. Bioinformatic analyses were implemented to identify differential gene and pathway regulation during Ts1Cje fetal brain development. In separate experiments, the Fox scale, ultrasonic vocalization and homing tests were used to investigate behavioral deficits in Ts1Cje pups (n = 29) versus WT littermates (n = 64) at postnatal days 3-21. Ts1Cje fetal brains displayed more differentially regulated genes (n = 71) than adult (n = 31) when compared to their age-matched euploid brains. Ts1Cje embryonic brains showed up-regulation of cell cycle markers and down-regulation of the solute-carrier amino acid transporters. Several cellular processes were dysregulated at both stages, including apoptosis, inflammation, Jak/Stat signaling, G-protein signaling, and oxidoreductase activity. In addition, early behavioral deficits in surface righting, cliff aversion, negative geotaxis, forelimb grasp, ultrasonic vocalization, and the homing tests were observed. The Ts1Cje mouse model exhibits abnormal gene expression during fetal brain development, and significant neonatal behavioral deficits in the pre-weaning period. In combination with human studies, this suggests that the Down syndrome phenotype manifests prenatally and provides a rationale for prenatal therapy to improve perinatal brain development and postnatal neurocognition.

The pathway not taken: understanding 'omics data in the perinatal context.

OBJECTIVE: 'Omics analysis of large datasets has an increasingly important role in perinatal research, but understanding gene expression analyses in the fetal context remains a challenge. We compared the interpretation provided by a widely used systems biology resource (ingenuity pathway analysis [IPA]) with that from gene set enrichment analysis (GSEA) with functional annotation curated specifically for the fetus (Developmental FunctionaL Annotation at Tufts [DFLAT]). STUDY DESIGN: Using amniotic fluid supernatant transcriptome datasets previously produced by our group, we analyzed 3 different developmental perturbations: aneuploidy (Trisomy 21 [T21]), hemodynamic (twin-twin transfusion syndrome [TTTS]), and metabolic (maternal obesity) vs sex- and gestational age-matched control subjects. Differentially expressed probe sets were identified with the use of paired t-tests with the Benjamini-Hochberg correction for multiple testing (P < .05). Functional analyses were performed with IPA and GSEA/DFLAT. Outputs were compared for biologic relevance to the fetus. RESULTS: Compared with control subjects, there were 414 significantly dysregulated probe sets in T21 fetuses, 2226 in TTTS recipient twins, and 470 in fetuses of obese women. Each analytic output was unique but complementary. For T21, both IPA and GSEA/DFLAT identified dysregulation of brain, cardiovascular, and integumentary system development. For TTTS, both analytic tools identified dysregulation of cell growth/proliferation, immune and inflammatory signaling, brain, and cardiovascular development. For maternal obesity, both tools identified dysregulation of immune and inflammatory signaling, brain and musculoskeletal development, and cell death. GSEA/DFLAT identified substantially more dysregulated biologic functions in fetuses of obese women (1203 vs 151). For all 3 datasets, GSEA/DFLAT provided more comprehensive information about brain development. IPA consistently provided more detailed annotation about cell death. IPA produced many dysregulated terms that pertained to cancer (14 in T21, 109 in TTTS, 26 in maternal obesity); GSEA/DFLAT did not. CONCLUSION: Interpretation of the fetal amniotic fluid supernatant transcriptome depends on the analytic program, which suggests that >1 resource should be used. Within IPA, physiologic cellular proliferation in the fetus produced many "false positive" annotations that pertained to cancer, which reflects its bias toward adult diseases. This study supports the use of gene annotation resources with a developmental focus, such as DFLAT, for 'omics studies in perinatal medicine.

Finding novel molecular connections between developmental processes and disease.

Identifying molecular connections between developmental processes and disease can lead to new hypotheses about health risks at all stages of life. Here we introduce a new approach to identifying significant connections between gene sets and disease genes, and apply it to several gene sets related to human development. To overcome the limits of incomplete and imperfect information linking genes to disease, we pool genes within disease subtrees in the MeSH taxonomy, and we demonstrate that such pooling improves the power and accuracy of our approach. Significance is assessed through permutation. We created a web-based visualization tool to facilitate multi-scale exploration of this large collection of significant connections (http://gda.cs.tufts.edu/development). High-level analysis of the results reveals expected connections between tissue-specific developmental processes and diseases linked to those tissues, and widespread connections to developmental disorders and cancers. Yet interesting new hypotheses may be derived from examining the unexpected connections. We highlight and discuss the implications of three such connections, linking dementia with bone development, polycystic ovary syndrome with cardiovascular development, and retinopathy of prematurity with lung development. Our results provide additional evidence that TGFB lays a key role in the early pathogenesis of polycystic ovary syndrome. Our evidence also suggests that the VEGF pathway and downstream NFKB signaling may explain the complex relationship between bronchopulmonary dysplasia and retinopathy of prematurity, and may form a bridge between two currently-competing hypotheses about the molecular origins of bronchopulmonary dysplasia. Further data exploration and similar queries about other gene sets may generate a variety of new information about the molecular relationships between additional diseases.

DFLAT: functional annotation for human development.

BACKGROUND: Recent increases in genomic studies of the developing human fetus and neonate have led to a need for widespread characterization of the functional roles of genes at different developmental stages. The Gene Ontology (GO), a valuable and widely-used resource for characterizing gene function, offers perhaps the most suitable functional annotation system for this purpose. However, due in part to the difficulty of studying molecular genetic effects in humans, even the current collection of comprehensive GO annotations for human genes and gene products often lacks adequate developmental context for scientists wishing to study gene function in the human fetus. DESCRIPTION: The Developmental FunctionaL Annotation at Tufts (DFLAT) project aims to improve the quality of analyses of fetal gene expression and regulation by curating human fetal gene functions using both manual and semi-automated GO procedures. Eligible annotations are then contributed to the GO database and included in GO releases of human data. DFLAT has produced a considerable body of functional annotation that we demonstrate provides valuable information about developmental genomics. A collection of gene sets (genes implicated in the same function or biological process), made by combining existing GO annotations with the 13,344 new DFLAT annotations, is available for use in novel analyses. Gene set analyses of expression in several data sets, including amniotic fluid RNA from fetuses with trisomies 21 and 18, umbilical cord blood, and blood from newborns with bronchopulmonary dysplasia, were conducted both with and without the DFLAT annotation. CONCLUSIONS: Functional analysis of expression data using the DFLAT annotation increases the number of implicated gene sets, reflecting the DFLAT's improved representation of current knowledge. Blinded literature review supports the validity of newly significant findings obtained with the DFLAT annotations. Newly implicated significant gene sets also suggest specific hypotheses for future research. Overall, the DFLAT project contributes new functional annotation and gene sets likely to enhance our ability to interpret genomic studies of human fetal and neonatal development.

Amniotic fluid RNA gene expression profiling provides insights into the phenotype of Turner syndrome.

Turner syndrome is a sex chromosome aneuploidy with characteristic malformations. Amniotic fluid, a complex biological material, could contribute to the understanding of Turner syndrome pathogenesis. In this pilot study, global gene expression analysis of cell-free RNA in amniotic fluid supernatant was utilized to identify specific genes/organ systems that may play a role in Turner syndrome pathophysiology. Cell-free RNA from amniotic fluid of five mid-trimester Turner syndrome fetuses and five euploid female fetuses matched for gestational age was extracted, amplified, and hybridized onto Affymetrix(®) U133 Plus 2.0 arrays. Significantly differentially regulated genes were identified using paired t tests. Biological interpretation was performed using Ingenuity Pathway Analysis and BioGPS gene expression atlas. There were 470 statistically significantly differentially expressed genes identified. They were widely distributed across the genome. XIST was significantly down-regulated (p < 0.0001); SHOX was not differentially expressed. One of the most highly represented organ systems was the hematologic/immune system, distinguishing the Turner syndrome transcriptome from other aneuploidies we previously studied. Manual curation of the differentially expressed gene list identified genes of possible pathologic significance, including NFATC3, IGFBP5, and LDLR. Transcriptomic differences in the amniotic fluid of Turner syndrome fetuses are due to genome-wide dysregulation. The hematologic/immune system differences may play a role in early-onset autoimmune dysfunction. Other genes identified with possible pathologic significance are associated with cardiac and skeletal systems, which are known to be affected in females with Turner syndrome. The discovery-driven approach described here may be useful in elucidating novel mechanisms of disease in Turner syndrome.

Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity.

Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many mediated by in utero microglial programming. As microglia remain inaccessible throughout development, identification of noninvasive biomarkers reflecting fetal brain microglial programming could permit screening and intervention. We used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells) in a mouse model of maternal diet-induced obesity, and single-cell RNA-seq to demonstrate shared transcriptional programs. Comparison with human datasets demonstrated conservation of placental resident macrophage signatures between mice and humans. Single-cell RNA-seq identified common alterations in fetal microglial and Hofbauer cell gene expression induced by maternal obesity, as well as sex differences in these alterations. We propose that Hofbauer cells, which are easily accessible at birth, provide insights into fetal brain microglial programs and may facilitate the early identification of offspring vulnerable to neurodevelopmental disorders.

The splicing factor hnRNPL demonstrates conserved myocardial regulation across species and is altered in heart failure.

Heart failure (HF) is highly prevalent. Mechanisms underlying HF remain incompletely understood. Splicing factors (SF), which control pre-mRNA alternative splicing, regulate cardiac structure and function. This study investigated regulation of the splicing factor heterogeneous nuclear ribonucleoprotein-L (hnRNPL) in the failing heart. hnRNPL protein increased in left ventricular tissue from mice with transaortic constriction-induced HF and from HF patients. In left ventricular tissue, hnRNPL was detected predominantly in nuclei. Knockdown of the hnRNPL homolog Smooth in Drosophila induced cardiomyopathy. Computational analysis of predicted mouse and human hnRNPL binding sites suggested hnRNPL-mediated alternative splicing of tropomyosin, which was confirmed in C2C12 myoblasts. These findings identify hnRNPL as a sensor of cardiac dysfunction and suggest that disturbances of hnRNPL affect alternative splicing in HF.

Current and future directions in network biology.

Summary: Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation: Not applicable.

From patterns to pathways: gene expression data analysis comes of age.

Many different biological questions are routinely studied using transcriptional profiling on microarrays. A wide range of approaches are available for gleaning insights from the data obtained from such experiments. The appropriate choice of data-analysis technique depends both on the data and on the goals of the experiment. This review summarizes some of the common themes in microarray data analysis, including detection of differential expression, clustering, and predicting sample characteristics. Several approaches to each problem, and their relative merits, are discussed and key areas for additional research highlighted.

Cell-specific imputation of drug connectivity mapping with incomplete data.

Drug repositioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. "Connectivity mapping" is a process that links drugs to diseases by identifying compounds whose impact on expression in a collection of cells reverses the disease's impact on expression in disease-relevant tissues. The LINCS project has expanded the universe of compounds and cells for which data are available, but even with this effort, many clinically useful combinations are missing. To evaluate the possibility of repurposing drugs despite missing data, we compared collaborative filtering using either neighborhood-based or SVD imputation methods to two naive approaches via cross-validation. Methods were evaluated for their ability to predict drug connectivity despite missing data. Predictions improved when cell type was taken into account. Neighborhood collaborative filtering was the most successful method, with the best improvements in non-immortalized primary cells. We also explored which classes of compounds are most and least reliant on cell type for accurate imputation. We conclude that even for cells in which drug responses have not been fully characterized, it is possible to identify unassayed drugs that reverse in those cells the expression signatures observed in disease.

Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity.

Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many mediated by in utero microglial programming. As microglia remain inaccessible throughout development, identification of noninvasive biomarkers reflecting fetal brain microglial programming could permit screening and intervention. We used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells) in a mouse model of maternal diet-induced obesity, and single-cell RNA-seq to demonstrate shared transcriptional programs. Comparison with human datasets demonstrated conservation of placental resident macrophage signatures between mice and humans. Single-cell RNA-seq identified common alterations in fetal microglial and Hofbauer cell gene expression induced by maternal obesity, as well as sex differences in these alterations. We propose that Hofbauer cells, which are easily accessible at birth, provide novel insights into fetal brain microglial programs, and may facilitate the early identification of offspring vulnerable to neurodevelopmental disorders in the setting of maternal exposures.

Novel neurodevelopmental information revealed in amniotic fluid supernatant transcripts from fetuses with trisomies 18 and 21.

Trisomies 18 and 21 are the two most common live born autosomal aneuploidies in humans. While the anatomic abnormalities in affected fetuses are well documented, the dysregulated biological pathways associated with the development of the aneuploid phenotype are less clear. Amniotic fluid (AF) cell-free RNA is a valuable source of biological information obtainable from live fetuses. In this study, we mined gene expression data previously produced by our group from mid-trimester AF supernatant samples. We identified the euploid, trisomy 18 and trisomy 21 AF transcriptomes, and analyzed them with a particular focus on the nervous system. We used multiple bioinformatics resources, including DAVID, Ingenuity Pathway Analysis, and the BioGPS Gene Expression Atlas. Our analyses confirmed that AF supernatant from aneuploid fetuses is enriched for nervous system gene expression and neurological disease pathways. Tissue analysis showed that fetal brain cortex and Cajal-Retzius cells were significantly enriched for genes contained in the AF transcriptomes. We also examined AF transcripts known to be dysregulated in aneuploid fetuses compared with euploid controls and identified several brain-specific transcripts among them. Many of these genes play critical roles in nervous system development. NEUROD2, which was downregulated in trisomy 18, induces neurogenic differentiation. SOX11, downregulated in trisomy 21, is a transcription factor that is essential for pan-neuronal protein expression and axonal growth of sensory neurons. Our results show that whole transcriptome analysis of cell-free RNA in AF from live pregnancies permits discovery of biomarkers of abnormal human neurodevelopment and advances our understanding of the pathophysiology of aneuploidy.

Comprehensive analysis of genes expressed by rare microchimeric fetal cells in the maternal mouse lung.

During pregnancy, cells from each fetus travel into the maternal circulation and organs, resulting in the development of microchimerism. Identification of the cell types in this microchimeric population would permit better understanding of possible mechanisms by which they affect maternal health. However, comprehensive analysis of fetal cells has been hampered by their rarity. In this study, we sought to overcome this obstacle by combining flow cytometry with multidimensional gene expression microarray analysis of fetal cells isolated from the murine maternal lung during late pregnancy. Fetal cells were collected from the lungs of pregnant female mice. cDNA was amplified and hybridized to gene expression microarrays. The resulting fetal cell core transcriptome was interrogated using multiple methods including Ingenuity Pathway Analysis, the BioGPS gene expression database, principal component analysis, the Eurexpress gene expression atlas, and primary literature. Here we report that small numbers of fetal cells can be flow sorted from the maternal lung, facilitating discovery-driven gene expression analysis. We additionally show that gene expression data can provide functional information about fetal cells. Our results suggest that fetal cells in the murine maternal lung are a mixed population, consisting of trophoblasts, mesenchymal stem cells, and cells of the immune system. Detection of trophoblasts and immune cells in the maternal lung may facilitate future mechanistic studies related to the development of immune tolerance and pregnancy-related complications, such as pre-eclampsia. Furthermore, the presence and persistence of mesenchymal stem cells in maternal organs may have implications for long-term postpartum maternal health.

Toward the dynamic interactome: it's about time.

Dynamic molecular interactions play a central role in regulating the functioning of cells and organisms. The availability of experimentally determined large-scale cellular networks, along with other high-throughput experimental data sets that provide snapshots of biological systems at different times and conditions, is increasingly helpful in elucidating interaction dynamics. Here we review the beginnings of a new subfield within computational biology, one focused on the global inference and analysis of the dynamic interactome. This burgeoning research area, which entails a shift from static to dynamic network analysis, promises to be a major step forward in our ability to model and reason about cellular function and behavior.