Cell-type-specific consequences of mosaic structural variants in hematopoietic stem and progenitor cells.

The functional impact and cellular context of mosaic structural variants (mSVs) in normal tissues is understudied. Utilizing Strand-seq, we sequenced 1,133 single-cell genomes from 19 human donors of increasing age, and discovered the heterogeneous mSV landscapes of hematopoietic stem and progenitor cells. While mSVs are continuously acquired throughout life, expanded subclones in our cohort are confined to individuals >60. Cells already harboring mSVs are more likely to acquire additional somatic structural variants, including megabase-scale segmental aneuploidies. Capitalizing on comprehensive single-cell micrococcal nuclease digestion with sequencing reference data, we conducted high-resolution cell-typing for eight hematopoietic stem and progenitor cells. Clonally expanded mSVs disrupt normal cellular function by dysregulating diverse cellular pathways, and enriching for myeloid progenitors. Our findings underscore the contribution of mSVs to the cellular and molecular phenotypes associated with the aging hematopoietic system, and establish a foundation for deciphering the molecular links between mSVs, aging and disease susceptibility in normal tissues.

Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches.

Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.

Functional analysis of structural variants in single cells using Strand-seq.

Somatic structural variants (SVs) are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We present a computational method, scNOVA, which uses Strand-seq to perform haplotype-aware integration of SV discovery and molecular phenotyping in single cells by using nucleosome occupancy to infer gene expression as a readout. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation, and consequences of SVs on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T cell acute lymphoblastic leukemia, which revealed c-Myb activation, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture, using a Notch inhibitor. By directly linking SVs to their functional effects, scNOVA enables systematic single-cell multiomic studies of structural variation in heterogeneous cell populations.

Dynamic landscape of long noncoding RNAs during leaf aging in Arabidopsis.

Leaf senescence, the last stage of leaf development, is essential for whole-plant fitness as it marks the relocation of nutrients from senescing leaves to reproductive or other developing organs. Temporally coordinated physiological and functional changes along leaf aging are fine-tuned by a highly regulated genetic program involving multi-layered regulatory mechanisms. Long noncoding RNAs (lncRNAs) are newly emerging as hidden players in many biological processes; however, their contribution to leaf senescence has been largely unknown. Here, we performed comprehensive analyses of RNA-seq data representing all developmental stages of leaves to determine the genome-wide lncRNA landscape along leaf aging. A total of 771 lncRNAs, including 232 unannotated lncRNAs, were identified. Time-course analysis revealed 446 among 771 developmental age-related lncRNAs (AR-lncRNAs). Intriguingly, the expression of AR-lncRNAs was regulated more dynamically in senescing leaves than in growing leaves, revealing the relevant contribution of these lncRNAs to leaf senescence. Further analyses enabled us to infer the function of lncRNAs, based on their interacting miRNA or mRNA partners. We considered functionally diverse lncRNAs including antisense lncRNAs (which regulate overlapping protein-coding genes), competitive endogenous RNAs (ceRNAs; which regulate paired mRNAs using miRNAs as anchors), and mRNA-interacting lncRNAs (which affect the stability of mRNAs). Furthermore, we experimentally validated the senescence regulatory function of three novel AR-lncRNAs including one antisense lncRNA and two mRNA-interacting lncRNAs through molecular and phenotypic analyses. Our study provides a valuable resource of AR-lncRNAs and potential regulatory networks that link the function of coding mRNA and AR-lncRNAs. Together, our results reveal AR-lncRNAs as important elements in the leaf senescence process.

Aß-induced mitochondrial dysfunction in neural progenitors controls KDM5A to influence neuronal differentiation.

Mitochondria in neural progenitors play a crucial role in adult hippocampal neurogenesis by being involved in fate decisions for differentiation. However, the molecular mechanisms by which mitochondria are related to the genetic regulation of neuronal differentiation in neural progenitors are poorly understood. Here, we show that mitochondrial dysfunction induced by amyloid-beta (Aß) in neural progenitors inhibits neuronal differentiation but has no effect on the neural progenitor stage. In line with the phenotypes shown in Alzheimer's disease (AD) model mice, Aß-induced mitochondrial damage in neural progenitors results in deficits in adult hippocampal neurogenesis and cognitive function. Based on hippocampal proteome changes after mitochondrial damage in neural progenitors identified through proteomic analysis, we found that lysine demethylase 5A (KDM5A) in neural progenitors epigenetically suppresses differentiation in response to mitochondrial damage. Mitochondrial damage characteristically causes KDM5A degradation in neural progenitors. Since KDM5A also binds to and activates neuronal genes involved in the early stage of differentiation, functional inhibition of KDM5A consequently inhibits adult hippocampal neurogenesis. We suggest that mitochondria in neural progenitors serve as the checkpoint for neuronal differentiation via KDM5A. Our findings not only reveal a cell-type-specific role of mitochondria but also suggest a new role of KDM5A in neural progenitors as a mediator of retrograde signaling from mitochondria to the nucleus, reflecting the mitochondrial status.

Multi-Omics-Based Autophagy-Related Untypical Subtypes in Patients with Cerebral Amyloid Pathology.

Recent multi-omics analyses paved the way for a comprehensive understanding of pathological processes. However, only few studies have explored Alzheimer's disease (AD) despite the possibility of biological subtypes within these patients. For this study, unsupervised classification of four datasets (genetics, miRNA transcriptomics, proteomics, and blood-based biomarkers) using Multi-Omics Factor Analysis+ (MOFA+), along with systems-biological approaches following various downstream analyses are performed. New subgroups within 170 patients with cerebral amyloid pathology (Aß+) are revealed and the features of them are identified based on the top-rated targets constructing multi-omics factors of both whole (M-TPAD) and immune-focused models (M-IPAD). The authors explored the characteristics of subtypes and possible key-drivers for AD pathogenesis. Further in-depth studies showed that these subtypes are associated with longitudinal brain changes and autophagy pathways are main contributors. The significance of autophagy or clustering tendency is validated in peripheral blood mononuclear cells (PBMCs; n = 120 including 30 Aß- and 90 Aß+), induced pluripotent stem cell-derived human brain organoids/microglia (n = 12 including 5 Aß-, 5 Aß+, and CRISPR-Cas9 apolipoprotein isogenic lines), and human brain transcriptome (n = 78). Collectively, this study provides a strategy for precision medicine therapy and drug development for AD using integrative multi-omics analysis and network modelling.

Single-cell analysis of structural variations and complex rearrangements with tri-channel processing.

Structural variation (SV), involving deletions, duplications, inversions and translocations of DNA segments, is a major source of genetic variability in somatic cells and can dysregulate cancer-related pathways. However, discovering somatic SVs in single cells has been challenging, with copy-number-neutral and complex variants typically escaping detection. Here we describe single-cell tri-channel processing (scTRIP), a computational framework that integrates read depth, template strand and haplotype phase to comprehensively discover SVs in individual cells. We surveyed SV landscapes of 565 single cells, including transformed epithelial cells and patient-derived leukemic samples, to discover abundant SV classes, including inversions, translocations and complex DNA rearrangements. Analysis of the leukemic samples revealed four times more somatic SVs than cytogenetic karyotyping, submicroscopic copy-number alterations, oncogenic copy-neutral rearrangements and a subclonal chromothripsis event. Advancing current methods, single-cell tri-channel processing can directly measure SV mutational processes in individual cells, such as breakage-fusion-bridge cycles, facilitating studies of clonal evolution, genetic mosaicism and SV formation mechanisms, which could improve disease classification for precision medicine.

Prognostic plasma protein panel for Aß deposition in the brain in Alzheimer's disease.

Alzheimer's disease (AD) is the most common age-associated dementia. Many studies have sought to predict cerebral amyloid deposition, the major pathological hallmark of AD, using body fluids such as blood or cerebral spinal fluid (CSF). The use of blood in diagnostic procedures is widespread in medicine; however, existing blood biomarkers for AD remain unreliable. We sought to discover blood biomarkers that discriminate Aß deposition status in the brain. This study used 107 individuals who were cognitively normal (CN), 107 patients with mild cognitive impairment (MCI), and 40 AD patients with Pittsburg compound B positron emission tomography (PiB-PET) amyloid imaging data available. We found five plasma biomarker candidates via mass spectrometry (MS) based-proteomic analysis and validated these proteins using enzyme-linked immunosorbent assay (ELISA). Our integrated models were highly predictive of brain amyloid deposition, exhibiting 0.871 accuracy with 79% sensitivity and 84% specificity overall, and 0.836 accuracy with 68% sensitivity and 90% specificity in patients with MCI. These results indicated that a combination of proteomic-based blood proteins might be a possible biomarker set for predicting cerebral amyloid deposition.

Comparative proteomic analysis of mouse models of pathological and physiological cardiac hypertrophy, with selection of biomarkers of pathological hypertrophy by integrative Proteogenomics.

To determine fundamental characteristics of pathological cardiac hypertrophy, protein expression profiles in two widely accepted models of cardiac hypertrophy (swimming-trained mouse for physiological hypertrophy and pressure-overload-induced mouse for pathological hypertrophy) were compared using a label-free quantitative proteomics approach. Among 3955 proteins (19,235 peptides, false-discovery rate < 0.01) identified in these models, 486 were differentially expressed with a log2 fold difference ≥ 0.58, or were detected in only one hypertrophy model (each protein from 4 technical replicates, p < .05). Analysis of gene ontology biological processes and KEGG pathways identified cellular processes enriched in one or both hypertrophy models. Processes unique to pathological hypertrophy were compared with processes previously identified in cardiac-hypertrophy models. Individual proteins with differential expression in processes unique to pathological hypertrophy were further confirmed using the results of previous targeted functional analysis studies. Using a proteogenomic approach combining transcriptomic and proteomic analyses, similar patterns of differential expression were observed for 23 proteins and corresponding genes associated with pathological hypertrophy. A total of 11 proteins were selected as early-stage pathological-hypertrophy biomarker candidates, and the results of western blotting for five of these proteins in independent samples confirmed the patterns of differential expression in mouse models of pathological and physiological cardiac hypertrophy.

Exploring the Complexity of Cortical Development Using Single-Cell Transcriptomics.

The developing neocortex in the mammalian brain is composed of multiple cell types including apical progenitors (AP), basal progenitors (BP), and neurons that populate three different layers, the ventricular zone (VZ), the subventricular zone (SVZ), and the cortical plate (CP). Despite recent advances, the diversity of the existing cell populations including those which are differentiating and mature, their biogenesis and the underlying gene regulatory mechanisms remain poorly known. Recent studies have taken advantage of the rapidly emerging single-cell technologies to decode the heterogeneity of cell populations at the transcriptome level during cortical development and their molecular details. Here we review these studies and provide an overview of the steps in single-cell transcriptomics including both experimental and computational analysis. We also discuss how single-cell genomics holds a big potential in future for brain research and discuss its possible applications and biological insights that can be achieved from these approaches. We conclude this review by discussing the current challenges in the implementation of single-cell techniques toward a comprehensive understanding of the genetic and epigenetic mechanisms underlying neocortex development.

Molecular bases for differential aging programs between flag and second leaves during grain-filling in rice.

Flag leaves (FL) and second leaves (SL) in rice show differential aging patterns during monocarpic senescence. Coordination of aging programs between FL and SL is important for grain yield and quality. However, the molecular bases for differential aging programs between FL and SL have not been systematically explored in rice. Here, we performed mRNA-sequencing of FL and SL at six time points during grain-filling and identified four molecular bases for differential aging programs between FL and SL: phenylpropanoid biosynthesis, photosynthesis, amino acid (AA) transport, and hormone response. Of them, photosynthesis (carbon assimilation) and AA transport (nitrogen remobilization) predominantly occurred in FL and SL, respectively, during grain-filling. Unlike other molecular bases, AA transport showed consistent differential expression patterns between FL and SL in independent samples. Moreover, long-distance AA transporters showed invariant differential expression patterns between FL and SL after panicle removal, which was consistent to invariant differential nitrogen contents between FL and SL after panicle removal. Therefore, our results suggest that the supplies of carbon and nitrogen to seeds is functionally segregated between FL and SL and that long-distance AA transport is an invariant core program for high nitrogen remobilization in SL.

Golgi Outpost Synthesis Impaired by Toxic Polyglutamine Proteins Contributes to Dendritic Pathology in Neurons.

Dendrite aberration is a common feature of neurodegenerative diseases caused by protein toxicity, but the underlying mechanisms remain largely elusive. Here, we show that nuclear polyglutamine (polyQ) toxicity resulted in defective terminal dendrite elongation accompanied by a loss of Golgi outposts (GOPs) and a decreased supply of plasma membrane (PM) in Drosophila class IV dendritic arborization (da) (C4 da) neurons. mRNA sequencing revealed that genes downregulated by polyQ proteins included many secretory pathway-related genes, including COPII genes regulating GOP synthesis. Transcription factor enrichment analysis identified CREB3L1/CrebA, which regulates COPII gene expression. CrebA overexpression in C4 da neurons restores the dysregulation of COPII genes, GOP synthesis, and PM supply. Chromatin immunoprecipitation (ChIP)-PCR revealed that CrebA expression is regulated by CREB-binding protein (CBP), which is sequestered by polyQ proteins. Furthermore, co-overexpression of CrebA and Rac1 synergistically restores the polyQ-induced dendrite pathology. Collectively, our results suggest that GOPs impaired by polyQ proteins contribute to dendrite pathology through the CBP-CrebA-COPII pathway.

Protein-Induced Pluripotent Stem Cells Ameliorate Cognitive Dysfunction and Reduce Aß Deposition in a Mouse Model of Alzheimer's Disease.

Transplantation of stem cells into the brain attenuates functional deficits in the central nervous system via cell replacement, the release of specific neurotransmitters, and the production of neurotrophic factors. To identify patient-specific and safe stem cells for treating Alzheimer's disease (AD), we generated induced pluripotent stem cells (iPSCs) derived from mouse skin fibroblasts by treating protein extracts of embryonic stem cells. These reprogrammed cells were pluripotent but nontumorigenic. Here, we report that protein-iPSCs differentiated into glial cells and decreased plaque depositions in the 5XFAD transgenic AD mouse model. We also found that transplanted protein-iPSCs mitigated the cognitive dysfunction observed in these mice. Proteomic analysis revealed that oligodendrocyte-related genes were upregulated in brains injected with protein-iPSCs, providing new insights into the potential function of protein-iPSCs. Taken together, our data indicate that protein-iPSCs might be a promising therapeutic approach for AD. Stem Cells Translational Medicine 2017;6:293-305.

Functional characterization of EI24-induced autophagy in the degradation of RING-domain E3 ligases.

Historically, the ubiquitin-proteasome system (UPS) and autophagy pathways were believed to be independent; however, recent data indicate that these pathways engage in crosstalk. To date, the players mediating this crosstalk have been elusive. Here, we show experimentally that EI24 (EI24, autophagy associated transmembrane protein), a key component of basal macroautophagy/autophagy, degrades 14 physiologically important E3 ligases with a RING (really interesting new gene) domain, whereas 5 other ligases were not degraded. Based on the degradation results, we built a statistical model that predicts the RING E3 ligases targeted by EI24 using partial least squares discriminant analysis. Of 381 RING E3 ligases examined computationally, our model predicted 161 EI24 targets. Those targets are primarily involved in transcription, proteolysis, cellular bioenergetics, and apoptosis and regulated by TP53 and MTOR signaling. Collectively, our work demonstrates that EI24 is an essential player in UPS-autophagy crosstalk via degradation of RING E3 ligases. These results indicate a paradigm shift regarding the fate of E3 ligases.

PiB-PET Imaging-Based Serum Proteome Profiles Predict Mild Cognitive Impairment and Alzheimer's Disease.

Development of a simple, non-invasive early diagnosis platform of Alzheimer's disease (AD) using blood is urgently required. Recently, PiB-PET imaging has been shown to be powerful to quantify amyloid-ß plaque loads leading to pathophysiological alterations in AD brains. Thus, there has been a need for serum biomarkers reflecting PiB-PET imaging data as an early diagnosis platform of AD. Here, using LC-MS/MS analysis coupled with isobaric tagging, we performed comprehensive proteome profiling of serum samples from cognitively normal controls, mild cognitive impairment (MCI), and AD patients, who were selected using PiB-PET imaging. Comparative analysis of the proteomes revealed 79 and 72 differentially expressed proteins in MCI and AD, respectively, compared to controls. Integrated analysis of these proteins with genomic and proteomic data of AD brain tissues, together with network analysis, identified three biomarker candidates representing the altered proteolysis-related process in MCI or AD: proprotein convertase subtilisin/kexin type 9 (PCSK9), coagulation factor XIII, A1 polypeptide (F13A1), and dermcidin (DCD). In independent serum samples of MCI and AD, we confirmed the elevation of the candidates using western blotting and ELISA. Our results suggest that these biomarker candidates can serve as a potential non-invasive early diagnosis platform reflecting PiB-PET imaging for MCI and AD.

Programming of Plant Leaf Senescence with Temporal and Inter-Organellar Coordination of Transcriptome in Arabidopsis.

Plant leaves, harvesting light energy and fixing CO2, are a major source of foods on the earth. Leaves undergo developmental and physiological shifts during their lifespan, ending with senescence and death. We characterized the key regulatory features of the leaf transcriptome during aging by analyzing total- and small-RNA transcriptomes throughout the lifespan of Arabidopsis (Arabidopsis thaliana) leaves at multidimensions, including age, RNA-type, and organelle. Intriguingly, senescing leaves showed more coordinated temporal changes in transcriptomes than growing leaves, with sophisticated regulatory networks comprising transcription factors and diverse small regulatory RNAs. The chloroplast transcriptome, but not the mitochondrial transcriptome, showed major changes during leaf aging, with a strongly shared expression pattern of nuclear transcripts encoding chloroplast-targeted proteins. Thus, unlike animal aging, leaf senescence proceeds with tight temporal and distinct interorganellar coordination of various transcriptomes that would be critical for the highly regulated degeneration and nutrient recycling contributing to plant fitness and productivity.

Novel Morphologic and Genetic Analysis of Cancer Cells in a 3D Microenvironment Identifies STAT3 as a Regulator of Tumor Permeability Barrier Function.

Tumor permeability is a critical determinant of drug delivery and sensitivity, but systematic methods to identify factors that perform permeability barrier functions in the tumor microenvironment are not yet available. Multicellular tumor spheroids have become tractable in vitro models to study the impact of a three-dimensional (3D) environment on cellular behavior. In this study, we characterized the spheroid-forming potential of cancer cells and correlated the resulting spheroid morphologies with genetic information to identify conserved cellular processes associated with spheroid structure. Spheroids generated from 100 different cancer cell lines were classified into four distinct groups based on morphology. In particular, round and compact spheroids exhibited highly hypoxic inner cores and permeability barriers against anticancer drugs. Through systematic and correlative analysis, we reveal JAK-STAT signaling as one of the signature pathways activated in round spheroids. Accordingly, STAT3 inhibition in spheroids generated from the established cancer cells and primary glioblastoma patient-derived cells altered the rounded morphology and increased drug sensitivity. Furthermore, combined administration of the STAT3 inhibitor and 5-fluorouracil to a mouse xenograft model markedly reduced tumor growth compared with monotherapy. Collectively, our findings demonstrate the ability to integrate 3D culture and genetic profiling to determine the factors underlying the integrity of the permeability barrier in the tumor microenvironment, and may help to identify and exploit novel mechanisms of drug resistance.

Mechanism of Cisplatin-Induced Cytotoxicity Is Correlated to Impaired Metabolism Due to Mitochondrial ROS Generation.

The chemotherapeutic use of cisplatin is limited by its severe side effects. In this study, by conducting different omics data analyses, we demonstrated that cisplatin induces cell death in a proximal tubular cell line by suppressing glycolysis- and tricarboxylic acid (TCA)/mitochondria-related genes. Furthermore, analysis of the urine from cisplatin-treated rats revealed the lower expression levels of enzymes involved in glycolysis, TCA cycle, and genes related to mitochondrial stability and confirmed the cisplatin-related metabolic abnormalities. Additionally, an increase in the level of p53, which directly inhibits glycolysis, has been observed. Inhibition of p53 restored glycolysis and significantly reduced the rate of cell death at 24 h and 48 h due to p53 inhibition. The foremost reason of cisplatin-related cytotoxicity has been correlated to the generation of mitochondrial reactive oxygen species (ROS) that influence multiple pathways. Abnormalities in these pathways resulted in the collapse of mitochondrial energy production, which in turn sensitized the cells to death. The quenching of ROS led to the amelioration of the affected pathways. Considering these observations, it can be concluded that there is a significant correlation between cisplatin and metabolic dysfunctions involving mROS as the major player.

Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) emerges as a promising tool to identify the ions (small molecules) indicative of disease states from the surface of patient tissues. In TOF-SIMS analysis, an enhanced ionization of surface molecules is critical to increase the number of detected ions. Several methods have been developed to enhance ionization capability. However, how these methods improve identification of disease-related ions has not been systematically explored. Here, we present a multi-dimensional SIMS (MD-SIMS) that combines conventional TOF-SIMS and metal-assisted SIMS (MetA-SIMS). Using this approach, we analyzed cancer and adjacent normal tissues first by TOF-SIMS and subsequently by MetA-SIMS. In total, TOF- and MetA-SIMS detected 632 and 959 ions, respectively. Among them, 426 were commonly detected by both methods, while 206 and 533 were detected uniquely by TOF- and MetA-SIMS, respectively. Of the 426 commonly detected ions, 250 increased in their intensities by MetA-SIMS, whereas 176 decreased. The integrated analysis of the ions detected by the two methods resulted in an increased number of discriminatory ions leading to an enhanced separation between cancer and normal tissues. Therefore, the results show that MD-SIMS can be a useful approach to provide a comprehensive list of discriminatory ions indicative of disease states.

Integration of proteomic and transcriptomic profiles identifies a novel PDGF-MYC network in human smooth muscle cells.

BACKGROUND: Platelet-derived growth factor-BB (PDGF-BB) has been implicated in the proliferation, migration and synthetic activities of smooth muscle cells that characterize physiologic and pathologic tissue remodeling in hollow organs. However, neither the molecular basis of PDGFR-regulated signaling webs, nor the extent to which specific components within these networks could be exploited for therapeutic benefit has been fully elucidated. RESULTS: Expression profiling and quantitative proteomics analysis of PDGF-treated primary human bladder smooth muscle cells identified 1,695 genes and 241 proteins as differentially expressed versus non-treated cells. Analysis of gene expression data revealed MYC, JUN, EGR1, MYB, RUNX1, as the transcription factors most significantly networked with up-regulated genes. Forty targets were significantly altered at both the mRNA and protein levels. Proliferation, migration and angiogenesis were the biological processes most significantly associated with this signature, and MYC was the most highly networked master regulator. Alterations in master regulators and gene targets were validated in PDGF-stimulated smooth muscle cells in vitro and in a model of bladder injury in vivo. Pharmacologic inhibition of MYC and JUN confirmed their role in SMC proliferation and migration. Network analysis identified the diaphanous-related formin 3 as a novel PDGF target regulated by MYC and JUN, which was necessary for PDGF-stimulated lamellipodium formation. CONCLUSIONS: These findings provide the first systems-level analysis of the PDGF-regulated transcriptome and proteome in normal smooth muscle cells. The analyses revealed an extensive cohort of PDGF-dependent biological processes and connected key transcriptional effectors to their regulation, significantly expanding current knowledge of PDGF-stimulated signaling cascades. These observations also implicate MYC as a novel target for pharmacological intervention in fibroproliferative expansion of smooth muscle, and potentially in cancers in which PDGFR-dependent signaling or MYC activation promote tumor progression.