ATM-deficiency-induced microglial activation promotes neurodegeneration in ataxia-telangiectasia.

While ATM loss of function has long been identified as the genetic cause of ataxia-telangiectasia (A-T), how it leads to selective and progressive degeneration of cerebellar Purkinje and granule neurons remains unclear. ATM expression is enriched in microglia throughout cerebellar development and adulthood. Here, we find evidence of microglial inflammation in the cerebellum of patients with A-T using single-nucleus RNA sequencing. Pseudotime analysis revealed that activation of A-T microglia preceded upregulation of apoptosis-related genes in granule and Purkinje neurons and that microglia exhibited increased neurotoxic cytokine signaling to granule and Purkinje neurons in A-T. To confirm these findings experimentally, we performed transcriptomic profiling of A-T induced pluripotent stem cell (iPSC)-derived microglia, which revealed cell-intrinsic microglial activation of cytokine production and innate immune response pathways compared to controls. Furthermore, A-T microglia co-culture with either control or A-T iPSC-derived neurons was sufficient to induce cytotoxicity. Taken together, these studies reveal that cell-intrinsic microglial activation may promote neurodegeneration in A-T.

Quantum processor-inspired machine learning in the biomedical sciences.

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired by recent advances in physical quantum processors, we evaluated several unconventional machine-learning (ML) strategies on actual human tumor data, namely "Ising-type" methods, whose objective function is formulated identical to simulated annealing and quantum annealing. We show the efficacy of multiple Ising-type ML algorithms for classification of multi-omics human cancer data from The Cancer Genome Atlas, comparing these classifiers to a variety of standard ML methods. Our results indicate that Ising-type ML offers superior classification performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences.

Smooth Muscle Cell Reprogramming in Aortic Aneurysms.

The etiology of aortic aneurysms is poorly understood, but it is associated with atherosclerosis, hypercholesterolemia, and abnormal transforming growth factor ß (TGF-ß) signaling in smooth muscle. Here, we investigated the interactions between these different factors in aortic aneurysm development and identified a key role for smooth muscle cell (SMC) reprogramming into a mesenchymal stem cell (MSC)-like state. SMC-specific ablation of TGF-ß signaling in Apoe-/- mice on a hypercholesterolemic diet led to development of aortic aneurysms exhibiting all the features of human disease, which was associated with transdifferentiation of a subset of contractile SMCs into an MSC-like intermediate state that generated osteoblasts, chondrocytes, adipocytes, and macrophages. This combination of medial SMC loss with marked increases in non-SMC aortic cell mass induced exuberant growth and dilation of the aorta, calcification and ossification of the aortic wall, and inflammation, resulting in aneurysm development.

Chronic mTOR activation induces a degradative smooth muscle cell phenotype.

Smooth muscle cell (SMC) proliferation has been thought to limit the progression of thoracic aortic aneurysm and dissection (TAAD) because loss of medial cells associates with advanced disease. We investigated effects of SMC proliferation in the aortic media by conditional disruption of Tsc1, which hyperactivates mTOR complex 1. Consequent SMC hyperplasia led to progressive medial degeneration and TAAD. In addition to diminished contractile and synthetic functions, fate-mapped SMCs displayed increased proteolysis, endocytosis, phagocytosis, and lysosomal clearance of extracellular matrix and apoptotic cells. SMCs acquired a limited repertoire of macrophage markers and functions via biogenesis of degradative organelles through an mTOR/ß-catenin/MITF-dependent pathway, but were distinguishable from conventional macrophages by an absence of hematopoietic lineage markers and certain immune effectors even in the context of hyperlipidemia. Similar mTOR activation and induction of a degradative SMC phenotype in a model of mild TAAD due to Fbn1 mutation greatly worsened disease with near-uniform lethality. The finding of increased lysosomal markers in medial SMCs from clinical TAAD specimens with hyperplasia and matrix degradation further supports the concept that proliferation of degradative SMCs within the media causes aortic disease, thus identifying mTOR-dependent phenotypic modulation as a therapeutic target for combating TAAD.

Endothelial TGF-ß signalling drives vascular inflammation and atherosclerosis.

Atherosclerosis is a progressive vascular disease triggered by interplay between abnormal shear stress and endothelial lipid retention. A combination of these and, potentially, other factors leads to a chronic inflammatory response in the vessel wall, which is thought to be responsible for disease progression characterized by a buildup of atherosclerotic plaques. Yet molecular events responsible for maintenance of plaque inflammation and plaque growth have not been fully defined. Here we show that endothelial TGFß signaling is one of the primary drivers of atherosclerosis-associated vascular inflammation. Inhibition of endothelial TGFß signaling in hyperlipidemic mice reduces vessel wall inflammation and vascular permeability and leads to arrest of disease progression and regression of established lesions. These pro-inflammatory effects of endothelial TGFß signaling are in stark contrast with its effects in other cell types and identify it as an important driver of atherosclerotic plaque growth and show the potential of cell-type specific therapeutic intervention aimed at control of this disease.

Endothelial ERK1/2 signaling maintains integrity of the quiescent endothelium.

To define the role of ERK1/2 signaling in the quiescent endothelium, we induced endothelial Erk2 knockout in adult Erk1-/- mice. This resulted in a rapid onset of hypertension, a decrease in eNOS expression, and an increase in endothelin-1 plasma levels, with all mice dying within 5 wk. Immunostaining and endothelial fate mapping showed a robust increase in TGFß signaling leading to widespread endothelial-to-mesenchymal transition (EndMT). Fibrosis affecting the cardiac conduction system was responsible for the universal lethality in these mice. Other findings included renal endotheliosis, loss of fenestrated endothelia in endocrine organs, and hemorrhages. An ensemble computational intelligence strategy, comprising deep learning and probabilistic programing of RNA-seq data, causally linked the loss of ERK1/2 in HUVECs in vitro to activation of TGFß signaling, EndMT, suppression of eNOS, and induction of endothelin-1 expression. All in silico predictions were verified in vitro and in vivo. In summary, these data establish the key role played by ERK1/2 signaling in the maintenance of vascular normalcy.

Enhancing Retrosynthetic Reaction Prediction with Deep Learning Using Multiscale Reaction Classification.

Chemical synthesis planning is a key aspect in many fields of chemistry, especially drug discovery. Recent implementations of machine learning and artificial intelligence techniques for retrosynthetic analysis have shown great potential to improve computational methods for synthesis planning. Herein, we present a multiscale, data-driven approach for retrosynthetic analysis with deep highway networks (DHN). We automatically extracted reaction rules (i.e., ways in which a molecule is produced) from a data set consisting of chemical reactions derived from U.S. patents. We performed the retrosynthetic reaction prediction task in two steps: first, we built a DHN model to predict which group of reactions (consisting of chemically similar reaction rules) was employed to produce a molecule. Once a reaction group was identified, a DHN trained on the subset of reactions within the identified reaction group, was employed to predict the transformation rule used to produce a molecule. To validate our approach, we predicted the first retrosynthetic reaction step for 40 approved drugs using our multiscale model and compared its predictive performance with a conventional model trained on all machine-extracted reaction rules employed as a control. Our multiscale approach showed a success rate of 82.9% at generating valid reactants from retrosynthetic reaction predictions. Comparatively, the control model trained on all machine-extracted reaction rules yielded a success rate of 58.5% on the validation set of 40 pharmaceutical molecules, indicating a significant statistical improvement with our approach to match known first synthetic reaction of the tested drugs in this study. While our multiscale approach was unable to outperform state-of-the-art rule-based systems curated by expert chemists, multiscale classification represents a marked enhancement in retrosynthetic analysis and can be easily adapted for use in a range of artificial intelligence strategies.

Melanocortin 4 Receptor Pathway Dysfunction in Obesity: Patient Stratification Aimed at MC4R Agonist Treatment.

Context: The hypothalamic melanocortin 4 receptor (MC4R) pathway serves a critical role in regulating body weight. Loss of function (LoF) mutations in the MC4R pathway, including mutations in the pro-opiomelanocortin (POMC), prohormone convertase 1 (PCSK1), leptin receptor (LEPR), or MC4R genes, have been shown to cause early-onset severe obesity. Methods: Through a comprehensive epidemiological analysis of known and predicted LoF variants in the POMC, PCSK1, and LEPR genes, we sought to estimate the number of US individuals with biallelic MC4R pathway LoF variants. Results: We predict ~650 α-melanocyte-stimulating hormone (MSH)/POMC, 8500 PCSK1, and 3600 LEPR homozygous and compound heterozygous individuals in the United States, cumulatively enumerating >12,800 MC4R pathway-deficient obese patients. Few of these variants have been genetically diagnosed to date. These estimates increase when we include a small subset of less rare variants: ß-MSH/POMC,PCSK1 N221D, and a PCSK1 LoF variant (T640A). To further define the MC4R pathway and its potential impact on obesity, we tested associations between body mass index (BMI) and LoF mutation burden in the POMC, PCSK1, and LEPR genes in various populations. We show that the cumulative allele burden in individuals with two or more LoF alleles in one or more genes in the MC4R pathway are predisposed to a higher BMI than noncarriers or heterozygous LoF carriers with a defect in only one gene. Conclusions: Our analysis represents a genetically rationalized study of the hypothalamic MC4R pathway aimed at genetic patient stratification to determine which obese subpopulations should be studied to elucidate MC4R agonist (e.g., setmelanotide) treatment responsiveness.

Aging and neurodegeneration are associated with increased mutations in single human neurons.

It has long been hypothesized that aging and neurodegeneration are associated with somatic mutation in neurons; however, methodological hurdles have prevented testing this hypothesis directly. We used single-cell whole-genome sequencing to perform genome-wide somatic single-nucleotide variant (sSNV) identification on DNA from 161 single neurons from the prefrontal cortex and hippocampus of 15 normal individuals (aged 4 months to 82 years), as well as 9 individuals affected by early-onset neurodegeneration due to genetic disorders of DNA repair (Cockayne syndrome and xeroderma pigmentosum). sSNVs increased approximately linearly with age in both areas (with a higher rate in hippocampus) and were more abundant in neurodegenerative disease. The accumulation of somatic mutations with age-which we term genosenium-shows age-related, region-related, and disease-related molecular signatures and may be important in other human age-associated conditions.

Shear-induced Notch-Cx37-p27 axis arrests endothelial cell cycle to enable arterial specification.

Establishment of a functional vascular network is rate-limiting in embryonic development, tissue repair and engineering. During blood vessel formation, newly generated endothelial cells rapidly expand into primitive plexi that undergo vascular remodeling into circulatory networks, requiring coordinated growth inhibition and arterial-venous specification. Whether the mechanisms controlling endothelial cell cycle arrest and acquisition of specialized phenotypes are interdependent is unknown. Here we demonstrate that fluid shear stress, at arterial flow magnitudes, maximally activates NOTCH signaling, which upregulates GJA4 (commonly, Cx37) and downstream cell cycle inhibitor CDKN1B (p27). Blockade of any of these steps causes hyperproliferation and loss of arterial specification. Re-expression of GJA4 or CDKN1B, or chemical cell cycle inhibition, restores endothelial growth control and arterial gene expression. Thus, we elucidate a mechanochemical pathway in which arterial shear activates a NOTCH-GJA4-CDKN1B axis that promotes endothelial cell cycle arrest to enable arterial gene expression. These insights will guide vascular regeneration and engineering.

FGF-dependent metabolic control of vascular development.

Blood and lymphatic vasculatures are intimately involved in tissue oxygenation and fluid homeostasis maintenance. Assembly of these vascular networks involves sprouting, migration and proliferation of endothelial cells. Recent studies have suggested that changes in cellular metabolism are important to these processes. Although much is known about vascular endothelial growth factor (VEGF)-dependent regulation of vascular development and metabolism, little is understood about the role of fibroblast growth factors (FGFs) in this context. Here we identify FGF receptor (FGFR) signalling as a critical regulator of vascular development. This is achieved by FGF-dependent control of c-MYC (MYC) expression that, in turn, regulates expression of the glycolytic enzyme hexokinase 2 (HK2). A decrease in HK2 levels in the absence of FGF signalling inputs results in decreased glycolysis, leading to impaired endothelial cell proliferation and migration. Pan-endothelial- and lymphatic-specific Hk2 knockouts phenocopy blood and/or lymphatic vascular defects seen in Fgfr1/Fgfr3 double mutant mice, while HK2 overexpression partly rescues the defects caused by suppression of FGF signalling. Thus, FGF-dependent regulation of endothelial glycolysis is a pivotal process in developmental and adult vascular growth and development.

Cell types differ in global coordination of splicing and proportion of highly expressed genes.

Balance in the transcriptome is regulated by coordinated synthesis and degradation of RNA molecules. Here we investigated whether mammalian cell types intrinsically differ in global coordination of gene splicing and expression levels. We analyzed RNA-seq transcriptome profiles of 8 different purified mouse cell types. We found that different cell types vary in proportion of highly expressed genes and the number of alternatively spliced transcripts expressed per gene, and that the cell types that express more variants of alternatively spliced transcripts per gene are those that have higher proportion of highly expressed genes. Cell types segregated into two clusters based on high or low proportion of highly expressed genes. Biological functions involved in negative regulation of gene expression were enriched in the group of cell types with low proportion of highly expressed genes, and biological functions involved in regulation of transcription and RNA splicing were enriched in the group of cell types with high proportion of highly expressed genes. Our findings show that cell types differ in proportion of highly expressed genes and the number of alternatively spliced transcripts expressed per gene, which represent distinct properties of the transcriptome and may reflect intrinsic differences in global coordination of synthesis, splicing, and degradation of RNA molecules.

Somatic mutation in single human neurons tracks developmental and transcriptional history.

Neurons live for decades in a postmitotic state, their genomes susceptible to DNA damage. Here we survey the landscape of somatic single-nucleotide variants (SNVs) in the human brain. We identified thousands of somatic SNVs by single-cell sequencing of 36 neurons from the cerebral cortex of three normal individuals. Unlike germline and cancer SNVs, which are often caused by errors in DNA replication, neuronal mutations appear to reflect damage during active transcription. Somatic mutations create nested lineage trees, allowing them to be dated relative to developmental landmarks and revealing a polyclonal architecture of the human cerebral cortex. Thus, somatic mutations in the brain represent a durable and ongoing record of neuronal life history, from development through postmitotic function.

Proteomic analysis and identification of cellular interactors of the giant ubiquitin ligase HERC2.

HERC2 is a large E3 ubiquitin ligase with multiple structural domains that has been implicated in an array of cellular processes. Mutations in HERC2 are linked to developmental delays and impairment caused by nervous system dysfunction, such as Angelman Syndrome and autism-spectrum disorders. However, HERC2 cellular activity and regulation remain poorly understood. We used a broad proteomic approach to survey the landscape of cellular proteins that interact with HERC2. We identified nearly 300 potential interactors, a subset of which we validated binding to HERC2. The potential HERC2 interactors included the eukaryotic translation initiation factor 3 complex, the intracellular transport COPI coatomer complex, the glycogen regulator phosphorylase kinase, beta-catenin, PI3 kinase, and proteins involved in fatty acid transport and iron homeostasis. Through a complex bioinformatic analysis of potential interactors, we linked HERC2 to cellular processes including intracellular protein trafficking and transport, metabolism of cellular energy, and protein translation. Given its size, multidomain structure, and association with various cellular activities, HERC2 may function as a scaffold to integrate protein complexes and bridge critical cellular pathways. This work provides a significant resource with which to interrogate HERC2 function more deeply and evaluate its contributions to mechanisms governing cellular homeostasis and disease.

nEASE: a method for gene ontology subclassification of high-throughput gene expression data.

UNLABELLED: High-throughput technologies can identify genes whose expression profiles correlate with specific phenotypes; however, placing these genes into a biological context remains challenging. To help address this issue, we developed nested Expression Analysis Systematic Explorer (nEASE). nEASE complements traditional gene ontology enrichment approaches by determining statistically enriched gene ontology subterms within a list of genes based on co-annotation. Here, we overview an open-source software version of the nEASE algorithm. nEASE can be used either stand-alone or as part of a pathway discovery pipeline. AVAILABILITY: nEASE is implemented within the Multiple Experiment Viewer software package available at http://www.tm4.org/mev. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Therapeutic implications of GIPC1 silencing in cancer.

GIPC1 is a cytoplasmic scaffold protein that interacts with numerous receptor signaling complexes, and emerging evidence suggests that it plays a role in tumorigenesis. GIPC1 is highly expressed in a number of human malignancies, including breast, ovarian, gastric, and pancreatic cancers. Suppression of GIPC1 in human pancreatic cancer cells inhibits in vivo tumor growth in immunodeficient mice. To better understand GIPC1 function, we suppressed its expression in human breast and colorectal cancer cell lines and human mammary epithelial cells (HMECs) and assayed both gene expression and cellular phenotype. Suppression of GIPC1 promotes apoptosis in MCF-7, MDA-MD231, SKBR-3, SW480, and SW620 cells and impairs anchorage-independent colony formation of HMECs. These observations indicate GIPC1 plays an essential role in oncogenic transformation, and its expression is necessary for the survival of human breast and colorectal cancer cells. Additionally, a GIPC1 knock-down gene signature was used to interrogate publically available breast and ovarian cancer microarray datasets. This GIPC1 signature statistically correlates with a number of breast and ovarian cancer phenotypes and clinical outcomes, including patient survival. Taken together, these data indicate that GIPC1 inhibition may represent a new target for therapeutic development for the treatment of human cancers.