Biology subjects

Li, A.

Publications and source records attributed to Li, A..

At least 271 records · Page 15Linked to original sources

bioRxiv2019

Serotonin Neurons in the Dorsal and Medial Raphe Nuclei: from Single-Cell Transcriptomes to Whole-Brain Projections

Serotonin neurons of the dorsal and medial raphe nuclei (DR and MR) collectively innervate the entire forebrain and midbrain, modulating diverse physiology and behavior. To gain a fundamental understanding of their molecular heterogeneity, we used plate-based single-cell RNA-sequencing to generate a comprehensive dataset comprising eleven transcriptomically distinct serotonin neuron clusters. Systematic in situ hybridization mapped specific clusters to the principal DR, caudal DR, or MR. These transcriptomic clusters differentially express a rich repertoire of neuropeptides, receptors, ion channels, and transcription factors. We generated novel intersectional viral-genetic tools to access specific subpopulations. Whole-brain axonal projection mapping revealed that DR serotonin neurons co-expressing vesicular glutamate transporter-3 preferentially innervate the cortex, whereas those co-expressing thyrotropin-releasing hormone innervate subcortical regions in particular the hypothalamus. Reconstruction of 50 individual DR serotonin neurons revealed segregated axonal projection patterns at the single-cell level. Together, these results provide a molecular foundation of the heterogenous serotonin neuronal phenotypes.

neuroscience↗

bioRxiv2019

In situ measurement of absolute concentrations by Normalized Raman Imaging

Cell mass and its chemical composition are important aggregate cellular variables for physiological processes including growth control and tissue homeostasis. Despite their central importance, it has been difficult to quantitatively measure these quantities from single cells in intact tissue. Here, we introduce Normalized Raman Imaging (NoRI), a Stimulated Raman Scattering (SRS) microscopy method that provides the local concentrations of protein, lipid and water from live or fixed tissue samples with high spatial resolution. Using NoRI, we demonstrate that single cell protein, lipid and water concentrations are maintained in a tight range in cells under same physiological conditions and are altered in different physiological states such as cell cycle stages, attachment to substrates of different stiffness, or by entering senescence. In animal tissues, protein and lipid concentration varies with cell types, yet an unexpected cell-to-cell heterogeneity was found in cerebellar Purkinje cells. Protein and lipid concentration profile provides a new means to quantitatively compare disease-related pathology as demonstrated using models of Alzheimers disease. Our demonstration shows that NoRI is a broadly applicable tool for probing the biological regulation of protein mass, lipid mass and water in cellular and tissue growth, homeostasis, and disease.

bioengineering↗

bioRxiv2019

Performance assessment of age-adapted SOFA, qSOFA, and PELOD-2, PCIS, P-MODS for Hand, Foot and Mouth Disease

ObjectivesHand, foot and mouth disease (HFMD) is a common infectious disease in children caused by intestinal virus and an important cause of child death. Early identification of critical HFMD and timely intervention are the key to reduce mortality. However, there is no available unified critical HFMD screening standard. This study aimed to explore the predictive evaluation of HFMD with critical illness scoring systems.\n\nMethodsA total of 31 patients with mild HFMD, 30 with severe HFMD, and 25 with critical HFMD were included. The platelet index in age-adapted sequential organ failure assessment score (SOFA) was re-assigned to constitute the SOFA for HFMD (H-SOFA). The results of age-adapted SOFA, quick SOFA (qSOFA), and pediatric logistic organ dysfunction score-2 (PELOD-2), pediatric multiple organ dysfunction score (P-MODS), pediatric critical illness score (PCIS), H-SOFA of the three groups were compared.\n\nResultsSignificant differences in the following parameters were found between severe group and critical group: enterovirus 71 positive, heart rate, respiration, vomiting, cold sweat, moist rales, disturbance in consciousness, platelet, and blood glucose (P<0.05), as well as all critical illness scoring data (P<0.001). age-adapted SOFA, qSOFA, and PELOD-2, P-MODS, H-SOFA were positively correlated with critical HFMD (odds ratio (OR): 3.213, 8.66, 2.64, 2.56, and 4.297 respectively; P<0.01), with area under the curve (AUC) values of 0.938, 0.823, 0.848, 0.910, and 0.956, respectively. PCIS was negatively correlated with critical HFMD (OR=0.76, P<0.001), with an AUC value of 0.865.\n\nConclusionIncrease in platelet count was related to the severity of HFMD. Age-adapted SOFA, qSOFA, and PELOD-2, P-MODS, PCIS, H-SOFA had high predictive value on critical HFMD, with H-SOFA being the highest.

epidemiology↗

bioRxiv2019

iEDGE: integration of Epi-DNA and Gene Expression and applications to the discovery of somatic copy number-associated drivers in cancer

The emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators - such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others - as predictive features of cancer outcome. However, identification of \"driver genes\" associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively.\n\nFirst, iEDGE identifies the cis and trans genes associated with the presence/absence of a particular epi-DNA alteration across samples. Tests of statistical mediation are then performed to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks.\n\nWe used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible at https://montilab.bu.edu/iEDGE.

bioinformatics↗

bioRxiv2019

Unsupervised Toxoplasma gondii Recognition by Fuzzy Cycle Generative Adversarial Network

Toxoplasma gondii, one of the worlds most common parasites, can infect all types of warm-blooded animals, including one-third of the worlds human population. Most current routine diagnostic methods are costly, time-consuming, and labor-intensive. Although T.gondii can be directly observed under the microscope in tissue or spinal fluid samples, this form of identification is difficult and requires well trained professionals. Nevertheless, the traditional identification of parasites under the microscope is still performed by a large number of laboratories. Novel, efficient and reliable methods of T.gondii identification are therefore needed, particularly in developing countries. To this end, we developed a novel transfer learning based microscopic image recognition method for T.gondii identification. This approach employs Fuzzy Cycle Generative Adversarial Network (FCGAN) with transfer learning utilizing knowledge gained by the parasitologists that Toxoplasma is in banana or crescent shaped form. Our approach aims to build connection between micro and macro associated objects by embedding fuzzy C-means cluster algorithm into Cycle Generative Adversarial Network (Cycle GAN). Our approach achieves 93.1% and 94.0% detection accuracy for 400X and 1000X Toxoplasma microscopic images respectively. We show the high accuracy and effectiveness of our approach in the newly collected unlabeled Toxoplasma microscopic images, comparing to other current available deep learning methods. This novel method for Toxoplasma microscopic image recognition will open a new window for developing cost-effective and scalable deep learning based diagnostic solution, potentially enabling broader clinical access in developing countries.

bioinformatics↗

bioRxiv2019

Systematic identification of regulators of antibody-drug conjugate toxicity using CRISPR-Cas9 screens

Antibody-drug conjugates (ADCs) selectively deliver highly toxic chemotherapeutic agents to target antigen-expressing cells and have become an important cancer treatment in recent years. However, the molecular mechanisms by which ADCs are internalized and activated within cells remain unclear. Here we use CRISPR-Cas9 screens to identify genes that control the toxicity of ADCs. Our results demonstrate critical roles for a range of known and novel endolysosomal trafficking regulators in ADC toxicity. We identify and characterize C18orf8/RMC1 as a regulator of ADC toxicity through its role in endosomal maturation. Through comparative analysis of CRISPR screens with ADCs bearing a noncleavable linker versus a cleavable valine-citrulline (VC) linker, we show that a subset of late endosomal and lysosomal regulators are selectively essential for toxicity of noncleavable linker ADCs. We further show that cleavable VC linkers are rapidly processed upon internalization and therefore surprisingly appear to bypass the requirement of lysosomal delivery. Lastly, we show that inhibition of sialic acid biosynthesis sensitizes cells to ADC treatment by increasing the rate of ADC internalization. This sensitization was observed using several ADCs targeting different antigens in diverse cancer cell types, including the FDA-approved ADC trastuzumab emtansine (T-DM1) in Her2-positive breast cancer cells. Together, these results reveal novel regulators of endolysosomal trafficking, provide important insights to guide future ADC design, and identify candidate combination therapy targets as well as potential mechanisms of ADC resistance.

cancer biology↗

bioRxiv2019

Evaluating Invasive EEG Implantations in Medically Refractory Epilepsy with Functional Scalp EEG Recordings and Structural Imaging Data

Seizures in patients with medically refractory epilepsy (MRE) epilepsy cannot be controlled with drugs. For focal MRE, seizures originate in the epileptogenic zone (EZ), which is the minimum amount of cortex that must be treated to be seizure free. Localizing the EZ is often a laborious process wherein clinicians first inspect scalp EEG recordings during several seizure events, and then formulate an implantation plan for subsequent invasive monitoring. The goal of implantation is to place electrodes into the brain region covering the EZ. Then, during invasive monitoring, clinicians visually inspect intracranial EEG recordings to more precisely localize the EZ. The EZ is then surgically removed. Unfortunately surgical success rates average at 50%. Such grim outcomes call for analytical assistance in creating more accurate implantation plans from scalp EEG. In this paper, we introduce a method that combines imaging data (CT and MRI scans) with scalp EEG to derive an implantation distribution. Specifically, scalp EEG data recorded over a seizure event is converted into a time-gamma frequency map, which is then processed to derive a spectrally annotated implantation distribution (SAID). The SAID represents a distribution of gamma power in each of the eight cortical lobe/hemisphere partitions. We applied this method to 4 MRE patients who underwent treatment, and found that the SAID distribution overlapped more with clinical implantations in success cases than in failed cases. These preliminary findings suggest that the SAID may help in improving EZ localization accuracy and surgical outcomes.

neuroscience↗

bioRxiv2019

Effects of Lactobacillus plantarum 15-1 and fructooligosaccharide on the response of broilers to pathogenic Escherichia coli O78 challenge

One-day-old broilers were randomly allocated to 5 treatments: basal diet challenged by saline (negative control, n-control); basal diet and challenged by E.coil O78 (positive control, p-control); supplementation with L. plantarum 15-1 at 1x108 CFU/kg challenged with E.coil O78 (LP); supplementation with FOS at 5 g/kg challenged with E.coil O78 (FOS); supplementation with L. plantarum 15-1 and FOS challenged with E.coil O78 (LP+FOS). L. plantarum 15-1 or FOS had a lowered effect (P<0.05) on crypt depth on d 14 compared with two controls, and L. plantarum 15-1, FOS and L. plantarum 15-1+FOS also reduced relative to p-control on d 21. L. plantarum 15-1 reduced the level of diamine oxidase (DAO) at d 14 and 21 compared with p-control (P<0.05), the broilers with L. plantarum 15-1 and FOS increased the concentration of IgA and IgG relative to two control, and decreased diamine oxidase (DAO) compared with p-control (P 0.05). L. plantarum 15-1 increased the concentration of acetic acid and total short-chain fatty acid (SCFA) in comparison with p-control at d 14 (P<0.05), FOS improved the level of valeric acid and total SCFA relative to p-control at d 21 (P<0.001), the broilers fed L. plantarum 15-1 and FOS were increased the level of butyric acid at d 14 (P<0.05). FOS enhanced bursal index of broilers at d 21 (P<0.05). L. plantarum 15-1 and FOS did no effect on the growth performance. In conclusion, FOS can promote average daily gain, L. plantarum 15-1 and FOS can improve intestinal morphology, and increase the concentration of SCFA in cecal contents in broilers challenged with E.coil O78. These results suggest that L. plantarum 15-1 and FOS have effective mitigation to E. coil O78 via lowing reducing the intestinal injury and enhancing the immune responses.

zoology↗

bioRxiv2019

Identification and mitigation of pervasive off-target activity in CRISPR-Cas9 screens for essential non-coding elements

Pooled CRISPR-Cas9 screens have recently emerged as a powerful method for functionally characterizing regulatory elements in the non-coding genome, but off-target effects in these experiments have not been systematically evaluated. Here, we conducted a genome-scale screen for essential CTCF loop anchors in the K562 leukemia cell line. Surprisingly, the primary drivers of signal in this screen were single guide RNAs (sgRNAs) with low specificity scores. After removing these guides, we found that there were no CTCF loop anchors critical for cell growth. We also observed this effect in an independent screen fine-mapping the core motifs in enhancers of the GATA1 gene. We then conducted screens in parallel with CRISPRi and CRISPRa, which do not induce DNA damage, and found that an unexpected and distinct set of off-targets also caused strong confounding growth effects with these epigenome-editing platforms. Promisingly, strict filtering of CRISPRi libraries using GuideScan specificity scores removed these confounded sgRNAs and allowed for the identification of essential enhancers, which we validated extensively. Together, our results show off-target activity can severely limit identification of essential functional motifs by active Cas9, while strictly filtered CRISPRi screens can be reliably used for assaying larger regulatory elements.

genomics↗

bioRxiv2019

Repertoire-Based Diagnostics Using Statistical Biophysics

A fundamental challenge in immunology is diagnostic classification based on repertoire sequence. We used the principle of maximum entropy (MaxEnt) to build compact representations of antibody (IgH) and T-cell receptor (TCR{beta}) CDR3 repertoires based on the statistical biophysical patterns latent in the frequency and ordering of repertoires constituent amino acids. This approach results in substantial advantages in quality, dimensionality, and training speed compared to MaxEnt models based solely on the standard 20-letter amino-acid alphabet. Descriptor-based models learn patterns that pure amino-acid-based models cannot. We demonstrate the utility of descriptor models by successfully classifying influenza vaccination status (AUC=0.97, p=4x10-3), requiring only 31 samples from 14 individuals. Descriptor-based MaxEnt modeling is a powerful new method for dissecting, encoding, and classifying complex repertoires.

immunology↗

bioRxiv2019

Longitudinal single cell profiling of regulatory T cells identifies IL-33 as a driver of tumor immunosuppression

Regulatory T cells (Tregs) can impair anti-tumor immune responses and are associated with poor prognosis in multiple cancer types. Tregs in human tumors span diverse transcriptional states distinct from those of peripheral Tregs, but their contribution to tumor development remains unknown. Here, we used single cell RNA-Seq to longitudinally profile conventional CD4+ T cells (Tconv) and Tregs in a genetic mouse model of lung adenocarcinoma. Tissue-infiltrating and peripheral CD4+ T cells differed, highlighting divergent pathways of activation during tumorigenesis. Longitudinal shifts in Treg heterogeneity suggested increased terminal differentiation and stabilization of an effector phenotype over time. In particular, effector Tregs had enhanced expression of the interleukin 33 receptor ST2. Treg-specific deletion of ST2 reduced effector Tregs, increased infiltration of CD8+ T cells into tumors, and decreased tumor burden. Our study shows that ST2 plays a critical role in Treg-mediated immunosuppression in cancer, highlighting new potential paths for therapeutic intervention.

cancer biology↗

bioRxiv2018

Molecular changes during extended neoadjuvant letrozole treatment of breast cancer: distinguishing acquired resistance from dormant tumours

BackgroundThe risk of recurrence for endocrine-treated breast cancer patients persists for many years or even decades following surgery and apparently successful adjuvant therapy. This period of dormancy and acquired resistance is inherently difficult to investigate, previous efforts have been limited to in vitro or in vivo approaches. In this study, sequential tumour samples from patients receiving extended neoadjuvant endocrine treatment were characterised as a novel clinical model.\n\nMethodsConsecutive tumour samples from 62 patients undergoing extended (4-45 months) neoadjuvant aromatase inhibitor, letrozole, therapy were subjected to transcriptomic and proteomic analysis, representing before ([≤]0), early-on (13-120 days) and long-term (>120 days) neoadjuvant letrozole treatment. Patients with at least a 40% initial reduction in tumour size by 4 months of treatment were included. Of these, 42 patients with no subsequent progression were classified as \"dormant\", and the remaining 20 patients as \"acquired resistant\".\n\nResultsChanges in gene expression in dormant tumours begin early and become more pronounced at later timepoints. Therapy-induced changes in resistant tumours were common features of treatment, rather than being specific to resistant phenotype. Comparative analysis of long-term treated dormant and resistant tumours highlighted changes in epigenetics pathways including DNA methylation and histone acetylation. DNA methylation marks 5-methylcytosine and 5-hydroxymethylcytosine were significantly reduced in resistant tumours compared to dormant tissues after extended letrozole treatment.\n\nConclusionsThis is the first patient-matched gene expression study investigating long-term aromatase inhibitor-induced dormancy and acquired resistance in breast cancer. Dormant tumours continue to change during treatment whereas acquired resistant tumours more closely resemble their diagnostic samples. Global loss of DNA methylation was observed in resistant tumours under extended treatment. Epigenetic alterations may lead to escape from dormancy and drive acquired resistance in a subset of patients supporting a potential role for therapy targeted at these epigenetic alterations in the management of endocrine resistant breast cancer.

cancer biology↗

bioRxiv2018

Analysis of microbial community structure of pit mud for Chinese strong-flavor liquor fermentation using next generation DNA sequencing of full-length 16S rRNA

The pit is the necessary bioreactor for brewing process of Chinese strong-flavor liquor. Pit mud in pits contains a large number of microorganisms and is a complex ecosystem. The analysis of bacterial flora in pit mud is of great significance to understand liquor fermentation mechanisms. To overcome taxonomic limitations of short reads in 16S rRNA variable region sequencing, we used high-throughput DNA sequencing of near full-length 16S rRNA gene to analyze microbial compositions of different types of pit mud that produce different qualities of strong-flavor liquor. The results showed that the main species in pit mud were Pseudomonas extremaustralis 14-3, Pseudomonas veronii, Serratia marcescens WW4, and Clostridium leptum in Ruminiclostridium. The microbial diversity of pit mud with different quality was significantly different. From poor to good quality of pit mud (thus the quality of liquor), the relative abundances of Ruminiclostridium and Syntrophomonas in Firmicutes was increased, and the relative abundance of Olsenella in Actinobacteria also increased, but the relative abundances of Pseudomonas and Serratia in Proteobacteria were decreased. The surprising findings of this study include that the diversity of intermediate level quality of N pit mud was the lowest, and the diversity levels of high quality pit mud G and poor quality pit mud B were similar. Correlation analysis showed that there were high positive correlations (r > 0.8) among different microbial groups in the flora. Based on the analysis of the microbial structures of pit mud in different quality, the good quality pit mud has a higher microbial diversity, but how this higher diversity and differential microbial compositions contribute to better quality of liquor fermentation remains obscure.

molecular biology↗

bioRxiv2018

A robust image registration interface for large volume brain atlas

Mapping the brain structures in three-dimensional accurately is critical for an in-depth understanding of the brain functions. By using the brain atlas as a hub, mapping detected datasets into a standard brain space enables efficiently use of various datasets. However, because of the heterogeneous and non-uniform characteristics of the brain structures at cellular level brought with the recently developed high-resolution whole-brain microscopes, traditional registration methods are difficult to apply to the robust mapping of various large volume datasets. Here, we proposed a robust Brain Spatial Mapping Interface (BrainsMapi) to address the registration of large volume datasets at cellular level by introducing the extract regional features of the anatomically invariant method and a strategy of parameter acquisition and large volume transformation. By performing validation on model data and biological images, BrainsMapi can not only achieve robust registration on sample tearing and streak image datasets, different individual and modality datasets accurately, but also are able to complete the registration of large volume dataset at cellular level which dataset size reaches 20 TB. Besides, it can also complete the registration of historical vectorized dataset. BrainsMapi would facilitate the comparison, reuse and integration of a variety of brain datasets.

neuroscience↗

bioRxiv2018

LRScaf: Improving Draft Genomes Using Long Noisy Reads

BackgroundThe advent of Third Generation Sequencing (TGS) technologies opens the door to improve genome assembly. Long reads are promised to enhance the quality of fragmental draft assemblies constructed from Next Generation Sequencing (NGS) technologies. To date, a few of algorithms, i.e., SSPACE-LongRead, OPERA-LG, SMIS, npScarf, DBG2OLC, Unicycler, and LINKS, have been released that are capable of improving draft assemblies. However, hybrid assembly on large genomes is still challenging.\n\nResultsWe develop a scalable and computationally efficient scaffolder, Long Reads Scaffolder (LRScaf), that is capable of boosting assembly contiguity to a large extent using long reads. In our experiment, our method significantly improves the contiguity of human draft assemblies, increasing the NG50 value of CHM1 from 127.5 Kb to 10.4 Mb using 20-fold coverage PacBio dataset and the NG50 value of NA12878 from 115.7 Kb to 17.4 Mb using 35-fold coverage Nanopore dataset. The run time for the scaffolding procedure using LRScaf is the shortest in all cases of our experiment. Compared with the run time of SSPACE-LongRead, LRScaf is faster 300 times for S. cerevisiae and 2,300 times for D. melanogaster. The peak RAM of LRScaf, by contrast, is more efficient than LINKS in our test. For the rice case, the peak RAM of LINKS (877.72 Gb) is about 196 times higher than LRScaf. For the experiment of human assembly, the peak RAM of LINKS is beyond the capacity of system memory (1 Tb) whereas LRScaf takes 20.28 and 41.20 Gb on CHM1 and NA12878 datasets.\n\nConclusionsThe new method, LRScaf, yields the best or at least moderate contiguity and accuracy of scaffolds in the shortest run time compared with the state-of-the-art methods. Furthermore, it offers a new opportunity for the hybrid assembly of large genomes.

bioinformatics↗

bioRxiv2018

Tributyltin induces a transcriptional response without a brite adipocyte signature in adipocyte models

Tributyltin (TBT), a peroxisome proliferator-activated receptor {gamma} (PPAR{gamma})/retinoid X receptor (RXR) ligand and founding member of the environmental obesogen chemical class, induces adipocyte differentiation and suppresses bone formation. A growing number of environmental PPAR{gamma} ligands are being identified. However, the potential for environmental PPAR{gamma} ligands to induce adverse metabolic effects has been questioned because PPAR{gamma} is a therapeutic target in treatment of type II diabetes. We evaluated the molecular consequences of TBT exposure during bone marrow multipotent mesenchymal stromal cell (BM-MSC) differentiation in comparison to rosiglitazone, a therapeutic PPAR{gamma} ligand, and LG100268, a synthetic RXR ligand. Mouse primary BM-MSCs (female, C57BL/6J) undergoing bone differentiation were exposed to maximally efficacious and human relevant concentrations of rosiglitazone (100 nM), LG100268 (100 nM) or TBT (80 nM) for 4 days. Gene expression was assessed using microarrays, and in silico functional annotation was performed using pathway enrichment analysis approaches. Pathways related to osteogenesis were downregulated by all three ligands, while pathways related to adipogenesis were upregulated by rosiglitazone and TBT. However, pathways related to mitochondrial biogenesis and brown-in-white (brite) adipocyte differentiation were more significantly upregulated in rosiglitazone-treated than TBT-treated cells. The lack of induction of genes involved in adipocyte energy dissipation by TBT was confirmed by an independent gene expression analysis in BM-MSCs undergoing adipocyte differentiation and by analysis of a publically available 3T3 L1 data set. Furthermore, rosiglitazone, but not TBT, induced mitochondrial biogenesis. This study is the first to show that an environmental PPAR{gamma} ligand has a limited capacity to induce health promoting activities of PPAR{gamma}.

pharmacology and toxicology↗

bioRxiv2018

The Carcinogenome Project: In-vitro Gene Expression Profiling of Chemical Perturbations to Predict Long-Term Carcinogenicity

Background: Most chemicals in commerce have not been evaluated for their carcinogenic potential. The current de-facto gold-standard approach to carcinogen testing adopts the two-year rodent bioassay, a time consuming and costly procedure. Alternative approaches, such as high-throughput in-vitro assays, show promise in addressing the limitations in carcinogen screening.\n\nObjectives: We developed a screening process for predicting chemical carcinogenicity and genotoxicity and characterizing modes of actions (MoAs) using in-vitro gene expression assays.\n\nMethods: We generated a large toxicogenomics resource comprising ~6,000 expression profiles corresponding to 330 chemicals profiled in HepG2 cells at multiple doses and in replicates. Predictive models of carcinogenicity were built using a Random Forest classifier. Differential pathway enrichment analysis was performed to identify pathways associated with carcinogen exposure. Signatures of carcinogenicity and genotoxicity were compared with external data sources including Drugmatrix and the Connectivity Map.\n\nResults: Among profiles with sufficient bioactivity, our classifiers achieved 72.2% AUC for predicting carcinogenicity and 82.3% AUC for predicting genotoxicity. Our analysis showed that chemical bioactivity, as measured by the strength and reproducibility of the transcriptional response, is not significantly associated with long-term carcinogenicity, as evidenced by the many carcinogenic chemicals that did not elicit substantial changes in gene expression at doses up to 40 M. However, sufficiently high transcriptional bioactivity is necessary for a chemical to be used for prediction of carcinogenicity. Pathway enrichment analysis revealed several pathways consistent with literature review of pathways that drive cancer, including DNA damage and DNA repair. These data are available for download via https://clue.io/CRCGN_ABC, and a web portal for interactive query and visualization of the data and results is accessible at https://carcinogenome.org.\n\nConclusions: We demonstrated a short-term in-vitro screening approach using gene expression profiling to predict long-term carcinogenicity and infer MoAs of chemical perturbations.

bioinformatics↗

bioRxiv2018

Estrogen Signaling in Arcuate Kiss1 Neurons Suppresses a Sex-Dependent Circuit That Promotes Dense Strong Bones in Female Mice

Central estrogen signaling coordinates energy expenditure, reproduction, and in concert with peripheral estrogen impacts skeletal homeostasis in female rodents. Here, we ablate estrogen receptor alpha (ER) in the medial basal hypothalamus and find a robust bone phenotype only in female mice that results in exceptionally strong trabecular and cortical bones, whose density surpasses other reported mouse models. Stereotaxic guided deletion of ER in the arcuate nucleus increases bone mass in both intact and estrogen-depleted females, confirming the central role of estrogen signaling in this sex-dependent bone phenotype. Loss of ER activity in kisspeptin (Kiss1)-expressing cells is sufficient to recapitulate the bone phenotype, identifying Kiss1 neurons as a critical node in this powerful neuroskeletal circuit. We propose that this newly identified female brain-to-bone pathway exists as a homeostatic regulator to divert calcium and energy stores from bone building when energetic demands are high. Our work reveals a previously unknown target for the treatment of age-related bone disease.

physiology↗