Corrigendum: Chromatin structure and context-dependent sequence features control prime editing efficiency

[This corrects the article DOI: 10.3389/fgene.2023.1222112.].

Novel SOX10 indel mutations drive schwannomas through impaired transactivation of myelination gene programs

BACKGROUND

Schwannomas are common peripheral nerve sheath tumors that can cause severe morbidity given their stereotypic intracranial and paraspinal locations. Similar to many solid tumors, schwannomas and other nerve sheath tumors are primarily thought to arise due to aberrant hyperactivation of the RAS growth factor signaling pathway. Here, we sought to further define the molecular pathogenesis of schwannomas.

METHODS

We performed comprehensive genomic profiling on a cohort of 96 human schwannomas, as well as DNA methylation profiling on a subset. Functional studies including RNA sequencing, chromatin immunoprecipitation-DNA sequencing, electrophoretic mobility shift assay, and luciferase reporter assays were performed in a fetal glial cell model following transduction with wildtype and tumor-derived mutant isoforms of SOX10.

RESULTS

We identified that nearly one-third of sporadic schwannomas lack alterations in known nerve sheath tumor genes and instead harbor novel recurrent in-frame insertion/deletion mutations in SOX10, which encodes a transcription factor responsible for controlling Schwann cell differentiation and myelination. SOX10 indel mutations were highly enriched in schwannomas arising from nonvestibular cranial nerves (eg facial, trigeminal, vagus) and were absent from vestibular nerve schwannomas driven by NF2 mutation. Functional studies revealed these SOX10 indel mutations have retained DNA binding capacity but impaired transactivation of glial differentiation and myelination gene programs.

CONCLUSIONS

We thus speculate that SOX10 indel mutations drive a unique subtype of schwannomas by impeding proper differentiation of immature Schwann cells.

Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation

Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-specific gene networks targeted by a set of variants from a genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenomics Mapping Consortium cell types, which we used to interpret regulatory single nucleotide polymorphisms (SNPs) in the NHGRI-EBI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including schizophrenia, coronary artery disease (CAD) and Crohn's disease. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes.

Chromatin structure and context-dependent sequence features control prime editing efficiency

Prime editing (PE) is a highly versatile CRISPR-Cas9 genome editing technique. The current constructs, however, have variable efficiency and may require laborious experimental optimization. This study presents statistical models for learning the salient epigenomic and sequence features of target sites modulating the editing efficiency and provides guidelines for designing optimal PEs. We found that both regional constitutive heterochromatin and local nucleosome occlusion of target sites impede editing, while position-specific G/C nucleotides in the primer-binding site (PBS) and reverse transcription (RT) template regions of PE guide RNA (pegRNA) yield high editing efficiency, especially for short PBS designs. The presence of G/C nucleotides was most critical immediately 5' to the protospacer adjacent motif (PAM) site for all designs. The effects of different last templated nucleotides were quantified and observed to depend on the length of both PBS and RT templates. Our models found AGG to be the preferred PAM and detected a guanine nucleotide four bases downstream of the PAM to facilitate editing, suggesting a hitherto-unrecognized interaction with Cas9. A neural network interpretation method based on nonextensive statistical mechanics further revealed multi-nucleotide preferences, indicating dependency among several bases across pegRNA. Our work clarifies previous conflicting observations and uncovers context-dependent features important for optimizing PE designs.

Chromatin structure and context-dependent sequence features control prime editing efficiency

Prime editor (PE) is a highly versatile CRISPR-Cas9 genome editing technique. The current constructs, however, have variable efficiency and may require laborious experimental optimization. This study presents statistical models for learning the salient epigenomic and sequence features of target sites modulating the editing efficiency and provides guidelines for designing optimal PEs. We found that both regional constitutive heterochromatin and local nucleosome occlusion of target sites impede editing, while position-specific G/C nucleotides in the primer binding site (PBS) and reverse transcription (RT) template regions of PE guide-RNA (pegRNA) yield high editing efficiency, especially for short PBS designs. The presence of G/C nucleotides was most critical immediately 5' to the protospacer adjacent motif (PAM) site for all designs. The effects of different last templated nucleotides were quantified and seen to depend on both PBS and RT template lengths. Our models found AGG to be the preferred PAM and detected a guanine nucleotide four bases downstream of PAM to facilitate editing, suggesting a hitherto-unrecognized interaction with Cas9. A neural network interpretation method based on nonextensive statistical mechanics further revealed multi-nucleotide preferences, indicating dependency among several bases across pegRNA. Our work clarifies previous conflicting observations and uncovers context-dependent features important for optimizing PE designs.

Spectral clustering of single-cell multi-omics data on multilayer graphs

MOTIVATION

Single-cell sequencing technologies that simultaneously generate multimodal cellular profiles present opportunities for improved understanding of cell heterogeneity in tissues. How the multimodal information can be integrated to obtain a common cell type identification, however, poses a computational challenge. Multilayer graphs provide a natural representation of multi-omic single-cell sequencing datasets, and finding cell clusters may be understood as a multilayer graph partition problem.

RESULTS

We introduce two spectral algorithms on multilayer graphs, spectral clustering on multilayer graphs (SCML) and the weighted locally linear (WLL) method, to cluster cells in multi-omic single-cell sequencing datasets. We connect these algorithms through a unifying mathematical framework that represents each layer using a Hamiltonian operator and a mixture of its eigenstates to integrate the multiple graph layers, demonstrating in the process that the WLL method is a rigorous multilayer spectral graph theoretic reformulation of the popular Seurat weighted nearest neighbor (WNN) algorithm. Implementing our algorithms and applying them to a CITE-seq dataset of cord blood mononuclear cells yields results similar to the Seurat WNN analysis. Our work thus extends spectral methods to multimodal single-cell data analysis.

AVAILABILITY

The code used in this study can be found at https://github.com/jssong-lab/sc-spectrum.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

SOX10 regulates melanoma immunogenicity through an IRF4-IRF1 axis

Loss-of-function mutations of JAK1/2 impair cancer cell responsiveness to IFN-γ and immunogenicity. Therefore, an understanding of compensatory pathways to activate IFN-γ signaling in cancer cells is clinically important for the success of immunotherapy. Here we demonstrate that the transcription factor SOX10 hinders immunogenicity of melanoma cells through the IRF4-IRF1 axis. Genetic and pharmacological approaches revealed that SOX10 repressed IRF1 transcription via direct induction of a negative regulator, IRF4. The SOX10-IRF4-IRF1 axis regulated PD-L1 expression independently of JAK-STAT pathway activity, and suppression of SOX10 increased the efficacy of combination therapy with an anti-PD-1 antibody and HDAC inhibitor against a clinically relevant melanoma model. Thus, the SOX10-IRF4-IRF1 axis serves as a potential target that can bypass JAK-STAT signaling to immunologically warm up melanoma with a "cold" tumor immune microenvironment.

The relationship between adverse outcomes and a full spectrum of ejection fraction in patients without acute myocardial infarction undergoing percutaneous coronary intervention

Background

There is insufficient study evaluating how the risk of adverse cardiovascular outcomes varies across the full range of LVEF in patients undergoing percutaneous coronary intervention (PCI). A more comprehensive evaluation could provide further understanding of prognosis and support the management of these patients.

Purpose

The present study aimed to assess the association between left ventricular ejection fraction (LVEF) and major adverse cardiovascular events (MACE) in patients with coronary artery diseases undergoing PCI.

Methods

A consecutive series of 9475 patients without acute myocardial infarction (AMI) undergoing PCI were enrolled into the study and followed up for a median 2.4 years. Patients were stratified into 8 groups by 5% intervals: under 40%, 40–45%, 45–50%, 50–55%, 55–60%, 60–65%, 65–70%, and over 70%. MACE, consisting of cardiac death, myocardial infarction, stent thrombosis, and revascularization, was evaluated as the outcome.

Results

MACE occurred in 594 (6.3%) patients. The MACE rates decreased before LVEF under 65% while increased after LVEF over 70%, showing a U-shaped pattern. The 65–70% group showed the lowest rate of MACE (4.5%). Consistently, in the Cox regression, the association between LVEF and MACE presented as a U-shaped pattern regardless of different age and sex groups, and after adjusted for clinical and procedural covariables. The LVEF categories lower than 65% or higher than 70% were independent predictors of MACE, compared with 65–70% group (Hazard ratios 1.311 to 2.657, all P value >0.05). Similar trend was also observed in restricted mean survival time analysis.

Conclusions

LVEF and MACE displayed a U-shaped association and patients with LVEF of 65–70% showed the lowest risk of MACE. The present study provided an insight into how the MACE risk changed in a wide spectrum of LVEF in patients without AMI undergoing PCI, which might improve preprocedural evaluation.

Funding Acknowledgement

Type of funding sources: None.

Chd1 protects genome integrity at promoters to sustain hypertranscription in embryonic stem cells

Stem and progenitor cells undergo a global elevation of nascent transcription, or hypertranscription, during key developmental transitions involving rapid cell proliferation. The chromatin remodeler Chd1 mediates hypertranscription in pluripotent cells but its mechanism of action remains poorly understood. Here we report a novel role for Chd1 in protecting genome integrity at promoter regions by preventing DNA double-stranded break (DSB) accumulation in ES cells. Chd1 interacts with several DNA repair factors including Atm, Parp1, Kap1 and Topoisomerase 2β and its absence leads to an accumulation of DSBs at Chd1-bound Pol II-transcribed genes and rDNA. Genes prone to DNA breaks in Chd1 KO ES cells are longer genes with GC-rich promoters, a more labile nucleosomal structure and roles in chromatin regulation, transcription and signaling. These results reveal a vulnerability of hypertranscribing stem cells to accumulation of endogenous DNA breaks, with important implications for developmental and cancer biology.

EVALUATION OF INTERNAL EXPOSURE TO RADIOACTIVE AEROSOL GENERATED FROM PLASMA MELTING SYSTEM USING THE BIDAS CODE

The radioactive aerosol generated by the Nuclear Power Plant (NPP) decommissioning process can be inhaled by workers and deposited inside the human body, resulting in internal exposure. Because internal exposure, unlike external exposure, is difficult to measure directly, it is all the more necessary to assess the dose workers receive as a result of internal exposure. Precise assessment of the internal exposure necessitates actual measurements in the work environment such as the workers' respiration rate, kind of nuclide and amount of captured nuclide. However, in the event of difficulties in securing these measurements, the internal exposure dose can be estimated based upon the recommended values by the ICRP (International Commission on Radiological Protection) such as the intake fraction and particle size. In this study, 5 μm was selected as the particle size as recommended by the ICRP, and both heavy and light respiratory rates were used in the calculation. With respect to the nuclides contained in the radioactive aerosol and their concentrations, the data captured for the aerosol in the melting facility on the Kozloduy NPP premises in Bulgaria were applied to estimate workers' internal exposure. As a result, each worker was found not to have received more than 20 mSv/yr, which is the maximum annual permissible dose for workers.

Functional analysis of low-grade glioma genetic variants predicts key target genes and transcription factors

BACKGROUND

Large-scale genome-wide association studies (GWAS) have implicated thousands of germline genetic variants in modulating individuals' risk to various diseases, including cancer. At least 25 risk loci have been identified for low-grade gliomas (LGGs), but their molecular functions remain largely unknown.

METHODS

We hypothesized that GWAS loci contain causal single nucleotide polymorphisms (SNPs) that reside in accessible open chromatin regions and modulate the expression of target genes by perturbing the binding affinity of transcription factors (TFs). We performed an integrative analysis of genomic and epigenomic data from The Cancer Genome Atlas and other public repositories to identify candidate causal SNPs within linkage disequilibrium blocks of LGG GWAS loci. We assessed their potential regulatory role via in silico TF binding sequence perturbations, convolutional neural network trained on TF binding data, and simulated annealing-based interpretation methods.

RESULTS

We built an interactive website (http://education.knoweng.org/alg3/) summarizing the functional footprinting of 280 variants in 25 LGG GWAS regions, providing rich information for further computational and experimental scrutiny. We identified as case studies PHLDB1 and SLC25A26 as candidate target genes of rs12803321 and rs11706832, respectively, and predicted the GWAS variant rs648044 to be the causal SNP modulating ZBTB16, a known tumor suppressor in multiple cancers. We showed that rs648044 likely perturbed the binding affinity of the TF MAFF, as supported by RNA interference and in vitro MAFF binding experiments.

CONCLUSIONS

The identified candidate (causal SNP, target gene, TF) triplets and the accompanying resource will help accelerate our understanding of the molecular mechanisms underlying genetic risk factors for gliomas.

Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

[Risk factors of massive introperative blood loss in posterior spinal fusion for adolescent idiopathic scoliosis]

Objective: To investigate the risk factors and prognostic impact of massive introperative blood loss in posterior spinal fusion (PSF) surgery for adolescent idiopathic scoliosis (AIS). Methods: Clinical data were collected of 1 896 AIS patients who underwent PSF surgery under general anesthesia in Drum Tower Hospital Affiliated to Nanjing University Medical School from November 2010 to October 2019 and retrospectively analyzed. According to the volume of intraoperative blood loss, the patients were divided into the massive introperative blood loss group [estimated blood loss (EBL)/estimated blood volume (EBV)≥30%] and the non-massive introperative blood loss group (EBL/EBV<30%). The perioperative parameters between the two groups were compared, single factor analysis and multivariate logistic regression analysis was performed to identify independent risk factors related to massive introperative blood loss in PSF surgery. Results: A total of 1 896 AIS patients who underwent PSF surgery were included in the study. There were 298 males and 1 598 females, with an age of (14.5±1.7) years. Among them, 633 (33%) experienced massive intraoperative blood loss. The factors significantly related to the massive blood loss during PSF surgery in this study are: sex, body mass index(BMI), preoperative blood platelet count (PLT), prothrombin time, international normalized ratio(INR), preoperative Cobb angle, duration of operation, the number of fused levels, the number of screws, thoracoplasty, intraoperative use of tranexamic acid and dexmedetomidine; The independent factors included duration of operation longer than 4 hours(OR=4.311,P<0.001), the number of fused levels to be more than 10(OR=4.044,P<0.001), thoracoplasty (OR=2.174,P=0.019), BMI lower than 18.1 kg/m²(OR=2.094,P<0.001), preoperative PLT less than 186.5×10⁹/L(OR=1.480,P=0.009), preoperative INR larger than 1 (OR=1.531,P=0.003) and preoperative Cobb angle larger than 53°(OR=1.306,P=0.036) ;Intraoperative use of tranexamic acid (OR=0.770, P=0.047) and dexmedetomidine (OR=0.653, P=0.008) are protective factors for massive intraoperative blood loss. In addition, in the massive intraoperative blood loss group, length of postoperative hospital stay (P<0.001), volume of postoperative incision drainage (P<0.001), postoperative allogeneic blood transfusion rate (22.7% vs 14.3%, P<0.001), incidence of postoperative hypoalbuminemia (90.3% vs 80.7%, P<0.001) and the number of rescue opioid analgesic requirements after surgery (P=0.006) were significantly higher than those in the non-massive introperative blood loss group. Conclusions: Longer operation duration, higher number of fusion levels, lower BMI, lower preoperative PLT, higher INR, larger preoperative Cobb angle and intraoperative thoraplasty are independent risk factors for massive intraoperative blood loss in AIS patients undergoing PSF surgery. Intraoperative use of tranexamic acid and dexmedetomidine can reduce the risk of massive blood loss in PSF surgery. Massive intraoperative blood loss significantly affects the patient's prognosis.

Adult diffuse glioma GWAS by molecular subtype identifies variants in D2HGDH and FAM20C

BACKGROUND

Twenty-five germline variants are associated with adult diffuse glioma, and some of these variants have been shown to be associated with particular subtypes of glioma. We hypothesized that additional germline variants could be identified if a genome-wide association study (GWAS) were performed by molecular subtype.

METHODS

A total of 1320 glioma cases and 1889 controls were used in the discovery set and 799 glioma cases and 808 controls in the validation set. Glioma cases were classified into molecular subtypes based on combinations of isocitrate dehydrogenase (IDH) mutation, telomerase reverse transcriptase (TERT) promoter mutation, and 1p/19q codeletion. Logistic regression was applied to the discovery and validation sets to test for associations of variants with each of the subtypes. A meta-analysis was subsequently performed using a genome-wide P-value threshold of 5 × 10-8.

RESULTS

Nine variants in or near D-2-hydroxyglutarate dehydrogenase (D2HGDH) on chromosome 2 were genome-wide significant in IDH-mutated glioma (most significant was rs5839764, meta P = 2.82 × 10-10). Further stratifying by 1p/19q codeletion status, one variant in D2HGDH was genome-wide significant in IDH-mutated non-codeleted glioma (rs1106639, meta P = 4.96 × 10-8). Further stratifying by TERT mutation, one variant near FAM20C (family with sequence similarity 20, member C) on chromosome 7 was genome-wide significant in gliomas that have IDH mutation, TERT mutation, and 1p/19q codeletion (rs111976262, meta P = 9.56 × 10-9). Thirty-six variants in or near GMEB2 on chromosome 20 near regulator of telomere elongation helicase 1 (RTEL1) were genome-wide significant in IDH wild-type glioma (most significant was rs4809313, meta P = 2.60 × 10-10).

CONCLUSIONS

Performing a GWAS by molecular subtype identified 2 new regions and a candidate independent region near RTEL1, which were associated with specific glioma molecular subtypes.

Epigenomic tensor predicts disease subtypes and reveals constrained tumor evolution

Understanding the epigenomic evolution and specificity of disease subtypes from complex patient data remains a major biomedical problem. We here present DeCET (decomposition and classification of epigenomic tensors), an integrative computational approach for simultaneously analyzing hierarchical heterogeneous data, to identify robust epigenomic differences among tissue types, differentiation states, and disease subtypes. Applying DeCET to our own data from 21 uterine benign tumor (leiomyoma) patients identifies distinct epigenomic features discriminating normal myometrium and leiomyoma subtypes. Leiomyomas possess preponderant alterations in distal enhancers and long-range histone modifications confined to chromatin contact domains that constrain the evolution of pathological epigenomes. Moreover, we demonstrate the power and advantage of DeCET on multiple publicly available epigenomic datasets representing different cancers and cellular states. Epigenomic features extracted by DeCET can thus help improve our understanding of disease states, cellular development, and differentiation, thereby facilitating future therapeutic, diagnostic, and prognostic strategies.

Leistico et al. apply tensor decomposition and classification methods to integrate information from hierarchical heterogenous epigenomic datasets and identify histone modification patterns that discriminate disease conditions, tissue types, and differentiation states. Leiomyomas are shown to possess alterations in distal enhancers and large-scale regions confined to chromatin contact domains.

Single-Cell Profiling Reveals Divergent, Globally Patterned Immune Responses in Murine Skin Inflammation

Inflammatory response heterogeneity has impeded high-resolution dissection of diverse immune cell populations during activation. We characterize mouse cutaneous immune cells by single-cell RNA sequencing, after inducing inflammation using imiquimod and oxazolone dermatitis models. We identify 13 CD45⁺ subpopulations, which broadly represent most functionally characterized immune cell types. Oxazolone pervasively upregulates Jak2/Stat3 expression across T cells and antigen-presenting cells (APCs). Oxazolone also induces Il4/Il13 expression in newly infiltrating basophils, and Il4ra and Ccl24, most prominently in APCs. In contrast, imiquimod broadly upregulates Il17/Il22 and Ccl4/Ccl5. A comparative analysis of single-cell inflammatory transcriptional responses reveals that APC response to oxazolone is tightly restricted by cell identity, whereas imiquimod enforces shared programs on multiple APC populations in parallel. These global molecular patterns not only contrast immune responses on a systems level but also suggest that the mechanisms of new sources of inflammation can eventually be deduced by comparison to known signatures.

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Oxazolone pervasively upregulates Jak2/Stat3 expression across T cells and APCs

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Il4/Il13 induction in skin by oxazolone is dominated by infiltrating basophils

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Imiquimod broadly increases Il17/Il22 and Ccl4/Ccl5, extending to non-T cells

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Oxazolone induces more highly compartmentalized immune cell responses than imiquimod

Riemannian geometry and statistical modeling correct for batch effects and control false discoveries in single-cell surface protein count data

Recent advances in next generation sequencing-based single-cell technologies have allowed high-throughput quantitative detection of cell-surface proteins along with the transcriptome in individual cells, extending our understanding of the heterogeneity of cell populations in diverse tissues that are in different diseased states or under different experimental conditions. Count data of surface proteins from the cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) technology pose new computational challenges, and there is currently a dearth of rigorous mathematical tools for analyzing the data. This work utilizes concepts and ideas from Riemannian geometry to remove batch effects between samples and develops a statistical framework for distinguishing positive signals from background noise. The strengths of these approaches are demonstrated on two independent CITE-seq data sets in mouse and human.

Abstract 1193: Adult diffuse glioma GWAS by molecular subtype identifies variants in D2HGDH, FAM20C and GMEB2

Background: Genome-wide association studies (GWAS) revealed that 25 regions in 24 genes are associated with adult diffuse glioma development. These regions were identified by performing GWAS of glioma overall and by pathology (GBM and nonGBM). The 2016 WHO Classification of Tumors of the Central Nervous System utilizes two somatic alterations to molecularly-classify adult diffuse glioma: IDH mutation and 1p/19q codeletion. We hypothesized that germline variants may increase susceptibility to, or interact with, these somatic alterations to accelerate the development of specific molecular subtypes of glioma. We further hypothesize that germline variants associated with IDH-mutated glioma might be associated with other IDH-mutated tumors, namely, cholangiocarcinoma, acute myeloid leukemia (AML) and melanoma.

Methods: We performed a GWAS by glioma molecular subtype - as defined by presence or absence of IDH somatic mutation and 1p/19q codeletion. A total of 1320 glioma cases and 1889 controls were used in the discovery set, and 799 glioma cases and 808 controls in the validation set. A meta-analysis was performed with a genome-wide p-value threshold of 5 × 10−8. GTEx data were used to perform an expression quantitative trait loci (eQTL) analysis. For germline variants that were significantly associated with IDH-mutated glioma, we evaluated pleiotropy with cholangiocarcinoma, AML and melanoma using TCGA and Mayo Biobank controls.

Results: Variants in or near D2HGDH on chromosome 2 were genome-wide significant in IDH-mutated glioma (meta p-value = 2.82 × 10−10). TCGA reported that the D2HGDH region was commonly deleted in IDH-mutated gliomas that do not have 1p/19q codeletion. In TCGA data for IDH-mutated, non-codeleted glioma, we observed that the D2HGDH variant was inversely associated with tumor deletions of D2HGDH (odds ratio=0.57, p-value=0.015). The eQTL analyses demonstrated significant associations between D2HGDH germline variant and expression of D2HGDH (p=2.2 × 10−11). Further stratifying IDH-mutated glioma by 1p/19q codeletion status, one variant near FAM20C on chromosome 7 was genome-wide significant in gliomas that have IDH mutation and 1p/19q codeletion (meta p-value=9.56 × 10−9). Analyses are currently underway to evaluate pleiotropy of these IDH-mutated glioma germline variants with other IDH-mutated tumors including cholangiocarcinoma, AML and melanoma. Variants in or near GMEB2 on chromosome 20 were genome-wide significant in IDH wild-type glioma (meta p-value=2.60 × 10−10). The most significant variant in the GMEB2 region remained significant after adjustment for the known RTEL1 glioma risk variant nearby on chromosome 20 (p=0.029).

Conclusions: We identified and validated novel germline variants in two genes that are associated with etiology of IDH-mutated and one gene that is associated with IDH wild-type adult diffuse glioma.

Citation Format: Jeanette E. Eckel-Passow, Kristen L. Drucker, Thomas M. Kollmeyer, Matthew L. Kosel, Paul A. Decker, Annette M. Molinaro, Terri Rice, Corrine E. Praska, Lauren E. Clark, Alissa A. Caron, Alexej Abyzov, Anthony Batzler, Jun S. Song, Melike Pekmezci, Helen M. Hansen, Lucie S. McCoy, Paige M. Bracci, Joseph Wiemels, John K. Wiencke, Stephen Francis, Terence C. Burns, Caterina Giannini, Daniel H. Lachance, Margaret Wrensch, Robert B. Jenkins. Adult diffuse glioma GWAS by molecular subtype identifies variants in D2HGDH, FAM20C and GMEB2 [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1193.

ABC-GWAS: Functional Annotation of Estrogen Receptor-Positive Breast Cancer Genetic Variants

Over the past decade, hundreds of genome-wide association studies (GWAS) have implicated genetic variants in various diseases, including cancer. However, only a few of these variants have been functionally characterized to date, mainly because the majority of the variants reside in non-coding regions of the human genome with unknown function. A comprehensive functional annotation of the candidate variants is thus necessary to fill the gap between the correlative findings of GWAS and the development of therapeutic strategies. By integrating large-scale multi-omics datasets such as the Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE), we performed multivariate linear regression analysis of expression quantitative trait loci, sequence permutation test of transcription factor binding perturbation, and modeling of three-dimensional chromatin interactions to analyze the potential molecular functions of 2,813 single nucleotide variants in 93 genomic loci associated with estrogen receptor-positive breast cancer. To facilitate rapid progress in functional genomics of breast cancer, we have created "Analysis of Breast Cancer GWAS" (ABC-GWAS), an interactive database of functional annotation of estrogen receptor-positive breast cancer GWAS variants. Our resource includes expression quantitative trait loci, long-range chromatin interaction predictions, and transcription factor binding motif analyses to prioritize putative target genes, causal variants, and transcription factors. An embedded genome browser also facilitates convenient visualization of the GWAS loci in genomic and epigenomic context. ABC-GWAS provides an interactive visual summary of comprehensive functional characterization of estrogen receptor-positive breast cancer variants. The web resource will be useful to both computational and experimental biologists who wish to generate and test their hypotheses regarding the genetic susceptibility, etiology, and carcinogenesis of breast cancer. ABC-GWAS can also be used as a user-friendly educational resource for teaching functional genomics. ABC-GWAS is available at http://education.knoweng.org/abc-gwas/.

Epigenetic engineering of yeast reveals dynamic molecular adaptation to methylation stress and genetic modulators of specific DNMT3 family members

Cytosine methylation is a ubiquitous modification in mammalian DNA generated and maintained by several DNA methyltransferases (DNMTs) with partially overlapping functions and genomic targets. To systematically dissect the factors specifying each DNMT's activity, we engineered combinatorial knock-in of human DNMT genes in Komagataella phaffii, a yeast species lacking endogenous DNA methylation. Time-course expression measurements captured dynamic network-level adaptation of cells to DNMT3B1-induced DNA methylation stress and showed that coordinately modulating the availability of S-adenosyl methionine (SAM), the essential metabolite for DNMT-catalyzed methylation, is an evolutionarily conserved epigenetic stress response, also implicated in several human diseases. Convolutional neural networks trained on genome-wide CpG-methylation data learned distinct sequence preferences of DNMT3 family members. A simulated annealing interpretation method resolved these preferences into individual flanking nucleotides and periodic poly(A) tracts that rotationally position highly methylated cytosines relative to phased nucleosomes. Furthermore, the nucleosome repeat length defined the spatial unit of methylation spreading. Gene methylation patterns were similar to those in mammals, and hypo- and hypermethylation were predictive of increased and decreased transcription relative to control, respectively, in the absence of mammalian readers of DNA methylation. Introducing controlled epigenetic perturbations in yeast thus enabled characterization of fundamental genomic features directing specific DNMT3 proteins.

SequencEnG: an interactive knowledge base of sequencing techniques

SUMMARY

Next-generation sequencing (NGS) techniques are revolutionizing biomedical research by providing powerful methods for generating genomic and epigenomic profiles. The rapid progress is posing an acute challenge to students and researchers to stay acquainted with the numerous available methods. We have developed an interactive online educational resource called Sequencing Techniques Engine for Genomics (SequencEnG) to provide a tree-structured knowledge base of 66 different sequencing techniques and step-by-step NGS data analysis pipelines comparing popular tools. SequencEnG is designed to facilitate barrier-free learning of current NGS techniques and provides a user-friendly interface for searching through experimental and analysis methods.

AVAILABILITY AND IMPLEMENTATION

SequencEnG is part of the project Knowledge Engine for Genomics (KnowEnG) and is freely available at http://education.knoweng.org/sequenceng/.

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

Transcriptional Programming of Normal and Inflamed Human Epidermis at Single-Cell Resolution

Perturbations in the transcriptional programs specifying epidermal differentiation cause diverse skin pathologies ranging from impaired barrier function to inflammatory skin disease. However, the global scope and organization of this complex cellular program remain undefined. Here we report single-cell RNA sequencing profiles of 92,889 human epidermal cells from 9 normal and 3 inflamed skin samples. Transcriptomics-derived keratinocyte subpopulations reflect classic epidermal strata but also sharply compartmentalize epithelial functions such as cell-cell communication, inflammation, and WNT pathway modulation. In keratinocytes, ~12% of assessed transcript expression varies in coordinate patterns, revealing undescribed gene expression programs governing epidermal homeostasis. We also identify molecular fingerprints of inflammatory skin states, including S100 activation in the interfollicular epidermis of normal scalp, enrichment of a CD1C⁺CD301A⁺ myeloid dendritic cell population in psoriatic epidermis, and IL1β^hiCCL3^hiCD14⁺ monocyte-derived macrophages enriched in foreskin. This compendium of RNA profiles provides a critical step toward elucidating epidermal diseases of development, differentiation, and inflammation.

Cheng et al. report single-cell RNA sequencing of normal and inflamed human epidermis, revealing a discrete set of specialized keratinocytes that exhibit a distinct composition at different anatomic sites. Myeloid dendritic cells and macrophages also vary sharply with epidermal anatomic site and inflammation, indicating dynamic programming of antigen-presenting cells.

Erratum: Author Correction: Targeted exon skipping with AAV-mediated split adenine base editors

[This corrects the article DOI: 10.1038/s41421-019-0109-7.].

Single-Cell Transcriptomics Reveals Spatial and Temporal Turnover of Keratinocyte Differentiation Regulators

Keratinocyte differentiation requires intricately coordinated spatiotemporal expression changes that specify epidermis structure and function. This article utilizes single-cell RNA-seq data from 22,338 human foreskin keratinocytes to reconstruct the transcriptional regulation of skin development and homeostasis genes, organizing them by differentiation stage and also into transcription factor (TF)–associated modules. We identify groups of TFs characterized by coordinate expression changes during progression from the undifferentiated basal to the differentiated state and show that these TFs also have concordant differential predicted binding enrichment in the super-enhancers previously reported to turn over between the two states. The identified TFs form a core subset of the regulators controlling gene modules essential for basal and differentiated keratinocyte functions, supporting their nomination as master coordinators of keratinocyte differentiation. Experimental depletion of the TFs ZBED2 and ETV4, both predicted to promote the basal state, induces differentiation. Furthermore, our single-cell RNA expression analysis reveals preferential expression of antioxidant genes in the basal state, suggesting keratinocytes actively suppress reactive oxygen species to maintain the undifferentiated state. Overall, our work demonstrates diverse computational methods to advance our understanding of dynamic gene regulation in development.

The Cancer-Associated Genetic Variant Rs3903072 Modulates Immune Cells in the Tumor Microenvironment

Genome-wide association studies (GWAS) have hitherto identified several germline variants associated with cancer susceptibility, but the molecular functions of these risk modulators remain largely uncharacterized. Recent studies have begun to uncover the regulatory potential of noncoding GWAS SNPs using epigenetic information in corresponding cancer cell types and matched normal tissues. However, this approach does not explore the potential effect of risk germline variants on other important cell types that constitute the microenvironment of tumor or its precursor. This paper presents evidence that the breast-cancer-associated variant rs3903072 may regulate the expression of CTSW in tumor-infiltrating lymphocytes. CTSW is a candidate tumor-suppressor gene, with expression highly specific to immune cells and also positively correlated with breast cancer patient survival. Integrative analyses suggest a putative causative variant in a GWAS-linked enhancer in lymphocytes that loops to the 3' end of CTSW through three-dimensional chromatin interaction. Our work thus poses the possibility that a cancer-associated genetic variant could regulate a gene not only in the cell of cancer origin but also in immune cells in the microenvironment, thereby modulating the immune surveillance by T lymphocytes and natural killer cells and affecting the clearing of early cancer initiating cells.

Targeted exon skipping with AAV-mediated split adenine base editors

Techniques for exclusion of exons from mature transcripts have been applied as gene therapies for treating many different diseases. Since exon skipping has been traditionally accomplished using technologies that have a transient effect, it is particularly important to develop new techniques that enable permanent exon skipping. We have recently shown that this can be accomplished using cytidine base editors for permanently disabling the splice acceptor of target exons. We now demonstrate the application of CRISPR-Cas9 adenine deaminase base editors to disrupt the conserved adenine within splice acceptor sites for programmable exon skipping. We also demonstrate that by altering the amino acid sequence of the linker between the adenosine deaminase domain and the Cas9-nickase or by coupling the adenine base editor with a uracil glycosylase inhibitor, the DNA editing efficiency and exon-skipping rates improve significantly. Finally, we developed a split base editor architecture compatible with adeno-associated viral packaging. Collectively, these results represent significant progress toward permanent in vivo exon skipping through base editing and, ultimately, a new modality of gene therapy for the treatment of genetic diseases.

Local genomic features predict the distinct and overlapping binding patterns of the bHLH-Zip family oncoproteins MITF and MYC-MAX

MITF and MYC are well-known oncoproteins and members of the basic helix-loop-helix leucine zipper (bHLH-Zip) family of transcription factors (TFs) recognizing hexamer E-box motifs. MITF and MYC not only share the core binding motif, but are also the two most highly expressed bHLH-Zip transcription factors in melanocytes, raising the possibility that they may compete for the same binding sites in select oncogenic targets. Mechanisms determining the distinct and potentially overlapping binding modes of these critical oncoproteins remain uncharacterized. We introduce computational predictive models using local sequence features, including a boosted convolutional decision tree framework, to distinguish MITF versus MYC-MAX binding sites with up to 80% accuracy genomewide. Select E-box locations that can be bound by both MITF and MYC-MAX form a separate class of MITF binding sites characterized by differential sequence content in the flanking region, diminished interaction with SOX10, higher evolutionary conservation, and less tissue-specific chromatin organization.

A unified computational framework for modeling genome-wide nucleosome landscape

Nucleosomes form the fundamental building blocks of eukaryotic chromatin, and previous attempts to understand the principles governing their genome-wide distribution have spurred much interest and debate in biology. In particular, the precise role of DNA sequence in shaping local chromatin structure has been controversial. This paper rigorously quantifies the contribution of hitherto-debated sequence features-including G+C content, 10.5 bp periodicity, and poly(dA:dT) tracts-to three distinct aspects of genome-wide nucleosome landscape: occupancy, translational positioning and rotational positioning. Our computational framework simultaneously learns nucleosome number and nucleosome-positioning energy from genome-wide nucleosome maps. In contrast to other previous studies, our model can predict both in vitro and in vivo nucleosome maps in Saccharomyces cerevisiae. We find that although G+C content is the primary determinant of MNase-derived nucleosome occupancy, MNase digestion biases may substantially influence this GC dependence. By contrast, poly(dA:dT) tracts are seen to deter nucleosome formation, regardless of the experimental method used. We further show that the 10.5 bp nucleotide periodicity facilitates rotational but not translational positioning. Applying our method to in vivo nucleosome maps demonstrates that, for a subset of genes, the regularly-spaced nucleosome arrays observed around transcription start sites can be partially recapitulated by DNA sequence alone. Finally, in vivo nucleosome occupancy derived from MNase-seq experiments around transcription termination sites can be mostly explained by the genomic sequence. Implications of these results and potential extensions of the proposed computational framework are discussed.

Disruption of the β1L Isoform of GABP Reverses Glioblastoma Replicative Immortality in a TERT Promoter Mutation-Dependent Manner

TERT promoter mutations reactivate telomerase, allowing for indefinite telomere maintenance and enabling cellular immortalization. These mutations specifically recruit the multimeric ETS factor GABP, which can form two functionally independent transcription factor species: a dimer or a tetramer. We show that genetic disruption of GABPβ1L (β1L), a tetramer-forming isoform of GABP that is dispensable for normal development, results in TERT silencing in a TERT promoter mutation-dependent manner. Reducing TERT expression by disrupting β1L culminates in telomere loss and cell death exclusively in TERT promoter mutant cells. Orthotopic xenografting of β1L-reduced, TERT promoter mutant glioblastoma cells rendered lower tumor burden and longer overall survival in mice. These results highlight the critical role of GABPβ1L in enabling immortality in TERT promoter mutant glioblastoma.

CRISPR-SKIP: programmable gene splicing with single base editors

CRISPR gene editing has revolutionized biomedicine and biotechnology by providing a simple means to engineer genes through targeted double-strand breaks in the genomic DNA of living cells. However, given the stochasticity of cellular DNA repair mechanisms and the potential for off-target mutations, technologies capable of introducing targeted changes with increased precision, such as single-base editors, are preferred. We present a versatile method termed CRISPR-SKIP that utilizes cytidine deaminase single-base editors to program exon skipping by mutating target DNA bases within splice acceptor sites. Given its simplicity and precision, CRISPR-SKIP will be broadly applicable in gene therapy and synthetic biology.

Quantum transport senses community structure in networks

Quantum time evolution exhibits rich physics, attributable to the interplay between the density and phase of a wave function. However, unlike classical heat diffusion, the wave nature of quantum mechanics has not yet been extensively explored in modern data analysis. We propose that the Laplace transform of quantum transport (QT) can be used to construct an ensemble of maps from a given complex network to a circle S^{1}, such that closely related nodes on the network are grouped into sharply concentrated clusters on S^{1}. The resulting QT clustering (QTC) algorithm is as powerful as the state-of-the-art spectral clustering in discerning complex geometric patterns and more robust when clusters show strong density variations or heterogeneity in size. The observed phenomenon of QTC can be interpreted as a collective behavior of the microscopic nodes that evolve as macroscopic cluster "orbitals" in an effective tight-binding model recapitulating the network. python source code implementing the algorithm and examples are available at https://github.com/jssong-lab/QTC.

Abstract 1220: Integrative genomic analysis discovers the causative regulatory mechanisms of a breast cancer-associated genetic variant

Genome-wide association studies (GWAS) have identified genetic variants that may significantly modulate breast cancer susceptibility. However, the precise molecular mechanisms behind these associations remain largely unknown; often, it is not even clear whether the GWAS variants are functional themselves or just genetically linked to other functional variants. We here provide an integrated method for identifying functional regulatory variants associated with breast cancer and their target genes by combining the analyses of expression quantitative trait loci (eQTL), a modified version of allele-specific expression (ASE) systematically utilizing haplotype information, transcription factor (TF) binding preference, and epigenetic information. Application of our method to the breast cancer susceptibility region in 5p12 demonstrates that the GWAS risk allele rs4415084-T is correlated with higher expression levels of the protein-coding gene MRPS30 and lncRNA RP11-53O19.1. We propose that an intergenic SNP, in linkage disequilibrium (LD) with the GWAS SNP rs4415084, is the predicted functional SNP. We provide multiple levels of evidence that the risk allele of the predicted functional SNP, in phase with the GWAS risk allele rs4415084-T, creates a GATA3 binding motif within a regulatory element, resulting in differential GATA3 binding and chromatin accessibility, which thereby promote the transcription of MRPS30 and RP11-53O19.1. MRPS30 encodes a member of the mitochondrial ribosomal proteins, implicating the risk SNP's role in modulating mitochondrial activities in breast cancer. Our computational framework can be extended to facilitate the rapid functional characterization of other genetic variants modulating cancer susceptibility and provides an effective way of integrating GWAS results with high-throughput genomic and epigenomic data.

Citation Format: Yi Zhang, Mohith Manjunath, Shilu Zhang, Deborah Chasman, Sushmita Roy, Jun S. Song. Integrative genomic analysis discovers the causative regulatory mechanisms of a breast cancer-associated genetic variant [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1220.

ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data

SUMMARY

Clustering is one of the most common techniques used in data analysis to discover hidden structures by grouping together data points that are similar in some measure into clusters. Although there are many programs available for performing clustering, a single web resource that provides both state-of-the-art clustering methods and interactive visualizations is lacking. ClusterEnG (acronym for Clustering Engine for Genomics) provides an interface for clustering big data and interactive visualizations including 3D views, cluster selection and zoom features. ClusterEnG also aims at educating the user about the similarities and differences between various clustering algorithms and provides clustering tutorials that demonstrate potential pitfalls of each algorithm. The web resource will be particularly useful to scientists who are not conversant with computing but want to understand the structure of their data in an intuitive manner.

AVAILABILITY

ClusterEnG is part of a bigger project called KnowEnG (Knowledge Engine for Genomics) and is available at http://education.knoweng.org/clustereng.

CONTACT

songi@illinois.edu.

Integrative Genomic Analysis Predicts Causative Cis-Regulatory Mechanisms of the Breast Cancer-Associated Genetic Variant rs4415084

Previous genome-wide association studies (GWAS) have identified several common genetic variants that may significantly modulate cancer susceptibility. However, the precise molecular mechanisms behind these associations remain largely unknown; it is often not clear whether discovered variants are themselves functional or merely genetically linked to other functional variants. Here, we provide an integrated method for identifying functional regulatory variants associated with cancer and their target genes by combining analyses of expression quantitative trait loci, a modified version of allele-specific expression that systematically utilizes haplotype information, transcription factor (TF)-binding preference, and epigenetic information. Application of our method to a breast cancer susceptibility region in 5p12 demonstrates that the risk allele rs4415084-T correlates with higher expression levels of the protein-coding gene mitochondrial ribosomal protein S30 (MRPS30) and lncRNA RP11-53O19.1 We propose an intergenic SNP rs4321755, in linkage disequilibrium (LD) with the GWAS SNP rs4415084 (r² = 0.988), to be the predicted functional SNP. The risk allele rs4321755-T, in phase with the GWAS rs4415084-T, created a GATA3-binding motif within an enhancer, resulting in differential GATA3 binding and chromatin accessibility, thereby promoting transcription of MRPS30 and RP11-53O19.1. MRPS30 encodes a member of the mitochondrial ribosomal proteins, implicating the role of risk SNP in modulating mitochondrial activities in breast cancer. Our computational framework provides an effective means to integrate GWAS results with high-throughput genomic and epigenomic data and can be extended to facilitate rapid functional characterization of other genetic variants modulating cancer susceptibility.Significance: Unification of GWAS results with information from high-throughput genomic and epigenomic profiles provides a direct link between common genetic variants and measurable molecular perturbations. Cancer Res; 78(7); 1579-91. ©2018 AACR.

[The clinical application status and progression of auditory steady-state evoked response]

Auditory steady-state evoked response(ASSR) is an objective method to evaluate hearing threshold. Recently, it was reported that CE-Chirp ASSR is more reliable than the conventional ASSR in predicting the behavioral hearing threshold. Many reseaches were conducted to improve the reliability of ASSR threshold and behavioral threshold. This paper reviewed the development, principle, clinical application status, and progress of ASSR.

Maximum entropy methods for extracting the learned features of deep neural networks

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

Deep learning is a state-of-the-art reformulation of artificial neural networks that have a long history of development. It can perform superbly well in diverse automated classification and prediction problems, including handwriting recognition, image identification, and biological pattern recognition. Its modern success can be attributed to improved training algorithms, clever network architecture, rapid explosion of available data, and advanced computing power–all of which have allowed the great expansion in the number of unknown parameters to be estimated by the model. These parameters, however, are so intricately connected through highly nonlinear functions that interpreting which essential features of given data are actually used by a deep neural network for its excellent performance has been difficult. We address this problem by using ideas from statistical physics to sample new unseen data that are likely to behave similarly to original data points when passed through the trained network. This synthetic data cloud around each original data point retains informative features while averaging out nonessential ones, ultimately allowing us to extract important network-learned features from the original data set and thus improving the human interpretability of deep learning methods. We demonstrate how our method can be applied to biological sequence analysis.

TeachEnG: a Teaching Engine for Genomics

Motivation

Bioinformatics is a rapidly growing field that has emerged from the synergy of computer science, statistics and biology. Given the interdisciplinary nature of bioinformatics, many students from diverse fields struggle with grasping bioinformatic concepts only from classroom lectures. Interactive tools for helping students reinforce their learning would be thus desirable. Here, we present an interactive online educational tool called TeachEnG (acronym for Teaching Engine for Genomics) for reinforcing key concepts in sequence alignment and phylogenetic tree reconstruction. Our instructional games allow students to align sequences by hand, fill out the dynamic programming matrix in the Needleman-Wunsch global sequence alignment algorithm, and reconstruct phylogenetic trees via the maximum parsimony, Unweighted Pair Group Method with Arithmetic mean (UPGMA) and Neighbor-Joining algorithms. With an easily accessible interface and instant visual feedback, TeachEnG will help promote active learning in bioinformatics.

Availability and implementation

TeachEnG is freely available at http://teacheng.illinois.edu. The source code is available from https://github.com/KnowEnG/TeachEnG under the Artistic License 2.0. It is written in JavaScript and compatible with Firefox, Safari, Chrome and Microsoft Edge.

Contact

songj@illinois.edu.

High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells

Digital holographic cytometry (DHC) permits label-free visualization of adherent cells. Dozens of cellular features can be derived from segmentation of hologram-derived images. However, the accuracy of single cell classification by these features remains limited for most applications, and lack of standardization metrics has hindered independent experimental comparison and validation. Here we identify twenty-six DHC-derived features that provide biologically independent information across a variety of mammalian cell state transitions. When trained on these features, machine-learning algorithms achieve blind single cell classification with up to 95% accuracy. Using classification accuracy to guide platform optimization, we develop methods to standardize holograms for the purpose of kinetic single cell cytometry. Applying our approach to human melanoma cells treated with a panel of cancer therapeutics, we track dynamic changes in cellular behavior and cell state over time. We provide the methods and computational tools for optimizing DHC for kinetic single adherent cell classification.

NF45 and NF90/NF110 coordinately regulate ESC pluripotency and differentiation

While years of investigation have elucidated many aspects of embryonic stem cell (ESC) regulation, the contributions of post-transcriptional and translational mechanisms to the pluripotency network remain largely unexplored. In particular, little is known in ESCs about the function of RNA binding proteins (RBPs), the protein agents of post-transcriptional regulation. We performed an unbiased RNAi screen of RBPs in an ESC differentiation assay and identified two related genes, NF45 (Ilf2) and NF90/NF110 (Ilf3), whose knockdown promoted differentiation to an epiblast-like state. Characterization of NF45 KO, NF90 + NF110 KO, and NF110 KO ESCs showed that loss of NF45 or NF90 + NF110 impaired ESC proliferation and led to dysregulated differentiation down embryonic lineages. Additionally, we found that NF45 and NF90/NF110 physically interact and influence the expression of each other at different levels of regulation. Globally across the transcriptome, NF45 KO ESCs and NF90 + NF110 KO ESCs show similar expression changes. Moreover, NF90 + NF110 RNA immunoprecipitation (RIP)-seq in ESCs suggested that NF90/NF110 directly regulate proliferation, differentiation, and RNA-processing genes. Our data support a model in which NF45, NF90, and NF110 operate in feedback loops that enable them, through both overlapping and independent targets, to help balance the push and pull of pluripotency and differentiation cues.

Emergent community agglomeration from data set geometry

In the statistical learning language, samples are snapshots of random vectors drawn from some unknown distribution. Such vectors usually reside in a high-dimensional Euclidean space, and thus the "curse of dimensionality" often undermines the power of learning methods, including community detection and clustering algorithms, that rely on Euclidean geometry. This paper presents the idea of effective dissimilarity transformation (EDT) on empirical dissimilarity hyperspheres and studies its effects using synthetic and gene expression data sets. Iterating the EDT turns a static data distribution into a dynamical process purely driven by the empirical data set geometry and adaptively ameliorates the curse of dimensionality, partly through changing the topology of a Euclidean feature space R^{n} into a compact hypersphere S^{n}. The EDT often improves the performance of hierarchical clustering via the automatic grouping information emerging from global interactions of data points. The EDT is not restricted to hierarchical clustering, and other learning methods based on pairwise dissimilarity should also benefit from the many desirable properties of EDT.

The effect of low-volatile organic compounds, water-based paint on aggravation of allergic disease in schoolchildren

Whether indoor painting aggravates preexisting allergic diseases remains unclear. We aimed to evaluate the impact of new classroom painting on aggravation of asthma, allergic rhinitis (AR), and atopic dermatitis (AD) in children. Studied school was previously painted with conventional water-based paint 20 years ago and had natural ventilation system. We identified a total of 172 children aged 10-12 years with allergic diseases in 17 classrooms, which were allocated to newly painted rooms with low-volatile organic compounds (VOC), water-based paint, or existing rooms. After painting, there was no intervention or internal airflow to influence indoor air environment in both classrooms. We prospectively assessed the symptom severity and serious events of allergic diseases between both classrooms at baseline and after one and eight weeks after painting. At one and eight weeks, there were no significant changes in the Childhood Asthma Control Test scores, the fractional nitric oxide levels, lung function in asthmatic children in either classroom. There were also no significant changes in the severity score of AR or AD, or serious events in all allergic diseases. These findings suggest classroom painting with this new paint at the levels encountered in this study might not be a major aggravating factor for school-aged children with allergic diseases.

Decellularized Human Dental Pulp as a Scaffold for Regenerative Endodontics

Teeth undergo postnatal organogenesis relatively late in life and only complete full maturation a few years after the crown first erupts in the oral cavity. At this stage, development can be arrested if the tooth organ is damaged by either trauma or caries. Regenerative endodontic procedures (REPs) are a treatment alternative to conventional root canal treatment for immature teeth. These procedures rely on the transfer of apically positioned stem cells, including stem cells of the apical papilla (SCAP), into the root canal system. Although clinical success has been reported for these procedures, the predictability of expected outcomes and the organization of the newly formed tissues are affected by the lack of an available suitable scaffold that mimics the complexity of the dental pulp extracellular matrix (ECM). In this study, we evaluated 3 methods of decellularization of human dental pulp to be used as a potential autograft scaffold. Tooth slices of human healthy extracted third molars were decellularized by 3 different methods. One of the methods generated the maximum observed decellularization with minimal impact on the ECM composition and organization. Furthermore, recellularization of the scaffold supported the proliferation of SCAP throughout the scaffold with differentiation into odontoblast-like cells near the dentinal walls. Thus, this study reports that human dental pulp from healthy extracted teeth can be successfully decellularized, and the resulting scaffold supports the proliferation and differentiation of SCAP. The future application of this form of an autograft in REPs can fulfill a yet unmet need for a suitable scaffold, potentially improving clinical outcomes and ultimately promoting the survival and function of teeth with otherwise poor prognosis.

Targeting and Immobilization of Bioactive Peptides on Dentin Matrix

The regeneration of structurally/functionally competent tooth root cementum is a critical step for the successful restoration of periodontal attachment. In this study, we tested whether a poly-glutamic acid-rich domain and glutamine-containing transglutaminase substrate can be used to target biologically active peptides to the mineralized root matrix and to bind such peptides covalently to the organic matrix. As a biologically active model molecule, the integrin-binding motif, RGD, was used. The effects of immobilization of such synthetic peptides to the dentin matrix on cementoblastic adhesion in vitro and cementogenesis in vivo were studied. In vitro, cementoblastic adhesion improved significantly when the dentin surface contained covalently bound peptides. In vivo, this bound peptide significantly increased cementum formation compared with that attained in control conditions. Transglutaminase-catalyzed covalent binding of bioactive peptides targeted to mineralized collagenous dentin matrix via the poly-glutamate domain can be readily achieved. This approach offers potential for clinical use in periodontal regeneration.

Inhibition of mTOR induces a paused pluripotent state

Cultured pluripotent stem cells are a cornerstone of regenerative medicine owing to their ability to give rise to all cell types of the body. Although pluripotent stem cells can be propagated indefinitely in vitro, pluripotency is paradoxically a transient state in vivo, lasting 2-3 days around the time of blastocyst implantation. The exception to this rule is embryonic diapause, a reversible state of suspended development triggered by unfavourable conditions. Diapause is a physiological reproductive strategy widely employed across the animal kingdom, including in mammals, but its regulation remains poorly understood. Here we report that the partial inhibition of mechanistic target of rapamycin (mTOR), a major nutrient sensor and promoter of growth, induces reversible pausing of mouse blastocyst development and allows their prolonged culture ex vivo. Paused blastocysts remain pluripotent and competent-able to give rise to embryonic stem (ES) cells and live, fertile mice. We show that both naturally diapaused blastocysts in vivo and paused blastocysts ex vivo display pronounced reductions in mTOR activity, translation, histone modifications associated with gene activity and transcription. Pausing can be induced directly in cultured ES cells and sustained for weeks without appreciable cell death or deviations from cell cycle distributions. We show that paused ES cells display a remarkable global suppression of transcription, maintain a gene expression signature of diapaused blastocysts and remain pluripotent. These results uncover a new pluripotent stem cell state corresponding to the epiblast of the diapaused blastocyst and indicate that mTOR regulates developmental timing at the peri-implantation stage. Our findings have implications in the fields of assisted reproduction, regenerative medicine, cancer, metabolic disorders and ageing.

Quantitative analysis and prediction of G-quadruplex forming sequences in double-stranded DNA

G-quadruplex (GQ) is a four-stranded DNA structure that can be formed in guanine-rich sequences. GQ structures have been proposed to regulate diverse biological processes including transcription, replication, translation and telomere maintenance. Recent studies have demonstrated the existence of GQ DNA in live mammalian cells and a significant number of potential GQ forming sequences in the human genome. We present a systematic and quantitative analysis of GQ folding propensity on a large set of 438 GQ forming sequences in double-stranded DNA by integrating fluorescence measurement, single-molecule imaging and computational modeling. We find that short minimum loop length and the thymine base are two main factors that lead to high GQ folding propensity. Linear and Gaussian process regression models further validate that the GQ folding potential can be predicted with high accuracy based on the loop length distribution and the nucleotide content of the loop sequences. Our study provides important new parameters that can inform the evaluation and classification of putative GQ sequences in the human genome.

Intratumoral Heterogeneity of the Epigenome

Investigation into intratumoral heterogeneity (ITH) of the epigenome is in a formative stage. The patterns of tumor evolution inferred from epigenetic ITH and genetic ITH are remarkably similar, suggesting widespread co-dependency of these disparate mechanisms. The biological and clinical relevance of epigenetic ITH are becoming more apparent. Rare tumor cells with unique and reversible epigenetic states may drive drug resistance, and the degree of epigenetic ITH at diagnosis may predict patient outcome. This perspective presents these current concepts and clinical implications of epigenetic ITH, and the experimental and computational techniques at the forefront of ITH exploration.