The UCSC Genome Browser database: 2019 update

Global quantification exposes abundant low-level off-target activity by base editors

Base editors are dedicated engineered deaminases that enable directed conversion of specific bases in the genome or transcriptome in a precise and efficient manner, and hold promise for correcting pathogenic mutations. A major concern limiting application of this powerful approach is the issue of off-target edits. Several recent studies have shown substantial off-target RNA activity induced by base editors and demonstrated that off-target mutations may be suppressed by improved deaminases versions or optimized guide RNAs. Here, we describe a new class of off-target events that are invisible to the established methods for detection of genomic variations and were thus far overlooked. We show that nonspecific, seemingly stochastic, off-target events affect a large number of sites throughout the genome or the transcriptome, and account for the majority of off-target activity. We develop and employ a different, complementary approach that is sensitive to the stochastic off-target activity and use it to quantify the abundant off-target RNA mutations due to current, optimized deaminase editors. We provide a computational tool to quantify global off-target activity, which can be used to optimize future base editors. Engineered base editors enable directed manipulation of the genome or transcriptome at single-base resolution. We believe that implementation of this computational approach would facilitate design of more specific base editors.

Ki-67 gene expression

Ki-67 serves as a prominent cancer marker. We describe how expression of the MKI67 gene coding for Ki-67 is controlled during the cell cycle. MKI67 mRNA and Ki-67 protein are maximally expressed in G₂ phase and mitosis. Expression is dependent on two CHR elements and one CDE site in the MKI67 promoter. DREAM transcriptional repressor complexes bind to both CHR sites and downregulate the expression in G₀/G₁ cells. Upregulation of MKI67 transcription coincides with binding of B-MYB-MuvB and FOXM1-MuvB complexes from S phase into G₂/M. Importantly, binding of B-MYB to the two CHR elements correlates with loss of CHR-dependent MKI67 promoter activation in B-MYB-knockdown experiments. In knockout cell models, we find that DREAM/MuvB-dependent transcriptional control cooperates with the RB Retinoblastoma tumor suppressor. Furthermore, the p53 tumor suppressor indirectly downregulates transcription of the MKI67 gene. This repression by p53 requires p21/CDKN1A. These results are consistent with a model in which DREAM, B-MYB-MuvB, and FOXM1-MuvB together with RB cooperate in cell cycle-dependent transcription and in transcriptional repression following p53 activation. In conclusion, we present mechanisms how MKI67 gene expression followed by Ki-67 protein synthesis is controlled during the cell cycle and upon induction of DNA damage, as well as upon p53 activation.

Identification of Potential circRNA-microRNA-mRNA Regulatory Network in Skeletal Muscle

Circular RNAs (circRNAs) are a newly discovered family of regulatory RNAs generated through backsplicing. Genome-wide profiling of circRNAs found that circRNAs are ubiquitously expressed and regulate gene expression by acting as a sponge for RNA-binding proteins (RBPs) and microRNAs (miRNAs). To identify circRNAs expressed in mouse skeletal muscle, we performed high-throughput RNA-sequencing of circRNA-enriched gastrocnemius muscle RNA samples, which identified more than 1,200 circRNAs. In addition, we have identified more than 14,000 and 15,000 circRNAs in aging human skeletal muscle tissue and satellite cells, respectively. A subset of abundant circRNAs was analyzed by RT-PCR, Sanger sequencing, and RNase R digestion assays to validate their expression in mouse skeletal muscle tissues. Analysis of the circRNA-miRNA-mRNA regulatory network revealed that conserved circNfix might associate with miR-204-5p, a suppressor of myocyte enhancer factor 2c (Mef2c) expression. To support the hypothesis that circNfix might regulate myogenesis by controlling Mef2c expression, silencing circNfix moderately reduced Mef2c mRNA expression and inhibited C2C12 differentiation. We propose that circNfix promotes MEF2C expression during muscle cell differentiation in part by acting as a sponge for miR-204-5p.

R-Loop Tracker: Web Access-Based Tool for R-Loop Detection and Analysis in Genomic DNA Sequences

R-loops are common non-B nucleic acid structures formed by a three-stranded nucleic acid composed of an RNA–DNA hybrid and a displaced single-stranded DNA (ssDNA) loop. Because the aberrant R-loop formation leads to increased mutagenesis, hyper-recombination, rearrangements, and transcription-replication collisions, it is regarded as important in human diseases. Therefore, its prevalence and distribution in genomes are studied intensively. However, in silico tools for R-loop prediction are limited, and therefore, we have developed the R-loop tracker tool, which was implemented as a part of the DNA Analyser web server. This new tool is focused upon (1) prediction of R-loops in genomic DNA without length and sequence limitations; (2) integration of R-loop tracker results with other tools for nucleic acids analyses, including Genome Browser; (3) internal cross-evaluation of in silico results with experimental data, where available; (4) easy export and correlation analyses with other genome features and markers; and (5) enhanced visualization outputs. Our new R-loop tracker tool is freely accessible on the web pages of DNA Analyser tools, and its implementation on the web-based server allows effective analyses not only for DNA segments but also for full chromosomes and genomes.

Pseudoexon activation in disease by non-splice site deep intronic sequence variation - wild type pseudoexons constitute high-risk sites in the human genome

Accuracy of pre-messenger RNA (pre-mRNA) splicing is crucial for normal gene expression. Complex regulation supports the spliceosomal distinction between authentic exons and the many seemingly functional splice sites delimiting pseudoexons. Pseudoexons are nonfunctional intronic sequences that can be activated for aberrant inclusion in mRNA, which may cause disease. Pseudoexon activation is very challenging to predict, in particular when activation occurs by sequence variants that alter the splicing regulatory environment without directly affecting splice sites. As pseudoexon inclusion often evades detection due to activation of nonsense-mediated mRNA decay, and because conventional diagnostic procedures miss deep intronic sequence variation, pseudoexon activation is a heavily underreported disease mechanism. Pseudoexon characteristics have mainly been studied based on in silico predicted sequences. Moreover, because recognition of sequence variants that create or strengthen splice sites is possible by comparison with well-established consensus sequences, this type of pseudoexon activation is by far the most frequently reported. Here we review all known human disease-associated pseudoexons that carry functional splice sites and are activated by deep intronic sequence variants located outside splice site sequences. We delineate common characteristics that make this type of wild type pseudoexons distinct high-risk sites in the human genome.

Spatial and temporal intratumour heterogeneity has potential consequences for single biopsy-based neuroblastoma treatment decisions

Intratumour heterogeneity is a major cause of treatment failure in cancer. We present in-depth analyses combining transcriptomic and genomic profiling with ultra-deep targeted sequencing of multiregional biopsies in 10 patients with neuroblastoma, a devastating childhood tumour. We observe high spatial and temporal heterogeneity in somatic mutations and somatic copy-number alterations which are reflected on the transcriptomic level. Mutations in some druggable target genes including ALK and FGFR1 are heterogeneous at diagnosis and/or relapse, raising the issue whether current target prioritization and molecular risk stratification procedures in single biopsies are sufficiently reliable for therapy decisions. The genetic heterogeneity in gene mutations and chromosome aberrations observed in deep analyses from patient courses suggest clonal evolution before treatment and under treatment pressure, and support early emergence of metastatic clones and ongoing chromosomal instability during disease evolution. We report continuous clonal evolution on mutational and copy number levels in neuroblastoma, and detail its implications for therapy selection, risk stratification and therapy resistance.

Yersinia remodels epigenetic histone modifications in human macrophages

Various pathogens systematically reprogram gene expression in macrophages, but the underlying mechanisms are largely unknown. We investigated whether the enteropathogen Yersinia enterocolitica alters chromatin states to reprogram gene expression in primary human macrophages. Genome-wide chromatin immunoprecipitation (ChIP) seq analyses showed that pathogen-associated molecular patterns (PAMPs) induced up- or down-regulation of histone modifications (HMod) at approximately 14500 loci in promoters and enhancers. Effectors of Y. enterocolitica reorganized about half of these dynamic HMod, with the effector YopP being responsible for about half of these modulatory activities. The reorganized HMod were associated with genes involved in immune response and metabolism. Remarkably, the altered HMod also associated with 61% of all 534 known Rho GTPase pathway genes, revealing a new level in Rho GTPase regulation and a new aspect of bacterial pathogenicity. Changes in HMod were associated to varying degrees with corresponding gene expression, e. g. depending on chromatin localization and cooperation of the HMod. In summary, infection with Y. enterocolitica remodels HMod in human macrophages to modulate key gene expression programs of the innate immune response.

Human pathogenic bacteria can affect epigenetic histone modifications to modulate gene expression in host cells. However, a systems biology analysis of this bacterial virulence mechanism in immune cells has not been performed. Here we analyzed genome-wide epigenetic histone modifications and associated gene expression changes in primary human macrophages infected with enteropathogenic Yersinia enterocolitica. We demonstrate that Yersinia virulence factors extensively modulate histone modifications and associated gene expression triggered by the pathogen-associated molecular patterns (PAMPs) of the bacteria. The epigenetically modulated genes are involved in several key pathways of the macrophage immune response, including the Rho GTPase pathway, revealing a novel level of Rho GTPase regulation by a bacterial pathogen. Overall, our findings provide an in-depth view of epigenetic and gene expression changes during host-pathogen interaction and might have further implications for understanding of the innate immune memory in macrophages.

Formation and persistence of polyglutamine aggregates in mistranslating cells

In neurodegenerative diseases, including pathologies with well-known causative alleles, genetic factors that modify severity or age of onset are not entirely understood. We recently documented the unexpected prevalence of transfer RNA (tRNA) mutants in the human population, including variants that cause amino acid mis-incorporation. We hypothesized that a mistranslating tRNA will exacerbate toxicity and modify the molecular pathology of Huntington's disease-causing alleles. We characterized a tRNA^Pro mutant that mistranslates proline codons with alanine, and tRNA^Ser mutants, including a tRNA^Ser_AGA G35A variant with a phenylalanine anticodon (tRNA^Ser_AAA) found in ∼2% of the population. The tRNA^Pro mutant caused synthetic toxicity with a deleterious huntingtin poly-glutamine (polyQ) allele in neuronal cells. The tRNA^Ser_AAA variant showed synthetic toxicity with proteasome inhibition but did not enhance toxicity of the huntingtin allele. Cells mistranslating phenylalanine or proline codons with serine had significantly reduced rates of protein synthesis. Mistranslating cells were slow but effective in forming insoluble polyQ aggregates, defective in protein and aggregate degradation, and resistant to the neuroprotective integrated stress response inhibitor (ISRIB). Our findings identify mistranslating tRNA variants as genetic factors that slow protein aggregation kinetics, inhibit aggregate clearance, and increase drug resistance in cellular models of neurodegenerative disease.

Regulated splicing of large exons is linked to phase-separation of vertebrate transcription factors

Although large exons cannot be readily recognized by the spliceosome, many are evolutionarily conserved and constitutively spliced for inclusion in the processed transcript. Furthermore, whether large exons may be enriched in a certain subset of proteins, or mediate specific functions, has remained unclear. Here, we identify a set of nearly 3,000 SRSF3-dependent large constitutive exons (S3-LCEs) in human and mouse cells. These exons are enriched for cytidine-rich sequence motifs, which bind and recruit the splicing factors hnRNP K and SRSF3. We find that hnRNP K suppresses S3-LCE splicing, an effect that is mitigated by SRSF3 to thus achieve constitutive splicing of S3-LCEs. S3-LCEs are enriched in genes for components of transcription machineries, including mediator and BAF complexes, and frequently contain intrinsically disordered regions (IDRs). In a subset of analyzed S3-LCE-containing transcription factors, SRSF3 depletion leads to deletion of the IDRs due to S3-LCE exon skipping, thereby disrupting phase-separated assemblies of these factors. Cytidine enrichment in large exons introduces proline/serine codon bias in intrinsically disordered regions and appears to have been evolutionarily acquired in vertebrates. We propose that layered splicing regulation by hnRNP K and SRSF3 ensures proper phase-separation of these S3-LCE-containing transcription factors in vertebrates.

CSC software corrects off-target mediated gRNA depletion in CRISPR-Cas9 essentiality screens

Off-target effects are well established confounders of CRISPR negative selection screens that impair the identification of essential genomic loci. In particular, non-coding regulatory elements and repetitive regions are often difficult to target with specific gRNAs, effectively precluding the unbiased screening of a large portion of the genome. To address this, we developed CRISPR Specificity Correction (CSC), a computational method that corrects for the effect of off-targeting on gRNA depletion. We benchmark CSC with data from the Cancer Dependency Map and show that it significantly improves the overall sensitivity and specificity of viability screens while preserving known essentialities, particularly for genes targeted by highly promiscuous gRNAs. We believe this tool will further enable the functional annotation of the genome as it represents a robust alternative to the traditional filtering strategy of discarding unspecific guides from the analysis. CSC is an open-source software that can be seamlessly integrated into current CRISPR analysis pipelines.

The control of polycomb repressive complexes by long noncoding RNAs

The polycomb repressive complexes 1 and 2 (PRCs; PRC1 and PRC2) are conserved histone-modifying enzymes that often function cooperatively to repress gene expression. The PRCs are regulated by long noncoding RNAs (lncRNAs) in complex ways. On the one hand, specific lncRNAs cause the PRCs to engage with chromatin and repress gene expression over genomic regions that can span megabases. On the other hand, the PRCs bind RNA with seemingly little sequence specificity, and at least in the case of PRC2, direct RNA-binding has the effect of inhibiting the enzyme. Thus, some RNAs appear to promote PRC activity, while others may inhibit it. The reasons behind this apparent dichotomy are unclear. The most potent PRC-activating lncRNAs associate with chromatin and are predominantly unspliced or harbor unusually long exons. Emerging data imply that these lncRNAs promote PRC activity through internal RNA sequence elements that arise and disappear rapidly in evolutionary time. These sequence elements may function by interacting with common subsets of RNA-binding proteins that recruit or stabilize PRCs on chromatin. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

Analysis of the prognostic significance and potential mechanisms of lncRNAs associated with m6A methylation in papillary thyroid carcinoma

BACKGROUND

m6A methylation-related long non-coding RNAs (lncRNAs) play a significant role in the progression of various tumors and can be used as prognostic markers. However, whether m6A-related lncRNAs also play the same function as prognostic markers in papillary thyroid carcinoma (PTC) remains unclear.

METHODS

Consensus cluster analysis was performed to divide PTC samples obtained from The Cancer Genome Atlas database into two clusters according to the expression of m6A-related lncRNAs. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to create and verify a prognostic model. Furthermore, the relationship among risk scores, clusters, programmed death-ligand 1 (PD-L1), tumor microenvironment (TME), clinicopathological characteristics, immune infiltration, immune checkpoint, and tumor mutation burden (TMB) was analyzed. In addition, a nomogram was created, and subsequently, the drug sensitivity of lncRNAs in the prognostic model was analyzed. Finally, the relationship between these lncRNAs and prognosis in pan-cancer was investigated.

RESULTS

The prognosis, RAS, BRAF, M, and TME were found to be different in two clusters. The prognostic model included three lncRNAs: PSMG3-AS1, BHLHE40-AS1, and AC016747.3. The risk score was associated with clusters, PD-L1, tumor microenvironment, clinicopathological characteristics, immune cell infiltration, immune checkpoint, and TMB, and thus, risk score was confirmed as useful prognostic indicator. Differentially expressed lncRNAs are involved in many malignancies and can be identified as cancer prognostic makers.

CONCLUSION

According to our research, we can regard m6A-related lncRNAs involved in the procession of PTC as a biomarker of progression-free survival for PTC patients, and pan-cancer.

Somatic variant analysis suite: copy number variation clonal visualization online platform for large-scale single-cell genomics

The recent advance of single-cell copy number variation (CNV) analysis plays an essential role in addressing intratumor heterogeneity, identifying tumor subgroups and restoring tumor-evolving trajectories at single-cell scale. Informative visualization of copy number analysis results boosts productive scientific exploration, validation and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, single-cell Somatic Variant Analysis Suite (scSVAS), for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell genomic analysis that provides an arsenal of unique functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may conduct scientific discoveries, share interactive visualizations and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing and publishing single-cell CNV profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.

RUNX1 regulates the proliferation and chemoresistance of colorectal cancer through the Hedgehog signaling pathway

Background: Chemoresistance is one of the main causes of recurrence in colorectal cancer (CRC) patients and leads to a poor prognosis. To characterize RUNX1 expression in colorectal cancer (CRC) and elucidate its mechanistic involvement in the tumor biology of this disease.

Methods: The expression of RUNX1 in CRC and normal tissues was detected by bioinformatics analysis. Cell proliferation was measured by CCK-8 and clonogenic assays. In vivo tumor progression was assessed with a xenograft mouse model. Cell drug sensitivity tests and flow cytometry were performed to analyze CRC cell chemoresistance. RUNX1, key molecules of the Hedgehog signaling pathway, and ABCG2 were detected by qRT-PCR and Western blotting.

Results: RUNX1 expression is upregulated in CRC tissues. RUNX1 enhanced CRC cell resistance to 5-fluorouracil (5-FU), promoted proliferation, and inhibited 5-FU-induced apoptosis. Mechanistically, RUNX1 can activate the Hedgehog signaling pathway and promote the expression of ABCG2 in CRC cells.

Conclusions: Our study demonstrated that RUNX1 promotes CRC proliferation and chemoresistance by activating the Hedgehog signaling pathway and ABCG2 expression.

Simplifying the development of portable, scalable, and reproducible workflows

Command-line software plays a critical role in biology research. However, processes for installing and executing software differ widely. The Common Workflow Language (CWL) is a community standard that addresses this problem. Using CWL, tool developers can formally describe a tool's inputs, outputs, and other execution details. CWL documents can include instructions for executing tools inside software containers. Accordingly, CWL tools are portable-they can be executed on diverse computers-including personal workstations, high-performance clusters, or the cloud. CWL also supports workflows, which describe dependencies among tools and using outputs from one tool as inputs to others. To date, CWL has been used primarily for batch processing of large datasets, especially in genomics. But it can also be used for analytical steps of a study. This article explains key concepts about CWL and software containers and provides examples for using CWL in biology research. CWL documents are text-based, so they can be created manually, without computer programming. However, ensuring that these documents conform to the CWL specification may prevent some users from adopting it. To address this gap, we created ToolJig, a Web application that enables researchers to create CWL documents interactively. ToolJig validates information provided by the user to ensure it is complete and valid. After creating a CWL tool or workflow, the user can create 'input-object' files, which store values for a particular invocation of a tool or workflow. In addition, ToolJig provides examples of how to execute the tool or workflow via a workflow engine. ToolJig and our examples are available at https://github.com/srp33/ToolJig.

TIGER: The gene expression regulatory variation landscape of human pancreatic islets

Genome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D.

The CD155/TIGIT axis promotes and maintains immune evasion in neoantigen-expressing pancreatic cancer

The CD155/TIGIT axis can be co-opted during immune evasion in chronic viral infections and cancer. Pancreatic adenocarcinoma (PDAC) is a highly lethal malignancy, and immune-based strategies to combat this disease have been largely unsuccessful to date. We corroborate prior reports that a substantial portion of PDAC harbors predicted high-affinity MHC class I-restricted neoepitopes and extend these findings to advanced/metastatic disease. Using multiple preclinical models of neoantigen-expressing PDAC, we demonstrate that intratumoral neoantigen-specific CD8+ T cells adopt multiple states of dysfunction, resembling those in tumor-infiltrating lymphocytes of PDAC patients. Mechanistically, genetic and/or pharmacologic modulation of the CD155/TIGIT axis was sufficient to promote immune evasion in autochthonous neoantigen-expressing PDAC. Finally, we demonstrate that the CD155/TIGIT axis is critical in maintaining immune evasion in PDAC and uncover a combination immunotherapy (TIGIT/PD-1 co-blockade plus CD40 agonism) that elicits profound anti-tumor responses in preclinical models, now poised for clinical evaluation.

Rare single nucleotide variants in COL5A1 promoter do not play a major role in keratoconus susceptibility associated with rs1536482

BACKGROUND

Keratoconus is a chronic degenerative disorder of the cornea characterized by thinning and cone-shaped protrusions. Although genetic factors play a key role in keratoconus development, the etiology is still under investigation. The occurrence of single-nucleotide polymorphisms (SNPs) associated with keratoconus in Russian patients is poorly studied. The purpose of this study was to validate whether three reported keratoconus-associated SNPs (rs1536482 near the COL5A1 gene, rs2721051 near the FOXO1 gene, rs1324183 near the MPDZ gene) are also actual for a Russian cohort of patients. Additionally, we investigated the COL5A1 promoter sequence for single-nucleotide variants (SNVs) in a subgroup of keratoconus patients with at least one rs1536482 minor allele (rs1536482+) to assess the role of these SNVs in keratoconus susceptibility associated with rs1536482.

METHODS

This case-control study included 150 keratoconus patients and two control groups (main and additional, 205 and 474 participants, respectively). We performed PCR targeting regions flanking SNVs and the COL5A1 promoter, followed by Sanger sequencing of amplicons. The additional control group was genotyped using an SNP array.

RESULTS

The minor allele frequency was significantly different between the keratoconus and control cohorts (main and combined) for rs1536482, rs2721051, and rs1324183 (p-value < 0.05). The rare variants rs1043208782 and rs569248712 were found in the COL5A1 promoter in two out of 94 rs1536482+ keratoconus patients.

CONCLUSION

rs1536482, rs2721051, and rs1324183 were associated with keratoconus in a Russian cohort. SNVs in the COL5A1 promoter do not play a major role in keratoconus susceptibility associated with rs1536482.

Development and validation of a ferroptosis-related prognostic model for the prediction of progression-free survival and immune microenvironment in patients with papillary thyroid carcinoma

BACKGROUND

Ferroptosis is an iron-dependent and regulated cell death that has been widely reported in a variety of malignancies. The overall survival of papillary thyroid cancer (PTC) is excellent, but the identification of patients with poor prognosis still faces challenges. Nevertheless, whether ferroptosis-related genes (FRGs) can be used to screen high-risk patients is not clear.

METHODS

We obtained the clinical data of patients with PTC and FRGs from the UCSC Xena platform and the FerrDb respectively. Differentially expressed genes (DEGs) of FRGs were obtained from the entire The Cancer Genome Atlas (TCGA). Subsequently, the entire TCGA dataset was randomly split into two subsets: training and test datasets. Based on DEGs, we constructed a predictive model which was tested in the test dataset and the entire TCGA dataset to predict progression-free survival (PFS). Patients were categorized into high- or low-risk groups based on their median risk score. We analyzed differences in some aspects, including pathway enrichment analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), tumor microenvironment (TME), human leukocyte antigen (HLA) genes, and tumor mutation burden (TMB) analyses, between high-risk and low-risk groups.

RESULTS

A predictive model with three FRGs (HSPA5, AURKA, and TSC22D3) was constructed. Patients in the high-risk group had worse PFS compared with patients in the low-risk group. Functional analysis results revealed that ssGSEA, immune cell infiltration, TME, HLA, and TMB were closely associated with ferroptosis.

CONCLUSION

The prognostic model constructed in this study can effectively predict PFS for patients with PTC.

Dissecting genomic determinants of positive selection with an evolution-guided regression model

In evolutionary genomics, it is fundamentally important to understand how characteristics of genomic sequences, such as gene expression level, determine the rate of adaptive evolution. While numerous statistical methods, such as the McDonald–Kreitman (MK) test, are available to examine the association between genomic features and the rate of adaptation, we currently lack a statistical approach to disentangle the independent effect of a genomic feature from the effects of other correlated genomic features. To address this problem, I present a novel statistical model, the MK regression, which augments the MK test with a generalized linear model. Analogous to the classical multiple regression model, the MK regression can analyze multiple genomic features simultaneously to infer the independent effect of a genomic feature, holding constant all other genomic features. Using the MK regression, I identify numerous genomic features driving positive selection in chimpanzees. These features include well-known ones, such as local mutation rate, residue exposure level, tissue specificity, and immune genes, as well as new features not previously reported, such as gene expression level and metabolic genes. In particular, I show that highly expressed genes may have a higher adaptation rate than their weakly expressed counterparts, even though a higher expression level may impose stronger negative selection. Also, I show that metabolic genes may have a higher adaptation rate than their nonmetabolic counterparts, possibly due to recent changes in diet in primate evolution. Overall, the MK regression is a powerful approach to elucidate the genomic basis of adaptation.

Decreased DHRS1 expression is a novel predictor of poor survival in patients with hepatocellular carcinoma

Aim: To investigate the effect of aberrant expression of DHRS1 on hepatocellular carcinoma (HCC). Materials & methods: Kaplan-Meier and Cox regression analyses were performed to evaluate the correlation between DHRS1 and overall survival. Gene set enrichment analysis was performed to explore the potential function of DHRS1 in HCC. Results: Multiple data analysis revealed that DHRS1 mRNA and protein expression level were remarkably lower in HCC than that in normal tissues. In survival analysis, patients with low DHRS1 expression presented a poorer prognosis, and was an independent risk factor for HCC. Conclusion: Decreased DHRS1 expression may be a potential predictor of poor prognosis in HCC.

Cancer DEIso: An integrative analysis platform for investigating differentially expressed gene-level and isoform-level human cancer markers

Transcript isoforms regulated by alternative splicing can substantially impact carcinogenesis, leading to a need to obtain clues for both gene differential expression and malfunctions of isoform distributions in cancer studies. The Cancer Genome Atlas (TCGA) project was launched in 2008 to collect cancer-related genome mutation raw data from the population. While many repositories tried to add insights into the raw data in TCGA, no existing database provides both comprehensive gene-level and isoform-level cancer stage marker investigation and survival analysis. We constructed Cancer DEIso to facilitate in-depth analyses for both gene-level and isoform-level human cancer studies. Patient RNA-seq data, sample sheets, patient clinical data, and human genome datasets were collected and processed in Cancer DEIso. And four functions to search differentially expressed genes/isoforms between cancer stages were implemented: (i) Search potential gene/isoform markers for a specified cancer type and its two stages; (ii) Search potentially induced cancer types and stages for a gene/isoform; (iii) Expression survival analysis on a given gene/isoform for some cancer; (iv) Gene/isoform stage expression comparison visualization. As an example, we demonstrate that Cancer DEIso can indicate potential colorectal cancer isoform diagnostic markers that are not easily detected when only gene-level expressions are considered. Cancer DEIso is available at http://cosbi4.ee.ncku.edu.tw/DEIso/.

Productive visualization of high-throughput sequencing data using the SeqCode open portable platform

Large-scale sequencing techniques to chart genomes are entirely consolidated. Stable computational methods to perform primary tasks such as quality control, read mapping, peak calling, and counting are likewise available. However, there is a lack of uniform standards for graphical data mining, which is also of central importance. To fill this gap, we developed SeqCode, an open suite of applications that analyzes sequencing data in an elegant but efficient manner. Our software is a portable resource written in ANSI C that can be expected to work for almost all genomes in any computational configuration. Furthermore, we offer a user-friendly front-end web server that integrates SeqCode functions with other graphical analysis tools. Our analysis and visualization toolkit represents a significant improvement in terms of performance and usability as compare to other existing programs. Thus, SeqCode has the potential to become a key multipurpose instrument for high-throughput professional analysis; further, it provides an extremely useful open educational platform for the world-wide scientific community. SeqCode website is hosted at http://ldicrocelab.crg.eu, and the source code is freely distributed at https://github.com/eblancoga/seqcode.

Comprehensive characterization genetic regulation and chromatin landscape of enhancer-associated long non-coding RNAs and their implication in human cancer

Long non-coding RNAs (lncRNAs) that emanate from enhancer regions (defined as enhancer-associated lncRNAs, or elncRNAs) are emerging as critical regulators in disease progression. However, their biological characteristics and clinical relevance have not been fully portrayed. Here, based on the traditional expression quantitative loci (eQTL) and our optimized residual eQTL method, we comprehensively described the genetic effect on elncRNA expression in more than 300 lymphoblastoid cell lines. Meanwhile, a chromatin atlas of elncRNAs relative to the genetic regulation state was depicted. By applying the maximum likelihood estimate method, we successfully identified causal elncRNAs for protein-coding gene expression reprogramming and showed their associated single nucleotide polymorphisms (SNPs) favor binding of transcription factors. Further epigenome analysis revealed two immune-associated elncRNAs AL662844.4 and LINC01215 possess high levels of H3K27ac and H3K4me1 in human cancer. Besides, pan-cancer analysis of 3D genome, transcriptome, and regulatome data showed they potentially regulate tumor-immune cell interaction through affecting MHC class I genes and CD47, respectively. Moreover, our study showed there exist associations between elncRNA and patient survival. Finally, we made a user-friendly web interface available for exploring the regulatory relationship of SNP-elncRNA-protein-coding gene triplets (http://bio-bigdata.hrbmu.edu.cn/elncVarReg). Our study provides critical mechanistic insights for elncRNA function and illustrates their implications in human cancer.

A computational approach for identification of core modules from a co-expression network and GWAS data

This protocol describes the application of the "omnigenic" model of the genetic architecture of complex traits to identify novel "core" genes influencing a disease-associated phenotype. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. This protocol leverages GWAS data, a co-expression network, and publicly available data, including the GTEx database and the International Mouse Phenotyping Consortium Database, to identify modules enriched for genes with "core-like" characteristics. For complete details on the use and execution of this protocol, please refer to Sabik et al. (2020).

SmProt: A Reliable Repository with Comprehensive Annotation of Small Proteins Identified from Ribosome Profiling

Small proteins specifically refer to proteins consisting of less than 100 amino acids translated from small open reading frames (sORFs), which were usually missed in previous genome annotation. The significance of small proteins has been revealed in current years, along with the discovery of their diverse functions. However, systematic annotation of small proteins is still insufficient. SmProt was specially developed to provide valuable information on small proteins for scientific community. Here we present the update of SmProt, which emphasizes reliability of translated sORFs, genetic variants in translated sORFs, disease-specific sORF translation events or sequences, and remarkably increased data volume. More components such as non-ATG translation initiation, function, and new sources are also included. SmProt incorporated 638,958 unique small proteins curated from 3,165,229 primary records, which were computationally predicted from 419 ribosome profiling (Ribo-seq) datasets or collected from literature and other sources from 370 cell lines or tissues in 8 species (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Saccharomyces cerevisiae, Caenorhabditis elegans, and Escherichia coli). In addition, small protein families identified from human microbiomes were also collected. All datasets in SmProt are free to access, and available for browse, search, and bulk downloads at http://bigdata.ibp.ac.cn/SmProt/.

CTCF knockout in zebrafish induces alterations in regulatory landscapes and developmental gene expression

Coordinated chromatin interactions between enhancers and promoters are critical for gene regulation. The architectural protein CTCF mediates chromatin looping and is enriched at the boundaries of topologically associating domains (TADs), which are sub-megabase chromatin structures. In vitro CTCF depletion leads to a loss of TADs but has only limited effects over gene expression, challenging the concept that CTCF-mediated chromatin structures are a fundamental requirement for gene regulation. However, how CTCF and a perturbed chromatin structure impacts gene expression during development remains poorly understood. Here we link the loss of CTCF and gene regulation during patterning and organogenesis in a ctcf knockout zebrafish model. CTCF absence leads to loss of chromatin structure and affects the expression of thousands of genes, including many developmental regulators. Our results demonstrate the essential role of CTCF in providing the structural context for enhancer-promoter interactions, thus regulating developmental genes.

Expression Profiles and Potential Functions of Long Non-Coding RNAs in the Heart of Mice With Coxsackie B3 Virus-Induced Myocarditis

Aims

Long non-coding RNAs (lncRNAs) are critical regulators of viral infection and inflammatory responses. However, the roles of lncRNAs in acute myocarditis (AM), especially fulminant myocarditis (FM), remain unclear.

Methods

FM and non-fulminant myocarditis (NFM) were induced by coxsackie B3 virus (CVB3) in different mouse strains. Then, the expression profiles of the lncRNAs in the heart tissues were detected by sequencing. Finally, the patterns were analyzed by Pearson/Spearman rank correlation, Kyoto Encyclopedia of Genes and Genomes, and Cytoscape 3.7.

Results

First, 1,216, 983, 1,606, and 2,459 differentially expressed lncRNAs were identified in CVB3-treated A/J, C57BL/6, BALB/c, and C3H mice with myocarditis, respectively. Among them, 88 lncRNAs were commonly dysregulated in all four models. Quantitative real-time polymerase chain reaction analyses further confirmed that four out of the top six commonly dysregulated lncRNAs were upregulated in all four models. Moreover, the levels of ENSMUST00000188819, ENSMUST00000199139, and ENSMUST00000222401 were significantly elevated in the heart and spleen and correlated with the severity of cardiac inflammatory infiltration. Meanwhile, 923 FM-specific dysregulated lncRNAs were detected, among which the levels of MSTRG.26098.49, MSTRG.31307.11, MSTRG.31357.2, and MSTRG.32881.28 were highly correlated with LVEF.

Conclusion

Expression of lncRNAs is significantly dysregulated in acute myocarditis, which may play different roles in the progression of AM.

Single-nuclei transcriptomes from human adrenal gland reveal distinct cellular identities of low and high-risk neuroblastoma tumors

Childhood neuroblastoma has a remarkable variability in outcome. Age at diagnosis is one of the most important prognostic factors, with children less than 1 year old having favorable outcomes. Here we study single-cell and single-nuclei transcriptomes of neuroblastoma with different clinical risk groups and stages, including healthy adrenal gland. We compare tumor cell populations with embryonic mouse sympatho-adrenal derivatives, and post-natal human adrenal gland. We provide evidence that low and high-risk neuroblastoma have different cell identities, representing two disease entities. Low-risk neuroblastoma presents a transcriptome that resembles sympatho- and chromaffin cells, whereas malignant cells enriched in high-risk neuroblastoma resembles a subtype of TRKB+ cholinergic progenitor population identified in human post-natal gland. Analyses of these populations reveal different gene expression programs for worst and better survival in correlation with age at diagnosis. Our findings reveal two cellular identities and a composition of human neuroblastoma tumors reflecting clinical heterogeneity and outcome.

Information content differentiates enhancers from silencers in mouse photoreceptors

Enhancers and silencers often depend on the same transcription factors (TFs) and are conflated in genomic assays of TF binding or chromatin state. To identify sequence features that distinguish enhancers and silencers, we assayed massively parallel reporter libraries of genomic sequences targeted by the photoreceptor TF cone-rod homeobox (CRX) in mouse retinas. Both enhancers and silencers contain more TF motifs than inactive sequences, but relative to silencers, enhancers contain motifs from a more diverse collection of TFs. We developed a measure of information content that describes the number and diversity of motifs in a sequence and found that, while both enhancers and silencers depend on CRX motifs, enhancers have higher information content. The ability of information content to distinguish enhancers and silencers targeted by the same TF illustrates how motif context determines the activity of cis-regulatory sequences.

Different cell types are established by activating and repressing the activity of specific sets of genes, a process controlled by proteins called transcription factors. Transcription factors work by recognizing and binding short stretches of DNA in parts of the genome called cis-regulatory sequences. A cis-regulatory sequence that increases the activity of a gene when bound by transcription factors is called an enhancer, while a sequence that causes a decrease in gene activity is called a silencer.

To establish a cell type, a particular transcription factor will act on both enhancers and silencers that control the activity of different genes. For example, the transcription factor cone-rod homeobox (CRX) is critical for specifying different types of cells in the retina, and it acts on both enhancers and silencers. In rod photoreceptors, CRX activates rod genes by binding their enhancers, while repressing cone photoreceptor genes by binding their silencers. However, CRX always recognizes and binds to the same DNA sequence, known as its binding site, making it unclear why some cis-regulatory sequences bound to CRX act as silencers, while others act as enhancers.

Friedman et al. sought to understand how enhancers and silencers, both bound by CRX, can have different effects on the genes they control. Since both enhancers and silencers contain CRX binding sites, the difference between the two must lie in the sequence of the DNA surrounding these binding sites.

Using retinas that have been explanted from mice and kept alive in the laboratory, Friedman et al. tested the activity of thousands of CRX-binding sequences from the mouse genome. This showed that both enhancers and silencers have more copies of CRX-binding sites than sequences of the genome that are inactive. Additionally, the results revealed that enhancers have a diverse collection of binding sites for other transcription factors, while silencers do not. Friedman et al. developed a new metric they called information content, which captures the diverse combinations of different transcription binding sites that cis-regulatory sequences can have. Using this metric, Friedman et al. showed that it is possible to distinguish enhancers from silencers based on their information content.

It is critical to understand how the DNA sequences of cis-regulatory regions determine their activity, because mutations in these regions of the genome can cause disease. However, since every person has thousands of benign mutations in cis-regulatory sequences, it is a challenge to identify specific disease-causing mutations, which are relatively rare. One long-term goal of models of enhancers and silencers, such as Friedman et al.’s information content model, is to understand how mutations can affect cis-regulatory sequences, and, in some cases, lead to disease.

Converting single nucleotide variants between genome builds: from cautionary tale to solution

Next-generation sequencing studies are dependent on a high-quality reference genome for single nucleotide variant (SNV) calling. Although the two most recent builds of the human genome are widely used, position information is typically not directly comparable between them. Re-alignment gives the most accurate position information, but this procedure is often computationally expensive, and therefore, tools such as liftOver and CrossMap are used to convert data from one build to another. However, the positions of converted SNVs do not always match SNVs derived from aligned data, and in some instances, SNVs are known to change chromosome when converted. This is a significant problem when compiling sequencing resources or comparing results across studies. Here, we describe a novel algorithm to identify positions that are unstable when converting between human genome reference builds. These positions are detected independent of the conversion tools and are determined by the chain files, which provide a mapping of contiguous positions from one build to another. We also provide the list of unstable positions for converting between the two most commonly used builds GRCh37 and GRCh38. Pre-excluding SNVs at these positions, prior to conversion, results in SNVs that are stable to conversion. This simple procedure gives the same final list of stable SNVs as applying the algorithm and subsequently removing variants at unstable positions. This work highlights the care that must be taken when converting SNVs between genome builds and provides a simple method for ensuring higher confidence converted data. Unstable positions and algorithm code, available at https://github.com/cathaloruaidh/genomeBuildConversion.

AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data

Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved.

Biomedical Data Commons (BMDC) prioritizes B-lymphocyte non-coding genetic variants in Type 1 Diabetes

The repurposing of biomedical data is inhibited by its fragmented and multi-formatted nature that requires redundant investment of time and resources by data scientists. This is particularly true for Type 1 Diabetes (T1D), one of the most intensely studied common childhood diseases. Intense investigation of the contribution of pancreatic β-islet and T-lymphocytes in T1D has been made. However, genetic contributions from B-lymphocytes, which are known to play a role in a subset of T1D patients, remain relatively understudied. We have addressed this issue through the creation of Biomedical Data Commons (BMDC), a knowledge graph that integrates data from multiple sources into a single queryable format. This increases the speed of analysis by multiple orders of magnitude. We develop a pipeline using B-lymphocyte multi-dimensional epigenome and connectome data and deploy BMDC to assess genetic variants in the context of Type 1 Diabetes (T1D). Pipeline-identified variants are primarily common, non-coding, poorly conserved, and are of unknown clinical significance. While variants and their chromatin connectivity are cell-type specific, they are associated with well-studied disease genes in T-lymphocytes. Candidates include established variants in the HLA-DQB1 and HLA-DRB1 and IL2RA loci that have previously been demonstrated to protect against T1D in humans and mice providing validation for this method. Others are included in the well-established T1D GRS2 genetic risk scoring method. More intriguingly, other prioritized variants are completely novel and form the basis for future mechanistic and clinical validation studies The BMDC community-based platform can be expanded and repurposed to increase the accessibility, reproducibility, and productivity of biomedical information for diverse applications including the prioritization of cell type-specific disease alleles from complex phenotypes.

Cytoplasmic long noncoding RNAs are differentially regulated and translated during human neuronal differentiation

The expression of long noncoding RNAs is highly enriched in the human nervous system. However, the function of neuronal lncRNAs in the cytoplasm and their potential translation remains poorly understood. Here we performed Poly-Ribo-Seq to understand the interaction of lncRNAs with the translation machinery and the functional consequences during neuronal differentiation of human SH-SY5Y cells. We discovered 237 cytoplasmic lncRNAs up-regulated during early neuronal differentiation, 58%-70% of which are associated with polysome translation complexes. Among these polysome-associated lncRNAs, we find 45 small ORFs to be actively translated, 17 specifically upon differentiation. Fifteen of 45 of the translated lncRNA-smORFs exhibit sequence conservation within Hominidea, suggesting they are under strong selective constraint in this clade. The profiling of publicly available data sets revealed that 8/45 of the translated lncRNAs are dynamically expressed during human brain development, and 22/45 are associated with cancers of the central nervous system. One translated lncRNA we discovered is LINC01116, which is induced upon differentiation and contains an 87 codon smORF exhibiting increased ribosome profiling signal upon differentiation. The resulting LINC01116 peptide localizes to neurites. Knockdown of LINC01116 results in a significant reduction of neurite length in differentiated cells, indicating it contributes to neuronal differentiation. Our findings indicate cytoplasmic lncRNAs interact with translation complexes, are a noncanonical source of novel peptides, and contribute to neuronal function and disease. Specifically, we demonstrate a novel functional role for LINC01116 during human neuronal differentiation.

lncExplore: a database of pan-cancer analysis and systematic functional annotation for lncRNAs from RNA-sequencing data

Over the past few years, with the rapid growth of deep-sequencing technology and the development of computational prediction algorithms, a large number of long non-coding RNAs (lncRNAs) have been identified in various types of human cancers. Therefore, it has become critical to determine how to properly annotate the potential function of lncRNAs from RNA-sequencing (RNA-seq) data and arrange the robust information and analysis into a useful system readily accessible by biological and clinical researchers. In order to produce a collective interpretation of lncRNA functions, it is necessary to integrate different types of data regarding the important functional diversity and regulatory role of these lncRNAs. In this study, we utilized transcriptomic sequencing data to systematically observe and identify lncRNAs and their potential functions from 5034 The Cancer Genome Atlas RNA-seq datasets covering 24 cancers. Then, we constructed the 'lncExplore' database that was developed to comprehensively integrate various types of genomic annotation data for collective interpretation. The distinctive features in our lncExplore database include (i) novel lncRNAs verified by both coding potential and translation efficiency score, (ii) pan-cancer analysis for studying the significantly aberrant expression across 24 human cancers, (iii) genomic annotation of lncRNAs, such as cis-regulatory information and gene ontology, (iv) observation of the regulatory roles as enhancer RNAs and competing endogenous RNAs and (v) the findings of the potential lncRNA biomarkers for the user-interested cancers by integrating clinical information and disease specificity score. The lncExplore database is to our knowledge the first public lncRNA annotation database providing cancer-specific lncRNA expression profiles for not only known but also novel lncRNAs, enhancer RNAs annotation and clinical analysis based on pan-cancer analysis. lncExplore provides a more complete pathway to highly efficient, novel and more comprehensive translation of laboratory discoveries into the clinical context and will assist in reinterpreting the biological regulatory function of lncRNAs in cancer research. Database URL http://lncexplore.bmi.nycu.edu.tw.

Systematic identification of non-canonical transcription factor motifs

Sequence-specific transcription factors (TFs) recognize motifs of related nucleotide sequences at their DNA binding sites. Upon binding at these sites, TFs regulate critical molecular processes such as gene expression. It is widely assumed that a TF recognizes a single “canonical” motif, although recent studies have identified additional “non-canonical” motifs for some TFs. A comprehensive approach to identify non-canonical DNA binding motifs and the functional importance of those motifs’ matches in the human genome is necessary for fully understanding the mechanisms of TF-regulated molecular processes in human cells. To address this need, we developed a statistical pipeline for in vitro HT-SELEX data that identifies and characterizes the distributions of non-canonical TF motifs in a stringent manner. Analyzing ~170 human TFs’ HT-SELEX data, we found non-canonical motifs for 19 TFs (11%). These non-canonical motifs occur independently of the TFs’ canonical motifs. Non-canonical motif occurrences in the human genome show similar evolutionary conservation to canonical motif occurrences, explain TF binding in locations without canonical motifs, and occur within gene promoters and epigenetically marked regulatory sequences in human cell lines and tissues. Our approach and collection of non-canonical motifs expand current understanding of functionally relevant DNA binding sites for human TFs.

ACE2 Netlas: In silico Functional Characterization and Drug-Gene Interactions of ACE2 Gene Network to Understand Its Potential Involvement in COVID-19 Susceptibility

Angiotensin-converting enzyme-2 (ACE2) receptor has been identified as the key adhesion molecule for the transmission of the SARS-CoV-2. However, there is no evidence that human genetic variation in ACE2 is singularly responsible for COVID-19 susceptibility. Therefore, we performed an integrative multi-level characterization of genes that interact with ACE2 (ACE2-gene network) for their statistically enriched biological properties in the context of COVID-19. The phenome-wide association of 51 genes including ACE2 with 4,756 traits categorized into 26 phenotype categories, showed enrichment of immunological, respiratory, environmental, skeletal, dermatological, and metabolic domains (p < 4e-4). Transcriptomic regulation of ACE2-gene network was enriched for tissue-specificity in kidney, small intestine, and colon (p < 4.7e-4). Leveraging the drug-gene interaction database we identified 47 drugs, including dexamethasone and spironolactone, among others. Considering genetic variants within ± 10 kb of ACE2-network genes we identified miRNAs whose binding sites may be altered as a consequence of genetic variation. The identified miRNAs revealed statistical over-representation of inflammation, aging, diabetes, and heart conditions. The genetic variant associations in RORA, SLC12A6, and SLC6A19 genes were observed in genome-wide association study (GWAS) of COVID-19 susceptibility. We also report the GWAS-identified variant in 3p21.31 locus, serves as trans-QTL for RORA and RORC genes. Overall, functional characterization of ACE2-gene network highlights several potential mechanisms in COVID-19 susceptibility. The data can also be accessed at https://gpwhiz.github.io/ACE2Netlas/.

Developmental and evolutionary dynamics of cis-regulatory elements in mouse cerebellar cells

Organ development is orchestrated by cell- and time-specific gene regulatory networks. In this study, we investigated the regulatory basis of mouse cerebellum development from early neurogenesis to adulthood. By acquiring snATAC-seq (single-nucleus assay for transposase accessible chromatin using sequencing) profiles for ~90,000 cells spanning 11 stages, we mapped cerebellar cell types and identified candidate cis-regulatory elements (CREs). We detected extensive spatiotemporal heterogeneity among progenitor cells and a gradual divergence in the regulatory programs of cerebellar neurons during differentiation. Comparisons to vertebrate genomes and snATAC-seq profiles for ∼20,000 cerebellar cells from the marsupial opossum revealed a shared decrease in CRE conservation during development and differentiation as well as differences in constraint between cell types. Our work delineates the developmental and evolutionary dynamics of gene regulation in cerebellar cells and provides insights into mammalian organ development.

Neuropeptide S receptor 1 is a nonhormonal treatment target in endometriosis

Endometriosis is a common chronic inflammatory condition causing pelvic pain and infertility in women, with limited treatment options and 50% heritability. We leveraged genetic analyses in two species with spontaneous endometriosis, humans and the rhesus macaque, to uncover treatment targets. We sequenced DNA from 32 human families contributing to a genetic linkage signal on chromosome 7p13-15 and observed significant overrepresentation of predicted deleterious low-frequency coding variants in NPSR1, the gene encoding neuropeptide S receptor 1, in cases (predominantly stage III/IV) versus controls (P = 7.8 × 10^-4). Significant linkage to the region orthologous to human 7p13-15 was replicated in a pedigree of 849 rhesus macaques (P = 0.0095). Targeted association analyses in 3194 surgically confirmed, unrelated cases and 7060 controls revealed that a common insertion/deletion variant, rs142885915, was significantly associated with stage III/IV endometriosis (P = 5.2 × 10^-5; odds ratio, 1.23; 95% CI, 1.09 to 1.39). Immunohistochemistry, qRT-PCR, and flow cytometry experiments demonstrated that NPSR1 was expressed in glandular epithelium from eutopic and ectopic endometrium, and on monocytes in peritoneal fluid. The NPSR1 inhibitor SHA 68R blocked NPSR1-mediated signaling, proinflammatory TNF-α release, and monocyte chemotaxis in vitro (P < 0.01), and led to a significant reduction of inflammatory cell infiltrate and abdominal pain (P < 0.05) in a mouse model of peritoneal inflammation as well as in a mouse model of endometriosis. We conclude that the NPSR1/NPS system is a genetically validated, nonhormonal target for the treatment of endometriosis with likely increased relevance to stage III/IV disease.

TERA-Seq: true end-to-end sequencing of native RNA molecules for transcriptome characterization

Direct sequencing of single, native RNA molecules through nanopores has a strong potential to transform research in all aspects of RNA biology and clinical diagnostics. The existing platform from Oxford Nanopore Technologies is unable to sequence the very 5′ ends of RNAs and is limited to polyadenylated molecules. Here, we develop True End-to-end RNA Sequencing (TERA-Seq), a platform that addresses these limitations, permitting more thorough transcriptome characterization. TERA-Seq describes both poly- and non-polyadenylated RNA molecules and accurately identifies their native 5′ and 3′ ends by ligating uniquely designed adapters that are sequenced along with the transcript. We find that capped, full-length mRNAs in human cells show marked variation of poly(A) tail lengths at the single molecule level. We report prevalent capping downstream of canonical transcriptional start sites in otherwise fully spliced and polyadenylated molecules. We reveal RNA processing and decay at single molecule level and find that mRNAs decay cotranslationally, often from their 5′ ends, while frequently retaining poly(A) tails. TERA-Seq will prove useful in many applications where true end-to-end direct sequencing of single, native RNA molecules and their isoforms is desirable.

ASL expression in ALDH1A1+ neurons in the substantia nigra metabolically contributes to neurodegenerative phenotype

Argininosuccinate lyase (ASL) is essential for the NO-dependent regulation of tyrosine hydroxylase (TH) and thus for catecholamine production. Using a conditional mouse model with loss of ASL in catecholamine neurons, we demonstrate that ASL is expressed in dopaminergic neurons in the substantia nigra pars compacta, including the ALDH1A1⁺ subpopulation that is pivotal for the pathogenesis of Parkinson disease (PD). Neuronal loss of ASL results in catecholamine deficiency, in accumulation and formation of tyrosine aggregates, in elevation of α-synuclein, and phenotypically in motor and cognitive deficits. NO supplementation rescues the formation of aggregates as well as the motor deficiencies. Our data point to a potential metabolic link between accumulations of tyrosine and seeding of pathological aggregates in neurons as initiators for the pathological processes involved in neurodegeneration. Hence, interventions in tyrosine metabolism via regulation of NO levels may be therapeutic beneficial for the treatment of catecholamine-related neurodegenerative disorders.

Whole-genome methylation analysis of testicular germ cells from cryptozoospermic men points to recurrent and functionally relevant DNA methylation changes

BACKGROUND

Several studies have reported an association between male infertility and aberrant sperm DNA methylation patterns, in particular in imprinted genes. In a recent investigation based on whole methylome and deep bisulfite sequencing, we have not found any evidence for such an association, but have demonstrated that somatic DNA contamination and genetic variation confound methylation studies in sperm of severely oligozoospermic men. To find out whether testicular germ cells (TGCs) of such patients might carry aberrant DNA methylation, we compared the TGC methylomes of four men with cryptozoospermia (CZ) and four men with obstructive azoospermia, who had normal spermatogenesis and served as controls (CTR).

RESULTS

There was no difference in DNA methylation at the whole genome level or at imprinted regions between CZ and CTR samples. However, using stringent filters to identify group-specific methylation differences, we detected 271 differentially methylated regions (DMRs), 238 of which were hypermethylated in CZ (binominal test, p < 2.2 × 10-16). The DMRs were enriched for distal regulatory elements (p = 1.0 × 10-6) and associated with 132 genes, 61 of which are differentially expressed at various stages of spermatogenesis. Almost all of the 67 DMRs associated with the 61 genes (94%) are hypermethylated in CZ (63/67, p = 1.107 × 10-14). As judged by single-cell RNA sequencing, 13 DMR-associated genes, which are mainly expressed during meiosis and spermiogenesis, show a significantly different pattern of expression in CZ patients. In four of these genes, the promoter is hypermethylated in CZ men, which correlates with a lower expression level in these patients. In the other nine genes, eight of which downregulated in CZ, germ cell-specific enhancers may be affected.

CONCLUSIONS

We found that impaired spermatogenesis is associated with DNA methylation changes in testicular germ cells at functionally relevant regions of the genome. We hypothesize that the described DNA methylation changes may reflect or contribute to premature abortion of spermatogenesis and therefore not appear in the mature, motile sperm.

Androgen signaling connects short isoform production to breakpoint formation at Ewing sarcoma breakpoint region 1

Ewing sarcoma breakpoint region 1 (EWSR1) encodes a multifunctional protein that can cooperate with the transcription factor ERG to promote prostate cancer. The EWSR1 gene is also commonly involved in oncogenic gene rearrangements in Ewing sarcoma. Despite the cancer relevance of EWSR1, its regulation is poorly understood. Here we find that in prostate cancer, androgen signaling upregulates a 5′ EWSR1 isoform by promoting usage of an intronic polyadenylation site. This isoform encodes a cytoplasmic protein that can strongly promote cell migration and clonogenic growth. Deletion of an Androgen Receptor (AR) binding site near the 5′ EWSR1 polyadenylation site abolished androgen-dependent upregulation. This polyadenylation site is also near the Ewing sarcoma breakpoint hotspot, and androgen signaling promoted R-loop and breakpoint formation. RNase H overexpression reduced breakage and 5′ EWSR1 isoform expression suggesting an R-loop dependent mechanism. These data suggest that androgen signaling can promote R-loops internal to the EWSR1 gene leading to either early transcription termination, or breakpoint formation.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

Development of a Prognostic Nomogram for Acute Myeloid Leukemia on IGHD Gene Family

Purpose

Acute myeloid leukaemia (AML) is a common haematological disease in adults. The overall survival (OS) remains unsatisfactory. It is critical to identify potential prognostic biomarkers and develop a nomogram that predicts overall survival in patients with AML.

Patients and Methods

We used gene expression dataset and clinical data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to identify differential expression analysis, survival analysis, and prognostic value of IGHD gene family (IGHDs) in AML patients. A risk score model was built through Lasso analysis and multivariate Cox regression. We also developed a nomogram and evaluated its accuracy with Harrell’s Harmony Index (C-index) and calibration curve. Last, the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database was used for external validation.

Results

IGHD1-20 mRNA expression level was an independent prognostic factor for patients with AML by multivariate analysis. After Lasso analysis and multivariate Cox regression, we constructed a 3-gene model (IGHD1-1, IGHD1-20, IGHD3-16) associated with OS in AML. Risk score and age were validated as independent risk factors for prognosis and were used to build a nomogram. The C index and calibration curve results show that its ability to predict 1-year, 3-year and 5-year overall survival is accurate.

Conclusion

The mRNA level of IGHDs was increased in AML patients. IGHD1-20 was an independent risk factor for OS in AML patients. The IGHDs risk model (IGHD1-1, IGHD1-20, IGHD3-16) relates to the OS of AML patients. The nomogram, including risk score and age, can conveniently and effectively predict the overall survival rate of patients.

Effect of 5-fluorouracil on mRNA expression of drug metabolizing enzyme and transporter genes in human hepatoma cell lines

Fluoropyrimidines such as 5-fluorouracil (5-FU) are well known to have drug-drug interactions with anticoagulant medications such as warfarin. This study investigated the mRNA expression of pharmacokinetic (PK)-related genes in response to 5-FU using the hepatocarcinoma cell lines after examining relevant gene expression via RNA sequencing. We used HepaRG cells for 5-FU treatment analysis because these cells displayed PK-related gene expression. 5-FU exposure significantly reduced cytochrome P450 3A4 (CYP3A4) mRNA expression. Additionally, the mRNA expression of nuclear receptor subfamily 1 group I member 2 (also known as pregnane X receptor), a nuclear receptor transcription factor that promotes the expression of many CYP genes, was also decreased in HepaRG cells following 5-FU treatment. The mRNA expressions of the CYP2B6 and ATP-binding cassette transporter genes were decreased after 5-FU treatment. This study revealed that 5-FU treatment reduced PK-related gene expression in HepaRG cells. These findings should be useful for further drug-drug interaction research.

Mitonuclear conflict in a macaque species exhibiting phylogenomic discordance

Speciation and hybridization are intertwined processes in the study of evolution. Hybridization between sufficiently diverged populations can result in genomic conflict within offspring, causing reduced viability and fertility, thus increasing divergence between populations. Conflicts between mitochondrial and nuclear genes are increasingly found to play a role in this process in various systems. We examine the possibility of this conflict in the bear macaque, Macaca arctoides (Primates: Cercopithecidae), a primate species exhibiting mitonuclear discordance due to extensive hybridization with species in the sinica and fascicularis groups. Here, divergence, introgression and natural selection of mitonuclear genes (N = 160) relative to nuclear control genes (N = 144) were analysed to determine whether there are evolutionary processes involved in resolving the potential conflict caused by mitonuclear discordance. Nucleotide divergence of mitonuclear genes is increased relative to control nuclear genes between M. arctoides and the species sharing its nuclear ancestry (p = 0.007), consistent with genetic conflict. However, measures of introgression and selection do not identify large-scale co-introgression or co-evolution as means to resolve mitonuclear conflict. Nonetheless, mitochondrial tRNA synthetases stand out in analyses using dN/dS and extended branch lengths as potential targets of selection. The methodology implemented provides a framework that can be used to examine the effects of mitonuclear co-introgression and co-evolution on a genomic scale in a variety of systems.

Investigation of LINC00493/SMIM26 Gene Suggests Its Dual Functioning at mRNA and Protein Level

The amount of human long noncoding RNA (lncRNA) genes is comparable to protein-coding; however, only a small number of lncRNAs are functionally annotated. Previously, it was shown that lncRNAs can participate in many key cellular processes, including regulation of gene expression at transcriptional and post-transcriptional levels. The lncRNA genes can contain small open reading frames (sORFs), and recent studies demonstrated that some of the resulting short proteins could play an important biological role. In the present study, we investigate the widely expressed lncRNA LINC00493. We determine the structure of the LINC00493 transcript, its cell localization and influence on cell physiology. Our data demonstrate that LINC00493 has an influence on cell viability in a cell-type-specific manner. Furthermore, it was recently shown that LINC00493 has a sORF that is translated into small protein SMIM26. The results of our knockdown and overexpression experiments suggest that both LINC00493/SMIM26 transcript and protein affect cell viability, but in the opposite manner.