RefSeq: an update on mammalian reference sequences

The Clinical Genome and Ancestry Report: An interactive web application for prioritizing clinically implicated variants from genome sequencing data with ancestry composition

Genome sequencing is positioned as a routine clinical work-up for diverse clinical conditions. A commonly used approach to highlight candidate variants with potential clinical implication is to search over locus- and gene-centric knowledge databases. Most web-based applications allow a federated query across diverse databases for a single variant; however, sifting through a large number of genomic variants with combination of filtering criteria is a substantial challenge. Here we describe the Clinical Genome and Ancestry Report (CGAR), an interactive web application developed to follow clinical interpretation workflows by organizing variants into seven categories: (1) reported disease-associated variants, (2) rare- and high-impact variants in putative disease-associated genes, (3) secondary findings that the American College of Medical Genetics and Genomics recommends reporting back to patients, (4) actionable pharmacogenomic variants, (5) focused reports for candidate genes, (6) de novo variant candidates for trio analysis, and (7) germline and somatic variants implicated in cancer risk, diagnosis, treatment and prognosis. For each variant, a comprehensive list of external links to variant-centric and phenotype databases are provided. Furthermore, genotype-derived ancestral composition is used to highlight allele frequencies from a matched population since some disease-associated variants show a wide variation between populations. CGAR is an open-source software and is available at https://tom.tch.harvard.edu/apps/cgar/.

Resolving the genetic paradox of invasions: Preadapted genomes and postintroduction hybridization of bigheaded carps in the Mississippi River Basin

The genetic paradox of biological invasions is complex and multifaceted. In particular, the relative role of disparate propagule sources and genetic adaptation through postintroduction hybridization has remained largely unexplored. To add resolution to this paradox, we investigate the genetic architecture responsible for the invasion of two invasive Asian carp species, bighead carp (Hypophthalmichthys nobilis) and silver carp (H. molitrix) (bigheaded carps) that experience extensive hybridization in the Mississippi River Basin (MRB). We sequenced the genomes of bighead and silver carps (~1.08G bp and ~1.15G bp, respectively) and their hybrids collected from the MRB. We found moderate-to-high heterozygosity in bighead (0.0021) and silver (0.0036) carps, detected significantly higher dN/dS ratios of single-copy orthologous genes in bigheaded carps versus 10 other species of fish, and identified genes in both species potentially associated with environmental adaptation and other invasion-related traits. Additionally, we observed a high genomic similarity (96.3% in all syntenic blocks) between bighead and silver carps and over 90% embryonic viability in their experimentally induced hybrids. Our results suggest intrinsic genomic features of bigheaded carps, likely associated with life history traits that presumably evolved within their native ranges, might have facilitated their initial establishment of invasion, whereas ex-situ interspecific hybridization between the carps might have promoted their range expansion. This study reveals an alternative mechanism that could resolve one of the genetic paradoxes in biological invasions and provides invaluable genomic resources for applied research involving bigheaded carps.

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge

Precise identification of causative variants from whole-genome sequencing data, including both coding and noncoding variants, is challenging. The Critical Assessment of Genome Interpretation 5 SickKids clinical genome challenge provided an opportunity to assess our ability to extract such information. Participants in the challenge were required to match each of the 24 whole-genome sequences to the correct phenotypic profile and to identify the disease class of each genome. These are all rare disease cases that have resisted genetic diagnosis in a state-of-the-art pipeline. The patients have a range of eye, neurological, and connective-tissue disorders. We used a gene-centric approach to address this problem, assigning each gene a multiphenotype-matching score. Mutations in the top-scoring genes for each phenotype profile were ranked on a 6-point scale of pathogenicity probability, resulting in an approximately equal number of top-ranked coding and noncoding candidate variants overall. We were able to assign the correct disease class for 12 cases and the correct genome to a clinical profile for five cases. The challenge assessor found genes in three of these five cases as likely appropriate. In the postsubmission phase, after careful screening of the genes in the correct genome, we identified additional potential diagnostic variants, a high proportion of which are noncoding.

ORSO (Online Resource for Social Omics): A data-driven social network connecting scientists to genomics datasets

High-throughput sequencing has become ubiquitous in biomedical sciences. As new technologies emerge and sequencing costs decline, the diversity and volume of available data increases exponentially, and successfully navigating the data becomes more challenging. Though datasets are often hosted by public repositories, scientists must rely on inconsistent annotation to identify and interpret meaningful data. Moreover, the experimental heterogeneity and wide-ranging quality of high-throughput biological data means that even data with desired cell lines, tissue types, or molecular targets may not be readily interpretable or integrated. We have developed ORSO (Online Resource for Social Omics) as an easy-to-use web application to connect life scientists with genomics data. In ORSO, users interact within a data-driven social network, where they can favorite datasets and follow other users. In addition to more than 30,000 datasets hosted from major biomedical consortia, users may contribute their own data to ORSO, facilitating its discovery by other users. Leveraging user interactions, ORSO provides a novel recommendation system to automatically connect users with hosted data. In addition to social interactions, the recommendation system considers primary read coverage information and annotated metadata. Similarities used by the recommendation system are presented by ORSO in a graph display, allowing exploration of dataset associations. The topology of the network graph reflects established biology, with samples from related systems grouped together. We tested the recommendation system using an RNA-seq time course dataset from differentiation of embryonic stem cells to cardiomyocytes. The ORSO recommendation system correctly predicted early data point sources as embryonic stem cells and late data point sources as heart and muscle samples, resulting in recommendation of related datasets. By connecting scientists with relevant data, ORSO provides a critical new service that facilitates wide-ranging research interests.

New sequencing technologies have rapidly transformed biomedical research. Public data repositories now contain millions of datasets, which have the potential to accelerate and bolster research projects. However, the sheer magnitude of available data makes navigation difficult. We created ORSO (Online Resource for Social Omics) to address these challenges. ORSO is a social network where entries are not status updates or tweets, but biological datasets. Users may add their own data to ORSO, joining 30,000 validated datasets that are already hosted, and other users may find these data through intuitive search functions and informative analytics. Users can then favorite datasets relevant to their interests or follow contributing users. ORSO also uses a recommendation system like those used on commercial websites to automatically recommend data to users based on user interactions and dataset similarities. By making data more accessible and by connecting users to relevant data, we anticipate that ORSO will be an important resource for scientists. ORSO may be the first of many applications that use methods originating in social media and ecommerce to enhance and further research projects in the life sciences.

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.

Beyond mass spectrometry, the next step in proteomics

Proteins can be the root cause of a disease, and they can be used to cure it. The need to identify these critical actors was recognized early (1951) by Sanger; the first biopolymer sequenced was a peptide, insulin. With the advent of scalable, single-molecule DNA sequencing, genomics and transcriptomics have since propelled medicine through improved sensitivity and lower costs, but proteomics has lagged behind. Currently, proteomics relies mainly on mass spectrometry (MS), but instead of truly sequencing, it classifies a protein and typically requires about a billion copies of a protein to do it. Here, we offer a survey that illuminates a few alternatives with the brightest prospects for identifying whole proteins and displacing MS for sequencing them. These alternatives all boast sensitivity superior to MS and promise to be scalable and seem to be adaptable to bioinformatics tools for calling the sequence of amino acids that constitute a protein.

Evaluation of polymorphisms in microRNA-binding sites and pancreatic cancer risk in Chinese population

As promising biomarkers and therapy targets, microRNAs (miRNAs) are involved in various physiological and tumorigenic processes. Genetic variants in miRNA‐binding sites can lead to dysfunction of miRNAs and contribute to disease. However, systematic investigation of the miRNA‐related single nucleotide polymorphisms (SNPs) for pancreatic cancer (PC) risk remains elusive. We performed integrative bioinformatics analyses to select 31 SNPs located in miRNA‐target binding sites using the miRNASNP v2.0, a solid database providing miRNA‐related SNPs for genetic research, and investigated their associations with risk of PC in two large case‐control studies totally including 1847 cases and 5713 controls. We observed that the SNP rs3802266 is significantly associated with increased risk of PC (odds ratio (OR) = 1.21, 95% confidence intervals (CI) = 1.11‐1.31, P = 1.29E‐05). Following luciferase reporter gene assays show that rs3802266‐G creates a stronger binding site for miR‐181a‐2‐3p in 3′ untranslated region (3′UTR) of the gene ZHX2. Expression quantitative trait loci (eQTL) analysis suggests that ZHX2 expression is lower in individuals carrying rs3802266‐G with increased PC risk. In conclusion, our findings highlight the involvement of miRNA‐binding SNPs in PC susceptibility and provide new clues for PC carcinogenesis.

Rapid molecular evolution of pain insensitivity in multiple African rodents

Noxious substances, called algogens, cause pain and are used as defensive weapons by plants and stinging insects. We identified four previously unknown instances of algogen-insensitivity by screening eight African rodent species related to the naked mole-rat with the painful substances capsaicin, acid (hydrogen chloride, pH 3.5), and allyl isothiocyanate (AITC). Using RNA sequencing, we traced the emergence of sequence variants in transduction channels, like transient receptor potential channel TRPA1 and voltage-gated sodium channel Na_v1.7, that accompany algogen insensitivity. In addition, the AITC-insensitive highveld mole-rat exhibited overexpression of the leak channel NALCN (sodium leak channel, nonselective), ablating AITC detection by nociceptors. These molecular changes likely rendered highveld mole-rats immune to the stings of the Natal droptail ant. Our study reveals how evolution can be used as a discovery tool to find molecular mechanisms that shut down pain.

Assessing concordance among human, in silico predictions and functional assays on genetic variant classification

MOTIVATION

A variety of in silico tools have been developed and frequently used to aid high-throughput rapid variant classification, but their performances vary, and their ability to classify variants of uncertain significance were not systemically assessed previously due to lack of validation data. This has been changed recently by advances of functional assays, where functional impact of genetic changes can be measured in single-nucleotide resolution using saturation genome editing (SGE) assay.

RESULTS

We demonstrated the neural network model AIVAR (Artificial Intelligent VARiant classifier) was highly comparable to human experts on multiple verified datasets. Although highly accurate on known variants, AIVAR together with CADD and PhyloP showed non-significant concordance with SGE function scores. Moreover, our results indicated that neural network model trained from functional assay data may not produce accurate prediction on known variants.

AVAILABILITY AND IMPLEMENTATION

All source code of AIVAR is deposited and freely available at https://github.com/TopGene/AIvar.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Environmental Exposures and Inner- and Intercity Traffic Flows of the Metro System May Contribute to the Skin Microbiome and Resistome

The skin functions as the primary interface between the human body and the external environment. To understand how the microbiome varies within urban mass transit and influences the skin microbiota, we profiled the human palm microbiome after contact with handrails within the Hong Kong Mass Transit Railway (MTR) system. Intraday sampling time was identified as the primary determinant of the variation and recurrence of the community composition, whereas human-associated species and clinically important antibiotic resistance genes (ARGs) were captured as p.m. signatures. Line-specific signatures were notably correlated with line-specific environmental exposures and city characteristics. The sole cross-border line appeared as an outlier in most analyses and showed high relative abundance and a significant intraday increment of clinically important ARGs (24.1%), suggesting potential cross-border ARG transmission, especially for tetracycline and vancomycin resistance. Our study provides an important reference for future public health strategies to mitigate intracity and cross-border pathogen and ARG transmission.

Codon use and aversion is largely phylogenetically conserved across the tree of life

Using parsimony, we analyzed codon usages across 12,337 species and 25,727 orthologous genes to rank specific genes and codons according to their phylogenetic signal. We examined each codon within each ortholog to determine the codon usage for each species. In total, 890,814 codons were parsimony informative. Next, we compared species that used a codon with species that did not use the codon. We assessed each codon's congruence with species relationships provided in the Open Tree of Life (OTL) and determined the statistical probability of observing these results by random chance. We determined that 25,771 codons had no parallelisms or reversals when mapped to the OTL. Codon usages from orthologous genes spanning many species were 1109× more likely to be congruent with species relationships in the OTL than would be expected by random chance. Using the OTL as a reference, we show that codon usage is phylogenetically conserved within orthologous genes in archaea, bacteria, plants, mammals, and other vertebrates. We also show how to use our provided framework to test different tree hypotheses by confirming the placement of turtles as sister taxa to archosaurs.

An expanded landscape of human long noncoding RNA

Long noncoding RNAs (lncRNAs) are emerging as key regulators of multiple essential biological processes involved in physiology and pathology. By analyzing the largest compendium of 14,166 samples from normal and tumor tissues, we significantly expand the landscape of human long noncoding RNA with a high-quality atlas: RefLnc (Reference catalog of LncRNA). Powered by comprehensive annotation across multiple sources, RefLnc helps to pinpoint 275 novel intergenic lncRNAs correlated with sex, age or race as well as 369 novel ones associated with patient survival, clinical stage, tumor metastasis or recurrence. Integrated in a user-friendly online portal, the expanded catalog of human lncRNAs provides a valuable resource for investigating lncRNA function in both human biology and cancer development.

eIF4A2 drives repression of translation at initiation by Ccr4-Not through purine-rich motifs in the 5'UTR

BACKGROUND

Regulation of the mRNA life cycle is central to gene expression control and determination of cell fate. miRNAs represent a critical mRNA regulatory mechanism, but despite decades of research, their mode of action is still not fully understood.

RESULTS

Here, we show that eIF4A2 is a major effector of the repressive miRNA pathway functioning via the Ccr4-Not complex. We demonstrate that while DDX6 interacts with Ccr4-Not, its effects in the mechanism are not as pronounced. Through its interaction with the Ccr4-Not complex, eIF4A2 represses mRNAs at translation initiation. We show evidence that native eIF4A2 has similar RNA selectivity to chemically inhibited eIF4A1. eIF4A2 exerts its repressive effect by binding purine-rich motifs which are enriched in the 5'UTR of target mRNAs directly upstream of the AUG start codon.

CONCLUSIONS

Our data support a model whereby purine motifs towards the 3' end of the 5'UTR are associated with increased ribosome occupancy and possible uORF activation upon eIF4A2 binding.

Immunosignature Screening for Multiple Cancer Subtypes Based on Expression Rule

Liquid biopsy (i.e., fluid biopsy) involves a series of clinical examination approaches. Monitoring of cancer immunological status by the “immunosignature” of patients presents a novel method for tumor-associated liquid biopsy. The major work content and the core technological difficulties for the monitoring of cancer immunosignature are the recognition of cancer-related immune-activating antigens by high-throughput screening approaches. Currently, one key task of immunosignature-based liquid biopsy is the qualitative and quantitative identification of typical tumor-specific antigens. In this study, we reused two sets of peptide microarray data that detected the expression level of potential antigenic peptides derived from tumor tissues to avoid the detection differences induced by chip platforms. Several machine learning algorithms were applied on these two sets. First, the Monte Carlo Feature Selection (MCFS) method was used to analyze features in two sets. A feature list was obtained according to the MCFS results on each set. Second, incremental feature selection method incorporating one classification algorithm (support vector machine or random forest) followed to extract optimal features and construct optimal classifiers. On the other hand, the repeated incremental pruning to produce error reduction, a rule learning algorithm, was applied on key features yielded by the MCFS method to extract quantitative rules for accurate cancer immune monitoring and pathologic diagnosis. Finally, obtained key features and quantitative rules were extensively analyzed.

An Overview of the Intrinsic Role of Citrullination in Autoimmune Disorders

A protein undergoes many types of posttranslation modification. Citrullination is one of these modifications, where an arginine amino acid is converted to a citrulline amino acid. This process depends on catalytic enzymes such as peptidylarginine deiminase enzymes (PADs). This modification leads to a charge shift, which affects the protein structure, protein-protein interactions, and hydrogen bond formation, and it may cause protein denaturation. The irreversible citrullination reaction is not limited to a specific protein, cell, or tissue. It can target a wide range of proteins in the cell membrane, cytoplasm, nucleus, and mitochondria. Citrullination is a normal reaction during cell death. Apoptosis is normally accompanied with a clearance process via scavenger cells. A defect in the clearance system either in terms of efficiency or capacity may occur due to massive cell death, which may result in the accumulation and leakage of PAD enzymes and the citrullinated peptide from the necrotized cell which could be recognized by the immune system, where the immunological tolerance will be avoided and the autoimmune disorders will be subsequently triggered. The induction of autoimmune responses, autoantibody production, and cytokines involved in the major autoimmune diseases will be discussed.

Realizing the potential of full-length transcriptome sequencing

Long-read sequencing holds great potential for transcriptome analysis because it offers researchers an affordable method to annotate the transcriptomes of non-model organisms. This, in turn, will greatly benefit future work on less-researched organisms like unicellular eukaryotes that cannot rely on large consortia to generate these transcriptome annotations. However, to realize this potential, several remaining molecular and computational challenges will have to be overcome. In this review, we have outlined the limitations of short-read sequencing technology and how long-read sequencing technology overcomes these limitations. We have also highlighted the unique challenges still present for long-read sequencing technology and provided some suggestions on how to overcome these challenges going forward. This article is part of a discussion meeting issue 'Single cell ecology'.

Comprehensive Identification and Characterization of Human Secretome Based on Integrative Proteomic and Transcriptomic Data

Secreted proteins (SPs) play important roles in diverse important biological processes; however, a comprehensive and high-quality list of human SPs is still lacking. Here we identified 6,943 high-confidence human SPs (3,522 of them are novel) based on 330,427 human proteins derived from databases of UniProt, Ensembl, AceView, and RefSeq. Notably, 6,267 of 6,943 (90.3%) SPs have the supporting evidences from a large amount of mass spectrometry (MS) and RNA-seq data. We found that the SPs were broadly expressed in diverse tissues as well as human body fluid, and a significant portion of them exhibited tissue-specific expression. Moreover, 14 cancer-specific SPs that their expression levels were significantly associated with the patients’ survival of eight different tumors were identified, which could be potential prognostic biomarkers. Strikingly, 89.21% of 6,943 SPs (2,927 novel SPs) contain known protein domains. Those novel SPs we mainly enriched with the known domains regarding immunity, such as Immunoglobulin V-set and C1-set domain. Specifically, we constructed a user-friendly and freely accessible database, SPRomeDB (www.unimd.org/SPRomeDB), to catalog those SPs. Our comprehensive SP identification and characterization gain insights into human secretome and provide valuable resource for future researches.

Exome-chip association analysis of intracranial aneurysms

OBJECTIVE

To investigate to what extent low-frequency genetic variants (with minor allele frequencies <5%) affect the risk of intracranial aneurysms (IAs).

METHODS

One thousand fifty-six patients with IA and 2,097 population-based controls from the Netherlands were genotyped with the Illumina HumanExome BeadChip. After quality control (QC) of samples and single nucleotide variants (SNVs), we conducted a single variant analysis using the Fisher exact test. We also performed the variable threshold (VT) test and the sequence kernel association test (SKAT) at different minor allele count (MAC) thresholds of >5 and >0 to test the hypothesis that multiple variants within the same gene are associated with IA risk. Significant results were tested in a replication cohort of 425 patients with IA and 311 controls, and results of the 2 cohorts were combined in a meta-analysis.

RESULTS

After QC, 995 patients with IA and 2,080 controls remained for further analysis. The single variant analysis comprising 46,534 SNVs did not identify significant loci at the genome-wide level. The gene-based tests showed a statistically significant association for fibulin 2 (FBLN2) (best p = 1 × 10^-6 for the VT test, MAC >5). Associations were not statistically significant in the independent but smaller replication cohort (p > 0.57) but became slightly stronger in a meta-analysis of the 2 cohorts (best p = 4.8 × 10^-7 for the SKAT, MAC ≥1).

CONCLUSION

Gene-based tests indicated an association for FBLN2, a gene encoding an extracellular matrix protein implicated in vascular wall remodeling, but independent validation in larger cohorts is warranted. We did not identify any significant associations for single low-frequency genetic variants.

JustOrthologs: a fast, accurate and user-friendly ortholog identification algorithm

Motivation

Orthologous gene identification is fundamental to all aspects of biology. For example, ortholog identification between species can provide functional insights for genes of unknown function and is a necessary step in phylogenetic inference. Currently, most ortholog identification algorithms require all-versus-all BLAST comparisons, which are time-consuming and memory intensive.

Results

In contrast to existing approaches, JustOrthologs exploits the conservation of gene structure by using the lengths of coding sequence regions and dinucleotide percentages to identify orthologs. In comparison to OrthoMCL, OMA and OrthoFinder, JustOrthologs decreases ortholog identification runtime by more than 96% and achieves comparable precision and recall scores. The computational speedup allowed us to conduct pairwise comparisons of 1197 complete genomes (780 eukaryotes and 417 archaea). We confirmed gene annotations for 384 120 genes, grouped 1 675 415 genes in previously unreported ortholog groups, and identified 51 429 potentially mislabeled genes across 622 843 ortholog groups.

Availability and implementation

JustOrthologs is an open source collaborative software package available in the GitHub repository: https://github.com/ridgelab/JustOrthologs/. All test FASTA files used for comparisons are freely available at https://github.com/ridgelab/JustOrthologs/comparisonFastaFiles/. Reference genomes used in this work are available for download from the NCBI repository: ftp://ftp.ncbi.nih.gov/genomes/.

Supplementary information

Supplementary data are available at Bioinformatics online.

BC200 overexpression contributes to luminal and triple negative breast cancer pathogenesis

Background

Long non coding RNAs (lncRNAs) are RNA molecules longer than 200 nucleotides that are not translated into proteins, but regulate the transcription of genes involved in different cellular processes, including cancer. Epidemiological analyses have demonstrated that parous women have a decreased risk of developing breast cancer in postmenopausal years if they went through a full term pregnancy in their early twenties. We here provide evidence of the role of BC200 in breast cancer and, potentially, in pregnancy’s preventive effect in reducing the lifetime risk of developing breast cancer.

Methods

Transcriptome analysis of normal breast of parous and nulliparous postmenopausal women revealed that several lncRNAs are differentially expressed in the parous breast. RNA sequencing of healthy postmenopausal breast tissue biopsies from eight parous and eight nulliparous women showed that there are 42 novel lncRNAs differentially expressed between these two groups. Screening of several of these 42 lncRNAs by RT-qPCR in different breast cancer cell lines, provided evidence that one in particular, lncEPCAM (more commonly known as BC200), was a strong candidate involved in cancer progression. Proliferation, migration, invasion and xerograph studies confirmed this hypothesis.

Results

The poorly studied oncogenic BC200 was selected to be tested in vitro and in vivo to determine its relevance in breast cancer and also to provide us with an understanding of its role in the increased susceptibility of the nulliparous women to cancer. Our results show that BC200 is upregulated in nulliparous women, and breast cancer cells and tissue. The role of BC200 is not completely understood in any of the breast cancer subtypes. We here provide evidence that BC200 has a role in luminal breast cancer as well as in the triple negative breast cancer subtype.

Conclusion

When overexpressed in luminal and triple negative breast cancer cell lines, BC200 shows increased proliferation, migration, and invasion in vitro. In vivo, overexpression of BC200 increased tumor size. Although treatment for cancer using lncRNAs as targets is in its infancy, the advancement in knowledge and technology to study their relevance in disease could lead to the development of novel treatment and preventive strategies for breast cancer.

Genome-wide quantification of ADAR adenosine-to-inosine RNA editing activity

Adenosine-to-inosine (A-to-I) RNA editing by the adenosine deaminase that acts on RNA (ADAR) enzymes is a common RNA modification, preventing false activation of the innate immune system by endogenous double-stranded RNAs. Methods for quantification of ADAR activity are sought after, due to an increasing interest in the role of ADARs in cancer and autoimmune disorders, as well as attempts to harness the ADAR enzymes for RNA engineering. Here, we present the Alu editing index (AEI), a robust and simple-to-use computational tool devised for this purpose. We describe its properties and demonstrate its superiority to current quantification methods of ADAR activity. The AEI is used to map global editing across a large dataset of healthy human samples and identify putative regulators of ADAR, as well as previously unknown factors affecting the observed Alu editing levels. These should be taken into account in future comparative studies of ADAR activity. The AEI tool is available at https://github.com/a2iEditing/RNAEditingIndexer.

SQuIRE reveals locus-specific regulation of interspersed repeat expression

Transposable elements (TEs) are interspersed repeat sequences that make up much of the human genome. Their expression has been implicated in development and disease. However, TE-derived RNA-seq reads are difficult to quantify. Past approaches have excluded these reads or aggregated RNA expression to subfamilies shared by similar TE copies, sacrificing quantitative accuracy or the genomic context necessary to understand the basis of TE transcription. As a result, the effects of TEs on gene expression and associated phenotypes are not well understood. Here, we present Software for Quantifying Interspersed Repeat Expression (SQuIRE), the first RNA-seq analysis pipeline that provides a quantitative and locus-specific picture of TE expression (https://github.com/wyang17/SQuIRE). SQuIRE is an accurate and user-friendly tool that can be used for a variety of species. We applied SQuIRE to RNA-seq from normal mouse tissues and a Drosophila model of amyotrophic lateral sclerosis. In both model organisms, we recapitulated previously reported TE subfamily expression levels and revealed locus-specific TE expression. We also identified differences in TE transcription patterns relating to transcript type, gene expression and RNA splicing that would be lost with other approaches using subfamily-level analyses. Altogether, our findings illustrate the importance of studying TE transcription with locus-level resolution.

Paternal impacts on development: identification of genomic regions vulnerable to oxidative DNA damage in human spermatozoa

STUDY QUESTION

Do all regions of the paternal genome within the gamete display equivalent vulnerability to oxidative DNA damage?

SUMMARY ANSWER

Oxidative DNA damage is not randomly distributed in mature human spermatozoa but is instead targeted, with particular chromosomes being especially vulnerable to oxidative stress.

WHAT IS KNOWN ALREADY

Oxidative DNA damage is frequently encountered in the spermatozoa of male infertility patients. Such lesions can influence the incidence of de novo mutations in children, yet it remains to be established whether all regions of the sperm genome display equivalent susceptibility to attack by reactive oxygen species.

STUDY DESIGN, SIZE, DURATION

Human spermatozoa obtained from normozoospermic males (n = 8) were split into equivalent samples and subjected to either hydrogen peroxide (H2O2) treatment or vehicle controls before extraction of oxidized DNA using a modified DNA immunoprecipitation (MoDIP) protocol. Specific regions of the genome susceptible to oxidative damage were identified by next-generation sequencing and validated in the spermatozoa of normozoospermic males (n = 18) and in patients undergoing infertility evaluation (n = 14).

PARTICIPANTS/MATERIALS, SETTING, METHODS

Human spermatozoa were obtained from normozoospermic males and divided into two identical samples prior to being incubated with either H2O2 (5 mm, 1 h) to elicit oxidative stress or an equal volume of vehicle (untreated controls). Alternatively, spermatozoa were obtained from fertility patients assessed as having high basal levels of oxidative stress within their spermatozoa. All semen samples were subjected to MoDIP to selectively isolate oxidized DNA, prior to sequencing of the resultant DNA fragments using a next-generation whole-genomic sequencing platform. Bioinformatic analysis was then employed to identify genomic regions vulnerable to oxidative damage, several of which were selected for real-time quantitative PCR (qPCR) validation.

MAIN RESULTS AND THE ROLE OF CHANCE

Approximately 9000 genomic regions, 150–1000 bp in size, were identified as highly vulnerable to oxidative damage in human spermatozoa. Specific chromosomes showed differential susceptibility to damage, with chromosome 15 being particularly sensitive to oxidative attack while the sex chromosomes were protected. Susceptible regions generally lay outside protamine- and histone-packaged domains. Furthermore, we confirmed that these susceptible genomic sites experienced a dramatic (2–15-fold) increase in their burden of oxidative DNA damage in patients undergoing infertility evaluation compared to normal healthy donors.

LIMITATIONS, REASONS FOR CAUTION

The limited number of samples analysed in this study warrants external validation, as do the implications of our findings. Selection of male fertility patients was based on high basal levels of oxidative stress within their spermatozoa as opposed to specific sub-classes of male factor infertility.

WIDER IMPLICATIONS OF THE FINDINGS

The identification of genomic regions susceptible to oxidation in the male germ line will be of value in focusing future analyses into the mutational load carried by children in response to paternal factors such as age, the treatment of male infertility using ART and paternal exposure to environmental toxicants.

STUDY FUNDING/COMPETING INTEREST(S)

Project support was provided by the University of Newcastle’s (UoN) Priority Research Centre for Reproductive Science. M.J.X. was a recipient of a UoN International Postgraduate Research Scholarship. B.N. is the recipient of a National Health and Medical Research Council of Australia Senior Research Fellowship. Authors declare no conflict of interest.

The CLN3 gene and protein: What we know

Background

One of the most important steps taken by Beyond Batten Disease Foundation in our quest to cure juvenile Batten (CLN3) disease is to understand the State of the Science. We believe that a strong understanding of where we are in our experimental understanding of the CLN3 gene, its regulation, gene product, protein structure, tissue distribution, biomarker use, and pathological responses to its deficiency, lays the groundwork for determining therapeutic action plans.

Objectives

To present an unbiased comprehensive reference tool of the experimental understanding of the CLN3 gene and gene product of the same name.

Methods

BBDF compiled all of the available CLN3 gene and protein data from biological databases, repositories of federally and privately funded projects, patent and trademark offices, science and technology journals, industrial drug and pipeline reports as well as clinical trial reports and with painstaking precision, validated the information together with experts in Batten disease, lysosomal storage disease, lysosome/endosome biology.

Results

The finished product is an indexed review of the CLN3 gene and protein which is not limited in page size or number of references, references all available primary experiments, and does not draw conclusions for the reader.

Conclusions

Revisiting the experimental history of a target gene and its product ensures that inaccuracies and contradictions come to light, long‐held beliefs and assumptions continue to be challenged, and information that was previously deemed inconsequential gets a second look. Compiling the information into one manuscript with all appropriate primary references provides quick clues to which studies have been completed under which conditions and what information has been reported. This compendium does not seek to replace original articles or subtopic reviews but provides an historical roadmap to completed works.

Genome-wide identification of DNA methylation QTLs in whole blood highlights pathways for cardiovascular disease

Identifying methylation quantitative trait loci (meQTLs) and integrating them with disease-associated variants from genome-wide association studies (GWAS) may illuminate functional mechanisms underlying genetic variant-disease associations. Here, we perform GWAS of >415 thousand CpG methylation sites in whole blood from 4170 individuals and map 4.7 million cis- and 630 thousand trans-meQTL variants targeting >120 thousand CpGs. Independent replication is performed in 1347 participants from two studies. By linking cis-meQTL variants with GWAS results for cardiovascular disease (CVD) traits, we identify 92 putatively causal CpGs for CVD traits by Mendelian randomization analysis. Further integrating gene expression data reveals evidence of cis CpG-transcript pairs causally linked to CVD. In addition, we identify 22 trans-meQTL hotspots each targeting more than 30 CpGs and find that trans-meQTL hotspots appear to act in cis on expression of nearby transcriptional regulatory genes. Our findings provide a powerful meQTL resource and shed light on DNA methylation involvement in human diseases.

Differentially methylated CpGs can inform on disease mechanisms and be useful as biomarkers. Here, the authors perform GWAS for DNA methylation in whole blood, cis- and trans-meQTL mapping, followed by Mendelian randomization analysis that links meQTLs with cardiovascular diseases.

Assessment of patient clinical descriptions and pathogenic variants from gene panel sequences in the CAGI-5 intellectual disability challenge

The Critical Assessment of Genome Interpretation-5 intellectual disability challenge asked to use computational methods to predict patient clinical phenotypes and the causal variant(s) based on an analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental manifestations (i.e. ID, autism, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) have been made available for this challenge. For each patient, predictors had to report the causative variants and which of the seven phenotypes were present. Since neurodevelopmental disorders are characterized by strong comorbidity, tested individuals often present more than one pathological condition. Considering the overall clinical manifestation of each patient, the correct phenotype has been predicted by at least one group for 93 individuals (62%). ID and ASD were the best predicted among the seven phenotypic traits. Also, causative or potentially pathogenic variants were predicted correctly by at least one group. However, the prediction of the correct causative variant seems to be insufficient to predict the correct phenotype. In some cases, the correct prediction has been supported by rare or common variants in genes different from the causative one.

Rare DEGS1 variant significantly alters de novo ceramide synthesis pathway

The de novo ceramide synthesis pathway is essential to human biology and health, but genetic influences remain unexplored. The core function of this pathway is the generation of biologically active ceramide from its precursor, dihydroceramide. Dihydroceramides have diverse, often protective, biological roles; conversely, increased ceramide levels are biomarkers of complex disease. To explore the genetics of the ceramide synthesis pathway, we searched for deleterious nonsynonymous variants in the genomes of 1,020 Mexican Americans from extended pedigrees. We identified a Hispanic ancestry-specific rare functional variant, L175Q, in delta 4-desaturase, sphingolipid 1 (DEGS1), a key enzyme in the pathway that converts dihydroceramide to ceramide. This amino acid change was significantly associated with large increases in plasma dihydroceramides. Indexes of DEGS1 enzymatic activity were dramatically reduced in heterozygotes. CRISPR/Cas9 genome editing of HepG2 cells confirmed that the L175Q variant results in a partial loss of function for the DEGS1 enzyme. Understanding the biological role of DEGS1 variants, such as L175Q, in ceramide synthesis may improve the understanding of metabolic-related disorders and spur ongoing research of drug targets along this pathway.

ATG5 cancer mutations and alternative mRNA splicing reveal a conjugation switch that regulates ATG12-ATG5-ATG16L1 complex assembly and autophagy

Autophagy is critical for maintaining cellular homeostasis during times of stress, and is thought to play important roles in both tumorigenesis and tumor cell survival. Formation of autophagosomes, which mediate delivery of cytoplasmic cargo to lysosomes, requires multiple autophagy-related (ATG) protein complexes, including the ATG12–ATG5-ATG16L1 complex. Herein, we report that a molecular ATG5 “conjugation switch”, comprised of competing ATG12 and ubiquitin conjugation reactions, integrates ATG12–ATG5-ATG16L1 complex assembly with protein quality control of its otherwise highly unstable subunits. This conjugation switch is tightly regulated by ATG16L1, which binds to free ATG5 and mutually protects both proteins from ubiquitin conjugation and proteasomal degradation, thereby instead promoting the irreversible conjugation of ATG12 to ATG5. The resulting ATG12–ATG5 conjugate, in turn, displays enhanced affinity for ATG16L1 and thus fully stabilizes the ATG12–ATG5-ATG16L1 complex. Most importantly, we find in multiple tumor types that ATG5 somatic mutations and alternative mRNA splicing specifically disrupt the ATG16L1-binding pocket in ATG5 and impair the essential ATG5-ATG16L1 interactions that are initially required for ATG12–ATG5 conjugation. Finally, we provide evidence that ATG16L2, which is overexpressed in several cancers relative to ATG16L1, hijacks the conjugation switch by competing with ATG16L1 for binding to ATG5. While ATG16L2 stabilizes ATG5 and enables ATG12–ATG5 conjugation, this endogenous dominant-negative inhibitor simultaneously displaces ATG16L1, resulting in its proteasomal degradation and a block in autophagy. Thus, collectively, our findings provide novel insights into ATG12–ATG5-ATG16L1 complex assembly and reveal multiple mechanisms wherein dysregulation of the ATG5 conjugation switch inhibits autophagy.

Identification of novel TGF-β regulated genes with pro-migratory roles

Transforming growth factor-β (TGF-β) signaling plays fundamental roles in the development and homeostasis of somatic cells. Dysregulated TGF-β signaling contributes to cancer progression and relapse to therapies by inducing epithelial-to-mesenchymal transition (EMT), enriching cancer stem cells, and promoting immunosuppression. Although many TGF-β-regulated genes have been identified, only a few datasets were obtained by next-generation sequencing. In this study, we performed RNA-sequencing analysis of MCF10A cells and identified 1166 genes that were upregulated and 861 genes that were downregulated by TGF-β. Gene set enrichment analysis revealed that focal adhesion and metabolic pathways were the top enriched pathways of the up- and downregulated genes, respectively. Genes in these pathways also possess significant predictive value for renal cancers. Moreover, we confirmed that TGF-β induced expression of MICAL1 and 2, and the histone demethylase, KDM7A, and revealed their regulatory roles on TGF-β-induced cell migration. We also show a critical effect of KDM7A in regulating the acetylation of H3K27 on TGF-β-induced genes. In sum, this study identified novel effectors that mediate the pro-migratory role of TGF-β signaling, paving the way for future studies that investigate the function of MICAL family members in cancer and the novel epigenetic mechanisms downstream TGF-β signaling.

DNMT3L facilitates DNA methylation partly by maintaining DNMT3A stability in mouse embryonic stem cells

DNMT3L (DNMT3-like), a member of the DNMT3 family, has no DNA methyltransferase activity but regulates de novo DNA methylation. While biochemical studies show that DNMT3L is capable of interacting with both DNMT3A and DNMT3B and stimulating their enzymatic activities, genetic evidence suggests that DNMT3L is essential for DNMT3A-mediated de novo methylation in germ cells but is dispensable for de novo methylation during embryogenesis, which is mainly mediated by DNMT3B. How DNMT3L regulates DNA methylation and what determines its functional specificity are not well understood. Here we show that DNMT3L-deficient mouse embryonic stem cells (mESCs) exhibit downregulation of DNMT3A, especially DNMT3A2, the predominant DNMT3A isoform in mESCs. DNA methylation analysis of DNMT3L-deficient mESCs reveals hypomethylation at many DNMT3A target regions. These results confirm that DNMT3L is a positive regulator of DNA methylation, contrary to a previous report that, in mESCs, DNMT3L regulates DNA methylation positively or negatively, depending on genomic regions. Mechanistically, DNMT3L forms a complex with DNMT3A2 and prevents DNMT3A2 from being degraded. Restoring the DNMT3A protein level in DNMT3L-deficient mESCs partially recovers DNA methylation. Thus, our work uncovers a role for DNMT3L in maintaining DNMT3A stability, which contributes to the effect of DNMT3L on DNMT3A-dependent DNA methylation.

ExtRamp: a novel algorithm for extracting the ramp sequence based on the tRNA adaptation index or relative codon adaptiveness

Different species, genes, and locations within genes use different codons to fine-tune gene expression. Within genes, the ramp sequence assists in ribosome spacing and decreases downstream collisions by incorporating slowly-translated codons at the beginning of a gene. Although previously reported as occurring in some species, no previous attempt at extracting the ramp sequence from specific genes has been published. We present ExtRamp, a software package that quickly extracts ramp sequences from any species using the tRNA adaptation index or relative codon adaptiveness. Different filters facilitate the analysis of codon efficiency and enable identification of genes with a ramp sequence. We validate the existence of a ramp sequence in most species by running ExtRamp on 229 742 339 genes across 23 428 species. We evaluate differences in reported ramp sequences when we use different parameters. Using the strictest ramp sequence cut-off, we show that across most taxonomic groups, ramp sequences are approximately 20–40 codons long and occur in about 10% of gene sequences. We also show that in Drosophila melanogaster as gene expression increases, a higher proportion of genes have ramp sequences. We provide a framework for performing this analysis on other species. ExtRamp is freely available at https://github.com/ridgelab/ExtRamp.

Transketolase and vitamin B1 influence on ROS-dependent neutrophil extracellular traps (NETs) formation

Neutrophil extracellular traps (NETs) are a recently identified, web-like, extracellular structure composed of decondensed nuclear DNA and associated antimicrobial granules. NETs are extruded into the extracellular environment via the reactive oxygen species (ROS)-dependent cell death pathway participating in inflammation and autoimmune diseases. Transketolase (TKT) is a thiamine pyrophosphate (vitamin B1)-dependent enzyme that links the pentose phosphate pathway with the glycolytic pathway by feeding excess sugar phosphates into the main carbohydrate metabolic pathways to generate biosynthetic reducing capacity in the form of NADPH as a substrate for ROS generation. In this work, TKT was selected as a lead candidate from 24 NET-associated proteins obtained by literature screening and knowledge gap assessment. Consequently, we determined whether TKT influenced NET formation in vitro. We firstly established that the release of ROS-dependent NETs was significantly decreased after purified human PMNs were pretreated with oxythiamine, a TKT inhibitor, and in a concentration dependent manner. As a cofactor for TKT reaction, we evaluated the release of NET formation either in vitamin B1 treatment or in combined use of oxythiamine and vitamin B1, and found that those treatments also exerted a significant suppressive effect on the amount of NET-DNA and ROS production. The regulation of TKT by oxythiamine and/or vitamin B1 may therefore be associated with response to the modulation of NET formation by preventing generation of excessive NETs in inflammatory diseases.

MicroPro: using metagenomic unmapped reads to provide insights into human microbiota and disease associations

We develop a metagenomic data analysis pipeline, MicroPro, that takes into account all reads from known and unknown microbial organisms and associates viruses with complex diseases. We utilize MicroPro to analyze four metagenomic datasets relating to colorectal cancer, type 2 diabetes, and liver cirrhosis and show that including reads from unknown organisms significantly increases the prediction accuracy of the disease status for three of the four datasets. We identify new microbial organisms associated with these diseases and show viruses play important prediction roles in colorectal cancer and liver cirrhosis, but not in type 2 diabetes. MicroPro is freely available at https://github.com/zifanzhu/MicroPro .

Genomic dissection of enhancers uncovers principles of combinatorial regulation and cell type-specific wiring of enhancer-promoter contacts

Genomic binding of transcription factors, like the glucocorticoid receptor (GR), is linked to the regulation of genes. However, as we show here, GR binding is a poor predictor of GR-dependent gene regulation even when taking the 3D organization of the genome into account. To connect GR binding sites to the regulation of genes in the endogenous genomic context, we turned to genome editing. By deleting GR binding sites, individually or in combination, we uncovered how cooperative interactions between binding sites contribute to the regulation of genes. Specifically, for the GR target gene GILZ, we show that the simultaneous presence of a cluster of GR binding sites is required for the activity of an individual enhancer and that the GR-dependent regulation of GILZ depends on multiple GR-bound enhancers. Further, by deleting GR binding sites that are shared between different cell types, we show how cell type-specific genome organization and enhancer-blocking can result in cell type-specific wiring of promoter–enhancer contacts. This rewiring allows an individual GR binding site shared between different cell types to direct the expression of distinct transcripts and thereby contributes to the cell type-specific consequences of glucocorticoid signaling.

Complete motif analysis of sequence requirements for translation initiation at non-AUG start codons

The initiation of mRNA translation from start codons other than AUG was previously believed to be rare and of relatively low impact. More recently, evidence has suggested that as much as half of all translation initiation utilizes non-AUG start codons, codons that deviate from AUG by a single base. Furthermore, non-AUG start codons have been shown to be involved in regulation of expression and disease etiology. Yet the ability to gauge expression based on the sequence of a translation initiation site (start codon and its flanking bases) has been limited. Here we have performed a comprehensive analysis of translation initiation sites that utilize non-AUG start codons. By combining genetic-reporter, cell-sorting, and high-throughput sequencing technologies, we have analyzed the expression associated with all possible variants of the -4 to +4 positions of non-AUG translation initiation site motifs. This complete motif analysis revealed that 1) with the right sequence context, certain non-AUG start codons can generate expression comparable to that of AUG start codons, 2) sequence context affects each non-AUG start codon differently, and 3) initiation at non-AUG start codons is highly sensitive to changes in the flanking sequences. Complete motif analysis has the potential to be a key tool for experimental and diagnostic genomics.

Depletion of Hemoglobin Transcripts and Long-Read Sequencing Improves the Transcriptome Annotation of the Polar Bear (Ursus maritimus)

Transcriptome studies evaluating whole blood and tissues are often confounded by overrepresentation of highly abundant transcripts. These abundant transcripts are problematic, as they compete with and prevent the detection of rare RNA transcripts, obscuring their biological importance. This issue is more pronounced when using long-read sequencing technologies for isoform-level transcriptome analysis, as they have relatively lower throughput compared to short-read sequencers. As a result, long-read based transcriptome analysis is prohibitively expensive for non-model organisms. While there are off-the-shelf kits available for select model organisms capable of depleting highly abundant transcripts for alpha (HBA) and beta (HBB) hemoglobin, they are unsuitable for non-model organisms. To address this, we have adapted the recent CRISPR/Cas9-based depletion method (depletion of abundant sequences by hybridization) for long-read full-length cDNA sequencing approaches that we call Long-DASH. Using a recombinant Cas9 protein with appropriate guide RNAs, full-length hemoglobin transcripts can be depleted in vitro prior to performing any short- and long-read sequencing library preparations. Using this method, we sequenced depleted full-length cDNA in parallel using both our Oxford Nanopore Technology (ONT) based R2C2 long-read approach, as well as the Illumina short-read based Smart-seq2 approach. To showcase this, we have applied our methods to create an isoform-level transcriptome from whole blood samples derived from three polar bears (Ursus maritimus). Using Long-DASH, we succeeded in depleting hemoglobin transcripts and generated deep Smart-seq2 Illumina datasets and 3.8 million R2C2 full-length cDNA consensus reads. Applying Long-DASH with our isoform identification pipeline, Mandalorion, we discovered ∼6,000 high-confidence isoforms and a number of novel genes. This indicates that there is a high diversity of gene isoforms within U. maritimus not yet reported. This reproducible and straightforward approach has not only improved the polar bear transcriptome annotations but will serve as the foundation for future efforts to investigate transcriptional dynamics within the 19 polar bear subpopulations around the Arctic.

Towards a complete map of the human long non-coding RNA transcriptome

Gene maps, or annotations, enable us to navigate the functional landscape of our genome. They are a resource upon which virtually all studies depend, from single-gene to genome-wide scales and from basic molecular biology to medical genetics. Yet present-day annotations suffer from trade-offs between quality and size, with serious but often unappreciated consequences for downstream studies. This is particularly true for long non-coding RNAs (lncRNAs), which are poorly characterized compared to protein-coding genes. Long-read sequencing technologies promise to improve current annotations, paving the way towards a complete annotation of lncRNAs expressed throughout a human lifetime.

A survey of localized sequence rearrangements in human DNA

Genomes mutate and evolve in ways simple (substitution or deletion of bases) and complex (e.g. chromosome shattering). We do not fully understand what types of complex mutation occur, and we cannot routinely characterize arbitrarily-complex mutations in a high-throughput, genome-wide manner. Long-read DNA sequencing methods (e.g. PacBio, nanopore) are promising for this task, because one read may encompass a whole complex mutation. We describe an analysis pipeline to characterize arbitrarily-complex 'local' mutations, i.e. intrachromosomal mutations encompassed by one DNA read. We apply it to nanopore and PacBio reads from one human cell line (NA12878), and survey sequence rearrangements, both real and artifactual. Almost all the real rearrangements belong to recurring patterns or motifs: the most common is tandem multiplication (e.g. heptuplication), but there are also complex patterns such as localized shattering, which resembles DNA damage by radiation. Gene conversions are identified, including one between hemoglobin gamma genes. This study demonstrates a way to find intricate rearrangements with any number of duplications, deletions, and repositionings. It demonstrates a probability-based method to resolve ambiguous rearrangements involving highly similar sequences, as occurs in gene conversion. We present a catalog of local rearrangements in one human cell line, and show which rearrangement patterns occur.

Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma

The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy.

Genome-wide mapping and profiling of γH2AX binding hotspots in response to different replication stress inducers

BACKGROUND

Replication stress (RS) gives rise to DNA damage that threatens genome stability. RS can originate from different sources that stall replication by diverse mechanisms. However, the mechanism underlying how different types of RS contribute to genome instability is unclear, in part due to the poor understanding of the distribution and characteristics of damage sites induced by different RS mechanisms.

RESULTS

We use ChIP-seq to map γH2AX binding sites genome-wide caused by aphidicolin (APH), hydroxyurea (HU), and methyl methanesulfonate (MMS) treatments in human lymphocyte cells. Mapping of γH2AX ChIP-seq reveals that APH, HU, and MMS treatments induce non-random γH2AX chromatin binding at discrete regions, suggesting that there are γH2AX binding hotspots in the genome. Characterization of the distribution and sequence/epigenetic features of γH2AX binding sites reveals that the three treatments induce γH2AX binding at largely non-overlapping regions, suggesting that RS may cause damage at specific genomic loci in a manner dependent on the fork stalling mechanism. Nonetheless, γH2AX binding sites induced by the three treatments share common features including compact chromatin, coinciding with larger-than-average genes, and depletion of CpG islands and transcription start sites. Moreover, we observe significant enrichment of SINEs in γH2AX sites in all treatments, indicating that SINEs may be a common barrier for replication polymerases.

CONCLUSIONS

Our results identify the location and common features of genome instability hotspots induced by different types of RS, and help in deciphering the mechanisms underlying RS-induced genetic diseases and carcinogenesis.

Retroposed copies of RET gene: a somatically acquired event in medullary thyroid carcinoma

BACKGROUND

Different pathogenic germline mutations in the RET oncogene are identified in MEN 2, a hereditary syndrome characterized by medullary thyroid carcinoma (MTC) and other endocrine tumors. Although genetic predisposition is recognized, not all RET mutation carriers will develop the disease during their lifetime or, likewise, RET mutation carriers belonging to the same family may present clinical heterogeneity. It has been suggested that a single germline mutation might not be sufficient for development of MEN 2-associated tumors and a somatic bi-allelic alteration might be required. Here we investigated the presence of somatic second hit mutation in the RET gene in MTC.

METHODS

We integrated Multiplex Ligation-dependent Probe Amplification (MLPA) and whole exome sequencing (WES) to search for copy number alteration (CNA) in the RET gene in MTC samples and medullary thyroid cell lines (TT and MZ-CR-1). We next found reads spanning exon-exon boundaries on RET, an indicative of retrocopy. We subsequently searched for RET retrocopies in the human reference genome (GRCh37) and in the 1000 Genomes Project data, by looking for reads reporting joined exons in the RET locus or distinct genomic regions. To determine RET retrocopy specificity and recurrence, DNA isolated from sporadic and MEN 2-associated MTC (n = 37), peripheral blood (n = 3) and papillary thyroid carcinomas with RET fusion (n = 10) samples were tested using PCR-sequencing methodology.

RESULTS

Through MLPA we have found evidence of CNA in the RET gene in MTC samples and MTC cell lines. WES analysis reinforced the presence of the CNA and hinted for a retroposed copy of RET not found in the human reference genome and 1.000 Genomes Project. Extended analysis confirmed the presence of a somatic MTC-related retrocopy of RET in both sporadic and hereditary tumors. We further unveiled a recurrent (28%) novel point mutation (p.G548 V) found exclusively in the retrocopy of RET. The mutation was also found in cDNA of mutated samples, suggesting it might be functional.

CONCLUSION

We here report a somatic specific RET retroposed copy in MTC samples and cell lines. Our results support the idea that generation of retrocopies in somatic cells is likely to contribute to MTC genesis and progression.

Umap and Bismap: quantifying genome and methylome mappability

Short-read sequencing enables assessment of genetic and biochemical traits of individual genomic regions, such as the location of genetic variation, protein binding and chemical modifications. Every region in a genome assembly has a property called 'mappability', which measures the extent to which it can be uniquely mapped by sequence reads. In regions of lower mappability, estimates of genomic and epigenomic characteristics from sequencing assays are less reliable. These regions have increased susceptibility to spurious mapping from reads from other regions of the genome with sequencing errors or unexpected genetic variation. Bisulfite sequencing approaches used to identify DNA methylation exacerbate these problems by introducing large numbers of reads that map to multiple regions. Both to correct assumptions of uniformity in downstream analysis and to identify regions where the analysis is less reliable, it is necessary to know the mappability of both ordinary and bisulfite-converted genomes. We introduce the Umap software for identifying uniquely mappable regions of any genome. Its Bismap extension identifies mappability of the bisulfite-converted genome. A Umap and Bismap track hub for human genome assemblies GRCh37/hg19 and GRCh38/hg38, and mouse assemblies GRCm37/mm9 and GRCm38/mm10 is available at https://bismap.hoffmanlab.org for use with genome browsers.

Bioinformatic analysis of bacteria and host cell dual RNA-sequencing experiments

Bacterial pathogens subvert host cells by manipulating cellular pathways for survival and replication; in turn, host cells respond to the invading pathogen through cascading changes in gene expression. Deciphering these complex temporal and spatial dynamics to identify novel bacterial virulence factors or host response pathways is crucial for improved diagnostics and therapeutics. Dual RNA sequencing (dRNA-Seq) has recently been developed to simultaneously capture host and bacterial transcriptomes from an infected cell. This approach builds on the high sensitivity and resolution of RNA sequencing technology and is applicable to any bacteria that interact with eukaryotic cells, encompassing parasitic, commensal or mutualistic lifestyles. Several laboratory protocols have been presented that outline the collection, extraction and sequencing of total RNA for dRNA-Seq experiments, but there is relatively little guidance available for the detailed bioinformatic analyses required. This protocol outlines a typical dRNA-Seq experiment, based on a Chlamydia trachomatis-infected host cell, with a detailed description of the necessary bioinformatic analyses with currently available software tools.

Heterogeneous Loop Model to Infer 3D Chromosome Structures from Hi-C

Adapting a well-established formalism in polymer physics, we develop a minimalist approach to infer three-dimensional folding of chromatin from Hi-C data. The three-dimensional chromosome structures generated from our heterogeneous loop model (HLM) are used to visualize chromosome organizations that can substantiate the measurements from fluorescence in situ hybridization, chromatin interaction analysis by paired-end tag sequencing, and RNA-seq signals. We demonstrate the utility of the HLM with several case studies. Specifically, the HLM-generated chromosome structures, which reproduce the spatial distribution of topologically associated domains from fluorescence in situ hybridization measurement, show the phase segregation between two types of topologically associated domains explicitly. We discuss the origin of cell-type-dependent gene-expression level by modeling the chromatin globules of α-globin and SOX2 gene loci for two different cell lines. We also use the HLM to discuss how the chromatin folding and gene-expression level of Pax6 loci, associated with mouse neural development, are modulated by interactions with two enhancers. Finally, HLM-generated structures of chromosome 19 of mouse embryonic stem cells, based on single-cell Hi-C data collected over each cell-cycle phase, visualize changes in chromosome conformation along the cell-cycle. Given a contact frequency map between chromatic loci supplied from Hi-C, HLM is a computationally efficient and versatile modeling tool to generate chromosome structures that can complement interpreting other experimental data.

AutoMLST: an automated web server for generating multi-locus species trees highlighting natural product potential

Understanding the evolutionary background of a bacterial isolate has applications for a wide range of research. However generating an accurate species phylogeny remains challenging. Reliance on 16S rDNA for species identification currently remains popular. Unfortunately, this widespread method suffers from low resolution at the species level due to high sequence conservation. Currently, there is now a wealth of genomic data that can be used to yield more accurate species designations via modern phylogenetic methods and multiple genetic loci. However, these often require extensive expertise and time. The Automated Multi-Locus Species Tree (autoMLST) was thus developed to provide a rapid 'one-click' pipeline to simplify this workflow at: https://automlst.ziemertlab.com. This server utilizes Multi-Locus Sequence Analysis (MLSA) to produce high-resolution species trees; this does not preform multi-locus sequence typing (MLST), a related classification method. The resulting phylogenetic tree also includes helpful annotations, such as species clade designations and secondary metabolite counts to aid natural product prospecting. Distinct from currently available web-interfaces, autoMLST can automate selection of reference genomes and out-group organisms based on one or more query genomes. This enables a wide range of researchers to perform rigorous phylogenetic analyses more rapidly compared to manual MLSA workflows.

Large-scale determination and characterization of cell type-specific regulatory elements in the human genome

Histone modifications have been widely elucidated to play vital roles in gene regulation and cell identity. The Roadmap Epigenomics Consortium generated a reference catalog of several key histone modifications across >100s of human cell types and tissues. Decoding these epigenomes into functional regulatory elements is a challenging task in computational biology. To this end, we adopted a differential chromatin modification analysis framework to comprehensively determine and characterize cell type-specific regulatory elements (CSREs) and their histone modification codes in the human epigenomes of five histone modifications across 127 tissues or cell types. The CSREs show significant relevance with cell type-specific biological functions and diseases and cell identity. Clustering of CSREs with their specificity signals reveals distinct histone codes, demonstrating the diversity of functional roles of CSREs within the same cell or tissue. Last but not least, dynamics of CSREs from close cell types or tissues can give a detailed view of developmental processes such as normal tissue development and cancer occurrence.

The Genome of the Steller Sea Lion (Eumetopias jubatus)

The Steller sea lion is the largest member of the Otariidae family and is found in the coastal waters of the northern Pacific Rim. Here, we present the Steller sea lion genome, determined through DNA sequencing approaches that utilized microfluidic partitioning library construction, as well as nanopore technologies. These methods constructed a highly contiguous assembly with a scaffold N50 length of over 14 megabases, a contig N50 length of over 242 kilobases and a total length of 2.404 gigabases. As a measure of completeness, 95.1% of 4104 highly conserved mammalian genes were found to be complete within the assembly. Further annotation identified 19,668 protein coding genes. The assembled genome sequence and underlying sequence data can be found at the National Center for Biotechnology Information (NCBI) under the BioProject accession number PRJNA475770.

Chromosome-Level Alpaca Reference Genome VicPac3.1 Improves Genomic Insight Into the Biology of New World Camelids

The development of high-quality chromosomally assigned reference genomes constitutes a key feature for understanding genome architecture of a species and is critical for the discovery of the genetic blueprints of traits of biological significance. South American camelids serve people in extreme environments and are important fiber and companion animals worldwide. Despite this, the alpaca reference genome lags far behind those available for other domestic species. Here we produced a chromosome-level improved reference assembly for the alpaca genome using the DNA of the same female Huacaya alpaca as in previous assemblies. We generated 190X Illumina short-read, 8X Pacific Biosciences long-read and 60X Dovetail Chicago^® chromatin interaction scaffolding data for the assembly, used testis and skin RNAseq data for annotation, and cytogenetic map data for chromosomal assignments. The new assembly VicPac3.1 contains 90% of the alpaca genome in just 103 scaffolds and 76% of all scaffolds are mapped to the 36 pairs of the alpaca autosomes and the X chromosome. Preliminary annotation of the assembly predicted 22,462 coding genes and 29,337 isoforms. Comparative analysis of selected regions of the alpaca genome, such as the major histocompatibility complex (MHC), the region involved in the Minute Chromosome Syndrome (MCS) and candidate genes for high-altitude adaptations, reveal unique features of the alpaca genome. The alpaca reference genome VicPac3.1 presents a significant improvement in completeness, contiguity and accuracy over VicPac2 and is an important tool for the advancement of genomics research in all New World camelids.