Genomic Repositories Research Articles

Scalable Newborn Screening Solutions: Bioinformatics and Next-Generation Sequencing.

Expansion of the newborn disorder panel requires the incorporation of new testing modalities. This is especially true for disorders lacking robust biomarkers for detection in primary screening methods and for disorders requiring genotyping or sequencing as a second-tier and/or diagnostic test. In this commentary, we discuss how next-generation sequencing (NGS) methods can be used as a secondary testing method in NBS. Additionally, we elaborate on the importance of genomic variant repositories for the annotation and interpretation of variants. Barriers to the incorporation of NGS and bioinformatics within NBS are discussed, and ideas for a regional bioinformatics model and shared variant repository are presented as potential solutions.

Model organism databases are in jeopardy.

Model organism databases are in jeopardy.

P13.04 Survival analyses for determining clinical relevance of molecular markers in translational glioblastoma research - a systematic review

Abstract BACKGROUND Pre-clinical glioblastoma studies can assess the relevance of their findings to patient survival using integrated clinical and genomic data. Validity of univariable analyses requires an assumption that molecular markers are randomly distributed across patient characteristics to mitigate the confounding effects of clinical variables. Multivariable survival analyses adjusting for clinical variables do not change the association if this assumption holds. We aimed to assess this by summarising the types of survival analyses and their results in translational glioblastoma research. MATERIAL AND METHODS We systematically searched Medline and Embase Jan 2008 to Feb 2021 for glioblastoma cell line or animal studies validating their molecular markers in The Cancer Genome Atlas (TCGA) or the Chinese Glioma Genome Atlas (CGGA) using survival analyses. Studies that exclusively used genomic data without laboratory findings were excluded. Two reviewers independently assessed study eligibility and extracted data. Data items included patient inclusion criteria, characteristics of survival analyses, and whether molecular markers had statistically significant association with overall survival. RESULTS Of 1,047 potentially eligible studies, we included 59 pre-clinical glioblastoma studies that tested the association between their molecular markers and survival using TCGA or CGGA data. All studies used TCGA data and 2 also used CGGA data. Sixteen (27%) studies specified their patient inclusion criteria from TCGA for survival analysis. Eight studies exclusively investigated sets of molecular markers, leaving 51 studies reporting 126 molecular markers. Eighteen (31%) studies used multivariable survival analysis in addition to univariable analyses. All molecular markers underwent univariable analyses, of which 12 (10%) molecular markers had additional multivariable survival analyses. In the 13 multivariable analyses on 12 molecular markers, four (31%) markers were associated with survival in the univariable analyses but not in the multivariable analyses. CONCLUSION Most pre-clinical studies used univariable survival analyses alone in public genomic repositories to assess the relevance of their results to patient survival. Our findings demonstrated that multivariable analyses are needed to account for confounding effects of clinical variables. Using relevant components from reporting guidelines for observational studies can improve the transparency and quality of translational studies.

CovidPhy: A tool for phylogeographic analysis of SARS-CoV-2 variation

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen responsible for the coronavirus disease 2019 (COVID-19) pandemic. SARS-CoV-2 genomes have been sequenced massively and worldwide and are now available in different public genome repositories. There is much interest in generating bioinformatic tools capable to analyze and interpret SARS-CoV-2 variation. We have designed CovidPhy (http://covidphy.eu), a web interface that can process SARS-CoV-2 genome sequences in plain fasta text format or provided through identity codes from the Global Initiative on Sharing Avian Influenza Data (GISAID) or GenBank. CovidPhy aggregates information available on the large GISAID database (>1.49 M genomes). Sequences are first aligned against the reference sequence and the interface provides different sources of information, including automatic classification of genomes into a pre-computed phylogeny and phylogeographic information, haplogroup/lineage frequencies, and sequencing variation, indicating also if the genome contains known variants of concern (VOC). Additionally, CovidPhy allows searching for variants and haplotypes introduced by the user and includes a list of genomes that are good candidates for being responsible for large outbreaks worldwide, most likely mediated by important superspreading events, indicating their possible geographic epicenters and their relative impact as recorded in the GISAID database.

GenoVault: a cloud based genomics repository

GenoVault is a cloud-based repository for handling Next Generation Sequencing (NGS) data. It is developed using OpenStack-based private cloud with various services like keystone for authentication, cinder for block storage, neutron for networking and nova for managing compute instances for the Cloud. GenoVault uses object-based storage, which enables data to be stored as objects instead of files or blocks for faster retrieval from different distributed object nodes. Along with a web-based interface, a JavaFX-based desktop client has also been developed to meet the requirements of large file uploads that are usually seen in NGS datasets. Users can store files in their respective object-based storage areas and the metadata provided by the user during file uploads is used for querying the database. GenoVault repository is designed taking into account future needs and hence can scale both vertically and horizontally using OpenStack-based cloud features. Users have an option to make the data shareable to the public or restrict the access as private. Data security is ensured as every container is a separate entity in object-based storage architecture which is also supported by Secure File Transfer Protocol (SFTP) for data upload and download. The data is uploaded by the user in individual containers that include raw read files (fastq), processed alignment files (bam, sam, bed) and the output of variation detection (vcf). GenoVault architecture allows verification of the data in terms of integrity and authentication before making it available to collaborators as per the user’s permissions. GenoVault is useful for maintaining the organization-wide NGS data generated in various labs which is not yet published and submitted to public repositories like NCBI. GenoVault also provides support to share NGS data among the collaborating institutions. GenoVault can thus manage vast volumes of NGS data on any OpenStack-based private cloud.

Developing Bioprospecting Strategies for Bioplastics Through the Large-Scale Mining of Microbial Genomes.

The accumulation of petroleum-based plastic waste has become a major issue for the environment. A sustainable and biodegradable solution can be found in Polyhydroxyalkanoates (PHAs), a microbially produced biopolymer. An analysis of the global phylogenetic and ecological distribution of potential PHA producing bacteria and archaea was carried out by mining a global genome repository for PHA synthase (PhaC), a key enzyme involved in PHA biosynthesis. Bacteria from the phylum Actinobacteria were found to contain the PhaC Class II genotype which produces medium-chain length PHAs, a physiology until now only found within a few Pseudomonas species. Further, several PhaC genotypes were discovered within Thaumarchaeota, an archaeal phylum with poly-extremophiles and the ability to efficiently use CO2 as a carbon source, a significant ecological group which have thus far been little studied for PHA production. Bacterial and archaeal PhaC genotypes were also observed in high salinity and alkalinity conditions, as well as high-temperature geothermal ecosystems. These genome mining efforts uncovered previously unknown candidate taxa for biopolymer production, as well as microbes from environmental niches with properties that could potentially improve PHA production. This in silico study provides valuable insights into unique PHA producing candidates, supporting future bioprospecting efforts toward better targeted and relevant taxa to further enhance the diversity of exploitable PHA production systems.

Contribution of "Omic" Studies to the Understanding of Cadasil. A Systematic Review.

CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy) is a small vessel disease caused by mutations in NOTCH3 that lead to an odd number of cysteines in the epidermal growth factor (EGF)-like repeat domain, causing protein misfolding and aggregation. The main symptoms are migraines, psychiatric disorders, recurrent strokes, and dementia. Omic technologies allow the massive study of different molecules for understanding diseases in a non-biased manner or even for discovering targets and their possible treatments. We analyzed the progress in understanding CADASIL that has been made possible by omics sciences. For this purpose, we included studies that focused on CADASIL and used omics techniques, searching bibliographic resources, such as PubMed. We excluded studies with other phenotypes, such as migraine or leukodystrophies. A total of 18 articles were reviewed. Due to the high prevalence of NOTCH3 mutations considered pathogenic to date in genomic repositories, one can ask whether all of them produce CADASIL, different degrees of the disease, or whether they are just a risk factor for small vessel disease. Besides, proteomics and transcriptomics studies found that the molecules that are significantly altered in CADASIL are mainly related to cell adhesion, the cytoskeleton or extracellular matrix components, misfolding control, autophagia, angiogenesis, or the transforming growth factor β (TGFβ) signaling pathway. The omics studies performed on CADASIL have been useful for understanding the biological mechanisms and could be key factors for finding potential drug targets.

Abstract 95: The landscape of extrachromosomal circular DNA in medulloblastoma subgroups

Abstract Extrachromosomal circular DNA (ecDNA) has been shown to be an important driver of particularly aggressive human cancers. However, it remains unknown to what extent and by which molecular mechanisms ecDNA promotes tumor development and progression in the different molecular subgroups of medulloblastoma (MB), the most common malignant pediatric brain tumor. To answer these questions, we have assembled a multi-institutional retrospective cohort of 472 MB patient samples with available whole genome sequencing (WGS) data, drawing from three non-overlapping public cancer genomic data repositories and covering all four MB subgroups (i.e. WNT, SHH, Group 3 and Group 4). Using genomic cloud computing platforms, local computing resources and recent computational methods for the detection and reconstruction of ecDNA, we find ecDNA in 66 patients (14%) and observe that the presence of ecDNA is associated with significantly poorer outcomes. By subgroup, ecDNA was found in 0/13 WNT (0%), 18/69 SHH (26%), 12/76 G3 (16%) and 14/123 G4 (11%) patients. Affected genomic loci harbor up to hundredfold amplification of oncogenes including MYC, MYCN, and TERT; as well as amplifications of novel putative driver genes involved in chromatin remodeling, DNA repair, and kinase signaling pathways. To investigate the frequency and properties of ecDNA in model systems of MB tumors, we further analyzed WGS data from 24 patient-derived xenografts (PDX) and four cell lines. ecDNA was substantially more frequent in these patient-derived models (17 of 29, 59%) than in our patient cohort. To elucidate the functional regulatory landscapes of ecDNAs in MB, we generated transcriptional (RNA-seq), accessible chromatin (ATAC-seq), and chromatin interaction (Hi-C) profiles from 6 MB tumor samples. In each case, we identify regulatory interactions that cross fusion breakpoints on the ecDNA, representing potential “enhancer rewiring” events that are likely to contribute to transcriptional activation of co-amplified oncogenes. To test this hypothesis, we are currently conducting in-vitro CRISPRi screens targeting regulatory regions on the ecDNA with the aim of testing whether enhancer-rewiring increases the transcription of co-amplified oncogenes and promotes proliferation and cell line growth. In summary, our study analyzes the frequency, diversity and functional relevance of regulatory enhancers co-amplified with oncogenes on ecDNA across MB subgroups and provides strong scientific justification for continued mechanistic studies of ecDNA in MB tumors and tumor models with the potential to uncover new therapeutic approaches. Citation Format: Owen S. Chapman, Shanqing Wang, Jens Luebeck, Alexandra Garancher, Jon D. Larson, Joshua Lange, John Crawford, Scott L. Pomeroy, Paul Mischel, Ernest Fraenkel, Robert J. Wechsler-Reya, Vineet Bafna, Jill P. Mesirov, Lukas Chavez. The landscape of extrachromosomal circular DNA in medulloblastoma subgroups [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 95.

OMIC-01. THE LANDSCAPE OF EXTRACHROMOSOMAL CIRCULAR DNA IN MEDULLOBLASTOMA SUBGROUPS

Extrachromosomal circular DNA (ecDNA) is an important driver of particularly aggressive human cancers. However, the prevalence of ecDNA, and its role in tumor development and progression in the different molecular subgroups of medulloblastoma (MB), remain unknown. To answer these questions, we have assembled a multi-institutional retrospective cohort of 472 MB patients with available whole genome sequencing (WGS) data, drawing from three cancer genomic data repositories and covering all MB subgroups (WNT, SHH, Group 3 and Group 4). Using recent computational methods to detect and reconstruct ecDNA, we find ecDNA in 66 patients (14%) and observe that the presence of ecDNA is associated with significantly poorer outcomes. By subgroup, ecDNA was found in 0/24 WNT (0%), 22/109 SHH (20%), 15/107 Group 3 (14%) and 20/181 Group 4 (11%) patients. Affected genomic loci harbor up to hundredfold amplification of oncogenes including MYC, MYCN, TERT, and other novel putative oncogenes. We further analyzed 24 patient-derived xenograft (PDX) and four cell line models of MB tumors. ecDNA was substantially more frequent in patient-derived models (17 of 29, 59%) than in our patient cohort. To elucidate the functional regulatory landscapes of ecDNAs in MB, we generated transcriptional (RNA-seq), accessible chromatin (ATAC-seq), and chromatin interaction (Hi-C) profiles of 6 MB tumor samples. In each case, we identify regulatory interactions that cross fusion breakpoints on the ecDNA, representing potential “enhancer rewiring” events which may contribute to transcriptional activation of co-amplified oncogenes. To test this hypothesis, we are currently conducting in-vitro CRISPRi screens targeting regulatory regions on the ecDNA of a MB cell line to determine whether these enhancers promote proliferation. In summary, our study analyzes the frequency, diversity and functional relevance of ecDNA across MB subgroups and provides strong justification for continued mechanistic studies of ecDNA in MB with the potential to uncover new therapeutic approaches.

Enlightening the taxonomy darkness of human gut microbiomes with a cultured biobank

BackgroundIn gut microbiome studies, the cultured gut microbial resource plays essential roles, such as helping to unravel gut microbial functions and host-microbe interactions. Although several major studies have been performed to elucidate the cultured human gut microbiota, up to 70% of the Unified Human Gastrointestinal Genome species have not been cultured to date. Large-scale gut microbial isolation and identification as well as availability to the public are imperative for gut microbial studies and further characterizing human gut microbial functions.ResultsIn this study, we constructed a human Gut Microbial Biobank (hGMB; homepage: hgmb.nmdc.cn) through the cultivation of 10,558 isolates from 31 sample mixtures of 239 fresh fecal samples from healthy Chinese volunteers, and deposited 1170 strains representing 400 different species in culture collections of the International Depository Authority for long-term preservation and public access worldwide. Following the rules of the International Code of Nomenclature of Prokaryotes, 102 new species were characterized and denominated, while 28 new genera and 3 new families were proposed. hGMB represented over 80% of the common and dominant human gut microbial genera and species characterized from global human gut 16S rRNA gene amplicon data (n = 11,647) and cultured 24 “most-wanted” and “medium priority” taxa proposed by the Human Microbiome Project. We in total sequenced 115 genomes representing 102 novel taxa and 13 previously known species. Further in silico analysis revealed that the newly sequenced hGMB genomes represented 22 previously uncultured species in the Unified Human Gastrointestinal Genome (UHGG) and contributed 24 representatives of potentially “dark taxa” that had not been discovered by UHGG. The nonredundant gene catalogs generated from the hGMB genomes covered over 50% of the functionally known genes (KEGG orthologs) in the largest global human gut gene catalogs and approximately 10% of the “most wanted” functionally unknown proteins in the FUnkFams database.ConclusionsA publicly accessible human Gut Microbial Biobank (hGMB) was established that contained 1170 strains and represents 400 human gut microbial species. hGMB expands the gut microbial resources and genomic repository by adding 102 novel species, 28 new genera, 3 new families, and 115 new genomes of human gut microbes.6-6epGnHGpob5sGsfPZJHQVideo abstract

GenoDedup: Similarity-Based Deduplication and Delta-Encoding for Genome Sequencing Data

The vast datasets produced in human genomics must be efficiently stored, transferred, and processed while prioritizing storage space and restore performance. Balancing these two properties becomes challenging when resorting to traditional data compression techniques. In fact, specialized algorithms for compressing sequencing data favor the former, while large genome repositories widely resort to generic compressors (e.g., GZIP) to benefit from the latter. Notably, human beings have approximately 99.9 percent of DNA sequence similarity, vouching for an excellent opportunity for deduplication and its assets: leveraging inter-file similarity and achieving higher read performance. However, identity-based deduplication fails to provide a satisfactory reduction in the storage requirements of genomes. In this article, we balance space savings and restore performance by proposing \sf GenoDedupGenoDedup, the first method that integrates efficient similarity-based deduplication and specialized delta-encoding for genome sequencing data. Our solution currently achieves 67.8 percent of the reduction gains of SPRING (i.e., the best specialized tool in this metric) and restores data 1.62×1.62× faster than SeqDB (i.e., the fastest competitor). Additionally, GenoDedupGenoDedup restores data 9.96×9.96× faster than SPRING and compresses files 2.05×2.05× more than SeqDB.

8 Variations in COMT Gene Are Not Associated with Patient-Reported Outcomes: A Multicenter Collaborative Proof of Concept Study

Abstract Introduction Variant single nucleotide polymorphisms (SNPs) in the catechol-O-methyl transferase (COMT) gene have previously been associated with pain thresholds, resiliency, and cognitive functioning. Known colloquially as the “warrior/worrier” gene, the GG variant is associated with higher resilience whereas the AA variant is associated with lower resilience. The aim of this study is to demonstrate the feasibility of correlating a genomic data repository with multicenter national longitudinal burn data. Methods From August 2018 to July 2020, participant cheek swabs were collected in-person or by mail at three burn centers and sent to the lead center for DNA isolation and storage. COMT SNPs were determined by polymerase chain reaction (PCR). SNP data were merged with a multicenter burn database. Outcomes of interest included the Patient-Reported Outcomes Measurement Information System (PROMIS-29), Community Integration Questionnaire (CIQ), Veterans RAND 12 (VR-12) physical (PCS) and mental component summary (MCS) scores, posttraumatic stress disorder (PTSD) measure, and Posttraumatic Growth Inventory (PTGI). Follow-up data were obtained at 6 months post-discharge. We used a Kruskal-Wallis test to determine associations between COMT SNPs and outcomes. Results Of the 126 cheek swabs obtained, DNA was successfully isolated from 124 swabs, and PCR SNP analysis was successful for 111 swabs. The distribution of COMT SNPs in our study population was consistent with the general population. Kruskal-Wallis analysis revealed no association between COMT SNPs and patient outcomes. Median PTGI scores trended toward significance, but in the opposite direction than predicted for COMT genotypes. Conclusions A national longitudinal database can be feasibly supplemented by a genomic data repository. Variation in the COMT gene did not correlate with health-related quality of life measures examined in a small cohort of burn survivors. Lack of statistical significance can be attributed to small sample size.

An Update on Advances in COVID-19 Laboratory Diagnosis and Testing Guidelines in India.

The declaration of COVID-19 as a global pandemic has warranted the urgent need for technologies and tools to be deployed for confirming diagnosis of suspected cases. Diagnostic testing for COVID-19 is critical for understanding epidemiology, contract-tracing, case management, and to repress the transmission of the SARS-CoV-2. Currently, the Nucleic Acid Amplification Test (NAAT)-based RT-PCR technique is a gold standard test used for routine diagnosis of COVID-19 infection. While there are many commercially available RT-PCR assay kits available in the market, selection of highly sensitive, specific, and validated assays is most crucial for the accurate diagnosis of COVID-19 infection. Laboratory diagnosis of SARS-CoV-2 is extremely important in the disease and outbreak management. Development of rapid point of care tests with better sensitivity and specificity is the critical need of the hour as this will help accurate diagnosis and aid in containing the spread of SARS-CoV-2 infection. Early detection of viral infection greatly enhances implementation of specific public health intervention, such as infection control, environmental decontamination, and the closure of specific high-risk zones. Large-scale sequencing of SARS-CoV-2 genome isolated from affected populations across the world needs to be carried to monitor mutations that might affect performance of molecular tests. Creation of genome repositories and open-source genetic databases for use by global researchers is clearly the way forward to manage COVID-19 outbreak and accelerate vaccine development. This review summarizes various molecular diagnostics methods, technical guidelines, and advanced testing strategies adopted in India for laboratory diagnosis of COVID-19.

Genomics Analyses Reveal Unique Classification, Population Structure and Novel Allele of Neo-Tetraploid Rice

BackgroundNeo-tetraploid rice (NTR) is a useful new germplasm that developed from the descendants of the autotetraploid rice (ATR) hybrids. NTR showed improved fertility and yield potential, and produced high yield heterosis when crossed with indica ATR for commercial utilization. However, their classification, population structure and genomic feature remain elusive.ResultsHere, high-depth genome resequencing data of 15 NTRs and 18 ATRs, together with 38 publicly available data of diploid rice accessions, were analyzed to conduct classification, population structure and haplotype analyses. Five subpopulations were detected and NTRs were clustered into one independent group that was adjacent to japonica subspecies, which maybe the reason for high heterosis when NTRs crossed with indica ATRs. Haplotype patterns of 717 key genes that associated with yield and other agronomic traits were revealed in these NTRs. Moreover, a novel specific SNP variation was detected in the first exon of HSP101, a known heat-inducible gene, which was conserved in all NTRs but absent in ATRs, 3KRG and RiceVarMap2 databases. The novel allele was named as HSP101–1, which was confirmed to be a heat response factor by qRT-PCR, and knockout of HSP101–1 significantly decreased the thermotolerance capacity of NTR. Interestingly, HSP101–1 was also specifically expressed in the anthers of NTR at pre-meiotic and meiosis stages under optimal environment without heat stress, and its loss-of-function mutant showed significant decrease in fertility of NTR.ConclusionThe construction of first genomic variation repository and the revelation of population structure provide invaluable information for optimizing the designs of tetraploid rice breeding. The detection of specific genomic variations offered useful genomic markers and new directions to resolve high fertility mechanism of NTR.

Toward better governance of human genomic data.

Here, we argue that, in line with the dramatic increase in the collection, storage and curation of human genomic data for biomedical research, genomic data repositories and consortia have adopted governance frameworks to both enable wide access and protect against possible harms. However, the merits and limitations of different governance frameworks in achieving these twin aims are a matter of ongoing debate in the scientific community; indeed, best practices and points for consideration are notably absent in devising governance frameworks for genomic databases. According to our collective experience in devising and assessing governance frameworks, we identify five key functions of ‘good governance’ (or ‘better governance’) and three areas in which trade-offs should be considered when specifying policies within those functions. We apply these functions as a benchmark to describe, as an example, the governance frameworks of six large-scale international genomic projects.

Cooperative co-evolution for feature selection in Big Data with random feature grouping

A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because of some limitations, such as not considering feature interactions, dealing with only an even number of features, and decomposing the dataset statically. In this paper, a novel random feature grouping (RFG) has been introduced with its three variants to dynamically decompose Big Data datasets and to ensure the probability of grouping interacting features into the same subcomponent. RFG can be used in CC-based FS processes, hence called Cooperative Co-Evolutionary-Based Feature Selection with Random Feature Grouping (CCFSRFG). Experiment analysis was performed using six widely used ML classifiers on seven different datasets from the UCI ML repository and Princeton University Genomics repository with and without FS. The experimental results indicate that in most cases [i.e., with naïve Bayes (NB), support vector machine (SVM), k-Nearest Neighbor (k-NN), J48, and random forest (RF)] the proposed CCFSRFG-1 outperforms an existing solution (a CC-based FS, called CCEAFS) and CCFSRFG-2, and also when using all features in terms of accuracy, sensitivity, and specificity.

Predicting DNA methylation from genetic data lacking racial diversity using shared classified random effects

Public genomic repositories are notoriously lacking in racially and ethnically diverse samples. This limits the reaches of exploration and has in fact been one of the driving factors for the initiation of the All of Us project. Our particular focus here is to provide a model-based framework for accurately predicting DNA methylation from genetic data using racially sparse public repository data. Epigenetic alterations are of great interest in cancer research but public repository data is limited in the information it provides. However, genetic data is more plentiful. Our phenotype of interest is cervical cancer in The Cancer Genome Atlas (TCGA) repository. Being able to generate such predictions would nicely complement other work that has generated gene-level predictions of gene expression for normal samples.We develop a new prediction approach which uses shared random effects from a nested error mixed effects regression model. The sharing of random effects allows borrowing of strength across racial groups greatly improving predictive accuracy. Additionally, we show how to further borrow strength by combining data from different cancers in TCGA even though the focus of our predictions is DNA methylation in cervical cancer. We compare our methodology against other popular approaches including the elastic net shrinkage estimator and random forest prediction. Results are very encouraging with the shared classified random effects approach uniformly producing more accurate predictions – overall and for each racial group.

Divergence of Peroxisome Membrane Gene Sequence and Expression Between Yeast Species.

Large population-genomic sequencing studies can enable highly-powered analyses of sequence signatures of natural selection. Genome repositories now available for Saccharomyces yeast make it a premier model for studies of the molecular mechanisms of adaptation. We mined the genomes of hundreds of isolates of the sister species S. cerevisiae and S. paradoxus to identify sequence hallmarks of adaptive divergence between the two. From the top hits we focused on a set of genes encoding membrane proteins of the peroxisome, an organelle devoted to lipid breakdown and other specialized metabolic pathways. In-depth population- and comparative-genomic sequence analyses of these genes revealed striking divergence between S. cerevisiae and S. paradoxus. And from transcriptional profiles we detected non-neutral, directional cis-regulatory variation at the peroxisome membrane genes, with overall high expression in S. cerevisiae relative to S. paradoxus. Taken together, these data support a model in which yeast species have differentially tuned the expression of peroxisome components to boost their fitness in distinct niches.

The Zmat2 gene in non-mammalian vertebrates: Organizational simplicity within a divergent locus in fish.

ZMAT2 is among the least-studied of mammalian proteins and genes, even though it is the ortholog of Snu23, a protein involved in pre-mRNA splicing in yeast. Here we have used data from genomic and gene expression repositories to examine the Zmat2 gene and locus in 8 terrestrial vertebrates, 10 ray-finned fish, and 1 lobe-finned fish representing > 500 million years of evolutionary diversification. The analyses revealed that vertebrate Zmat2 genes are similar to their mammalian counterparts, as in 16/19 species studied they contain 6 exons, and in 18/19 encode a single conserved protein. However, unlike in mammals, no Zmat2 pseudogenes were identified in these vertebrates, although an expressed Zmat2 paralog was characterized in flycatcher that resembled a DNA copy of a processed and retro-transposed mRNA, and thus could be a proto-pseudogene captured during its evolutionary journey from active to inert. The Zmat2 locus in terrestrial vertebrates, and in spotted gar and coelacanth, also shares additional genes with its mammalian counterparts, including Histidyl-tRNA synthetase (Hars), Hars2, and others, but these are absent from the Zmat2 locus in teleost fish, in which Stem-loop-binding protein 2 (Slbp2) and Lymphocyte cytosolic protein 2a (Lcp2a) are present instead. Taken together, these observations argue that a recognizable Zmat2 was present in the earliest vertebrate ancestors, and postulate that during chromosomal tetraploidization and subsequent re-diploidization during modern teleost evolution, the duplicated Zmat2 gene was retained and the original lost. This study also highlights how information from genomic resources can be leveraged to reveal new biologically significant insights.

Structure and expression of the long noncoding RNA gene MIR503 in humans and non-human primates

Recent technical and other advances in genomics provide unique opportunities to improve our understanding of human physiology and disease predisposition through a detailed analysis of gene structure and expression by examining data in public genome and gene-expression repositories. Yet, the vast majority of human genes remain understudied. This is particularly true of genes for long noncoding RNAs (lncRNAs). Here, we describe the detailed characterization of MIR503HG, a lncRNA gene found on the X chromosome in humans. Using information extracted from public databases, we show that human MIR503HG is a 5-exon gene, and that it is highly conserved among 5 non-human primates spanning over 85 million years ago of evolutionary diversification. MIR503HG is transcribed and processed into multiple distinct RNAs in each of these species through differential exon use and alternative RNA splicing, with a higher abundance of transcripts being found in reproductive tissues, especially during the early stages of ovary and testis development, indicating a possible role in reproductive biology. Furthermore, in select reproductive system cancers, MIR503HG transcripts are downregulated, with higher levels of RNA expression being associated with clinical outcomes. Collectively, these investigations show how the use of genomic, gene expression, and other genetic resources can lead to new insights about human biology and disease, and argue that MIR503HG is worthy of additional study.