Integrating Genome Sequence Research Articles

Predicting RNA polymerase II transcriptional elongation pausing and associated histone code.

RNA Polymerase II (Pol II) transcriptional elongation pausing is an integral part of the dynamic regulation of gene transcription in the genome of metazoans. It plays a pivotal role in many vital biological processes and disease progression. However, experimentally measuring genome-wide Pol II pausing is technically challenging and the precise governing mechanism underlying this process is not fully understood. Here, we develop RP3 (RNA Polymerase II Pausing Prediction), a network regularized logistic regression machine learning method, to predict Pol II pausing events by integrating genome sequence, histone modification, gene expression, chromatin accessibility, and protein-protein interaction data. RP3 can accurately predict Pol II pausing in diverse cellular contexts and unveil the transcription factors that are associated with the Pol II pausing machinery. Furthermore, we utilize a forward feature selection framework to systematically identify the combination of histone modification signals associated with Pol II pausing. RP3 is freely available at https://github.com/AMSSwanglab/RP3.

Integrating genomic and multiomic data for Angelica sinensis provides insights into the evolution and biosynthesis of pharmaceutically bioactive compounds

Angelica sinensis roots (Angelica roots) are rich in many bioactive compounds, including phthalides, coumarins, lignans, and terpenoids. However, the molecular bases for their biosynthesis are still poorly understood. Here, an improved chromosome-scale genome for A. sinensis var. Qinggui1 is reported, with a size of 2.16 Gb, contig N50 of 4.96 Mb and scaffold N50 of 198.27 Mb, covering 99.8% of the estimated genome. Additionally, by integrating genome sequencing, metabolomic profiling, and transcriptome analysis of normally growing and early-flowering Angelica roots that exhibit dramatically different metabolite profiles, the pathways and critical metabolic genes for the biosynthesis of these major bioactive components in Angelica roots have been deciphered. Multiomic analyses have also revealed the evolution and regulation of key metabolic genes for the biosynthesis of pharmaceutically bioactive components; in particular, TPSs for terpenoid volatiles, ACCs for malonyl CoA, PKSs for phthalide, and PTs for coumarin biosynthesis were expanded in the A. sinensis genome. These findings provide new insights into the biosynthesis of pharmaceutically important compounds in Angelica roots for exploration of synthetic biology and genetic improvement of herbal quality.

Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data

Millions of single nucleotide variants (SNVs) exist in the human genome; however, it remains challenging to identify functional SNVs associated with diseases. We propose a non-encoding SNVs analysis tool bpb3, BayesPI-BAR version 3, aiming to identify the functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data. The identified FMBs display high frequency SNVs, significant changes in transcription factors (TFs) binding affinity and are nearby the regulatory regions of differentially expressed genes. A two-level Bayesian approach with a biophysical model for protein-DNA interactions is implemented, to compute TF-DNA binding affinity changes based on clustered position weight matrices (PWMs) from over 1700 TF-motifs. The epigenetic data, such as the DNA methylome can also be integrated to scan FMBs. By testing the datasets from follicular lymphoma and melanoma, bpb3 automatically and robustly identifies FMBs, demonstrating that bpb3 can provide insight into patho-mechanisms, and therapeutic targets from transcriptomic and genomic data.

Comprehensive Genome Characterization of Childhood T-ALL Links Oncogene Activation Mechanism and Subtypes to Prognosis

Background: Children with relapsed T-cell acute lymphoblastic leukemia (T-ALL) have poor prognosis, and identification of patients at risk for recurrence is required to prevent relapse. Prior genomic studies of T-ALL have failed to identify genetic alterations that are prognostic independent of minimal residual disease (MRD), in part due to limited cohort size, exclusion of patients with refractory disease, and lack of comprehensive, integrated genome and transcriptome sequencing, which is important as many T-ALL drivers occur in non-coding regions of the genome. Aim: To identify all coding and non-coding alterations and identify genomic predictors of relapse or refractory disease. Methods: We performed whole genome, exome, and transcriptome sequencing of 1,313 cases enrolled on the Children's Oncology Group AALL0434 trial of childhood T-ALL. ATAC-seq and HiChIP were used to investigate the consequences of non-coding mutations and structural variants (SV). We used transcriptome profiling by Uniform Manifold Approximation and Projection (UMAP), Leiden algorithm clustering, hierarchical clustering of oncogene expression, and analysis of sequence and structural DNA variants to identify subtypes of T-ALL cases, their driver and associated genomic alterations. Results: Using integrative genomic analysis, we identified the clonal subtype-defining putative drivers in 94% of samples, 60% of which were non-coding regions and required WGS for identification in 28% of cases. We identified 16 T-ALL subtypes, several of which were previously unrecognized and involved partitioning of known subgroups into groups with distinct gene expression and driver alterations. We identified 4 subtypes with deregulation of the TAL1/LMO1/LMO2 core transcriptional circuitry: TAL1-RA, TAL1-RB, TAL1-RB/RPL10, LMO2-refractory (15.5%, 20.4%, 2.8%, 0.9% of all cases). These subtypes were separated by the lower expression of T cell maturation genes CD4/CD8 and RAG in TAL1-RA and more mature double positive TAL1-RB. TAL1-RA was characterized by multiple activation mechanisms for TAL1, LMO1 and/or LMO2; and TAL1-RB with alterations of TAL1, LMO1, LMO2,LYL1 and, TAL2 including rearrangements of these genes to TCR enhancers, chimeric fusion oncoproteins and a diverse range of enhancer hijacking, amplification and SNV events. Activation mechanisms differed between the groups; TAL1-RA commonly had STIL::TAL1 fusion with frequently co-occurring LMO2/LMO1 SNV/Indels or LMO2 enhancer gains, whereas TAL1-RB had frequent TAL1/LMO2/TAL2/LYL1 TCR rearrangements, TAL1 enhancer gains or various other TAL1 rearrangements. Two additional subgroups included 39 cases with TAL1/LMO2 activation and RPL10 mutations (TAL1-RB/RPL10) and the other characterized by refractory disease (LMO2-refractory, day 29 MRD >5%, 8 cases) with deregulation of LMO2 by hijacking of the BCL11B enhancer. We observed striking association between early T-cell precursor phenotype and driver lesions, with several groups highly enriched for ETP-ALL cases. One group (11.5% of all cases, 41.9% of ETP ALL cases, Fig. 1A) had distinct gene expression and deregulation of HOXA13 by SVs, ZFP36L2 or ETV6 by chimeric fusions and MED12 by SNVs. A second group (7.3% cases, 30.4% ETP) had HOXA9 deregulation by HOXA9 SVs or NUP98/NUP214/KMT2/MLLT10 chimeric fusions. Selective HOXA13 deregulation could be explained by genomic breakpoints occurring in different chromatin compartments than rearrangements deregulating other HOXA genes such as HOXA9. Novel HOXA13 SVs include rearrangements to MIR181A1HG and MED13 loci. Event-free survival (EFS) varied by subtype (Fig1B. log rank P<0.0001). Adverse risk subtypes were SPI1-R (P<0.0001, subtype vs. rest of samples), HOXA9 (P<0.0001), LMO2-refractory (P<0.0001) and favorable risk TAL1-RB/RPL10 (P=0.046), NKX2-1 (P=0.0099), TLX1 (P=0.013), TLX3 (P=0.028) and KMT2A-R (P=0.003) subtypes (Fig. 1B). Outcome was associated with key oncogenic pathways: for example, absence of Notch pathway alterations was associated with poor outcome if D29 MRD≥0.01% (5-year EFS <75%), whereas, patients with Notch alterations had good outcome regardless of Day 29 MRD. Conclusion: Comprehensive definition of the genomic landscape of T-ALL requires integrated WGS and RNAseq, and identifies multiple subgroups and driver lesions associated with immunophenotype and outcome. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Microalgae biomass as a sustainable source for biofuel, biochemical and biobased value-added products: An integrated biorefinery concept

Microalgal biomass has been proved to be a sustainable source for biofuels including bio-oil, biodiesel, bioethanol, biomethane, etc. One of the collateral benefits of integrating the use of microalgal technologies in the industry is microalgae’s ability to capture carbon dioxide during the application and biomass production process and consequently reducing carbon dioxide emissions. Although microalgae are a feasible source of biofuel, industrial microalgae applications face energy and cost challenges. To overcome these challenges, researchers have been interested in applying the bio-refinery approach to extract the important components encapsulated in microalgae. This review discusses the key steps of microalgae-based biorefinery including cultivation and harvesting, cell disruption, biofuel and value-added compound extraction along with the detailed technologies associated with each step of biorefinery. This review found that suitable microalgae species are selected based on their carbohydrate, lipid and protein contents and selecting the suitable species are crucial for high-quality biofuel and value-added products production. Microalgae species contain carbohydrates, proteins and lipids in the range of 8% to 69.7%, 5% to 74% and 7% to 65% respectively which proved their ability to be used as a source of value-added commodities in multiple industries including agriculture, animal husbandry, medicine, culinary, and cosmetics. This review suggests that lipid and value-added products from microalgae can be made more economically viable by integrating upstream and downstream processes. Therefore, a systematically integrated genome sequencing and process-scale engineering approach forimproving the extraction of lipids and co-products iscritical in the development of futuremicroalgal biorefineries.

Integrating genome sequence and structural data for statistical learning to predict transcription factor binding sites.

We report an approach to predict DNA specificity of the tetracycline repressor (TetR) family transcription regulators (TFRs). First, a genome sequence-based method was streamlined with quantitative P-values defined to filter out reliable predictions. Then, a framework was introduced to incorporate structural data and to train a statistical energy function to score the pairing between TFR and TFR binding site (TFBS) based on sequences. The predictions benchmarked against experiments, TFBSs for 29 out of 30 TFRs were correctly predicted by either the genome sequence-based or the statistical energy-based method. Using P-values or Z-scores as indicators, we estimate that 59.6% of TFRs are covered with relatively reliable predictions by at least one of the two methods, while only 28.7% are covered by the genome sequence-based method alone. Our approach predicts a large number of new TFBs which cannot be correctly retrieved from public databases such as FootprintDB. High-throughput experimental assays suggest that the statistical energy can model the TFBSs of a significant number of TFRs reliably. Thus the energy function may be applied to explore for new TFBSs in respective genomes. It is possible to extend our approach to other transcriptional factor families with sufficient structural information.

Associating divergent lncRNAs with target genes by integrating genome sequence, gene expression and chromatin accessibility data.

Recent RNA knockdown experiments revealed that a dozen divergent long noncoding RNAs (lncRNAs) positively regulate the transcription of genes in cis. Here, to understand the regulatory mechanism of divergent lncRNAs, we proposed a computational model IRDL (Identify the Regulatory Divergent LncRNAs) to associate divergent lncRNAs with target genes. IRDL took advantage of the cross-tissue paired expression and chromatin accessibility data in ENCODE and a dozen experimentally validated divergent lncRNA target genes. IRDL integrated sequence similarity, co-expression and co-accessibility features, battled the scarcity of gold standard datasets with an increasingly learning framework and identified 446 and 977 divergent lncRNA-gene regulatory associations for mouse and human, respectively. We found that the identified divergent lncRNAs and target genes correlated well in expression and chromatin accessibility. The functional and pathway enrichment analysis suggests that divergent lncRNAs are strongly associated with developmental regulatory transcription factors. The predicted loop structure validation and canonical database search indicate a scaffold regulatory model for divergent lncRNAs. Furthermore, we computationally revealed the tissue/cell-specific regulatory associations considering the specificity of lncRNA. In conclusion, IRDL provides a way to understand the regulatory mechanism of divergent lncRNAs and hints at hundreds of tissue/cell-specific regulatory associations worthy for further biological validation.

Adding color to a century-old enigma: multi-color chromosome identification unravels the autotriploid nature of saffron (Crocus sativus) as a hybrid of wild Crocus cartwrightianus cytotypes.

Saffron crocus (Crocus sativus) is the source of the most expensive spice of the world, produced from manually harvested stigmas, thus serving as a cash crop for rural communities. However, despite its economic importance, its genome and chromosomes are poorly studied. C.sativus is a sterile triploid species harboring eight chromosome triplets, and propagated only as a clonal lineage by corms. Saffron's evolutionary origin, parental species and allo- or autotriploidy has been a matter of discussion for almost a century. We performed a survey sequencing of the saffron genome and selected cytogenetic landmark sequences consisting of major tandem repeats, which we used as probes in comparative multicolor fluorescent insitu hybridization (FISH). We tagged 92 chromosomal positions and resolved the chromosomal composition of saffron triplets. By comparative FISH of six Crocus species from 11 accessions, we demonstrate that C.sativus is an autotriploid hybrid derived from heterogeneous Crocuscartwrightianus cytotypes. The FISH reference karyotype of saffron is crucial for integrating genome sequencing data with chromosomes and for investigating the relationship among Crocus species. We provide an evolutionary model of the saffron emergence; the knowledge of the parental origin offers a route towards the resynthesis of C.sativus from C.cartwrightianus to broaden saffron's gene pool.

Identification of Common and Rare Genetic Variation Associated With Plasma Protein Levels Using Whole-Exome Sequencing and Mass Spectrometry.

Identifying genetic variation associated with plasma protein levels, and the mechanisms by which they act, could provide insight into alterable processes involved in regulation of protein levels. Although protein levels can be affected by genetic variants, their estimation can also be biased by missense variants in coding exons causing technical artifacts. Integrating genome sequence genotype data with mass spectrometry-based protein level estimation could reduce bias, thereby improving detection of variation that affects RNA or protein metabolism. Here, we integrate the blood plasma protein levels of 664 proteins from 165 participants of the Tromsø Study, measured via tandem mass tag mass spectrometry, with whole-exome sequencing data to identify common and rare genetic variation associated with peptide and protein levels (protein quantitative trait loci [pQTLs]). We additionally use literature and database searches to prioritize putative functional variants for each pQTL. We identify 109 independent associations (36 protein and 73 peptide) and use genotype data to exclude 49 (4 protein and 45 peptide) as technical artifacts. We describe 2 particular cases of rare variation: 1 associated with the complement pathway and 1 with platelet degranulation. We identify putative functional variants and show that pQTLs act through diverse molecular mechanisms that affect both RNA and protein metabolism. We show that although the majority of pQTLs exert their effects by modulating RNA metabolism, many affect protein levels directly. Our work demonstrates the extent by which pQTL studies are affected by technical artifacts and highlights how prioritizing the functional variant in pQTL studies can lead to insights into the molecular steps by which a protein may be regulated.

Sustainable dairy cattle selection in the genomic era.

Genomic selection offers considerable flexibility to increase genetic trends in dairy cattle breeding. It is also an opportunity for more sustainable breeding, in terms of breeding goal and genetic variability. With a shorter generation interval, there is a big risk of increasing inbreeding if semen dissemination policy of elite bulls is not changed. However, using a large number of young bulls as service bulls and bull sires is a solution for both maximizing genetic trend while reducing inbreeding trend. Female genotyping is a key challenge for within-herd selection but, more importantly, for selection of new traits and for replacement of current reference populations based upon progeny-tested bulls. Genomic selection also opens new avenues for more customized breeding across herds or production systems. A big challenge is to reduce the dependency of genomic predictions on relationship between candidates and the reference population. A strong effort is presently dedicated to integrating genome sequence information into predictions, to improve their accuracy and persistency. In the longer term, further customization of selection will be possible by accounting for G × E interactions. Important developments are also necessary to decrease loss of favourable alleles through genetic drift.

Integrated Genome Sequencing Reveals Frequent Loss of Function Alterations of ARID1A and Other Epigenetic Modifiers in Sezary Syndrome

IntroductionSezary Syndrome (SS) is an aggressive mature T-cell neoplasm characterized by erythroderma, generalized lymphadenopathy and circulating T-cells. SS cells are characterized by complex karyotypes with numerous structural alterations and copy-number variants. Previous studies have implicated disruption and/or haploinsufficiency of TP53, CDKN2A, RB1 and PTEN in SS. Nevertheless, the comprehensive genetic alterations underlying the pathogenesis of SS are unknown. In this study, we used an integrated genomic sequencing approach to characterize the genetic basis of SS.MethodsSS cases that fulfilled established diagnostic criteria including characteristic cytologic, immunophenotypic and karyotypic features were analyzed. We performed genomic analysis on 84 primary SS cases using whole genome sequencing (WGS; n = 6), high-resolution array comparative genome hybridization (aCGH using NimbleGen 270K Feature Array; n = 80) and whole exome sequencing (WES) using Illumina TruSeq Exome Capture (n = 66). We filtered this combined dataset to identify 463 genes within regions of recurrent copy-number variations that were also impacted by protein coding mutations. We focused on 16 of these genes and confirmed 75 mutations in these genes by Sanger sequencing. For 35 of these mutations, normal tissue was available to confirm somatic acquisition.ResultsWGS revealed a complex structural genome with numerous regions of chromothrypsis and aneuploidy with over 1,000 inter- and intra-chromosomal translocations in the 6 SS genomes but did not reveal recurrent structural alterations. Array CGH identified many aneuploidies reported previously including 8+, 10-, 17p-/iso17 and 19q-. These results were corroborated by aCGH analysis which also revealed recurrent genomic losses at 9p21.3 (CDKN2A), 10p11.22 (PTEN) and 13q14.2 (RB1). Importantly, exome sequencing further identified several novel somatic, deleterious mutations in these three genes. Strikingly, in addition to confirming loss of function alterations of genes previously implicated in SS pathogenesis, we identified genetic alterations in genes involved in epigenetic modification including chromatin/histone modification (including ARID1A, ARID5B, MLL, MLL2, MLL4, SETD1A, SETD1B, SETD6) and DNA methylation (including NCOR1, TET1, TET2, DNMT3A, DNMT3B) not previously implicated in SS pathogenesis.Among the most frequent and most restricted regions of recurrent deletion was the 1p36.11 region. Of the 18 genes within this region, ARID1A showed the highest frequency of disruption (27/80 showing deletion; 7/66 showing novel mutations including a frameshift mutation at residue 1449 within the GR binding domain of ARID1A). Of the SS genomes for which both WES and aCGH analysis were performed, 25/62 (40.3%) showed disruption of ARID1A either by deletion or mutation or both. Moreover ARID5B was also targeted by deletions of region 10q21.2 in 23/80 genomes and 3/66 SS genomes showed mutations. We also identified a narrow region of recurrent aneuploidy affecting the SWI/SNF family-member of SMARC genes known to interact with ARID-family SMARCC1at 3p21.31. This gene was recurrently targeted by a deletion in 17/80 (21.3%) SS genomes by highly restricted deletional events (<4Mb).Altogether, deletions and/or mutations in ARID1A, ARID5B or SMARCC1 were identified in 38 of 62 (61.3%) of SS genomes. Further analysis revealed that other members of the ARID- and SMARC-families, ARID1B, ARID2 and ARID4A, were mutated. Moreover, ARID3A and SMARCA4 (both located on 19p) were targeted by recurrent deletions in 29/80 and 18/80 SS genomes, respectively. Altogether, deletions and/or mutations in at least one of the ARID- or SMARC-family genes were identified in 91.9% of SS.ConclusionThis study is the first to comprehensively profile the genomic landscape in SS. Integration of high-resolution aCGH with mutational analysis reveals alterations in epigenetic regulators through loss-of-function lesions in ARID- and SMARC-family genes in over 90% of SS. The results of our study offer further insights into the pathogenesis of SS and provide support for use of drugs targeting deregulated chromatin remodeling pathways in the treatment of SS patients. DisclosuresNo relevant conflicts of interest to declare.

A systematic approach to the reporting of medically relevant findings from whole genome sequencing.

BackgroundThe MedSeq Project is a randomized clinical trial developing approaches to assess the impact of integrating genome sequencing into clinical medicine. To facilitate the return of results of potential medical relevance to physicians and patients participating in the MedSeq Project, we sought to develop a reporting approach for the effective communication of such findings.MethodsGenome sequencing was performed on the Illumina HiSeq platform. Variants were filtered, interpreted, and validated according to methods developed by the Laboratory for Molecular Medicine and consistent with current professional guidelines. The GeneInsight software suite, which is integrated with the Partners HealthCare electronic health record, was used for variant curation, report drafting, and delivery.ResultsWe developed a concise 5–6 page Genome Report (GR) featuring a single-page summary of results of potential medical relevance with additional pages containing structured variant, gene, and disease information along with supporting evidence for reported variants and brief descriptions of associated diseases and clinical implications. The GR is formatted to provide a succinct summary of genomic findings, enabling physicians to take appropriate steps for disease diagnosis, prevention, and management in their patients.ConclusionsOur experience highlights important considerations for the reporting of results of potential medical relevance and provides a framework for interpretation and reporting practices in clinical genome sequencing.Electronic supplementary materialThe online version of this article (doi:10.1186/s12881-014-0134-1) contains supplementary material, which is available to authorized users.

Recessive mutations in a distal PTF1A enhancer cause isolated pancreatic agenesis.

The contribution of cis-regulatory mutations to human disease remains poorly understood. Whole genome sequencing can identify all non-coding variants, yet discrimination of causal regulatory mutations represents a formidable challenge. We used epigenomic annotation in hESC-derived embryonic pancreatic progenitor cells to guide the interpretation of whole genome sequences from patients with isolated pancreatic agenesis. This uncovered six different recessive mutations in a previously uncharacterized ~400bp sequence located 25kb downstream of PTF1A (pancreas-specific transcription factor 1a) in ten families with pancreatic agenesis. We show that this region acts as a developmental enhancer of PTF1A and that the mutations abolish enhancer activity. These mutations are the most common cause of isolated pancreatic agenesis. Integrating genome sequencing and epigenomic annotation in a disease-relevant cell type can uncover novel non-coding elements underlying human development and disease.