Integration Of Sequencing Data Research Articles

Comprehensive analysis of the mitochondrial genome of Ziziphus mauritiana Lam.: Investigating sequence structure, repeat-mediated recombination, chloroplast-derived transfer, RNA editing, and evolution

Ziziphus mauritiana Lam. is a valuable semi-deciduous tropical fruit tree with economic, medicinal, nutritional, and environmental significance. While the chloroplast genome of Z. mauritiana has been studied, the decoding of its mitochondrial genome (mtGenome) is crucial for various research areas, including molecular phylogeny and functional genomics. In this study, we extracted high-quality genomic DNA from the green leaves of Z. mauritiana and assembled the mtGenome by integrating sequencing data from both long and short reads. The Z. mauritiana mtGenome spans 324,124 bp with a 45.43 % GC content and exhibits six distinct conformations alongside the main circular sequence due to three pairs of repetitive fragments that facilitate recombination. RNA editing events resulted in changes in the physicochemical properties of amino acids, with genes expressed at higher levels showing an increased frequency of editing sites. Chloroplast-to-mitochondria horizontal gene transfers occurred in Z. mauritiana; however, these transferred fragments had few mutations, posing a challenge in determining the exact timing of these transfers. Phylogenetic analysis highlights a slower evolution rate of mitochondrial genes compared to chloroplast genes. These insights greatly enhance our understanding of the dynamics of the Z. mauritiana mtGenome, including recombination, gene expression, evolutionary processes, and more. The data and results of this study will serve as valuable mtGenome data resources within the Rhamnaceae family, thereby facilitating further genomics-based studies on Z. mauritiana.

Unravelling phylogenetic relationships of the tribe Cereeae using target enrichment sequencing.

Cactaceae are succulent plants, quasi-endemic to the American continent, and one of the most endangered plant groups in the world. Molecular phylogenies have been key to unravelling phylogenetic relationships among major cactus groups, previously hampered by high levels of morphological convergence. Phylogenetic studies using plastid markers have not provided adequate resolution for determining generic relationships within cactus groups. This is the case for the tribe Cereeae s.l., a highly diverse group from tropical America. Here we aimed to reconstruct a well-resolved phylogenetic tree of tribe Cereeae and update the circumscription of suprageneric and generic groups in this tribe. We integrated sequence data from public gene and genomic databases with new target sequences (generated using the customized Cactaceae591 probe set) across representatives of this tribe, with a denser taxon sampling of the subtribe Cereinae. We inferred concatenated and coalescent phylogenetic trees and compared the performance of both approaches. Six well-supported suprageneric clades were identified using different datasets. However, only genomic datasets, especially the Cactaceae591, were able to resolve the contentious relationships within the subtribe Cereinae. We propose a new taxonomic classification within Cereeae based on well-resolved clades, including new subtribes (Aylosterinae subtr. nov., Uebelmanniinae subtr. nov. and Gymnocalyciinae subtr. nov.) and revised subtribes (Trichocereinae, Rebutiinae and Cereinae). We emphasize the importance of using genomic datasets allied with coalescent inference to investigate evolutionary patterns within the tribe Cereeae.

A universal framework for single-cell multi-omics data integration with graph convolutional networks.

Single-cell omics data are growing at an unprecedented rate, whereas effective integration of them remains challenging due to different sequencing methods, quality, and expression pattern of each omics data. In this study, we propose a universal framework for the integration of single-cell multi-omics data based on graph convolutional network (GCN-SC). Among the multiple single-cell data, GCN-SC usually selects one data with the largest number of cells as the reference and the rest as the query dataset. It utilizes mutual nearest neighbor algorithm to identify cell-pairs, which provide connections between cells both within and across the reference and query datasets. A GCN algorithm further takes the mixed graph constructed from these cell-pairs to adjust count matrices from the query datasets. Finally, dimension reduction is performed by using non-negative matrix factorization before visualization. By applying GCN-SC on six datasets, we show that GCN-SC can effectively integrate sequencing data from multiple single-cell sequencing technologies, species or different omics, which outperforms the state-of-the-art methods, including Seurat, LIGER, GLUER and Pamona.

Analysis of KRAS Mutations in Gastrointestinal Tract Adenocarcinomas Reveals Site-Specific Mutational Signatures

Adenocarcinomas of the luminal gastrointestinal tract and pancreatobiliary system often show histologic and immunohistochemical overlap, making delineation of the primary site in a metastatic setting difficult. Previous studies have shown that site-specific missense mutations in the oncogene KRAS could be used in conjunction with immunohistochemistry to differentiate metastatic pancreatic adenocarcinoma from primary lung adenocarcinoma. In this study, we assessed the patterning of KRAS mutations across sites in the gastrointestinal and pancreatobiliary system. By integrating sequencing data from 44 separate studies, we assessed 2523 KRAS mutations in 7382 distinct cases of adenocarcinoma, including those from the esophagus, stomach, ampulla, biliary system, pancreas, and colon. We found that gastrointestinal adenocarcinomas demonstrate a marked regional variation in the frequency of KRAS mutations, with the most frequent KRAS mutation observed in pancreatic adenocarcinoma (up to 94.9%), whereas the frequency is much lower in adenocarcinomas from the esophagus and stomach (5.4% and 8.7%, respectively). Intriguingly, the pattern of missense mutations showed site specificity as well, with c.35G>T (p.G12V) and c.34G>C (p.G12R) mutations enriched in pancreatic primaries and codon 13 and non–codon 12/13 alterations enriched in gastric primaries (specificity of 98.9% and 93.2%, respectively, with a negative predictive value of 93.6% and 92.93% against pancreatic adenocarcinoma). Furthermore, we found that esophageal and gastric adenocarcinomas show an enrichment in transitional mutations, whereas other sites showed an equal distribution. Importantly, the examination of a validation cohort from our own institution revealed similar trends. These findings indicate that, in addition to providing therapeutic and diagnostic information, KRAS mutational analysis may also prove useful in delineating the site of origin in gastrointestinal adenocarcinomas that share morphologic and immunohistochemical overlap. Moreover, transitional mutations are more frequent in esophageal and gastric adenocarcinomas, reiterating the role of chronic inflammation in the pathogenesis of foregut adenocarcinomas.

Circular RNA expression and the competitive endogenous RNA network in pathological, age-related macular degeneration events: A cross-platform normalization study.

Age-related macular degeneration (AMD) causes irreversible blindness in people aged over 50 worldwide. The dysfunction of the retinal pigment epithelium is the primary cause of atrophic AMD. In the current study, we used ComBat and Training Distribution Matching to integrate data obtained from the Gene Expression Omnibus database. Integrated sequencing data were analyzed by Gene Set Enrichment Analysis. Peroxisome and tumor necrosis factor-α (TNF-α) signaling via nuclear factor kappa B (NF-κB) were among the top 10 pathways and were selected to construct AMD cell models to identify differentially expressed circular RNAs (circRNAs). A competing endogenous RNA network, related to differentially expressed circRNAs, was then constructed. This network included seven circRNAs, 15 microRNAs, and 82 mRNAs. The Kyoto Encyclopedia of Genes and Genomes analysis of mRNAs in this network showed that the hypoxia-inducible factor-1 (HIF-1) signaling pathway is a common downstream event. The results of the current study may provide insights into the pathological processes that cause atrophic AMD.

PRPS2 mutations drive acute lymphoblastic leukemia relapse through influencing PRPS1/2 hexamer stability.

Tumor relapse is the major cause of treatment failure in childhood acute lymphoblastic leukemia (ALL), yet the underlying mechanisms are still elusive. Here, we demonstrate that phosphoribosyl pyrophosphate synthetase 2 (PRPS2) mutations drive ALL relapse through influencing PRPS1/2 hexamer stability. Ultra-deep sequencing was performed to identify PRPS2 mutations in ALL samples. The effects of PRPS2 mutations on cell survival, cell apoptosis, and drug resistance were evaluated. In vitro PRPS2 enzyme activity and ADP/GDP feedback inhibition of PRPS enzyme activity were assessed. Purine metabolites were analyzed by ultra-performance liquid-chromatography tandem mass spectrometry (UPLC-MS/MS). Integrating sequencing data with clinical information, we identified PRPS2 mutations only in relapsed childhood ALL with thiopurine therapy. Functional PRPS2 mutations mediated purine metabolism specifically on thiopurine treatment by influencing PRPS1/2 hexamer stability, leading to reduced nucleotide feedback inhibition of PRPS activity and enhanced thiopurine resistance. The 3-amino acid V103-G104-E105, the key difference between PRPS1 and PRPS2, insertion in PRPS2 caused severe steric clash to the interface of PRPS hexamer, leading to its low enzyme activity. In addition, we demonstrated that PRPS2 P173R increased thiopurine resistance in xenograft models. Our work describes a novel mechanism by which PRPS2 mutants drive childhood ALL relapse and highlights PRPS2 mutations as biomarkers for relapsed childhood ALL.

Whole-Transcriptome Sequencing Analyses of Nuclear Antixoxidant-1 in Endothelial Cells: Role in Inflammation and Atherosclerosis.

Inflammation, oxidative stress, and copper (Cu) play an important role in cardiovascular disease, including atherosclerosis. We previously reported that cytosolic Cu chaperone antioxidant-1 (Atox1) translocates to the nucleus in response to inflammatory cytokines or exogenous Cu and that Atox1 is localized at the nucleus in the endothelium of inflamed atherosclerotic aorta. However, the roles of nuclear Atox1 and their function are poorly understood. Here we showed that Atox1 deficiency in ApoE−/− mice with a Western diet exhibited a significant reduction of atherosclerotic lesion formation. In vitro, adenovirus-mediated overexpression of nuclear-targeted Atox1 (Ad-Atox1-NLS) in cultured human endothelial cells (ECs) increased monocyte adhesion and reactive oxygen species (ROS) production compared to control cells (Ad-null). To address the underlying mechanisms, we performed genome-wide mapping of Atox1-regulated targets in ECs, using an unbiased systemic approach integrating sequencing data. Combination of ChIP-Seq and RNA-Seq analyses in ECs transfected with Ad-Atox1-NLS or Ad-null identified 1387 differentially expressed genes (DEG). Motif enrichment assay and KEGG pathway enrichment analysis revealed that 248 differentially expressed genes, including inflammatory and angiogenic genes, were regulated by Atox1-NLS, which was then confirmed by real-time qPCR. Among these genes, functional analysis of inflammatory responses identified CD137, CSF1, and IL5RA as new nuclear Atox1-targeted inflammatory genes, while CD137 is also a key regulator of Atox1-NLS-induced ROS production. These findings uncover new nuclear Atox1 downstream targets involved in inflammation and ROS production and provide insights into the nuclear Atox1 as a potential therapeutic target for the treatment of inflammatory diseases such as atherosclerosis.

Integrated Sequencing Data, Annotation, and Targeting Analysis of mRNAs and MicroRNAs from Tea Leaf During Infection by Tea Leaf Spot Pathogen, Epicoccum nigrum.

Integrated Sequencing Data, Annotation, and Targeting Analysis of mRNAs and MicroRNAs from Tea Leaf During Infection by Tea Leaf Spot Pathogen, Epicoccum nigrum.

A Quartet Network Analysis Identifying Mechanically Responsive Long Noncoding RNAs in Bone Remodeling.

Mechanical force, being so ubiquitous that it is often taken for granted and overlooked, is now gaining the spotlight for reams of evidence corroborating their crucial roles in the living body. The bone, particularly, experiences manifold extraneous force like strain and compression, as well as intrinsic cues like fluid shear stress and physical properties of the microenvironment. Though sparkled in diversified background, long noncoding RNAs (lncRNAs) concerning the mechanotransduction process that bone undergoes are not yet detailed in a systematic way. Our principal goal in this research is to highlight the potential lncRNA-focused mechanical signaling systems which may be adapted by bone-related cells for biophysical environment response. Based on credible lists of force-sensitive mRNAs and miRNAs, we constructed a force-responsive competing endogenous RNA network for lncRNA identification. To elucidate the underlying mechanism, we then illustrated the possible crosstalk between lncRNAs and mRNAs as well as transcriptional factors and mapped lncRNAs to known signaling pathways involved in bone remodeling and mechanotransduction. Last, we developed combinative analysis between predicted and established lncRNAs, constructing a pathway–lncRNA network which suggests interactive relationships and new roles of known factors such as H19. In conclusion, our work provided a systematic quartet network analysis, uncovered candidate force-related lncRNAs, and highlighted both the upstream and downstream processes that are possibly involved. A new mode of bioinformatic analysis integrating sequencing data, literature retrieval, and computational algorithm was also introduced. Hopefully, our work would provide a moment of clarity against the multiplicity and complexity of the lncRNA world confronting mechanical input.

A comparative analysis of single cell small RNA sequencing data reveals heterogeneous isomiR expression and regulation

MicroRNAs (miRNAs) are non-coding small RNAs which play a critical role in the regulation of gene expression in cells. It is known that miRNAs are often expressed as multiple isoforms, called isomiRs, which may have alternative regulatory functions. Despite the recent development of several single cell small RNA sequencing protocols, these methods have not been leveraged to investigate isomiR expression and regulation to better understand their role on a single cell level. Here we integrate sequencing data from three independent studies and find substantial differences in isomiR composition that suggest that cell autonomous mechanisms may drive isomiR processing. We also find evidence of altered regulatory functions of different classes of isomiRs, when compared to their respective wild-type miRNA, which supports a biological role for many of the isomiRs that are expressed.

Genomic epidemiology reveals multiple introductions of SARS-CoV-2 followed by community and nosocomial spread, Germany, February to May 2020.

BackgroundIn the SARS-CoV-2 pandemic, viral genomes are available at unprecedented speed, but spatio-temporal bias in genome sequence sampling precludes phylogeographical inference without additional contextual data.AimWe applied genomic epidemiology to trace SARS-CoV-2 spread on an international, national and local level, to illustrate how transmission chains can be resolved to the level of a single event and single person using integrated sequence data and spatio-temporal metadata.MethodsWe investigated 289 COVID-19 cases at a university hospital in Munich, Germany, between 29 February and 27 May 2020. Using the ARTIC protocol, we obtained near full-length viral genomes from 174 SARS-CoV-2-positive respiratory samples. Phylogenetic analyses using the Auspice software were employed in combination with anamnestic reporting of travel history, interpersonal interactions and perceived high-risk exposures among patients and healthcare workers to characterise cluster outbreaks and establish likely scenarios and timelines of transmission.ResultsWe identified multiple independent introductions in the Munich Metropolitan Region during the first weeks of the first pandemic wave, mainly by travellers returning from popular skiing areas in the Alps. In these early weeks, the rate of presumable hospital-acquired infections among patients and in particular healthcare workers was high (9.6% and 54%, respectively) and we illustrated how transmission chains can be dissected at high resolution combining virus sequences and spatio-temporal networks of human interactions.ConclusionsEarly spread of SARS-CoV-2 in Europe was catalysed by superspreading events and regional hotspots during the winter holiday season. Genomic epidemiology can be employed to trace viral spread and inform effective containment strategies.

The evolutionary history of sedges (Cyperaceae) in Madagascar

AbstractAimMadagascar is renowned for its unparalleled biodiversity and endemism. With many ecosystems under threat, research is urgently needed on its unique plant diversity. This applies both to Madagascar's forests and treeless vegetation types. Sedges (Cyperaceae) are among the top 10 species‐richest angiosperm families in Madagascar (310 native species, 38% endemic), of which two‐thirds occur in open habitats. We aimed to infer the evolutionary history of sedges in Madagascar, by estimating the number, age and origins of endemic lineages, and how they diversified on the island. We tested contrasting hypotheses of (a) few colonizations but important in situ radiations against (b) a high number of anagenetic colonizations.LocationMadagascar and the surrounding Indian Ocean islands, integrated within a global dataset.TaxonSedge family Cyperaceae.MethodsWe estimated time‐calibrated molecular phylogenies encompassing a large proportion of Madagascar's known sedge flora (incl. 55% of native species), integrating sequence data for 1,382 accessions representing almost 25% of the c. 5,600 sedge species worldwide, combined with ancestral area reconstruction, diversification analyses and Bayesian stochastic mapping.ResultsCyperaceae lineages arrived in Madagascar from c. 40 Mya with many arriving more recently. About 20 endemic lineages of Cyperaceae occur on the island, of which only six encompass more than five species. All except one of the endemic lineages that diversified in Madagascar use the C3 photosynthetic pathway. The main biogeographical links of Madagascar's sedge flora are to Southern and Tropical Africa.Main conclusionsThe biogeographical history of Cyperaceae in Madagascar is a chronicle of relatively recent multiple in and out processes of long‐distance dispersal colonizations constrained by distance. Also, the Madagascar region is not only a ‘sink’ for immigrant taxa, in situ diversification and dispersal to other regions also occurred. Some of the most diverse endemic lineages show clear adaptation to local environments.

Integration of large‐scale molecular networks and exomic data can identify Alzheimer's disease genes

AbstractBackgroundWe introduce a new approach to identify gene variants linked to common complex diseases. This method combines whole exome sequencing and large‐scale molecular network analysis. For proof of principle, we studied the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Alzheimer’s Disease Sequencing Project (ADSP) cohorts. In each, we evaluated the coding variants of 16,087 genes in the context of their ∼6,471,000 gene‐gene interactions, and prioritized genes that sorted cases from controls.MethodThe deleterious functional effect of every coding variant was quantified based on an Evolutionary Action (EA) model for the impact of similar variants across evolutionary history. For each gene, this impact was averaged over every individual from the ADNI (or the ADSP) cohort, and this average was used to modulate the edge weights of the STRING network of protein‐protein interactions. This process was carried out in cases and separately in controls, to yield two modified copies of the original STRING network, one representing the mutational perturbation from individuals affected with Alzheimer’s and, the other, representing those found in healthy individuals. We systematically compared these two graphs in order to identify, region by region and gene by gene, which parts of the network was significantly mutationally perturbed.ResultAmong the 100 top‐ranked in ADSP and, separately, in ADNI, a quarter are identical. This overlap indicates a robustness surpassing Genome‐wide association studies. One such gene, for example, is TOMM40, which is known to be relevant to AD. More broadly, these top‐ranked genes also significantly overlap, are significantly enriched for, or significantly diffuse to known gold standard AD genes and AD‐related diseases and pathways. These criteria for success show the top‐ranked genes are not random and are relevant to AD.ConclusionThis approach to integrate sequencing data with network information promises to be a useful new tool to elucidate disease genes in AD and other complex, common diseases.

Planet Microbe: a platform for marine microbiology to discover and analyze interconnected 'omics and environmental data.

In recent years, large-scale oceanic sequencing efforts have provided a deeper understanding of marine microbial communities and their dynamics. These research endeavors require the acquisition of complex and varied datasets through large, interdisciplinary and collaborative efforts. However, no unifying framework currently exists for the marine science community to integrate sequencing data with physical, geological, and geochemical datasets. Planet Microbe is a web-based platform that enables data discovery from curated historical and on-going oceanographic sequencing efforts. In Planet Microbe, each ‘omics sample is linked with other biological and physiochemical measurements collected for the same water samples or during the same sample collection event, to provide a broader environmental context. This work highlights the need for curated aggregation efforts that can enable new insights into high-quality metagenomic datasets. Planet Microbe is freely accessible from https://www.planetmicrobe.org/.

Genomic Signature of Mismatch Repair Deficiency in Areca Nut–Related Oral Cancer

Areca nut (AN) chewing contributes to an increase of oral squamous cell carcinoma (OSCC) cases in South and Southeast Asia; however, genomic events underlying the carcinogenesis process of AN-related OSCC remain unclear. Here, we comprehensively describe the genomic and transcriptome alterations of 113 Chinese OSCC patients (89 AN related and 24 AN negative) by whole-exome sequencing and RNA sequencing, and we compared the genomic differences between AN-related and AN-negative samples by integrating sequencing data of 325 OSCC patients from The Cancer Genome Atlas database and 50 from a published Taiwanese study. We identified 11 significantly mutated genes for OSCC, including 4 novel ones (ATG2A, WEE1, DST, and TSC2), of which WEE1 and ATG2A mutated with significantly higher rates in AN-related samples (P = 0.04 and P = 0.003, respectively). Mutational signature analysis revealed that AN-related OSCCs were specially characterized by the genomic signature of mismatch repair deficiency (dMMR), which could also predict the prognosis status of AN-related OSCC. In addition, an elevated PD-L1 expression was also observed in both AN-related patients (P = 3.71 × 10−11) and those with a high dMMR level (P = 1.99 × 10−4). Further differential expression analysis and in vitro experiments confirmed the role of dMMR in the development of OSCC induced by AN exposure. Taken together, this study first revealed the molecular profiles and highlighted the role of dMMR in AN-related OSCC among the Chinese population and identified that AN-related OSCC may represent a potential cohort for effective anti-PD-1/L1 immunotherapy.

A chromosome-level genome assembly of Pyropia haitanensis (Bangiales, Rhodophyta).

Pyropia haitanensis (Bangiales, Rhodophyta), a major economically important marine crop, is also considered as an ideal research model of Rhodophyta to address several major biological questions such as sexual reproduction and adaptation to intertidal abiotic stresses. However, comparative genomic analysis to decipher the underlying molecular mechanisms is hindered by the lack of high‐quality genome information. Therefore, we integrated sequencing data from Illumina short‐read sequencing, PacBio single‐molecule sequencing and BioNano optical genome mapping. The assembled genome was approximately 53.3 Mb with an average GC% of 67.9%. The contig N50 and scaffold N50 were 510.3 kb and 5.8 Mb, respectively. Additionally, 10 superscaffolds representing 80.9% of the total assembly (42.7 Mb) were anchored and orientated to the 5 linkage groups based on markers and genetic distance; this outcome is consistent with the karyotype of five chromosomes (n = 5) based on cytological observation in P. haitanensis. Approximately 9.6% and 14.6% of the genomic region were interspersed repeat and tandem repeat elements, respectively. Based on full‐length transcriptome data generated by PacBio, 10,903 protein‐coding genes were identified. The construction of a genome‐wide phylogenetic tree demonstrated that the divergence time of P. haitanensis and Porphyra umbilicalis was ~204.4 Ma. Interspecies comparison revealed that 493 gene families were expanded and that 449 were contracted in the P. haitanensis genome compared with those in the Po. umbilicalis genome. The genome identified is of great value for further research on the genome evolution of red algae and genetic adaptation to intertidal stresses.

VisFeature: a stand-alone program for visualizing and analyzing statistical features of biological sequences.

Many efforts have been made in developing bioinformatics algorithms to predict functional attributes of genes and proteins from their primary sequences. One challenge in this process is to intuitively analyze and to understand the statistical features that have been selected by heuristic or iterative methods. In this paper, we developed VisFeature, which aims to be a helpful software tool that allows the users to intuitively visualize and analyze statistical features of all types of biological sequence, including DNA, RNA and proteins. VisFeature also integrates sequence data retrieval, multiple sequence alignments and statistical feature generation functions. VisFeature is a desktop application that is implemented using JavaScript/Electron and R. The source codes of VisFeature are freely accessible from the GitHub repository (https://github.com/wangjun1996/VisFeature). The binary release, which includes an example dataset, can be freely downloaded from the same GitHub repository (https://github.com/wangjun1996/VisFeature/releases). Supplementary data are available at Bioinformatics online.

Atheroprotective Flow Upregulates ITPR3 (Inositol 1,4,5-Trisphosphate Receptor 3) in Vascular Endothelium via KLF4 (Krüppel-Like Factor 4)-Mediated Histone Modifications.

Objective- The topographical distribution of atherosclerosis in vasculature underscores the importance of shear stress in regulating endothelium. With a systems approach integrating sequencing data, the current study aims to explore the link between shear stress-regulated master transcription factor and its regulation of endothelial cell (EC) function via epigenetic modifications. Approach and Results- H3K27ac (acetylation of histone 3 lysine 27)-ChIP-seq (chromatin immunoprecipitation followed by high throughput sequencing), ATAC-seq (an assay for transposase-accessible chromatin-sequencing), and RNA-seq (RNA-sequencing) were performed to investigate the genome-wide epigenetic regulations in ECs in response to atheroprotective pulsatile shear stress (PS). In silico prediction revealed that KLF4 binding motifs were enriched in the PS-enhanced H3K27ac regions. By integrating PS- and KLF4-modulated H3K27ac, we identified 18 novel PS-upregulated genes. The promoter regions of these genes showed an overlap between the KLF4-enhanced assay for transposase-accessible chromatin signals and the PS-induced H3K27ac peaks. Experiments using ECs isolated from mouse aorta, lung ECs from EC-KLF4-TG versus EC-KLF4-KO mice, and atorvastatin-treated ECs showed that ITPR3 (inositol 1,4,5-trisphosphate receptor 3) was robustly activated by KLF4 and statins. KLF4 ATAC-qPCR (quantitative polymerase chain reaction) and ChIP-qPCR further demonstrated that a specific locus in the promoter region of the ITPR3 gene was essential for KLF4 binding, H3K27ac enrichment, chromatin accessibility, RNA polymerase II recruitment, and ITPR3 transcriptional activation. Deletion of this KLF4 binding locus in ECs by using CRISPR-Cas9 resulted in blunted calcium influx, reduced expression of endothelial nitric oxide synthase, and diminished nitric oxide bioavailability. Conclusions- These results from a novel multiomics study suggest that KLF4 is crucial for PS-modulated H3K27ac that allow the transcriptional activation of ITPR3. This novel mechanism contributes to the Ca2+-dependent eNOS (endothelial nitric oxide synthase) activation and EC homeostasis.

MAGI: A Method for Metabolite Annotation and Gene Integration.

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (that is, gene annotations). Metabolite Annotation and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network. This is calculated by a method that emphasizes consensus between metabolites and genes via biochemical reactions. To demonstrate the potential of this method, we applied MAGI to integrate sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. Our findings suggest that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations in this organism. MAGI also made biochemical predictions for poorly annotated genes that were consistent with the extensive literature on this important organism. This limited analysis suggests that using metabolomics data has the potential to improve annotations in sequenced organisms and also provides testable hypotheses for specific biochemical functions. MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with source code available at https://github.com/biorack/magi .

Nextstrain: real-time tracking of pathogen evolution.

SummaryUnderstanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualization platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, open to health professionals, epidemiologists, virologists and the public alike.Availability and implementationAll code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.