Aging and obesity prime the methylome and transcriptome of adipose stem cells for disease and dysfunction

The epigenome of stem cells occupies a critical interface between genes and environment, serving to regulate expression through modification by intrinsic and extrinsic factors. We hypothesized that aging and obesity, which represent major risk factors for a variety of diseases, synergistically modify the epigenome of adult adipose stem cells (ASCs). Using integrated RNA- and targeted bisulfite-sequencing in murine ASCs from lean and obese mice at 5- and 12-months of age, we identified global DNA hypomethylation with either aging or obesity, and a synergistic effect of aging combined with obesity. The transcriptome of ASCs in lean mice was relatively stable to the effects of age, but this was not true in obese mice. Functional pathway analyses identified a subset of genes with critical roles in progenitors and in diseases of obesity and aging. Specifically, Mapt, Nr3c2, App, and Ctnnb1 emerged as potential hypomethylated upstream regulators in both aging and obesity (AL vs. YL and AO vs. YO), and App, Ctnnb1, Hipk2, Id2, and Tp53 exhibited additional effects of aging in obese animals. Furthermore, Foxo3 and Ccnd1 were potential hypermethylated upstream regulators of healthy aging (AL vs. YL), and of the effects of obesity in young animals (YO vs. YL), suggesting that these factors could play a role in accelerated aging with obesity. Finally, we identified candidate driver genes that appeared recurrently in all analyses and comparisons undertaken. Further mechanistic studies are needed to validate the roles of these genes capable of priming ASCs for dysfunction in aging- and obesity-associated pathologies

Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction

Background Ribosomal RNA (rRNA) comprises at least 90% of total RNA extracted from mammalian tissue or cell line samples. Informative transcriptional profiling using massively parallel sequencing technologies requires either enrichment of mature poly-adenylated transcripts or targeted depletion of the rRNA fraction. The latter method is of particular interest because it is compatible with degraded samples such as those extracted from FFPE and also captures transcripts that are not poly-adenylated such as some non-coding RNAs. Here we provide a cross-site study that evaluates the performance of ribosomal RNA removal kits from Illumina, Takara/Clontech, Kapa Biosystems, Lexogen, New England Biolabs and Qiagen on intact and degraded RNA samples. Results We find that all of the kits are capable of performing significant ribosomal depletion, though there are differences in their ease of use. All kits were able to remove ribosomal RNA to below 20% with intact RNA and identify ~ 14,000 protein coding genes from the Universal Human Reference RNA sample at >1FPKM. Analysis of differentially detected genes between kits suggests that transcript length may be a key factor in library production efficiency. Conclusions These results provide a roadmap for labs on the strengths of each of these methods and how best to utilize them. Keywords: RNAseqr; RNA depletion; Illumina; NGS; ABRF; TranscriptomicsNational Cancer Institute (U.S.) (Grant P30-CA14051)National Institute of Environmental Health Sciences (Grant P30-ES002109

Tumor Biology and Immune Infiltration Define Primary Liver Cancer Subsets Linked to Overall Survival After Immunotherapy

Primary liver cancer is a rising cause of cancer deaths in the US. Although immunotherapy with immune checkpoint inhibitors induces a potent response in a subset of patients, response rates vary among individuals. Predicting which patients will respond to immune checkpoint inhibitors is of great interest in the field. In a retrospective arm of the National Cancer Institute Cancers of the Liver: Accelerating Research of Immunotherapy by a Transdisciplinary Network (NCI-CLARITY) study, we use archived formalin-fixed, paraffin-embedded samples to profile the transcriptome and genomic alterations among 86 hepatocellular carcinoma and cholangiocarcinoma patients prior to and following immune checkpoint inhibitor treatment. Using supervised and unsupervised approaches, we identify stable molecular subtypes linked to overall survival and distinguished by two axes of aggressive tumor biology and microenvironmental features. Moreover, molecular responses to immune checkpoint inhibitor treatment differ between subtypes. Thus, patients with heterogeneous liver cancer may be stratified by molecular status indicative of treatment response to immune checkpoint inhibitors

Nitrogen metabolism and transport in the Arbuscular mycorrhizal interaction

The Arbuscular Mycorrhiza is arguably the world's most important interaction. The roots of over 70 % of all known land plant species form this mutualistic interaction with fungi of the phylum Glomeromycota. Arbuscular mycorrhizal (AM) fungi can act as biofertilizers, bioprotectors and bioregulators of plants and have gained increasing attention for their potential role in sustainable agriculture and in the restoration and bioremediation of contaminated and disturbed sites. In this interaction plants take up mineral nutrients from the soil through their associated AM fungi and transfer in exchange for their beneificial effect on nutrient uptake photosynthetically fixed carbon to the fungus. Nitrogen (N) is known to be transferred from the fungus to the plant in the AM interaction, yet its metabolism, storage and transport in the symbiosis are poorly understood. Here, we report new findings about the N metabolism and transport in the AM symbiosis by analyzing fungal gene expression with quantitative polymerase chain reaction (Q-PCR). In vitro mycorrhizas of Glomus intraradices and Ri T-DNA-transformed carrot roots were grown in two-compartment Petri dishes. Different experiments were carried out to measure the effect of different carbon (C) or nitrogen (N) sources on fungal gene expression. The RNA was extracted from the ERM and processed for Q-PCR using gene specific primers. Inorganic nitrogen is taken up by the fungus with its extraradical mycelium (ERM), is incorporated into amino acids, and translocated from the ERM to the intraradical mycelium (IRM) as arginine, where it is broken down to an inorganic form via the catabolic arm of the urea cycle and transferred to the plant without C (Govindarajulu et al., 2005; Jin et al., 2005). Consistent with the proposed mechanism, the genes involved primarily in nitrogen assimilation were highly expressed in the ERM whereas the mRNA transcripts levels of genes associated with the breakdown of arginine were low in the ERM. The expression levels of these genes are regulated by the C availability for the mycorrhizal fungus and by an exogenous supply of N to the ERM.M.S.Includes bibliographical references (p. 34-38)by Sulbha Choudhar

Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads

Due to advancements in sequencing technology, sequence data production is no longer a constraint in the field of microbiology and has made it possible to study uncultured microbes or whole environments using metagenomics. However, these new technologies introduce different biases in metagenomic sequencing, affecting the nucleotide distribution of resulting sequence reads. Here, we illustrate such biases using two methods. One is based on phylogenetic heatmaps (PGHMs), a novel approach for compact visualization of sequence composition differences between two groups of sequences containing the same phylogenetic groups. This method is well suited for finding noise and biases when comparing metagenomics samples. We apply PGHMs to detect noise and bias in the data produced with different DNA extraction protocols, different sequencing platforms and different experimental frameworks. In parallel, we use principal component analysis displaying different clustering of sequences from each sample to support our findings and illustrate the utility of PGHMs. We considered contributions of the read length and GC-content variation and observed that in most cases biases were generally due to the GC-content of the reads

Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads

Due to advancements in sequencing technology, sequence data production is no longer a constraint in the field of microbiology and has made it possible to study uncultured microbes or whole environments using metagenomics. However, these new technologies introduce different biases in metagenomic sequencing, affecting the nucleotide distribution of resulting sequence reads. Here, we illustrate such biases using two methods. One is based on phylogenetic heatmaps (PGHMs), a novel approach for compact visualization of sequence composition differences between two groups of sequences containing the same phylogenetic groups. This method is well suited for finding noise and biases when comparing metagenomics samples. We apply PGHMs to detect noise and bias in the data produced with different DNA extraction protocols, different sequencing platforms and different experimental frameworks. In parallel, we use principal component analysis displaying different clustering of sequences from each sample to support our findings and illustrate the utility of PGHMs. We considered contributions of the read length and GC-content variation and observed that in most cases biases were generally due to the GC-content of the reads

Sequence composition diversity in Alaskan glacier and other metagenomes

Abstract Metagenomics by next generation sequencing has become an important tool for interrogating complex microbial communities. In this study we analyzed several pairs of metagenomic samples obtained by different methods and observed biases, resulting in different nucleotide composition of the sequenced reads. The pairwise sample comparison was based on the principal component analysis of dinucleotide word frequencies in sequences obtained from different platforms. We found bias in the sequences obtained from the different platforms for the amplified hypervariable regions in 16S rRNA but not in shotgun metagenome reads aligned to such hypervariable regions. The differences and consistency of the distributions of the nucleotides suggest that the biases are likely due to a combination of biases introduced by PCR and different sequencing protocols, and they are related to the GC content of the reads produced. For this reason, caution should be exercised when interpreting the results of comparative metagenomics studies, as they may vary depending on the sequencing technology

Changes in the core endophytic mycobiome of carrot taproots in response to crop management and genotype

Abstract Fungal endophytes can influence production and post-harvest challenges in carrot, though the identity of these microbes as well as factors affecting their composition have not yet been determined, which prevents growers from managing these organisms to improve crop performance. Consequently, we characterized the endophytic mycobiome in the taproots of three carrot genotypes that vary in resistance to two pathogens grown in a trial comparing organic and conventional crop management using Illumina sequencing of the internal transcribed spacer (ITS) gene. A total of 1,480 individual operational taxonomic units (OTUs) were identified. Most were consistent across samples, indicating that they are part of a core mycobiome, though crop management influenced richness and diversity, likely in response to differences in soil properties. There were also differences in individual OTUs among genotypes and the nematode resistant genotype was most responsive to management system indicating that it has greater control over its endophytic mycobiome, which could potentially play a role in resistance. Members of the Ascomycota were most dominant, though the exact function of most taxa remains unclear. Future studies aimed at overcoming difficulties associated with isolating fungal endophytes are needed to identify these microbes at the species level and elucidate their specific functional roles

Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study

Publisher Copyright: © 2021, The Author(s).With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.Peer reviewe