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Summary: Combinatorial signaling by proinflammatory cytokines synergizes to exacerbate toxicity to cells and tissue injury during acute infections. To explore synergism at the gene-regulatory level, we investigated the dynamics of transcription and chromatin signaling in response to dual cytokines by integrating nascent RNA imaging mass spectrometry, RNA sequencing, amplification-independent mRNA quantification, assay for transposase-accessible chromatin using sequencing (ATAC-seq), and transcription factor profiling. Costimulation with interferon-gamma (IFNγ) and tumor necrosis factor alpha (TNFα) synergistically induced a small subset of genes, including the chemokines CXCL9, -10, and -11. Gene induction coincided with increased chromatin accessibility at non-coding regions enriched for p65 and STAT1 binding sites. To discover coactivator dependencies, we conducted a targeted chemogenomic screen of transcriptional inhibitors followed by modeling of inhibitor dose-response curves. These results identified high efficacy of either p300/CREB-binding protein (CBP) or bromodomain and extra-terminal (BET) bromodomain inhibitors to disrupt induction of synergy genes. Combination p300/CBP and BET bromodomain inhibition at half-maximal inhibitory concentrations (subIC50) synergistically abrogated IFNγ/TNFα-induced chemokine gene and protein levels.

Summary: Genomic and transcriptomic analysis has furthered our understanding of many tumors. Yet, thyroid cancer management is largely guided by staging and histology, with few molecular prognostic and treatment biomarkers. Here, we utilize a large cohort of 251 patients with 312 samples from two tertiary medical centers and perform DNA/RNA sequencing, spatial transcriptomics, and multiplex immunofluorescence to identify biomarkers of aggressive thyroid malignancy. We identify high-risk mutations and discover a unique molecular signature of aggressive disease, the Molecular Aggression and Prediction (MAP) score, which provides improved prognostication over high-risk mutations alone. The MAP score is enriched for genes involved in epithelial de-differentiation, cellular division, and the tumor microenvironment. The MAP score also identifies aggressive tumors with lymphocyte-rich stroma that may benefit from immunotherapy. Future clinical profiling of the stromal microenvironment of thyroid cancer could improve prognostication, inform immunotherapy, and support development of novel therapeutics for thyroid cancer and other stroma-rich tumors.

Single-cell sequencing have been widely used to characterize cellular heterogeneity. Sample multiplexing where multiple samples are pooled together for single-cell experiments, attracts wide attention due to its benefits of increasing capacity, reducing costs, and minimizing batch effects. To analyze multiplexed data, the first crucial step is to demultiplex, the process of assigning cells to individual samples. Inaccurate demultiplexing will create false cell types and result in misleading characterization. We propose scDemultiplex, which models hashtag oligo (HTO) counts with beta-binomial distribution and uses an iterative strategy for further refinement. Compared with seven existing demultiplexing approaches, scDemultiplex achieved great performance in both high-quality and low-quality data. Additionally, scDemultiplex can be combined with other approaches to improve their performance.

Introduction: Salt sensitivity of blood pressure is a phenomenon in which blood pressure changes according to dietary sodium intake. Our previous studies found that high salt activates antigen presenting cells, resulting in the development of hypertension. The mechanisms by which salt-induced immune cell activation is regulated in salt sensitivity of blood pressure are unknown. In the current study, we investigated dietary salt-induced effects on the renin-angiotensin-aldosterone system (RAAS) gene expression in myeloid immune cells and their impact on salt sensitive hypertension in humans.Methods: We performed both bulk and single-cell sequencing analysis on immune cells with in vitro and in vivo high dietary salt treatment in humans using a rigorous salt-loading/depletion protocol to phenotype salt-sensitivity of blood pressure. We also measured plasma renin and aldosterone using radioimmunoassay.Results: We found that while in vitro high sodium exposure downregulated the expression of renin, renin binding protein and renin receptor, there were no significant changes in the genes of the renin-angiotensin system in response to dietary salt loading and depletion in vivo. Plasma renin in salt sensitive individuals tended to be lower with a blunted response to the salt loading/depletion challenge as previously reported.Discussion: These findings suggest that unlike systemic RAAS, acute changes in dietary salt intake do not regulate RAAS expression in myeloid immune cells.

The molecular and cellular underpinnings of cribriform prostate cancer aggressiveness remain to be explored. Here, the authors perform single-cell RNA-sequencing, TCR sequencing and histology and reveal cancer cell intrinsic pathways and an immunosuppressive tumour microenvironment.

Abstract Aim Deregulated signaling pathways are a hallmark feature of oncogenesis and driver of tumor progression. Dual specificity protein phosphatase 4 (DUSP4) is a critical negative regulator of the mitogen-activated protein kinase (MAPK) pathway and is often deleted or epigenetically silenced in tumors. DUSP4 alterations lead to hyperactivation of MAPK signaling in many cancers, including breast cancer, which often harbor mutations in cell cycle checkpoint genes, particularly in TP53. Methods Using a genetically engineered mouse model, we generated mammary-specific Dusp4-deleted primary epithelial cells to investigate the necessary conditions in which DUSP4 loss may drive breast cancer oncogenesis. Results We found that Dusp4 loss alone is insufficient in mediating tumorigenesis, but alternatively converges with loss in Trp53 and MYC amplification to induce tumorigenesis primarily through chromosome 5 amplification, which specifically upregulates Dbf4, a cell cycle gene that promotes cellular replication by mediating cell cycle checkpoint escape. Conclusions This study identifies a novel mechanism for breast tumorigenesis implicating Dusp4 loss and p53 mutations in cellular acquisition of Dbf4 upregulation as a driver of cellular replication and cell cycle checkpoint escape.

Previous studies report that mutant p53 proteins have gain-of-function activities and cause oncogenic phenotypes. Herein, the authors engineered two isogenic epithelial cell lines to express wild-type or missense mutant p53 or be deficient for p53 protein and show that aneuploidy drives several of the GOF phenotypes previously ascribed to mutant p53.

Background The systemic inflammation that occurs after exposure to cardiopulmonary bypass (CPB), which is especially severe in neonatal patients, is associated with poorer outcomes and is not well understood. In order to gain deeper insight into how exposure to bypass activates inflammatory responses in circulating leukocytes, we studied changes in microRNA (miRNA) expression during and after exposure to bypass. miRNAs are small noncoding RNAs that have important roles in modulating protein levels and function of cells. Methods and Results We performed miRNA‐sequencing on leukocytes isolated from neonatal patients with CPB (n=5) at 7 time points during the process of CPB, including before the initiation of bypass, during bypass, and at 3 time points during the first 24 hours after weaning from bypass. We identified significant differentially expressed miRNAs using generalized linear regression models, and miRNAs were defined as statistically significant using a false discovery rate–adjusted P

Motivation: Extracellular vesicles (EVs) are produced and released by most cells and are now recognized to play a role in intercellular communication through the delivery of molecular cargo, including proteins, lipids, and RNA. Small RNA sequencing (small RNA-seq) has been widely used to characterize the small RNA content in EVs. However, there is a lack of a systematic assessment of the quality, technical biases, RNA composition, and RNA biotypes enrichment for small RNA profiling of EVs across cell types, biofluids, and conditions. Methods: We collected and reanalyzed small RNA-seq datasets for 2756 samples from 83 studies involving 55 with EVs only and 28 with both EVs and matched donor cells. We assessed their quality by the total number of reads after adapter trimming, the overall alignment rate to the host and non-host genomes, and the proportional abundance of total small RNA and specific biotypes, such as miRNA, tRNA, rRNA, and Y RNA. Results: We found that EV extraction methods varied in their reproducibility in isolating small RNAs, with effects on small RNA composition. Comparing proportional abundances of RNA biotypes between EVs and matched donor cells, we discovered that rRNA and tRNA fragments were relatively enriched, but miRNAs and snoRNA were depleted in EVs. Except for the export of eight miRNAs being context-independent, the selective release of most miRNAs into EVs was study-specific. Conclusion: This work guides quality control and the selection of EV isolation methods and enhances the interpretation of small RNA contents and preferential loading in EVs.

Objective Small RNA (sRNA) sequencing has revealed new sRNA classes beyond microRNAs (miRNAs). These sRNAs can regulate genes and act as biomarkers. The aim of this study was to determine if the endogenous plasma sRNA landscape is altered in patients with rheumatoid arthritis (RA) compared with control subjects and to determine its association with disease‐related parameters in RA. Methods sRNA sequencing was performed on plasma from 165 RA and 90 control subjects who were frequency‐matched for age, race, and sex. Endogenous sRNAs, such as miRNAs, isomiRs, sRNAs derived from small nuclear RNAs (snDRs), small nucleolar RNAs (snoDRs), Y RNAs (yDRs), transfer‐derived RNAs (tDRs), long noncoding RNAs (lncDRs) as well as miscellaneous sRNAs (miscRNAs), were quantified using Tools for Integrative Genome analysis of Extracellular sRNAs (TIGER). Individual and categories of sRNAs were compared between RA and controls, and significantly altered sRNAs and sRNA categories were correlated with disease activity and general laboratory measures in RA. Results Patients with RA had more miRNAs (1.42‐fold, P = 0.01), more tDRs (1.14‐fold, P = 0.04), and fewer yDRs (−1.41‐fold, P = 0.009) compared with control subjects. Disease duration was inversely associated with yDRs. Disease‐related parameters, such as Disease Activity Score‐28 (DAS28), swollen joint count, and inflammatory markers were significantly positively associated with tDRs and miscRNAs, and miR‐22‐3p and related sequences and isomiRs were most significantly associated with DAS28. Conclusion Endogenous plasma sRNAs are altered in RA compared with control subjects. Although individual miRNAs have been well studied and many are excellent biomarkers in RA, several non‐miRNA sRNAs were significantly associated with disease‐related parameters as classes and may represent novel biomarkers for RA.

Objective: Type 2 diabetes is characterized by hyperglycemia and inflammation. Prostaglandin E2, which signals through four G protein-coupled receptors (EP1-4), is a mediator of inflammation and is upregulated in diabetes. We have shown previously that EP3 receptor blockade promotes β-cell proliferation and survival in isolated mouse and human islets ex vivo. Here, we analyzed whether systemic EP3 blockade could enhance β-cell mass and identity in the setting of type 2 diabetes using mice with a spontaneous mutation in the leptin receptor (Leprdb). Methods: Four- or six-week-old, db/+, and db/db male mice were treated with an EP3 antagonist daily for two weeks. Pancreata were analyzed for α-cell and β-cell proliferation and β-cell mass. Islets were isolated for transcriptomic analysis. Selected gene expression changes were validated by immunolabeling of the pancreatic tissue sections. Results: EP3 blockade increased β-cell mass in db/db mice through enhanced β-cell proliferation. Importantly, there were no effects on α-cell proliferation. EP3 blockade reversed the changes in islet gene expression associated with the db/db phenotype and restored the islet architecture. Expression of the GLP-1 receptor was slightly increased by EP3 antagonist treatment in db/db mice. In addition, the transcription factor nuclear factor E2-related factor 2 (Nrf2) and downstream targets were increased in islets from db/db mice in response to treatment with an EP3 antagonist. The markers of oxidative stress were decreased. Conclusions: The current study suggests that EP3 blockade promotes β-cell mass expansion in db/db mice. The beneficial effects of EP3 blockade may be mediated through Nrf2, which has recently emerged as a key mediator in the protection against cellular oxidative damage.

Summary: In this study low-input RNA-sequencing was used to annotate the molecular identity of endothelial cells isolated and immunopurified with CD144 microbeads. Using this technique, comparative gene expression profiling from healthy subjects and patients with type 2 diabetes mellitus identified both known and novel pathways linked with EC dysfunction. Modeling of diabetes by treating cultured ECs with high glucose identified shared changes in gene expression in diabetic cells. Overall, the data demonstrate how purified ECs from patients can be used to generate new hypotheses about mechanisms of human vascular disease. Key Words: endothelial cells, gene expression, diabetes mellitus, endothelial cell dysfunction

Summary: We report that p73 is expressed in ovarian granulosa cells and that loss of p73 leads to attenuated follicle development, ovulation, and corpus luteum formation, resulting in decreased levels of circulating progesterone and defects in mammary gland branching. Ectopic progesterone in p73-deficient mice completely rescued the mammary branching and partially rescued the ovarian follicle development defects. Performing RNA sequencing (RNA-seq) on transcripts from murine wild-type and p73-deficient antral follicles, we discovered differentially expressed genes that regulate biological adhesion programs. Through modulation of p73 expression in murine granulosa cells and transformed cell lines, followed by RNA-seq and chromatin immunoprecipitation sequencing, we discovered p73-dependent regulation of a gene set necessary for cell adhesion and migration and components of the focimatrix (focal intra-epithelial matrix), a basal lamina between granulosa cells that promotes follicle maturation. In summary, p73 is essential for ovarian folliculogenesis and functions as a key regulator of a gene network involved in cell-to-cell adhesion and migration. : Molecular Network; Functional Aspects of Cell Biology; Developmental Biology; Omics Subject Areas: Molecular Network, Functional Aspects of Cell Biology, Developmental Biology, Omics

Very short tandem repeats bear substantial genetic, evolutional, and pathological significance in genome analyses. Here, we compiled a census of tandem mono-nucleotide/di-nucleotide/tri-nucleotide repeats (MNRs/DNRs/TNRs) in GRCh38, which we term "polytracts" in general. Of the human genome, 144.4 million nucleotides (4.7%) are occupied by polytracts, and 0.47 million single nucleotides are identified as polytract hinges, i.e., break-points of tandem polytracts. Preliminary exploration of the census suggested polytract hinge sites and boundaries of AAC polytracts may bear a higher mapping error rate than other polytract regions. Further, we revealed landscapes of polytract enrichment with respect to nearly a hundred genomic features. We found MNRs, DNRs, and TNRs displayed noticeable difference in terms of locational enrichment for miscellaneous genomic features, especially RNA editing events. Non-canonical and C-to-U RNA-editing events are enriched inside and/or adjacent to MNRs, while all categories of RNA-editing events are under-represented in DNRs. A-to-I RNA-editing events are generally under-represented in polytracts. The selective enrichment of non-canonical RNA-editing events within MNR adjacency provides a negative evidence against their authenticity. To enable similar locational enrichment analyses in relation to polytracts, we developed a software Polytrap which can handle 11 reference genomes. Additionally, we compiled polytracts of four model organisms into a Track Hub which can be integrated into USCS Genome Browser as an official track for convenient visualization of polytracts.

Abstract Background One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. Results To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference’s distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/. Conclusions RnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.

Abstract Background High throughput sequencing technology enables the both the human genome and transcriptome to be screened at the single nucleotide resolution. Tools have been developed to infer single nucleotide variants (SNVs) from both DNA and RNA sequencing data. To evaluate how much difference can be expected between DNA and RNA sequencing data, and among tissue sources, we designed a study to examine the single nucleotide difference among five sources of high throughput sequencing data generated from the same individual, including exome sequencing from blood, tumor and adjacent normal tissue, and RNAseq from tumor and adjacent normal tissue. Results Through careful quality control and analysis of the SNVs, we found little difference between DNA-DNA pairs (1%–2%). However, between DNA-RNA pairs, SNV differences ranged anywhere from 10% to 20%. Conclusions Only a small portion of these differences can be explained by RNA editing. Instead, the majority of the DNA-RNA differences should be attributed to technical errors from sequencing and post-processing of RNAseq data. Our analysis results suggest that SNV detection using RNAseq is subject to high false positive rates.

Abstract Background Triple negative breast cancer (TNBC) is a heterogeneous disease that lacks unifying molecular alterations that can guide therapy decisions. We previously identified distinct molecular subtypes of TNBC (TNBCtype) using gene expression data generated on a microarray platform using frozen tumor specimens. Tumors and cell lines representing the identified subtypes have distinct enrichment in biologically relevant transcripts with differing sensitivity to standard chemotherapies and targeted agents. Since our initial discoveries, RNA-sequencing (RNA-seq) has evolved as a sensitive and quantitative tool to measure transcript abundance. Methods To demonstrate that TNBC subtypes were similar between platforms, we compared gene expression from matched specimens profiled by both microarray and RNA-seq from The Cancer Genome Atlas (TCGA). In the clinical care of patients with TNBC, tumor specimens collected for diagnostic purposes are processed by formalin fixation and paraffin-embedding (FFPE). Thus, for TNBCtype to eventually have broad and practical clinical utility we performed RNA-seq gene expression and molecular classification comparison between fresh-frozen (FF) and FFPE tumor specimens. Results Analysis of TCGA showed consistent subtype calls between 91% of evaluable samples demonstrating conservation of TNBC subtypes across microarray and RNA-seq platforms. We compared RNA-seq performed on 21-paired FF and FFPE TNBC specimens and evaluated genome alignment, transcript coverage, differential transcript enrichment and concordance of TNBC molecular subtype calls. We demonstrate that subtype accuracy between matched FF and FFPE samples increases with sequencing depth and correlation strength to an individual TNBC subtype. Conclusions TNBC subtypes were reliably identified from FFPE samples, with highest accuracy if the samples were less than 4 years old and reproducible subtyping increased with sequencing depth. To reproducibly subtype tumors using gene expression, it is critical to select genes that do not vary due to platform type, tissue processing or RNA isolation method. The majority of differentially expressed transcripts between matched FF and FFPE samples could be attributed to transcripts selected for by RNA enrichment method. While differentially expressed transcripts did not impact TNBC subtyping, they will provide guidance on determining which transcripts to avoid when implementing a gene set size reduction strategy. Trial registration NCT00930930 07/01/2009.

RNA editing is phenomenon that occurs in both protein coding and non-coding RNAs. Increasing evidence have shown that adenosine-to-inosine RNA editing can potentially rendering substantial functional effects throughout the genome. Using RNA editing datasets from two large consortiums: The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) project, we quantitatively analyzed human genome-wide RNA editing events derived from tumor or normal tissues. Generally, a common RNA editing site tends to have a higher editing level in tumors as compared to normal samples. Of the 14 tumor-normal-paired cancer types examined, Eleven of the 14 cancers tested had overall increased RNA editing levels in the tumors. The editomes in cancer or normal tissues were dissected by genomic locations, and significant RNA editing locational difference was found between cancerous and healthy subjects. Additionally, our results indicated a significant correlation between the RNA editing rate and the gene density across chromosomes, highlighted hyper RNA editing clusters through visualization of running RNA editing rates along chromosomes, and identified hyper RNA edited genes (protein-coding genes, lincRNAs, and pseudogenes) that embody a large portion of cancer prognostic predictors. This study reinforces the potential functional effects of RNA editing in protein-coding genes, and also makes a strong foundation for further exploration of RNA editing’s roles in non-coding regions.

To comprehensively study extracellular small RNAs (sRNA) by sequencing (sRNA-seq), we developed a novel pipeline to overcome current limitations in analysis entitled, “Tools for Integrative Genome analysis of Extracellular sRNAs (TIGER)”. To demonstrate the power of this tool, sRNA-seq was performed on mouse lipoproteins, bile, urine and livers. A key advance for the TIGER pipeline is the ability to analyse both host and non-host sRNAs at genomic, parent RNA and individual fragment levels. TIGER was able to identify approximately 60% of sRNAs on lipoproteins and >85% of sRNAs in liver, bile and urine, a significant advance compared to existing software. Moreover, TIGER facilitated the comparison of lipoprotein sRNA signatures to disparate sample types at each level using hierarchical clustering, correlations, beta-dispersions, principal coordinate analysis and permutational multivariate analysis of variance. TIGER analysis was also used to quantify distinct features of exRNAs, including 5ʹ miRNA variants, 3ʹ miRNA non-templated additions and parent RNA positional coverage. Results suggest that the majority of sRNAs on lipoproteins are non-host sRNAs derived from bacterial sources in the microbiome and environment, specifically rRNA-derived sRNAs from Proteobacteria. Collectively, TIGER facilitated novel discoveries of lipoprotein and biofluid sRNAs and has tremendous applicability for the field of extracellular RNA.

Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such as expansion or shrinkage or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes and regional specification, identification and definition of altered cell infiltrates in tumorigenesis, and benchmarking batch-correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions and has broad utility for gaining insight into tissue-level perturbations at the single-cell resolution.

Bromodomain and extra-terminal domain (BET) family inhibitors offer an approach to treating hematological malignancies. We used precision nuclear run-on transcription sequencing (PRO-seq) to create high-resolution maps of active RNA polymerases across the genome in t(8;21) acute myeloid leukemia (AML), as these polymerases are exceptionally sensitive to BET inhibitors. PRO-seq identified over 1,400 genes showing impaired release of promoter-proximal paused RNA polymerases, including the stem cell factor receptor tyrosine kinase KIT that is mutated in t(8;21) AML. PRO-seq also identified an enhancer 3′ to KIT. Chromosome conformation capture confirmed contacts between this enhancer and the KIT promoter, while CRISPRi-mediated repression of this enhancer impaired cell growth. PRO-seq also identified microRNAs, including MIR29C and MIR29B2, that target the anti-apoptotic factor MCL1 and were repressed by BET inhibitors. MCL1 protein was upregulated, and inhibition of BET proteins sensitized t(8:21)-containing cells to MCL1 inhibition, suggesting a potential mechanism of resistance to BET-inhibitor-induced cell death.

Parenteral prostacyclin therapy is the most efficacious pharmacologic treatment for pulmonary arterial hypertension (PAH), but clinical response is variable. We sought to identify clinical, hemodynamic, and genetic associations with response to prostacyclin therapy. We performed a retrospective analysis of patients within a de-identified electronic health record and associated DNA biobank. Patients with PAH and a right heart catheterization (RHC) in the six months before initiation of a parenteral prostacyclin were included. Responders were defined a priori by attainment of World Health Organization (WHO) functional class (FC) 2 or better at the time of repeat RHC within two years. We performed exploratory analyses to identify genomic associations with prostacyclin response. Of 129 patients identified, 54 met our criteria for “responders.” These patients were younger, more likely to be male, and were less likely to have connective tissue disease-related PAH. At follow-up, responders had improved hemodynamics, 6-min walk distance, and long-term survival. Baseline PA oxygen saturation (hazard ratio [HR] 0.568 [0.34–0.95]) and follow-up FC (HR = 2.57 [1.22–5.43]) were associated with survival. Prostacyclin responders were enriched in alleles related to cell development and circulatory system development and pathways related to aldosterone metabolism, cAMP signaling, and vascular smooth muscle contraction ( P

The recently-introduced Orbitrap Fusion mass spectrometry permits various types of MS2 acquisition methods. To date, these different MS2 strategies and the optimal data interpretation approach for each have not been adequately evaluated. This study comprehensively investigated the four MS2 strategies: HCD-OT (higher-energy-collisional-dissociation with Orbitrap detection), HCD-IT (HCD with ion trap, IT), CID-IT (collision-induced-dissociation with IT) and CID-OT on Orbitrap Fusion. To achieve extensive comparison and identify the optimal data interpretation method for each technique, several search engines (SEQUEST and Mascot) and post-processing methods (score-based, PeptideProphet, and Percolator) were assessed for all techniques for the analysis of a human cell proteome. It was found that divergent conclusions could be made from the same dataset when different data interpretation approaches were used and therefore requiring a relatively fair comparison among techniques. Percolator was chosen for comparison of techniques because it performs the best among all search engines and MS2 strategies. For the analysis of human cell proteome using individual MS2 strategies, the highest number of identifications was achieved by HCD-OT, followed by HCD-IT and CID-IT. Based on these results, we concluded that a relatively fair platform for data interpretation is necessary to avoid divergent conclusions from the same dataset, and HCD-OT and HCD-IT may be preferable for protein/peptide identification using Orbitrap Fusion.

Background. Proper rRNA depletion is crucial for the successful utilization of FFPE specimens when studying gene expression. We performed a study to evaluate two major rRNA depletion methods: Ribo-Zero and RNase H. RNAs extracted from 4 samples were treated with the two rRNA depletion methods in duplicate and sequenced (N=16). We evaluated their reducibility, ability to detect RNA, and ability to molecularly subtype these triple negative breast cancer specimens. Results. Both rRNA depletion methods produced consistent data between the technical replicates. We found that the RNase H method produced higher quality RNAseq data as compared to the Ribo-Zero method. In addition, we evaluated the RNAseq data generated from the FFPE tissue samples for noncoding RNA, including lncRNA, enhancer/super enhancer RNA, and single nucleotide variation (SNV). We found that the RNase H is more suitable for detecting high-quality, noncoding RNAs as compared to the Ribo-Zero and provided more consistent molecular subtype identification between replicates. Unfortunately, neither method produced reliable SNV data. Conclusions. In conclusion, for FFPE specimens, the RNase H rRNA depletion method performed better than the Ribo-Zero. Neither method generates data sufficient for SNV detection.

RNAseq and microarray methods are frequently used to measure gene expression level. While similar in purpose, there are fundamental differences between the two technologies. Here, we present the largest comparative study between microarray and RNAseq methods to date using The Cancer Genome Atlas (TCGA) data. We found high correlations between expression data obtained from the Affymetrix one-channel microarray and RNAseq (Spearman correlations coefficients of ∼0.8). We also observed that the low abundance genes had poorer correlations between microarray and RNAseq data than high abundance genes. As expected, due to measurement and normalization differences, Agilent two-channel microarray and RNAseq data were poorly correlated (Spearman correlations coefficients of only ∼0.2). By examining the differentially expressed genes between tumor and normal samples we observed reasonable concordance in directionality between Agilent two-channel microarray and RNAseq data, although a small group of genes were found to have expression changes reported in opposite directions using these two technologies. Overall, RNAseq produces comparable results to microarray technologies in term of expression profiling. The RNAseq normalization methods RPKM and RSEM produce similar results on the gene level and reasonably concordant results on the exon level. Longer exons tended to have better concordance between the two normalization methods than shorter exons.

Type 2 diabetes and its chronic complications have become a worldwide epidemic nowadays. However, its molecular mechanism is still unknown. We have previously identified a novel variant rs12742393 of NOS1AP for type 2 diabetes susceptibility in the Chinese population. In this study, we analyzed the total serum profiling among three genotypes of rs12742393 to discover potential crosstalk under the variant and the disease through proteomic analyses for the first time. We used OFFGEL peptide fractionation, LC-MS/MS analysis, and label-free quantification to profile the fasting human serum samples of the genotypes in rs12742393 (n=4, for CC, AC, and AA, resp.). Four proteins were identified, including apoA4, alpha1-ACT, HABP2, and keratin 10, with blood levels changed significantly between CC and AA homozygotes of rs12742393. Compared with AA group, the levels of apoA4 increased (P=0.000265), whereas the concentration of alpha1-ACT, HABP2, and keratin 10 decreased in CC group (P=0.011116, 0.021175, and 0.015661, resp.). Then we selected additional fasting serum samples for ELISA and western blot validation. However, no significant differences were identified by neither ELISA nor western blot (P>0.05). The protein profiling changes between the genotypes of rs12742393 indicated that this SNP might play a role in the development of type 2 diabetes.