Patterns of Unwanted Biological and Technical Expression Variation Among 49 Human Tissues.

Tissue gene expression studies are impacted by biological and technical sources of variation, which can be broadly classified into wanted and unwanted variation. The latter, if not addressed, results in misleading biological conclusions. Methods have been proposed to reduce unwanted variation, such as normalization and batch correction. A more accurate understanding of all causes of variation could significantly improve the ability of these methods to remove unwanted variation while retaining variation corresponding to the biological question of interest. We used 17,282 samples from 49 human tissues in the Genotype-Tissue Expression data set (v8) to investigate patterns and causes of expression variation. Transcript expression was transformed to z-scores, and only the most variable 2% of transcripts were evaluated and clustered based on coexpression patterns. Clustered gene sets were assigned to different biological or technical causes based on histologic appearances and metadata elements. We identified 522 variable transcript clusters (median: 11 per tissue) among the samples. Of these, 63% were confidently explained, 16% were likely explained, 7% were low confidence explanations, and 14% had no clear cause. Histologic analysis annotated 46 clusters. Other common causes of variability included sex, sequencing contamination, immunoglobulin diversity, and compositional tissue differences. Less common biological causes included death interval (Hardy score), disease status, and age. Technical causes included blood draw timing and harvesting differences. Many of the causes of variation in bulk tissue expression were identifiable in the Tabula Sapiens data set of single-cell expression. This is among the largest explorations of the underlying sources of tissue expression variation. It uncovered expected and unexpected causes of variable gene expression and demonstrated the utility of matched histologic specimens. It further demonstrated the value of acquiring meaningful tissue harvesting metadata elements to use for improved normalization, batch correction, and analysis of both bulk and single-cell RNA-seq data.

Spatial Proteomic Approach to Characterize Skeletal Muscle Myofibers.

Skeletal muscle myofibers have differential protein expression resulting in functionally distinct slow- and fast-twitch types. While certain protein classes are well-characterized, the depth of all proteins involved in this process is unknown. We utilized the Human Protein Atlas (HPA) and the HPASubC tool to classify mosaic expression patterns of staining across 49,600 unique tissue microarray (TMA) images using a visual proteomic approach. We identified 2164 proteins with potential mosaic expression, of which 1605 were categorized as "likely" or "real." This list included both well-known fiber-type-specific and novel proteins. A comparison of the 1605 mosaic proteins with a mass spectrometry (MS)-derived proteomic dataset of single human muscle fibers led to the assignment of 111 proteins to fiber types. We additionally used a multiplexed immunohistochemistry approach, a multiplexed RNA-ISH approach, and STRING v11 to further assign or suggest fiber types of newly characterized mosaic proteins. This visual proteomic analysis of mature skeletal muscle myofibers greatly expands the known repertoire of twitch-type-specific proteins.

Toward the human cellular microRNAome.

MicroRNAs are short RNAs that serve as regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell-autonomously, and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data have caused considerable confusion, and comprehensive cell-level data do not yet exist. Here, we establish the landscape of human cell-specific microRNA expression. This project evaluated 8 billion small RNA-seq reads from 46 primary cell types, 42 cancer or immortalized cell lines, and 26 tissues. It identified both specific and ubiquitous patterns of expression that strongly correlate with adjacent superenhancer activity. Analysis of unaligned RNA reads uncovered 207 unknown minor strand (passenger) microRNAs of known microRNA loci and 495 novel putative microRNA loci. Although cancer cell lines generally recapitulated the expression patterns of matched primary cells, their isomiR sequence families exhibited increased disorder, suggesting DROSHA- and DICER1-dependent microRNA processing variability. Cell-specific patterns of microRNA expression were used to de-convolute variable cellular composition of colon and adipose tissue samples, highlighting one use of these cell-specific microRNA expression data. Characterization of cellular microRNA expression across a wide variety of cell types provides a new understanding of this critical regulatory RNA species.

Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes.

Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted "noncoding RNAs" to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.

A draft map of the human proteome.

The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.

Phosphotyrosine profiling of curcumin-induced signaling.

BACKGROUND: Curcumin, derived from the rhizome Curcuma longa, is a natural anti-cancer agent and has been shown to inhibit proliferation and survival of tumor cells. Although the anti-cancer effects of curcumin are well established, detailed understanding of the signaling pathways altered by curcumin is still lacking. In this study, we carried out SILAC-based quantitative proteomic analysis of a HNSCC cell line (CAL 27) to investigate tyrosine signaling in response to curcumin. RESULTS: Using high resolution Orbitrap Fusion Tribrid Fourier transform mass spectrometer, we identified 627 phosphotyrosine sites mapping to 359 proteins. We observed alterations in the level of phosphorylation of 304 sites corresponding to 197 proteins upon curcumin treatment. We report here for the first time, curcumin-induced alterations in the phosphorylation of several kinases including TNK2, FRK, AXL, MAPK12 and phosphatases such as PTPN6, PTPRK, and INPPL1 among others. Pathway analysis revealed that the proteins differentially phosphorylated in response to curcumin are known to be involved in focal adhesion kinase signaling and actin cytoskeleton reorganization. CONCLUSIONS: The study indicates that curcumin may regulate cellular processes such as proliferation and migration through perturbation of the focal adhesion kinase pathway. This is the first quantitative phosphoproteomics-based study demonstrating the signaling events that are altered in response to curcumin. Considering the importance of curcumin as an anti-cancer agent, this study will significantly improve the current knowledge of curcumin-mediated signaling in cancer.

Quantitative phosphoproteomic analysis of IL-33-mediated signaling.

Interleukin-33 (IL-33) is a novel member of the IL-1 family of cytokines that plays diverse roles in the regulation of immune responses. IL-33 exerts its effects through a heterodimeric receptor complex resulting in the production and release of proinflammatory cytokines. A detailed understanding of the signaling pathways activated by IL-33 is still unclear. To gain insights into the IL-33-mediated signaling mechanisms, we carried out a SILAC-based global quantitative phosphoproteomic analysis that resulted in the identification of 7191 phosphorylation sites derived from 2746 proteins. We observed alterations in the level of phosphorylation in 1050 sites corresponding to 672 proteins upon IL-33 stimulation. We report, for the first time, phosphorylation of multiple protein kinases, including mitogen-activated protein kinase activated protein kinase 2 (Mapkapk2), receptor (TNFRSF) interacting serine-threonine kinase 1 (Ripk1), and NAD kinase (Nadk) that are induced by IL-33. In addition, we observed IL-33-induced phosphorylation of several protein phosphatases including protein tyrosine phosphatase, nonreceptor-type 12 (Ptpn12), and inositol polyphosphate-5-phosphatase D (Inpp5d), which have not been reported previously. Network analysis revealed an enrichment of actin binding and cytoskeleton reorganization that could be important in macrophage activation induced by IL-33. Our study is the first quantitative analysis of IL-33-regulated phosphoproteome. Our findings significantly expand the understanding of IL-33-mediated signaling events and have the potential to provide novel therapeutic targets pertaining to immune-related diseases such as asthma where dysregulation of IL-33 is observed. All MS data have been deposited in the ProteomeXchange with identifier PXD000984 (http://proteomecentral.proteomexchange.org/dataset/PXD000984).

Macrophage migration inhibitory factor - a therapeutic target in gallbladder cancer.

BACKGROUND: Poor prognosis in gallbladder cancer is due to late presentation of the disease, lack of reliable biomarkers for early diagnosis and limited targeted therapies. Early diagnostic markers and novel therapeutic targets can significantly improve clinical management of gallbladder cancer. METHODS: Proteomic analysis of four gallbladder cancer cell lines based on the invasive property (non-invasive to highly invasive) was carried out using the isobaric tags for relative and absolute quantitation labeling-based quantitative proteomic approach. The expression of macrophage migration inhibitory factor was analysed in gallbladder adenocarcinoma tissues using immunohistochemistry. In vitro cellular assays were carried out in a panel of gallbladder cancer cell lines using MIF inhibitors, ISO-1 and 4-IPP or its specific siRNA. RESULTS: The quantitative proteomic experiment led to the identification of 3,653 proteins, among which 654 were found to be overexpressed and 387 were downregulated in the invasive cell lines (OCUG-1, NOZ and GB-d1) compared to the non-invasive cell line, TGBC24TKB. Among these, macrophage migration inhibitory factor (MIF) was observed to be highly overexpressed in two of the invasive cell lines. MIF is a pleiotropic proinflammatory cytokine that plays a causative role in multiple diseases, including cancer. MIF has been reported to play a central role in tumor cell proliferation and invasion in several cancers. Immunohistochemical labeling of tumor tissue microarrays for MIF expression revealed that it was overexpressed in 21 of 29 gallbladder adenocarcinoma cases. Silencing/inhibition of MIF using siRNA and/or MIF antagonists resulted in a significant decrease in cell viability, colony forming ability and invasive property of the gallbladder cancer cells. CONCLUSIONS: Our findings support the role of MIF in tumor aggressiveness and suggest its potential application as a therapeutic target for gallbladder cancer.

Proteomic analysis of human vitreous humor.

BACKGROUND: The vitreous humor is a transparent, gelatinous mass whose main constituent is water. It plays an important role in providing metabolic nutrient requirements of the lens, coordinating eye growth and providing support to the retina. It is in close proximity to the retina and reflects many of the changes occurring in this tissue. The biochemical changes occurring in the vitreous could provide a better understanding about the pathophysiological processes that occur in vitreoretinopathy. In this study, we investigated the proteome of normal human vitreous humor using high resolution Fourier transform mass spectrometry. RESULTS: The vitreous humor was subjected to multiple fractionation techniques followed by LC-MS/MS analysis. We identified 1,205 proteins, 682 of which have not been described previously in the vitreous humor. Most proteins were localized to the extracellular space (24%), cytoplasm (20%) or plasma membrane (14%). Classification based on molecular function showed that 27% had catalytic activity, 10% structural activity, 10% binding activity, 4% cell and 4% transporter activity. Categorization for biological processes showed 28% participate in metabolism, 20% in cell communication and 13% in cell growth. The data have been deposited to the ProteomeXchange with identifier PXD000957. CONCLUSION: This large catalog of vitreous proteins should facilitate biomedical research into pathological conditions of the eye including diabetic retinopathy, retinal detachment and cataract.

Brooklyn plots to identify co-expression dysregulation in single cell sequencing.

Altered open chromatin regions, impacting gene expression, is a feature of some human disorders. We discovered it is possible to detect global changes in genomically-related adjacent gene co-expression within single cell RNA sequencing (scRNA-seq) data. We built a software package to generate and test non-randomness using 'Brooklyn plots' to identify the percent of genes significantly co-expressed from the same chromosome in ∼10 MB intervals across the genome. These plots establish an expected low baseline of co-expression in scRNA-seq from most cell types, but, as seen in dilated cardiomyopathy cardiomyocytes, altered patterns of open chromatin appear. These may relate to larger regions of transcriptional bursting, observable in single cell, but not bulk datasets.

miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs.

MicroRNA-seq data is produced by aligning small RNA sequencing reads of different miRNA transcript isoforms, called isomiRs, to known microRNAs. Aggregation to microRNA-level counts discards information and violates core assumptions of differential expression (DE) methods developed for mRNA-seq data. We establish miRglmm, a DE method for microRNA-seq data, that uses a generalized linear mixed model of isomiR-level counts, facilitating detection of miRNA with differential expression or differential isomiR usage. We demonstrate that miRglmm outperforms current DE methods in estimating DE for miRNA, whether or not there is significant isomiR variability, and simultaneously provides estimates of isomiR-level DE.

A curated human cellular microRNAome based on 196 primary cell types.

BACKGROUND: An incomplete picture of the expression distribution of microRNAs (miRNAs) across human cell types has long hindered our understanding of this important regulatory class of RNA. With the continued increase in available public small RNA sequencing datasets, there is an opportunity to more fully understand the general distribution of miRNAs at the cell level. RESULTS: From the NCBI Sequence Read Archive, we obtained 6,054 human primary cell datasets and processed 4,184 of them through the miRge3.0 small RNA sequencing alignment software. This dataset was curated down, through shared miRNA expression patterns, to 2,077 samples from 196 unique cell types derived from 175 separate studies. Of 2,731 putative miRNAs listed in miRBase (v22.1), 2,452 (89.8%) were detected. Among reasonably expressed miRNAs, 108 were designated as cell specific/near specific, 59 as infrequent, 52 as frequent, 54 as near ubiquitous, and 50 as ubiquitous. The complexity of cellular microRNA expression estimates recapitulates tissue expression patterns and informs on the miRNA composition of plasma. CONCLUSIONS: This study represents the most complete reference, to date, of miRNA expression patterns by primary cell type. The data are available through the human cellular microRNAome track at the UCSC Genome Browser (https://genome.ucsc.edu/cgi-bin/hgHubConnect) and an R/Bioconductor package (https://bioconductor.org/packages/microRNAome/).

Dissecting Plasmodium yoelii Pathobiology: Proteomic Approaches for Decoding Novel Translational and Post-Translational Modifications.

Malaria is a vector-borne disease. It is caused by Plasmodium parasites. Plasmodium yoelii is a rodent model parasite, primarily used for studying parasite development in liver cells and vectors. To better understand parasite biology, we carried out a high-throughput-based proteomic analysis of P. yoelii. From the same mass spectrometry (MS)/MS data set, we also captured several post-translational modified peptides by following a bioinformatics analysis without any prior enrichment. Further, we carried out a proteogenomic analysis, which resulted in improvements to some of the existing gene models along with the identification of several novel genes. Analysis of proteome and post-translational modifications (PTMs) together resulted in the identification of 3124 proteins. The identified PTMs were found to be enriched in mitochondrial metabolic pathways. Subsequent bioinformatics analysis provided an insight into proteins associated with metabolic regulatory mechanisms. Among these, the tricarboxylic acid (TCA) cycle and the isoprenoid synthesis pathway are found to be essential for parasite survival and drug resistance. The proteogenomic analysis discovered 43 novel protein-coding genes. The availability of an in-depth proteomic landscape of a malaria pathogen model will likely facilitate further molecular-level investigations on pre-erythrocytic stages of malaria.

miRge3.0: a comprehensive microRNA and tRF sequencing analysis pipeline.

MicroRNAs and tRFs are classes of small non-coding RNAs, known for their roles in translational regulation of genes. Advances in next-generation sequencing (NGS) have enabled high-throughput small RNA-seq studies, which require robust alignment pipelines. Our laboratory previously developed miRge and miRge2.0, as flexible tools to process sequencing data for annotation of miRNAs and other small-RNA species and further predict novel miRNAs using a support vector machine approach. Although miRge2.0 is a leading analysis tool in terms of speed with unique quantifying and annotation features, it has a few limitations. We present miRge3.0 that provides additional features along with compatibility to newer versions of Cutadapt and Python. The revisions of the tool include the ability to process Unique Molecular Identifiers (UMIs) to account for PCR duplicates while quantifying miRNAs in the datasets, correct erroneous single base substitutions in miRNAs with miREC and an accurate mirGFF3 formatted isomiR tool. miRge3.0 also has speed improvements benchmarked to miRge2.0, Chimira and sRNAbench. Finally, miRge3.0 output integrates into other packages for a streamlined analysis process and provides a cross-platform Graphical User Interface (GUI). In conclusion miRge3.0 is our third generation small RNA-seq aligner with improvements in speed, versatility and functionality over earlier iterations.

Mapping Post-Translational Modifications in Brain Regions in Alzheimer's Disease Using Proteomics Data Mining.

Alzheimer's disease (AD) is a leading cause of dementia and a neurodegenerative disease. Proteomics and post-translational modification (PTM) analyses offer new opportunities for a comprehensive understanding of pathophysiology of brain in AD. We report here multiple PTMs in patients with AD, harnessing publicly available proteomics data from nine brain regions and at three different Braak stages of disease progression. Specifically, we identified 7190 peptides with PTMs, corresponding to 2545 proteins from brain regions with intermediate tangles, and 6864 peptides with PTMs corresponding to 2465 proteins from brain regions with severe tangles. A total of 103 proteins with PTMs were expressed uniquely to intermediate tangles and severe tangles compared to no tangles. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis suggested the association of these proteins in AD progression through platelet activation. These modified proteins were also found to be enriched for the tricarboxylic acid (TCA) cycle, respiratory electron cycle, and detoxification of reactive oxygen species. The multi-PTM data reported here contribute to our understanding of the neurobiology of AD and highlight the prospects of omics systems science research in neurodegenerative diseases. The present study provides a region-wise classification for the proteins with PTMs along with their differential expression patterns, providing insights into the localization of these proteins upon modification. The catalog of multi-PTMs identified in the context of AD from different brain regions provides a unique platform for generating newer hypotheses in understanding the putative role of specific PTMs in AD pathogenesis.

Single cell RNA-seq analysis of the flexor digitorum brevis mouse myofibers.

BACKGROUND: Skeletal muscle myofibers can be separated into functionally distinct cell types that differ in gene and protein expression. Current single cell expression data is generally based upon single nucleus RNA, rather than whole myofiber material. We examined if a whole-cell flow sorting approach could be applied to perform single cell RNA-seq (scRNA-seq) in a single muscle type. METHODS: We performed deep, whole cell, scRNA-seq on intact and fragmented skeletal myofibers from the mouse fast-twitch flexor digitorum brevis muscle utilizing a flow-gated method of large cell isolation. We performed deep sequencing of 763 intact and fragmented myofibers. RESULTS: Quality control metrics across the different gates indicated only 171 of these cells were optimal, with a median read count of 239,252 and an average of 12,098 transcripts per cell. scRNA-seq identified three clusters of myofibers (a slow/fast 2A cluster and two fast 2X clusters). Comparison to a public skeletal nuclear RNA-seq dataset demonstrated a diversity in transcript abundance by method. RISH validated multiple genes across fast and slow twitch skeletal muscle types. CONCLUSION: This study introduces and validates a method to isolate intact skeletal muscle myofibers to generate deep expression patterns and expands the known repertoire of fiber-type-specific genes.

Correction: Targeting focal adhesion kinase overcomes erlotinib resistance in smoke induced lung cancer by altering phosphorylation of epidermal growth factor receptor.

[This corrects the article DOI: 10.18632/oncoscience.395.].

Plant Omics: Metabolomics and Network Pharmacology of Liquorice, Indian Ayurvedic Medicine Yashtimadhu.

Plant omics is an emerging field of systems science and offers the prospects of evidence-based evaluation of traditional herbal medicines in human diseases. To this end, the powdered root of Yashtimadhu (Glycyrrhiza glabra L.), commonly known as liquorice, is frequently used in Indian Ayurvedic medicine with an eye to neuroprotection but its target proteins, mechanisms of action, and metabolites remain to be determined. Using a metabolomics and network pharmacology approach, we identified 98,097 spectra from positive and negative polarities that matched to ∼1600 known metabolites. These metabolites belong to terpenoids, alkaloids, and flavonoids, including both novel and previously reported active metabolites such as glycyrrhizin, glabridin, liquiritin, and other terpenoid saponins. Novel metabolites were also identified such as quercetin glucosides, coumarin derivatives, beta-carotene, and asiatic acid, which were previously not reported in relation to liquorice. Metabolite-protein interaction-based network pharmacology analyses enriched 107 human proteins, which included dopamine, serotonin, and acetylcholine neurotransmitter receptors among other regulatory proteins. Pathway analysis highlighted the regulation of signaling kinases, growth factor receptors, cell cycle, and inflammatory pathways. In vitro validation confirmed the regulation of cell cycle, MAPK1/3, PI3K/AKT pathways by liquorice. The present data-driven, metabolomics and network pharmacology study paves the way for further translational clinical research on neuropharmacology of liquorice and other traditional medicines.

CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets.

Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes. Some of these coding variants play an important role in activating specific pathways that drive proliferation. Coding variants present on cancer cell surfaces by the major histocompatibility complex serve as neo-antigens and result in immune activation. The success of immune therapy in patients is attributed to neo-antigen load on cancer cell surfaces. However, which coding variants are expressed at the protein level can't be predicted based on genomic data. Complementing genomic data with proteomic data can potentially reveal coding variants that are expressed at the protein level. However, identification of variant peptides using mass spectrometry data is still a challenging task due to the lack of an appropriate tool that integrates genomic and proteomic data analysis pipelines. To overcome this problem, and for the ease of the biologists, we have developed a graphical user interface (GUI)-based tool called CusVarDB. We integrated variant calling pipeline to generate sample-specific variant protein database from next-generation sequencing datasets. We validated the tool with triple negative breast cancer cell line datasets and identified 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38 datasets, respectively.