RNA-seq reveals more consistent reference genes for gene expression studies in human non-melanoma skin cancers

StCoExpNet: a global co-expression network analysis facilitates identifying genes underlying agronomic traits in potatoes

KEY MESSAGE

We constructed a gene expression atlas and co-expression network for potatoes and identified several novel genes associated with various agronomic traits. This resource will accelerate potato genetics and genomics research. Potato (Solanum tuberosum L.) is the world's most crucial non-cereal food crop and ranks third in food production after wheat and rice. Despite the availability of several potato transcriptome datasets at public databases like NCBI SRA, an effort has yet to be put into developing a global transcriptome atlas and a co-expression network for potatoes. The objectives of our study were to construct a global expression atlas for potatoes using publicly available transcriptome datasets, identify housekeeping and tissue-specific genes, construct a global co-expression network and identify co-expression clusters, investigate the transcriptional complexity of genes involved in various essential biological processes related to agronomic traits, and provide a web server (StCoExpNet) to easily access the newly constructed expression atlas and co-expression network to investigate the expression and co-expression of genes of interest. In this study, we used data from 2299 publicly available potato transcriptome samples obtained from 15 different tissues to construct a global transcriptome atlas. We found that roughly 87% of the annotated genes exhibited detectable expression in at least one sample. Among these, we identified 281 genes with consistent and stable expression levels, indicating their role as housekeeping genes. Conversely, 308 genes exhibited marked tissue-specific expression patterns. We exemplarily linked some co-expression clusters to important agronomic traits of potatoes, such as self-incompatibility, anthocyanin biosynthesis, tuberization, and defense responses against multiple pathogens. The dataset compiled here constitutes a new resource (StCoExpNet), which can be accessed at https://stcoexpnet.julius-kuehn.de . This transcriptome atlas and the co-expression network will accelerate potato genetics and genomics research.

Application of OpenArray Technology to Assess Changes in the Expression of Functionally Significant Genes in the Substantia Nigra of Mice in a Model of Parkinson's Disease

Studying the molecular mechanisms of the pathogenesis of Parkinson's disease (PD) is critical to improve PD treatment. We used OpenArray technology to assess gene expression in the substantia nigra (SN) cells of mice in a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) model of PD and in controls. Among the 11 housekeeping genes tested, Rps27a was taken as the reference gene due to its most stable expression in normal and experimental conditions. From 101 genes encoding functionally significant proteins of nigrostriatal dopaminergic neurons, 57 highly expressed genes were selected to assess their expressions in the PD model and in the controls. The expressions of Th, Ddc, Maoa, Comt, Slc6a3, Slc18a2, Drd2, and Nr4a2 decreased in the experiment compared to the control, indicating decreases in the synthesis, degradation, and transport of dopamine and the impaired autoregulation of dopaminergic neurons. The expressions of Tubb3, Map2, Syn1, Syt1, Rab7, Sod1, Cib1, Gpx1, Psmd4, Ubb, Usp47, and Ctsb genes were also decreased in the MPTP-treated mice, indicating impairments of axonal and vesicular transport and abnormal functioning of the antioxidant and ubiquitin-proteasome systems in the SN. The detected decreases in the expressions of Snca, Nsf, Dnm1l, and Keap1 may serve to reduce pathological protein aggregation, increase dopamine release in the striatum, prevent mitophagy, and restore the redox status of SN cells.

Galectin-7 reprograms skin carcinogenesis by fostering innate immune evasive programs

Non-melanoma skin cancer (NMSC) has risen dramatically as a result of chronic exposure to sunlight ultraviolet (UV) radiation, climatic changes and clinical conditions associated with immunosuppression. In spite of considerable progress, our understanding of the mechanisms that control NMSC development and their associated molecular and immunological landscapes is still limited. Here we demonstrated a critical role for galectin-7 (Gal-7), a β-galactoside-binding protein preferentially expressed in skin tissue, during NMSC development. Transgenic mice (Tg46) overexpressing Gal-7 in keratinocytes showed higher number of papillomas compared to WT mice or mice lacking Gal-7 (Lgals7^-/-) when subjected to a skin carcinogenesis protocol, in which tumor initiator 7,12-dimethylbenz[a]anthracene (DMBA) and tumor promoter 12-O-tetradecanoyl-phorbol-13-acetate (TPA) were sequentially administered. RNAseq analysis of Tg46 tumor lesions revealed a unique profile compatible with cells of the myelomonocytic lineage infiltrating these tumors, an effect that was substantiated by a higher number of CD11b⁺Gr1⁺ cells in tumor-draining lymph nodes. Heightened c-Met activation and Cxcl-1 expression in Tg46 lesions suggested a contribution of this pathway to the recruitment of these cells. Remarkably, Gal-7 bound to the surface of CD11b⁺Ly6C^hiLy6G^lo monocytic myeloid cells and enhanced their immunosuppressive activity, as evidenced by increased IL-10 and TGF-β₁ secretion, and higher T-cell inhibitory activity. In vivo, carcinogen-treated Lgals7^-/- animals adoptively transferred with Gal-7-conditioned monocytic myeloid cells developed higher number of papillomas, whereas depletion of these cells in Tg46-treated mice led to reduction in the number of tumors. Finally, human NMSC biopsies showed increased LGALS7 mRNA and Gal-7 protein expression and displayed transcriptional profiles associated with myeloid programs, accompanied by elevated CXCL1 expression and c-Met activation. Thus, Gal-7 emerges as a critical mediator of skin carcinogenesis and a potential therapeutic target in human NMSC.

DNA Methylome and Transcriptome Study of Triterpenoid CDDO in TPA-Mediated Skin Carcinogenesis Model

Overexposure to ultraviolet radiation and environmental carcinogens drive skin cancer development through redox imbalance and gene mutation. Antioxidants such as triterpenoids have exhibited anti-oxidative and anti-inflammatory potentials to alleviate skin carcinogenesis. This study investigated the methylome and transcriptome altered by tumor promoter 12-O-tetradecanoylphorbol-13-acetate (TPA) or TPA with 2-cyano 2,3-dioxoolean-1,9-dien-28-oic acid (CDDO). The results show that CDDO blocks TPA-induced transformation dose dependently. Several differential expressed genes (DEGs) involved in skin cell transformation, while counteracted by CDDO, were revealed by differential expression analysis including Lyl1, Lad1, and Dennd2d. In CpG methylomic profiles, the differentially methylated regions (DMRs) in the promoter region altered by TPA while showing the opposite methylation status in the CDDO treatment group were identified. The correlation between DNA methylation and RNA expression has been established and DMRs showing inverse correlation were further studied as potential therapeutic targets. From the CpG methylome and transcriptome results, CDDO significantly restored gene expression of NAD(P)H:quinone oxidoreductase 1 (Nqo1) inhibited by TPA by decreasing their promoter CpG methylation. Ingenuity Pathways Analysis (IPA) shows that CDDO neutralized the effect of TPA through modulating cell cycles, cell migration, and inflammatory and immune response regulatory pathways. Notably, Tumor Necrosis Factor Receptor 2 (TNFR2) signaling was significantly downregulated by CDDO potentially contributing to prevention of TPA-induced cell transformation. Overall, incorporating the transcriptome, CpG methylome, and signaling pathway network, we reveal potential therapeutic targets and pathways by which CDDO could reverse TPA-induced carcinogenesis. The results could be useful for future human study and targets development for skin cancer.

MAB21L4 Deficiency Drives Squamous Cell Carcinoma via Activation of RET

Epithelial squamous cell carcinomas (SCC) most commonly originate in the skin, where they display disruptions in the normally tightly regulated homeostatic balance between keratinocyte proliferation and terminal differentiation. We performed a transcriptome-wide screen for genes of unknown function that possess inverse expression patterns in differentiating keratinocytes compared with cutaneous SCC (cSCC), leading to the identification of MAB21L4 (C2ORF54) as an enforcer of terminal differentiation that suppresses carcinogenesis. Loss of MAB21L4 in human cSCC organoids increased expression of RET to enable malignant progression. In addition to transcriptional upregulation of RET, deletion of MAB21L4 preempted recruitment of the CacyBP-Siah1 E3 ligase complex to RET and reduced its ubiquitylation. In SCC organoids and in vivo tumor models, genetic disruption of RET or selective inhibition of RET with BLU-667 (pralsetinib) suppressed SCC growth while inducing concomitant differentiation. Overall, loss of MAB21L4 early during SCC development blocks differentiation by increasing RET expression. These results suggest that targeting RET activation is a potential therapeutic strategy for treating SCC.

SIGNIFICANCE

Downregulation of RET mediated by MAB21L4-CacyBP interaction is required to induce epidermal differentiation and suppress carcinogenesis, suggesting RET inhibition as a potential therapeutic approach in squamous cell carcinoma.

Obtaining Reliable RT-qPCR Results in Molecular Diagnostics—MIQE Goals and Pitfalls for Transcriptional Biomarker Discovery

In this review, we discuss the development pipeline for transcriptional biomarkers in molecular diagnostics and stress the importance of a reliable gene transcript quantification strategy. Hence, a further focus is put on the MIQE guidelines and how to adapt them for biomarker discovery, from signature validation up to routine diagnostic applications. First, the advantages and pitfalls of the holistic RNA sequencing for biomarker development will be described to establish a candidate biomarker signature. Sequentially, the RT-qPCR confirmation process will be discussed to validate the discovered biomarker signature. Examples for the successful application of RT-qPCR as a fast and reproducible quantification method in routinemolecular diagnostics are provided. Based on the MIQE guidelines, the importance of “key steps” in RT-qPCR is accurately described, e.g., reverse transcription, proper reference gene selection and, finally, the application of automated RT-qPCR data analysis software. In conclusion, RT-qPCR proves to be a valuable tool in the establishment of a disease-specific transcriptional biomarker signature and will have a great future in molecular diagnostics or personalized medicine.

RNA-Seq is not required to determine stable reference genes for qPCR normalization

Assessment of differential gene expression by qPCR is heavily influenced by the choice of reference genes. Although numerous statistical approaches have been proposed to determine the best reference genes, they can give rise to conflicting results depending on experimental conditions. Hence, recent studies propose the use of RNA-Seq to identify stable genes followed by the application of different statistical approaches to determine the best set of reference genes for qPCR data normalization. In this study, however, we demonstrate that the statistical approach to determine the best reference genes from commonly used conventional candidates is more important than the preselection of ‘stable’ candidates from RNA-Seq data. Using a qPCR data normalization workflow that we have previously established; we show that qPCR data normalization using conventional reference genes render the same results as stable reference genes selected from RNA-Seq data. We validated these observations in two distinct cross-sectional experimental conditions involving human iPSC derived microglial cells and mouse sciatic nerves. These results taken together show that given a robust statistical approach for reference gene selection, stable genes selected from RNA-Seq data do not offer any significant advantage over commonly used reference genes for normalizing qPCR assays.

RTqPCR is a powerful technique that is widely used to quantify gene expression in research and diagnostics of different diseases. The technique involves making multiple copies (amplification) of a specific target DNA. The amplified target DNA binds to a molecule that emits fluorescence upon binding. The extent of fluorescence correlates to the amount of DNA present. To precisely quantify this fluorescence (and thus the quantities of target DNA), internal control genes also called as reference genes need to be determined. Such genes, in principle, do not have varied expression across samples and would exhibit the same fluorescence in all samples. They can thus be used to normalize the expression of the Target DNA. Unfortunately, choosing the right reference gene is very tricky and poor choice of reference genes results in unreliable data both in research and in diagnostics. In this study, we validate a statistical approach to find stably expressed reference genes for any experimental setting using a given set of candidates. We compare our approach to RNA sequencing which quantifies the expression of thousands of genes at the same time. We highlight the advantages of our approach which is cost effective and saves a lot of time when compared to sequencing.

Genome-wide screening of novel RT-qPCR reference genes for study of GLRaV-3 infection in wine grapes and refinement of an RNA isolation protocol for grape berries

BACKGROUND

Grapevine, as an essential fruit crop with high economic values, has been the focus of molecular studies in diverse areas. Two challenges exist in the grapevine research field: (i) the lack of a rapid, user-friendly and effective RNA isolation protocol for mature dark-skinned berries and, (ii) the lack of validated reference genes that are stable for quantification of gene expression across desired experimental conditions. Successful isolation of RNA with sufficient yield and quality is essential for downstream analyses involving nucleic acids. However, ripe berries of dark-skinned grape cultivars are notoriously challenging in RNA isolation due to high contents of polyphenolics, polysaccharides, RNase and water.

RESULTS

We have optimized an RNA isolation protocol through modulating two factors at the lysis step that could impact results of RNA isolation - 2-ME concentration and berry mass. By finding the optimal combination among the two factors, our refined protocol was highly effective in isolating total RNA with high yield and quality from whole mature berries of an array of dark-skinned wine grape cultivars. Our protocol takes a much shorter time to complete, is highly effective, and eliminates the requirement for hazardous organic solvents. We have also shown that the resulting RNA preps were suitable for multiple downstream analyses, including the detection of viruses and amplification of grapevine genes using reverse transcription-polymerase chain reaction (RT-PCR), gene expression analysis via quantitative reverse transcription PCR (RT-qPCR), and RNA Sequencing (RNA-Seq). By using RNA-Seq data derived from Cabernet Franc, we have identified seven novel reference gene candidates (CYSP, NDUFS8, YLS8, EIF5A2, Gluc, GDT1, and EF-Hand) with stable expression across two tissue types, three developmental stages and status of infection with grapevine leafroll-associated virus 3 (GLRaV-3). We evaluated the stability of these candidate genes together with two conventional reference genes (actin and NAD5) using geNorm, NormFinder and BestKeeper. We found that the novel reference gene candidates outperformed both actin and NAD5. The three most stable reference genes were CYSP, NDUFS8 and YSL8, whereas actin and NAD5 were among the least stable. We further tested if there would be a difference in RT-qPCR quantification results when the most stable (CYSP) and the least stable (actin and NAD5) genes were used for normalization. We concluded that both actin and NAD5 led to erroneous RT-qPCR results in determining the statistical significance and fold-change values of gene expressional change.

CONCLUSIONS

We have formulated a rapid, safe and highly effective protocol for isolating RNA from recalcitrant berry tissue of wine grapes. The resulting RNA is of high quality and suitable for RT-qPCR and RNA-Seq. We have identified and validated a set of novel reference genes based on RNA-Seq dataset. We have shown that these new reference genes are superior over actin and NAD5, two of the conventional reference genes commonly used in early studies.

Systematic Identification and Validation of Housekeeping and Tissue-Specific Genes in Allotetraploid Chenopodium quinoa

Quinoa is a gluten-free food crop that contains all the essential amino acids and vitamins. The selection of proper housekeeping and tissue-specific genes is the crucial prerequisite for gene expression analysis using the common approach, real-time quantitative PCR (RT-qPCR). In this study, we identified 40 novel candidate housekeeping genes by the minimum transcript per million (TPM), coefficient of variation (CV) and maximum fold change (MFC) methods and 19 candidate tissue-specific genes by the co-expression network method based on an RNA-seq dataset that included 53 stem, leaf, flower and seed samples, as well as additional shoot and root samples under different stresses. The expression stability of 12 housekeeping and tissue-specific genes, as well as that of another two traditionally used housekeeping genes, was further evaluated using qPCR and ranked using NormFinder, BestKeeper and the comparative delta-Ct method. The results demonstrated that MIF, RGGA, VATE and UBA2B were ranked as the top four most stable candidate housekeeping genes. qPCR analysis also revealed three leaf-specific genes and five root-specific genes, but no stem-specific gene was identified. Gene Ontology (GO) enrichment analysis identified that housekeeping genes were mainly enriched in the small molecule metabolic process, organonitrogen compound metabolic process, NAD binding and ligase activity. In addition, tissue-specific genes are closely associated with the major functions of a specific tissue. Specifically, GO terms “photosynthesis” and “thylakoid” were most significantly overrepresented in candidate leaf-specific genes. The novel housekeeping and tissue-specific genes in our study will enable better normalization and quantification of transcript levels in quinoa.

Xyloglucan processing machinery in Xanthomonas pathogens and its role in the transcriptional activation of virulence factors

Xyloglucans are highly substituted and recalcitrant polysaccharides found in the primary cell walls of vascular plants, acting as a barrier against pathogens. Here, we reveal that the diverse and economically relevant Xanthomonas bacteria are endowed with a xyloglucan depolymerization machinery that is linked to pathogenesis. Using the citrus canker pathogen as a model organism, we show that this system encompasses distinctive glycoside hydrolases, a modular xyloglucan acetylesterase and specific membrane transporters, demonstrating that plant-associated bacteria employ distinct molecular strategies from commensal gut bacteria to cope with xyloglucans. Notably, the sugars released by this system elicit the expression of several key virulence factors, including the type III secretion system, a membrane-embedded apparatus to deliver effector proteins into the host cells. Together, these findings shed light on the molecular mechanisms underpinning the intricate enzymatic machinery of Xanthomonas to depolymerize xyloglucans and uncover a role for this system in signaling pathways driving pathogenesis.

Modeling transcriptomic age using knowledge-primed artificial neural networks

The development of 'age clocks', machine learning models predicting age from biological data, has been a major milestone in the search for reliable markers of biological age and has since become an invaluable tool in aging research. However, beyond their unquestionable utility, current clocks offer little insight into the molecular biological processes driving aging, and their inner workings often remain non-transparent. Here we propose a new type of age clock, one that couples predictivity with interpretability of the underlying biology, achieved through the incorporation of prior knowledge into the model design. The clock, an artificial neural network constructed according to well-described biological pathways, allows the prediction of age from gene expression data of skin tissue with high accuracy, while at the same time capturing and revealing aging states of the pathways driving the prediction. The model recapitulates known associations of aging gene knockdowns in simulation experiments and demonstrates its utility in deciphering the main pathways by which accelerated aging conditions such as Hutchinson-Gilford progeria syndrome, as well as pro-longevity interventions like caloric restriction, exert their effects.

Stable gene expression for normalisation and single-sample scoring

Gene expression signatures have been critical in defining the molecular phenotypes of cells, tissues, and patient samples. Their most notable and widespread clinical application is stratification of breast cancer patients into molecular (PAM50) subtypes. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical application of thousands of existing gene signatures captured in repositories such as the Molecular Signature Database. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across thousands of samples, allowing signature scoring and supporting general data normalisation for transcriptomic data. Our new method, stingscore, quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these ‘stably-expressed genes’. We show that our list of stable genes has better stability across cancer and normal tissue data than previously proposed gene sets. Additionally, we show that signature scores computed from targeted transcript measurements using stingscore can predict docetaxel response in breast cancer patients. This new approach to gene expression signature analysis will facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

RNA sequencing of whole blood in dogs with primary immune-mediated hemolytic anemia (IMHA) reveals novel insights into disease pathogenesis

Immune-mediated hemolytic anemia (IMHA) is a life-threatening autoimmune disorder characterized by a self-mediated attack on circulating red blood cells. The disease occurs naturally in both dogs and humans, but is significantly more prevalent in dogs. Because of its shared features across species, dogs offer a naturally occurring model for studying IMHA in people. In this study, we used RNA sequencing of whole blood from treatment-naïve dogs to study transcriptome-wide changes in gene expression in newly diagnosed animals compared to healthy controls. We found many overexpressed genes in pathways related to neutrophil function, coagulation, and hematopoiesis. In particular, the most highly overexpressed gene in cases was a phospholipase scramblase, which mediates the externalization of phosphatidylserine from the inner to the outer leaflet of cell membranes. This family of genes has been shown to be critically important for programmed cell death of erythrocytes as well as the initiation of the clotting cascade. Unexpectedly, we found marked underexpression of many genes related to lymphocyte function. We also identified groups of genes that are highly associated with the inflammatory response and red blood cell regeneration in affected dogs. We did not find any genes that distinguished dogs that lived vs. those that died at 30 days following diagnosis, nor did we find any relevant genomic signatures of microbial organisms in the blood of affected animals. Future studies are warranted to validate these findings and assess their implication in developing novel therapeutic approaches for dogs and humans with IMHA.

The UVR Filter Octinoxate Modulates Aryl Hydrocarbon Receptor Signaling in Keratinocytes via Inhibition of CYP1A1 and CYP1B1

Ultraviolet radiation (UVR) is a consistent part of the environment that has both beneficial and harmful effects on human health. UVR filters in the form of commercial sunscreens have been widely used to reduce the negative health effects of UVR exposure. Despite their benefit, literature suggests that some filters can penetrate skin and have off-target biological effects. We noted that many organic filters are hydrophobic and contain aromatic rings, making them potential modulators of Aryl hydrocarbon Receptor (AhR) signaling. We hypothesized that some filters may be able to act as agonists or antagonists on the AhR. Using a luciferase reporter cell line, we observed that the UVR filter octinoxate potentiated the ability of the known AhR ligand, 6-formylindolo[3,2-b]carbazole (FICZ), to activate the AhR. Cotreatments of keratinocytes with octinoxate and FICZ lead to increased levels of cytochrome P4501A1 (CYP1A1) and P4501B1 (CYP1B1) mRNA transcripts, in an AhR-dependent fashion. Mechanistic studies revealed that octinoxate is an inhibitor of CYP1A1 and CYP1B1, with IC50 values at approximately 1 µM and 586 nM, respectively. In vivo topical application of octinoxate and FICZ also elevated CYP1A1 and CYP1B1 mRNA levels in mouse skin. Our results show that octinoxate is able to indirectly modulate AhR signaling by inhibiting CYP1A1 and CYP1B1 enzyme function, which may have important downstream consequences for the metabolism of various compounds and skin integrity. It is important to continue studying the off-target effects of octinoxate and other UVR filters, because they are used on skin on a daily basis world-wide.

Systematic analysis of 1298 RNA-Seq samples and construction of a comprehensive soybean (Glycine max) expression atlas

Soybean (Glycine max [L.] Merr.) is a major crop in animal feed and human nutrition, mainly for its rich protein and oil contents. The remarkable rise in soybean transcriptome studies over the past 5 years generated an enormous amount of RNA-seq data, encompassing various tissues, developmental conditions and genotypes. In this study, we have collected data from 1298 publicly available soybean transcriptome samples, processed the raw sequencing reads and mapped them to the soybean reference genome in a systematic fashion. We found that 94% of the annotated genes (52 737/56 044) had detectable expression in at least one sample. Unsupervised clustering revealed three major groups, comprising samples from aerial, underground and seed/seed-related parts. We found 452 genes with uniform and constant expression levels, supporting their roles as housekeeping genes. On the other hand, 1349 genes showed heavily biased expression patterns towards particular tissues. A transcript-level analysis revealed that 95% (70 963 of 74 490) of the assembled transcripts have intron chains exactly matching those from known transcripts, whereas 3256 assembled transcripts represent potentially novel splicing isoforms. The dataset compiled here constitute a new resource for the community, which can be downloaded or accessed through a user-friendly web interface at http://venanciogroup.uenf.br/resources/. This comprehensive transcriptome atlas will likely accelerate research on soybean genetics and genomics.

Characterization of a Myeloid Activation Signature that Correlates with Survival in Melanoma Patients

Understanding the cellular interactions within the tumor microenvironment (TME) of melanoma paved the way for novel therapeutic modalities, such as T cell-targeted immune checkpoint inhibitors (ICI). However, only a limited fraction of patients benefits from such therapeutic modalities, highlighting the need for novel predictive and prognostic biomarkers. As myeloid cells orchestrate the tumor-specific immune response and influence the efficacy of ICI, assessing their activation state within the TME is of clinical relevance. Here, we characterized a myeloid activation (MA) signature, comprising the three genes Cxcl11, Gbp1, and Ido1, from gene expression data of human myeloid cells stimulated with poly(I:C) or cGAMP. This MA signature positively correlated to overall survival in melanoma. In addition, increased expression of the MA signature was observed in melanoma patients responding to ICI (anti-PD-1), as compared to non-responders. Furthermore, the MA signature was validated in the murine B16F10 melanoma model where it was induced and associated with decreased tumor growth upon intratumoral administration of poly(I:C) and cGAMP. Finally, we were able to visualize co-expression of the MA signature genes in myeloid cells of human melanoma tissues using RNAscope in situ hybridization. In conclusion, the MA signature indicates the activation state of myeloid cells and represents a prognostic biomarker for the overall survival in melanoma patients.

Reference gene selection and validation for mRNA expression analysis by RT-qPCR in murine M1- and M2-polarized macrophage

Murine bone marrow-derived macrophages (M0) and M1- and M2-polarized macrophages are being widely used as a laboratory model for polarized macrophages related molecular mechanism analysis. Gene expression analysis based on reference gene normalization using RT-qPCR was a powerful way to explore the molecular mechanism. But little is known about reference genes in these cell models. So, the goal of this study was to identify reference genes in these types of macrophages. Candidate reference genes in murine bone marrow-derived and polarized macrophages were selected from microarray data using Limma linear model method and evaluated by determining the stability value using five algorithms: BestKeeper, NormFinder, GeNorm, Delta CT method, and RefFinder. Finally, the selected stable reference genes were validated by testing three important immune and inflammatory genes (NLRP1, IL-1β, and TNF-α) in the cell lines. Our study has clearly shown that Ubc followed by Eef1a1 and B2m respectively were recognized as the three ideal reference genes for gene expression analysis in murine bone marrow-derived and polarized macrophages. When three reference genes with strong different stability were used for validation, a large variation of a gene expression level of IL-1β, TNF-α and NLRP1 were obtained which provides clear evidence of the need for careful selection of reference genes for RT-qPCR analysis. Normalization of mRNA expression level with Ubc rather than Actb or Gusb by qPCR in macrophages and polarized macrophages is required to ensure the accuracy of the qPCR analysis.

Alternative Splicing Events as Indicators for the Prognosis of Uveal Melanoma

Growing evidence has revealed that abnormal alternative splicing (AS) events are closely related to carcinogenic processes. However, the comprehensive study on the prognostic value of splicing events involved in uveal melanoma (UM) is still lacking. Therefore, splicing data of 80 UM patients were obtained from the Cancer Genome Atlas (TCGA) SpliceSeq and RNA sequence data of UM and patient clinical features were downloaded from the Cancer Genome Atlas (TCGA) database to identify survival related splicing events in UM. As a result, a total of 37996 AS events of 17911 genes in UM were detected, among which 5299 AS events of 3529 genes were significantly associated with UM patients’ survival. Functional enrichment analysis revealed that this survival related splicing genes are corelated with mRNA catabolic process and ribosome pathway. Based on survival related splicing events, seven types of prognostic markers and the final overall prognostic signature could independently predict the overall survival of UM patients. Finally, an 11 spliced gene was identified in the final signature. On the basis of these 11 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. The results showed that the 11 genes could determine short- and long-term survival with a predicted accuracy of 97.5%. Besides, the splicing factors and alternative splicing events correlation network was constructed to serve as therapeutic targets for UM treatment. Thus, our study depicts a comprehensive landscape of alternative splicing events in the prognosis of UM. The correlation network and associated pathways would provide additional potential targets for therapy and prognosis.

Multiplatform biomarker identification using a data-driven approach enables single-sample classification

BACKGROUND

High-throughput gene expression profiles have allowed discovery of potential biomarkers enabling early diagnosis, prognosis and developing individualized treatment. However, it remains a challenge to identify a set of reliable and reproducible biomarkers across various gene expression platforms and laboratories for single sample diagnosis and prognosis. We address this need with our Data-Driven Reference (DDR) approach, which employs stably expressed housekeeping genes as references to eliminate platform-specific biases and non-biological variabilities.

RESULTS

Our method identifies biomarkers with "built-in" features, and these features can be interpreted consistently regardless of profiling technology, which enable classification of single-sample independent of platforms. Validation with RNA-seq data of blood platelets shows that DDR achieves the superior performance in classification of six different tumor types as well as molecular target statuses (such as MET or HER2-positive, and mutant KRAS, EGFR or PIK3CA) with smaller sets of biomarkers. We demonstrate on the three microarray datasets that our method is capable of identifying robust biomarkers for subgrouping medulloblastoma samples with data perturbation due to different microarray platforms. In addition to identifying the majority of subgroup-specific biomarkers in CodeSet of nanoString, some potential new biomarkers for subgrouping medulloblastoma were detected by our method.

CONCLUSIONS

In this study, we present a simple, yet powerful data-driven method which contributes significantly to identification of robust cross-platform gene signature for disease classification of single-patient to facilitate precision medicine. In addition, our method provides a new strategy for transcriptome analysis.

Clonal replacement of tumor-specific T cells following PD-1 blockade

Immunotherapies that block inhibitory checkpoint receptors on T cells have transformed the clinical care of patients with cancer1. However, whether the T cell response to checkpoint blockade relies on reinvigoration of pre-existing tumor-infiltrating lymphocytes or on recruitment of novel T cells remains unclear2-4. Here we performed paired single-cell RNA and T cell receptor sequencing on 79,046 cells from site-matched tumors from patients with basal or squamous cell carcinoma before and after anti-PD-1 therapy. Tracking T cell receptor clones and transcriptional phenotypes revealed coupling of tumor recognition, clonal expansion and T cell dysfunction marked by clonal expansion of CD8+CD39+ T cells, which co-expressed markers of chronic T cell activation and exhaustion. However, the expansion of T cell clones did not derive from pre-existing tumor-infiltrating T lymphocytes; instead, the expanded clones consisted of novel clonotypes that had not previously been observed in the same tumor. Clonal replacement of T cells was preferentially observed in exhausted CD8+ T cells and evident in patients with basal or squamous cell carcinoma. These results demonstrate that pre-existing tumor-specific T cells may have limited reinvigoration capacity, and that the T cell response to checkpoint blockade derives from a distinct repertoire of T cell clones that may have just recently entered the tumor.

Optimal use of statistical methods to validate reference gene stability in longitudinal studies

Multiple statistical approaches have been proposed to validate reference genes in qPCR assays. However, conflicting results from these statistical methods pose a major hurdle in the choice of the best reference genes. Recent studies have proposed the use of at least three different methods but there is no consensus on how to interpret conflicting results. Researchers resort to averaging the stability ranks assessed by different approaches or attributing a weighted rank to candidate genes. However, we report here that the suitability of these validation methods can be influenced by the experimental setting. Therefore, averaging the ranks can lead to suboptimal assessment of stable reference genes if the method used is not suitable for analysis. As the respective approaches of these statistical methods are different, a clear understanding of the fundamental assumptions and the parameters that influence the calculation of reference gene stability is necessary. In this study, the stability of 10 candidate reference genes (Actb, Gapdh, Tbp, Sdha, Pgk1, Ppia, Rpl13a, Hsp60, Mrpl10, Rps26) was assessed using four common statistical approaches (GeNorm, NormFinder, Coefficient of Variation or CV analysis and Pairwise ΔCt method) in a longitudinal experimental setting. We used the development of the cerebellum and the spinal cord of mice as a model to assess the suitability of these statistical methods for reference gene validation. GeNorm and the Pairwise ΔCt were found to be ill suited due to a fundamental assumption in their stability calculations. Highly correlated genes were given better stability ranks despite significant overall variation. NormFinder fares better but the presence of highly variable genes influences the ranking of all genes because of the algorithm’s construct. CV analysis estimates overall variation, but it fails to consider variation across groups. We thus highlight the assumptions and potential pitfalls of each method using our longitudinal data. Based on our results, we have devised a workflow combining NormFinder, CV analysis along with visual representation of mRNA fold changes and one-way ANOVA for validating reference genes in longitudinal studies. This workflow proves to be more robust than any of these methods used individually.

Pan-Cancer Analysis of TCGA Data Revealed Promising Reference Genes for qPCR Normalization

Quantitative PCR (qPCR) remains the most widely used technique for gene expression evaluation. Obtaining reliable data using this method requires reference genes (RGs) with stable mRNA level under experimental conditions. This issue is especially crucial in cancer studies because each tumor has a unique molecular portrait. The Cancer Genome Atlas (TCGA) project provides RNA-Seq data for thousands of samples corresponding to dozens of cancers and presents the basis for assessment of the suitability of genes as reference ones for qPCR data normalization. Using TCGA RNA-Seq data and previously developed CrossHub tool, we evaluated mRNA level of 32 traditionally used RGs in 12 cancer types, including those of lung, breast, prostate, kidney, and colon. We developed an 11-component scoring system for the assessment of gene expression stability. Among the 32 genes, PUM1 was one of the most stably expressed in the majority of examined cancers, whereas GAPDH, which is widely used as a RG, showed significant mRNA level alterations in more than a half of cases. For each of 12 cancer types, we suggested a pair of genes that are the most suitable for use as reference ones. These genes are characterized by high expression stability and absence of correlation between their mRNA levels. Next, the scoring system was expanded with several features of a gene: mutation rate, number of transcript isoforms and pseudogenes, participation in cancer-related processes on the basis of Gene Ontology, and mentions in PubMed-indexed articles. All the genes covered by RNA-Seq data in TCGA were analyzed using the expanded scoring system that allowed us to reveal novel promising RGs for each examined cancer type and identify several "universal" pan-cancer RG candidates, including SF3A1, CIAO1, and SFRS4. The choice of RGs is the basis for precise gene expression evaluation by qPCR. Here, we suggested optimal pairs of traditionally used RGs for 12 cancer types and identified novel promising RGs that demonstrate high expression stability and other features of reliable and convenient RGs (high expression level, low mutation rate, non-involvement in cancer-related processes, single transcript isoform, and absence of pseudogenes).

Genome-Wide Identification and Evaluation of New Reference Genes for Gene Expression Analysis Under Temperature and Salinity Stresses in Ciona savignyi

Rapid adaptation/accommodation to changing environments largely contributes to maximal survival of invaders during biological invasions, usually leading to success in crossing multiple barriers and finally in varied environments in recipient habitats. Gene expression is one of the most important and rapid ways during responses to environmental stresses. Selection of proper reference genes is the crucial prerequisite for gene expression analysis using the common approach, real-time quantitative PCR (RT-qPCR). Here we identified eight candidate novel reference genes from the RNA-Seq data in an invasive model ascidian Ciona savignyi under temperature and salinity stresses. Subsequently, the expression stability of these eight novel reference genes, as well as other six traditionally used reference genes, was evaluated using RT-qPCR and comprehensive tool RefFinder. Under the temperature stress, two traditional reference genes, ribosomal proteins S15 and L17 (RPS15, RPL17), and one novel gene Ras homolog A (RhoA), were recommended as the top three stable genes, which can be used to normalize target genes with a high and moderate expression level, respectively. Under the salinity stress, transmembrane 9 superfamily member (TMN), MOB kinase activator 1A-like gene (MOB) and ubiquitin-conjugating enzyme (UBQ2) were suggested as the top three stable genes. On the other hand, several commonly used reference genes such as α-tubulin (TubA), β-tubulin (TubB) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) showed unstable expressions, thus these genes should not be used as internal controls for gene expression analysis. We also tested the expression level of an important stress response gene, large proline-rich protein bag6-like gene (BAG) using different reference genes. As expected, we observed different results and conclusions when using different normalization methods, thus suggesting the importance of selection of proper reference genes and associated normalization methods. Our results provide a valuable reference gene resource for the normalization of gene expression in the study of environmental adaptation/accommodation during biological invasions using C. savignyi as a model.

Perinatal phthalate and high-fat diet exposure induce sex-specific changes in adipocyte size and DNA methylation

Environmental factors such as diet and endocrine-disrupting chemicals have individually been shown to mediate metabolic function. However, the underlying mechanism by which the combination disrupts adipocyte morphology and fat storage remains unknown. The current study evaluated early-life programming by diet and phthalate exposure. During gestation and lactation, pregnant Long-Evans hooded rat dams were fed either a control (C) or high-fat (HF) diet and were orally administered one of three phthalate dosages (0, 200 or 1000 μg/kg/day), yielding six groups of offspring: C-0, C-200, C-1000, HF-0, HF-200 and HF-1000. On postnatal day (PND) 90, gonadal fat pads were collected and analyzed for histology, gene expression and DNA methylation. Differences in body weight were observed only in males. Hematoxylin and eosin staining revealed larger adipocyte size in HF-0 vs. C-0 females. Exposure to 200 or 1000 μg/kg/day phthalates modulated diet-induced changes in adipose morphology. Compared to C-0 females, HF-0 females also had higher expression of the adipogenesis gene Wnt receptor, frizzled 1 (Fzd1) and the triglyceride cleaving enzyme lipoprotein lipase (Lpl). These increases in gene expression were accompanied by lower DNA methylation surrounding the transcription start sites of the two genes. Diet-driven effects were observed in unexposed females but not in phthalate-treated rats. Results suggest a sex-specific association between perinatal HF diet and body weight, adipocyte size and DNA methylation. Perinatal phthalate exposure appears to produce a phenotype that more closely resembles HF-fed animals.

Human Skin Equivalents Demonstrate Need for Neuro-Immuno-Cutaneous System

A variety of human skin equivalents (HSEs) has been designed for clinical use or for exploratory skin research. In vitro HSE models have been used to target relationships between the skin and nervous or immune systems but have not yet considered the neuro-immuno-cutaneous (NIC) system. In this study, HSEs are described, with and without neural and immune components, to discern these types of effects. These systems are composed of only primary human cells and contain an epidermis, dermis, hypodermis (with immune cells), and human induced neural stem cells for the neuronal component. RNA sequencing is utilized to confirm differences between sample groups and to identify unique or important genes with respect to sample type. Only samples with both neural and immune components result in the upregulation of genes in all the key biological pathways explored. The analysis of protein secretion confirms that this group has measurable functions related to all key cell types. Overall, this novel skin tissue system confirms that designing HSEs that include the NIC system results in a tissue model that reflects key functions. These systems could be used to identify selected targets of interest in skin research related to healthy or diseased states.

Using RNA-seq data to select reference genes for normalizing gene expression in apple roots

Gene expression in apple roots in response to various stress conditions is a less-explored research subject. Reliable reference genes for normalizing quantitative gene expression data have not been carefully investigated. In this study, the suitability of a set of 15 apple genes were evaluated for their potential use as reliable reference genes. These genes were selected based on their low variance of gene expression in apple root tissues from a recent RNA-seq data set, and a few previously reported apple reference genes for other tissue types. Four methods, Delta Ct, geNorm, NormFinder and BestKeeper, were used to evaluate their stability in apple root tissues of various genotypes and under different experimental conditions. A small panel of stably expressed genes, MDP0000095375, MDP0000147424, MDP0000233640, MDP0000326399 and MDP0000173025 were recommended for normalizing quantitative gene expression data in apple roots under various abiotic or biotic stresses. When the most stable and least stable reference genes were used for data normalization, significant differences were observed on the expression patterns of two target genes, MdLecRLK5 (MDP0000228426, a gene encoding a lectin receptor like kinase) and MdMAPK3 (MDP0000187103, a gene encoding a mitogen-activated protein kinase). Our data also indicated that for those carefully validated reference genes, a single reference gene is sufficient for reliable normalization of the quantitative gene expression. Depending on the experimental conditions, the most suitable reference genes can be specific to the sample of interest for more reliable RT-qPCR data normalization.