High-throughput Expression Research Articles

Normalization Methods for the Analysis of Unbalanced Transcriptome Data: A Review.

Dozens of normalization methods for correcting experimental variation and bias in high-throughput expression data have been developed during the last two decades. Up to 23 methods among them consider the skewness of expression data between sample states, which are even more than the conventional methods, such as loess and quantile. From the perspective of reference selection, we classified the normalization methods for skewed expression data into three categories, data-driven reference, foreign reference, and entire gene set. We separately introduced and summarized these normalization methods designed for gene expression data with global shift between compared conditions, including both microarray and RNA-seq, based on the reference selection strategies. To our best knowledge, this is the most comprehensive review of available preprocessing algorithms for the unbalanced transcriptome data. The anatomy and summarization of these methods shed light on the understanding and appropriate application of preprocessing methods.

High Throughput Immunophenotyping and Expression Profiling at Single Cell Level Reveal BCR-ABL1 Dependent Surface Markers of Chronic Myeloid Leukemia Stem Cells

BACKGROUND: Bcr-abl1 oncogene targeted treatment with tyrosine kinase inhibitors (TKI) showed an impressive efficacy against proliferating chronic myeloid leukemia (CML) cells. However, rapid relapses in more than half of CML patients after discontinuation of the treatment suggest a presence of quiescent leukemic stem cells inherently resistant to BCR-ABL1 inhibition. Understanding the heterogeneity of CML stem cell compartment is crucial for preventing the treatment failure. Specificity of already established leukemic stem cell (LSC) markers has been tested mainly in bulk CD34+CD38- populations at diagnosis. Phenotypes and molecular signatures of therapy resistant BCR ABL1 positive stem cells is however yet to be established. AIMS: Identification of BCR-ABL1 dependent LSC markers at single cell level by direct comparison their surface and transcript expression with the levels and the presence of BCR-ABL1 transcript at diagnosis and after administration of TKI treatment. METHODS: Total number of 375 cells were obtained from bone marrow and peripheral blood of 4 chronic phase CML patients. Cells were collected prior any treatment and three months after TKI treatment initiation. Normal bone marrow cells and BCR-ABL1 positive K562 cell line were used as controls. Indexed immuno-phenotyping and sorting of CD34+CD38- single cells was performed using a panel of 11 specific surface markers. Collected single cells were lysed and cDNA was enriched for 11 targets using 22 cycle pre-amplification. Expression profiling was carried on SmartChip real-time PCR system (Takara Bio) detecting following genes: BCR-ABL1, CD26, CD25, IL1-Rap, CD56, CD90, CD93, CD69, KI67, and control genes GUS and HPRT. Unsupervised clustering was performed using principal component analysis (PCA). Correlations were measured by Spearman rank method. RESULTS: At diagnosis, majority of BCR-ABL1+ C34+CD38- stem cells co-express IL1-Rap, CD26, and CD69 on their surface (88%, 82%, 78% overlap). Only 56% of BCR-ABL1+ cells positive for aforementioned markers co-express CD25, 28% CD93 and 16% CD56. The expression of these markers could also be detected in 4-11% of BCR-ABL1- cell, although this could be technical inaccuracy caused by the single cell profiling. CD90 marker did not show any correlation with BCR-ABL1 expression. At transcript level the expression of IL-1Rap, CD26, CD25 and CD56 was observed in 62%, 52% 45% and 16% BCR-ABL1+ cells, and up to 7% of BCR-ABL1- cells. CD69 expression was observed in 90% of BCR-ABL+ cells at transcript level, but also in 71% BCR-ABL- cells. BCR-ABL1 independent expression was observed for cKIT. (60% vs. 76 % in positive vs negative). Finally proliferation marker KI67 was expressed only in 6% of the BCR-ABL1+ cells. PCA analysis divided cells into several distinct clusters with BCR-ABL1 as the main contributor, and cKIT, CD69 and CD26, IL-1RAP as other significant factors. Interestingly BCR-ABL1+ cells collected during TKI treatment showed persistent surface expression of IL-1Rap and CD26, while CD56, CD69 and CD93 were only on part of the BCR-ABL1+ cells. CD25 was significantly deregulated during TKI treatment. CONCLUSION: At diagnosis up to 80% of LSC co-express 3 specific surface markers - IL-1RAP, CD26 and CD69. Variable portion of LSC co-express additional markers such are CD25, CD56 and CD93. During TKI treatment the surface expression of majority of markers is decreased, where the best correlated LSC marker is IL-1Rap, followed by CD26 and CD69. CD56 marker seems to persist in the same proportion of cells while CD25 disappears. cKIT is highly expressed in normal BM and HSC from CML patients, but also in some LSC. CD34+CD38- cells show non-proliferating phenotype. Disclosures Mayer: AOP Orphan Pharmaceuticals AG: Research Funding.

Exercise-induced changes of gene expression in the cerebellum of aged mice

PurposeExercise has been prescribed to the elderly based on its effect on increasing muscle strength and protein synthesis that prevent sense of balance and/or cognitive functions. However, a few molecular mechanism researches has been conducted on how the vestibular organs, cerebellum, and hippocampus, which are responsible for the deterioration and balance of spatial learning memory due to aging, are affected by exercise. MethodsThe 9-week old and 84-week old C57Bl/6 were assigned randomly to Young-Control (YC), Young-Exercise (YE), Old-Control (OC) and Old-Exercise (OE) groups for 4 -week treadmill running. A Rotarod test was used to evaluate motor coordination function. Moreover, a high-throughput whole transcript expression RNA array approach was applied to the cerebellum of aged mice to explain the novel molecular mechanism of beneficial effect of exercise. ResultsAs results, the motor coordination function was significantly improved in exercise-aged mice. The RNA sequencing analysis showed that the expression of cerebellar genes was significantly changed by aging rather than exercise. Especially, Cers1 was up-regulated in sedentary aged mice and down-regulated in exercise aged mice. Fumonisin B1, inhibition of Cers1, mitigates neuronal cell death induced by doxorubicin. ConclusionThese results provide unraveling specific transcripts and understanding of the exercise-related cerebellum transcriptome in aged mice. Well-designed exercise program might prevent the motor coordination defect in aged model, which development of the exercise protocol for elderly population based on these markers.

Improving personalised therapy in metastatic breast cancer by implementing RNA sequencing based gene expression signatures

Abstract Background Breast cancer is one of the most common cancers worldwide and - despite significant advances in prevention and treatment - contributes significantly to the overall mortality from cancer. This emphasizes the need for better treatments and improved patient stratification.Histologic as well as genetic testing is able to substantially improve treatment selection and benefit as well as patient management. Within the CATCH (Comprehensive assessment of clinical features and biomarkers to identify patients with advanced or metastatic breast cancer (mBC) for marker-driven trials in humans) program, whole-genome and RNA sequence analyses are performed to identify biomarkers. With these high-throughput approaches, genetic variants and/or expression events are frequently discovered for which the predictive value is not yet known. Methods The R-packages GSVA (Gene Set Variation Analysis) and PROGENy (Pathway RespOnsive GENes) were used to calculate enrichment scores from RNA sequencing data. Results Here, we show that RNA sequencing based pathway analyses of metastatic breast cancer samples has the potential to guide patient stratification by providing an additional layer of information that may help to interpret the observed events. This approach is particularly interesting for challenging clinical samples greatly extending the impact of single-gene-based analyses. We analyzed 115 metastases by RNA sequencing for which histologic subtype and matched whole genome sequencing data are available. We test the usefulness of several published predictive gene expression signatures in metastatic breast cancer, which we exemplify by the IFN-y signature that has the potential to stratify patients for immune checkpoint inhibitor therapy. However, there are important limitations when these approaches are applied: Both breast cancer subtypes as well as the amount of normal tissue present in the material used can affect the results. Therefore, careful comparison to subtype and tissue matched reference cohorts is critical. Conclusions In the future, integration with clinical outcome data will add further information on the sensitivity and specificity of the approach. Pathway enrichment analysis may emerge as a fast and cost-effective tool to identify patients that benefit from certain targeted therapies such as immune checkpoint inhibitors. Legal entity responsible for the study The authors. Funding German Cancer Research Center and University Hospital Heidelberg. Disclosure All authors have declared no conflicts of interest.

Identification of Breast Cancer Subtype Specific MicroRNAs Using Survival Analysis to Find Their Role in Transcriptomic Regulation.

The microRNA (miRNA) biomolecules have a significant role in the development of breast cancer, and their expression profiles are different in each subtype of breast cancer. Thus, our goal is to use the Next Generation Sequencing provided high-throughput miRNA expression and clinical data in an integrated fashion to perform survival analysis in order to identify breast cancer subtype specific miRNAs, and analyze associated genes and transcription factors. We select top 100 miRNAs for each of the four subtypes, based on the value of hazard ratio and p-value, thereafter, identify 44 miRNAs that are related to all four subtypes, which we call as four-star miRNAs. Moreover, 12, 14, 9, and 15 subtype specific, viz. one-star miRNAs, are also identified. The resulting miRNAs are validated by using machine learning methods to differentiate tumor cases from controls (for four-star miRNAs), and subtypes (for one-star miRNAs). The four-star miRNAs provide 95% average accuracy, while in case of one-star miRNAs 81% accuracy is achieved for HER2-Enriched. Differences in expression of miRNAs between cancer stages is also analyzed, and a subset of eight miRNAs is found, for which expression is increased in stage II relative to stage I, including hsa-miR-10b-5p, which contributes to breast cancer metastasis. Subsequently we prepare regulatory networks in order to identify the interactions among miRNAs, their targeted genes and transcription factors (TFs), that are targeting those miRNAs. In this way, key regulatory circuits are identified, where genes such as TP53, ESR1, BRCA1, MYC, and others, that are known to be important genetic factors for the cause of breast cancer, produce transcription factors that target the same genes as well as interact with the selected miRNAs. To provide further biological validation the Protein-Protein Interaction (PPI) networks are prepared and Kyoto Encyclopedia of Genes and Genomes pathway and gene ontology (GO) enrichment analysis are performed. Among the enriched pathways many are breast cancer-related, such as PI3K-Akt or p53 signaling pathways, and contain proteins such as TP53, also present in the regulatory networks. Moreover, we find that the genes are enriched in GO terms associated with breast cancer. Our results provide detailed analysis of selected miRNAs and their regulatory networks.

Multiple Synthetic Routes to the Mini-Protein Omomyc and Coiled-Coil Domain Truncations.

The Myc transcription factor represents an "undruggable" target of high biological interest due to its central role in various cancers. An abbreviated form of the c-Myc protein, called Omomyc, consists of the Myc DNA-binding domain and a coiled-coil region to facilitate dimerization of the 90 amino acid polypeptide. Here we present our results to evaluate the synthesis of Omomyc using three complementary strategies: linear Fmoc solid-phase peptide synthesis (SPPS) using several advancements for difficult sequences, native chemical ligation from smaller peptide fragments, and a high-throughput bacterial expression and assay platform for rapid mutagenesis. This multifaceted approach allowed access to up to gram quantities of the mini-protein and permitted in vitro and in vivo SAR exploration of this modality. DNA-binding results and cellular activity confirm that Omomyc and analogues presented here, are potent binders of the E-box DNA engaged by Myc for transcriptional activation and that this 90-amino acid mini-protein is cell permeable and can inhibit proliferation of Myc-dependent cell lines. We also present additional results on covalent homodimerization through disulfide formation of the full-length mini-protein and show the coiled-coil region can be truncated while preserving both DNA binding and cellular activity. Altogether, our results highlight the ability of advanced peptide synthesis to achieve SAR tractability in a challenging synthetic modality.

Unraveling the Regulatory G-Quadruplex Puzzle: Lessons From Genome and Transcriptome-Wide Studies.

G-quadruplexes (G4s) are among the best-characterized DNA secondary structures and are enriched in regulatory regions, especially promoters, of several prokaryote and eukaryote genomes, indicating a possible role in cis regulation of genes. Many studies have focused on evaluating the impact of specific G4-forming sequences in the promoter regions of genes. However, the lack of correlation between the presence of G4s and the functional impact on cis gene regulation, evidenced by the variable expression fold change in the presence of G4 stabilizers, shows that not all G4s affect transcription in the same manner. This indicates that the regulatory effect of the G4 is significantly influenced by its position, the surrounding DNA topology, and other environmental factors within the cell. In this review, we compare individual gene studies with high-throughput differential expression studies to highlight the importance of formulating a combined approach that can be applied in humans, bacteria, and viruses to better understand the effect of G4-mediated gene regulation.

Selection of High Producers From Combinatorial Libraries for the Production of Recombinant Proteins in Escherichia coli and Vibrio natriegens.

The optimization of recombinant protein production in bacteria is an important stage of process development, especially for difficult-to-express proteins that are particularly sensitive or recalcitrant. The optimal expression level must be neither too low, which would limit yields, nor too high, which would promote the formation of insoluble inclusion bodies. Expression can be optimized by testing different combinations of elements such as ribosome binding sites and N-terminal affinity tags, but the rate of protein synthesis is strongly dependent on mRNA secondary structures so the combined effects of these elements must be taken into account. This substantially increases the complexity of high-throughput expression screening. To address this limitation, we generated libraries of constructs systematically combining different ribosome binding sites, N-terminal affinity tags, and periplasmic translocation sequences representing two secretion pathways. Each construct also contained a green fluorescent protein (GFP) tag to allow the identification of high producers and a thrombin cleavage site enabling the removal of fusion tags. To achieve proof of principle, we generated libraries of 200 different combinations of elements for the expression of an antimicrobial peptide (AMPs), an antifungal peptide, and the enzyme urate oxidase (uricase) in Escherichia coli and Vibrio natriegens. High producers for all three difficult-to-express products were enriched by fluorescence-activated cell sorting. Our results indicated that the E. coli ssYahJ secretion signal is recognized in V. natriegens and efficiently mediates translocation to the periplasm. Our combinatorial library approach therefore allows the cross-species direct selection of high-producer clones for difficult-to-express proteins by systematically evaluating the combined impact of multiple construct elements.

Genome-wide analysis of multi-view data of miRNA-seq to identify miRNA biomarkers for stomach cancer.

Stomach cancer is one of the leading causes of cancer-related deaths worldwide. More than 80% diagnosis of this cancer occur at later stages leading to low 5-year survival rate. This emphasizes the need to have better prognostic techniques for stomach cancer. In this regard, the Next-Generation Sequencing of whole genome and multi-view approach to omics may reveal the underlying molecular complexity of stomach cancer using high throughput expression data of miRNA. Generally, miRNAs are small, non-coding RNAs, which cause downregulation of target mRNAs. They also show differential expression for a specific biological condition like stage or histological type of stomach cancer, highlighting their importance as potential biomarkers. Analyzing miRNA expression data is a challenging task due to the existence of large number of miRNAs and less sample size. A small set of miRNAs will be helpful in designing efficient diagnostic and prognostic tool. In this regard, here a computational framework is proposed that selects different sets of miRNAs for five different categories of clinical outcomes viz. condition, clinical stage, age, histological type, and survival status. First, the miRNAs are ranked using four feature ranking methods. These ranks are used to find an ensemble rank based on adaptive weight. Second, the top 100 miRNAs from each category are used to find the miRNAs that are common to all categories as well as miRNAs that belong to only one category. Finally, the results have been validated quantitatively and through biological significance analysis.

The lignin toolbox of the model grass Setaria viridis.

The core set of biosynthetic genes potentially involved in developmental lignification was identified in the model C4 grass Setaria viridis. Lignin has been recognized as a major recalcitrant factor negatively affecting the processing of plant biomass into bioproducts. However, the efficient manipulation of lignin deposition in order to generate optimized crops for the biorefinery requires a fundamental knowledge of several aspects of lignin metabolism, including regulation, biosynthesis and polymerization. The current availability of an annotated genome for the model grass Setaria viridis allows the genome-wide characterization of genes involved in the metabolic pathway leading to the production of monolignols, the main building blocks of lignin. Here we performed a comprehensive study of monolignol biosynthetic genes as an initial step into the characterization of lignin metabolism in S. viridis. A total of 56 genes encoding bona fide enzymes catalyzing the consecutive ten steps of the monolignol biosynthetic pathway were identified in the S. viridis genome. A combination of comparative phylogenetic studies, high-throughput expression analysis and quantitative RT-PCR analysis was further employed to identify the family members potentially involved in developmental lignification. Accordingly, 14 genes clustered with genes from closely related species with a known function in lignification and showed an expression pattern that correlates with lignin deposition. These genes were considered the "core lignin toolbox" responsible for the constitutive, developmental lignification in S. viridis. These results provide the basis for further understanding lignin deposition in C4 grasses and will ultimately allow the validation of biotechnological strategies to produce crops with enhanced processing properties.

Identification of Stress Associated microRNAs in Solanum lycopersicum by High-Throughput Sequencing.

Tomato (Solanum lycopersicum) is one of the most important crops around the world and also a model plant to study response to stress. High-throughput sequencing was used to analyse the microRNA (miRNA) profile of tomato plants undergoing five biotic and abiotic stress conditions (drought, heat, P. syringae infection, B. cinerea infection, and herbivore insect attack with Leptinotarsa decemlineata larvae) and one chemical treatment with a plant defence inducer, hexanoic acid. We identified 104 conserved miRNAs belonging to 37 families and we predicted 61 novel tomato miRNAs. Among those 165 miRNAs, 41 were stress-responsive. Reverse transcription quantitative PCR (RT-qPCR) was used to validate high-throughput expression analysis data, confirming the expression profiles of 10 out of 11 randomly selected miRNAs. Most of the differentially expressed miRNAs were stress-specific, except for sly-miR167c-3p upregulated in B. cinerea and P. syringae infection, sly-newmiR26-3p upregulated in drought and Hx treatment samples, and sly-newmiR33-3p, sly-newmiR6-3p and sly-newmiR8-3p differentially expressed both in biotic and abiotic stresses. From mature miRNAs sequences of the 41 stress-responsive miRNAs 279 targets were predicted. An inverse correlation between the expression profiles of 4 selected miRNAs (sly-miR171a, sly-miR172c, sly-newmiR22-3p and sly-miR167c-3p) and their target genes (Kinesin, PPR, GRAS40, ABC transporter, GDP and RLP1) was confirmed by RT-qPCR. Altogether, our analysis of miRNAs in different biotic and abiotic stress conditions highlight the interest to understand the functional role of miRNAs in tomato stress response as well as their putative targets which could help to elucidate plants molecular and physiological adaptation to stress.

Colorectal tumor-on-a-chip system: A 3D tool for precision onco-nanomedicine.

Awareness that traditional two-dimensional (2D) in vitro and nonrepresentative animal models may not completely emulate the 3D hierarchical complexity of tissues and organs is on the rise. Therefore, posterior translation into successful clinical application is compromised. To address this dearth, on-chip biomimetic microenvironments powered by microfluidic technologies are being developed to better capture the complexity of in vivo pathophysiology. Here, we describe a "tumor-on-a-chip" model for assessment of precision nanomedicine delivery on which we validate the efficacy of drug-loaded nanoparticles in a gradient fashion. The model validation was performed by viability studies integrated with live imaging to confirm the dose-response effect of cells exposed to the CMCht/PAMAM nanoparticle gradient. This platform also enables the analysis at the gene expression level, where a down-regulation of all the studied genes (MMP-1, Caspase-3, and Ki-67) was observed. This tumor-on-chip model represents an important development in the use of precision nanomedicine toward personalized treatment.

Extracting predictors for lung adenocarcinoma based on Granger causality test and stepwise character selection

BackgroundLung adenocarcinoma is the most common type of lung cancer, with high mortality worldwide. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, DNA methylation, and miRNA quantification. However, the hub genes, which can be served as bio-markers for discriminating cancer and healthy individuals, are not well screened.ResultHere we present a new method for extracting gene predictors, aiming to obtain the least predictors without losing the efficiency. We firstly analyzed three different expression microarrays and constructed multi-interaction network, since the individual expression dataset is not enough for describing biological behaviors dynamically and systematically. Then, we transformed the undirected interaction network to directed network by employing Granger causality test, followed by the predictors screened with the use of the stepwise character selection algorithm. Six predictors, including TOP2A, GRK5, SIRT7, MCM7, EGFR, and COL1A2, were ultimately identified. All the predictors are the cancer-related, and the number is very small fascinating diagnosis. Finally, the validation of this approach was verified by robustness analyses applied to six independent datasets; the precision is up to 95.3% ∼ 100%.ConclusionAlthough there are complicated differences between cancer and normal cells in gene functions, cancer cells could be differentiated in case that a group of special genes expresses abnormally. Here we presented a new, robust, and effective method for extracting gene predictors. We identified as low as 6 genes which can be taken as predictors for diagnosing lung adenocarcinoma.

A High-Throughput Platform for the Generation of Synthetic Ab Clones by Single-Strand Site-Directed Mutagenesis.

Current developments in meta-data analysis and predictive computational models offer alternative routes for the identification of antibodies. In silico-based technologies and NGS data analysis from Ab phage-display selections offer expanded selections of Ab candidates. Accordingly, the identified de novo Abs with predicted selectivity for a target antigen must undergo rapid gene synthesis for downstream Ab characterizations. Here we describe a high-throughput strategy for the generation of synthetic Ab clones for expression as Fab proteins in Escherichia coli. Our approach utilizes simultaneous single-stranded site-directed mutagenesis of diversified Ab regions of a phagemid template with engineered complementary determining regions that contain multiple stop codon and restriction enzyme sites. Subsequently, we perform rapid screening of Ab DNA clones for correct gene assemblies by high-throughput Ab-phage protein expression screens. Identified sequences are corroborated by Sanger DNA sequencing analysis. In summary, our work describes a rapid and cost-effective platform for the high-throughput synthesis of synthetic Ab genes as Fab proteins for implementation into downstream proteinvalidation pipelines.

FOXP1 circular RNA sustains mesenchymal stem cell identity via microRNA inhibition.

Stem cell identity and plasticity are controlled by master regulatory genes and complex circuits also involving non-coding RNAs. Circular RNAs (circRNAs) are a class of RNAs generated from protein-coding genes by backsplicing, resulting in stable RNA structures devoid of free 5’ and 3’ ends. Little is known of the mechanisms of action of circRNAs, let alone in stem cell biology. In this study, for the first time, we determined that a circRNA controls mesenchymal stem cell (MSC) identity and differentiation. High-throughput MSC expression profiling from different tissues revealed a large number of expressed circRNAs. Among those, circFOXP1 was enriched in MSCs compared to differentiated mesodermal derivatives. Silencing of circFOXP1 dramatically impaired MSC differentiation in culture and in vivo. Furthermore, we demonstrated a direct interaction between circFOXP1 and miR-17–3p/miR-127–5p, which results in the modulation of non-canonical Wnt and EGFR pathways. Finally, we addressed the interplay between canonical and non-canonical Wnt pathways. Reprogramming to pluripotency of MSCs reduced circFOXP1 and non-canonical Wnt, whereas canonical Wnt was boosted. The opposing effect was observed during generation of MSCs from human pluripotent stem cells. Our results provide unprecedented evidence for a regulatory role for circFOXP1 as a gatekeeper of pivotal stem cell molecular networks.

An Integrated Regulatory Network Based on Comprehensive Analysis of mRNA Expression, Gene Methylation and Expression of Long Non-coding RNAs (lncRNAs) in Myelodysplastic Syndromes.

Myelodysplastic syndromes (MDS) are a heterogeneous group of disorders characterized by ineffective hematopoiesis, defective differentiation of hematopoietic precursors, and expansion of the abnormal clones. The prevalence of MDS has raised great concerns worldwide, but its pathogenetic mechanisms remain elusive. To provide insights on novel biomarkers for the diagnosis and therapy of MDS, we performed high-throughput genome-wide mRNA expression profiling, DNA methylation analysis, and long non-coding RNAs (lncRNA) analysis on bone marrows from four MDS patients and four age-matched healthy controls. We identified 1,937 differentially expressed genes (DEGs), 515 methylated genes, and 214 lncRNA that showed statistically significant differences. As the most significant module-related DEGs, TCL1A, PTGS2, and MME were revealed to be enriched in regulation of cell differentiation and cell death pathways. In addition, the GeneGo pathway maps identified by top DEGs were shown to converge on cancer, immunoregulation, apoptosis and regulation of actin cytoskeleton, most of which are known contributors in MDS etiology and pathogenesis. Notably, as potential biomarkers for diagnosis of MDS, four specific genes (ABAT, FADD, DAPP1, and SMPD3) were further subjected to detailed pathway analysis. Our integrative analysis on mRNA expression, gene methylation and lncRNAs profiling facilitates further understanding of the pathogenesis of MDS, and may promote the diagnosis and novel therapeutics for this disease.

Construction of prognostic risk prediction model based on high-throughput sequencing expression profile data in childhood acute myeloid leukemia

This study aimed to identify critical prognostic molecular markers in Childhood acute myeloid leukemia (AML) and construct nomogram-based model for prognostic prediction. The RNA-sequencing profiles and corresponding clinical information were downloaded from TCGA database. Differential expressed genes (DEG) were screened using limma package, subsequently following by GO and KEGG pathway analysis. Univariate and multivariate cox regression analysis were performed to screen critical DEGs. Nomogram-based prediction model were constructed to identify clinical factors with independent prognostic values, and the accuracy of this model was validated. A total of 214 DEGs were identified from relapse AML samples compared with non-relapse samples. These DEGs were mainly involved in twenty GO terms and three signaling pathways, such as chromatin assembly or disassembly, cytokine-cytokine receptor interaction, and JAK-STAT signaling pathway. Among these genes, Univariate and multivariate cox regression analysis results showed that relapse and risk score were significantly correlated with survival outcomes. Finally, the accuracy ability of nomogram-based prediction model was validated. These six DEGs (ABCA5, CYP7A1, HERC5, etc.) play major roles in AMLs progression. Our nomogram-based prognostic predictive model might be an effective method to estimate survival probability of AML patients with different risk status.

Network meta-analysis correlates with analysis of merged independent transcriptome expression data

BackgroundUsing meta-analysis, high-dimensional transcriptome expression data from public repositories can be merged to make group comparisons that have not been considered in the original studies. Merging of high-dimensional expression data can, however, implicate batch effects that are sometimes difficult to be removed. Removing batch effects becomes even more difficult when expression data was taken using different technologies in the individual studies (e.g. merging of microarray and RNA-seq data). Network meta-analysis has so far not been considered to make indirect comparisons in transcriptome expression data, when data merging appears to yield biased results.ResultsWe demonstrate in a simulation study that the results from analyzing merged data sets and the results from network meta-analysis are highly correlated in simple study networks. In the case that an edge in the network is supported by multiple independent studies, network meta-analysis produces fold changes that are closer to the simulated ones than those obtained from analyzing merged data sets. Finally, we also demonstrate the practicability of network meta-analysis on a real-world data example from neuroinfection research.ConclusionsNetwork meta-analysis is a useful means to make new inferences when combining multiple independent studies of molecular, high-throughput expression data. This method is especially advantageous when batch effects between studies are hard to get removed.

SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles.

Characterization of individual cell types is fundamental to the study of multicellular samples. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task. Currently, most of the scRNA-seq data analyses are commenced with unsupervised clustering. Clusters are often assigned to different cell types based on the enriched canonical markers. However, this process is inefficient and arbitrary. In this study, we present a technical framework of training the expandable supervised-classifier in order to reveal the single-cell identities as soon as the single-cell expression profile is input. Using multiple scRNA-seq datasets we demonstrate the superior accuracy, robustness, compatibility and expandability of this new solution compared to the traditional methods. We use two examples of the model upgrade to demonstrate how the projected evolution of the cell-type classifier is realized.

High-throughput sequencing and differential expression analysis of miRNAs in response to Brassinosteroid treatment in Arabidopsis thaliana.

Brassinosteroids are a class of phytohormones that play crucial roles in improving stress tolerance in plants. Many biochemical and physiological changes in response to abiotic stress are related to regulation of gene expression and accumulation of associated proteins. MicroRNAs (miRNAs) are class of small non-coding RNAs that regulate gene expression post-transcriptionally. Roles of these regulatory RNAs in brassinosteroid (BR) signalling have however remained elusive. In this study using high-throughput small RNA sequencing method, we present a comprehensive compilation of BR-induced differentially expressed microRNAs in root and shoots of Arabidopsis thaliana seedlings. We identified 229 known miRNAs belonging to 102 families and 27 novel miRNAs that express in response to exogenous BR treatment. Out of 102 families, miRNAs belonging to known 48 families and out of 27 novel miRNAs, 23 were observed to be differentially expressed in response to BR treatment. Among the conserved miRNAs, all members of miR169 were observed to be downregulated in both shoot and root samples. While, auxin-responsive factors were predicted to be direct targets of some novel miRNAs that are upregulated in shoots and suppressed in roots. The BR-responsive tissue-specific miRNome characterized in this study can be used as a starting point by investigators for functional validation studies that will shed light on the underlying molecular mechanism of BR-mediated stress tolerance at the level of post-transcriptional gene regulation.