A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes

TEnGExA: an R package based tool for tissue enrichment and gene expression analysis

RNA-seq data analysis with rapidly advancing high-throughput sequencing technology, nowadays provides large number of transcripts or genes to perform downstream analysis including functional annotation and pathway analysis. However for the data from multiple tissues, downstream analysis with tissue-specific or tissue-enriched transcripts is highly preferable. However, there is still a need of tool for quickly performing tissue-enrichment and gene expression analysis irrespective of number of input genes or tissues at various fragments per kilobase of transcript per million fragments mapped (FPKM) thresholds. To fulfill this need, we presented a freely available R package and web-interface tool, TEnGExA, which allows tissue-enrichment analysis (TEA) for any number of genes or transcripts for any species provided only a read-count or FPKM-value matrix as input. Based on the different FPKM value and fold thresholds, TEnGExA classifies the user provided gene lists into tissue-enriched or tissue-specific transcripts along with other standard classes. By analyzing the published sample data from human, plant and microorganism, we signifies that TEnGExA can easily handle complex or large data from any species to provided tissue-enriched gene list for downstream analysis in quick time. In summary, TEnGExA is quick, easy to use and an efficient tool for TEA. The R package is freely available at https://github.com/ubagithub/TEnGExA/ and the GUI web interface is accessible at http://webtom.cabgrid.res.in/tissue_enrich/.

A Preliminary Study on the Characteristics of microRNAs in Ovarian Stroma and Follicles of Chuanzhong Black Goat during Estrus

microRNAs (miRNAs) play a significant role in ovarian follicular maturity, but miRNA expression patterns in ovarian stroma (OS), large follicles (LF), and small follicles (SF) have been rarely explored. We herein aimed to identify miRNAs, their target genes and signaling pathways, as well as their interaction networks in OS, LF, and SF of Chuanzhong black goats at the estrus phase using small RNA-sequencing. We found that the miRNA expression profiles of LF and SF were more similar than those of OS—32, 16, and 29 differentially expressed miRNAs were identified in OS vs. LF, OS vs. SF, and LF vs. SF, respectively. Analyses of functional enrichment and the miRNA-targeted gene interaction network suggested that miR-182 (SMC3), miR-122 (SGO1), and miR-206 (AURKA) were involved in ovarian organogenesis and hormone secretion by oocyte meiosis. Furthermore, miR-202-5p (EREG) and miR-485-3p (FLT3) were involved in follicular maturation through the MAPK signaling pathway, and miR-2404 (BMP7 and CDKN1C) played a key role in follicular development through the TGF-β signaling pathway and cell cycle; nevertheless, further research is warranted. To our knowledge, this is the first study to investigate miRNA expression patterns in OS, LF, and SF of Chuanzhong black goats during estrus. Our findings provide a theoretical basis to elucidate the role of miRNAs in follicular maturation. These key miRNAs might provide candidate biomarkers for the diagnosis of follicular maturation and will assist in developing new therapeutic targets for female goat infertility.

Identifying Cell-Type Specific Genes and Expression Rules Based on Single-Cell Transcriptomic Atlas Data

Single-cell sequencing technologies have emerged to address new and longstanding biological and biomedical questions. Previous studies focused on the analysis of bulk tissue samples composed of millions of cells. However, the genomes within the cells of an individual multicellular organism are not always the same. In this study, we aimed to identify the crucial and characteristically expressed genes that may play functional roles in tissue development and organogenesis, by analyzing a single-cell transcriptomic atlas of mice. We identified the most relevant gene features and decision rules classifying 18 cell categories, providing a list of genes that may perform important functions in the process of tissue development because of their tissue-specific expression patterns. These genes may serve as biomarkers to identify the origin of unknown cell subgroups so as to recognize specific cell stages/states during the dynamic process, and also be applied as potential therapy targets for developmental disorders.

Identifying Microbiota Signature and Functional Rules Associated With Bacterial Subtypes in Human Intestine

Gut microbiomes are integral microflora located in the human intestine with particular symbiosis. Among all microorganisms in the human intestine, bacteria are the most significant subgroup that contains many unique and functional species. The distribution patterns of bacteria in the human intestine not only reflect the different microenvironments in different sections of the intestine but also indicate that bacteria may have unique biological functions corresponding to their proper regions of the intestine. However, describing the functional differences between the bacterial subgroups and their distributions in different individuals is difficult using traditional computational approaches. Here, we first attempted to introduce four effective sets of bacterial features from independent databases. We then presented a novel computational approach to identify potential distinctive features among bacterial subgroups based on a systematic dataset on the gut microbiome from approximately 1,500 human gut bacterial strains. We also established a group of quantitative rules for explaining such distinctions. Results may reveal the microstructural characteristics of the intestinal flora and deepen our understanding on the regulatory role of bacterial subgroups in the human intestine.

Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes

Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression–methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes.

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

Tissue differences revealed by gene expression profiles of various cell lines

Mechanisms through which tissues are formed and maintained remain unknown but are fundamental aspects in biology. Tissue-specific gene expression is a valuable tool to study such mechanisms. But in many biomedical studies, cell lines, rather than human body tissues, are used to investigate biological mechanisms Whether or not cell lines maintain their tissue-specific characteristics after they are isolated and cultured outside the human body remains to be explored. In this study, we applied a novel computational method to identify core genes that contribute to the differentiation of cell lines from various tissues. Several advanced computational techniques, such as Monte Carlo feature selection method, incremental feature selection method, and support vector machine (SVM) algorithm, were incorporated in the proposed method, which extensively analyzed the gene expression profiles of cell lines from different tissues. As a result, we extracted a group of functional genes that can indicate the differences of cell lines in different tissues and built an optimal SVM classifier for identifying cell lines in different tissues. In addition, a set of rules for classifying cell lines were also reported, which can give a clearer picture of cell lines in different issues although its performance was not better than the optimal SVM classifier. Finally, we compared such genes with the tissue-specific genes identified by the Genotype-tissue Expression project. Results showed that most expression patterns between tissues remained in the derived cell lines despite some uniqueness that some genes show tissue specificity.

The next generation personalized models to screen hidden layers of breast cancer tumorigenicity

BACKGROUND

Breast cancer (BC) is a challenging disease and major cause of death amongst women worldwide who die due to tumor relapse or sidelong diseases. BC main complexity comes from the heterogeneous nature of breast tumors that demands customized treatments in the form of personalized medicine.

REVIEW OF THE LITERATURE AND DISCUSSION

Spatiotemporally dynamic and heterogeneous nature of BC tumors is shaped by their clonal evolution and sub-clonal selections and shapes resistance to collective or group therapies that drives cancer recurrence and tumor metastasis. Personalized intervention promises to administer medications that selectively target each individual patient tumor and even further each colonized secondary tumor. Such personalized regimens will require creation of in vitro and in vivo models genuinely recapitulating characteristics of each tumor type as initiating platforms for two main purposes: to closely monitor the tumorigenic processes that shape tumor heterogeneity and evolution as the main driving forces behind tumor chemo-resistance and relapse, and subsequently to establish patient-specific preventive and therapeutic measures. While application of tumor modeling for personalized drug screening and design requires a separate review, here we discuss the personalized utilities of xenograft modeling in investigating BC tumor formation and progression toward metastasis. We will further elaborate on the impact of innovative technologies on personalized modeling of BC tumorigenicity at improved resolution.

CONCLUSION

Heterogeneous nature of each BC tumor requires personalized intervention implying that modeling breast tumors is inevitable for better disease understanding, detection and cure. Patient-derived xenografts are just the initiating piece of the puzzle for ideal management of breast cancer. Emerging technologies promise to model BC more personalized than before.