Tools and strategies for RNA-sequencing data analysis

RNA-Sequencing (RNA-seq) has enabled the in-depth study of the transcriptome, becoming the primary research method in the field of molecular biology. The typical aim of RNA-seq is to quantify and detect differentially expressed (DE) and differentially spliced (DS) genes. Numerous methodologies and tools have been developed in recent years to assist in analyzing RNA-seq data. However, it is difficult for researchers to decide which methods or strategies they should adopt to optimize the analysis of their datasets. In this Thesis, in Study I, we applied the gene-level DE analysis approach to detect the androgen-regulated genes between cancerous and benign samples in 48 primary prostate cancer patients. Combined with other measurements from the same samples, our analysis indicated that patients having TMPRSS-ERG gene fusion had distinct intratumoral androgen profiles compared to TMPRSS-ERG negative tumors. However, the DE can remain undetected when the expression varies across the gene due to reasons such as alternative splicing. Hence, to account for this problem, an alternate analysis approach has been suggested in which the statistical testing of lower feature levels (e.g. transcripts, transcript compatibility counts, or exons) is performed initially, followed by aggregating the results to the gene level. In Study II, we tested this alternate approach on these lower features and compared the results to those from the conventional gene-level approach. In the alternate approach, two methods (Lancaster method and empirical brown method (ebm)) were tested for aggregating the feature-level results to gene-level results. Our results suggest that the exon-level estimates improve the detection of the DE genes when the ebm method is used for aggregating the results. Accordingly, R/Bioconductor package EBSEA was developed using the winning approach. RNA-seq data can also be used to find DS events between conditions. However, the detection of DS is more challenging than the detection of DE. In Study III, a comprehensive comparison of ten DS tools was performed. We concluded that exonbased and event-based methods (rMATS and MAJIQ) performed overall best across the different evaluation metrics considered. Furthermore, we observed overall low concordance between the results reported by the different tools, making it recommendable to use more than one tool when performing DS analysis, and to concentrate on the overlapping results.Työkaluja ja strategioita RNA-sekvensointidatan analyysiin RNA-sekvensointi (RNA-seq) on mahdollistanut transkriptomin yksityiskohtaisen tarkastelun ja siitä on tullut hyvin suosittu työkalu molekyylibiologian tutkimuksessa. RNA-sekvensointitutkimusten tyypillinen tarkoitus on selvittää näyteryh- mien välillä eriävästi ilmentyviä ja silmukoituvia geenejä. RNA-sekvensointidatojen analyysiin on kehitetty runsaasti työkaluja ja usein on haastavaa valita näiden joukosta optimaaliset välineet tietyn aineiston käsittelyyn. Tässä väitöstyössä osajulkaisussa I tunnistettiin androgeenihormonien säätelemiä eriävästi ilmentyviä geenejä syöpäkudoksen ja terveen kudoksen välillä 48 eturauhassyöpäpotilaalla. Kun nämä tulokset yhdistettiin muihin samojen potilaiden käytettävissä oleviin mittausarvoihin, havaittiin, että TMPRSS-ERG-geenifuusion omaavien potilaiden syöpäkudoksen androgeenihormonigeenien ilmentymisprofiili poikkesi verrattuna niihin potilaisiin, joilta ei löytynyt vastaavaa geenifuusiota. On kuitenkin mahdollista, että tällä lähestymistavalla eriävä ilmentyminen jää joidenkin geenien osalta havaitsematta, jos ilmentymistaso vaihtelee geenin eri osissa, esimerkiksi vaihtoehtoisen silmukoinnin vaikutuksen vuoksi. Ratkaisuksi tähän on esitetty uudenlaista lähestymistapaa, jossa tilastollinen testaus näyteryhmien välillä suoritetaan geenin rakenteen osalta hienojakoisemmalla tasolla (esimerkiksi transkriptien, transkriptiyhteensopivien mittausyksiköiden tai eksonien tasolla) ja vasta näin saadut osatulokset yhdistetään geenitason kokonaistulokseksi. Julkaisussa II verrattiin tätä lähestymistapaa perinteiseen geenitason analyysiin testaamalla kahta eri menetelmää tulosten yhdistämiseen takaisin geenitasolle: 1) Lancaster- menetelmää ja 2) empiiristä Brown-menetelmää (ebm). Tulosten perusteella eksonitason mittausarvojen käyttö yhdistettynä ebm-menetelmään paransi eriävästi ilmentyvien geenien tunnistusta. Tämä lähestymistapa on sisällytetty väitöstyössä kehitettyyn geenien eriävää ilmentymistä analysoivaan R/Bioconductor -analyysipakettiin EBSEA. RNA-sekvensointidataa voidaan käyttää myös eriävien silmukointitapahtumien tunnistamiseen näyteryhmien välillä. Tämä on kuitenkin haastavampaa kuin geenien eriävän ilmentymisen analyysi. Julkaisussa III vertailtiin kymmentä eriävien silmukointitapahtumien tunnistamiseen kehitettyä työkalua. Näistä työkaluista eksoniperustaiset ja silmukointitapahtumaperustaiset työkalut (erityisesti rMATS ja MAJIQ) tuottivat parhaat kokonaistulokset käytetyillä vertailukriteereillä. Työkalujen tuottamien tulosten välillä havaittiin kuitenkin merkittäviä eroja, minkä johdosta tulosten jatkotarkastelussa on hyödyllistä keskittyä niihin tuloksiin, jotka ovat löydettävissä useammalla kuin yhdellä työkalulla

Exon-level estimates improve the detection of differentially expressed genes in RNA-seq studies

Detection of differentially expressed genes (DEGs) between different biological conditions is a key data analysis step of most RNA-sequencing studies. Conventionally, computational tools have used gene-level read counts as input to test for differential gene expression between sample condition groups. Recently, it has been suggested that statistical testing could be performed with increased power at a lower feature level prior to aggregating the results to the gene level. In this study, we systematically compared the performance of calling the DEGs when using read count data at different levels (gene, transcript, and exon) as input, in the context of two publicly available data sets. Additionally, we tested two different methods for aggregating the lower feature-level p-values to gene-level: Lancaster and empirical Brown's method. Our results show that detection of DEGs is improved compared to the conventional gene-level approach regardless of the lower feature-level used for statistical testing. The overall best balance between accuracy and false discovery rate was obtained using the exon-level approach with empirical Brown's aggregation method, which we provide as a freely available Bioconductor package EBSEA (https://bioconductor.org/packages/release/bioc/html/EBSEA.html)

Comparison of methods to detect differentially expressed genes between single-cell populations

We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications.</p

Systematic evaluation of differential splicing tools for RNA-seq studies

Differential splicing (DS) is a post-transcriptional biological process with critical, wide-ranging effects on a plethora of cellular activities and disease processes. To date, a number of computational approaches have been developed to identify and quantify differentially spliced genes from RNA-seq data, but a comprehensive intercomparison and appraisal of these approaches is currently lacking. In this study, we systematically evaluated 10 DS analysis tools for consistency and reproducibility, precision, recall and false discovery rate, agreement upon reported differentially spliced genes and functional enrichment. The tools were selected to represent the three different methodological categories: exon-based (DEXSeq, edgeR, JunctionSeq, limma), isoform-based (cuffdiff2, DiffSplice) and event-based methods (dSpliceType, MAJIQ, rMATS, SUPPA). Overall, all the exon-based methods and two event-based methods (MAJIQ and rMATS) scored well on the selected measures. Of the 10 tools tested, the exon-based methods performed generally better than the isoform-based and event-based methods. However, overall, the different data analysis tools performed strikingly differently across different data sets or numbers of samples

Epigenetic control of CD1D expression as a mechanism of resistance to immune checkpoint therapy in poorly immunogenic melanomas

Immune Checkpoint Therapies (ICT) have revolutionized the treatment of metastatic melanoma. However, only a subset of patients reaches complete responses. Deficient β2-microglobulin (β2M) expression impacts antigen presentation to T cells, leading to ICT resistance. Here, we investigate alternative β2M-correlated biomarkers that associate with ICT resistance. We shortlisted immune biomarkers interacting with human β2M using the STRING database. Next, we profiled the transcriptomic expression of these biomarkers in association with clinical and survival outcomes in the melanoma GDC-TCGA-SKCM dataset and a collection of publicly available metastatic melanoma cohorts treated with ICT (anti-PD1). Epigenetic control of identified biomarkers was interrogated using the Illumina Human Methylation 450 dataset from the melanoma GDC-TCGA-SKCM study. We show that β2M associates with CD1d, CD1b, and FCGRT at the protein level. Co-expression and correlation profile of B2M with CD1D, CD1B, and FCGRT dissociates in melanoma patients following B2M expression loss. Lower CD1D expression is typically found in patients with poor survival outcomes from the GDC-TCGA-SKCM dataset, in patients not responding to anti-PD1 immunotherapies, and in a resistant anti-PD1 pre-clinical model. Immune cell abundance study reveals that B2M and CD1D are both enriched in tumor cells and dendritic cells from patients responding to anti-PD1 immunotherapies. These patients also show increased levels of natural killer T (NKT) cell signatures in the tumor microenvironment (TME). Methylation reactions in the TME of melanoma impact the expression of B2M and SPI1, which controls CD1D expression. These findings suggest that epigenetic changes in the TME of melanoma may impact β2M and CD1d-mediated functions, such as antigen presentation for T cells and NKT cells. Our hypothesis is grounded in comprehensive bioinformatic analyses of a large transcriptomic dataset from four clinical cohorts and mouse models. It will benefit from further development using well-established functional immune assays to support understanding the molecular processes leading to epigenetic control of β2M and CD1d. This research line may lead to the rational development of new combinatorial treatments for metastatic melanoma patients that poorly respond to ICT

High intratumoral dihydrotestosterone is associated with antiandrogen resistance in VCaP prostate cancer xenografts in castrated mice

Antiandrogen treatment resistance is a major clinical concern in castration-resistant prostate cancer (CRPC) treatment. Using xenografts of VCaP cells we showed that growth of antiandrogen resistant CRPC tumors were characterized by a higher intratumor dihydrotestosterone (DHT) concentration than that of treatment responsive tumors. Furthermore, the slow tumor growth after adrenalectomy was associated with a low intratumor DHT concentration. Reactivation of androgen signaling in enzalutamide-resistant tumors was further shown by the expression of several androgen-dependent genes. The data indicate that intratumor DHT concentration and expression of several androgen-dependent genes in CRPC lesions is an indication of enzalutamide treatment resistance and an indication of the need for further androgen blockade. The presence of an androgen synthesis, independent of CYP17A1 activity, has been shown to exist in prostate cancer cells, and thus, novel androgen synthesis inhibitors are needed for the treatment of enzalutamide-resistant CRPC tumors that do not respond to abiraterone.Peer reviewe

Antioxidant potential and α-glucosidase inhibitory activity of onion (Allium cepa L.) Peel and bulb extracts

Allium cepa L. is a commonly consumed vegetable that belongs to the Amaryllidaceae family and contains nutrients and antioxidants in ample amounts. In spite of the valuable food applications of onion bulb, its peel and outer fleshy layers are generally regarded as waste and exploration of their nutritional and therapeutic potential is still in progress with a very slow progression rate. The present study was designed with the purpose of doing a comparative analysis of the antioxidant potential of two parts of Allium cepa, i.g., bulb (edible part) and outer fleshy layers and dry peels (inedible part). Moreover, the inhibitory effect of the onion bulb and peel extracts on rat intestinal α-glucosidase and pancreatic α-amylase of porcine was also evaluated. The antioxidant potential of onion peel and bulb extracts were evaluated using 2,2-diphenyl- 1-picryl hydrazyl (DPPH), ferric-reducing antioxidant power assay (FRAP), 2,2’-azino-bis- 3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical scavenging assay, H2O2 radical scavenging activity and Fe2+ chelating activity. Total flavonoids and phenolic content of ethanolic extract of onion peel were significantly greater as compared to that of onion bulb. Ethanolic extract of onion peel also presented better antioxidant and free-radical scavenging activity as compared to the ethanolic extract of bulb, while the aqueous extract of bulb presented weakest antioxidative potential. Onion peel extract’s α-glucosidase inhibition potential was also correlated with their phenolic and flavonoid contents. The current findings presented onion peel as a possible source of antioxidative agents and phenolic compounds that might be beneficial against development of various common chronic diseases that might have an association with oxidative stress. Besides, outer dry layers and fleshy peels of onion exhibited higher phenolic content and antioxidant activities, compared to the inner bulb. The information obtained by the present study can be useful in promoting the use of vegetable parts other than the edible mesocarp for several future food applications, rather than these being wasted.Peer reviewe

Adrenals Contribute to Growth of Castration-Resistant VCaP Prostate Cancer Xenografts

The role of adrenal androgens as drivers for castration-resistant prostate cancer (CRPC) growth in humans is generally accepted; however, the value of preclinical mouse models of CRPC is debatable, because mouse adrenals do not produce steroids activating the androgen receptor. In this study, we confirmed the expression of enzymes essential for de novo synthesis of androgens in mouse adrenals, with high intratissue concentration of progesterone (P-4) and moderate levels of androgens, such as androstenedione, testosterone, and dihydrotestosterone, in the adrenal glands of both intact and orchectomized (ORX) mice. ORX alone had no effect on serum P-4 concentration, whereas orchectomized and adrenalectomized (ORX + ADX) resulted in a significant decrease in serum P-4 and in a further reduction in the Low levels of serum androgens (androstenedione, testosterone, and dihydrotestosterone), measured by mass spectrometry. In line with this, the serum prostate-specific antigen and growth of VCaP xenografts in mice after ORX + ADX were markedly reduced compared with ORX alone, and the growth difference was not abolished by a glucocorticoid treatment. Moreover, ORX + ADX altered the androgen-dependent gene expression in the tumors, similar to that recently shown for the enzalutamide treatment. These data indicate that in contrast to the current view, and similar to humans, mouse adrenals synthesize significant amounts of steroids that contribute to the androgen receptor dependent growth of CRPC.Peer reviewe

Machine Learning Methods for the Classification of Endometriosis

Endometriosis is a benign disorder estimated to affect 10% of women during their reproductive years. The main method for the diagnosis of endometriosis currently used by the clinicians is laparoscopic inspection followed by histological confirmation. Non-invasive methods such as ultrasound or magnetic resonance imaging do not have enough diagnostic power. The aim of this study was to find a potential panel of serum biomarkers for the diagnostic classification of endometriosis. A total of 35 serum biomarkers were measured in cases and controls. The controls in the study were relatively older women who come to the clinic for sterilization, which resulted in left- skewed patient population for age. In order to evaluate sensitivity in respect to this confounder, a matched and non-matched subset of subjects were compared in the analysis. The data was further stratified randomly into training and test data sets. Machine learning analysis was performed using multivariate approaches (Naive Bayes, Support Vector Machine using linear or polynomial kernel, Random Forest, Elastic net, Artificial Neural Network) in training and test data sets separately to train and validate the findings using different cross validation techniques. The comparison of the machine learning approaches suggested that Random Forest and Elastic Net perform particularly well in comparison to the other methods. The predictive biomarkers identified in the study included not only the conventional endometriosis marker CA125, but also novel potential biomarkers which can refine currently utilized practices in the diagnosis and classification of endometriosis