Long-read transcriptome sequencing analysis with IsoTools

Long-read transcriptome sequencing (LRTS) holds the promise to boost our understanding of alternative splicing. Recent advances in accuracy and throughput have diminished the major limitations and enabled the direct quantification of isoforms. Considering the complexity of the data and the broad range of potential applications, it is clear that highly flexible, accurate analysis tools are crucial. Here, we present IsoTools, a comprehensive Python-based analysis package, for the improvement of alternative and differential splicing analysis. Iso-Tools provides a comprehensive data structure that integrates genomic information from LRTS transcripts together with the reference annotation, and enables broad functionality to quality control, visualize and analyze the data. Additionally, we implemented a graph-based method for the identification of alternative splicing events and a statistical approach based on the beta binomial distribution for the detection of differential events. To demonstrate our methods, we generated PacBio Iso-Seq data of human hepatocytes treated with the HDAC inhibitor valproic acid, a compound known to induce widespread transcriptional changes. Contrasted with short read RNA-Seq of the same samples, this analysis shows that LRTS provides valuable additional insights for a better understanding of alternative splicing, in particular with respect to complex novel and differential splicing events. IsoTools is made available for the community along with extensive documentation at https://github.com/MatthiasLienhard/isotools

Hypoxic oligodendrocyte precursor cell-derived VEGFA is associated with blood–brain barrier impairment

Abstract Cerebral small vessel disease is characterised by decreased cerebral blood flow and blood–brain barrier impairments which play a key role in the development of white matter lesions. We hypothesised that cerebral hypoperfusion causes local hypoxia, affecting oligodendrocyte precursor cell—endothelial cell signalling leading to blood–brain barrier dysfunction as an early mechanism for the development of white matter lesions. Bilateral carotid artery stenosis was used as a mouse model for cerebral hypoperfusion. Pimonidazole, a hypoxic cell marker, was injected prior to humane sacrifice at day 7. Myelin content, vascular density, blood–brain barrier leakages, and hypoxic cell density were quantified. Primary mouse oligodendrocyte precursor cells were exposed to hypoxia and RNA sequencing was performed. Vegfa gene expression and protein secretion was examined in an oligodendrocyte precursor cell line exposed to hypoxia. Additionally, human blood plasma VEGFA levels were measured and correlated to blood–brain barrier permeability in normal-appearing white matter and white matter lesions of cerebral small vessel disease patients and controls. Cerebral blood flow was reduced in the stenosis mice, with an increase in hypoxic cell number and blood–brain barrier leakages in the cortical areas but no changes in myelin content or vascular density. Vegfa upregulation was identified in hypoxic oligodendrocyte precursor cells, which was mediated via Hif1α and Epas1. In humans, VEGFA plasma levels were increased in patients versus controls. VEGFA plasma levels were associated with increased blood–brain barrier permeability in normal appearing white matter of patients. Cerebral hypoperfusion mediates hypoxia induced VEGFA expression in oligodendrocyte precursor cells through Hif1α/Epas1 signalling. VEGFA could in turn increase BBB permeability. In humans, increased VEGFA plasma levels in cerebral small vessel disease patients were associated with increased blood–brain barrier permeability in the normal appearing white matter. Our results support a role of VEGFA expression in cerebral hypoperfusion as seen in cerebral small vessel disease

Pharmacological inhibition of 17β-hydroxysteroid dehydrogenase impairs human endometrial cancer growth in an orthotopic xenograft mouse model

Endometrial cancer (EC) is the most common gynaecological tumor in developed countries and its incidence is increasing. Approximately 80% of newly diagnosed EC cases are estrogen-dependent. Type 1 17β-hydroxysteroid dehydrogenase (17β-HSD-1) is the enzyme that catalyzes the final step in estrogen biosynthesis by reducing the weak estrogen estrone (E1) to the potent estrogen 17β-estradiol (E2), and previous studies showed that this enzyme is implicated in the intratumoral E2 generation in EC. In the present study we employed a recently developed orthotopic and estrogen-dependent xenograft mouse model of EC to show that pharmacological in-hibition of the 17β-HSD-1 enzyme inhibits disease development. Tumors were induced in one uterine horn of athymic nude mice by  intrauterine injection of  the  well-differentiated human endometrial adenocarcinoma Ishikawa cell line, modified to express human 17β-HSD-1 in levels comparable to EC, and the luciferase and green fluorescent protein reporter genes. Controlled estrogen exposure in ovariectomized mice was achieved using subcutaneous MedRod implants that released either the low active estrone (E1) precursor or vehicle. A subgroup of E1 supplemented mice received daily oral gavage of FP4643, a well-characterized 17β-HSD-1 in-hibitor. Bioluminescence imaging (BLI) was used to measure tumor growth non-invasively. At sacrifice, mice receiving E1  and  treated with the  FP4643 inhibitor showed a  significant reduction in  tumor growth by approximately 65% compared to mice receiving E1. Tumors exhibited metastatic spread to the peritoneum, to the  lymphovascular space (LVI), and  to  the  thoracic cavity. Metastatic spread and  LVI  invasion were both significantly reduced in the inhibitor-treated group. Transcriptional profiling of tumors indicated that FP4643 treatment reduced the oncogenic potential at the mRNA level. In conclusion, we show that 17β-HSD-1 inhibition represents a promising novel endocrine treatment for EC.   </div

A novel, integrated in vitro carcinogenicity test to identify genotoxic and non-genotoxic carcinogens using human lymphoblastoid cells

Human exposure to carcinogens occurs via a plethora of environmental sources, with 70–90% of cancers caused by extrinsic factors. Aberrant phenotypes induced by such carcinogenic agents may provide universal biomarkers for cancer causation. Both current in vitro genotoxicity tests and the animal-testing paradigm in human cancer risk assessment fail to accurately represent and predict whether a chemical causes human carcinogenesis. The study aimed to establish whether the integrated analysis of multiple cellular endpoints related to the Hallmarks of Cancer could advance in vitro carcinogenicity assessment. Human lymphoblastoid cells (TK6, MCL-5) were treated for either 4 or 23 h with 8 known in vivo carcinogens, with doses up to 50% Relative Population Doubling (maximum 66.6 mM). The adverse effects of carcinogens on wide-ranging aspects of cellular health were quantified using several approaches; these included chromosome damage, cell signalling, cell morphology, cell-cycle dynamics and bioenergetic perturbations. Cell morphology and gene expression alterations proved particularly sensitive for environmental carcinogen identification. Composite scores for the carcinogens’ adverse effects revealed that this approach could identify both DNA-reactive and non-DNA reactive carcinogens in vitro. The richer datasets generated proved that the holistic evaluation of integrated phenotypic alterations is valuable for effective in vitro risk assessment, while also supporting animal test replacement. Crucially, the study offers valuable insights into the mechanisms of human carcinogenesis resulting from exposure to chemicals that humans are likely to encounter in their environment. Such an understanding of cancer induction via environmental agents is essential for cancer prevention

Maternal and paternal genomes differentially affect myofibre characteristics and muscle weights of bovine fetuses at midgestation

Postnatal myofibre characteristics and muscle mass are largely determined during fetal development and may be significantly affected by epigenetic parent-of-origin effects. However, data on such effects in prenatal muscle development that could help understand unexplained variation in postnatal muscle traits are lacking. In a bovine model we studied effects of distinct maternal and paternal genomes, fetal sex, and non-genetic maternal effects on fetal myofibre characteristics and muscle mass. Data from 73 fetuses (Day153, 54% term) of four genetic groups with purebred and reciprocal cross Angus and Brahman genetics were analyzed using general linear models. Parental genomes explained the greatest proportion of variation in myofibre size of Musculus semitendinosus (80–96%) and in absolute and relative weights of M. supraspinatus, M. longissimus dorsi, M. quadriceps femoris and M. semimembranosus (82–89% and 56–93%, respectively). Paternal genome in interaction with maternal genome (P<0.05) explained most genetic variation in cross sectional area (CSA) of fast myotubes (68%), while maternal genome alone explained most genetic variation in CSA of fast myofibres (93%, P<0.01). Furthermore, maternal genome independently (M. semimembranosus, 88%, P<0.0001) or in combination (M. supraspinatus, 82%; M. longissimus dorsi, 93%; M. quadriceps femoris, 86%) with nested maternal weight effect (5–6%, P<0.05), was the predominant source of variation for absolute muscle weights. Effects of paternal genome on muscle mass decreased from thoracic to pelvic limb and accounted for all (M. supraspinatus, 97%, P<0.0001) or most (M. longissimus dorsi, 69%, P<0.0001; M. quadriceps femoris, 54%, P<0.001) genetic variation in relative weights. An interaction between maternal and paternal genomes (P<0.01) and effects of maternal weight (P<0.05) on expression of H19, a master regulator of an imprinted gene network, and negative correlations between H19 expression and fetal muscle mass (P<0.001), suggested imprinted genes and miRNA interference as mechanisms for differential effects of maternal and paternal genomes on fetal muscle.Ruidong Xiang, Mani Ghanipoor-Samami, William H. Johns, Tanja Eindorf, David L. Rutley, Zbigniew A. Kruk, Carolyn J. Fitzsimmons, Dana A. Thomsen, Claire T. Roberts, Brian M. Burns, Gail I. Anderson, Paul L. Greenwood, Stefan Hiendlede

Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, both have limited efficiency and are ineffective in early prognosis. Due to such limitations, we propose to use RNA-Seq data for evaluating putative higher accuracy biomarkers at the transcript level that could help in early prognosis. METHODS: To identify such potential transcript biomarkers, RNA-Seq data for healthy liver and various HCC cell models were subjected to five different machine learning algorithms: random forest, K-nearest neighbor, Naïve Bayes, support vector machine, and neural networks. Various metrics, namely sensitivity, specificity, MCC, informedness, and AUC-ROC (except for support vector machine) were evaluated. The algorithms that produced the highest values for all metrics were chosen to extract the top features that were subjected to recursive feature elimination. Through recursive feature elimination, the least number of features were obtained to differentiate between the healthy and HCC cell models. RESULTS: From the metrics used, it is demonstrated that the efficiency of the known protein biomarkers for HCC is comparatively lower than complete transcriptomics data. Among the different machine learning algorithms, random forest and support vector machine demonstrated the best performance. Using recursive feature elimination on top features of random forest and support vector machine three transcripts were selected that had an accuracy of 0.97 and kappa of 0.93. Of the three transcripts, two were protein coding (PARP2–202 and SPON2–203) and one was a non-coding transcript (CYREN-211). Lastly, we demonstrated that these three selected transcripts outperformed randomly taken three transcripts (15,000 combinations), hence were not chance findings, and could then be an interesting candidate for new HCC biomarker development. CONCLUSION: Using RNA-Seq data combined with machine learning approaches can aid in finding novel transcript biomarkers. The three biomarkers identified: PARP2–202, SPON2–203, and CYREN-211, presented the highest accuracy among all other transcripts in differentiating the healthy and HCC cell models. The machine learning pipeline developed in this study can be used for any RNA-Seq dataset to find novel transcript biomarkers. Code: www.github.com/rajinder4489/ML_biomarkers SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08704-9

Towards the development of an omics data analysis framework

The use of various omics techniques for scientific research is increasing. While toxicogenomics studies have already produced substantial data on diverse omics platforms, to date there has been little routine application in regulatory toxicology. This is despite the promises and excitement of 20 years ago when it was widely speculated that omics methods would reduce or even replace animal use and allow a much enhanced understanding of hazard and susceptibility. One of the reasons for this has been a trepidation about relying on the produced data. It has been argued that omics outputs might not be sufficiently reliable for regulatory application because the techniques, bioinformatics and interpretation can vary. For these reasons the robustness of the obtained results is questioned. This reticence to trust omics data is further magnified by the lack of internationally agreed upon guidelines and protocols for both the generation and processing of omics data. One way forward would be to reach a consensus on an omics data analysis framework (ODAF) for regulatory application (R-ODAF) based on rigorous data analysis. The authors of this article are involved in a Long-Range Research Initiative (LRI) project that will propose an R-ODAF for transcriptomics data. The R-ODAF will then be reviewed and evaluated by the main regulatory agencies and consensus forums such as the Organization for Economic Co-operation and Development (OECD). This work builds on The MicroArray Quality Control work that developed standards for the generation of data from microarrays and sequencing but not for reporting or analysis