Data from a pre-publication independent replication initiative examining ten moral judgement effects

We present the data from a crowdsourced project seeking to replicate findings in independent laboratories before (rather than after) they are published. In this Pre-Publication Independent Replication (PPIR) initiative, 25 research groups attempted to replicate 10 moral judgment effects from a single laboratory's research pipeline of unpublished findings. The 10 effects were investigated using online/lab surveys containing psychological manipulations (vignettes) followed by questionnaires. Results revealed a mix of reliable, unreliable, and culturally moderated findings. Unlike any previous replication project, this dataset includes the data from not only the replications but also from the original studies, creating a unique corpus that researchers can use to better understand reproducibility and irreproducibility in science

The pipeline project: Pre-publication independent replications of a single laboratory's research pipeline

This crowdsourced project introduces a collaborative approach to improving the reproducibility of scientific research, in which findings are replicated in qualified independent laboratories before (rather than after) they are published. Our goal is to establish a non-adversarial replication process with highly informative final results. To illustrate the Pre-Publication Independent Replication (PPIR) approach, 25 research groups conducted replications of all ten moral judgment effects which the last author and his collaborators had “in the pipeline” as of August 2014. Six findings replicated according to all replication criteria, one finding replicated but with a significantly smaller effect size than the original, one finding replicated consistently in the original culture but not outside of it, and two findings failed to find support. In total, 40% of the original findings failed at least one major replication criterion. Potential ways to implement and incentivize pre-publication independent replication on a large scale are discussed

Circular RNAs: Potential Applications as Therapeutic Targets and Biomarkers in Breast Cancer

Circular RNAs (circRNAs) are a class of non-coding RNAs that form a covalently closed loop. A number of functions and mechanisms of action for circRNAs have been reported, including as miRNA sponge, exerting transcriptional and translational regulation, interacting with proteins, and coding for peptides. CircRNA dysregulation has also been implicated in many cancers, such as breast cancer. Their relatively high stability and presence in bodily fluids makes cancer-associated circRNAs promising candidates as a new biomarker. In this review, we summarize the research undertaken on circRNAs associated with breast cancer, discuss circRNAs as biomarkers, and present circRNA-based therapeutic approaches

Recent Advances in Oligonucleotide Therapeutics in Oncology

Cancer is one of the leading causes of death worldwide. Conventional therapies, including surgery, radiation, and chemotherapy have achieved increased survival rates for many types of cancer over the past decades. However, cancer recurrence and/or metastasis to distant organs remain major challenges, resulting in a large, unmet clinical need. Oligonucleotide therapeutics, which include antisense oligonucleotides, small interfering RNAs, and aptamers, show promising clinical outcomes for disease indications such as Duchenne muscular dystrophy, familial amyloid neuropathies, and macular degeneration. While no approved oligonucleotide drug currently exists for any type of cancer, results obtained in preclinical studies and clinical trials are encouraging. Here, we provide an overview of recent developments in the field of oligonucleotide therapeutics in oncology, review current clinical trials, and discuss associated challenges

Single-Cell RNA-Seq Reveals Heterogeneous lncRNA Expression in Xenografted Triple-Negative Breast Cancer Cells

Breast cancer is the most commonly diagnosed cancer in the world, with triple-negative breast cancer (TNBC) making up 12% of these diagnoses. TNBC tumours are highly heterogeneous in both inter-tumour and intra-tumour gene expression profiles, where they form subclonal populations of varying levels of aggressiveness. These aspects make it difficult to study and treat TNBC, requiring further research into tumour heterogeneity as well as potential therapeutic targets and biomarkers. Recently, it was discovered that the majority of the transcribed genome comprises non-coding RNAs, in particular long non-coding RNAs (lncRNAs). LncRNAs are transcripts of >200 nucleotides in length that do not encode a protein. They have been characterised as regulatory molecules and their expression can be associated with a malignant phenotype. We set out to explore TNBC tumour heterogeneity in vivo at a single cell level to investigate whether lncRNA expression varies across different cells within the tumour, even if cells are coming from the same cell line, and whether lncRNA expression is sufficient to define cellular subpopulations. We applied single-cell expression profiling due to its ability to capture expression signals of lncRNAs expressed in small subpopulations of cells. Overall, we observed most lncRNAs to be expressed at low, but detectable levels in TNBC xenografts, with a median of 25 lncRNAs detected per cell. LncRNA expression alone was insufficient to define a subpopulation of cells, and lncRNAs showed highly heterogeneous expression patterns, including ubiquitous expression, subpopulation-specific expression, and a hybrid pattern of lncRNAs expressed in several, but not all subpopulations. These findings reinforce that transcriptionally defined tumour cell subpopulations can be identified in cell-line derived xenografts, and uses single-cell RNA-seq (scRNA-seq) to detect and characterise lncRNA expression across these subpopulations in xenografted tumours. Future studies will aim to investigate the spatial distribution of lncRNAs within xenografts and patient tissues, and study the potential of subclone-specific lncRNAs as new therapeutic targets and/or biomarkers

The lncRNA Toolkit: Databases and In Silico Tools for lncRNA Analysis

Long non-coding RNAs (lncRNAs) are a rapidly expanding field of research, with many new transcripts identified each year. However, only a small subset of lncRNAs has been characterized functionally thus far. To aid investigating the mechanisms of action by which new lncRNAs act, bioinformatic tools and databases are invaluable. Here, we review a selection of computational tools and databases for the in silico analysis of lncRNAs, including tissue-specific expression, protein coding potential, subcellular localization, structural conformation, and interaction partners. The assembled lncRNA toolkit is aimed primarily at experimental researchers as a useful starting point to guide wet-lab experiments, mainly containing multi-functional, user-friendly interfaces. With more and more new lncRNA analysis tools available, it will be essential to provide continuous updates and maintain the availability of key software in the future

Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data

Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing or single-cell DNA sequencing and are based on calling copy number alterations and single nucleotide variants (SNVs). Single-cell RNA sequencing (scRNA-seq) is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this article, we demonstrate for the first time that scRNA-seq data contain sufficient evolutionary signal and can also be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Both standardized expression values and SNVs appear to be equally capable of reconstructing a similar pattern of phylogenetic relationship. This pattern is stable even when phylogenetic uncertainty is taken in account. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer

Antisense Oligonucleotide-Mediated Splice Switching: Potential Therapeutic Approach for Cancer Mitigation

Splicing is an essential process wherein precursor messenger RNA (pre-mRNA) is reshaped into mature mRNA. In alternative splicing, exons of any pre-mRNA get rearranged to form mRNA variants and subsequently protein isoforms, which are distinct both by structure and function. On the other hand, aberrant splicing is the cause of many disorders, including cancer. In the past few decades, developments in the understanding of the underlying biological basis for cancer progression and therapeutic resistance have identified many oncogenes as well as carcinogenic splice variants of essential genes. These transcripts are involved in various cellular processes, such as apoptosis, cell signaling and proliferation. Strategies to inhibit these carcinogenic isoforms at the mRNA level are promising. Antisense oligonucleotides (AOs) have been developed to inhibit the production of alternatively spliced carcinogenic isoforms through splice modulation or mRNA degradation. AOs can also be used to induce splice switching, where the expression of an oncogenic protein can be inhibited by the induction of a premature stop codon. In general, AOs are modified chemically to increase their stability and binding affinity. One of the major concerns with AOs is efficient delivery. Strategies for the delivery of AOs are constantly being evolved to facilitate the entry of AOs into cells. In this review, the different chemical modifications employed and delivery strategies applied are discussed. In addition to that various AOs in clinical trials and their efficacy are discussed herein with a focus on six distinct studies that use AO-mediated exon skipping as a therapeutic strategy to combat cancer

Clustered termination sites enhance transcription and are required for chromatin looping.

<p>(A) Overview to the stably integrated rDNA minigenes and the locations of the PCR amplicons. (B) Chromatin-immunoprecipitation (ChIP) assays on stably integrated rDNA reporter genes using the indicated antibodies. Occupancies were measured by qPCR, calculated as percentage of input chromatin and background signals as determined from control IPs with unspecific antibodies (α-HA or α-IgG) were subtracted. At least three independent biological replicates were performed. Error bars indicate the standard error of the mean. For statistical analysis, a two-sided, homoscedatic student's t-test was performed, stars denote significances. * p<0.05, ** p<0.01, *** p< = 0.001. (C) ChIP experiment using an rDNA reporter in which the Pol I spacer promoter, core promoter and enhancer regions of a pT₁₀ reporter construct were replaced by a Pol II promoter containing a canonical TBP binding site. The experiment was performed as described in (B).</p

Multiple termination sites enhance transcription <i>in vivo</i>.

<p>(A) Reporter plasmids containing the rDNA promoter, <i>Firefly</i> luciferase and either no (pTΔ), one (pT₁), two (pT₂), ten (pT₁₀) termination sites and T₁ and T_1–10 in reverse orientation (pT_1r and pT_10r) were co-transfected with a <i>Renilla</i> luciferase encoding plasmid (pRL-TK) into CHO cells. As a control, empty pBluescript vector was co-transfected. Transcriptional activities were analysed using a dual luciferase reporter assay. The ratio <i>of Firefly</i>/<i>Renilla</i> relative light units (RLU) of three independent experiments is given. Error bars indicate standard deviations. The functional elements and the sizes of the reporter plasmids are depicted. (B) Reporter plasmids were co-transfected with a GFP-TTFΔN348 expression vector and analysed as described in (A). (C) Reporter plasmids were co-transfected with a GFP-TTFΔN470 expression vector and analysed as described in (A). (D) RNA FISH using CHO cell lines with stably integrated rDNA minigenes. CHO-pT₁₀ cells containing an rDNA minigene with a full terminator, were stained with DAPI (in blue in the middle panel), with α-B23 antibody staining the nucleoli (left panel; shown in red in the middle panel), and integrated reporter gene transcripts were visualized by FISH (right panel; shown in green in the middle panel). Bar: 5 µm. (E) Transcription levels of genomically inserted pT₁ and pT₁₀ constructs were assayed using RT-qPCR. Comparative quantitation was performed and RNA levels of the <i>Firefly</i> luciferase sequence were normalized to β-actin expression. Relative transcript levels of three independent experiments are given in relation to non-transfected CHO Flp-In cells (control), error bars denote standard deviations.</p