Adenosine-to-inosine (A-to-I) RNA editing is a common post transcriptional modification. It has a critical role in protecting against false activation of innate immunity by endogenous double stranded RNAs and has been associated with various regulatory processes and diseases such as autoimmune and cardiovascular diseases as well as cancer. In addition, the endogenous A-to-I editing machinery has been recently harnessed for RNA engineering. The study of RNA editing in humans relies heavily on the usage of cell lines as an important and commonly-used research tool. In particular, manipulations of the editing enzymes and their targets are often developed using cell line platforms. However, RNA editing in cell lines behaves very differently than in normal and diseased tissues, and most cell lines exhibit low editing levels, requiring over-expression of the enzymes. Here, we explore the A-to-I RNA editing landscape across over 1000 human cell lines types and show that for almost every editing target of interest a suitable cell line that mimics normal tissue condition may be found. We provide CLAIRE, a searchable catalogue of RNA editing levels across cell lines available at http://srv00.recas.ba.infn.it/atlas/claire.html, to facilitate rational choice of appropriate cell lines for future work on A-to-I RNA editing.

Long range regulatory interactions among distal enhancers and target genes are important for tissue-specific gene expression. Genome-scale identification of these interactions in a cell line-specific manner, especially using the fewest possible datasets, is a significant challenge. We develop a novel computational approach, Regulatory Interaction Prediction for Promoters and Long-range Enhancers (RIPPLE), that integrates published Chromosome Conformation Capture (3C) data sets with a minimal set of regulatory genomic data sets to predict enhancer-promoter interactions in a cell line-specific manner. Our results suggest that CTCF, RAD21, a general transcription factor (TBP) and activating chromatin marks are important determinants of enhancer-promoter interactions. To predict interactions in a new cell line and to generate genome-wide interaction maps, we develop an ensemble version of RIPPLE and apply it to generate interactions in five human cell lines. Computational validation of these predictions using existing ChIA-PET and Hi-C data sets showed that RIPPLE accurately predicts interactions among enhancers and promoters. Enhancer-promoter interactions tend to be organized into subnetworks representing coordinately regulated sets of genes that are enriched for specific biological processes and cis-regulatory elements. Overall, our work provides a systematic approach to predict and interpret enhancer-promoter interactions in a genome-wide cell-type specific manner using a few experimentally tractable measurements.

Using libraries of replication origins generated previously, we identified three clones that supported the autonomous replication of their respective plasmids in transformed, but not in normal cells. Assessment of their in vivo replication activity by in situ chromosomal DNA replication assays revealed that the chromosomal loci corresponding to these clones coincided with chromosomal replication origins in all cell lines, which were more active by 2-3-fold in the transformed by comparison to the normal cells. Evaluation of pre-replication complex (pre-RC) protein abundance at these origins in transformed and normal cells by chromatin immunoprecipitation assays, using anti-ORC2, -cdc6 and -cdt1 antibodies, showed that they were bound by these pre-RC proteins in all cell lines, but a 2-3-fold higher abundance was observed in the transformed by comparison to the normal cells. Electrophoretic mobility shift assays (EMSAs) performed on the most efficiently replicating clone, using nuclear extracts from the transformed and normal cells, revealed the presence of a DNA replication complex in transformed cells, which was barely detectable in normal cells. Subsequent supershift EMSAs suggested the presence of transformation-specific complexes. Mass spectrometric analysis of these complexes revealed potential new protein players involved in DNA replication that appear to correlate with cellular transformation.

Adenosine deaminases acting on RNA (ADARs) are involved in RNA editing that converts adenosines to inosines in double-stranded RNAs. ADAR1 was demonstrated to be functional on different viruses exerting either antiviral or proviral effects. Concerning HIV-1, several studies showed that ADAR1 favors viral replication. The aim of this study was to investigate the composition of the ADAR1 ribonucleoprotein complex during HIV-1 expression. By using a dual-tag affinity purification procedure in cells expressing HIV-1 followed by mass spectrometry analysis, we identified 14 non-ribosomal ADAR1-interacting proteins, most of which are novel. A significant fraction of these proteins were previously demonstrated to be associated to the Long INterspersed Element 1 (LINE1 or L1) ribonucleoparticles and to regulate the life cycle of L1 retrotransposons that continuously re-enter host-genome.Hence, we investigated the function of ADAR1 in the regulation of L1 activity.By using different cell-culture based retrotransposition assays in HeLa cells, we demonstrated a novel function of ADAR1 as suppressor of L1 retrotransposition. Apparently, this inhibitory mechanism does not occur through ADAR1 editing activity. Furthermore, we showed that ADAR1 binds the basal L1 RNP complex. Overall, these data support the role of ADAR1 as regulator of L1 life cycle.

Condensin I and condensin II are multi-subunit complexes that are known for their individual roles in genome organization and preventing genomic instability. However, interactions between condensin I and condensin II subunits and cooperative roles for condensin I and condensin II, outside of their genome organizing functions, have not been reported. We previously discovered that condensin II cooperates with Gamma Interferon Activated Inhibitor of Translation (GAIT) proteins to associate with Long INterspersed Element-1 (LINE-1 or L1) RNA and repress L1 protein expression and the retrotransposition of engineered L1 retrotransposition in cultured human cells. Here, we report that the L1 3'UTR is required for condensin II and GAIT association with L1 RNA, and deletion of the L1 RNA 3'UTR results in increased L1 protein expression and retrotransposition. Interestingly, like condensin II, we report that condensin I also binds GAIT proteins, associates with the L1 RNA 3'UTR, and represses L1 retrotransposition. We provide evidence that the condensin I protein, NCAPD2, is required for condensin II and GAIT protein association with L1 RNA. Furthermore, condensin I and condensin II subunits interact to form a L1-dependent super condensin complex (SCC) which is located primarily within the cytoplasm of both transformed and primary epithelial cells. These data suggest that increases in L1 expression in epithelial cells promote cytoplasmic condensin protein associations that facilitate a feedback loop in which condensins may cooperate to mediate L1 repression.

LINE-1 expression damages host DNA via insertions and endonuclease-dependent DNA double-strand breaks (DSBs) that are highly toxic and mutagenic. The predominant tissue of LINE-1 expression has been considered to be the germ line. We show that both full-length and processed L1 transcripts are widespread in human somatic tissues and transformed cells, with significant variation in both L1 expression and L1 mRNA processing. This is the first demonstration that RNA processing is a major regulator of L1 activity. Many tissues also produce translatable spliced transcript (SpORF2). An Alu retrotransposition assay, COMET assays and 53BP1 foci staining show that the SpORF2 product can support functional ORF2 protein expression and can induce DNA damage in normal cells. Tests of the senescence-associated beta-galactosidase expression suggest that expression of exogenous full-length L1, or the SpORF2 mRNA alone in human fibroblasts and adult stem cells triggers a senescence-like phenotype, which is one of the reported responses to DNA damage. In contrast to previous assumptions that L1 expression is germ line specific, the increased spectrum of tissues exposed to L1-associated damage suggests a role for L1 as an endogenous mutagen in somatic tissues. These findings have potential consequences for the whole organism in the form of cancer and mammalian aging.

Long interspersed element-1 (LINE-1, L1) composes ∼17% of the human genome. However, genetic interactions between L1 and human immunodeficiency virus type 1 (HIV-1) remain poorly understood. In this study, we found that HIV-1 suppresses L1 retrotransposition. Notably, HIV-1 Vpr strongly inhibited retrotransposition without inhibiting L1 promoter activity. Since Vpr is known to regulate host cell cycle, we examined the possibility whether Vpr suppresses L1 retrotransposition in a cell cycle dependent manner. We showed that the inhibitory effect of a mutant Vpr (H71R), which is unable to arrest the cell cycle, was significantly relieved compared with that of wild-type Vpr, suggesting that Vpr suppresses L1 mobility in a cell cycle dependent manner. Furthermore, a host cell cycle regulator p21Waf1 strongly suppressed L1 retrotransposition. The N-terminal kinase inhibitory domain (KID) of p21 was required for this inhibitory effect. Another KID-containing host cell cycle regulator p27Kip1 also strongly suppressed L1 retrotransposition. We showed that Vpr and p21 coimmunoprecipitated with L1 ORF2p and they suppressed the L1 reverse transcriptase activity in LEAP assay, suggesting that Vpr and p21 inhibit ORF2p-mediated reverse transcription. Altogether, our results suggest that viral and host cell cycle regulatory machinery limit L1 mobility in cultured cells.

Approximately 17% of the human genome is comprised of long interspersed nuclear element 1 (LINE-1, L1) non-LTR retrotransposons. L1 retrotransposition is known to be the cause of several genetic diseases, such as hemophilia A, Duchene muscular dystrophy, and so on. The L1 retroelements are also able to cause colon cancer, suggesting that L1 transposition could occur not only in germ cells, but also in somatic cells if innate immunity would not function appropriately. The mechanisms of L1 transposition restriction in the normal cells, however, are not fully defined. We here show that antiretroviral innate proteins, human APOBEC3 (hA3) family members, from hA3A to hA3H, differentially reduce the level of L1 retrotransposition that does not correlate either with antiviral activity against Vif-deficient HIV-1 and murine leukemia virus, or with patterns of subcellular localization. Importantly, hA3G protein inhibits L1 retrotransposition, in striking contrast to the recent reports. Inhibitory effect of hA3 family members on L1 transposition might not be due to deaminase activity, but due to novel mechanism(s). Thus, we conclude that all hA3 proteins act to differentially suppress uncontrolled transposition of L1 elements.

Long interspersed nuclear element 1 is an autonomous non-long terminal repeat retrotransposon that comprises ∼17% of the human genome. Its spontaneous retrotransposition and the accumulation of heritable L1 insertions can potentially result in genome instability and sporadic disorders. Moloney leukemia virus 10 homolog (MOV10), a putative RNA helicase, has been implicated in inhibiting L1 replication, although its underlying mechanism of action remains obscure. Moreover, the physiological relevance of MOV10-mediated L1 regulation in human disease has not yet been examined. Using a proteomic approach, we identified RNASEH2 as a binding partner of MOV10. We show that MOV10 interacts with RNASEH2, and their interplay is crucial for restricting L1 retrotransposition. RNASEH2 and MOV10 co-localize in the nucleus, and RNASEH2 binds to L1 RNAs in a MOV10-dependent manner. Small hairpin RNA-mediated depletion of either RNASEH2A or MOV10 results in an accumulation of L1-specific RNA-DNA hybrids, suggesting they contribute to prevent formation of vital L1 heteroduplexes during retrotransposition. Furthermore, we show that RNASEH2-MOV10-mediated L1 restriction downregulates expression of the rheumatoid arthritis-associated inflammatory cytokines and matrix-degrading proteinases in synovial cells, implicating a potential causal relationship between them and disease development in terms of disease predisposition.

MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of cell-type specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org.

Long Interspersed Element-1 (LINE-1) retrotransposition contributes to inter- and intra-individual genetic variation and occasionally can lead to human genetic disorders. Various strategies have been developed to identify human-specific LINE-1 (L1Hs) insertions from short-read whole genome sequencing (WGS) data; however, they have limitations in detecting insertions in complex repetitive genomic regions. Here, we developed a computational tool (PALMER) and used it to identify 203 non-reference L1Hs insertions in the NA12878 benchmark genome. Using PacBio long-read sequencing data, we identified L1Hs insertions that were absent in previous short-read studies (90/203). Approximately 81% (73/90) of the L1Hs insertions reside within endogenous LINE-1 sequences in the reference assembly and the analysis of unique breakpoint junction sequences revealed 63% (57/90) of these L1Hs insertions could be genotyped in 1000 Genomes Project sequences. Moreover, we observed that amplification biases encountered in single-cell WGS experiments led to a wide variation in L1Hs insertion detection rates between four individual NA12878 cells; under-amplification limited detection to 32% (65/203) of insertions, whereas over-amplification increased false positive calls. In sum, these data indicate that L1Hs insertions are often missed using standard short-read sequencing approaches and long-read sequencing approaches can significantly improve the detection of L1Hs insertions present in individual genomes.

Membrane fusion plays a key role in many biological processes including vesicle trafficking, synaptic transmission, fertilization or cell entry of enveloped viruses. As a common feature the fusion process is mediated by distinct membrane proteins. We describe here 'Fusoselect', a universal procedure allowing the identification and engineering of molecular determinants for cell-cell fusion-activity by directed evolution. The system couples cell-cell fusion with the release of retroviral particles, but can principally be applied to membrane proteins of non-viral origin as well. As a model system, we chose a gamma-retroviral envelope protein, which naturally becomes fusion-active through proteolytic processing by the viral protease. The selection process evolved variants that, in contrast to the parental protein, mediated cell-cell fusion in absence of the viral protease. Detailed analysis of the variants revealed molecular determinants for fusion competence in the cytoplasmic tail (CT) of retroviral Env proteins and demonstrated the power of Fusoselect.

microRNAs play an important roles in cell growth, differentiation, proliferation and apoptosis. They can function either as tumor suppressors or oncogenes. We found that the overexpression of miR-192 inhibited cell proliferation in A549, H460 and 95D cells, and inhibited tumorigenesis in a nude mouse model. Both caspase-7 and the PARP protein were activated by the overexpression of miR-192, thus suggesting that miR-192 induces cell apoptosis through the caspase pathway. Further studies showed that retinoblastoma 1 (RB1) is a direct target of miR-192. Over-expression of miR-192 decreased RB1 mRNA and protein levels and repressed RB1-3'-UTR reporter activity. Knockdown of RB1 using siRNA resulted in a similar cell morphology as that observed for overexpression of miR-192. Additionally, RB1-siRNA treatment inhibited cell proliferation and induced cell apoptosis in lung cancer cells. Analysis of miRNA expression in clinical samples showed that miR-192 is significantly downregulated in lung cancer tissues compared to adjacent non-cancerous lung tissues. In conclusion, our results demonstrate that miR-192 is a tumor suppressor that can target the RB1 gene to inhibit cell proliferation and induce cell apoptosis in lung cancer cells. Furthermore, miR-192 was expressed at low levels in lung cancer samples, indicating that it might be a promising therapeutic target for lung cancer treatment.

The accumulation of large epigenomics data consortiums provides us with the opportunity to extrapolate existing knowledge to new cell types and conditions. We propose Epitome, a deep neural network that learns similarities of chromatin accessibility between well characterized reference cell types and a query cellular context, and copies over signal of transcription factor binding and modification of histones from reference cell types when chromatin profiles are similar to the query. Epitome achieves state-of-the-art accuracy when predicting transcription factor binding sites on novel cellular contexts and can further improve predictions as more epigenetic signals are collected from both reference cell types and the query cellular context of interest.

Characterization of the epigenetic status of individual cells remains a challenge. Current sequencing approaches have limited coverage, and it is difficult to assign an epigenetic status to the transcription state of individual gene alleles in the same cell. To address these limitations, a targeted microscopy-based epigenetic visualization assay (EVA) was developed for detection and quantification of epigenetic marks at genes of interest in single cells. The assay is based on an in situ biochemical reaction between an antibody-conjugated alkaline phosphatase bound to the epigenetic mark of interest, and a 5'-phosphorylated fluorophore-labeled DNA oligo tethered to a target gene by gene-specific oligonucleotides. When the epigenetic mark is present at the gene, phosphate group removal by the phosphatase protects the oligo from λ-exonuclease activity providing a quantitative fluorescent readout. We applied EVA to measure 5-methylcytosine (5mC) and H3K9Ac levels at different genes and the HIV-1 provirus in human cell lines. To link epigenetic marks to gene transcription, EVA was combined with RNA-FISH. Higher 5mC levels at the silenced compared to transcribed XIST gene alleles in female somatic cells validated this approach and demonstrated that EVA can be used to relate epigenetic marks to the transcription status of individual gene alleles.

Conventional high-throughput genomic technologies for mapping regulatory element activities in bulk samples such as ChIP-seq, DNase-seq and FAIRE-seq cannot analyze samples with small numbers of cells. The recently developed low-input and single-cell regulome mapping technologies such as ATAC-seq and single-cell ATAC-seq (scATAC-seq) allow analyses of small-cell-number and single-cell samples, but their signals remain highly discrete or noisy. Compared to these regulome mapping technologies, transcriptome profiling by RNA-seq is more widely used. Transcriptome data in single-cell and small-cell-number samples are more continuous and often less noisy. Here, we show that one can globally predict chromatin accessibility and infer regulatory element activities using RNA-seq. Genome-wide chromatin accessibility predicted by RNA-seq from 30 cells can offer better accuracy than ATAC-seq from 500 cells. Predictions based on single-cell RNA-seq (scRNA-seq) can more accurately reconstruct bulk chromatin accessibility than using scATAC-seq. Integrating ATAC-seq with predictions from RNA-seq increases the power and value of both methods. Thus, transcriptome-based prediction provides a new tool for decoding gene regulatory circuitry in samples with limited cell numbers.

Combinatorial interactions of transcription modulators are critical to regulate cell-specific expression and to drive direct cell reprogramming (e.g. trans-differentiation). However, the identification of key transcription modulators from myriad of candidate genes is laborious and time consuming. To rapidly identify key regulatory factors involved in direct cell reprogramming, we established a multiplex single-cell screening system using a fibroblast-to-monocyte transition model. The system implements a single-cell 'shotgun-transduction' strategy followed by nested-single-cell-polymerase chain reaction (Nesc-PCR) gene expression analysis. To demonstrate this, we simultaneously transduced 18 monocyte-enriched transcription modulators in fibroblasts followed by selection of single cells expressing monocyte-specific CD14 and HLA-DR cell-surface markers from a heterogeneous population. Highly multiplex Nesc-PCR expression analysis revealed a variety of gene combinations with a significant enrichment of SPI1 (86/86) and a novel transcriptional modulator, HCLS1 (76/86), in the CD14(+)/HLA-DR(+) single cells. We could further demonstrate the synergistic role of HCLS1 in regulating monocyte-specific gene expressions and phagocytosis in dermal fibroblasts in the presence of SPI1. This study establishes a platform for a multiplex single-cell screening of combinatorial transcription modulators to drive any direct cell reprogramming.

Engineering mammalian cell lines that stably express many transgenes requires the precise insertion of large amounts of heterologous DNA into well-characterized genomic loci, but current methods are limited. To facilitate reliable large-scale engineering of CHO cells, we identified 21 novel genomic sites that supported stable long-term expression of transgenes, and then constructed cell lines containing one, two or three 'landing pad' recombination sites at selected loci. By using a highly efficient BxB1 recombinase along with different selection markers at each site, we directed recombinase-mediated insertion of heterologous DNA to selected sites, including targeting all three with a single transfection. We used this method to controllably integrate up to nine copies of a monoclonal antibody, representing about 100 kb of heterologous DNA in 21 transcriptional units. Because the integration was targeted to pre-validated loci, recombinant protein expression remained stable for weeks and additional copies of the antibody cassette in the integrated payload resulted in a linear increase in antibody expression. Overall, this multi-copy site-specific integration platform allows for controllable and reproducible insertion of large amounts of DNA into stable genomic sites, which has broad applications for mammalian synthetic biology, recombinant protein production and biomanufacturing.

CRISPR/dCas9-based labeling has allowed direct visualization of genomic regions in living cells. However, poor labeling efficiency and signal-to-background ratio have limited its application to visualize genome organization using super-resolution microscopy. We developed (Po)STAC (Polycistronic SunTAg modified CRISPR) by combining CRISPR/dCas9 with SunTag labeling and polycistronic vectors. (Po)STAC enhances both labeling efficiency and fluorescence signal detected from labeled loci enabling live cell imaging as well as super-resolution fixed-cell imaging of multiple genes with high spatiotemporal resolution.

Recent developments of single cell RNA-sequencing technologies lead to the exponential growth of single cell sequencing datasets across different conditions. Combining these datasets helps to better understand cellular identity and function. However, it is challenging to integrate different datasets from different laboratories or technologies due to batch effect, which are interspersed with biological variances. To overcome this problem, we have proposed Single Cell Integration by Disentangled Representation Learning (SCIDRL), a domain adaption-based method, to learn low-dimensional representations invariant to batch effect. This method can efficiently remove batch effect while retaining cell type purity. We applied it to thirteen diverse simulated and real datasets. Benchmark results show that SCIDRL outperforms other methods in most cases and exhibits excellent performances in two common situations: (i) effective integration of batch-shared rare cell types and preservation of batch-specific rare cell types; (ii) reliable integration of datasets with different cell compositions. This demonstrates SCIDRL will offer a valuable tool for researchers to decode the enigma of cell heterogeneity.