identification-functionally-relevant-lentivirus-integration-sites-an

Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library

The extent of functional sequences within the human genome is a pivotal yet debated topic in biology. Although high-throughput reverse genetic screens ...

Xiamen University

Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long noncoding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the noncoding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual unambiguous classification framework results in a number of challenges in the annotation and interpretation of noncoding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel the function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then, we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets. 

The Landscape of long noncoding RNA classification.

Functionality of the non-coding transcripts encoded by the human genome is the coveted goal of the modern genomics research. While commonly relied on the classical methods of forward genetics, integration of different genomics datasets in a global Systems Biology fashion presents a more productive avenue of achieving this very complex aim. Here we report application of a Systems Biology-based approach to dissect functionality of a newly identified vast class of very long intergenic non-coding (vlinc) RNAs. Using highly quantitative FANTOM5 CAGE dataset, we show that these RNAs could be grouped into 1542 novel human genes based on analysis of insulators that we show here indeed function as genomic barrier elements. We show that vlinc RNAs genes likely function in cisto activate nearby genes. This effect while most pronounced in closely spaced vlinc RNA-gene pairs can be detected over relatively large genomic distances. Furthermore, we identified 101 vlinc RNA genes likely involved in early embryogenesis based on patterns of their expression and regulation. We also found another 109 such genes potentially involved in cellular functions also happening at early stages of development such as proliferation, migration and apoptosis. Overall, we show that Systems Biology-based methods have great promise for functional annotation of non-coding RNAs.

Functional annotation of the vlinc class of non-coding RNAs using systems biology approach.

The past decade has seen an explosion of interest in long noncoding RNAs (lncRNAs). However, despite the massive volume of scientific data implicating these transcripts in a plethora of molecular and cellular processes, a great deal of controversy surrounds these RNAs. One of the main reasons for this lies in the multiple unique features of lncRNAs which limit the available methods used to characterize them. Combined with their vast numbers and inadequate classification, comprehensive annotation of these transcripts becomes a daunting task. The solution to this complex challenge likely lies in deep understanding of the strengths and weaknesses of each computational and empirical approach, and integration of multiple strategies to reduce noise, authenticate the results, and classify lncRNAs. We review here both the advantages and caveats of strategies commonly used for functional characterization and annotation of lncRNAs in the context of emerging conceptual guidelines for their application.

Strategies to Annotate and Characterize Long Noncoding RNAs: Advantages and Pitfalls.

Single-strand breaks (SSBs) represent the major form of DNA damage, yet techniques to map these lesions genome-wide with nucleotide-level precision are limited. Here, we present a method, termed SSiNGLe, and demonstrate its utility to explore the distribution and dynamic changes in genome-wide SSBs in response to different biological and environmental stimuli. We validate SSiNGLe using two very distinct sequencing techniques and apply it to derive global profiles of SSBs in different biological states. Strikingly, we show that patterns of SSBs in the genome are non-random, specific to different biological states, enriched in regulatory elements, exons, introns, specific types of repeats and exhibit differential preference for the template strand between exons and introns. Furthermore, we show that breaks likely contribute to naturally occurring sequence variants. Finally, we demonstrate strong links between SSB patterns and age. Overall, SSiNGLe provides access to unexplored realms of cellular biology, not obtainable with current approaches.

Novel approach reveals genomic landscapes of single-strand DNA breaks with nucleotide resolution in human cells.

Long non-coding (lnc) RNAs represent a fascinating class of transcripts that remains highly controversial mainly due to ambiguity surrounding overall biological relevance of these RNAs. Multitude of reverse genetics studies showing functionality of lncRNAs are unfortunately based on assays that are either plagued by non-specific effects and/or cannot unambiguously assign observed phenotypes to the transcript per se. Here, we show application of the novel CRISPR/Cas13 RNA knockdown system that has superior specificity compared to other transcript-targeting knockdown methods like RNAi. We applied this method to a novel widespread subclass of nuclear lncRNAs - very long intergenic non-coding (vlinc) RNAs - in a high-throughput phenotypic assay based on survival challenge in response to anticancer drug treatments. We used multiple layers of controls including mismatch control for each targeting gRNA to ensure uncovering true phenotype-transcript relationships. We found evidence supporting importance for cellular survival for up to 60% of the tested protein-coding mRNAs and, importantly, 64% of vlincRNAs. Overall, this study demonstrates utility of CRISPR/Cas13 as a highly sensitive and specific tool for reverse genetics study of both protein-coding genes and lncRNAs. Furthermore, importantly, this approach provides evidence supporting biological significance of the latter transcripts in anticancer drug response.

A CRISPR/Cas13-based approach demonstrates biological relevance of vlinc class of long non-coding RNAs in anticancer drug response.

Long non-coding RNAs (lncRNAs) represent a major fraction of the transcriptome in multicellular organisms. Although a handful of well-studied lncRNAs are broadly recognized as biologically meaningful, the fraction of such transcripts out of the entire collection of lncRNAs remains a subject of vigorous debate. Here we review the evidence for and against biological functionalities of lncRNAs and attempt to arrive at potential modes of lncRNA functionality that would reconcile the contradictory conclusions. Finally, we discuss different strategies of phenotypic analyses that could be used to investigate such modes of lncRNA functionality.

Reverse-genetics studies of lncRNAs-what we have learnt and paths forward.

Although naturally occurring catalytic RNA molecules-ribozymes-have attracted a great deal of research interest, very few have been identified in humans. Here, we developed a genome-wide approach to discovering self-cleaving ribozymes and identified a naturally occurring ribozyme in humans. The secondary structure and biochemical properties of this ribozyme indicate that it belongs to an unidentified class of small, self-cleaving ribozymes. The sequence of the ribozyme exhibits a clear evolutionary path, from its appearance between ~130 and ~65 million years ago (Ma), to acquiring self-cleavage activity very recently, ~13-10 Ma, in the common ancestors of humans, chimpanzees and gorillas. The ribozyme appears to be functional in vivo and is embedded within a long noncoding RNA belonging to a class of very long intergenic noncoding RNAs. The presence of a catalytic RNA enzyme in lncRNA creates the possibility that these transcripts could function by carrying catalytic RNA domains.

Hovlinc is a recently evolved class of ribozyme found in human lncRNA.

The majority of the human genome is transcribed in the form of long non-coding (lnc) RNAs. While these transcripts have attracted considerable interest, their molecular mechanisms of function and biological significance remain controversial. One of the main reasons behind this lies in the significant challenges posed by lncRNAs requiring the development of novel methods and concepts to unravel their functionality. Existing methods often lack cross-validation and independent confirmation by different methodologies and therefore leave significant ambiguity as to the authenticity of the outcomes. Nonetheless, despite all the caveats, it appears that lncRNAs may function, at least in part, by regulating other genes via chromatin interactions. Therefore, the function of a lncRNA could be inferred from the function of genes it regulates. In this work, we present a genome-wide functional annotation strategy for lncRNAs based on identification of their regulatory networks via the integration of three distinct types of approaches: co-expression analysis, mapping of lncRNA-chromatin interactions, and assaying molecular effects of lncRNA knockdowns obtained using an inducible and highly specific CRISPR/Cas13 system.

Very long intergenic non-coding (vlinc) RNAs directly regulate multiple genes in cis and trans.

DNA damage plays a critical role in biology and diseases; however, how different types of DNA lesions affect cellular functions is far from clear mostly due to the paucity of high-resolution methods that can map their locations in complex genomes, such as those of mammals. Here, we present the development and validation of SSiNGLe-AP method, which can map a common type of DNA damage, abasic (AP) sites, in a genome-wide and high-resolution manner. We apply this method to six different tissues of mice with different ages and human cancer cell lines. We find a nonrandom distribution of AP sites in the mammalian genome that exhibits dynamic enrichment at specific genomic locations, including single-nucleotide hotspots, and is significantly influenced by gene expression, age and tissue type in particular. Overall, these results suggest that we are only starting to understand the true complexities in the genomic patterns of DNA damage.

Complex genomic patterns of abasic sites in mammalian DNA revealed by a high-resolution SSiNGLe-AP method.

The proper replication of mitochondrial DNA is key to the maintenance of this crucial organelle. Multiple studies aimed at understanding the mechanisms of replication of the mitochondrial genome have been conducted in the past several decades; however, while highly informative, they were conducted using relatively low-sensitivity techniques. Here, we established a high-throughput approach based on next-generation sequencing to identify replication start sites with nucleotide-level resolution and applied it to the genome of mitochondria from different human and mouse cell types. We found complex and highly reproducible patterns of mitochondrial initiation sites, both previously annotated and newly discovered in this work, that showed differences among different cell types and species. These results suggest that the patterns of the replication initiation sites are dynamic and might reflect, in some yet unknown ways, the complexities of mitochondrial and cellular physiology. Overall, this work suggests that much remains unknown about the details of mitochondrial DNA replication in different biological states, and the method established here opens up a new avenue in the study of the replication of mitochondrial and potentially other genomes.

Dynamic Patterns of Mammalian Mitochondrial DNA Replication Uncovered Using SSiNGLe-5'ES.

Conversion or editing of adenosine (A) into inosine (I) catalyzed by specialized cellular enzymes represents one of the most common post-transcriptional RNA modifications with emerging connection to disease. A-to-I conversions can happen at specific sites and lead to increase in proteome diversity and changes in RNA stability, splicing, and regulation. Such sites can be detected as adenine-to-guanine sequence changes by next-generation RNA sequencing which resulted in millions reported sites from multiple genome-wide surveys. Nonetheless, the lack of extensive independent validation in such endeavors, which is critical considering the relatively high error rate of next-generation sequencing, leads to lingering questions about the validity of the current compendiums of the editing sites and conclusions based on them.

Lessons from discovery of true ADAR RNA editing sites in a human cell line.

Endogenous single-stranded DNA (essDNA) can form in a mammalian genome as the result of a variety of molecular processes and can both play important roles inside the cell as well as have detrimental consequences to genome integrity, much of which remains to be fully understood. Here, we established the SSiNGLe-P1 approach based on limited digestion by P1 endonuclease for high-throughput genome-wide identification of essDNA regions. We applied this method to profile essDNA in both human mitochondrial and nuclear genomes. In the mitochondrial genome, the profiles of essDNA provide new evidence to support the strand-displacement model of mitochondrial DNA replication. In the nuclear genome, essDNA regions were found to be enriched in certain types of functional genomic elements, particularly, the origins of DNA replication, R-loops, and to a lesser degree, in promoters. Furthermore, interestingly, many of the essDNA regions identified by SSiNGLe-P1 have not been annotated and thus could represent yet unknown functional elements.

Genome-Wide Profiling of Endogenous Single-Stranded DNA Using the SSiNGLe-P1 Method.

Fraction of functional sequence in the human genome remains a key unresolved question in Biology and the subject of vigorous debate. While a plethora of studies have connected a significant fraction of human DNA to various biochemical processes, the classical definition of function requires evidence of effects on cellular or organismal fitness that such studies do not provide. Although multiple high-throughput reverse genetics screens have been developed to address this issue, they are limited to annotated genomic elements and suffer from non-specific effects, arguing for a strong need to develop additional functional genomics approaches.

Evidence for widespread existence of functional novel and non-canonical human transcripts.

The identification of novel age-related biomarkers represents an area of intense research interest. Despite multiple studies associating DNA damage with aging, there is a glaring paucity of DNA damage-based biomarkers of age, mainly due to the lack of precise methods for genome-wide surveys of different types of DNA damage. Recently, we developed two techniques for genome-wide mapping of the most prevalent types of DNA damage, single-strand breaks and abasic sites, with nucleotide-level resolution. Herein, we explored the potential of genomic patterns of DNA damage identified by these methods as a source of novel age-related biomarkers using mice as a model system. Strikingly, we found that models based on genomic patterns of either DNA lesion could accurately predict age with higher precision than the commonly used transcriptome analysis. Interestingly, the informative patterns were limited to relatively few genes and the DNA damage levels were positively or negatively correlated with age. These findings show that previously unexplored high-resolution genomic patterns of DNA damage contain useful information that can contribute significantly to both practical applications and basic science.

Genome-wide profiles of DNA damage represent highly accurate predictors of mammalian age.

We recently developed two high-resolution methods for genome-wide mapping of two prominent types of DNA damage, single-strand DNA breaks (SSBs) and abasic (AP) sites and found highly complex and non-random patterns of these lesions in mammalian genomes. One salient feature of SSB and AP sites was the existence of single-nucleotide hotspots for both lesions.

Common occurrence of hotspots of single strand DNA breaks at transcriptional start sites.

Accurate identification of the correct, biologically relevant RNA structures is critical to understanding various aspects of RNA biology since proper folding represents the key to the functionality of all types of RNA molecules and plays pivotal roles in many essential biological processes. Thus, a plethora of approaches have been developed to predict, identify, or solve RNA structures based on various computational, molecular, genetic, chemical, or physicochemical strategies. Purely computational approaches hold distinct advantages over all other strategies in terms of the ease of implementation, time, speed, cost, and throughput, but they strongly underperform in terms of accuracy that significantly limits their broader application. Nonetheless, the advantages of these methods led to a steady development of multiple in silico RNA secondary structure prediction approaches including recent deep learning-based programs. Here, we compared the accuracy of predictions of biologically relevant secondary structures of dozens of self-cleaving ribozyme sequences using seven in silico RNA folding prediction tools with tasks of varying complexity. We found that while many programs performed well in relatively simple tasks, their performance varied significantly in more complex RNA folding problems. However, in general, a modern deep learning method outperformed the other programs in the complex tasks in predicting the RNA secondary structures, at least based on the specific class of sequences tested, suggesting that it may represent the future of RNA structure prediction algorithms.

Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes.

Accurate and comprehensive genomic annotation, including the full list of protein-coding genes, is vital for understanding the molecular mechanisms of human biology. We have previously shown that the genome contains a multitude of yet hidden functional exons and transcripts, some of which might represent novel mRNAs. These results resonate with those from other groups and strongly argue that two decades after the completion of the first draft of the human genome sequence, the current annotation of human genes and transcripts remains far from being complete.

Article	Year
The Landscape of long noncoding RNA classification. Trends in genetics : TIG\| PubMed ID: 25869999	2015
Functional annotation of the vlinc class of non-coding RNAs using systems biology approach. Nucleic acids research\| PubMed ID: 27001520	2016
Strategies to Annotate and Characterize Long Noncoding RNAs: Advantages and Pitfalls. Trends in genetics : TIG\| PubMed ID: 30017313	2018
Novel approach reveals genomic landscapes of single-strand DNA breaks with nucleotide resolution in human cells. Nature communications\| PubMed ID: 31862872	2019
A CRISPR/Cas13-based approach demonstrates biological relevance of vlinc class of long non-coding RNAs in anticancer drug response. Scientific reports\| PubMed ID: 32020014	2020
Reverse-genetics studies of lncRNAs-what we have learnt and paths forward. Genome biology\| PubMed ID: 32290841	2020
Hovlinc is a recently evolved class of ribozyme found in human lncRNA. Nature chemical biology\| PubMed ID: 33753927	2021
Very long intergenic non-coding (vlinc) RNAs directly regulate multiple genes in cis and trans. BMC biology\| PubMed ID: 34016118	2021
Complex genomic patterns of abasic sites in mammalian DNA revealed by a high-resolution SSiNGLe-AP method. Nature communications\| PubMed ID: 36198706	2022
Dynamic Patterns of Mammalian Mitochondrial DNA Replication Uncovered Using SSiNGLe-5'ES. International journal of molecular sciences\| PubMed ID: 37298662	2023
Lessons from discovery of true ADAR RNA editing sites in a human cell line. BMC biology\| PubMed ID: 37468903	2023
Genome-Wide Profiling of Endogenous Single-Stranded DNA Using the SSiNGLe-P1 Method. International journal of molecular sciences\| PubMed ID: 37569439	2023
Evidence for widespread existence of functional novel and non-canonical human transcripts. BMC biology\| PubMed ID: 38001496	2023
Genome-wide profiles of DNA damage represent highly accurate predictors of mammalian age. Aging cell\| PubMed ID: 38391092	2024
Common occurrence of hotspots of single strand DNA breaks at transcriptional start sites. BMC genomics\| PubMed ID: 38622509	2024
Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes. Genomics, proteomics & bioinformatics\| PubMed ID: 39317944	2024
A novel human protein-coding locus identified using a targeted RNA enrichment technique. BMC biology\| PubMed ID: 39593153	2024

Philipp Kapranov

Biography

JoVE Journal Publications

Other Publications