Dataset Information

Hidden RNA pairings counteract the "first-come, first-served" splicing principle to drive stochastic choice in Dscam1 splice variants.

ABSTRACT: Drosophila melanogaster Dscam1 encodes 38,016 isoforms via mutually exclusive splicing; however, the regulatory mechanism behind this is not fully understood. Here, we found a set of hidden RNA secondary structures that balance the stochastic choice of Dscam1 splice variants (designated balancer RNA secondary structures). In vivo mutational analyses revealed the dual function of these balancer interactions in driving the stochastic choice of splice variants, through enhancement of the inclusion of distal exon 6s by cooperating with docking site-selector pairing to form a stronger multidomain pre-mRNA structure and through simultaneous repression of the inclusion of proximal exon 6s by antagonizing their docking site-selector pairings. Thus, we provide an elegant molecular model based on competition and cooperation between two sets of docking site-selector and balancer pairings, which counteracts the "first-come, first-served" principle. Our findings provide conceptual and mechanistic insight into the dynamics and functions of long-range RNA secondary structures.

SUBMITTER: Dong H

PROVIDER: S-EPMC8791459 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Hidden RNA pairings counteract the "first-come, first-served" splicing principle to drive stochastic choice in Dscam1 splice variants.

Dong Haiyang H Xu Bingbing B Guo Pengjuan P Zhang Jian J Yang Xi X Li Lei L Fu Ying Y Shi Jilong J Zhang Shixin S Zhu Yanda Y Shi Yang Y Zhou Fengyan F Bian Lina L You Wendong W Shi Feng F Yang Xiaofeng X Huang Jianhua J He Haihuai H Jin Yongfeng Y

Science advances 20220126 4

Drosophila melanogaster Dscam1 encodes 38,016 isoforms via mutually exclusive splicing; however, the regulatory mechanism behind this is not fully understood. Here, we found a set of hidden RNA secondary structures that balance the stochastic choice of Dscam1 splice variants (designated balancer RNA secondary structures). In vivo mutational analyses revealed the dual function of these balancer interactions in driving the stochastic choice of splice variants, through enhancement of ...[more]

PMID: 35080968

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Project description:BackgroundHuman populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a OhiddenO population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process.Methodology/principal findingsHere, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits.ConclusionsSCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics.

Dataset Information

Hidden RNA pairings counteract the "first-come, first-served" splicing principle to drive stochastic choice in Dscam1 splice variants.

Publications

Hidden RNA pairings counteract the "first-come, first-served" splicing principle to drive stochastic choice in <i>Dscam1</i> splice variants.

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