Dataset Information

Fuzzy Interactions Form and Shape the Histone Transport Complex.

ABSTRACT: Protein transport into the nucleus is mediated by transport receptors. Import of highly charged proteins, such as histone H1 and ribosomal proteins, requires a dimer of two transport receptors. In this study, we determined the cryo-EM structure of the Imp7:Imp?:H1.0 complex, showing that the two importins form a cradle that accommodates the linker histone. The H1.0 globular domain is bound to Imp?, whereas the acidic loops of Imp? and Imp7 chaperone the positively charged C-terminal tail. Although it remains disordered, the H1 tail serves as a zipper that closes and stabilizes the structure through transient non-specific interactions with importins. Moreover, we found that the GGxxF and FxFG motifs in the Imp7 C-terminal tail are essential for Imp7:Imp? dimerization and H1 import, resembling importin interaction with nucleoporins, which, in turn, promote complex disassembly. The architecture of many other complexes might be similarly defined by rapidly exchanging electrostatic interactions mediated by disordered regions.

SUBMITTER: Ivic N

PROVIDER: S-EPMC6436938 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Fuzzy Interactions Form and Shape the Histone Transport Complex.

Ivic Nives N Potocnjak Mia M Solis-Mezarino Victor V Herzog Franz F Bilokapic Silvija S Halic Mario M

Molecular cell 20190226 6

Protein transport into the nucleus is mediated by transport receptors. Import of highly charged proteins, such as histone H1 and ribosomal proteins, requires a dimer of two transport receptors. In this study, we determined the cryo-EM structure of the Imp7:Impβ:H1.0 complex, showing that the two importins form a cradle that accommodates the linker histone. The H1.0 globular domain is bound to Impβ, whereas the acidic loops of Impβ and Imp7 chaperone the positively charged C-terminal tail. Althou ...[more]

PMID: 30824373

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Project description:The present paper explores nonnative (L2) phonological encoding of lexical entries and dissociates the difficulties associated with L2 phonological and phonolexical encoding by focusing on similarly sounding L2 words that are not differentiated by difficult phonological contrasts. We test two main claims of the fuzzy lexicon hypothesis: (1) L2 fuzzy phonolexical representations are not fully specified and lack details at both phonological and phonolexical levels of representation (Experiment 1); and (2) fuzzy phonolexical representations can lead to establishing incorrect form-to-meaning mappings (Experiment 2). The Russian-English Translation Judgment Task (Experiment 1, TJT) explores how the degree of phonolexical similarity between a word and its lexical competitor affects lexical access of Russian words. Words with smaller phonolexical distance (e.g., parent-parrot) show longer reaction times and lower accuracy compared to words with a larger phonolexical distance (e.g., parent-parchment) in lower-proficiency nonnative speakers, and, to a lesser degree, higher-proficiency speakers. This points to a lack of detail in nonnative phonolexical representations necessary for efficient lexical access. The Russian Pseudo-Semantic Priming task (Experiment 2, PSP) addresses the vulnerability of form-to-meaning mappings as a consequence of fuzzy phonolexical representations in L2. We primed the target with a word semantically related to its phonological competitor, or a potentially confusable word. The findings of Experiment 2 extend the results of Experiment 1 that, unlike native speakers, nonnative speakers do not properly encode phonolexical information. As a result, they are prone to access an incorrect lexical representation of a competitor word, as indicated by a slowdown in the judgments to confusable words. The study provides evidence that fuzzy phonolexical representations result in unfaithful form-to-meaning mappings, which lead to retrieval of incorrect semantic content. The results of the study are in line with existing research in support of less detailed L2 phonolexical representations, and extend the findings to show that the fuzziness of phonolexical representations can arise even when confusable words are not differentiated by difficult phonological contrasts.

Project description:BackgroundTo date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy.ResultsAdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF), and using 19 transcription factors (TFs), we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms.ConclusionAdaFuzzy successfully integrates multiple types of data (sequence, ChIP, and microarray) to predict transcriptional regulatory networks. The validated success in the prediction results implies that AdaFuzzy can be applied to uncover TIs in yeast.

Dataset Information

Fuzzy Interactions Form and Shape the Histone Transport Complex.

Publications

Fuzzy Interactions Form and Shape the Histone Transport Complex.

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