The coronavirus nucleocapsid protein (N) controls viral genome packaging and contains numerous phosphorylation sites located within unstructured regions. Binding of phosphorylated SARS-CoV N to the host 14-3-3 protein in the cytoplasm was reported to regulate nucleocytoplasmic N shuttling. All seven isoforms of the human 14-3-3 are abundantly present in tissues vulnerable to SARS-CoV-2, where N can constitute up to ∼1% of expressed proteins during infection. Although the association between 14-3-3 and SARS-CoV-2 N proteins can represent one of the key host-pathogen interactions, its molecular mechanism and the specific critical phosphosites are unknown. Here, we show that phosphorylated SARS-CoV-2 N protein (pN) dimers, reconstituted via bacterial co-expression with protein kinase A, directly associate, in a phosphorylation-dependent manner, with the dimeric 14-3-3 protein, but not with its monomeric mutant. We demonstrate that pN is recognized by all seven human 14-3-3 isoforms with various efficiencies and deduce the apparent K D to selected isoforms, showing that these are in a low micromolar range. Serial truncations pinpointed a critical phosphorylation site to Ser197, which is conserved among related zoonotic coronaviruses and located within the functionally important, SR-rich region of N. The relatively tight 14-3-3/pN association could regulate nucleocytoplasmic shuttling and other functions of N via occlusion of the SR-rich region, and could also hijack cellular pathways by 14-3-3 sequestration. As such, the assembly may represent a valuable target for therapeutic intervention.

The coronavirus nucleocapsid protein (N) controls viral genome packaging and contains numerous phosphorylation sites located within unstructured regions. Binding of phosphorylated SARS-CoV N to the host 14-3-3 protein in the cytoplasm was reported to regulate nucleocytoplasmic N shuttling. All seven isoforms of the human 14-3-3 are abundantly present in tissues vulnerable to SARS-CoV-2, where N can constitute up to ~1% of expressed proteins during infection. Although the association between 14-3-3 and SARS-CoV-2 N proteins can represent one of the key host-pathogen interactions, its molecular mechanism and the specific critical phosphosites are unknown. Here, we show that phosphorylated SARS-CoV-2 N protein (pN) dimers, reconstituted via bacterial co-expression with protein kinase A, directly associate, in a phosphorylation-dependent manner, with the dimeric 14-3-3 protein, but not with its monomeric mutant. We demonstrate that pN is recognized by all seven human 14-3-3 isoforms with various efficiencies and deduce the apparent KD to selected isoforms, showing that these are in a low micromolar range. Serial truncations pinpointed a critical phosphorylation site to Ser197, which is conserved among related zoonotic coronaviruses and located within the functionally important, SR-rich region of N. The relatively tight 14-3-3/pN association can regulate nucleocytoplasmic shuttling and other functions of N via occlusion of the SR-rich region, while hijacking cellular pathways by 14-3-3 sequestration. As such, the assembly may represent a valuable target for therapeutic intervention.HighlightsSARS-CoV-2 nucleocapsid protein (N) binds to all seven human 14-3-3 isoforms. This association with 14-3-3 strictly depends on phosphorylation of N. The two proteins interact in 2:2 stoichiometry and with the Kd in a μM range. Affinity of interaction depends on the specific 14-3-3 isoform. Conserved Ser197-phosphopeptide of N is critical for the interaction.

Bilateral cochlear implantation allowed for better speech recognition in noise relative to unilateral performance for a group of 12 children who underwent sequential bilateral cochlear implantation at various ages. There was not a statistically significant relationship between speech recognition in noise benefit, which was defined as the difference in performance between the first implanted ear and the bilateral condition and the age at which the second implant was received. Children receiving bilateral cochlear implants younger than 4 years of age achieved better speech recognition in quiet performance for the later implanted ear as compared with children receiving their second cochlear implant after 4 year of age.

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Rapid growth over the past two decades in digitized textual information represents untapped potential for methodological innovations in the adaptation governance literature that draw on machine learning approaches already being applied in other areas of computational social sciences. This Focus Article explores the potential for text mining techniques, specifically topic modeling, to leverage this data for large-scale analysis of the content of adaptation policy documents. We provide an overview of the assumptions and procedures that underlie the use of topic modeling, and discuss key areas in the adaptation governance literature where topic modeling could provide valuable insights. We demonstrate the diversity of potential applications for topic modeling with two examples that examine: (a) how adaptation is being talked about by political leaders in United Nations Framework Convention on Climate Change; and (b) how adaptation is being discussed by decision-makers and public administrators in Canadian municipalities using documents collected from 25 city council archives. This article is categorized under: Vulnerability and Adaptation to Climate Change > Institutions for Adaptation K E Y W O R D S climate change adaptation, governance, policy, quantitative text analysis, topic models 1 | INTRODUCTION Text-based research methods have been a cornerstone of qualitative social science methods since the 1950s (Lasswell, 1952). These approaches see documents as meaningful artifacts that can be analyzed for their thematic and semantic content (Krippendorff, 2013), and they form a core component of the climate change adaptation governance literature. In lieu of directly observable and measurable indicators such as greenhouse gas emissions, adaptation governance research relies on written records, surveys, and interviews as its primary information sources about how different actors are responding to climate change impacts. Content analysis methods are commonly applied to sources such as government reports, strategic planning documents, peer reviewed and gray literature, and media stories (Araos et al.