Evolutionary remodelling of N-terminal domain loops fine-tunes SARS-CoV-2 spike

The emergence of SARS-CoV-2 variants has exacerbated the COVID-19 global health crisis. Thus far, all variants carry mutations in the spike glycoprotein, which is a critical determinant of viral transmission being responsible for attachment, receptor engagement and membrane fusion, and an important target of immunity. Variants frequently bear truncations of flexible loops in the N-terminal domain (NTD) of spike; the functional importance of these modifications has remained poorly characterised. We demonstrate that NTD deletions are important for efficient entry by the Alpha and Omicron variants and that this correlates with spike stability. Phylogenetic analysis reveals extensive NTD loop length polymorphisms across the sarbecoviruses, setting an evolutionary precedent for loop remodelling. Guided by these analyses, we demonstrate that variations in NTD loop length, alone, are sufficient to modulate virus entry. We propose that variations in NTD loop length act to fine-tune spike; this may provide a mechanism for SARS-CoV-2 to navigate a complex selection landscape encompassing optimisation of essential functionality, immune-driven antigenic variation and ongoing adaptation to a new host

Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization

Early life inter-kingdom interactions shape the immunological environment of the airways

Background: There is increasing evidence that the airway microbiome plays a key role in the establishment of respiratory health by interacting with the developing immune system early in life. While it has become clear that bacteria are involved in this process, there is a knowledge gap concerning the role of fungi. Moreover, the inter-kingdom interactions that infuence immune development remain unknown. In this prospective exploratory human study, we aimed to determine early post-natal microbial and immunological features of the upper airways in 121 healthy newborns. Results: We found that the oropharynx and nasal cavity represent distinct ecological niches for bacteria and fungi. Breastfeeding correlated with changes in microbiota composition of oropharyngeal samples with the greatest impact upon the relative abundance of Streptococcus species and Candida. Host transcriptome profling revealed that genes with the highest expression variation were immunological in nature. Multi-omics factor analysis of host and microbial data revealed unique co-variation patterns. Conclusion: These data provide evidence of a diverse multi-kingdom microbiota linked with local immunological characteristics in the frst week of life that could represent distinct trajectories for future respiratory health.Céline Pattaroni, Matthew Macowan, Roxanne Chatzis, Carmel Daunt, Adnan Custovic, Michael D. Shields, Ultan F. Power, Jonathan Grigg, Graham Roberts, Peter Ghazal, Jürgen Schwarze, Mindy Gore, Steve Turner, Andrew Bush, Sejal Saglani, Clare M. Lloyd, and Benjamin J. Marslan

Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization