Biology subjects

Belmonte, A.

Publications and source records attributed to Belmonte, A..

3 recordsLinked to original sources

bioRxiv2024

Sensory perception of fluctuating light in Arabidopsis

When exposed to shade from neighbours, competitive plants modify their growth patterns to improve access to light. In dense plant stands, ranging from forests to humid grasslands and crops, shade is interrupted by sunflecks penetrating the canopy. Relatively infrequent, minute-scale interruptions can significantly contribute to the daily light input. However, given the short duration and the time gap between these low frequency sunflecks (LFS), whether plants can sense them was unknown. Here we show that phytochrome B (phyB), cryptochrome 1 (cry1), cry2, and UV RESISTANCE LOCUS 8 (UVR8) cooperatively perceive LFS to reduce hypocotyl growth in Arabidopsis thaliana. LFS also enhanced the expression of photosynthetic and photo-protective genes and initiated pre-emptive acclimation to water restriction. Repeated LFS increased the nuclear abundance of cry1 and UVR8. This positive feedback enhanced the sensitivity to subsequent LFS and even to the shade between LFS. LFS reduced the nuclear abundance of the growth regulator PHYTOCHROME INTERACTING FACTOR 4 (PIF4), which only slowly recovered upon return to shade, further amplifying the signal. The dynamics of the photo-sensory system under fluctuating light helps adjust plants to the prevailing conditions.

plant biology↗

bioRxiv2021

CHARM: COVID-19 Health Action Response for Marines - association of antigen-specific interferon-gamma and IL2 responses with asymptomatic and symptomatic infections after a positive qPCR SARS-CoV-2 test

SARS-CoV-2 T cell responses are associated with COVID-19 recovery, and Class I- and Class II-restricted epitopes have been identified in the spike (S), nucleocapsid (N) and membrane (M) proteins and others. This prospective COVID-19 Health Action Response for Marines (CHARM) study enabled assessment of T cell responses in symptomatic and asymptomatic SARS-CoV-2 infected participants. At enrollment all participants were negative by qPCR; follow-up occurred biweekly and then bimonthly for the next 6 weeks. Study participants who tested positive by qPCR SARS-CoV-2 test were asked to enroll in an immune response sub-study. FluoroSpot interferon-gamma (IFN-{gamma}) and IL2 responses following qPCR-confirmed infection at enrollment (day 0), day 7 and 14 and more than 28 days later were measured using pools of 17mer peptides covering S, N, and M proteins, or CD4+CD8 peptide pools containing predicted epitopes from multiple SARS-CoV-2 antigens. Among 124 asymptomatic and 105 symptomatic participants, SARS-CoV-2 infection generated IFN-{gamma} responses to the S, N and M proteins that persisted longer in asymptomatic cases. IFN-{gamma} responses were significantly (p=0.001) more frequent to the N pool (51.4%) than the M pool (18.9%) among asymptomatic subjects; however, the difference was not statistically significant (p=0.06) for symptomatic subjects (N pool: 44.4%; M pool: 25.9%). In asymptomatic participants IFN-{gamma} responders to the CD4+CD8 pool responded more frequently to the S pool (55.6%) and N pool (57.1%), than the M pool (7.1%), but symptomatic participants, IFN-{gamma} responses were more frequent to the S pool (75.0%) than N pool (33.3%) and M pool (33.3%). The frequencies of IFN-{gamma} responses to the S and N+M pools peaked 7 days after the positive qPCR test among asymptomatic (S pool: 22.2%; N+M pool: 28.7%) and symptomatic (S pool: 15.3%; N+M pool 21.9%) participants and dropped by >28 days. Magnitudes of post-infection IFN-{gamma} and IL2 responses to the N+M pool were significantly correlated with IFN-{gamma} and IL2 responses to the N and M pools. These data further support the central role of Th1-biased cell mediated immunity IFN-{gamma} and IL2 responses, particularly to the N protein, in controlling COVID-19 symptoms, and justify T cell-based COVID-19 vaccines that include the N and S proteins.

immunology↗

bioRxiv2017

Modeling of cytometry data in logarithmic space: when is a bimodal distribution not bimodal?

Recent efforts in systems immunology lead researchers to build quantitative models of cell activation and differentiation. One goal is to account for the distributions of proteins from single-cell measurements by flow cytometry or mass cytometry as a readout of biological regulation. In that context, large cell-to-cell variability is often observed in biological quantities. We show here that these readouts, viewed in logarithmic scale may result in two easily-distinguishable modes, while the underlying distribution (in linear scale) is unimodal. We introduce a simple mathematical test to highlight this mismatch. We then dissect the flow of influence of cell-to-cell variability proposing a graphical model which motivates higher-dimensional analysis of the data. Finally we show how acquiring additional biological information can be used to reduce uncertainty introduced by cell-to-cell variability, helping to clarify whether the data is uni- or bimodal. This communication has cautionary implications for manual and automatic gating strategies, as well as clustering and modeling of single-cell measurements.

immunology↗