Biology subjects

Fernandez, C. I.

Publications and source records attributed to Fernandez, C. I..

3 recordsLinked to original sources

bioRxiv2026

Estimating mean growth trajectories when measurements are sparse and age is uncertain

Background and objectivesComparing childrens growth across the world and at different moments in history can yield insight into both health challenges and healthy morphological variation in our species. A difficulty of such comparative analyses is that, in marginalized populations, there are often logistical complications to obtaining repeat measures of individual childrens height and weight. The problem is even more acute for historical populations: bioarchaeological datasets comprise single measures of individuals at death. Additionally, for both contemporary and historical populations, there is often non-trivial uncertainty about childrens ages. Both of these factors complicate estimation of growth trajectories. Here we evaluate the degree to which we can accurately estimate a population-mean growth trajectory using only a small number of (randomly) uncertain measurements, like those that compose many contemporary and bioarchaeological datasets. MethodologyWe recently derived a causal model of human growth from fundamental principles of metabolism and allometry, permitting exploration of genetic and environmental contributions to childrens growth. Here, we fit this model in a Bayesian framework to simulated cross-sectional and longitudinal datasets of varying size, where age is uncertain. ResultsWe show that, for large-scale comparative purposes, reasonably accurate population-mean growth trajectories may be obtained from single height measures of 100 children. However, detailed analyses of pubertal growth spurts and the metabolic and allometric parameters underlying growth require more extensive longitudinal datasets. Conclusions and implicationsWe conclude that this new model and estimation strategy constitute a potentially useful toolkit for comparing mean growth trajectories across contemporary and historical populations.

developmental biology↗

bioRxiv2024

Large-pore connexin hemichannels function as molecule transporters independently of ion conduction

Connexin hemichannels were identified as the first members of the eukaryotic large-pore channel family that mediate permeation of both atomic ions and small molecules between the intracellular and extracellular environments. The conventional view is that their pore is a large passive conduit through which both ions and molecules diffuse in a similar manner. In stark contrast to this notion, we demonstrate that the permeation of ions and of molecules in connexin hemichannels can be uncoupled and differentially regulated. We find that human connexin mutations that produce pathologies and were previously thought to be loss-of-function mutations due to the lack of ionic currents are still capable of mediating the passive transport of molecules with kinetics close to those of wild-type channels. This molecular transport displays saturability in the micromolar range, selectivity, and competitive inhibition, properties that are tuned by specific interactions between the permeating molecules and the N-terminal domain that lies within the pore -- a general feature of large-pore channels. We propose that connexin hemichannels and, likely, other large-pore channels, are hybrid channel/transporter-like proteins that might switch between these two modes to promote selective ion conduction or autocrine/paracrine molecular signaling in health and disease processes.

biophysics↗

bioRxiv2022

Causal models of human growth and their estimation using temporally-sparse data

Existing models of human growth provide little insight into underlying mechanisms responsible for inter-individual and inter-population variation in childrens growth trajectories. Building on general theories linking growth to metabolic rates, we develop a causal parametric model of height and weight growth incorporating a representation of human body allometry and a process-partitioned representation of ontogeny. This model permits separation of metabolic causes of growth variation, potentially influenced by nutrition and disease, from allometric factors, potentially under stronger genetic control. We estimate model parameters using a Bayesian multilevel statistical design applied to temporally-dense height and weight measurements of U.S. children, and temporally-sparse measurements of Indigenous Amazonian children. This facilitates a comparison of the contributions of metabolism and allometry to observed cross-cultural variation in the growth trajectories of the two populations, and permits simulation of the effects of healthcare interventions on growth. This theoretical model provides a new framework for exploring the causes of growth variation in our species, while potentially guiding the development of appropriate, and desired, healthcare interventions in societies confronting growth-related health challenges, such as malnutrition and stunting.

developmental biology↗