Biology subjects

Muldoon, S.

Publications and source records attributed to Muldoon, S..

3 recordsLinked to original sources

bioRxiv2026

Binary partitioning of human brain organization due to divergent human cytoskeletal evolution

CHRFAM7A is a biallelic uniquely human fusion gene present in 99.3% of humans. The direct and inverted alleles likely emerged independently in Africa and East Asia. We uncovered that the inverted allele regulates ULK4 expression through genetic epistasis. The increase in long to short ULK4 isoform ratio enhances -tubulin acetylation leading to microtubule (MT) cytoskeleton phenotypes (cell body: microtubule rich projections; neurite: de-bundling; growth cone: fanning with MT invasion). The MT cytoskeleton gain of function enhances neuronal arborization and functional connectivity in the human brain. Considering that the previously reported direct allele leads to actin cytoskeleton gain of function, we propose that CHRFAM7A alleles represent binary human genetic background through divergent evolution of the cytoskeleton. In the human brain the two alleles represent distinct brain organization: the direct allele enhances microstructure as measured by diffusion tensor imaging while the inverted allele increases functional connectivity with increased small world propensity. The two types of brain organization likely represent different susceptibility to neuropsychiatric disease and may underlie the allelic disease associations. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/725959v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@a84e75org.highwire.dtl.DTLVardef@1b712d9org.highwire.dtl.DTLVardef@367c01org.highwire.dtl.DTLVardef@1074dc4_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

A protocol for rapid generation cycling (speed breeding) of hemp (Cannabis sativa) for research and agriculture

Hemp (Cannabis sativa) is a highly versatile multi-purpose crop with a multitude of applications, from textiles, biofuel and building material to high-value food products for consumer markets. Furthermore, non-hallucinogenic cannabinoids like cannabidiol (CBD), which can be extracted from female hemp flowers, are potentially valuable pharmacological compounds. In addition, hemp has high carbon sequestration potential due to its rapid growth rate. Therefore, the hemp industry is gaining more traction and breeding hemp cultivars adapted to local climate conditions or bred for specific applications is becoming increasingly important. Here, we present a method for rapid generation cycling (speed breeding) for hemp. The speed breeding protocol makes use of the photoperiod sensitivity of Cannabis. It encompasses vegetative growth of the plants for two weeks under continuous light, followed by flower induction, pollination and seed development for four weeks under short-day conditions and a seed ripening phase under continuous light and water stress. With the protocol introduced here, a generation time of under nine weeks (61 days) from seed to seed can be achieved. Our method furthermore synchronises flowering time of different hemp cultivars, thus facilitating crosses between cultivars. The extremely short generation time will enable hemp researchers and breeders to perform crosses in a time-efficient way and generate new hemp cultivars with defined genetic characteristics in a shorter amount of time.

plant biology↗

bioRxiv2017

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can be used to identify communities or modules in brain graphs: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the utility of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

neuroscience↗