Ay, A. | Search bioRxiv

bioRxiv2024

Subconcussive head impacts sustained during American football alter gut microbiome diversity and composition

Subconcussive head impacts (SHIs) are a public health concern amongst at-risk populations. SHIs are hits to the head that do not typically generate symptoms and are unlikely to meet diagnostic criteria for mild traumatic brain injury (mTBI). Changes in the gut microbiome have been associated with mTBIs and implicated in both acute recovery from and neurodegenerative pathologies associated with repeated mTBI. This study monitored the gut microbiomes and head impact exposure of collegiate American football players across a competition season to determine if SHIs lead to acute and longer-term changes to the gut microbiome. SHI exposure correlates with changes in microbial diversity and composition three days post-exposure, the athletes gut microbiomes change significantly across the season, and mixed effects modeling provides evidence for cumulative effects of SHIs. These data provide the first evidence for a link between SHIs and changes in the diversity and composition of the gut microbiome.

microbiology↗

bioRxiv2018

Characterizing Building Blocks of Resource Constrained Biological Networks

Identification of motifs-recurrent and statistically significant patterns-in biological networks is the key to understand the design principles, and to infer governing mechanisms of biological systems. This, however, is a computationally challenging task. This task is further complicated as biological interactions depend on limited resources, i.e., a reaction takes place if the reactant molecule concentrations are above a certain threshold level. This biochemical property implies that network edges can participate in a limited number of motifs simultaneously. Existing motif counting methods ignore this problem. This simplification often leads to inaccurate motif counts (over-or under-estimates), and thus, wrong biological interpretations. In this paper, we develop a novel motif counting algorithm, Partially Overlapping MOtif Counting (POMOC), that considers capacity levels for all interactions in counting motifs. Our experiments on real and synthetic networks demonstrate that motif count using the POMOC method significantly differs from the existing motif counting approaches, and our method extends to large-scale biological networks in practical time. Our results also show that our method makes it possible to characterize the impact of different stress factors on cells organization of network. In this regard, analysis of a S. cerevisiae transcriptional regulatory network using our method shows that oxidative stress is more disruptive to organization and abundance of motifs in this network than mutations of individual genes. Our analysis also suggests that by focusing on the edges that lead to variation in motif counts, our method can be used to find important genes, and to reveal subtle topological and functional differences of the biological networks under different cell states.

bioinformatics↗