bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.10.09.757869

When can site-specific glycoform compositions resolve biosynthetic rates?

Matsui, Y.·Himori, K.·Zhang, B.·Kitani, A.
View original source ↗Export metadata ↓

Abstract

Mass spectrometry measures site-specific glycoform compositions, but when these measurements can resolve upstream biosynthetic rates remains unclear. We assess whether small changes in effective rates, each representing the processing capacity of a group of biosynthetic reactions, can be distinguished near specified baseline rates with secretion held fixed. Because proportions sum to one, m jointly normalised glycoform classes provide at most m-1 independent quantities. Distinguishing changes in k rates therefore requires at least k+1 classes, although this count alone does not ensure identifiability. In a canonical N-glycan network with eight rates, exhaustive evaluation of nine-class panels identified panels that could locally distinguish all eight rates. Among these panels, class choice changed the allowable measurement error by approximately 18-fold for the same target uncertainty in inferred rates. A separate simulation within the fixed network showed that estimating rate-change combinations with weaker composition responses reduced bias but amplified measurement noise. We applied the framework to plasma data from 40 controls and 70 patients across 14 inherited glycosylation-disorder groups. A prespecified composition score for the expected pathway disturbance ranked among the top two in 13 of 14 groups. Network responses linked observed composition shifts to biosynthetic reaction classes. Expanding site models from five to eight reaction classes improved control-composition fit but reduced the number of sites at which all modelled rates were locally distinguishable and increased noise sensitivity. Rate inference thus requires sufficient panel size, separable rate responses, adequate measurement precision and a model that fits the composition. These requirements guide measurement design and help determine whether compositions support pathway-level interpretation or individual-rate inference.

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Matsui, Y., Himori, K., Zhang, B., Kitani, A.. 2026-10-13. When can site-specific glycoform compositions resolve biosynthetic rates?. https://doi.org/10.64898/2026.10.09.757869

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Gut microbially produced phenolic acids have differential and sex-dependent effects on host metabolic health

Gut microbiota-derived metabolites are increasingly recognized as important regulators of host metabolism, but the specific functions of individual metabolites remain poorly understood. We therefore investigated the metabolic effects of two structurally related, phenylalanine-derived microbial metabolites phenylacetic acid (PAA) and phenylpropionic acid (PPA) in a mouse model of diet-induced obesity. Although clinical studies have linked PAA and its downstream product phenylacetylglutamine to metabolic disease, PAA did not alter body mass or adiposity in this model. Instead, it improved glucose tolerance selectively in male mice and markedly remodeled lipid metabolism in both sexes, reducing circulating triglycerides and acylcarnitines and activating hepatic PPAR--associated transcriptional programs associated with fatty acid {omega}-hydroxylation and peroxisomal lipid metabolism. Despite this extensive metabolic remodeling, PAA did not worsen hepatic steatosis, fibrosis, or markers of liver injury. By contrast, PPA had minimal effects on hepatic metabolic pathways but produced pronounced sex-dependent physiological responses, increasing adiposity in female mice and improving glucose tolerance in male mice. These findings show that structurally related microbial metabolites can have distinct, sex-dependent metabolic effects and highlight the need to define the actions of individual microbiome-derived metabolites when developing targeted therapies for metabolic disease.

systems biology↗
bioRxiv2026

Polarization of regulatory inputs reveals structural constraints in reconstructed pathway-associated regulatory networks

Activating and inhibitory interactions jointly shape cellular regulation, yet it remains unclear whether their allocation among individual targets exhibits systematic organization beyond basic structural constraints. Here we introduce antagonistic--cooperative polarization, a target-wise principle in which the sign of each regulatory interaction is determined by the class of its target. This formulation yields a node-separable graph polarization index and decomposes departures from perfect polarization exactly into localized conflicts at targets receiving both activating and inhibitory inputs. We applied the framework to 98 pathway-associated regulatory subnetworks reconstructed from a unified signed regulatory network. Target-wise sign consistency was generally high, but the key evidence for non-random organization came from constrained null models: mixed-input targets were depleted relative to both global sign shuffling and degree-preserving directed rewiring, with depletion increasing systematically with pathway size. This association was robust to rewiring-mobility and pathway-overlap analyses, and indegree-stratified analysis identified the clearest statistically supported depletion among targets receiving exactly two signed inputs. Mixed-input organization was pathway-specific but recurrent across genes; recurrent mixed-input targets remained enriched for Cancer Gene Census genes after accounting for pathway opportunity. Mixed-input occurrences were also preferentially associated with signed feedback motifs, particularly negative feedback, whereas downstream reach was not increased. Together, these results reveal a prevalent polarized backbone punctuated by localized sites of opposing regulation whose abundance departs from expectations under complementary null models controlling for network topology, sign composition, degree structure, and source-specific regulatory output.

systems biology↗
bioRxiv2026

SHAPPEing agent-based models to spatial data: Sequential Hooke's law Algorithm for Point Pattern reconstruction with Environmental constraints

Agent-based models (ABMs) are widely used to study biological systems consisting of interacting species, such as tumour and immune cells, but their predictions depend on how agents are initially placed. High-resolution imaging techniques can capture the spatial configurations of various biological systems, but their use is costly. Furthermore, they only provide small regions of interest that may be too small to initialise most ABMs directly. Here, we present the Sequential Hooke's law Algorithm for Point Pattern reconstruction with Environmental constraints, or SHAPPE, a multitype point pattern reconstruction algorithm that generates synthetic reconstructions of arbitrary size and shape by moving points under spring-like forces with data-derived target distances. As it enforces a first-order structure through environmental constraints estimated with density grids, SHAPPE can be applied to both homogeneous and inhomogeneous point patterns. Applied to 87 glioblastoma IMC samples, we show that SHAPPE reduces dissimilarity in density distributions by a median of 53% and errors in inter-cell-type exposure levels by 34% relative to a random baseline. We then initialise a glioblastoma ABM with SHAPPE reconstructions instead of random configurations and find reduced tumour-CD8+ T cell interaction frequency with SHAPPE. This decrease translates into an over 20% decrease in the average number of tumour cells killed by CD8+ T cells over 50 days. Altogether, SHAPPE shows the potential to ease the integration of high-resolution imaging techniques into computational biology ABMs and ground initial conditions in data.

systems biology↗