Biology subjects

Mach, M.

Publications and source records attributed to Mach, M..

5 recordsLinked to original sources

bioRxiv2026

Development and design rules for a modular human peptide-tag adaptor CAR T cell system

CAR T cells are highly effective in haematological malignancies, but limited post-infusion control and tumour antigen heterogeneity remain major challenges. Modular CAR systems address these limitations using interchangeable adaptor molecules (AM) to control CAR T cell engagement and redirect targeting toward different antigens, yet the design principles underlying effective modular function remain undefined. We present our modular platform, BRiDGE CAR, and systematically examine how CAR and adaptor configuration influence activity. BRiDGE CAR activity was influenced by CAR hinge length and adaptor format and valency; a medium-length hinge combined with a full-length, bivalent adaptor supported potent activity across multiple tumour antigens and consistently controlled tumour growth across the tested xenograft models. During repeated antigen challenge, CD33-directed BRiDGE CAR T cells retained less-differentiated phenotypes than direct CD33-targeting CAR T cells. In vivo, BRiDGE CAR T cells persisted during adaptor withdrawal and regained antitumour activity upon adaptor re-administration, while IL-18 armouring enhanced tumour control at a reduced CAR T-cell dose. We identified key design parameters governing rules for adaptor-mediated CAR activity, with antigen targeting and CAR T cell function remaining independently modifiable, establishing BRiDGE CAR as a flexible and controllable modular platform.

cancer biology↗

bioRxiv2023

Connectome embedding in multidimensional graph-invariant spaces

The topological organization of brain networks, or connectomes, can be quantified using graph theory. Here, we investigated brain networks in higher dimensional spaces defined by up to ten node-level graph theoretical invariants. Nodal invariants are intrinsic nodal properties which reflect the topological characteristics of the nodes with respect to the whole network, including segregation (e.g., clustering coefficient) and centrality (e.g., betweenness centrality) measures. Using 100 healthy unrelated subjects from the Human Connectome Project, we generated multiple types of connectomes (structural/functional networks and binary/weighted networks) and embedded the corresponding network nodes (brain regions) into multidimensional graph spaces defined by the invariants. First, we observed that nodal invariants are correlated between them (i.e., they carry similar network information) at a whole-brain and subnetwork level. Second, we conducted a machine learning analysis to test whether brain regions embedded in multidimensional graph spaces can be accurately classified into higher order (association, subcortical and cerebellar) and lower order (visual, somatomotor, attention) areas. Brain regions of higher order and lower order brain circuits were classified with an 80-87% accuracy in a 10-dimensional (10D) space. 10D graph metrics performed better than 2D and 3D graph metrics, and non-linear Gaussian kernels performed better than linear kernels. This suggests a non-linear brain network information gain in a high-dimensional graph space. Finally, we quantified the inter-subject Euclidean distance of each brain region embedded in the multidimensional graph space. The inter-individual distance was largest for regions of the default mode and frontoparietal networks, providing a new avenue for subject-specific network coordinates in a multidimensional space. To conclude, we propose a new framework for quantifying connectome features in multidimensional spaces defined by graph invariants, providing a new avenue for subject-specific network coordinates and inter-individual distance analyses.

neuroscience↗

bioRxiv2022

The evolution of information transmission in mammalian brain networks

Brain communication, defined as information transmission through white-matter connections, is at the foundation of the brains computational capacities that subtend almost all aspects of behavior: from sensory perception shared across mammalian species, to complex cognitive functions in humans. How did communication strategies in macroscale brain networks adapted across evolution to accomplish increasingly complex functions? By applying a graph- and information-theory approach to assess information-related pathways in mouse, macaque and human brains, we show a brain communication gap between selective information transmission in non-human mammals, where brain regions share information through single polysynaptic pathways, and parallel information transmission in humans, where regions share information through multiple parallel pathways. In humans, parallel transmission acts as a major connector between unimodal and transmodal systems. The layout of information-related pathways is unique to individuals across different mammalian species, pointing at the individual-level specificity of information routing architecture. Our work provides evidence that different communication patterns are tied to the evolution of mammalian brain networks.

neuroscience↗

bioRxiv2021

Prioritizing Management for Cumulative Impacts

Determining where environmental management is best applied, either through regulating single sectors of human activities or across sectors, is complicated by interactions between human impacts and the environment. In this article, we show how an explicit representation of human-environment interactions can help, via "impact networks" including activities (e.g. shipping), stressors (e.g. ship strikes), species (e.g. humpback whales) or ecosystem services (e.g. marine recreation). Impact networks can enable the identification of "leverage nodes", which, if present, can direct managers to the activities and stressors crucial for reducing risk to important ecosystem components. Exploring an impact network for a coastal ecosystem in British Columbia, Canada, we seek to identify these leverage nodes using a new approach employing Bayesian Belief Networks of risks to ecosystems. In so doing, we address three key questions: (1) Do leverage nodes exist? (2) Do management plans for species correctly identify leverage nodes? (3) Does the management of leverage nodes for certain species realize benefits for other species and ecosystem services? We show that there are several leverage nodes across all species investigated, and show that preconceptions about the regulation of risk to species can misidentify leverage nodes, potentially leading to ineffective management. Notably, we show that managing fisheries does not reduce overall risk to herring whereas managing diverse cumulative impacts including nutrient runoff, oil spills, and marine debris can reduce risk to herring, additional species, and related ecosystem services. Thus, by targeting leverage nodes, managers can efficiently mitigate risks for whole communities, ecosystems, and ecosystem services.

ecology↗

bioRxiv2019

Influence of human cytomegalovirus glycoprotein O polymorphism on the inhibitory effect of soluble forms of trimer- and pentamer-specific entry receptors

Human cytomegalovirus (HCMV) envelope glycoprotein complexes, gH/gL/gO-trimer and gH/gL/UL128L-pentamer, are important for cell-free HCMV entry. While soluble Nrp2-Fc (sNrp2-Fc) interferes with epithelial/endothelial cell entry through UL128, soluble PDGFR-Fc (sPDGFR-Fc) interacts with gO thereby inhibiting infection of all cell types. Since gO is the most variable subunit we investigated the influence of gO polymorphism on the inhibitory capacities of sPDGFR-Fc and sNRP2-Fc.\n\nAccordingly, gO genotype 1c (GT1c) sequence was fully or partially replaced by gO GT2b, GT3, GT5 sequences in TB40-BAC4-luc background. All mutants were tested for fibroblast and epithelial cell infectivity, for virions gO and gH content, and for infection inhibition by sPDGFR-Fc and sNrp2-Fc.\n\nFull-length and partial gO GT swapping may strongly alter the virions gO and gH levels associated with enhanced epithelial cell infectivity. All gO GT mutants except recombinant gO GT1c/3 displayed a near-complete inhibition at 1.25 g/ml sPDGFR-Fc on epithelial cells (98% versus 91%) and all on fibroblasts ([≥] 99%). While gO GT replacement did not influence sNrp2-Fc inhibition at 1.25 g/ml on epithelial cells (96%-98%), it rendered mutants with low gO levels moderately accessible to fibroblasts inhibition (20%-40%). In contrast to the steep sPDGFR-Fc inhibition curves (slope >1.0), sNrp2-Fc dose-response curves on epithelial cells displayed slopes of ~1.0 suggesting functional differences between these entry inhibitors.\n\nOur findings suggest that targeting of gO-trimer rather than UL128-pentamer might be a promising target to inhibit infectivity independent of the cell type, gO polymorphism, and gO/gH content. However, intragenic gO recombination may lead to moderate resistence to sPDGFR-Fc inhibition.\n\nImportanceHuman cytomegalovirus (HCMV) is known for its broad cell tropism as reflected by the different organs and tissues affected by HCMV infection. Hence, inhibition of HCMV entry into distinct cell types could be considered as a promising therapeutic option to limit cell-free HCMV infection. Soluble forms of cellular entry receptor PDGFR rather than those of entry receptor neuropilin-2 inhibit infection of multiple cell types. sPDGFR specifically interacts with gO of the trimeric gH/gL/gO envelope glycoprotein complex. HCMV strains may differ with respect to the virions amount of trimer and the highly polymorphic gO sequence. In this study, we show that gO polymorphism rather than gO levels may affect the inhibitory capacity of sPDGFR. The finding that gO intragenic recombination may lead to moderate evasion from sPDGFR inhibition is of major value to the development of potential anti-HCMV therapeutic compounds based on sPDGFR.

microbiology↗