Loon, A. R. | bioRxiv Science

bioRxiv2022

Distinct effects of disease-associated TREM2 R47H/+ and T66M mutations on iPSC-derived microglia.

Genetic findings have highlighted key roles for microglia in the pathology of neurodegenerative conditions such as Alzheimers disease (AD). Distinct mutations in the microglial protein TREM2 (triggering receptor expressed on myeloid cells 2) are associated with different forms of neurodegeneration in humans; R47H/+ mutations increase AD risk, while loss-of-function mutations such as TREM2 T66M result in more severe forms of neurodegeneration. We employed gene editing and stem cell models to gain insight into the effects of these mutations on human iPSC-derived microglia. We found divergent effects of TREM2 R47H/+ and T66M mutations on gene expression, with R47H/+ cells exhibiting a pro-inflammatory gene expression signature. Both the TREM2 R47H/+ and T66M mutations caused similar impairments in microglial movement and the uptake of multiple substrates, while R47H/+ microglia were hyper-responsive to inflammatory stimuli, consistent with their gene expression signature. We developed an in vitro laser-induced injury model in neuron-microglia co-cultures, finding an impaired injury response by TREM2 R47H/+ microglia. Furthermore, in xenotransplantation experiments, mouse brains transplanted with TREM2 R47H/+ microglia exhibited reduced synaptic density. Consistently, we observed upregulation of multiple complement cascade components in TREM2 R47H/+ microglia, suggesting that inappropriate synaptic pruning may underlie the effect. Thus, these findings identify shared and distinct effects of these two TREM2 mutations on microglial gene expression and function. While the TREM2 T66M mutation impairs microglial movement and uptake processes, the TREM2 R47H/+ mutation additionally confers multiple potentially detrimental effects on human microglia, likely to underlie its association with AD.

neuroscience↗

bioRxiv2021

Cellular intelligence: dynamic specialization through non-equilibrium multi-scale compartmentalization

Intelligence is usually associated with the ability to perceive, retain and use information to adapt to changes in ones environment. In this context, systems of living cells can be thought of as intelligent entities. Here, we show that the concepts of non-equilibrium tuning and compartmentalization are sufficient to model manifestations of cellular intelligence such as specialization, division, fusion and communication using the language of operads. We implement our framework as an unsupervised learning algorithm, IO_SCPLOWNTC_SCPLOWCO_SCPLOWYTC_SCPLOW, which we show is able to memorize, organize and abstract reference machine-learning datasets through generative and self-supervised tasks. Overall, our learning framework captures emergent properties programmed in living systems, and provides a powerful new approach for data mining. Structured abstractO_ST_ABSBackgroundC_ST_ABSAlthough intelligence has been given many definitions, we can associate it with the ability to perceive, retain, and use information to adapt to changes in ones environment. In this context, systems of living cells can be thought of as intelligent entities. While one can reasonably describe their adaptive abilities within the realm of homeostatic mechanisms, it is challenging to comprehend the principles governing their metabolic intelligence. In each organism, cells have indeed developed as many ways to adapt as there are cell types, and elucidating the impetus of their evolutionary behaviors could be the key to understanding life processes and likely diseases. AdvancesThe goal of this article is to propose principles for understanding cellular intelligence. Specifically, we show that the concepts of non-equilibrium tuning and compartmentalization are enough to recover cellular adaptive behaviors such as specialization, division, fusion, and communication. Our model has the advantage to encompass all scales of life, from organelles to organisms through systems of organs and cell assemblies. We achieve this flexibility using the language of operads, which provides an elegant framework for reasoning about nested systems and, as an emergent behavior, non-equilibrium compartmentalization. To demonstrate the validity and the practical utility of our model, we implement it in the form of an unsupervised learning algorithm, IO_SCPLOWNTC_SCPLOWCO_SCPLOWYTC_SCPLOW, and apply it to reference machine learning datasets through generative and self-supervised tasks. We find that IO_SCPLOWNTC_SCPLOWCO_SCPLOWYTC_SCPLOWs interpretability, plasticity and accuracy surpass that of a wide range of machine learning algorithms, thus providing a powerful approach for data mining. OutlookOur results indicate that the nested hierarchical language of operads captures the emergent properties of programmed cellular metabolism in the development of living systems, and provide a new biologically-inspired, yet practical and lightweight, computational paradigm for memorizing, organizing and abstracting datasets.

systems biology↗