Biology subjects

Buchser, W.

Publications and source records attributed to Buchser, W..

9 recordsLinked to original sources

bioRxiv2026

An Isogenic iPSC Model of LMNA p.R482W Recapitulates Adipocyte Dysfunction in Familial Partial Lipodystrophy Type 2

Familial partial lipodystrophy type 2 (FPLD2) is a rare autosomal dominant disorder caused by pathogenic variants in LMNA, most commonly p.R482W, and is characterized by selective loss of subcutaneous adipose tissue, severe insulin resistance, and dyslipidemia. The cellular mechanisms underlying adipocyte dysfunction in FPLD2 remain incompletely understood, in part because existing in vitro models rely on patient-derived, non-isogenic cell lines or require non- physiologic transgene overexpression to achieve adipogenic differentiation. Here, we describe a CRISPR/Cas9-edited isogenic iPSC model of FPLD2 in which the heterozygous LMNA c.1444C>T (p.R482W) variant was introduced into a healthy donor-derived iPSC line. Using a transgene-free mesenchymal progenitor differentiation protocol, we generated LMNAp.R482W and isogenic control adipocytes and characterized their transcriptomic and functional phenotypes. Bulk RNA- sequencing demonstrated strong concordance in adipogenesis gene expression between genotypes during differentiation (Pearson {rho} = 0.968), yet LMNAp.R482W adipocytes exhibited significant downregulation of key adipogenic, lipid-handling, and extracellular matrix genes compared to isogenic controls. Functionally, LMNAp.R482W adipocytes showed markedly impaired glucose uptake under both basal and insulin-stimulated conditions, consistent with severe insulin resistance. Seahorse extracellular flux analysis revealed a globally quiescent metabolic state with significantly reduced oxygen consumption rate (126.5 vs. 261.5 pmol/min, p < 0.0001) and extracellular acidification rate (27.6 vs. 46.7 mpH/min, p < 0.0001). To validate these findings in human tissue, gene set enrichment analysis results from the iPSC model were compared to single-nucleus RNA-sequencing data from subcutaneous white adipose tissue biopsies of individuals with FPLD2 carrying the LMNA p.R482W variant. Across 37 mutually significant pathways, 59.5% were concordant between the iPSC model and human adipose tissue, including the pathways most central to FPLD2 pathophysiology: adipogenesis, fatty acid metabolism, oxidative phosphorylation, and inflammatory response. These findings establish this isogenic iPSC system as a faithful cellular model of FPLD2 adipocyte biology and a platform for functional characterization of LMNA variants of uncertain significance.

genomics↗

bioRxiv2026

Intellectual disability risk gene RFX4 regulates cortical neurogenesis by restraining neuronal differentiation

Despite the recent identification of RFX4 as a neurodevelopmental disorder risk gene, its role in cortical development remained unclear. Here, we identified both shared and lineage-specific RFX4 requirements for human cortical development using new human stem cell models of deficiency and pathogenic mutation. We found that RFX4 restrains neurogenesis by acting cooperatively with NOTCH signaling, specifically repressing pro-neuronal and synaptic gene expression in neural progenitors. We also determined that genome-wide binding of RFX3, another neurodevelopmental disorder risk gene, depends upon RFX4 to regulate synaptic gene expression. Furthermore, we identified lineage-specific functions for RFX4 in regulating proliferation during cortical inhibitory neuron development. Ultimately, we demonstrated that RFX4 deficiency persistently dysregulates neuronal gene expression through neuronal differentiation and disrupts cortical neuron stratification in organoid models. These consequences were absent in neurons generated by direct differentiation, confirming that neuronal phenotypes resulted from unconstrained neurogenesis. Finally, we modeled pathogenic missense mutation of the RFX4 DNA-binding domain. While this mutation strongly reduced DNA binding, it dysregulated synaptic gene expression distinctly from our deficiency models, supporting pathogenic mechanisms distinct from haploinsufficiency. Together, this work identified both shared and lineage-specific requirements for RFX4 during cortical development, building a necessary foundation for elucidating the etiology of RFX4-associated disorders.

developmental biology↗

bioRxiv2025

SARM1 is an essential component of neuronal Parthanatos

The NAD+ hydrolase SARM1 is the central executioner of pathological axon degeneration. SARM1 is allosterically activated by an increased NMN/NAD+ ratio resulting from depletion of NAD+ or accumulation of its precursor, NMN, typically due to loss of the labile NAD+ synthetase NMNAT2 following axon injury. Another NAD+ hydrolase, PARP1, is hyperactivated by DNA damage, triggering the Parthanatos cell death pathway. We demonstrate that multiple mechanistically-distinct DNA-damaging agents lead to SARM1 activation and axon degeneration following PARP1 activation. Remarkably, SARM1 is required for key steps downstream of PARP1 activation by DNA damage that are pathognomonic of Parthanatos, including mitochondrial depolarization, nuclear translocation of AIF (apoptosis-inducing factor), and cell death. Moreover, SARM1 mediates glutamate excitotoxicity, a clinically significant pathomechanism attributed to Parthanatos. The identification of SARM1 as an essential component of neuronal Parthanatos, a major contributor to cell death in neurodegenerative disease, greatly expands the potential clinical utility of SARM1 inhibitors.

neuroscience↗

bioRxiv2024

BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica, an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica

bioinformatics↗

bioRxiv2022

Loss of estrogen unleashing neuro-inflammation increases the risk of Alzheimer's disease in women

The risk of Alzheimers disease (AD) in women is about 2 times greater than in men. The estrogen hypothesis is being accepted as the essential sex factor causing the sex difference in AD. Also, the recent meta-analysis using large-scale medical records data indicated estrogen replacement therapy. However, the underlying molecular targets and mechanisms explaining this sex difference in AD disease development remain unclear. In this study, we identified that estrogen treatment can strongly inhibition of neuro-inflammation signaling targets, using the systems pharmacology model; and identified ESR1/ESR2 (the receptors of estrogen) are topologically close to the neuroinflammation biomarker genes using signaling network analysis. Moreover, the estrogen level in women decreased to an extremely lower level than in men after age 55. Pooling together the multiple pieces of evidence, it is concluded that the loss of estrogen unleashing neuro-inflammation increases the womens risk of Alzheimers disease. These analysis results provide novel supporting evidence explaining the potential mechanism of the anti-neuroinflammation role of estrogen causing the sex difference of AD. Medications boosting the direct downstream signaling of ESR1/ESR2, or inhibiting upstream signaling targets of neuroinflammation, like JAK2 inhibitors, on the signaling network can be potentially effective or synergistic combined with estrogen for AD prevention and treatment.

neuroscience↗

bioRxiv2021

Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

Cellular phenotypes emerge from a hierarchy of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be extracted from the statistical pattern of proteome variation as measured across thousands of bacteria and that these hierarchies reflect the emergence of complex bacterial phenotypes. We describe the mathematics underlying our statistical approach and validate our results through gene-set enrichment analysis and comparison to existing experimentally-derived hierarchical databases. We demonstrate the biological utility of our unbiased hierarchical models by creating a model of motility in Pseudomonas aeruginosa and using it to discover a previously unappreciated genetic effector of twitch-based motility. Overall, our approach, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), predicts hierarchies of protein interaction networks describing emergent biological function using only the statistical pattern of bacterial proteome variation.

systems biology↗

bioRxiv2021

Weakly activated core inflammation pathways were identified as a central signaling mechanism contributing to the chronic neurodegeneration in Alzheimer's disease

Neuro-inflammation signaling has been identified as an important hallmark of Alzheimers disease (AD) in addition to amyloid {beta} plaques (A{beta}) and neurofibrillary tangles (NFTs). However, our knowledge of neuro-inflammation is very limited; and the core signaling pathways associated with neuro-inflammation are missing. From a novel perspective, i.e., investigating weakly activated molecular signals (rather than the strongly activated molecular signals), in this study, we uncovered the core neuro-inflammation signaling pathways in AD. Our novel hypothesis is that weakly activated neuro-inflammation signaling pathways can cause neuro-degeneration in a chronic process; whereas, strongly activated neuro-inflammation often cause acute disease progression like in COVID-19. Using the two large-scale genomics datasets, i.e., Mayo Clinic (77 control and 81 AD samples) and RosMap (97 control and 260 AD samples), our analysis identified 7 categories of signaling pathways implicated on AD and related to virus infection: immune response, x-core signaling, apoptosis, lipid dysfunctional, biosynthesis and metabolism, and mineral absorption signaling pathways. More interestingly, most of genes in the virus infection, immune response and x-core signaling pathways, are associated with inflammation molecular functions. Specifically, the x-core signaling pathways were defined as a group of 9 signaling proteins: MAPK, Rap1, NF-kappa B, HIF-1, PI3K-Akt, Wnt, TGF-beta, Hippo and TNF, which indicated the core neuro-inflammation signaling pathways responding to the low-level and weakly activated inflammation and hypoxia, and leading to the chronic neuro-degeneration. The core neuro-inflammation signaling pathways can be used as novel therapeutic targets for effective AD treatment and prevention.

neuroscience↗

bioRxiv2021

VCP protects neurons from proteopathic seeding

Uptake and spread of proteopathic seeds, such as S, Tau, and TDP-43, contribute to neurodegeneration. The cellular machinery necessary for this process is poorly understood. Using a genome-wide CRISPR-Cas9 screen, we identified Valosin Containing Protein (VCP) as a suppressor of S seeding. Dominant mutations in VCP cause multisystem proteinopathy (MSP) with muscle and neuronal degeneration. VCP inhibition or disease mutations increase S seeding in cells and neurons. This is not associated with an increase in seed uptake and is similar to treatment with the lysosomal damaging agent, LLoME. Intrastriatal injection of S seeds into VCP disease mice enhances seeding efficiency compared with controls. This is not specific to S since VCP inhibition or disease mutations increased TDP-43 seeding in neurons. These data support that VCP protects against proteopathic spread of pathogenic aggregates. The spread of distinct aggregate species may dictate pleiotropic phenotypes and pathologies in VCP associated MSP.

neuroscience↗

bioRxiv2020

Integrative omics data analysis uncovers biomarker genes and potential candidate drugs for G3 medulloblastoma

Medulloblastoma (MB) is the most common malignant brain tumor in infants and children. Four molecular subtypes of MB are recognized: WNT, SHH, Group 3 (G3), and Group 4 (G4). Compared with WNT and SHH subtypes, G3 MBs exhibit significantly worse outcomes and higher metastatic rates, and there is no effective treatment yet. Moreover, G3 and G4 MBs are much more common in boys than girls, i.e., sex bias, which also plays important roles in cancer prognosis and drug response. However, the molecular mechanism of G3 remains unclear, and there are no well-identified biomarker genes associated with these phenotypes, i.e., worse survival rate, higher metastasis rate, and sex bias. In this exploratory study, we aim to identify potential biomarkers associated with the three phenotypes using integrative analysis of gene expression, methylation and copy number variation datasets. In the results, we identified a set of biomarker genes and linked them into a network signature. The network signature showed better performance in the separation of G3 MB patients into subtypes with a significant difference in terms of the three phenotypes. To identify potentially effective drugs for G3 MBs, a set of drugs with diverse targets were prioritized, which can potentially inhibit the network signature. These drugs or combinations thereof might be effective for G3 treatment.

bioinformatics↗