Citations

ABSTRACT Symmetry refers to properties that remain invariant upon mathematical transformations. The principles of symmetry have guided numerous important discoveries in physics and chemistry but not in biology and medicine. Here, we aim to explore the presence of symmetry relationships at the gene expression level as a mean to distinguish between healthy and disease states. We deployed Learning-Based Invariant Feature Engineering - LIFE, a hybrid machine learning approach implemented with two symmetric invariant feature functions (IFFs) to identify Invariant Feature Genes (IFGs), which are gene pairs whose IFF single-value outputs remain invariant across individual samples in a given biological phenotype. Our multiclass classification results across the transcriptomes of 25 normal organs, 25 cancer types, and blood samples obtained from 4 different types of neurodegenerative diseases revealed the presence of unique phenotype-specific IFGs. We constructed networks using these IFGs (IF-Nets) and intriguingly, we demonstrated that the hubs could serve as information encoders, capable of reconstructing sample-wise expression values in relation to their counterpart genes. More importantly, we found that hubs of cancer IF-Nets were enriched with both approved and clinical trial drugs, highlighting “symmetry breaking” as a novel approach for treating diseases.

ABSTRACT His domain protein tyrosine phosphatase (HD-PTP; also known as PTPN23) facilitates function of the endosomal sorting complexes required for transport (ESCRTs) during multivesicular body (MVB) formation. To uncover its role in physiological homeostasis, embryonic lethality caused by a complete lack of HD-PTP was bypassed through generation of hypomorphic mice expressing reduced protein, resulting in animals that are viable into adulthood. These mice exhibited marked lipodystrophy and decreased receptor-mediated signaling within white adipose tissue (WAT), involving multiple prominent pathways including RAS/MAPK, phosphoinositide 3-kinase (PI3K)/AKT and receptor tyrosine kinases (RTKs), such as EGFR. EGFR signaling was dissected in vitro to assess the nature of defective signaling, revealing decreased trans-autophosphorylation and downstream effector activation, despite normal EGF binding. This corresponds to decreased plasma membrane cholesterol and increased lysosomal cholesterol, likely resulting from defective endosomal maturation necessary for cholesterol trafficking and homeostasis. The ESCRT components Vps4 and Hrs have previously been implicated in cholesterol homeostasis; thus, these findings expand knowledge on which ESCRT subunits are involved in cholesterol homeostasis and highlight a non-canonical role for HD-PTP in signal regulation and adipose tissue homeostasis.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy with a dismal 5-year survival rate, highlighting the urgent need to improve drug response and overcome primary or acquired (secondary) resistance to conventional chemotherapy. Glycogen synthase kinase 3β (GSK-3β) is a target in PDAC and the GSK-3 inhibitor, elraglusib, has been shown to improve chemosensitivity of PDAC and survival in preclinical cancer models. In addition, data from phase II clinical trials in patients with previously untreated metastatic pancreatic cancer is demonstrating preliminary evidence of clinical benefit in patients receiving a combination of gemcitabine/nab-paclitaxel. While this early evidence is highly encouraging, the biological determinants of elraglusib response are unknown. Using genome wide CRISPR/Cas9 library screening, we identify aurora kinase A (AURKA), an oncogenic kinase with major roles in mitosis, as a critical regulator for elraglusib sensitivity. Elraglusib treatment activates AURKA by inducing a G2/M cell cycle arrest. We show that knocking out AURKA reduces the phosphorylation of polo like kinase 1 (PLK1), arresting cells in mitosis, and leading to mitotic catastrophe and significant apoptosis after elraglusib treatment. Strikingly, the AURKA inhibitor, alisertib, works synergistically with elraglusib to inhibit PDAC cell line growth and induce apoptosis. In summary, our results demonstrate that targeting AURKA is an effective approach to sensitize PDAC tumors to elraglusib. Citation Format: Li Ding, Easton Maeder, Cheng Zhang, Taylor Weiskittel, Daniel Schmitt, Andrew Mazar, Hu Li, Daniel Billadeau. Genome wide CRISPR/Cas9 library screening identifies aurora kinase A as a regulator of elraglusib sensitivity in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4658.

Background: Cellular senescence has been associated with neurodegenerative disease and clearance of senescent cells using genetic or pharmaceutical strategies (senolytics) has demonstrated beneficial effects in mouse models investigating individual disease etiologies of Alzheimer’s disease (AD). However, it has remained unclear if senescent cell clearance in a mouse model exhibiting both plaque and tau pathologies modifies the disease state (3xTg). Objective: To investigate the effects of senescent cell clearance in the 3xTg mouse model. Methods: 3xTg mice were treated with senolytics (ABT263 (navitoclax; NAVI), a combination of dasatinib and quercetin (D+Q)), or subjected to transgene-mediated removal of p16-expressing cells (via INK-ATTAC). Results: Senolytic treatments consistently reduced microgliosis and ameliorated both amyloid and tau pathology in 3xTg mice. Using RNA sequencing, we found evidence that synaptic dysfunction and neuroinflammation were attenuated with treatment. These beneficial effects were not observed with short-term senolytic treatment in mice with more advanced disease. Conclusions: Overall, our results further corroborate the beneficial effects senescent cell clearance could have on AD and highlight the importance of early intervention for the treatment of this debilitating disease.

AbstractBackgroundContext‐specific networks can capture functionally important genes that may not be differentially expressed. Leveraging seven different gene expression datasets of Alzheimer’s disease (AD) and healthy control brains, we constructed a novel pipeline that includes such network analysis, augmented by in vivo validations and in silico drug repurposing to facilitate prioritization of AD therapeutic targets.MethodWe utilized publicly available gene expression data from 733 AD and 454 control brains across 7 datasets and the NetDecoder (PMID: 26975659) network biology platform to build context‐specific networks. Based on the known protein‐protein interaction and gene expression correlations, information flow from the top differentially expressed genes (DEG) to transcriptional regulator genes was calculated using a process‐guided flow algorithm. Genes with the greatest informational flow changes across AD cohorts were systematically prioritized. We next validated these genes in a medium‐throughput screen of a tau‐transgenic Drosophila model (PMID: 11408621). Further, we identified existing small‐molecule drugs to target the candidate genes utilizing curated databases.ResultsA total of 14 context‐specific networks were constructed across 7 datasets. We found AD‐related genes, such as APP, to have high flow differences between AD and control networks and high connectivity. Notably, many such genes were not DEGs between AD and control, suggesting that the information flow captures novel aspects of AD pathophysiology. We nominated 224 unique genes with the greatest flow differences for functional validation. We screened 111 of the 224 candidate genes with readily available, high‐confidence orthologs in Drosophila. We identified genes that suppress (N = 68) or enhance (N = 27) tau‐related neurodegeneration in this model. We confirmed the effects of previously nominated genes, such as HDAC1, which enhances tau‐neurodegeneration if overexpressed and suppresses it if knocked down. We also identified and validated additional therapeutic targets. Lastly, we applied a network‐proximity‐based algorithm and identified small molecule drugs for the candidate genes.ConclusionWe present a novel pipeline that integrates analytic approaches, experimental validation, and drug repurposing to facilitate therapeutic discovery in AD. We applied this pipeline and nominated previously known and novel genes implicated in AD pathogenesis. Our pipeline demonstrates a cross‐species, systems‐level approach to therapeutic nomination in AD and related neurodegenerative diseases.

Background: The existence and contribution of microglia with senescent-like alterations in the pathogenesis of age-related neurodegenerative diseases like Alzheimer’s disease (AD) have been suggested in recent years. However, the identification of this distinct microglial population in vivo has proven challenging, largely due to overlaps in the inflammatory phenotype of activated and senescent microglia. Furthermore, attempts at recapitulating senescence in microglia in vitro are limited. Objective: To identify and characterize senescent microglia that occur in vivo in an animal model of neurodegeneration driven by pathologic tau. Methods: We analyzed the RNA expression patterns of individual microglia from normal mice and the pathogenic tau P301 S PS19 mouse model. We have previously demonstrated that p16-expressing senescent microglia occur in these mice when neurodegeneration has occurred. Results: Here we identify a subset of disease-associated microglia with senescent features, notably characterized by the expression of Ccl4 . This signature overlaps with established markers of senescence from other cell types. Conclusion: Our characterization of senescent microglia can be used to better understand the role of senescent microglia in various age-related contexts, including whether clearance of senescent microglia represents a viable therapeutic option.

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.

Anticipating and understanding cancers’ need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors’ dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

Abstract Histological and lineage immunofluorescence examination revealed that healthy conducting airways of humans and animals harbor sporadic poorly differentiated epithelial patches mostly in the dorsal noncartilage regions that remarkably manifest squamous differentiation. In vitro analysis demonstrated that this squamous phenotype is not due to intrinsic functional change in underlying airway basal cells. Rather, it is a reversible physiological response to persistent Wnt signaling stimulation during de novo differentiation. Squamous epithelial cells have elevated gene signatures of glucose uptake and cellular glycolysis. Inhibition of glycolysis or a decrease in glucose availability suppresses Wnt-induced squamous epithelial differentiation. Compared with pseudostratified airway epithelial cells, a cascade of mucosal protective functions is impaired in squamous epithelial cells, featuring increased epithelial permeability, spontaneous epithelial unjamming, and enhanced inflammatory responses. Our study raises the possibility that the squamous differentiation naturally occurring in healthy airways identified herein may represent “vulnerable spots” within the airway mucosa that are sensitive to damage and inflammation when confronted by infection or injury. Squamous metaplasia and hyperplasia are hallmarks of many airway diseases, thereby expanding these areas of vulnerability with potential pathological consequences. Thus, investigation of physiological and reversible squamous differentiation from healthy airway basal cells may provide critical knowledge to understand pathogenic squamous remodeling, which is often nonreversible, progressive, and hyperinflammatory.