Yun Gong

Supplementary Figure S1 shows results from toxicity evaluation of WAY262611, the effect of WAY262611 on the growth of the primary tumor and on mouse survival, as well as examples of metastatic lesions.

Supplementary Table S1: Top 10 GO Biological Processes Enriched in Treated vs Control

Osteosarcoma is the most common primary malignant bone tumor in childhood. Patients who present with metastatic disease at diagnosis or relapse have a very poor prognosis, and this has not changed over the past four decades. The Wnt signaling pathway plays a role in regulating osteogenesis and is implicated in osteosarcoma pathogenesis. DKK-1 inhibits the canonical Wnt signaling pathway, causing inhibition of osteoblast differentiation and disordered bone repair. Our lab previously demonstrated that an mAb against DKK-1 prevented metastatic disease in a mouse model. This study expands upon those findings by demonstrating similar results with a small-molecule inhibitor of DKK-1, WAY262611, both in vitro and in vivo. WAY262611 was evaluated in vitro on osteosarcoma cell lines, including proliferation, caspase activation, cell-cycle analysis, and signaling pathway activation. We utilized our orthotopic implantation/amputation model of osteosarcoma metastasis in vivo to determine the impact of WAY262611 on primary tumor progression and metastatic outgrowth of disseminated tumor cells. Differentiation status was determined using single-cell RNA sequencing. We show here that WAY262611 activates canonical Wnt signaling, enhances nuclear localization and transcriptional activity of β-catenin, and slows proliferation of osteosarcoma cell lines. We also show that WAY262611 induces osteoblastic differentiation of a patient-derived xenograft of osteosarcoma in vivo, as well as inhibiting metastasis. This work credentials DKK-1 as a therapeutic target in osteosarcoma, allowing for manipulation of the Wnt signaling pathway and providing preclinical justification for the development of new biologics for the prevention of osteosarcoma metastasis.

This study investigated the genetic regulation of microRNA (miRNA) expression in monocytes and its potential role in musculoskeletal diseases. We mapped expression quantitative trait loci (eQTLs) for miRNAs using data from 281 Caucasian (CAU) and 170 African American (AA) individuals, constructed ancestry-specific models to impute miRNA expression from genotype data, and applied these models to test associations with osteoporosis and sarcopenia. Analysis identified 468 and 2653 independent eQTLs for 61 miRNAs in CAU and 25 in AA, respectively, the majority of which were ancestry-specific. Association analyses identified 22 and 26 miRNAs associated with osteoporosis and sarcopenia, respectively, in the CAU population; corresponding findings in the African American population were 26 and 14 miRNAs. Analysis of their target genes revealed 1238 and 741 genes that were nominally associated with osteoporosis and sarcopenia in CAU; with 524 genes associated with osteoporosis and 891 associated with sarcopenia in AA. Functional enrichment analysis indicated that the target genes of the identified miRNAs are involved in disease-relevant biological processes—cell migration and motility in osteoporosis, and immune/cytokine responses in sarcopenia. This work provides insights into the genetic architecture of miRNA expression and implicates monocyte miRNAs in musculoskeletal diseases, underscoring the importance of including diverse ancestral backgrounds in genomic studies.

Supplementary Table S1: Top 10 GO Biological Processes Enriched in Treated vs Control

Supplementary Figure S1 shows results from toxicity evaluation of WAY262611, the effect of WAY262611 on the growth of the primary tumor and on mouse survival, as well as examples of metastatic lesions.

Osteosarcoma is the most common primary malignant bone tumor in childhood. Patients who present with metastatic disease at diagnosis or relapse have a very poor prognosis, and this has not changed over the past four decades. The Wnt signaling pathway plays a role in regulating osteogenesis and is implicated in osteosarcoma pathogenesis. DKK-1 inhibits the canonical Wnt signaling pathway, causing inhibition of osteoblast differentiation and disordered bone repair. Our lab previously demonstrated that an mAb against DKK-1 prevented metastatic disease in a mouse model. This study expands upon those findings by demonstrating similar results with a small-molecule inhibitor of DKK-1, WAY262611, both in vitro and in vivo. WAY262611 was evaluated in vitro on osteosarcoma cell lines, including proliferation, caspase activation, cell-cycle analysis, and signaling pathway activation. We utilized our orthotopic implantation/amputation model of osteosarcoma metastasis in vivo to determine the impact of WAY262611 on primary tumor progression and metastatic outgrowth of disseminated tumor cells. Differentiation status was determined using single-cell RNA sequencing. We show here that WAY262611 activates canonical Wnt signaling, enhances nuclear localization and transcriptional activity of β-catenin, and slows proliferation of osteosarcoma cell lines. We also show that WAY262611 induces osteoblastic differentiation of a patient-derived xenograft of osteosarcoma in vivo, as well as inhibiting metastasis. This work credentials DKK-1 as a therapeutic target in osteosarcoma, allowing for manipulation of the Wnt signaling pathway and providing preclinical justification for the development of new biologics for the prevention of osteosarcoma metastasis.

Background: Aging significantly elevates the risk for Alzheimer’s disease (AD), contributing to the accumulation of AD pathologies, such as amyloid‐β (Aβ), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease.Method: In this study, for the first time, we employed a cutting‐edge spatial transcriptome platform, SpaTial Enhanced Resolution Omics‐sequencing (Stereo‐seq), to generate the first comprehensive, subcellular resolution spatial transcriptome atlas of the PFC from six AD cases at various neuropathological stages and six age, sex, and ethnicity matched controls.Result: Our analyses revealed distinct transcriptional alterations across six neocortex layers, highlighted the AD‐associated disruptions in laminar architecture, and identified changes in layer‐to‐layer interactions as AD progresses. Further, throughout the progression from NA to various stages of AD, we discovered specific genes that were significantly upregulated in neurons experiencing high stress and in nearby non‐neuronal cells, compared to cells distant from the source of stress. Notably, the cell‐cell interactions between the neurons under the high stress and adjacent glial cells that promote Aβ clearance and neuroprotection were diminished in AD in response to stressors compared to NA. Through cell‐type specific gene co‐expression analysis, we identified three modules in excitatory and inhibitory neurons associated with neuronal protection, protein dephosphorylation, and negative regulation of Aβ plaque formation. These modules negatively correlated with AD progression, indicating a reduced capacity for toxic substance clearance in AD subject samples. Moreover, we have discovered a novel transcription factor, ZNF460, that regulates all three modules, establishing it as a potential new therapeutic target for AD.Conclusion: Overall, utilizing the latest spatial transcriptome platform, our study developed the first transcriptome‐wide atlas with subcellular resolution for assessing the molecular alterations in the human PFC due to AD. This atlas sheds light on the potential mechanisms underlying the progression from NA to AD.

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can reveal the underlying disease mechanisms at the molecular level. In this study, we developed a novel deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian cancer patients. Our model achieved significantly better prognosis prediction than the conventional Cox Proportional Hazard model and other competitive deep learning approaches in various settings. Moreover, an interpretation approach was applied to tackle the “black-box” nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that made important contributions to distinguishing predicted high- and low-risk patients. The identified associations were partially supported by previous studies.

With the development of neuroimaging techniques, a growing amount of multi-modal brain imaging data are collected, facilitating comprehensive study of the brain. In this paper, we jointly analyzed functional magnetic resonance imaging (fMRI) collected under different paradigms in order to understand cognitive behaviors of an individual. To this end, we proposed a novel multi-view learning algorithm called structure-enforced collaborative regression (SCoRe) to extract co-expressed discriminative brain regions under the guidance of anatomical structure of the brain. An advantage of SCoRe over its predecessor collaborative regression (CoRe) lies in its incorporation of group structures in the brain imaging data, which makes the model biologically more meaningful. Results from real data analysis has confirmed that by incorporating prior knowledge of brain structure, SCoRe can deliver better prediction performance and is less sensitive to hyper-parameters than CoRe. After validation with simulation experiments, we applied SCoRe to fMRI data collected from the Philadelphia Neurodevelopmental Cohort and adopted the scores from the wide range achievement test (WRAT) to evaluate an individual’s cognitive skills. We located 14 relevant brain regions that can efficiently predict WRAT scores and these brain regions were further confirmed by other independent studies.