Emerging studies have begun to demonstrate that reprogrammed stromal cells play pivotal roles in tumor growth, metastasis, and resistance to therapy. However, the contribution of stromal cells to non-small-cell lung cancer (NSCLC) has remained underexplored. We used an orthotopic model of Kras-driven NSCLC to systematically dissect the contribution of specific hematopoietic stromal cells in lung cancer. RNA deep-sequencing analysis of individually sorted myeloid lineage and tumor epithelial cells revealed cell-type-specific differentially regulated genes, indicative of activated stroma. We developed a computational model for crosstalk signaling discovery based on ligand-receptor interactions and downstream signaling networks and identified known and novel tumor-stroma paracrine and tumor autocrine crosstalk-signaling pathways in NSCLC. We provide cellular and molecular insights into components of the lung cancer microenvironment that contribute to carcinogenesis. This study has the potential for development of therapeutic strategies that target tumor-stroma interactions and may complement conventional anti-cancer treatments.

Chemotherapy-induced diarrhea (CID), with clinical high incidence, adversely affects the efficacy of cancer treatment and patients' quality of life. Our study demonstrates that the citrus flavonoid hesperetin (Hst) has a superior potential as a new agent to prevent and alleviate CID. In the animal model for irinotecan (CPT-11) induced CID, Hst could selectively inhibit intestinal carboxylesterase (CES2) and thus reduce the local conversion of CPT-11 to cytotoxic SN-38 which causes intestinal toxicity. Oral administration of Hst manifested an excellent anti-diarrhea efficacy, prohibiting 80% of severe and 100% of mild diarrhea in the CPT-11 administered tumor-bearing mice. In addition, a significant attenuation of intestinal inflammation contributed to the anti-diarrhea effect of Hst. Moreover, Hst was found to work synergistically with CPT-11 in tumor inhibition by suppressing the tumor's STAT3 activity and recruiting tumoricidal macrophages into the tumor microenvironment. The anti-intestinal inflammation and anti-STAT3 properties of Hst would contribute its broad benefits to the management of diarrhea caused by other chemo or targeted agents, and more importantly, enhance and reinforce the anti-tumor effects of these agents, to improve patient outcomes.

Convincing epidemiological data suggest an inverse association between cancer and neurodegeneration, including Alzheimer's disease (AD). Since both AD and cancer are characterized by abnormal, but opposing cellular behavior, i.e., increased cell death in AD while excessive cell growth occurs in cancer, this motivates us to initiate the study into unraveling the shared genes and cell signaling pathways linking AD and glioblastoma multiform (GBM). In this study, a comprehensive bioinformatics analysis on clinical microarray datasets of 1,091 GBM and 524 AD cohorts was performed. Significant genes and pathways were identified from the bioinformatics analyses - in particular ERK/MAPK signaling, up-regulated in GBM and Angiopoietin Signaling pathway, reciprocally up-regulated in AD - connecting GBM and AD (P < 0.001), were investigated in details for their roles in GBM growth in an AD environment. Our results showed that suppression of GBM growth in an AD background was mediated by the ERK-AKT-p21-cell cycle pathway and anti-angiogenesis pathway.

Gene network information has been used to improve gene selection in microarray-based studies by selecting marker genes based both on their expression and the coordinate expression of genes within their gene network under a given condition. Here we propose a new network-embedded gene selection model. In this model, we first address the limitations of microarray data. Microarray data, although widely used for gene selection, measures only mRNA abundance, which does not always reflect the ultimate gene phenotype, since it does not account for post-transcriptional effects. To overcome this important (critical in certain cases) but ignored-in-almost-all-existing-studies limitation, we design a new strategy to integrate together microarray data with the information of microRNA, the major post-transcriptional regulatory factor. We also handle the challenges led by gene collaboration mechanism. To incorporate the biological facts that genes without direct interactions may work closely due to signal transduction and that two genes may be functionally connected through multi paths, we adopt the concept of diffusion distance. This concept permits us to simulate biological signal propagation and therefore to estimate the collaboration probability for all gene pairs, directly or indirectly-connected, according to multi paths connecting them. We demonstrate, using type 2 diabetes (DM2) as an example, that the proposed strategies can enhance the identification of functional gene partners, which is the key issue in a network-embedded gene selection model. More importantly, we show that our gene selection model outperforms related ones. Genes selected by our model 1) have improved classification capability; 2) agree with biological evidence of DM2-association; and 3) are involved in many well-known DM2-associated pathways.

Acquisition and quantitative analysis of high resolution images of dendritic spines are challenging tasks but are necessary for the study of animal models of neurological and psychiatric diseases. Currently available methods for automated dendritic spine detection are for the most part customized for 2D image slices, not volumetric 3D images. In this work, a fully automated method is proposed to detect and segment dendritic spines from 3D confocal microscopy images of medium-sized spiny neurons (MSNs). MSNs constitute a major neuronal population in striatum, and abnormalities in their function are associated with several neurological and psychiatric diseases. Such automated detection is critical for the development of new 3D neuronal assays which can be used for the screening of drugs and the studies of their therapeutic effects. The proposed method utilizes a generalized gradient vector flow (GGVF) with a new smoothing constraint and then detects feature points near the central regions of dendrites and spines. Then, the central regions are refined and separated based on eigen-analysis and multiple shape measurements. Finally, the spines are segmented in 3D space using the fast marching algorithm, taking the detected central regions of spines as initial points. The proposed method is compared with three popular existing methods for centerline extraction and also with manual results for dendritic spine detection in 3D space. The experimental results and comparisons show that the proposed method is able to automatically and accurately detect, segment, and quantitate dendritic spines in 3D images of MSNs.

The profibrotic adhesion molecule, osteopontin (OPN), is upregulated in kidneys of humans and mice with diabetes. The thiazolidinedione (TZD) insulin sensitizers decrease albuminuria in diabetic nephropathy (DN) and reduce OPN expression in vascular and cardiac tissue. To examine whether OPN is a critical mediator of DN we treated db/db mice with insulin, rosiglitazone, or pioglitazone to achieve similar fasting plasma glucose levels. The urine albumin-to-creatinine ratio and glomerular OPN expression were increased in diabetic mice, but both were reduced by the TZDs more than by insulin. We administered streptozotocin to OPN-null and OPN-wild-type mice, and OPN-null mice were bred into both type 1 (Ins2(akita/+)) and 2 (db/db) diabetic mice. In each case, OPN deletion decreased albuminuria, mesangial area, and glomerular collagen IV, fibronectin and transforming growth factor (TGF)-beta in the diabetic mice compared with their respective controls. In cultured mouse mesangial cells, TZDs but not insulin decreased angiotensin II-induced OPN expression, while recombinant OPN upregulated TGF-beta, ERK/MAPK, and JNK/MAPK signaling. These studies show that OPN expression in DN mouse models enhances glomerular damage, likely through the expression of TGF-beta, while its deletion protects against disease progression, suggesting that OPN might serve as a therapeutic target.

The fate of disseminated tumor cells is largely determined by microenvironment (ME) niche. The osteogenic niche promotes cancer cell proliferation and bone metastasis progression. We investigated the underlying mechanisms using pre-clinical models and analyses of clinical data. We discovered that the osteogenic niche serves as a calcium (Ca) reservoir for cancer cells through gap junctions. Cancer cells cannot efficiently absorb Ca from ME, but depend on osteogenic cells to increase intracellular Ca concentration. The Ca signaling, together with previously identified mammalian target of rapamycin signaling, promotes bone metastasis progression. Interestingly, effective inhibition of these pathways can be achieved by danusertib, or a combination of everolimus and arsenic trioxide, which provide possibilities of eliminating bone micrometastases using clinically established drugs.

Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.

Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients' survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship in TME. The SIO pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among tumor, immune, and stromal cells that coordinate to influence overall survival rates in HGSC patients. In addition, SIO integrates IMC data with microdissected tumor and stromal transcriptomes from the same patients to identify novel signaling networks, which would lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients.

Infections by viruses including severe acute respiratory syndrome coronavirus 2 could cause organ inflammations such as myocarditis, pneumonia and encephalitis. Innate immunity to viral nucleic acids mediates antiviral immunity as well as inflammatory organ injury. However, the innate immune mechanisms that control viral induced organ inflammations are unclear.

Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P < 5 × 10-8) not previously associated with postchallenge insulin resistance, eight of which were shown to share their genetic architecture with type 2 diabetes in colocalization analyses. We investigated candidate genes at a subset of associated loci in cultured cells and identified nine candidate genes newly implicated in the expression or trafficking of GLUT4, the key glucose transporter in postprandial glucose uptake in muscle and fat. By focusing on postprandial insulin resistance, we highlighted the mechanisms of action at type 2 diabetes loci that are not adequately captured by studies of fasting glycemic traits.

African Americans (AAs) have the highest prevalence of hypertension among United States racial/ethnic groups. Regulators of blood pressure, such as aldosterone and endothelin-1, impact glucose regulation. The relationship between these factors and incident diabetes is not well elucidated among AAs.

T cell infiltration into white adipose tissue (WAT) drives obesity-induced adipose inflammation, but the mechanisms of obesity-induced T cell infiltration into WAT remain unclear. Our single-cell RNA sequencing reveals a significant impact of adipose stem cells (ASCs) on T cells. Transplanting ASCs from obese mice into WAT enhances T cell accumulation. C-C motif chemokine ligand 5 (CCL5) is upregulated in ASCs as early as 4 weeks of high-fat diet feeding, coinciding with the onset of T cell infiltration into WAT during obesity. ASCs and bone marrow transplantation experiments demonstrate that CCL5 from ASCs plays a crucial role in T cell accumulation during obesity. The production of CCL5 in ASCs is induced by tumor necrosis factor alpha via the nuclear factor κB pathway. Overall, our findings underscore the pivotal role of ASCs in regulating T cell accumulation in WAT during the early phases of obesity, emphasizing their importance in modulating adaptive immunity in obesity-induced adipose inflammation.

Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients' outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities.

Personalized genomics instability, e.g., somatic mutations, is believed to contribute to the heterogeneous drug responses in patient cohorts. However, it is difficult to discover personalized driver mutations that are predictive of drug sensitivity owing to diverse and complex mutations of individual patients. To circumvent this problem, a novel computational method is presented to discover potential drug sensitivity relevant cancer subtypes and identify driver mutation modules of individual subtypes by coupling differentially expressed genes (DEGs) based subtyping analysis with the driver mutation network analysis.

The role of epithelial-to-mesenchymal transition (EMT) in metastasis is a longstanding source of debate, largely owing to an inability to monitor transient and reversible EMT phenotypes in vivo. Here we establish an EMT lineage-tracing system to monitor this process in mice, using a mesenchymal-specific Cre-mediated fluorescent marker switch system in spontaneous breast-to-lung metastasis models. We show that within a predominantly epithelial primary tumour, a small proportion of tumour cells undergo EMT. Notably, lung metastases mainly consist of non-EMT tumour cells that maintain their epithelial phenotype. Inhibiting EMT by overexpressing the microRNA miR-200 does not affect lung metastasis development. However, EMT cells significantly contribute to recurrent lung metastasis formation after chemotherapy. These cells survived cyclophosphamide treatment owing to reduced proliferation, apoptotic tolerance and increased expression of chemoresistance-related genes. Overexpression of miR-200 abrogated this resistance. This study suggests the potential of an EMT-targeting strategy, in conjunction with conventional chemotherapies, for breast cancer treatment.

PPARgamma ligands have been shown to have antiproliferative effects on many cell types. We herein report that a synthetic dominant-negative (DN) PPARgamma mutant functions like a growth factor to promote cell cycle progression and cell proliferation in human coronary artery smooth muscle cells (CASMCs). In quiescent CASMCs, adenovirus-expressed DN-PPARgamma promoted G1-->S cell cycle progression, enhanced BrdU incorporation, and increased cell proliferation. DN-PPARgamma expression also markedly enhanced positive regulators of the cell cycle, increasing Rb and CDC2 phosphorylation and the expression of cyclin A, B1, D1, and MCM7. Conversely, overexpression of wild-type (WT) or constitutively-active (CA) PPARgamma inhibited cell cycle progression and the activity and expression of positive regulators of the cell cycle. DN-PPARgamma expression, however, did not up-regulate positive cell cycle regulators in PPARgamma-deficient cells, strongly suggesting that DN-PPARgamma effects on cell cycle result from blocking the function of endogenous wild-type PPARgamma. DN-PPARgamma expression enhanced phosphorylation of ERK MAPKs. Furthermore, the ERK specific-inhibitor PD98059 blocked DN-PPARgamma-induced phosphorylation of Rb and expression of cyclin A and MCM7. Our data thus suggest that DN-PPARgamma promotes cell cycle progression and cell growth in CASMCs by modulating fundamental cell cycle regulatory proteins and MAPK mitogenic signaling pathways in vascular smooth muscle cells (VSMCs).

Reconstruction of the central surface representation of the cerebral cortex is an important means to study the structure and function of the human brain. In this paper, we propose a novel method based on an elastic transform vector field to drive a deformable model for the reconstruction of the central cortical surface. Both simulated brain cortexes and real brain images are used to evaluate this approach. We applied the surface reconstruction method and a hybrid volumetric and surface registration algorithm to detect simulated brain atrophy. Experimental results show that the central cortical surface representation has better performance in detecting simulated atrophy than the traditionally used inner or outer cortical surface representations.

This study aims to discover genes with prognostic potential for glioblastoma (GBM) patients' survival in a patient group that has gone through standard of care treatments including surgeries and chemotherapies, using tumor gene expression at initial diagnosis before treatment. The Cancer Genome Atlas (TCGA) GBM gene expression data are used as inputs to build a deep multilayer perceptron network to predict patient survival risk using partial likelihood as loss function. Genes that are important to the model are identified by the input permutation method. Univariate and multivariate Cox survival models are used to assess the predictive value of deep learned features in addition to clinical, mutation, and methylation factors. The prediction performance of the deep learning method was compared to other machine learning methods including the ridge, adaptive Lasso, and elastic net Cox regression models. Twenty-seven deep-learned features are extracted through deep learning to predict overall survival. The top 10 ranked genes with the highest impact on these features are related to glioblastoma stem cells, stem cell niche environment, and treatment resistance mechanisms, including POSTN, TNR, BCAN, GAD1, TMSB15B, SCG3, PLA2G2A, NNMT, CHI3L1 and ELAVL4.

The majority of breast cancer models for drug discovery are based on orthotopic or subcutaneous tumours. Therapeutic responses of metastases, especially microscopic metastases, are likely to differ from these tumours due to distinct cancer-microenvironment crosstalk in distant organs. Here, to recapitulate such differences, we established an ex vivo bone metastasis model, termed bone-in-culture array or BICA, by fragmenting mouse bones preloaded with breast cancer cells via intra-iliac artery injection. Cancer cells in BICA maintain features of in vivo bone micrometastases regarding the microenvironmental niche, gene expression profile, metastatic growth kinetics and therapeutic responses. Through a proof-of-principle drug screening using BICA, we found that danusertib, an inhibitor of the Aurora kinase family, preferentially inhibits bone micrometastases. In contrast, certain histone methyltransferase inhibitors stimulate metastatic outgrowth of indolent cancer cells, specifically in the bone. Thus, BICA can be used to investigate mechanisms involved in bone colonization and to rapidly test drug efficacies on bone micrometastases.