CGPE: A user-friendly gene and pathway explore webserver for public cancer transcriptional data

Digitized for IUPUI ScholarWorks inclusion in 2021.High throughput technology has been widely used by researchers to understand diseases at the molecular level. Database and servers for downloading and analyzing these publicly data is available as well. But there is still lacking tools for facilitating researchers to study the function of genes in pathways views by integrated public omics data

Questionnaire Data From the Revision of a Chinese Version of Free Will and Determinism Plus Scale

Funding statement: This work was supported by National Nature Science Foundations of China No. 31471001 to Kaiping Peng. All data, together with their codebooks and manipulation code, are available at osf.io/t2nsw/.Peer reviewedPublisher PD

Highly robust model of transcription regulator activity predicts breast cancer overall survival

Background: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. Methods: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. Result: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. Conclusion: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression

Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice

Background Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary. Methods We implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients. Results A five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis. Conclusions Transcription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective

Efficient Energy Conversion through Vortex Arrays in the Turbulent Magnetosheath

Turbulence is often enhanced when transmitted through a collisionless plasma shock. We investigate how the enhanced turbulent energy in the Earth's magnetosheath effectively dissipates via vortex arrays. This research topic is of great importance as it relates to particle energization at astrophysical shocks across the universe. Wave modes and intermittent coherent structures are the key candidate mechanisms for energy conversion in turbulent plasmas. Here, by comparing in-situ measurements in the Earth's magnetosheath with a theoretical model, we find the existence of vortex arrays at the transition between the downstream regions of the Earth's bow shock. Vortex arrays consist of quasi-orthogonal kinetic waves and exhibit both high volumetric filling factors and strong local energy conversion, thereby showing a greater dissipative energization than traditional waves and coherent structures. Therefore, we propose that vortex arrays are a promising mechanism for efficient energy conversion in the sheath regions downstream of astrophysical shocks

The various substrates of Usnea aurantiaco-atra and its algal sources in the Fildes Peninsula, Antarctica

The lichen species Usnea aurantiaco-atra (Jacq.) Bory is the most dominant vegetation on the Fildes Peninsula, Antarctica. Most individuals grow on rocks, and some are found with mosses. During the 27th and 28th Chinese National Antarctic Research expeditions of the Great Wall Station, U. aurantiaco-atra was observed growing on the lichen thallus of Umbilicaria antarctica Frey & I.M. Lamb, or on wood, which indicated that Usnea aurantiaco-atra could grow on various substrates. The diversities of the symbionts in U. aurantiaco-atra collected in the Fildes Peninsula were investigated using ITS rDNA sequences. The results showed that the sequences from mycobionts of U. aurantiaco-atra growing on various substrates did not exhibit significant differences. All photobionts in this lichen species were the green algae Trebouxia jamesii (Hildreth & Ahmadjian) Gärtner. The identical sequences from the photobionts of both Umbilicaria antarctica and Usnea aurantiaco-atra indicated there was an algae pool in this area and different mycobionts could obtain their algal partners from this pool. The variety of substrates for U. aurantiaco-atra suggested its photobiont could be obtained from a mature lichen thallus by vegetative propagation; from other lichen thalli (e.g. Umbilicaria antarctica); or from the surroundings. This study will promote understanding of the distribution of photobionts and the process of lichenization

Sesn3 protects against diet‐induced nonalcoholic steatohepatitis in mice via suppression of the TGFβ signal transduction

Sesn3 belongs to the three‐member sestrin protein family. Sestrins have been implicated in anti‐oxidative stress, AMPK and mTOR signal transduction, and metabolic homeostasis. However, the role of Sesn3 in the development of nonalcoholic steatohepatitis (NASH) has not been previously studied. In this work, we generated Sesn3 whole‐body knockout and liver‐specific transgenic mice to investigate the hepatic function of Sesn3 in diet‐induced NASH. With only 4 weeks of dietary treatment, Sesn3 knockout mice developed severe NASH phenotype as characterized by hepatic steatosis, inflammation, and fibrosis. Strikingly, after 8‐week feeding with a NASH‐inducing diet, Sesn3 transgenic mice were largely protected against NASH development. Transcriptomic analysis revealed that multiple extracellular matrix related processes were upregulated including TGFβ signaling and collagen production. Further biochemical and cell biological analyses have illustrated a critical control of the TGFβ‐Smad pathway by Sesn3 at the TGFβ receptor and Smad3 levels. First, Sesn3 inhibits the TGFβ receptor through an interaction with Smad7; second, Sesn3 directly inhibits the Smad3 function through protein‐protein interaction and cytosolic retention

Generation of the tumor-suppressive secretome from tumor cells

Rationale: The progression of cancer cells depends on the soil and building an inhibitory soil might be a therapeutic option. We previously created tumor-suppressive secretomes by activating Wnt signaling in MSCs. Here, we examined whether the anti-tumor secretomes can be produced from tumor cells. Methods: Wnt signaling was activated in tumor cells by overexpressing β-catenin or administering BML284, a Wnt activator. Their conditioned medium (CM) was applied to cancer cells or tissues, and the effects of CM were evaluated. Tumor growth in the mammary fat pad and tibia in C57BL/6 female mice was also evaluated through μCT imaging and histology. Whole-genome proteomics analysis was conducted to determine and characterize novel tumor-suppressing proteins, which were enriched in CM. Results: The overexpression of β-catenin or the administration of BML284 generated tumor-suppressive secretomes from breast, prostate and pancreatic cancer cells. In the mouse model, β-catenin-overexpressing CM reduced tumor growth and tumor-driven bone destruction. This inhibition was also observed with BML284-treated CM. Besides p53 and Trail, proteomics analysis revealed that CM was enriched with enolase 1 (Eno1) and ubiquitin C (Ubc) that presented notable tumor-suppressing actions. Importantly, Eno1 immunoprecipitated CD44, a cell-surface adhesion receptor, and its silencing suppressed Eno1-driven tumor inhibition. A pan-cancer survival analysis revealed that the downregulation of MMP9, Runx2 and Snail by CM had a significant impact on survival outcomes (p < 0.00001). CM presented a selective inhibition of tumor cells compared to non-tumor cells, and it downregulated PD-L1, an immune escape modulator. Conclusions: The tumor-suppressive secretome can be generated from tumor cells, in which β-catenin presented two opposing roles, as an intracellular tumor promoter in tumor cells and a generator of extracellular tumor suppressor in CM. Eno1 was enriched in CM and its interaction with CD44 was involved in Eno1's anti-tumor action. Besides presenting a potential option for treating primary cancers and metastases, the result indicates that aggressive tumors may inhibit the growth of less aggressive tumors via tumor-suppressive secretomes

INPP5D expression is associated with risk for Alzheimer’s disease and induced by plaque-associated microglia

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, robust microgliosis, neuroinflammation, and neuronal loss. Genome-wide association studies recently highlighted a prominent role for microglia in late-onset AD (LOAD). Specifically, inositol polyphosphate-5-phosphatase (INPP5D), also known as SHIP1, is selectively expressed in brain microglia and has been reported to be associated with LOAD. Although INPP5D is likely a crucial player in AD pathophysiology, its role in disease onset and progression remains unclear. We performed differential gene expression analysis to investigate INPP5D expression in AD and its association with plaque density and microglial markers using transcriptomic (RNA-Seq) data from the Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) cohort. We also performed quantitative real-time PCR, immunoblotting, and immunofluorescence assays to assess INPP5D expression in the 5xFAD amyloid mouse model. Differential gene expression analysis found that INPP5D expression was upregulated in LOAD and positively correlated with amyloid plaque density. In addition, in 5xFAD mice, Inpp5d expression increased as the disease progressed, and selectively in plaque-associated microglia. Increased Inpp5d expression levels in 5xFAD mice were abolished entirely by depleting microglia with the colony-stimulating factor receptor-1 antagonist PLX5622. Our findings show that INPP5D expression increases as AD progresses, predominantly in plaque-associated microglia. Importantly, we provide the first evidence that increased INPP5D expression might be a risk factor in AD, highlighting INPP5D as a potential therapeutic target. Moreover, we have shown that the 5xFAD mouse model is appropriate for studying INPP5D in AD